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离骚屈原〔先秦〕帝高阳之苗裔兮，朕皇考曰伯庸。
摄提贞于孟陬兮，惟庚寅吾以降。
皇览揆余初度兮，肇锡余以嘉名：名余曰正则兮，字余曰灵均。
纷吾既有此内美兮，又重之以修能。
扈江离与辟芷兮，纫秋兰以为佩。
汩余若将不及兮，恐年岁之不吾与。
朝搴阰之木兰兮，夕揽洲之宿莽。
日月忽其不淹兮，春与秋其代序。
惟草木之零落兮，恐美人之迟暮。
(惟通：唯)不抚壮而弃秽兮，何不改此度？(改此度一作：改乎此度)乘骐骥以驰骋兮，来吾道夫先路！昔三后之纯粹兮，固众芳之所在。
杂申椒与菌桂兮，岂惟纫夫蕙茝！彼尧、舜之耿介兮，既遵道而得路。
何桀纣之猖披兮，夫唯捷径以窘步。
惟夫党人之偷乐兮，路幽昧以险隘。
岂余身之惮殃兮，恐皇舆之败绩！忽奔走以先后兮，及前王之踵武。
荃不查余之中情兮，反信谗而齌怒。
余固知謇謇之为患兮，忍而不能舍也。
指九天以为正兮，夫唯灵修之故也。
曰黄昏以为期兮，羌中道而改路！初既与余成言兮，后悔遁而有他。
余既不难夫离别兮，伤灵修之数化。
余既滋兰之九畹兮，又树蕙之百亩。
畦留夷与揭车兮，杂杜衡与芳芷。
冀枝叶之峻茂兮，愿俟时乎吾将刈。
虽萎绝其亦何伤兮，哀众芳之芜秽。
众皆竞进以贪婪兮，凭不厌乎求索。
羌内恕己以量人兮，各兴心而嫉妒。
忽驰骛以追逐兮，非余心之所急。
老冉冉其将至兮，恐修名之不立。
朝饮木兰之坠露兮，夕餐秋菊之落英。
苟余情其信姱以练要兮，长顑颔亦何伤。
掔木根以结茝兮，贯薜荔之落蕊。
矫菌桂以纫蕙兮，索胡绳之纚纚。
謇吾法夫前修兮，非世俗之所服。
虽不周于今之人兮，愿依彭咸之遗则。
长太息以掩涕兮，哀民生之多艰。
余虽好修姱以鞿羁兮，謇朝谇而夕替。
既替余以蕙纕兮，又申之以揽茝。
亦余心之所善兮，虽九死其犹未悔。
怨灵修之浩荡兮，终不察夫民心。
众女嫉余之蛾眉兮，谣诼谓余以善淫。
固时俗之工巧兮，偭规矩而改错。
背绳墨以追曲兮，竞周容以为度。
忳郁邑余侘傺兮，吾独穷困乎此时也。
宁溘死以流亡兮，余不忍为此态也。
鸷鸟之不群兮，自前世而固然。
何方圜之能周兮，夫孰异道而相安？屈心而抑志兮，忍尤而攘诟。
Author: 青州 seo汉字区位码表a — bao 所谓汉字编码，就是采用一种科学可行的办法，为每个汉字编一个唯一的代码，以便计算机辨认、接收和处理。
在此介绍的是《国家标准信息交换汉字编码》这种编码经过加。
工整理一律以汉语拼音的字母为序，音节相同的字以使用频率为序，其查找方法与一般汉语字典的汉字拼音音节索引查找法相同。
（ 1）按音序查常用汉字按音序几乎都可查到，例如：白” 字，首先，按其发音 bai 查在汉字编码表“ 中的位置，然后在 bai 范围内查“ 白” 字，找到“ 白” 字后，其汉字右侧的数字 1655 就是“白”的汉字编码。
（ 2）关于多音字的查找由于汉字编码是一种无重码的汉字编码，所以多音字只有一个编码。
在查多音字时，如果用某个音查不到，可换另外的音去查。
例如：重庆的“重”和重量的“重” 字同音不，同，汉字“ 重” 的编码是按 Zhong 音编码的。
a 啊 1601 阿 1602 吖 6325 嗄 6436 腌 7571 锕 7925 ai 埃 1603 挨1604 哎 1605 唉 1606 哀 1607 皑 1608 癌 1609 蔼 1610 矮 1611 艾 1612 碍1613 爱 1614隘 1615 捱 6263 嗳 6440 嗌 6441 嫒 7040 瑷 7208 暧 7451 砹 7733 锿7945 霭 8616 an 鞍 1616 氨 1617 安 1618 俺 1619 按 1620 暗 1621 岸 1622 胺 1623 案 1624谙 5847 埯 5991 揞 6278 犴 6577 庵 6654 桉 7281 铵 7907 鹌 8038 黯8786 ang 肮 1625 昂 1626 盎 1627 ao 凹 1628 敖 1629 熬 1630 翱 1631 袄1632 傲 1633奥 1634 懊 1635 澳 1636 坳 5974 拗 6254 嗷 6427 岙 6514 廒 6658 遨6959 媪 7033 骜 7081 獒 7365 聱 8190 螯 8292 鏊 8643 鳌 8701 鏖 8773 ba 芭 1637 捌 1638扒 1639 叭 1640 吧 1641 笆 1642 八 1643 疤 1644 巴 1645 拔 1646 跋1647 靶 1648 把 1649 耙 1650 坝 1651 霸 1652 罢 1653 爸 1654 茇 6056 菝6135 岜 6517 灞 6917钯 7857 粑 8446 鲅 8649 魃 8741 bai 白 1655 柏 1656 百 1657 摆 1658 佰 1659 败 1660 拜 1661 稗 1662 捭 6267 呗 6334 掰 7494 ban 斑 1663 班1664 搬 1665扳 1666 般 1667 颁 1668 板 1669 版 1670 扮 1671 拌 1672 伴 1673 瓣1674 半 1675 办 1676 绊 1677 阪 5870 坂 5964 钣 7851 瘢 8103 癍 8113 舨8418 bang 邦 1678帮 1679 梆 1680 榜 1681 膀 1682 绑 1683 棒 1684 磅 1685 蚌 1686 镑1687 傍 1688 谤 1689 蒡 6182 浜 6826 bao 苞 1690 胞 1691 包 1692 褒 1693 剥 1694 薄 17011 Author: 寿光 seobao—ce 雹 1702 保 1703 堡 1704 饱 1705 宝 1706 抱 1707 报 1708 暴1709 豹 1710 鲍 1711 爆 1712 葆 6165 孢 7063 煲 7650 鸨 8017 褓 8157 趵8532 龅 8621 bei 杯 1713 碑 1714 悲 1715 卑 1716 北 1717 辈 1718 背 1719 贝 1720 钡 1721 倍 1722 狈 1723 备 1724 惫 1725 焙 1726 被 1727 孛 5635 陂 5873 邶 5893 埤 5993 萆 6141 蓓 6177 悖 6703 碚 7753 鹎 8039褙 8156 鐾 8645 鞴 8725 ben 奔 1728 苯 1729 本 1730 笨 1731 畚 5946 坌 5948 贲 7458 锛 7928 beng 崩 1732 绷 1733 甭 1734 泵 1735 蹦 1736 迸1737 嘣 6452 甏 7420 bi 逼 1738 鼻 1739 比 1740 鄙 1741 笔 1742 彼 1743 碧 1744 蓖 1745 蔽 1746 毕 1747 毙 1748 毖 1749 币 1750 庇 1751 痹 1752 闭 1753 敝 1754 弊 1755 必 1756 辟 1757 壁 1758 臂 1759 避 1760 陛 1761 匕 5616 俾 5734 芘 6037 荜 6074 荸 6109 薜 6221 吡 6333 哔 6357 狴 6589 庳 6656 愎 6725 滗 6868 濞 6908 弼 6986 妣 6994 婢 7030 嬖 7052 璧 7221 睥 7802 畀 7815 铋 7873 秕 7985 裨 8152 筚 8357 箅 8375 篦 8387 舭 8416 襞 8437 跸 8547 髀 8734 bian 鞭 1762 边 1763 编 1764 贬 1765 扁 1766 便1767 变 1768 卞 1769 辨 1770 辩 1771 辫 1772 遍 1773 匾 5650 弁 5945 苄6048 忭 6677 汴 6774 缏 7134 煸 7652 砭 7730 碥 7760 窆 8125 褊 8159 蝙8289 笾 8354 鳊 8693 biao 标 1774 彪 1775 膘 1776 表 1777 婊 7027 骠7084 杓 7228 飑 7609 飙 7613 飚 7614 镖 7958 镳 7980 瘭 8106 裱 8149 鳔8707 髟 8752 bie 鳖 1778 憋 1779 别 1780 瘪 1781 蹩 8531 bin 彬 1782 斌1783 濒 1784 滨 1785 宾 1786 摈 1787 傧 5747 豳 6557 缤 7145 玢 7167bin-chan 槟 7336 殡 7375 膑 7587 镔 7957 髌 8738 鬓 8762 bing 兵 1788 冰1789 柄 1790 丙 1791 秉 1792 饼 1793 炳 1794 病 1801 并 1802 禀 5787 邴5891 摒 6280 bo 玻 1803 菠 1804 播 1805 拨 1806 钵 1807 波 1808 博 1809 勃 1810 搏 1811 铂 1812 箔 1813 伯 1814 帛 1815 舶 1816 脖 1817 膊 1818 渤 1819 泊 1820 驳 1821 亳 5781 啵 6403 饽 6636 檗 7362 擘 7502 礴 7771 钹 7864 鹁 8030 簸 8404 跛 8543 踣 8559 bu 捕 1822 卜 1823 哺 1824 补1825 埠 1826 不 1827 布 1828 步 1829 簿 1830 部 1831 怖 1832 卟 6318 逋6945 瓿 7419 晡 7446 钚 7848 钸 7863 醭 8519 ca 擦 1833 嚓 6474 礤 7769 cai 猜 1834 裁 1835 材 1836 才 1837 财 1838 睬 1839 踩 1840 采 1841 彩1842 菜 1843 蔡 1844 can 餐 1845 参 1846 蚕 1847 残 1848 惭 1849 惨 1850 灿 1851 孱 6978 骖 7078 璨 7218 粲 8451 黪 8785 cang 苍 1852 舱 1853 仓1854 沧 1855 藏 1856 cao 操 1857 糙 1858 槽 1859 曹 1860 草 1861 嘈 6448 漕 6878 螬 8309 艚 8429 ce 厕 1862 策 1863 侧 1864 册 1865 测 1866 恻66922 Author: 寿光 seocen—chun cen 岑 6515 涔 6825 ceng 层 1867 蹭 1868 噌 6465 cha 插1869 叉 1870 茬 1871 茶 1872 查 1873 碴 1874 搽 1875 察 1876 岔 1877 差1878 诧 1879 猹 6610 馇 6639 汊 6766 姹 7017 杈 7230 楂 7311 槎 7322 檫7363 锸 7942 镲 7979 衩 8135 chai 拆 1880 柴 1881 豺 1882 侪 5713 钗7846 瘥 8091 虺 8219 chan 搀 1883 掺 1884 蝉 1885 馋 1886 谗 1887 缠1888 铲 1889 产 1890 阐 1891 颤 1892 冁 5770 谄 5838 谶 5863 蒇 6159 廛6660 忏 6667 潺 6893 澶 6904 羼 6981 婵 7031 骣 7086 觇 7472 禅 7688 镡7966 蟾 8324 躔 8580 chang 昌 1893 猖 1894 场 1901 尝 1902 常 1903 长1904 偿 1905 肠 1906 厂 1907 敞 1908 畅 1909 唱 1910 倡 1911 伥 56866 鬯 5943 苌 6041 菖 6137 徜 6568 怅 6674 惝 6714 阊 6749 娼 7029嫦 7047 昶 7438 氅 7509 鲳 8680 chao 超 1912 抄 1913 钞 1914 朝 1915 嘲1916 潮 1917 巢 1918 吵 1919 炒 1920 怊 6687 晁 7443 焯 7644 耖 8173 che 车 1921 扯 1922 撤 1923 掣 1924 彻 1925 澈 1926 坼 5969 砗 7726 chen 郴1927 臣 1928 辰 1929 尘 1930 晨 1931 忱 1932 沉 1933 陈 1934 趁 1935 衬1936 伧 5687 谌 5840 抻 6251 嗔 6433 宸 6923 琛 7201 榇 7320 碜 7755 龀8619 cheng 撑 1937 称 1938 城 1939 橙 1940 成 1941 呈 1942 乘 1943 程1944 惩 1945 澄 1946 诚 1947 承 1948 逞 1949 骋 1950 秤 1951 丞 5609 埕5984 枨 7239 柽 7263 塍 7583 瞠 7810 铖 7881 铛 7885 裎 8146 蚴 8242 酲8508 chi 吃 1952 痴 1953 持 1954 匙 1955 池 1956 迟 1957 弛 1958 驰 1959 耻 1960 齿 1961 侈 1962 尺 1963 赤 1964 翅 1965 斥 1966 炽 1967 傺 5749 坻 5970 墀 6015 茌 6061 叱 6319 哧 6374 啻 6420 嗤 6445 彳 6560 饬 6633 媸 7042 敕 7523 眙 7784 眵 7787 鸱 8023 瘛 8101 褫 8161 蚶 8232 螭 8304 笞 8355 篪 8388 豉 8489 踟 8556 魑 8746 chong 充 1968 冲 1969 虫 1970 崇1971 宠 1972 茺 6091 忡 6671 憧 6731 铳 7905 舂 8409 艟 8430 chou 抽1973 酬 1974 畴 1975 踌 1976 稠 1977 愁 1978 筹 1979 仇 1980 绸 1981 瞅1982 丑 1983 臭 1984 俦 5717 帱 6492 惆 6716 瘳 8112 雠 8637 chu 初 1985 出 1986 橱 1987 厨 1988 躇 1989 锄 1990 雏 1991 滁 1992 除 1993 楚 1994础 2001 储 2002 矗 2003 搐 2004 触 2005 处 2006 亍 5601 刍 5927 怵 6680 憷 6732 绌 7109 杵 7238 楮 7290 樗 7343 褚 8150 蜍 8260 蹰 8573 黜 8777 chuai 搋 6285 啜 6408 嘬 6460 膪 7590 踹 8563 chuan 揣 2007 川 2008 穿2009 椽 2010 传 2011 船 2012 喘 2013 串 2014 舛 6622 遄 6955 巛 7161 氚7516 钏 7843 舡 8413 chuang 疮 2015 窗 2016 幢 2017 床 2018 闯 2019 创2020 怆 6675 chui 吹 2021 炊 2022 捶 2023 锤 2024 垂 2025 陲 5879 缍7122 棰 7302 槌 7319 chun 春 20263 Author: 寿光 seochun—diao 椿 2027 醇 2028 唇 2029 淳 2030 纯 2031 蠢 2032 莼 6127 鹑 8040 蝽 8277 chuo 戳 2033 绰 2034 辍 7401 踔 8554 龊 8626 ci 疵 2035 茨 2036 磁 2037 雌 2038 辞 2039 慈 2040 瓷 2041 词 2042 此 2043 刺 2044 赐 2045 次 2046 茈 6075 呲 6358 祠 7684 鹚 8043 糍 8457 cong 聪 2047 葱2048 囱 2049 匆 2050 从 2051 丛 2052 苁 6042 淙 6840 骢 7085 琮 7193 璁7214 枞 7240 cou 凑 2053 楱 7308 辏 7403 腠 7577 cu 粗 2054 醋 2055 簇2056 促 2057 蔟 6193 徂 6562 猝 6607 殂 7367 酢 8501 蹙 8530 蹴 8577 cuan 蹿 2058 篡 2059 窜 2060 氽 5764 撺 6305 爨 7664 镩 7973 cui 摧 2061 崔 2062 催 2063 脆 2064 瘁 2065 粹 2066 淬 2067 翠 2068 萃 6145 啐 6393 悴 6718 璀 7213 榱 7333 毳 7505 隹 8631 cun 村 2069 存 2070 寸 2071 忖6666 皴 8169 cuo 磋 2072 撮 2073 搓 2074 措 2075 挫 2076 错 2077 厝 5640 嵯 6547 脞 7566 锉 7917 矬 7983 痤 8078 鹾 8526 蹉 8567 da 搭 2078 达2079 答 2080 瘩 2081 打 2082 大 2083耷 6239 哒 6353 嗒 6410 怛 6682 妲 7007 沓 7719 疸 8067 褡 8155 笪8346 靼 8716 鞑 8718 dai 呆 2084 歹 2085 傣 2086 戴 2087 带 2088 殆 2089 代 2090 贷 2091 袋 2092 待 2093 逮 2094 怠 2101 埭 6004 甙 6316 呔 6330 岱 6523 迨 6942 骀 7070 绐 7110 玳 7173 黛 8776 dan 耽 2102 担 2103 丹2104 单 2105 郸 2106 掸 2107 胆 2108 旦 2109 氮 2110 但 2111 惮 2112 淡2113 诞 2114 弹 2115 蛋 2116 儋 5757 萏 6144 啖 6402 澹 6903 殚 7373 赕7470 眈 7781 瘅 8087 聃 8185 箪 8376 dang 当 2117 挡 2118 党 2119 荡2120 档 2121 谠 5852 凼 5942 菪 6148 宕 6920 砀 7724 裆 8141 dao 刀 2122 捣 2123 蹈 2124 倒 2125 岛 2126 祷 2127 导 2128 到 2129 稻 2130 悼 2131 道 2132 盗 2133 叨 6322 忉 6665 氘 7514 焘 7666 纛 8478 de 德 2134 得2135 的 2136 锝 7929 deng 蹬 2137 灯 2138 登 2139 等 2140 瞪 2141 凳2142 邓 2143 噔 6466 嶝 6556 戥 7413 磴 7767 镫 7975 簦 8403 di 堤 2144 低 2145 滴 2146 迪 2147 敌 2148 笛 2149 狄 2150 涤 2151 嫡 2153 抵 2154 底 2155 地 2156 蒂 2157 第 2158 帝 2159 弟 2160 递 2161 缔 2162 氐 5621 籴 5765 诋 5814 谛 5848 邸 5901 荻 6122 嘀 6454 娣 7023 绨 7116 柢 7260 棣 7306 觌 7475 砥 7738 碲 7758 睇 7791 镝 7965 羝 8438 骶 8730 dia 嗲6439 dian 颠 2163 掂 2164 滇 2165 碘 2166 点 2167 典 2168 靛 2169 垫2170 电 2171 佃 7172 甸 2173 店 2174 惦 2175 奠 2176 淀 2177 殿 2178 阽5871 坫 5967 巅 6559 玷 7172 钿 7868 癜 8116 癫 8118 簟 8401 踮 8558 diang 仃 5674 啶 6404 diao 碉 2179 叼 2180 雕 2181 凋 2182 刁 2183 掉2184 吊 2185 钓 2186 调 2187 铞 7886 铫 7902 貂 85854 Author: 寿光 seodiao—fen 鲷 8684 die 跌 2188 爹 2189 碟 2190 蝶 2191 迭 2192 谍2193 叠 2194 佚 5693 垤 5976 堞 6006 揲 6273 喋 6409 牒 7526 瓞 8012 耋8183 蹀 8562 鲽 8688 ding 丁 2201 盯 2202 叮 2203 钉 2204 顶 2205 鼎2206 锭 2207 定 2208 订 2209 玎 7164 腚 7575 碇 7754 町 7814 铤 7890 疔8059 耵 8184 酊 8490 diu 丢 2210 铥 7891 dong 东 2211 冬 2212 董 2213 懂2214 动 2215 栋 2216 侗 2217 恫 2218 冻 2219 洞 2220 垌 5799 咚 6343 岽6520 峒 6528 氡 7517 胨 7543 胴 7556 硐 7747 鸫 8020 dou 兜 2221 抖 2222 斗 2223 陡 2224 豆 2225 逗 2226 痘 2227 蔸 6190 窦 8128 蚪 8229 篼 8391 du 都 2228 督 2229 毒 2230 犊 2231 独 2232 读 2233 堵 2234 睹 2235 赌2236 杜 2237 镀 2238 肚 2239 度 2240 渡 2241 妒 2242 芏 6022 嘟 6429 渎6834 椟 7292 牍 7525 蠹 8328 笃 8338 髑 8739 黩 8782 duan 端 2243 短2244 锻 2245 段 2246 断 2247 缎 2248 椴 7318 煅 7649 簖 8393 dui 堆 2249 兑 2250 队 2251 对 2252 怼 7701 憝 7713 碓 7752 镦 7970 dun 墩 2253 吨2254 蹲 2255 敦 2256 顿 2257 囤 2258 钝 2259 盾 2260 遁 2261 沌 6771 炖7632 砘 7727 礅 7766 盹 7779 趸 8527 duo 掇 2262 哆 2263 多 2264 夺 2265 垛 2266 躲 2267 朵 2268 跺 2269 舵 2270 剁 2271 惰 2272 堕 2273 咄 6345 哚 6365 沲 6785 柁 7262 铎 7876 裰 8154 踱 8566 e 蛾 2274 峨 2275 鹅 2276 俄 2277 额 2278 讹2279 娥 2280 恶 2281 厄 2282 扼 2283 遏 2284 鄂 2285 饿 2286 噩 5612 谔5844 垩 5949 苊 6035 莪 6113 萼 6164 呃 6332 愕 6721 阏 6753 屙 6977 婀7025 轭 7378 腭 7581 锇 7916 锷 7941 鹗 8042 颚 8206 颛 8207 鳄 8689 ei 诶 5832 en 恩 2287 蒽 6176 摁 6284 er 而 2288 儿 2289 耳 2290 尔 2291 饵2292 洱 2293 二 2294 贰 2301 佴 5706 迩 6939 珥 7177 铒 7879 鸸 8025 鲕8660 fa 发 2302 罚 2303 筏 2304 伐 2305 乏 2306 阀 2307 法 2308 珐 2309 垡 5950 砝 7732 fan 藩 2310 帆 2311 番 2312 翻 2313 樊 2314 矾 2315 钒2316 繁 2317 凡 2318 烦 2319 反 2320 返 2321 范 2322 贩 2323 犯 2324 饭2325 泛 2326 蕃 6212 蘩 6232 幡 6506 梵 7283 燔 7660 畈 7818 蹯 8576 fang 坊 2327 芳 2328 方 2329 肪 2330 房 2331 防 2332 妨 2333 仿 2334 访2335 纺 2336 放 2337 邡 5890 彷 6561 枋 7242 钫 7853 舫 8419 鲂 8648 fei 菲 2338 非 2339 啡 2340 飞 2341 肥 2342 匪 2343 诽 2344 吠 2345 肺 2346 废 2347 沸 2348 费 2349 芾 6032 狒 6584 悱 6713 淝 6839 妃 6990 绯 7119 榧 7328 腓 7572 斐 7619 扉 7673 镄 7948 痱 8082 蜚 8267 篚 8385 翡 8468 霏 8613 鲱 8678 fen 芬 2350 酚 2351 吩 2352 氛 2353 分 2354 纷 2355 坟2356 焚 2357 汾 2358 粉 2359 奋 23605 Author: 寿光 seofen—gu 份 2361 忿 2362 愤 2363 粪 2364 偾 5739 瀵 6915 棼 7291 鲼8687 鼢 8787 feng 丰 2365 封 2366 枫 2367 蜂 2368 峰 2369 锋 2370 风2371 疯 2372 烽 2373 逢 2374 冯 2375 缝 2376 讽 2377 奉 2378 凤 2379 俸5726 酆 5926 葑 6155 唪 6384 沣 6767 砜 7731 fo 佛 2380 fou 否 2381 缶8330 fu 夫 2382 敷 2383 肤 2384 孵 2385 扶 2386 拂 2387 辐 2388 幅 2389 氟 2390 符 2391 伏 2392 俘 2393 服 2394 浮 2401 涪 2402 福 2403 袱 2404 弗 2405 甫 2406 抚 2407 辅 2408 俯 2409 釜 2410 斧 2411 脯 2412 腑 2413 府 2414 腐 2415 赴 2416 副 2417 覆 2418 赋 2419 复 2420 傅 2421 付 2422 阜 2423 父 2424 腹 2425 负 2426 富 2427 讣 2428 附 2429 妇 2430 缚 2431 咐 2432 匐 5775 凫 5776 郛 5914 芙 6029 苻 6062 茯 6082 莩 6119 菔 6142 拊 6252 呋 6327 幞 6505 怫 6686 滏 6870 艴 6985 孚 7058 驸 7066 绂 7106 绋 7108 桴 7285 赙 7471 祓 7680 砩 7741 黻 7774 黼 7775 罘 7823 稃 7991 馥 8005 蜉 8261 蝠 8280 蝮 8283 麸 8479 趺 8535 跗 8538 鲋 8654 鳆 8691 ga 噶 2433 嘎 2434 伽 5704 尬 6246 尕 7056 尜 7057 旮 7424 钆 7837 gai 该 2435 改 2436 概 2437 钙 2438 盖 2439 溉 2440 丐 5604 陔 5875 垓 5982 戤 7414 赅 7464 gan 干 2441 甘 2442 杆 2443 柑 2444 竿 2445 肝 2446 赶2447 感 2448 秆 2449 敢 2450 赣 2451 坩 5965 苷 6053 尴 6247 擀 6306 泔6779 淦 6838 澉 6887 绀 7104 橄 7347 旰 7426 矸 7723 疳 8065 酐 8491 gang 冈 2452 刚 2453 钢 2454 缸 2455 肛 2456 纲 2457 岗 2458 港 2459 杠2460 戆 7716 罡 7824 筻 8364 gao 篙 2461 皋 2462 高 2463 膏 2464 羔 2465 糕 2466 搞 2467 镐 2468 稿 2469。
清代-曹雪芹《芙蓉女儿诔》原文、译文及注释原文：芙蓉女儿诔清代-曹雪芹维太平不易之元，蓉桂竞芳之月，无可奈何之日，怡红院浊玉，谨以群花之蕊，冰鲛之縠，沁芳之泉，枫露之茗，四者虽微，聊以达诚申信，乃致祭于白帝宫中抚司秋艳芙蓉女儿之前曰：窃思女儿自临浊世，迄今凡十有六载。
其先之乡籍姓氏，湮沦而莫能考者久矣。
而玉得于衾枕栉沐之间，栖息宴游之夕，亲昵狎亵，相与共处者，仅五年八月有畸。
忆女儿曩生之昔，其为质则金玉不足喻其贵，其为性则冰雪不足喻其洁，其为神则星日不足喻其精，其为貌则花月不足喻其色。
姊娣悉慕媖娴，妪媪咸仰惠德。
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镜分鸾别，愁开麝月之奁；梳化龙飞，哀折檀云之齿。
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桐阶月暗，芳魂与倩影同销；蓉帐香残，娇喘共细言皆绝。
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芳名未泯，檐前鹦鹉犹呼；艳质将亡，槛外海棠预萎。
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抛残绣线，银笺彩缕谁裁？褶断冰丝，金斗御香未熨。
昨承严命，既趋车而远涉芳园；今犯慈威，复拄杖而近抛孤柩。
及闻櫘棺被燹，惭违共穴之盟；石椁成灾，愧迨同灰之诮。
尔乃西风古寺，淹滞青燐，落日荒丘，零星白骨。
楸榆飒飒，蓬艾萧萧。
隔雾圹以啼猿，绕烟塍而泣鬼。
集腋成裘jí yè chéng qiú×12 危如累卵wēi rú lěi luǎn√3 蹿红cuān hóng√4 拓扑学tuò pū xué√5 菽粟shū sù×6 刮痧guā shā×7 鳕鱼xuě yú×8 芦笙lú shēng×9 羸弱léi ruò√10 豇豆jiāng dòu√11 荸荠bí qi √12 锒铛入狱láng dāng rù yù√13 莞尔一笑wǎn ěr yī xiào×14 金兀术Jīn Wù Zhú×15 甘霖gān lín√16 怙恶不悛hù è bù quān√17 蛲虫náo chóng ×18 苏洵Sū Xún√19 髭须zī xū×20 耄耋之年mào dié zhī nián√21 鳄梨è lí×22 戗面馒头 qiàng miàn mán tou √23 戎马倥偬róng mǎ kǒng zǒng√24 谄谀chǎn yú√25 痈疽yōng jū×26 扶乩fújī√27 东施效颦dōng shī xiào pín√28 缱绻qiǎn quǎn√29 膻腥shān xīng√30 蓬荜生辉péng bì shēng huī√31 旖旎yǐ nǐ√32 忝列门墙tiǎn liè mén qiáng×33 鸸鹋ér miáo √34 卖官鬻爵mài guān yù jué√35 芒砀山Máng Dàng Shān×36 荫翳/阴翳yīn yì√37 礌石léi shí√38 皋陶Gāo Yáo×39 袍笏登场páo hù dēng chǎng√40 干哕gān yue√41 祭酹jì lèi ×42 龙骧虎峙lóng xiāng hǔ zhì×43 谢道韫Xiè Dào Yùn ×第3期复赛第三场中国汉字听写大会 复赛第一场未w èi 雨y ǔ 绸ch óu 缪m óu绸缪：紧密缠缚。
1 Pricing Differentiated Services:A Game-Theoretic ApproachEitan Altman Dhiman Barman Rachid El Azouzi David Ros Bruno TuffinAbstract—The goal of this paper is to study pricing of differentiated services and its impact on the choice of service priority at equilibrium.We consider both TCP connections as well as non controlled(real time) connections.The performance measures(such as throughput and loss rates)are determined according to the operational parameters of a RED buffer management. The latter is assumed to be able to give differentiated services to the applications according to their choice of service class.We consider a best effort type of service differentiation where the QoS of connections is not guaranteed,but by choosing a better(more expensive) service class,the QoS parameters of a session can improve (as long as the service class of other sessions arefixed). The choice of a service class of an application will depend both on the utility as well as on the cost it has to pay.We first study the performance of the system as a function of the connections’parameters and their choice of service classes.We then study the decision problem of how to choose the service classes.We model the problem as a noncooperative game.We establish conditions for an equilibrium to exist and to be uniquely defined.We further provide conditions for convergence to equilibrium from non equilibria initial states.Wefinally study the pricing problem of how to choose prices so that the resulting equilibrium would maximize the network benefit.Keywords:TCP,Buffer Management,RED/AQM, Nash equilibrium,Pricing,Mathematical program-ming/optimization,EconomicsI.I NTRODUCTIONWe study in this paper the performance of com-peting connections that share a bottleneck link.Both TCP connections with controlled rate as well as CBR Address:INRIA,B.P.93,2004Route des Lucioles,06902, Sophia-Antipolis Cedex.The work of these authors was supported by a research contract with France Telecom R&D001B001.111Cummington Street,Dept.of Computer Science,Boston University,Boston,MA02215,USA.The work of this author was performed during internship at INRIA,financed by the INRIA’s PrixNet ARC collaboration projectGET/ENST Bretagne,Rue de la chˆa taigneraie CS17607,35567 Cesson S´e vign´e Cedex,FranceIRISA/INRIA,Campus Universitaire de Beaulieu,35042Rennes Cedex,France (Constant Bit Rate)connections are considered.A RED buffer management is used for early drop of packets. We allow for service differentiation between the con-nections through the rejection probability(as a function of the average queue size),which may depend on the connection(or on the connection class).More specif-ically,we consider a buffer management scheme that uses a single averaged queue length to determine the rejection probabilities(similar to the way it is done in the RIO-C(coupled RIO)buffer management,see[9]); for any given averaged queue size,packets belonging to connections with higher priority have smaller probability of being rejected than those belonging to lower priority classes.To obtain this differentiation in loss probabilities, we assume that the loss curve of RED is scaled by a factor that represents the priority level of the application. We obtain various performance measures of interest such as the throughput,the average queue size and the average drop probability.We then address the question of the choice of pri-orities.Given utilities that depend on the performance measures on one hand and on the cost for a given priority on the other hand,the sessions at the system are faced with a non-cooperative game in which the choice of priority of each session has an impact on the quality of services of other sessions.For the case of CBR traffic, we establish conditions for an equilibrium to exist.We further provide conditions for convergence to equilibrium from non equilibria initial states.We shallfinally study numerically the pricing problem of how the network should choose prices so that the resulting equilibrium would maximize its benefit.We briefly mention some recent work in that area. Reference[5]has considered a related problem where the traffic generated by each session was modeled as a Poisson process,and the service time was exponentially distributed.The decision variables were the input rates and the performance measure was the goodput(output rates).The paper restricted itself to symmetric users and symmetric equilibria and the pricing issue was not considered.In this framework,with a common RED buffer,it was shown that an equilibrium does not exist. An equilibrium was obtained and characterized for an2 alternative buffer management that was proposed,calledVLRED.We note that in contrast to[5],since we alsoinclude in the utility of CBR traffic a penalty for losses(which is supported by studies of voice quality in packet-based telephony[6]),we do obtain an equilibrium whenusing RED.For other related papers,see for instance[8] (in which a priority game is considered for competing connections sharing a drop-tail buffer),[1]as well as the survey[2].In[13],the authors present mechanisms (e.g.,AIMD of TCP)to control end-user transmission rate into differentiated services Internet through poten-tial functions and corresponding convergence to Nash equilibrium.The approach of our pricing problem is related to the Stackelberg methodology for hierarchical optimization: for afixed pricing strategy one seeks the equilibrium among the users(the optimization level corresponding to the“follower”),and then the network(considered as the“leader”)optimizes the pricing strategy.This type of methodology has been used in other contexts of networking in[3],[7].The structure of this paper is as follows.In Section II we describe the model of RED,then in Section III we compute the throughputs and the loss probabilities of TCP and of CBR connections for given priorities chosen by the connections.In Section IV we introduce the model for competition between connections at given prices.In section V we focus on the game in the case of only CBR connections or only TCP connections and provide properties of the equilibrium:existence,uniqueness and convergence.In section VI we provide an algorithm for computing Nash equilibrium for symmetric case.The optimal pricing is then discussed in Section VII.We present numerical examples in sectionVIII to validate the model.II.T HE MODELRED is based on the following idea:there are two thresholds and such that the drop probability is0if the average queue length is less than,1 if it is above,and if it is with;the latter is the conges-tion avoidance mode of operation.This is illustrated in Figure1.We consider a set containing TCPflows(or aggregate offlows)and a set containing real time flows that can be differentiated by RED;they all share a common buffer yet RED treats them differently1.We assume that they all have common values of and 1RED punishes aggressiveflows more by dropping more packets from thoseflows dropprobability1q qmin maxaverage queue length p(i)Fig.1.Drop probability in RED as functionbut eachflow may have a different value of, which is the value of the drop probability as the average queue tends to(from the left).In other words,the slope of the linear part of the curve in Figure1depends theflow:(1)where and are TCPflow’s round trip time and drop probability,respectively.is typically taken as (when the delayed ack option is disabled)or(when it is enabled).We shall assume throughout the paper that the queueing delay is negligible with respect to for the TCP connections.In contrast,the rates,for,of real timeflows are not controlled and are assumed to befixed.Ifwe assume throughout the paper that(unless otherwise specified),otherwise the RED buffer is not a bottleneck.Similarly,if we assume that TCP senders are not limited by the receiver window.In general,since the bottleneck queue is seen as afluid queue,we can writeIf we operate in the linear part of the RED curve then this leads to the system of equations:3 with()unknowns:(average queue length),and,where,is given by(1).Substituting(1)and(2) into thefirst equation of the above set,we obtain a single equation for:,then(3)can be written as a cubic equation in:(4) whereNote that,in the case of only real-time connections ()operating in the linear region,we have(6) (Recall that,throughout the paper,when considering this case we shall assume that.)In the case of only TCP connections()operating in the linear region,we haveand(7)(8)IV.U TILITY,PRICING AND EQUILIBRIUMWe denote a strategy vector by t for allflows such that th entry is.By(),we define a strategy whereflow uses and all otherflows use from vector.We associate toflow a utility.The utility will be a function of the QoS parameters and the price payed byflow,and is determined by the actions of allflows. More precisely,is given bywhere thefirst term stands for the utility for the goodput, the second term stands for the dis-utility for the loss rate and the last term corresponds to the price to be paid byflow to the network.In particular,wefind it natural to assume that a TCP flow has(as lost packets are retransmitted anyhow,and their impact is already taken into account in the throughput).Moreover,since for TCP already includes the loss term,the utility function of TCP is assumed to be4We assume that the strategies or actions available to session are given by a compact set of the form: Eachflow of the network strives tofind its best strategy so as to maximize its own objective function. Nevertheless its objective function depends upon its own choice but also upon the choices of the otherflows.In this situation,the solution concept widely accepted is the concept of Nash equilibrium.Definition1:A Nash equilibrium of the game is a strategy profile wherefrom which noflow have any incentive to deviate.More precisely the strategy profile,is a Nash equilibrium,if the following holds true for anyis the bestflow can do if the otherflows choose the strategies.Note that the network income is given by. Since the’s are functions of and, can include pricing per volume of traffic successfully transmitted.In particular,we allow for to depend on the uncontrolled arrival rates of real-time sessions(but since these are constants,we do not make them appear as an argument of the function).We shall sometimesfind it more convenient to rep-resent the control action of connection as instead of as.Clearly,properties such as existence or uniqueness of equilibrium in terms of directly imply the corresponding properties with respect to.V.E QUILIBRIUM FOR ONLY R EAL-T IME SESSIONSOR ONLY TCP CONNECTIONSWe assume throughout thatfor all connections.The bound for is given so that we have.From(2)we see that with equality obtained only for the case.2In our analysis,we are interested mainly in the lin-ear region.For only real-time sessions or only TCP connections,we state the assumptions and describe the conditions for linear region operations and we show the existence of a Nash equilibrium.2Note that if the assumption does not hold then for some value we would already have for some so one could redefine to be.An important feature in our model is that the queue length beyond which should be the same for all.Theorem1:A sufficient condition for the system to operate in linear region is that for all:1-For only real time connections:and(10)where and.Proof:The condition(9)(resp.(10))will ensure that the value of obtained in the linear region(see(5) (resp.(7)))is not larger that.Indeed,for real time connections,(9)implies thatThe following result establishes the existence of Nash equilibrium for only real time sessions or only TCP connections.Theorem2:Assume that the functions are convex in.Then a Nash equilibrium exists. Proof:See Appendix X-B.A.Supermodular GamesIn Theorem3(resp.Theorem5)we present alterna-tive conditions that provide sufficient conditions for a supermodular structure for real-time connections(resp. for only TCP connections).This implies in particular the existence of an equilibrium.Another implication of su-permodularity is that a simple,so-called tatˆo nnement or Round Robin scheme,for best responses converges to the equilibrium.To describe it,we introduce the following asynchronous dynamic greedy algorithm(GA). Greedy Algorithm:Assume a given initial choice for allflows.At some strictly increasing times, ,flows update their actions;the actions at time are obtained as follows.A singleflow at time updates its so as to optimizewhere is the vector of actions of the otherflows .We assume that eachflow updates its actionsinfinitely often.In particular,for the case of only real time sessions,we update as follows:(11) where in(11)is given by(6).For the TCP-only case,we update as follows:which will lead to update of as follows,and corresponds to utility function of real time session.Then is given by:ifififotherwiseTheorem3:For the case of only real-time connections we assume that,,andwhere and. Then there is smallest equilibrium,and the GA dynamic algorithm converges toleading toIt is non-positive if and only if.A sufficient condition is that Thus the game is super-modular.The result then follows from standard theory of super-modular games[11],[12].(13) Then the game is super-modular.Proof:See Appendix X-D.where denotes(with some abuse of notation)the strat-egy where allflows use,and where the maximization is taken with respect to.Then is a symmetric equilibrium ifTheorem6:Consider real time connections operating in linear region.The symmetric equilibrium satisfies:(15)where andwhich gives when taking the derivativewe obtain(15).To ensure that the symmetric TCPflows operate inthe linear region,we satisfy the condition on.E.Real-time connections and TCPflowsIn this experiment,we combine both real-time andTCP connections.We have,,Mbps,RTT=10ms,Mbps.The highest network revenue is achieved at.In the simulations,we9IX.C ONCLUSIONS AND F UTURE W ORKWe have studied in this paper afluid model of the RED buffer management algorithm with different drop probabilities applied to both UDP and TCP traffic.We first computed the performance measures forfixed drop policies.We then investigated how the drop policies are also convergence properties of best-response dynamics). The equilibrium depends on the pricing strategy of the network provider.Wefinally addressed the problem of optimizing the revenue of the network provider. Concerning the future work,we are working on de-riving sufficient and necessary conditions for operating at the linear region when there are both real time and10other versions of RED will be considered as the gentle-RED variant).We will also examine well thefluid model is suitable for the packet-level that it approximates.R EFERENCEST.Alpcan and T.Basar,“A game-theoretic framework for congestion control in a general topology networks”,41st IEEE Conference on Decision and Control,Las Vegas,Nevada,Dec. 10-13,2002.E.Altman,T.Boulogne,R.El Azouzi,T.Jimenez and L.Wynter,“A survey on networking games”, Telecommunication Systems,2000,under revision.Available at http://www-sop.inria.fr/mistral/personnel/ Eitan.Altman/ntkgame.htmlT.Basar and R.Srikant,“A Stackelberg network game with a large number of followers”,J.Optimization Theory and Applications,115(3):479-490,December2002F.Bernstein and A.Federgruen,“A general equilib-rium model for decentralized supply chains with price-and service-competition”,Available at http://faculty./˜fernando/bio/D.Dutta,A.Goel and J.Heidemann,“Oblivious AQM and Nash Equilibria”,IEEE Infocom,2003.J.Janssen,D.De Vleeschauwer,M.B¨u chli and G.H.Petit,“Assessing voice quality in packet-based telephony”,IEEE Internet Computing,pp.48–56,May–June,2002.Y.A.Korilis,zar and A.Orda,“Achieving network optima using Stackelberg routing strategies”,IEEE/ACM Trans-actions on Networking,5(1),pp.161–173,1997.M.Mandjes,“Pricing strategies under heterogeneous service requirements”,Computer Networks42,pp.231–249,2003. P.Pieda,J.Ethridge,M.Baines and F.Shallwani,A Network Simulator Differentiated Services Implementation,Open IP, Nortel Networks,July,2000.Available at http://www.isi.edu/nsnam/nsJ.B.Rosen,“Existence and uniqueness of equilibrium points for concave N-person games”,Econometrica,33:153–163, 1965.D.Topkis,“Equilibrium points in nonzero-sum n-person sub-modular games”,SIAM J.Control and Optimization,17:773–787,Nov.1979.D.D.Yao,“S-modular games with queueing applications”, Queueing Systems,21:449–475,1995.Youngmi Jin and Geroge Kesidis,“Nash equilibria of a generic networking game with applications to circuit-switched networks”,IEEE INFOCOM’03Numerical Recipes in C,The Art of Scientific Computing,2nd Edition,Section5.6/webRoot/ Books/Numerical_Recipes/bookc.html1X.A PPENDIXProof of part2of Theorem1For only TCP connections,we have,11From equation(7),we get the following sufficient and necessary condition for:or equivalently,A sufficient condition for the latter iswhich is convex in.Hence are concave in and continuous in.The existence then follows from[10]. For TCP connection,we have(18)where.On the other hand,(1)impliesandThen(18)becomes(19) Since the function is convex in,then form(19), it suffices to show that the second derivative of with respect to is non-positive.We havewhere and.Now,we must prove that the second derivative of the functions and are non-positive for all and.We begin by taking the second derivative of.After some simplification,we obtain12 which is positive.For the second function,since thefunction is positive,it suffices to show that the secondderivative of function is non-positive,we havewhich is non-positive.C.Proof of Theorem4Under supermodular condition,to show the unique-ness of Nash equilibrium,it suffices to show that[4],(20) or equivalently,(21) For the case of only real time sessions,.We have,This leads to the sufficient condition:.It follows that Thus a sufficient condition for supermodularity (。
声律启蒙上卷一东云y ún 对du ì雨y ǔ，雪xu ě 对du ì风f ēng ，晚w ǎn 照zh ào 对du ì晴q íng 空k ōng 。
来l ái 鸿h ïng 对du ì去q ù 燕y àn ，宿s ù 鸟ni ǎo 对du ì 鸣m íng 虫ch ïng 。
三s ān 尺ch ǐ剑ji àn ，六li ù钧j ūn 弓g ōng ，岭l ǐng 北b ěi 对du ì江ji āng 东d ōng 。
人r ãn 间ji ān 清q īng 暑sh ǔ殿di àn ，天ti ān 上sh àng 广gu ǎng 寒h án 宫g ōng 。
两li ǎng 岸àn 晓xi ǎo 烟y ān 杨y áng 柳li ǔ 绿l ǜ，一y ì 园yu án 春ch ūn 雨y ǔ 杏x ìng 花hu ā 红h ïng。
两li ǎng 鬓b ìn 风f ēng 霜shu āng ，途t ú 次c ì早z ǎo 行x íng 之zh ī 客k â；一y ì 蓑su ō 烟y ān 雨y ǔ，溪x ī 边bi ān 晚w ǎn 钓di ào 之zh ī 翁w ēng 。
沿y án 对du ì 革g ã，异y ì 对du ì 同t ïng ，白b ái 叟s ǒu 对du ì 黄hu áng 童t ïng 。
江ji āng 风f ēng 对du ì 海h ǎi 雾w ù，牧m ù 子z ǐ对du ì 渔y ú 翁w ēng 。
离lí 骚sāo先xiān 秦qín · 屈qū 原yuán帝dì 高gāo 阳yáng 之zhī 苗miáo 裔yì 兮xī ， 朕zhèn 皇huáng 考kǎo 曰yuē 伯bó 庸yōng。
摄shè 提tí 贞zhēn 于yú 孟mèng 陬zōu 兮xī ， 惟wéi 庚gēng 寅yín吾wú 以yǐ 降hōng。
皇huáng 览lǎn 揆kuí 余yú 初chū 度dù 兮xī ， 肇zhào 锡xī 余yú 以yǐ嘉jiā 名míng。
名míng 余yú 曰yuē 正zhèng 则zé 兮xī ， 字zì 余yú 曰yuē 灵líng均jūn。
纷fēn 吾wú 既jì 有yǒu 此cǐ 内nèi 美měi 兮xī ， 又yòu 重chóng 之zhī以yǐ 修xiū 能n éng。
扈hù 江jiāng 离lí 与yǔ 辟pì 芷zhǐ 兮xī ， 纫rèn 秋qiū 兰lán 以yǐ为wéi 佩pèi。
汩gǔ 余yú 若ruò 将jiāng 不bù 及jí 兮xī ， 恐kǒng 年nián 岁suì之zhī 不bù 吾wú 与yǔ。
朝cháo 搴qiān 阰pí 之zhī 木mù 兰lán 兮xī ， 夕xī 揽lǎn 洲zhōu之zhī 宿xiǔ 莽mǎng。
声sh ēng 律l ǜ启q ǐ蒙m ēng 拼p īn 音y īn一y ì 东d ōng云y ún 对du ì雨y ǔ，雪xu ě 对du ì风f ēng ，晚w ǎn 照zh ào 对du ì晴q íng 空k ōng 。
来l ái 鸿h ïng 对du ì去q ù 燕y àn ，宿s ù 鸟ni ǎo 对du ì 鸣m íng 虫ch ïng 。
三s ān 尺ch ǐ剑ji àn ，六li ù钧j ūn 弓g ōng ，岭l ǐng 北b ěi 对du ì江ji āng 东d ōng 。
人r ãn 间ji ān 清q īng 暑sh ǔ殿di àn ，天ti ān 上sh àng 广gu ǎng 寒h án 宫g ōng 。
两li ǎng 岸àn 晓xi ǎo 烟y ān 杨y áng 柳li ǔ 绿l ǜ，一y ì 园yu án 春ch ūn 雨y ǔ 杏x ìng 花hu ā 红h ïng。
两li ǎng 鬓b ìn 风f ēng 霜shu āng ，途t ú 次c ì早z ǎo 行x íng 之zh ī 客k â；一y ì 蓑su ō 烟y ān 雨y ǔ，溪x ī 边bi ān 晚w ǎn 钓di ào 之zh ī 翁w ēng 。
沿y án 对du ì 革g ã，异y ì 对du ì 同t ïng ，白b ái 叟s ǒu 对du ì 黄hu áng 童t ïng 。
NTE5461 thru NTE5468Silicon Controlled Rectifier (SCR)10 AmpDescription:The NTE5461 through NTE5468 series silicon controlled rectifiers are designed primarily for half–wave AC control applications such as motor controls, heating controls, and power supplies; or wher-ever half–wave silicon gate–controlled, solid–state devices are needed. These devices are supplied in a TO220 type package.Features;D Glass Passivated Junctions and Center Gate Fire for Greater Parameter Uniformity and Stability D Small, Rugged, Thermowatt Construction for Low Thermal Resistance, High Heat Dissipation,and DurabilityD Blocking Voltage to 800 VoltsAbsolute Maximum Ratings:Peak Repetitive Reverse Voltage; Peak Repetitive Off–State Voltage (Note 1), V RRM, V DRM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .NTE546150V . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .NTE5462100V NTE5463200V . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .NTE5465400V NTE5466600V . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .NTE5468800V Non–Repetitive Peak Reverse Voltage; Non–Repetitive Off–State Voltage, V RSM, V DSM. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .NTE546175V NTE5462125V . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .NTE5463250V . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .NTE5465500V . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .NTE5466700V . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .NTE5468900V. . . . . . . . . . . . . . . . . . . . . . RMS Forward Current (All Conducting Angles, T C = +75°C), I T(RMS)10A. . . . . . . . . . . . . . Peak Forward Surge Current (1 Cycle, Sine Wave, 60Hz, T C = +80°C), I TSM100A. . . . . . . . . . . . . . . . . Circuit Fusing Considerations (T J = –65° to +100°C, t = 1 to 8.3ms), I2t40A2s. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Forward Peak gate Power (t ≤ 10µs), P GM16W. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Forward Average Gate Power, P G(AV)500mW. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Operating Junction Temperature Range, T J–40° to +100°C. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Storage Temperature Range, T stg–40° to +150°C. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Thermal Resistance, Junction–to–Case, R thJC2°C/W Note 1.V DRM and V RRM for all types can be applied on a continuous DC basis without incurring dam-age. Ratings apply for zero or negative gate voltage. Devices shall not have a positive bias applied to the gate concurrently with a negative potential on the anode.Electrical Characteristics: (T= +25°C unless otherwise specified)。
《声律启蒙》注音版《声律启蒙》注音版(上)一yī 东dōng云yún 对duì 雨yǔ，雪xuě 对duì 风fēng ，晚wǎn 照zhào 对duì 晴qíng 空kōng 。
来l ái 鸿hïng 对duì 去qù 燕yàn ，宿su 鸟niǎo 对duì 鸣míng 虫chïng 。
三sān 尺chǐ 剑jiàn ，六liù 钧jūn 弓gōng ，岭lǐng 北běi 对duì 江jiāng 东dōng 。
人rãn 间jiān 清qīng 暑shǔ 殿diàn ，天tiān 上shàng 广guǎng 寒hán 宫gōng 。
两liǎng 岸àn 晓xiǎo 烟yān 杨yáng 柳liǔ 绿lǜ，一yī 园yuán 春chūn 雨yǔ 杏xìng 花huā 红hïng 。
两liǎng 鬓bìn 风fēng 霜shuāng ， 途tú 次cì早zǎo 行xing 之zhī 客kâ；一yī蓑suō烟yān 雨yǔ，溪xī边biān 晚wǎn 钓diào 之zhī 翁wēng 。
沿yán 对duì 革gã，异yì 对duì 同tong ，白bái 叟sǒu 对duì 黄huáng 童tïng 。
江jiāng 风fēng 对duì海hǎi 雾wù，牧mù 子zǐ 对duì 渔yú翁wēng 。
颜yán 巷xiàng 陋lîu ，阮ruǎn 途tú 穷qiïng ，冀jì 北běi 对duì 辽liáo 东dōng 。
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Marginal Boosting1 Gunnar R¨atsch2Manfred K.Warmuth3NeuroCOL T2Technical Report SeriesNC2-TR-2001-097August7,2001Produced as part of the ESPRIT Working Group in Neural and ComputationalLearning II,NeuroCOL T227150For more information see the NeuroCOLT websiteor email neurocolt@1Supported by DFG grant MU987/1-1,EU-Neurocolt II and NSF grant CCR9821087. 2raetsch@first.gmd.de GMD FIRST,Kekul´e str.7,12489Berlin,Germany3manfred@ University of California at Santa Cruz,CA95060,USAIntroduction1AbstractAdaBoost produces a linear combination of weak hypotheses.It has been observed in practice that the generalization error of the algorithm continues toimprove even after all examples are classified correctly by the current linear com-bination,i.e.by a hyperplane in feature space where each weak hypothesis is adimension.The improvement is attributed to the experimental observation thatthe distances(margins)of the examples to the separating hyperplane are increas-ing even when the training error is already zero,i.e.all examples are on thecorrect side of the hyperplane.We give an iterative version of AdaBoost that ex-plicitly maximizes the minimum margin of the examples.We bound the numberof iterations and the number of hypotheses used in thefinal linear combinationwhich approximates the maximum margin hyperplane with a certain precision.This result is shown to be independent from the size of the hypothesis class–even infinite hypothesis classes are allowed.1IntroductionIn the most common version of boosting the algorithm is given afixed set of labeledtraining examples.In each stage the algorithm produces a probability weighting on theexamples.It then is given a weak hypothesis whose error(probability of wrong clas-sification)is slightly below50%,which is used to update the distribution.Intuitively,the hard examples receive high weights.At end of each stage the weak hypothesis isadded to the linear combination,which forms the current hypothesis of the boostingalgorithm.The most well known boosting algorithm is AdaBoost[4].It adapts the linear co-efficient of the weak hypothesis to the error of the weak hypothesis.Earlier work onboosting includes[12,2].AdaBoost has two redeeming properties.First,along withearlier boosting algorithms[12],it has the property that its training error convergesexponentially fast to zero.More precisely,if the training error of the-th weak learneris,then an upper bound on the training error of the linear combination isreduced by a factor of at stage.Second,it has been observed experimentallythat AdaBoost continues to“learn”even after the training error of the linear combina-tion is zero[13],i.e.in experiments the generalization error is continuing to improve.When the training error is zero,then all examples are on the“right side”of the linearcombination(viewed as a hyperplane in a feature space,where each base hypothesisis one dimension).The margin of an example is the signed distance to the hyperplanetimes its label.As soon as the training error is zero,the examples are on the right side and have positive margin.It has also been observed that the margins of the ex-amples continue to increase even after the training error is zero.There are theoreticalbounds on the generalization error of linear classifiers(e.g.[13,1])that improve withthe size of the minimum margin of the examples.So the fact that the margins improveexperimentally seems to explain why AdaBoost still learns after the training error iszero.There is one shortfall in this argument.AdaBoost has not been proven to maximizethe minimum margin of the examples.In fact,in our experiments in Section4weobserve that AdaBoost does not seem to maximize the margin.Breiman[1]proposeda modified algorithm–Arc-GV(Arc ing-G ame V alue)–suitable for this task andMarginal Boosting2 showed that it asymptotically maximizes the margin.In this paper we propose an algorithm that maximizes the margin up to a given accuracy.We prove exponential convergence rates to the maximum margin solution in terms of and the sample size .To our knowlegde,this is thefirst result on the non-asymptotical convergence of a boosting algorithm to the maximum margin solution.The paper is structured as follows:In Section2wefirst extend the original Ada-Boost algorithm leading to AdaBoost.Then we propose Marginal AdaBoost,which uses AdaBoost as a subroutine.In Section3we give a more detailed analysis of both algorithms.First,we prove that if the training error of the-th weak learner is ,then an upper bound on the fraction of examples with margin smaller than is reduced by a factor of at stage of AdaBoost(cf.Theorem2 and Corollary3).Exploiting this property,we prove the exponential convergence rate of our algorithm(cf.Theorem4).We complete the paper with experiments confirming our theoretical analysis(Section4)and a conclusion.2Marginal BoostingFor AdaBoost it has been shown that it quickly generates a combined hypothesis Algorithm1The AdaBoost algorithm.,,andMarginal Boosting3 ),AdaBoost would converge fast to a combined hypothesis with a near maximum margin.The details are given in Section3.2.Since one does not know the value of beforehand,one also needs tofind. We propose an algorithm that constructs a sequence converging to:A fast way tofind a real value up to a certain accuracy on the interval is to use a binary search–one needs only steps.Our idea is to use the binary search tofind,where we call Algorithm1to decide whether the current guess is larger or smaller than.This leads to Algorithm2.The algorithm proceeds in iterations,where is determined by the accuracy that we would like to reach:In each iteration it calls AdaBoost(cf.step3a in Algorithm2),where is chosen to be in the middle of an interval(cf.step3c). Based on the success of AdaBoost to achieve a large enough margin,the interval is updated(cf.step3b).We can show that the interval is chosen such that it always contains,the unknown maximal margin,while the length of the interval is almost reduced by a factor of two.Finally,in the last step of the algorithm,one has reached a good estimate of and calls AdaBoost for generating a combined hypothesis with margin at least.In the next section we will give a detailed analysis of how the algorithm works and how many calls to the base learner are needed to approximate:in the worst case one needs about times more iterations than in the case where we already know .Since AdaBoost is used as a sub-routine and starts from the beginning in each round,one can think of several speed-ups,which might help to reduce the compu-tation time.For instance,one could store the base hypotheses of previous iterations and instead of calling the base learner,onefirst sifts through these previously used hy-potheses.Furthermore,one may stop AdaBoost,when the combined hypothesis has reached a margin of or the base learner returns a hypothesis with edge lower than.1.Input:,Accuracy2.Initialize:,,,,.3.Do for,(a)(b)if,then,else,(c)Detailed Analysis4 3Detailed Analysis3.1Weak learning and marginsThe standard assumption made on the base learning algorithm in the PAC-Boosting setting is that it returns a hypothesis from afixed set that is slightly better than random guessing on any training set.1More formally this means that the error rate is consistently smaller than.Note that the error rate of1In the PAC setting,the base learner is allowed to fail with probability.Since we are seeking for simple presentation,we ignore this fact here.Our algorithm can be extended to this case.Detailed Analysis5 Note,for AdaBoost it sofar has only been shown that it asymptotically achievesa margin of at least[11].We aim tofind an algorithm that approximates themaximum margin solution up to any precision in few iterations.3.2Convergence properties of AdaBoostWe now analyze a slightly generalized version of Algorithm1,where is notfixed but could be adapted in each iteration.We therefore consider sequences,which might either be specified before running the algorithm or computed based on results during the algorithm.For instance,the idea proposed by[1]is to set,which leads to Arc-GV.We are answering the question how good AdaBoost is able to increase the margin and bound the fraction of examples, which have a margin smaller than say.This leads to a theorem generalizing Thm.5 in[4]for the case:Theorem2([11,9]).Let be the edges of that are generated by Algorithm1and for.Then for all(3)where is the binary relative entropy.Thus,the algorithm makes progress reducing the rhs.of(2),if the term under the square-root is smaller then one.This is e.g.the case if large compared to and or,by(3),if(cf.step4in Algorithm1).The larger,the more progress one makes.Suppose we would like the reach a margin on all training examples,where we obviously need to assume.Then the question arises,which sequence ofone should use tofind a combined hypothesis in as few iterations as possible.One can boundsteps.Detailed Analysis6 Proof.We use(3)for,yielding(3)where we use a bound on the binary entropy.If,there is no example left with margin smaller than,which proves the corollary.3.3Convergence of Marginal AdaBoostSo far we have always considered the case where we already know some proper value of.Let us now assume the case that the maximum achievable margin is and we would like to achieve a margin of.Our algorithm guesses a value of starting with.We need to understand what happens if our guess is too high,i.e.,or too low,i.e.?First,if,then one cannot reach the margin of since the maximum achiev-able margin is.By Corollary3,if AdaBoost has not reached a margin of at least in steps,we can conclude that(cf.step(3b)in Algorithm2).This is the worst case.The better case is,if the distribution generated by AdaBoost will be too difficult for the base learner and it eventually fails to achieve an edge of at least and AdaBoost will stop(cf.stopping condition in Algorithm1).Second,assume is chosen to low,say,then one achieves a margin of in a few steps by Corollary3.Since the maximum margin is always greater that a certain achieved margin,one can conclude that(cf.step(3b)in Algorithm2).Note that there is a small gap in the proposed binary search procedure:We are not able to identify the case efficiently.This means that we cannot reduce the length of the search interval by exactly a factor of two in each iteration.This makes the analysis slightly more difficult,but eventually leads to the following theorem on the worst case performance of Marginal AdaBoost:Theorem4.Assume the base learner always achieves an edge.Then Algo-rithm2willfind a combined hypothesis that maximizes the margin up to accuracyin at mostbase hypotheses.Proof.See Algorithm2for definitions of.We claim that in any iteration.We show,if, then for all.It holds(induction start).By assumption and we may set.By Theorem1holds for all and,hence,we may set.We have to consider two cases.(a)and(b).In case(b)we have an additional term in,which follows from,justified byand Theorem2.By construction,the length of interval is(almost)decreased in each itera-tion by a factor of two.We show.In case(a)the interval is reduced by at least a factor of two.The worst case is if always(b)happens:Detailed Analysis7Experimental Illustration8 Theorem6(Strong Min-Max).If is compact,then .In general this requirement can be fulfilled by base learning algorithms whose outputs continuously depend on the distribution.Furthermore,the outputs of the hy-potheses need to be bounded(cf.step3a in Algorithm1).Thefirst requirement might be a problem with base learning algorithms such as some variants of decision stumps or decision trees.However,there is a simple trick to avoid this problem:Roughly speaking,at each point with discontinuity,one adds all hypotheses to H that are limit points of,where is an arbitrary sequence converging to and denotes the hypothesis returned by the base learning algorithm for weighting and training sample[9].4Experimental IllustrationFirst of all,we would like to note that we are aware of the fact that maximizing the margin of the ensemble does not lead in all cases to an improved generalization perfor-mance.For fairly noisy data sets even the opposite has been reported(cf.[8,1,5,11]).However,at least for well separable data the theory applies for hard margins.Hence, one should be able to measure differences in the generalization error,if one function approximately maximizes the margin while another function does not,as similar re-sults in[13]on a multi-class optical character recognition problem.Here we can report experiments on artificial data onlygorithm works and(b)how it compares to AdaBoost.Our data is100dimensional and contains98nuisancedimensions with uniform noise.The other two dimen-sions are plotted exemplary in Figure1.For training weuse only100examples and there is obviously the needto carefully control the capacity of the ensemble.As base learning algorithm we use C4.5decision treesprovided by Ross Quinlan[7]using an option to con-trol the number of nodes in the tree.We have set it suchthat C4.5generates trees with about three nodes.Oth-erwise,the base learner often classifies all training ex-amples correctly and over-fits the data already.Further-Figure1:The two discriminative dimensions of our separable one hundred dimensional data set.more,since in this case the margin is already maximal(equal to1),both algorithms would stop since.We therefore need to limit the complexity of the base learner, in good agreement with the bounds on the generalization error[13].In Figure2(left)we see a typical run of Marginal AdaBoost for.It calls AdaBoost three times.Thefirst call of AdaBoost for already stops after four iterations,since it has generated a consistent combined hypothesis.The lower bound on as computed by our algorithm is and the upper bound is(cf.step 3b in Algorithm2).The second time is chosen to be in the middle of the interval and AdaBoost reaches the margin of after80iterations.The interval is now .Since the length of the interval is small enough,Marginal AdaBoost leaves the loop through exit condition4,calls AdaBoost the last time forExperimental Illustration9 Table1Estimated generalization performances and margins with confidence intervalsfor decision trees(C4.5),AdaBoost(AB)and Marginal AB on the toy data.The lastrow shows the number of times the algorithm had the smallest error.All numbers areaveraged over200splits into100training and19900test examples.andfinally achieves a margin of.For comparison we also plot the margins of the hypotheses generated by AdaBoost(cf.Figure2(right)).Oneobserves that it is not able to achieve a large margin efficiently(after1000iterations).PSfragConclusion10 5ConclusionWe proposed a 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A：啊:21834 阿:38463 埃:22467 挨:25384 哎:21710 唉:21769 哀:21696 皑:30353 癌:30284蔼:34108 矮:30702 艾:33406 碍:30861 爱:29233 隘:38552 鞍:38797 氨:27688 安:23433俺:20474 按:25353 暗:26263 岸:23736 胺:33018 案:26696 肮:32942 昂:26114 盎:30414凹:20985 敖:25942 熬:29100 翱:32753 袄:34948 傲:20658 奥:22885 懊:25034 澳:28595B:扒:25170 叭:21485 吧:21543 笆:31494 八:20843 疤:30116 巴:24052 拔:25300 跋:36299靶:38774 把:25226 耙:32793 坝:22365 霸:38712 罢:32610 爸:29240 白:30333 柏:26575百:30334 摆:25670 佰:20336 败:36133 拜:25308 稗:31255 斑:26001 班:29677 搬:25644扳:25203 般:33324 颁:39041 板:26495 版:29256 扮:25198 拌:25292 伴:20276 瓣:29923半:21322 办:21150 绊:32458 邦:37030 帮:24110 梆:26758 榜:27036 膀:33152 绑:32465棒:26834 磅:30917 蚌:34444 镑:38225 傍:20621 谤:35876 苞:33502 胞:32990 包:21253孢:23394 褒:35090 剥:21093 薄:34180 雹:38649 保:20445 堡:22561 饱:39281 宝:23453抱:25265 报:25253 暴:26292 豹:35961 鲍:40077 爆:29190 杯:26479 碑:30865 悲:24754卑:21329 北:21271 辈:36744 背:32972 贝:36125 钡:38049 倍:20493 狈:29384 备:22791惫:24811 焙:28953 被:34987 奔:22868 苯:33519 本:26412 笨:31528 崩:23849 绷:32503甭:29997 泵:27893 蹦:36454 迸:36856 逼:36924 鼻:40763 比:27604 鄙:37145 笔:31508彼:24444 碧:30887 蓖:34006 蔽:34109 毕:27605 毙:27609 毖:27606 币:24065 庇:24199痹:30201 闭:38381 敝:25949 弊:24330 必:24517 辟:36767 壁:22721 臂:33218 避:36991陛:38491 鞭:38829 边:36793 编:32534 贬:36140 扁:25153 便:20415 变:21464 卞:21342辨:36776 辩:36777 辫:36779 遍:36941 标:26631 彪:24426 膘:33176 表:34920 鳖:40150憋:24971 别:21035 瘪:30250 彬:24428 斌:25996 濒:28626 滨:28392 宾:23486 摈:25672兵:20853 冰:20912 柄:26564 丙:19993 秉:31177 饼:39292 炳:28851 病:30149 并:24182玻:29627 菠:33760 播:25773 拨:25320 钵:38069 波:27874 博:21338 勃:21187 搏:25615铂:38082 箔:31636 伯:20271 帛:24091 舶:33334 脖:33046 膊:33162 渤:28196 泊:27850驳:39539 捕:25429 卜:21340 哺:21754 补:34917 埠:22496 不:19981 布:24067 步:27493簿:31807 部:37096 怖:24598C：擦:25830 猜:29468 裁:35009 材:26448 才:25165 财:36130 睬:30572 踩:36393 采:37319彩:24425 菜:33756 蔡:34081 餐:3***4 参:21442 蚕:34453 残:27531 惭:24813 惨:24808灿:28799 苍:33485 舱:33329 仓:20179 沧:27815 藏:34255 操:25805 糙:31961 槽:27133曹:26361 草:33609 厕:21397 策:31574 侧:20391 册:20876 测:27979 层:23618 蹭:36461插:25554 叉:21449 茬:33580 茶:33590 查:26597 碴:30900 搽:25661 察:23519 岔:23700差:24046 诧:35815 拆:25286 柴:26612 豺:35962 搀:25600 掺:25530 蝉:34633 馋:39307谗:35863 缠:32544 铲:38130 产:20135 阐:38416 颤:39076 昌:26124 猖:29462 场:22330尝:23581 常:24120 长:38271 偿:20607 肠:32928 厂:21378 敞:25950 畅:30021 唱:21809倡:20513 超:36229 抄:25220 钞:38046 朝:26397 嘲:22066 潮:28526 巢:24034 吵:21557炒:28818 车:36710 扯:25199 撤:25764 掣:25507 彻:24443 澈:28552 郴:37108 臣:33251 辰:36784 尘:23576 晨:26216 忱:24561 沉:27785 陈:38472 趁:36225 衬:34924 撑:25745 称:31216 城:22478 橙:27225 成:25104 呈:21576 乘:20056 程:31243 惩:24809 澄:28548 诚:35802 承:25215 丞:19998 逞:36894 骋:39563 秤:31204 吃:21507 痴:30196 持:25345 匙:21273 池:27744 迟:36831 弛:24347 驰:39536 耻:32827 齿:40831 侈:20360 尺:23610 赤:36196 翅:32709 斥:26021 炽:28861 充:20805 冲:20914 虫:34411 崇:23815 宠:23456 抽:25277 酬:37228 畴:30068 踌:36364 稠:31264 愁:24833 筹:31609 仇:20167 绸:32504 瞅:30597 丑:19985 臭:33261 初:21021 出:20986 橱:27249 厨:21416 躇:36487 锄:38148 雏:38607 滁:28353 除:38500 楚:26970 础:30784 储:20648 矗:30679 搐:25616 触:35302 处:22788 揣:25571 川:24029 穿:31359 椽:26941 传:20256 船:33337 喘:21912 串:20018 疮:30126 窗:31383 幢:24162 床:24202 闯:38383 创:21019 吹:21561 炊:28810 捶:25462 锤:38180 垂:22402 春:26149 椿:26943 醇:37255 唇:21767 淳:28147 纯:32431 蠢:34850 戳:25139 绰:32496 疵:30133 茨:33576 磁:30913 雌:38604 辞:36766 慈:24904 瓷:29943 词:35789 此:27492 刺:21050 赐:36176 次:27425 聪:32874 葱:33905 囱:22257 匆:21254 从:20174 丛:19995 凑:20945 粗:31895 醋:37259 簇:31751 促:20419 蹿:36479 篡:31713 窜:31388 摧:25703 崔:23828 催:20652 脆:33030 瘁:30209 粹:31929 淬:28140 翠:32736 村:26449 存:23384 寸:23544 磋:30923 撮:25774 搓:25619 措:25514 挫:25387 错:38169 D:搭:25645 达:36798 答:31572 瘩:30249 打:25171 大:22823 呆:21574 歹:27513 傣:20643 戴:25140 带:24102 殆:27526 代:20195 贷:36151 袋:34955 待:24453 逮:36910 怠:24608 耽:32829 担:25285 丹:20025 单:21333 郸:37112 掸:25528 胆:32966 旦:26086 氮:27694 但:20294 惮:24814 淡:28129 诞:35806 弹:24377 蛋:34507 当:24403 挡:25377 党:20826 荡:33633 档:26723 刀:20992 捣:25443 蹈:36424 倒:20498 岛:23707 祷:31095 导:23548 到:21040 稻:31291 悼:24764 道:36947 盗:30423 德:24503 得:24471 的:30340 蹬:36460 灯:28783 登:30331 等:31561 瞪:30634 凳:20979 邓:37011 堤:22564 低:20302 滴:28404 迪:36842 敌:25932 笛:31515 狄:29380 涤:28068 翟:32735 嫡:23265 抵:25269 底:24213 地:22320 蒂:33922 第:31532 帝:24093 弟:24351 递:36882 缔:32532 颠:39072 掂:25474 滇:28359 碘:30872 点:28857 典:20856 靛:38747 垫:22443 电:30005 佃:20291 甸:30008 店:24215 惦:24806 奠:22880 淀:28096 殿:27583 碉:30857 叼:21500 雕:38613 凋:20939 刁:20993 掉:25481 吊:21514 钓:38035 调:35843 跌:36300 爹:29241 碟:30879 蝶:34678 迭:36845 谍:35853 叠:21472 丁:19969 盯:30447 叮:21486 钉:38025 顶:39030 鼎:40718 锭:38189 定:23450 订:35746 丢:20002 东:19996 冬:20908 董:33891 懂:25026 动:21160 栋:26635 侗:20375 恫:24683 冻:20923 洞:27934 兜:20828 抖:25238 斗:26007 陡:38497 豆:35910 逗:36887 痘:30168 都:37117 督:30563 毒:27602 犊:29322 独:29420 读:35835 堵:22581 睹:30585 赌:36172 杜:26460 镀:38208 肚:32922 度:24230 渡:28193 妒:22930 端:31471 短:30701 锻:38203 段:27573 断:26029 缎:32526 堆:22534 兑:20817 队:38431 对:23545 墩:22697 吨:21544 蹲:36466 敦:25958 顿:39039 囤:22244 钝:38045 盾:30462 遁:36929 掇:25479 哆:21702 多:22810 夺:22842 垛:22427 躲:36530 朵:26421 跺:36346 舵:33333 剁:21057 惰:24816 堕:22549E:蛾:34558 峨:23784 鹅:40517 俄:20420 额:39069 讹:35769 娥:23077 恶:24694 厄:21380 扼:25212 遏:36943 鄂:37122 饿:39295 恩:24681 而:32780 儿:20799 耳:32819 尔:23572 饵:39285 洱:27953 二:20108 贰:36144F:发:21457 罚:32602 筏:31567 伐:20240 乏:20047 阀:38400 法:27861 珐:29648 藩:34281 帆:24070 番:30058 翻:32763 樊:27146 矾:30718 钒:38034 繁:32321 凡:20961 烦:28902 反:21453 返:36820 范:33539 贩:36137 犯:29359 饭:39277 泛:27867 坊:22346 芳:33459 方:26041 肪:32938 房:25151 防:38450 妨:22952 仿:20223 访:35775 纺:32442 放:25*** 菲:33778 非:38750 啡:21857 飞:39134 肥:32933 匪:21290 诽:35837 吠:21536 肺:32954 废:24223 沸:27832 费:36153 芬:33452 酚:37210 吩:21545 氛:27675 分:20998 纷:32439 坟:22367 焚:28954 汾:27774 粉:31881 奋:22859 份:20221 忿:24575 愤:24868 粪:31914 丰:20016 封:23553 枫:26539 蜂:34562 峰:23792 锋:38155 风:39118 疯:30127 烽:28925 逢:36898 冯:20911 缝:32541 讽:35773 奉:22857 凤:20964 佛:20315 否:21542 夫:22827 敷:25975 肤:32932 孵:23413 扶:25206 拂:25282 辐:36752 幅:24133 氟:27679 符:31526 伏:20239 俘:20440 服:26381 浮:28014 涪:28074 福:31119 袱:34993 弗:24343 甫:29995 抚:25242 辅:36741 俯:20463 釜:37340 斧:26023 脯:33071 腑:33105 府:24220 腐:33104 赴:36212 副:21103 覆:35206 赋:36171 复:22797 傅:20613 付:20184 阜:38428 父:29238 腹:33145 负:36127 富:23500 讣:35747 附:38468 妇:22919 缚:32538 咐:21648G:噶:22134 嘎:22030 尬:23596 该:35813 改:25913 概:27010 钙:38041 盖:30422 溉:28297 干:24178 甘:29976 杆:26438 尴:23604 柑:26577 竿:31487 肝:32925 赶:36214 感:24863 秆:31174 敢:25954 赣:36195 冈:20872 刚:21018 钢:38050 缸:32568 肛:32923 纲:32434岗:23703 港:28207 杠:26464 篙:31705 皋:30347 高:39640 膏:33167 羔:32660 糕:31957 搞:25630 镐:38224 稿:31295 告:21578 哥:21733 歌:27468 搁:25601 戈:25096 鸽:40509 胳:33011 疙:30105 割:21106 革:38761 葛:33883 格:26684 蛤:34532 阁:38401 隔:38548 铬:38124 个:20010 各:21508 给:32473 根:26681 跟:36319 耕:32789 更:26356 庚:24218 羹:32697 埂:22466 耿:32831 梗:26775 工:24037 攻:25915 功:21151 恭:24685 龚:40858 供:20379 躬:36524 公:20844 宫:23467 弓:24339 巩:24041 汞:27742 拱:25329 贡:36129 共:20849 钩:38057 勾:21246 沟:27807 苟:33503 狗:29399 垢:22434 构:26500 购:36141 够:22815 辜:36764 菇:33735 咕:21653 箍:31629 估:20272 沽:27837 孤:23396 姑:22993 鼓:40723 古:21476 蛊:34506 骨:39592 谷:35895 股:32929 故:25925 顾:39038 固:22266 雇:38599 刮:21038 瓜:29916 剐:21072 卦:21350 寡:23521 挂:25346 褂:35074 乖:20054 拐:25296 怪:24618 棺:26874 关:20851 官:23448 冠:20896 观:35266 管:31649 馆:39302 罐:32592 惯:24815 灌:28748 贯:36143 光:20809 广:24191 逛:36891 瑰:29808 规:35268 圭:22317 硅:30789 归:24402 龟:40863 闺:38394 轨:36712 鬼:39740 诡:35809 癸:30328 桂:26690 柜:26588 跪:36330 贵:36149 刽:21053 辊:36746 滚:28378 棍:26829 锅:38149 郭:37101 国:22269 果:26524 裹:35065 过:36807J：桨:26728 奖:22870 讲:35762 匠:21280 酱:37233 降:38477 蕉:34121 椒:26898 礁:30977 焦:28966 胶:33014 交:20132 郊:37066 浇:27975 骄:39556 娇:23047 嚼:22204 搅:25605 铰:38128 矫:30699 侥:20389 脚:33050 狡:29409 角:35282 饺:39290 缴:32564 绞:32478 剿:21119 教:25945 酵:37237 轿:36735 较:36739 叫:21483 窖:31382 揭:25581 接:25509 皆:30342 秸:31224 街:34903 阶:38454 截:25130 劫:21163 节:33410 桔:26708 杰:26480 捷:25463 睫:30571 竭:31469 洁:27905 结:32467 解:35299 姐:22992 戒:25106 藉:34249 芥:33445 界:30028 借:20511 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晤:26212 物:29289 勿:21247 务:21153 悟:24735 误:35823X:昔:26132 熙:29081 析:26512 西:35199 硒:30802 矽:30717 晰:26224 嘻:22075 吸:21560 锡:38177 牺:29306 稀:31232 息:24687 希:24076 悉:24713 膝:33181 夕:22805 惜:24796 兮:20846熄:29060 烯:28911 溪:28330 汐:27728 犀:29312 檄:27268 袭:34989 席:24109 习:20064 媳:23219 喜:21916 铣:38115 洗:27927 系:31995 隙:38553 戏:25103 细:32454 瞎:30606 虾:34430 匣:21283 霞:38686 辖:36758 暇:26247 峡:23777 侠:20384 狭:29421 下:19979 厦:21414 夏:22799 吓:21523 掀:25472 锨:38184 先:20808 仙:20185 鲜:40092 纤:32420 咸:21688 贤:36132 衔:34900 舷:33335 闲:38386 涎:28046 弦:24358 嫌:23244 显:26174 险:38505 现:29616 献:29486 县:21439 腺:33146 馅:39301 羡:32673 宪:23466 陷:38519 限:38480 线:32447 相:30456 厢:21410 镶:38262 香:39321 箱:31665 襄:35140 湘:28248 乡:20065 翔:32724 祥:31077 详:35814 想:24819 响:21709 享:20139 项:39033 巷:24055 橡:27233 像:20687 向:21521 象:35937 萧:33831 硝:30813 霄:38660 削:21066 哮:21742 嚣:22179 销:38144 消:28040 宵:23477 淆:28102 晓:26195 小:23567 孝:23389 校:26657 肖:32*** 啸:21880 笑:31505 效:25928 楔:26964 些:20123 歇:27463 蝎:34638 鞋:38795 协:21327 挟:25375 携:25658 邪:37034 斜:26012 胁:32961 谐:35856 写:20889 械:26800 卸:21368 蟹:34809 懈:25032 泄:27844 泻:27899 谢:35874 屑:23633 薪:34218 芯:33455 锌:38156 欣:27427 辛:36763 新:26032 忻:24571 心:24515 馨:39336信:20449 衅:34885 星:26143腥:33125 猩:29481 惺:24826 兴:20852 刑:21009 型:22411 形:24418 邢:37026 行:34892 醒:37266 幸:24184 杏:26447 性:24615 姓:22995 兄:20804 凶:20982 胸:33016 匈:21256 汹:27769 雄:38596 熊:29066 休:20241 修:20462 羞:32670 朽:26429 嗅:21957 锈:38152 秀:31168 袖:34966 绣:32483 墟:22687 戌:25100 需:38656 虚:34394 嘘:22040 须:39035 徐:24464 许:35768 蓄:33988 酗:37207 叙:21465 旭:26093 序:24207 畜:30044 恤:24676 絮:32110 婿:23167 绪:32490 续:32493 轩:36713 喧:21927 宣:23459 悬:24748 旋:26059 玄:29572 选:36873 癣:30307 眩:30505 绚:32474 靴:38772 薛:34203 学:23398 穴:31348 雪:38634 血:34880 勋:21195 熏:29071 循:24490 旬:26092 询:35810 寻:23547 驯:39535 巡:24033 殉:27529 汛:27739 训:35757 讯:35759 逊:36874 迅:36805Y:压:21387 押:25276 鸦:40486 鸭:40493 呀:21568 丫:20011 芽:33469 牙:29273 蚜:34460 崖:23830 衙:34905 涯:28079 雅:38597 哑:21713 亚:20122 讶:35766 焉:28937 咽:21693 阉:38409 烟:28895 淹:28153 盐:30416 严:20005 研:30740 蜒:34578 岩:23721 延:24310言:35328 颜:39068 阎:38414 炎:28814 沿:27839 奄:22852 掩:25513 眼:30524 衍:34893 演:28436 艳:33395 堰:22576 燕:29141 厌:21388 砚:30746 雁:38593 唁:21761 彦:24422 焰:28976 宴:23476 谚:35866 验:39564 餍:3***1殃:27523 央:22830 鸯:40495 秧:31207 杨:26472扬:25196 佯:20335 疡:30113 羊:32650 洋:27915 阳:38451 氧:27687 仰:20208 痒:30162 养:20859 样:26679 漾:28478 邀:36992 腰:33136 妖:22934 瑶:29814 摇:25671 尧:23591 遥:36965 窑:31377 谣:35875 姚:23002 咬:21676 舀:33280 药:33647 要:35201 耀:32768 椰:26928 噎:22094 耶:32822 爷:29239 野:37326 冶:20*** 也:20063 页:39029 掖:25494 业:19994 叶:21494 曳:26355 腋:33099 夜:22812 液:28082 一:19968 壹:22777 医:21307 揖:25558 铱:38129 依:20381 伊:20234 衣:34915 颐:39056 夷:22839 遗:36951 移:31227 胰:33008 疑:30097 沂:27778 宜:23452 姨:23016 彝:24413 椅:26885 蚁:34433倚:20506 已:24050 乙:20057 矣:30691 以:20197 艺:33402 抑:25233 易:26131 邑:37009 屹:23673 亿:20159 役:24441 臆:33222 逸:36920 肄:32900 疫:30123 亦:20134 裔:35028 意:24847 毅:27589 忆:24518 义:20041 益:30410 溢:28322 诣:35811 议:35758 谊:35850 羿:32703 弈:24328译:35793 异:24322 翼:32764 翌:32716 绎:32462 茵:33589 荫:33643 因:22240 殷:27575 音:38899 阴:38452 姻:23035 吟:21535 银:38134 淫:28139 寅:23493 饮:39278 尹:23609 引:24341 隐:38544 印:21360 英:33521 樱:27185 婴:23156 鹰:40560 应:24212 缨:32552 莹:33721 萤:33828 营:33829 荧:33639 蝇:34631 迎:36814 赢:36194 盈:30408 影:24433 颖:39062 硬:30828 映:26144 哟:21727 拥:25317 佣:20323 臃:33219 痈:30152 庸:24248 雍:38605 踊:36362 蛹:34553 咏:21647 泳:27891 涌:28044 永:27704 恿:24703 勇:21191 甬:29996用:29992 幽:24189 优:20248 悠:24736 忧:24551 尤:23588 由:30001 邮:37038 铀:38080 犹:29369 油:27833 游:28216 酉:37193 有:26377 友:21451 右:21491 佑:20305 釉:37321 诱:35825 又:21448 幼:24188 迂:36802 淤:28132 于:20110 盂:30402 榆:27014 虞:34398 愚:24858 舆:33286 余:20313 俞:20446 逾:36926 鱼:40060 愉:24841 渝:28189 渔:28180 隅:38533 予:20104 娱:23089 雨:38632 与:19982 屿:23679 禹:31161 宇:23431 语:35821 羽:32701 玉:29577 域:22495 芋:33419 郁:37057 吁:21505 遇:36935 喻:21947 峪:23786 御:24481 愈:24840 欲:27442 狱:29425 育:32946 誉:35465 浴:28020 寓:23507 裕:35029 预:39044 豫:35947 钰:38064 驭:39533 鸳:40499 渊:28170 冤:20900 元:20803 垣:22435 袁:34945 原:21407 援:25588 辕:36757 园:22253 员:21592 圆:22278 猿:29503 源:28304 缘:32536 远:36828 苑:33489 愿:24895 怨:24616 院:38498 曰:26352 约:32422 越:36234 跃:36291 钥:38053 岳:23731 粤:31908 月:26376 悦:24742 阅:38405 耘:32792 云:20113 郧:37095 匀:21248 陨:38504 允:20801 运:36816 蕴:34164 酝:37213 晕:26197 韵:38901 孕:23381Z:匝:21277 砸:30776 杂:26434 栽:26685 哉:21705 灾:28798 宰:23472 载:36733 再:20877 在:22312 咱:21681 攒:25874 暂:26242 赞:36190 赃:36163 脏:33039 葬:33900 遭:36973 糟:31967 凿:20991 藻:34299 枣:26531 早:26089 澡:28577 蚤:34468 躁:36481 噪:22122 造:36896 皂:30338 灶:28790 燥:29157 责:36131 择:25321 则:21017 泽:27901 贼:36156 怎:24590 增:22686 憎:24974 曾:26366 赠:36192 扎:25166 喳:21939 渣:28195 札:26413 轧:36711 铡:38113 闸:38392 眨:30504 栅:26629 榨:27048 咋:21643 乍:20045 炸:28856 诈:35784 摘:25688 斋:25995 宅:23429 窄:31364 债:20538 寨:23528 瞻:30651 毡:27617 詹:35449 粘:31896 沾:27838 盏:30415 斩:26025 辗:36759 崭:23853 展:23637 蘸:34360栈:26632 占:21344 战:25112 站:31449 湛:28251 绽:32509 樟:27167 章:31456 彰:24432 漳:28467 张:24352 掌:25484 涨:28072 杖:26454 丈:19976 帐:24080 账:36134 仗:20183 胀:32960 瘴:30260 障:38556 招:25307 昭:26157 找:25214 沼:27836 赵:36213 照:29031 罩:32617 兆:20806 肇:32903 召:21484 遮:36974 折:25240 哲:21746 蛰:34544 辙:36761 者:32773 锗:38167 蔗:34071 这:36825 浙:27993 珍:29645 斟:26015 真:30495 甄:29956 砧:30759 臻:33275 贞:36126 针:38024 侦:20390 枕:26517 疹:30137 诊:35786 震:38663 振:25391 镇:38215 阵:38453 蒸:33976 挣:25379 睁:30529 征:24449 狰:29424 争:20105。
高考语文词语拼音高考语文词语拼音141 召唤 zh a o 142 觇视 ch t r143鞭鞘 sh t o 144 潲水 sh a o145 翘首 qi a o146 炽热 ch i 147 盘飧 s U r 148 偌大 ru? 149 谲诈 ju? 150 鹰隼 s u r 151 给养 j i152 福祉zh i 153 铜臭xi u154 对峙 zh i 155 消弭m i156 印玺x i157 妊娠shyr158 弹弓 d a r 159 慑服 sha160 踝骨 hu1 拗断d o2 柏油 b d i3 焙烧 bai4 鱼鳔 bi a o5.熬白菜to 6.淬火cu i 7 佞臣 r i rg 8 恫吓 ha 9 俳优 p a i 10 扼腕 w a r 11山坳a o12黄檗 b?13 黄柏 b?14 绷脸 bt rg15 葳蕤 wyiru16 脖颈 gt ng17 坼裂 cha18 .褫夺ch i 19 忖量 c u r 20 砖坯 p? 21觊觎y u 22夹杂 ji t 23 发酵 ji a o 24 皈依 gu? 25 执拗 ri u 26傍晚 b a ng 27濒危 b?r 28 .掣肘 chazh 29 .挫败 cu? 30 蚍蜉 P i 31黑魆魆x U 32绩效 j i 33 .弄堂 l?ng 34 噱头 xu?35 果脯 f u 36襁褓 qi d ng 37剥花生 b t o 38 绷直 barg byrg39 煸炒 bi t r40 屏退 b i rg 41称职 chan 42汤匙ch i 43 横财 harg 44 眼睑 ji d r 45 框架 ku a rg 46 书坊f trig47苔藓 t a i 48 刨冰 b a o 49 剥落 b o 50 荸荠 b i?q i 51摒除b irg 52谶语 char 53 .处理ch u 54 屏除 b i rg 55 箭镞 z u56间杂 ji a r 57散漫 s d r 58 磨坊 f a rg 59 头癣 xu d r60 背篓byi61 薄脆bao 62 .匕首b i 63 褊狭 bi d r 64 香饽饽 b??bo 65 谄谀 ch d r66 称誉 chyrg 67 遄飞 chu a r 68 豆荚 ji a 69 .倥偬 zcng 70 诨号h u r71 僭越 ji a r 72 度量衡 d u 73 游舫 f d rg 74 漩涡 w o 75 薄暮b?76 豁免 hu? 77 纰缪 mi u 78 模具 m u 79 口讷 ra 80 妍媸 ch?81皴裂 c U r82勖勉 x u83 劲旅 j i rg 84 氛围 fyr 85 潦草 li a86 符合 f u87 乖蹇 ji d r88 睚眦 z i 89 发怵 ch u 90 白术 zh u 91嫩绿 rar 92漱口 sh u 93 敕封 ch i 94 夹生 ji t 95 及笄j?96兽槛 ji a r 97胚胎 pyi 98 较劲 j i r 99 祗敬 zh? 100 攫取 ju? 101 沏茶 q? 102 胸脯 p u 103 役使 y i 104 泥淖 r a o 105 脱脂 zh? 106 .挑唆su o 107 箴言 zhyr 108 咯血 k d 109 剖析 p o u 110 后嗣 s i 111 昳丽 y i 112 蒙骗 myrg 113 .颓靡 tu i 114 龃龉 y u 115 举隅 y u 116 卷帙 zh i 117 狡黠 xi a118 市侩 ku a i119 回溯 s u 120 富庶 sh u 121 .远岫xi u 122霉菌 j U r 123 巨擘 b? 124 靓妆 j i rg 125 稼穑 sa126 诘问 ji? 127伶俜丨irgp?rg128 .渐染ji t 129 骄矜 j?r 130 浸润 ji r131角斗 ju?132角色 ju? 133 揩汗 k t i 134 铿锵 qi t rg 135 拊掌f u136 解送 jia137 宽宥 y?u138 哈达 h d 139 亲昵 r i140 胳臂 gy?bei、给下列词语中加点的汉字注音熟悉课文内容，掌握常用字词读音）161 .掉色sh a i 162．辅弼b i163．伺候c i164 ．包扎z t165．作坊zu o?fang 166 .刨床b co 167．择菜zh o i 168．与会y i169．酽茶y c n 170 ．熨帖y i 171 .冠心病gu tn 172?疏浚jin 173．症结zhyng 174 ．答应d t175．盘桓hu o n176 ?譬喻p i177 .啁啾ji u178 ．省亲x i ng 179 .汲取j i180 ．堤岸d? 181 .咫尺zh i182 ．缇骑j i183．媲美p i184．飙升bi t o 185 ．湍急tu t n 186 .筵席y on 187 ．瞳孔t?ng 188．船舷xi o n 189 .遴选丨in 190．夙愿s i191 .跂望q i192 ．腈纶j?ng 193．入殓li c n 194．收讫q i195 ．菡萏d c n 196.节骨眼jiygu 197．金刚钻zu c n 198 ．投掷zh i199 .凫水f d200．马厩ji i201 ．攻讦ji? 202．鳜鱼gu i203．诤友zhang 304．氤氲y u n 205．滂沱p t ng 206 ?稽首q i207 ．吊唁y c n 208．信笺ji t n 209．玷污di c n 210．杜撰zhu c n 211 ．缔结d i212 ．场所ch a ng 213 ．腠理c?u 214．黜免ch i215．脸颊ji o 216 ．聒噪z c o 217．搭讪sh c n 2 1 8 ．天台山t t i 219．床铺p i220 ．遽然j i 221 .羁縻m i222．打擂台lai 223．浸渍z i224．挟制xi? 225 .翁媪a o 226．旮旯g t l o227 ．阴鸷zh i228 ?神祇q 229．撇嘴pit 230 .檄文x i 231．宫绦t t o 232 ．簇拥c i233．一场雨ch o ng234．赏赉l c i 235 ．齑粉j? 236．赍志j? 237 ．镌刻ju t n 238 ．扶乩j? 239．岑寂c?n 240 ．神龛k t n 241 ．轻飏y o ng 242 ．札记zh o243 ．一服药f i244．悲恸t?ng 245 ?商贾g u 246．关卡qi a247．豢养hu c n 248．阴霾m o i 249．采撷xi? 250．逡巡q u n 251 ．苑囿y?u 252 ．骁勇xi t o 253．孝悌t i254．靛青di c n 255．霰弹xi c n 256 ．艄公sh t o 257 .绮罗q i258．弹劾h? 259．重创chu t ng 260．濯足zhu? 261 ．迫击炮p a i 262．水泵bang 263 .雾霭a i 264．鳊鱼bi t n 265．蛤蚌g?b c ng 266．龅牙b t o o o267．袍泽p o o 268．同胞b t o 269.血泊p o270 ．甜薄脆b 271 ?黄芪q it o272 ．十里堡p i273．印把子b c274．大伯子b a i 275．黄骠马bi276．一场球ch a ng 281 ．傧相b?n 277．车马炮j u282 ．髌骨b i n278．辟邪b i283 ．宝藏z c n279 .偏裨p i280 ．骠骑pi c og 284 ．电势差ch t 285 ．羼杂ch c n286．镐京h c o 287 ．轧账g o288 ．惩罚ch?ng289．豆豉ch i290 ．奢侈ch i291 ．炽盛ch i292 ．舂米ch o ng 293．冲模ch?ng 294．撺掇cu t n 295．处决ch u 296．畜力ch i297 ．傣族d a i 298．抽搐ch i299．阔绰chu? 300 ．瑕疵c? 301 ．雌雄c i302．伺隙s i303．大黄d c 3 0 4 ．瓦窑堡b u305 ．畜产x u306．嚼舌ji o o 307 ．倔脾气jua 308 ．馏馒头li u309．落架l c o 3 1 0 ．唱片儿pi t n311 ．自个儿gt 312 ．镐头g a o 313 ．封诰g c o 3 1 4．排子车p a i 315．膀肿p t ng316．朴树p? 317 ．打烊y c ng 318 ．佣金y?ng 319．一宿xi u320 ．吁请y u321 ．刷白shu c322 ．骨殖g u?shi 323 ．踏实t t?shi 324 ．海参崴w a i 325．叶韵xi? 326．体己话t? 327．肯綮q i ng 328 ．亲家公q i ng 329．嬗变sh c n 330．流觞sh t■■y331 ．氓隶m?ng 332．西畴ch?u333 ．尚飨xi a ng 334．忏悔ch c n 335．貂蝉ch o n336．呜咽ya 337 ．桌帏wt i 338 ．窨井盖y i n 339．再一强ji c ng340 ．砧板zhyn钉扣子d ing342 ．干禄 g u nl u 343．跌宕 diy344．骨节 g u ji? 345.晃眼 hu a ng 牵累lt i347．拉破手 l a 348．撩起 li U o349．淋盐 l i n 350.搂柴火 l o u问难 n a n 352 ．眼泡 p U D 353 ．疱疹 p a o 354.喷香 pan 355 .劈开p i 漂白 pi a o 357．腥臊 s a o 358．煞白 sh a359．趟地 t U ng 360 ．一帖药 tit 一通话t?ng 362．挑战 ti a o 363．旋风 xu a n364．要功 y U o 365 ．荫凉 y i n应声 y i ng367 ．晕场 y u n 368 ．炸糕 z h a369．辟谣 p i 370．矿藏 c a ng 树行子 h a ng 372 ．剿说 ch U o 373 ．拚命 p a n 374 .折腾 zhy375．标识 zh i 档案 d a ng 377 ．靓仔 z a i 378．桦树 hu a 379．从容 c?ng 380 .骨髓 su i 拙劣 zhu o 382 ．环绕 r a o383．渠道 q u 384．袭击x 1 385．蜿蜒w U n 祈祷q id a o 387 ．召开zh a o 388 ．狗彘 zh i 389.逻辑 lu??j i 390 ．山冈 g U ng 傀儡 ku ilt i392．掳掠 l u l?a 393．蹼掌 p u394．宽绰 chu ? 395.饥荒 j??huang 虫豸 zh i 397 ．秸秆 jiy 398．发酵 ji a o 399 .盎司a r g 400．粗糙 c U o 赁屋 l i n 402．晌午 sh a ng 403．绥靖 su 1 404. 毒枭 xi U o 405．蜕化 tu i 卜筮 b u 407 ．吮吸sh u n 408 ．酗酒 x u409. 沼气 zh a o 410 ．绚烂 xu a n 玻璃碴ch a412 ．荞麦 qi a o413．粗犷 gu a ng414 . 肚脐 q i 415 ．喑哑 y?n蔓菁 m a n?jing417 ．央浼 mt i 418．偌大 ru? 419. 笃厚 d u 420 ．碘酒 di a n汗涔涔c?n 422 ．盥洗 gu a n 423 ．薅草 h U o 424.汾酒 f?n 425 ．挑剔 t?．划豁拳 hu a427．刽子手 gu i 428 ．股肱 g o ng429 ．刹那 ch430．拂晓 f u日晷 gu 1 432．雪茄 ji U 433 ．抓阄 ji434 ．诳语 ku a ng 435．乐阕 qua掮客 qi a n437 ．菜畦 q i 438．酾酒 sh? 439．山岚 l a n 440．牛虻 m?ng 悭吝 qi U n 442 ．唱喏 rt 443．嗾使 s o u 444 ．歃血 sh a 445．饕餮 t U o 趿拉 t U447 ．倾轧 y a 448 ．栅栏 zh a 449 ．阋墙 x i 450．癫痫 di U n 游弋 y i452 ．渊薮s o u453 ．恣睢 su? 454．狩猎 sh?u 455 ．囟门 x i n 怨艾 y i457 ．帷幄 w?458．糯米 nu?459．笑靥 ya460．扁舟 pi U墙垣 yu a n 462 ．贮存 zh u 463 ．苎麻 zh u 464．造诣 y i 465．甲醛 qu a n 悖谬 baimi u467．朝觐 j in468．孢子 b U o469 ．保镖 bi U o 470．寒碜 h a n?chen 磐石 p a n 472 ．疲沓 p i?ta 473 ．情愫 s u 474 ．舢板 sh U n 475 .什锦 sh i 推诿 wt i 477 ．魍魉 li a ng 478 ．翕动 x?479 ．撅嘴 juy480．糊口 h u马扎zh a 482．跖骨zh i 483．抵牾d 1w u 484 .蒺藜丨i 485．纨绔 w a n 赝品 y a n 487 ．剥皮 b U o 48 8 ．眯缝眼 m??feng 489．茎叶 j?ng 490．瑰丽 gu? 斡旋 w? 492．惭怍 zu? 493 ．诤言zhang 494．肇事 zh a o 495．伛偻 y u 怃然 w u497．粮囤 d u n498．壳菜 qi a o499．翘楚 qi a o500 ．三岔口 ch的士 d i 502 ．砝码 f a m a503．鸟铳 ch?ng504．惩创 chu a ng 505．椽子 chu a潭柘寺 zha507．编纂 zu a n 508．莳秧 sh i 509 ．方遒 qi u 510．靑荇 x i ng 跫音 qi?ng512 ．宁谧 m i 513 ．敛裾 j u514 ．花蕊 ru 1515．坍圮 t U n341． 346． 351． 356． 361． 366． 371． 376． 381． 38 6． 391． 396． 401． 406． 411． 416． 421． 426 a431．u 436． 441． 456．451． 456． n 461．466． 471． 476． 481． 486． 491． 496．a501． n 506． 511．zh u 、给下列成语中加点的汉字注音1 ．不着边际zhu? 2．杀鸡吓猴xi a3．空心吃药k?ng 4．笑容可掬j t5．倾箱倒箧q ia6 ．螳臂当车d u ng 7．博闻强识zh i8．车载斗量z a i 9 ．人才济济j M 1 0．返璞归真p u11 ．彬彬有礼b?n 12 ．荷枪实弹ha 13 ．抚髀长叹b i14 ．捕风捉影b u15 ．重足而立ch?ng16 ．噤若寒蝉j i n 17 ．鞭辟入里p i18 ．既往不咎ji u19 ．俾众周知b M20 ．高屋建瓴l mg21 ．解铃系铃x i22 ．峨冠博带gu u n 23 ．喁喁私语y u24．心急火燎li d o 25 ．呶呶不休n a o26 ．手上燎泡li a o 27．额手称庆?28 ．扑朔迷离shu? 29 ．虚与委蛇y i30 ．顺蔓摸瓜w a n31 ．踽踽独行j u32 ．缄口不言ji u n 33 ．自古洎今j i34．剑拔弩张n u35 ．桀骜不驯a o36．情不自禁j?n 37 ．怨声载道z a i 38 ?白雪皑皑a i 39．一哄而散h?ng 40 ．厝火积41 ．暴殄天物ti d n 42．朝暾夕月t t n 43 ．吞言咽理y a n44．伛偻提携l u45 ．未雨绸缪m?u46 ．不胫而走j i ng 47．饮马长城y i n 48．高瞻远瞩zh u49 ．黄发垂髫ti a o 50 ．敬谢不敏m M n51 ．不值一哂sht n52．偃仰啸歌xi a o 53．阮囊羞涩sa54．卖官鬻爵y u55 ．残碑断碣ji?56．管窥蠡测1157．循规蹈矩d d o 58 ．刮目相看gu u59．锃光瓦亮zang 60 ．流水浅61 ．集腋成裘ya 62．扺掌而谈zh M63．负笈之志j i64．信而有征zhyng 65 ．猝不及防c u66 ．亟来问讯q i67．泾渭分明j?ng 68．心劳日拙zhu c69．醍醐灌顶1170 ．言简意516 .瘐毙y u517 .发轫ran 518.薄赆j i n 519.巉岩ch a n 521 .孱头c a n 522 .畏葸x i523.竹篁hu a ng 524.指摘zh u i 526.饭甑zang 527.冲压ch?ng 528.似的sh i529 .杉篙sh u531 .哭丧脸kt?sang 532 .雪橇qi to 533.蛲虫n a o 534 .豆萁q i 536.投奔ban 537 .眯了眼m i538.道行h?ng 539．镏金li u 541 .痉挛j i ng 542 .捋胡子丨u543 .捋袖子lu o544．吐蕃b c 546 .抡大锤丨tn 547 .鲰生zcu 548.两涘s i549 .笏板h u 551 .刀俎z u552.骖乘shang 553.萌蘖nia 554 ．熹微x? 556.歆享x?n 557.形骸h a i 558.陈抟tu a n 559 ．蹙额c ung561 .歹毒d d562 .参省x M g 563.鸡豚t u n 564．跂望q i 566.牌坊f u ng 567.掀起x?u n 568 .哪吒n? 520 .坟茔y m g 525．仓颉j i? 530 ?无射钟y i 535 .鮎鱼ni a n 540.揪捽zu? 545.鄂伦春a 550 ?槲寄生h u 555.阙秦ju? 560 .横样子ha565 .锋镝d i赅g u i71 .器宇轩昂xu u n 72.寅吃卯粮m ao 73.岿然不动ku? 74.安土重迁zh?ng 75.海市蜃楼shan76 .否极泰来p i77.响遏仃云a78.自吹自擂l?i 79.饮鸩止渴zhan80.扛鼎之力g u ng81 .哈雷彗星hu i82.纵横捭阖 b a83.仃伍出身h ong 84.涸辙之鲋h? 85.冥顽不灵m ing86 .为虎作伥wai 87.为丛驱雀c?ng 88.万箭攒心cu on 89 .藏头露尾l n90.半身不遂su 191 .沐猴而冠gu cn 92.闷声闷气myn 93 .相时而动xi c ng 94 .解衣衣人y i95.与闻其事y n96.风雪载途z c i 97.强词夺理qi a ng 98.跑马卖解xia 99.掀起高潮xi u n 100.楔形文字xiy101 .因噎废食yy 102.乌烟瘴气zh c ng 103 .眩于名利xu c n 104 .陟罚臧否zh i105.杀一儆百j i ng106 .含英咀华j U107 .瓮中捉鳖wang 108 .凤冠霞帔pai 109 .勒紧腰带lyi 110.炙手可热zh i111.越俎代庖p o o 112 .面露愠色y n n 113 .箪食壶浆s i 114.一抔黄土p o u 115 .生死攸关y o u116.谑而不虐xua 117.如丧考妣b i118.命运多舛chu a n 119 蜂虿有毒ch c i 120.飞扬跋扈h n121 .椎心泣血chu 11司晨p in126 .发扬踔厉chu o127.假途灭虢gu o128 .时乖命蹇jii a n 129.雨后初霁j i 130.揆情度理ku i131 .广袤无垠m c o 132.削足适履xuy 133.囿于成见y?u 134.千乘之国shang135 .鹬蚌相争y n136 .颐指气使y i 137.怙恶不悛h n138 .具体而微wyi 139.安步当车d c ng 140.借箸代筹zh n141 .休戚与共q? 142.犯而不校ji c o 143.胶柱鼓瑟sa 144 .胼手胝足zh? 145.宵衣旰食g c n146 .狗尾续貂di u o147.徇私舞弊x n n 148 .孤注一掷zh i149 .无耻谰言l o n 150.曲突徙薪x i151 .拾遗补缺quy 152 .倚马千言y i153 .铤而走险t i ng 154 .天真罄露q i ng 155.长歌当哭d c ng156 .桀骜不驯x n n 157.殒身不恤y tn 158 .万姓胪欢l u159 .义愤填膺y?ng 160.忐忑不安t a n161 .放诞无礼d c n 162.懵懂顽童mt ng 163 .殛鲧用禹j i164 .便宜从事bi c n 165.读书在庠xi o ng166 .流觞曲水sh u ng167 .残羹冷炙gyng 168 .牛山濯濯zhu? 169 .间或一轮ji c n 170.沸反盈22.枹止响腾f u123.荦荦大端lu? 124 ．风声鹤唳125.牝鸡天 y ing171.咬文嚼字ji CD 172 ?锱铢必较z? 173 ?值得商榷qua 174 ?韬光养晦t u o 175 ?不落言筌 qu on176．岁在癸丑 gu i 177 .阿谀奉承y 178 ．妃嫔媵嫱 p i n179 ．鼎铛玉石 chyng 180．瓮牖绳枢y o u181 ．天高地迥 ji o ng182．天罡地煞 g u ng 183．骖騑上路fyi 184．睇眄中天 d i 1 85．宗悫长风 qua186 ．叨陪鲤对 t u o 187．骐骥一跃 j i 188．锲而舍之 qia 189 ．驽马十驾 n u 1 90．六艺经传 zhu a n191 ．在庾粟粒 y u 192 ．倚叠如山 y i 193．率赂秦耶 shu a i194．当与秦较 t 3ng 1 95 ．自我徂尔 c u 196 ．擢我素手 zhu? 197 ．羁鸟念林 j? 198 ．青青子衿 j?n 199 ．契阔谈讌 y a n 200．客谈瀛洲 y i ng201 ．渌水荡漾 l u 202．脚著谢屐j? 203．轻拢慢捻ni 3n 204．呕哑嘲哳zh u 205．时已惘然 w 3ng206．镜中衰鬓 b i n 207 ．雕栏玉砌 q i 208．一抹彩霞 m o 209．骤雨初歇 zh?u 210．还酹江月 lai211 ．金风玉露 l u 212 ．舞榭歌台 xia 213 ．荠麦青青 j i 214 ．女娲炼石 w u 2 1 5．双双鹧鸪 zha216 ．有谁堪摘 zh u i217．门衰祚薄 zu? 218．责臣逋慢 b u 219．除臣洗马 xi 3n 220 ．生当陨首 y u n221 ．孤舟嫠妇 l i 222．横槊赋诗 shu? 223．匏樽相属 p c o 224 ．相与枕藉 jia 225．余音袅袅 ni 3o226．冯虚御风 p i ng 227 ．猿猱愁攀 n c o 228 ．砯崖转石 p?ng 229 ．钟鼓馔玉 zhu a n 230．烹羊宰牛 pyng231 ．耶娘妻子 y? 232．荆杞丛生 q i 233 ．谗人间之 ji a n 234．濯淖污泥 zhu? 235．蝉蜕浊秽tu i下蹑履 nia 245．摧藏悲哀 z a ng246．欸乃一声3 i 247．酒旗斜矗ch u 248．聊消溽暑r u 249 ．投缳道路 hu 0250 .蹈死不顾 d 3o251．赠谥美显 sh i 252 ．面面相觑q u253．虎贲猛士 byn254 ．孙子膑脚 b i n 255．餔糟啜醨b u256.洗涤旧迹 d i 257.半点漪沦y? 258 .佝偻脊背gcu 259 .踟蹰旋转zhu a n260 .塵战正酣0 o 261 .窸窣有声 x?s u 262 .繁芜丛杂 w u 263 .浅尝辄止 zh? 264.污蔑诅咒 mia 265.侮蔑诽谤 w u266 .奚落揶揄x? 267 .第一要着zh u 268 .悬揣苦思chu 3269 .绰有余裕chu? 270 .陈抟老祖 tu c n236.茕茕孑立殊q U241 .眄柯怡颜 ji? 237.沧海一粟 mi 巾242 .躬耕西畴 238 ?鹤汀凫渚 t?ng 239 .游目骋怀 cht ng240 .趣舍万ch?u 243 .肇锡嘉名 c i244.足306．艰难险巇x? 307．视如敝屣x i308 ．妖妖趫趫qi a o 309．难以省识x i ng 310 ．久旱云霓n i311．鲛帕鸾绦ji u o 312．铰扇套ji a o 313．泽被后世bai 314．手里攥着zu a n 315．懵懂顽童mt ng316．聚讼纷纭s?ng317 ．槁项黄馘x u318．秩秩斯干i a n 319．味同嚼蜡ji a o 320．差可拟ch u321．饬令查办ch i322．敕令封赏ch i323．老骥伏枥j i324．草菅人命ji u n 325．捉虱子sh?326．扪参历井m?n 327 ．猿猱欲度n a o 328 ．臭名昭著zh u o 329 ．溘然长逝ka 330 ．夷为平地 y丨331．惶悚不安s c ng 332 ．敛声屏气b i ng 333．杜撰不少zhu a n 334．唇吻翕辟x? 335．不遑辞候hu a ng336．牛羊蹄躈qi a o 337．俾入邑庠b i338 ．直龁敌领h? 339 ．咫尺天涯zh i 340 ．以蠹贫 d u341．自增惭怍zu?342．坐贻聋瞽g u343 ．幢节玲珑chu a ng 344．绡縠参差h u 345 ．应举下第y i ng346．撝退辞谢hu? 347 ．绣闼雕甍t a m?ng 34 8 ．潦水尽l a o 349．新州懿范y i 350 W VT7．恶乎待哉w u566．约二斤肉y u o 567．智者乐山y a o271.绞丝银镯盈天faizhu? 272．碑帖拓本t a 273 ．雪褥草甸r u 274．草窠狼窝ky 275．沸反276.胡子拉碴人li a o 281.涂泥抹墙戆脑g a ng 286.不忍觳觫笄zh i ch um?277．瓦釜雷鸣282．戆直可嘉h us u 287 .齐国褊小f u 278．尥蹶子li a ojut 279．撂挑子li a o 280．春色撩zhu a ng 283 .实属赘疣y?u 284 .忤逆不孝w u 285 .戆头bi a n 288．盍反其本h? 289．口占一绝zh u n 290．盥漱栉291．女郎行h a ng 292．逑qi u296．恁时节nan 297．玉醅pyi301．莺莺张珙碚b? 官吏每l 谨赓一绝gcng 302 ?苌弘化碧?mengyng293 .忝列门墙298.揾英雄泪ch a ng 303 .盗跖颜渊ti an 294 .舞丹墀ch i 295 .君子好wan 299 ．恓恓惶惶x? 300 ．溶溶zh i 304 .前合后偃y a n 305 .踉跄踬。
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穷年累月→ 月明星稀→ 稀奇古怪→ 怪声怪气→ 气壮河山→ 山遥水远→远交近攻→ 攻苦食淡→ 淡妆浓抹→ 抹月秕风→ 风花雪月→ 月露风云→ 云游四海→ 海底捞针→ 针锋相对→ 对床夜语→ 语无伦次→ 次第花开→ 开花结果→ 果熟蒂落→ 落花流水→ 水性杨花→ 花貎蓬心→ 心口如一→ 一步一鬼→ 鬼头鬼脑→ 脑满肠肥→ 肥头大耳→ 耳食之谈→ 谈笑风生→ 生死相依→ 依依不舍→ 舍己为人→ 人命危浅→ 浅尝辄止→ 止暴禁非→ 非同寻常→ 常年累月→ 月晕而风→ 风风雨雨→ 雨打风吹→ 吹灰之力→ 力倍功半→ 半筹莫展→ 展翅高飞→ 飞蛾赴焰→ 焰火冲天→ 天罗地网→ 网开一面→ 面红耳赤→ 赤胆忠心→ 心高气傲→ 傲然屹立→ 立功赎罪→ 罪魁祸首→ 首善之区→ 区闻陬见→ 见兔放鹰→ 鹰击长空→ 空穴来风→ 风卷残云→ 云消雾散→ 散马休牛→ 牛毛细雨→ 雨过天青→ 青红皂白→ 白日做梦→ 梦寐以求→ 求志达道→ 道同义合→ 合两为一→ 一举成名→ 名垂后世→ 世世代代→ 代代相传→ 传道授业→ 业峻鸿绩→ 绩学之士→ 士饱马腾→ 腾达飞黄→ 黄道吉日→ 日薄西山→ 山光水色→ 色授魂与→ 与人为善→ 善男信女→ 女织男耕→ 耕当问奴→ 奴颜婢膝→ 膝痒搔背→ 背信弃义→ 义结金兰→ 兰质蕙心→ 心口如一→ 一举两得→ 得未曾有→ 有口无心→ 心口如一→ 一步登天→ 天南地北→ 北鄙之声→ 声东击西→ 西窗剪烛→ 烛照数计→ 计不反顾→ 顾影自怜→ 怜香惜玉→ 玉洁冰清→ 清风明月→ 月明星稀→ 稀世之宝→ 宝刀不老→ 老之将至→ 至亲好友→ 友风子雨→ 雨过天晴→ 晴空万里→ 里应外合→ 合浦珠还→ 还淳反古→ 古色古香→ 香车宝马→ 马到成功→ 功败垂成→ 成千上万→ 万众一心→ 心口如一→ 一见如故→ 故伎重演→ 演武修文→ 文武之道→ 道听途说→ 说长道短→ 短斤缺两→ 两全其美→ 美不胜收→ 收之桑榆→ 榆次之辱→ 辱国丧师→ 师出无名→ 名不副实→ 实逼处此→ 此伏彼起→ 起承转合→ 合胆同心→ 心不两用→ 用尽心机→ 机变如神→ 神安气集→ 集思广益→ 益寿延年→ 年高望重→ 重于泰山→ 山盟海誓→ 誓死不二→ 二姓之好→ 好景不长→ 长生不老→ 老调重弹→ 弹冠相庆→ 庆吊不行→ 行将就木→ 木已成舟→ 舟车劳顿→ 顿学累功→ 功成名就→ 就地正法→ 法网难逃→ 逃之夭夭→ 夭桃秾李→ 李代桃僵→ 僵持不下→ 下不为例→ 例行公事→ 事出有因→ 因小失大→ 大义灭亲→ 亲如手足→ 足智多谋→ 谋事在人→ 人定胜天→ 天涯海角→ 角巾东路→ 路见不平→ 平起平坐→ 坐怀不乱→ 乱作一团→ 团花簇锦→ 锦上添花→ 花前月下→ 下落不明→ 明哲保身→ 身体力行→ 行云流水→ 水涨船高→ 高傲自大→ 大得人心→ 心口如一→ 一飞冲天→ 天末凉风→ 风吹草动→ 动魄惊心→ 心惊胆战→ 战无不胜→ 胜友如云→ 云消雾散→ 散兵游勇→ 勇猛精进→ 进寸退尺→ 尺二秀才→ 才高八斗→ 斗转星移→ 移天换日→ 日新月异→ 异口同声→ 声色犬马→ 马首是瞻→ 瞻前顾后→ 后顾之忧→ 忧国忧民→ 民不聊生→ 生龙活虎→ 虎落平阳→ 阳关大道→ 道听途说→ 说一不二→ 二话不说→ 说长道短→ 短寿促命→ 命里注定→ 定国安邦→ 邦国殄瘁→ 瘁心劬力→ 力不胜任→ 任劳任怨→ 怨声载道→ 道义之交→ 交口称赞→ 赞不绝口→ 口若悬河→ 河倾月落→ 落落大方→ 方枘圆凿→ 凿壁偷光→ 光天化日→ 日积月累→ 累月经年→ 年深日久→ 久假不归→ 归心似箭→ 箭在弦上→ 上情下达→ 达官贵人→ 人极计生→ 生寄死归→ 归正守丘→ 丘山之功→ 功高震主→ 主贤臣良→ 良辰吉日→ 日薄桑榆→ 榆木疙瘩→ 瘩背啃膝→ 膝痒搔背→ 背城一战→ 战天斗地→ 地动山摇→ 摇头摆尾→ 尾大不掉→ 掉舌鼓唇→唇枪舌剑→ 剑胆琴心→ 心口如一→ 一丝不苟→ 苟且偷安→ 安分守己→ 己饥己溺→ 溺心灭质→ 质而不俚→ 俚俗不堪→ 堪以告慰→ 慰情胜无→ 无奇不有→ 有口无心→ 心口如一→ 一举成名→ 名扬四海→ 海纳百川→ 川泽纳污→ 污手垢面→ 面目全非→ 非分之财→ 财大气粗→ 粗衣淡饭→ 饭囊酒甕→ 甕牖桑枢→ 枢机之发→ 发奋图强→ 强本节用→ 用心竭力→ 力均势敌→ 敌国外患→ 患难之交→ 交臂相失→ 失惊打怪→ 怪力乱神→ 神工鬼力→ 力能扛鼎→ 鼎足之势→ 势穷力竭→ 竭尽心力→ 力透纸背→ 背水一战→ 战战兢兢→ 兢兢翼翼→ 翼翼小心→ 心急如火→ 火上浇油→ 油腔滑调→ 调墨弄笔→ 笔走龙蛇→ 蛇口蜂针→ 针锋相对→ 对牛弹琴→ 琴瑟和谐→ 谐声韵事→ 事必躬亲→ 亲如骨肉→ 肉跳神惊→ 惊弓之鸟→ 鸟尽弓藏→ 藏器待时→ 时不再来→ 来日方长→ 长生不老→ 老马识途→ 途遥日暮→ 暮气沉沉→ 沉鱼落雁→ 雁过留声→ 声色货利→ 利令志惛→ 惛惛罔罔→ 罔极之恩→ 恩重如山→ 山盟海誓→ 誓海盟山→ 山川米聚→ 聚精会神→ 神采飞扬→ 扬名四海→ 海阔天空→ 空穴来风→ 风华绝代→ 代代相传→ 传道授业→ 业峻鸿绩→ 绩学之士→ 士饱马腾→ 腾达飞黄→ 黄卷青灯→ 灯火辉煌→ 辉煌金碧→ 碧血丹心→ 心口如一→ 一举两得→ 得心应手→ 手不释卷→ 卷土重来→ 来日大难→ 难解难分→ 分秒必争→ 争猫丢牛→ 牛刀割鸡→ 鸡犬不宁→ 宁缺毋滥→ 滥用职权→ 权衡轻重→ 重见天日→ 日新月异→ 异口同声→ 声嘶力竭→ 竭尽全力→ 力不胜任→ 任劳任怨→ 怨天尤人→ 人存政举→ 举世闻名→ 名不虚传→ 传为佳话→ 话不投机→ 机不可失→ 失之交臂→ 臂有四肘→ 肘行膝步→ 步步为营→ 营私舞弊→ 弊绝风清→ 清风明月→ 月明星稀→ 稀世之珍→ 珍馐美馔→ 馔玉炊珠→ 珠联璧合→ 合浦珠还→ 还淳反古→ 古色古香→ 香车宝马→ 马到成功→ 功败垂成→ 成千上万→ 万众一心→ 心急如焚→ 焚膏继晷→ 晷日长度→ 度日如年→ 年深岁久→ 久别重逢→ 逢凶化吉→ 吉光片羽→ 羽毛未丰→ 丰功伟绩→ 绩勋卓著→ 著书立说→ 说长论短→ 短斤缺两→ 两全其美→ 美不胜收→ 收之桑榆→ 榆木疙瘩→ 瘩背啃膝→ 膝行而前→ 前赴后继→ 继往开来→ 来之不易→ 易如反掌→ 掌上明珠→ 珠围翠绕→ 绕梁之音→ 音容凄断→ 断章取义→ 义薄云天→ 天南地北→ 北窗高卧→ 卧薪尝胆→ 胆大如斗→ 斗转星移→ 移天换日→ 日薄西山→ 山珍海味→ 味如嚼蜡→ 腊尽春回→ 回天倒日→ 日暮途穷→ 穷山恶水→ 水到渠成→ 成王败寇→ 寇不可玩→ 玩世不恭→ 恭恭敬敬→ 敬如上宾→ 宾至如归→ 归心似箭→ 箭拔弩张→ 张冠李戴→ 戴罪立功→ 功高震主→ 主情造意→ 意气风发→ 发扬光大→ 大功告成→ 成一家言→ 言三语四→ 四时八节→ 节用裕民→ 民不堪命→ 命世之才→ 才高行厚→ 厚貌深辞→ 辞严意正→ 正气凛然→ 然糠自照→ 照功行赏→ 赏贤罚暴→ 暴虎冯河→ 河清海晏→ 晏然自若→ 若明若暗→ 暗中盘算→ 算无遗策→ 策名委质→ 质而不俚→ 俚俗不堪→ 堪以告慰→ 慰情胜无→ 无奇不有→ 有口无心→ 心口如一→ 一丝不挂→ 挂一漏万→ 万古长青→ 青天白日。
故障描述：
客户送修一G460笔记本故障为充电只能充到80%，然后显示电源已连接未充电。
维修过程：
更换电池，重装系统后故障依旧，为客户申请主板，更换后故障依旧。
解决方案：
更换电池主板后仍有故障，考虑到备件DOA的可能性很小，怀疑软件问题进联想电源管理；
然后点击右下角中间的设置按钮如下图；
将电池模式更改为最长续航时间（下图），故障消失；
此模式作用是保护电池，延长电池使用寿命，因为电池的使用寿命和充电周期有关，所以在使用“最长电池寿命”时，当充电电量达到一定值时会自动切断电池充电以保护电池。
