01_Handon_Guide(COIL Landscape and HANA Studio)

仪器仪表常用词汇英语翻译pH 计pH meterX 射线衍射仪X-ray diffractometerX 射线荧光光谱仪X-ray fluoresce nee spectrometer 力测量仪表force measuring instrument孑L板orifice plate文丘里管venturi tube水表water meter力口速度仪accelerometer可编程序控制器programmable controller平衡机balancing machine皮托管Pitot tube皮带秤belt weigher光线示波器light beam oscillograph光学高温计optical pyrometer光学显微镜optical microscope光谱仪器optical spectrum instrument吊车秤crane weigher地中衡platform weigher字符图形显示器character and graphic display 位移测量仪表displacement measuring instrument 巡迴检测装置data logger波纹管bellows 长度测量工具dimensional measuring instrument长度传感器linear transducer厚度计thickness gauge差热分析仪differential thermal analyzer扇形磁场质谱计sector magnetic field mass spectrometer 料斗秤hopper weigher核磁共振波谱仪nuclear magnetic resonance spectrometer气相色谱仪gas chromatograph浮球调节阀float adjusting valve真空计vacuum gauge动圈仪表moving-coil instrument基地式调节仪表local-m oun ted con troller密度计densitometer液位计liquid level meter组装式仪表package system减压阀pressure reducing valve测功器dynamometer紫外和可见光分光光度计ultraviolet-visible spectrometer顺序控制器sequence controller微处理器microprocessor温度调节仪表temperature controller煤气表gas meter节流阀throttle valve电子自动平衡仪表electronic self-balanee instrument电子秤electronic weigher电子微探针electron microprobe电子显微镜electron microscope弹簧管bourdon tube数字式显示仪表digital display instrument热流计heat-flow meter热量计heat flux meter热电阻resista nee temperature热电偶thermocouple膜片和膜盒diaphragm and diaphragm capsule调节阀regulating valve噪声计noise meter应变仪strain measuring instrument湿度计hygrometer声级计sound lever meter黏度计viscosimeter转矩测量仪表torque measuring instrument转速测量仪表tachometer露点仪dew-point meter变送器transmitter仪器仪表常用术语中英文对照带注释版性能特性performanee characteristic :确定仪器仪表功能和能力的有关参数及其定量的表述。

CODE CATALOG #THIS DO CUMENT IS THE PRO PERTY O F CARRIER CO RPO RATIO N AND IS DELIVERED UPO N THE EXPRESS CO NDITIO N THAT THE CO NTENTS WILL NO T BE DISCLO SED O R USED WITHOUT CARRIER CORPORATION’S WRITTEN CONSENT.PRODUCT INFORMATION IS SUBJECT TO CHANGE WITHOUT NOTICE.09/27/2021JOB NAME JOB NUMBER LOCATION BUYERBUYER #REVISIONDRAWING NUMBERSUBMISSIO N O F THESE DRAWINGS O R DO CU-MENTS D O ES N O T CONSTITUTE PART PER-FO RMANCE O R ACCEP-TANCE OF CONTRACT.DOAS UNIT SIZE 2WITH ELECTRIC HEAT &ELECTRONIC CONTROLS45Q1 OF 145Q -202-28SHEETBPLAN VIEW - LEFT HAND UNIT(Downstream Side Induced Coil Connection Shown)STANDARD FEATURES: 20GA. Galvanized Steel ConstructionSteel Control Enclosure for Electronic Components1/2’’ Thick Dual Density Fiberglass InsulationMeeting NFPA 90A and UL181 Safety Requirements1/3 HP ECM Motor with Permanently Lubricated BallBearings and Constant Airflow Program0-10Vdc Remote Control Manual Control 2-10Vdc Remote Control120V 208/240V 277VBottom Access Panel for ServiceSensible Cooling Coil Factory Installed on Induced Air Inlet Supplied with Drip Tray **4 Quadrant, Center Averaging Inlet Flow SensorFactory Supplied 24 Volt Control Transformer forElectronic ControlsETL ListedPerformance Data per AHRI Standard 880Construction Type Air Filter – 19” x 8 5/8” x 1” x 2* Check NEC for Unit Clearance Requirements OPTIONAL FEATURES:Liners 1/2’’ Cellular Insulation 1/2’’ Foil Encapsulated Fiberglass Insulation No Liner Cooling Coil Connection Upstream Side Left Hand ControlsMotor Toggle Disconnect Switch Dust Tight Control Enclosure 24VAC SSR LineaHeat Controlled SSR HeatDischarge Temperature Sensor Door Interlocking Disconnect Switch Fused Merv Type 8 Air Filter – 19’’ x 8 5/8’’ x 1’’ x 2Dual Access Panels for ServiceCam-Locks (Dual Access Panel Only)。

Provides the ultimate solution for measuring and monitoring precise tension on web process or wire machinery in demanding industrial environments•For use with pillow block bearingsand rotating shaft installations.•Washdown-duty, withstanding impinging liquids and wet environments.•Designed to comply withNEMA 4x, IP65/67 standards.•Completely sealed. Corrosion-resisting and chemical-resisting (Stainless Steel 410 or Anodized Aluminum Alloy 6061).•Competitively priced againstcommon non-sealed,non-corrosive and chemical resistive designs.•Compact low-profile design fits easily into tight places.•With an end connector design(instead of on the side), no need for a left hand and right hand configuration.•Available in a variety of loadcapacities (25 to 30,000 lb.)and sizes (6.5 to 17 inches long),suiting a wide range of applications.•Provides 500% overload protection.•Easily mounted at any angle.•Convenient mounting plate for easy installation of pillow block bearing.Performance BenefitsThe UPB (Under-Pillow-Block)Washdown-Duty LC (load cell) is part of the Cleveland-Kidder ®Tension Transducer product family. It sets the standard for under-pillow-block tension transducers for the web process industry.The UPB Washdown-Duty LC has a completely sealed corrosion-resisting design, making it ideal for use in demanding industrial environments,including the production of paper, steel,textiles, roofing shingles, linoleum,rubber, foil, and food products.The UPB Washdown-Duty LC can be mounted at any angle. Its web force direction is not restricted to being either parallel or perpendicular to the UPB top surface (common with other load cell designs). Its compact low profile design makes it perfect for use in both retrofit and OEM applications.The UPB Washdown-Duty LC is a solid one-piece design, providing a high natural frequency response with a high level, linear output signal.Design FeaturesThe Cleveland-Kidder UPB Washdown-Duty LC is an under-pillow-block load cell that has been designed formeasuring and monitoring tension on web process and wire machinery in demanding environments.The UPB Washdown-Duty LC is made from a solid block that results in a completely sealed design with a very low profile. It is typically applied in pairs,one under each of the supporting guide roll’s pillow block bearings. Mounting the pillow block bearing to the UPB Washdown-Duty LC is simple and convenient:Rather than having to drill into the top of the load cell, the UPB Washdown-Duty LC uses a convenient mounting plate, making installation andreplacement easier. Held in place by four corner bolts, the mounting plate is easily removed, drilled and tapped to match the pillow block mounting dimensions. The plate is then remounted and the pillow block bearing is bolted into place.To assure maximum corrosion and chemical resistance, the UPBWashdown-Duty LC is made from either Stainless Steel or Aluminum Alloy depending on the size rating.Displacement from loads is negligible (typically 0.002 in.) and the output is temperature compensated.UNDER PILLOW BLOCK WASHDOWN-DUTY LOAD CELLUPB Washdown-DutyLCSIZE RATING (LB) * ULTIMATE OVERLOAD (%)UPB125 to 1000500UPB21000 to 1000050020000250UPB310000 to 30000500INDUSTRIAL PRODUCTSDIMENSION TABLESRATINGSSize Dimensions in Inches*Maximum No.Working Force (lb.)A B C D E F(Max.)G H J(Max.)KL M N WUPB1 1.50.55 5.8 1.55/16(4) 5.8 1.6 1.951/2 1.40 6.5 1.3 2.2 2.2 25, 50, 100, 250, 500, 1000UPB230.8010.0 3.01/2 (4)10.0 3.0 2.473/4 1.6811.0 3.3 3.95 4.01000, 2500, 5000, 10000, 20000UPB3 4.51.6315.04.51 (4)15.6 5.04.21-1/8 2.8017.04.85.26.510000, 20000, 30000Size Dimensions in Millimeters*MaximumNo.Working Force (N)**A B C DE F(Max.)G H J(Max.)KLM N WUPB13814147.538M814740.549.5M1235.5165335656 110, 225, 450, 1100, 2250, 4500UPB2762025476M122547663M2042.5279.584100101.54500, 11000, 22000, 45000, 90000UPB311441.5381114M24396127106.5M307143212213216545000, 90000, 135000Weight lb. (kg.) Each UPB1UPB2UPB33.7 (1.7)22 (10)102 (47)*Ultimate overload: Maximum force applied on the transducer without risking permanentdeformation. For the Washdown duty UPB the output is linear up to the point of the ultimate overload.*Allow 2.5 inches clearance (64mm) for connector.**Approximate rating in Newtons.INDUSTRIAL PRODUCTS SPECIFICATIONSMaterial: Body: Size 1- Anodized Aluminum Alloy 6061Size 2 & 3 Stainless Steel 410Mounting Plates: Stainless Steel 304Bolts for Mounting Plates: Stainless Steel Gage Resistance:Each transducer contains a half-bridge,having a nominal end-to-end resistance of440-480 Ohms.Gage Factor:100 nominalExcitation Voltage:10 VDC or VAC (rms) maximumOutput Signal@ Rated M.W.F.: 100 mV nominal / transducer200 mV nominal / pair(With 10 VDC or VAC rms excitationvoltage)OperatingTemperature Range:0˚F to 200˚FSensitivity Changewith Temp.Less than 0.02% of rated outputtypicalHumidity:95% R.H.CombinedNon-linearityAnd hysteresis:±0.5% maximum of rated output Repeatability:±0.2% maximum of rated output“MS” Connectors:MS-3102E-10SL-3P(Sealed 3 Pin Connector)Input Impedancerequired:5K Ohms per transducer(TransducerSignal Amplifierif not CMCsupplied)Output impedance:880 Ohms for UPB2 and UPB3120 Ohms for UPB1UNDERPILLOWBLOCKWASHDOWN-DUTYLOAD CELLUPBWashdown-DutyLCProcedure for Mounting the UPB Load Cellto the Machine Frame (see picture above)Remove the pillow block mounting plate (it is held in place by four stainless steel corner bolts) in order to gain access to the four load cell mounting holes. Drill and tap the machine frame to match the load cell mounting holes. Note: The UPB must be oriented so that the resultant tension force direction (bisector of the wrap angle) isin the same quadrant as the load direction arrow on the side of the UPB.Bolt the load cell in place. The UPB load cell is designed so that either imperial or metric mounting bolts can be used when mounting the load cell to the machine frame. Refer to E in the Dimensions Table for the proper bolt size. Before remounting the pillow block mounting plate refer to the procedure below.Procedure for Mounting the Pillow Block Bearing to the UPB Load Cell (see picture above)Mounting the pillow block bearing to the UPB is simple and convenient. The UPB is offered with a pillow block mounting plate. The mounting plate is held in place by four stainless steel corner bolts. Remove the mounting plate, then drill and tap it to match the pillow block mounting dimensions. A centerline mark is provided on the mounting plate. The plate is to be drilled and tapped by utilizing this centerline mark to insure that the pillow block bearing is centered on the plate. Remount the plate and bolt the pillow block bearing to it. The mounting plate is 304 Stainless Steel, which is amenable to drilling but offers corrosive and chemical resistance. Refer to Jin the Dimensions Table for the maximum bolt diameterrecommended for bolting the pillow blockbearing to the mounting plate.INDUSTRIAL PRODUCTSUNDER PILLOW BLOCK WASHDOWN-DUTY LOAD CELLUPB LCSIZING CALCULATIONSIZING CALCULATION:ORDERING PROCEDURE:SELECTION CHARTT =Max Tension A =Wrap Angle (Degrees)W =Roll Weight B =Angle of Tension Force (Degrees)K =Overload for Transients(Nominally 1.4 for most applications)MWF = Maximum Working Force(This is used to select the proper force rating of the transducer)C =Mounting Angle H =Bearing Height + DSIZE L (in.) D (in.)UPB1 2.50.98UPB2 4.5 1.25UPB36.52.10A[2KT sin —][HsinB + LcosB] ±W[L cosC – HsinC]**2*The MWF calculation defines the force on each individual load cell.**If Angle B is below horizontal use + in calculation. If Angle B is above horizontal use - in calculation Note: Consult CMC for assistance in sizing the load cell to your specific application.1. Calculate the Maximum Working Force (MWF)rating based upon your calculations from the sizing calculation equation.2. From the Selection Chart,determine the part number for the UPB Washdown-Duty LC. Select a MWF rating that equals or exceeds the MWF from your sizing calculation. Then, make sure that your pillow block bearing fits the UPB type that you selected (UPB 1, 2,or 3).Example: If you calculate a MWF of 2,204 lbs., select a UPB 2 rated at 2,500 lbs.MWF from the Selection Chart. Your part number is M846-12172-100. Then,from the Dimensions Table,make sure that your pillow bearing fits on the Size 2transducer. If it does not,please consult factory.3. Obtain pricing and delivery information by contacting a CMC sales representative,distributor, or the factory.ACCESSORIESACCESSORIES CHART*Load Cell Cable The load cell cable end is provided with a straight or right angle connector as specified. The controller end is provided with tinned leads. The controller end of the cable can be cut to length if the standard length provided is not the exact length required.MWF* =2LTransducer MWF (lbs.)UPB1Rating25501002505001000Part No. M846-12171-000100200300400500UPB2Rating1000250050001000020000Part No. M846-12172-000100200300400UPB3Rating100002000030000Part No. M846-12173-000100200Cable Length Part Number - Straight Connector Part Number - Right Angle Connector 20 Feet MO-01948-020MO-01957-02025 Feet MO-01948-025MO-01957-02550 Feet MO-01948-050MO-01957-05075 Feet MO-01948-075MO-01957-075100 Feet MO-01948-100MO-09157-100150 FeetMO-01948-150MO-01957-150*Cables are not washdown-duty. For washdown-duty cables consult CMC.。

The Allen-Bradley® GuardShield™ 450L-E Safety Light Curtains fromRockwell Automation are based on a unique patented transceiver technology which allows each stick to be used as a transmitter or as a receiver. The full functionality of a transceiver is provided by plug-ins inserted at the bottom of the sticks. By using only one stick type with the optimal plug-ins selected based on the requirements of the application, the GuardShield 450L portfolio is a simple, cost effective solution that offers enhanced flexibility while maintaining the highest level of safety.The enhanced Integrated Laser Alignment System (ILAS) of the GuardShield 450L-E reduces installation time by providing multiple visible laser points that optimize setup with a simple touch of the ILAS symbol on the front window of the stick. Plus, the compact design and full length protective field make it easy to integrate a GuardShield 450L-E system in hand and finger protection applications from 150 mm (5.9 in.) up to 1950 mm (76.7 in.) in increments of 150 mm (0.5 ft).The enhanced GuardShield 450L-E light curtain system is also ideal for special applications requiring advanced functions such as muting and blanking,easily set up through DIP switches located on the dedicated plug-in modules. For muting, the common set ups like 4-sensor or 2-sensor with L- and T-configuration and override function can be selected. Blanking, reducedresolution, floating blanking and teach-in fixed blanking features are available. Configure up to eight protection zones via CCW software. A cascading plug-in can also be installed for series connection of additional GuardShield 450L Safety Light Curtain systems for multi-sided machine guarding (up to four sides). All these special functions combined with the inherent flexibility of the GuardShield 450L transceiver design help to simplify your engineering logistics and minimize the stock required to address your full range of applications.For configuration, monitoring and troubleshooting, our free Connected Components Workbench software is available at our website. A separate optical interface tool is required if using Connected Components Workbench software for diagnostic information.Features and Benefits• Extended features and functionality compared to the 450L-B such as cascading, built-in muting,blanking and multiple applications configuration• Embedded functions configured quickly and easily via DIP switches or software, significantly reducing engineering effort. These include:– M uting, blanking, start mode, external device monitoring (EDM), scanning range• Leverages patented transceiver technology – each stick can be used as a transmitter or receiver via innovative plug-in modules• Enhanced Integrated Laser Alignment System (ILAS) for quick installation and reliable operation• Active protective field provides sensing over the entire length of a transceiver• Compact design 30 mm x 30 mm (1.18 in. x 1.18 in.)• Wide range of protection heights 150…1950 mm (5.9…76.7 in.) in increments of 150 mm (0.5 ft)• Resolutions:– F inger resolution (14 mm): 0.5 to 9 m (1.64…29.53 ft)– H and resolution (30 mm): 0.9 to 16.2 m (2.95…53.15 ft)• Supports cascading of multiple systems in series• Flexible mounting options allow for quick and easy installation• Configure, monitor and troubleshoot via Connected Components Workbench (CCW) software.Pre-configure multiple configurations. • IP65 enclosure rating• TÜV certified Type 4 IEC 61496-1/-2, Ple, SILcl3 per EN ISO 13849-1,IEC 62061Allen-Bradley GuardShield 450L-E Safety Light CurtainEnhanced Flexibility and Advanced Features in a Cost-Effective Safety SolutionRequired Accessories 1Replace the x with 2 (6.6 ft), 5 (16.4 ft), 10 (32.8 ft), 15 (49.2 ft), 20 (65.6 ft), or 30 (98.4 ft) for available lengths in metersOptional Accessories*Requires 450L fw version 4.00x and CCW R12 at minimum.1xxxx = 0150…1950 mm (0.5…6.4 ft) in increments of 150 mm (0.5 ft)Innovative plug-in modulesestablish transceiver as an emitter orreceiver and provide other advanced functions.Integrated Laser Alignment System accelerates setup for optimal performance at the touch of a button.General Ordering InformationLight Curtain System: Order two identical transceivers/catalog numbers. Plug-in Modules: Order one transmitter and one receiver plug-in with the desired functionality for one system – or – Order two universal plug-ins for one system. Each universal plug-in can be used as a transmitter or a receiver. To cascade systems or for muting options use thecascading plug-in.1xxxx = 0150 … 1950 mm (0.5 … 6.4 ft) in increments of 150 mm (0.5 ft).For example: “450L-E4HL 0900YD” indicates an order for 900 mm hand detection light curtain transceiver.2Optional side mounting bracket kit is available below.1 The 8-pin transmitter plug-in option allows two 8-pin cordsets to be used in one system.2Order two universal plug-ins for one system. Each universal plug-in can be used as a transmitter or a receiver.Publication 450L-PP002B-EN-P – May 2020 | Supersedes Publication 450L-PP002A-EN-P – January 2018Copyright © 2020 Rockwell Automation, Inc. All Rights Reserved. Printed in USA.Allen-Bradley, Connected Components Workbench, Expanding human potential and GuardShield are trademarks of Rockwell Automation, Inc.Trademarks not belonging to Rockwell Automation are property of their respective companies.Connect with us.。

桂林2024年09版小学3年级英语全练全测(含答案)考试时间：90分钟（总分:120）A卷考试人：_________题号一二三四五总分得分一、综合题(共计100题)1、填空题：I have a soft ________ (玩具熊) that I hug every night before I sleep.2、填空题：My uncle is a great __________ (故事讲述者).3、填空题：The process of photosynthesis takes place in the ______ of plants.(光合作用发生在植物的叶子中。

)4、听力题：The dog is _____ (大).5、What is the primary ingredient in a salad?A. MeatB. VegetablesC. FruitD. Grains答案:B6、填空题：I enjoy going to the ________ (游乐场) during summer.7、What is the shape of a basketball?A. SquareB. RectangleC. CircleD. Oval8、填空题：A lemur is known for its large ______ (眼睛).I like ___ (swimming) in summer.10、听力题：A chemical reaction can produce ______.11、What is the name of the vast region containing thousands of galaxies?A. SuperclusterB. Galaxy ClusterC. NebulaD. Cosmic Web12、填空题：The __________ (历史的记录保持) ensures accuracy.13、How many fingers do you have on one hand?A. FourB. FiveC. SixD. Seven14、What do we call the place where we go to see books?A. LibraryB. StoreC. MuseumD. Gym15、填空题：The ________ was a significant battle in World War II.16、填空题：The __________ (历史的共同体) fosters belonging.17、填空题：My family loves to watch movies ____.18、What do you call a large area of land covered with grass?A. DesertB. PrairieC. ForestD. Swamp答案:B19、听力题：I have __________ apples in my bag.An electronegative atom pulls electrons towards itself during a ______ bond.21、听力题：The chemical formula for cobalt(II) nitrate is _____.22、What do we call a tool used to measure length?A. ThermometerB. ScaleC. Tape measureD. Ruler答案: D. Ruler23、What is the term for a planet that is too hot for liquid water?A. Desert PlanetB. Gas GiantC. Hot JupiterD. Ice Giant24、Which planet is known for its beautiful rings?A. JupiterB. SaturnC. UranusD. Neptune25、填空题：The phone is ringing ________ (响).26、什么是美国著名作家，因其小说《了不起的盖茨比》而闻名？A. F. Scott FitzgeraldB. Ernest HemingwayC. Mark TwainD. John Steinbeck答案: A27、What is the name of the famous bear who loves to go on adventures with his friends?A. Paddington BearB. Winnie the PoohC. Yogi BearD. Baloo答案: B28、填空题：Many plants have _____ (独特的特征) that help them survive.The _____ (donkey) is braying.30、Which season comes after summer?A. WinterB. FallC. SpringD. Rainy答案:B31、填空题：The _____ (松鼠) is gathering nuts for the winter.32、填空题：I want to _____ (grow) my own vegetables.33、听力题：The cat is _____ on the windowsill. (sitting)34、What is the opposite of open?A. CloseB. ShutC. BlockD. Both A and B答案:D35、听力题：The process of making beer involves fermentation of _______.36、填空题：I enjoy playing ________ (电子游戏) on my computer.37、填空题：A ______ (温室气候) is controlled for optimal plant growth.38、填空题：I like to _______ (参加) sports teams at school.39、填空题：My favorite sport is _______ (滑雪).40、听力题：The sun is _______ (shining) through the trees.A chemical reaction can change the physical and chemical ______ of substances.42、填空题：The __________ (历史的思考方式) shape our discourse.43、oasis) is a fertile area in a desert. 填空题：The ____44、填空题：The ______ (植物纤维) can be used for making clothes.45、填空题：The ______ (小鸟) makes a beautiful nest.46、What do you call a person who studies human behavior?A. PsychologistB. SociologistC. AnthropologistD. Historian答案:A47、听力题：We need to ________ our homework.48、What is 5 + 7?A. 10B. 11C. 12D. 13答案:C49、What is the main purpose of a museum?A. To collect taxesB. To showcase art and historyC. To provide entertainmentD. To promote tourism答案: B50、填空题：The __________ (边界) is marked by a river.51、听力题：An object that floats on water displaces its own ______ (weight) in water.I go to school by ______.53、填空题：My friend is very __________ (自信).54、What do we call a young eel?A. FryB. ElverC. KitD. Pup答案:B. Elver55、听力题：An electromagnet is created by passing electricity through a ______ (coil).56、听力题：The chemical formula for neodymium oxide is _____.57、What is the opposite of "young"?A. OldB. NewC. FreshD. Young58、填空题：The __________ (环境科学) informs conservation efforts.59、What is the name of the famous Scottish lake said to be home to a monster?A. Loch NessB. Lake SuperiorC. Lake TahoeD. Lake Victoria答案：A60、听力题：The __________ is a large area of land used for agriculture.61、What do you call the process of changing waste into reusable materials?A. RecyclingB. CompostingC. DisposingD. Collecting答案: AI love _____ (exploring) different habitats.63、What do you call a large area of water surrounded by land?A. SeaB. OceanC. LakeD. Pond答案:C64、听力填空题：My favorite season is __________ because I enjoy __________.65、填空题：The parrot can be very ________________ (吵闹).66、What is the name of the famous tower in Paris?A. Eiffel TowerB. Louvre MuseumC. Arc de TriompheD. Palais Garnier答案: A. Eiffel Tower67、填空题：Learning new languages is very ________ (有趣).68、听力题：A __________ is a reaction that involves the combination of substances.69、选择题：What is the capital of Norway?A. OsloB. BergenC. StavangerD. Tromsø70、What is the capital city of Brazil?A. Rio de JaneiroB. BrasiliaC. Sao PauloD. Salvador71、填空题：My _______ (狗) loves to play fetch with a stick.72、What do we call the lines on a globe that run from north to south?A. LatitudeB. LongitudeC. EquatorD. Prime Meridian答案:B73、填空题：A _____ (peony) bush is stunning in bloom.74、听力题：Astronomers use radio telescopes to study ______ waves from space.75、填空题：A ______ (休闲园) offers a peaceful escape.76、听力题：The chemical process of photosynthesis converts sunlight into _____.77、听力题：The __________ is the lowest point of a river.78、听力题：Glaciers can shape the landscape by ______ the ground beneath them.79、填空题：The __________ (南北战争) ended in 1865.80、听力题：I have a _____ (拼图) to solve.81、What is 20 ÷ 4?A. 4B. 5C. 6D. 7答案:B82、填空题：The first successful heart surgery was performed in ________ (20世纪).83、填空题：The _____ (空气) around us is important for plants to breathe.84、Which one is a holiday celebrated in December?A. HalloweenB. ThanksgivingC. ChristmasD. Easter85、填空题：I love to go ______ (滑沙) in the desert.86、听力题：The _______ of sound can create echoes in certain locations.87、听力题：The bear has thick _____ fur.88、填空题：__________ (化学信息) is critical for advancing research and development.89、填空题：The ______ (蛇) can be found in many different colors.90、填空题：We have a ______ (快乐的) family tradition for holidays.91、填空题：The _____ (种植) of trees helps the environment.92、填空题：I think animals are very _______ (形容词). They bring joy and _______ (快乐) to our lives.93、What do we call a journey to a place for pleasure?A. TripB. BusinessC. CommuteD. Expedition94、填空题：The ancient Greeks held their festivals in honor of ______ (宙斯).95、What is the name of the currency used in the United States?A. EuroB. PesoC. DollarD. Yen答案:C96、填空题：The _____ (小鸭) paddles in the pond with its family.97、填空题：I have a stuffed ________ (动物名称) as my favorite ________ (玩具).98、填空题：The __________ (历史的情感表达) forge connections.99、How do you spell "cat"?A. KatB. CatC. KattD. Catt答案:B100、听力题：I like to _____ (参观) historical sites.。

Secrets of Optical Flow Estimation and Their PrinciplesDeqing Sun Brown UniversityStefan RothTU DarmstadtMichael J.BlackBrown UniversityAbstractThe accuracy of opticalflow estimation algorithms has been improving steadily as evidenced by results on the Middlebury opticalflow benchmark.The typical formula-tion,however,has changed little since the work of Horn and Schunck.We attempt to uncover what has made re-cent advances possible through a thorough analysis of how the objective function,the optimization method,and mod-ern implementation practices influence accuracy.We dis-cover that“classical”flow formulations perform surpris-ingly well when combined with modern optimization and implementation techniques.Moreover,wefind that while medianfiltering of intermediateflowfields during optimiza-tion is a key to recent performance gains,it leads to higher energy solutions.To understand the principles behind this phenomenon,we derive a new objective that formalizes the medianfiltering heuristic.This objective includes a non-local term that robustly integratesflow estimates over large spatial neighborhoods.By modifying this new term to in-clude information aboutflow and image boundaries we de-velop a method that ranks at the top of the Middlebury benchmark.1.IntroductionThefield of opticalflow estimation is making steady progress as evidenced by the increasing accuracy of cur-rent methods on the Middlebury opticalflow benchmark [6].After nearly30years of research,these methods have obtained an impressive level of reliability and accuracy [33,34,35,40].But what has led to this progress?The majority of today’s methods strongly resemble the original formulation of Horn and Schunck(HS)[18].They combine a data term that assumes constancy of some image property with a spatial term that models how theflow is expected to vary across the image.An objective function combin-ing these two terms is then optimized.Given that this basic structure is unchanged since HS,what has enabled the per-formance gains of modern approaches?The paper has three parts.In thefirst,we perform an ex-tensive study of current opticalflow methods and models.The most accurate methods on the Middleburyflow dataset make different choices about how to model the objective function,how to approximate this model to make it com-putationally tractable,and how to optimize it.Since most published methods change all of these properties at once, it can be difficult to know which choices are most impor-tant.To address this,we define a baseline algorithm that is“classical”,in that it is a direct descendant of the original HS formulation,and then systematically vary the model and method using different techniques from the art.The results are surprising.Wefind that only a small number of key choices produce statistically significant improvements and that they can be combined into a very simple method that achieves accuracies near the state of the art.More impor-tantly,our analysis reveals what makes currentflow meth-ods work so well.Part two examines the principles behind this success.We find that one algorithmic choice produces the most signifi-cant improvements:applying a medianfilter to intermedi-ateflow values during incremental estimation and warping [33,34].While this heuristic improves the accuracy of the recoveredflowfields,it actually increases the energy of the objective function.This suggests that what is being opti-mized is actually a new and different ing ob-servations about medianfiltering and L1energy minimiza-tion from Li and Osher[23],we formulate a new non-local term that is added to the original,classical objective.This new term goes beyond standard local(pairwise)smoothness to robustly integrate information over large spatial neigh-borhoods.We show that minimizing this new energy ap-proximates the original optimization with the heuristic me-dianfiltering step.Note,however,that the new objective falls outside our definition of classical methods.Finally,once the medianfiltering heuristic is formulated as a non-local term in the objective,we immediately recog-nize how to modify and improve it.In part three we show how information about image structure andflow boundaries can be incorporated into a weighted version of the non-local term to prevent over-smoothing across boundaries.By in-corporating structure from the image,this weighted version does not suffer from some of the errors produced by median filtering.At the time of publication(March2010),the re-sulting approach is ranked1st in both angular and end-point errors in the Middlebury evaluation.In summary,the contributions of this paper are to(1)an-alyze currentflow models and methods to understand which design choices matter;(2)formulate and compare several classical objectives descended from HS using modern meth-ods;(3)formalize one of the key heuristics and derive a new objective function that includes a non-local term;(4)mod-ify this new objective to produce a state-of-the-art method. In doing this,we provide a“recipe”for others studying op-ticalflow that can guide their design choices.Finally,to en-able comparison and further innovation,we provide a public M ATLAB implementation[1].2.Previous WorkIt is important to separately analyze the contributions of the objective function that defines the problem(the model) and the optimization algorithm and implementation used to minimize it(the method).The HS formulation,for example, has long been thought to be highly inaccurate.Barron et al.[7]reported an average angular error(AAE)of~30degrees on the“Yosemite”sequence.This confounds the objective function with the particular optimization method proposed by Horn and Schunck1.When optimized with today’s meth-ods,the HS objective achieves surprisingly competitive re-sults despite the expected over-smoothing and sensitivity to outliers.Models:The global formulation of opticalflow intro-duced by Horn and Schunck[18]relies on both brightness constancy and spatial smoothness assumptions,but suffers from the fact that the quadratic formulation is not robust to outliers.Black and Anandan[10]addressed this by re-placing the quadratic error function with a robust formula-tion.Subsequently,many different robust functions have been explored[12,22,31]and it remains unclear which is best.We refer to all these spatially-discrete formulations derived from HS as“classical.”We systematically explore variations in the formulation and optimization of these ap-proaches.The surprise is that the classical model,appropri-ately implemented,remains very competitive.There are many formulations beyond the classical ones that we do not consider here.Significant ones use oriented smoothness[25,31,33,40],rigidity constraints[32,33], or image segmentation[9,21,41,37].While they deserve similar careful consideration,we expect many of our con-clusions to carry forward.Note that one can select among a set of models for a given sequence[4],instead offinding a “best”model for all the sequences.Methods:Many of the implementation details that are thought to be important date back to the early days of op-1They noted that the correct way to optimize their objective is by solv-ing a system of linear equations as is common today.This was impractical on the computers of the day so they used a heuristic method.ticalflow.Current best practices include coarse-to-fine es-timation to deal with large motions[8,13],texture decom-position[32,34]or high-orderfilter constancy[3,12,16, 22,40]to reduce the influence of lighting changes,bicubic interpolation-based warping[22,34],temporal averaging of image derivatives[17,34],graduated non-convexity[11]to minimize non-convex energies[10,31],and medianfilter-ing after each incremental estimation step to remove outliers [34].This medianfiltering heuristic is of particular interest as it makes non-robust methods more robust and improves the accuracy of all methods we tested.The effect on the objec-tive function and the underlying reason for its success have not previously been analyzed.Least median squares estima-tion can be used to robustly reject outliers inflow estimation [5],but previous work has focused on the data term.Related to medianfiltering,and our new non-local term, is the use of bilateralfiltering to prevent smoothing across motion boundaries[36].The approach separates a varia-tional method into twofiltering update stages,and replaces the original anisotropic diffusion process with multi-cue driven bilateralfiltering.As with medianfiltering,the bi-lateralfiltering step changes the original energy function.Models that are formulated with an L1robust penalty are often coupled with specialized total variation(TV)op-timization methods[39].Here we focus on generic opti-mization methods that can apply to any model andfind they perform as well as reported results for specialized methods.Despite recent algorithmic advances,there is a lack of publicly available,easy to use,and accurateflow estimation software.The GPU4Vision project[2]has made a substan-tial effort to change this and provides executablefiles for several accurate methods[32,33,34,35].The dependence on the GPU and the lack of source code are limitations.We hope that our public M ATLAB code will not only help in un-derstanding the“secrets”of opticalflow,but also let others exploit opticalflow as a useful tool in computer vision and relatedfields.3.Classical ModelsWe write the“classical”opticalflow objective function in its spatially discrete form asE(u,v)=∑i,j{ρD(I1(i,j)−I2(i+u i,j,j+v i,j))(1)+λ[ρS(u i,j−u i+1,j)+ρS(u i,j−u i,j+1)+ρS(v i,j−v i+1,j)+ρS(v i,j−v i,j+1)]}, where u and v are the horizontal and vertical components of the opticalflowfield to be estimated from images I1and I2,λis a regularization parameter,andρD andρS are the data and spatial penalty functions.We consider three different penalty functions:(1)the quadratic HS penaltyρ(x)=x2;(2)the Charbonnier penaltyρ(x)=√x2+ 2[13],a dif-ferentiable variant of the L1norm,the most robust convexfunction;and(3)the Lorentzianρ(x)=log(1+x22σ2),whichis a non-convex robust penalty used in[10].Note that this classical model is related to a standard pairwise Markov randomfield(MRF)based on a4-neighborhood.In the remainder of this section we define a baseline method using several techniques from the literature.This is not the“best”method,but includes modern techniques and will be used for comparison.We only briefly describe the main choices,which are explored in more detail in the following section and the cited references,especially[30].Quantitative results are presented throughout the remain-der of the text.In all cases we report the average end-point error(EPE)on the Middlebury training and test sets,de-pending on the experiment.Given the extensive nature of the evaluation,only average results are presented in the main body,while the details for each individual sequence are given in[30].3.1.Baseline methodsTo gain robustness against lighting changes,we follow [34]and apply the Rudin-Osher-Fatemi(ROF)structure texture decomposition method[28]to pre-process the in-put sequences and linearly combine the texture and struc-ture components(in the proportion20:1).The parameters are set according to[34].Optimization is performed using a standard incremental multi-resolution technique(e.g.[10,13])to estimateflow fields with large displacements.The opticalflow estimated at a coarse level is used to warp the second image toward thefirst at the nextfiner level,and aflow increment is cal-culated between thefirst image and the warped second im-age.The standard deviation of the Gaussian anti-aliasingfilter is set to be1√2d ,where d denotes the downsamplingfactor.Each level is recursively downsampled from its near-est lower level.In building the pyramid,the downsampling factor is not critical as pointed out in the next section and here we use the settings in[31],which uses a factor of0.8 in thefinal stages of the optimization.We adaptively de-termine the number of pyramid levels so that the top level has a width or height of around20to30pixels.At each pyramid level,we perform10warping steps to compute the flow increment.At each warping step,we linearize the data term,whichinvolves computing terms of the type∂∂x I2(i+u k i,j,j+v k i,j),where∂/∂x denotes the partial derivative in the horizon-tal direction,u k and v k denote the currentflow estimate at iteration k.As suggested in[34],we compute the deriva-tives of the second image using the5-point derivativefilter1 12[−180−81],and warp the second image and its deriva-tives toward thefirst using the currentflow estimate by bicu-bic interpolation.We then compute the spatial derivatives ofAvg.Rank Avg.EPEClassic-C14.90.408HS24.60.501Classic-L19.80.530HS[31]35.10.872BA(Classic-L)[31]30.90.746Adaptive[33]11.50.401Complementary OF[40]10.10.485Table1.Models.Average rank and end-point error(EPE)on the Middlebury test set using different penalty functions.Two current methods are included for comparison.thefirst image,average with the warped derivatives of the second image(c.f.[17]),and use this in place of∂I2∂x.For pixels moving out of the image boundaries,we set both their corresponding temporal and spatial derivatives to zero.Af-ter each warping step,we apply a5×5medianfilter to the newly computedflowfield to remove outliers[34].For the Charbonnier(Classic-C)and Lorentzian (Classic-L)penalty function,we use a graduated non-convexity(GNC)scheme[11]as described in[31]that lin-early combines a quadratic objective with a robust objective in varying proportions,from fully quadratic to fully robust. Unlike[31],a single regularization weightλis used for both the quadratic and the robust objective functions.3.2.Baseline resultsThe regularization parameterλis selected among a set of candidate values to achieve the best average end-point error (EPE)on the Middlebury training set.For the Charbonnier penalty function,the candidate set is[1,3,5,8,10]and 5is optimal.The Charbonnier penalty uses =0.001for both the data and the spatial term in Eq.(1).The Lorentzian usesσ=1.5for the data term,andσ=0.03for the spa-tial term.These parameters arefixed throughout the exper-iments,except where mentioned.Table1summarizes the EPE results of the basic model with three different penalty functions on the Middlebury test set,along with the two top performers at the time of publication(considering only published papers).The clas-sic formulations with two non-quadratic penalty functions (Classic-C)and(Classic-L)achieve competitive results de-spite their simplicity.The baseline optimization of HS and BA(Classic-L)results in significantly better accuracy than previously reported for these models[31].Note that the analysis also holds for the training set(Table2).At the time of publication,Classic-C ranks13th in av-erage EPE and15th in AAE in the Middlebury benchmark despite its simplicity,and it serves as the baseline below.It is worth noting that the spatially discrete MRF formulation taken here is competitive with variational methods such as [33].Moreover,our baseline implementation of HS has a lower average EPE than many more sophisticated methods.Avg.EPE significance p-value Classic-C0.298——HS0.38410.0078Classic-L0.31910.0078Classic-C-brightness0.28800.9453HS-brightness0.38710.0078Classic-L-brightness0.32500.2969Gradient0.30500.4609Table2.Pre-Processing.Average end-point error(EPE)on the Middlebury training set for the baseline method(Classic-C)using different pre-processing techniques.Significance is always with respect to Classic-C.4.Secrets ExploredWe evaluate a range of variations from the baseline ap-proach that have appeared in the literature,in order to illu-minate which may be of importance.This analysis is per-formed on the Middlebury training set by changing only one property at a time.Statistical significance is determined using a Wilcoxon signed rank test between each modified method and the baseline Classic-C;a p value less than0.05 indicates a significant difference.Pre-Processing.For each method,we optimize the regu-larization parameterλfor the training sequences and report the results in Table2.The baseline uses a non-linear pre-filtering of the images to reduce the influence of illumina-tion changes[34].Table2shows the effect of removing this and using a standard brightness constancy model(*-brightness).Classic-C-brightness actually achieves lower EPE on the training set than Classic-C but significantly lower accuracy on the test set:Classic-C-brightness= 0.726,HS-brightness=0.759,and Classic-L-brightness =0.603–see Table1for comparison.This disparity sug-gests overfitting is more severe for the brightness constancy assumption.Gradient only imposes constancy of the gra-dient vector at each pixel as proposed in[12](i.e.it robustly penalizes Euclidean distance between image gradients)and has similar performance in both training and test sets(c.f. Table8).See[30]for results of more alternatives. Secrets:Some form of imagefiltering is useful but simple derivative constancy is nearly as good as the more sophisti-cated texture decomposition method.Coarse-to-fine estimation and GNC.We vary the number of warping steps per pyramid level andfind that3warping steps gives similar results as using10(Table3).For the GNC scheme,[31]uses a downsampling factor of0.8for non-convex optimization.A downsampling factor of0.5 (Down-0.5),however,has nearly identical performance Removing the GNC step for the Charbonnier penalty function(w/o GNC)results in higher EPE on most se-quences and higher energy on all sequences(Table4).This suggests that the GNC method is helpful even for the con-vex Charbonnier penalty function due to the nonlinearity ofAvg.EPE significance p-value Classic-C0.298——3warping steps0.30400.9688Down-0.50.2980 1.0000w/o GNC0.35400.1094Bilinear0.30200.1016w/o TA VG0.30600.1562Central derivativefilter0.30000.72667-point derivativefilter[13]0.30200.3125Bicubic-II0.29010.0391GC-0.45(λ=3)0.29210.0156GC-0.25(λ=0.7)0.2980 1.0000MF3×30.30500.1016MF7×70.30500.56252×MF0.3000 1.00005×MF0.30500.6875w/o MF0.35210.0078Classic++0.28510.0078 Table3.Model and Methods.Average end-point error(EPE)on the Middlebury training set for the baseline method(Classic-C) using different algorithm and modelingchoices.Figure1.Different penalty functions for the spatial terms:Char-bonnier( =0.001),generalized Charbonnier(a=0.45and a=0.25),and Lorentzian(σ=0.03).the data term.Secrets:The downsampling factor does not matter when using a convex penalty;a standard factor of0.5isfine. Some form of GNC is useful even for a convex robust penalty like Charbonnier because of the nonlinear data term. Interpolation method and derivatives.Wefind that bicu-bic interpolation is more accurate than bilinear(Table3, Bilinear),as already reported in previous work[34].Re-moving temporal averaging of the gradients(w/o TA VG), using Central differencefilters,or using a7-point deriva-tivefilter[13]all reduce accuracy compared to the base-line,but not significantly.The M ATLAB built-in function interp2is based on cubic convolution approximation[20]. The spline-based interpolation scheme[26]is consistently better(Bicubic-II).See[30]for more discussions. Secrets:Use spline-based bicubic interpolation with a5-pointfilter.Temporal averaging of the derivatives is proba-bly worthwhile for a small computational expense. Penalty functions.Wefind that the convex Charbonnier penalty performs better than the more robust,non-convex Lorentzian on both the training and test sets.One reason might be that non-convex functions are more difficult to op-timize,causing the optimization scheme tofind a poor local(a)With medianfiltering(b)Without medianfilteringFigure2.Estimatedflowfields on sequence“RubberWhale”using Classic-C with and without(w/o MF)the medianfiltering step. Color coding as in[6].(a)(w/MF)energy502,387and(b)(w/o MF)energy449,290.The medianfiltering step helps reach a so-lution free from outliers but with a higher energy.optimum.We investigate a generalized Charbonnier penalty functionρ(x)=(x2+ 2)a that is equal to the Charbon-nier penalty when a=0.5,and non-convex when a<0.5 (see Figure1).We optimize the regularization parameterλagain.Wefind a slightly non-convex penalty with a=0.45 (GC-0.45)performs consistently better than the Charbon-nier penalty,whereas more non-convex penalties(GC-0.25 with a=0.25)show no improvement.Secrets:The less-robust Charbonnier is preferable to the Lorentzian and a slightly non-convex penalty function(GC-0.45)is better still.Medianfiltering.The baseline5×5medianfilter(MF 5×5)is better than both MF3×3[34]and MF7×7but the difference is not significant(Table3).When we perform5×5medianfiltering twice(2×MF)orfive times(5×MF)per warping step,the results are worse.Finally,removing the medianfiltering step(w/o MF)makes the computedflow significantly less accurate with larger outliers as shown in Table3and Figure2.Secrets:Medianfiltering the intermediateflow results once after every warping iteration is the single most important secret;5×5is a goodfilter size.4.1.Best PracticesCombining the analysis above into a single approach means modifying the baseline to use the slightly non-convex generalized Charbonnier and the spline-based bicu-bic interpolation.This leads to a statistically significant improvement over the baseline(Table3,Classic++).This method is directly descended from HS and BA,yet updated with the current best optimization practices known to us. This simple method ranks9th in EPE and12th in AAE on the Middlebury test set.5.Models Underlying Median FilteringOur analysis reveals the practical importance of median filtering during optimization to denoise theflowfield.We ask whether there is a principle underlying this heuristic?One interesting observation is thatflowfields obtained with medianfiltering have substantially higher energy than those without(Table4and Figure2).If the medianfilter is helping to optimize the objective,it should lead to lower energies.Higher energies and more accurate estimates sug-gest that incorporating medianfiltering changes the objec-tive function being optimized.The insight that follows from this is that the medianfil-tering heuristic is related to the minimization of an objective function that differs from the classical one.In particular the optimization of Eq.(1),with interleaved medianfiltering, approximately minimizesE A(u,v,ˆu,ˆv)=(2)∑i,j{ρD(I1(i,j)−I2(i+u i,j,j+v i,j))+λ[ρS(u i,j−u i+1,j)+ρS(u i,j−u i,j+1)+ρS(v i,j−v i+1,j)+ρS(v i,j−v i,j+1)]}+λ2(||u−ˆu||2+||v−ˆv||2)+∑i,j∑(i ,j )∈N i,jλ3(|ˆu i,j−ˆu i ,j |+|ˆv i,j−ˆv i ,j |),whereˆu andˆv denote an auxiliaryflowfield,N i,j is the set of neighbors of pixel(i,j)in a possibly large area andλ2 andλ3are scalar weights.The term in braces is the same as theflow energy from Eq.(1),while the last term is new. This non-local term[14,15]imposes a particular smooth-ness assumption within a specified region of the auxiliary flowfieldˆu,ˆv2.Here we take this term to be a5×5rectan-gular region to match the size of the medianfilter in Classic-C.A third(coupling)term encouragesˆu,ˆv and u,v to be the same(c.f.[33,39]).The connection to medianfiltering(as a denoising method)derives from the fact that there is a direct relation-ship between the median and L1minimization.Consider a simplified version of Eq.(2)with just the coupling and non-local terms,where E(ˆu)=λ2||u−ˆu||2+∑i,j∑(i ,j )∈N i,jλ3|ˆu i,j−ˆu i ,j |.(3)While minimizing this is similar to medianfiltering u,there are two differences.First,the non-local term minimizes the L1distance between the central value and allflow values in its neighborhood except itself.Second,Eq.(3)incorpo-rates information about the data term through the coupling equation;medianfiltering theflow ignores the data term.The formal connection between Eq.(3)and medianfil-tering3is provided by Li and Osher[23]who show that min-2Bruhn et al.[13]also integrated information over a local region in a global method but did so for the data term.3Hsiao et al.[19]established the connection in a slightly different way.Classic-C 0.5890.7480.8660.502 1.816 2.317 1.126 1.424w/o GNC 0.5930.7500.8700.506 1.845 2.518 1.142 1.465w/o MF0.5170.7010.6680.449 1.418 1.830 1.066 1.395Table 4.Eq.(1)energy (×106)for the optical flow fields computed on the Middlebury training set .Note that Classic-C uses graduated non-convexity (GNC),which reduces the energy,and median filtering,which increases it.imizing Eq.(3)is related to a different median computationˆu (k +1)i,j=median (Neighbors (k )∪Data)(4)where Neighbors (k )={ˆu (k )i ,j }for (i ,j )∈N i,j and ˆu (0)=u as well as Data ={u i,j ,u i,j ±λ3λ2,u i,j±2λ3λ2···,u i,j ±|N i,j |λ32λ2},where |N i,j |denotes the (even)number of neighbors of (i,j ).Note that the set of “data”values is balanced with an equal number of elements on either side of the value u i,j and that information about the data term is included through u i,j .Repeated application of Eq.(4)converges rapidly [23].Observe that,as λ3/λ2increases,the weighted data val-ues on either side of u i,j move away from the values of Neighbors and cancel each other out.As this happens,Eq.(4)approximates the median at the first iterationˆu (1)i,j ≈median (Neighbors (0)∪{u i,j }).(5)Eq.(2)thus combines the original objective with an ap-proximation to the median,the influence of which is con-trolled by λ3/λ2.Note in practice the weight λ2on thecoupling term is usually small or is steadily increased from small values [34,39].We optimize the new objective (2)by alternately minimizingE O (u ,v )=∑i,jρD (I 1(i,j )−I 2(i +u i,j ,j +v i,j ))+λ[ρS (u i,j −u i +1,j )+ρS (u i,j −u i,j +1)+ρS (v i,j −v i +1,j )+ρS (v i,j −v i,j +1)]+λ2(||u −ˆu ||2+||v −ˆv ||2)(6)andE M (ˆu ,ˆv )=λ2(||u −ˆu ||2+||v −ˆv ||2)(7)+∑i,j ∑(i ,j )∈N i,jλ3(|ˆu i,j −ˆu i ,j |+|ˆv i,j −ˆv i ,j |).Note that an alternative formulation would drop the cou-pling term and impose the non-local term directly on u and v .We find that optimization of the coupled set of equations is superior in terms of EPE performance.The alternating optimization strategy first holds ˆu ,ˆv fixed and minimizes Eq.(6)w.r.t.u ,v .Then,with u ,v fixed,we minimize Eq.(7)w.r.t.ˆu ,ˆv .Note that Eqs.(3)andAvg.EPE significancep -value Classic-C0.298——Classic-C-A0.30500.8125Table 5.Average end-point error (EPE)on the Middlebury train-ing set is shown for the new model with alternating optimization (Classic-C-A ).(7)can be minimized by repeated application of Eq.(4);weuse this approach with 5iterations.We perform 10steps of alternating optimizations at every pyramid level and change λ2logarithmically from 10−4to 102.During the first and second GNC stages,we set u ,v to be ˆu ,ˆv after every warp-ing step (this step helps reach solutions with lower energy and EPE [30]).In the end,we take ˆu ,ˆv as the final flow field estimate.The other parameters are λ=5,λ3=1.Alternatingly optimizing this new objective function (Classic-C-A )leads to similar results as the baseline Classic-C (Table 5).We also compare the energy of these solutions using the new objective and find the alternat-ing optimization produces the lowest energy solutions,as shown in Table 6.To do so,we set both the flow field u ,v and the auxiliary flow field ˆu ,ˆv to be the same in Eq.(2).In summary,we show that the heuristic median filter-ing step in Classic-C can now be viewed as energy min-imization of a new objective with a non-local term.The explicit formulation emphasizes the value of robustly inte-grating information over large neighborhoods and enables the improved model described below.6.Improved ModelBy formalizing the median filtering heuristic as an ex-plicit objective function,we can find ways to improve it.While median filtering in a large neighborhood has advan-tages as we have seen,it also has problems.A neighborhood centered on a corner or thin structure is dominated by the surround and computing the median results in oversmooth-ing as illustrated in Figure 3(a).Examining the non-local term suggests a solution.For a given pixel,if we know which other pixels in the area be-long to the same surface,we can weight them more highly.The modification to the objective function is achieved by introducing a weight into the non-local term [14,15]:∑i,j ∑(i ,j )∈N i,jw i,j,i ,j (|ˆu i,j −ˆu i ,j |+|ˆv i,j −ˆv i ,j |),(8)where w i,j,i ,j represents how likely pixel i ,j is to belongto the same surface as i,j .。

台州英语导游词台州英语导游词台州历史悠久，是江南翼龙化石的发现地和五千多年前新石器时代的下汤文化的发祥地。

以下是店铺整理的台州导游词，欢迎阅读。

台州英语导游词Taizhou, China's golden coastline a young coastal city, located in the central coastal Zhejiang and Shanghai Economic Zone in the southern wing. Taizhou City at 122 degrees east longitude, north latitude 28 degrees, is a subtropical monsoon climate model. Urban-based Jiaojiang, Huangyan, road and bridge three areas. Under the jurisdiction of the sea, Wenling cities, Yuhuan, rooftops, Xianju, three four counties. The city's land area of 9411 square kilometers, sea area of 80000 square km, population 5.46 million. Which the urban area is 1536 square kilometers, population 1.4 million.Taizhou has a long history is a pterosaur fossil found in southern and in more than 5000 years ago, the Neolithic culture of the birthplace of the next soup. Established after the First Emperor unified China Urago back to the Han Dynasty (BC 85 years) to establish back to Pu County, the Three Kingdoms period (AD 257) set Linhai County, the Tang Dynasty (622 years) from Taizhou said.After the founding of New China, the Civil Administration has been formed in 1994, the State Council approved the establishment of eight menstrual Taizhou. Taizhou have both mountain and ocean interests, history, and there were "sea mountain" is laudatory.Taizhou, Zhejiang, one of the main producing area of grain. Is China's first super-jin of rice per mu, super-jin in two places.Taizhou is also China's major Shuiguozhixiang given name Yuhuan pomelo fruit Huangyan tangerine and foreign well-known. Taiwan is one of China's major fishing areas, with vast and rich East China Sea fishing grounds, fishery production ranks first in Zhejiang Province.Taizhou developed economy, the market prosperity is the cradle of China's stock cooperative system. Of reform and opening up 20 years ago, Taizhou, People's emancipation of the mind, and work hard to forge ahead, to find a suitable situation for economic development city road characteristics was initially set up a socialist market economic structure, so that a relatively backward agricultural areas of pure fast completed the early stages of industrialization into the ranks of the coastal economically developed cities. Taizhou convenient transportation and advanced communication. Haimen Hong Kong has always been an important diplomatic port, 230 AD, the earliest opening of the mainland and Taiwan on the route.Today's Taizhou, with ports, airports, 104 national highway, the coastal highway, sea and air has been initially built three-dimensional traffic system. Taizhou style of study since ancient times, the prosperity of state for culture. Customs are pure and honest, and social stability. Beautiful mountain and ocean scenery, numerous attractions. Education, science and technology developed, talented people. Wu Hua Tian Bao, in order to bring China's affluent Gold Coast land.In 1999, the State Council's approval, Taizhou, Zhejiang, identified as the pattern of urbanization in the big cities and an economic sub-district. In 2000, Taizhou municipal government proposed "the development of large industries, development of major ports, to build a large transportation, building large cities,"the strategic goal, located in Taizhou, prosperous, civilized, beautiful modern port city. Taizhou second goal is to take off: in 2020, Branch Trade and Industry developed a modern coastal city. Taizhou mountainous coastal, mountain and ocean scenery both. Since the Han and Jin, way Buddha taught two special-sheng, Chongshan valleys among the temple after another, adding to a number of cultural relics.The People's Republic of China is established, and open up a number of scenic spots. Scenic region, can be divided into three categories: mainly in Taishan and kuocang shan mountain attractions, including the Huangyan city, Wenling County Moroyama, natural scenery and the integration of a number of monuments; to the main Linhai taozhu , and the snake-coil Sanmen Island and cents caves, Wenling County, Shitang fishing village, Yuhuan County dalu dao, Chen Jiaojiang City Island and other coastal attractions, a great potential for development; all cities and counties Chengxiang suburban scenic spots, Most have been turned into parks.Tiantai Mountain in 1985, eight of which menstrual Provincial People's Government declared as a provincial-level spots in 1988, eight menstrual approved as a national key scenic spots. Taozhu scenic spots, Qing Feng Geng Snow "Tainan-dong Lin" has been documented, 80, began to attract attention, and there are many new discoveries. There are a lot of scenic spots throughout the region to be developed.台州中文导游词台州是中国黄金海岸线上一个年轻的滨海城市,位于浙江沿海中部,上海经济区的最南翼。

Phase diagramHello everybody, welcome to my class. Today, we will talk about phase diagram and Gibbs phase rule, as well as how to calculate the corresponding proportion of liquid phase and solid phase.译文：大家好，欢迎来到我的课程。

今天，我们将讨论相图，吉布斯相律，以及如何计算液相和固相的相对含量。

First of all, let’s introduce the definition of phase. Phase is defined as a homogeneous part or aggregation of material. This homogenous part is distinguished from another part due to difference in structure, composition, or both. The different structures form an interface to difference in structure and composition. （这里要注意相的概念，相是指在结构和组成方面与其它部分不同的均匀体。

）译文：我们首先学习相的定义。

相是指在一种材料中，结构、组成，或两者同时不同于其他部分的均匀体或聚集体部分。

不同部分间形成界面，也就是相与相之间的分界面。

Some solid materials have the capability of changing their crystal structure under the varying conditions of pressure and temperature, causing an ability of phase-change.译文：一些固体材料随着压力和温度条件的改变而发生结晶结构变化，具有相变的能力。