Node-failure Tolerance of Topology in Wireless
Sensor Networks
Liang-min Wang1,2, Jian-feng Ma2, Yuan-bo Guo3, SangJae Moon4
1 The Computer School,
Jiangsu University, Zhenjiang, 212013, China
2 Key Laboratory of Computer Network and Information Security of Education
Ministry,
Xidian University, Xi’an Shaanxi, P.C. China, 710071
3 School of electronic technology,
Engineering university of PLA, Zhengzhou, 450004, China
4 Mobile Network Security Technology Research Center
Kyungpook National University, Sankyuk-dong, Buk-ku, Daeyu, Korea,
702-701
(Corresponding author: Liang-min Wang, Email:liangminwang@https://www.doczj.com/doc/d317608208.html,) Abstract: Three basic questions are presented and answered in researching on node-failure topology in wireless sensor network. First, what is the definition of node-failure tolerance? Second, how to evaluate this tolerance ability? Third, which type of topologies is more efficient in tolerating node-failure? Introduction: Wireless sensor networks usually were deployed in remote and hostile surroundings, and people cannot attend the sensor nodes. When some nodes are failure, such as batteries exhausted, hardware faulted and intrusion from attackers, these unattended nodes cannot be changed or repaired. The failure nodes may lead to network partition which decreases the cover ratio, reduces the availability of the network and even produces network failure. So network topology should tolerate node-failure to avoid network partition.
We think the following three questions should be answered when researching node-failure tolerance of wireless sensor networks.
z What is the definition of node-failure tolerance for topology in WSN?
z How to evaluate the tolerance abilities of topologies?
z Which topologies are more efficient than the others in tolerating failure nodes?
Review: The existing research [1-4] on tolerance topologies for WSN is concentrated on finding multiply connected network with minimum energy by using power control. But we argue that it is not the truth.
Considering graph G with grid structure in Fig.1, we regard a point of intersection as a sensor node, and denote the number of the sensors as n, i.e. |V|=n.
Firstly, the node-connectivity of graph Fig.1 (a) is 2, but it can tolerate 3 or more failure nodes when n are big enough. After 2 black nodes are deleted from Fig.1 (a), the gray node becomes isolate, and the connected graph is divided into two partitions. According to the existing work[1-4], graph (a) can only tolerate 1 failure nodes. But 3 unavailable nodes in Fig.1 (a) have less effect on the large-scale sensor networks. Thus the number of failure nodes that topologies can tolerate is not equivalent to the node-connectivity of the graph.
Secondly, the graphs in Fig.1 (a) and Fig.1 (b) are 2-connected, but the availability of the entire network is significantly different after 2 black nodes are deleted. When 2 black nodes are deleted, only three nodes are not available in Fig.1 (a), but more than 50% nodes are unavailable in Fig.1 (b). That is to say, although node connectivity of these two graphs is 2, one can tolerate 2 failure
nodes, but the other cannot.
----Unavailable Node
(a) (b)
Fig. 1 Effect of Node-failure on Availability of WSN
Now we conclude that fault-tolerance topology is not the same concept as multiply connected graph, and using multiply connectivity to evaluate tolerance ability is not appropriate.
Tolerating Node-failure: We think that the effect on the availability of the network taken by the failure nodes should be considered defining node-failure tolerance for topologies of wireless sensor network. We describe the tolerance ability of G as the number of nodes which are deleted without the effect on availability of the network.
Definition 1: Graph G=(V, E) is connected, where |V|=n . After k (n ) nodes are deleted, the residual graph has r connected component, which are G 1 = (V 1, E 1), G 2 = (V 2, E 2), … G r = (V r , E r ), 1 ≤ r ≤ k (n )＋1. Then the node number of maximum connected component is A k (n )=max(|V 1|, |V 2|, … |V r |), and the cover rate of available nodes is denoted as C k (n ). If it satisfies
()1lim )(lim ==∞→∞→n
n A n C k n k n (1) Then we call graph G can tolerate k (n ) failure nodes.
Tolerance of Fault and Intrusion : We use an extension network model with
Bernoulli nodes uniform distributing in a unit area to describe topologies for WSN. In this model, an additional assumption is introduced to graph G(V, E) that all nodes are elected as heads independently with probability p for some constant 0 < p ≤ 1[5]. Flat and hierarchical topologies of wireless sensor network can be illustrated by graph over Bernoulli nodes. In flat topologies, p = 1, all nodes are heads. In hierarchical topologies, 0 < p < 1, there are n · p (n ) heads in the network.
In fact, node failure of WSN falls into two classes, one is fault caused by error, and the other is intrusion brought by attack. Because Error happens at random, but attack is hostile and selective[6], so fault happens in all nodes with the same probability, but intrusion happens only in the head nodes for their important roles in the hierarchy topology of wireless sensor network. Let the Bernoulli probability of the network model be p (n ), then fault happens in all the nodes, but intrusion only attack these n · p (n ) heads.
If the failure node number is k (n ), we write the number of failure heads as k 1(n ) and the number of failure ordinary nodes as k 2(n ).
)()()(n k n k n k 21+= (2)
p 1(n ) is denoted as the failure-head ratio of all the failure nodes:
)()
()(11n k n k n p = (3)
If node-failure is fault, then p 1(n ) = p (n ). If node-failure is intrusion, then p 1(n ) =1. From Definition 1, we can define fault tolerance and intrusion tolerance as the maximum number of tolerating failure nodes, which is the
standards to evaluate tolerance abilities of topologies.
Definition 2: G=(V, E) is the network module with Bernoulli nodes, where |V|=n and the active probability is p (n ). If k (n ) nodes are arbitrarily selected, in which k (n )· p 1(n ) nodes are heads, where p 1(n ) is defined by equation(2) and equation(3). If graph G can tolerate these k (n ) failure nodes, we call G can tolerate k (n ) fault nodes when p 1(n )=p (n ), and we call G can tolerate k (n ) intrusion nodes when p 1(n )=1.
Definition 3: G=(V, E) can tolerate k (n ) fault nodes. When m (n ) nodes are deleted from G, where m (n )= ?(k (n )), the ratio of available nodes C m (n ) satisfies:
()1lim )(lim ≠=∞→∞→n
n A n C m n m n (4) Then we call G is θ(k (n )) fault tolerance, also call fault tolerance of G is θ(k (n )), write as FTOL ＝θ(k (n )).
Definition 4: G=(V, E) can tolerate k (n ) intrusion nodes. When m (n ) nodes are deleted from G, where m (n )= ?(k (n )), the ratio of available nodes C m (n ) satisfies equation(4). Then we call G is θ(k (n )) intrusion tolerance, also call intrusion tolerance of G is θ(k (n )), written as ITOL ＝θ(k (n )).
In definition 3 and 4, the symbol ? and θ are described by the following equation:
f (n )= θ(
g (n ))
? Exist constant c 1, c 2 and n 0, such that c 1 g (n )≤f (n ) ≤ c 2g (n ), where n >n 0 f (n )= ?(g (n ))
?≥ Exist constant c , and n 0, such that f (n ) c g (n ), where n >n 0
(a) (b)
Fig. 2 Hierarchy Topology
Tolerance of Hierarchy Topology: There are n nodes in the network model with Bernoulli probability p(n), which means each node has probability p(n) to be cluster, then there are n·p(n) cluster, and each cluster has a head and average (1/p(n)-1) ordinary nodes, as shown in Fig.2(a). If an ordinary node is failure, then only a node is unavailable. If a head is failure, then the head and its (1/p(n)-1) ordinary nodes are unavailable. If some heads are failure, they may make some other heads partitioned, then the failure heads and their ordinary nodes, the partitioned heads and their ordinary nodes are unavailable, As shown in Fig.2 (b), black failure nodes make a head gray, then all the ordinary neighbors of these black or gray heads are unavailable.
x
y
(a)
Let F f(n) be the unavailable nodes brought by k1(n) heads. To attack the network, attackers try their best to make F f(n) reach the biggest value. In Fig.3 (a), k1(n) black failure nodes form a connected curve and divide the network to
two parts. Then the number of unavailable gray nodes reaches the biggest value. We treat F f (n ) as the maximum area of shading region in Fig.3 (b), and we compute the maximum of F f (n ) with restriction of equation(6).
∫==00)()(x
x f dx x f n F (5) ∫=ds n k )(1 (6)
Then the maximum of F f (n ):
π)()(21n k n F f = (7)
from (2) and (7), A k (n ) residual nodes is available.
))()1)(1()(()(2n k n p n F n n A f k +??
???????= (8) If equation (1) is true, the topology can tolerate k (n ) failure nodes. From (8) and (1):
?????????=??∈?????=?=????1)()()1,0()())(11,)()(min(0)()(11
2/12/111111222/112/1111
12n p n n p c n p n n p c n n p n p c n p n c n k εεεε (9)
Where p 1(n ) is defined by equation(3), c , ε1 and ε2 are constants, ε1, ε2∈2
1,0(. Now Theorem is proved. Theorem : G=(V, E) is a network model with Bernoulli nodes, in which each node has probability p (n ) to be cluster head, and each failure node is a head with probability p 1(n ). Then the network topology can tolerate θ(k (n )) failure nodes, where θ(k (n )) is defined by equation (9).
Corollary : In hierarchical structure of WSN topology, let the probability of head be c , then fault tolerance decreases with c , but intrusion tolerance increases with c .
Proof: From equation(9), in this structure:
)(θFTOL 2/12/1ε???=n c (10)
))(θ(ITOL 2/1ε??=n c (11)
Where ε is a very small constant, and ε∈2
1,0(. From (10) and (11):
0)21(θ)FTOL(2/12/3
0)θ()ITOL(2/1>?=+?εn c dc
c d # Related Work: The popular researching on tolerance of WSN topology is looking for k -connected graph with minimum energy consumed that is far away from our work. The earlier paper [6] studies tolerance of wired network, in which the residual wired links after some nodes are deleted are used to evaluate the tolerance ability of the topologies. And it conclude that the topology whose wired links are centralized in minority nodes, has better fault tolerance, but less intrusion tolerance. This conclusion is consistent with our corollary.
Conclusion: We point out that the node-failure tolerance of WSN topologies is not the same as the connectivity of graphs, and present the definition of tolerating k failure nodes. Then the tolerance of fault and intrusion tolerance are presented in network model with Bernoulli nodes to evaluate the tolerance ability. Finally, fault and intrusion tolerance of hierarchical topology are studied, and the rules of fault and intrusion tolerance with head ratio of hierarchical topology are achieved.
Reference
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2 Ning Li, Jennifer C. Hou, FLSS: A Fault-Tolerant Topology Control Algorithm for Wireless Networks. Proceedings of the 10th annual international conference on Mobile computing and networking(MobiCOM04), September, 2004.
3Paolo Santi, Topology Control in Wireless Ad Hoc and Sensor Networks, ACM Comp. Surveys (to appear2005).
4 Yong Chen, Sang H. Son, A Fault Tolerant Topology Control in Wireless Sensor Networks. ACS/IEEE International Conference on Computer Systems and Applications, Cairo, Egypt, January 2005.
5 Peng-jun Wan, Chih-wei Yi, Asymptotic Critical Transmission Range for Connectivity in Wireless Ad Hoc Networks with Bernouli Nodes. Proceedings of the 5th ACM international symposium on Mobile ad hoc networking and computing (MOBIHOC 2004), Tokyo, Japan, 2004.
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如何写先进个人事迹 篇一：如何写先进事迹材料 如何写先进事迹材料 一般有两种情况：一是先进个人，如先进工作者、优秀党员、劳动模范等；一是先进集体或先进单位，如先进党支部、先进车间或科室，抗洪抢险先进集体等。无论是先进个人还是先进集体，他们的先进事迹，内容各不相同，因此要整理材料，不可能固定一个模式。一般来说，可大体从以下方面进行整理。 (1)要拟定恰当的标题。先进事迹材料的标题，有两部分内容必不可少，一是要写明先进个人姓名和先进集体的名称，使人一眼便看出是哪个人或哪个集体、哪个单位的先进事迹。二是要概括标明先进事迹的主要内容或材料的用途。例如《王鬃同志端正党风的先进事迹》、《关于评选张鬃同志为全国新长征突击手的材料》、《关于评选鬃处党支部为省直机关先进党支部的材料》等。 (2)正文。正文的开头，要写明先进个人的简要情况，包括：姓名、性别、年龄、工作单位、职务、是否党团员等。此外，还要写明有关单位准备授予他(她)什么荣誉称号，或给予哪种形式的奖励。对先进集体、先进单位，要根据其先进事迹的主要内容，寥寥数语即应写明，不须用更多的文字。 然后，要写先进人物或先进集体的主要事迹。这部分内容是全篇材料
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HRA HRC HRA HRC HRA HRC HRA HRC 86.6 70.0103778.555.059937070.540.037726928.027486.3 69.5101778.254.558936570.339.537226627.527186.1 69.099777.954.057936070.039.036726327.026885.8 68.597877.753.557035538.536226026.526485.5 68.095977.453.056135038.035125726.026185.2 67.594177.152.555134537.535225425.525885.0 67.092376.952.054334137.034725125.025584.7 66.590676.651.553433636.534224824.525284.4 66.088950176.351.052533236.033824524.024984.1 65.587249476.150.551732735.533324223.524683.9 65.085648875.850.050932335.032924023.024383.6 64.584048175.549.550131834.532423722.524083.3 64.082547475.349.049331434.032023422.023783.1 63.581046875.048.548531033.531623221.523482.8 63.079546174.748.047830633.031222921.023182.5 62.578045574.547.547030232.530822720.522982.2 62.076644974.247.046329832.030422520.022682.0 61.575244273.946.545629431.530022219.522381.7 61.073943673.746.044929131.029622019.022181.4 60.572643073.445.544328730.529221818.521881.2 60.071342473.245.043628330.028921618.021680.9 59.570041872.944.542928029.528521417.521480.6 59.068841372.644.042327629.028121117.021180.3 58.567640772.443.541727328.527880.1 58.066440172.143.041179.8 57.565339671.842.540579.5 57.064239171.642.039979.3 56.563138571.341.539379.0 56.062038071.141.038878.755.560937570.840.5382黑色金属材料 硬度值换算表 布氏硬度 HB 洛氏硬度 维氏硬度HV 布氏硬度HB 洛氏硬度维氏硬度HV 布氏硬度HB 维氏硬度HV 注:1.布氏硬度:主要用来测定铸件、锻件、有色金属制件、热轧坯料及退火件的硬度,测定范围≯HB450。 2.洛氏硬度:HRA 主要用于高硬度试件,测定硬度高于HRC67以上的材料和表面硬度,如硬质合金、氮化钢等，测定范围HRA>70。HRC 主要用于钢制件(如碳钢、工具钢、合金钢等）淬火或回火后的硬度测定,测定范围HRC20~67。 3.维氏硬度:用来测定薄件和钢板制件的硬度,也可用来测定渗碳、氰化、氮化等表面硬化制件的硬度。 洛氏硬度维氏硬度HV 布氏硬度HB 洛氏硬度
How to manage time Time treats everyone fairly that we all have 24 hours per day. Some of us are capable to make good use of time while some find it hard to do so. Knowing how to manage them is essential in our life. Take myself as an example. When I was still a senior high student, I was fully occupied with my studies. Therefore, I hardly had spare time to have fun or develop my hobbies. But things were changed after I entered university. I got more free time than ever before. But ironically, I found it difficult to adjust this kind of brand-new school life and there was no such thing called time management on my mind. It was not until the second year that I realized I had wasted my whole year doing nothing. I could have taken up a Spanish course. I could have read ten books about the stories of successful people. I could have applied for a part-time job to earn some working experiences. B ut I didn’t spend my time on any of them. I felt guilty whenever I looked back to the moments that I just sat around doing nothing. It’s said that better late than never. At least I had the consciousness that I should stop wasting my time. Making up my mind is the first step for me to learn to manage my time. Next, I wrote a timetable, setting some targets that I had to finish each day. For instance, on Monday, I must read two pieces of news and review all the lessons that I have learnt on that day. By the way, the daily plan that I made was flexible. If there’s something unexpected that I had to finish first, I would reduce the time for resting or delay my target to the next day. Also, I would try to achieve those targets ahead of time that I planed so that I could reserve some more time to relax or do something out of my plan. At the beginning, it’s kind of difficult to s tick to the plan. But as time went by, having a plan for time in advance became a part of my life. At the same time, I gradually became a well-organized person. Now I’ve grasped the time management skill and I’m able to use my time efficiently.
Adaptive Online Scheduling in Storm Leonardo Aniello aniello@dis.uniroma1.it Roberto Baldoni baldoni@dis.uniroma1.it Leonardo Querzoni querzoni@dis.uniroma1.it Research Center on Cyber Intelligence and Information Security and Department of Computer,Control,and Management Engineering Antonio Ruberti Sapienza University of Rome ABSTRACT Today we are witnessing a dramatic shift toward a data-driven economy,where the ability to e?ciently and timely analyze huge amounts of data marks the di?erence between industrial success stories and catastrophic failures.In this scenario Storm,an open source distributed realtime com-putation system,represents a disruptive technology that is quickly gaining the favor of big players like Twitter and Groupon.A Storm application is modeled as a topology,i.e. a graph where nodes are operators and edges represent data ?ows among such operators.A key aspect in tuning Storm performance lies in the strategy used to deploy a topology, i.e.how Storm schedules the execution of each topology component on the available computing infrastructure.In this paper we propose two advanced generic schedulers for Storm that provide improved performance for a wide range of application topologies.The?rst scheduler works o?ine by analyzing the topology structure and adapting the de-ployment to it;the second scheduler enhance the previous approach by continuously monitoring system performance and rescheduling the deployment at run-time to improve overall performance.Experimental results show that these algorithms can produce schedules that achieve signi?cantly better performances compared to those produced by Storm’s default scheduler. Categories and Subject Descriptors D.4.7[Organization and Design]:Distributed systems Keywords distributed event processing,CEP,scheduling,Storm 1.INTRODUCTION In the last few years we are witnessing a huge growth in information production.IBM claims that“every day,we create2.5quintillion bytes of data-so much that90%of the data in the world today has been created in the last two Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for pro?t or commercial advantage and that copies bear this notice and the full citation on the?rst page.To copy otherwise,to republish,to post on servers or to redistribute to lists,requires prior speci?c permission and/or a fee. DEBS’13,June29–July3,2013,Arlington,Texas,USA. Copyright2013ACM978-1-4503-1758-0/13/06...$15.00.years alone”[15].Domo,a business intelligence company, has recently reported some?gures[4]that give a perspective on the sheer amount of data that is injected on the internet every minute,and its heterogeneity as well:3125photos are added on Flickr,34722likes are expressed on Facebook, more than100000tweets are done on Twitter,etc.This apparently unrelenting growth is a consequence of several factors including the pervasiveness of social networks,the smartphone market success,the shift toward an“Internet of things”and the consequent widespread deployment of sensor networks.This phenomenon,know with the popular name of Big Data,is expected to bring a strong growth in economy with a direct impact on available job positions;Gartner says that the business behind Big Data will globally create4.4 million IT jobs by2015[1]. Big Data applications are typically characterized by the three V s:large volumes(up to petabytes)at a high veloc-ity(intense data streams that must be analyzed in quasi real-time)with extreme variety(mix of structured and un-structured data).Classic data mining and analysis solutions quickly showed their limits when faced with such loads.Big Data applications,therefore,imposed a paradigm shift in the area of data management that brought us several novel approaches to the problem represented mostly by NoSQL databases,batch data analysis tools based on Map-Reduce, and complex event processing engines.This latter approach focussed on representing data as a real-time?ow of events proved to be particularly advantageous for all those appli-cations where data is continuously produced and must be analyzed on the?https://www.doczj.com/doc/d317608208.html,plex event processing engines are used to apply complex detection and aggregation rules on intense data streams and output,as a result,new events.A crucial performance index in this case is represented by the average time needed for an event to be fully analyzed,as this represents a good?gure of how much the application is quick to react to incoming events. Storm[2]is a complex event processing engine that,thanks to its distributed architecture,is able to perform analytics on high throughput data streams.Thanks to these character-istics,Storm is rapidly conquering reputation among large companies like Twitter,Groupon or The Weather Chan-nel.A Storm cluster can run topologies(Storm’s jargon for an application)made up of several processing components. Components of a topology can be either spouts,that act as event producers,or bolts that implement the processing logic.Events emitted by a spout constitute a stream that can be transformed by passing through one or multiple bolts where its events are processed.Therefore,a topology repre-
英语演讲稿：未来的工作 这篇《英语演讲稿范文：未来的工作》，是特地，希望对大家有所帮助！ 热门演讲推荐：竞聘演讲稿 | 国旗下演讲稿 | 英语演讲稿 | 师德师风演讲稿 | 年会主持词 | 领导致辞 everybody good afternoon:. first of all thank the teacher gave me a story in my own future ideal job. everyone has a dream job. my dream is to bee a boss, own a pany. in order to achieve my dreams, i need to find a good job, to accumulate some experience and wealth, it is the necessary things of course, in the school good achievement and rich knowledge is also very important. good achievement and rich experience can let me work to make the right choice, have more opportunities and achievements. at the same time, munication is very important, because it determines whether my pany has a good future development. so i need to exercise their municative ability. i need to use all of the free time to learn
小学生个人读书事迹简介怎么写800字 书,是人类进步的阶梯,苏联作家高尔基的一句话道出了书的重要。书可谓是众多名人的“宠儿”。历来,名人说出关于书的名言数不胜数。今天小编在这给大家整理了小学生个人读书事迹,接下来随着小编一起来看看吧! 小学生个人读书事迹1 “万般皆下品,惟有读书高”、“书中自有颜如玉,书中自有黄金屋”,古往今来,读书的好处为人们所重视,有人“学而优则仕”,有人“满腹经纶”走上“传道授业解惑也”的道路……但是,从长远的角度看,笔者认为读书的好处在于增加了我们做事的成功率,改善了生活的质量。 三国时期的大将吕蒙,行伍出身,不重视文化的学习,行文时,常常要他人捉刀。经过主君孙权的劝导,吕蒙懂得了读书的重要性,从此手不释卷,成为了一代儒将,连东吴的智囊鲁肃都对他“刮目相待”。后来的事实证明,荆州之战的胜利,擒获“武圣”关羽,离不开吕蒙的“运筹帷幄,决胜千里”,而他的韬略离不开平时的读书。由此可见,一个人行事的成功率高低,与他的对读书,对知识的重视程度是密切相关的。 的物理学家牛顿曾近说过,“如果我比别人看得更远,那是因为我站在巨人的肩上”,鲜花和掌声面前,一代伟人没有迷失方向,自始至终对读书保持着热枕。牛顿的话语告诉我们,渊博的知识能让我们站在更高、更理性的角度来看问题,从而少犯错误,少走弯路。
读书的好处是显而易见的,但是,在社会发展日新月异的今天,依然不乏对读书,对知识缺乏认知的人,《今日说法》中我们反复看到农民工没有和用人单位签订劳动合同,最终讨薪无果;屠户不知道往牛肉里掺“巴西疯牛肉”是犯法的;某父母坚持“棍棒底下出孝子”,结果伤害了孩子的身心,也将自己送进了班房……对书本,对知识的零解读让他们付出了惨痛的代价,当他们奔波在讨薪的路上,当他们面对高墙电网时,幸福,从何谈起?高质量的生活,从何谈起? 读书,让我们体会到“锄禾日当午,汗滴禾下土”的艰辛;读书,让我们感知到“四海无闲田,农夫犹饿死”的无奈;读书,让我们感悟到“为报倾城随太守,西北望射天狼”的豪情壮志。 读书的好处在于提高了生活的质量,它填补了我们人生中的空白,让我们不至于在大好的年华里无所事事,从书本中,我们学会提炼出有用的信息,汲取成长所需的营养。所以,我们要认真读书,充分认识到读书对改善生活的重要意义,只有这样,才是一种负责任的生活态度。 小学生个人读书事迹2 所谓读一本好书就是交一个良师益友,但我认为读一本好书就是一次大冒险,大探究。一次体会书的过程,真的很有意思,咯咯的笑声,总是从书香里散发;沉思的目光也总是从书本里透露。是书给了我启示,是书填补了我无聊的夜空,也是书带我遨游整个古今中外。所以人活着就不能没有书,只要爱书你就是一个爱生活的人,只要爱书你就是一个大写的人,只要爱书你就是一个懂得珍惜与否的人。可真所谓
表面粗糙度与标准公差表 无论用何种加工方法加工，在零件表面总会留下微细的凸凹不平的刀痕，出现交错起伏的峰谷现象，粗加工后的表面用肉眼就能看到，精加工后的表面用放大镜或显微镜仍能观察到。这就是零件加工后的表 面粗糙度。过去称为表面光洁度。 国家规定表面粗糙度的参数由高度参数、间距参数和综合参数组成。 高度参数共有三个: 轮廓的平均算术偏差(Ra)如图1所示，通过零件的表面轮廓作一中线m ，将一定长度的轮廓分成两部分，使中线两侧轮廓线与中线之间所包含的面积相等，即 F1+F3+……+Fn-1=F2+F4+……+Fn 图1 轮廓的平均算术偏差 轮廓的平均算术偏差值Ra，就是在一定测量长度l 范围内，轮廓上各点至中线距离绝对值的平均算术偏 差。用算式表示为 Ra＝dx 或近似写成 Ra≈ ?不平度平均高度(Rz)就是在基本测量长度范围内，从平行于中线的任意线起，自被测轮廓上五个最高点至 五个最低点的平均距离(图2)，即 RZ= 图2 不平度平均高度 ?轮廓最大高度Ry，就是在取样长度内，轮廓峰顶线和轮廓谷底线之间的距离。 间距参数共有两个： 轮廓单峰平均间距S，就是在取样长度内，轮廓单峰间距的平均值。而轮廓单峰间距，就是两相邻轮廓 单峰的最高点在中线上的投影长度Si。 轮廓微观不平度的平均间距Sm。含有一个轮廓峰和相邻轮廓谷的一段中线长度Smi，称轮廓微观不平间距。 综合参数只有一个，就是轮廓支承长度率tp。它是轮廓支承长度np与取样长度l之比。 在原有的国家标准中，表面光洁度分为14级，其代号为V1、V2……V14。V后的数字越大，表面光洁度 就越高，即表面粗糙度数值越小。 在车间生产中，常根据表面粗糙度样板和加工出来的零件表面进行比较，用肉眼或手指的感觉，来判断零件表面粗糙度的等级。此外，还有很多测量光洁度的仪器。
关于坚持的英语演讲稿 Results are not important, but they can persist for many years as a commemoration of. Many years ago, as a result of habits and overeating formed one of obesity, as well as indicators of overall physical disorders, so that affects my work and life. In friends to encourage and supervise, the participated in the team Now considered to have been more than three years, neither the fine rain, regardless of winter heat, a day out with 5:00 time. The beginning, have been discouraged, suffering, and disappointment, but in the end of the urging of friends, to re-get up, stand on the playground. 成绩并不重要，但可以作为坚持多年晨跑的一个纪念。多年前，由于庸懒习惯和暴饮暴食，形成了一身的肥胖，以及体检指标的全盘失常，以致于影响到了我的工作和生活。在好友的鼓励和督促下，参加了晨跑队伍。现在算来，已经三年多了，无论天晴下雨，不管寒冬酷暑，每天五点准时起来出门晨跑。开始时，也曾气馁过、痛苦过、失望过，但最后都在好友们的催促下，重新爬起来，站到了操场上。 In fact, I did not build big, nor strong muscles, not a sport-born people. Over the past few years to adhere to it, because I have a team behind, the strength of a strongteam here, very grateful to our team, for a long time, we encourage each other, and with sweat, enjoying common health happy. For example, Friends of the several run in order to maintain order and unable to attend the 10，000 meters race, and they are always concerned about the brothers and promptly inform the place and time, gives us confidence and courage. At the same time, also came on their own inner desire and pursuit for a good health, who wrote many of their own log in order to refuel for their own, and inspiring. 其实我没有高大身材，也没健壮肌肉，天生不属于运动型的人。几年来能够坚持下来，因为我的背后有一个团队，有着强大团队的力量，在这里，非常感谢我们的晨跑队，长期以来，我们相互鼓励着，一起流汗，共同享受着健康带来的快
1.一孔尺寸的要求为φ30+0052,我们在加工时最理想的目标值是（ D ） A.30 B.30.052 C.30.040 D.30.026 2.下列标准公差中,精度最低的是（ D ） A.ITO B.IT01 C.IT10 D.IT18 3.某轴段标注尺寸为,现对四个完工零件进行测量,得到的数据分别为φ55.000mm,φ5 4.998mmφ5 5.042mm,φ55.023mm,则合格的零件有（ A ） A1个 B.2个 C.3个 D.4个 4.已知某轴的公称尺寸为φ50加工后测得的实际尺寸为φ50.022mm,则实际偏差为（ D ） A.0.051mm B.0.050mm C.0.025mm D.0.022mm 5.关于φ20R6、φ20R7、φ20R8三个公差带,下列说法正确的是（ C ） A,上、下极限偏差均不相同 B.上、下极限偏差均相同 C.上极限偏差相同,但下极限偏差不同
D.上极限偏差不同,但下极限偏差相同 6.表面结构中,加工纹理呈两斜向交叉且与视图所在的投影面相交的是（ C ） 7.表面结构中,加工纹理垂直于视图所在的投影面的是（ B ） 8.以下不属于表面粗糙度对零件的使用性能影响的主要表现是（ C ） A.对配合性质的影响 B对耐磨性的影响奥面 C.对材料塑性变形的影响 D.对抗腐蚀性的影响 9.Ra值测量最常用的方法是（ C ） A.样板比较法 B.显微镜比较法,度 C电动轮廓仪比较法D.光切显微镜测量法工眼 10.表面粗糙度是( )误差。（ B ） A.宏观几何形状 B.微观几何形状 C.宏观相互位置 D.微观相互位置 11选择表面粗糙度评定参数值时,下列论述不正确的有（ A ） A.同一零件上工作表面应比非工作表面参数值大 B摩擦表面应比非摩擦表面的参数值小 C配合质量要求高,表面粗糙度参数值应小 D.受交变载荷的表面,表面粗糙度参数值应小
1.How to build a business that lasts100years 0:11Imagine that you are a product designer.And you've designed a product,a new type of product,called the human immune system.You're pitching this product to a skeptical,strictly no-nonsense manager.Let's call him Bob.I think we all know at least one Bob,right?How would that go? 0:34Bob,I've got this incredible idea for a completely new type of personal health product.It's called the human immune system.I can see from your face that you're having some problems with this.Don't worry.I know it's very complicated.I don't want to take you through the gory details,I just want to tell you about some of the amazing features of this product.First of all,it cleverly uses redundancy by having millions of copies of each component--leukocytes,white blood cells--before they're actually needed,to create a massive buffer against the unexpected.And it cleverly leverages diversity by having not just leukocytes but B cells,T cells,natural killer cells,antibodies.The components don't really matter.The point is that together,this diversity of different approaches can cope with more or less anything that evolution has been able to throw up.And the design is completely modular.You have the surface barrier of the human skin,you have the very rapidly reacting innate immune system and then you have the highly targeted adaptive immune system.The point is,that if one system fails,another can take over,creating a virtually foolproof system. 1:54I can see I'm losing you,Bob,but stay with me,because here is the really killer feature.The product is completely adaptive.It's able to actually develop targeted antibodies to threats that it's never even met before.It actually also does this with incredible prudence,detecting and reacting to every tiny threat,and furthermore, remembering every previous threat,in case they are ever encountered again.What I'm pitching you today is actually not a stand-alone product.The product is embedded in the larger system of the human body,and it works in complete harmony with that system,to create this unprecedented level of biological protection.So Bob,just tell me honestly,what do you think of my product? 2:47And Bob may say something like,I sincerely appreciate the effort and passion that have gone into your presentation,blah blah blah-- 2:56(Laughter) 2:58But honestly,it's total nonsense.You seem to be saying that the key selling points of your product are that it is inefficient and complex.Didn't they teach you 80-20?And furthermore,you're saying that this product is siloed.It overreacts, makes things up as it goes along and is actually designed for somebody else's benefit. I'm sorry to break it to you,but I don't think this one is a winner.
Storm简介
Storm简介 ?实时计算需要解决一些什么问题?实现一个实时计算系统?Storm基本概念 ?Storm使用场景 ?Storm分组机制
Storm简介 ?实时计算需要解决一些什么问题 伴随着信息科技日新月异的发展，信息呈现出爆发式的膨胀，人们获取信息的途径也更加多样、更加便捷，同时对于信息的时效性要求也越来越高。举个搜索场景中的例子，当一个卖家发布了一条宝贝信息时，他希望的当然是这个宝贝马上就可以被卖家搜索出来、点击、购买啦，相反，如果这个宝贝要等到第二天或者更久才可以被搜出来，估计这个大哥就要骂娘了。再举一个推荐的例子，如果用户昨天在淘宝上买了一双袜子，今天想买一副泳镜去游泳，但是却发现系统在不遗余力地给他推荐袜子、鞋子，根本对他今天寻找泳镜的行为视而不见，估计这哥们心里就会想推荐你妹呀。其实稍微了解点背景知识的码农们都知道，这是因为后台系统做的是每天一次的全量处理，而且大多是在夜深人静之时做的，那么你今天白天做的事情当然要明天才能反映出来啦。
Storm简介 ?实现一个实时计算系统 全量数据处理使用的大多是鼎鼎大名的hadoop或者hive，作为一个批处理系统，hadoop 以其吞吐量大、自动容错等优点，在海量数据处理上得到了广泛的使用。但是，hadoop不擅长实时计算，因为它天然就是为批处理而生的，这也是业界一致的共识。否则最近这两年也不会有 s4,storm,puma这些实时计算系统如雨后春笋般冒出来啦。先抛开s4,storm,puma这些系统不谈，我们首先来看一下，如果让我们自己设计一个实时计算系统，我们要解决哪些问题。