A Similar Duplicate Data Detection Method Based on Fuzzy Clustering for Topology Formation

Transcription

1 Lejang GUO 1, We WANG 2, Fangxn CHEN 1, Xao TANG 1,2, Weang WANG 1 Ar Force Radar Acadey (1), Wuhan Unversty (2) A Slar Duplcate Data Detecton Method Based on Fuzzy Clusterng for Topology Foraton Abstract. The changng nforaton technology akes data ncrease exponentally n all areas, the qualty of the huge aounts of data s the core probles. Data cleanng s an effectve technology to solve data qualty probles. Ths paper focuses on the duplcate data cleanng technques. It studes the qualty of the data fro the archtectural level, the nstance-level probles, the ult-source sngle-source probles, duplcated records cleanng applcaton platfor and the evaluaton crtera. In these studes, a proved novel detecton ethod adopts the fuzzy clusterng algorth wth the Levenshten dstance cobnaton to data cleanng.it can accurately and quckly detect and reove duplcate raw data. The proved ethod ncludes a slar duplcate records detecton process, the ajor syste fraework desgn, syste functon odules of the pleentaton process and results analyss n the paper. The precson and recall rates are hgher than several other data cleanng ethods. These coparsons confr the valdty of the ethod. The experental results exhbt that the proposed ethod s effectve n data detecton and cleanng process. Streszczene. Artykuł proponuje nowe etody czyszczena danych z uwzględnene lczby przypadków, welu źródeł, podwójnych rekordów nnych kryterów oceny. Ulepszona etoda detekcj wykorzystuje algoryt rozytego klastrowana w dystanse Levenshtena. W ten sposób szybko wykrywane są usuwane podwójne wersze danych. (Metoda detekcj podwójnych danych bazująca na rozyty klastrowanu) Keywords: Approxate Duplcate Data, Fuzzy Clusterng, Data Cleanng, Levenshten Dstance Słowa kluczowe: czyszczene danych, rozyte klastrowane. Introducton The changng nforaton technology akes data of all areas ncrease extreely fast, whch akes data becoe the focus. The accurate analyss of data s of sgnfcance because the decson, whch decson-akers ade, s hghly depend on t. However, the data receved fro external sources n the data warehouse s usually drty, ncoplete and nconsstent. There are two reasons: frstly, the lack of effectve data analyss technques; the second, the data s of not hgh qualty, dfferent data sources on the sae data ay volate the value of the data tself. The drty data n the DW (Data Warehouse) wll affect the accuracy of the extracted nforaton, akng all knds of data analyss, such as OLAP (On-Lne Analytcal Processng), data nng applcatons have abguous or wrong conclusons, akng decsons lose the support. Therefore, the qualty of DW data s a proble can t be gnored. Data cleanng s desgned to guarantee hgh qualty data, to elnate error data redundant data and nconsstent nforaton n data warehouse or database, whch can enhance the qualty of decson. Data cleanng a s not only on elnatng data errors, redundant and nconsstent nforaton, ts fundaental purpose s to unfed a varety of data sets fro dfferent, ncopatble rules, whch are the necessary eleents to construct DW and KDD (Knowledge Dscovery n Databases). Snce the data nconsstences wll be a varety of possbltes, and data s of a large aount, data anoaly detecton usually reles on hghly effcent data converson algorth [1]. Database technology and data s large aount are undergong a revoluton fro quantty to qualty. In the past, people coplete the data query and analyss n a seautoatc way. Those data cannot satsfy people s analyss and arguent of affars n varous felds hde behnd the data. KDD and data nng technques generate and develop based on the needs of the county, t also create uch new felds, such as research and applcaton n data warehouse technology [2]. Those technologes ake people use data effectvely on a ore convenent platfor, whch lad a sold foundaton for decson analyss. Intalzng the varous data of dfferent data sources s a very portant part n the KDD; t has an portant pact on the fnal result. Enttes of the real world n dfferent data sources often use dfferent syntax fors. The way to reove data of dfferent data sources s dffcult snce the data n dfferent fors ay lead to the stuaton of only-two or only-three result. The a large nuber of fact has proved that, slar to large-scale ntellgent systes, such as data nng syste, data pre-processng consttute 60% to 80% of the entre workload, the data cleanng s one of the part of data pre-processng, whch has a very portant role n the ntellgent syste. Therefore, the study on syste, whch could produce odel fro a large nuber of useful nforaton, becoes ncreasngly portant. Data cleansng s a work-related feld, whch work around specfc areas. The data-cleanng fraework for all odes s not avalable, whch akes the research of general data cleanng syste becoe a hot spot. The Study and Iproveent of Fuzzy Clusterng Algorth The Status of Fuzzy Clusterng Algorth Pattern recognton and achne recognton s regular n nosy or coplex envronents, whch s the search n data structure. In pattern recognton, the set of data s called a cluster. In realty, data s not evenly dstrbuted, so the rule or structure ay not be accurately defned. In other words, pattern recognton s an nexact scence. It s to deal wth soe fuzzy ssue. For exaple, the boundary between clusters ay be vague, and not certan, that s, a data ay belong to two or ore dfferent levels of cluster ebers. So the key ssue s to fnd a set of data ponts, whch s a cluster. The current study on fuzzy clusterng algorth located n the followng areas: the convergence local nu of the fuzzy c-eans clusterng algorth; creaton of dfferent types of objectve functons; dvdng the fuzzy space n dfferent ways; leadng the optal value of real world nto fuzzy clusterng algorth; nzng the dstance of fuzzy c-eans clusterng algorth; clusterng a large nuber types of data. Ths paper studes how to optze the ost classc fuzzy c-eans clusterng algorth n fuzzy clusterng algorth, and apply the proved algorth to the detecton of approxately duplcate data. Fuzzy c-eans Clusterng Algorth Fuzzy C-eans (FCM) clusterng algorth allows a group of data belongng to two or ore clusters. Ths ethod was rased by Dunn n 1973, and developed by Bezdek n 1981, and s coonly used n pattern recognton [3]. It s a clusterng algorth based on dvson, 26 PRZEGLĄD ELEKTROTECHNICZNY (Electrcal Revew), ISSN , R. 88 NR 1b/2012

2 and t as at led the slarty between objects whch are dvded nto the sae class (ebers of group) to be axu, whereas the slarty between objects of dfferent classes nu. It s based on the objectve functon s nzng, whch lst as follows: N C (1) J u x c 1 j1 j 2 where s any real nuber greater than 1, whch was set to 2.00 by Bezdek. u s a j level of x eber cluster; x s the densonal easureents; C j s a densonal clusters, * center s to express the slarty between any crtera, any easureent center. The objectve functon whch fuzzy partton optzed through teratve functon lst as equaton (1), updatng ebers u and the cluster center C j : 1 (2) 2 (3) u x c c j k1 xck C N u 1 N 1 u x 1 The teraton stops when eet the equaton (4). (k1) k (4) ax u u where s between 0 and 1, k s the teraton step. The process converges to a local nu or saddle pont of J.The algorth has the followng steps: 1. transfor U nto atrx u, U (0) 2. the k-step, accordng to equaton(3),and calculate the atrx center vector c (k) 3. calculate U (k), U ( k 1) accordng to equaton (1) stop calculate If U ( k 1) U ( k), return to step 2. Ths algorth proposed n ths paper wll be leaded as the fundaental algorth to detect the slar and duplcate data nto the database cleanng. The Probles of the Exstng Algorth Research fro the above algorth, t knows that data s bound to each cluster s ebershp functon, whch represents the fuzzy way of the algorth. To acheve ths goal, t only needs to create a atrx naedu, a nuber between 0 and 1 representng connecton degree between the data and cluster canters. Gven a partcular data set, assung that s an axs dstrbuton. One-densonal data clusterng ebershp s shown n Fg.1, the connecton degree represent the locaton of slar data between slar data eber to a certan extent. In the FCM approach, the sae orgn does not belong to only one welldefned cluster, but t can be placed n ddle. In ths case, as the ebershp functon shown n Fg.2, represented wth a sooth curve, each base ay belong to dfferent groups of ebers. Fg.1 One-densonal data clusterng ebershp Fro the clusterng ebershp of FCM ethod s data n Fg.2, there are soe tradtonal clusterng algorths, whch have been appled to the detecton of slar or duplcate data by scholar, but there are soe probles [4]. In the appng process of character feld, f there exsts spaces n the d-pont, they wll lose the correspondng effectve feld nforaton. If DBSCAN algorth s used alone n dealng wth character data, the general s to add ASCII value of all character n turn, t can easly lead to sjudgeent. Such as bg and gbe, they can be consdered to be duplcate data, those two dfferent records ay be classfed nto the sae class, and the clusterng result obtaned n the cluster set s not entrely slar record. Fg.2 Data clusterng ethod FCM ebershp The Slar Duplcate Data Detecton Based on Fuzzy Clusterng Through research and analyss of the prevous secton, these wll have ore or less the exstence of proble by the use of a sngle algorth, and t wll affect the fnal result of data cleanng. Broadly speakng, data cleanng s the process of data sources, and akng the processed data effcent and useful; n the narrow sense, data cleanng specfcs the process to buld DW and data nng to ake the data source accurate coplete consstence after that the data source s relable [5]. Based on the coparson the prevous ethod, ths paper presents a new ethod of detectng slar or duplcate data. The ethod uses a cobnaton of the fuzzy c-eans clusterng algorth and the Levenshten dstance to process drty data, and gets the needed data accurately effectvely and quckly. So that slar or duplcate data wll all be throw nto trash. There are two steps n the ethod proposed n ths paper, frstly usng fuzzy c-eans clusterng algorth to gather the ebers of greatest slarty nto a class as far as possble together, and then copare the ebers of each class by Levenshten dstance, fnally delete the sae ebers of each class whch are naely slar data [6].Levenshten dstance s the splest edt dstance whch s a coonly used easure of the strng dstance. Ths ethod s the nterpretaton for nzng the nuber of operatons the fro the source strng to the destnaton strng by nsert (nsert characters n a poston of a strng), reove (delete one character for strng), replace (replace a character wth another character n the strng). Ths ethod has a wde range of applcatons n deternng the slarty of two strngs. Edt dstance s defned as follows: assung all strngs s created fro a lted set of characters. Two strngs such as T the length of the strng, are respectvely S, T. PRZEGLĄD ELEKTROTECHNICZNY (Electrcal Revew), ISSN , R. 88 NR 1b/

3 D (. represents the edt dstance between the frst characters of the strng S and the frst j. Characters of the strng T ; Obvously, D ( T,0.0) 0. D (. recursvely defned as follows: D(. D( 1, j 1) n D(, j 1) 1 D( 1, 1 the edt dstance of Strng S, Strng T : D( T ) D( S, T (5) ) 1 T tos j nsertt DelS Through the above defnton, you can wrte a progra to calculate the edt dstance between two strngs, the algorth's te coplexty s O( S T ). For exaple: transfor Strng s = "Acaday" nto a strng t = "Acadey", s s the source strng and t s the target strng, the operatons are shown n table 1: Table 1.Exaple edt dstance S t Operaton Acaday Acadey A caday A cadey Ac aday Ac adey Aca day Aca dey Acad ay Acad ey Acada y Acade y Replace a to Acada y Acade y Acada y Acade y Del Acaday Acadey If defne the cost of each edt operaton s 1, In Table 1 t shows, edt dstance between "Acaday" and "Acadey" s 2, calculate result of slarty after the standardzaton (to take a longer strng length) s 0.75.If defne the cost of each edt operaton s 1, that s the Levenshten dstance. Based on the above analyss, the edt dstance has a certan effect on atchng of the sspelled strngs. For exaple, edt dstance between "Coputer Network" and "Coputer Ntwork" s only 1 and slarty value s ; t also has soe effect on nserton and deleton of the short words, for exaple, edt dstance between "Lenovo" and "Lenovo Co" s 3 and the slarty value s Steps of the Iproved Algorth In ths paper, the proved algorth s dvded nto two phases: The frst phase: Clusterng the data sets by the fuzzy c- eans clusterng algorth, so that a large data set s dvded nto a nuber of sall classes (groups), ost of the slar or duplcate records s clustered nto dfferent sall classes. And duplcate records n each class are located closely postons, whch create a relatvely better condton for the accurate detecton of these records. The second phase: start second cluster, t anly prove the clusterng accuracy. Most of the slar or duplcate records are clustered nto dfferent sall classes n the frst stage, but no accurate results of detecton are acqured [7]. By usng the Levenshten dstance ethod n the second clusterng ethod, one can contrast the characterstcs of each eber one by one to fnd duplcate records, and ake test results ore accurate. The Feasblty of the Iproved Algorth If fuzzy c-eans clusterng s used ndvdually, te coplexty wll drectly related to the aount of nput data, ths wll lead to that ths algorth has a bg syste j overhead. In other words, after the frst cluster, t wll have a re-cluster on each class that contans slar or duplcate records, but the aount of nput data has not been reduced, the te overhead s stll large, and t wll be very slow to reach the convergence [8]. Then copare data by usng Levenshten dstance ethod ndvdually, the te coplexty s O ( n 2 ), t s not a good optsaton ethod, ether. Frstly, ths algorth does cluster analyss through the fuzzy c-eans, after the copleton of the frst cluster, another part of the error data whch are caused by the change of character poston are cluster nto a eber group (cluster). So t s needed for the Levenshten dstance ethod to do the re-cluster. We can know fro the prevous analyss, Levenshten dstance ethod has a certan effect on atchng the sspelled strngs. At the sae te, the nuber of each class s records has been relatvely sall after the frst cluster, so t does not really ncrease the coputng te. And there wll be soe ncrease n accuracy. Meanwhle, the ethod can not only ake up for shortcongs caused by detect the slar or duplcate data ndvdually, but also get ore precse results. The Ipleentaton of the Slar or Duplcate Data Detectng Method Based on Fuzzy Clusterng Desgn of Syste Process Ipleent data cleanng platfor through the prevous Iproved algorth, akng data of low qualty n the data warehouse can be cleaned by ths data cleanng platfor, akng the nforaton syste data ore accurate, consstent, to support the rght decsons [9]. Syste desgn process chart s shown n Fg.3. Orgnal Data Data cleanng strateges Matheatcal Statstcs Data Fg.3 Syste Desgn Process Property Cleanng Duplcaton of data cleanng The Data to eet data qualty requreents Steps of Ipleentaton Based on the above dscusson of data cleanng, the process of cleanng data can be dvded nto the followng steps; each step can be subdvded nto a nuber of tasks. These steps can be descrbed as Fg Data Preparaton Data analyss s the frst pvotal step of data preparaton. The an works of analyss are located n the source fle, dentfcaton and separaton of ndvdual data eleents. Data analyss akes t easy to do the transforaton, standardzaton and data atchng, because t allows coparsons between ndvdual data coponents, rather than between a lot of long and coplex strngs. It s needed to analyss the school logo, school nae and unt coponent nto a fxed forat packet n ths data source. Ths s a key step for detecton of duplcated records, and even for all of the data cleanng process. Data converson eans akng data of sae property have the sae data type by convertng, whch can also be called the type transforaton. If an applcaton stores data n a data type, ths forat of data ake sense n ths syste, but f these data are appled to a new syste, these data ay be unavalable, and even lead to errors. The an process s lst as follows: dentfy data sources, port the data to 28 PRZEGLĄD ELEKTROTECHNICZNY (Electrcal Revew), ISSN , R. 88 NR 1b/2012

4 database engne. Accordng to data types, length, the value and dscrete values, frequency, dversty, unqueness, null values, typcal strng pattern of etadata and derved, provdng a exact pont of all knds propertes qualty probles. In ths paper, we regard null values, as the record s not coplete. And ncoplete records wll be ported nto the detecton odule to re-detect, only had ths off can the data be ported nto the data source and used. Do the class defnton of the sae type of thng, and then, each record s an object entty. After the class defnton, hypostatze the data source whch wll be processed, each record corresponds to a sngle object entty. 2. The pleentaton of fuzzy C-eans clusterng Step through the operaton on the data source data has been copleted the data analyss, converson, the process of standardzaton, and lack of data to do a prelnary cleanng, the sae type of data encapsulated nto classes, accordng to fuzzy c-eans clusterng algorth to gather type of analyss, allows the experental data source 4000 records are dvded nto two clusters x, x s uch saller than 4000, and ths x nuber of clusters n each cluster s bascally to slarty as possble between slar, as dfferences between dfferent classes. In short, the pleentaton of fuzzy c-eans clusterng algorths, large data sources can be dvded nto a relatvely sall data set, whch focused on a sall data set ost of the duplcated records. The steps of data processng, the dvson has good results, but for the boundary value, can not dstngush between good, the next step wll use the Levenshten dstance algorth further dentfcaton. 3. The pleentaton of Levenshten dstance ethod Copared usng Levenshten dstance algorth for each class of record for the second cluster, the step has been possble to repeat the record set slar to a class, so the records n each cluster slarty s relatvely hgh. Based on the foregong analyss of the Levenshten dstance, t deterne the slarty of two strngs, especally strngs to have sspelled words and between the nsert and reove the short sde there s a wde range of applcatons. The data table n the sae class labelled data clusterng copared one by one, untl the cluster fro the frst record to the last record so far. Then repeat for the second clusterng steps untl all the data n the data table coparng copleted. 4. Reove duplcate records It s well known that n our country everyone's ID nuber s unque on the network card's MAC address s unque, then our data n the database are unque to dfferent enttes. Follow the steps we need to thnk ore over the ebers of the sae class can be cleared out. In the cleanng process, you can use three dfferent ethods for a anual by a professonal decson based on experence and qualfcatons of what nforaton would be deleted. Progress to the second than the frst se-autoatc, and t randoly selected by calculatng the slarty to delete the data, the accuracy s hgher. The thrd type s autoatc, the coputer do t theselves, no need to worry about you, but can not guarantee accuracy. Durng ths step, you can based on the envronent, the scope, purpose and other selecton ethods on the lne. Sulaton and Analyss of Experental Results We evaluate the perforance of our proposed ethod (EELTC) va MATLAB. For splcty, an deal MAC layer and error-free councaton lnks are assued. The sulaton paraeters are gven n Table.2. Frst, we obtan axu length of furthest level (R0) whch s a predefned value and ust be set prarly. Table 2. The sulaton paraeter Paraeter Value Network sze 200*200 BS locaton 100,350 Nuber of Sensors 200 Intal Energy 0.5 J Eele 50 nj/bt εfs 2 10 pj/bt/ εp pj/bt/ EDA 5 nj/bt/sngnal Data Packet Sze 4000 bts ω t s In the broadcast-based ode, the nodes do not establsh gradents n the nterest broadcast phase. Instead, the agnetc charge s ncluded n the data beng dssenated. The recevng node can tell fro the charge carred n the data where the data s fro and whether to forward further down-strea. When a node receves data, t checks f t has any atchng entry. If t does, t copares the agnetc charge n the entry wth the agnetc charge of the data. If the forer s greater than the data, t sets the agnetc charge of the data t n the entry and then broadcasts the data. Ths eans the data s sent fro the node whose agnetc charge s lower than the nteredate node, and the nteredate node repeats ths process. Fg.4 Data propagaton wth colluson. The node receves duplcate data, or data whose agnetc charge s greater than that n the entry, the data wll be dscarded. In Fg.4 (a), the agnetc charge of every node s estab- lshed; the snk s charge s 7. In Fg.4 (b), when the source wants to send data to the snk, t wll broadcast the data to ts neghbor nodes. In Fg.4(c), the nodes wth charge strength 6 broadcast the data because the agnetc charge of the nodes s greater than that of the data. Thus, the snk receves the data. Fro the tools Menu, one can select the opton Sulate Lnk Breaks. In ths ode, f a lnk on the graph s clcked, the lnk s broken. If the algorth s a parent table drven one, where the alternatve parents are saved n lsts, the algorth drectly changes to the alternatve parent. If t s not case, the algorth re-routes the graph wthout usng the broken lnk n Fg.5. If the Sulate Lnk Breaks opton s not selected, for the algorths that generate parent tables, these tables can be dsplayed by clckng on the nodes. In Fg.5, Sulaton of lnk breaks. The lnk of node 12 towards the root node s broken. Wth the fuzzy Algorth, a re-routng takes place and any nodes change ther actve lnks shown n redon the left). On the other hand, as 2- SafeLnks Algorth s an algorth that creates parent tables, only the node loosng the lnk (node 12) changes ts actve lnk, the rest of the lnks (n black) rean the sae (on the rght). PRZEGLĄD ELEKTROTECHNICZNY (Electrcal Revew), ISSN , R. 88 NR 1b/

5 Fg.5 The generated routng trees wth dfferent algorths We copare the aount of energy consued by CHs n three protocols for 20 rounds (Fg.6). The energy consued by CHs per round n EELTC s uch lower than that n LEACH, and s alost the sae as EEUC. In LEACH because CHs send ther data to the BS va sngle hop councaton, the energy consupton s uch hgher. In EELTC and EEUC, CHs transt ther data to the BS va ult- hop, so a consderable aount of energy s saved. Fg.6 Mnu dstance between CHs We copare energy consupton just n setup phase wthout consderng energy consupton n steady state of three protocols n 20 rounds n Fg.7. EELTC saved consderable aount of energy by consderng of the te for each canddate to start broadcastng advertseent essage. EEUC protocol consued hgher energy than LEACH and EELTC because t has ore control overhead n setup phase. In EELTC clusters creaton and nter-cluster ulthop routng are accoplshed by a sngle ADV essage. But n EEUC ths s done by too any control essages exchange. Fnally, we exane the energy effcency of three protocols by evaluatng of the te untl the frst node des and the te untl the last node des. It s clear that EEUC ncreases network lfe te copared wth LEACH, and EEUC due to havng uch lower overhead and usng ulthop transsson data. Concluson Wth the developent of nforaton technology, data cleanng process has becoe an portant part of data ntegraton. Slar to the detecton of duplcate records n a data cleansng has been the focus of the study. Exstng duplcate record detecton algorth s slar n ost cases, Feld atchng algorth and the record atchng algorth s the core of ths approach. Exstng feld atchng algorth does not have the versatlty, and record atchng algorths usually fnd feld values usng weghted ethod to calculate the slarty of accuracy s poor.in ths paper, we develop an accurate and coprehensve energy odel for the front-end of a wreless netowork. For a dsjont ultpath confguraton whose patterned fal- ure reslence s coparable to that of braded ultpaths, the braded ultpaths have about 50% hgher reslence to solated falures and a thrd of the overhead for alternate path antenance.ths ore n-depth study of the exstng duplcated records detecton ethod to analyze the advantages and dsadvantages of varous detecton ethods. Exstng detecton ethods for calculatng the slarty values recorded shortcongs, the proved fuzzy clusterng algorth s ntroduced to prove the detecton of approxately duplcate records, for specfc ssues, constructed for the detecton of duplcated records cleanng platfor to prove a slar accuracy of detecton of duplcate records. REFERENCES [1] Stojenovc, X. Ln. Power-aware localzed routng n wreless networks, (2001) 12,No.11, [2] Huseyn Ozgur Tan, Ibrah Korpeogle. Power Effcent Data Gatherng and Aggregaton n Wreless Sensor Networks, (2003) 32,No.4, [3] Jan Yu,Mn-Shen Yang.A Generalzed Fuzzy Clusterng Regularzaton Model Wth Optalty Tests and Model Coplexty Analyss, IEEE Transactons on Fuzzy Systes, (2007) 15,No.5, [4] Chatzs, S.,Varvargou, T..Factor Analyss Latent Subspace Modelng and Robust Fuzzy Clusterng Usng t-dstrbutons, IEEE Transactons on Fuzzy Systes, (2009) 17, [5] M. Carde, J. Wu, M. Lu. Iprovng network lfete usng sensors wth adjustable sensng ranges, Sensor Networks, (2006) 10,No.2,41-49 [6] Luo H., Luo J., Lu Y.,, Das S. K..Adaptve Data Fuson for Energy Effcent Routng n Wreless Sensor Networks, IEEE Trans. on Coputers, (2006) 18,No.4, [7] Yao Shen, Yunze Ca, Xaong Xu.A shortest-path-based topology control algorth n wreless ulthop networks, Coputer Councaton Revew, (2007) 37,No.5, [8] M. Zunga, B. Krshnaachar. Analyzng the transtonal regon n low power wreless lnks, IEEE Secon 04, [9] K. Seada,M. Zunga,A. Hely,B.Krshnaachar,Energy effcent fowwardng strateges for geographc routng n wreless sensor networks,n ACM Sensys 04, Baltore, MD, Nov Authors: dr Lejang Guo, a Senor IEEE eber, the departent of Early Warnng Survellance Intellgence, Ar Force Radar Acadey, Wuhan, , Chna. E-al: radar_boss@163.co. dr We Wang, school of power and echancal engneerng,wuhan unversty,wuhan, ,Chna. E-al: leezbaby@163.co. Prof Fangxn Chen, the departent of Early Warnng Survellance Intellgence, Ar Force Radar Acadey, Wuhan,430019, Chna. E- al:pxal@sn.co. Fg.7 The aount of energy consued by CHs 30 PRZEGLĄD ELEKTROTECHNICZNY (Electrcal Revew), ISSN , R. 88 NR 1b/2012