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201O 2nd Inteational Confrence on Education Technolog and Computer (ICETC)

Multi objective optimization for object recognition

dlh lp

Science & Reseach Brach, Islamic Azad University

CE DeptmentTehr, Ira hosseinalipoor@srbiau.ac.ir

Absac- Relevant with some important subjects like targetrecognition, sensor fusion systems can be considered as one ofthe main issues highlighting here. Environmental condition,target characteristic and sensor eciency are three parameterswhich can impress on sensor value in target recognition so forrecognizing targets, a group of sensors which have morerecognition rates, must be selected intelligently. Utilizing manysensors to acquire the highest object recognition rate wouldhave extra cost and decrease energy of mobile sensors rapidly.Therefore make a tradeo between sensor numbers and object

recognition rate would be imperatively. This paper attempts todesign a multi objective optimization service by usingoptimization algorithm and neural network. This servicespecies highest recognition rate for each distinct sensornumbers. We propose multi objective optimization algorithmto help accessing the best sensory conguration for a deniteenvironment regarding to the environmental conditions,sensors performance, and object features. Our multi objectiveoptimization algorithm has two functions. Genetic algorithm isused to perform as one of functions to speci objectrecognition rates of each sensor group. Neural network is usedto perform as tness function of each genetic algorithmchromosomes. Another function is sensor numbersdeterminant. Highest recognition rate and lowest sensornumbers are two objects which multi objective optimization

algorithm wants to make a balance between them. We dene dierent scenarios for 6 dierent sensors in dierentconditions. Object recognition rate of each sensor is collected.These rates are used for neural networks training process. Bydening new scenario and run multi objective optimizationalgorithm in this scenario, this algorithm makes a Pareto frontbetween sensor numbers and object recognition rate. Finallythis algorithm nds, by distinct numbers of sensor, whichsensors by which recognition ability must be used to reach thehighest recognition rate.

Kord Automac Sensor Manement; Intelligent Sensor

Selecon; Objecs Recognion Rate; lti objetive

opmizaon; Genetic Algothm; Neural Network

I. NTRODUTON

The achitecture of object recognition is based on networks of sensors d data sion. Each of ese networkscovers a pa of environment d includes senso nodes ad

 their sion operations. Most of ese operations must beautomated for accelerating eir necess decision maing.This accelerates observation, orientation, decision, d action(OODA) cycle ad also developing common operational

 pictre (COP) for each agent in environmental monitoring.

Mhd h

Maleke Ashta University

CE DepatmentTehr, Irafeshaai@mut.ac.ir

Senso network nodes ca be dierent kinds of sensors like rada, ined cera, laser ad etc. In order to decrease the processing time ad also increase the aweness ofenvironment, we need tomated sensor magement. Automatic sensor maagement is performed in level 4 of eL model ned process maement [1]. The process

 maagement stage is ongoing assessment of e othersion stages to ensure that the data acquisition d sion is

 being perfoed in a way that will give optimal results. iscould also improve results by adjusting e peters in e

sion process, establishing a tget priori or selecting thesensors to give improved recognition rates [1]. ree processes ae done in this level:1. Situation measurement, which is in relation with

 trasmission of senso platform to the best possible point in order to receive the most accurate ad the leastexpensive information of environment.

2. Scheduling of sensors tasks which is used for multinctional sensors.

3. Sensors selection, which is done in multi sensor d multi-taget environments. The best sensors selection task for object recognition is perfoed by is part.

The sensor selection operation determines e best sensor

conguration in e current situation.  this paper we focuson e last process of automated sensor maagement to use

 the best senso conguration in y sitation automatically.For each platfo to perfo senso data sion, rst,sensors ae selected considering environmental conditions,

 taget chaacteristics ad sensor eciency. Aer thatintelligent sensor selection performed, data sion results

 have maximum accuracy based on the group of selectedsensors; Because of not using inexact or noisy sensorsinformation.

II. SENSOR SELETON OPTMZATON SERVE

Due to dierent eciency of sensors ad also mayeects of sensor numbers on sensor selection, multiobjective optimizion for sensor selection is so import.Figure 1 depicts the object recognition rate based on e

 number of sensors.

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20 0 2nd nternational Coerence on Education Technolog and Computer (ICET

High

Object

Recntn

Rate

Low

ow gNumr f Snr

FIG I. OBJECT RECOGNITION ATE I OUR CONDITIONS

 According to gure 1, at e rst ad second states,

efciency of system is decreased because of choosing badsensors for recognizing tgets. Why the second state has a

 poor eciency is sensor selection without considering theenvironmental conditions, target chaacteristics, ad sensoreciency.  the third ad four states, using the optimizedsensors caused to increase system efciency. In the thirdstate, because of using more sensors in recognizing task, theinfoation processing is time consuming and also has

 redundt results.  e fourh state, only a group of sensors, which have the most recognition rates, are used for sensorsion.  this paper, our goal is to present such service toaccess the highest recognition rate by lowest sensorsautomatically.

May techniques have been developed for general

optimized solution seaching for sensor selection. ese techniques rge om applying constraints on the objectivenction to seamline the optimization process [2] toapplying advced aricial aalysis techniques such asgenetic algorithms d simulated anealing algorims [3].Unique optimization techniques have been proposed such as picle swa optimization [4] d cuting ad suogateconsaint aalysis [5]. The point of this section is not to

 review each sech solution or optimization technique but toidenti the vie of algorithms available. Optimization

 techniques are well documented [6]. Genetic algorithm usesa set of chromosomes to present possible solutions forsolving problems. Each chromosome contains substringscalled genes, expressing vits of the problem space. In

 this paper, genetic algorithm is one of two multi objectiveoptimization nction, used for ding the highest

 recognition rate of each sensors group. Figure 2(A)illusates a chromosome at is a set of sensors deed asone possible solution. Each chromosome has 6 genes dshows the recognition rate of each sensor.  this gure,Sensors 3, 5, 6 are used d had 0.95, 1, 0.99 recognition rates

respectively

� SII

� S2I

� S3I

� S4 I

� S5  � S6

095 099

Fi2 A

(,� 

Fig.2. (B)

FIG2 (A) ROMOSOME STRUCTURE FIG2 8 E GENETIC AGORITMPROCESS

Figure 2(B) illustrates the whole process of sensorselection using genetic algorithm. The contribution of is paper is the use of neural netork as an estimator to evaluate the tness value of each genetic algorithm chromosomes. Inall previous methods at rst made a tness nction d then

 use optimization approach to d e optimal solution so is meod is the rst utilizing neural network within aoptimization algorithm for sensor selection in environmental

 monitoring or tget recognition [3, 6 ad 7].

For implementing optimization algorim d showing their results use TLAB Optimization Tools. In geneticalgorim, stochastic unifo method is used for selectionnction. This method lays out a line in which each pentcoesponds to a section of the line of leng proporional toits expectation. The algorim moves along e line in stepsof equal size, one step for each parent. At each step, thealgorim allocates a parent om e section it lands on. erst step is a uniform radom number less than the step size.For scaling, nction rk meod is used. is methodscales the raw scores based on the rk of each individual,

 rather than its score. The r of an individual is its positionin the sored scores. The r of the ttest individual is 1, e

 next ttest is 2, and so on. Rank ess scaling removes the

effect of the spread of the raw scores. For crossover nction tow point nction wi 0.8 crossover action is used. Thisnction selects two random integers 'm ad 'n between 1and Number of viables. e algorim selects genes

 numbered less or equal to m om the rst parent,selects genes numbered om m+ 1 to n om the second

 pent, d selects genes numbered greater th n om therst paent. The algorithm then concatenates these genes tofo a single chromosome. Constraint dependent nction isalso used for mutation wi 0.2 mutation action [, 11].

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