Mesopic Visual Performance of Cockpit s Interior based on Artificial Neural Network

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1 Mesoic Visual Perforance of Cockit s Interior based on Artificial Neural Network Dongdong WEI Fudan University Det of Mechanics & Science Engineering Shanghai, China Abstract The abient light of cockit is usually under esoic vision, and it s ainly related to the cockit s interior. In this aer, a SB odel is coe u to silify the relationshi between the esoic luinous efficiency and the different hotoetric and colorietric variables in the cockit. Self- Organizing Ma (SOM) network is deonstrated classifying and selecting sales. A Back-Proagation (BP) network can autoatically learn the relationshi between aterial characteristics and esoic luinous efficiency. Coaring with the MOVE odel, SB odel can quickly calculate the esoic luinous efficiency with certain accuracy. Keywords- coonent; Mesoic Vision; Cockit; Artificial Neural Network; BP; SOM. I. INTRODUCTION A. Cockit s Interior Ergonoics Visual cofort occuies an increasing iortant lace in our everyday life, but also in the field of aeronautics which is the subect of this aer. Modern science and technology is eole-oriented. More and ore huan factors were taken into consideration on the develoent of odern civil airlane. A subect of alying ergonoics into an-achine relationshi develos gradually, which has brought about ore and ore attention. The abient light of cockit consists of natural sunlight, instruents anels, inside lighting systes and interior s reflecting light. The quality of abient light in the an-achine syste should eet the requireents, as well as roviding eole visual inforation about activity both in quality and quantity. It should eet ergonoics requireents of ercetive inforation, aiing at aking eole cofortable and leasant. The cockit s cofort lays an iortant role in ilot's ob, esecially the visual erforance. About 80% outside inforation is received though vision, which akes vision the ost iortant channel to counicate with eternal world [1]. Visual cofort is the sychological feeling about cofort level in abient light. So, visual cofort is a quantity of sychological feeling. Uncofortable vision will cause a series of sytos, usually aears as redness and swelling, ain, itching, tears, dizziness or even intestines and stoach robles. Cofortable environent of cockit would guarantee the ilot kee a noral state in the rocess of work, to avoid flight accidents caused by visual factors. Gang SUN Fudan University Det of Mechanics & Science Engineering Shanghai, China In the an-achine ergonoics study, establish the inner relationshi between different aterials roerties and light source, and the received luinance, light intensity, contrast and color in the secific conditions. Siulate different characteristics of efficiency of different light source and different aterials. The influences of these ultidiscilinary factors are not indeendent. They re of colicated nonlinear relation. Past studies doestic and oversea are ostly aied at single factor of variables, without considering the nonlinear relationshi between ultile factors and the couling echanis. The established design standards and nors cannot coletely eet the ilot s ergonoics requireents in the real flight environent, thus increasing the design difficulty of a cockit ergonoics syste. There s a long history according to the visual ergonoics research in cockit. Britain and Aerica have already done a lot of eeriental and theoretical research, and established design standards of illuination and colorietry. However, there s only a little study for the interior syste of ergonoics roble. The doestic related research is scattered, ainly aused in the qualitative subective evaluation level, which cannot for a systeatic theory. Through the research of aircraft cockit s couling echanis, characteristics of the ilot s visual ercetion and corehensive effect with ultivariable factors, we can build the civil aircraft cockit s interior ergonoics theory odel and alication echanis. Figure 1. Cockit s visual syste odel OPTIS established a cockit s visual syste odel [2] by CATIA based on ergonoic design criteria and a certain tye of aircraft cockit, which is shown in Figure.1. Then various otical roerties could be set, including the characteristics of 15 P a g e

2 light source sectral and otical aterials which articiating in the rocess of light transission. After that, they can be alied to the otical tracking syste to siulate the light rocess. For safety reasons, visual inforation ust be seen as cofortable as ossible by the aircraft ilot in any light conditions. In this aer, we focus on the cockit s esoic visual erforance based on the ANN ethod. B. Mesoic Vision Mesoic light levels are those between the hotoic (daytie) and scotoic (etreely low) light levels. The esoic luinance range covers brightness between about and 3 cd/ 2. Most night-tie outdoor and traffic lighting environents and soe indoor lighting are in the esoic range [3]. As huan eyes have different ercetions on light fusions fro different frequency, there coes to be different brightness, for observers, between lights of different wavelength even with the sae ower. Luinous efficiency function curves indicate such a huan eye character. Photoic luinous efficiency function V (in Figure2), which is fit for delineating the sectral resonse within a 2- degree range of huan eyes in a higher brightness, is the ost widely used function in this field and was brought forward on the 6th conference of CIE in CIE has successively brought forward the function with a wider range of 10 degree and also the luinous efficiency function environents of low brightness less than 0.001cd/ 2. V ' for levels [4]. The EC roect MOVE [5] (Mesoic Otiisation of Visual Efficiency) was carried out during in the EC Fifth Fraework rograe (G6RD-CT ). The obective of the roect was to define relevant sectral sensitivity functions for the luinance range of cd/ 2, where standardisation is ost urgently needed. The TC1-58 Technology Coittee found in 2000 also by CIE cae u with a better odel using the ethod of visual erforance and such a function below was brought forward [6]: Where 1 ' M V V V (1) V reresents the esoic luinous efficiency function under the environent of a certain backdro brightness, M is the noralization factor ofv, is a araeter which is located between 0 and 1 based on backdro brightness and sectral ower. =1 is in hotoic conditions while =0 in scotoic conditions. Illuination is defined as the transarent flu on unit area, and flu is available fro function below: M P V d Where stands for the total flu, arrangeent function of light source, (2) efficiency function on certain brightness, noralization factor. s/ /. s P is the sectral V is the luinous M is the Figure 2. Photoic luinous efficiency function luinous efficiency function V and scotoic V '. Mesoic hotoetry has a long history. Most esoic hotoetry odels have concentrated on brightness evaluation. The early works began in the 1950s and then a secial research ut forward by the Coittee of Mesoic Version Photoetry in CIE started in A nuber of studies of visual erforance at esoic light levels have been conducted, which underscore the iortance of recognizing the distinction between hotoetry and a colete characterization of visual resonses at esoic Figure 3. The rocess of calculating In this aer, a new ethod has been used into huan vision odel to silify the nonlinear relationshi between light characteristics and esoic vision. In real tie scenario, there are any factors taken into consideration. However, we focus on soe doinant variable in this aer. II. ARTIFICIAL NEURAL NETWORK An artificial neural network (ANN), usually called neural network (NN), is a atheatical odel or coutational odel that is insired by the structure and/or functional asects of biological neural networks. A neural network consists of an interconnected grou of artificial neurons, and it rocesses inforation using a connectionist aroach to coutation. Fro 1940s, with huan being s fully understanding of the brain structure, coosition and the ost basic unit, Artificial Neural Network was arose [7]. Silified odel 16 P a g e

3 (ANN) was established after cobining atheatics, hysics and inforation rocessing ethod, and aking neural network abstracted. As an active arginal subect, the research and alication of neural network is becoing a hot ot of artificial intelligence, cognitive science, neurohysiology, nonlinear dynaics, and other related subects. In last ten years, acadeic research according to neural network is very active, and uts forward alost a hundred of neural network odel. Neural network is also widely used in analysis of inut and outut with ultile variables. In the rocess of aircraft design, using the neural network to otiize neuatic araeters has obtained soe rogress. In this aer, SOM network is used to coress a set of high-diensional inut araeters that contain aterial characteristics onto a two-diensional SOM grid. SOM network is different fro other artificial neural networks in the sense that it uses a neighborhood function to reserve the toological roerties of the inut sace. The neurons will classify the sace, each neuron reresenting a artition of that sace. SOM network eloys an Winner-Takes-All (WTA) oeration, which only the winner is allowed to adust the weight connecting to the inut. The training rocess includes sequential stes[8]. a) Initialization: Randoly secify the weight value W, 1,2,, ( stands for the nuber of neurons on coetitive layer) and give the initial value of Learning Rate 0 N. and Radius of Neighbor * 0 b) Coetition: Select a certain sale: X X1, X 2,, X n (3) Then calculate the resonses by (4) to all the neurons on * coetitive layer. Find the winning neuron with the largest resonse. T Y W X, 1,2,, (4) c) Adation: Calculate the radius of neighbor neurons N t, and all neuron weights in the radius will be * adusted by (2). 1, 1,2,, ; * wi t wi t t N i wi t i n N t Where the Learning Rate will be the function of training tie t and the radius of neighbor N t, N t e N 0, t t t t t 0 1, t t t t t (5) (6) In which, t is the total cycles for training and t is the cycle tie keeing the original learning rate. If t declines to a tolerance or the training tie t is long enough, then the training is finished, or else go to ste b) continuing another sale. After the whole training, the network will be sensitive differently to the sales in database. The sales with close characteristics will generate siilar resonse on the outut a, which roves to classify different tyes of sales successfully. BP network odel is the ost tyical artificial neutral network (ANN) odel, widely used as a ulti-layer feedforward network, which includes inut layer, hidden layer and outut layer. In this aer, we give color coordinate and brightness as inut and esoic luinous efficiency function as teacher signal, so that the network will be caable of estiating any esoic luinous efficiency by giving a single light characteristics. The training rocess follows 7 stes: a) Initial all network weights to sall rando values. X X 1, X 2,, X n b) Inut a certain sale to the network, and calculate the outut of all the neurons. ), calculate the error ter ( L). c) For each neuron k on outut layer (l L k t o o 1 o (7) ( L) k k k k k d) Where, tk is the teacher signal and o k is the outut of neuron k. e) For each neuron on hidden layer ( 2 l L 1 ), ( ) calculate the error ters L. o 1o w ( l) ( l 1) ( l 1) k k k f) Udate the network weights as follows: 1 w n w n n o n ( ) ( ) ( ) ( 1) k k k (9) g) Go to ste b) for another inut sale until the terination condition is et. Figure 4. SB odel (8) 17 P a g e

4 A new odel cobines SOM and BP network naed SB odel, shown as in Figure 4. It uses SOM to classify different sales while BP network to siulate nonlinear relationshi between aterial characteristics and esoic luinous efficiency. III. EXPERIMENTAL RESULTS To study the esoic vision erforance of ilot in cockit, we gathered easureent variable into an initial database, shown as in Figure 5. In the condition of eerient, inut variable including aterial reflectivity, aterial transissivity, aterial absortivity, luinous flu, color teerature, aterial coordinate and aterial coordinate y, as well as outut variable including brightness, light intensity, contrast, color and color y. (IJARAI) International Journal of Advanced Research in Artificial Intelligence, Figure 7. CIE 1931 Chroaticity Diagra Figure 5. Initial database After observing, aterial reflectivity, aterial transissivity, and light intensity are invalid. In the condition of color teerature equals 5000K and luinous flu equals 1000l. Initial database can be tried. The sale database shows as Figure 6. Chroaticity coordinates of soe coon illuinants used as a white reference. TABLE I. Reference White coordinate y coordinate CIE-E CIE-A CIE-C CIE-D After calculation, the doinant wavelengths of color coordinates are shown in Table 2. TABLE II. DOMINANT WAVELENGTH OF COLOR COORDINATES A. Doinant Wavelength Calculate the doinant wavelength with -y chroaticity coordinates in a Chroaticity Diagra [9], ust illustrated in Figure 7. Construct a line between the chroaticity coordinates of the reference white oint on the diagra (for instance, CIE-E) and the chroaticity coordinates, and then etraolates the line fro the end that terinates at the filter oint. The wavelength associated with the oint on the horseshoe-shaed curve at which the etraolated line intersects is the doinant wavelength. Table 1 gives soe coon illuinants used as a white reference. color color y wavelength P a g e

5 B. Mesoic Luinous Efficiency Then the hotoic luinous efficiency and the scotoic luinous efficiency could be obtained by doinant wavelength. The araeter in forula (1) could be calculated according to Table 4 in reference [5]. According to the brightness and s/ ratios, could be aroiately calculated in certain conditions as shown in Table 2. The s/ ratio is chosen in the condition of tyical overcast sky, which is 2.36 [10]. Linear fitting function s used to fulfill the -value. TABLE III. X-VALUE FOR MOVE MODEL C. SOM Network Results According to the design requireent, a total of 12 sales are selected, including brightness, color and color y. For initialization, we give the initial learning rate And the radius of neighbor is defined as following eression: 0.9e 10 / 2000 N t t t (10) According to the test result, total cycles of training t is defined as wavelength brightness M is a noralizing function such that V attains a aiu value of 1, shown as in Figure 8. Figure 8. -value and M distribution results The MOVE odel is alicable for other conditions such as tyical sunlight sky and tyical direct sunlight sky. Figure 9. 2d-resonse by SOM After the whole training, all 12 sales are classified into 4 grous on a 2 2 a. Table 4 shows the resulting SOM with cluster grous considering the all 3 characteristics. Figure 9 deonstrates the resonses to different sales. The nuber is ranked fro left to right and u to down, sign as 001 to 012. The ore siilar the characteristics of sales are, the closer the colored as are. Fro the colored result, we can easily get that ost of the sales erfored like the sae in grou 1. No.012, 014 and 015 are the ost distinguishing ones P a g e

6 TABLE IV. Grou 1 Grou 2 Grou 3 Grou 4 001, 003, ,009, , , ,014,015 D. BP Network Results Construct a BP network, with color coordinate and brightness as inut and esoic luinous efficiency as teacher signal (target). The nuber of hidden nodes deends on the nuber, scale and coleity of sales. To confir the nuber of hidden nodes, we take the forula as follow: n l (5) where is the nuber of hidden nodes, n is the nuber of inut nodes, l is the nuber of outut nodes, and is a constant between 1 and 10. (hidden lr (learning ratio), ingrad = 1e-10 The ain araeters of BP network are of 3 nodes), 0.15 (iniu gradient), and eochs Figure 10. Error density distribution function MOVE SB_Model After the whole training, the result is shown in Figure 10. The black square line reresents error density distribution function of MOVE odel, as well as the red circle line reresents error density distribution function of SB odel. Observing the eak of these two odels, esoic luinous efficiency function of SB odel is ore concentrated than MOVE odel. CONCLUSIONS This aer ut forward a SB odel, which uses SOM network to classified different sales into different grous. Chose a certain grou, the training result is better than before. Use BP network to silify the relationshi between the esoic luinous efficiency, and the different hotoetric and colorietric variables in the cockit. After coaring with the MOVE odel, SB odel takes advantage of ANN to silify the relationshi, and convenient to calculate esoic luinous function, as well as has ore concentrate error density distribution. To ake research ore accurate, we will take ore research on siulating huan s eye, and construct corresonding odel. Photoic vision and scotoic vision are taken into consideration in the net as well. ACKNOWLEDGMENT The work is sonsored by National Basic Research Progra ( 973 Progra) of China, Issue Nuber 2010CB REFERENCES [1] Wei Liu, Xiugan Yuan, Haiyan Lin, Synthetical Evaluation of Ergonoics Study in Visual Inforation Flow Syste of Pilots, Journal of Beiing University of Aeronautics and Astronautics, 27(2), [2] Jacques Delacour, Develoent of a new siulation software for visual ergonoy of a cockit, Otis Cororation, 2002 [3] Yandan Lin, Dahua Chen, Wencheng Chen, The significance of esoic visual erforance and its use in develoing a esoic hotoetry syste, Building and Environent, 41, 2006, [4] MS Rea, JD Bullough, JP Freyssinier-Nova, and A Bieran, A roosed unified syste of hotoetry, Lighting Research and Technology, June 2004; vol. 36, 2: [5] Elohola M, HMonen L, Perforance based odel for esoic hotoetry, MOVE Proect Reort, 2005,Finland. [6] Yong Yang, Zuoun Bao, Chuanzheng Zhu, Lei Wang, Study on the Mesoic Vision Theory Used in Road Tunnel Lighting Measureent, 2011 Third International Conference on Measuring Technology and Mechatronics Autoation. [7] Daqi Zhu, The Research Progress and Prosects of Artificial Neural Networks, Journal of Southern Yangtze University (Natural Science Edition), 3(1), 2004 [8] Jie Chen, Gang Sun, Xin Jin, Intelligent Aerodynaic Design for Airfoil Based on Artificial Neural NetworkMethod, Couter and Autoation Engineering (ICCAE), 2010 The 2nd International Conference on, Feb. 2010, [9] Turan Erdogan, How to calculate luinosity, doinant wavelength, and ecitation urity, Serock White Paer Series [10] S.M. Beran, Energy Efficiency Consequences of Scotoic Sensitivity, Journal of the Illuinating Engineering Society, P a g e