A Particle Swarm Optimization of Natural Ventilation Parameters in a ...

Sensors & Transducers Volume 102, Issue 3 March 2009

www.sensorsportal.com

ISSN 1726-5479

Editor-in-Chief: professor Sergey Y. Yurish, phone: +34 696067716, fax: +34 93 4011989, e-mail: [email protected] Editors for Western Europe Meijer, Gerard C.M., Delft University of Technology, The Netherlands Ferrari, Vittorio, Universitá di Brescia, Italy Editor South America Costa-Felix, Rodrigo, Inmetro, Brazil

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Sensors & Transducers Journal (ISSN 1726-5479) is a peer review international journal published monthly online by International Frequency Sensor Association (IFSA). Available in electronic and CD-ROM. Copyright © 2009 by International Frequency Sensor Association. All rights reserved.

Sensors & Transducers Journal

Contents Volume 102 Issue 3 March 2009

www.sensorsportal.com

ISSN 1726-5479

Research Articles Smart Sensor Systems: Book Review …………………………………………………………………..

I

Design of a Smart and High Precision Industrial Temperature Measurement and Monitoring System Using K-type Thermocouple and SPI-compatible Temperature Sensor Utpal Sarma, Digbijoy Chakraborty, P. K. Boruah..............................................................................

1

IEEE 1451.0-2007 Compatible Smart Sensor Readout with Error Compensation Using FPGA J. Kamala and B. Umamaheswari ......................................................................................................

10

Predicting the Deflections of Micromachined Electrostatic Actuators Using Artificial Neural Network (ANN) Hing Wah Lee, Mohd. Ismahadi Syono and Ishak Hj. Abd. Azid........................................................

22

Conception and Development of a Portable Electronic Nose System for Classification of Raw Milk Using Principal Component Analysis Approach Hing Wah Lee, Mohd. Ismahadi Syono and Ishak Hj. Abd. Azid .......................................................

33

Viscosity Measurement Using Microcontroller to Study the Thermal Degradation of Edible Oil Neelameagam Periyasamy, Rubalya Valantina Sathianathan and Murugananthan Krishnamoorthy. .......................................................................................................

45

Problems of Terminology in the Field of Measuring Instruments with Elements of Artificial Intelligence Roald Taymanov, Ksenia Sapozhnikova............................................................................................

51

Microcontroller Based Closed Loop PMDC Motor Position Control System Subrata Chattopadhyay, Utpal Chakraborty, Arindam Bhakta and Sagarika Pal ..............................

62

Discrete Time Sliding Mode Control Using Fast Output Sampling Feedback for Piezoelectric Actuated Structures L. R. Karl Marx, M. Umapathy, A. Girija, D. Ezhilarasi. ......................................................................

71

A Particle Swarm Optimization of Natural Ventilation Parameters in a Greenhouse with Continuous Roof Vents Abdelhafid Hasni, Belkacem Draoui, Thierry Boulard, Rachid Taibi and Brahim Dennai ..................

84

Experimental and Computational Study of Two-phase (Air–Palm Oil) Flow through Pipe and Control Valve in Series Arivazhagan M., Pugalenthi, Krishna Karthik K., Rani Hemamalini, Sundaram S.............................

94

The Effect on Pressure Drop across Control Valve for Two Phase Flow (Air-Water) Arivazhagan M, Krishna Karthik K, Sundaram S................................................................................

105

RBIC-Lite – a Family of Signal Conditioning ICs of ZMD Krauss Gudrun, Krauss Mathias ........................................................................................................

115

Authors are encouraged to submit article in MS Word (doc) and Acrobat (pdf) formats by e-mail: [email protected] Please visit journal’s webpage with preparation instructions: http://www.sensorsportal.com/HTML/DIGEST/Submition.htm International Frequency Sensor Association (IFSA).

Sensors & Transducers Journal, Vol. 102, Issue 3, March 2009, pp. 84-93

Sensors & Transducers ISSN 1726-5479 © 2009 by IFSA http://www.sensorsportal.com

A Particle Swarm Optimization of Natural Ventilation Parameters in a Greenhouse with Continuous Roof Vents 1

Abdelhafid HASNI, 1Belkacem DRAOUI, 2Thierry BOULARD, 1Rachid TAIBI and 1Brahim DENNAI 1

Institut de Génie Mécanique, Centre Universitaire de Béchar B. P. 417, 08000 Béchar, Algérie Tel.: 213 49 81 55 81/91, fax: 213 49 81 52 44 2 INRA-URIH 400, route des Chappes, BP 167, 06903 Sophia Antipolis, France Tel.: (0)492386663, Fax: (0)493653318 E-.mail: [email protected], [email protected]

Received: 15 December 2008 /Accepted: 24 March 2009 /Published: 31 March 2009 Abstract: Although natural ventilation plays an important role in the affecting greenhouse climate, as defined by temperature, humidity and CO2 concentration, particularly in Mediterranean countries, little information and data are presently available on full-scale greenhouse ventilation mechanisms. In this paper, we present a new method for selecting the parameters based on a particle swarm optimization (PSO) algorithm which optimize the choice of parameters by minimizing a cost function. The simulator was based on a published model with some minor modifications as we were interested in the parameter of ventilation. The function is defined by a reduced model that could be used to simulate and predict the greenhouse environment, as well as the tuning methods to compute their parameters. This study focuses on the dynamic behavior of the inside air temperature and humidity during ventilation. Our approach is validated by comparison with some experimental results. Various experimental techniques were used to make full-scale measurements of the air exchange rate in a 400 m2 plastic greenhouse. The model which we propose based on natural ventilation parameters optimized by a particle swarm optimization was compared with the measurements results. Copyright © 2009 IFSA. Keywords: Optimization, Particle swarm optimization, Greenhouses, Temperature, Humidity, Hydric model, Climate models, Cooling fog system, Metaheuristics

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1. Introduction Greenhouse ventilation is a key function in the control of greenhouse parameters such as air temperature and air humidity, and influences strongly the growth and development of the crops. In spite of this importance, especially in warm regions, the available knowledge on greenhouse ventilation is scarce. The air exchanged between inside and outside is still predicted with a large uncertainty ascribed to the difficulties of performing accurate measurements. Among the few works published in the literature, some are related to wind tunnel experiments on small-scale greenhouse with both roof and side openings. Other reported measurements on full-scale multispan greenhouses equipped with roof ventilators used the tracer gas techniques [1, 2, 7]. Particle swarm optimization (PSO), first introduced by Kennedy and Eberhart [3, 4], is one of the modernheuristic algorithms. The PSO technique can generate a high-quality solution within shorter calculation time and stable convergence characteristics than other stochastic methods [5]. Much research is still in progress for proving the potential of the PSO in solving complex optimization problems.

2. Problem Formulation Our objective is to optimize a reduced greenhouse model in which the controlled variables are, indoor temperature ( Ti , °C) and water vapour pressure ( Pi , Pa) and the actuators are, the fog system ( ϕ l , power of the evaporative cooling fog system,Wm-2), the vent opening ( s , vents opening surface m2), the soil heat flux ( Qs , Wm-2) and the air heating ( Qa ,Wm-2). Heat and water vapour balances have been first formulated in order to obtain the main equation of the whole model. Then particular equations have been added to complete the model.

3. Block Diagram and Open Loop Results The block diagram of the greenhouse is shown in (Fig. 1) together with the four actuators s , Qa , Qs and ϕ l ; five input variables have also been considered, Te (external temperature, °C), Pe (external vapour pressure, Pa), R g (outside global radiation, Wm-2), V (wind speed, ms-1) and PTi (saturated vapour pressure at temperature Ti , Pa) these are mainly considered as disturbances in the control loop. Some simulations have been carried out to study the dynamic behavior of the controlled variables Ti and Pi . In these tests, we have also considered the initial conditions for indoor temperature Ti (n ) and water vapour pressure Pi (n ) (Fig. 1) [2].

4. Mathematical Model of the Indoor Water Vapour Pressure In this case a water vapour balance inside the greenhouse is carried out, and the result is [1, 2]:

⎛ Rg ⎞ ⎜ ⎟ ⎛ r S B γ τ ′ χ γ S B γ S ⎞ ⎜ Pe ⎟ Pi (n +1) = Pi (n ) exp(− ζ ∆t ) + (1 − exp(− ζ ∆t ))⎜ ⎟ ⎜ ⎟ ξ ξ ξ ⎠ ⎜ PTi ⎟ , ⎝ ξ ⎜ ⎟ ⎝ ϕl ⎠

(1)

85

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where

(

) (

)

⎛ ⎛ ⎞⎞ ⎜⎜ Al C sV + Al C s0V + d 0 + ⎜ BγS ⎟⎟ ρC p ⎠ ⎟ ⎝ ⎝ ⎠ ζ = ,

(2)

v

(

) (

) (

)

ξ = ρC p Al C s V + ρC p Al C s0 V + ρC p d 0 + Bγ S ,

(3)

χ = ξ − Bγ S ,

(4)

and

Fig. 1. Block diagram of the controlled green house.

In these equations ∆t is the discretisation time step (s), r the ratio A B (Pa m2 W-1), with A parameter of the model of transpiration (.), B a parameter of the transpiration model (Wm-2 hPa-1), S the exchange surface between two constituents of the greenhouse (m2), γ the psychrometric constant (hPa K-1), τ ′ the greenhouse cover transmissivity (.), Al and C parameters of the natural ventilation model (.), s0 the leakage surface (m2), d 0 the wind independent leakage rate (m3 s-1), ρ the air density (kg m-3) and C p the thermal capacity of the greenhouse air (kg-1K-1). All fluxes are expressed per m2 greenhouse soil area.

5. Mathematical Model of the Indoor Temperature In this case an energy balance inside the greenhouse is performed from which the temperature is obtained as:

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Ti (n +1) =

h v

⎛ ν − h α 1 Kl − Kl ⎞ ⎟⎟ v v v v v ⎠ ⎝

Tm (n +1) + ⎜⎜

⎛ Te ⎞ ⎜ ⎟ ⎜ Rg ⎟ ⎜ ⎟ ⎜ Qa ⎟ , ⎜ Pe ⎟ ⎜ ⎟ ⎜P ⎟ ⎝ i ⎠

(5)

where

⎛ Te ⎞ ⎜ ⎟ ⎜ Rg ⎟ ⎜ ⎟ Qs ⎛ ∆t ⎞ ⎞ ⎛ ∆t ⎞ ⎛ Tm (n +1) = Tm (n ) exp⎜ − ⎟ + ⎜1 − exp⎜ − ⎟ ⎟ × ℘ × ⎜ ⎟ , ⎜ Qa ⎟ ⎝ τ ⎠⎠ ⎝ τ ⎠ ⎝ ⎜ ⎟ ⎜ Pe ⎟ ⎜P ⎟ ⎝ i ⎠

(6)

and

⎛ αh + βν Kl − Kl ⎞ 1 ν ⎟ ⎜ h(K + K s ) h(K + K s ) (K + K s ) (K + K s ) (K + K ) ⎟ , l ⎠ ⎝

℘ = ⎜1

(7)

In these expressions, h is the air/soil convective exchange coefficient (W m-2 K-1), v the greenhouse volume (m2), α the absorption of global radiation by the aerial compartment of the greenhouse (.), β the absorption of global radiation by the thermal mass compartment of the greenhouse (.), the two parameters α and β are fractions of the outside incident global radiation ( Rg ) collectively absorbed by the structure and crop in the former case, and by the soil (thermal mass) in the latter, K l the latent heat transfer coefficient driven by ventilation (W m-2 hPa-1), τ the time constant or characteristic time (s), K the overall heat loss coefficient through the greenhouse cover (W m-2 K-1) and K s the sensible heat transfer coefficient driven by ventilation (W m-2 K-1). Qs the soil heat flux (W m-2) and Qa the air heat input (W m-2) [1, 2].

6. Ventilation Ventilation may be either forced (mechanically, as by fans) or natural (caused by thermal buoyancy and/or wind pressures) Mechanical ventilation is typically designed to provide a maximum air exchange rate suitable for the local climate. Wind-driven ventilation is linearly proportional to wind speed and can be vigorous. Ventilation by thermal buoyancy depends on air temperature difference and the elevation difference between inlets and outlets but is not likely to be vigorous in practical applications. However, it can be adequate for greenhouse ventilation if properly designed and controlled. A significantly greater understanding of the subtleties of natural ventilation has developed over the past decade. New greenhouse designs allow for adequate side and roof ventilation. The extreme is the open roof greenhouse, which is most useful in gutter-connected greenhouses that cover large areas and are without sufficient sidewall area to provide adequate inlet area. The critical factor in designing natural ventilation is properly sized inlets and outlets. As a first rule, total inlet area should 87

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be equivalent to total outlet area. For example, upwind and downwind sidewall vents should be approximately the same area. Or, for thermal buoyancy ventilation, sidewall vents should have approximately the same combined area as the roof (ridge) vent. The ventilation rate Gv is a linear function of wind speed V , the vent opening s , as well as leakages both dependant ( s0 ) and independent of the wind speed ( d 0 ). Thus, we have:

Gv =

( s + s0 ) Al C V + d0 , 2

(8)

The four remaining parameters of the temperature and pressure balance equations to be optimized are: Al C , s0 , d 0 , β . The values identified for these parameters using the classical algorithm during a one week sequence are Al C = 0,2 , s 0 = 0 , d 0 = 0 , β = 0 [6].

With the Simulated annealing Algorithm, once we have chosen the parameters to be optimized, one must define also their numerical limits. Thus, we have defined the search space for the different parameters as shown in Table 1 [1, 2]. Table 1. Search space of the parameters to be identified.

Min Max

Al C

s0

d0

β

0 0.3

0 1

0 1

0 0.3

7. Particle Swarm Optimization PSO is one of the optimization techniques and a kind of evolutionary computation technique. The method has been found to be robust in solving problems featuring nonlinearity and nondifferentiability, multiple optima, and high dimensionality through adaptation, which is derived from the social-psychological theory. The features of the method are as follows: 1. The method is developed from research on swarm such as fish schooling and bird flocking. 2. It is based on a simple concept. Therefore, the computation time is short and requires few memories [8, 10, 11]. 3. It was originally developed for nonlinear optimization problems with continuous variables. It is easily expanded to treat a problem with discrete variables. According to the research results for birds flocking are finding food by flocking. PSO is basically developed through simulation of bird flocking in two-dimension space. The position of each agent is represented by XY axis position and also the velocity is expressed by v x (the velocity of X axis) and v y (the velocity of Y axis). Modification of the agent position is realized by the position and velocity information. Bird flocking optimizes a certain objective function. Each agent knows its best value so far (pbest) and its XY position. This information is analogy of personal experiences of each agent. Moreover, each agent knows the best value so farin the group (gbest) among pbest. This information is analogy of knowledge of how the other agents around them have performed. Namely, each agent tries to modify its position using the following information [8, 12, 13, 15]: – The current positions (x, y), – The current velocities ( v x , v y ), 88

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– The distance between the current position and pbest – The distance between the current position and gbest. This modification can be represented by the concept of velocity. Velocity of each agent can be modified by the following equation:

(

)

(

)

vik +1 = wvik + c1 rand1 × pbest i − sik + c 2 rand 2 × gbest i − s ik ,

(9)

where vik velocity of agent i at iteration k; w weighting function; ci weighting factor; rand random number

between 0 and 1; sik current position of agent i at iteration k; pbest i pbest of agent I; gbest gbest of the group. The following weighting function is usually utilized in (9). w=−

wmax − wmin × iter , itermax

(10)

where wmax initial weight; wmin final weight; itermax maximum iteration number; iter current iteration number. Using Eqs. (9) and (10) a certain velocity, which gradually gets close to pbest and gbest can be calculated. The current position can be modified by the following equation: s ik +1 = sik + vik +1 ,

(11)

Eq. (9) consists of three terms: the first one depends on the particle’s previous speed, the second term depends on the distance between the particle’s best previous and current position. The last term shows the effect of the swarm’s best experience on the velocity of each individual in the group. This effect is considered through the distance between swarm’s best experience (the position of the best particle in the swarm) and the ith particle’s current position. Eq. (11) simulates the flying of the particle toward a new position. The role of the inertia weight w is considered very important in PSO convergence behavior [9, 15]. The inertia weight is employed to control the impact of the previous history of velocities on the current velocity. In this way, the parameter wp regulates the trade-off between the global and local exploration abilities of the swarm. A large inertia weight facilitates global exploration (searching new areas), while a small one tends to facilitate local exploration, i.e. finetuning the current search area. A suitable value for the inertia weight w usually provides balance between global and local exploration abilities and consequently a reduction on the number of iterations required to locate the optimum solution.

8. Results and Conclusions The inside air temperature and humidity simulation models were identified using the described approaches for a greenhouse between 14 and 22 May 1991 located near Avignon in south-east France. The greenhouse had a tomato-crop area of 416 m2, in a double roof plastic house. Several actuators and sensors were installed and connected to an acquisition and control system based on a personal computer and a data acquisition and control card using a sampling interval of 1 hour. Only few seconds are required to identify the parameters of the reduced model with a personal computer. 89

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Since the PSO algorithm depends only on the objective function to guide the search, it must be defined before the PSO algorithm is initialized. With experimental to (5), a Mean Quadratic Errors (MQE) is chosen as the objective function in this study defined by [14]: MQE =

1 N

∑ [T ( j ) − T ( j )] N

j =1

2

,

i exp

i

(12)

where N is the number of data; Ti the indoor temperature calculates Ti exp the indoor temperature experimental The contribution of this paper is to apply the proposed PSO algorithm to minimize the MQE value. There are two general conditions to terminate the PSO algorithm: (a) the objective function of the global best is less than a pre-specified value or (b) the number of iterations achieves the maximum allowable number Nitr . In this study, the second criterion is adopted to terminate the search process. In the present simulations, the packet software of Matlab is programmed to implement the above PSO algorithm, the related values assigned to the variables of the PSO algorithm are given by sampling number N = 192, lower and upper bounds are lb = [0 0 0 0] and lb = [ 0,3 1 1 0 ,3] , the number of the population particles = 2000, the velocity decline parameter w = 0, the strength parameter for the local attractors and the global attractor c1=2, c2=2, and number of iterations N iter = 1000 in the current search. As shown in Fig. 3, the average of the differences between the experimental data of the temperature and with the air temperature values given by the model identified by the classical algorithm was 4.7 °C, with a maximum difference of 10.0832. And comparing the experimental data of the temperature and with the air temperature values given by the model identified by the PSO, the average of differences was 2.02°C, with a maximum difference of 8.4985 as shown in Fig. 4. The selection of models is done comparing the errors between the experimental data and the model identified by a classical algorithm and the errors between the experimental, and the calculus with the model identified by the PSO, calculating the Mean Relative Error (MRE), the Mean Absolute Error (MAE), the Standard Error (SE) and the Mean Quadratic Errors (MQE). The four-error measures are given by the following relations: MAE =

MRE =

MQE =

1 N

1 N

∑ T ( j) − T ( j) ,

N

Ti ( j ) − Ti exp ( j )

∑

N

j =1

i exp

i

Ti ( j )

j =1

[

(13)

× 100 ,

(14)

]

(15)

1 N 2 Ti ( j ) − Ti exp ( j ) , ∑ N − 1 j =1

The best results obtained by the Genetic Algorithm are given in Table 2. Fig. 2 compares the results given by the PSO and Classical Algorithms with the experimental values. Good agreement can be seen between the experimental results and the simulation obtained from the Genetic Algorithm, both in terms of dynamics and intensity of the signal. In order to estimate the validity of our algorithm, we have calculated the errors between the experimental and simulated results. We can see (Table 3) that, with respect to the classical algorithm, the PSO Algorithm improves very significantly the precision of 90

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the simplified greenhouse model. Identification of the physical parameters of a simplified model describing the interactions between crop and climate in a horticultural greenhouse can be seriously improved in terms of calculation time and accuracy of the results, by using a PSO algorithm instead of the classical Marquardt Algorithm. In this paper, we have successfully applied the PSO algorithm to identified parameters of Natural Ventilation in a Greenhouse with Continuous Roof Vents. In the model of greenhouses estimation of PSO-based algorithm, a set of ventilation parameters is refereed to as a particle, then the velocity and position updating formulas are performed on the particles to force them toward better positions. At the same time, the pre-specified objective function MQE can be minimized. To demonstrate the estimation performance, several examining conditions are considered, including different sizes of noises and different random sets of initial populations. The simulation results obtained from the PSO and classical methods are compared. They clearly reveal the effectiveness of the proposed PSO algorithm in estimating parameters of Natural Ventilation in a Greenhouse with Continuous Roof Vents. 35

30

Temperature°C

25

20

15

10

5 0

20

40

60

80

100 Hours

120

140

160

180

200

Fig. 2. Temperature inside greenhouse: experimental temperature inside─(continuous line); Model identified by classic algorithm ─(dot line); Model identified by PSO ─(dash line). a

12

Difference of Temperature°C

10

8

6

4

2

0 0

20

40

60

80

100 Hours

120

140

160

180

200

Fig. 3. Difference of temperature between the experimental, and the calculus with the model identified by a classical algorithm.

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9 8

Difference of Temperature°C

7 6 5 4 3 2 1 0 0

20

40

60

80

100 Hours

120

140

160

180

200

Fig. 4. Difference of temperature between the experimental and the calculus with the model identified by the PSO.

Table 2. Best parameter values identified by the Genetic Algorithm.

Min

Al C

s0

d0

β

0

0

0

0

Table 3. Statistical accuracy measures. The errors Model identified by classical algorithm Model identified by PSO algorithm

MQE 0.3771 0.2481

MAE 4.7015 2.7622

MRE 0.0014 5.9928e-004

SE 0.3790 0.2494

References [1]. Boulard, T. and Draoui, B., Natural ventilation of greenhouse with continuous roof vents: Measurements and data analysis, Journal of Agricultural Engineering Research, 61, 1995, pp. 27-36. [2]. Hasni, A., Draoui, B., Bounaama, F., Tamali, M., and Boulard, T., Evolutionary algorithms in the optimization of natural ventilation parameters in a greenhouse with continuous roof vents, In: J. J. PérezParra, J. I. Montero and B. J. Bailey (Eds), International Symposium on Greenhouse Cooling, Almeria (ESP), April 24-27, 2006. ISHS - Acta Horticulturae, 719, 2006, pp. 49-55. [3]. Kennedy J, Eberhart R., Particle swarm optimization, in Proc. of IEEE Int. Conf. on Neural Networks, Perth, Australia, Vol. IV, 1995, pp. 1942–8. [4]. Eberhart R. C., Shi Y., Comparison between genetic algorithms and particle swarm optimization, in Proc. IEEE Int. Conf. on Evol. Comput., Anchorage, AK, May 1998, p. 611–6. [5]. Angeline P. J., Using selection to improve particle swarm optimization, in Proc. of IEEE Int. Conf. Evol. Comput., Anchorage, AK, May 1998, pp. 84–89. [6]. Draoui, B., Caractérisation et analyse du comportement thermohydrique d’une serre horticole (Identification in-situ des paramètres d’un modèle dynamique). Thèse de doctorat de l’université de Nicesophia antipolis, France, 1994.

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[7]. T. Boulard, Greenhouse Natural Ventilation Modelling: A Survey Of The Different Approaches, In J. J. Pérez-Parra, J. I. Montero and B. J. Bailey (Eds), International Symposium on Greenhouse Cooling, Almeria (ESP), April 24-27, 2006, ISHS - Acta Horticulturae, 719, pp. 29-40. [8]. EL-Zonkoly, A. M., Optimal tuning of power systems stabilizers and AVR gains using particle swarm optimization, International Journal of Expert Systems with Applications, Vol. 31, 939, 2006, pp. 551–557. [9]. Y. Shi, R. Eberhart, Parameter selection in particle swarm optimization, in Proceedings of 7th Annual Conference on Evolutionary Program, March 1998, pp. 591–600. [10].Chau K. W., Particle swarm optimization training algorithm for ANNs in stage prediction of Shing Mun River, Journal of Hydrology, 329, 2006, pp. 363–7. [11].Geethanjali M, Mary Raja Slochanal S, Bhavani R., PSO trained ANN-based differential protection scheme for power transformers, Neurocomputing, 2007, in press (corrected proof). [12].Liao C. J., Tseng C. T., Luarn P., A discrete version of particle swarm optimization for flowshop scheduling problems, Computer & Operations Research, 34, 2007, pp. 3099–111. [13].Tasgetiren M. F., Liang Y.-C., Sevkli M., Gencyilmaz G., A particle swarm optimization algorithm for makespan and total flowtime minimization in permutation flowshop sequencing problem, European Journal of Operational Research, 177, 2007, pp. 1930–47. [14].Yih-Lon Lin and al, A particle swarm optimization approach to nonlinear rational filter modeling, Expert Systems with Applications, 2007, in press (corrected proof). [15].Long Liu and al., Culture conditions optimization of hyaluronic acid production by Streptococcus zooepidemicus based on radial basis function neural network and quantum-behaved particle swarm optimization algorithm, Enzyme and Microbial Technology, 44, 2009, pp. 24–32. ___________________ 2009 Copyright ©, International Frequency Sensor Association (IFSA). All rights reserved. (http://www.sensorsportal.com)

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