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Advanced Computational Intelligence (IWACI), 2010 Third International Workshop on

Date 25-27 Aug. 2010

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Displaying Results 1 - 25 of 133
  • Stacker-reclaimer scheduling for raw material yard operation

    Publication Year: 2010 , Page(s): 432 - 436
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1075 KB) |  | HTML iconHTML  

    Raw material yards at dry bulk terminals act as temporary buffers for inbound and outbound raw materials. Both discharging and charging processes are supported by raw material yards in most cases. Stacker-reclaimers are dedicated equipments in yards for raw material handling. The efficiency of yard operation depends to a great extent on the productivity of stacker-reclaimers. Stacker-reclaimer scheduling problem is discussed in this paper. Given a set of handling operations in a yard, the objective is to find an optimal operation sequence on each stacker-reclaimer so as to minimize the makespan. A mixed integer programming model is formulated. Since the considered problem is NP-hard in nature, a parthenogenetic algorithm is developed to obtain near optimal solutions. Computational experiments are conducted to examine the presented model and the solution algorithm. The computational results show that the proposed parthenogenetic algorithm is effective. View full abstract»

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  • A Specialized Particle Swarm Optimization for global path planning of mobile robots

    Publication Year: 2010 , Page(s): 271 - 276
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1131 KB) |  | HTML iconHTML  

    A specialized global path planning algorithm for mobile robot based on Guaranteed Convergence Particle Swarm Optimization (GCPSO) is proposed. An environmental map was set up and a path connecting the start node and the goal node was coded as a particle. Then, a particular “active region” for particles was mapped out according to the location of obstacles. The initial particle population was generated within this region and particles flied in the “active region” to search for the optimum path. In the search process, both acceleration coefficients and inertia weight of particle swarm optimization algorithm are self-adaptively adjusted and invalid particles are replaced by global optima or local optima in the adjacent area. The simulation studies in both simple environment and complicated environment are carried out and the simulation results show that the proposed algorithm has advantages such as faster search speed and higher search quality. View full abstract»

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  • Global exponential stability of a class of BAM neural networks with distributed delays

    Publication Year: 2010 , Page(s): 11 - 15
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (579 KB) |  | HTML iconHTML  

    Based on contraction mapping principle, inequality technique, global exponential stability of a class of BAM neural networks with distributed delays is considered. Some sufficient conditions are derived which ensure the existence, uniqueness, global exponential stability of equilibrium points of the neural networks. Finally, the obtained results are demonstrated with a numerical example. View full abstract»

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  • An improved fuzzy k-means clustering with k-center initialization

    Publication Year: 2010 , Page(s): 157 - 161
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1187 KB) |  | HTML iconHTML  

    Initialization of fuzzy k-means algorithm decreases the convergent rate of clustering and leads to plenty of calculation. Thus, we propose an improved fuzzy k-means clustering based on k-center algorithm and binary tree in this paper, which firstly reduces redundant attributes while too many irrespective attributes affect the efficiency of clustering. Secondly, we remove the differences of units of dimensions, and then adopt k-center clustering to initialize k means of clusters, which means that we choose first mean randomly and others obtained according to distance subsequently. The binary tree is composed of k means in order to find its closest mean easily. Finally, the proposed algorithm is applied on Iris dataset, Pima-Indians-Diabetes dataset and Segmentation dataset, and results show that the proposed algorithm has higher efficiency and greater precision, and reduces the amount of calculation. View full abstract»

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  • An Online Self-organizing Neuro-Fuzzy System from training data

    Publication Year: 2010 , Page(s): 26 - 31
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1722 KB) |  | HTML iconHTML  

    In this paper, we design a novel Online Self-constructing Neuro-Fuzzy System (OSNFS) based on the proposed generalized ellipsoidal basis functions (GEBF). Due to the flexibility and dissymmetry of the GEBF, the partitioning made by GEBFs in the input space is more flexible and more economical, and therefore results in a parsimonious neuro-fuzzy system (NFS) with high performance under the online learning algorithm. The geometric growing criteria and the error reduction ratio (ERR) method are used as growing and pruning strategies respectively to realize the structure learning algorithm which implements an optimal and compact network structure. The proposed OSNFS starts with no fuzzy rules and does not need to partition the input space a priori. In addition, all the free parameters in premises and consequents are adjusted online based on the ε-completeness of fuzzy rules and the linear least square (LLS) approach, respectively. The performance of the proposed OSNFS is compared with other well-known algorithms on a benchmark problem in nonlinear dynamic system identification. Simulation results demonstrate that the proposed OSNFS approach can facilitate a compact and economical NFS with better approximation performance. View full abstract»

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  • A stereo pairs disparity matching algorithm by mean-shift segmentation

    Publication Year: 2010 , Page(s): 639 - 642
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (688 KB) |  | HTML iconHTML  

    A stereo pair reconstruction algorithm is proposed which utilizes color mean-shift segmentation on the reference image and local matching based on windows is employed. The scene structure is modeled by a set of planar surface patches, which are needed to be stored for each segment rather than each pixel. Instead of assigning a disparity value to each pixel, a disparity plane is assigned to each segment by which it can be reduce complexity of computing. Test results show that the presented algorithm can reconstruct the scene depths efficiently. View full abstract»

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  • Check image binary with morphological method

    Publication Year: 2010 , Page(s): 643 - 647
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1318 KB) |  | HTML iconHTML  

    During the past few years, automatic check processing has become a popular topic in the field of document image analysis (DIA). Image binary is an important but hard task in automatic check image processing system. Difficulties in binary procedure derive mainly from the different types and positions of seal imprints. In this paper, we proposed a new binary method which is based on the fact that objects in a character image are mostly dark with thin strokes. In order to remove large block backgrounds, we firstly designed a set of morphological perorations to enhance the local feature of thin objects. Then a global threshold is selected and applied to the new enhanced image. Experiment results on 2745 real-life Chinese check images demonstrate the efficiency of our method compared with other commonly used binary methods. The recognition success rate has achieved 91.5%. View full abstract»

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  • A hybrid control for elevator group system

    Publication Year: 2010 , Page(s): 491 - 495
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1223 KB) |  | HTML iconHTML  

    In order to increase the elevators running efficiency and quality of service, the optimizing control strategy of elevators is studied. In this paper a new hybrid control method which optimizes passenger service in an elevator group is described. It is capable of optimizing the neural-controller based on Particle Swarm Optimization (PSO) of an elevator group controller. Starting from the operation characteristics of elevator group control system, the architecture and the traffic pattern of an elevator group control system are described, and the optimization cost criterion function is proposed. The PSO algorithm is used to optimize the weights and biases of the neural network. Some weighted parameters of the Radial Basis Function (RBF) neural network can be modified based on the PSO, so that different weight settings and their influence on the elevator supervisory group control (ESGC) performance can be tested. It can reduce the passenger's average waiting time by allocating an appropriate number of elevator cars to the lobby floor. The results prove that the hybrid method is effective. View full abstract»

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  • A study of solar energy collector system based on fuzzy logic control for seawater desalination

    Publication Year: 2010 , Page(s): 220 - 222
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1068 KB) |  | HTML iconHTML  

    This paper proposes a kind of fuzzy logic control scheme according to the time-varying, lagging and nonlinear characteristics of temperature control and the proplem of more complicated control system. It includes the selection of control variables, the definitions of fuzzy sets, the division of domain levels, the choice of membership functions and setting the fuzzy logic control rules. MATLAB was used for the simulation analyse of the system and the results validated that the the fuzzy logic control to the system is effective and reasonable. View full abstract»

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  • A novel artificial neural network ensemble model based on K-nn nonparametric estimation for rainfall forecasting

    Publication Year: 2010 , Page(s): 76 - 80
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (565 KB) |  | HTML iconHTML  

    In this paper, we propose a novel artificial neural network ensemble rainfall forecasting model based K-nearest neighbor (K-nn) nonparametric estimation of regression. In this model, original data set are partitioned into some different training subsets via Bagging technology. Then using different ANNs algorithms and different network architecture generate diverse individual neural network ensemble by training subsets, Thirdly, the partial least square regression is adopted to extract ensemble members. Finally, the K-nn nonparametric regression is used for ensemble model. Empirical results obtained reveal that the prediction by using the nonparametric ensemble model is generally better than those obtained using other models presented in this study in terms of the same evaluation measurements. Our findings reveal that the nonparametric ensemble model proposed here can be used as an alternative forecasting tool for a Meteorological application in achieving greater forecasting accuracy and improving prediction quality further. View full abstract»

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  • Common fixed point for weakly compatible mappings in fuzzy metric spaces

    Publication Year: 2010 , Page(s): 135 - 138
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (525 KB) |  | HTML iconHTML  

    In this paper we discuss weakly compatible mappings in fuzzy metric spaces, and give some common fixed point theorems for weakly compatible mappings in fuzzy metric spaces. Our main results improve and extend the corresponding result. View full abstract»

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  • An efficient Symbiotic Taguchi-based Differential Evolution for neuro-fuzzy network design

    Publication Year: 2010 , Page(s): 179 - 184
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1196 KB) |  | HTML iconHTML  

    In this paper, we proposed a functional-link-based neural fuzzy network to improve the traditional TSK-type neural fuzzy network. Besides, an efficient evolutionary learning algorithm, called the Symbiotic Taguchi-based Modified Differential Evolution (STMDE), is proposed for the neural fuzzy networks design. Firstly, in order to avoid trapping in a local optimal solution and to ensure the searching capability of near global optimal solution, the STMDE adopts the Taguchi method to effectively search towards the best individual and employs an adaptive parameter control to adjust scaling factor which is called the Taguchi method. Moreover, the proposed STMDE introduces the concept of symbiotic evolution to improve the individual structure. Unlike the traditional individual that uses each one in a population as a full solution to a given problem, symbiotic evolution assumes that each individual in a population represents only a partial solution, while complex solutions combine several individuals in the population. View full abstract»

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  • Extension of the VIKOR method to dynamic intuitionistic fuzzy multiple attribute decision making

    Publication Year: 2010 , Page(s): 189 - 195
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (647 KB) |  | HTML iconHTML  

    The aim of this paper is to extend the VIKOR method for dynamic intuitionistic fuzzy multiple attribute decision making (DIF-MADM). Two new aggregation operators called dynamic intuitionistic fuzzy weighted geometric (DIFWG) operator and uncertain dynamic intuitionistic fuzzy weighted geometric (UDIFWG) operator are presented. A procedure based on the DIFWG operator is developed to solve the dynamic intuitionistic fuzzy multiple attribute decision making (DIF-MADM) problems where all the decision information about values takes the form of intuitionistic fuzzy numbers collected at different periods, a procedure based on the UDIFWG operator is developed for DIF-MADM under interval uncertainty in which all the decision information about attribute values takes the form of interval-valued intuitionistic fuzzy numbers collected at different periods. Finally, a numerical example is used to illustrate the applicability of the proposed approach. View full abstract»

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  • Existence of solutions for the impulsive semilinear fuzzy integrodifferential equations on (EnN, dε)

    Publication Year: 2010 , Page(s): 145 - 150
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (619 KB) |  | HTML iconHTML  

    In this paper, we study the existence and uniqueness of solutions for the impulsive semilinear fuzzy integrodifferential equations with nonlocal conditions and forcing term with memory in n-dimensional fuzzy vector space (EnN, dε) by using Banach fixed point theorem. That is an extension of the result of Kwun et al. [9] to impulsive system. View full abstract»

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  • Fuzzy reasoning method of warships combat problems based on Fuzzy Offering Degree

    Publication Year: 2010 , Page(s): 185 - 188
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (585 KB) |  | HTML iconHTML  

    Warship combat can be viewed as a complex system as it involves various factors and complicated relationship. To give full play to the function of experts' discussion via metasynthesis is an effective research approach. As the critical section of this method, reasoning technology has drawn wide-spread attention. With reference to the disadvantages of traditional reasoning methods, the author puts forward herein the concept “Fuzzy Offering Degree” based on the establishment condition of logical relationship between the rules' reason and result, on the basis of which reasoning rules and relevant reasoning method are estimated. According to the practical examples in this paper, new method fully takes logical relationship in warship combat into account, which makes it reliable and feasible. View full abstract»

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  • Multistability of almost periodic solutions of neural networks with discontinuous activation functions

    Publication Year: 2010 , Page(s): 16 - 20
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (953 KB) |  | HTML iconHTML  

    In this paper, we investigate the multistablility of almost periodic solutions of neural networks with a class of discontinuous activation functions. It shows that the n-neuron neural networks can have (r + 1)n (r ≥ 1) exponentially stable almost periodic solutions. As special cases, the multiperiodicity and multistability of neural networks with periodic or constant coefficients are derived respectively. Furthermore, an example is presented to illustrate the effectiveness of our results. View full abstract»

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  • A novel particle filter for tracking fast target

    Publication Year: 2010 , Page(s): 288 - 293
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1646 KB) |  | HTML iconHTML  

    This paper proposes a fast target tracking method in which particle filter is improved using Gaussian kernel and evolutionary strategy. We use Gaussian kernel function to replace the Dirac kernel function, which can decrease the degeneracy problem of the traditional particle filter partly. To further improve the performance of particle filter, we introduce evolutionary strategy into the process of Gaussian kernel particle filtering. It uses only mutation operation, which has less computation than genetic algorithm. And it can prevent the impoverishment problem and steer the particles towards local mode of posterior probability effectively. The proposed method can track fast target robustly using fewer particles than the standard particle filter and Gaussian kernel particle filter. View full abstract»

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  • Intelligent diagnosis algorithm of power equipment based on acoustic signal processing

    Publication Year: 2010 , Page(s): 661 - 665
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1120 KB) |  | HTML iconHTML  

    The operational state determination of power equipment is a key prerequisite to realize maintenance. On studying the relationship between power equipment state and its acoustic wave mutation character, a new diagnosis scheme of power equipment fault has been put forward. After the running acoustic signal acquired, MFCC coefficient has been selected the acoustic signal various band energy feature, and dynamic time warping (DTW) is utilized to determine equipment type. Then local energy band based wavelet packet decomposition is used in fault feature extraction. According to these feature parameters values and expert experience scoring, the knowledge based of fault database was established to diagnosis power equipment state and its fault level. Lastly, By 200 group transformer measured acoustic signal analysis experiments have been completed, and the results show the series acoustic treatment of methods is effective, and the diagnosis scheme of equipment failures have great practical value. View full abstract»

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  • Optimal sliding mode design for nonlinear systems

    Publication Year: 2010 , Page(s): 414 - 419
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (700 KB) |  | HTML iconHTML  

    This paper designs a time-varying nonlinear switching manifold in an optimal fashion for a class of nonlinear systems by developing the successive approximation approach of differential equation into infinite-time horizon. Based on the reaching law approach, we obtain a reach the nonlinear sliding surface. The stability of the nonlinear sliding mode is analyzed. The convergence velocity of every state trajectory on the ideal sliding surface can be adjusted through choosing quadratic performance index Simulation example is employed to test the validity of the proposed design algorithm. View full abstract»

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  • A novel nonlinear RBF neural network ensemble model for financial time series forecasting

    Publication Year: 2010 , Page(s): 86 - 90
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (577 KB) |  | HTML iconHTML  

    In this paper, a novel nonlinear Radial Basis Function Neural Network (RBF-NN) ensemble model based on ν-Support Vector Machine (SVM) regression is presented for financial time series forecasting. In the process of ensemble modeling, the first stage the initial data set is divided into different training sets by used Bagging and Boosting technology. In the second stage, these training sets are input to the different individual RBF-NN models, and then various single RBF-NN predictors are produced based on diversity principle. In the third stage, the Partial Least Square (PLS) technology is used to choosing the appropriate number of neural network ensemble members. In the final stage, ν-Support Vector Machine (SVM) regression is used for ensemble of the RBF-NN to prediction purpose. For testing purposes, this paper compare the new ensemble model's performance with some existing neural network ensemble approaches in terms of two financial time series: S & P 500 and Nikkei 225. Experimental results reveal that the predictions using the proposed approach are consistently better than those obtained using the other methods presented in this study in terms of the same measurements. Those results show that the proposed nonlinear ensemble technique provides a promising alternative to financial time series prediction. View full abstract»

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  • A novel updating strategy for associative memory scheme in cyclic dynamic environments

    Publication Year: 2010 , Page(s): 32 - 39
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1152 KB) |  | HTML iconHTML  

    Associative memory schemes have been developed for Evolutionary Algorithms (EAs) to solve Dynamic Optimization Problems (DOPs), and demonstrated powerful performance. In these schemes, how to update the memory could be important for their performance. However, little work has been done about the associative memory updating strategies. In this paper, a novel updating strategy is proposed for associative memory schemes. In this strategy, the memory point whose associated environmental information is most similar to the current environmental information is first picked out from the memory. Then, the selected memory individual is updated according to the fitness value, and the associated environmental information is updated according to the matching degree between environmental information and individuals. This updating strategy is embedded into a state-of-the-art algorithm, i.e. the MPBIL, and tested by experiments. Experimental results demonstrate that the proposed updating strategy is helpful for associative memory schemes to enhance their search ability in cyclic dynamic environments. View full abstract»

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  • Enhancing Particle Swarm Optimization via probabilistic models

    Publication Year: 2010 , Page(s): 254 - 259
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1125 KB) |  | HTML iconHTML  

    Particle Swarm Optimization (PSO) has gained much success particularly in continuous optimization. However, like other black box optimizations, PSO lacks an explicit mechanism for exploiting problem specific interactions among variables, which is crucial for discouraging premature convergence. In this paper, we propose two strategies to enhance PSO via probabilistic models. Firstly, we exploit problem structures in PSO to repel premature convergence, where problem specific interactions among variables are represented as a mixture of multivariate normal distributions. Secondly, the authors propose a hybrid constraint handling method for PSO via combining “feasibility and dominance” (FAD) rules with sampling from a mixture of Truncated Multivariate Normal Distributions (mixed TMNDs), where the constraints are restricted to linear inequalities and represented as mixed TMNDs. Results for test problems indicate that the proposed enhancements significantly improve the performance of PSO. View full abstract»

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  • A new LMS algorithm with application to fixed satellite communications

    Publication Year: 2010 , Page(s): 72 - 75
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (398 KB) |  | HTML iconHTML  

    A distinctive new LMS algorithm is proposed in this paper. Both fast convergence rate and small steady state error can be obtained through non-linear weighting N errors of the current moment and past moments in this algorithm. The algorithm analyzed that the ideal step size is achieved by increasing the error information, building new nonlinear relations between step size factor and error function and eliminating the effect of correlated noise sequences. Then the new algorithm is applied to the fixed satellite communications system in which the channel is affected by multipath effect and weather conditions attenuation. Simulation results show that the new algorithm converges faster than usual algorithms under approximate steady state performance. The signal BER performance is greatly improved with the new adaptive algorithm equalizer in the fixed satellite communications system. View full abstract»

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  • BPNN and RBFNN based modeling analysis and comparison for cement calcination process

    Publication Year: 2010 , Page(s): 101 - 106
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1162 KB) |  | HTML iconHTML  

    In order to improve the production stability of cement Precalciner Kiln calcination process, it is necessary to conduct in-depth analysis of the calcination process, knowledge of the process in running state and laws. To save energy and achieve stable production, we establish the simulation model of the calcination process used to find effective control methods. In view of the calcination process parameters of complex mathematical model is difficult, so we expressed directly using neural network method to establish the simulation model of the calcination process. Choosing reasonable state and control variables and collecting actual operation data to train neural network weights. Constructed two types of neural network BPNN and RBFNN based models, both achieved good fitting results. RBFNN based model can reach very high fitting results, but the BPNN based model has good generalization ability. So the BPNN based model can be used as simulation model of the calcination process for exploring new control algorithms. View full abstract»

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  • Design of Wireless Sensor Networks in prevention of combustion on coal gangue based on pseudo-parallel genetic algorithms

    Publication Year: 2010 , Page(s): 294 - 298
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1150 KB) |  | HTML iconHTML  

    Wireless Sensor Network (WSN) technology is employed in monitoring system to prevent the spontaneous combustion of coal gangue. A temperature monitoring system was designed, which monitor environment of gangue and prevent the occurrence of spontaneous combustion networking strategy through the collection of temperature in real-time or fix time. According to the energy-constrained problem of WSN, pseudo-parallel genetic algorithms (PPGA) is used in the multi-objective optimization problem of coal gangue detection with WSN. It's for designing networking strategy of coal gangue detection system. Appropriate encoding mechanism is worked out. Combining with various important parameters, the fitness function is established. Simulation results show: Energy management is optimized with the WSN base on PPGA and guarantee maximum life span of the network. View full abstract»

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