Optimal sensor deployment scheme for heterogeneous wireless sensor networks

For the high density of distributed heterogeneity nodes in wireless sensor networks (WSNs), the optimal sensor deployment is studied. A cost optimal heterogeneous sensor deployment scheme based on genetic algorithm is proposed. The cost of sensor node deployment is used as objective function for optimization computation subjected to network coverage and fault tolerance to obtain the suitable types and positions of the sensors. The algorithm can be used for boolean sensing model as well as probabilistic sensing model. The simulation results demonstrate that the proposed algorithm converges fast toward the optimal solutions and reduces the cost of sensor deployment, which is feasible for heterogenous nodes deployment in WSNs.     

Control strategy and parameters optimization outsourcing production inventory system under uncertainty

This paper proposes a control strategy for the optimal control of the outsourcing production inventory system based on production point and production batch. The statistical probability distribution is used to describe the uncertainty of the production capacity of cooperative enterprises and the transportation time. The optimization model of the control parameters is developed with random production capacity for cooperative enterprises and random transportation time in order to minimize the holding cost,setup cost as well as the cost of production capacity waste. A simulation-based control parameters optimization algorithm is designed to solve the proposed model. The control strategy of production inventory system and the control parameters optimization algorithm proposed are validated by an example. We analyze the influence of the variation of the production point and production batch to the performance of the outsourcing production inventory system.     

THE K-WEAK MINIMAL CUT SET OF NETWORKS AND FAST SEARCHING ALGORITHM

A new concept dafincd as k-weak minimal cut set, which, is used to characterize the weakness of the weak link set, is established in this paper. Based on this concept, a fast algcrithm or searching k-weak minimal cut set in large scale networks is proposed. From the analysis of computation complexity, it is shown that the frequency count of the algorithm is much lower and the algorithm is powerful to adequacy evaluation of the transmission capacity in large scale networks.     

Adaptive relay selection algorithm in decode-and-forward cooperative communication networks

An adaptive relay selection algorithm(ARS) is proposed to optimize the performance of decode-and-forward cooperative communication networks, both system outage probability and average numbers of cooperative relays are analyzed. When the direct transmission between source and destination is fail, according to the channel quality, the destination orders relay nodes in the decode set, and the ordered relays are included in cooperation in turn until the instantaneous received SNR at destination is not smaller than the preset SNR threshold. Simulation results show that, the proposed algorithm can get optimal outage performance, decrease the average numbers of cooperative relays efficiently, to incrense bandwidth efficiency and reduce system overhead.     

The Multi-objective Fuzzy Optimal Power Flow Model and its Neural Network Algorithm

This paper has presented a multi-objective fuzzy optimal power flow medel.Inthe model , the multiple objectives, such as the minimum generation cost and the minimum powerloss, have been considered simultaneously, A new algorithm based on neural network models is aisopresented,in which the neural networks are employed to express, the membership function of fuzzysets and solve the optimization problems. The validity of model and algorithm is verified with numerical examples.     

Using a Neural Network with Nonlinear Self-feedback to Solve the Maximum Clique Problem

As the MCP is NP-hard, an efficient approach to treating this problem is to design appropriate recurrent neural networks. We develop a new algorithm for the MCP, which can, to a certain extent, prevent the associated neural network from falling into local optimal points. The proposed algorithm incorporates nonlinear self-feedback into the SLDN algorithm and has distinguished dynamical characteristics. Simulation results show that the performance of proposed algorithm is statistically superior to the SLDN algorithm.     

A NONLINEAR PROGRAMMING MODEL AND ITS ALGORITHM FOR TRANSMISSION NETWORK PLANNING

The problem of transmission network planning is studied thoroughly by applying the mathmatical optimal tecqhniue in this paper. A novel approach is put forward for computer aided transmission network planning, which is quite different from what is based on D.C. load flow equations. A nonlinear transmission planning model is set up by regarding A.C. load flow equations as the system model. A practical model which can be solved more easily, and an united algorithm are obtained by applying the SUMT to the nonlinear model. The results of some practical examples indicate that this method can not only make the planning results more accurate, but also provide the voltage qualities and produce planning plans autometically and seek the plans optimally and fast.     

Generalized Damage Reliability and Sensitivity Analysis on Rolled-Concrete Gravity Dam

Based on fuzzy self-adapting stochastic damage model, the characteristics of generalized damage stress field on material crack tip are studied. The results express that the numerical algorithm is reliable. Sensitivity of rolled-concrete gravity dam under fuzzy-stochastic damage gaining is implemented by considering the spatial variation of material parameters. It is found that the compression strength of rolled-concrete is the governing index on gravity dam reliability. The activation simulations on fuzzy self-adapting stochastic damage model were carried by adopting different fuzzy distributions, which indicated the governing characteristics of the distribution on generalized damage evolution of rock-like material.     

Optimal bidding models of power plants considering transmission congestion

In order to achieve optimal bidding strategy of power plants in electricity market, the authors build optimal bidding model by predicting the bidding coefficients of competitors, and the optimal bidding coefficient without considering transmission capacity is achieved. Based on the bidding coefficients, the market clearing model is built, and the expected market clearing price is achieved. According to the expected values, the authors calculate the power flow. When forecasting the transmission congestion, this paper presents an optimal bidding model taking transmission congestion into account. Based on sensitivity analysis, the output regulation of power plants with minimum cost of eliminating congestion is achieved. Simulation results demonstrate that the proposed method can help power plants achieve the optimal profit.     

Distribution Network Structure Planning Based on Improved Immune Genetic Algorithm

Distribution Network Structure planning is a complex combinatorial optimization problem,which is difficult to solve properly by using traditional optimization methods.In order to solve this problem,Improved Immune Genetic Algorithm is introduced to the distribution network optimal planning. Improved Immune Genetic Algorithm draws into the immune diversity and antibody's density mechanism to maintain the individual's diversity and remains evolution algorithm's global stochastic searching ability,so it can promote diversity and the whole optimal-searching ability of genetic algorithm.The optimal module takes the minimum annual cost as its object,and the capacity and voltage drop of feeder and the radiation of distribution network as its restrictions.According to the require of radiation of distribution network,the spanning tree of the alternative network is taken as the initial solution to speed up the calculation.And the branch-exchange method is used in designing crossover operator and mutation operator to avoid the radiation checking and enhance the optimizing ability.This algorithm has been illustrated effectively by examples,at the same time,the calculation example demonstrates that,the algorithm has higher calculation speed than the traditional immune genetic algorithm.     

An optimal equivalent subchannel algorithm for low voltage power line communication system

According to the low voltage power line channel features,combined with adaptive bit loading and power allocation algorithm,an optimal equivalent subchannel algorithm is proposed.Under the condition of fixed amount of transmitted bits and the minimum transmitted power model,impact of optimal equivalent subchannel on the system performance is studied.The optimization algorithm sorts subchannels with similar transmission features into groups,thus the signaling load can be reduced,and the spectrum can be better used.The simulation results show that system can get lower bit error rate(BER) by adopting optimal equivalent subchannel algorithm.     

Fast Algorithm of Loads Equalization for Distribution Networks Based on Partition-hierarchical Model

Along with enlargement of scope of distribution, algorithms of reconfiguration for distribution networks become difficult to satisfy real-time require of engineering applications. This paper provides a new algorithm a fast algorithm based on partition-hierarchical model. It is found that partition of loads is relation of the smallest load of vertex that is bigger than average and the biggest load of vertex that is smaller than average. The method reduces the numbers of switches combination by searching smallest load of vertex that is bigger than average and the biggest load of vertex that is smaller than average and realize a fast algorithm of the loads global optimal equalization for distribution networks by transferring the loads. Results of test show that the method presented is applicable and efficient.     

Chaotic Time Series Analysis Based on Radial Basis Function Networks

A method based on radial basis function networks for forecasting chaotic time series is proposed.The nonlinear time series identification problem is formulated with a nonlinear autoregressive moving average(NARMAX)model then a new identification algorithm based on dynamic radial basis function networks is proposed.Then this method is applied to the estimation of embedding dimension for chaotic time series of Henon mapping and the confirmation of the chaotic phenomena in stock markets of China,from which one can get the desired results.Further research directions are also pointed out.     

Improved Genetic Algorithm of Float Encoding and Its Application

As an optimal method, Genetic Algorithm has obvious advantages, which is based on the nature selection and genetic transmission mechanisms such as high collateral,stochastic,self-reliance. but when in practical application, it usually has problems of premature convergence and result swing near optimum value.To solve the problem of premature convergence, the method called Monte-Carlo is adopted to prevent the algorithm from local optimal, and to the problem of result swing, the method changing the hunting zone dynamically is proposed to improve the accuracy of the optimal result. Further more, it devises programs to optimize the test functions of two famous optimal methods. The test results indicate that the improved Genetic Algorithm is valid, which can not only avoid local optimal but also improve the accuracy of the optimal result.     

Outage probability and power allocation in cognitive relay networks under imperfect channel state information

Considering the interference introduced by primary user, the outage probability of cognitive relay networks under imperfect channel state information is analyzed based, on the relay selection for the maximum channel gain. In addition, the mathematical optimization model of maximizing the spectrum efficiency under the interference constraint of primary user and the QoS requirement of cognitive user is proposed for cognitive relay networks. Then, the optimal solution of the model is obtained with the Lagrangian method. The proposed scheme improves the spectrum efficiency of cognitive relay networks on the premise of guaranteeing the transmission performance of primary user. Simulation results show that the proposed power allocation scheme improves the system performance compared with the traditional equal power allocation scheme. Meanwhile, it indicates that the approximate spectrum efficiency can be obtained under the perfect channel state and the imperfect channel state. Under the imperfect channel state, the feedback information and the complexity of implementation will be reduced, which is beneficial to the engineering application.     

Algorithm of removing outliers in SVR

The outlier and the measurement that an outlier does not fit the theoretical model in the regression problems are defined. The relationship between the theoretical model and the regression model in the regression problem is analyzed. An approximate theorem is proposed and verified by deleting outlier one by one to construct SVR to approximate the theoretical model. An algorithm of detecting outliers in the SVR problems is constructed based on the approximate theorem. The theoretical analysis of the convergence and effectiveness of the proposed algorithm is given. Then, the step-by-step search algorithm is introduced to improve the outlier removing algorithm to remove outliers in SVR with large-scale samples. The theoretical analysis shows that the improved algorithm is convergent and effective. Finally, the samples produced by two test functions and the samples in UCI data set are used for simulation, and the results show that the proposed algorithm is effective and robust.     

Improved BP Algorithm in Vibration Failure Diagosis of Steam Turbine-Generator Set

A failure diagnosis model of neural networks for the vibration failure feature of steam turbine-generator set is established on the basis of the improved BP algorithm ,and used for diagnosing practical generator set,the results of verification show that the method is effective.     

Thermal process model identification based on radial basisfunction neural networks

In order to accurately reflect the dynamic behavior and realize the whole optimal control of the thermal process, a novel modeling method of the RBF NN (Radial Basis Function Neural Networks) model is proposed to build nonlinear model. This method is based on entropy clustering and competitive learning algorithm, combined with nonlinear autoregressive moving average (NARMA) model to identify the RBF NN stucture, and the power vector is gotten by the least square algorithm. Two simulation experiments show that the proposed method of the identification based on NARMA model and RBF NN can accurately describe the non linearity of the process and has less hidden nodes.     

MDBPSO算法优化的全波段光谱数据协同冠层SIF监测小麦条锈病

为了从全波段光谱数据中提取对小麦条锈病敏感的特征参量,提高小麦条锈病遥感探测模型的运行效率和精度,本文首先从惯性权重和粒子更新方式两个方面对传统离散粒子群算法(discretebinaryparticleswarmoptimization, DBPSO)进行改进,利用改进离散粒子群算法(modified discrete binary particle swarm optimization, MDBPSO)从全波段光谱数据中优选遥感探测小麦条锈病严重度的特征变量,然后与冠层日光诱导叶绿素荧光(solar-inducedchlorophyllfluorescence,SIF)数据相结合作为自变量分别利用随机森林(randomforest,RF)和后向传播(backpropagation,BP)神经网络算法构建小麦条锈病遥感探测模型,并将其与相关系数(correlationcoefficient,CC)分析法和DBPSO算法提取特征参量构建模型的精度进行对比分析。结果表明:(1) MDBPSO算法比传统DBPSO算法具有更快的收敛速度和更高的寻优精度,改进前后其迭代次数从395次减少到156次,最优适应度函数(optimumfitnessvalue,OFV)值从0.145减小到0.127。(2)采用MDBPSO算法选择特征变量时,RF和BP神经网络两种方法构建的模型精度均高于CC分析法和DBPSO算法,其中RF算法预测病情指数(diseaseindex,DI)值和实测DI值间的检验集决定系数(validation set determination coefficient, R2V)比CC分析法和DBPSO算法分别提高了9%和3%,均方根误差(validation set root mean square error, RMSEV)分别降低了28%和11%, BP神经网络算法预测DI值和实测DI值间的R2V比CC分析法和DBPSO算法分别提高了13%和6%,RMSEV分别降低了21%和10%,利用MDBPSO算法优选特征参量能够提高小麦条锈病的遥感探测精度。(3)在MDBPSO、DBPSO和CC分析法3种特征选择算法中,RF算法构建的模型精度均高于BP神经网络算法,其中RF模型预测DI值和实测DI值间的R2V比BP神经网络算法至少提高了7%,平均提高了9%,RMSEV至少降低了15%,平均降低了20%。以MDBPSO算法优选的特征参量为自变量利用RF方法构建的小麦条锈病遥感探测的MDBPSO-RF模型是小麦条锈病遥感探测适宜模型,该研究结果为进一步实现作物健康状况大面积高精度遥感监测提供了新的思路。