Kybernetes

Nengchao Lv; Xinping Yan; Kun Xu; Chaozhong Wu

doi: 10.1108/03684921011063501pmid: N/A

Purpose – The purpose of this paper is to propose a bi‐level programming optimization model to reduce traffic congestion of transportation network while evacuating people to safe shelters during disasters or special events. Design/methodology/approach – The previous optimization model for contra flow configuration only considered the character of the manager. However, the traffic condition is not only controlled by managers, but also depended on the root choice of travelers. A bi‐level programming optimization model, which considered managers and evacuees' character, is proposed to optimize the contra flow of transportation network in evacuation during special events. The upper level model aims to minimize the total evacuation time, while the lower level based on user equilibrium assignment. A solution method based on discrete particle swarm optimization and Frank‐Wolfe algorithm is employed to solve the bi‐level programming problem. Findings – It is found that the bi‐level programming based contra flow optimization model can improve evacuation efficiency and decrease evacuation time 30 per cent or more. With the increase of traffic demand, the evacuation time will decrease significantly by contra flow configuration. Research limitations/implications – In the optimization model, the background traffic is ignored for simplification and the contra flow is configured absolutely as 0 or 1, which ensures vehicles do not go back into the evacuation area. Practical implications – An efficient optimization model for traffic managers to reduce congestion and evacuation time of evacuation network. Originality/value – The new bi‐level programming model not only considers managers' character, but also considers evacuees' reaction. The paper is aimed to optimize contra flow for transportation network.

Zhenghong Peng; Bin Song

doi: 10.1108/03684921011063510pmid: N/A

Purpose – The purpose of this paper is to define a new method (grey relational analysis (GRA)) for extracting pattern samples of dissolved gases in power transformer oil, then a hybrid algorithm of the back‐propagation (BP) network and fuzzy genetic algorithm‐artificial neural network (FGA‐ANN) is used to power transformer fault diagnosis based on extracted pattern samples. Design/methodology/approach – The existing manners (e.g. international electro technical commission triple‐ratio method), in practice, have certain faultiness due to the ambiguity of the inference and insufficient standard for judgment. So GRA method is chosen to solve a problem of optimal pattern samples data, then a hybrid algorithm of the BP network and FGA‐ANN is developed to optimize initial weights and to enable fast convergence of the BP network, and lastly, this algorithm is applied to the classification of dissolved gas analysis (DGA) data and power transformer fault diagnosis. Findings – If possible, the results should be accompanied by significance. For comparative studies, the proposed scheme does not require the three ratio code and high diagnosis accuracy is obtained. In addition, useful information is provided for future fault trends and multiple faults analysis. Research limitations/implications – Accessibility and availability of data are the main limitations which model will be applied. Practical implications – This paper provides useful advice for power transformer fault diagnosis method based on DGA data. Originality/value – The new method of optimal choice of options of pattern samples due to GRA. The paper is aimed at optimized samples data classified and abandons the traditional ratio method.

Yong‐fen Ran; Guang‐chi Xiong; Shi‐sheng Li; Liao‐yuan Ye

doi: 10.1108/03684921011063529pmid: N/A

Purpose – The purpose of this paper is to improve back propagation neural network (BPNN) modeling in order to promote the forecast calculation precision of landslide deformation. Design/methodology/approach – The genetic algorithm is adopted to optimize the architectural parameter of BPNN so as to avoided errors occurrence while using the trial‐and‐error method. Furthermore, the Sigmoid function is improved and revised to expand the output range of change‐over function from unipolar (only positive) to ambipolar (may be positive or negative), then the convergence time is reduced and the neural network can express more artificial intelligence. Findings – The modeling can effectively reduce the probability to get into the local minima while employing neural networks to forecast the landslide deformation. It significantly promotes the forecast precision. Research limitations/implications – The improved BPNN modeling, which is very good in learning and processing information, can work out the complex non‐linear relation by learning model and using the present data or reciprocity of surroundings. Practical implications – The revised BPNN modeling in this paper can be used to predict and calculate landslide deformation. Originality/value – The paper demonstrates that the modeling can meet the demand of calculation precision.

Baolin Sun; Wenxiang Wang; Xing Xie; Qianqing Qin

doi: 10.1108/03684921011063538pmid: N/A

Purpose – The purpose of this paper is to describe a new satellite mission scheduling (SMS) problem based on an genetic algorithm (GA). Design/methodology/approach – The SMS involves scheduling tasks to be performed by a satellite, where new task requests can arrive non‐deterministically (i.e. at any time) and must be scheduled in real‐time. This paper investigates algorithmic approaches for determining an optimal or near‐optimal sequence of tasks, allocated to a satellite payload over time, with dynamic tasking considerations. Findings – Simulation results show that the proposed approach is effective and efficient when utilized in actual cases. Originality/value – This paper shows how to adopt the GA search approach to generate an SMS within allowable computation time.

Sun Jingchun; Lv Ding; Wu Fan

doi: 10.1108/03684921011063547pmid: N/A

Purpose – The purpose of this paper is to model the trend of natural gas supply and demand in China in different circumstances from 1990 to 2050. Design/methodology/approach – The related factors were selected from references and classified into three categories such as endogenous, exogenous, and excluded factors. The three sub‐models of supply, demand and their interconnection were built and integrated. The impacts of natural gas resources, the investment of gas industry and the energy structure over natural gas supply and demand were analyzed based on scenario analysis. Findings – The impact of energy structure in China is more evident compared to natural gas resources and the investment level. Research limitations/implications – Import and transportation of natural gas will have growing impacts on the supply and demand in China when the model is applied. Practical implications – A very useful method to analyze the equilibrium of natural gas supply and demand. Originality/value – The paper presents a new prediction model of natural gas supply and demand in system dynamics. The paper is aimed at the researchers and decision makers in energy industries, especially in the fields of energy prices.

Chuangxin Guo; Yijia Cao; Yuezhong Tang; Zhenxiang Han

doi: 10.1108/03684921011063556pmid: N/A

Purpose – The purpose of this paper is to design an open architecture of an interconnected communication system (ICS) for multi‐level electric power control centers (EPCC) based on Tele‐control Application Service Element (TASE.2), which possesses specialties of high performances, robustness, cost‐efficiency, quick‐restoration, and easy‐maintenance. Design/methodology/approach – Based on the hierarchy and structure of TASE.2, the overall architecture of the ICS for multi‐level EPCC is put forward at first. As the key devices in the system, the structures of the communication gateway (CG) and common interface are designed. Then, the logical procession flows in CG and the con modes, for both CG and IC are analyzed in detail. The web‐based software configuration of remote maintenance and fault diagnosis is discussed conceptually. Findings – As a standardized, well‐developed, and efficient protocol, TASE.2 is considered to be the most suitable protocol to support the ICS for multi‐level EPCC. Research limitations/implications – The performance of the ICS needs to be further simulated. Practical implications – Practical architecture for ICS for multi‐level EPCC with robustness and cost‐efficient specialty is designed in principle, which is very useful for manufacturers to develop pilot devices or even products. Originality/value – This paper proposes a new ICS scheme for multi‐level EPCC based on TASE.2 is proposed.

Shuping Wan

doi: 10.1108/03684921011063565pmid: N/A

Purpose – The purpose of this paper is to research stochastic dynamic investment games with stochastic interest rate model in continuous time between two investors. The market interest rate has the dynamics of Duffie‐Kan interest rate. Design/methodology/approach – Recently, there has been an increasing interest in financial market models whose key parameters, such as the bank interest rate, stocks appreciation rates, and volatility rates, are modulated by stochastic interest rate. This paper uses the Duffie‐Kan stochastic interest rate model to develop stochastic differential portfolio games. By the HJB optimality equation, a general result in optimal control for a stochastic differential game with a general utility payoff function is obtained. Findings – Derive a general result in optimal control for a stochastic differential game with a general utility payoff function. The explicit optimal strategies and value of the games are obtained for the constant relative risk aversion utility games of fixed duration. Research limitations/implications – Accessibility and availability of stochastic interest rate data are the main limitations, which apply. Practical implications – The results obtained in this paper could be used as a guide to actual portfolio games. Originality/value – This paper presents a new approach for the optimal portfolio model under compound jump processes. The paper is aimed at actual portfolio games.

Wang Junguo; Zhou Jianzhong; Peng Bing

doi: 10.1108/03684921011063574pmid: N/A

Purpose – The purpose of this paper is to improve forecasting accuracy for short‐term load series. Design/methodology/approach – A forecasting method based on chaotic time series and optimal diagonal recurrent neural networks (DRNN) is presented. The input of the DRNN is determined by the embedding dimension of the reconstructed phase space, and adaptive dynamic back propagation (DBP) algorithm is used to train the network. The connection weights of the DRNN are optimized via modified genetic algorithms, and the best results of optimization are regarded as initial weights for the network. The new method is applied to predict the actual short‐term load according to its chaotic characteristics, and the forecasting results also validate the feasibility. Findings – For the chaos time series, the hybrid neural genetic method based on phase space reconstruction can carry out the short‐term prediction with the higher accuracy. Research limitations/implications – The proposed method is not suited to medium and long‐term load forecasting. Practical implications – The accuracy of the load forecasting is important to the economic and secure operation of power systems; also, the neural genetic method can improve forecasting accuracy. Originality/value – This paper will help overcome the defects of traditional neural network and make short‐term load forecasting more accurate and fast.

Kang Wang; Xingcheng Liu; Paul Cull

doi: 10.1108/03684921011063583pmid: N/A

Purpose – The purpose of this paper is to propose a novel decoding algorithm, to decrease the complexity in decoding conventional block turbo codes. Design/methodology/approach – In this algorithm, the signal‐to‐noise ratio (SNR) values of channels are adaptively estimated. After analyzing the relationship between the statistics of the received vectors R and the channel SNR, an adaptive method of tuning the decoding complexity is presented. Findings – Simulation results show that the proposed algorithm has greatly decreased the decoding complexity and sped up the decoding process while achieving better bit error rate performance. Originality/value – Simulation experiments described in this paper show that the proposed algorithm can decrease the decoding complexity, shorten the decoding time and achieve good decoding performance.

Fan Wen; Zhenshen Qu; Changhong Wang

doi: 10.1108/03684921011063592pmid: N/A

Purpose – The purpose of this paper is to describe how, in order to fulfill the specific missions under some special environments without people participating, a multi‐robot object tracking and docking systems are designed based on networked control frames. Design/methodology/approach – In the process of target recognition and tracking, the tracking robot obtains the target robot's position and poses information by means of multi‐sensors, and tracking the target robot uses a data fusion algorithm based on network‐delay. In the phase of docking, the exterior parameters of the CCD camera installed on the tracking robot can be calculated in‐phase by recognizing the coded target in a place on the target robot. Finally, the relative position and pose parameters between the tracking robot and the target robot can be derived using the coordinate rotation parameters. Findings – The experiment results indicated that the relative position measure error is less than 1.5 percent, and the relative pose measure error less than 1° within 1.5‐10 m. The research results show that the system can actualize object tracing and docking missions accurately and timely. Research limitations/implications – This paper is devoted to multi‐robot object tracking and docking systems. Practical implications – The main applications are in the exploration in the seabed, consignment in the workshop, formation of spacecrafts, docking of spacecrafts, and so on. Originality/value – The system can actualize object tracing and docking missions accurately, and the system is of reliable, real‐time, and robust capabilities. This will aid all developers and researchers to enhance their technicality.