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A Novel Energy Saving Strategy with N-Policy Sleep Mode in Cognitive Radio Networks

JOURNAL OF INTERNET TECHNOLOGY 2018年第5期19卷 1371-1382页

作者： Li, Lixia Jin, Shunfu Huo, Zhanqiang Yanshan Univ Sch Informat Sci & Engn Qinhuangdao Hebei Peoples R China Yanshan Univ Key Lab Comp Virtual Technol & Syst Integrat Hebe Qinhuangdao Hebei Peoples R China Henan Polytech Univ Coll Comp Sci & Technol Jiaozuo Henan Peoples R China

Energy consumption is significant even when the base station is idle in traditional cognitive radio networks. Taking into account both the energy saving in the base station and the latency of the secondary user packets, we propose an energy saving strategy with N-policy sleep mode in this paper. From the perspective of an overlay structure, we establish a two-dimensional continuous-time Markov chain model to capture the stochastic behavior of the proposed strategy. The stable condition of the system is obtained accordingly. Using the method of matrix geometric solution method, we derive the performance measures, such as energy saving rate of system and average latency of secondary user packets. Moreover, we provide simulation statistics as well as analysis results to verify the effectiveness of the proposed strategy and the accuracy of the performance estimation. Finally, we present an artificial bee colony algorithm to optimize the sleep parameter in the proposed strategy.

关键词： artificial bee colony algorithm Cognitive radio networks N-policy Overlay Sleep mode

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Blood glucose control using an ABC algorithm-based fuzzy-PID controller

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2018年第1期26卷 172-183页

作者： Soylu, Selim Danisman, Kenan Erciyes Univ Fac Engn Dept Elect & Elect Engn Kayseri Turkey Erciyes Univ Clin Engn Applicat & Res Ctr Kayseri Turkey

In this paper, a Mamdani-type fuzzy controller is proposed as the controller part of an artificial pancreas. The controller is optimized with the artificial bee colony optimization algorithm. The glucose insulin regulatory system, based on a nonlinear differential model in the presence of delay, is used both for virtual patient and healthy person data. The main target of the controller is to mimic a blood glucose concentration profile of the healthy person with exogenous insulin infusion. Simulations are performed to assess the control function in terms of tracking the blood glucose concentration profile of the healthy person and minimizing errors. To show robustness, a group of three tests are implemented. These tests include unusual glucose intake, sensor noise, and uncertainty in the clearance rate parameter. The simulation results demonstrate that the adopted method is more effective than similar studies in the literature.

关键词： artificial bee colony algorithm artificial pancreas fuzzy control Type 1 diabetes mellitus

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Improved ABC algorithm Optimizing the Bridge Sensor Placement

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SENSORS 2018年第7期18卷 2240页

作者： Yang, Jianhui Peng, Zhenrui Lanzhou Jiaotong Univ Sch Elect & Informat Engn Lanzhou 730070 Gansu Peoples R China Lanzhou Jiaotong Univ Sch Mechatron Engn Lanzhou 730070 Gansu Peoples R China

Inspired by sensor coverage density and matching & preserving strategy, this paper proposes an Improved artificial bee colony (IABC) algorithm which is designed to optimize bridge sensor placement. We use dynamic random coverage coding method to initialize colony to ensure the diversity and effectiveness. In addition, we randomly select the factors with lower trust value to search and evolve after food source being matched in order that the relatively high trust point factor is retained in the exploitation of food sources, which reduces the blindness of searching and improves the efficiency of convergence and the accuracy of the algorithm. According to the analysis of the modal data of the Ha-Qi long span railway bridge, the results show that IABC algorithm has faster convergence rate and better global search ability when solving the optimal placement problem of bridge sensor. The final analysis results also indicate that the IABC's solution accuracy is 76.45% higher than that of the ABC algorithm, and the solution stability is improved by 86.23%. The final sensor placement mostly covers the sensitive monitoring points of the bridge structure and, in this way, the IABC algorithm is suitable for solving the optimal placement problem of large bridge and other structures.

关键词： sensor placement artificial bee colony algorithm dynamic random coverage coding matching & preserving strategy Ha-Qi long span railway bridge

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Cost and risk aggregation in multi-objective route planning for hazardous materials transportation-A neuro-fuzzy and artificial bee colony approach

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EXPERT SYSTEMS WITH APPLICATIONS 2016年 65卷 1-15页

作者： Pamucar, Dragan Ljubojevic, Srdan Kostadinovic, Dragan Dorovic, Boban Univ Def Belgrade Dept Logist Pavla Jurisica Sturma 33 Belgrade 11000 Serbia

This paper proposes a new approach for cost and risk assessment in the multi-objective selection of routes for the transport of hazardous materials (hazmat) on a network of city roads. The model is based on the application of an Adaptive Neuro Fuzzy Inference System (ANFIS). The values of the cost and risk criteria are, using an adaptive neuro-fuzzy network trained with an artificial bee colony (ABC) algorithm, integrated into a single CR value by means of which the worthiness of each branch in the network is expressed, and after which the selection of the route is made using Dijkstra's algorithm. The ANFIS adequately treats a number of uncertainties and ambiguities in the input data and enables the inclusion of the knowledge of experts and the preferences of the decision makers. The procedure is also applicable in cases in which the decision maker does not have high quality data available. The proposed model is tested in a real urban route planning problem, in a case study of the distribution of oil and oil derivatives in Belgrade, Serbia. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Hazardous materials routing Adaptive neuro fuzzy inference system artificial bee colony algorithm Dijkstra's algorithm

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Identification and nonlinear model predictive control of MIMO Hammerstein system with constraints

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Journal of Central South University 2017年第2期24卷 448-458页

作者：李大字贾元昕李全善靳其兵 Department of Automation Beijing University of Chemical Technology Beijing 100029 China Beijing Century Robust Technology Co. Ltd. Beijing 100020 China

This work is concerned with identification and nonlinear predictive control method for MIMO Hammerstein systems with constraints. Firstly, an identification method based on steady-state responses and sub-model method is introduced to MIMO Hammerstein system. A modified version of artificial bee colony algorithm is proposed to improve the prediction ability of Hammerstein model. Next, a computationally efficient nonlinear model predictive control algorithm(MGPC) is developed to deal with constrained problem of MIMO system. The identification process and performance of MGPC are shown. Numerical results about a polymerization reactor validate the effectiveness of the proposed method and the comparisons show that MGPC has a better performance than QDMC and basic GPC.

关键词： model predictive control system identification constrained systems Hammerstein model polymerization reactor artificial bee colony algorithm

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Thermoeconomic analysis and multi-objective optimisation of MED-TVC and *** systems using ABC algorithm

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INTERNATIONAL JOURNAL OF EXERGY 2018年第3期26卷 329-358页

作者： Beyralvand, Davood Yaghoubi, Mahmood Shiraz Univ Sch Mech Engn Mollasadra Ave Shiraz *** Iran

In this study, 4E analysis tor feasible integrated systems for cogeneration of fresh water was investigated. Steam generation provided by a steam boiler, designed to supply the thermal heat requirements of MED-TVC and *** systems. Parametric analysis is made to determine CO2 emission rate, gain output ratio, exergy destruction rate and special heat rate for each system. By using artificial bee colony algorithm, systems were optimised for maximum exergy efficiency and minimum cost of the produced distilled water. The results showed that by selecting final optimum solutions, the distillated water cost reduced by 18.1% and 28.8% for MED-TVC and MED-TVC .FH systems respectively. Also exergy efficiency increased from 3.2% and 4.04% in the base case to 3.63% and 4.47% for MED-TVC and *** respectively in the optimum case. The system with feed water preheater, has more exergy efficiency and less CO2 emission, also the cost of distilled water reduced by 7. 86.%

关键词： 4E analysis MED-TVC MED-TVC.FH CO2 emission exergoeconomic multi objective optimisation artificial bee colony algorithm TRR method

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Interval Type-2 Neural Fuzzy Controller-Based Navigation of Cooperative Load-Carrying Mobile Robots in Unknown Environments

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SENSORS 2018年第12期18卷 4181页

作者： Lin, Chun-Hui Wang, Shyh-Hau Lin, Cheng-Jian Nation Cheng Kung Univ Dept Comp Sci & Informat Engn Tainan 701 Taiwan Natl Chin Yi Univ Technol Dept Comp Sci & Informat Engn Taichung 411 Taiwan

In this paper, a navigation method is proposed for cooperative load-carrying mobile robots. The behavior mode manager is used efficaciously in the navigation control method to switch between two behavior modes, wall-following mode (WFM) and goal-oriented mode (GOM), according to various environmental conditions. Additionally, an interval type-2 neural fuzzy controller based on dynamic group artificial bee colony (DGABC) is proposed in this paper. Reinforcement learning was used to develop the WFM adaptively. First, a single robot is trained to learn the WFM. Then, this control method is implemented for cooperative load-carrying mobile robots. In WFM learning, the proposed DGABC performs better than the original artificial bee colony algorithm and other improved algorithms. Furthermore, the results of cooperative load-carrying navigation control tests demonstrate that the proposed cooperative load-carrying method and the navigation method can enable the robots to carry the task item to the goal and complete the navigation mission efficiently.

关键词： evolutionary robot navigation control fuzzy control wall-following control cooperative carrying interval type-2 neural fuzzy controller artificial bee colony algorithm grouping strategy

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Extreme learning machine-based surrogate model for analyzing system reliability of soil slopes

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EUROPEAN JOURNAL OF ENVIRONMENTAL AND CIVIL ENGINEERING 2017年第11期21卷 1341-1362页

作者： Kang, Fei Li, Jing-Shuang Wang, Yuan Li, Junjie Dalian Univ Technol Sch Hydraul Engn Fac Infrastruct Engn Dalian Peoples R China Dalian Univ Technol State Key Lab Struct Anal Ind Equipment Dalian Peoples R China

Geotechnical engineering problems are characterised by many sources of uncertainty, and reliability analysis is needed to take the uncertainties into account. An intelligent surrogate model based on extreme learning machine is proposed for slope system reliability analysis. The weights and bias which play an important role in the performance of ELM are optimised by a nature inspired artificial bee colony algorithm. The system failure probability of soil slopes is estimated by Monte Carlo simulation via the proposed surrogate model. Experimental results show that the proposed method is feasible, effective and simple to implement system reliability analysis of soil slopes.

关键词： slope reliability analysis extreme learning machine artificial bee colony algorithm surrogate model

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Study on two-stage uncertain programming based on uncertainty theory

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JOURNAL OF INTELLIGENT MANUFACTURING 2017年第3期28卷 633-642页

作者： Zheng, Mingfa Yi, Yuan Wang, Zutong Chen, Jeng-Fung Xi An Jiao Tong Univ Sch Math & Stat Xian 710049 Peoples R China Air Force Engn Univ Mat Management & Safety Engn Coll Xian 710051 Peoples R China Feng Chia Univ Dept Ind Engn & Syst Management 100 Wenhwa Rd Taichung 40724 Taiwan

In this paper, based on uncertainty theory, we first present a new class of two-stage uncertain programming model and give its deterministic equivalent programming problem. Then some fundamental properties of the two-stage uncertain programming problem, including the convexity of feasible set as well as objective function, are investigated. In addition, a solution method by employing an efficiently heuristic algorithm, called artificial bee colony algorithm, is applied to solve the two-stage uncertain programming problem. Finally, some numerical examples are provided to illustrate the novel method introduced in this paper.

关键词： Uncertainty theory Two-stage uncertain programming Convexity artificial bee colony algorithm

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Design of multiplierless prototype filter for two-channel filter bank using hybrid method in FCSD space

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IET CIRCUITS DEVICES & SYSTEMS 2017年第1期11卷 29-40页

作者： Sharma, Ila Kumar, Anil Singh, Girish Kumar Lee, Heung-No PDPM Indian Inst Informat Technol Design & Mfg Ja Jabalpur 482011 MP India Indian Inst Technol Roorkee Roorkee 247667 Uttrakhand India Gwangju Inst Sci & Technol GIST Korea Sch Elect Engn & Comp Sci EECS Gwangju 61005 South Korea

In this work, a hybrid method-based design of multiplierless two-channel filter bank has been proposed with a given stopband attenuation (A(s)) and roll-off factor. Windowing method has been used for efficient design of a prototype filter with novelty of exploiting quantised coefficients in canonical sign digit (CSD) and factorised canonical sign digit (FCSD) space by merging the concept of particle swarm optimisation and artificial bee colony algorithm. The quantised filter coefficients are optimised by varying cut-off frequency such that the magnitude response of prototype filter is approximately reduced to 0.707 at quadrature frequency. The implemented filter is synthesised using target field programmable gate arrays XC3S500E-4-FG320 on Xilinx Spartan 3E starter board. The performances of designed prototype filter is compared with the earlier published works in terms of reconstruction error, amplitude distortion, slices, flip-flops, four-input lookup tables and adders. The synthesis results demonstrate that the significant reduction in hardware is achieved in term of adder gain. For filter order, N = 32, and word length 12, the adder gain achieved in CSD and FCSD is 41.77 and 43.07%, respectively, while for N = 30, it is 35.44% in CSD and 36.70% in FCSD, respectively.

关键词： channel bank filters particle swarm optimisation ant colony optimisation circuit optimisation multiplierless prototype filter design FCSD space hybrid method-based design multiplierless two-channel filter bank stopband attenuation roll-off factor canonical sign digit space factorised canonical sign digit space particle swarm optimisation artificial bee colony algorithm quantised filter coefficients target field programmable gate arrays XC3S500E-4-FG320 Xilinx Spartan 3E starter board windowing method

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