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Intelligent integrated optimization of mining and ore-dressing grades in metal mines

SOFT COMPUTING 2018年第1期22卷 283-299页

作者： He, Yong Liao, Nuo Bi, Jiajing Guangdong Univ Technol Sch Management Guangzhou 510520 Guangdong Peoples R China Univ Delaware Ctr Energy & Environm Policy Newark DE 19716 USA

An intelligent integrated method is proposed for optimizing the head grade and dressing grade in the mining and ore-dressing management of metal mines, beginning with the establishment of a nonlinear constrained optimization model with the objective function of economic benefit, two constraints comprising of the resource utilization rate and the output of concentrate, along with head grade and dressing grade as the decision variables. Particle swarm optimization (pso) algorithm is then integrated with artificial neural networks to create a pso-ANN algorithm capable of identifying the optimal grade combination. The outer layer of pso-ANN uses the pso algorithm to carry out a global search, with the head grade and dressing grade being combined as swarm particles for evolutionary computation. The constraint handling techniques of feasibility-based rules are used to update the historical best location of each particle (pbest) and the global best location of the swarm (gbest) to guide the particles toward the optimum. The inner layer uses regression model, BPNN and RBFNN to calculate the loss rate, ore-dressing metal recovery rate and costs, respectively, to facilitate the further calculation of the resource utilization rate, the concentrate output and the economic benefit of each particle. Finally, the proposed method is tested by carrying out a case study based upon Daye Iron Mine to indicate its effectiveness and reliability.

关键词： Mining and ore-dressing grades Intelligent constrained optimization pso algorithm Artificial neural networks

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Tracking of moving magnetic target based on magnetic gradient system with total field magnetometers

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SENSOR REVIEW 2018年第4期38卷 501-508页

作者： Fan, Liming Kang, Xiyuan Zheng, Quan Zhang, Xiaojun Liu, Xuejun Geng, Zhoushan Kang, Chong Harbin Engn Univ Coll Sci Harbin Heilongjiang Peoples R China Harbin Engn Univ Coll Comp Sci & Technol Harbin Heilongjiang Peoples R China

Purpose This paper aims to focus on the tracking of a moving magnetic target by using total field magnetometers and to present a tracking method based on the gradient of a magnetic anomaly. In the tracking, it is assumed that the motion of the target is equivalent to a first-order Markov process. And the unit direction vector of the magnetic moment from the gradient of the magnetic anomaly can be obtained. According to the unit direction vector, the inverse problem is turned into an optimization problem to estimate the parameters of the target. The particle swarm optimization algorithm is used to solve this optimization problem. The proposed method is validated by the numerical simulation and real data. The parameters of the target can be calculated rapidly using the proposed method. And the results show that the estimated parameters of the mobile target using the proposed method are very close to the true values. Design/methodology/approach The authors focus on the tracking of a moving magnetic target by using total field magnetometers and present a tracking method based on the gradient of a magnetic anomaly. Findings The paper provides an effective method for tracking the magnetic target based on an array with total field sensors. Originality/value Comparing with a vector magnetic sensor, the measurement of the scalar magnetic sensor is almost not influenced by its orientation. In this paper, a moving magnetic target was tracked by using total field magnetometers and a tracking method presented based on the gradient of a magnetic anomaly.

关键词： Optimization problem pso algorithm tracking magnetic gradient magnetic target

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Parameter estimation of nonlinear thermoelectric structures using particle swarm optimization

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SIMULATION MODELLING PRACTICE AND THEORY 2018年 81卷 1-10页

作者： Ojeda, Daniel R. G. de Almeida, Luiz A. L. Vilcanqui, Omar A. C. Univ Fed Abc Ctr Engn Modelagem & Ciencias Sociais Aplicadas Ave Estados5001 BR-09210580 Santo Andre SP Brazil

The purpose of this investigation is motivated mainly for thermal energy harvesting devices and temperature feedback controllers interacting with electronic circuits. Mostly intended to design Linear Quadratic Gaussian (LQG) type controllers, suitable for uncertain dynamical systems, in which not all state variables are measured and available for proper feedback. We present a methodology for modeling and estimation of several internal parameters for a proposed thermal characterization apparatus that employs thermoelectric modules (TEMs). Repeated random vector sampling, similar to Monte Carlo method, is combined with particle swarm optimization (pso) algorithm for parameter estimation. For the intended applications, is mandatory to drive apparatus that have embedded TEMs, not only using direct current powering, as usually done in literature, but also with alternate current signals over a large range of relevant frequencies. For exciting the many nonlinear and linear states during the identification procedure, a single embedded TEM is injected with a proper random electrical current signal having power spectral density of a band-limited white noise. Sensitivity to both initial conditions and different sets of random excitation, brings uncertainty in the estimated 21 parameters of our mechanical apparatus with two embedded TEMs and their corresponding dynamics. Simulation are presented showing the effectiveness of the proposed estimation technique, with convergence performance and parameter statistical distribution over a set of uncorrelated random current vector excitation. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Energy Harvesting Thermoelectric module pso algorithm Genetic algorithm Simulated annealing System identification Nonlinear LQG controller Thermal controller for PCR cycling Parameter estimation Optimization

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Personalized Lane-Change Assistance System With Driver Behavior Identification

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2018年第11期67卷 10293-10306页

作者： Zhu, Bing Yan, Shude Zhao, Jian Deng, Weiwen Jilin Univ State Key Lab Automot Simulat & Control Changchun 130012 Jilin Peoples R China Jilin Univ Coll Automot Engn Changchun 130012 Jilin Peoples R China Beihang Univ Dept Transportat Sci & Engn Beijing 100083 Peoples R China

An in-depth study on driving habits and personalized driving-assistance systems is conducive to the realization of vehicle safety and intelligent driving. In this paper, we present a personalized vehicle lane-change assistance system integrated with a driver-behavior identification strategy. First, the driver-behavior data-acquisition system is designed and established. Based on this, the input data of different kinds of drivers along with vehicle signals are collected under typical working conditions. The drivers are classified utilizing factor analysis and a fuzzy c-means clustering algorithm, and the identification of driver behavior is realized using a backpropagation neural network optimized by a particle swarm optimization algorithm. Then, personalized warning, planning, and control systems are designed for lane changing. The proposed personalized lane-change assistance system can provide more personalized recommendations to the drivers, increasing the potential for more widespread acceptance and use of advanced driver-assistance system. Finally, the correctness of the proposed personalized lane-change system is evaluated by conducting computer simulations and a driver-in-the-loop- simulation under various conditions. And the results show that the lane-change assistance system based on driver behavior can meet the driving needs of different drivers without sacrificing safety.

关键词： Personalized lane-change assistance system driver behavior identification personalized lane-change warning BP neural network pso algorithm

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Fast codebook design method for image vector quantisation

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IET IMAGE PROCESSING 2018年第12期12卷 2311-2318页

作者： Fan, Hang-Yu Lu, Zhe-Ming Zhejiang Univ Sch Aeronaut & Astronaut Hangzhou 310027 Zhejiang Peoples R China

Vector quantisation (VQ) is a widely used method for data compression or data clustering. This study proposes a fast codebook design method for image VQ. General particle swarm optimisation (pso)-K-means hybrid methods for codebook design take too much time. To deal with this problem, the authors present a new clustering method, which takes a very short time and generates a pretty good codebook. This method contains four parts, i.e. pooling, pso algorithm, reverse cast, and K-means algorithm. Pooled images are used for the pso process while original images are used for K-means fine-tuning, and these two processes connected by the reverse cast process. In the authors' experiment, this method can dramatically reduce the computation time by using big sizes of pooling window or enhance the codebook quality by using small sizes of pooling window. They can make the calculation time almost one-tenth of that of the pso-K-means hybrid method, and the scheme is also faster than the K-means algorithm. Experimental results demonstrate that the main advantages of the proposed algorithm lie in the fact that it can reduce the computation time and enhance the quality of codebooks.

关键词： vector quantisation particle swarm optimisation data compression image coding pattern clustering pso algorithm pooled images pso process original images reverse cast process computation time pooling window codebook quality hybrid method fast codebook design method image vector quantisation data compression image VQ general particle swarm optimisation clustering method

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Parameters tuning of power system stabilizer PSS4B using hybrid particle swarm optimization algorithm

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2018年第9期28卷

作者： Wang, Delin Ma, Ningning Wei, Mengxi Liu, Yingchao Southwest Jiaotong Univ Sch Elect Engn Chengdu 610031 Sichuan Peoples R China

The new power system stabilizer PSS4B demonstrates good performance on suppressing low-frequency power oscillation. However, the numerous interdependent parameters make their tunings rather difficult. In this paper, a modified particle swarm optimization algorithm is applied to tune the parameters of PSS4B. The influence level of each block for the frequency characteristic is different. Hence, there are 2 steps for the optimizing parameters in this paper. The first step optimizes the parameters of hybrid block and gain block, and the parameters of lead-lag block are optimized in the second step. To evaluate the effectiveness of the proposed method, several simulations are performed under very challenging conditions by MATLAB/Simulink. Moreover, the simulation results are compared with the conventional tuning method. The comparison indicates that the proposed method can provide damping characteristics and suppress low-frequency power oscillation effectively.

关键词： parameter optimization power system stabilizer pso algorithm PSS4B

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Solar Concentrator Consisting of Multiple Aspheric Reflectors

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ENERGIES 2019年第21期12卷

作者： Cao, Guobin Qin, Hua Ramachandran, Rajan Liu, Bo Beijing Railway Century Soil Engn Co LTD Yangfangdian Rd Beijing 100038 Peoples R China Shandong Univ Technol Sch Phys & Optoelect Engn Lab Funct Mol & Mat Zibo 255000 Shandong Peoples R China

This paper presents an off-axis-focused solar concentrator system consisting of 190 aspheric reflectors, where the aperture radius of each reflector is 10 cm, and vertices of all reflectors are orderly arranged in the same plane. The aspheric parameters controlling the curvature of the reflectors are determined using coordinate transformations and the particle swarm optimization (pso) algorithm. Based on these aspheric parameters, the light distribution of focal plane was calculated by the ray tracing method. The analyses show that the designed concentrator system has a spot radius of less than 1 cm and the concentration ratio over 3300:1 is achieved using only one reflection. The design results have been verified with the optical design software Zemax.

关键词： concentrating solar power system aspheric reflector off-axis point focusing pso algorithm

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Application of swarm optimisation-based modified algorithm for selective harmonic elimination in reduced switch count multilevel inverter

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IET POWER ELECTRONICS 2018年第8期11卷 1472-1482页

作者： Panda, Kaibalya Prasad Panda, Gayadhar Natl Inst Technol Meghalaya Dept Elect Engn Bijni Complex Shillong Meghalaya India

Multilevel inverters (MLIs) are nowadays extensively used in integration with renewable energy sources and in drives applications. The converter cost and voltage quality improvement are the utmost importance in such application of MLI. A six-switch seven-level reduced switch symmetrical MLI (6S-7L MLI) is proposed here. Compared to the conventional and similar existing MLI topologies, the proposed MLI uses less active switches and has less driver circuit requirement. A particle swarm optimisation (pso)-based modified selected harmonic elimination technique is derived and analysed for computing optimal switching angles of proposed 6S-7L MLI to eliminate third- and fifth-order harmonics. Moreover, the performance of the proposed algorithm is compared with the two most commonly used pso variants. The analysis shows the modified version of pso is most suitable for optimising the output voltage of proposed MLI through targeted harmonic elimination. The proposed topology is investigated through simulation by applying the calculated switching angles using modified pso algorithm. Finally, a single-phase experimental prototype is designed to verify the validity of the proposed structure.

关键词： invertors renewable energy sources harmonics suppression particle swarm optimisation network topology switching convertors harmonic distortion power supply quality pso algorithm selective harmonic elimination reduced switch count multilevel inverter renewable energy sources voltage quality improvement active switches driver circuit requirement particle swarm optimisation harmonic elimination technique optimal switching angles fifth-order harmonics switching angles swarm optimisation-based modified algorithm seven-level reduced switch symmetrical MLI topology

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A new method based on Type-2 fuzzy neural network for accurate wind power forecasting under uncertain data

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RENEWABLE ENERGY 2018年 120卷 220-230页

作者： Sharifian, Amir Ghadi, M. Jabbari Ghavidel, Sahand Li, Li Zhang, Jiangfeng Guilan Reg Elect Co POB 41377-18775 Rasht Iran Univ Technol Sydney Fac Engn & Informat Technol POB 123 Broadway NSW 2007 Australia

Nowadays, due to some environmental restrictions and decrease of fossil fuel sources, renewable energy sources and specifically wind power plants have a major part of energy generation in the industrial countries. To this end, the accurate forecasting of wind power is considered as an important and influential factor for the management and planning of power systems. In this paper, a novel intelligent method is proposed to provide an accurate forecast of the medium term and long-term wind power by using the uncertain data from an online supervisory control and data acquisition (SCADA) system and the numerical weather prediction (NWP). This new method is based on the particle swarm optimization (pso) algorithm and applied to train the Type-2 fuzzy neural network (T2FNN) which is called T2FNN-pso. The presented method combines both of fuzzy system's expert knowledge and the neural network's learning capability for accurate forecasting of the wind power. In addition, the T2FNN-pso can appropriately handle the uncertainties associated with the measured parameters from SCADA system, the numerical weather prediction and measuring tools. The proposed method is applied on a case study of a real wind farm. The obtained simulation results validate effectiveness and applicability of the proposed method for a practical solution to an accurate wind power forecasting in a power system control center. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Type-2 fuzzy neural network pso algorithm Medium-term and long-term wind power forecasting Uncertain information

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Optimization for the Following Operation of a High-Speed Train Under the Moving Block System

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2018年第10期19卷 3406-3413页

作者： Liu, Hong-En Yang, Hui Cai, Bai-Gen East China Jiaotong Univ Key Lab Adv Control & Optimizat Jiangxi Prov Nanchang 330013 Jiangxi Peoples R China Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing 100044 Peoples R China

Efficient and safe following operation of a high-speed train (HST) under the moving block system (MBS) has been the trend for high-speed railway (HSR) transportation. However, due to the complex operating environment and changeable operation state of HST during the following operation process, it becomes inapplicable for conventional train control approaches to satisfy HST's multi-objective operation demand including safety, punctuality, energy efficiency, and ride comfort. To resolve this problem, in this paper, practical models for the real-world characteristics of HSR's line parameters and HST's following control, as well as the multi-objective optimization model with some novel metrics, are established for achieving the optimal following control of HST under MRS. Besides, the multi-objective particle swarm optimization algorithm is modified with the preference information of control sensitivity and energy efficiency, so as to efficiently obtain the optimal following control strategy based on the real-time data of HST like velocity, location, and speed restriction. Furthermore, the convergence performance of our proposed method is demonstrated by comparative tests which also help select the proper algorithm parameters. Finally, the efficiency and feasibility of the proposed framework are illustrated by showing some experimental results.

关键词： High-speed train moving block system following operation optimization convergence performance pso algorithm

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