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An Improved particle Filter With a Novel Hybrid Proposal Distribution for Quantitative Analysis of Gold Immunochromatographic Strips

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IEEE TRANSACTIONS ON NANOTECHNOLOGY 2019年 18卷 819-829页

作者： Zeng, Nianyin Wang, Zidong Zhang, Hong Kim, Kee-Eung Li, Yurong Liu, Xiaohui Xiamen Univ Dept Instrumental & Elect Engn Xiamen 361005 Fujian Peoples R China Brunel Univ Dept Comp Sci Uxbridge UB8 3PH Middx England Korea Adv Inst Sci & Technol Dept Comp Sci Daejeon 305701 South Korea Fuzhou Univ Coll Elect Engn & Automat Fuzhou 350002 Fujian Peoples R China Fujian Key Lab Med Instrumentat & Pharmaceut Tech Fuzhou 350002 Fujian Peoples R China

In this paper, a novel statistical pattern recognition method is proposed for accurately segmenting test and control lines from the gold immunochromatographic strip (GICS) images for the benefits of quantitative analysis. A new dynamic state-space model is established, based on which the segmentation task of test and control lines is transformed into a state estimation problem. Especially, the transition equation is utilized to describe the relationship between contour points on the upper and the lower boundaries of test and control lines, and a new observation equation is developed by combining the contrast of between-class variance and the uniformity measure. Then, an innovative particle filter (PF) with a hybrid proposal distribution, namely, deep-belief-network-based particle filter (DBN-PF) is put forward, where the deep belief network (DBN) provides an initial recognition result in the hybrid proposal distribution, and the particle swarm optimization algorithm moves particles to regions of high likelihood. The performance of proposed DBN-PF method is comprehensively evaluated on not only an artificial dataset but also the GICS images in terms of several indices as compared to the PF and DBN methods. It is demonstrated via experiment results that the proposed approach is effective in quantitative analysis of GICS.

关键词： Gold immunochromatographic strip particle filter proposal distribution deep belief network dynamical model image segmentation particle swarm optimization algorithm Monte Carlo

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optimization of Power Allocation Based on particle swarm optimization in Wireless Location Network 9th

Optimization of Power Allocation Based on Particle Swarm Opt...

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9th China Satellite Navigation Conference (CSNC) - Location, Time of Augmentation

作者： Lin, Jinrui Li, Guangxia Tian, Shiwei Suo, Longlong Army Engn Univ PLA Coll Commun Engn Nanjing Jiangsu Peoples R China

ISBN: (纸本)9789811300295;9789811300288

Wireless location network combines data communication, contextual data collection and navigation. The purpose of this network is to determine the positions of agents based on measurements between nodes and nodes. In order to improve the positioning accuracy of wireless location network, the usual method is to increase the density of nodes, especially the density of anchor nodes, so as to optimize the topology of the network. However, the wireless location network node power is limited, especially the wireless sensor network. It is necessary to increase power consumption to meet the needs of a large number of nodes. However, it is required that a certain level of power consumption should be set in the wireless location network, and even require further reduction in power consumption in daily life. At the same time, we need to maintain a certain positioning accuracy, which requires the optimization of power allocation in the network, to make sure that power and positioning accuracy of the network can meet the needs of the actual living. In this paper, an optimization of power allocation based on particle swarm optimization in wireless location network is proposed to optimize the power allocation. The square position error bound is introduced as the evaluation standard of the network positioning accuracy. Through the power optimization, the positioning accuracy of the network is improved.

关键词： Wireless location network Power optimization allocation Square position error bound particle swarm optimization algorithm

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A comparative investigation of deterministic and metaheuristic algorithms for node localization in wireless sensor networks

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WIRELESS NETWORKS 2019年第5期25卷 2789-2803页

作者： Kulkarni, Vaishali R. Desai, Veena Kulkarni, Raghavendra V. MS Ramaiah Univ Appl Sci Bengaluru India KLS Gogte Inst Technol Belagavi India

Location-based services in wireless sensor networks demand precise information of locations of sensor nodes. Range-based localization, a problem formulated as a two-dimensional optimization problem, has been addressed in this paper as a multistage exercise using bio-inspired metaheuristics. A modified version of the shuffled frog leaping algorithm (MSFLA) has been developed for accurate sensor localization. The results of MSFLA have been compared with those of geometric trilateration, artificial bee colony and particle swarm optimization algorithms. Dependance of localization accuracies achieved by these algorithms on the environmental noise has been investigated. Simulation results show that MSFLA delivers the estimates of the locations over 30% more accurately than the geometric trilateration method does in noisy environments. However, they involve higher computational expenses. The MSFLA delivers the most accurate localization results;but, it requires the longest computational time.

关键词： Artificial bee colony algorithm particle swarm optimization algorithm Sensor localization Shuffled frog leaping algorithm Trilateration Wireless sensor networks

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Control strategy based on Fourier transformation and intelligent optimization for planar Pendubot

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INFORMATION SCIENCES 2019年 491卷 279-288页

作者： Wu, Jundong Wang, Yawu Ye, Wenjun Su, Chun-Yi Concordia Univ Fac Engn & Comp Sci Montreal PQ H3G 1M8 Canada China Univ Geosci Sch Automat Wuhan 430074 Hubei Peoples R China Hubei Key Lab Adv Control & Intelligent Automat C Wuhan 430074 Hubei Peoples R China

This paper presents a new control strategy based on Fourier transformation and intelligent optimization for a planar Pendubot with a passive second link, which can be treated as a second-order nonholonomic system whose control has been an open and challenging issue. A controller acting within a time corresponding to the frequency of its fundamental harmonic term is designed to realize the system control objective, which is to move the system from its initial position to the target position. By employing Fourier transformation, a general expression of the controller composed of a constant term and harmonic terms is obtained. Next, the constant term is obtained by the angular momentum theorem, and the particle swarm optimization algorithm is employed to obtain the harmonic terms of the controller. A feedback control strategy based on a nonlinear disturbance observer is then applied to overcome the uncertainties/disturbances in the system. Finally, simulation results prove the validity of this control method. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Planar Pendubot Nonholonomic systems Fourier transformation particle swarm optimization algorithm Nonlinear disturbance observer

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Integrated Strategy of the Output Planning and Economic Operation of the Combined System of Wind Turbines-Pumped-Storage-Thermal Power Units

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IEEE ACCESS 2019年 7卷 20567-20576页

作者： Ma, Liye Wang, Zhiqiang Lu, Zhigang Lu, Xiaoyu Wan, Furui Yanshan Univ Sch Elect Engn Qinhuangdao 066004 Peoples R China Guangdong Med Device Qual Supervis & Inspect Inst Guangzhou 510663 Guangdong Peoples R China

Aiming at the problem that the large-scale renewable energy is integrated into the grid, which causes the instability of the system output power, the optimal scheduling of the power system during operation, system stability, and power quality. Based on the construction of wind power-pumped storage combined generator set by using water resources, combined with the power generation of a thermal power unit, a scheduling strategy based on the dynamic Kriging model for the integrated strategy of output planning and the economic operation of a wind power-pumped storage-thermal power combined system is proposed. The strategy uses the hypercube sampling method and the particle swarm optimization algorithm after improving the inertia weight to solve the model. The simulation results verify the correctness and effectiveness of the proposed strategy. Through the strategy proposed in this paper, the best output operation scheme of the combined system can be obtained at the same time, which reduces the total investment cost of the system and increases the consumption of renewable energy.

关键词： Renewable energy particle swarm optimization algorithm Kriging model hypercube sampling method economic operation

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A study on multi-ASC scheduling method of automated container terminals based on graph theory

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COMPUTERS & INDUSTRIAL ENGINEERING 2019年 129卷 404-416页

作者： Lu, Houjun Wang, Sai Shanghai Maritime Univ Coll Logist Engn 1550 Haigang Ave Shanghai 201306 Peoples R China SAIC Motor Commercial Vehicle Tech Ctr Dept Informat Technol Shanghai Peoples R China

Compared with traditional terminals, two automated stacking cranes (ASCs) are configured for each container block of automated container terminal (ACT), which interact with automated guided vehicles (AGVs) and container trucks at the two ends of a container block individually. To increase the capacity, container yards with multiple rows of blocks perpendicular to the terminal's shoreline are considered. To utilize the yard spaces, twin ASCs are set to share transport tracks installed at the two sides of a block, while interferences between the ASCs causes the routing and sequencing operations. In order to control the scheduling of twin ASCs, the interference model is established by analyzing the time overlap between tasks. Considering the influence of AGV transportation time, the model are then established to sequence the container handling tasks under the minimization of waiting time and makespan. A particle swarm optimization algorithm (PSO) based on graph theory model is design to solve the problem. Numerical experiments show that the algorithm is more competitive than traditional algorithm. Based on the model and experimental result, the practical significance of applying the algorithm to the actual situation is discussed.

关键词： Automated container terminal Automated stacking crane Automated guided vehicle particle swarm optimization algorithm Graph theory

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A robust approach for a green periodic competitive VRP under uncertainty: DE and PSO algorithms

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2019年第6期36卷 5213-5225页

作者： Fallah, M. Tavakkoli-Moghaddam, R. Alinaghian, M. Salamatbakhsh-Varjovi, A. Islamic Azad Univ Dept Ind Engn Cent Tehran Branch Tehran Iran Univ Tehran Coll Engn Sch Ind Engn Tehran Iran Arts & M Tiers ParisTech LCFC Metz France USERN Tehran Iran Isfahan Univ Technol Dept Ind & Syst Engn Esfahan Iran Islamic Azad Univ Dept Ind Engn Sci & Res Branch Tehran Iran

The purpose of this paper is to examine and evaluate a new mathematical model of vehicle routing problem in order to optimize fuel consumption and maximize commercial profitability under the conditions of uncertainty of distributor service to customers using robust approach under scenario. According to the real world, distribution companies are interested in minimizing consumption of fuel in the distribution of goods for two reasons: the first reason is that reducing the consumption of fuel will reduce the current costs of distribution companies and ultimately increase their profits. The second reason is that reducing fuel consumption will reduce the harmful effects of greenhouse gases and air pollution. Other words, distribution companies operate in a competitive environment that has more than one distributor in the distribution network, and start time for serving customers has a significant impact on the profitability of the distributors. To calculate the efficiency of the proposed model, we used differential evolution (DE) algorithm and particle swarm optimization (PSO), and the results were compared in small and medium scales with the results of the exact solution method. To verify proceeds of proposed algorithms in large scales, a number of sample problems were created in large scales and the figures were evaluated. The computational results indicate that DE algorithm has a better computational function, but the PSO algorithm has better computational time.

关键词： Green periodic VRP robust optimization Fuel consumption particle swarm optimization algorithm

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Modeling of compressive strength of cemented sandy soil

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JOURNAL OF ADHESION SCIENCE AND TECHNOLOGY 2019年第8期33卷 791-807页

作者： Choobbasti, Asskar Janalizadeh Kutanaei, Saman Soleimani Afrakoti, Mobina Taslimi Paein Babol Univ Technol Dept Civil Engn Babol Sar Iran Rouzbahan Inst Higher Educ Dept Civil Engn Sari *** Iran

This paper attempted to show the application of particle swarm optimization in the prediction of the compressive strength of cement sandy soil from the curing period, porosity of sample and percentage of cement. The results of the study show that the unconfined compressive strength of the cement stabilized sandy soil increases with an increasing cement content curing time period. Moreover the compressive strength decreases with an increasing porosity. The compressive strength improvement due to cement treatment has a larger increase in samples with less porosity. In addition, particle swarm optimization algorithm is and accurate technique in estimation of compressive strength of cement stabilized sandy soil. In order to compare of existing correlations, a total number of 100 unconfined compressive tests and 15 scanning electron microscope tests have been conducted on cemented Babolsar sand. It can be concluded that compared to existing correlations models, particle swarm optimization algorithm models give more reliable prediction about compressive strength of cement satblized sandy soil. Moreover, the sensitivity analysis of the polynomial model shows that cement content and porosity have significant impact on predicting unconfined compressive strength.

关键词： Cement unconfined compressive strength particle swarm optimization algorithm Sand SEM

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Research on Optimal Scheduling of Energy Storage System Based on particle swarm optimization 3

Research on Optimal Scheduling of Energy Storage System Base...

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3rd IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)

作者： Wang Licui Tianjin City Vocat Coll Tianjin Peoples R China

ISBN: (纸本)9781538645093

In the power system, the power load of users will show a large gap between peak and valley, which may leads to the imbalance between supply and demand of power system. In this paper, the particle swarm optimization (PSO) algorithm is used to optimize the battery energy storage system to reduce the impact of peak load on the power grid, and help the power grid to smooth the power load fluctuation, and reduce the cost of the users.

关键词： particle swarm optimization algorithm optimal scheduling battery energy storage peak load shifting

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Deterministic and stochastic modelling of greenhouse microclimate

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SYSTEMS SCIENCE & CONTROL ENGINEERING 2019年第3期7卷 65-72页

作者： Yang, Hua Liu, Qi-Fang Yang, Huai-Qing Shanxi Agr Univ Coll Informat Sci & Engn Taigu Shanxi Peoples R China

In order to improve the yield and quality of greenhouse crops, it is necessary to develop a reliable model to predict and control the microclimate of greenhouse. In this paper, the problem of deterministic and stochastic modelling for greenhouse microclimate defined by the variables of temperature and humidity is considered. Experiments were conducted in a naturally ventilated single-sloped greenhouse without crops in north China. Firstly, a mechanism model is adopted and the assumed unknown parameters are derived by using increased convergence factor particle swarm optimization algorithm. Secondly, considered the disturbance is independent identically distributed white noise, a stochastic dynamic model is constructed and the parameters are obtained by using maximum likelihood estimate. Finally, a comparison of measured and simulated data is given to show that the proposed models can reasonably forecast internal greenhouse microclimate.

关键词： Modelling greenhouse particle swarm optimization algorithm maximum likelihood estimate

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