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Proceedings of the 8th International Conference on Industrial and Business Engineering

作者： Guangying Jin Wei Feng Qingpu Meng School of Maritime Economics and Management Dalian Maritime University China

ISBN: (纸本)9781450397582

The development of the container transportation industry has made a huge contribution to the development of the regional economy. Container transportation saves cost and time and increases transportation benefits. Container throughput forecasts are very important for logistics companies, shipping companies, port authorities and shipyards. Such forecasts enable shipping companies and port operators to develop appropriate short- to medium-term strategies to remain competitive, while playing a fundamental role in allocating resources to the market. In this paper, particle swarm algorithm (PSO) is used to optimize the BP neural network model, so as to construct the PSO-BP neural network model, write the PSO-BP code and collect the historical throughput data of Shenzhen Port containers, and set the relevant parameters to use the PSO-BP model to Predicting the container throughput of Shenzhen Port, the experimental results show that the accuracy of the PSO-BP neural network model is higher than that of the unoptimized BP neural network model.

关键词： particle swarm algorithm BP Neural Network Container PSO-BP Neural Network Container Throughput

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Steam-Water Modelling and the Coal-Saving Scheduling Strategy of Combined Heat and Power Systems

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ENERGIES 2022年第1期15卷 141-141页

作者： Guo, Junshan Zheng, Wei Cong, Zhuang Shang, Panfeng Wang, Congyu Song, Jiwei Zhongshi Yitong Grp Jinan 250003 Peoples R China State Grid Shandong Elect Power Res Inst Jinan 250003 Peoples R China Shandong Univ Inst Thermal Sci & Technol Jinan 250061 Peoples R China

China aims to peak carbon emissions by 2030. As a result, small-scale coal-fired combined heat and power (CHP) units and self-provided units are gradually shut down, and large-scale coal-fired CHP units are a solution to undertake the industrial heat loads. From the perspective of the industrial heat load allocation during the non-heating season, the problems regarding the coal-saving scheduling strategy of coal-fired CHP units are addressed. The steam-water equations of CHP units are established to analyze the heat-power coupling characteristics. The energy utilization efficiency, exergy efficiency and the coal consumption are analyzed. The optimization model of saving coal consumption is established and the adaptive mutation particle swarm optimization (AMPSO) is introduced to solve the above model. The 330 MW coal-fired CHP unit is taken as an example, and the results show that for the constant main flow rate, each increase of 1 t/h industrial steam extraction will reduce the power output by about 0.321 MW. The energy utilization efficiency and the exergy are mainly influenced by industrial steam supply and the power load, respectively. For the CHP system with two parallel CHP units, the unequal allocation of industrial heat load between two units saves more coal than equal allocation. The coal consumption can be reduced when the unit with lower power load undertakes more industrial heat load. In the typical day, the total coal consumption after optimization is 3203.92 tons, a decrease of 14.66 tons compared to the optimization before. The two CHP units in the case can benefit about 5,612,700 CHY extra in one year.

关键词： combined heat and power steam-water equation load optimal allocation particle swarm algorithm

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Multi-scale assessment and multi-objective optimization of a novel solid oxide fuel cell hybrid power system fed by bio-syngas

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JOURNAL OF POWER SOURCES 2022年 524卷 231047-231047页

作者： Zhao, Zhongkai Shi, Xiaomin Zhang, Mingliang Ouyang, Tiancheng Guangxi Univ Sch Mech Engn Nanning 530004 Peoples R China Guangxi Univ Guangxi Key Lab Petrochem Resource Proc & Proc In Nanning 530004 Peoples R China

Nowadays, thermal power generation using fossil fuels is still the main way for electricity supply, resulting in serious environmental pollution and energy consumption. The research on new power generation and alternative energy is urgently needed to conduct. In this paper, a new power supply system combined biomass and fuel cell with waste heat recovery is proposed and analyzed. The system uses agricultural waste as energy source, providing gas fuel for solid oxide fuel cell through the biomass gasification. In addition, the homogeneous charge compression ignition engine can utilize unreacted fuel, and the waste heat energy in flue gas is recovered by the combination of supercritical carbon dioxide cycle and absorption refrigeration cycle. Under selecting working condition, the engine and bottom cycles brings an improvement of 11.10% in thermal efficiency, and the total fuel efficiency can reach 59.35%. Besides, the payback time of this system is nearly 60% shorter than the traditional system, with a minimum of 1.77 years. The whole system realizes the energy cascade utilization, being more efficient and cleaner, and has satisfactory performance.

关键词： Agro-waste Waste heat recovery Homogeneous charge compression ignition engine particle swarm algorithm Multi-perspective evaluation

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Optimal sensor placement methodology of hydraulic control system for fault diagnosis

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2022年 174卷 109069-109069页

作者： Kong, Xiangdi Cai, Baoping Liu, Yonghong Zhu, Hongmin Liu, Yiqi Shao, Haidong Yang, Chao Li, Haojie Mo, Tianyang China Univ Petr Coll Mech & Elect Engn Qingdao 266580 Shandong Peoples R China Imperial Coll London Dept Aeronaut Exhibit Rd London England South China Univ Technol Sch Automat Sci & Engn Wushang Rd Guangzhou 510640 Peoples R China Hunan Univ Coll Mech & Vehicle Engn Changsha 410082 Peoples R China

During the state monitoring and fault diagnosis of hydraulic control system, different kinds of sensors are used to collect fault signals. The arrangement of a limited number of sensors in the most reasonable positions of the hydraulic system, that is, to solve the problem on the optimal placement of sensors, is the key to improving efficiency of fault diagnosis. Aiming at fault diagnosis of hydraulic control system, this paper proposes a sensor placement methodology of hydraulic control system to determine the optimal number and position of sensors based on a discrete particle swarm algorithm. First, the model of fault propagation and sensor response time is evaluated by a simulation model. Second, a discrete optimization model for sensor placement is established. Finally, a discrete particle swarm optimization algorithm is used to calculate the optimal solution for the optimal placement of sensors. In the iterative process, a Monte Carlo simulation-based comparison algorithm is used for the evaluation and comparison of particle. The simulation case of typical multi-circuit hydraulic control systems proves that the proposed method has fast convergence speed and optimization results. A real case of a subsea blowout preventer control system shows that the proposed method reduces the number of sensors and data redundancy effectively. Compared with the traditional method, the robustness of the proposed system under the optimal solution is improved.

关键词： Optimal sensor placement particle swarm algorithm Subsea blowout preventer Fault diagnosis

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Correlation analysis of roughness surface height distribution parameters and maximum mises stress

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SURFACE TOPOGRAPHY-METROLOGY AND PROPERTIES 2022年第1期10卷 015046-015046页

作者： Yang Duo Wang Qibo Tang Jinyuan Xia Fujia Zhou Wei Wen Yuqin Cent South Univ State Key Lab High Performance Complex Mfg Changsha Hunan Peoples R China Cent South Univ Coll Mech & Elect Engn Changsha Hunan Peoples R China Hunan Univ Sci & Technol Hunan Prov Key Lab High Efficiency & Precis Machi Xiangtan Peoples R China

Roughness surfaces contact analysis is cutting-edge research in interface design. The 3D rough surface amplitude distribution characterized by height distribution parameters( Sq (root mean square), Ssk (skewness), Sku (kurtosis)) has a great influence on the extreme value and distribution of the interface contact stress. However, the relationship between height distribution parameters and surface maximum mises stress ( sigma ( max )) is still unclear and lacks of in-depth study. Through BP(Back Propagation) neural network, global sensitivity qualitative (Morris) and quantitative (Sobol) analysis methods, the relationship between Sq , Ssk , Sku and sigma ( max ) under different loads is studied. Based on complete polynomial and permutation combination method, the optimal correlation model between height distribution parameters and sigma ( max ) was established, and particle swarm algorithm was introduced to analyze sigma ( max ) extreme values under different Sq . The results show that: (1) Under different loads, the order about height distribution parameters influence on surface contact stress is: Sq > Ssk > Sku , and as the load increases, the influence of Ssk and Sku gradually decreases. (2) In different roughness surfaces, the influence of Ssk and Sku on the contact performance is significantly full of discrepancy. The research results provide reference and technical support for active design of rough surface microstructure to improved contact performance.

关键词： roughness surface height distribution contact stress regression analysis particle swarm algorithm

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Research on Improvement of particle swarm Optimization

Research on Improvement of Particle Swarm Optimization

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International Conference on Cyber Security Intelligence and Analytics (CSIA)

作者： Chang, Chunguang Wu, Xi Shenyang Jianzhu Univ Sch Management Shenyang 110168 Liaoning Peoples R China

ISBN: (纸本)9783030152352;9783030152345

Although the particle swarm optimization algorithm has simple principle, few parameters and easy implementation, the particle swarm optimization algorithm is easy to fall into local optimum on multi-mode function and the local search ability is relatively weak. In this paper, the improvement of these two defects is carried out. The particle motion formula with learning model is added, and the generation strategy of a guided vector is added to improve the particle swarm optimization algorithm. The improved algorithm has a two-layer structure, and finally the research direction is prospected.

关键词： particle swarm algorithm Two-layer structure Improvement

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PSO-BP neural network based on physical health perception of the elderly in retirement home

PSO-BP neural network based on physical health perception of...

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第35届中国控制与决策会议

作者： Jiujun Bai Ying Zhao Xuebo Chen School of Electronic and Information Engineering University of Science and Technology Liaoning China Broadcast Liaoning Network School of Management Anshan Normal University

The recreational community retirement home staff can keep abreast of the elderly's health conditions and provide personalised ***,how health status is collected and judged is more traditional,and there is a lack of prediction of the healthy development of the *** information and intelligent upgrading of the elderly recreation system is imperative,and neural network modelling based on the Internet of Things and its intelligent optimisation methods is one of the means to do *** on 5G IoT technology,this paper adopts BP neural network and particle swarm optimization modelling methods to use the physical health and long-term living environment health indicators collected by IoT terminals as model input *** selecting evaluation indicators,the construction of evaluation models,the establishment of a primary database and the determination of evaluation criteria,intelligent analysis and optimisation processes are carried out to form a health posture awareness system for the elderly in recreational community retirement home.A comparison of the simulation results shows that the proposed geriatric health situational awareness system has improved the efficiency and accuracy of its geriatric health awareness capabilities.

关键词： BP neural network particle swarm algorithm physical health perception Internet of Things artificial intelligence 5G

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An Electromagnetic Parameter Estimation Method for Microwave Photonic Signal Based on PSO

An Electromagnetic Parameter Estimation Method for Microwave...

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2021 CIE International Conference on Radar, Radar 2021

作者： Xie, Yiyuan Gao, Yuexin Xing, Mengdao Xidian University National Laboratory of Radar Signal Processing Xi'an China School of Aerospace Science and Technology Xidian University Xi'an China

ISBN: (纸本)9781665498142

Microwave photonics radar generates cross-band signals, which provides a basis for precise electromagnetic characteristics description and accurate identification of targets. As a result, a corresponding electromagnetic model parameter extraction method is urgently required under the cross-band and wide-angle condition. This paper proposes a parameter estimation method based on an improved particle swarm Optimization (PSO). The representation dictionary without range-azimuth coupling of scattering behavior for the data processed by PFA is firstly formed based on the attributed scattering center (ASC) model. Based on the proposed scattering behavior dictionary, the improved PSO algorithm and the CLEAN algorithm are implemented to estimate the electromagnetic parameters of each scattering center. Further, the simulated annealing (SA) algorithm is introduced to improve the method's capacity to find the global optimum. Finally, the structure of each scattering center can be identified according to the parameter estimation results. The processing results of simulation data verify the effectiveness of the algorithm. Compared with traditional algorithms, the proposed algorithm is insensitive to the initialization value and does not require image segmentation, which reduces the complexity of computation. © 2021 IEEE.

关键词： Attributed scattering center Cross-band radar imaging Parameter estimation particle swarm algorithm Polar Format algorithm

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Intelligent Neural Network with Parallel Salp swarm algorithm for Power Load Prediction

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JOURNAL OF INTERNET TECHNOLOGY 2022年第4期23卷 643-657页

作者： Zhou, Jin-Liang Chu, Shu-Chuan Tian, Ai-Qing Peng, Yan-Jun Pan, Jeng-Shyang Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao Shandong Peoples R China Flinders Univ S Australia Coll Sci & Engn Adelaide SA Australia Chaoyang Univ Technol Dept Informat Management Taichung Taiwan

The neural network runs slowly and lacks accuracy in power load forecasting, so it is optimized using meta-heuristic algorithm. Salp swarm algorithm (SSA) is a novel meta-heuristic algorithm that simulates the salp foraging process. In this paper, a parallel salp swarm algorithm (PSSA) is proposed to improve the performance of SSA. It not only improves local development capabilities, but also accelerates global exploration. Through 23 test functions, PSSA performs better than other algorithms and can effectively explore the whole search space. Finally, PSSA is used to optimize the weights as well as the thresholds of the neural network. Using the optimized neural network to predict the power load in a certain region, the results show that PS SA can better optimize the neural network and increase its prediction accuracy.

关键词： Power load forecast Salp swarm algorithm Artificial neural network particle swarm algorithm

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Simulation of tennis serve behavior based on video image processing and wireless sensor technology

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EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING 2020年第1期2020卷 1-16页

作者： Li, Xiaozhong Huang, Pinglin Guangdong Baiyun Univ Fac Educ & Sports Guangzhou 510450 Peoples R China

Video-based human motion analysis is an important research direction in the field of computer vision. It detects moving objects from video sequences, extracts key parts of the human body, and obtains useful information for human movements. In this paper, the joints of the teeing arm are first color-coded. The tennis teeing video is collected by a high-speed camera. The coordinates of the tacking points in each frame are used instead of the knuckles to study the trajectory of the teeing arm. In the process of video processing, after constructing a dictionary for a series of noise maps, the sparse representation idea was used to reconstruct an interference-free service diagram, and a mixture of Gaussian background modeling was used to extract the foreground of the motion. After obtaining the motion foreground, the marker points are extracted through the color features, and binarization operations are performed on the marker points. Next, the outline of the marker points is searched, the outline is surrounded by the minimum circle, and the returned circle center coordinates are used as the joint point coordinates. Taking the trajectory of the shoulder marking point as a research object, a tennis serving model based on an improved support vector machine was established.

关键词： Wireless sensor Sparse representation Best hitting point Support vector machine particle swarm algorithm

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