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Photon dosimetry using selective data sampling with particle swarm optimization algorithm based on NaI(Tl) scintillation detector

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KERNTECHNIK 2022年第3期87卷 364-372页

作者： Taheri Balanoji, Seyed Mortaza Zaki Dizaji, Hossein Abdi Saray, Akbar Imam Hossein Comprehens Univ Fac Sci Tehran Iran Urmia Univ Phys Dept Fac Sci Orumiyeh Iran

Sodium Iodide doped with Thallium NaI(Tl) scintillation detectors have potential for the development of an active dosimeter for photon radiation. We aim to show that the photon dosimetry response for NaI(Tl) scintillation detector may be optimized by employing the particle swarm optimization algorithm, when the selective data sampling is applied for the detector readout. In this work, Sodium Iodide doped with Thallium NaI(Tl) scintillation detector is considered due to being highly sensitive to gamma radiation, and one of the affordable room temperature detectors. In this research, we intend to measure the dosimetry response of the NaI(Tl) detector for various gamma sources, as an example, by measuring the ambient dose equivalent H*(10) for different gamma radioactive sources. Furthermore, we demonstrate that the photon dosimetry response may be well optimized for various energies, especially at lower energies, by increasing the energy interval number in data sampling over the NaI(Tl) scintillation detector readout with the help of an optimization algorithm. The simulation software Geant4 has been used for determining the NaI(Tl) scintillation detector readout. To this end, experimental ambient dose equivalent measurements for gamma radiation sources are compared with the theoretical results. As three and six energy intervals are considered for the selective data sampling along with an optimization algorithm based on NaI(Tl) detector output, the error percentage will be less than 20 and 10%, respectively.

关键词： ambient dose equivalent H*(10) Geant4 NaI(Tl) scintillation detector particle swarm optimization algorithm photon dosimetry response selective data sampling

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A Reactive Power optimization Method for AC Metro Power Supply System Based on particle swarm optimization algorithm 19

A Reactive Power Optimization Method for AC Metro Power Supp...

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20th IEEE Vehicle Power and Propulsion Conference (IEEE VPPC)

作者： Feng, Ding Zhou, Haiqi Lin, Sheng Southwest Jiaotong Univ Sch Elect Engn Chengdu Peoples R China

ISBN: (纸本)9798350344455

Aiming at the problem of reactive power imbalance in AC metro, the equivalent circuit model is established by investigating the topology AC metro power supply system, and the change of reactive power in the whole day of the system is analyzed in combination with the actual operation data of the metro. The reactive power evaluation indices are proposed to evaluate the influence of reactive power on the system. To solve the problem that the power factor of the system at night does not meet the requirements and there is no available device in the line for reactive power optimization, the particle swarm optimization (PSO) is used to obtain the optimal configuration of reactive power compensation devices. The results show that the balance and optimization of reactive power can be achieved through reasonable configuration of compensation devices.

关键词： AC metro power supply system reactive power optimization particle swarm optimization algorithm

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Ship Heading Control Based on Improved particle swarm optimization algorithm 1st

Ship Heading Control Based on Improved Particle Swarm Optimi...

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World Conference on Intelligent and 3-D Technologies (WCI3DT)

作者： Liang, Hao Wang, Huaming Fan, Lingmei Wang, Xinshuai Chen, Xiaoyu Zhejiang Ocean Univ Sch Marine Engn Equipment Zhoushan Peoples R China Zhejiang Ocean Univ Sch Naval Architecture & Maritime Zhoushan Peoples R China

ISBN: (纸本)9789811971860;9789811971846;9789811971839

Ship course control is designed by means of ship response mathematical model, servo system of steering engine, wave interference model, and PID controller, and particle swarm optimization algorithm (PSO) is added to the course control to obtain the optimal PID parameter value. Considering that the number of PSO iterations and the objective function in the search process have a certain influence on the PID parameter value, this paper mainly studies changing the iteration number and objective function to compare the corresponding PID parameters and then obtains the optimal control effect according to different PID parameters corresponding to different control effects.

关键词： Response mathematical model particle swarm optimization algorithm Iteration times The objective function

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STEEL LAZY WAVE RISER optimization USING particle swarm optimization algorithm 42

STEEL LAZY WAVE RISER OPTIMIZATION USING PARTICLE SWARM OPTI...

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ASME 42nd International Conference on Ocean, Offshore and Arctic Engineering (OMAE)

作者： Lal, Mayank Yenduri, Anurag Sebastian, Abhilash TechnipFMC Houston TX 77044 USA TechnipFMC Chennai Tamil Nadu India

ISBN: (纸本)9780791886854

Steel lazy wave risers have become a preferred riser solution as offshore oil and gas developments are happening in deeper waters and harsher environments. Steel lazy wave risers work well with FPSOs even though FPSO motions are worse compared to other floating platforms such as Spars and Semisubmersibles. FPSOs offer a tremendous advantage compared to other floating platforms as they can be used in regions with inadequate pipeline infrastructure to transport oil and gas to shore. The targeted placement of buoyancy modules on steel lazy wave risers improves the strength and fatigue performance compared to steel catenary risers making them feasible for use with FPSO's. Also, buoyancy modules on steel lazy wave risers help reduce the top tension compared to steel catenary risers which gains significance in ultra-deepwater developments where top tensions for steel catenary risers make them an infeasible riser solution. Steel lazy wave riser configuration design is dependent on a few parameters such as hang-off angle, length to start of buoyancy section, length of buoyancy section, type of buoyancy, and buoyancy factor. Theoretically, there are infinite solutions for a steel lazy wave riser configuration. Hence, the design of steel lazy wave riser is an optimization problem in which the parameters that govern the design can be varied to get an optimized configuration. In this paper, the particle swarm algorithm which is a bio-inspired algorithm is used to find an optimal solution for steel lazy wave riser configuration. The solution space is searched for an optimal steel lazy wave riser configuration based on an objective function which is a function of strength response, fatigue response, and cost. Solutions with poor interference response are penalized in the algorithm and rejected as part of the solution space search process. Finding an optimal solution automatically will help reduce the overall cost of risers which is a significant part of any offshore development.

关键词： SLWR optimization particle swarm optimization algorithm

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Optimal Energy Consumption optimization in a Smart House by Considering Electric Vehicles and Demand Response via a Hybrid Gravitational Search and particle swarm optimization algorithm

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Energy Engineering 2022年第6期119卷 2489-2511页

作者： Rongxin Zhang Chengying Yang Xuetao Li School of Economics and Management Hubei University of Automotive TechnologyShiyan442000China School of Management Universiti Sains MalaysiaPenang11800Malaysia School of Science Hubei University of Automotive TechnologyShiyan442002China

Buildings are the main energy consumers across the world,especially in urban *** smartization,or the smartification of housing,therefore,is a major step towards energy grid smartization *** controlling the energy consumption of lighting,heating,and cooling systems,energy consumption can be *** or some part of the energy consumed in future smart buildings must be supplied by renewable energy sources(RES),which mitigates environmental impacts and reduces peak demand for electrical *** this paper,a new optimization algorithm is applied to solve the optimal energy consumption problem by considering the electric vehicles and demand response in smart *** this way,large power stations that work with fossil fuels will no longer be *** current study modeled and evaluated the performance of a smart house in the presence of electric vehicles(EVs)with bidirectional power exchangeability with the power grid,an energy storage system(ESS),and solar ***,the solar RES and ESS for predicting solar-generated power prediction uncertainty have been considered in this *** case studies,including the sales of electrical energy resulting from PV panels’generated power to the power grid,time-variable loads such as washing machines,and different demand response(DR)strategies based on energy price variations were taken into account to assess the economic and technical effects of EVs,BESS,and solar *** proposed model was simulated in MATLAB.A hybrid particle swarm optimization(PSO)and gravitational search(GS)algorithm were utilized for *** generation and reduction were performed via LHS and backward methods,*** results demonstrate that the proposed model minimizes the energy supply cost by considering the stochastic time of use(STOU)loads,EV,ESS,and PV *** on the results,the proposed model markedly reduced the electricity costs of the smart house.

关键词： Energy management smart house particle swarm optimization algorithm gravitational search algorithm demand response electric vehicle

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optimization of PID Controller for Underwater Autonomous Vehicle Based on particle swarm optimization algorithm 3

Optimization of PID Controller for Underwater Autonomous Veh...

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3rd International Conference on Electrical Engineering and Control Science, IC2ECS 2023

作者： Li, Hongna Fan, Yiqun Tianjin University of Technology Maritime College Tianjin University of Technology Tianjin China

ISBN: (纸本)9798350382426

In order to ensure that the underwater autonomous vehicle can maintain the stability of control performance when performing unknown tasks, this paper combines the particle swarm optimization algorithm with PID controller, and compares the particle swarm optimization PID simulation with fuzzy PID simulation and traditional PID simulation, and concludes that the use of particle swarm optimization algorithm to optimize the PID parameters has a certain feasibility, which improves the stability and control performance of the system. © 2023 IEEE.

关键词： particle swarm optimization algorithm PID controller underwater autonomous vehicle

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Hybridizing salp swarm algorithm with particle swarm optimization algorithm for recent optimization functions

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EVOLUTIONARY INTELLIGENCE 2022年第1期15卷 23-56页

作者： Singh, Narinder Singh, S. B. Houssein, Essam H. Punjabi Univ Dept Math Patiala Punjab India Minia Univ Fac Comp & Informat El Minia Governorate Egypt

The salp swarm algorithm (SSA) has shown its fast search speed in several challenging problems. Research shows that not every nature-inspired approach is suitable for all applications and functions. Additionally, it does not provide the best exploration and exploitation for each function during the search process. Therefore, there were several researches attempts to improve the exploration and exploitation of the meta-heuristics by developing the newly hybrid approaches. This inspired our current research and therefore, we developed a newly hybrid approach called hybrid salp swarm algorithm with particle swarm optimization for searching the superior quality of optimal solutions of the standard and engineering functions. The hybrid variant integrates the advantages of SSA and PSO to eliminate many disadvantages such as the trapping in local optima and the unbalanced exploitation. We have used the velocity phase of the PSO approach in salp swarm approach in order to avoid the premature convergence of the optimal solutions in the search space, escape from ignoring in local minima and improve the exploitation tendencies. The new approach has been verified on different dimensions of the given functions. Additionally, the proposed technique has been compared with a wide range of algorithms in order to confirm its efficiency in solving standard CEC 2005, CEC 2017 test suits and engineering problems. The simulation results show that the proposed hybrid approach provides competitive, often superior results as compared to other existing algorithms in the research community.

关键词： Standard functions Heuristic hybridization Salp swarm algorithm particle swarm optimization algorithm Exploration and exploitation

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Dynamic Estimation Method of Construction Cost Based on particle swarm optimization algorithm 4

Dynamic Estimation Method of Construction Cost Based on Part...

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4th IEEE Global Conference for Advancement in Technology, GCAT 2023

作者： Zhang, Xiaotao Ma, Fuxin Gansu Institute of Mechanical & Electrical Engineering Gansu Tianshui741001 China Tianshui Normal University Gansu Tianshui741001 China

ISBN: (纸本)9798350305258

The dynamic estimation method of project cost based on particle swarm optimization algorithm is a new method of project cost evaluation. This method mainly introduces particle swarm optimization algorithm into the process of project cost evaluation to optimize the parameters and weight distribution of the evaluation model, so as to improve the accuracy and reliability of the evaluation results. The main feature of this method is its dynamism, as it can adjust the evaluation model in real-time according to different engineering changes to achieve more accurate dynamic cost estimation. Therefore, this method can better cope with the complexity and variability of engineering construction. The specific realization of this method is to introduce the particle swarm optimization algorithm, set the optimization model according to the estimation goal and range, and calculate and adjust the optimization goal of each parameter as the objective function. This method can be solved through multiple iterations to obtain the optimal solution, thereby achieving accurate calculation of dynamic estimation of engineering costs. In a word, the dynamic estimation method of project cost based on particle swarm optimization algorithm can provide more accurate cost estimation results of buildings and related projects, and solve the problem that there is a large deviation between the accuracy of cost estimation and the actual cost in the industry. This method can not only improve the accuracy of engineering budgets, but also improve the efficiency and quality of construction, making positive contributions to the sustainable development of the construction industry. © 2023 IEEE.

关键词： Construction cost Dynamic cost estimation particle swarm optimization algorithm

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Design of Electric Carbon Emission Assessment System Based on particle swarm optimization algorithm 3

Design of Electric Carbon Emission Assessment System Based o...

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3rd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Wu, Fangquan Liu, Yichi Tang, Chengjia Hu, Junhan Guizhou Power Grid Guizhou China

ISBN: (纸本)9798350317022

The traditional electric carbon emission assessment model can no longer meet the increasingly complex and changeable needs of enterprises. Based on big data analysis and big data technology, a dynamic planning model suitable for electric carbon emission assessment system is constructed. Then, we describe in detail the construction and solution method of dynamic programming model of electric carbon emission assessment system based on particle swarm optimization algorithm. The model will comprehensively consider key factors through big data algorithms. Finally, we verify the effectiveness and practicality of the model through case studies and empirical analysis. The results show that the dynamic planning model of electric carbon emission assessment system based on particle swarm optimization algorithm can help enterprises predict demand more accurately, allocate reasonably, and improve performance and organizational efficiency. The application of this model can effectively improve the electric carbon emission assessment system efficiency and decision-making ability, thereby enhancing the competitiveness and sustainable development of enterprises. © 2023 IEEE.

关键词： Big Data Decision Support Electric Carbon Emission Assessment System Optimal Configuration particle swarm optimization algorithm

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optimization and Performance Analysis of particle swarm optimization algorithm in Learning Behavior System

Optimization and Performance Analysis of Particle Swarm Opti...

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2023 International Conference on Evolutionary algorithms and Soft Computing Techniques, EASCT 2023

作者： Guo, Miaoxiu Shandong Institute of Commerce and Technology Jinan China

ISBN: (纸本)9798350313413

With the rapid development of intelligent control technology, artificial intelligence has become an indispensable part of people's life. The particle swarm optimization algorithm has incomparable advantages. It obtains the optimal solution through information exchange between groups. In order to strengthen the learning efficiency of college students and improve their abilities, this paper uses the particle swarm algorithm to calculate the learning behavior of college students, and designs and optimizes the analysis module, strategy module and model of the system. In this paper, a genetic optimization model based on particle swarm optimization algorithm is established, and it is designed and simulated in a computer environment. For the iterative method, performance analysis and parameter estimation are proposed, and the entire learning process can be completed efficiently by continuously adjusting its own internal parameters. And by means of experimental analysis and algorithm performance comparison, the ability of particle swarm optimization algorithm is obtained. The experimental results show that the effectiveness of the improved multi-objective optimization particle swarm optimization algorithm in the learning behavior analysis system reaches 93%, and it is the most effective algorithm among the four algorithms. Therefore, in this system, a variety of particle swarm optimization algorithms can be combined to make full use of the MOPSO algorithm to improve system performance. © 2023 IEEE.

关键词： learning behavior optimization system particle swarm optimization algorithm performance analysis

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