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Fast reconstruction method for defect profiles of ferromagnetic materials based on metal magnetic memory technique

MEASUREMENT 2023年第1期215卷

作者： Li, Junting Su, Sanqing Wang, Wei Liu, Xinwei Zuo, Fuliang Xian Univ Architecture & Technol Sch Civil Engn Xian 710055 Shaanxi Peoples R China

In this paper, a fast reconstruction method for surface defect profiles of ferromagnetic materials is proposed based on the metal magnetic memory technology. An improved magnetic charge model that can adapt to rectangular and V-shaped defect profiles and a new particle swarm optimization algorithm based on a chaotic initial distribution, sigmoid inertia weight coefficient, and sine cosine acceleration coefficients are established as the forward model and iterative means of the method, respectively. The proposed method is verified with theoretical and experimental data, and the influence of noise is considered. The reconstruction method has good accuracy, repeatability, and robustness.

关键词： Metal magnetic memory Defect reconstruction Magnetic charge model particle swarm optimization algorithm

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Using particle swarm optimization algorithm in Multimedia CDN Content Placement

Using Particle Swarm Optimization Algorithm in Multimedia CD...

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5th International Symposium on Parallel Architectures, algorithms and Programming (PAAP)

作者： Chen, Jian-Bo Chen, Chu-Chuan Ming Chuan Univ Dept Informat & Telecommun Engn Tao Yuan Taiwan

ISBN: (纸本)9780769548982;9781467345668

Multimedia Content Delivery Networks (CDN) is used to improve the performance and reliability on Internet. In CDN architecture, the multimedia contents are replicated from the origin server to replica servers in order to improve the performance and minimize the use of network bandwidth. Efficient placing the multimedia contents in CDN is a challenging problem. There are five factors that can be used to determine the placement of multimedia contents, they are bandwidth availability, connection availability, storage availability, CPU availability, and memory availability. In this paper, a particle swarm optimization (PSO) algorithm is adopted to solve this issue. PSO algorithm uses these five different input parameters as different dimensions. In this five dimension searching space, PSO algorithm can find out the global optimal solution. With this global optimal solution, it is the most appropriate replica server that must place the multimedia content. The simulation results show that the PSO algorithm can achieve a better performance than other algorithms.

关键词： component CDN Content Placement particle swarm optimization algorithm

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Multi-criteria optimal sizing and analysis of PV/wind/fuel cell/battery/diesel generator for rural electrification: A case study in Chad

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INTERNATIONAL JOURNAL OF RENEWABLE ENERGY DEVELOPMENT-IJRED 2024年第3期13卷 491-507页

作者： Abdoulaye, Mahamat Adoum Waita, Sebastian Wekesa, Cyrus Wabuge Mwabora, Julius Mwakondo Univ Nairobi Dept Phys POB 30197-00100 Nairobi Kenya Korea Inst Energy Res Jeju Global Res Ctr Distributed Energy Team 200 Haemajihaean Ro Jeju 63357 South Korea Univ Eldoret Sch Engn POB 1125-30100 Eldoret Kenya

. Access to sustainable, clean, affordable, and reliable electricity is crucial for social and economic development, yet Sub-Saharan Africa (SSA) struggles significantly in this context. In CHAD, only 11.3% of the population is able to access electricity, making it one of the least electrified countries in SSA with the lowest clean energy access. In rural areas, electricity access falls to just 1.3%. This research applies and executes a MultiObjective particle swarm optimization (MOPSO) algorithm using MATLAB R2023b to assess the techno-economic, environmental, and social impacts of a hybrid system based on optimal PV/Wind/Battery/Fuel Cell (FC)/Diesel generator (DG) sizing for rural electrification in CHAD. The proposed system's self-sufficiency index (SSSI) and the Annualized System Cost (ASC) were chosen as objective functions to guarantee the economic feasibility of the system, higher self-sufficiency, and lower dependence on external energy sources (DG). The simulation results show that the optimal size of the proposed system supplies the load demand by 100% of the renewable energy sources (RES) fraction, and the optimal capacities of the main components to supply the load demand are: Solar Power (493 KW), Wind Turbine (166 KW), Battery Energy Charge/Discharge (229180 kWh /221300 kWh), Hydrogen tank storage energy (83 874 kWh), Electrolyzer size (202 KW), Fuel cell size (144 KW). The evelized cost of electricity (LCOE) of 0.2982 $/kWh, which is 51.12% lower than the national unit production costs of electricity in rural areas of CHAD (0.61 $/kWh). This LCOE is also the lowest compared to previous works done using HOMER Pro for the country of CHAD. The results also give a levelized cost of hydrogen (LCOH) of 3.8563 US $/kg, lower than for all studies found in the literature for the country of Chad. The proposed system's yearly avoided greenhouse gas (GHG) emission is 374 640 kg. The proposed system will create five (5) new jobs (JCO) and improve the Human Develo

关键词： system particular Avoided greenhouse gas (GHG) social assessment Hybrid energy system Optimal sizing rural electrification particle swarm optimization algorithm zero-carbon electricity

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Assessment of MV XLPE cable aging state based on PSO-XGBoost algorithm

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ELECTRIC POWER SYSTEMS RESEARCH 2023年第1期221卷

作者： Pan, Qiaosheng Zhang, Chi Wei, Xinyuan Wan, Aode Wei, Zeping Hefei Univ Technol Sch Instrument Sci & Optoelect Engn Hefei 230009 Anhui Peoples R China Sichuan Changhong Elect Co Ltd Mianyang 621000 Sichuan Peoples R China Anhui Univ Technol Sch Elect & Informat Engn Maanshan 243032 Peoples R China

Cable aging is one of the main security risks to power systems. With the widely used cables in power systems, the accurate assessment of cable aging status is increasingly important. This study proposes an efficient assessment model based on the PSO-XGBoost algorithm, which integrates the particle swarm optimization (PSO) algorithm and the extreme gradient boosting (XGBoost) algorithm. The XGBoost model is established to assess the cable aging status with the inputs of partial discharge, operating life, corrosion condition and load condition. The PSO algorithm automatically optimizes parameters during XGBoost model training. Then, the standard performance evaluation metrics of the proposed assessment model are compared with four advanced classification models. The accuracy, precision, recall and F1-score of the assessment model are above 98%, indicating that the proposed PSO-XGBoost model can accurately assess the cable aging state. Furthermore, these calculation results of the proposed model are better than the other four benchmark models, which shows that the proposed model performs better in cable aging status assessment than the existing models.

关键词： Cable aging status assessment Extreme gradient boosting algorithm particle swarm optimization algorithm

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Active power filter module function to improve power quality conditions using GWO and PSO techniques for solar photovoltaic arrays and battery energy storage systems

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JOURNAL OF ENERGY STORAGE 2023年 72卷

作者： Abou Houran, Mohamad Sabzevari, Kiomars Hassan, Alaaeldien Oubelaid, Adel Tostado-Veliz, Marcos Khosravi, Nima Xi An Jiao Tong Univ Sch Elect Engn Xian 710049 Peoples R China Tech & Vocat Univ TVU Dept Elect Engn Tehran Iran Univ Bejaia Fac Technol Lab Technol Ind & Informat Bejaia 06000 Algeria Univ Jaen Dept Elect Engn Linares 23700 Spain R&D Management NPC Dept Elect & Instrumentat Engn Tehran Iran MOP Deputy Res & Dev Tehran Iran

This study aims to unbalanced power quality (PQ) conditions analysis of solar photovoltaic arrays and battery energy storage system (PV-BESS) integrated active power filter module (APFM). Here, the APFM's role is to mitigate the PQ issues that existed by the nonlinear loads. The standalone PV-APFM design is negligibly reliable approximated to a hybrid PV-BESS system because of its fluctuation and high environmental reliance. Further, here the research challenge is to optimise the APFM controller gain coefficients by the grey wolf optimization (GWO) algorithm and apply this technique to current and voltage harmonic loops (CHL-VHL) to achieve the best answer to enhance the unbalanced PQ conditions. The multi-objective functions (MOFs) to crack the problem include the total harmonic distortion (THD) of current and voltage components. The particle swarm optimization (PSO) approach will be considered a complementary method for validation and comparison with the outcomes obtained from the GWO procedure. Four case studies in imbalance conditions have been considered to confirm the proposed method in MATLAB-Simulink software.

关键词： Harmonic condition Total harmonic distortion Active power filter module Grey wolf optimization particle swarm optimization algorithm

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Research on site selection of agricultural internet of things nodes based on rapid terrain sampling

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2023年 204卷

作者： Xie, Jiaxing Liang, Gaotian Gao, Peng Wang, Weixing Yin, Dongxiao Li, Jun South China Agr Univ Coll Elect Engn Coll Artificial Intelligence Guangzhou 510642 Peoples R China South China Agr Univ Guangdong Lab Lingnan Modern Agr Guangzhou 510642 Peoples R China Luoding Polytech Yunfu 527200 Peoples R China South China Agr Univ Coll Engn Guangzhou 510642 Peoples R China

With the development of Internet of Things (IoT) technology, modern agriculture is moving in the direction of 4.0. The Agricultural IoT is inseparable from wireless communication. However, in traditional agricultural IoT router and gateway site selection, the influence of the actual terrain on transmission loss is not considered, which results in node power wastage and increased maintenance costs. Based on a multi-sensor fusion algorithm, a fast terrain sampler is designed in this study to collect point-cloud data of the experimental site terrain. A reasonable objective function is then designed under the premise of consideration of the electromagnetic wave free-space and diffraction losses, and the locations of the routers and gateway are optimized based on k-means and particle swarm optimization (PSO) algorithm. Simulations show that the running time of the PSO algorithm is very sensitive to the changes in the execution parameters, and the improved PSO algorithm converges faster than the genetic algorithm (GA) in all three initialization methods. After collecting field terrain data, five interpolation methods were compared, and the nearest-neighbor algorithm is used to obtain the terrain model. On-site collection of received signal-strength indication (RSSI) shows that the communication quality of the optimal point selected via this algorithm is significantly higher than those of nearby points. At the same time, it is proved that the RSSI data has serious discontinuities, so the traditional gradient descent method is not suitable for solving the objective function in this work. Therefore, the algorithm used herein is of great significance for the site selection of agricultural Internet of Things nodes. However, since there are many ways to calculate the diffraction loss, the objective function of this work still needs more correction studies. The tool proposed in this work can obtain a 3D model of farmland faster than traditional surveying and mapping methods. Wit

关键词： S ite selection planning Fast terrain sampling modeling particle swarm optimization algorithm Wireless transmission loss Extended Kalman filter

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Thermodynamic Multi-Field Coupling optimization of Microsystem Based on Artificial Intelligence

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MICROMACHINES 2023年第2期14卷 411-411页

作者： Shan, Guangbao Wu, Xudong Li, Guoliang Xing, Chaoyang Zhang, Shengchang Fu, Yu Xidian Univ Sch Microelect Xian 710071 Peoples R China Beijing Inst Aerosp Control Devices Beijing 100039 Peoples R China China Acad Aerosp Standardizat & Prod Assurance Beijing 100071 Peoples R China

An efficient multi-objective optimization method of temperature and stress for a microsystem based on particle swarm optimization (PSO) was established, which is used to map the relationship between through-silicon via (TSV) structural design parameters and performance objectives in the microsystem, and complete optimization temperature, stress and thermal expansion deformation efficiently. The relationship between the design and performance parameters is obtained by a finite element method (FEM) simulation model. The neural network is built and trained in order to understand the mapping relationship. Then, the design parameters are iteratively optimized using the PSO algorithm, and the FEM results are used to verify the efficiency and reliability of the optimization methods. When the optimization target of peak temperature, bump temperature, TSV temperature, maximum stress and maximum thermal deformation are set as 100 degrees C, 55 degrees C, 35 degrees C, 180 Mpa and 12 mu m, the optimization results are as follows: the peak temperature is 97.90 degrees C, the bump temperature is 56.01 degrees C, the TSV temperature is 31.52 degrees C, the maximum stress is 247.4 Mpa and the maximum expansion deformation is 11.14 mu m. The corresponding TSV structure design parameters are as follows: the radius of TSV is 10.28 mu m, the pitch is 65 mu m and the thickness of SiO2 is 0.83 mu m. The error between the optimization result and the target temperature is 2.1%, 1.8%, 9.9%, 37.4% and 7.2% respectively. The PSO method has been verified by regression analysis, and the difference between the temperature and deformation optimization results of the FEM method is not more than 3%. The stress error has been analyzed, and the reliability of the developed method has been verified. While ensuring the accuracy of the results, the proposed optimization method reduces the time consumption of a single simulation from 2 h to 70 s, saves a lot of time and human resources, greatly improves

关键词： TSV optimization particle swarm optimization algorithm microsystem

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A novel mapping algorithm for three-dimensional network on chip based on quantum-behaved particle swarm optimization

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Frontiers of Computer Science 2017年第4期11卷 622-631页

作者： Cui HUANG Dakun ZHANG Guozhi SONG School of Computer Science and Software Engineering Tianjin Polytechnic University Tianjin 300387 China

Mapping of three-dimensional network on chip is a key problem in the research of three-dimensional network on chip. The quality of the mapping algorithm used di- rectly affects the communication efficiency between IP cores and plays an important role in the optimization of power consumption and throughput of the whole chip. In this paper, ba- sic concepts and related work of three-dimensional network on chip are introduced. Quantum-behaved particle swarm op- timization algorithm is applied to the mapping problem of three-dimensional network on chip for the first time. Sim- ulation results show that the mapping algorithm based on quantum-behaved particle swarm algorithm has faster con- vergence speed with much better optimization performance compared with the mapping algorithm based on particle swarm algorithm. It also can effectively reduce the power consumption of mapping of three-dimensional network on chip.

关键词： three-dimensional network on chip mapping al-gorithm quantum-behaved particle swarm optimization al-gorithm particle swarm optimization algorithm low powerconsumption

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An improved Fourier Ptychography algorithm for ultrasonic array imaging

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COMPUTERS IN BIOLOGY AND MEDICINE 2023年第1期163卷 107157-107157页

作者： Yang, Yaoyao Wu, Runcong Chen, Dongdong Fei, Chunlong Li, Di Yang, Yintang Xidian Univ Sch Microelect Xian 710071 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China

Inspired by the optical imaging algorithm, the Fourier Ptychography (FP) algorithm is adopted to improve the resolution of ultrasonic array imaging. In the FP algorithm, the steady-state spectrum is utilized to recover the high-resolution ultrasonic images. Meanwhile, the parameters of FP algorithm are empirical, which can affect the imaging quality of ultrasonic array. Then the particle swarm optimization (PSO) algorithm is used to optimize the parameters of FP algorithm to further improve the imaging quality of ultrasonic array. The tungsten imaging experiments and pig eye imaging experiments are conducted to demonstrate the feasibility and effectiveness of the developed algorithm. In addition, the proposed algorithm and the coherent wave superposition (CWS) al-gorithm are both based on single plane wave (SPW) algorithms and they are then compared. The results show that the CWS algorithm and FP algorithm have good longitudinal and lateral resolutions, respectively. The particle swarm optimization-based FP (PSOFP) imaging algorithm has both excellent lateral and longitudinal resolutions. The average lateral resolution of PSOFP imaging algorithm is improved by 34.47% compared with CWS imaging algorithm in the tungsten wires experiments, and the lateral boundary structure width of the lens is improved by 49.48% in the pig eye experiments. The proposed algorithm can effectively improve the ultrasonic imaging quality for medical application.

关键词： Fourier Ptychography Ultrasonic imaging particle swarm optimization algorithm Medical imaging

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Network traffic prediction model based on improved VMD and PSO-ELM

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2023年第7期36卷 e5448-e5448页

作者： Shi, Jinmei Zhou, Jinghe Feng, Junying Chen, Huandong Hainan Vocat Univ Sci & Technol Coll Informat Engn Haikou Peoples R China Hainan Normal Univ Coll Informat Sci & Technol Haikou Peoples R China Hainan Vocat Univ Sci & Technol Coll Informat Engn Haikou 571126 Hainan Peoples R China

The rapid update of computing power leads to exponential data traffic growth, and the incidence of network attacks is also increasing. It is significantly important to analyze and predict network traffic accurately in the early stage and take corresponding preventive measures. The existing network flow integrated forecasting models still have some bottlenecks that are difficult to solve, for example, the slow optimization speed of modal decomposition parameters, easy falling into local optimal solutions, the slow convergence speed of the training process, and poor generalization capability. In this paper, particle swarm optimization (PSO) is utilized to improve the parameters selection process of the variational mode decomposition (VMD) algorithm and the extreme learning machine (ELM) algorithm. First, the PSO-VMD combined with multi-scale permutation entropy (MPE) is utilized to decompose the original network flow, and multiple eigenmode components are obtained. Second, the PSO-ELM is utilized to train the network traffic prediction model, and the PSO parameters in PSO-ELM are updated through adaptive weight adjustment and synchronous learning factors to increase the training and prediction speed, and the component prediction results are reconstructed to get a high-precision network flow forecasting result. Finally, through the prediction and verification of the public network flow data of the WIDE backbone, the result of this experiment indicates that the VMD-PSO-ELM can break through the bottlenecks of slow optimization speed of VMD decomposition parameters, reduce the computational complexity of ELM, accelerate the convergence speed, and increase the forecasting accuracy.

关键词： extreme learning machine multi-scale permutation entropy network traffic prediction particle swarm optimization algorithm variational mode decomposition

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