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An algorithm-assisted high-resolution D-TOF imaging system with reconfigurable macropixel-based SPAD image sensor 认领 引用
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作者 Zhe Wang Jia-xing Song +8 位作者 Na Tian Xing-jia Ni Xu Yang Run-jiang Dou Peng Feng Jian Liu Nan-jian Wu Li-yuan Liu Shuang-ming Yu 《Journal of Semiconductors》 EI CAS CSCD 2026年第7期61-71,共11页
Single-photon avalanche diode(SPAD)image sensors are widely used in direct time-of-flight(D-TOF)imaging,but their ranging performance is often constrained by limited laser power.This article presents a SPAD-based D-TO... Single-photon avalanche diode(SPAD)image sensors are widely used in direct time-of-flight(D-TOF)imaging,but their ranging performance is often constrained by limited laser power.This article presents a SPAD-based D-TOF imaging system that combines a reconfigurable macro-pixel sensor architecture with a lightweight depth completion algorithm to achieve long-range depth imaging with enhanced spatial resolution under low optical power.The proposed sensor adopts a back-side illuminated(BSI)3D-stacked architecture with programmable macro-pixels that enhance detection sensitivity and enable flexible sensitivity–resolution trade-offs.An injection-locked ring-oscillator-based time-to-digital converter(RO-TDC)array achieves a time resolution of 152.5 ps,enabling accurate TOF measurement at an optical power of 10 mW.To compensate for macropixel-induced resolution loss,a probabilistic normalized convolutional neural network(pNCNN)is employed for depth completion using sparse depth inputs only.Experimental results demonstrate that up to 30×effective resolution enhancement of the system can be achieved via the depth completion algorithm without changing the physical resolution of the sensor.Additionally,the proposed system achieves a maximum ranging distance of 90 m and a range-to-power figure-of-merit(FOM)of9 m/mW,which validates the effectiveness of the system. 展开更多
关键词 SPAD reconfigurable macro-pixel time-to-digital converter(TDC) depth completion algorithm
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Structural Damage Diagnosis Based onMulti-Stage Sparrow Search Algorithm 认领 引用
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作者 Lijun Yang Qiuwei Yang 《Computers, Materials & Continua》 SCIE EI 2026年第9期2449-2468,共20页
This study proposes a Multi-Stage Sparrow Search Algorithm(MS-SSA)for precise structural damage identification.Initially,the structural static displacement sensitivity formulation is derived via the Sherman-Morrison-W... This study proposes a Multi-Stage Sparrow Search Algorithm(MS-SSA)for precise structural damage identification.Initially,the structural static displacement sensitivity formulation is derived via the Sherman-Morrison-Woodbury formula,and an objective function is constructed by integrating the sensitivity equations with the L2-norm penalty.Subsequently,MS-SSA is implemented to sequentially achieve preliminary damage localization and accurate quantification.In the localization phase,a constrained narrow-bound search space is predefined to identify potential damage regions.Leveraging this feedback,the sensitivity equations are condensed,and the search boundaries are adaptively refined for the quantification phase,where SSA is reapplied to precisely determine damage severity while mitigating misjudgments.TheMS-SSA framework exhibits two distinct advantages:(i)Phase I localization accelerates convergence by constraining the search space,as it does not target precise quantification;and(ii)the significant reduction in unknowns achieved by excluding intact elements in Phase II enables rapid convergence to the global optimum.Comparative studies against the GreyWolf Optimizer(GWO),Whale Optimization Algorithm(WOA),and standard SSA demonstrate that the proposed method effectively overcomes computational instability,slowconvergence,and large errors inherent in swarm intelligence optimization for damage identification.Specifically,numerical case studies reveal that the identification error is reduced to merely 9%~22%of that associated with existing methods,with experimental validation confirming reductions to 18%~22%.Overall,the proposed approach achieves high-fidelity damage identification while eliminating false positives and false negatives. 展开更多
关键词 Damage diagnosis static displacement sensitivity Sparrow Search Algorithm(SSA) narrow search range
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Optimization of Truss Structures Using Nature-Inspired Algorithms with Frequency and Stress Constraints 认领 引用 被引量：1
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作者 Sanjog Chhetri Sapkota Liborio Cavaleri +3 位作者 Ajaya Khatri Siddhi Pandey Satish Paudel Panagiotis G.Asteris 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第1期436-464,共29页
Optimization is the key to obtaining efficient utilization of resources in structural design.Due to the complex nature of truss systems,this study presents a method based on metaheuristic modelling that minimises stru... Optimization is the key to obtaining efficient utilization of resources in structural design.Due to the complex nature of truss systems,this study presents a method based on metaheuristic modelling that minimises structural weight under stress and frequency constraints.Two new algorithms,the Red Kite Optimization Algorithm(ROA)and Secretary Bird Optimization Algorithm(SBOA),are utilized on five benchmark trusses with 10,18,37,72,and 200-bar trusses.Both algorithms are evaluated against benchmarks in the literature.The results indicate that SBOA always reaches a lighter optimal.Designs with reducing structural weight ranging from 0.02%to 0.15%compared to ROA,and up to 6%–8%as compared to conventional algorithms.In addition,SBOA can achieve 15%–20%faster convergence speed and 10%–18%reduction in computational time with a smaller standard deviation over independent runs,which demonstrates its robustness and reliability.It is indicated that the adaptive exploration mechanism of SBOA,especially its Levy flight–based search strategy,can obviously improve optimization performance for low-and high-dimensional trusses.The research has implications in the context of promoting bio-inspired optimization techniques by demonstrating the viability of SBOA,a reliable model for large-scale structural design that provides significant enhancements in performance and convergence behavior. 展开更多
关键词 Optimization truss structures nature-inspired algorithms meta-heuristic algorithms red kite opti-mization algorithm secretary bird optimization algorithm
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Residual Strength Prediction of Corroded Pipelines Based on Sparrow Search Algorithm-Optimized Kernel Extreme Learning Machine 认领 引用
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作者 Zixuan Zong Tingting Long +3 位作者 Huaqing Dong Guoqiang Huang Xiao Meng Mohammadamin Azimi 《Structural Durability & Health Monitoring》 EI 2026年第3期480-495,共16页
This paper proposes a novel approach for predicting the residual strength of corroded pipelines by combining the Kernel Extreme Learning Machine(KELM)with Sparrow Search Algorithm(SSA)optimization.The proposed SSA-KEL... This paper proposes a novel approach for predicting the residual strength of corroded pipelines by combining the Kernel Extreme Learning Machine(KELM)with Sparrow Search Algorithm(SSA)optimization.The proposed SSA-KELM model addresses the limitations of traditional evaluation methods and single machine learning models in residual strength prediction.A dataset comprising 80 samples from burst tests and finite element simulations was used to validate the model.Results demonstrate that the SSA-KELM model achieves superior prediction accuracy with a maximum relative error of 13.54%and minimum relative error of 0.20%.The model’s mean absolute error(MAE),root mean square error(RMSE),and mean absolute percentage error(MAPE)are 0.658%,0.780%,and 4.38%,respectively,significantly outperforming conventional machine learning models and traditional assessment methods.This research provides a reliable tool for evaluating pipeline integrity and maintenance planning. 展开更多
关键词 Pipeline corrosion residual strength prediction kernel extreme learning machine sparrow search algorithm machine learning
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Sparse optimization and multi-performance enhancement of a high frequency to very high frequency wideband radio array using a genetic algorithm 认领 引用
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作者 Yifeng Qin Jiarui Di +8 位作者 Liang Dong Zhang Ning Yan Liu Lesheng He Abidin Zamri Zainal Huanhuan Xie Latef Abdul Tarik Wei He Othman Mohamadariff 《Astronomical Techniques and Instruments》 CSCD 2026年第3期235-249,共15页
The High Frequency(HF, 3–30 MHz) to Very High Frequency(VHF, 30–300 MHz) band is a critical observational window in radio astronomy, playing a key role in the study of early-universe reionization, space weather moni... The High Frequency(HF, 3–30 MHz) to Very High Frequency(VHF, 30–300 MHz) band is a critical observational window in radio astronomy, playing a key role in the study of early-universe reionization, space weather monitoring, and solar physics. We determine whether a genetic algorithm-optimized sparse configuration of a 64-element planar radio antenna array can minimize the peak sidelobe level and enhance performance within the 10–90 MHz frequency range, compared with a regular configuration. The sparse-optimized array achieves a 1.04 dB reduction in peak sidelobe level across the frequency band compared with the regular array. Sensitivity improves significantly at all frequency points, with increases of up to 56% at 10 MHz and 45% at 50 MHz. At 90 MHz, the sensitivity matches that of the regular array. At three representative frequencies(50 MHz, 60 MHz, and 70 MHz), grating lobe suppression tests at different scan angles show that the regular array shows prominent grating lobes at specific scan angles(θ =53° at 50 MHz, θ = 30° at 60 MHz, and θ = 15° at 70 MHz). By contrast, the sparse array shows no observable grating lobes, confirming its superior suppression capability. At wide bandwidths, a sparse array optimized with a genetic algorithm outperforms a regular array in peak sidelobe level, sensitivity, and scanning range, supporting its use as a better technical solution for radio astronomical observations. 展开更多
关键词 HF-VHF band Sparse array Genetic algorithm Peak side lobe level Sensitivity
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A Novel Hybrid Sine Cosine-Flower Pollination Algorithm for Optimized Feature Selection 认领 引用
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作者 Sumbul Azeem Shazia Javed +3 位作者 Farheen Ibraheem Uzma Bashir Nazar Waheed Khursheed Aurangzeb 《Computers, Materials & Continua》 SCIE EI 2026年第5期1916-1930,共15页
Data serves as the foundation for training and testing machine learning and artificial intelligencemodels.The most fundamental part of data is its attributes or features.The feature set size changes from one dataset t... Data serves as the foundation for training and testing machine learning and artificial intelligencemodels.The most fundamental part of data is its attributes or features.The feature set size changes from one dataset to another.Only the relevant features contributemeaningfully to classificationaccuracy.The presence of irrelevant features reduces the system’s effectiveness.Classification performance often deteriorates on high-dimensional datasets due to the large search space.Thus,one of the significant obstacles affecting the performance of the learning process in the majority of machine learning and data mining techniques is the dimensionality of the datasets.Feature selection(FS)is an effective preprocessing step in classification tasks.The aim of applying FS is to exclude redundant and unrelated features while retaining the most informative ones to optimize classification capability and compress computational complexity.In this paper,a novel hybrid binary metaheuristic algorithm,termed hSC-FPA,is proposed by hybridizing the Flower Pollination Algorithm(FPA)and the Sine Cosine Algorithm(SCA).Hybridization controls the exploration capacity of SCA and the exploitation behavior of FPA to maintain a balanced search process.SCA guides the global search in the early iterations,while FPA’s local pollination refines promising solutions in later stages.A binary conversion mechanism using a threshold function is implemented to handle the discrete nature of the feature selection problem.The functionality of the proposed hSC-FPA is authenticated on fourteen standard datasets from the UCI repository using the K-Nearest Neighbors(K-NN)classifier.Experimental results are benchmarked against the standalone SCA and FPA algorithms.The hSC-FPA consistently achieves higher classification accuracy,selects a more compact feature subset,and demonstrates superior convergence behavior.These findings support the stability and outperformance of the hybrid feature selection method presented. 展开更多
关键词 Classification algorithms feature selection process flower pollination algorithm hybrid model metaheuristics multi-objective optimization search algorithm sine cosine algorithm
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An NOMA-VLC power allocation scheme for multi-user based on sparrow search algorithm 认领 引用 被引量：1
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作者 WANG Xing WANG Haitao +3 位作者 DONG Zhenliang XIONG Yingfei SHI Huili WANG Ping 《Optoelectronics Letters》 EI 2025年第5期278-283,共6页
A non-orthogonal multiple access(NOMA) power allocation scheme on the basis of the sparrow search algorithm(SSA) is proposed in this work. Specifically, the logarithmic utility function is utilized to address the pote... A non-orthogonal multiple access(NOMA) power allocation scheme on the basis of the sparrow search algorithm(SSA) is proposed in this work. Specifically, the logarithmic utility function is utilized to address the potential fairness issue that may arise from the maximum sum-rate based objective function and the optical power constraints are set considering the non-negativity of the transmit signal, the requirement of the human eyes safety and all users' quality of service(Qo S). Then, the SSA is utilized to solve this optimization problem. Moreover, to demonstrate the superiority of the proposed strategy, it is compared with the fixed power allocation(FPA) and the gain ratio power allocation(GRPA) schemes. Results show that regardless of the number of users considered, the sum-rate achieved by SSA consistently outperforms that of FPA and GRPA schemes. Specifically, compared to FPA and GRPA schemes, the sum-rate obtained by SSA is increased by 40.45% and 53.44% when the number of users is 7, respectively. The proposed SSA also has better performance in terms of user fairness. This work will benefit the design and development of the NOMA-visible light communication(VLC) systems. 展开更多
关键词 NOMA logarithmic utility function VLC Sparrow Search Algorithm sparrow search algorithm ssa fairness issue power allocation Sum Rate
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Propagation Alongside Crossover:An Evolutionary Algorithm for Continuous Optimization and Feature Selection 认领 引用
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作者 Najibeh Farzi-Veijouyeh Vahideh Sahargahi Neda Matin 《Journal of Bionic Engineering》 SCIE EI CSCD 2026年第2期1112-1175,共64页
Researchers continuously advance optimization algorithms,recognizing that no meta-heuristic can solve all problem types,as stated by the No Free Lunch theorem.This paper introduces the Propagation alongside Crossover(... Researchers continuously advance optimization algorithms,recognizing that no meta-heuristic can solve all problem types,as stated by the No Free Lunch theorem.This paper introduces the Propagation alongside Crossover(PAC)algorithm to address continuous optimization challenges.The primary goal of PAC is to structure the algorithmic phases in a manner that achieves a robust balance between exploration and exploitation through appropriately designed mechanisms at each stage.PAC simultaneously leverages the benefits of propagation,crossover,and mutation.Three independent operators are defined to generate new candidate solutions separately,and a novel selection strategy allows individuals produced by each operator,along with members of the current population,to independently enter the next generation.This design preserves population diversity,prevents all individuals from converging toward a single point,and enhances the algorithm’s ability to explore the solution space effectively.A key innovation of PAC is its three-mode propagation mechanism,which comprises local search,linear propagation toward the target point,and tear-drop shaped propagation toward the target point.Tear-drop propagation provides a precise and adaptive search around promising solutions,increasing diversity and preventing entrapment in local optima.The target point is typically set as the global optimum;however,when propagating the global optimum itself,a random point is used as the target to further enhance exploration and escape from local optima.The initial population is generated using chaotic mapping to ensure broad coverage of the search space.PAC was rigorously evaluated on 51 benchmark functions and three engineering problems,considering scalability,convergence,sensitivity,and computational efficiency.Comparative analyses with established optimization algorithms demonstrate PAC’s superior performance,as confirmed by Wilcoxon signed-rank and Friedman statistical tests.Furthermore,PAC was applied as a feature selection method on four diverse datasets,achieving substantial dimensionality reduction while outperforming comparative methods in classification accuracy.These results highlight PAC’s versatility,robustness,and practical effectiveness. 展开更多
关键词 Optimization algorithms Meta-heuristic algorithms Continuous optimization Propagation alongside crossover algorithm Intrusion detection
Low-complexity APSK demodulation algorithm based on K-means clustering in LEO satellite communication systems 认领 引用
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作者 Guangfu Wu Xiangrui Meng +1 位作者 Changlin Chen Biqun Xiang 《Digital Communications and Networks》 SCIE EI CSCD 2026年第2期343-353,共11页
Amplitude Phase Shift Keying(APSK)is more suitable for the nonlinear channels of Low Earth Orbit(LEO)satellite communication systems compared to Quadrature Amplitude Modulation(QAM).To tackle challenges posed by Direc... Amplitude Phase Shift Keying(APSK)is more suitable for the nonlinear channels of Low Earth Orbit(LEO)satellite communication systems compared to Quadrature Amplitude Modulation(QAM).To tackle challenges posed by Direct Current(DC)interference and high demodulation complexity,we propose an APSK demodulation algorithm based on K-means clustering.Initially,static DC components are calculated and removed from the received APSK signals.Subsequently,the estimated APSK constellation points serve as initial centers for K-means clustering.These centers are refined through the K-means process and act as theoretical APSK constellation points for the Max-Log-MAP demodulation algorithm,effectively eliminating residual DC.We then introduce a low-complexity APSK demodulation algorithm that utilizes the symmetry of constellation points along with the Euclidean distance between DC-eliminated signals and these constellation points to minimize the set of constellation points.Simulation results indicate that for 32-APSK,our proposed demodulation submodule reduces computational complexity to approximately one-third that of the Max-Log-MAP algorithm while improving Bit Error Rate(BER)performance by about 0.23 dB.Furthermore,end-to-end simulation experiments conducted within LEO satellite communication systems demonstrate that our approach not only maintains this complexity advantage but also enhances BER performance by approximately 1.1 dB. 展开更多
关键词 DC elimination APSK demodulation LEO satellite communication K-means algorithm Max-Log-MAP algorithm
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A Metaheuristic Football Optimization Algorithm Integrated with Large Language Models for Automated Seismic Time-Series Modeling 认领 引用
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作者 Amal H.Alharbi Marwa M.Eid +2 位作者 Nima Khodadadi Ebrahim A.Mattar Sayed Elkenawy 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第5期947-987,共41页
Seismic time series forecasting remains challenging due to the nonlinearity,non-stationarity,and noise of earthquake data,and because deep learning models are sensitive to preprocessing and hyperparameter settings.Alt... Seismic time series forecasting remains challenging due to the nonlinearity,non-stationarity,and noise of earthquake data,and because deep learning models are sensitive to preprocessing and hyperparameter settings.Although recent studies have improved neural architectures and optimization techniques,preprocessing is often treated as a fixed or manually designed stage,with limited integration into model optimization.To address this,this paper proposes an integrated,data-driven modelling framework that combines guided preprocessing with systematic hyperparameter optimization for seismic prediction,specifically forecasting earthquake magnitude from seismic catalog time-series data,with experiments conducted on Canadian seismic records.The method uses a Large Language Model to guide data preparation and feature engineering,rather than fully automate them,and applies deep learning-based forecasting with the N-HITS architecture,optimized via metaheuristic-assisted feature selection and hyperparameter tuning.The Football Optimization Algorithm(FbOA),employed as a metaheuristic optimization strategy in this study,is evaluated and compared with several well-known optimizers under identical conditions.The results show significant performance gains,with FbOA achieving superior accuracy,robustness,and convergence compared to baseline and competing methods.Notably,error metrics are reduced(MSE 3.10×10-7,RMSE 5.57×103),with high performance indicators(r=0.982,R2=0.979,NSE=0.981,WI=0.985).These results highlight the value of integrating guided preprocessing with optimization and demonstrate a scalable framework for high-precision time-series prediction in geophysical and related domains. 展开更多
关键词 Seismic time-series forecasting large language models metaheuristic algorithms football optimization algorithm earthquake modeling
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A Quantum-Inspired Algorithm for Clustering and Intrusion Detection 认领 引用
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作者 Gang Xu Lefeng Wang +5 位作者 Yuwei Huang Yong Lu Xin Liu Weijie Tan Zongpeng Li Xiu-Bo Chen 《Computers, Materials & Continua》 SCIE EI 2026年第4期1180-1215,共36页
The Intrusion Detection System(IDS)is a security mechanism developed to observe network traffic and recognize suspicious or malicious activities.Clustering algorithms are often incorporated into IDS;however,convention... The Intrusion Detection System(IDS)is a security mechanism developed to observe network traffic and recognize suspicious or malicious activities.Clustering algorithms are often incorporated into IDS;however,conventional clustering-based methods face notable drawbacks,including poor scalability in handling high-dimensional datasets and a strong dependence of outcomes on initial conditions.To overcome the performance limitations of existing methods,this study proposes a novel quantum-inspired clustering algorithm that relies on a similarity coefficient-based quantum genetic algorithm(SC-QGA)and an improved quantum artificial bee colony algorithm hybrid K-means(IQABC-K).First,the SC-QGA algorithmis constructed based on quantum computing and integrates similarity coefficient theory to strengthen genetic diversity and feature extraction capabilities.For the subsequent clustering phase,the process based on the IQABC-K algorithm is enhanced with the core improvement of adaptive rotation gate and movement exploitation strategies to balance the exploration capabilities of global search and the exploitation capabilities of local search.Simultaneously,the acceleration of convergence toward the global optimum and a reduction in computational complexity are facilitated by means of the global optimum bootstrap strategy and a linear population reduction strategy.Through experimental evaluation with multiple algorithms and diverse performance metrics,the proposed algorithm confirms reliable accuracy on three datasets:KDD CUP99,NSL_KDD,and UNSW_NB15,achieving accuracy of 98.57%,98.81%,and 98.32%,respectively.These results affirm its potential as an effective solution for practical clustering applications. 展开更多
关键词 Intrusion detection clustering quantum artificial bee colony algorithm K-means quantum genetic algorithm
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PID Steering Control Method of Agricultural Robot Based on Fusion of Particle Swarm Optimization and Genetic Algorithm 认领 引用 被引量：4
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作者 ZHAO Longlian ZHANG Jiachuang +2 位作者 LI Mei DONG Zhicheng LI Junhui 《农业机械学报》 EI CAS CSCD 北大核心 2026年第1期358-367,共10页
Aiming to solve the steering instability and hysteresis of agricultural robots in the process of movement,a fusion PID control method of particle swarm optimization(PSO)and genetic algorithm(GA)was proposed.The fusion... Aiming to solve the steering instability and hysteresis of agricultural robots in the process of movement,a fusion PID control method of particle swarm optimization(PSO)and genetic algorithm(GA)was proposed.The fusion algorithm took advantage of the fast optimization ability of PSO to optimize the population screening link of GA.The Simulink simulation results showed that the convergence of the fitness function of the fusion algorithm was accelerated,the system response adjustment time was reduced,and the overshoot was almost zero.Then the algorithm was applied to the steering test of agricultural robot in various scenes.After modeling the steering system of agricultural robot,the steering test results in the unloaded suspended state showed that the PID control based on fusion algorithm reduced the rise time,response adjustment time and overshoot of the system,and improved the response speed and stability of the system,compared with the artificial trial and error PID control and the PID control based on GA.The actual road steering test results showed that the PID control response rise time based on the fusion algorithm was the shortest,about 4.43 s.When the target pulse number was set to 100,the actual mean value in the steady-state regulation stage was about 102.9,which was the closest to the target value among the three control methods,and the overshoot was reduced at the same time.The steering test results under various scene states showed that the PID control based on the proposed fusion algorithm had good anti-interference ability,it can adapt to the changes of environment and load and improve the performance of the control system.It was effective in the steering control of agricultural robot.This method can provide a reference for the precise steering control of other robots. 展开更多
关键词 agricultural robot steering PID control particle swarm optimization algorithm genetic algorithm
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Intelligent sequential multi-impulse collision avoidance method for non-cooperative spacecraft based on an improved search tree algorithm 认领 引用 被引量：3
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作者 Xuyang CAO Xin NING +4 位作者 Zheng WANG Suyi LIU Fei CHENG Wenlong LI Xiaobin LIAN 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2025年第4期378-393,共16页
The problem of collision avoidance for non-cooperative targets has received significant attention from researchers in recent years.Non-cooperative targets exhibit uncertain states and unpredictable behaviors,making co... The problem of collision avoidance for non-cooperative targets has received significant attention from researchers in recent years.Non-cooperative targets exhibit uncertain states and unpredictable behaviors,making collision avoidance significantly more challenging than that for space debris.Much existing research focuses on the continuous thrust model,whereas the impulsive maneuver model is more appropriate for long-duration and long-distance avoidance missions.Additionally,it is important to minimize the impact on the original mission while avoiding noncooperative targets.On the other hand,the existing avoidance algorithms are computationally complex and time-consuming especially with the limited computing capability of the on-board computer,posing challenges for practical engineering applications.To conquer these difficulties,this paper makes the following key contributions:(A)a turn-based(sequential decision-making)limited-area impulsive collision avoidance model considering the time delay of precision orbit determination is established for the first time;(B)a novel Selection Probability Learning Adaptive Search-depth Search Tree(SPL-ASST)algorithm is proposed for non-cooperative target avoidance,which improves the decision-making efficiency by introducing an adaptive-search-depth mechanism and a neural network into the traditional Monte Carlo Tree Search(MCTS).Numerical simulations confirm the effectiveness and efficiency of the proposed method. 展开更多
关键词 Non-cooperative target Collision avoidance Limited motion area Impulsive maneuver model Search tree algorithm Neural networks
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Calculation algorithm for the space charge force of a train with infinite bunches 认领 引用 被引量：1
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作者 San-Hai Ren Hong-Yu Li +5 位作者 Jia-Ru Shi Hao Zha Wei-Hang Gu Qiang Gao Qian Tan Huai-Bi Chen 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2025年第6期13-25,共13页
Industrial linear accelerators often contain many bunches when their pulse widths are extended to microseconds.As they typically operate at low electron energies and high currents,the interactions among bunches cannot... Industrial linear accelerators often contain many bunches when their pulse widths are extended to microseconds.As they typically operate at low electron energies and high currents,the interactions among bunches cannot be neglected.In this study,an algorithm is introduced for calculating the space charge force of a train with infinite bunches.By utilizing the ring charge model and the particle-in-cell(PIC)method and combining analytical and numerical methods,the proposed algorithm efficiently calculates the space charge force of infinite bunches,enabling the accurate design of accelerator parameters and a comprehensive understanding of the space charge force.This is a significant improvement on existing simulation software such as ASTRA and PARMELA that can only handle a single bunch or a small number of bunches.The PIC algorithm is validated in long drift space transport by comparing it with existing models,such as the infinite-bunch,ASTRA single-bunch,and PARMELA several-bunch algorithms.The space charge force calculation results for the external acceleration field are also verified.The reliability of the proposed algorithm provides a foundation for the design and optimization of industrial accelerators. 展开更多
关键词 Particle-in-cell method Space charge force Algorithm Infinite bunches
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Derivative Free and Dispatch Algorithm-Based Optimization and Power System Assessment of a Biomass-PV-Hydrogen Storage-Grid Hybrid Renewable Microgrid for Agricultural Applications 认领 引用 被引量：1
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作者 Md.Fatin Ishraque Akhlaqur Rahman +5 位作者 Kamil Ahmad Sk.A.Shezan Md.Meheraf Hossain Sheikh Rashel Al Ahmed Md.Iasir Arafat Noor E Nahid Bintu 《Energy Engineering》 EI 2025年第8期3347-3375,共29页
In this research work,the localized generation from renewable resources and the distribution of energy to agricultural loads,which is a local microgrid concept,have been considered,and its feasibility has been assesse... In this research work,the localized generation from renewable resources and the distribution of energy to agricultural loads,which is a local microgrid concept,have been considered,and its feasibility has been assessed.Two dispatch algorithms,named Cycle Charging and Load Following,are implemented to find the optimal solution(i.e.,net cost,operation cost,carbon emission.energy cost,component sizing,etc.)of the hybrid system.The microgrid is also modeled in the DIgSILENT Power Factory platform,and the respective power system responses are then evaluated.The development of dispatch algorithms specifically tailored for agricultural applications has enabled to dynamically manage energy flows,responding to fluctuating demands and resource availability in real-time.Through careful consideration of factors such as seasonal variations and irrigation requirements,these algorithms have enhanced the resilience and adaptability of the microgrid to dynamic operational conditions.However,it is revealed that both approaches have produced the same techno-economic results showing no significant difference.This illustrates the fact that the considered microgrid can be implemented with either strategy without significant fluctuation in performance.The study has shown that the harmful gas emission has also been limited to only 17,928 kg/year of CO2,and 77.7 kg/year of Sulfur Dioxide.For the proposed microgrid and load profile of 165.29 kWh/day,the net present cost is USD 718,279,and the cost of energy is USD 0.0463 with a renewable fraction of 97.6%.The optimal sizes for PV,Bio,Grid,Electrolyzer,and Converter are 1494,500,999,999,500,and 495 kW,respectively.For a hydrogen tank(HTank),the optimal size is found to be 350 kg.This research work provides critical insights into the techno-economic feasibility and environmental impact of integrating biomass-PV-hydrogen storage-Grid hybrid renewable microgrids into agricultural settings. 展开更多
关键词 Renewable energy derivative-free algorithm optimization hybrid system energy storage
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Information Diffusion Models and Fuzzing Algorithms for a Privacy-Aware Data Transmission Scheduling in 6G Heterogeneous ad hoc Networks 认领 引用 被引量：1
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作者 Borja Bordel Sánchez Ramón Alcarria Tomás Robles 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第2期1214-1234,共21页
In this paper,we propose a new privacy-aware transmission scheduling algorithm for 6G ad hoc networks.This system enables end nodes to select the optimum time and scheme to transmit private data safely.In 6G dynamic h... In this paper,we propose a new privacy-aware transmission scheduling algorithm for 6G ad hoc networks.This system enables end nodes to select the optimum time and scheme to transmit private data safely.In 6G dynamic heterogeneous infrastructures,unstable links and non-uniform hardware capabilities create critical issues regarding security and privacy.Traditional protocols are often too computationally heavy to allow 6G services to achieve their expected Quality-of-Service(QoS).As the transport network is built of ad hoc nodes,there is no guarantee about their trustworthiness or behavior,and transversal functionalities are delegated to the extreme nodes.However,while security can be guaranteed in extreme-to-extreme solutions,privacy cannot,as all intermediate nodes still have to handle the data packets they are transporting.Besides,traditional schemes for private anonymous ad hoc communications are vulnerable against modern intelligent attacks based on learning models.The proposed scheme fulfills this gap.Findings show the probability of a successful intelligent attack reduces by up to 65%compared to ad hoc networks with no privacy protection strategy when used the proposed technology.While congestion probability can remain below 0.001%,as required in 6G services. 展开更多
关键词 6G networks ad hoc networks privacy scheduling algorithms diffusion models fuzzing algorithms
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Method for Estimating the State of Health of Lithium-ion Batteries Based on Differential Thermal Voltammetry and Sparrow Search Algorithm-Elman Neural Network 认领 引用 被引量：3
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作者 Yu Zhang Daoyu Zhang TiezhouWu 《Energy Engineering》 EI 2025年第1期203-220,共18页
Precisely estimating the state of health(SOH)of lithium-ion batteries is essential for battery management systems(BMS),as it plays a key role in ensuring the safe and reliable operation of battery systems.However,curr... Precisely estimating the state of health(SOH)of lithium-ion batteries is essential for battery management systems(BMS),as it plays a key role in ensuring the safe and reliable operation of battery systems.However,current SOH estimation methods often overlook the valuable temperature information that can effectively characterize battery aging during capacity degradation.Additionally,the Elman neural network,which is commonly employed for SOH estimation,exhibits several drawbacks,including slow training speed,a tendency to become trapped in local minima,and the initialization of weights and thresholds using pseudo-random numbers,leading to unstable model performance.To address these issues,this study addresses the challenge of precise and effective SOH detection by proposing a method for estimating the SOH of lithium-ion batteries based on differential thermal voltammetry(DTV)and an SSA-Elman neural network.Firstly,two health features(HFs)considering temperature factors and battery voltage are extracted fromthe differential thermal voltammetry curves and incremental capacity curves.Next,the Sparrow Search Algorithm(SSA)is employed to optimize the initial weights and thresholds of the Elman neural network,forming the SSA-Elman neural network model.To validate the performance,various neural networks,including the proposed SSA-Elman network,are tested using the Oxford battery aging dataset.The experimental results demonstrate that the method developed in this study achieves superior accuracy and robustness,with a mean absolute error(MAE)of less than 0.9%and a rootmean square error(RMSE)below 1.4%. 展开更多
关键词 Lithium-ion battery state of health differential thermal voltammetry Sparrow Search Algorithm
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Distributed Economic Dispatch Algorithms of Microgrids Integrating Grid-Connected and Isolated Modes 认领 引用 被引量：1
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作者 Zhongxin Liu Yanmeng Zhang +1 位作者 Yalin Zhang Fuyong Wang 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2025年第1期86-98,共13页
The economic dispatch problem(EDP) of microgrids operating in both grid-connected and isolated modes within an energy internet framework is addressed in this paper. The multi-agent leader-following consensus algorithm... The economic dispatch problem(EDP) of microgrids operating in both grid-connected and isolated modes within an energy internet framework is addressed in this paper. The multi-agent leader-following consensus algorithm is employed to address the EDP of microgrids in grid-connected mode, while the push-pull algorithm with a fixed step size is introduced for the isolated mode. The proposed algorithm of isolated mode is proven to converge to the optimum when the interaction digraph of microgrids is strongly connected. A unified algorithmic framework is proposed to handle the two modes of operation of microgrids simultaneously, enabling our algorithm to achieve optimal power allocation and maintain the balance between power supply and demand in any mode and any mode switching. Due to the push-pull structure of the algorithm and the use of fixed step size,the proposed algorithm can better handle the case of unbalanced graphs, and the convergence speed is improved. It is documented that when the transmission topology is strongly connected and there is bi-directional communication between the energy router and its neighbors, the proposed algorithm in composite mode achieves economic dispatch even with arbitrary mode switching.Finally, we demonstrate the effectiveness and superiority of our algorithm through numerical simulations. 展开更多
关键词 Consensus algorithm distributed optimization economic dispatch(ED) energy router(ER) multi-agent systems
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An improved Alpha-shape algorithm for extracting section contours of the super-high steel bridge tower using point clouds 认领 引用 被引量：2
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作者 ZHANG Yiming ZHAO Tianhao +2 位作者 LIAO Ruixuan LI Haoqing WANG Hao 《Journal of Southeast University(English Edition)》 EI CAS 2026年第1期26-35,共10页
The virtual preassembly of super-high steel bridge towers faces a challenge in the efficient and precise extraction of complex cross-sectional features.Factors such as fabrication errors,gravity-induced deformations,a... The virtual preassembly of super-high steel bridge towers faces a challenge in the efficient and precise extraction of complex cross-sectional features.Factors such as fabrication errors,gravity-induced deformations,and temperature fluctuations can compromise the accuracy of contour extraction.To address these limitations,an improved Alpha-shape-based point cloud contour extraction method is proposed.The proposed approach uses a hierarchical strategy to process three-dimensional laser scanning point clouds.The processed data are then subjected to curvatureadaptive voxel filtering to reduce acquisition noise.In addition,an enhanced iterative closest point(ICP)variant with correspondence validation accurately aligns the discrete point cloud segments.The proposed curvature-responsive Alpha-shape framework enables multiscale contour delineation through topology-adaptive threshold modulation,which resolves boundary ambiguities in geometrically complex cross-sections.The method was experimentally validated using field-acquired measurement datasets from the Zhangjinggao Yangtze River Bridge tower segments,confirming its capability to reconstruct noncanonical cross-sectional geometries.Three contour extraction methods,including Poisson reconstruction,the conventional Alpha-shape algorithm,and random sample consensus with ICP(RANSAC-ICP),were compared to evaluate the performance of the proposed Alpha-shape algorithm.The results demonstrate that the proposed method achieves superior contour extraction accuracy and data reduction efficiency,highlighting its effectiveness in contour extraction tasks. 展开更多
关键词 super-high steel bridge tower point cloud contour extraction improved Alpha-shape algorithm
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Gekko Japonicus Algorithm:A Novel Nature-inspired Algorithm for Engineering Problems and Path Planning 认领 引用
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作者 Ke Zhang Hongyang Zhao +2 位作者 Xingdong Li Chengjin Fu Jing Jin 《Journal of Bionic Engineering》 SCIE EI CSCD 2026年第1期431-471,共41页
This paper introduces a novel nature-inspired metaheuristic algorithm called the Gekko japonicus algorithm.The algo-rithm draws inspiration mainly from the predation strategies and survival behaviors of the Gekko japo... This paper introduces a novel nature-inspired metaheuristic algorithm called the Gekko japonicus algorithm.The algo-rithm draws inspiration mainly from the predation strategies and survival behaviors of the Gekko japonicus.The math-ematical model is developed by simulating various biological behaviors of the Gekko japonicus,such as hybrid loco-motion patterns,directional olfactory guidance,implicit group advantage tendencies,and the tail autotomy mechanism.By integrating multi-stage mutual constraints and dynamically adjusting parameters,GJA maintains an optimal balance between global exploration and local exploitation,thereby effectively solving complex optimization problems.To assess the performance of GJA,comparative analyses were performed against fourteen state-of-the-art metaheuristic algorithms using the CEC2017 and CEC2022 benchmark test sets.Additionally,a Friedman test was performed on the experimen-tal results to assess the statistical significance of differences between various algorithms.And GJA was evaluated using multiple qualitative indicators,further confirming its superiority in exploration and exploitation.Finally,GJA was utilized to solve four engineering optimization problems and further implemented in robotic path planning to verify its practical applicability.Experimental results indicate that,compared to other high-performance algorithms,GJA demonstrates excep-tional performance as a powerful optimization algorithm in complex optimization problems.We make the code publicly available at:http://gffzz188fe103f8f1460asc5v50kbobvbo6v9u.ffgz.tsg.suse.edu.cn/zhy1109/Gekko-japonicusalgorithm. 展开更多
关键词 Gekko japonicus algorithm Metaheuristic algorithm Exploration and exploitation Engineering optimization Path planning
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