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Data-Driven Algorithms for Finite-Horizon and Infinite-Horizon Indefinite Linear Quadratic Stochastic Optimal Control Problems 认领 引用
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作者 Guangchen Wang Heng Zhang 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第6期1459-1469,共11页
This paper is devoted to devising data-driven algorithms for finite-horizon and infinite-horizon linear quadratic stochastic optimal control(LQSOC)problems.In our study,the diffusion terms of system dynamics are permi... This paper is devoted to devising data-driven algorithms for finite-horizon and infinite-horizon linear quadratic stochastic optimal control(LQSOC)problems.In our study,the diffusion terms of system dynamics are permitted to hinge upon both control and state variables,and the weighting matrices of cost functionals are allowed to be indefinite.It is acknowledged that the optimal controls of finite-horizon and infinite-horizon indefinite LQSOC problems are correlated with a generalized differential Riccati equation(GDRE)and a generalized algebraic Riccati equation(GARE).Herein,we propose two data-driven algorithms to approximate the solutions of these Riccati equations,and thereby determine optimal controls,without leveraging the information of all system parameters.Additionally,we prove the convergence of these algorithms and examine the impact of computational errors.Finally,we validate the performance of these data-driven algorithms via three simulation examples. 展开更多
关键词 Data-driven algorithm generalized algebraic Riccati equation (GARE) generalized differential Riccati equation (GDRE) indefinite problem linear quadratic stochastic optimal control (LQSOC)
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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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Auxiliary-qubit-free quantum approximate optimization algorithm for the minimum dominating set problem 认领 引用
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作者 Guanghui Li Xiaohui Ni +5 位作者 Junjian Su Sujuan Qin Fenzhuo Guo Bingjie Xu Wei Huang Fei Gao 《Chinese Physics B》 SCIE EI CAS CSCD 2026年第5期326-344,共19页
Quantum approximate optimization algorithm(QAOA)is a promising framework for solving combinatorial optimization problems on near-term quantum devices.One such problem is the minimum dominating set(MDS),which is known ... Quantum approximate optimization algorithm(QAOA)is a promising framework for solving combinatorial optimization problems on near-term quantum devices.One such problem is the minimum dominating set(MDS),which is known to be NP-hard.Existing QAOA algorithms for this problem typically require numerous auxiliary qubits,increasing circuit overhead and hardware requirements.In this paper,we propose an auxiliary-qubit-free QAOA algorithm based on Hamiltonian evolution(AQFH-QAOA)for the MDS problem.Unlike previous studies that require numerous auxiliary qubits,our algorithm eliminates the need for auxiliary qubits,thereby significantly reducing circuit overhead.In addition,we present an auxiliary-qubit-free optimized implementation of the previously proposed Guerrero's QAOA algorithm(AQFG-QAOA)by utilizing gate decomposition techniques.Through a detailed analysis of gate complexity,we evaluate the applicability of these two algorithms.Numerical experiments demonstrate that our proposed algorithm achieves competitive solution quality compared with existing QAOA algorithms,making it a promising candidate for implementation on near-term quantum devices. 展开更多
关键词 quantum algorithm quantum approximate optimization algorithm minimum dominating set Boolean algebra identities
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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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Pedestrian detection of infrared images based on an improved FCOS algorithm 认领 引用
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作者 Fuzhen ZHU Hao HAN +1 位作者 Hengfei JIA Bing ZHU 《Optoelectronics Letters》 EI 2026年第2期105-110,共6页
The current infrared image pedestrian detectors have problems with high rates of false positives and false negatives. To solve these problems, we proposed an improved anchor-free fully convolutional one-stage object d... The current infrared image pedestrian detectors have problems with high rates of false positives and false negatives. To solve these problems, we proposed an improved anchor-free fully convolutional one-stage object detection(FCOS) algorithm. Firstly, we introduced the channel attention module squeeze excitation(SE)-Block in the FCOS backbone network, which was used to learn how to model the relative importance between different feature channels, and to achieve the weight recalibration of the features extracted from the convolution neural network, and improve the weight values that are more important for pedestrian target detection. Secondly, soft non-maximum suppression(Soft-NMS) replaced the conventional NMS within the algorithm's post-processing phase, which was used to reduce the probability of missed detection for occluded pedestrians. The experimental results show that our improved FCOS algorithm improves the average precision(AP) by 6.71% on the original dataset and 7.97% on the augmented KAIST pedestrian dataset compared with the original FCOS algorithm. Our improvements effectively meet the real-time requirements and there is no significant decrease in speed compared with the original FCOS algorithm, and decreased the false positives and false negatives for infrared image pedestrian detection. 展开更多
关键词 weight recalibration pedestrian detection infrared image pedestrian detectors channel attention module learn how model relative importance different feature channels improved FCOS algorithm infrared images Squeeze Excitation SE block
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Methods,Algorithms,Codes,and Software for Theoretical and Computational Chemistry 认领 引用
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作者 Bin Jiang 《Chinese Journal of Chemical Physics》 SCIE EI CAS CSCD 2026年第3期301-302,共2页
Theoretical and computational chemistry has profoundly impacted a wide range of disciplines,from chemistry and physics to biology and materials science.In recent years,remarkable advances in electronic structure theor... Theoretical and computational chemistry has profoundly impacted a wide range of disciplines,from chemistry and physics to biology and materials science.In recent years,remarkable advances in electronic structure theory,molecular dynamics,and machine learning methods——coupled with increasingly powerful algorithms and software—have equipped chemists with an unprecedented arsenal of tools to tackle complex chemical problems. 展开更多
关键词 theoretical computational chemistry electronic structure theorymolecular dynamicsand algorithms codes software methods materials sciencein theoretical chemistry
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A Hybrid Approach for Query-Based Data Extraction Using Ensemble BERT Model with Walrus Optimization Algorithm 认领 引用
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作者 Poluru Eswaraiah Uddagiri Sirisha +3 位作者 Shaik Abdul Nabi Revathi Durgam Pallavi Malavath Gilakara Muni Nagamani 《Computers, Materials & Continua》 SCIE EI 2026年第8期1580-1606,共27页
The growing volume of digital text complicates the extraction of relevant information from unstructured data.Transformer models such as BERT,ALBERT,and RoBERTa are powerful,but they may face challenges in hyperparamet... The growing volume of digital text complicates the extraction of relevant information from unstructured data.Transformer models such as BERT,ALBERT,and RoBERTa are powerful,but they may face challenges in hyperparameter optimization and adaptation to new domains.To address this issue,a hybrid ensemble BERT model is suggested,optimized using the Walrus Optimization Algorithm(WaOA).The framework applies PCA to reduce dimensionality,ontology normalization,and K-means clustering to improve semantic comprehension.Experimental results on the SQuAD 2.0 and MS MARCO datasets show that the proposed model outperforms the baseline models.WaOA(Weighted Average of Attention)can improve convergence,reduce training time,and enhance prediction accuracy.The model also improves the semantic relevance of the extracted information.Attention maps visualize the model’s focus on relevant query terms.The method enhances efficiency and cuts redundancy.It also provides a more generalized approach to different query types.The framework promotes consistent and reliable performance across different data conditions,including varying input formats and varying noise levels.It can be generalized to multilingual and domain-specific applications.Overall,the framework provides a scalable and reliable solution to real-world information extraction. 展开更多
关键词 Query-based information extraction ensemble BERT walrus optimization algorithm metaheuristic learning PCA K-means clustering ROUGE t-SNE attention visualization
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Optimal setting and placement of FACTS devices using strength Pareto multi-objective evolutionary algorithm 认领 引用 被引量：2
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作者 Amin Safari Hossein Shayeghi Mojtaba Bagheri 《Journal of Central South University》 SCIE EI CAS CSCD 2017年第4期829-839,共11页
This work proposes a novel approach for multi-type optimal placement of flexible AC transmission system(FACTS) devices so as to optimize multi-objective voltage stability problem. The current study discusses a way for... This work proposes a novel approach for multi-type optimal placement of flexible AC transmission system(FACTS) devices so as to optimize multi-objective voltage stability problem. The current study discusses a way for locating and setting of thyristor controlled series capacitor(TCSC) and static var compensator(SVC) using the multi-objective optimization approach named strength pareto multi-objective evolutionary algorithm(SPMOEA). Maximization of the static voltage stability margin(SVSM) and minimizations of real power losses(RPL) and load voltage deviation(LVD) are taken as the goals or three objective functions, when optimally locating multi-type FACTS devices. The performance and effectiveness of the proposed approach has been validated by the simulation results of the IEEE 30-bus and IEEE 118-bus test systems. The proposed approach is compared with non-dominated sorting particle swarm optimization(NSPSO) algorithm. This comparison confirms the usefulness of the multi-objective proposed technique that makes it promising for determination of combinatorial problems of FACTS devices location and setting in large scale power systems. 展开更多
关键词 strength Pareto multi-objective evolutionary algorithm static var compensator (SVC) thyristor controlled series capacitor (TCSC) static voltage stability margin optimal location
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Active micro-vibration control based on improved variable step size LMS algorithm 认领 引用 被引量：1
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作者 Li Xiangmin Fang Yubin +2 位作者 Zhu Xiaojin Huang Yonghui Zhou Yijia 《High Technology Letters》 EI CAS 2020年第2期178-187,共10页
The contradiction of variable step size least mean square(LMS)algorithm between fast convergence speed and small steady-state error has always existed.So,a new algorithm based on the combination of logarithmic and sym... The contradiction of variable step size least mean square(LMS)algorithm between fast convergence speed and small steady-state error has always existed.So,a new algorithm based on the combination of logarithmic and symbolic function and step size factor is proposed.It establishes a new updating method of step factor that is related to step factor and error signal.This work makes an analysis from 3 aspects:theoretical analysis,theoretical verification and specific experiments.The experimental results show that the proposed algorithm is superior to other variable step size algorithms in convergence speed and steady-state error. 展开更多
关键词 adaptive filtering variable step size least mean square(LMS)algorithm logarithmic and symbolic functions convergence and steady state error active control of micro vibration
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An Efficient Evolutionary Algorithm for Few-for-Many Optimization 认领 引用
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作者 Ke Shang Hisao Ishibuchi +1 位作者 Zexuan Zhu Qingfu Zhang 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第6期1362-1377,共16页
Few-for-many(F4M)optimization,recently introduced as a novel paradigm in multi-objective optimization,aims to find a small set of solutions that effectively handle a large number of conflicting objectives.Unlike tradi... Few-for-many(F4M)optimization,recently introduced as a novel paradigm in multi-objective optimization,aims to find a small set of solutions that effectively handle a large number of conflicting objectives.Unlike traditional many-objective optimization methods,which typically attempt comprehensive coverage of the Pareto front,F4M optimization emphasizes finding a small representative solution set to efficiently address highdimensional objective spaces.Motivated by the computational complexity and practical relevance of F4M optimization,this paper proposes a new evolutionary algorithm explicitly tailored for efficiently solving F4M optimization problems.Inspired by Smetric selection evolutionary multi-objective optimization algorithm(SMS-EMOA),our proposed approach employs a(μ+1)-evolution strategy guided by the objective of F4M optimization.Furthermore,to facilitate rigorous performance assessment,we propose a novel benchmark test suite specifically designed for F4M optimization by leveraging the similarity betw een the R2indicator and F4M formulations.Our test suite is highly flexible,allowing any existing multi-objective optimization problem to be transformed into a corresponding F4M instance via scalarization using the weighted Tchebycheff function.Comprehensive experimental evaluations on benchmarks demonstrate the superior performance of our algorithm compared to existing state-of-the-art algorithms,especially on instances involving a large number of objectives.The source code of the proposed algorithm will be released publicly.Source code is available at http://gffzz188fe103f8f1460asbcx5ubwqp60f6uvb.ffgz.tsg.suse.edu.cn/MOL-SZU/SoM-EMOA. 展开更多
关键词 Evolutionary algorithm few-for-many optimization many-objective optimization (MOO) multi-objective optimization
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Effect of a region-wide incorporation of an algorithm based on the 2012 international consensus guideline on the practice pattern for the management of pancreatic cystic neoplasms in an integrated health system 认领 引用
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作者 Andrew Khoi Nguyen Agathon Girgis +12 位作者 Timnit Tekeste Karen Chang Mopelola Adeyemo Armen Eskandari Emilio Alonso Priyanka Yaramada Charles Chaya Albert Ko Edmund Burke Isaiah Roggow Rebecca Butler Aniket Kawatkar Brian S Lim 《World Journal of Clinical Cases》 SCIE 2018年第13期624-631,共8页
AIM To examine the practice pattern in Kaiser Permanente Southern California(KPSC), i.e., gastroenterology(GI)/surgery referrals and endoscopic ultrasound(EUS), for pancreatic cystic neoplasms(PCNs) after the regionwi... AIM To examine the practice pattern in Kaiser Permanente Southern California(KPSC), i.e., gastroenterology(GI)/surgery referrals and endoscopic ultrasound(EUS), for pancreatic cystic neoplasms(PCNs) after the regionwide dissemination of the PCN management algorithm.METHODS Retrospective review was performed; patients with PCN diagnosis given between April 2012 and April 2015(18 mo before and after the publication of the algorithm) in KPSC(integrated health system with 15 hospitals and 202 medical offices in Southern California) were identified.RESULTS2558(1157 pre-and 1401 post-algorithm) received a new diagnosis of PCN in the study period. There was no difference in the mean cyst size(pre-19.1 mm vs post-18.5 mm, P = 0.119). A smaller percentage of PCNs resulted in EUS after the implementation of the algorithm(pre-45.5% vs post-34.8%, P < 0.001). A smaller proportion of patients were referred for GI(pre-65.2% vs post-53.3%, P < 0.001) and surgery consultations(pre-24.8% vs post-16%, P < 0.001) for PCN after the implementation. There was no significant change in operations for PCNs. Cost of diagnostic care was reduced after the implementation by 24%, 18%, and 36% for EUS, GI, and surgery consultations, respectively, with total cost saving of 24%.CONCLUSION In the current healthcare climate, there is increased need to optimize resource utilization. Dissemination of an algorithm for PCN management in an integrated health system resulted in fewer EUS and GI/surgery referrals, likely by aiding the physicians ordering imaging studies in the decision making for the management of PCNs. This translated to cost saving of 24%, 18%, and 36% for EUS, GI, and surgical consultations, respectively, with total diagnostic cost saving of 24%. 展开更多
关键词 Pancreatic cyst Algorithm incorporation Pancreas Optimization of resource utilization Fukuoka criteria Sendai criteria Pancreatic cancer Intraductal papillary mucinous neoplasm Practice management Algorithm
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Pigeon-Inspired Optimization Algorithm:Definition,Variants,and Its Applications in Unmanned Aerial Vehicles 认领 引用
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作者 Yu-Xuan Zhou Kai-Qing Zhou +2 位作者 Wei-Lin Chen Zhou-Hua Liao Di-Wen Kang 《Computers, Materials & Continua》 SCIE EI 2026年第4期186-225,共40页
ThePigeon-InspiredOptimization(PIO)algorithmconstitutes ametaheuristic method derived fromthe homing behaviour of pigeons.Initially formulated for three-dimensional path planning in unmanned aerial vehicles(UAVs),the ... ThePigeon-InspiredOptimization(PIO)algorithmconstitutes ametaheuristic method derived fromthe homing behaviour of pigeons.Initially formulated for three-dimensional path planning in unmanned aerial vehicles(UAVs),the algorithmhas attracted considerable academic and industrial interest owing to its effective balance between exploration and exploitation,coupled with advantages in real-time performance and robustness.Nevertheless,as applications have diversified,limitations in convergence precision and a tendency toward premature convergence have become increasingly evident,highlighting a need for improvement.This reviewsystematically outlines the developmental trajectory of the PIO algorithm,with a particular focus on its core applications in UAV navigation,multi-objective formulations,and a spectrum of variantmodels that have emerged in recent years.It offers a structured analysis of the foundational principles underlying the PIO.It conducts a comparative assessment of various performance-enhanced versions,including hybrid models that integrate mechanisms from other optimization paradigms.Additionally,the strengths andweaknesses of distinct PIOvariants are critically examined frommultiple perspectives,including intrinsic algorithmic characteristics,suitability for specific application scenarios,objective function design,and the rigor of the statistical evaluation methodologies employed in empirical studies.Finally,this paper identifies principal challenges within current PIO research and proposes several prospective research directions.Future work should focus on mitigating premature convergence by refining the two-phase search structure and adjusting the exponential decrease of individual numbers during the landmark operator.Enhancing parameter adaptation strategies,potentially using reinforcement learning for dynamic tuning,and advancing theoretical analyses on convergence and complexity are also critical.Further applications should be explored in constrained path planning,Neural Architecture Search(NAS),and other real-worldmulti-objective problems.For Multi-objective PIO(MPIO),key improvements include controlling the growth of the external archive and designing more effective selection mechanisms to maintain convergence efficiency.These efforts are expected to strengthen both the theoretical foundation and practical versatility of PIO and its variants. 展开更多
关键词 Pigeon-inspired optimization metaheuristic algorithm algorithmvariants swarmintelligence variants UAVs convergence analysis
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Optimized VMD algorithm for noise reduction of absorption spectra of CO2 认领 引用
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作者 Ru Zhang Junfen Wang +3 位作者 Xuemei Shi Mingliang Li Zhanmin Zhao Han Wang 《Optoelectronics Letters》 EI 2026年第1期23-28,共6页
The end tidal carbon dioxide(EtCO2)is crucial for monitoring patients respiratory function,which reflects the status of lung ventilation and gas exchange.Therefore,achieving accurate measurements of EtCO2holds s... The end tidal carbon dioxide(EtCO2)is crucial for monitoring patients respiratory function,which reflects the status of lung ventilation and gas exchange.Therefore,achieving accurate measurements of EtCO2holds significant importance in clinical practice.The measurements of EtCO2based on wavelength modulation-direct absorption spectroscopy(WM-DAS)had great advantages and the noise reduction of spectrum was very important.An optimized variational mode decomposition(VMD)algorithm improved by the dung beetle optimization algorithm and wavelet packet denoising algorithm was proposed to enhance the measurement accuracy of EtCO2concentration.The dung beetle optimization algorithm was used to obtain the optimal number of decomposition mode layers K and secondary penalty factorα.The optimal parameters were used to decompose the original transmitted light intensity signal with noise,and a series of intrinsic mode functions(IMFs)were obtained.Pearson correlation coefficient(R)was used to select the pure signal and the noisy signal,and the noisy signal was denoised by wavelet packet denoising algorithm.The transmitted light intensity signal was reconstructed by the signal processed by wavelet packet denoising algorithm and the pure signal.The results showed that the proposed algorithm could effectively remove the noise of signal of transmitted light intensity and improve the accuracy of concentration measurements of EtCO2. 展开更多
关键词 monitoring patients respiratory functionwhich Variational Mode Decomposition Wavelet Packet Denoising noise reduction spectrum end tidal carbon dioxide etco Dung Beetle Optimization Algorithm dung be lung ventilation
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An improved YOLOv8 apple leaf disease detection algorithm 认领 引用
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作者 Xinyu PEI Wei YUAN +1 位作者 Yuexiu ZHANG Lianjun SONG 《Optoelectronics Letters》 EI 2026年第5期314-320,共7页
This paper suggests an improved you only look once version 8n(YOLOv8n)algorithm for apple leaf disease detection,abbreviated as ALWB-YOLOv8n.The model is comprised of four essential components.Initially,arbitrary kern... This paper suggests an improved you only look once version 8n(YOLOv8n)algorithm for apple leaf disease detection,abbreviated as ALWB-YOLOv8n.The model is comprised of four essential components.Initially,arbitrary kernel convolution(AKConv)replaces the convolution module,which significantly decreases both the model’s parameter count and its overall size.Secondly,the large selective kernel network(LSKNet)attention mechanism is added in the Backbone,which can dynamically adjust the spatial sensory domain,and experiments have proved that this method is extremely advantageous for small target detection.Third,a weighted bi-directional feature pyramid network is introduced,which enables the model to achieve multi-scale feature fusion and is more concise and faster.Finally,wise intersection over union(WIoU)is used to replace complete intersection over union(CIoU)in YOLOv8,and the idea of focal loss is introduced,which effectively solves the detection problems in cases such as apple leaves occluding each other and blurred boundaries of diseased leaves.The improved algorithm exhibits superior performance compared to other common object detection algorithms.Compared with YOLOv8n,the improved algorithm achieves 2.3%improvement in precision,3.8%improvement in recall,and 2.5%and 2.7%improvement in mAP0.5 and mAP0.5:0.95,respectively.Compared with YOLOv8n,the improved model reduces the number of parameters and size of the model and realizes real-time monitoring with a frames per second(FPS)of 50.5. 展开更多
关键词 large selective kernel network arbitrary kernel convolution large selective kernel network lsknet attention kernel convolution akconv replaces weighted bi directional feature pyramid network adjust spatial sensory domainand improved YOLOv n algorithm apple leaf disease detection
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Enhanced Euclid Algorithm for Modular Multiplicative Inverse and Its Application in Cryptographic Protocols 认领 引用 被引量：3
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作者 Boris S. Verkhovsky 《International Journal of Communications, Network and System Sciences》 2010年第12期901-906,共6页
Numerous cryptographic algorithms (ElGamal, Rabin, RSA, NTRU etc) require multiple computations of modulo multiplicative inverses. This paper describes and validates a new algorithm, called the Enhanced Euclid Algorit... Numerous cryptographic algorithms (ElGamal, Rabin, RSA, NTRU etc) require multiple computations of modulo multiplicative inverses. This paper describes and validates a new algorithm, called the Enhanced Euclid Algorithm, for modular multiplicative inverse (MMI). Analysis of the proposed algorithm shows that it is more efficient than the Extended Euclid algorithm (XEA). In addition, if a MMI does not exist, then it is not necessary to use the Backtracking procedure in the proposed algorithm;this case requires fewer operations on every step (divisions, multiplications, additions, assignments and push operations on stack), than the XEA. Overall, XEA uses more multiplications, additions, assignments and twice as many variables than the proposed algorithm. 展开更多
关键词 Extended-Euclid Algorithm Modular Multiplicative Inverse Public-Key Cryptography RSA Cryptocol Rabin Information Hiding Algorithm ElGamal Encryption/Decryption NTRU Cryptosystem Computer Simulation Low Memory Devices
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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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ADMM-based Distributed Algorithm for Economic Dispatch in Power Systems With Both Packet Drops and Communication Delays 认领 引用 被引量：13
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作者 Qing Yang Gang Chen Ting Wang 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2020年第3期842-852,共11页
By virtue of alternating direction method of multipliers(ADMM), Newton-Raphson method, ratio consensus approach and running sum method, two distributed iterative strategies are presented in this paper to address the e... By virtue of alternating direction method of multipliers(ADMM), Newton-Raphson method, ratio consensus approach and running sum method, two distributed iterative strategies are presented in this paper to address the economic dispatch problem(EDP) in power systems. Different from most of the existing distributed ED approaches which neglect the effects of packet drops or/and time delays, this paper takes into account both packet drops and time delays which frequently occur in communication networks. Moreover, directed and possibly unbalanced graphs are considered in our algorithms, over which many distributed approaches fail to converge. Furthermore, the proposed schemes can address the EDP with local constraints of generators and nonquadratic convex cost functions, not just quadratic ones required in some existing ED approaches. Both theoretical analyses and simulation studies are provided to demonstrate the effectiveness of the proposed schemes. 展开更多
关键词 Alternating direction method of multipliers(ADMM) average consensus directed graph(digraph) distributed algorithm economic dispatch packet drops time delays
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An Overall Optimization Model Using Metaheuristic Algorithms for the CNN-Based IoT Attack Detection Problem 认领 引用 被引量：1
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作者 Le Thi Hong Van Le Duc Thuan +1 位作者 Pham Van Huong Nguyen Hieu Minh 《Computers, Materials & Continua》 SCIE EI 2026年第4期1934-1964,共31页
Optimizing convolutional neural networks(CNNs)for IoT attack detection remains a critical yet challenging task due to the need to balance multiple performance metrics beyond mere accuracy.This study proposes a unified... Optimizing convolutional neural networks(CNNs)for IoT attack detection remains a critical yet challenging task due to the need to balance multiple performance metrics beyond mere accuracy.This study proposes a unified and flexible optimization framework that leverages metaheuristic algorithms to automatically optimize CNN configurations for IoT attack detection.Unlike conventional single-objective approaches,the proposed method formulates a global multi-objective fitness function that integrates accuracy,precision,recall,and model size(speed/model complexity penalty)with adjustable weights.This design enables both single-objective and weightedsum multi-objective optimization,allowing adaptive selection of optimal CNN configurations for diverse deployment requirements.Two representativemetaheuristic algorithms,GeneticAlgorithm(GA)and Particle Swarm Optimization(PSO),are employed to optimize CNNhyperparameters and structure.At each generation/iteration,the best configuration is selected as themost balanced solution across optimization objectives,i.e.,the one achieving themaximum value of the global objective function.Experimental validation on two benchmark datasets,Edge-IIoT and CIC-IoT2023,demonstrates that the proposed GA-and PSO-based models significantly enhance detection accuracy(94.8%–98.3%)and generalization compared with manually tuned CNN configurations,while maintaining compact architectures.The results confirm that the multi-objective framework effectively balances predictive performance and computational efficiency.This work establishes a generalizable and adaptive optimization strategy for deep learning-based IoT attack detection and provides a foundation for future hybrid metaheuristic extensions in broader IoT security applications. 展开更多
关键词 Genetic algorithm(GA) particle swarm optimization(PSO) multi-objective optimization convolutional neural network—CNN IoT attack detection metaheuristic optimization CNN configuration
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Binary Gravitational Search based Algorithm for Optimum Siting and Sizing of DG and Shunt Capacitors in Radial Distribution Systems 认领 引用
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作者 N. A. Khan S. Ghosh S. P. Ghoshal 《Energy and Power Engineering》 2013年第4期1005-1010,共6页
This paper presents a binary gravitational search algorithm (BGSA) is applied to solve the problem of optimal allotment of DG sets and Shunt capacitors in radial distribution systems. The problem is formulated as a no... This paper presents a binary gravitational search algorithm (BGSA) is applied to solve the problem of optimal allotment of DG sets and Shunt capacitors in radial distribution systems. The problem is formulated as a nonlinear constrained single-objective optimization problem where the total line loss (TLL) and the total voltage deviations (TVD) are to be minimized separately by incorporating optimal placement of DG units and shunt capacitors with constraints which include limits on voltage, sizes of installed capacitors and DG. This BGSA is applied on the balanced IEEE 10 Bus distribution network and the results are compared with conventional binary particle swarm optimization. 展开更多
关键词 Normal Load Flow Radial Distribution System Distributed Generation Shunt Capacitors Binary Particle Swarm Optimization Binary Gravitational Search Algorithm Total line Loss Total Voltage Deviation
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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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