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Data-Driven Algorithms for Finite-Horizon and Infinite-Horizon Indefinite Linear Quadratic Stochastic Optimal Control Problems 认领 引用
1
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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Collaborative optimization of well operations and adjustment strategies in waterflooding reservoirs using an enhanced adaptive differential evolution algorithm 认领 引用
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作者 Xian-Min Zhang Jian-Gang Yang +2 位作者 Qi-Hong Feng Ya-Wei Hou Lei Zhang 《Petroleum Science》 SCIE EI CAS CSCD 2026年第5期2735-2757,共23页
Efficient optimization of well operations andadjustment strategies in large-scale waterflooding reservoirs is a high-dimensional and complex challenge due to strong decision coupling and reservoir heterogeneity.This s... Efficient optimization of well operations andadjustment strategies in large-scale waterflooding reservoirs is a high-dimensional and complex challenge due to strong decision coupling and reservoir heterogeneity.This study proposes a collaborative optimization framework that integrates multiple adjustment strategies,includinginfillwell drilling,shut-in of low-efficiency wells,and injectionproduction well conversion.A penalty mechanism is introduced tobalance cumulative oil production maximization with minimum production constraints for infill wells.The core contribution is the development of a multi-strategy enhancedadaptive differential evolution algorithm(E-ADE),which incorporates the follower update mechanism of the SparrowSearch Algorithm(SSA)and the logarithmic spiral search strategy of the Whale Optimization Algorithm(WOA)into the differential evolution(DE)framework.By dynamically adjusting differential evolution vectors and adaptively regulating population size across optimization stages,E-ADE effectively balances global exploration and local exploitation,leading to significantlyimproved convergence speed and optimization accuracy.Benchmark tests on nine multimodalfunctions demonstrate that E-ADEconsistently outperforms classical algorithms,includingDE,GA,PSO,WOA,and SSA.The method is further applied to the PUNQ-S3 reservoir model and the S4 block of the W12-2 oilfield under high water-cut conditions.The results indicate that E-ADE enables adaptive optimization of infillwell placement,shut-in schemes,and welltype conversions,achieving coordinated improvements in both field-scale production andsingle-well performance,and substantially enhancing the efficiency of waterflooding development. 展开更多
关键词 Waterflooding reservoir Collaborative optimization Enhanced adaptive differential evolution algorithm Logarithmic spiral search Shannon entropy
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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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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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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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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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基于遗传算法和最小二乘支持向量机的织物剪切性能预测 认领 引用 被引量：2
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作者 卢桂馥 王勇 +1 位作者 窦易文 Gui-fu Yi-wen 《计量学报》 北大核心 2009年第6期
提出了一种基于最小二乘支持向量机的织物剪切性能预测模型,并且采用遗传算法进行最小二乘支持向量机的参数优化,将获得的样本进行归一化处理后,将其输入预测模型以得到预测结果.仿真结果表明,基于最小二乘支持向量机的预测模型比BP神... 提出了一种基于最小二乘支持向量机的织物剪切性能预测模型,并且采用遗传算法进行最小二乘支持向量机的参数优化,将获得的样本进行归一化处理后,将其输入预测模型以得到预测结果.仿真结果表明,基于最小二乘支持向量机的预测模型比BP神经网络和线性回归方法具有更高的精度和范化能力.Abstract：A new method is proposed to predict the fabric shearing property with least square support vector machines ( LS-SVM ). The genetic algorithm is investigated to select the parameters of LS-SVM models as a means of improving the LS- SVM prediction. After normalizing the sampling data, the sampling data are inputted into the model to gain the prediction result. The simulation results show the prediction model gives better forecasting accuracy and generalization ability than BP neural network and linear regression method. 展开更多
关键词 基于遗传算法 最小二乘支持向量机 织物 剪切 性能预测模型 Support Vector Machines sampling data support vector machines generalization ability simulation results linear regression genetic algorithm BP neural network prediction model 线性回归方法 LS-SVM least square 归一化处理 new method 预测结果
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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://gffzz188fe103f8f1460ascoup6n5n6bf96k55.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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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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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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Deep Transfers of p-Class Tower Groups 认领 引用
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作者 Daniel C. Mayer 《Journal of Applied Mathematics and Physics》 2018年第1期36-50,共15页
Let p be a prime. For any finite p-group G, the deep transfers T H,G ' : H / H ' → G ' / G " from the maximal subgroups H of index (G:H) = p in G to the derived subgroup G ' are introduced as an ... Let p be a prime. For any finite p-group G, the deep transfers T H,G ' : H / H ' → G ' / G " from the maximal subgroups H of index (G:H) = p in G to the derived subgroup G ' are introduced as an innovative tool for identifying G uniquely by means of the family of kernels ùd(G) =(ker(T H,G ')) (G: H) = p. For all finite 3-groups G of coclass cc(G) = 1, the family ùd(G) is determined explicitly. The results are applied to the Galois groups G =Gal(F3 (∞)/ F) of the Hilbert 3-class towers of all real quadratic fields F = Q(√d) with fundamental discriminants d > 1, 3-class group Cl3(F) □ C3 × C3, and total 3-principalization in each of their four unramified cyclic cubic extensions E/F. A systematic statistical evaluation is given for the complete range 1 d 7, and a few exceptional cases are pointed out for 1 d 8. 展开更多
关键词 Hilbert p-Class Field Towers p-Class Groups p-Principalization Quadratic Fields Dihedral Fields of Degree 2p Finite p-Groups Two-Step Centralizers Polarization Principle Descendant Trees p-Group Generation Algorithm p-Multiplicator Rank Relation Rank Generator Rank Deep Transfers Shallow Transfers Partial Order and Monotony Principle of Artin Patterns Parametrized Polycyclic pc-Presentations Commutator Calculus
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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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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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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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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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New Regularization Algorithms for Solving the Deconvolution Problem in Well Test Data Interpretation 认领 引用 被引量：1
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作者 Vladimir Vasin Georgy Skorik +1 位作者 Evgeny Pimonov Fikri Kuchuk 《Applied Mathematics》 2010年第5期387-399,共13页
Two new regularization algorithms for solving the first-kind Volterra integral equation, which describes the pressure-rate deconvolution problem in well test data interpretation, are developed in this paper. The main ... Two new regularization algorithms for solving the first-kind Volterra integral equation, which describes the pressure-rate deconvolution problem in well test data interpretation, are developed in this paper. The main features of the problem are the strong nonuniform scale of the solution and large errors (up to 15%) in the input data. In both algorithms, the solution is represented as decomposition on special basic functions, which satisfy given a priori information on solution, and this idea allow us significantly to improve the quality of approximate solution and simplify solving the minimization problem. The theoretical details of the algorithms, as well as the results of numerical experiments for proving robustness of the algorithms, are presented. 展开更多
关键词 Deconvolution Problem Volterra Equations Well Test Regularization Algorithm Quasi-Solutions Method Tikhonov Regularization A Priori Information Discrete Approximation Non-Quadratic Stabilizing Functional Special Basis
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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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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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RRT*-GSQ:A hybrid sampling path planning algorithm for complex orchard scenarios 认领 引用
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作者 ZHU Qingzhen ZHAO Jiamuyang +1 位作者 DAI Xu YU Yang 《农业工程学报》 EI CAS CSCD 北大核心 2026年第3期13-25,共13页
Traditional sampling-based path planning algorithms,such as the rapidly-exploring random tree star(RRT*),encounter critical limitations in unstructured orchard environments,including low sampling efficiency in narr... Traditional sampling-based path planning algorithms,such as the rapidly-exploring random tree star(RRT*),encounter critical limitations in unstructured orchard environments,including low sampling efficiency in narrow passages,slow convergence,and high computational costs.To address these challenges,this paper proposes a novel hybrid global path planning algorithm integrating Gaussian sampling and quadtree optimization(RRT*-GSQ).This methodology aims to enhance path planning by synergistically combining a Gaussian mixture sampling strategy to improve node generation in critical regions,an adaptive step-size and direction optimization mechanism for enhanced obstacle avoidance,a Quadtree-AABB collision detection framework to lower computational complexity,and a dynamic iteration control strategy for more efficient convergence.In obstacle-free and obstructed scenarios,compared with the conventional RRT*,the proposed algorithm reduced the number of node evaluations by 67.57%and 62.72%,and decreased the search time by 79.72%and 78.52%,respectively.In path tracking tests,the proposed algorithm achieved substantial reductions in RMSE of the final path compared to the conventional RRT*.Specifically,the lateral RMSE was reduced by 41.5%in obstacle-free environments and 59.3%in obstructed environments,while the longitudinal RMSE was reduced by 57.2%and 58.5%,respectively.Furthermore,the maximum absolute errors in both lateral and longitudinal directions were constrained within 0.75 m.Field validation experiments in an operational orchard confirmed the algorithm's practical effectiveness,showing reductions in the mean tracking error of 47.6%(obstacle-free)and 58.3%(with obstructed),alongside a 5.1%and 7.2%shortening of the path length compared to the baseline method.The proposed algorithm effectively enhances path planning efficiency and navigation accuracy for robots,presenting a superior solution for high-precision autonomous navigation of agricultural robots in orchard environments and holding significant value for engineering applications. 展开更多
关键词 robot path planning orchard improved RRT*algorithm Gaussian sampling autonomous navigation
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