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Distributed Iterative Learning Control for Load Balancing in Flexible AC/DC Hybrid Distribution Systems 认领 引用
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作者 Hong Zhang Bin Xu +4 位作者 Jinzhong Li Xiaoxiao Meng Cheng Qian Wei Ma Yuguang Xie 《Energy Engineering》 EI 2026年第5期298-316,共19页
The increasing integration of distributed renewable energy sources in the distribution network leads to unbalanced load rates in the distribution network.The traditional load balancing methods are mainly based on netw... The increasing integration of distributed renewable energy sources in the distribution network leads to unbalanced load rates in the distribution network.The traditional load balancing methods are mainly based on network reconfiguration,which have problems such as a long time scale and poor adaptability.In response to these issues,this paper proposes a distributed iterative learning control(ILC)strategy for load balancing in flexible AC/DC hybrid distribution systems.This method combines the consensus algorithm with the ILC mechanism to construct a multi-terminal AC/DC flexible interconnection system model.It is only necessary to measure the load rate of adjacent units without observing the overall system status,which greatly reduces complexity and enhances robustness.In this paper,a new energy photovoltaic and energy storage integrated system was built through MATLAB/Simulink simulation,and the effectiveness of the proposed strategy under normal working conditions and port faults was verified through this system.Through comparative studies with event-triggered control and traditional consensus algorithms,as well as real-time simulations on the RT-LAB simulation platform,it has been confirmed that this method has superior performance in terms of convergence speed,steady-state accuracy,and dynamic response,and has the potential to be applied in practical models.It is suitable for application in medium and low voltage distribution systems with new energy access. 展开更多
关键词 AC/DC hybrid distribution systems iterative learning algorithm load rate
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An Iterated Greedy Algorithm with Memory and Learning Mechanisms for the Distributed Permutation Flow Shop Scheduling Problem 认领 引用
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作者 Binhui Wang Hongfeng Wang 《Computers, Materials & Continua》 SCIE EI 2025年第1期371-388,共18页
The distributed permutation flow shop scheduling problem(DPFSP)has received increasing attention in recent years.The iterated greedy algorithm(IGA)serves as a powerful optimizer for addressing such a problem because o... The distributed permutation flow shop scheduling problem(DPFSP)has received increasing attention in recent years.The iterated greedy algorithm(IGA)serves as a powerful optimizer for addressing such a problem because of its straightforward,single-solution evolution framework.However,a potential draw-back of IGA is the lack of utilization of historical information,which could lead to an imbalance between exploration and exploitation,especially in large-scale DPFSPs.As a consequence,this paper develops an IGA with memory and learning mechanisms(MLIGA)to efficiently solve the DPFSP targeted at the mini-malmakespan.InMLIGA,we incorporate a memory mechanism to make a more informed selection of the initial solution at each stage of the search,by extending,reconstructing,and reinforcing the information from previous solutions.In addition,we design a twolayer cooperative reinforcement learning approach to intelligently determine the key parameters of IGA and the operations of the memory mechanism.Meanwhile,to ensure that the experience generated by each perturbation operator is fully learned and to reduce the prior parameters of MLIGA,a probability curve-based acceptance criterion is proposed by combining a cube root function with custom rules.At last,a discrete adaptive learning rate is employed to enhance the stability of the memory and learningmechanisms.Complete ablation experiments are utilized to verify the effectiveness of the memory mechanism,and the results show that this mechanism is capable of improving the performance of IGA to a large extent.Furthermore,through comparative experiments involving MLIGA and five state-of-the-art algorithms on 720 benchmarks,we have discovered that MLI-GA demonstrates significant potential for solving large-scale DPFSPs.This indicates that MLIGA is well-suited for real-world distributed flow shop scheduling. 展开更多
关键词 Distributed permutation flow shop scheduling makespan iterated greedy algorithm memory mechanism cooperative reinforcement learning
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Iterative Learning Fault Diagnosis Algorithm for Non-uniform Sampling Hybrid System 认领 引用 被引量：2
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作者 Hongfeng Tao Dapeng Chen Huizhong Yang 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2017年第3期534-542,共9页
For a class of non-uniform output sampling hybrid system with actuator faults and bounded disturbances,an iterative learning fault diagnosis algorithm is proposed.Firstly,in order to measure the impact of fault on sys... For a class of non-uniform output sampling hybrid system with actuator faults and bounded disturbances,an iterative learning fault diagnosis algorithm is proposed.Firstly,in order to measure the impact of fault on system between every consecutive output sampling instants,the actual fault function is transformed to obtain an equivalent fault model by using the integral mean value theorem,then the non-uniform sampling hybrid system is converted to continuous systems with timevarying delay based on the output delay method.Afterwards,an observer-based fault diagnosis filter with virtual fault is designed to estimate the equivalent fault,and the iterative learning regulation algorithm is chosen to update the virtual fault repeatedly to make it approximate the actual equivalent fault after some iterative learning trials,so the algorithm can detect and estimate the system faults adaptively.Simulation results of an electro-mechanical control system model with different types of faults illustrate the feasibility and effectiveness of this algorithm. 展开更多
关键词 Equivalent fault model fault diagnosis iterative learning algorithm non-uniform sampling hybrid system virtual fault
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Iterative Learning Control for Discrete-time Stochastic Systems with Quantized Information 认领 引用 被引量：11
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作者 Dong Shen Yun Xu 《IEEE/CAA Journal of Automatica Sinica》 EI 2016年第1期59-67,共9页
An iterative learning control (ILC) algorithm using quantized error information is given in this paper for both linear and nonlinear discrete-time systems with stochastic noises. A logarithmic quantizer is used to gua... An iterative learning control (ILC) algorithm using quantized error information is given in this paper for both linear and nonlinear discrete-time systems with stochastic noises. A logarithmic quantizer is used to guarantee an adaptive improvement in tracking performance. A decreasing learning gain is introduced into the algorithm to suppress the effects of stochastic noises and quantization errors. The input sequence is proved to converge strictly to the optimal input under the given index. Illustrative simulations are given to verify the theoretical analysis. © 2014 Chinese Association of Automation. 展开更多
关键词 Algorithms Digital control systems Discrete time control systems Iterative methods Learning algorithms Stochastic control systems Stochastic systems
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An optimal method for nonlinear iterative learning control systems with constraint and model uncertainty 认领 引用
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作者 LI Heng-jie HAO Xiao-hong XU Wei-tao 《通讯和计算机（中英文版）》 2008年第1期58-61,66,共4页
Clonal selection algorithm is improved and proposed as a method to implement nonlinear optimal iterative learning control algorithm. In the method, more priori information was coded in a clonal selection algorithm to ... Clonal selection algorithm is improved and proposed as a method to implement nonlinear optimal iterative learning control algorithm. In the method, more priori information was coded in a clonal selection algorithm to decrease the size of the search space and to deal with constraint on input. Another clonal selection algorithm is used as a model modifying device to cope with uncertainty in the plant model. Finally, simulations show that the convergence speed is satisfactory regardless of the nature of the plant and whether or not the plant model is precise. 展开更多
关键词 非线性迭代学习控制系统 无性选择算法 最优化方法 模型不确定性
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Data-Driven Learning Control Algorithms for Unachievable Tracking Problems 认领 引用 被引量：4
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作者 Zeyi Zhang Hao Jiang +1 位作者 Dong Shen Samer S.Saab 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2024年第1期205-218,共14页
For unachievable tracking problems, where the system output cannot precisely track a given reference, achieving the best possible approximation for the reference trajectory becomes the objective. This study aims to in... For unachievable tracking problems, where the system output cannot precisely track a given reference, achieving the best possible approximation for the reference trajectory becomes the objective. This study aims to investigate solutions using the Ptype learning control scheme. Initially, we demonstrate the necessity of gradient information for achieving the best approximation.Subsequently, we propose an input-output-driven learning gain design to handle the imprecise gradients of a class of uncertain systems. However, it is discovered that the desired performance may not be attainable when faced with incomplete information.To address this issue, an extended iterative learning control scheme is introduced. In this scheme, the tracking errors are modified through output data sampling, which incorporates lowmemory footprints and offers flexibility in learning gain design.The input sequence is shown to converge towards the desired input, resulting in an output that is closest to the given reference in the least square sense. Numerical simulations are provided to validate the theoretical findings. 展开更多
关键词 Data-driven algorithms incomplete information iterative learning control gradient information unachievable problems
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具有双资源约束的分布式装配混合流水车间调度 认领 引用
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作者 赵才 吴亮红 《中国机械工程》 EI CAS CSCD 北大核心 2026年第7期1725-1733,共9页
针对具有双资源约束的分布式装配混合流水车间调度问题,提出一种基于知识驱动的迭代贪婪(KDIG)算法。采用基于知识的NEH(Nawaz-Enscore-Ham)初始化策略,生成初始解决方案。基于问题特征设计了4种局部搜索算子。局部搜索算子与Q学习机制... 针对具有双资源约束的分布式装配混合流水车间调度问题,提出一种基于知识驱动的迭代贪婪(KDIG)算法。采用基于知识的NEH(Nawaz-Enscore-Ham)初始化策略,生成初始解决方案。基于问题特征设计了4种局部搜索算子。局部搜索算子与Q学习机制的结合使个体在迭代更新时动态选择最优的局部搜索算子,从而显著提升个体在搜索过程中的效率。KDIG算法与5种主流算法在81个大型实例上的测试结果表明,KDIG算法优于其他对比算法。 展开更多
关键词 分布式装配混合流车间调度 知识驱动 迭代贪婪算法 Q学习
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An Interpretable Temporal Convolutional Framework for Granger Causality Analysis 认领 引用
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作者 Aoxiang Dong Andrew Starr Yifan Zhao 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第3期665-679,共15页
Most existing parametric approaches for detecting linear or nonlinear Granger causality(GC)face challenges in estimating appropriate time delays,a critical factor for accurate GC detection.This issue becomes particula... Most existing parametric approaches for detecting linear or nonlinear Granger causality(GC)face challenges in estimating appropriate time delays,a critical factor for accurate GC detection.This issue becomes particularly pronounced in nonlinear complex systems,which are often opaque and consist of numerous components or variables.In this paper,we propose a novel temporal convolutional network(TCN)-based end-to-end GC detection approach called the interpretable temporal convolutional framework(ITCF).Unlike conventional deep learning models,which act like a“black box”and are difficult to analyse the interactions between variables,the proposed ITCF is able to detect both linear and nonlinear GC and automatically estimate time delay during the multivariant time series prediction.Specifically,GC is obtained by employing the least absolute shrinkage and selection operator(Lasso)regression during the prediction of multivariate time series using TCN.Then,time delays can be estimated by interpreting the TCN kernels.We propose a convolutional hierarchical group Lasso(cHGL),a hierarchical regularisation approach to effectively utilise temporal information within each TCN channel for enhanced GC detection.Additionally,as far as we are concerned,this paper is the first to integrate the Iterative Soft-Thresholding Algorithm into the backpropagation of TCN to optimise the proposed cHGL,which enables causal channel selection and induces sparsity within each TCN channel to remove redundant temporal information,ultimately creating an end-to-end GC detection framework.The testing results of four experiments,involving two simulations and two real data,demonstrate that the proposed ITCF,in comparison with state-ofthe-art,offers a more reliable estimation of GC relationships in complex systems featuring intricate dynamics,limited data lengths,or numerous variables. 展开更多
关键词 Granger causality(GC) interpretable deep learning iterative soft-thresholding algorithm(ISTA) least absolute shrinkage and selection operator(Lasso) temporal convolutional network(TCN)
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深度学习重建算法在低剂量冠状动脉CT血管成像中的图像质量与辐射剂量优化:一项前瞻性随机对照研究 认领 引用 被引量：2
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作者 杨欢 周永霞 +1 位作者 丁昭军 刘文罡 《中国医学装备》 2026年第2期13-17,共5页
目的:比较低管电流结合深度学习重建(DLIR)算法与低管电流结合混合迭代重建(HIR)算法和常规扫描方案结合HIR算法所获得的冠状动脉CT血管成像(CCTA)的图像,评估DLIR算法在CCTA中的临床应用价值。方法:前瞻性纳入2023年8月至2024年5月重... 目的:比较低管电流结合深度学习重建(DLIR)算法与低管电流结合混合迭代重建(HIR)算法和常规扫描方案结合HIR算法所获得的冠状动脉CT血管成像(CCTA)的图像,评估DLIR算法在CCTA中的临床应用价值。方法:前瞻性纳入2023年8月至2024年5月重庆医科大学附属永川医院收治的100例拟行回顾性门控CCTA检查的患者,采用随机数表法将其分为常规剂量组(50例)和低剂量组(50例)。常规剂量组采用160 mAs扫描,并以迭代重建算法进行图像重建。低剂量组采用60 mAs扫描,将该组患者的扫描数据分别采用两种不同的算法进行重建,又分为A组和B组,A组采用Karl 3D、B组使采用DLIR算法。比较常规剂量组、A组和B组3组的辐射剂量、主观图像质量评价、客观图像质量测量值图像噪声(SD)、信噪比(SNR)、对比噪声比(CNR)。结果:低剂量组(A和B组)有效辐射剂量(4.29±0.90)m Sv显著低于常规组(9.38±1.90)m Sv(t=17.10,P0.05),常规剂量组和B组的图像质量均优于A组,差异有统计学意义(x2=39.71、46.22,P0.05);常规剂量组、A组和B组噪声比较差异有统计学意义(F=176.39,P<0.05),冠状动脉各节段[右冠状动脉(RCA);左前降支(LAD);左旋支(LCX)]SNR和CNR比较差异均具有统计学意义(F=132.79、129.36、133.37和161.23、170.68、169.64,P<0.05)。结论:低管电流结合DLIR算法应用于回顾性门控CCTA中,可以显著降低辐射剂量,并进一步提高图像质量。 展开更多
关键词 冠状动脉CT血管成像(CCTA) 深度学习重建(DLIR)算法 混合迭代重建(HIR) 辐射剂量
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Dual-Objective Mixed Integer Linear Program and Memetic Algorithm for an Industrial Group Scheduling Problem 认领 引用 被引量：13
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作者 Ziyan Zhao Shixin Liu +1 位作者 MengChu Zhou Abdullah Abusorrah 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2021年第6期1199-1209,共11页
Group scheduling problems have attracted much attention owing to their many practical applications.This work proposes a new bi-objective serial-batch group scheduling problem considering the constraints of sequence-de... Group scheduling problems have attracted much attention owing to their many practical applications.This work proposes a new bi-objective serial-batch group scheduling problem considering the constraints of sequence-dependent setup time,release time,and due time.It is originated from an important industrial process,i.e.,wire rod and bar rolling process in steel production systems.Two objective functions,i.e.,the number of late jobs and total setup time,are minimized.A mixed integer linear program is established to describe the problem.To obtain its Pareto solutions,we present a memetic algorithm that integrates a population-based nondominated sorting genetic algorithm II and two single-solution-based improvement methods,i.e.,an insertion-based local search and an iterated greedy algorithm.The computational results on extensive industrial data with the scale of a one-week schedule show that the proposed algorithm has great performance in solving the concerned problem and outperforms its peers.Its high accuracy and efficiency imply its great potential to be applied to solve industrial-size group scheduling problems. 展开更多
关键词 Insertion-based local search iterated greedy algorithm machine learning memetic algorithm nondominated sorting genetic algorithm II(NSGA-II) production scheduling
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Feature-Based Aggregation and Deep Reinforcement Learning:A Survey and Some New Implementations 认领 引用 被引量：18
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作者 Dimitri P.Bertsekas 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2019年第1期1-31,共31页
In this paper we discuss policy iteration methods for approximate solution of a finite-state discounted Markov decision problem, with a focus on feature-based aggregation methods and their connection with deep reinfor... In this paper we discuss policy iteration methods for approximate solution of a finite-state discounted Markov decision problem, with a focus on feature-based aggregation methods and their connection with deep reinforcement learning schemes. We introduce features of the states of the original problem, and we formulate a smaller "aggregate" Markov decision problem, whose states relate to the features. We discuss properties and possible implementations of this type of aggregation, including a new approach to approximate policy iteration. In this approach the policy improvement operation combines feature-based aggregation with feature construction using deep neural networks or other calculations. We argue that the cost function of a policy may be approximated much more accurately by the nonlinear function of the features provided by aggregation, than by the linear function of the features provided by neural networkbased reinforcement learning, thereby potentially leading to more effective policy improvement. 展开更多
关键词 Reinforcement learning dynamic programming Markovian decision problems aggregation feature-based architectures policy iteration deep neural networks rollout algorithms
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基于点云配准的变电站三维地图构建方法 认领 引用
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作者 李军 吴喜春 +2 位作者 余浩睿 向晨光 李海丰 《电气传动》 2026年第5期70-75,共6页
针对变电站三维地图构建中因设备遮挡导致的真实标签数据稀缺问题,提出了一种基于半监督深度点云配准的三维地图构建方法。首先,采用迭代最近点算法为配准的点云数据生成伪标签,通过选取合适伪标签有效应对复杂遮挡场景;其次,结合少量... 针对变电站三维地图构建中因设备遮挡导致的真实标签数据稀缺问题,提出了一种基于半监督深度点云配准的三维地图构建方法。首先,采用迭代最近点算法为配准的点云数据生成伪标签,通过选取合适伪标签有效应对复杂遮挡场景;其次,结合少量真实标签和伪标签数据,通过交替迭代的方法逐步提升点云配准模型的精度和收敛性;最后,在ModelNet40数据集上对模型进行了训练和测试,实验结果表明,所提出的基于半监督深度点云配准方法优于传统方法,特别是在真实标签数据稀缺的情况下效果突出。 展开更多
关键词 变电站三维地图 半监督学习 深度点云配准 伪标签 迭代最近点算法
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基于强化学习改进的渐进不规则三角网加密滤波算法 认领 引用
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作者 沈求峰 胡翔云 徐嘉伯 《测绘地理信息》 CSCD 2026年第4期86-91,共6页
机载激光雷达(airborne LiDAR scanning,ALS)点云地面滤波是三维地理信息处理的关键步骤,但现有方法仍存在地形适应性不足的问题。渐进不规则三角网(triangular irregular network,TIN)加密(progressive TIN densification,PTD)算法虽... 机载激光雷达(airborne LiDAR scanning,ALS)点云地面滤波是三维地理信息处理的关键步骤,但现有方法仍存在地形适应性不足的问题。渐进不规则三角网(triangular irregular network,TIN)加密(progressive TIN densification,PTD)算法虽具有计算成本低、性能稳定等优点,但其固定参数设置导致在地形陡峭、近地物与地面点特征相似区域易产生误判。为此,本文提出一种基于强化学习改进的PTD滤波方法。该方法通过强化学习机制动态感知局部地形特征,自主调整传统PTD算法中的迭代参数,突破原有固定参数对复杂地形的适应性限制。同时建立加权决策模型,将传统PTD算法与强化学习输出的空间特征进行融合判别,兼顾算法稳定性与地形适应能力。在包含陡坡、低矮植被等典型复杂场景的实验中,改进方法较传统PTD算法总体精度有较大提升,验证了强化学习机制对参数动态优化的有效性。该方法为复杂地形下的地面滤波提供了新的技术路径。 展开更多
关键词 三维点云 地面滤波 PTD算法 强化学习 迭代参数 数字高程模型
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多频域迭代与物理约束协同的电磁逆散射算法 认领 引用
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作者 包立远 李明杰 卞士纪 《佳木斯大学学报（自然科学版）》 CAS 2026年第5期155-158,共4页
传统电磁逆散射问题求解方法在处理高对比度、复杂散射体时,收敛速度慢、计算成本高以及对噪声敏感,为解决这些问题,研究提出了一种融合多频域迭代与物理约束的电磁逆散射问题求解算法。该算法利用多频点信息构建频域迭代算法,以提高求... 传统电磁逆散射问题求解方法在处理高对比度、复杂散射体时,收敛速度慢、计算成本高以及对噪声敏感,为解决这些问题,研究提出了一种融合多频域迭代与物理约束的电磁逆散射问题求解算法。该算法利用多频点信息构建频域迭代算法,以提高求解精度和稳定性;引入物理约束,利用物理规律指导求解过程,进一步提升求解效率和精度。实验结果表明,该算法的重构精度为0.02,仅需50次迭代即可达到预定精度,平均计算时间为0.35秒/迭代,显著优于其他算法。在存在噪声时,该算法的信噪比为20,表现出更好的鲁棒性。研究为电磁逆散射问题的高效、准确求解提供了一种新的方法,满足实际应用中对高精度、高效率和高稳定性的需求。 展开更多
关键词 电磁逆散射 多频域迭代 物理约束 深度学习 求解算法
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Consensus control for heterogeneous uncertain multi-agent systems with hybrid nonlinear dynamics via iterative learning algorithm 认领 引用 被引量：4
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作者 XIE Jin CHEN JiaXi +2 位作者 LI JunMin CHEN WeiSheng ZHANG Shuai 《Science China(Technological Sciences)》 SCIE EI CAS CSCD 2023年第10期2897-2906,共10页
In this study,We propose a compensated distributed adaptive learning algorithm for heterogeneous multi-agent systems with repetitive motion,where the leader's dynamics are unknown,and the controlled system's p... In this study,We propose a compensated distributed adaptive learning algorithm for heterogeneous multi-agent systems with repetitive motion,where the leader's dynamics are unknown,and the controlled system's parameters are uncertain.The multiagent systems are considered a kind of hybrid order nonlinear systems,which relaxes the strict requirement that all agents are of the same order in some existing work.For theoretical analyses,we design a composite energy function with virtual gain parameters to reduce the restriction that the controller gain depends on global information.Considering the stability of the controller,we introduce a smooth continuous function to improve the piecewise controller to avoid possible chattering.Theoretical analyses prove the convergence of the presented algorithm,and simulation experiments verify the effectiveness of the algorithm. 展开更多
关键词 multi-agent systems adaptive iterative learning control hybrid nonlinear dynamics composite energy function consensus algorithm
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基于改进哈里斯鹰算法的机器人路径规划研究 认领 引用 被引量：7
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作者 白宇鑫 陈振亚 +3 位作者 石瑞涛 苏蔚涛 马卓强 杨尚进 《系统仿真学报》 EI CAS CSCD 北大核心 2025年第3期742-752,共11页
为提升哈里斯鹰优化算法收敛精度,解决易陷入局部最优等问题,提出了一种基于迭代混沌精英反向学习和黄金正弦策略的哈里斯鹰优化算法(gold sine HHO,GSHHO)。利用无限迭代混沌映射初始化种群,运用精英反向学习策略筛选优质种群,提高种... 为提升哈里斯鹰优化算法收敛精度,解决易陷入局部最优等问题,提出了一种基于迭代混沌精英反向学习和黄金正弦策略的哈里斯鹰优化算法(gold sine HHO,GSHHO)。利用无限迭代混沌映射初始化种群,运用精英反向学习策略筛选优质种群,提高种群质量,增强算法的全局搜索能力;使用一种收敛因子调整策略重新计算猎物能量,平衡算法的全局探索和局部开发能力;在哈里斯鹰的开发阶段引入黄金正弦策略,替换原有的位置更新方法,提升算法的局部开发能力;在9个测试函数和不同规模的栅格地图上评估GSHHO的有效性。实验结果表明:GSHHO在不同测试函数中具有较好的寻优精度和稳定性能,在2次机器人路径规划中路径长度较原始HHO算法分别减少4.4%、3.17%,稳定性分别提升52.98%、63.12%。 展开更多
关键词 哈里斯鹰优化算法 迭代混沌 精英反向学习 黄金正弦算法 栅格法 路径规划
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低管电流联合深度学习算法在副鼻窦CT成像中的对比研究 认领 引用 被引量：5
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作者 孙宇飞 钟朝辉 +4 位作者 李向明 周万博 许晨思 邓淼 张立新 《CT理论与应用研究（中英文）》 2025年第3期351-358,共8页
目的:探讨低管电流联合深度学习算法在副鼻窦CT成像中的应用效果,评估其在图像质量与辐射剂量方面的优势。方法:回顾性收集2024年3月至2024年11月在首都医科大学附属北京友谊医院接受副鼻窦CT检查的患者,将其分为3组:常规剂量组、低管电... 目的:探讨低管电流联合深度学习算法在副鼻窦CT成像中的应用效果,评估其在图像质量与辐射剂量方面的优势。方法:回顾性收集2024年3月至2024年11月在首都医科大学附属北京友谊医院接受副鼻窦CT检查的患者,将其分为3组:常规剂量组、低管电流CI组以及CV组。分别对3组图像的下鼻甲黏膜、翼内肌、颞窝脂肪区域进行CT值、SD值、信噪比(SNR)和对比噪声比(CNR)的测量与计算,客观评估图像质量。同时,由两名头颈影像专业医师基于最薄层厚图像,采用4分法对3组图像进行主观质量评分。比较常规剂量组与低管电流组的辐射剂量。结果:本研究共纳入80例患者。其中常规剂量组40例,低管电流CI组和CV组40例。3组间下鼻甲黏膜、翼内肌、颞窝脂肪CT值差异无统计学意义。在常规剂量组与CI组之间,SD值、信噪比(SNR)及对比噪声比(CNR)的差异无统计学意义。然而,常规剂量组与CV组在下鼻甲黏膜、翼内肌、颞窝脂肪区域的SD值及信噪比(SNR)方面,差异具有统计学意义。同样,CI组与CV组在相应区域的SD值及信噪比(SNR)方面,差异具有统计学意义。对于对比噪声比(CNR),常规剂量组与CV组在下鼻甲黏膜、翼内肌区域的差异具有统计学意义,CI组与CV组在相应区域对比噪声比(CNR)的差异亦具有统计学意义。在图像主观评分方面,常规剂量组和CI组的得分分别为(3.93±0.26)分和(3.88±0.33)分,显著高于CV组的(2.70±0.46)分,差异具有统计学意义。此外,低管电流组的辐射剂量相较于常规剂量组降低约73%,差异具有统计学意义。结论:低管电流联合深度学习算法在副鼻窦CT成像中,能够在保证图像质量前提下,显著降低辐射剂量。 展开更多
关键词 深度学习算法 迭代算法 副鼻窦CT 低剂量CT
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基于相似日与VMD-DBO-KELM的分布式光伏发电功率预测方法 认领 引用 被引量：11
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作者 方朝雄 郑洁云 +3 位作者 张章煌 陈若晨 周霞 王子博 《高电压技术》 EI CAS CSCD 北大核心 2025年第7期3477-3487,共11页
为了降低气象因素对分布式光伏发电功率预测精度的影响,提出一种基于相似日与变分模态分解(variational mode decomposition,VMD),结合蜣螂算法优化核极限学习机(dung beetle optimizer kernel extreme learning machine,DBO-KELM)的分... 为了降低气象因素对分布式光伏发电功率预测精度的影响,提出一种基于相似日与变分模态分解(variational mode decomposition,VMD),结合蜣螂算法优化核极限学习机(dung beetle optimizer kernel extreme learning machine,DBO-KELM)的分布式光伏发电功率预测方法。首先,采用改进的迭代自组织数据分析算法(iterative self-organizing data analysis techniques algorithm,ISODATA)将历史分布式光伏发电功率数据划分为不同的相似日类;然后,通过变分模态分解将光伏发电功率序列分解为不同的模态分量,并将其输入采用蜣螂优化算法优化的核极限学习机预测模型中,对每个分量分别进行预测;再对预测分量进行重构,进而实现基于VMD-DBO-KELM的高精度分布式光伏发电功率预测;最后,采用某分布式光伏站点实测数据进行算例分析。结果表明:所提方法在不同相似日下都具有较高的预测精度,具有较强的适应性。 展开更多
关键词 分布式光伏发电 功率预测 蜣螂优化算法 核极限学习机 迭代自组织数据分析算法 变分模态分解
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多智能体系统的一致性数据驱动最优迭代学习控制 认领 引用 被引量：2
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作者 耿燕 常杜辉 贺兴时 《西安工程大学学报》 CAS 2025年第2期118-126,共9页
为提高多智能体系统的跟踪性能和放宽算法的收敛性条件,设计一种数据驱动的最优迭代学习控制策略。针对一类线性时不变的多智能体系统,通过最小化预测输出与实际输出的残差与相邻估计差值的和,构建参数估计算法来估计系统参数。以虚拟... 为提高多智能体系统的跟踪性能和放宽算法的收敛性条件,设计一种数据驱动的最优迭代学习控制策略。针对一类线性时不变的多智能体系统,通过最小化预测输出与实际输出的残差与相邻估计差值的和,构建参数估计算法来估计系统参数。以虚拟领导者来替代期望轨迹,在通讯拓扑的基础上,通过优化智能体一致跟踪误差与控制差值和的指标函数,并将估计的参数嵌入到学习律中,设计了最优迭代学习控制律。结果表明参数估计误差有界,系统的跟踪误差单调收敛。通过数值仿真验证了设计的控制策略的有效性。 展开更多
关键词 迭代学习控制 多智能体系统 数据驱动 参数估计算法 最优控制
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智能回收模式下逆向物流车辆路径问题研究 认领 引用 被引量：1
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作者 王勇 孟亚雷 +1 位作者 罗思妤 许茂增 《计算机集成制造系统》 EI CSCD 北大核心 2025年第5期1872-1891,共20页
针对智能回收模式下逆向物流车辆路径问题研究在多频次回收和车辆共享调度相结合方面存在的不足,提出了智能回收模式下基于多频次回收和车辆共享的逆向物流车辆路径优化策略。首先,构建了包含运输成本、车辆租赁与维修成本、回收品处理... 针对智能回收模式下逆向物流车辆路径问题研究在多频次回收和车辆共享调度相结合方面存在的不足,提出了智能回收模式下基于多频次回收和车辆共享的逆向物流车辆路径优化策略。首先,构建了包含运输成本、车辆租赁与维修成本、回收品处理成本、违反时间窗惩罚成本和环境外部性收益的逆向物流运营成本最小化和回收车辆使用数最小化的双目标优化模型。其次,设计了一种两阶段CW-SLNSGA-Ⅱ算法对模型进行求解。该算法第一阶段将Clarke-Wright节约算法和Sweep扫描算法相结合生成初始解,第二阶段将自学习机制嵌入非支配排序遗传算法(NSGA-Ⅱ)中,使个体的交叉概率和变异概率可以根据适应度值的变化进行动态调整,并应用精英迭代策略保留了适应度值较优的个体,提高了算法的搜索性能。然后,通过与多目标蚁群算法(MOACO)、多目标鲸鱼优化算法(MOWOA)和基于分解的多目标进化算法(MOEAD)的对比分析,验证了算法的有效性。最后,通过实例对所提模型和算法进行了验证,并结合精英迭代策略和自学习机制对所提算法进行了消融实验研究,进而探讨了回收中心选择不同容量的回收车辆进行服务时车辆使用数与逆向物流运营成本的变化情况。研究结果表明,所提出的模型和算法可以有效降低逆向物流车辆调度成本和减少车辆使用数,并可实现多频次回收的车辆共享调度,进而为智能回收模式下的逆向物流网络构建和智慧城市建设提供理论支持和决策参考。 展开更多
关键词 智能回收模式 车辆路径问题 资源共享 CW-SLNSGA-Ⅱ算法 精英迭代
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