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12th International Conference on Computer and Communications Management

作者： Murayama, Mao Ito, Nobuhiro Otsuka, Takanobu Nagoya Inst Technol Dept Comp Sci Nagoya Aichi Japan

ISBN: (纸本)9798400718038

Recent social conditions have caused to rise of raw material prices in the manufacturing industry. In addition, efforts to realize a decarbonized society are gaining momentum worldwide. Therefore, the manufacturing industry is required to reduce manufacturing costs and energy consumption by improving production efficiency. The objective of this study is to equalize machine utilization rates to reduce product retention on the production line which causes lower production efficiency. To deal with this, this study aims to develop a production scheduling system. For this purpose, we propose and implement the system that optimizes production schedules by considering the start time of job processing and the processing order of jobs using the production line simulation. Experiments have shown that the proposed system can produce production schedules with shorter product retention time than those used in real factories.

关键词： Production scheduling optimization algorithm Parameter Product retention time

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Deep learning-driven interval uncertainty propagation for aeronautical structures

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Chinese Journal of Aeronautics 2024年第12期37卷 71-86页

作者： Yan SHI Michael BEER Institute for Risk and Reliability Leibniz Universitat HannoverHannover 30167Germany Department of Civil and Environmental Engineering University of LiverpoolLiverpool L693BXUnited Kingdom International Joint Research Center for Resilient Infrastructure&International Joint Research Center for Engineering Reliability and Stochastic Mechanics Tongji UniversityShanghai 200092China

Interval Uncertainty Propagation(IUP)holds significant importance in quantifying uncertainties in structural outputs when confronted with interval input *** the aviation field,the precise determination of probability models for input parameters of aeronautical structures entails substantial costs in both time and *** an alternative,the use of interval variables to describe input parameter uncertainty becomes a pragmatic *** complex task of solving the IUP for aeronautical structures,particularly in scenarios marked by pronounced nonlinearity and multiple outputs,necessitates innovative *** study introduces an efficient deep learning-driven approach to address the challenges associated with *** proposed approach combines the Deep Neural Network(DNN)with intelligent optimization algorithms for dealing with the IUP in aeronautical *** inventive extremal value-oriented weighting technique is presented,assigning varying weights to different training samples within the loss function,thereby enhancing the computational accuracy of the DNN in predicting extremal values of structural ***,an adaptive framework is established to strategically balance the global exploration and local exploitation capabilities of the DNN,resulting in a predictive model that is both robust and *** illustrate the effectiveness of the developed approach,various applications are explored,including a high-dimensional numerical example and two aeronautical *** obtained results highlight the high computational accuracy and efficiency achieved by the proposed approach,showcasing its potential for addressing complex IUP challenges in aeronautical engineering.

关键词： Uncertainty propagation Interval variable Deep learning optimization algorithm Aeronautical structure

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A dueling double deep Q network assisted cooperative dual-population coevolutionary algorithm for multi-objective combined economic and emission dispatch problems

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SWARM AND EVOLUTIONARY COMPUTATION 2025年 95卷

作者： Yu, Xiaobing Wang, Yuan Zhang, Wen Nanjing Univ Informat Sci & Technol Collaborat Innovat Ctr Forecast & Evaluat Meteorol Nanjing 210044 Peoples R China Nanjing Univ Informat Sci & Technol Sch Management Sci & Engn Res Inst Risk Governance & Emergency Decis Making Nanjing 210044 Peoples R China

With the increasing demand for electricity and the awareness of environmental protection, requirements for economic efficiency and controlling environmental impact of the power system are increasing. However, traditional power system scheduling usually focuses on ensuring the stability of the power supply, which neglects the optimization of cost and emissions. Therefore, combined economic and emission dispatch (CEED) problem is proposed to overcome this challenge. Due to nonlinear and nonconvex objective functions and narrow feasible regions, the optimization of multi-objective CEED problem encounters many difficulties. A dueling double deep Q network-assisted cooperative dual-population coevolutionary algorithm (D3QN-CDCA) is developed to solve multi-objective CEED problems. The proposed algorithm utilizes D3QN to select operators dynamically and adaptively for two populations of coevolutionary algorithm, thus enhancing its adaptability to different practical constrained multi-objective problems and satisfying search needs of different iteration stages. The introduction of D3QN is to overcome the inherent overestimation of DQN and improve the learning efficiency of the network. To comprehensively evaluate its performance, we tested D3QN-CDCA on benchmark function sets and applied it to CEED problems in comparison with other competitive algorithms. Results demonstrate that D3QN-CDCA outperforms existing methods with an average IGD+ ranking of 1.4286 and an average HV ranking of 1.321 in benchmark function sets. Meanwhile, the proposed algorithm achieves an average improvement of 23.86 % in 6unit, 23.12 % in 11-unit and 13.51 % in 14-unit CEED problems. The improvement in solution quality demonstrates the effectiveness of D3QN-CDCA in solving high-dimensional multi-objective optimization problems, particularly in CEED, highlighting its potential for real-world energy management applications.

关键词： Combined economic and emission dispatch Coevolutionary algorithm Deep Q learning optimization algorithm

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Set-membership estimation for T-S fuzzy complex networks: a dynamic coding-decoding mechanism

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NONLINEAR DYNAMICS 2025年第13期113卷 17041-17056页

作者： Hu, Changzhen Ding, Sanbo Rong, Nannan Hebei Univ Technol Sch Artificial Intelligence Tianjin 300401 Peoples R China Hebei Univ Technol Control Engn Technol Res Ctr Hebei Prov Tianjin 300401 Peoples R China Tiangong Univ Sch Artificial Intelligence Tianjin 300387 Peoples R China

This paper explores the set-membership estimation (SME) of T-S fuzzy complex networks (TSFCNs) under unknown but bounded process and measurement noises. To facilitate digital transmission, a dynamic coding-decoding mechanism is integrated into each communication channel between nodes. It employs a dynamic auxiliary variable to adjust the coding interval. Meanwhile, under a directed communication topology, a fuzzy estimator is designed for each node. This estimator utilizes relative measurement outputs from the node itself and its neighbors to determine an ellipsoidal set that encapsulates the system state at each instant. Furthermore, two optimization problems are formulated to establish sufficient conditions for the desired estimators, ensuring that specified performance requirements are satisfied. Finally, numerical simulations are provided to validate the effectiveness of the SME method for TSFCNs.

关键词： T-S fuzzy complex networks Set-membership estimation Dynamic coding-decoding mechanism optimization algorithm

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A novel predictive-reactive scheduling method for parallel batch processor lot-sizing and scheduling with sequence-dependent setup time

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COMPUTERS & INDUSTRIAL ENGINEERING 2024年 189卷

作者： Qiu, Junhao Liu, Jianjun Peng, Chengfeng Chen, Qingxin Guangdong Univ Technol Sch Electromech Engn State Key Lab Precis Elect Mfg Technol & Equipment Guangdong CIM Prov Key Lab Guangzhou Peoples R China Xiangnan Univ Sch Phys & Elect Elect Engn Chenzhou 423000 Peoples R China

In practical printed circuit boards (PCB) drilling production systems, multiple spindle processing capabilities and uncertainties are commonplace. This paper addresses predictive-reactive scheduling, considering parallel batch processor lot-sizing and scheduling with sequence-dependent setup times in a dynamic environment. We propose a predictive scheduling algorithm named S-AGAIG to balance machine spindle utilization and order tardiness in steps. Multiple splitting solutions are first formed by using a split heuristic algorithm to size the sub -lots of orders. Then the adaptive genetic algorithm with iterated greedy search is applied to select the solutions and scheduling. Furthermore, we present a schedule repair strategy based on sub -lot co-processing considering the impact of critical sub -lots, and construct a scheduling stability metric for rescheduling. In 36 sets of case experiments encompassing diverse load and machine type configurations, S-AGAIG showcases a 21.27% enhancement in average tardiness performance when compared to its closest rival. Across 28 cases involving varying disturbances, the framework demonstrates exceptional robustness.

关键词： Predictive-reactive scheduling Lot-sizing and scheduling Multi-spindle batch processor Sequence-dependent setup optimization algorithm

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Optimizing electric vehicle routing under traffic congestion: A comprehensive energy consumption model considering drivetrain losses

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COMPUTERS & OPERATIONS RESEARCH 2024年 168卷

作者： Xiong, Hao Xu, Yumiao Yan, Huili Guo, Haoying Zhang, Chen Hainan Univ Int Business Sch Haikou 570228 Hainan Peoples R China Hainan Univ Coll Int Tourism & Publ Adm Haikou 570228 Hainan Peoples R China Southeast Univ Sch Econ & Management Nanjing 211189 Jiangsu Peoples R China

Energy consumption is a critical concern within the electric vehicle (EV) industry. Despite its substantial impact, drivetrain losses have been inadequately addressed in some studies. This paper introduces drivetrain losses as a nonlinear function of velocity, incorporating them into the comprehensive energy consumption model. Considering the influence of traffic congestion on vehicle speed, our proposed model integrates drivetrain losses, aerodynamic losses, tire losses, kinetic energy losses, and potential energy losses. The objective is to optimize routing with the aim of minimizing energy consumption. To tackle this problem, we present a dedicated adaptive large neighborhood search algorithm. Our numerical experiments reveal key findings: (1) Drivetrain losses represent a higher percentage of total energy consumption compared to aerodynamic and tire losses;(2) Drivetrain losses influenced by traffic congestion constitute a significant share, ranging between 63.82% and 72.08% of total energy consumption;(3) Energy consumption experiences notable increases under the impact of traffic congestion.

关键词： Electric vehicles routing Drivetrain losses Energy consumption model Traffic congestion optimization algorithm

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Efficient and Robust Sparse Linear Discriminant Analysis for Data Classification

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2025年第1期9卷 617-629页

作者： Liu, Jingjing Feng, Manlong Xiu, Xianchao Liu, Wanquan Zeng, Xiaoyang Shanghai Univ Sch Microelect Shanghai Key Lab Automobile Intelligent Network In Shanghai 200444 Peoples R China Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200444 Peoples R China Sun Yat Sen Univ Sch Intelligent Syst Engn Guangzhou 528406 Peoples R China Fudan Univ Sch Microelect State Key Lab Integrated Chips & Syst Shanghai 201203 Peoples R China

Sparse linear discriminant analysis (LDA) is a popular machine learning method that improves the accuracy of data classification by introducing sparsity. However, its performance often degrades seriously when encountering noise. To address this issue, this paper proposes a new method called efficient and robust sparse linear discriminant analysis (ERSLDA). The core idea is to characterize the local pixel corruptions by integrating L-p-norm (0< p< 1), and to describe the global structured sparsity by enforcing L-2,L-p-norm (0< p< 1), thereby improving the ability of feature selection. Compared with existing L-1-norm and L-2,L-1-norm, the involved L-p-norm and L-2,L-p-norm can bring higher robustness and better accuracy. Moreover, an additional matrix with Frobenius norm is embedded to represent Gaussian noise, which further enhances the robustness in different scenarios. In algorithms, an iterative optimization scheme is developed based on the alternating direction method of multipliers (ADMM) to solve the proposed ERSLDA, and the resulting subproblems can be calculated efficiently. Extensive numerical comparisons are performed with nine state-of-the-art LDA-based methods on seven benchmark image datasets. The experimental results validate that the proposed method is efficient for data classification and robust to noise.

关键词： Data classification linear discriminant analysis (LDA) L-p-norm optimization algorithm

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Coupled Vehicle and Trajectory Design optimization for Boost-Glide Hypersonic Systems

Coupled Vehicle and Trajectory Design Optimization for Boost...

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AIAA SciTech Forum

作者： Gottlieb, Jonah R. Mines, John Mark Garmendia, Daniel C. Emhoff, Jerold W. Clemens, Daniel E. Johns Hopkins Univ Appl Phys Lab Air & Missile Def Sect Laurel MD 20723 USA Johns Hopkins Univ Appl Phys Lab Space Explorat Sect Laurel MD 20723 USA

ISBN: (数字)9781624107115

ISBN: (纸本)9781624107115

Multidisciplinary design optimization approaches have been successfully applied to many flight vehicle design problems in academia and are now becoming more common in industry. Typically, the vehicle design is optimized given a constrained trajectory, or the trajectory is optimized given a fixed vehicle design;however, simultaneous vehicle/trajectory optimization is very challenging. Certain applications, even for conceptual design, demand higher fidelity in the vehicle model and/or trajectory solver, which is possible given sufficient computational resources. This paper describes missile conceptual design optimization methods and software tools under development for more than a decade at the Johns Hopkins University Applied Physics Laboratory. These methods are applied to the problem of simultaneous vehicle and trajectory design optimization to demonstrate how that coupling drives performance for a hypothetical boost-glide hypersonic vehicle.

关键词： Boost Glide Multidisciplinary Design and optimization Hypersonic Systems Optimal Control Problem Launch Vehicle Design optimization algorithm Suborbital Spaceplane Flight Vehicle Design Software Tool Propellant

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Structural Deformation Warning Method for Coal Mine Roadway Surrounding Rock Based on Evidential Reasoning

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Zhang, Zehui Liu, Hanfeng Xu, Xiaobin Xie, Zhemin Huo, Huining Chang, Leilei Tao, Zhigang Hangzhou Dianzi Univ China Austria Belt & Rd Joint Lab Artificial Intel Hangzhou 310018 Peoples R China China Univ Min & Technol Beijing State Key Lab Tunnel Engn Beijing 100083 Peoples R China

The structural deformation measuring of coal mine roadways is an effective method to prevent dynamic disasters and avoid loss of civilian life and property. Previous studies on structural deformation early warning, however, failed on multisource uncertain data integration, dynamic adaptability, and limiting the updating ability of models. To address the above issues, this article proposes a structural deformation warning method for coal mine roadway surrounding rock based on evidential reasoning. The evidential reasoning theory is used to construct the models for predicting the deformation value and evaluating the stability level of the surrounding rock through multiple-source data fusion. Moreover, the online optimization mechanism is proposed to improve the performance of the evidence reasoning (ER)-based models. The proposed deformation warning method is evaluated on real coal mine roadway surrounding rock data. Experiment results demonstrate that the proposed method model performs well in deformation prediction with the advantage of dynamic updating and the roadway surrounding stability evaluation.

关键词： Deformation Rocks optimization Monitoring Predictive models Cognition Reliability Linear programming Deformable models Coal mining Coal mine roadway surrounding rock deformation prediction early warning evidential reasoning optimization algorithm

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A CNN-Based Approach for Classical Music Recognition and Style Emotion Classification

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IEEE ACCESS 2025年 13卷 20647-20666页

作者： Shi, Yawen Henan Univ Sci & Technol Sch Art & Design Luoyang 471000 Henan Peoples R China

Music recognition refers to the process of automatically recognizing and classifying the musical content in audio signals using computer technology and algorithms. Music recognition technology can help people recognize information such as the music title, artist, musical style, rhythm, and the emotions conveyed by the music in the audio, thus enabling applications like automated music information retrieval and recommendation systems. Classical music, due to its vast quantity, diverse types, and time span covering several centuries, presents challenges that existing music recognition software and traditional music recognition algorithms cannot effectively address. In this study, The model based on convolutional neural networks (CNNs) is proposed, allowing people to recognize the classical music title, style, and emotions contained in a piece of music. The proposed model is particularly beneficial for individuals who are interested in classical music but lack extensive knowledge about it, as it provides essential information about the pieces. By extracting multidimensional features from classical music, the model can recognize the title, style, and emotions expressed. To improve the model's recognition accuracy, various noises are introduced to the dataset. Meanwhile, in this study, a novel loss function has been devised to more effectively assess the model's performance. For searching for optimal performance of the model, a novel optimization algorithm also be proposed to find optimal hyperparameters of loss function. The experiment results show average title recognition accuracy is 0.98, average style recognition accuracy is 0.89 and average emotion recognition accuracy is 0.93. These results adequately demonstrate that the proposal model significantly enhances the model's ability to accurately recognize the titles, styles, and emotions of classical music, achieving high recognition rates even in noisy environments.

关键词： Emotion recognition Accuracy Feature extraction Multiple signal classification Classification algorithms Noise Deep learning Recommender systems Proposals Heuristic algorithms CNN deep learning music recognition music retrieval optimization algorithm

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