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Multi-sensor Deep Clustering Networks with Joint Learning Strategy for Fault Diagnosis Considering Consensus-specific Constraint

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IEEE Sensors Journal 2025年

作者： Xu, Yihao Lu, Mingfeng Wang, Gang Fang, Fang University of Science and Technology of China School of Management Hefei230026 China Hefei University of Technology School of Management Hefei230009 China Hefei University of Technology School of Management Key Laboratory of Process Optimization and Intelligent Decision-Making of the Ministry of Education Ministry of Education Engineering Research Center for Intelligent Decision-Making and Information System Technologies Hefei230009 China

The deep clustering method has been successfully employed for fault diagnosis due to its remarkable ability to extract deep representation features, particularly in scenarios where labeled data is unavailable. However, the separate learning strategy utilized in deep clustering-based fault diagnosis methods leads to sub-optimal fault diagnosis results, and the consensus representation feature present in multi-sensor industrial data is not fully explored to improve the effectiveness of fault diagnosis. To address the above problems, a novel deep clustering method named Multi-sensor Deep Clustering Networks with joint learning strategy (MsDCN) is proposed for fault diagnosis, which can comprehensively utilize the industrial multi-sensor data and enable joint optimization. First, the Commonality-net uses a sensor-aware attentive encoder (SaAE) to integrate multi-sensor industrial data and extract the consensus representation feature. Second, the Individuality-net is established to extract the specific representation features of multi-sensor industrial data. Finally, a joint multisensor deep clustering learning strategy including consensus-specific constraints is designed to optimize the networks jointly and learn the consensus representation feature with consistent and complementary information. Extensive experiments on the gearbox and bearing datasets validate the proposed MsDCN. The results indicate that the proposed MsDCN can generate a reliable consensus representation feature to achieve better fault diagnosis performance than other benchmark methods. Moreover, its capability to enhance multi-sensor fault diagnosis with unlabeled data highlights its potential for real-world applications. © 2001-2012 IEEE.

关键词： Labeled data

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Data-driven group decision making for diagnosis of thyroid nodule

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Science China(Information Sciences) 2019年第11期62卷 151-173页

作者： Chao FU Wenjun CHANG Weiyong LIU Shanlin YANG School of Management Hefei University of Technology Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Department of Ultrasound The First Affiliated Hospital of USTC Division of Life Sciences and MedicineUniversity of Science and Technology of China

Emerging information technologies' integration into various fields has enhanced the development of these fields. Large volumes of data have been accumulated in this process. The accumulated data offer opportunities and challenges for people facing practical problems. On the one hand, it is essential to depend on a group's capabilities rather than an individual's capabilities to handle practical problems because the individual may lack sufficient expertise and experience to use data. In this situation, the practical problems can be considered as group decision making(GDM) problems. On the other hand, the accumulated data can help generate quality solutions to GDM problems. To obtain such solutions under the assumption that the accumulated data regarding a specific decision problem are available, this paper proposes a data-driven GDM method. In the method, decision makers' weights are learned from historical overall assessments and the corresponding gold standards, while criterion weights are learned from historical overall assessments and the corresponding decision matrices. The learned expert weights and criterion weights are used to produce the aggregated assessments, from which alternatives are compared or the overall conclusion is made. In a tertiary hospital located in Hefei, Anhui Province, China, the proposed method is applied to aid radiologists in diagnosing thyroid nodules.

关键词： data-driven group decision making interval number learning of expert weights learning of criterion weights diagnosis of thyroid nodule

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The selection of project management software by FAHP and FMCDM in automobile R&D process

The selection of project management software by FAHP and FMC...

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2nd International Conference on Networking and Digital Society, lCNDS 2010

作者： Fu, Lei Shi, Li Yang, Ying Yu, Bengong Key Laboratory of Process Optimization and Intelligent Decision-making School of Management Hefei University of Technology Hefei China

ISBN: (纸本)9781424451616

The selection of project management (PM) software is a complex issue and has significant impacts to the efficiency of the automobile research & development (R&D) process. Given two alternatives based on critical path method (CPM) and critical chain project management (CCPM) respectively, decision-makers usually cause confusion. This paper analyzed the characteristic of automobile R&D project and developed an evaluation model based on the fuzzy analytic hierarchy process (FAHP) and fuzzy multiple criteria decision making (FMCDM) methods, which can help the managers in automobile industry to select more appropriate software in the course of product R&D. In particular, the FAHP method is used to obtain the weights of evaluation criteria, and the FMCDM method is used to determine the final rank of the software. The uncertainty and vagueness in evaluation procedure were presented as the fuzzy triangular numbers. The proposed method is applied to the case study of evaluating the performance of PM software based on CPM and CCPM respectively for a car manufacturer. The result of evaluation not only assisted managers in choosing suitable software but also outline the trend in development of CCPM software. ©2010 IEEE.

关键词： Managers

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A hybrid multi-chromosome genetic algorithm for the cutting stock problem

A hybrid multi-chromosome genetic algorithm for the cutting ...

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International Conference on Information Management, Innovation Management and Industrial Engineering, ICIII

作者： Jin, Peng Zhang, Shu Chu School of Management Hefei University of Technology Key Laboratory of Process Optimization and Intelligent Decision-making Hefei China

ISBN: (纸本)9780769542799

This paper presents a hybrid multi-chromosome genetic algorithm (HMCGA) to solve an in integer linear programming formulation of the Cutting Stock Problem (CSP). The CSP is an important class combinatorial problem. It is appropriate to minimize the raw material used by industries for fulfilling customer's demands. In such cases, classic models for solving the cutting stock problem are useless. HMCGA differ from previous application of genetic algorithm to CSP in that our coding contain tow chromosome, one represents the cutting pattern and another represents the frequencies of the cutting patterns in the first chromosome, rather than relying on a traditional genetic algorithm to decode each individual solution. Results obtained from computational experiments for ten benchmarks demonstrate that the performance of HMCGA is compared to that obtained using existing mete-heuristic algorithm. © 2010 IEEE.

关键词： Genetic algorithms

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Solving the Observing and Downloading Integrated Scheduling Problem of Earth Observation Satellite with a Quantum Genetic Algorithm

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Journal of Systems Science and Information 2018年第5期9卷 399-420页

作者： Ye ZHANG Xiaoxuan HU Waiming ZHU Peng JIN School of Management Hefei University of Technology Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education

This paper addresses the integrated Earth observation satellite scheduling problem. It is a complicated problem because observing and downloading operations are both involved. We use an acyclic directed graph model to describe the observing and downloading integrated scheduling *** on the model which considering energy constraints and storage capacity constraints, we develop an efficient solving method using a novel quantum genetic algorithm. We design a new encoding and decoding scheme that can generate feasible solution and increase the diversity of the *** results of the simulation experiments show that the proposed method solves the integrated Earth observation satellite scheduling problem with good performance and outperforms the genetic algorithm and greedy algorithm on all instances.

关键词： Earth observation satellite integrated scheduling quantum genetic algorithm

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Hierarchical method of task assignment for multiple cooperating UAV teams

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Journal of Systems Science & Complexity 2015年第5期28卷 1000-1009页

作者： Xiaoxuan Hu Huawei Ma Qingsong Ye He Luo School of Management Hefei University of Technology Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education

The problem of task assignment for multiple cooperating unmanned aerial vehicle(UAV) teams is considered. Multiple UAVs forming several small teams are needed to perform attack tasks on a set of predetermined ground targets. A hierarchical task assignment method is presented to address the problem. It breaks the original problem down to three levels of sub-problems: target clustering, cluster allocation and target assignment. The first two sub-problems are centrally solved by using clustering algorithms and integer linear programming, respectively, and the third sub-problem is solved in a distributed and parallel manner, using a mixed integer linear programming model and an improved ant colony algorithm. The proposed hierarchical method can reduce the computational complexity of the task assignment problem considerably, especially when the number of tasks or the number of UAVs is large. Experimental results show that this method is feasible and more efficient than non-hierarchical methods.

关键词： unmanned aerial vehicle (UAV) task assignment clustering integer linear programming ant colony optimization(ACO) algorithm

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Heuristic algorithms for scheduling on uniform parallel machines with heads and tails

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Journal of Systems Engineering and Electronics 2011年第3期22卷 462-467页

作者： Kai Li Shanlin Yang School of Management Hefei University of TechnologyHefei 230009P.R.China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of EducationHefei University of TechnologyHefei 230009P.R.China

This paper considers the uniform parallel machine scheduling problem with unequal release dates and delivery times to minimize the maximum completion *** this NP-hard problem,the largest sum of release date,processing time and delivery time first rule is designed to determine a certain machine for each job,and the largest difference between delivery time and release date first rule is designed to sequence the jobs scheduled on the same machine,and then a novel algorithm for the scheduling problem is *** evaluate the performance of the proposed algorithm,a lower bound for the problem is *** accuracy of the proposed algorithm is tested based on the data with problem size varying from 200 jobs to 600 *** computational results indicate that the average relative error between the proposed algorithm and the lower bound is only 0.667%,therefore the solutions obtained by the proposed algorithm are very accurate.

关键词： scheduling parallel machine uniform release date/head delivery time/tail.

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Pricing and production strategies in remanufacturing from the perspectives of supply chain: Review and future directions

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Frontiers of Engineering Management 2024年第1期11卷 1-15页

作者： Kai LI Yan LI Nenggui ZHAO School of Management Hefei University of TechnologyHefei 230009China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of EducationHefei 230009China

Remanufacturing is widely recognized as beneficial to the environment and a circular economy. However, remanufacturing is more complex than traditional manufacturing due to the effects of government policy, uncertainty of consumer preferences, competition and cooperation among firms, and so on. These factors motivate academics to optimize remanufacturing outcomes, especially for product pricing and production. This study reviews the published literature on pricing and production strategies in remanufacturing from four perspectives of supply chain, namely, government policy, consumer characteristics, relationships among firms, and supply chain structures. Review results can benefit scholars/practitioners in the future by highlighting the challenges and opportunities in remanufacturing strategies.

关键词： remanufacturing reverse and closed-loop supply chain circular economy

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Electricity load forecasting in smart grid based on residual GM (1, 1) model

Electricity load forecasting in smart grid based on residual...

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2013 International Conference on Mechatronics and Automatic Control Systems, ICMS 2013

作者： Shen, Jianxin Wang, Haijiang Yang, Shanlin Key Lab of Process Optimization and Intelligent Decision-making Hefei University of Technology Hefei China

ISBN: (纸本)9783319012728

The construction of smart grid has put forward higher requirements on deployment accuracy of the energy. Power generation and electricity sectors have carried out more accurate data analysis and forecasting. In this context, a residual GM (1, 1) model is proposed. This model can overcome the lack of traditional grey model and make accurate forecasting of electricity consumption in smart grid. Finally, numerical examples demonstrate that this method can efficiently improve the prediction accuracy. © Springer International Publishing Switzerland 2014.

关键词： Smart power grids

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A Fast Generation Algorithm of Communication Topology for Three-dimensional Optimally Persistent Formation 33

A Fast Generation Algorithm of Communication Topology for Th...

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第33届中国控制与决策会议

作者： Guoqiang Wang Qianwei Lv Xiaoduo Li Xin Cao He Luo School of Management Hefei University of Technology Key Laboratory of Process Optimization and Intelligent Decision-Making Ministry of Education Engineering Research Center for Intelligent Decision-making & Information Systems Technologies Ministry of Education Key Laboratory of Urban ITS Technology Optimization and Integration Ministry of Public Security of China

ISBN: (纸本)9781665431293

To solve the communication topology generation problem of three-dimensional optimally persistent formation with leader constraint,a fast generation algorithm based on minimum cost arborescence,arc addition and path reverse operation is *** with the existing algorithms,this algorithm has a shorter calculation ***,the simulation results further verify the correctness and effectiveness of the algorithm.

关键词： Communication topology Leader constraint Three-dimensional optimal persistent formation Minimum cost arborescence Arc addition Path reverse operation

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