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Differential privacy histogram publishing method based on dynamic sliding window

Frontiers of Computer Science 2023年第4期17卷 209-220页

作者： Qian CHEN Zhiwei NI Xuhui ZHU Pingfan XIA School of Management Hefei University of TechnologyHefei 230009China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of EducationHefei 230009China

Differential privacy has recently become a widely recognized strict privacy protection model of data *** privacy histogram publishing can directly show the statistical data distribution under the premise of ensuring user privacy for data query,sharing,and *** dynamic data release is a study with a wide range of current industry ***,the amount of data varies considerably over different *** data processing will result in the risk of users’information leakage and unavailability of the ***,we designed a differential privacy histogram publishing method based on the dynamic sliding window of LSTM(DPHP-DL),which can improve data availability on the premise of guaranteeing data ***-DL is integrated by DSW-LSTM and DPHK+.DSW-LSTM updates the size of sliding windows based on data value prediction via long shortterm memory(LSTM)networks,which evenly divides the data stream into several ***+heuristically publishes non-isometric histograms based on k-mean++clustering of automatically obtaining the optimal K,so as to achieve differential privacy histogram publishing of dynamic *** experiments on real-world dynamic datasets demonstrate the superior performance of the DPHP-DL.

关键词： differential privacy dynamic data histogram publishing sliding window

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Short-term optimal scheduling of hydro–wind–PV and multi-storage complementary systems based on opposition-based learning PSO algorithm

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Applied Energy 2025年 394卷

作者： Yaoyao He Ning Xian School of Management Hefei University of Technology Hefei 230009 China Key Laboratory of Process Optimization and Intelligent Decision-Making (Hefei University of Technologu) Ministry of Education Hefei Anhui China

The introduction of energy storage systems in multi-energy complementary systems ensures efficient energy use and distribution, enhancing the system’s economic benefits. However, current research not only lacks the application of energy storage in large-scale hydro–wind–PV hybrid systems, but also uses only one type of energy storage system in the complementary system, neglecting the synergistic effect between various energy storage systems. To address this research gap, this study proposes a hydro–wind–PV joint scheduling model that considers the coordinated optimization of pumped storage and battery storage. Through this synergy, the energy storage systems can further optimize the exploitation of energy storage potential and improve energy utilization. Additionally, a particle swarm optimization algorithm based on opposite-based learning (PSO-OBL) is proposed, tailored for short-term optimization. The model and algorithm are validated through their application to a power grid in the southwest region of China. The results demonstrate that the integration of pumped storage and battery storage significantly enhances the system’s economic efficiency, and the PSO-OBL algorithm outperforms traditional algorithms in both convergence and solution quality. By analyzing 4 typical days, the findings show that multiple energy storage systems can effectively cooperate under varying environmental conditions, further improving energy self-sufficiency and maximizing the benefits of energy storage. Compared with the traditional model, the system’s economic efficiency can be improved by a maximum of 3.01 %, and the load self-sufficiency rate is increased by 2.32 %. This study provides practical reference for optimal scheduling of multiple energy storage systems.

关键词： Particle swarm optimization (PSO)

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A multi-UAV deployment method for border patrolling based on Stackelberg game

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Journal of Systems Engineering and Electronics 2023年第1期34卷 99-116页

作者： LEI Xing HU Xiaoxuan WANG Guoqiang LUO He School of Management Hefei University of TechnologyHefei 230009China Key Laboratory of Process Optimization&Intelligent Decision-making Ministry of EducationHefei 230009China Engineering Research Center for Intelligent Decision-making&Information Systems Technologies Ministry of EducationHefei 230009China

To strengthen border patrol measures, unmanned aerial vehicles(UAVs) are gradually used in many countries to detect illegal entries on borders. However, how to efficiently deploy limited UAVs to patrol on borders of large areas remains challenging. In this paper, we first model the problem of deploying UAVs for border patrol as a Stackelberg game. Two players are considered in this game: The border patrol agency is the leader,who optimizes the patrol path of UAVs to detect the illegal immigrant. The illegal immigrant is the follower, who selects a certain area of the border to pass through at a certain time after observing the leader’s strategy. Second, a compact linear programming problem is proposed to tackle the exponential growth of the number of leader’s strategies. Third, a method is proposed to reduce the size of the strategy space of the follower. Then, we provide some theoretic results to present the effect of parameters of the model on leader’s utilities. Experimental results demonstrate the positive effect of limited starting and ending areas of UAV’s patrolling conditions and multiple patrolling altitudes on the leader ’s utility, and show that the proposed solution outperforms two conventional patrol strategies and has strong robustness.

关键词： border patrol unmanned aerial vehicle(UAV) Stackelberg game compact linear programming dominated strategy elimination

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optimization of communication topology for persistent formation in case of communication faults

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Chinese Physics B 2023年第7期32卷 610-624页

作者：王国强罗贺胡笑旋台建玮 School of Management Hefei University of TechnologyHefei 230009China Key Laboratory of Process Optimization&Intelligent Decision-making Ministry of EducationHefei 230009China Engineering Research Center for Intelligent Decision-making&Information Systems Technologies Ministry of EducationHefei 230009China

To address the optimization problem of communication topology for persistent formation in the case of communication faults such as link interruption,transmitter failure,and receiver failure a two-stage model including fast reconstruction of communication topology and re-optimization of communication topology is ***,a fast reconstruction algorithm of communication topology for persistent formation(FRA-CT-PF),based on optimally rigid graph,arc addition operation,and path reversal operation,is proposed,which can quickly generate a feasible reconstructed communication topology after communication faults,thus ensuring the safety of the agents and maintaining the formation shape of persistent ***,a re-optimization algorithm of communication topology for persistent formation(ROA-CTPF),based on agent position exchange,is proposed,which can further obtain a reoptimized communication topology to minimize the formation communication cost while still maintaining the formation shape of persistent *** time complexities of these two algorithms are also ***,the effectiveness of the above algorithms is verified by numerical *** with existing algorithms,FRA-CT-PF can always obtain feasible reconstructed communication topology in much less time under all communication fault scenarios,and ROA-CT-PF can obtain a reoptimized communication topology to further reduce the formation communication cost in a shorter time.

关键词： persistent formation communication topology formation communication cost communication fault

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Multicriteria game approach to air-to-air combat tactical decisions for multiple UAVs

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Journal of Systems Engineering and Electronics 2023年第6期34卷 1447-1464页

作者： JIANG Ruhao LUO He MA Yingying WANG Guoqiang School of Management Hefei University of TechnologyHefei 230009China Key Laboratory of Process Optimization&Intelligent Decision-making Ministry of EducationHefei 230009China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei 230009China

Air-to-air combat tactical decisions for multiple unmanned aerial vehicles(ACTDMU)are a key decision-making step in beyond visual range *** influencing factors,strong antagonism and real-time requirements need to be considered in the ACTDMU *** this paper,we propose a multicriteria game approach to *** approach consists of a multicriteria game model and a Pareto Nash equilibrium *** this model,we form the strategy profiles for the integration of air-to-air combat tactics and weapon target assignment strategies by considering the correlation between them,and we design the vector payoff functions based on predominance *** propose a algorithm of Pareto Nash equilibrium based on preference relations using threshold constraints(PNE-PRTC),and we prove that the solutions obtained by this algorithm are refinements of Pareto Nash equilibrium *** numerical experiments indicate that PNE-PRTC algorithm is considerably faster than the baseline algorithms and the performance is *** on large-scale instances,the Pareto Nash equilibrium solutions can be calculated by PNEPRTC algorithm at the second *** simulation experiments show that the multicriteria game approach is more effective than one-side decision approaches such as multiple-attribute decision-making and randomly chosen decisions.

关键词： tactical decision multicriteria game preference relation Pareto Nash equilibrium.

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A BSC-ANP Model for Evaluation of Strategic Fit of New Product Development Projects

A BSC-ANP Model for Evaluation of Strategic Fit of New Produ...

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第25届中国控制与决策会议

作者： Ying Yang ShanLin Yang Jian Yang School of Management Hefei University of Technology Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education

ISBN: (纸本)9781467355339

New product development (NPD) projects have been major forces driving company competitive advantage. All of them should align with corporate strategy so that the limited resources may be allocated effectively. However, few studies have been conducted to measure the extent that the NPD project matches with strategy. The paper identifies the cause-effect relationships between the strategy BSC measures for NPD projects based on strategy map and presents a BSC-ANP model to evaluate the strategic fit of projects. The causal relationships and interdependencies between projects and criteria are integrated with ANP model to produce the final priorities of projects with respect to their strategic fit. The proposed model was tested against empirical data drawn from NPD projects in an automobile company.

关键词： Evaluation, Strategic fit, New product development project, BSC-ANP

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A composite quantile regression long short-term memory network with group lasso for wind turbine anomaly detection

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Journal of Ambient Intelligence and Humanized Computing 2023年第3期14卷 2261-2274页

作者： Xu, Qifa Wu, Dongdong Jiang, Cuixia Wang, Xiangxiang School of Management Hefei University of Technology Hefei230009 China Key Laboratory of Process Optimization and Intelligent Decision-Making Ministry of Education Hefei230009 China

Wind turbine anomaly detection is an important but challenging work. Though deep learning is promising in this filed, it may fail for large error distributions and often lacks interpretability due to its complex structure. To this end, we develop a composite quantile regression long short-term memory network with group lasso (CQR-LSTM-GL). The CQR-LSTM-GL model extends the LSTM network to the quantile regression framework, which makes LSTM always valid regardless of the error distribution. Additionally, the CQR-LSTM-GL model adopts the group lasso method to compress the network structure and select key features. To illustrate its efficacy, we conduct a numerical experiment on the public server machine datasets. The experimental results show that the CQR-LSTM-GL model is not only slightly better than several competitive models in anomaly detection performance, but also far ahead in sparsity performance. The sparsity of CQR-LSTM-GL is 1.63 times that of CQR-LSTM-L1, and much more than that of CQR-LSTM. We then apply the CQR-LSTM-GL model to the anomaly detection of a wind turbine using the real Supervisory Control and Data Acquisition data, where all three anomalies are identified. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Long short-term memory

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An Adaptive Local Grid Nesting-based Genetic Algorithm for Multi-earth Observation Satellites' Area Target Observation

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Journal of Systems Science and Systems Engineering 2024年第2期33卷 232-258页

作者： Ligang Xing Wei Xia Xiaoxuan Hu Waiming Zhu Yi Wu School of Management Hefei University of TechnologyHefei 230009China Key Laboratory of Process Optimization and Inteligent Decision-making Ministry of EducationHefei 230009China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei 230009China

The Scheduling of the Multi-EOSs Area Target Observation(SMEATO)is an EOS resource schedul-ing problem highly coupled with computational *** advances in EOS technology and the ex-pansion of wide-area remote sensing applications have increased the practical significance of *** this paper,an adaptive local grid nesting-based genetic algorithm(ALGN-GA)is proposed for developing SMEATO ***,a local grid nesting(LGN)strategy is designed to discretize the target area into parts,so as to avoid the explosive growth of calculations.A genetic algorithm(GA)framework is then used to share reserve information for the population during iterative evolution,which can generate high-quality solutions with low computational *** this basis,an adaptive technique is introduced to determine whether a local region requires nesting and whether the grid scale is *** effectiveness of the proposed model is assessed experimentally with nine randomly generated tests at different *** results show that the ALGN-GA offers advantages over several conventional algorithms in 88.9%of instances,especially in large-scale *** fully demonstrate the high efficiency and stability of the ALGN-GA.

关键词： Multi-EOSs scheduling area target observation adaptive genetic algorithm local grid nesting

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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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