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2nd International Conference on Frontiers of intelligent Manufacturing and Automation, CFIMA 2024

作者： Li, Ling Chen, Yulu Shen, Ziming Mo, Hangjie Li, Xiaojian Hefei University of Technology Anhui Hefei China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Anhui Hefei China

ISBN: (纸本)9798400710681

By overlaying preoperative models and their internal structures onto endoscopic images, cross-modal registration methods restore a surgeon’s ability to perceive three-dimensional information in laparoscopic scenes, providing precise surgical navigation. Many cross-modal registration and surgical navigation techniques are heavily reliant on manually annotated landmark information. An automated preoperative-to-intraoperative cross-modal registration pipeline for 3D-2D registration in laparoscopic liver interventions is proposed in this paper, eliminating manual intervention. Firstly, two convolutional neural networks are trained to autonomously obtain contours as landmarks from preoperative models and intraoperative endoscopic images. Secondly, an improved Random Sample Consensus-based perspective-n-points method with segmentation constraints is proposed to calculate the transformation from the preoperative model to the endoscopic coordinate system using marked points of key anatomical structures. Finally, the preoperative model is projected onto the intraoperative coordinate system to achieve augmented reality visual guidance. In experiments conducted on a public liver registration dataset, the method proposed in this paper achieves comparable results to registration methods based on manual annotations, validating the effectiveness of the proposed pipeline. © 2024 Copyright held by the owner/author(s).

关键词： Augmented reality

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Generative method for aerodynamic optimization based on classifier-free guided denoising diffusion probabilistic model

arXiv

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arXiv 2025年

作者： Deng, Shisong Zhang, Qiang Cai, Zhengyang School of Management Hefei University of Technology Hefei China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Hefei China

Recently, inverse design approach, which directly generates optimal aerodynamic shape with neural network models to meet designated performance targets, has drawn enormous attention. However, the current state-of-the-art inverse design approach for airfoils, which is based on generative adversarial network, demonstrates insufficient precision in its generating and training processes and struggles to reveal the coupling relationship among specified performance indicators. To address these issues, the airfoil inverse design framework based on the classifier-free guided denoising diffusion probabilistic model (CDDPM) is proposed innovatively in this paper. First, the CDDPM can effectively capture the correlations among specific performance indicators and, by adjusting the classifier-free guide coefficient, generate corresponding upper and lower surface pressure coefficient distributions based on designated pressure features. These distributions are then accurately translated into airfoil geometries through a mapping model. Experimental results using classical transonic airfoils as examples show that the inverse design based on CDDPM can generate a variety of pressure coefficient distributions, which enriches the diversity of design results. Compared with current state-of-the-art Wasserstein generative adversarial network methods, CDDPM achieves a 33.6% precision improvement in airfoil generating tasks. Moreover, a practical method to readjust each performance indicator value is proposed based on global optimization algorithm in conjunction with active learning strategy, aiming to provide rational value combination of performance indicators (i.e. meeting aerodynamic constraints) for the inverse design framework. This work is not only suitable for the airfoils design, but also has the capability to apply to optimization process of general product parts targeting selected performance indicators. Copyright © 2025, The Authors. All rights reserved.

关键词： Generative adversarial networks

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A blockchain-based self-adaptive collaboration mechanism for service-oriented manufacturing system

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International Journal of Computer Integrated Manufacturing 2025年

作者： Shao, Liangjia Ren, Minglun School of Economics and Management Jiangsu Maritime Institute Nanjing China School of Management Hefei University of Technology Hefei China Key Laboratory of Process Optimization and Intelligence Decision-Making Ministry of Education Hefei China

The service-oriented manufacturing system, based on the cloud manufacturing platform, is adversely affected by factors such as diverse entities, geographical dispersion, and distributed collaboration. The system faces challenges like a lack of trust among entities and operational process uncertainty, thereby significantly reducing service collaboration efficiency. Blockchain, a promising technology in the information era, can address these issues by enhancing the authenticity of service data and trust among entities. In this paper, a blockchain-based operational mode is proposed to establish a completely trustworthy manufacturing environment. The potential abnormal events during collaborative service processes and their impacts are illustrated. According to these analyses, a self-adaptive service collaboration process is designed utilizing the blockchain, and a trust-based service collaboration model involving multiple entities is developed to enhance the system adjustment efficiency. This model, integrated with the solving algorithm, is deployed in smart contracts for automatic adjustment of service entities. An experimental study is conducted to verify the effectiveness of the proposed self-adaptive collaboration process and adjustment model. The results show that the proposed method can effectively boost the efficiency of the service-oriented manufacturing system. © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Smart manufacturing

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A bi-level programming problem of medicine procurement and production optimization

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Journal of Global optimization 2025年 1-35页

作者： Fan, Wenjuan Zhang, Jingwei Shao, Kaining Lan, Shaowen Zhu, Shuwan School of Management Hefei University of Technology Hefei China Key Laboratory of Process Optimization and Intelligent Decision-Making of Ministry of Education Hefei China Science and Technology Department Chuzhou University Chuzhou China School of Management Hangzhou Dianzi University Hangzhou China School of Economics and Management Fuzhou University Fuzhou China

The emerging medical alliance in some countries enables the joint optimization for its members through timely necessary information exchanging and sharing. This paper investigates a joint decision-making optimization problem between a focal hospital and multiple pharmaceutical manufacturers in a medical alliance, to optimize both parties’ objectives from the supply chain management perspective. Specifically, the hospital makes the decision on the medicine procurement from multiple candidate pharmaceutical manufacturers to minimize its total procurement cost, and each chosen pharmaceutical manufacturer determines the production plan for the different types of medicine procured by the hospital to maximize its total profit. Given that the hospital and pharmaceutical manufacturers make decisions under their objective functions and constraints while the decisions affect each other’s objective mutually, a bi-level programming mathematical model is established, with the hospital acting as the leader and the pharmaceutical manufacturers as multiple independent followers. We propose an improved hierarchical loop framework, including the upper solving, the lower solving, and the improvement module. Specifically, we embed the Variable Neighborhood Search (VNS) algorithm in the lower-solving module and apply a set of heuristic rules in the improvement module. To test the performance of the proposed algorithm, we replace the VNS algorithm in the lower solving module with PSO, GA, and SA, and compare their performance in small, medium, and large-scale instances. Besides, we also validate the effectiveness of the improvement module by comparing the performance of the algorithm with and without the module. The computational experiments prove the effectiveness and efficiency of the proposed approach. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： process planning

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Robust decision making for UAV air-to-ground attack under severe uncertainty

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Journal of Central South University 2015年第11期22卷 4263-4273页

作者：胡笑旋陈意罗贺 School of Management Hefei University of Technology Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education

As unmanned aerial vehicles(UAVs) are used more and more in military operations, increasing their level of autonomous decision making becomes necessary. In uncertain battlefield environments, when making sovereign decisions, UAVs must choose low-risk options. An integrated framework is proposed for UAV robust decision making in air-to-ground attack missions under severe uncertainty. In the offline part of the framework, the battlefield scenarios are analyzed and an influence diagram is built to represent the decision situation. In the online part, the UAV evaluates the alternative actions for every scenario, and then the optimal robust action is chosen, using the robust decision model. Results of simulation show that the proposed approach is feasible and effective. The framework can support UAVs in making independent robust decisions under circumstances which require immediate responses under severe uncertainty, and it can also be extended to applications in more complex situations.

关键词： unmanned aerial vehicle severe uncertainty robust

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Generation of optimal persistent formations for heterogeneous multi-agent systems with a leader constraint

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Chinese Physics B 2018年第2期27卷 645-654页

作者：王国强罗贺胡笑旋 School of Management Hefei University of Technology Key Laboratory of Process Optimization & Intelligent Decision-making Ministry of Education

In this study, we consider the generation of optimal persistent formations for heterogeneous multi-agent systems, with the leader constraint that only specific agents can act as leaders. We analyze three modes to control the optimal persistent formations in two-dimensional space, thereby establishing a model for their constrained generation. Then, we propose an algorithm for generating the optimal persistent formation for heterogeneous multi-agent systems with a leader constraint （LC-HMAS-OPFGA）, which is the exact solution algorithm of the model, and we theoretically prove its validity. This algorithm includes two kernel sub-algorithms, which are optimal persistent graph generating algorithm based on a minimum cost arborescence and the shortest path （MCA-SP-OPGGA）, and the optimal persistent graph adjusting algorithm based on the shortest path （SP-OPGAA）. Under a given agent formation shape and leader constraint, LC-HMAS-OPFGA first generates the network topology and its optimal rigid graph corresponding to this formation shape. Then, LC-HMAS- OPFGA uses MCA-SP-OPGGA to direct the optimal rigid graph to generate the optimal persistent graph. Finally, LC- HMAS-OPFGA uses SP-OPGAA to adjust the optimal persistent graph until it satisfies the leader constraint. We also demonstrate the algorithm, LC-HMAS-OPFGA, with an example and verify its effectiveness.

关键词： leader constraint heterogeneous multi-agent system optimal persistent formation minimum costarborescence

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Hierarchical method of task assignment for multiple cooperating UAV teams

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Journal of Systems Engineering and Electronics 2015年第5期26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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A MILP Model and Two-Stage Heuristic Algorithm for Vehicle-Assisted Multi-Drone Inspection Routing Problem

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IET intelligent Transport Systems 2025年第1期19卷

作者： Xia, Yuanqing Luo, He Dong, Rui Yin, Youlong Lin, Shizhong Wang, Guoqiang School of Management Hefei University of Technology Hefei China Key Laboratory of Process Optimization & Intelligent Decision-Making Ministry of Education Hefei China Engineering Research Center for Intelligent Management of Aerospace System Anhui Province Hefei China Management Center of UAV Inspection Operation State Grid Anhui Electric Power Co. Ltd Hefei China Anhui Power Transmission & Transformation Engineering Co. Ltd Hefei China

This paper introduces a vehicle-assisted multi-drone inspection routing problem (VAMDIRP), which enables the vehicle to repeatedly traverse roads, thereby reducing task completion time. Firstly, a mixed-integer linear programming (MILP) model is constructed for the problem using a series of decision variables and auxiliary variables. The model can be solved by GUROBI for small-scale instances. Secondly, a two-stage heuristic algorithm is proposed to solve large-scale instances. Experimental results show that this algorithm improves the solution quality by an average of 20.71% and reduces the running time by an average of 60.31% compared to existing algorithms. Finally, a sensitivity analysis is conducted on relevant parameters, revealing that changes in the number and speed of drones significantly affect the solution quality. © 2025 The Author(s). IET intelligent Transport Systems published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.

关键词： Sensitivity analysis

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Fuzziness parameter selection in fuzzy c-means: The perspective of cluster validation

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Science China(Information Sciences) 2014年第11期57卷 252-259页

作者： ZHOU KaiLe FU Chao YANG ShanLin School of Management Hefei University of Technology Key Laboratory of Process Optimization and Intelligent Decision-Making Ministry of Education

Fuzzy c-means(FCM) algorithm is an important clustering method in pattern recognition, while the fuzziness parameter, m, in FCM algorithm is a key parameter that can significantly affect the result of clustering. Cluster validity index(CVI) is a kind of criterion function to validate the clustering results, thereby determining the optimal cluster number of a data set. From the perspective of cluster validation, we propose a novel method to select the optimal value of m in FCM, and four well-known CVIs, namely XB, VK, VT, and SC, for fuzzy clustering are used. In this method, the optimal value of m is determined when CVIs reach their minimum values. Experimental results on four synthetic data sets and four real data sets have demonstrated that the range of m is [2, 3.5] and the optimal interval is [2.5, 3].

关键词： clustering fuzziness parameter fuzzy c-means(FCM) cluster validation cluster validity index

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Agile Earth Observation Satellite Scheduling Algorithm for Emergency Tasks Based on Multiple Strategies

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Journal of Systems Science and Systems Engineering 2021年第5期30卷 626-646页

作者： Haiquan Sun Wei Xia Zhilong Wang Xiaoxuan Hu aSchool of Management Hefei University of TechnologyHefeiChina Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of EducationHefeiChina

During the execution of imaging tasks,satellites are often required to observe natural disasters,local wars,and other emergencies,which regularly interferes with the execution of existing ***,rapid satellite scheduling is urgently *** a new generation of three degree-of-freedom(roll,pitch,and yaw)satellites,agile earth observation satellites(AEOSs)have longer variable-pitch visible time windows for ground targets and are capable of observing at any time within the time ***,they are very suitable for emergency ***,current task scheduling models and algorithms ignore the time,storage and energy consumed by ***,these cannot make full use of the AEOS capabilities to optimize the scheduling for emergency *** this study,we present a fine scheduling model and algorithm to realize the AEOS scheduling for emergency ***,a novel time window division method is proposed to convert a variable-pitch visible time window to multiple fixed-pitch visible time ***,a model that considers flexible pitch and roll capabilities is ***,a scheduling algorithm based on merging insertion,direct insertion,shifting insertion,deleting insertion,and reinsertion strategies is proposed to solve conflicting problems *** verify the effectiveness of the algorithm,48 groups of comparative experiments are carried *** experimental results show that the model and algorithm can improve the emergency task completion efficiency of AEOSs and reduce the disturbance measure of the ***,the proposed method can support hybrid satellite resource scheduling for emergency tasks.

关键词： Agile earth observation satellites emergency tasks merging insertion shifting insertion

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