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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 branch-price-and-cut algorithm for the time-dependent multiple truck–drone routing problem

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Computers and Operations Research 2025年 182卷

作者： Zang, Xiaoning Jiang, Li Liang, Changyong Fang, Xiang School of Management Hefei University of Technology Hefei China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education China Business School University of Colorado Denver DenverCO United States

Time-varying traffic conditions are crucial features of urban logistics. Overlooking these conditions will pose a high coordination risk for drone-assisted routing problems. In this paper, a time-dependent multiple truck–drone routing problem (TD-MTDRP), which captures the time-varying traffic conditions as time-dependent travel times, is introduced. In the problem, trucks with time-dependent travel times travel through the urban roads, while drones fly over the urban areas, unaffected by the urban traffic, resulting in time-independent travel times. Our objective is to the total duration of all routes. We present the ready time function on a truck–drone path level, which is proven to satisfy the FIFO property. A route-based model is introduced based on this function to formulate the problem. The model decomposes the set-partitioning problem into a master problem (MP) and a time-dependent shortest truck–drone path problem with resource constraints (TDSTDPPRC). A branch-price-and-cut algorithm is also developed to solve the model. In this algorithm, a column-and-row generation is introduced to solve the MP, and a labeling algorithm incorporating new label extension and dominance rules is presented to address the TDSTDPPRC. For the numerical results, the approaches are evaluated on two sets of instances derived from the benchmark instances, demonstrating the effectiveness of the exact method in solving the TD-MTDRP. © 2025

关键词： Travel time

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Condition-based maintenance policy for a two-component balanced system with a multimode protective device

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Reliability Engineering and System Safety 2025年 262卷

作者： Fang, Haojun Zheng, Rui Xia, Xiangde Hu, Chiwei School of Management Hefei University of Technology Hefei230009 China Key Laboratory of Process Optimization and Intelligent Decision-Making Ministry of Education Hefei University of Technology Hefei230009 China

This paper presents a condition-based maintenance policy for a two-component balanced system equipped with a multimode protective device. The balanced components follow the same deterioration process characterized as a continuous-time Markov chain. The system fails if a component jumps into the failure state or the two components are unbalanced, incurring costly corrective replacement. The protective device has three modes (0, 1, and 2) to enhance reliability and maintain balance. One protective mode is selected at an inspection epoch, where preventive replacement is not performed, for the next inspection interval. If the protective device fails, it can be repaired at the next inspection epoch. The goal is to determine the optimal policy that minimizes the average maintenance cost rate. This optimization problem is formulated within a semi-Markov decision process framework and solved using a policy iteration algorithm. A matrix-based method is developed to calculate the system's transition probabilities. A case study demonstrates the effectiveness of the proposed approach. © 2025 Elsevier Ltd

关键词： Preventive maintenance

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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 novel centralized cross-efficiency evaluation via explainable artificial intelligence in the context of big data

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European Journal of Operational Research 2025年

作者： Yang, Min Wang, Zixuan Liang, Liang Management School Hefei University of Technology Hefei230009 China Key Laboratory of Process Optimization and Intelligent Decision-Making Ministry of Education Hefei230009 China Intelligent Interconnection System Anhui Provincial Laboratory Hefei230009 China NSFC Fundamental Research Center on Smart Interconnected System Engineering Hefei230009 China

Cross-efficiency evaluation in data envelopment analysis (DEA) assumes that decision making units (DMUs) have full flexibility in choosing weights according to their individual preferences. However, this total autonomy may be inapplicable in some centralized organizational scenarios. To address this problem, this paper introduces a novel centralized cross-efficiency evaluation which considers both individual and organizational preferences with assistance of explainable artificial intelligence (XAI) in the context of big data. Specifically, XAI is first applied to approach the organizational efficiency function and then calculate the marginal contribution of each variable as the variable importance, which represents the organizational preference. Furthermore, we propose a centralized secondary goal model to select the unique optimal weight profile from the candidate weights that remain self-efficiency as Pareto-optimal, such that the deviation between individual and organizational preferences is minimized. In addition, a centralization factor is introduced to ensure that the model's centralization degree corresponds to the actual centralization level in organizational management. Finally, the proposed method is applied to evaluate the efficiency of DMUs within three different centralized organizations sequentially. The results verify that the proposed method yields more discriminative and robust efficiency scores within organizations compared to several previous cross-efficiency evaluation methods. © 2025 Elsevier B.V.

关键词： Risk perception

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A deep learning model integrating domain-specific features for enhanced glaucoma diagnosis

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BMC Medical Informatics and decision making 2025年第1期25卷 1-10页

作者： Xu, Jie Jing, Erkang Chai, Yidong Beijing Institute of Ophthalmology Beijing Tongren Eye Center Beijing Tongren Hospital Capital Medical University Beijing China School of Management of Hefei Key Laboratory of Process Optimization and Intelligence Decision Making University of Technology Minister of Education Hefei China

Glaucoma is a group of serious eye diseases that can cause incurable blindness. Despite the critical need for early detection, over 60% of cases remain undiagnosed, especially in less developed regions. Glaucoma diagnosis is a costly task and some models have been proposed to automate diagnosis based on images of the retina, specifically the area known as the optic cup and the associated disc where retinal blood vessels and nerves enter and leave the eye. However, diagnosis is complicated because both normal and glaucoma-affected eyes can vary greatly in appearance. Some normal cases, like glaucoma, exhibit a larger cup-to-disc ratio, one of the main diagnostic criteria, making it challenging to distinguish between them. We propose a deep learning model with domain features (DLMDF) to combine unstructured and structured features to distinguish between glaucoma and physiologic large cups. The structured features were based upon the known cup-to-disc ratios of the four quadrants of the optic discs in normal, physiologic large cups, and glaucomatous optic cups. We segmented each cup and disc using a fully convolutional neural network and then calculated the cup size, disc size, and cup-to-disc ratio of each quadrant. The unstructured features were learned from a deep convolutional neural network. The average precision (AP) for disc segmentation was 98.52%, and for cup segmentation it was also 98.57%. Thus, the relatively high AP values enabled us to calculate the 15 reliable features from each segmented disc and cup. In classification tasks, the DLMDF outperformed other models, achieving superior accuracy, precision, and recall. These results validate the effectiveness of combining deep learning-derived features with domain-specific structured features, underscoring the potential of this approach to advance glaucoma diagnosis.

关键词： Deep learning Disease diagnosis Glaucoma diagnosis Image segmentation Medical image analysis Physiologic large cups

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Probabilistic-linguistic-information-driven decision-making method based on weight extraction and information aggregation

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Journal of Control and decision 2025年

作者： Fu, Chao Jia, Xuefei Lu, Guangyan Chang, Wenjun School of Management Hefei University of Technology Hefei China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Hefei China Ministry of Education Engineering Research Center for Intelligent Decision-Making & Information System Technologies Hefei China Department of Operation Management CRRC Qishuyan Instiute Co. Ltd Changzhou China

In this paper, a probabilistic-linguistic-information-driven decision-making method is proposed to help decision makers make decisions with historical data. Historical individual assessments and collective assessments represented by probabilistic linguistic term sets (PLTSs) are used to extract criterion weights to train a decision model in the proposed method. To support the extraction of criterion weights, a new difference measure between PLTSs is developed, which considers semantics of linguistic terms and all possible difference cases except for intersection between PLTSs. The arithmetic, geometric, and quadratic mean operators are developed to iteratively combine individual assessments to generate the aggregated assessments, in which the problem of combinatorial explosion is effectively avoided. The three operators accomplish the training of the decision model based on the extracted criterion weights. The proposed method is used to help radiologists diagnose thyroid nodules, in which its applicability is verified and the performance of the three aggregation operators is compared. © 2025 Northeastern University, China.

关键词： aggregation operator diagnosis of thyroid nodules difference measure historical decision information Probabilistic-linguistic-information-driven decision-making

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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年第11期25卷 19921-19936页

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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An optimal self-healing policy with discrete resources

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Reliability Engineering and System Safety 2025年 262卷

作者： Zheng, Rui Shen, Jingyuan School of Management Hefei University of Technology Hefei230009 China Key Laboratory of Process Optimization and Intelligent Decision-Making Ministry of Education Hefei University of Technology Hefei230009 China School of Economics and Management Nanjing University of Science and Technology Nanjing210094 China

intelligent systems capable of detecting and repairing damage autonomously hold significant promise across various domains, such as space exploration, autonomous vehicles, and robotics. Integrating self-healing mechanisms is pivotal in enhancing these systems’ durability, reliability, and efficiency. This paper introduces a novel self-healing policy with discrete self-healing resources. Self-inspections at equidistant time epochs reveal the deterioration of the system. Various actions, including doing nothing, self-healing, and stopping, can be selected after inspection. A self-healing action reduces deterioration to a random lower level, with the degree of reduction depending on the amount of healing resources used. Therefore, it is crucial to determine the optimal number of healing resources to allocate during self-healing. The goal is to identify the policy that minimizes the expected average cost. This optimization problem is formulated within the semi-Markov decision process framework. The structural properties of the optimal policy are examined. A policy iteration algorithm is developed based on a discretization approach. A numerical example is used to show how to apply the proposed approach, and the obtained policy provides valuable insights to support the operation of intelligent systems. © 2025 Elsevier Ltd

关键词： Policy iteration Policy structure Self-healing Semi-Markov decision process

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