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5th IEEE Conference on Energy Internet and Energy System Integration, EI2 2021

作者： Zhou, Kaile Fei, Zhineng Lu, Xinhui Key Laboratory of Process Optimization and Intelligent Decision-making School of Management Hefei University of Technology Hefei230009 China

ISBN: (纸本)9781665434256

More and more internet data centers (IDCs) are trying to use renewable energy sources (RESs). However, powering IDCs with intermittent RESs presents a significant challenge. In addition, power and workload management in IDCs has great potential to reduce energy consumption, carbon footprints and energy cost. This study proposes an optimal load dispatch model for an IDC with battery energy storage system (BESS), which aims to lower the total costs. To accommodate the uncertainties of wind power, a data-driven distributionally robust optimization (DDRO) method is adopted. Then the column-and-constraint generation method (C&CG) is used to solve the corresponding optimization problem. In the experiment with the real-world traces of workload arrival and wholesale electricity price, three scheduling scenarios are investigated. Moreover, robust optimization and stochastic programming are implemented for comparison. Experimental results reveal that total costs of the IDC can be effectively reduced by adopting BESS and implementing workload dispatch. Meanwhile, the results also demonstrate the effectiveness of DDRO approach. © 2021 IEEE

关键词： Stochastic programming

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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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A Force-driven and Vision-driven Hybrid Control Method of Autonomous Laparoscope-Holding Robot

A Force-driven and Vision-driven Hybrid Control Method of Au...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Jin Fang Ling Li Xiaojian Li Hangjie Mo Pengxin Guo Xilin Xiao Yanwei Qu School of Management Hefei University of Technology Hefei China Key Laboratory of Process Optimization and Intelligent Decision-Making (Ministry of Education) Hefei University of Technology Hefei China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Laparoscope-holding robots significantly enhance the stability and precision of visualization in minimally invasive surgeries. Most existing robots of this kind depend on visual servo systems and struggle with efficient, rapid adjustments in the field-of-view (FOV), especially when identifying organs and needles outside the FOV. This paper presents a laparoscope-holding robot system capable of employing both vision-driven and force-driven mechanisms for continuous and large-scale FOV adjustments, respectively. The system features an integrated tactile handle, enabling the reception of human-robot interaction forces during surgical navigation. We propose a hybrid control method that leverages both force and vision inputs for laparoscopic FOV adjustments. This approach integrates a virtual wrench, generated from visual information, and an interaction wrench, obtained from the tactile handle, into the robot's dynamic model, which complies with remote center of motion constraints. The interaction wrench's gain is adjusted with the gripping force on the integrated tactile handle, ensuring that unintended movements caused by accidental contacts are prevented, thus safeguarding operational safety. The proposed method eliminates the need to switch control modes, enabling simultaneous visual tracking and tactile interaction guidance. Experimental results demonstrate that the proposed method not only allows for FOV adjustments with surgical instrument guiding but also adapts well to large-scale FOV adjustment tasks.

关键词： Laparoscopes Visualization Tracking Instruments Force Human-robot interaction Switches

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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 195页

作者： Jie Xu Erkang Jing Yidong Chai 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. School of Management of Hefei Key Laboratory of Process Optimization and Intelligence Decision Making University of Technology Minister of Education Hefei China. chaiyd@***.

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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Exploring the impact of COVID-19 on travel behavior based on EV charging big data 5

Exploring the impact of COVID-19 on travel behavior based on...

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5th IEEE Conference on Energy Internet and Energy System Integration, EI2 2021

作者： Zhou, Kaile Hu, Dingding Li, Fangyi Key Laboratory of Process Optimization and Intelligent Decision-making of Ministry of Education School of Management Hefei University of Technology Hefei230009 China

ISBN: (纸本)9781665434256

The COVID-19 pandemic has forced many governments around the world to implement strict lockdown measures and order citizens to stay at home, which has caused a major change in travel patterns. This study leveraged electric vehicle charging big data in Hefei, Anhui Province, China to estimate electric vehicle charging demand in the absence of the COVID-19 pandemic using multi-layer perceptron model, which quantified the impact of the COVID-19 pandemic. In addition, we employed the vector autoregressive model to investigate the dynamic relationships between the changes in charging demand and various explanatory factors. The results suggest that the daily average charging demand in Hefei decreased by 78.3% compared to the predicted value during the pandemic. Furthermore, according to the variance decomposition and impulse response function analysis, national confirmed COVID-19 cases play a dominant role in reducing charging demand. The number of daily hospitalizations and Migration Scale Index also have significant and robust effect on the decrease in charging demand. The Air Quality Index and Baidu Index are susceptible to external factors and do not have a direct impact on the change in charging demand. Findings support a better understanding of changes in travel behavior during the pandemic and provide policy makers with references to deal with similar events. © 2021 IEEE

关键词： COVID-19

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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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Competition in durable goods markets with product failures into consideration 9

Competition in durable goods markets with product failures i...

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9th International Symposium on Project management, ISPM 2021

作者： Tian, Jianfei Qian, Xiaofei School of management Hefei University of Technology Anhui China Key Laboratory of Process Optimization and Intelligent Decision-making of the Ministry of Education China

ISBN: (纸本)9781921712777

Few studies on competition in dual channel for durable goods take the product failures into consideration. However, the factors related to preventive maintenance (PM) and minimal maintenance (MM) have an important impact on the optimal profits of the manufacturer and retailer. Consequently, we develop three game models (Single Selling channel (SS), Manufacturer Stackelberg (MS) and Cooperation Model (CM)) to study the players’ equilibrium solutions and analyze the impact of the initial point, numbers and level of preventive maintenance on equilibrium price and profits. We find that the equilibrium wholesale and retail price under SS tend to increase when the manufacturer decides to raise the repair charge, while in MS the manufacturer and retailer will select a higher level for the wholesale price, retail price and rental charge when manufacturer chooses to put off the initial point for PM. In addition, numerical examples are provided to supplement our conclusions. The results show that the manufacturer in MS will set the numbers and level of PM to the maximum threshold in order to maximize its profit. © ISPM *** right reserved.

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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Generative Adversarial Network-Based Optimal Energy Storage Capacity Configuration for Newly–Build Microgrids

SSRN

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SSRN 2024年

作者： Zhou, Kunshu Zhou, Kaile Yang, Shanlin School of Management Hefei University of Technology Hefei230009 China Key Laboratory of Process Optimization and Intelligent Decision-making of Ministry of Education Hefei University of Technology Hefei230009 China

In the initial stage of microgrid construction, historical data is limited. It is challenging to address the issues of the small amount of data and high uncertainty of renewable energy when proceeding with capacity configuration. This study performs Wasserstein distance-based generative adversarial network with gradient penalty (WGAN-GP) to enhance the original 30 days PV power generation scenario data. Furthermore, the improved data is used to build a distributionally robust optimization model based on various discrete scenarios to determine the optimal capacity configuration of energy storage in newly–built microgrids. The results obtained from the solution are compared with those of traditional stochastic optimization and robust optimization, and the robustness of the results is verified by sensitivity analysis. The comparison results demonstrate that the strategy employed in this study can better address the issues of limited data and uncertainty in renewable energy in the capacity configuration process of newly-built microgrids. © 2024, The Authors. All rights reserved.

关键词： Sensitivity analysis

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Earth Observation Satellite Scheduling Problem for Multitemporal Revisit Tasks: A Variable Neighborhood Search Algorithm

SSRN

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SSRN 2024年

作者： Xing, Ligang Hu, Xiaoxuan Zhu, Waiming Zhu, Xutong Xia, Wei School of Management Hefei University of Technology Hefei230009 China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Hefei230009 China Engineering Research Center for Intelligent Management of Aerospace System Hefei230009 China

After natural disasters such as floods and earthquakes, Earth observation satellites (EOSs) often need to revisit affected areas multiple times to acquire multitemporal images. Such observation tasks are referred to as multitemporal revisit tasks. In this paper, we study the Earth observation satellite scheduling problem for multitemporal revisit tasks, which is a combinatorial optimization problem in continuous space. This problem exhibits more complex constraints and is more challenging to solve than the traditional Earth observation satellite scheduling problem. Firstly, we formally define the problem and formulate it with a mixed integer nonlinear programming model. Secondly, we develop a variable neighborhood search algorithm to search for the optimal solution. This algorithm embeds a dynamic greedy heuristic, which can efficiently generate a schedule for satellites. Thirdly, computational experiments demonstrate the efficiency and stability of the algorithm. Moreover, the CPU time of the algorithm increases linearly with both task count and revisit number, indicating good scalability. © 2024, The Authors. All rights reserved.

关键词： Integer programming

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