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2010 2nd IEEE International Conference on Information Management and Engineering, ICIME 2010

作者： Luo, He Hu, Xiao-Jian Hu, Xiao-Xuan School of Management Hefei University of Technology Hefei China Key Laboratory of Ministry of Education on Process Optimization and Intelligent Decision Making Hefei China

ISBN: (纸本)9781424452644

In the multi agent system, negotiation between different agents becomes more and more complex when the distributed tasks need to be allocated among them. Usually, this process has been done in the centralized way where agents have all the information of both the tasks and the environment. However, in the real application such as satellite scheduling problem or production scheduling problem, not all the information can be acquired, some of them even cannot be known. Therefore, in this paper, we propose a multi agent negotiation model for distributed task allocation in a priority based environment. This process is modeled as a finite Markov decision process (F-MDP), and a coordinated negotiation protocol is also suggested to support the model. The experiments show that tasks are allocated in an efficient way, and it can also reduce the communication cost. © 2010 IEEE.

关键词： Multi agent systems

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POLYNOMIAL DYNAMIC PROGRAMMING ALGORITHMS FOR LOT SIZING MODELS WITH BOUNDED INVENTORY AND STOCKOUT AND/OR BACKLOGGING

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Journal of Systems Science and Systems Engineering 2016年第3期25卷 370-397页

作者： Jinhong Zhong Feng Chu Chengbin Chu Shanlin Yang School of Management Hefei University of Technology Hefei 230009 China Laboratoire IBISC Universite d'Evry-Val d'Essone France Laboratoire Genie Industriel Ecole Centrale Paris France Key Laboratory of Process Optimization and Intelligent Decision-making of Ministry of Education Hefei China

This paper addresses a dynamic lot sizing problem with bounded inventory and stockout where both no backlogging and backlogging allowed cases are considered. The stockout option means that there is outsourcing in a period only when the inventory level at that period is non-positive. The production capacity is unlimited and production cost functions are linear but with fixed charges. The problem is that of satisfying all demands in the planning horizon at minimal total cost. We show that the no backlogging case can be solved in O（T^2） time with general concave inventory holding and outsourcing cost functions where T is the length of the planning horizon. The complexity can be reduced to O（T） when the inventory holding cost functions are also linear and have some realistic properties, even if the outsourcing cost functions remain general concave functions. When the inventory holding and outsourcing cost functions are linear, the backlogging case can be solved in O（ T^3 logT） time whether the outsourcing level at each period is bounded by the sum of the demand of that period and backlogging level from previous periods, or only by the demand of that period.

关键词： Dynamic lot sizing problem bounded inventory outsourcing backlogging stockout,dynamic programming

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Smart surgical telementoring system with patch-to-pixel point tracking method

EngMedicine

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EngMedicine 2025年第1期2卷

作者： Yanwei Qu Ling Li Tongtong Li Jieyu Zheng Wentao Tang Xiaojian Li School of Management Hefei University of Technology Hefei 230009 Anhui China Philosophy and Social Sciences Laboratory of Data Science and Smart Society Governance Ministry of Education Hefei 230009 Anhui China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Hefei 230009 Anhui China Hefei Haochuan Information Technology Co. Ltd Hefei 230088 Anhui China Ministry of Education Engineering Research Center for Intelligent Decision-Making Information System Technologies Hefei 230009 Anhui China

The utilization of surgical telementoring has become increasingly prevalent in enhancing the surgical standards of grassroots hospitals in contemporary times. In the traditional framework of surgical telementoring, remote doctors guide grassroots doctors via video and audio communication, which may encounter obstacles such as inadequate information transfer and challenges in accurately pinpointing the surgical site and path. To mitigate these issues, this study introduces an intelligent surgical telementoring system based on edge computing. This system enables the transmission of points marked by remote doctors to grassroots doctors, updating these points’ coordinates in real-time on the local endoscopic video. In this system, a novel method named P2PTracking (Patch-to-Pixel Point Tracking) is implemented. This process begins with the tracking of a square patch surrounding the point marked by the remote doctor using SiameseFC. Following this, feature matching is performed on the tracked patch in the current frame and the square patch in the labelled frame or template frame. The affine transformation is then calculated based on the feature matching results. Lastly, the point tracking result is derived using the computed affine transformation. Experimental results indicate that the proposed system has a transmission speed of 1.99M/s and a transmission latency of 171 ms when transmitting video at a resolution of 1920 × 1080px, while the proposed method can achieve an accuracy of 96.6 % when the pixel error is 4. The code and data are available at https://***/hfut66/*** .

关键词： Surgical telementoring Patch tracking Point tracking Feature matching Template update mechanism

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Multi-keyframe Guided Points Tracking Method for Enhanced Robotic Surgery

Multi-Keyframe Guided Points Tracking Method for Enhanced Ro...

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2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Li, Xiaojian Li, Tongtong Ma, Xiang Li, Ling Mo, Hangjie Zheng, Jieyu School of Management Hefei University of Technology Hefei China Hefei University of Technology Key Laboratory of Process Optimization and Intelligent Decision-Making Hefei China

ISBN: (纸本)9781665481090

Points tracking provides target guidance for the basic tasks of robotic surgery, such as autonomous cutting, autonomous suturing, and surgical navigation. Existing invivo tracking algorithms mainly consider region-level tracking, making it difficult to achieve pixel-level accuracy. This paper proposes a multi-keyframe guided tracking method (MKTracking) for robotic surgery, which achieves long-term dynamic tracking of any point on endoscopic images. An incremental keyframe pool building block based on time interval and image appearance is proposed, which contains keyframe basic information, tracking patch and tracking points. Besides, an online multi-keyframe query mechanism is designed to efficiently retrieves the three most similar templates from the keyframe pool based on the number of feature matches within the target patch. This mechanism can realize the identification of target moving out and the re-tracking of target reappearance. Points tracking result is obtained by fusing the optical flow results within the target patch across multiple frames. We evaluated MKTracking using ground-truth data, and the results demonstrated that our approach achieved superior tracking performance. © 2024 IEEE.

关键词： Optical flows

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Independent or Social Driven decision? A Counterfactual Refinement Strategy for Graph-Based Social Recommendation

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ACM Transactions on Information Systems 2025年第3期43卷

作者： Dongyang Li Jianshan Sun Chongming Gao Fuli Feng Kun Yuan School of Management Hefei University of Technology China School of Management Hefei University of Technology Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education China University of Science and Technology of China Intelligent Interconnected Systems Laboratory of Anhui Province (Hefei University of Technology) China University of Science and Technology of China China Hefei University of Technology Key Laboratory of Philosophy and Social Sciences for Cyberspace Behaviour and Management China

Social recommendation models have traditionally relied on social homophily to enhance user preference prediction by incorporating information from socially connected friends. However, this approach neglects the diverse nature of social relationships. Some individuals with independent personalities often prioritize their own interests over friends’ advice when making purchase decisions. Conversely, those who seek advice from others are more susceptible to social influence. Moreover, existing methods tend to overlook redundant and noisy social relationships within the network, hindering their ability to achieve accurate recommendations. In response, this paper proposes a novel counterfactual method to understand the causal factors driving purchase behaviors, thereby identifying the influence of users’ friends on their purchase decisions. By answering counterfactual questions about the influence of a friend's purchase behavior on the user's choices, we develop a causal model to represent social influence in the network. Our proposed refinement strategy, grounded in causal inference, generates counterfactual purchase behavior and guides the refinement of the social graph. Moreover, we present tailored graph refinement methods at various levels, ensuring fine-grained improvements. Experimental results on benchmark data demonstrate that the application of our strategy to different social recommendation models significantly enhances their predictive performance. The source code has been made available on https://***/LDY911/CFRSSR-Code.

关键词： social recommendation graph denoising causal inference

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Energy storage resources management:Planning,operation,and business model

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Frontiers of Engineering Management 2022年第3期9卷 373-391页

作者： Kaile ZHOU Zenghui ZHANG Lu LIU Shanlin YANG School of Management Hefei University of TechnologyHefei 230009China Key Laboratory of Process Optimization and Intelligent Decision-making of Ministry of Education Hefei University of TechnologyHefei 230009China

With the acceleration of supply-side renewable energy penetration rate and the increasingly diversified and complex demand-side loads,how to maintain the stable,reliable,and efficient operation of the power system has become a challenging issue requiring *** of the feasible solutions is deploying the energy storage system(ESS)to integrate with the energy system to stabilize ***,considering the costs and the input/output characteristics of ESS,both theinitial configuration process and the actual operation process require efficient *** study presents a comprehensive reviewof managing ESs from the perspectives of planning,operation,and business *** of all,in terms of planning and configuration,it is investigated from capacity planning,location planning,as well as capacity and location combined *** process is generally the first step in deploying ***,it explores operation management of ESS from the perspectives of state assessment and operation *** so-called state assessment refers to the assessment of three aspects:The state of charge(SOC),the state of health(SOH),and the remaining useful life(RUL).The operation optimization includes ESS operation strategy optimization and joint operation ***,it discusses the business models of *** business models involve ancillary services and load transfer,while emerging business models include electric vehicle(EV)as energy storage and shared energy storage.

关键词： energy storage system energy storage resources management planning configuration operational management business model

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Application of blockchain in the field of intelligent manufacturing:Theoretical basis,realistic plights,and development suggestions

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Frontiers of Engineering Management 2020年第4期7卷 578-591页

作者： Qiang ZHANG Baoyu LIAO Shanlin YANG School of Management Hefei University of TechnologyHefei 230009China Key Laboratory of Process Optimization and Intelligent Decision-making(Ministry of Education) Hefei University of TechnologyHefei 230009China

Blockchain technology is considered one of the promising technologies of the information technology *** core features of blockchain,such as decentralization,transparency,high security,and tamper-proof nature,bring great convenience for large-scale social cooperation and data *** has a broad application prospect in the field of intelligent *** key issues of this field,such as distributed collaborative production,industrial big data sharing and security,transparent logistics,and supply chain,are naturally consistent with the core characteristics of the blockchain *** study aims to analyze the application of blockchain in the field of intelligent ***,we introduce the basic connotation and applications of ***,we propose the theoretical basis for the application of blockchain in the field of intelligent ***,we point out the realistic plights and provide some suggestions to promote the application of blockchain in the field of intelligent manufacturing.

关键词： blockchain technology intelligent manufacturing networked collaborative manufacturing full lifecycle management manufacturing model innovation

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Photovoltaic Power Forecasting Based on SVM Optimized by Improved ABC 2

Photovoltaic Power Forecasting Based on SVM Optimized by Imp...

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2nd International Conference on Environmental Prevention and Pollution Control Technologies, EPPCT 2020

作者： Liu, Lin Lu, Yanyun Li, Shuqin School of Management Hefei University of Technology Hefei230009 China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Hefei230009 China

In order to improve the accuracy of photovoltaic power output prediction, a photovoltaic power prediction method based on similar days and improved artificial bee colony support vector machine is proposed. Firstly, through calculating the Euclidean distance of history day and measured day meteorological factors to determine similar days. Secondly, select historical data of photovoltaic power output, temperature, humidity and daily radiation on the slope of similar days and temperature, humidity and daily radiation on the slope of test date as input variables of support vector machine. And we adopt the improved artificial bees colony to optimize kernel function parameters and the penalty factor of support vector machine. Finally get the output in each period of photovoltaic power prediction. The experimental results showed that the proposed method can effectively improve the prediction accuracy of photovoltaic power. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Support vector machines

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Industrial False Signal Recognition Based on Deep Learning with Focal Loss

Industrial False Signal Recognition Based on Deep Learning w...

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作者： Xu, Qifa Cheng, Qiliang 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

False signals often occur in actual industrial environments. They are however treated as abnormal ones, which will lead to unexpected equipment downtime and production loses. Consider the memory limitations of edge computing, how to recognize them accurately with lightweight models is an important but difficult work. To this end, we develop a novel scheme based on deep learning with focal loss. This scheme first converts original one-dimensional waveform signals into time-frequency graphs through continuous wavelet transform. These graphs are then fed into a convolutional neural network (CNN) for binary classification. Finally, focal loss function is introduced into the CNN model to solve the problem of different types of misclassification costs in recognizing false signals. We verify the effectiveness of this scheme on a real-world application. The results show that our scheme is a high-quality classifier with an accuracy of 98.42%, a Precision of 98.68%, a Recall of 98.18%, and a specificity of 98.68%, which is efficient in recognizing false signals and performs better than several competing models. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Convolutional neural networks

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Short-term power load forecasting based on self-adapting PSO-BP neural network model

Short-term power load forecasting based on self-adapting PSO...

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4th International Conference on Computational and Information Sciences, ICCIS 2012

作者： He, Yaoyao Xu, Qifa School of Management Hefei University of Technology Hefei 230009 China Key Laboratory of Process Optimization and Intelligent Decision-Making Ministry of Education Hefei 230009 China

ISBN: (纸本)9780769547893

To resolve the problem of short-term power load forecasting, we propose a self-adapting particle swarm optimization (PSO) algorithm to optimize the error back propagation (BP) neural network model. The proposed model is called PSO-BP model which employs PSO to adjust control parameters of BP neural network. In order to verify the performance of PSO-BP, the practical datum of a city in China are selected in the experiments. Simulation results show that our approach outperforms simple BP neural network. © 2012 IEEE.

关键词： Neural network models

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