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4th International Conference on Computer Science and Application Engineering, CSAE 2020

作者： Yang, Wujuan Qian, Xiaofei School of Management and Key Laboratory of Process Optimization and Intelligent Decision-making of the Ministry of Education Hefei University of Technology Hefei China

ISBN: (纸本)9781450377720

The ubiquity of vulnerability in software products poses a serious threat to network security and user system. Patching for the software is a common tool to ease the security issue. Whereas, not all users will choose to patch due to the consideration on compatibility and inconvenience. Thus, it is significant to induce users to patch for the software. Based on the background of this practical problem, this paper proposes two models (a benchmark model and a price discount model) to analyze the user's buying and patching behavior, the pricing strategy of the software vendor, and the vendor's benefits. By comparing the results of the benchmark model and the price discount model, we find that the price discount policy will result in a higher group security level and more benefits for the vendor. © 2020 ACM.

关键词： Game theory

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A Credit Management Mechanism for Peer-to-Peer Electricity Trading in Blockchain Environment

A Credit Management Mechanism for Peer-to-Peer Electricity T...

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IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Jinhuan Guo Kaile Zhou Jie Chong Key Laboratory of Process Optimization and Intelligent Decision-making School of Management Hefei University of Technology Hefei China

ISBN: (纸本)9781665434263

With the rapid development of distributed generation, peer-to-peer (P2P) electricity trading between microgrids in blockchain environment has become one of the indispensable ways to consume the local renewable energy. The existing credit management for P2P electricity trading generally realized through the smart contract of penalty, which cannot show the risk identification and risk aversion behaviors of each microgrid. Therefore, this study proposes a newly credit management mechanism to reduce the defaults, which achieves credit-based risk control optimization and credit-based continuous double auctions. In the first stage, microgrids make robust control optimize to submit a trading strategy that follows contracts as far as possible. In the second stage, which realizes rewards and penalties through credit-based equivalent price conversion. The experimental results show that the proposed credit management mechanism can improve microgrids' interests and ensure fair environment.

关键词： Robust control Renewable energy sources Smart contracts Microgrids System integration Power markets Real-time systems

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Distributionally Robust optimization for Internet Data Center with Renewable Energy

Distributionally Robust Optimization for Internet Data Cente...

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IEEE Conference on Energy Internet and Energy System Integration (EI2)

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

ISBN: (纸本)9781665434263

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.

关键词： Renewable energy sources Data centers Costs Uncertainty Stochastic processes System integration Wind power generation

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Optimal Operation Of Battery Energy Storage System In Industrial Park

Optimal Operation Of Battery Energy Storage System In Indust...

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IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Kaile Zhou Zenghui Zhang Xinhui Lu Key Laboratory of Process Optimization and Intelligent Decision-making Schoolof Management Hefei University of Technology Hefei China

ISBN: (纸本)9781665434263

An industrial park containing distributed generations (DGs) can be seen as a microgrid. Due to the uncertainty and intermittency of the output of DGs, it is necessary to add battery energy storage system (BESS) in industrial parks. The battery state of health (SOH) is an important indicator of battery life. It is necessary to fully consider the battery SOH during the energy optimization of industrial parks. In this work, a two-stage model suitable for charge and discharge optimization of BESSs in industrial park microgrids is proposed. The first stage of the model is a charge and discharge scheduling of a BESS considering the SOH. This stage mainly schedules the charge and discharge time and the total amount of time. The second stage of the model is employed to further determine the charge and discharge allocation of all battery packs to achieve BESS life prolongation. The experimental results show that the introduction of SOH makes the scheduling results more realistic. Also, it balances the changes in the SOHs of all battery packs.

关键词： Schedules Job shop scheduling Uncertainty Rain Microgrids System integration Discharges (electric)

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A Transfer-Based Decision-Making Method with Criterion Weight and Reliability Transfer

SSRN

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SSRN 2023年

作者： Jia, Xuefei Chang, Wenjun Liao, Baoyu School of Management Hefei University of Technology Hefei Box 270 Anhui Hefei230009 China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Anhui Hefei230009 China Ministry of Education Engineering Research Center for Intelligent Decision-Making & Information System Technologies Hefei230009 China

The accumulated historical data not only brings benefits to social life, but also increases the complexity of decision-making. Data-driven multi-criteria decision-making (D2MCDM) can extract parameter information from historical data to help decision-makers generate acceptable and satisfactory decision solutions. When little historical data in one domain is available, the extracted parameter information may be inaccurate or inconsistency. To deal with this issue, the parameter-based transfer learning is considered to integrate into D2MCDM. The domain with little historical data is called the target domain (TD). One related domain with sufficient data is found, which is called the SD (SD). The sufficient data in the SD can help extract accurate parameter information of the TD. This paper proposes a transfer-based decision-making (TBDM) method with criterion weight and reliability transfer based on the Tri-training algorithm. The Tri-training algorithm is used to construct a data selection stage for the SD, which improves the data similarity between the SD and TD. The data-driven decision-making models are then constructed to learn criterion weights and reliabilities. The proposed TBDM method not only deals with the problem of insufficient data for decision model training, but also improves the acceptability of decision solutions to decision-makers. A case study of the diagnosis of breast lesions is analyzed to demonstrate the advantages of the TBDM method. The UCI datasets are used to conduct the comparative experiments to further validate the advantages of the TBDM method. © 2023, The Authors. All rights reserved.

关键词： Reliability

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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： Semantics

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Probabilistic Electricity Price Forecasting and Uncertainty Estimation Using Deep Bayesian Model with Multi-Distribution Fusion

SSRN

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SSRN 2024年

作者： Shao, Zhen Han, Yating Fu, Chao Zha, Jianrui Yang, Shanlin School of Management Hefei University of Technology Hefei230009 China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Anhui Hefei230009 China Philosophy and Social Sciences Laboratory of Data Science and Smart Society Governance Hefei University of Technology Ministry of Education Hefei230009 China Ministry of Education Engineering Research Center for Intelligent Decision-Making & Information System Technologies Hefei230009 China

Probabilistic electricity price forecasting (EPF) is paramount for stakeholder scheduling and trading in deregulated energy markets. However, during the process of establishing a probability prediction model, the consequential aleatoric and epistemic uncertainties are indistinguishable. Moreover, identifying epistemic uncertainty rather than aleatoric uncertainty is significant for conducting credible probabilistic EPF in cases with expected and unexpected fluctuations. This article proposes a hybrid approach under a Bayesian probability framework for performing reliable and accurate probabilistic forecasting. Specifically, it applies Bayesian deep learning model to convey the latent probability distribution of electricity prices, which represents the desirable epistemic uncertainty and inevitable aleatoric uncertainty. The embedded architecture employs a multi-head attention mechanism (MHAM) to allocate previous hidden states of long short-term memory (LSTM) cells, detecting both the local and global price patterns simultaneously. Considering the forecasting bias arising from aleatoric uncertainty in the process of establishing Bayesian networks, a new Wasserstein distance-based multi-distribution fusion mechanism driven by evidence theory is proposed and employed to identify and mitigate the aleatoric uncertainty of credible probabilistic EPF problems. The effectiveness of the proposed approach is evaluated on two real-world electricity price datasets and compared with twelve state-of-the-art models. © 2024, The Authors. All rights reserved.

关键词： Bayesian networks

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Towards Trustworthy Web Attack Detection: An Uncertainty-Aware Ensemble Deep Kernel Learning Model

arXiv

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arXiv 2024年

作者： Zhou, Yonghang Zhu, Hongyi Chai, Yidong Jiang, Yuanchun Liu, Yezheng School of Management Hefei University of Technology Anhui Hefei230009 China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Anhui Hefei230009 China Department of Information Systems and Cyber Security University of Texas at San Antonio 1 UTSA Circle San AntonioTX78249 United States

Web attacks are one of the major and most persistent forms of cyber threats, which bring huge costs and losses to web application-based businesses. Various detection methods, such as signature-based, machine learning-based, and deep learning-based, have been proposed to identify web attacks. However, these methods either (1) heavily rely on accurate and complete rule design and feature engineering, which may not adapt to fast-evolving attacks, or (2) fail to estimate model uncertainty, which is essential to the trustworthiness of the prediction made by the model. In this study, we proposed an Uncertainty-aware Ensemble Deep Kernel Learning (UEDKL) model to detect web attacks from HTTP request payload data with the model uncertainty captured from the perspective of both data distribution and model parameters. The proposed UEDKL utilizes a deep kernel learning model to distinguish normal HTTP requests from different types of web attacks with model uncertainty estimated from data distribution perspective. Multiple deep kernel learning models were trained as base learners to capture the model uncertainty from model parameters perspective. An attention-based ensemble learning approach was designed to effectively integrate base learners' predictions and model uncertainty. We also proposed a new metric named High Uncertainty Ratio-F Score Curve to evaluate model uncertainty estimation. Experiments on BDCI and SRBH datasets demonstrated that the proposed UEDKL framework yields significant improvement in both web attack detection performance and uncertainty estimation quality compared to benchmark models. © 2024, CC BY.

关键词： Adversarial machine learning

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

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

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IEEE Conference on Energy Internet and Energy System Integration (EI2)

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

ISBN: (纸本)9781665434263

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.

关键词： COVID-19 Reactive power Pandemics Atmospheric modeling Big Data Air quality Electric vehicle charging

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Room Impulse Response Reshaping-Based Blind Source Separation for an Underdetermined Reverberant Environment

SSRN

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SSRN 2024年

作者： Xie, Yuan Zou, Tao Yang, Junjie Sun, Weijun Xie, Shengli School of Mechanical and Electrical Engineering Guangzhou University Guangdong-Hong Kong-Macao Key Laboratory of Multi-scale Information Fusion and Collaborative Optimization Control of Complex Manufacturing Process Guangzhou510006 China School of Automation Guangdong University of Technology Joint International Research Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education of the P.R.C. Guangzhou510006 China School of Automation Guangdong University of Technology Guangdong Key Laboratory of IoT Information Processing Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China School of Automation Guangdong University of Technology Key Laboratory of Intelligent Detection and the IoT in Manufacturing Ministry of Education 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangzhou510006 China

Blind source separation under an underdetermined reverberation environment is a very challenging issue. The classical method is based on the expectation maximization algorithm. However, it is limited to high reverberation environments, resulting in bad or even invalid separation performance. To eliminate this restriction, a room impulse response reshaping-based expectation maximization method is designed to solve the problem of blind signal separation in an underdetermined reverberant environment. Firstly, a room impulse response reshaping technology is designed to eliminate the influence of audible echo on the reverberant environment, improving the quality of the received signals. Then, a new mathematical model of time-frequency mixing signals is established to reduce the approximation error of model transformation caused by high reverberation. Furthermore, an improved expectation maximization method is proposed for real-time update learning rules of model parameters, and then the sources are separated using the estimators provided by the improved expectation maximization method. Experimental results based on source separation of speech and music mixtures demonstrate that the proposed algorithm achieves better separation performance while maintaining much better robustness than popular expectation maximization methods. © 2024, The Authors. All rights reserved.

关键词： Reverberation

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