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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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Research of large scale manifold learning based on MapReduce

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Xitong Gongcheng Lilun yu Shijian/System Engineering Theory and Practice 2014年 34卷 151-157页

作者： Xue, Yong-Jian Ni, Zhi-Wei School of Management Hefei University of Technology Hefei230009 China The MOE Key Laboratory of Process Optimization and Intelligent Decision-Making Hefei230009 China

With the rapid development of the information technology, it is challenging for the traditional machine learning and data mining algorithms to deal with large scale explosive growth data. Manifold learning is a dimensionality reduction algorithm which can overcome some shortages of traditional linear dimensionality reduction methods. However, it is not useful for large scale data because of high complexity. In order to deal with the dimensionality reduction of large scale data, a distributed manifold learning algorithm is proposed based on MapReduce. Local sensitive hash functions are used to map the similarity points to the same bucket, then the geodesic distance between points in the same bucket can be computed by Euclidean norm according to the local homeomorphisms of Euclidean spaces of manifold and the geodesic distance among points between buckets can be computed by the modified geodesic distance formula which takes use of central points and edge points. Experiments on large scale of manmade dataset and real dataset show that this distributed manifold learning algorithm can approximate the geodesic distance between points effectively and it is useful for large scale dimensionality reduction.

关键词： MapReduce

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Case-based safety assessment of thermal power plants: A framework

Case-based safety assessment of thermal power plants: A fram...

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International Conference on Business Management and Electronic Information (BMEI)

作者： Changyong Liang Dongxiao Gu Isabelle Bichindaritz Chunrong Zuo Fei Cheng School of Management Hefei University of Technology Hefei Anhui China Research Center of Intelligent Decision Making and Information System Technologies Ministry of Education of China Hefei Anhui China Department of Computer Science University of Illinois Chicago Chicago IL USA Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education of China Hefei Anhui China Institute of Technology University of Washington Tacoma WA USA

Security assessment of Thermal Power Plants (TPPs) is one of the important means to guarantee the safety of production in thermal power production enterprises. Essentially, the evaluation of power plant systems relies to a large extent on the knowledge and length of experience of the experts. Therefore in this domain Case-Based Reasoning (CBR) is introduced for the security assessment of TPPs since this methodology models expertise through experience management. Taking the management system of TPPs as breakthrough point, this paper presents a case-based approach for the security assessment decision support of TPPs (SATPP). First, this paper reviews commonly used approaches for TPPs security assessment and the current general evaluation process of TPPs security assessment. Then a framework for the Management System Security Assessment of Thermal Power Plants (MSSATPP) is constructed and an intelligent decision Support System for MSSATPP (IDSS-MSSATPP) is functionally designed. MSSATPP involves several key technologies and methods such as knowledge representation and case matching.

关键词： Security Safety Production Power generation Power systems decision making Indexes

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Integration scheduling optimization problem of production and transportation in supply chain

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Nanjing Li Gong Daxue Xuebao/Journal of Nanjing University of Science and Technology 2012年第SUPPL.2期36卷 292-298页

作者： Pei, Jun Liu, Xin-Bao Fan, Wen-Juan Liu, Lin Zhang, Yu-Lei School of Management Hefei University of Technology Hefei 230009 China Key Laboratory of Process Optimization and Intelligent Decision-Making of Ministry of Education Hefei 230009 China

To study two-tier supply chain scheduling models comprised by multi suppliers and multi manufacturers, the scheduling optimization theory is applied into the supply chain domain. A math model is built by taking the minimization of total flow time of work pieces and total cost of delivery as optimization objectives. The dynamic programming algorithm is used to solve the problem based on analyzing the features of its optimum solution. Multi-objectives fusion is realized through analytic hierarchy process in the algorithm iterative process. The feasibility of this model and algorithm is illustrated by a numerical example.

关键词： Analytic hierarchy process

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Impacts of Cross-Channel Recommendation for Online-Niche Products

SSRN

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SSRN 2022年

作者： Qiu, Ju Zhao, Ju Hu, Xiaojian Zhou, Yongwu Min, Jie School of Management Hefei University of Technology Anhui Hefei230009 China Key Laboratory of Process Optimization and Intelligent Decision-Making Ministry of Education Hefei230009 China Ministry of Education Engineering Research Center for Intelligent Decision-Making & Information System Technologies Hefei230009 China School of Business Administration South China University of Technology Guangdong Guangzhou510640 China Department of Mathematics Anhui Jianzhu University Anhui Hefei230601 China

This paper investigates a newly emerging recommendation strategy for online niche products, that is, a multichannel retailer recommends online niche products for instore shoppers. Such cross-channel recommendations make more customers aware of niche products, generating a positive information effect for the online channel. It also increases the interaction between online and offline channels. However, it is unclear how the provision of cross-channel recommendations influences the channel relationship when online and offline channels sell common products. Our analysis reveals that the cross-channel recommendation can increase the offline price and sales of common products, which benefits the offline channel. But the online sales of common products are decreased, causing a cannibalization effect on the online channel. The negative effect on common products can be dominated by the positive information effect on niche products when recommending a high-price niche product. In such case, the online channel benefits from cross-channel recommendations and channel synergy is achieved. From the perspective of a multichannel retailer, recommending high-price niche products to in-store shoppers can increase the retailer’s overall profit although the market for common product might shrink. Focusing on the target market with a high potential market scale of recommended customers and increasing the recommendation precision can make cross-channel recommendations more effective. © 2022, The Authors. All rights reserved.

关键词： Sales

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Impacts of Cross-Channel Recommendation for Online-Niche Products

SSRN

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SSRN 2022年

关键词： Sales

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SFF-DA: Sptialtemporal Feature Fusion for Detecting Anxiety Nonintrusively

arXiv

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arXiv 2022年

作者： Mo, Haimiao Li, Yuchen Yang, Shanlin Zhang, Wei Ding, Shuai The School of Management Hefei University of Technology Anhui Hefei23009 China The Key Laboratory of Process Optimization and Intelligent Decision-Making Ministry of Education China The National Engineering Laboratory for Big Data Distribution and Exchange Technologies Shanghai200135 China The West China Hospital of Sichuan University Sichuan Chengdu610041 China

Early detection of anxiety is crucial for reducing the suffering of individuals with mental disorders and improving treatment outcomes. Utilizing an mHealth platform for anxiety screening can be particularly practical in improving screening efficiency and reducing costs. However, the effectiveness of existing methods has been hindered by differences in mobile devices used to capture subjects' physical and mental evaluations, as well as by the variability in data quality and small sample size problems encountered in real-world settings. To address these issues, we propose a framework with spatiotemporal feature fusion for detecting anxiety nonintrusively. We use a feature extraction network based on a 3D convolutional network and long short-term memory ("3DCNN+LSTM") to fuse the spatiotemporal features of facial behavior and noncontact physiology, which reduces the impact of uneven data quality. Additionally, we design a similarity assessment strategy to address the issue of deteriorating model accuracy due to small sample sizes. Our framework is validated with a crew dataset from the real world and two public datasets: the University of Burgundy Franche-Comté Psychophysiological (UBFC-Phys) dataset and the Smart Reasoning for Well-being at Home and at Work for Knowledge Work (SWELL-KW) dataset. The experimental results indicate that our framework outperforms the comparison methods. © 2022, CC BY-NC-SA.

关键词： mHealth

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Independent innovation capability evaluation and analysis on wanjiang city-zone

Independent innovation capability evaluation and analysis on...

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International Conference on Computer Science and Service System (CSSS)

作者： Dengzhou Zhang Yuanyuan Gao Zhanglin Peng School of Management Hefei University of Technology Hefei China finance Department China electronics technology group corporation Beijing China Institute of Computer Network System Hefei University of Technology Hefei China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Hefei China

As the capacity of independent innovation is a vital measure standard for the success of a city's construction, the goal of this paper is to analyze and comment it. In order to make the research result more objective and precise, a system of evaluating indicator which includes the ability of innovation input, management, guarantee and output has been established. Moreover, by using the method of Principal Component Analysis, the capacity for independent innovation of nine cities in the Wanjiang city-region is clearly realized. Finally, hoping that this paper would be helpful to the construction of Wanjiang city-region for industrial transfer and provide a policy basis for the macroscopic readjustment and control as well as management.

关键词： Technological innovation Cities and towns Security Economic indicators Industries Appraisal Finance

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COS-training: A new semi-supervised learning method for keyphrase extraction based on co-training and SMOTE

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ICIC Express Letters, Part B: Applications 2015年第1期6卷 233-238页

作者： Wang, Gang Yang, Shanlin Ma, Jian School of Management Hefei University of Technology No. 193 Tunxi Road Hefei China Ministry of Education Key Laboratory of Process Optimization and Intelligent Decision-Making Hefei University of Technology No. 193 Tunxi Road Hefei China Department of Information Systems City University of Hong Kong Tat Chee Avenue Kowloon Hong Kong

As keyphrase is a small set of words that can best represent a document, they play significant roles in varieties of text-related tasks. In recent years, many unsupervised and supervised methods have been proposed for keyphrase extraction. However, keyphrase extraction is an imbalanced classification problem in nature and contains many unlabeled data, which have not been paid attention to in the previous studies. In this research, a new semi-supervised learning method, COS-training, is proposed for keyphrase extraction based on co-training and SMOTE. For the testing and illustration purpose, a keyphrase extraction dataset is selected to verify the effectiveness of the proposed method. Empirical results reveal that COS-training is a potential solution for keyphrase extraction. Among the compared methods, COS-training gets the best result. Al l these results illustrate that COS-training can be used as an alternative method for keyphrase extraction.

关键词： Extraction

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RS-LSSVM: A new ensemble method for sentiment classification based on random subspace

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ICIC Express Letters, Part B: Applications 2014年第6期5卷 1773-1778页

作者： Wang, Gang Yang, Shanlin Ma, Jian School of Management Hefei University of Technology No. 193 Tunxi Road Hefei China Key Laboratory of Process Optimization and Intelligent Decision-Making Ministry of Education Hefei University of Technology No. 193 Tunxi Road Hefei China Department of Information Systems City University of Hong Kong Tat Chee Avenue Kowloon Hong Kong

As analyzing and predicting the polarity of the sentiment plays an important role in understanding social phenomena and general society trends, sentiment classification problem has become a popular topic in academia and industry in recent years. However, comparing with Bagging and Boosting, another popular ensemble method, i.e., Random Subspace, is paid much less attention to the sentiment classification problem. In this research, we propose a new ensemble method, RS-LSSVM, for sentiment classification based on Random Subspace and LSSVM. Ten public sentiment classification datasets are used to verify the effectiveness of the proposed RS-LSSVM. Experimental results reveal that RS-LSSVM can get the better results than the four base learners, Bagging, and Boosting. All these results indicate that RS-LSSVM can be used as an alternative method for sentiment classification.

关键词： Support vector machines

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