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A study on DS-SVM-based forecast model from the view of information fusion

International Journal of u- and e- Service, Science and Technology 2015年第2期8卷 395-404页

作者： Zhu, Weidong Sun, Yibo Wu, Yong Liu, Jingyu School of Economics Hefei University of Technology HefeiAnhui China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education HefeiAnhui China Institute of Industrial Information Hefei University of Technology HefeiAnhui China School of Management Hefei University of Technology HefeiAnhui China School of Management Hefei University of Technology HefeiAnhui China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education HefeiAnhui China School of Management Hefei University of Technology HefeiAnhui China

Dempster-Shafer Theory is specially advantaged in information fusion, while Support Vector Machine (SVM) can well deal with high-dimensional limited sample data. This Article firstly forecasts the data samples by categories with multiple SVMs, and hence based thereon, fuses the resulting information from multiple SVM models by using DS theory. At the end, Anderson's Iris data set is used to simulate the system of the created DS-SVM model, which shows that the approaches proposed in this Article can not only increase the accuracy rate of the SVM forecasting model, but also improve the propensity scores of the SVM forecasting model. © 2015 SERSC.

关键词： Support vector machines

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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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2011 International Conference on Business management and Electronic Information, BMEI 2011 - 2011 International Conference on Supernetworks and System management, ICSSM 2011

作者： Liang, Changyong Gu, Dongxiao Bichindaritz, Isabelle Zuo, Chunrong Cheng, Fei School of Management Hefei University of Technology Hefei Anhui 230009 China Key Laboratory of Process Optimization Intelligent Decision-making of Ministry of Education of China Hefei Anhui 230009 China Institute of Technology University of Washington Tacoma WA 98402 United States Department of Computer Science University of Illinois at Chicago Chicago IL 60607 United States Research Center of Intelligent Decision Making Information System Technologies of Ministry of Education of China Hefei Anhui 230009 China

ISBN: (纸本)9781612841069

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. © 2011 IEEE.

关键词： Case based reasoning

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3DGS-based Tracking of Articulated Instruments for Instruments-Tissue Interaction Estimation in Robot-assisted Minimally Invasive Surgery

3DGS-based Tracking of Articulated Instruments for Instrumen...

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IEEE International Conference on Robotics and Biomimetics

作者： Xiaojian Li Lili Shen Ling Li Hangjie Mo Xin Wang Xiang Ma Jieyu Zheng School of Management Hefei University of Technology Hefei China Key Laboratory of Process Optimization and Intelligent Decision-Making Hefei University of Technology Hefei China West China Hospital Sichuan University Sichuan China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

3D information of surgical scene potentially enhances the accuracy of surgical navigation and facilitate the automation of robot-assisted surgery. However, current research seldom considers the relative 3D position between instruments and tissue. In this paper, the instruments-tissue interaction estimation method is proposed to obtain the relative distance between instruments and tissue. The 3D Gaussian representation of instruments is established preoperatively, making the pose of instruments the only parameter to be learned and updated. Based on this, the instrument pose estimation method is proposed, which applies 3DGS (3D Gaussian Splatting) to the instrument Gaussian model to obtain rendered color images, projection masks, and depth maps. These are aligned with the input images, segmented masks and depth from stereo depth estimation to construct the target loss. By backpropagating through the target loss, the optimal instrument pose is obtained. The L1 loss and smooth loss based on TPS (the Thin Plate Spline) are used to complete the depth of tissue occluded by instruments. The instruments-tissue interaction is estimated by the nearest distance between instrument and tissue. The experimental results indicate an average error of 0.94 mm and an accuracy of 90.40% with an error less than 2mm in estimating relative 3D distance between instruments and tissue.

关键词： Solid modeling Three-dimensional displays Accuracy Minimally invasive surgery Navigation Instruments Pose estimation Real-time systems Splines (mathematics) Robots

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Uncertainty Forecasting for Streamflow based on Support Vector Regression Method with Fuzzy Information Granulation

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Energy Procedia 2019年 158卷 6189-6194页

作者： Yaoyao He Yudong Yan Xu Wang Chao Wang School of Management Hefei University of Technology Hefei 230009 China Laboratory of Process Optimization and Intelligent Decision-Making Ministry of Education Hefei 230009 China China Institute of Water Resources and Hydropower Research Beijing 100048 China

Accurate and comprehensive forecasting of streamflow plays an important role in the uncertainly analysis of the hydrologic system. It is widely accepted that prediction interval (PI) can provide more precise and detailed information than deterministic forecasting when the uncertainty level of streamflow increases. Support vector regression (SVR) is a supervised learning model for classification and regression analysis based on associated learning algorithms. In this paper, fuzzy information granulation (FIG) is combined with SVR model (FIG-SVR) for uncertainty forecasting of streamflow. On behalf of evaluating the performance of the forecasting results, the evaluation metrics of point forecasting and interval prediction results are introduced. The real streamflow data from the Three Gorges in the Yangtze River are used to validate the proposed method based on the proposed method. The results show that the proposed method provides the high-quality point predictand and PIs, and the uncertainly of streamflow can be well handled.

关键词： Streamflow forecasting Support vector regression Fuzzy information Granulation Triangular fuzzy particles

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A Multimodal Data-driven Framework for Anxiety Screening

arXiv

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arXiv 2023年

作者： Mo, Haimiao Ding, Shuai Hui, Siu Cheung 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 School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore

Early screening for anxiety and appropriate interventions are essential to reduce the incidence of self-harm and suicide in patients. Due to limited medical resources, traditional methods that overly rely on physician expertise and specialized equipment cannot simultaneously meet the needs for high accuracy and model interpretability. Multimodal data can provide more objective evidence for anxiety screening to improve the accuracy of models. The large amount of noise in multimodal data and the unbalanced nature of the data make the model prone to overfitting. However, it is a non-differentiable problem when high-dimensional and multimodal feature combinations are used as model inputs and incorporated into model training. This causes existing anxiety screening methods based on machine learning and deep learning to be inapplicable. Therefore, we propose a multimodal data-driven anxiety screening framework, namely MMD-AS, and conduct experiments on the collected health data of over 200 seafarers by smartphones. The proposed framework’s feature extraction, dimension reduction, feature selection, and anxiety inference are jointly trained to improve the model’s performance. In the feature selection step, a feature selection method based on the Improved Fireworks Algorithm is used to solve the non-differentiable problem of feature combination to remove redundant features and search for the ideal feature subset. The experimental results show that our framework outperforms the comparison methods. © 2023, CC BY-NC-SA.

关键词： Feature Selection

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Effectiveness Evaluation Method of Constellation Satellite Communication System with Acceptable Consistency and Consensus Under Probability Hesitant Intuitionistic Fuzzy Preference Relationship

Research Square

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Research Square 2022年

作者： Wang, Yanjun Hu, Xiaoxuan Wang, Lidong School of Management Hefei University of Technology Hefei230009 China Key Laboratory of Process Optimization and Intelligent Decision Making Ministry of Education Hefei230009 China Department of Mathematics Dalian Maritime University Dalian116026 China

System effectiveness evaluation is an important part of constellation satellite communication system research, with applications in project verification and optimization as well as tactical and technical measurement argumentation. This paper presents a systematic and comprehensive effectiveness evaluation method for a constellation satellite communication system under a probabilistic hesitant intuitionistic fuzzy preference relationship (PHIFPR), aiming to better address the fuzziness and uncertainty in effectiveness evaluation. First, a proposed definition of PHIFPR describes the hesitancy of evaluators, provides hesitancy distribution information, and depicts the worst negative information and risk preferences in effectiveness evaluation. Then, we deduce the approximate consistency index of PHIFPR and establish a mathematical programming model to increase individual consistency when the approximate consistency index does not reach a predetermined level. In the sequel, a proposed group consensus index uses the PHIFPR-based Hausdorff distance to measure the closeness between evaluators’ judgements. Afterwards, a consistency and consensus improvement model is designed to retain the original opinions of evaluators to make the consistency and consensus of PHIFPRs acceptable. Moreover, a goal programming model is established to gain the reliable scheme priority weights by regarding the approximate consistency condition of a PHIFPR as a fuzzy constraint. Finally, an experimental example is offered to highlight the practicability and feasibility of the proposed method, and some comparative analyses with other methods offer insights into the designed method. © 2022, CC BY.

关键词： Satellites

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Performance evaluation of China’s industry: a generalized equilibrium data envelopment analysis approach with fixed-sum undesirable output

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INFOR: Information Systems and Operational Research 2021年第2期59卷 290-308页

作者： Min Yang Qingxian An Dan Hu Liang Liang a School of Management Hefei University of Technology Hefei P.R. China b Key Laboratory of Process Optimization and Intelligent Decision-Making Ministry of Education Hefei University of Technology Hefei P.R.China c School of Business Central South University Changsha P.R. China

With the rapid development of economy, China faces a considerable challenge from environmental problems especially with the waste of gas pollution emitted by China’s industry. To assess the environmental performance of China’s industry, this paper proposes a new generalized equilibrium efficiency frontier data envelopment analysis (GEEFDEA) approach that considers waste gases as undesirable outputs. It is assumed that the total amount of waste gases that the industry is permitted to emit is limited (or fixed), that is, increasing air pollution of one area should reduce that of other areas for offset. Compared with the prior evaluation approaches, the proposed approach is the only one that satisfies the following conditions simultaneously. 1) The approach considers the property of the “fixed sum” of undesirable outputs. 2) The approach avoids the case in prior approaches (e.g., fixed sum output and zero sum gains DEA approaches) that an infeasible solution may occur when accessing decision making unites (DMUs) with fixed-sum undesirable outputs. 3) The proposed approach inherits all advantages of the GEEFDEA approach such as achieving an equilibrium state by only one step and obtaining a complete ranking order. Finally, the proposed approach is applied to evaluate the performance in China’s industry from 2008 to 2013. Results show China’s industry has significant regional characteristics.

关键词： Generalized equilibrium efficient frontier data envelopment analysis fixed-sum undesirable outputs China’s industry

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Financing constraints, principal-agent and Chinese listed firms’ output efficiency

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International Journal of u- and e- Service, Science and Technology 2016年第8期9卷 189-200页

作者： Zhu, Weidong Wang, Yiling Liu, Jingyu School of Economics Hefei University of Technology HefeiAnhui230009 China School of Management Hefei University of Technology HefeiAnhui230009 China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education HefeiAnhui230009 China

It is shown that output under financing constraints and principal-agent can be modeled as a one-side deviation from a frictionless output level. The effects of financing constraints and principal-agent can be identified and quantified by imposing a distribution assumption on the effects. Panel data on Chinese listed firms between 2008 and 2013 are used in the estimation. It is found that: (1) the output efficiency of Chinese listed firms is significantly affected by financing constraints and principal-agent;(2) the severity of financing constraints and principal-agent of firms in different regions varies;(3) firms’ output efficiency in east China varies greatly, and the mean efficiency is the lowest of the three regions. © 2016 SERSC.

关键词： Efficiency

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Hierarchically-Trusted Evidential Fusion Method with Consistency Learning for Multimodal Language Understanding

SSRN

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SSRN 2024年

作者： Yang, Ying Yan-Qiu, Yang Ren, Gang School of Management Hefei University of Technology Hefei230009 China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Hefei230009 China Ministry of Education Engineering Research Centre for Intelligent Decision-Making & Information System Technologies Hefei230009 China

Human communication in the real world is based on multimodal data. Multimodal language understanding tasks such as multimodal intention recognition and multimodal emotion recognition are key steps in many applications, such as dialogue systems and social media. Although many studies have explored multimodal fusion methods and applied these methods to multimodal language understanding tasks including intent recognition and sentiment analysis, it is still a knotty problem whether the classification results are trusted. Therefore, this paper proposes a hierarchically-trusted evidential fusion method (HTEF) for multimodal language understanding. We construct multiple views from multi-modal data in a hierarchical manner, and extract multiple pieces of evidence from them. Then, we introduce Dempster-Shafer evidence theory(DST) to fuse evidence obtained from multimodal data to ensure the reduction of overall uncertainty. In addition, we explore the feature-level consistency and decision-level consistency of multi-modal data to avoid the conflict across information captured from different modalities. The method is evaluated on the public multimodal intent recognition dataset MIntRec and the emotion recognition dataset MELD. The experimental results show that our model outperforms both existing single-modal and multimodal baseline models across multiple evaluation metrics, which verifies the effectiveness of the proposed method. © 2024, The Authors. All rights reserved.

关键词： Emotion Recognition

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An Evidence-Based Multimodal Fusion Approach for Predicting Review Helpfulness with Human-Ai Complementarity

SSRN

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SSRN 2023年

作者： Yang, Ying Zhang, Ling-Feng Ren, Gang Wang, Gang School of Management Hefei University of Technology 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

Online product reviews have been increasingly adopted by consumers to express their evaluation for products on e-commercial platforms. Consumers are encouraged to evaluate the review quality by helpful votes according to their human intelligence, and considerable machine learning approaches have been developed to help detect helpful reviews automatically. However, previous studies largely depend on machine intelligence and neglect the complementary strength between machine algorithms and human intelligence. This study proposes an evidence-based multimodal fusion approach for predicting the helpfulness of reviews by aggregating the human intelligence from user votes and machine intelligence from texts and images. Review helpfulness is measured by a belief distribution and represented by the evidence obtained from each modality data. An evidential reasoning rule-based fusion strategy is proposed to combine the hybrid intelligence considering their complementarity. A new dataset is also developed for the task of multimodal review helpfulness prediction by crawling online product reviews from *** and annotated by human scoring. Experimental results on the dataset show that the proposed approach for predicting review helpfulness has superior predictive power and outperforms existing methods. This study also contributes to e-commerce by providing an enhanced method for predicting review helpfulness from both human and machine intelligence. © 2023, The Authors. All rights reserved.

关键词： Forecasting

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