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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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A method based on manifold learning and Bagging for text classification

A method based on manifold learning and Bagging for text cla...

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International Conference on Artificial Intelligence, management Science and Electronic Commerce (AIMSEC)

作者： FengGang Li JiLi Fan Li Wang HuLin Zhang Rui Duan Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Hefei University of Technology Hefei China Hefei University of Technology Hefei Anhui CN

In order to solve the problem of high dimension in text classification, the paper proposes a method based on manifold learning and Bagging for text classification which imports manifold learning algorithm for dimension reduction. And Bagging algorithm is introduced when training classifier to improve the accuracy of text classification. Experimental results demonstrate that effect of text dimension reduction by manifold learning algorithm in the pretreatment of text classification is better, and the performance of the classifier has improved significantly.

关键词： Classification algorithms Text categorization Manifolds Training Bagging Support vector machine classification Euclidean distance

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

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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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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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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

引用

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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Understanding Information Sensitivity Perceptions and its Impact on Information Privacy Concerns in E-Commerce Services: Insights from China

SSRN

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SSRN 2023年

作者： Tao, Shouzheng Liu, Yezheng Sun, Chunhua 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 Key Laboratory of Philosophy and Social Sciences for Cyberspace Behaviour and Management Anhui Hefei230009 China

Although research topics related to information privacy in e-commerce services have been extensively studied, we still lack knowledge about consumers' perceived information sensitivity to personal information disclosure at the information type level and its effect on information privacy concerns. This study focused on common personal information types required by e-commerce services and investigated 625 Chinese consumers based on an online questionnaire about their information sensitivity to disclosing these personal information types. Rooted in the literature on information sensitivity and privacy concerns, we explored the impact of individual and situational factors on information sensitivity and the impact of information sensitivity of different personal information types on e-commerce privacy concerns. Our research identified which personal information types are considered insensitive and which are mediumsensitive or high-sensitive in e-commerce services and found a consensus among consumers on which personal information types are more sensitive. At the individual level, privacy experience, risk propensity, and gender affect information sensitivity. At the situational level, website social presence, website familiarity, and perceived privacy control affect information sensitivity. Perceived information sensitivity of medium-sensitive and high-sensitive personal information types affect e-commerce privacy concerns. Our research deepens the understanding of information sensitivity and privacy concerns and provides practical insights for privacy policymakers and e-commerce service providers. © 2023, The Authors. All rights reserved.

关键词： Websites

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An Empirical Analysis of Carbon Emission Price in China

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Energy Procedia 2018年 152卷 823-828页

作者： Kaile Zhou Yiwen Li School of Management Hefei University of Technology Hefei 230009 China Key Lab of Process Optimization and Intelligent Decision-making Ministry of Education Hefei 230009 China Department of Public Policy City University of Hong Kong Kowloon Hong Kong SAR China

Carbon emission trading, as an effective economic tool to deal with climate change issues, has received widespread attention in recent years. As a major carbon emitter, China plays an important role in global climate change. This paper uses the Vector Error Correction (VEC) model to explore the dynamic relationship between energy price, macroeconomic indicators, air quality, and carbon emission trading price. The results show that there is a long-term equilibrium relationship between these selected indicators and carbon emission trading price. In addition, this paper also uses the Generalized Auto-Regressive Conditional Heteroskedasticity (GARCH) model to analyze the carbon price fluctuation characteristics. It is found that there is a positive leverage effect on the price fluctuation of the selected carbon emission return series. External bad news will have a greater impact on carbon price fluctuation than good news.

关键词： Carbon market Carbon emission trading GARCH model Price fluctuation

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Point Tracking in Surgery–The 2024 Surgical Tattoos in Infrared (STIR) Challenge

arXiv

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arXiv 2025年

作者： Schmidt, Adam Karaoglu, Mert Asim Sinha, Soham Jang, Mingang Ha, Ho-Gun Jung, Kyungmin Gu, Kyeongmo Ullah, Ihsan Lee, Hyunki Šerých, Jonáš Neoral, Michal Matas, Jiří Zhou, Rulin He, Wenlong Wang, An Ren, Hongliang Silva, Bruno Queirós, Sandro Lima, Estêvão Vilaça, João L. Kikuchi, Shunsuke Kouno, Atsushi Matsuzaki, Hiroki Li, Tongtong Chen, Yulu Li, Ling Ma, Xiang Li, Xiaojian Zeinoddin, Mona Sheikh Wang, Xu Tandogdu, Zafer Shaw, Greg Mazomenos, Evangelos Stoyanov, Danail Chen, Yuxin Wu, Zijian Ladikos, Alexander DiMaio, Simon Salcudean, Septimiu E. Mohareri, Omid Intuitive Surgical Inc. Sunnyvale United States ImFusion GmbH Munich Germany Technical University of Munich Munich Germany NVIDIA Santa Clara United States Korea Republic of CMP Visual Recognition Group Faculty of Electrical Engineering Czech Technical University in Prague Czech Republic Shenzhen Research Institute China The Chinese University of Hong Kong China Shenzhen University China School of Medicine University of Minho Braga Portugal ICVS/3B’s - PT Government Associate Laboratory Braga Guimarães Portugal 2Ai –School of Technology IPCA Barcelos Portugal LASI – Associate Laboratory of Intelligent Systems Guimarães Portugal Jmees Inc Japan United States School of Management Hefei University of Technology Hefei230009 China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Hefei China Hawkes Institute University College London London United Kingdom Dept of Urology University College London Hospitals United Kingdom University of British Columbia Canada

Understanding tissue motion in surgery is crucial to enable applications in downstream tasks such as segmentation, 3D reconstruction, virtual tissue landmarking, autonomous probe-based scanning, and subtask autonomy. Labeled data are essential to enabling algorithms in these downstream tasks since they allow us to quantify and train algorithms. This paper introduces a point tracking challenge to address this, wherein participants can submit their algorithms for quantification. The submitted algorithms are evaluated using a dataset named surgical tattoos in infrared (STIR), with the challenge aptly named the STIR Challenge 2024. The STIR Challenge 2024 comprises two quantitative components: accuracy and efficiency. The accuracy component tests the accuracy of algorithms on in vivo and ex vivo sequences. The efficiency component tests the latency of algorithm inference. The challenge was conducted as a part of MICCAI EndoVis 2024. In this challenge, we had 8 total teams, with 4 teams submitting before and 4 submitting after challenge day. This paper details the STIR Challenge 2024, which serves to move the field towards more accurate and efficient algorithms for spatial understanding in surgery. In this paper we summarize the design, submissions, and results from the challenge. The challenge dataset is available here: https://***/records/14803158, and the code for baseline models and metric calculation is available here: https://***/athaddius/STIRMetrics Copyright © 2025, The Authors. All rights reserved.

关键词： Endoscopy

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Forecasting of electricity price subsidy based on installed cost of distributed photovoltaic in China

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Energy Procedia 2019年 158卷 3393-3398页

作者： Changhui Yang Rui Yao Kaile Zhou School of Management Hefei University of Technology Baohe District Hefei 230009 China Key Laboratory of Process Optimization and Intelligent Decision-Making ( Ministry of Education) Hefei University of Technology Baohe District Hefei 230009 China Department of Public Policy City University of Hong Kong Kowloon Hong Kong

In China, the installed cost of distributed photovoltaic (DPV) is declining rapidly. However, the level of subsidy per kWh for DPV is adjusted less frequently by the Chinese government. This paper proposed a linkage model to forecast subsidy per kWh of DPV based on net present value (NPV) method. The results show that the increase of subsidy duration, internal consumption proportion and sunshine hours will reduce subsidy level. Therefore, it is suggested that the Chinese government should take varied and targeted measures, such as reducing different subsidies for new DPV users who install DPV in different years, choosing a longer subsidy duration and setting different subsidy levels in different regions. These varied and targeted measures are beneficial to both government and users, because they can reduce the annual financial burden for the government and the surcharges of electricity purchase for users.

关键词： Distributed photovoltaic subsidy NPV method China

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