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Multi-sensor Deep Clustering Networks with Joint Learning Strategy for Fault Diagnosis Considering Consensus-specific Constraint

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IEEE Sensors Journal 2025年第11期25卷 19921-19936页

作者： Xu, Yihao Lu, Mingfeng Wang, Gang Fang, Fang University of Science and Technology of China School of Management Hefei230026 China Hefei University of Technology School of Management Hefei230009 China Hefei University of Technology School of Management Key Laboratory of Process Optimization and Intelligent Decision-Making of the Ministry of Education Ministry of Education Engineering Research Center for Intelligent Decision-Making and Information System Technologies Hefei230009 China

The deep clustering method has been successfully employed for fault diagnosis due to its remarkable ability to extract deep representation features, particularly in scenarios where labeled data is unavailable. However, the separate learning strategy utilized in deep clustering-based fault diagnosis methods leads to sub-optimal fault diagnosis results, and the consensus representation feature present in multi-sensor industrial data is not fully explored to improve the effectiveness of fault diagnosis. To address the above problems, a novel deep clustering method named Multi-sensor Deep Clustering Networks with joint learning strategy (MsDCN) is proposed for fault diagnosis, which can comprehensively utilize the industrial multi-sensor data and enable joint optimization. First, the Commonality-net uses a sensor-aware attentive encoder (SaAE) to integrate multi-sensor industrial data and extract the consensus representation feature. Second, the Individuality-net is established to extract the specific representation features of multi-sensor industrial data. Finally, a joint multisensor deep clustering learning strategy including consensus-specific constraints is designed to optimize the networks jointly and learn the consensus representation feature with consistent and complementary information. Extensive experiments on the gearbox and bearing datasets validate the proposed MsDCN. The results indicate that the proposed MsDCN can generate a reliable consensus representation feature to achieve better fault diagnosis performance than other benchmark methods. Moreover, its capability to enhance multi-sensor fault diagnosis with unlabeled data highlights its potential for real-world applications. © 2001-2012 IEEE.

关键词： Labeled data

A Force-driven and Vision-driven Hybrid Control Method of Autonomous Laparoscope-Holding Robot

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A Force-driven and Vision-driven Hybrid Control Method of Au...

IEEE International Conference on Robotics and Automation (ICRA)

作者： Jin Fang Ling Li Xiaojian Li Hangjie Mo Pengxin Guo Xilin Xiao Yanwei Qu School of Management Hefei University of Technology Hefei China Key Laboratory of Process Optimization and Intelligent Decision-Making (Ministry of Education) Hefei University of Technology Hefei China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Laparoscope-holding robots significantly enhance the stability and precision of visualization in minimally invasive surgeries. Most existing robots of this kind depend on visual servo systems and struggle with efficient, rapid adjustments in the field-of-view (FOV), especially when identifying organs and needles outside the FOV. This paper presents a laparoscope-holding robot system capable of employing both vision-driven and force-driven mechanisms for continuous and large-scale FOV adjustments, respectively. The system features an integrated tactile handle, enabling the reception of human-robot interaction forces during surgical navigation. We propose a hybrid control method that leverages both force and vision inputs for laparoscopic FOV adjustments. This approach integrates a virtual wrench, generated from visual information, and an interaction wrench, obtained from the tactile handle, into the robot's dynamic model, which complies with remote center of motion constraints. The interaction wrench's gain is adjusted with the gripping force on the integrated tactile handle, ensuring that unintended movements caused by accidental contacts are prevented, thus safeguarding operational safety. The proposed method eliminates the need to switch control modes, enabling simultaneous visual tracking and tactile interaction guidance. Experimental results demonstrate that the proposed method not only allows for FOV adjustments with surgical instrument guiding but also adapts well to large-scale FOV adjustment tasks.

关键词： Laparoscopes Visualization Tracking Instruments Force Human-robot interaction Switches

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

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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

Predicting Helpfulness of Multimodal Reviews with Customer Confirmation Bias: A Hierarchically-Trusted Multi-View Deep Learning Method

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SSRN 2024年

作者： Yang, Ying Tang, Si 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 Center for Intelligent Decision-Making & Information System Technologies Hefei230009 China

Accurately identifying high quality online-reviews is beneficial to both consumers and e-commerce platforms. Consumers usually exhibit confirmation bias phenomenon when evaluating the helpfulness of product reviews. However, there has been limited research on simulating and incorporating this information processing bias into predicting multimodal review helpfulness. To address this challenge, we combine confirmation bias theory with deep learning techniques, and propose a hierarchically-trusted multi-view deep learning method for predicting the helpfulness of multimodal reviews. Drawing upon the elaborate likelihood model and confirmation bias theory, multiple views are designed to represent consumers’ initial beliefs, central cues and peripheral cues of multimodal reviews. A hierarchically-trusted multi-view fusion strategy is designed to dynamically aggregate the different views considering their interactions. Experimental results on the *** review dataset demonstrate that the proposed method effectively improves the accuracy of evaluating the values of online reviews. This research contributes to the improvement of platform service operations and enhances consumer experience. © 2024, The Authors. All rights reserved.

关键词： Deep learning

Exploring the impact of COVID-19 on travel behavior based on EV charging big data 5

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

5th IEEE Conference on Energy Internet and Energy System Integration, EI2 2021

作者： Zhou, Kaile Hu, Dingding Li, Fangyi Key Laboratory of Process Optimization and Intelligent Decision-making of Ministry of Education School of Management Hefei University of Technology Hefei230009 China

ISBN: (纸本)9781665434256

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. © 2021 IEEE

关键词： COVID-19

Generative Adversarial Network-Based Optimal Energy Storage Capacity Configuration for Newly–Build Microgrids

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SSRN 2024年

作者： Zhou, Kunshu Zhou, Kaile Yang, Shanlin School of Management Hefei University of Technology Hefei230009 China Key Laboratory of Process Optimization and Intelligent Decision-making of Ministry of Education Hefei University of Technology Hefei230009 China

In the initial stage of microgrid construction, historical data is limited. It is challenging to address the issues of the small amount of data and high uncertainty of renewable energy when proceeding with capacity configuration. This study performs Wasserstein distance-based generative adversarial network with gradient penalty (WGAN-GP) to enhance the original 30 days PV power generation scenario data. Furthermore, the improved data is used to build a distributionally robust optimization model based on various discrete scenarios to determine the optimal capacity configuration of energy storage in newly–built microgrids. The results obtained from the solution are compared with those of traditional stochastic optimization and robust optimization, and the robustness of the results is verified by sensitivity analysis. The comparison results demonstrate that the strategy employed in this study can better address the issues of limited data and uncertainty in renewable energy in the capacity configuration process of newly-built microgrids. © 2024, The Authors. All rights reserved.

关键词： Sensitivity analysis

Competition in durable goods markets with product failures into consideration 9

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Competition in durable goods markets with product failures i...

9th International Symposium on Project Management, ISPM 2021

作者： Tian, Jianfei Qian, Xiaofei School of management Hefei University of Technology Anhui China Key Laboratory of Process Optimization and Intelligent Decision-making of the Ministry of Education China

ISBN: (纸本)9781921712777

Few studies on competition in dual channel for durable goods take the product failures into consideration. However, the factors related to preventive maintenance (PM) and minimal maintenance (MM) have an important impact on the optimal profits of the manufacturer and retailer. Consequently, we develop three game models (Single Selling channel (SS), Manufacturer Stackelberg (MS) and Cooperation Model (CM)) to study the players’ equilibrium solutions and analyze the impact of the initial point, numbers and level of preventive maintenance on equilibrium price and profits. We find that the equilibrium wholesale and retail price under SS tend to increase when the manufacturer decides to raise the repair charge, while in MS the manufacturer and retailer will select a higher level for the wholesale price, retail price and rental charge when manufacturer chooses to put off the initial point for PM. In addition, numerical examples are provided to supplement our conclusions. The results show that the manufacturer in MS will set the numbers and level of PM to the maximum threshold in order to maximize its profit. © ISPM *** right reserved.

关键词： Preventive maintenance

A branch-price-and-cut algorithm for the time-dependent multiple truck–drone routing problem

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Computers and Operations Research 2025年 182卷

作者： Zang, Xiaoning Jiang, Li Liang, Changyong Fang, Xiang School of Management Hefei University of Technology Hefei China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education China Business School University of Colorado Denver DenverCO United States

Time-varying traffic conditions are crucial features of urban logistics. Overlooking these conditions will pose a high coordination risk for drone-assisted routing problems. In this paper, a time-dependent multiple truck–drone routing problem (TD-MTDRP), which captures the time-varying traffic conditions as time-dependent travel times, is introduced. In the problem, trucks with time-dependent travel times travel through the urban roads, while drones fly over the urban areas, unaffected by the urban traffic, resulting in time-independent travel times. Our objective is to the total duration of all routes. We present the ready time function on a truck–drone path level, which is proven to satisfy the FIFO property. A route-based model is introduced based on this function to formulate the problem. The model decomposes the set-partitioning problem into a master problem (MP) and a time-dependent shortest truck–drone path problem with resource constraints (TDSTDPPRC). A branch-price-and-cut algorithm is also developed to solve the model. In this algorithm, a column-and-row generation is introduced to solve the MP, and a labeling algorithm incorporating new label extension and dominance rules is presented to address the TDSTDPPRC. For the numerical results, the approaches are evaluated on two sets of instances derived from the benchmark instances, demonstrating the effectiveness of the exact method in solving the TD-MTDRP. © 2025

关键词： Travel time

A Two-Dimensional Time-Aware Cloud Service Recommendation Approach with Enhanced Similarity and Trust

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SSRN 2023年

作者： Tang, Chunhua Zhao, Shuangyao Chen, Binbin Lu, Xiaonong Zhang, Qiang School of Management Hefei University of Technology Hefei230009 China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Hefei230009 China

Owing to an increasing number of functionally equivalent services on the cloud computing platform, quality-of-service (QoS) prediction and service recommendation have been developed using collaborative filtering (CF) techniques. However, each individual QoS is related to service status and user perceptions and thus instantly varies over time. QoS instantaneity often makes service predicted QoS deviate from actual values in traditional CF approaches. In addition, existing CF approaches ignore inauthentic QoS values given by untrustworthy users. To address these problems, we develop a two-dimensional time-aware and trust-aware service recommendation approach (TaTruSR). First, considering QoS instantaneity, both time weight (time dimension) and temporal certainty (QoS dimension) are designed to determine the contribution of co-invoked services. Second, a set of most similar and trusted neighbors can be identified from the view of the time-aware similarity model and trust model. In models, the direct similarity and local trust are calculated based on the QoS ratings and contribution of co-invoked services to improve the prediction accuracy and eliminate unreliable QoS. The indirect similarity and global trust are estimated based on the social network to alleviate the data sparsity problem. Finally, missing QoS prediction and service recommendation for the active user can be achieved based on enhanced similarity and trust. A case study and experimental evaluation on real-world datasets demonstrate the practicality and accuracy of the proposed approach. © 2023, The Authors. All rights reserved.

关键词： Forecasting