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International Conference on Computer Supported Cooperative Work in Design

作者： Hanlin Liu Jiantao Zhou Hua Li College of Computer Science Inner Mongolia University Hohhot China Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Engineering Research Center of Ecological Big Data Hohhot China

Anomaly detection refers to the identification of data objects that deviate from the general data distribution. One of the important challenges in anomaly detection is handling high-dimensional data, especially when it contains a large number of redundant attributes. Although traditional Isolation Forest (iForest) and Extended Isolation Forest (EIF) have performed well in anomaly detection, the detection accuracy is still extremely influenced by redundant attributes. In this paper, we propose a two-tier architecture named RS-EIF, to alleviate the negative impact of redundant attributes. Specifically, the RS-EIF combines the rough set theory and EIF algorithm and provides an effective basis for the EIF-based anomaly detection method by removing redundant attributes with rough set theory. Experimental results show that the RS-EIF algorithm performs better than the iForest and EIF algorithms in terms of anomaly detection rate, and the results of the combination of rough set theory and EIF are more prominent compared to the other combination methods.

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Integrated Optimization of Traffic Signals and Vehicle Trajectories for Mixed Traffic at Signalized Intersections: A Two-Level Hierarchical Control Framework

SSRN

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SSRN 2024年

作者： Zou, Yangang Zheng, Fangfang Liu, Can Liu, Xiaobo School of Transportation and Logistics National Engineering Laboratory of Integrated Transportation Big Data Application Technology National United Engineering Laboratory of Integrated and Intelligent Transportation Southwest Jiaotong University Western Hi-tech Zone Sichuan Chengdu611756 China

With the rapid advancement of connected and automated vehicle (CAV) technology, the integrated optimization of traffic signals and vehicle trajectories has emerged as a promising approach to enhance intersection performance. However, the complexity of this integrated optimization problem requires substantial computational resources, rendering existing methods impractical for real-time applications. To address this challenge, this paper presents a two-level hierarchical control framework for mixed traffic, which provides a computationally efficient solution to the integrated optimization problem of traffic signals and vehicle trajectories, while maintaining high performance. At the upper level, instead of considering individual vehicles, this study introduces a platoon-based mixed integer linear programming (PMILP) model to jointly optimize signal timing and desired arrival times, with the main objective of minimizing traffic delay. Building upon the optimized desired arrival times, a Nash-based distributed model predictive control (DMPC) method is developed at the lower level to optimize CAV trajectories for eco-driving. Numerical experiments are conducted to assess the performance of the proposed method. The results demonstrate that the integrated optimization approach significantly reduces traffic delay, fuel consumption, and idling time, while simultaneously enhancing driving comfort. Notably, the proposed method achieves these improvements with a high level of computational efficiency. © 2024, The Authors. All rights reserved.

关键词： Traffic signals

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Monotonicity-Induced Health Indicator for Axle-Box Bearings of Urban Rail Vehicles 6th

Monotonicity-Induced Health Indicator for Axle-Box Bearings ...

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6th International Conference on Electrical engineering and Information Technologies for Rail Transportation, EITRT 2023

作者： Wang, Yiran Xin, Ge An, Guoping Li, Yilei School of Traffic and Transportation Beijing Jiaotong University Beijing100044 China Center of Safety Technology National Railway Administration Beijing100160 China School of Mechanical Electronic and Control Engineering Beijing Jiaotong University Beijing100044 China Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9789819993147

Health indicators of axle-box bearings play an important role in failure prediction for urban rail vehicles. Vibration-based signal processing technology is a promising tool to reveal the degradation process with fruitful time-frequency information. However, the processes usually have non-stationary features caused by complex working conditions, which may be confused with the unknown temporal variations under different frequency bands. To address this issue, this paper proposes a new index, named monotonicity-induced health indicator, which is adaptive to weight the optimal frequency bands. First, frequency domain analysis is used to extract features from vibration signals. The monotonicity of these features is then estimated and their weights are optimized by means of maximizing the monotonicity. Eventually, all the features and weights are fused together to obtain a health indicator. The experimental result proves that the proposed health indicator shows its superiority in terms of monotonicity while accurately characterizing the degradation process of the bearings. © 2024, Beijing Paike Culture Commu. Co., Ltd.

关键词： Frequency domain analysis

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From a Social Cognitive Perspective: Context-aware Visual Social Relationship Recognition

arXiv

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arXiv 2024年

作者： Wu, Shiwei Zhang, Chao Chen, Joya Xu, Tong Wu, Likang Hu, Yao Chen, Enhong The Anhui Province Key Lab of Big Data Analysis and Application School of Data Science and School of Computer Science University of Science and Technology of China China The College of Design and Engineering National University of Singapore Singapore The Xiaohongshu.Inc. China

People's social relationships are often manifested through their surroundings, with certain objects or interactions acting as symbols for specific relationships, e.g., wedding rings, roses, hugs, or holding hands. This brings unique challenges to recognizing social relationships, requiring understanding and capturing the essence of these contexts from visual appearances. However, current methods of social relationship understanding rely on the basic classification paradigm of detected persons and objects, which fails to understand the comprehensive context and often overlooks decisive social factors, especially subtle visual cues. To highlight the social-aware context and intricate details, we propose a novel approach that recognizes Contextual Social Relationships (ConSoR) from a social cognitive perspective. Specifically, to incorporate social-aware semantics, we build a lightweight adapter upon the frozen CLIP to learn social concepts via our novel multi-modal side adapter tuning mechanism. Further, we construct social-aware descriptive language prompts (e.g., scene, activity, objects, emotions) with social relationships for each image, and then compel ConSoR to concentrate more intensively on the decisive visual social factors via visual-linguistic contrasting. Impressively, ConSoR outperforms previous methods with a 12.2% gain on the People-in-Social-Context (PISC) dataset and a 9.8% increase on the People-in-Photo-Album (PIPA) benchmark. Furthermore, we observe that ConSoR excels at finding critical visual evidence to reveal social relationships. © 2024, CC BY.

关键词： Modal analysis

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A novel semi-supervised multi-view clustering framework for screening Parkinson’s disease

arXiv

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arXiv 2020年

作者： Zhang, Xiaobo Zhai, Donghai Yang, Yan Zhang, Yiling Wang, Chunlin School of Information Science and Technology Southwest Jiaotong University Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China

In recent years, there are many research cases for the diagnosis of Parkinson's disease (PD) with the brain magnetic resonance imaging (MRI) by utilizing the traditional unsupervised machine learning methods and the supervised deep learning models. However, unsupervised learning methods are not good at extracting accurate features among MRIs and it is difficult to collect enough data in the field of PD to satisfy the need of training deep learning models. Moreover, most of the existing studies are based on single-view MRI data, of which data characteristics are not sufficient enough. In this paper, therefore, in order to tackle the drawbacks mentioned above, we propose a novel semi-supervised learning framework called Semi-supervised Multi-view learning Clustering architecture technology (SMC). The model firstly introduces the sliding window method to grasp different features, and then uses the dimensionality reduction algorithms of Linear Discriminant analysis (LDA) to process the data with different features. Finally, the traditional single-view clustering and multi-view clustering methods are employed on multiple feature views to obtain the results. Experiments show that our proposed method is superior to the state-of-art unsupervised learning models on the clustering effect. As a result, it may be noted that, our work could contribute to improving the effectiveness of identifying PD by previous labeled and subsequent unlabeled medical MRI data in the realistic medical environment. Copyright © 2020, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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EG-KGR: A Knowledge Graph Reasoning Model Based on Enhanced Graph Sample and Aggregate Inductive Learning Algorithm

EG-KGR: A Knowledge Graph Reasoning Model Based on Enhanced ...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Yuejia Wu Jian-Tao Zhou Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing College of Computer Science Inner Mongolia University National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Engineering Research Center of Ecological Big Data Hohhot China

Knowledge Graph is an important research field that involves the storage and management of knowledge, but the incompleteness and sparsity of Knowledge Graphs hinder their application in many fields. Knowledge Graph Reasoning aims to alleviate this problem by completing missing paths or identifying wrong paths between entities. Graph Convolution Network (GCN) based methods are one of the state-of-the-art approaches to this work. However, it is difficult to directly generalize to unknown nodes and utilizes valid information from the local neighborhood which results in poor flexibility and extensibility and will loss of important information. This paper presents EG-KGR, a plug-and-play knowledge reasoning model based on enhanced graph sampling and aggregate inductive learning algorithm to relieve the above problems and enhance existing GCN-based methods. Specifically, EG-KGR supports incremental characteristics, uses inductive learning to replace transductive learning, and designs random sampling and local information sampling optimization methods to improve the model's generalization ability, prediction accuracy, and running speed. Extensive experimental results show that our EG-KGR can achieve optimal results.

关键词： Training Knowledge engineering Convolution Aggregates Optimization methods Knowledge graphs Learning (artificial intelligence)

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Neural multi-atlas label fusion: application to cardiac MR images

arXiv

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arXiv 2017年

作者： Yang, Heran Sun, Jian Li, Huibin Wang, Lisheng Xu, Zongben School of Mathematics and Statistics Xi’an Jiaotong University China National Engineering Laboratory for Big Data Algorithm and Analysis Technology China Department of Automation Shanghai Jiaotong University China

Multi-atlas segmentation approach is one of the most widely-used image segmentation techniques in biomedical applications. There are two major challenges in this category of methods, i.e., atlas selection and label fusion. In this paper, we propose a novel multi-atlas segmentation method that formulates multi-atlas segmentation in a deep learning framework for better solving these challenges. The proposed method, dubbed deep fusion net (DFN), is a deep architecture that integrates a feature extraction subnet and a non-local patch-based label fusion (NL-PLF) subnet in a single network. The network parameters are learned by end-to-end training for automatically learning deep features that enable optimal performance in a NL-PLF framework. The learned deep features are further utilized in defining a similarity measure for atlas selection. By evaluating on two public cardiac MR datasets of SATA-13 and LV-09 for left ventricle segmentation, our approach achieved 0.833 in averaged Dice metric (ADM) on SATA-13 dataset and 0.95 in ADM for epicardium segmentation on LV-09 dataset, comparing favorably with the other automatic left ventricle segmentation methods. We also tested our approach on Cardiac Atlas Project (CAP) testing set of MICCAI 2013 SATA Segmentation Challenge, and our method achieved 0.815 in ADM, ranking highest at the time of writing. Copyright © 2017, The Authors. All rights reserved.

关键词： Image segmentation

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A Graph Sequence Generator and Multi-head Self-attention Mechanism based Knowledge Graph Reasoning Architecture

A Graph Sequence Generator and Multi-head Self-attention Mec...

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International Conference on Computer Supported Cooperative Work in Design

作者： Yuejia Wu Jian-Tao Zhou College of Computer Science Inner Mongolia University National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Engineering Research Center of Ecological Big Data Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot China

Knowledge Graph (KG) is an essential research direction that involves storing and managing knowledge data, but its incompleteness and sparsity hinder its development in various applications. Knowledge Graph Reasoning (KGR) is an effective method to solve this limitation via reasoning missing knowledge based on existing knowledge. The graph Convolution Network (GCN) based method is one of the state-of-the-art approaches to this work. However, there are still some problems, such as the insufficient ability to perceive graph structure and the poor effect of learning data features which may limit the reasoning accuracy. This paper proposes a KGR architecture based on a graph sequence generator and multi-head self-attention mechanism, named GaM-KGR, to improve the above problems and enhance prediction accuracy. Specifically, the GaM-KGR first introduces the graph generation model into the field of KGR for graph representation learning to obtain the hidden features in the data so that enhancing the reasoning effect and then embeds the generated graph sequence into the multi-head self-attention mechanism for subsequent processing to improve the graph structure perception ability of the proposed architecture, so that it can process the graph structure data more appropriately. Extensive experimental results show that the GaM-KGR architecture can achieve the state-of-the-art prediction results of current GCN-based models.

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Screening of energy-saving technologies for cement production based on boston consulting group matrix 21st

Screening of energy-saving technologies for cement productio...

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21st Chinese Materials Conference, CMC 2020

作者： Zhang, Yuchen Gong, Xianzheng Liu, Yu Zhou, Liwei Faculty of Materials and Manufacturing Beijing University of Technology No.100 Ping Le Yuan Chaoyang District Beijing100124 China National Engineering Laboratory for Industrial Big-data Application Technology No.100 Ping Le Yuan Chaoyang District Beijing100124 China China Building Material Federation No.11 San Li He Road Haidian District Beijing100831 China

ISBN: (纸本)9783035717389

Based on the analysis of the development situation of China’s cement industry and the BCG Matrix method, this paper classifies and screens 31 energy-saving technologies in China's cement production, and puts forward guiding suggestions for the application of energy-saving technologies for cement enterprises. The screening results show that the "Energy-efficient powder separation technology" and "New low-calcium cement clinker and production technology" are "double-high" technologies with excellent on both screening indicators. The investment energy saving rates are 17.5 tce/10,000 yuan (tons of standard coal equivalent per 10,000 yuan) and 10 tce/10,000 yuan. The promotion ratio increments are 35% and 25%. "Cement clinker energy-saving nitrogen reduction firing technology", "Fan drive mode adopts high voltage frequency conversion speed control technology", "Pure low temperature waste heat power generation technology for cement kiln" are "single-high" technologies with excellent on one of screening indicators. The investment energy saving rates are 0.3tce/10,000 yuan, 4.1tce/10,000 yuan, 3.9tce/10,000 yuan respectively. The promotion ratio increments are 19%, 35%, 31.5% respectively. © 2021 Trans Tech Publications Ltd, Switzerland.

关键词： Investments

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A Revised Model for Sea-Surface Wind Inversion data with Joint Interpolation Optimization and Residual Network

A Revised Model for Sea-Surface Wind Inversion Data with Joi...

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International Conference on Intelligent System and Knowledge engineering, ISKE

作者： Weihong Lin Lijun You Tianqiang Huang Liqing Huang Haifeng Luo Chao Xu College of Computer and Cyber Security Fujian Normal University Fujian Provincal Engineering Research Center of Big Data Analysis and Application Digital Fujian Institute of Big Data Security Technology Fuzhou China Fujian Meteorological Information Center Fujian Key Laboratory of Severe Weather Fuzhou China

The sea-surface wind fields are pivotal for studies in both oceanography and meteorology. However, the acquisition of ocean wind data is often challenged by practical constraints, especially near coasts, leading to significant data gaps. This paper introduces a two-stage approach that combines interpolation optimization with a residual network to improve satellite-derived sea-surface wind fields data. Initially, the Kriging interpolation method is employed to fill missing regions. Subsequently, a network structure similar to ResNet-18 is tailored to revise the interpolated data using buoy anemometer observations. By establishing a correlation model between the interpolated results and ground truth through supervised training, the approach ensures the interpolated results gravitate towards the actual measurements. The efficacy of the proposed model is corroborated through experimental visual analysis and error cure assessments.

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