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RNaAE: A Novel Approach for Identifying Unseen Railway Anomalies

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RNaAE: A Novel Approach for Identifying Unseen Railway Anoma...

International Conference on Signal Processing Proceedings (ICSP)

作者： Jiayu Zhang Qingji Guan Junbo Liu Yaping Huang Jianyong Guo Beijing Key Laboratory of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing China Infrastructure Inspector Center China Academy of Railway Science Corporation Limited Beijing China Tangshan Research Institute of Beijing Jiaotong University Tangshan China Urban Rail Transit National Engineering Research Center for Digital Construction and Evaluation Tianjin China

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

Detecting abnormal objects in railway track inspection images using vision-based technology is a crucial task for ensuring the safety of railway transportation. Traditional supervised object detection methods fail to achieve satisfactory results due to the diverse categories of abnormal objects and the lack of abnormal samples. Even though the widely used Autoencoder can leverage reconstruction errors to detect anomalies without using abnormal data, they tend to generate a relatively high number of false positives. In this paper, we address the task in an unsupervised manner and propose a novel Random Network-Assisted Autoencoder, called RNaAE, for identifying unseen abnormal objects. Specifically, we first design a learnable network to fit a randomly initialized stochastic network with fixed weights, where the difference between two predictions can then be used to estimate whether the candidate object is anomalous. After combined with a traditional Autoencoder, a Gaussian mixture model is then used to classify the candidate box into normal and abnormal by anomaly scores. Extensive experiments conducted on our collected railway anomaly dataset demonstrate that the proposed RNaAE exceeds previous stateof-the-art methods, achieving 98.23% and 92.02% in terms of AUROC and F1-score.

关键词： Autoencoders Transportation Stochastic processes Object detection Signal processing Inspection Rail transportation Safety Object recognition Anomaly detection

Biomass Energy May Mitigate Energy Shortages and Reduce Carbon Emissions in Asia

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SSRN

SSRN 2022年

作者： Li, Yong Wang, Zhaosheng Huang, Mei Gong, He Sun, Hongfu Zhu, Lin National Ecosystem Science Data Center Key Laboratory of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China College of Geoscience and Surveying Engineering China University of Mining and Technology Beijing100083 China National Satellite Meteorological Center China Meteorological Administration Beijing China

Biomass energy is a key component of renewable new energy, which is very important for energy security and environmental protection. Meanwhile, Asia is a region with intensive global energy consumption. It is of great significance to clarify its biomass energy potential and its role in energy and environment. So, we used MODIS NPP to evaluate potential biomass energy reserves and their temporal and spatial changes in Asia. The results show that the total potential biomass energy in Asia is 327.68 ± 11.11 EJ, which is approximately equal to the total energy use in all of Asia over the four years. In addition, we used the AHP and cluster analysis to investigate the degree of energy shortage in Asian *** supplying biomass energy for energy use in various countries, the number of countries with severe, moderate and mild energy shortages in Asia will decrease by 8.5%, 12.7% and 6.4% respectively. In addition, if 50% of conventional energy in Asia is replaced by biomass, its total carbon dioxide emissions will be reduced by 33% to 44%. Taken together, this shows that Asia's abundant biomass energy reserves can help alleviate energy shortages in various countries and reduce regional carbon dioxide emissions. © 2022, The Authors. All rights reserved.

关键词： Energy security

Fog-assisted multi-user swipt networks: Local computing or offloading

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arXiv 2019年

作者： Zheng, Haina Xiong, Ke Fan, Pingyi Zhong, Zhangdui Letaief, Khaled Ben School of Computer and Information Technology Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing China Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing Jiaotong University Beijing100044 China National Laboratory for Information Science and Technology Department of Electronic Engineering Tsinghua University Beijing100084 China State Key Lab of Rail Traffic Control and Safety Beijing Engineering Research Center of High-speed Railway Broadband Mobile Communications Beijing Jiaotong University Beijing100044 China Clear Water Bay Hong Kong

This paper investigates a fog computing-assisted multi-user simultaneous wireless information and power transfer (SWIPT) network, where multiple sensors with power splitting (PS) receiver architectures receive information and harvest energy from a hybrid access point (HAP), and then process the received data by using local computing mode or fog offloading mode. For such a system, an optimization problem is formulated to minimize the sensors' required energy while guaranteeing their required information transmissions and processing rates by jointly optimizing the multi-user scheduling, the time assignment, the sensors' transmit powers and the PS ratios. Since the problem is a mixed integer programming (MIP) problem and cannot be solved with existing solution methods, we solve it by applying problem decomposition, variable substitutions and theoretical analysis. For a scheduled sensor, the closed-form and semi-closedform solutions to achieve its minimal required energy are derived, and then an efficient multi-user scheduling scheme is presented, which can achieve the suboptimal user scheduling with low computational complexity. Numerical results demonstrate our obtained theoretical results, which show that for each sensor, when it is located close to the HAP or the fog server (FS), the fog offloading mode is the better choice;otherwise, the local computing mode should be selected. The system performances in a frame-by-frame manner are also simulated, which show that using the energy stored in the batteries and that harvested from the signals transmitted by previous scheduled sensors can further decrease the total required energy of the sensors. Copyright © 2019, The Authors. All rights reserved.

关键词： Energy harvesting

Mvqs: Robust Multi-View Instance-Level Cost-Sensitive Learning Method for Imbalanced data Classification

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SSRN

SSRN 2023年

作者： Hou, Zhaojie Tang, Jingjing Li, Yan Fu, Saiji Tian, Yingjie School of Business Administration Faculty of Business Administration Southwestern University of Finance and Economics Chengdu611130 China Institute of Big Data Southwestern University of Finance and Economics Chengdu611130 China School of Economics and Management Beijing University of Posts and Telecommunications Beijing100876 China School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China MOE Social Science Laboratory of Digital Economic Forecasts and Policy Simulation UCAS Beijing100190 China

During multi-view imbalanced learning, data is collected from different sources and class labels are heavily skewed. Multi-view imbalanced learning has been studied extensively, with two main categories: multi-view cost-sensitive learning and multi-view ensemble learning. However, those methods are subject to certain limitations: 1) omission of either consensus or complementary information, 2) complex preprocessing of multi-view ensemble learning and manual assignment of misclassification costs of multi-view cost-sensitive learning, and 3) limited ability to handle noisy data. Therefore, we aim to design a concise and unified framework to grapple with the multi-view representations, imbalanced classes and noisy samples simultaneously. Inspired by the elegant metrics of SVM-style models and asymmetric quadratic type squared error loss function (QTSELF), we propose a robust multi-view instance-level cost-sensitive support vector machine for imbalanced data classification called MVQS. The consensus regularization term and combination weight strategy are employed to fully exploit multi-view information. The QTSELF loss can not only adaptively impose instance-level penalties to samples, but also make MVQS more robust to noisy samples. We solve MVQS with the gradient descent and the alternating direction method of multipliers algorithms. The comprehensive experimental results on various datasets validate that MVQS is more competitive and robust than other benchmark approaches. © 2023, The Authors. All rights reserved.

关键词： Support vector machines

A hierarchical and dynamic large-scale railway-track modeling solution for high-speed train real-time visual simulation

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ICIC Express Letters, Part B: Applications 2016年第12期7卷 2705-2711页

作者： Liu, Shijian Pan, Jeng-Shyang Zou, Zheng Zou, Beiji Liao, Shenghui Shcool of Information Science and Engineering Fujian University of Technology No. 3 Xueyuan Road University Town Minhou Fuzhou350118 China Key Laboratory of Big Data Mining and Applications of Fujian Province Fujian University of Technology No. 3 Xueyuan Road University Town Minhou Fuzhou350118 China The Key Laboratory for Automotive Electronics and Electric Drive of Fujian Province Fujian University of Technology No. 3 Xueyuan Road University Town Minhou Fuzhou350118 China Shcool of Information Science and Engineering Central South University No. 932 South Lushan Road Changsha410083 China

Large-scale railway-track modeling is a key and challenging problem for real- time visual simulation of the high-speed train. The railway-track can generally be described by its central line, whose length and shape vary dramatically between different ones. Therefore, modeling of a large-scale specified railway-track is not a trivial work. A new hierarchical and dynamic modeling framework is proposed to deal with the challenges. Unlike existing top-down approach, this bottom-up solution treats the entire railway-track as the combination of multiple repetitive regularity patterns, namely the elements referred by us. It is an optimized strategy for large model decomposition and is superior in modeling local details. Experiments, including the usage of the proposed method for track assembling and its applications to high-speed train real-time visual simulation, demonstrate the effect and efficiency of our work. Though designed for railway-track modeling, this method can be easily extended to other transportation applications as well. © 2016 ISSN 2185-2766.

关键词： Railroads

Signal demodulation with machine learning methods for physical layer visible light communications: Prototype platform, open dataset and algorithms

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arXiv 2019年

作者： Ma, Shuai Dai, Jiahui Lu, Songtao Li, Hang Zhang, Han Du, Chun Li, Shiyin School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China State Key Laboratory of Integrated Services Networks Xidian University Xi'an710071 China Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States Shenzhen Research Institute of Big Data Shenzhen518172 China Department of Electrical and Computer Engineering University of California DavisCA95616 United States

In this paper, we investigate the design and implementation of machine learning (ML) based demodulation methods in the physical layer of visible light communication (VLC) systems. We build a flexible hardware prototype of an end-to-end VLC system, from which the received signals are collected as the real data. The dataset is available online, which contains eight types of modulated signals. Then, we propose three ML demodulators based on convolutional neural network (CNN), deep belief network (DBN), and adaptive boosting (AdaBoost), respectively. Specifically, the CNN based demodulator converts the modulated signals to images and recognizes the signals by the image classification. The proposed DBN based demodulator contains three restricted Boltzmann machines (RBMs) to extract the modulation features. The AdaBoost method includes a strong classifier that is constructed by the weak classifiers with the knearest neighbor (KNN) algorithm. These three demodulators are trained and tested by our online open dataset. Experimental results show that the demodulation accuracy of the three datadriven demodulators drops as the transmission distance increases. A higher modulation order negatively influences the accuracy for a given transmission distance. Among the three ML methods, the AdaBoost modulator achieves the best performance. Copyright © 2019, The Authors. All rights reserved.

关键词： Adaptive boosting

Robust and Fast Measure of Information via Low-rank Representation

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arXiv 2022年

作者： Dong, Yuxin Gong, Tieliang Yu, Shujian Chen, Hong Li, Chen School of Computer Science and Technology Xi’an Jiaotong University Xi’an710049 China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Ministry of Education Xi’an710049 China Machine Learning Group UiT - The Arctic University of Norway Norway College of Science Huazhong Agriculture University Wuhan430070 China Engineering Research Center of Intelligent Technology for Agriculture Ministry of Education Wuhan430070 China

The matrix-based Rényi’s entropy allows us to directly quantify information measures from given data, without explicit estimation of the underlying probability distribution. This intriguing property makes it widely applied in statistical inference and machine learning tasks. However, this information theoretical quantity is not robust against noise in the data, and is computationally prohibitive in large-scale applications. To address these issues, we propose a novel measure of information, termed low-rank matrix-based Rényi’s entropy, based on low-rank representations of infinitely divisible kernel matrices. The proposed entropy functional inherits the specialty of of the original definition to directly quantify information from data, but enjoys additional advantages including robustness and effective calculation. Specifically, our low-rank variant is more sensitive to informative perturbations induced by changes in underlying distributions, while being insensitive to uninformative ones caused by noises. Moreover, low-rank Rényi’s entropy can be efficiently approximated by random projection and Lanczos iteration techniques, reducing the overall complexity from O(n3) to O(n2s) or even O(ns2), where n is the number of data samples and s n. We conduct large-scale experiments to evaluate the effectiveness of this new information measure, demonstrating superior results compared to matrix-based Rényi’s entropy in terms of both performance and computational efficiency. Copyright © 2022, The Authors. All rights reserved.

关键词： Entropy

Undirected graphs: Is the shift-enabled condition trivial or necessary?

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arXiv 2018年

作者： Chen, Liyan Cheng, Samuel He, Kanghang Stankovic, Lina Stankovic, Vladimir Department of Computer Science and Technology Tongji University Shanghai201804 Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province Zhejiang Ocean University Zhoushan Zhejiang316022 School of Electrical and Computer Engineering University of Oklahoma OK74105 United States Department of Electronic and Electrical Engineering University of Strathclye GlasgowG1 1XW United Kingdom

It has recently been shown that, contrary to the wide belief that a shift-enabled condition (necessary for any shift-invariant filter to be representable by a graph shift matrix) can be ignored because any non-shift-enabled matrix can be converted to a shift-enabled matrix, such a conversion in general may not hold for a directed graph with non-symmetric shift matrix. This letter extends this prior work, focusing on undirected graphs where the shift matrix is generally symmetric. We show that while, in this case, the shift matrix can be converted to satisfy the original shift-enabled condition, the converted matrix is not associated with the original graph, that is, it does not capture anymore the structure of the graph signal. We show via a counterexample, that a non-shift-enabled matrix cannot be converted to a shift-enabled one and still maintain the topological structure of the underlying graph, which is necessary to facilitate localized signal processing. Copyright © 2018, The Authors. All rights reserved.

关键词： Signal processing

Impact of Consumer Characteristics on IP Product Purchase: Empirical Evidence from a Chinese Chain Retailer

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Procedia Computer Science 2023年 221卷 933-940页

作者： Jiayu Tang Tingyan Wang Xin Tian Qiang Cai Research Center on Fictitious Economy and Data Science. Chinese Academy of Sciences Beijing 100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China Sino-Danish College University of Chinese Academy of Sciences Beijing 100190 China Nuffield Department of Medicine University of Oxford South Parks Road Oxford OX1 3SY United Kingdom NIHR Oxford Biomedical Research Centre Big Data Institute University of Oxford Old Road Campus Oxford OX3 7LF United Kingdom School of Economics and Management Ningbo University of Technology Ningbo 315211 China

Developing IP product has become a common differentiation strategy for retailers to attract customers and remain competence strength. This study uses empirical model to relate consumer characteristics with IP product purchase. The results show that consumers shopping on weekends are more likely to buy IP products, and consumers who have stocking behavior are less likely to purchase IP products. Consumers who are members of the retailer also have lower probability to buy IP products. These findings provide suggestions for the retailers who want to apply differentiation strategy by introducing co-branded IP products.

关键词： IP product Weekend Stock Membership