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International Conference on Ubiquitous Robots and Ambient Intelligence

作者： Yuan Lan Weiwei Zong Xiaojian Ding Xiaoyan Xiong Xiaohang Han Jiahai Huang Bing Ma Key Laboratory of Ministry of Education in Advance Transducers and Intelligent Control System Institute of Mechatronics Engineering under School of Mechanical Engineering Taiyuan University of Technology Department of Computer Science Wayne State University Science and Technology on Information Systems Engineering Laboratory The Second Research Institute of China Electronic Technology Group Corporation

ISBN: (纸本)9781509008223

Rolling element bearings constitute the key parts on rotating machinery and their fault diagnosis are of great importance. In this paper, a novel Two-Step fault diagnosis framework is proposed to diagnose the status of rolling element bearings with imbalanced data. The Wavelet Packet Transform (WPT) is used to determine the feature vectors. 16-dimensional wavelet packet node energies were extracted from the original datasets as the feature vectors prepared to input to the classifiers. Next, our proposed framework consists of two steps for the fault diagnosis, where Step One makes use of Weighted Extreme Learning Machine (weighted ELM) in an effort to classify the normal or abnormal categories, and Step Two further diagnoses the underlying anomaly in details. The effectiveness of our proposed approach is testified on the raw data collected from the rolling element bearing experiments conducted in our Institute, and the empirical results showed that our approach is really fast and can achieve the diagnosis accuracies more than 95%.

关键词： Two-Step fault diagnosis framework Weighed ELM ELM Bearing fault diagnosis

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A Two-Step Fault Diagnosis Framework for Rolling Element Bearings with Imbalanced Data

A Two-Step Fault Diagnosis Framework for Rolling Element Bea...

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2016 13th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI)

作者： Yuan Lan Weiwei Zong Xiaojian Ding Xiaoyan Xiong Xiaohang Han Jiahai Huang Bing Ma Key Laboratory of Ministry of Education in Advance Transducers and Intelligent Control System Institute of Mechatronics Engineering under School of Mechanical EngineeringTaiyuan University of Technology Department of Computer Science Wayne State University Science and Technology on Information Systems Engineering Laboratory The Second Research Institute of China Electronic Technology Group Corporation

Rolling element bearings constitute the key parts on rotating machinery and their fault diagnosis are of great importance. In this paper, a novel Two-Step fault diagnosis framework is proposed to diagnose the status of rolling element bearings with imbalanced data. The Wavelet Packet Transform(WPT) is used to determine the feature vectors. 16-dimensional wavelet packet node energies were extracted from the original datasets as the feature vectors prepared to input to the ***, our proposed framework consists of two steps for the fault diagnosis, where Step One makes use of Weighted Extreme Learning Machine(weighted ELM) in an effort to classify the normal or abnormal categories, and Step Two further diagnoses the underlying anomaly in details. The effectiveness of our proposed approach is testified on the raw data collected from the rolling element bearing experiments conducted in our Institute,and the empirical results showed that our approach is really fast and can achieve the diagnosis accuracies more than 95%.

关键词： Two-Step fault diagnosis framework Weighed ELM ELM Bearing fault diagnosis

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Quality Assessment of Electrocardiogram Signals Using Contrastive Learning

Quality Assessment of Electrocardiogram Signals Using Contra...

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IEEE International Symposium on information (IT) in Medicine and Education, ITME

作者： Meiping Zhong Zuoyong Li ChuanSheng Wang Jiayan Huang WanQi Zhao Haoyi Fan College of Computer and Big Data Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou China Department of Automatic Control Technical Polytechnic University of Catalonia Barcelona Spain New Engineering Industry College Putian University Putian China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou China

Quality assessment of electrocardiogram (ECG) signals holds significant importance in reducing the high-frequency false alarms in automated arrhythmia diagnosis. Existing quality assessment methods primarily rely on empirical threshold setting or the calculation of relevant statistical values to formulate decision rules. However, the thresholds set in this manner are typically based on experience and knowledge, resulting in decision rules that lack flexibility and struggle to adapt to changes in the dataset. To address this issue, many researchers have turned to deep learning algorithms for ECG signal quality assessment. Nevertheless, most existing deep learning methods usually require large-scale and labeled ECG data for training, whose acquiring is typically challenging. Therefore, this paper proposes an efficient method for ECG signal quality assessment. First, we use a contrast learning framework to construct sample pairs through multilevel data augmentation. Second, a densely connected convolutional network (DenseNet) is used to improve the model's ability of extracting local features in ECG signals, and a Squeeze-and-Excitation attention module is introduced to make the model pay more attention to critical features. Finally, the feature representation is learned by minimizing the contextual contrast loss to bring similar samples closer and push dissimilar samples farther away. In this way, we can utilize the contrastive learning framework to extract temporal representations from unlabeled data and transfer them to downstream ECG signal quality assessment tasks. Extensive experiments on the Physionet/CinC Challenge 2011 dataset demonstrate that our approach outperforms most existing quality assessment methods.

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UIILD: A Unified Interpretable Intelligent Learning Diagnosis Framework for Intelligent Tutoring systems

arXiv

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arXiv 2022年

作者： Wang, Zhifeng Yan, Wenxing Zeng, Chunyan Tian, Yuan Dong, Shi School of Educational Information Technology Faculty of Artificial Intelligence in Education Central China Normal University Wuhan430079 China Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System Hubei University of Technology Wuhan430068 China

Intelligent learning diagnosis is a critical engine of intelligent tutoring systems, which aims to estimate learners' current knowledge mastery status and predict their future learning performance. The significant challenge with traditional learning diagnosis methods is the inability to balance diagnostic accuracy and interpretability. Although the existing psychometric-based learning diagnosis methods provide some domain interpretation through cognitive parameters, they have insufficient modeling capability with a shallow structure for large-scale learning data. While the deep learning-based learning diagnosis methods have improved the accuracy of learning performance prediction, their inherent black-box properties lead to a lack of interpretability, making their results untrustworthy for educational applications. To settle the above problem, the proposed unified interpretable intelligent learning diagnosis (UIILD) framework, which benefits from the powerful representation learning ability of deep learning and the interpretability of psychometrics, achieves a better performance of learning prediction and provides interpretability from three aspects: cognitive parameters, learner-resource response network, and weights of self-attention mechanism. Within the proposed framework, this paper presents a two-channel learning diagnosis mechanism LDM-ID as well as a three-channel learning diagnosis mechanism LDM-HMI. Experiments on two real-world datasets and a simulation dataset show that our method has higher accuracy in predicting learners' performances compared with the state-of-the-art models, and can provide valuable educational interpretability for applications such as precise learning resource recommendation and personalized learning tutoring in intelligent tutoring systems. Copyright © 2022, The Authors. All rights reserved.

关键词： Data mining

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An Igdt-Based a Low-Carbon Dispatch Strategy of Urban Integrated Energy system Considering Intermittent Features of Renewable Energy

SSRN

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SSRN 2023年

作者： Tian, Chongyi Kong, Xiangshun Peng, Bo Zhang, Feng Ma, Xin School of Information and Electrical Engineering Shandong Jianzhu University No.1000 Fengming Road Ji’nan250101 China Key Laboratory of Power System Intelligent Dispatch and Control Ministry of Education Shandong University No.17923 Jingshi Road Ji’nan250061 China

The current global energy crisis has prompted a significant rise in the use of low carbon units, making it a crucial research trend in the field of urban energy system, especially in terms of addressing the intermittency of renewable energy generation. This study proposes an integrated energy system planning model (UIES) for urban areas. In addition, to tackle the issue of carbon emissions, a reward and punishment ladder-type carbon transaction model is presented, which aims to control the level of carbon emissions within urban areas. To evaluate the performance of the UIES model, the authors develop an objective function that integrates the sum of equipment operation and maintenance costs, equipment daily acquisition costs, purchased energy costs, carbon trading costs, and integrated demand response (IDR) costs. The stochastic features of renewable energy sources are taken into account in the proposed approach, and an information gap decision theory (IGDT) based strategy is designed to quantify the uncertainty interval and intermittent fluctuations associated with renewable energy. This approach also aims to mitigate any negative effects and variations that may occur during the urban energy system's IDR process. Finally, the simulation tests to demonstrate that the average decrease in operating cost of both models based on IGDT strategy are 7.36%, and the peak of electrical (EL), heating (HL) and cooling load (CL) are reduced by 20.45%, 12.38% and 5.02% respectively. in conclusion, the energy service providers in urban areas can effectively manage renewable energy fluctuations by independently adjusting the operation sequence of equipment, and the economy and stability of the UIES can be effectively improved by the proposed dispatch strategy. © 2023, The Authors. All rights reserved.

关键词： Renewable energy resources

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TDL based Digital Equalization Method for Frequency Flatness of Ultra-Wideband mm Wave Channel Emulator

TDL based Digital Equalization Method for Frequency Flatness...

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Antennas and Propagation & USNC/URSI National Radio Science Meeting

作者： Haobo Zhang Chen Chen Dan Fei Hao Zhou Zhuangfei Gui Bo Ai School of Electronic and Information Engineering Beijing Jiaotong University Beijing China Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong University Beijing China State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China

ISBN: (数字)9798350369908

ISBN: (纸本)9798350369915

To avoid the deterioration of in-band flatness caused by radio frequency front-ends such as frequency converters in ultra-wideband millimeter-wave channel emulation systems, this paper proposes a digital equalization method that combines filter coefficients with channel time-varying impulse responses. The channel emulation and flatness filter compensation functions are simultaneously realized through the tapped delay line model without the need to add additional high-order filters, which greatly reduces the consumption of hardware resources and simplifies the structure of the baseband system.

关键词： Time-frequency analysis Filtering Millimeter wave technology Emulation Millimeter wave propagation Frequency conversion Hardware

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Editorial

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International Journal on Artificial Intelligence Tools 2012年第3期21卷

作者： Cui, Zhihua Shi, Zhongzhi Alex, Rajan Complex System and Computational Intelligence Laboratory School of Computer Science and Technology Taiyuan University of Science and Technology Shanxi 030024 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences No. 6 Kexueyuan South Road Zhongguancun Haidian District Beijing 100190 China Department of Engineering and Computer Science West Texas AandM University Canyon TX 79015 United States

Swarm intelligence is an umbrella for amount optimization algorithms. This discipline deals with natural and artificial systems composed of many individuals that coordinate their activities using decentralized control and self-organization. In general, multi-agent systems that use some swarm intelligence are said to be swarm intelligent systems. They are mostly used as search engines and optimization tools. The goal of this special issue has been to offer a wide spectrum of sample works throughout the world about innovative methodologies of swarm intelligence. The issue should be useful both for beginners and experienced researchers in the field of computational intelligence. © 2012 World Scientific Publishing Company.

关键词： Swarm intelligence

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Effect of main part size and disparity distribution on visual comfort of Stereoscopic images

Effect of main part size and disparity distribution on visua...

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2015 International Conference on Materials Engineering and information Technology Applications(MEITA 2015)

作者： Zhibin Su Fangju Zou Ren Hui School of Information and Engineering Communication University of China Key Laboratory of Acoustic Visual Technology and Intelligent Control System Ministry of Culture Beijing Key Laboratory of Modern Entertainment Technology

ISBN: (纸本)9781510812055

Visual comfort is one of the important indexes to evaluate the image quality and viewing experience in the process of viewing stereoscopic video and images. This paper mainly investigated the effect of the main part size and disparity distribution type of stereoscopic images on visual comfort through two subjective evaluation experiments. The experiment results confirmed that the main part size and disparity space distribution type are two important factors which can affect visual comfort, and they also proved the influence trend of these factors on visual comfort in different disparity conditions. Our experiments have important guiding significance for the establishment and improvement of the relevant objective evaluation experimental model and acquisition and display technology of stereoscopic images.

关键词： stereoscopic images main part size disparity subject assessment

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Performance Evaluation of 5G-R Physical Layer Using Hardware-in-the-Loop Simulation Platform

Performance Evaluation of 5G-R Physical Layer Using Hardware...

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Antennas and Propagation & USNC/URSI National Radio Science Meeting

作者： Feng Yuan Dan Fei Peng Zheng Chen Chen Weirong Liu Nan Wang Bo Ai School of Electronic and Information Engineering Beijing Jiaotong University Beijing China State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong University Beijing China

ISBN: (数字)9798350369908

ISBN: (纸本)9798350369915

5G network for railway (5G-R) emerges as a promising train-ground communication solution, concurrently facing ubiquitous connectivity and reliable transmission design challenges. This paper focuses on the impact of different channel models and Doppler effects on the physical layer transmission performance of the 5G-R communication system. Based on the 5G-R channel hardware-in-the-loop simulation platform, we measured and compared the performance of the 5G-R system in terms of bit error rate (BER) and error vector magnitude (EVM) across different scenarios. The measurement results reveal that non-line-of-sight propagation and Doppler effects during high-speed movement contribute to the deterioration of the system's BER and EVM. Nevertheless, enhancing the signal-to-noise ratio can mitigate the impact of these factors to some extent.

关键词： Time-frequency analysis Communication systems Hardware-in-the-loop simulation Bit error rate Physical layer Reliability engineering Vectors

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A Low-Carbon Dispatch Strategy of Urban Integrated Energy system Considering Intermittent Features of Renewable Energy

SSRN

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SSRN 2023年

The current global energy crisis has prompted a significant rise in the use of renewable energy devices, making it a crucial research trend in the field of urban energy system, especially in terms of addressing the intermittency of renewable energy generation. This study proposes an integrated energy system planning model (UIES) for urban areas. In addition, to tackle the issue of carbon emissions, a reward and punishment ladder-type carbon transaction model is presented, which aims to control the level of carbon emissions within urban areas. To evaluate the performance of the UIES model, the authors develop an objective function that integrates the sum of equipment operation and maintenance costs, equipment daily acquisition costs, purchased energy costs, carbon trading costs, and integrated demand response (IDR) costs. The stochastic features of renewable energy sources are taken into account in the proposed approach, and an information gap decision theory (IGDT) based strategy is designed to quantify the uncertainty interval and intermittent fluctuations associated with renewable energy. This approach also aims to mitigate any negative effects and variations that may occur during the urban energy system's IDR process. Finally, the simulation tests demonstrate that energy service providers in urban areas can effectively manage renewable energy fluctuations by independently adjusting the working sequence of equipment, and the operation economy and stability of the urban integrated energy system can be effectively improved by the proposed UIES model and dispatching strategy. © 2023, The Authors. All rights reserved.

关键词： Renewable energy resources

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