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Optimized Complex Convolutional Neural Network for Steady-State Visual Evoked Potentials Signal classification

Optimized Complex Convolutional Neural Network for Steady-St...

引用

System Simulation technology and its Application (CCSSTA), IEEE China Conference on

作者： Qingbin Li Chunxiao Han Yanli Liu Junxiu Liu Tianjin Key Laboratory of Information Sensing & Intelligent Control School of Automation and Electrical Engineering Tianjin University of Technology and Education Tianjin China

ISBN: (数字)9798350366600

ISBN: (纸本)9798350366617

The steady-state visual evoked potential (SSVEP) is one of the most commonly used paradigms in the BCI system, which produces a significant response to visual stimuli of a specific frequency. How to better improve the classification performance of SSVEP signals is currently the main task. In this paper, a modified C-CNN-SVM model of EEG signal is proposed, aiming to enhance the precision of SSVEP signal classification. Firstly, spectral features are obtained from the SSVEP signal using fast fourier transform (FFT) as input to the model. FFT obtains 128 data lengths of the SSVEP signal, including 64 real part data and 64 complex part data. Then, CNN is used to further extract features, and finally, support vector machine (SVM) is used for classification. The experimental results show that the modified C-CNN-SVM model achieves an average accuracy of 93.86%, which is better than the traditional CCA, FBCCA and TRCA algorithms.

关键词： Support vector machines Visualization Fast Fourier transforms Pattern classification Brain modeling Feature extraction Electroencephalography Classification algorithms Steady-state Convolutional neural networks

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A DRL-enabled Adaptive Sparsity Promoting Wide-area Damping controller for Power System with Time-delay and Packet Dropout

A DRL-enabled Adaptive Sparsity Promoting Wide-area Damping ...

引用

2023 Panda Forum on Power and Energy, PandaFPE 2023

作者： Cao, Di Zhang, Guozhou Hu, Jiaxiang Hu, Weihao Huang, Yuehui Chen, Zhe State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems China Electric Power Research Institute Chengdu China School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu China Aalborg University Department of Energy Technology Aalborg Denmark

ISBN: (纸本)9798350321173

This paper proposes a deep reinforcement learning (DRL) based adaptive sparsity promoting wide-area damping controller for power system. Different from traditional fixed wide-area control structure, the proposed method employs DRL algorithm to identify the optimal sparsity promoting control structure and obtain corresponding parameter settings under different operating conditions considering the time-delay and packet drop. Simulation are carried out on 10-machine 39-bus system to demonstrate the effectiveness of the proposed approach. © 2023 IEEE.

关键词： Time delay

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Analysis of Bamboo Resource Survey Application Based on Remote Sensing Images 6

Analysis of Bamboo Resource Survey Application Based on Remo...

引用

6th International Conference on Computer Information Science and Application technology, CISAT 2023

作者： Liu, Xiaohong Yan, Kai Sun, Maosheng College of Big Data and Intelligent Engineering Southwest Forestry University Kunming650000 China Key Laboratory of Metallurgical Equipment and Control Technology Ministry of Education Wuhan University of Science and Technology Wuhan430081 China College of Forestry Southwest Forestry University Kunming650224 China

ISBN: (纸本)9781510668546

Understanding the distribution range of bamboo resources in Yiliang county is important for the ecological services and economic benefits of the county. In this paper, we investigated the distribution of bamboo resources by using 3S (GPS, RS, GIS) technology with remote sensing images and field survey. The survey results show that: the total area of bamboo forest in the county is 65863.72×10-2 km2;the origin of bamboo resources is mainly plantation and natural forest;the types of bamboo forest are pure bamboo forest, tree forest and mixed bamboo forest;the applications are mainly nature reserve bamboo forest, protective bamboo forest, timber bamboo forest and bamboo forest for bamboo shoots;There are 9 kinds of bamboo species, including Jinfo Mountain Fang, Xiaochaoba Fang, Yushan Bamboo, Cixi Bamboo, Mao Bamboo, Bambusa Bamboo, Shui Bamboo, Yongsan Fang and Siban Bamboo;the protection level is Ⅰ, Ⅱ, Ⅲ and Ⅳ respectively. © 2023 SPIE.

关键词： Bamboo

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Design of a Capsule Robot System for Gastric Hemorrhage Detection using Luminol 20

Design of a Capsule Robot System for Gastric Hemorrhage Dete...

引用

20th IEEE International Conference on Mechatronics and Automation, ICMA 2023

作者： Mao, Yuchen Guo, Jian Guo, Shuxiang Fu, Qiang Tianjin University of Technology Tianjin Key Laboratory for Control Theory & Application Complicated Systems Intelligent Robot Laboratory School of Electrical Engineering and Automation Binshui Xidao Extension 391 Tianjin300384 China Shenzhen Institute of Advanced Biomedical Robot Co. Ltd No.12 Ganli Sixth Road Jihua Street Longgang District Shenzhen518100 China Key Laboratory of Convergence Medical Engineering System and Healthcare Technology The Ministry of Industry and Information Technology School of Life Science Beijing Institute of Technology No.5 Zhongguancun South Street Beijing100081 China

ISBN: (纸本)9798350320831

Gastric hemorrhage in upper gastrointestinal bleeding is an obvious indicator of deteriorating gastric disease. This paper proposes a capsule robot system for detecting bleeding in the stomach. The patient ingests a capsule robot containing luminol reagent, which detects bleeding in the stomach via chemiluminescence. The MCU receives the optical sensor signal, and the data is transmitted via radio frequency. The external lower computer receives and forwards the data to the upper computer for processing and display. The data is used to determine the presence and amount of stomach bleeding. Chemiluminescence is an excellent sensor, requiring no external energy supply, and demonstrating excellent sensitivity and selectivity. Combining chemical and electronic sensors presents opportunities for further development. Capsule robot detection reduces the discomfort and risk of secondary injury associated with traditional endoscopy. This paper establishes an experimental platform using porcine blood to simulate human blood, demonstrating the feasibility of the system. © 2023 IEEE.

关键词： Chemiluminescence

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A Multi-Time-Scale Prediction Method for Blast Furnace Gas Utilization Rate Based on Causality Analysis

A Multi-Time-Scale Prediction Method for Blast Furnace Gas U...

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2023 China Automation Congress, CAC 2023

作者： Zhao, Guoyu An, Jianqi Guo, Yunpeng Yang, Chenglin He, Yong School of Future Technology China University of Geosciences China School of Automation China University of Geosciences China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

ISBN: (纸本)9798350303759

Blast furnace operating parameters regulate the gas utilization rate (GUR), and different operating parameters affect the GUR on different time scales. However, the existing methods only analyze and model the prediction on a single-time scale, and the accuracy of the model needs to be improved. In this paper, a multi-time-scale prediction method of blast furnace gas utilization rate based on causality is proposed. Firstly, according to the production characteristics of the blast furnace, burden and blast supply are the operation means to regulate the blast furnace on different time scales. Then, this paper presents a blast furnace operation parameter extraction method fusing correlation coefficient and information entropy, and selects two most informative parameters to characterize the operation. On this basis, the causal analysis method is used to prove that the selected operation parameters affect the development trend of GUR. Finally, the long and short time scale prediction models of GUR are established, which are verified by the data of the actual blast furnace production process, improving the prediction accuracy of the GUR efficiently. © 2023 IEEE.

关键词： Blast furnaces

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A Method of Frequency Drift Fitting Correction for Distributed Optical Fiber Acoustic Sensing technology 3

A Method of Frequency Drift Fitting Correction for Distribut...

引用

3rd International Conference on Optics and Image Processing, ICOIP 2023

作者： Peng, Zijian Zhang, Yanmin Li, Zhengying School of Information Engineering Wuhan University of Technology Wuhan430070 China National Engineering Research Center of Fiber Optic Sensing Technology and Networks Wuhan University of Technology Wuhan430070 China Hubei Key Laboratory of Marine Electromagnetic Detection and Control Wuhan University of Technology Wuhan430064 China Wuhan Second Ship Design and Research Institute Wuhan430064 China

ISBN: (纸本)9781510667426

Distributed Acoustic Sensing (DAS), which utilizes phase change sensing, has higher signal-to-noise ratio and sensitivity compared to other optical fiber sensing technologies, and is often used in the field of marine acoustic detection and seismic wave measurement. However, the frequency fluctuation of the output light of the narrow linewidth laser used in the DAS system and the parasitic interference phenomenon in the sensing fiber can generate low-frequency noise, which ultimately leads to very low frequency drift noise in the demodulated signal. Aiming at the problem of frequency drift in long-distance transmission with parasitic interference as the main source, time-varying filtering empirical mode decomposition is used to extract frequency drift for fiber length segmentation, and combined with improved de trend fluctuation analysis to evaluate the intrinsic mode function, correct and fit to solve the frequency drift in long-distance transmission. © 2023 SPIE. All rights reserved.

关键词： Intrinsic mode functions

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Resilience-oriented critical load restoration for railway distribution systems in remote areas by coordination of photovoltaics and energy storage systems 3

Resilience-oriented critical load restoration for railway di...

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2024 3rd International Conference on Green Energy and Power Systems, ICGEPS 2024

作者： Wang, Ying Liu, Weizhi Ma, Yingtao School of Electrical Engineering Beijing Jiaotong University Beijing100044 China Beijing Zongheng Electro-mechanical Technology Co. Ltd. State Key Laboratory for Traction and Control System EMU and Locomotive China Academy of Railway Sciences Corporation Limited Beijing100081 China

The utility power infrastructure is relatively weak and susceptible to extreme conditions in remote areas, so the resilience enhancement of the railway distribution system is essential to ensure the service to critical loads, such as communication and signal devices, to prevent undesirable crash events. After extreme events, distributed resources such as distributed photovoltaics (PVs) and energy storage systems (ESSs) can be coordinated to restore critical loads as soon as possible. In this paper, a critical load restoration method for railway distribution systems in remote areas is proposed. The PVs and ESSs are coordinated to restore the critical loads distributed along the railway line. The problem is formulated as a mixed integer linear program and solved by off-the-shelf solvers. Finally, the effectiveness of the method is validated through a real-world case in China. © Published under licence by IOP Publishing Ltd.

关键词： Energy storage

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Prediction of exosomal piRNAs based on deep learning for sequence embedding with attention mechanism

Prediction of exosomal piRNAs based on deep learning for seq...

引用

2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Liu, Yajun Ding, Yulian Li, Aimin Fei, Rong Guo, Xie Wu, Fangxiang Xi'an University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China University of Saskatchewan Division of Biomedical Engineering Saskatoon Canada Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an China University of Saskatchewan Division of Biomedical Engineering Department of Computer Science Department of Mechanical Engineering Saskatoon Canada

ISBN: (纸本)9781665468190

PIWI-interacting RNAs (piRNAs) are a type of small non-coding RNAs which bind with the PIWI proteins to exert biological effects in various regulatory mechanisms. A growing amount of evidence reveals that exosomal piRNAs are potential biomarkers for diagnosis and treatment of complex diseases. Effective methods for the prediction of exosomal piRNAs are the foundation of piRNA functional research. In this study, we propose an end-to-end deep network for identifying exosomal piRNAs based on features learned from natural language processing (NLP) models for sequence embedding with attention mechanism. First, a benchmark dataset is constructed by processing piRNA subcellular localization annotated data and sequence data. Moreover, bagging positive unlabeled learning is applied to get the reliable negative set. Finally, we treat a piRNA sequence as a sentence and its k-mer subsequence as a token. Sequence embedding models with self-attention mechanism is designed to extract features from exosome piRNA sequences, which are used for the prediction task. Compared with three competing methods, our model achieves the best performance and reveals the key factors of exosomal piRNA sequences by the attention mechanism. Our model characterizes exosomal piRNAs and could be beneficial for researchers to investigate exosomal piRNAs' functions. © 2022 IEEE.

关键词： Embeddings

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Research on Seismic Bracing Monitoring System Based on Wireless Transmission

Research on Seismic Bracing Monitoring System Based on Wirel...

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IEEE International Conference on Mechatronics and Automation

作者： Lujia Wang Baofeng Zhang Wei Wang Zhiwei Li Junchao Zhu Tianjin Key Laboratory of Control Theory & Application of Complex Systems School of Electrical Engineering and Automation Tianjin University of Technology Tianjin China

ISBN: (数字)9798350388077

ISBN: (纸本)9798350388084

Real-time data acquisition and monitoring of seismic bracings can help to maintain their effectiveness during service life and to improve the seismic resistance and disaster mitigation effects when strong earthquakes occur. In order to monitor real-time states of the seismic bracing, an intelligent monitoring system based on wireless transmission technology was designed. The system used CC2530 Zigbee module to establish a ZigBee wireless LAN, and combined MEMS vibration sensors and strain gauges. Meanwhile, the system used ESP32 to upload the measurement data to OneNET cloud platform through MQTT wireless communication protocol. The function of data visualization was designed so as to realize real-time monitoring of seismic bracing states. The experimental results showed that the proposed wireless monitoring system can reliably execute real-time data collection, storage, display, and hazard warning to guarantee the effectiveness of seismic bracings.

关键词： Wireless communication Vibrations Wireless sensor networks Wireless LAN Disasters Data visualization Zigbee

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A Flow-Guided Anomaly Detection Method For Substation Equipment in Changing Scenarios

A Flow-Guided Anomaly Detection Method For Substation Equipm...

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2021 China Automation Congress, CAC 2021

作者： Zhang, Qingwei Wang, Jing Wang, Peng Zhou, Wenhao Li, Yingxuan Zhao, Chunhui State Key Laboratory of Smart Grid Protection and Control NAPJ Technology Development Company Limited Nanjing China State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China

ISBN: (纸本)9781665426473

The anomaly detection for substation equipment is of great significance to maintain the safe operation of substations. With the development of deep learning, many anomaly detection methods based on deep convolutional neural networks (CNN) have been proposed. However, the challenge lies in the diversity of the environment and the variety of anomaly types in the substation. This paper proposes a Flow-Guided Anomaly Detection Method, which contains a data augmentation module and a flow-guided feature fusion module. The data augmentation module utilizes CycleGAN to transfer the style of the images to adapt to changing scenarios, such as different illumination and season conditions. The flow-guided feature fusion module extracts the optical flow between the test image and the template image by a pre-trained FlowNet, which represents the change information between the image pair and is universal for different anomaly types. Then the appearance information and change information of the image are aggregated to obtain the fused feature, which contains more semantic information. Finally, the fused feature is utilized to accurately detect a wider range of abnormal types. Experiments show that our proposed method has superior performance in anomaly detection for substation equipment and is more robust to changing scenarios. © 2021 IEEE

关键词： Anomaly detection

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