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One-dimensional convolutional Multi-branch Fusion network for EEG-Based Motor Imagery Classification

IRBM 2023年第6期44卷 100812-100812页

作者： Liu, Xiaoguang Zhang, Mingjin Xiong, Shicheng Wang, Xiaodong Liang, Tie Li, Jun Xiong, Peng Wang, Hongrui Liu, Xiuling Hebei Univ Coll Elect & Informat Engn Baoding Hebei Peoples R China Hebei Univ Key Lab Digital Med Engn Hebei Prov Baoding Hebei Peoples R China Hebei Univ Affiliated Hosp Baoding Hebei Peoples R China

The Brain-Computer Interface (BCI) system based on motor imagery (MI) is a hot research topic nowadays, which can control external devices through the brain and has a wide range of applications in rehabilitation, gaming, and entertainment. due to the non-smooth, non-linear, and low signal-to-noise ratio of the MI EEG signal, it is challenging to accurately decode the MI task intention. A new end-to-end deep learning method is proposed to decode raw MI EEG signals without preprocessing, such as filtering and feature reinforcement. The 1d convolution is used to learn the time-frequency features in MI signals, and a four-branch fusion network is used as the main body to add a 1d CNN-AE block and 1d SE-block to enhance the algorithm's performance. Experiments on two publicly available datasets demonstrate that our proposed algorithm outperforms the current state-of-the-art methods. It achieves 86.11% and 89.51% on the BCI Competition IV-2a and the BCI Competition IV-2b datasets, respectively, and a 6.9% improvement in the generalizability test. The proposed data enhancement method can effectively alleviate the overfitting of the algorithm and improve the decoding performance. Further analysis shows that 1d convolution can effectively extract the features associated with the MI task.(c) 2023 AGBM. Published by Elsevier Masson SAS. All rights reserved.

关键词： Brain-Computer Interface (BCI) Electroencephalography (EEG) Motor imagery (MI) 1d convolutional neural network

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A deep learning model for bearing fault diagnosis based on convolution neural network with multi-channel and residual network 33

A deep learning model for bearing fault diagnosis based on c...

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33rd Chinese Control and decision Conference (CCdC)

作者： Tuo, Jianyong Hu, Yu Ma, Xin Wang, Youqing Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing 10029 Peoples R China Shandong Univ Sci & Technol Coll Elect Engn & Automat Qingdao 266590 Peoples R China

ISBN: (纸本)9781665440899

Traditional bearing fault diagnosis algorithms mostly rely on expert experience and prior knowledge, which can no longer meet the actual requirements of industrial big data. This paper proposes a new deep learning model that combines the multi-channel and wide first layer structures, and uses dropout technology, regularization, batch normalization, and other methods to solve the problem of overfitting in the network structure problem, and the introduction of the residual network to solve the problem of network degradation. Experimental results show that the model has an average accuracy of 100% in the bearing data set of Western Reserve University, showing good adaptive ability. The comparison results with mainstream diagnostic algorithms shows that the proposed method has good anti-noise ability.

关键词： Multi-channel Residual network 1d convolutional neural network Rolling Bearing Intelligent Fault diagnosis

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A dynamic-inner convolutional autoencoder for process monitoring

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COMPUTERS & CHEMICAL ENGINEERING 2022年 158卷 107654-107654页

作者： Zhang, Shuyuan Qiu, Tong Tsinghua Univ Dept Chem Engn Beijing 100084 Peoples R China Tsinghua Univ Beijing Key Lab Ind Big Data Syst & Applicat Beijing 100084 Peoples R China

Modern manufacturing industries are urgently demanding intelligent process monitoring systems for plant maintenance and accident prevention in the Industry 4.0 era. With the rapid development of deep learning, data-driven process monitoring methods are attracting wide attention and have been applied to many processes. However, most deep learning methods do not model process latent dynamics and are deficient to detect dynamic variations. In this work, a novel dynamic-inner convolutional autoencoder (diCAE) is proposed. Unlike previous autoencoders that only focus on input reconstruction, diCAE innovatively integrates a vector autoregressive model into a 1-dimensional convolutional autoencoder to monitor nonlinear processes, as well as capture process dynamics. When applied to a numerical simulation, diCAE could detect the dynamic variation and distinguish different process data into separate clusters with an intuitive visualization, while other conventional methods cannot. The effectiveness of diCAE is also demonstrated on the benchmark Tennessee Eastman process. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： dynamic data modeling 1d convolutional neural network Vector autoregressive model Process monitoring Fault detection

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Multivariate Abnormal detection for Industrial Control Systems Using 1d CNN and GRU

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IEEE ACCESS 2020年 8卷 88348-88359页

作者： Xie, Xin Wang, Bin Wan, Tiancheng Tang, Wenliang East China Jiaotong Univ Sch Informat Engn Nanchang 330013 Jiangxi Peoples R China

Currently, most anomaly detection approaches in industrial control systems (ICSs) use network event logs to build models, and current unsupervised machine learning methods rarely use spatiotemporal correlations and other dependencies between multiple variables (sensors/actuators) in a system to detect anomalies. Most of the existing anomaly detection technologies simply compare the current states with the predicted normal range. due to the highly dynamic characteristic of industrial control systems, it is insufficient to simply compare the current states with the predicted normal range. As a result, these approaches have low detection rates for unknown or new types of attacks. In view of these shortcomings, this paper presents a network model for predicting sensor/controller parameters in industrial control systems. To predict the parameter values of the sensors and controllers more accurately, the 1d convolutional neural network (1d & x005F;CNN) and gated recurrent unit (GRU) are combined to fully learn the spatiotemporal correlation and other dependencies between the parameter values of the sensors and controllers at each moment. An abnormal state detection method based on the calculation of the statistical deviation is proposed to realize the anomaly detection of industrial control systems. The model is validated on the Secure Water Treatment (SWaT) dataset. The precision, recall and F1 scores are used to evaluate the effectiveness of this method in anomaly detection on the SWaT dataset. The experimental results show that the average precision and recall of this method are 0.99 and 0.85, respectively, and that the average F1 score is 0.91. The experimental results show that the proposed method can be successfully applied to anomaly detection systems in industrial control systems with lower false positive rates.

关键词： Industrial control Feature extraction Anomaly detection Intrusion detection Integrated circuit modeling Correlation Auto-encoder 1d convolutional neural network gated recurrent unit industrial control system anomaly detection SWaT dataset

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Hyperparameter Optimization Method Based on Harmony Search Algorithm to Improve Performance of 1d CNN Human Respiration Pattern Recognition System

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SENSORS 2020年第13期20卷 3697.-3697.页

作者： Kim, Seong-Hoon Geem, Zong Woo Han, Gi-Tae Gachon Univ Dept Comp Engn Seongnam 13120 South Korea Gachon Univ Dept Energy IT Seoongnam 13120 South Korea

In this study, we propose a method to find an optimal combination of hyperparameters to improve the accuracy of respiration pattern recognition in a 1d (dimensional) convolutional neural network (CNN). The proposed method is designed to integrate with a 1d CNN using the harmony search algorithm. In an experiment, we used the depth of the convolutional layer of the 1d CNN, the number and size of kernels in each layer, and the number of neurons in the dense layer as hyperparameters for optimization. The experimental results demonstrate that the proposed method provided a recognition rate for five respiration patterns of approximately 96.7% on average, which is an approximately 2.8% improvement over an existing method. In addition, the number of iterations required to derive the optimal combination of hyperparameters was 2,000,000 in the previous study. In contrast, the proposed method required only 3652 iterations.

关键词： 1d convolutional neural network ultra-wideband radar respiration patterns harmony search algorithm hyperparameter optimization

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Automatic Classification of Swedish Metadata Using dewey decimal Classification:A Comparison of Approaches

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Journal of data and Information Science 2020年第1期5卷 18-38页

作者： Koraljka Golub Johan Hagelback Anders Ardo Department of Cultural Sciences Faculty of Arts and HumanitiesLinnaeus UniversityVaxjoSweden Department of Computer Science and Media Technology Faculty of TechnologyLinnaeus UniversityKalmarSweden Department of Electrical and Information Technology Lund UniversityLundSweden

Purpose:With more and more digital collections of various information resources becoming available,also increasing is the challenge of assigning subject index terms and classes from quality knowledge organization *** the ultimate purpose is to understand the value of automatically produced dewey decimal Classification(ddC)classes for Swedish digital collections,the paper aims to evaluate the performance of six machine learning algorithms as well as a string-matching algorithm based on characteristics of ***/methodology/approach:State-of-the-art machine learning algorithms require at least 1,000 training examples per *** complete data set at the time of research involved 143,838 records which had to be reduced to top three hierarchical levels of ddC in order to provide sufficient training data(totaling 802 classes in the training and testing sample,out of 14,413 classes at all levels).Findings:Evaluation shows that Support Vector Machine with linear kernel outperforms other machine learning algorithms as well as the string-matching algorithm on average;the string-matching algorithm outperforms machine learning for specific classes when characteristics of ddC are most suitable for the *** embeddings combined with different types of neural networks(simple linear network,standard neural network,1 d convolutional neural network,and recurrent neural network)produced worse results than Support Vector Machine,but reach close results,with the benefit of a smaller representation *** of features in machine learning shows that using keywords or combining titles and keywords gives better results than using only titles as *** only marginally improves the *** stop-words reduced accuracy in most cases,while removing less frequent words increased it *** greatest impact is produced by the number of training examples:81.90%accuracy on the training set is achieved when at least 1,000 records per class are available

关键词： LIBRIS dewey decimal Classification Automatic classification Machine learning Support Vector Machine Multinomial Naive Bayes Simple linear network Standard neural network 1d convolutional neural network Recurrent neural network Word embeddings String matching

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1d convolutional context-aware architectures for acoustic sensing and recognition of passing vehicle type 24

1D convolutional context-aware architectures for acoustic se...

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24th IEEE Conference on Signal Processing: Algorithms, Architectures, Arrangements, and Applications (IEEE SPA)

作者： Kurowski, Adam Zaporowski, Szymon Czyzewski, Andrzej Gdansk Univ Technol Multimedia Syst Dept Fac Elect Telecommun & Informat Narutowicza 11-12 PL-80233 Gdansk Poland

ISBN: (纸本)9788362065394

A network architecture that may be employed to sensing and recognition of a type of vehicle on the basis of audio recordings made in the proximity of a road is proposed in the paper. The analyzed road traffic consists of both passenger cars and heavier vehicles. Excerpts from recordings that do not contain vehicles passing sounds are also taken into account and marked as ones containing silence. The neural network architecture employed for these tasks is a Id convolutional network. Two types of classifiers are tested: one analyzing only the current audio frame and one analyzing three consecutive audio frames that allow us to take into account the context of the middle frame occurrence. The neural network is trained on datasets derived for four frame lengths, namely 50 ms, 100 ms, 200 ms, and 400 ms. Results of statistical analysis of both network classification accuracy are presented. The context-aware variant of a neural network performed better in a statistically significant manner for three out of four investigated frame lengths.

关键词： 1d convolutional neural network Vehicle Recognition Intelligent Transportation System

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Intelligent System for detection of Wild Animals Using HOG and CNN in Automobile Applications 11

Intelligent System for Detection of Wild Animals Using HOG a...

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11th International Conference on Information, Intelligence, Systems and Applications (IISA)

作者： Munian, Yuvaraj Martinez-Molina, Antonio Alamaniotis, Miltiadis Univ Texas San Antonio Dept Elect Engn San Antonio TX 78249 USA Univ Texas San Antonio Dept Architecture San Antonio TX USA

ISBN: (纸本)9780738123462

Animal Vehicle Collision, commonly called as roadkill, is an emerging threat to humans and wild animals with increasing fatalities every year. Amid Vehicular crashes, animal actions (i.e. deer) are unpredictable and erratic on roadways. This paper unveils a newer dimension for wild animals' auto-detection during active nocturnal hours using thermal image processing over camera car mount in the vehicle. To implement effective hot spot and moving object detection, obtained radiometric images are transformed and processed by an intelligent system. This intelligent system extracts the features of the image and subsequently detects the existence of an object of interest (i.e. deer). The main technique to extract the features of wild animals is the Histogram of Oriented Gradient (HOG) transform. The features are detected by normalizing the radiometric image and then processed by finding the magnitude and gradient of a pixel. The extracted features are given as an input to the basic deep learning model, a one-dimensional convolutional neural network (1d-CNN), where binary cross-entropy is used to detect the existence of the object. This intelligent system has been tested on a set of real scenarios and gives approximately 91% accuracy in the correct detection of the wild animals on roadsides from the city of San Antonio, TX, in the USA.

关键词： deer vehicle crashes thermal images histogram of oriented gradients (HOG) 1d convolutional neural network

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Process curve analysis with machine learning on the example of screw fastening and press-in processes

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Procedia CIRP 2021年 97卷 166-171页

作者： Moritz Meiners Andreas Mayr Jörg Franke

With the increasing degree of digitalization in manufacturing industry, advanced data analysis techniques such as machine learning (ML) are moving into focus. In several production processes like machining or welding, ML applications already show high potential for process monitoring, optimization, or control. Although screw fastening and press-in processes are frequently employed in modern assembly lines, there are only few research approaches addressing the application of ML so far. Therefore, this paper starts with an overview of conventional and first ML-based approaches to process curve monitoring. Following this, it is shown how ML can be used to detect and classify errors in screw fastening by analyzing the resulting torque curve. In addition, an assembly line consisting of several press-in processes is used to demonstrate that ML also allows the analysis of cross-process interactions by evaluating several process curves at once. In this way, defective parts can be detected and rejected at an early stage, eliminating further processing and testing steps while simultaneously reducing costs.

关键词： Machine learning deep learning 1d convolutional neural network Screw fastening Press-in Production Assembly

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Gait Analysis Based Parkinson's disease Auxiliary diagnosis System

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JOURNAL OF INTERNET TECHNOLOGY 2021年第5期22卷 991-999页

作者： Chen, Fangzhe Fan, Xuwei Li, Jianpeng Zou, Min Huang, Lianfen Xiamen Univ Dept Sch Informat Xiamen Peoples R China Xiamen Univ Dept Affiliated Hosp 1 Xiamen Peoples R China Fujian Med Univ Dept Clin Sch Med Fujian Peoples R China

Parkinson's disease (Pd) is a neurodegenerative disease that often occurs in elderly people. Its symptoms are static tremor and slow movement, which affect the life of the patient seriously. With the development of medical technology, the early diagnosis of Pd has attracted widespread attention. Many studies have shown that abnormal gait characteristics are potential bases for judging whether suffering from Parkinson's disease. If Pd can be diagnosed in the early stage, it will benefit the control of the disease and subsequent treatment. However, the diagnosis of Pd is a complex task which often relies on the doctor's experience and subjective evaluation. In this stage, because of the lack of professional knowledge of doctors or errors in subjective judgment, it is easy to misdiagnose and miss the best treatment time. In response to this problem, this paper designs an auxiliary diagnosis system for Pd based on abnormal gait, composed of embedded devices, mobile terminals and servers. The embedded device uses the accelerometer to collect the patient's six-dimensional gait data, then the data are transmitted to the mobile phone via Bluetooth and sent to the server. The server analyzes the data by 1d convolutional neural network model and monitors the abnormality of the patient's gait. Herein, we proved that the use of 1d convolutional neural network for analysis has better performance with five-fold cross-validation, and its recognition accuracy rate reaches 91.4%.

关键词： Embedded devices Gait analysis Parkinson's disease 1d convolutional neural network

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