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IEEE International Conference on Digital Futures and Transformative Technologies (ICoDT2)

作者： Rani, Mehwish Latif, Seemab Tahir, Muhaammad Ali Mumtaz, Rafia Natl Univ Sci & Technol NUST Sch Elect Engn & Comp Sci SEECS Islamabad Pakistan

ISBN: (纸本)9781665412858

With the advent of social media, e-commerce and video sharing websites, a large amount of textual data depicting people's opinions is being generated. This text has a great impact on other web users, product sellers and content creators. Text mining and emotion detection using text analysis has prospered a lot in recent years. Sentiment Analysis is the method of mining emotions, reviews and opinions of web users. In this paper, a survey of text Sentiment Analysis techniques is presented. Sentiment Analysis is applied to comments extracted from Pakistani news videos. Different machine learning algorithms previously surveyed in this paper have been applied to this dataset. Our results have shown that transfer learning can be effectively used for Sentiment Analysis.

关键词： Sentiment Analysis deep learning algorithm Natural Language Processing LSTM GRU and Transfer learning

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A Laboratory Set-Up for Cyber Attacks Simulation Using Protocol Analyzer and RTU Hardware Applying Semi-Supervised Detection algorithm

A Laboratory Set-Up for Cyber Attacks Simulation Using Proto...

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IEEE Texas Power and Energy Conference (TPEC)

作者： Parizad, Ali Hatziadoniu, Constantine Southern Illinois Univ Dept Elect & Comp Engn Carbondale IL 62901 USA

ISBN: (纸本)9781728186122

The integration of Information and Communication Technologies (ICT) into the modern power system makes it a complicated cyber-physical system (CPS). In this case, an adversary may find some loopholes, penetrate to CPS layer, compromise data, and consequently result in security and stability issues. In this paper, we proposed a laboratory set up to emulate the attacker's behavior and then detect the injected false data. To this end, RTU hardware and software are used to simulate a typical SCADA system. A protocol analyzer software is also employed to simulate a cyber-attack, inject false data, and send it to the control center. In the second stage, we developed a two-stage framework to detect FDIA. First, the LSTM, as a supervised learning algorithm, is utilized to build a predictive model. In this process, hyperparameter optimization is implemented to improve the accuracy of the developed model. In the second stage, an unsupervised scoring algorithm is applied to the real-time data to find the sequences of injected false data. Also, a penalty factor is considered during the detection procedure to prevent the algorithm from greedy search behavior. Simulation results on a real-world data set (Chicago load/weather) show the proposed method's effectiveness in the cyberattack implementation and FDIA detection problem.

关键词： Cyber Security Laboratory Set-Up RTU Hardware/Software Protocol Analyzer False Data Injection Attack (FDIA) deep learning algorithm Long Short Term Memory (LSTM) Supervised/Unsupervised learning Detection algorithm

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Application for Recognizing Sign Language Gestures Based on an Artificial Neural Network

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SENSORS 2022年第24期22卷 9864-9864页

作者： Kozyra, Kamil Trzyniec, Karolina Popardowski, Ernest Stachurska, Maria Ailleron SA Jana Pawla 2 43b PL-31864 Krakow Poland Agr Univ Krakow Dept Machinery Exploitat Ergon & Prod Proc Balicka 116B PL-30149 Krakow Poland Poznan Univ Tech Fac Management Engn Inst Safety & Qual Engn J Rychlewskiego 2 PL-60965 Poznan Poland

This paper presents the development and implementation of an application that recognizes American Sign Language signs with the use of deep learning algorithms based on convolutional neural network architectures. The project implementation includes the development of a training set, the preparation of a module that converts photos to a form readable by the artificial neural network, the selection of the appropriate neural network architecture and the development of the model. The neural network undergoes a learning process, and its results are verified accordingly. An internet application that allows recognition of sign language based on a sign from any photo taken by the user is implemented, and its results are analyzed. The network effectiveness ratio reaches 99% for the training set. Nevertheless, conclusions and recommendations are formulated to improve the operation of the application.

关键词： convolutional neural networks deep learning algorithm machine image recognition systems

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Identifying Dike-Pond System Using an Improved Cascade R-CNN Model and High-Resolution Satellite Images

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REMOTE SENSING 2022年第3期14卷 717页

作者： Ma, Yintao Zhou, Zheng She, Xiaoxiong Zhou, Longyu Ren, Tao Liu, Shishi Lu, Jianwei Huazhong Agr Univ Sch Resources & Environm Wuhan 430070 Peoples R China Minist Agr Key Lab Arable Land Conservat Middle & Lower Reac Wuhan 430070 Peoples R China Minist Ecol Environm Ecol Environm Monitoring & Sci Res Ctr Yangtze River Basin Ecol Environm Supervis & Adm Wuhan 430014 Peoples R China

The dike-pond system (DPS) is the integration of a natural or man-made pond and crop cultivation on dikes, widely distributed in the Pearl River Delta and Jianghan plain in China. It plays a key role in preserving biodiversity, enhancing the nutrient cycle, and increasing crop production. However, DPS is rarely mapped at a large scale with satellite data, due to the limitations in the training dataset and traditional classification methods. This study improved the deep learning algorithm Cascade Region Convolutional Neural Network (Cascade R-CNN) algorithm to detect the DPS in Qianjiang City using high-resolution satellite data. In the proposed mCascade R-CNN, the regular convolution layer in the backbone was modified into the deformable convolutional layer, which was more suitable for learning the features of DPS with variable shapes and orientations. The mCascade R-CNN yielded the most accurate detection of DPS, with an average precision (AP) value that was 2.71% higher than Cascade R-CNN and 11.84% higher than You Look Only Once-v4 (YOLOv4). The area of oilseed rape growing on the dikes accounted for 3.42% of the total oilseed rape planting area. This study demonstrates the potential of the deep leaning methods combined with high-resolution satellite images in detecting integrated agriculture systems.

关键词： dike-pond detection high-resolution satellite deep learning algorithm Cascade R-CNN YOLOv4

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A Contactless Glucose Solution Concentration Measurement System Based on Improved High Accurate FMCW Radar algorithm

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SENSORS 2022年第11期22卷 4126-4126页

作者： Liu, Chengjie Du, Yuan Du, Li Nanjing Univ Sch Elect Sci & Engn Nanjing 210000 Peoples R China

To reduce the pain and the probability of cross-infection caused by the invasive blood glucose testing instruments, the ex vivo glucose measurement is of high significance. The electrical property of blood varies with the density of the glucose, which can be sensed by measuring its reflected coefficient in millimeter-wave. In this article, we built a contactless glucose solution concentration measurement system based on 77-GHz FMCW radar. Several preliminary signal processing algorithms are cascaded with a deep neural network to improve the accuracy of glucose solution concentration measurement. Our experiment shows that the resolution of this ex vivo glucose measurement can achieve up to 0.1 mg/mL.

关键词： FMCW radar wavelet transform deep learning algorithm PSD sparrow search algorithm convolutional neural network

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A novel oil pipeline leakage detection method based on the sparrow search algorithm and CNN

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MEASUREMENT 2022年 204卷

作者： Li, Qi Shi, Yaru Lin, Ruiqi Qiao, Wenxu Ba, Wei Dalian Univ Technol Fac Elect Informat & Elect Engn Dalian 116023 Peoples R China Dalian Univ Technol Key Lab Intelligent Control Optimizat Ind Equipment Minist Educ Dalian 116023 Peoples R China Dalian Jiaotong Univ Coll Elect & Informat Engn Dalian 116023 Peoples R China

A novel sparrow search algorithm and convolutional neural network (SSA-CNN) method is proposed for oil pipeline leakage detection. Firstly, the proposed SSA-CNN method converts the input data from time series to two-dimensional matrix, and the classification conditions of different convolution kernel sizes and different pooling sizes are compared. Then, the SSA algorithm is used to optimize the parameters of the CNN. The simulation results show that, compared with the traditional machine learning method, using two-dimensional data as input can enhance the neural network's extraction of eigenvalues. The proposed SSA-CNN method was able to accurately classify 148 sample points in 150 test sets with an accuracy rate of 98.67%, which is not only higher than traditional machine learning methods, but also further improves the classification capability of CNN, while the SSA-CNN method can use the parameters already learned in the test set to ensure real-time detection.

关键词： Oil pipeline leakage deep learning algorithm Convolutional neural network Fault diagnosis Sparrow search algorithm

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A Systematic Approach to Building Autoencoders for Intrusion Detection 1st

A Systematic Approach to Building Autoencoders for Intrusion...

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1st Silicon Valley Cybersecurity Conference

作者： Song, Youngrok Hyun, Sangwon Cheong, Yun-Gyung Sungkyunkwan Univ Suwon South Korea Myongji Univ Yongin South Korea

ISBN: (纸本)9783030727246;9783030727253

Network Intrusion Detection Systems (NIDS) have been the most effective defense mechanism against various network attacks. As attack patterns have been intelligently and dynamically evolving, the deep learning-based NIDSs have been widely adopted to improve intrusion detection accuracy. Autoencoders, one of the unsupervised neural networks, are generative deep learning models that learn to represent the data as compressed vectors without class labels. Recently, various autoencoder-generative deep learning models-have been used for NIDS in order to efficiently alleviate the laborious labeling and to effectively detect unknown types of attacks (i.e. zero-day attacks). In spite of the effectiveness of autoencoders in detecting intrusions, it requires tremendous effort to identify the optimal model architecture of the autoencoders that results in the best performance, which is an obstacle for practical applications. To address this challenge, this paper rigorously studies autoencoders with two important factors using real network data. We investigate how the size of a latent layer and the size of the model influence the detection performance. We evaluate our autoencoder model using the IDS benchmark data sets and present the experimental findings.

关键词： IDS Dimension reduction Autoencoder PCA Semi-supervised machine learning algorithm (One-class) unsupervised learning algorithm deep learning algorithm

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Modulation recognition method of satellite communication based on CLDNN model 30

Modulation recognition method of satellite communication bas...

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30th IEEE International Symposium on Industrial Electronics (ISIE)

作者： Jiang, Jiahao Wang, Zhelong Zhao, Hongyu Qiu, Sen Li, Jiaxi Dalian Univ Technol Sch Control Sci & Engn Dalian 116024 Peoples R China

ISBN: (纸本)9781728190235

Modulation recognition as an important part of signal processing has been widely used in the field of satellite communication. Since the present existing modulation recognition method is still requires manual processing by professionals, automatic modulation recognition (AMR) is proposed. It has adaptive modulation capabilities to sense and learn environments and make corresponding adjustments. In this paper, we proposes a deep learning classification algorithm called Convolutional Long-Short Term deep Neural Network (CLDNN) to implement AMR. This model integrates architectures of Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) and deep neural networks (DNN) model. It was trained on the RadioML2016.10a dataset that composed of eleven commonly used modulation modes with different signal to noise ratio(SNR). The signal was generated in a real system using GNU radio to classify modulation. We adopt dropout instead of pooling operation to achieve higher recognition accuracy. In addition, comparisons with Residual Neural Network(ResNet) and Visual Geometry Group(VGG) models are presented. Experimental results demonstrate the significant performance advantage and application feasibility of the CLDNN model for AMR.

关键词： satellite communication automatic modulation recognition deep learning algorithm cognitive radio signal to noise ratio

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EMI Radiation Prediction and Structure Optimization of Packages by deep learning

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IEEE ACCESS 2019年 7卷 93772-93780页

作者： Jin, Hang Ma, Hanzhi Butala, Mark D. Liu, En-Xiao Li, Er-Ping Zhejiang Univ Zhejiang Univ Univ Illinois Urbana Champaign Inst Coll Informat Sci & Elect Engn Hangzhou 310027 Zhejiang Peoples R China Agcy Sci Technol & Res Inst High Performance Comp Singapore 138632 Singapore

With a rapid increase in operating frequency and package complexity, conventional analysis methods cannot efficiently cope with current complex electromagnetic interference (EMI) issues. In this paper, a new method built on a deep neural network (DNN) model is proposed to accurately and rapidly predict the maximum radiated electric field at 3-meters of a wire-bond ball grid array (WB-BGA) package. The key hyper-parameters of the DNN model, such as the learning rate and type of optimizers, are discussed in depth so as to attain optimal performance. Predicted radiation results by the DNN model and the results of the full-wave simulation show good agreement. Once DNN is trained, the prediction time is in the order of milliseconds and the model size is in megabytes, which can acquire the predicted radiation quickly and accurately and save storage space. Furthermore, to prevent the radiation from exceeding requirements, package structures are optimized by adjusting those parameters sensitive to radiation and disregarding insensitive parameters. The sensitivity of these WB-BGA package structural parameters to EMI radiation can be analyzed quickly based on data with different deviations generated by the trained DNN model. The sensitive parameters are adjusted according to their correlation with EMI radiation. The effectiveness and feasibility of the optimization method are verified by the WB-BGA package with two sets of different structural parameters.

关键词： deep learning algorithm deep neural network electromagnetic interference radiation prediction sensitivity analysis structure optimization WB-BGA package

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Pedestrian tracking and counting based on YOLOv4 and deepSORT in Subway Stations

Pedestrian tracking and counting based on YOLOv4 and DeepSOR...

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第35届中国控制与决策会议

作者： Hui Zhang Jie Xu Xiaowen Zhang State Key Lab of Rail Traffic Control and Safety Beijing Jiaotong University

Real walking parameters of pedestrians are the basis of research on pedestrian *** detection and tracking technology provides the possibility of accurate walking *** tackle the problem of lower detection accuracy of small-scale pedestrians,the YOLOv4 algorithm has been improved in two ***,the improved YOLOv4 model acts as the detector of deepSORT algorithm for pedestrian tracking and *** the improvement,the YOLOv4 algorithm performs well in both the public and private *** mAP is 90.61% in the private dataset,raising about 3.42%.Detection speeds reach 28.2 and 18.2 fps for processing images and video,*** on the YOLOv4-deepSORT algorithm,trajectories and walking parameters of pedestrians can be *** show the average speed of pedestrians on stairs in subway stations follows the normal distribution,the mean value is 1.27 m/s,and the standard deviation is 0.33 m/s.

关键词： deep learning algorithm pedestrian detection pedestrian tracking trajectories walking parameters

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