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technology research of φ-OTDR fiber Perimeter intrusion System based on CNN-LSTM algorithm 3

Technology research of φ-OTDR fiber Perimeter intrusion Sys...

引用

3rd international conference on Advanced Algorithms and Neural Networks, AANN 2023

作者： Li, Zhixing Wang, Zhengqi Li, Jiahui Yang, Huan Liang, Guodong Hao, Zhihao Wang, Guangyan Tianjin University of Commerce Tian Jin300130 China

ISBN: (纸本)9781510668355

The fiber optic sensing perimeter security system has broad application prospects due to its unique characteristics such as long transmission distance, wide monitoring range, high sensitivity, and strong real-time performance. based on DWT and STFT, this paper extracts the features of the intrusion source signal and uses the CNN-LSTM deep learning algorithm to intelligently classify the signal, improving the separability of disturbance events and accurately judging the intrusion source type. The average accuracy rate of classification can reach more than 96%, with excellent intelligence and low false alarm performance. This technology is a perfect replacement for traditional perimeter alarm systems such as infrared beams and electronic fences, especially applicable for real-time measurement of vibration-related events such as long-distance, omnidirectional, and multi-point illegal intrusion, illegal destruction, and structural damage. In addition, the application of distributed optical fiber sensing system in the industrial chain can improve product quality, ensure production safety, and realize production line *** research will improve the reliability and accuracy of the perimeter security system, improve the public security environment, save social resources, and improve social security. © 2023 SPIE. All rights reserved.

关键词： image recognition

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Isolated Arabic Characters Recognition System based On Combined Features And DNN 7

Isolated Arabic Characters Recognition System Based On Combi...

引用

7th international conference on image and signal Processing and their Applications, ISPA 2022

作者： Houdalatrache Lallouanibouchakour Farizameziani Mustaphayahiaoui Algiers Algeria

ISBN: (纸本)9781665480420

this paper presents a system for recognizing the isolated printed Arabic characters. In this type of systems, the feature extraction step is considered as the most important stage to better recognize the characters. For this purpose, we have proposed in this work to use a combined feature methods in order to improve the character recognition rate. The proposed system is based on the combined features extraction, which are: the Discrete Cosine Transform (DCT), Zernike moments and the Weighted Gradient Local Binary Pattern (WGLBP) and the Deep Neural Network (DNN) classifier. Experiments are carried out on the Printed Arabic Text set *** result confirm that the use of the features combination improves the recognition accuracy rate. © 2022 IEEE.

关键词： Discrete cosine transforms

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Tomato Leaf Disease Detection through Machine Learning based Parallel Convolutional Neural Networks 7

Tomato Leaf Disease Detection through Machine Learning based...

引用

7th international conference on Intelligent Computing and Control systems, ICICCS 2023

作者： Shetty, Sahana Selvanayaki, S. Sumathi, T. Nageswari, C.Shobana Nisha Devi, K. Suganyadevi, S. FET-Jain Department of CSE-AIML Cyber Security School of CSE Jayanagar Bangalore India Saveetha Engineering College Department of Artificial Intelligence and Data Science Chennai India Dr.Mahalingam College of Engineering and Technology Department of Information Technology Pollachi India R.M.D. Engineering College Department of ECE Kavaraipettai601206 India Bannari Amman Institute of Technology Department: Artificial Intelligence and Data Science Sathyamangalam India KPR Institute of Engineering and Technology Department of ECE Coimbatore India

ISBN: (纸本)9798350397253

Agriculture is the primary economic activity in a significant number of nations. Yet, plant-related illnesses are the most difficult obstacle for farmers to overcome. The leaf is the portion of the plant that is noticed the most, but the process of segmenting an image of a sick lesion on a leaf suffers from challenges related to uneven lighting and a crowded, complicated natural environment. The combination of colour balancing and superpixel is the foundation of the approach that has been suggested as a solution to these issues. To begin, the picture that is being read in will be converted into a color-balanced image so that the effects of uneven lighting may be removed. Second, using the superpixel operation, compact areas are produced from the modified picture. An experimentally calculated threshold is then applied to the Histogram of Gradients (HOG) and colour channels of the superpixel to separate the undesirable backdrop from the image of the leaf. In order to identify a sick or contaminated picture, K-means clustering is employed. Pyramid of HOG (PHOG), an expanded version of HOG, together with Grey Level Co-occurrence Matrix (GLCM) characteristics are used to depict a sick infected portion of the body. In the end, many disease classifiers are evaluated against one another, and Random Forest (RF) is chosen as the best option. An experiment is carried out to verify the effectiveness of the proposed approach's accuracy using natural photographs obtained from a variety of farms, images downloaded from the internet, and the Plant-Village colour dataset. This is done so that the resilience of the method can be evaluated. © 2023 IEEE.

关键词： Color

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Smart Sociolinguistic Intelligent Analysis Framework based on Feature Extraction and Matching of Structural Data 4

Smart Sociolinguistic Intelligent Analysis Framework Based o...

引用

4th international conference on Smart systems and Inventive technology, ICSSIT 2022

作者： Lin, Wei Wang, Wenpu School of Foreign Languages and International Education Chengdu Technology University Sichuan Chengdu611730 China

ISBN: (纸本)9781665401180

Smart sociolinguistic intelligent analysis framework based on feature extraction and the matching of structural data is designed in this paper. Since data mining has added annotations to the features of the search data, when searching and searching data, it is faster and more accurate than traditional statistical analysis methods. This paper uses the SUSAN features, the method is simple, the anti-noise ability is strong, and the processing speed is fast. The matching similarity measurement should also consider the feature point direction information to integrate the structure features. For target matching, the side direction information can be directly represented by the feature vector formed by the two-directional cross template response, and the framework is then completed. The model is validated through the public data and the collected data from the internet. © 2022 IEEE

关键词： Feature extraction

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A new fringe analysis method based on shared bottom network for single frame 3D reconstruction 15

A new fringe analysis method based on shared bottom network ...

引用

15th international conference on Information Optics and Photonics, CIOP 2024

作者： Lu, Jin Zhang, Min Jie Li, Mu H. Li, Mu J. Wang, Jin Wu, Pei Y. Sun, Wei Li, Lin School of Communication and Information Engineering XI'AN University of Posts&Telecommunications 618 Chang'an West St. Shaanxi Xi'an710121 China School of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an China China Academy of Industrial Internet Electronic city technology research and development center Chaoyang Beijing100020 China College of Engineering Ohio State University ColumbusOH43210 United States Wuhan University of Technology 1178 Heping Avenue Wuchang Hube Wuhan430061 China

ISBN: (数字)9781510686175

ISBN: (纸本)9781510686168

Recently, deep learning-based methods have been employed in optical measurement. The fringe to phase method based on deep learning can achieve high-precision 3D topography measurement and is applied to various optical metrology tasks, including phase extraction, phase unwrapping, fringe order determination, depth estimation, and other crucial steps. However, it appears simplistic to obtain images of each metrological task from a single fringe pattern. This paper proposes a novel network that effectively extracts the semantic features of fringe patterns by incorporating the design architecture of Transformer while retaining the advantages of convolutional networks. The architecture primarily consists of a backbone, decoder, and feature extraction block which enhance the features at different frequencies within a single fringe pattern. The backbone and decoder are specifically designed for wrapped phase prediction tasks. Experimental results demonstrate that the network accurately predicts the wrapped phase from a single fringe pattern. In comparison with previous methods, this paper's approach offers several contributions: an efficient utilization of a new type of encoder for extracting high-level semantic features from fringe patterns;moreover, only a single grayscale image is required as input for the network without relying on color composite images or additional prior information. © 2024 SPIE.

关键词： Fringe analysis Feature extraction Convolution Semantics Network architectures Phase unwrapping 3D modeling Deep learning Phase shifts Projection systems

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Insights into the Emotional Landscape of Bangladeshi Sports Fans: Deep Learning based Sentiment Analysis of Sports-Related Bengali Comments on Facebook

Insights into the Emotional Landscape of Bangladeshi Sports ...

引用

international IEEE conference on signal-image Technologies and internet-based System

作者： Anwarul Karim Anik Islam Sadia Sharmin Department of Computer Science and Engineering Bangladesh University of Engineering and Technology Dhaka Bangladesh

People around the globe find sports to be one of the most intriguing topics to discuss on Facebook. Through their comments on sports-related posts, mostly about Cricket and Football, they show strong and immediately noticeable feelings. To predict the sentiments and opinions conveyed by users in Bengali, particularly within the sports context, sentiment analysis can play a vital role. So our research study focuses on conducting sentiment analysis on comments obtained from sports-related posts on Facebook pages, aiming to gain valuable insights into the sentiments expressed by the audience. The methodology involves the collection of a comprehensive dataset from the comment sections of many sports-related Facebook posts, followed by applying data pre-processing techniques to eliminate noise and other unnecessary information. Subsequently, a range of machine learning and deep learning models, including Random Forest, Decision Tree, KNN, SVM, MNB, as well as deep learning models such as Bi-LSTM, CNN-LSTM hybrid, MultiLayer Perceptron with Embedding, Bi-LSTM GRU hybrid, and Bangla Bert are employed for comparative analysis of the sentiments expressed in the comments. based on the results, we discovered that, out of all the models we trained, RBF SVM was the most stable and well-fitting. The findings of this study will provide significant insights into the overall sentiment prevailing in sports-related discussions on Facebook, contributing to a more profound understanding of users’ emotional engagement in the sports domain.

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A Deep Learning-based Multi-language Document Layout Analysis Method 7

A Deep Learning-based Multi-language Document Layout Analysi...

引用

7th international conference on Vision, image and signal Processing, ICVISP 2023

作者： Yang, Xieliu Men, Guowen Yin, Chenyu School of Mechanical Engineering Shenyang Jianzhu University Shenyang China

ISBN: (纸本)9781839539848

Layout analysis of complex layout documents is significant for document understanding systems and is still a challenging task. A deep learning-based layout analysis method is proposed for contemporary printed documents in this paper. The network is developed from Mask-RCNN and the standard convolution structure in Mask-RCNN is replaced by deformable convolution network (DCN) to improve the location accuracy of the segmentation contour. Transfer learning is employed to solve the few-shot problem and the fine-tune mode is investigated. The network is pre-trained on the PubLayNet dataset and fine-tuned on the ICDAR2009 dataset. Experiments show that the proposed method outperforms the tested traditional method and is insensitive to language. © The Institution of Engineering & technology 2023.

关键词： Convolution

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Binary QC-LDPC Codes based on Whiteman's Generalized Cyclotomy 8

Binary QC-LDPC Codes Based on Whiteman's Generalized Cycloto...

引用

8th international conference on Communication, image and signal Processing, CCISP 2023

作者： Wang, Tao Shi, Zhiping Deng, Li Yang, Juan Chengdu China UESTC NKLWC Chengdu China School of Electronic Information and Automation Guilin University of Aerospace Technology Guilin China

ISBN: (纸本)9798350305838

This study introduces a construction method for QC-LDPC codes, ensuring their absence of four-cycles by utilizing Whiteman's generalized cyclotomic (GC) classes. An analysis of the cycle structure within the Tanner graph for the proposed codes, specifically focusing on cycles with length six, is conducted. Moreover, this paper presents the necessary and sufficient conditions for generating GC-QC-LDPC codes with a minimum girth of eight, based on the analysis. Simulation results demonstrate the superior bit error rate (BER) performance of the constructed QC-LDPC codes over the AWGN channel. Compared to other QC-LDPC codes, QC-LDPC codes constructed using this method have a reduced base matrix size for long code lengths. This attribute augments their suitability for integration within storage systems. © 2023 IEEE.

关键词： Bit error rate

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Research on Infrared Detection Method for Distribution Equipment based on Cloud Edge Collaboration 5

Research on Infrared Detection Method for Distribution Equip...

引用

5th IEEE international conference on Power, Intelligent Computing and systems, ICPICS 2023

作者： Hengfu, Feng Bo, Zhang Chongyou, Xu Meng, Zhou State Grid Shanghai Energy Internet Research Institute Co. Ltd. Shanghai China State Grid Zhejiang Electric Power Co. Ltd. Ningbo Power Supply Company Ningbo China

ISBN: (纸本)9798350333442

Infrared imaging is the main method for detecting power equipment and a key means to ensure the safe and reliable operation of the power grid. Traditional infrared imaging devices are bulky, inconvenient to carry, and unfriendly to operate, which affects their practical application effectiveness. In recent years, infrared imaging online detection and portable infrared imaging technology have been continuously developed, and advanced technologies such as 'big cloud moving intelligence' have been rapidly applied, providing new ideas for infrared imaging detection. This article proposes mobile phone infrared imaging detection and perforated infrared imaging online detection technology solutions based on the characteristics of overhead lines and distribution station buildings in the distribution network, and designs an infrared imaging detection architecture based on cloud edge collaborative distribution equipment. based on the cloud/edge collaboration mechanism, the inference calculation is moved forward to edge devices such as edge gateways or mobile phones. The model is trained in the cloud, and the model algorithm is updated before being distributed and deployed to the edge, reducing network transmission and cloud computing pressure. The cloud/edge updates independently. Finally, experiments have shown that the infrared imaging detection architecture for power distribution equipment based on cloud/edge collaboration effectively reduces network transmission load and cloud computing pressure, is easy to operate, and has good implementation effects. It provides a new solution to solve the series of problems existing in existing infrared imaging detection. © 2023 IEEE.

关键词： image recognition

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A Real-Time Computer Vision-based Green Intensity Estimation Method for the Arecanut Tree Pesticide Spraying System

A Real-Time Computer Vision-Based Green Intensity Estimation...

引用

international conference on VLSI, signal Processing, Power Electronics, IoT, Communication and Embedded systems, VSPICE 2023

作者： Nagaraja Hebbar, N. Sandeep, Bhat Artificial Intelligence and Data Science Department Srinivas Institute of Technology Mangaluru India Computer Science and Engineering Department Srinivas Institute of Technology Mangaluru India

ISBN: (纸本)9789819746569

In India, one of the commercial crops is arecanut. The majority of arecanut growers depend on the arecanut production. However, they are also having a great deal of difficulty finding skilled workers to do pesticide spraying jobs. This effort uses computer vision technologies to address the aforementioned problem. This work compares the HSV estimation with the green pixel estimate approach. This makes it easier to position the robot at the right height, where the pesticide spray will work best. In this case, the HSV estimation identified a better way to manage the robot’s motion. Using a wireless camera, the experimental setup achieves an incredibly innovative performance to capture an image of the arecanut crop. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Pixels

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