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Proceedings of the 2022 5th International Conference on Sensors, Signal and Image Processing

作者： Yuanwei Li Guangdong Industry Polytechnic China

ISBN: (纸本)9781450397124

This paper explored the application of deep learning target detection methods in the field of X-ray security screening. Faster R-CNN is a fully supervised deep learning method that uses only abnormal images containing dangerous goods as the training set, thus making it difficult to learn the features of normal images. It results in its high false detection rate when detecting normal images. In view of the above problems, combined with the characteristics of most of the X-ray security images are normal images, the author proposed a pre-classified head X-ray security image recognition method to reduce the false detection rate, while improving the performance and efficiency of dangerous goods detection, and more suitable for real X-ray security application scenarios.

关键词： Dangerous foods identification deep learning algorithm Target detection

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Audio Surveillance System(ASS): Merging of Acoustic Events Recognition and Classification through deep learning

Audio Surveillance System(ASS): Merging of Acoustic Events R...

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Signal, Control and Communication (SCC), International Conference on

作者： Safiah Endargiri Sara Alsubhi Ahad Alkabsani Pr.Dr. Kaouther laabidi Omri College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia College of Computer Science and Engineering University of Jeddah Tunis University University of El Manar Jeddah Saudi Arabia

ISBN: (数字)9781728135960

ISBN: (纸本)9781728135977

Despite the widespread use of Closed-Circuit TV for security purposes, CCTVs still include several weak points that can affect the quality of the provided surveillance services. Scientists and engineers are taking advantage of the continuous advancements in computer science by utilizing technology to improve acoustic recognition & classification approaches with the most efficient manners but at the cost of higher complexities. In this research, we focused on overcoming missing data paired with the use of existent surveillance approaches by adding acoustic surveillance features. We propose a low-complexity fully optimized algorithm that combines acoustic's recognition and classification algorithms. The proposed Audio Surveillance System (ASS) algorithm uses acoustic input which is then processed through the algorithm using the employed deep learning approaches to extract irregular patterns and classify them into appropriate categories to be used as a surveillance input.

关键词： acoustic ASR DNN deep learning algorithm ASS algorithm Acoustics Speech recognition aquifer storage and recovery Taxonomy Audio computer science

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A Novel 1-D Convolution Neural Network With SVM Architecture for Real-Time Detection Applications

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IEEE SENSORS JOURNAL 2018年第2期18卷 724-731页

作者： Lekha, S. Suchetha, M. Vellore Inst Technol Sch Elect Engn Madras 600037 Tamil Nadu India

To enhance the performance and sensitivity of continuous monitoring systems for detection of chronic diseases, selection of optimal machine learning algorithms is pivotal. Presently, the commonly used algorithms face constraints, such as high computational cost and lack of optimal feature selection on application to real time signals thereby reducing the efficiency of such analysis. deep learning approaches, such as the convolution neural network, overcome these drawbacks by calculating automated features from raw signal and classifying the derived features. This architecture shows good merits. However, the use of fully connected multi-layer perceptron algorithms have shown low classification performance. This paper proposes to develop a modified deep learning convolution neural network algorithm integrated with support vector machines to address the drawbacks present in multi-layer perceptron and thereby improving the overall performance of real-time detection applications. The system is validated on real-time breath signals for non-invasive detection of diabetes. The performance of this proposed algorithm is evaluated and compared with the existing technique.

关键词： deep learning algorithm multi-layer perceptron support vector machines detection systems diabetes

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A Spatial-Temporal Multi-Feature Network (STMF-Net) for Skeleton-Based Construction Worker Action Recognition

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SENSORS 2024年第23期24卷 7455页

作者： Tian, Yuanyuan Lin, Sen Xu, Hejun Chen, Guangchong Wuyi Univ Sch Civil Engn & Architecture Jiangmen 529020 Peoples R China East China Univ Sci & Technol Sch Business Shanghai 200231 Peoples R China Jiangsu Univ Sci & Technol Sch Civil Engn & Architecture Zhenjiang 212100 Peoples R China Shanghai Univ Sch Management Shanghai 200444 Peoples R China

Globally, monitoring productivity, occupational health, and safety of construction workers has long been a significant concern. To address this issue, there is an urgent need for efficient methods to continuously monitor construction sites and recognize workers' actions in a timely manner. Recently, advances in electronic technology and pose estimation algorithms have made it easier to obtain skeleton and joint trajectories of human bodies. deep learning algorithms have emerged as robust and automated tools for extracting and processing 3D skeleton information on construction sites, proving effective for workforce action assessment. However, most previous studies on action recognition have primarily focused on single-stream data, which limited the network's ability to capture more comprehensive worker action features. Therefore, this research proposes a Spatial-Temporal Multi-Feature Network (STMF-Net) designed to utilize six 3D skeleton-based features to monitor and capture the movements of construction workers, thereby recognizing their actions. The experimental results demonstrate an accuracy of 79.36%. The significance of this work lies in its potential to enhance management models within the construction industry, ultimately improving workers' health and work efficiency.

关键词： construction worker action recognition 3D skeleton deep learning algorithm

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UGC-YOLO:Underwater Environment Object Detection Based on YOLO with a Global Context Block

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Journal of Ocean University of China 2023年第3期22卷 665-674页

作者： YANG Yuyi CHEN Liang ZHANG Jian LONG Lingchun WANG Zhenfei School of Information and Electrical Engineering Hunan University of Science and TechnologyXiangtan 411201China

With the continuous development and utilization of marine resources,the underwater target detection has gradually become a popular research topic in the field of underwater robot operations and target ***,it is difficult to combine the environmental semantic information and the semantic information of targets at different scales by detection algorithms due to the complex underwater *** this paper,a cascade model based on the UGC-YOLO network structure with high detection accuracy is *** YOLOv3 convolutional neural network is employed as the baseline *** fusing the global semantic information between two residual stages in the parallel structure of the feature extraction network,the perception of underwater targets is improved and the detection rate of hard-to-detect underwater objects is ***,the deformable convolution is applied to capture longrange semantic dependencies and PPM pooling is introduced in the highest layer network for aggregating semantic ***,a multi-scale weighted fusion approach is presented for learning semantic information at different *** are conducted on an underwater test dataset and the results have demonstrated that our proposed algorithm could detect aquatic targets in complex degraded underwater *** with the baseline network algorithm,the Common Objects in Context(COCO)evaluation metric has been improved by 4.34%.

关键词： object detection underwater environment semantic information semantic features deep learning algorithm

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Developing the network social media in graphic design based on artificial neural network

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2021年第4期12卷 640-653页

作者： Liu, Yaxuan Luxun Acad Fine Arts Dalian 116650 Peoples R China

The purposes are to effectively solve the graphic design problems, develop an easy-to-use supporting design program, and make graphic design more reliable and accurate. Based on the analysis of the current graphic design framework, graphic design data are obtained from the network social media. A system of surrounding rock classification and support optimization design is developed by a deep neural network structure. The model's effectiveness is verified by more than 3000 road conditions data. The results show that the three-layer network's errors are 0.0062 with a training time of 12,455, and the five-layer network's errors are 0.00019 with a training time of 69,895. With the input layer, hidden layer, and the output layer of 8, 15, and 5 respectively, the model performs best. In the deep learning algorithm, the deep backpropagation neural network (deep BPNN) can obtain the best training effects with less training time. Therefore, the roadway drawing system's application based on the deep learning algorithm to the roadway support design can improve design efficiency and scientificity.

关键词： deep learning algorithm Weight coefficient Bias matrix Accuracy requirement Roadway support design

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Sustainable Sea of Internet of Things: Wind Energy Harvesting System for Unmanned Surface Vehicles

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ACS APPLIED MATERIALS & INTERFACES 2024年第22期16卷 28694-28708页

作者： Cao, Hao Tang, Hongjie Zhang, Zutao Kong, Lingji Tang, Minfeng Du, Xinru Mutsuda, Hidemi Tairab, Alaeldin M. Southwest Jiaotong Univ Sch Mech Engn Chengdu 610031 Peoples R China Chengdu Technol Univ Chengdu 611730 Peoples R China Southwest Jiaotong Univ Sch Informat Sci & Technol Chengdu 610031 Peoples R China Southwest Jiaotong Univ Yibin Res Inst Yibin Peoples R China Hiroshima Univ Grad Sch Adv Sci & Engn Higashi Hiroshima 7390046 Japan

Harvesting wind energy from the environment and integrating it with the internet of things and artificial intelligence to enable intelligent ocean environment monitoring are effective approach. There are some challenges that limit the performance of wind energy harvesters, such as the larger start-up torque and the narrow operational wind speed range. To address these issues, this paper proposes a wind energy harvesting system with a self-regulation strategy based on piezoelectric and electromagnetic effects to achieve state monitoring for unmanned surface vehicles (USVs). The proposed energy harvesting system comprises eight rotation units with centrifugal adaptation and four piezoelectric units with a magnetic coupling mechanism, which can further reduce the start-up torque and expand the wind speed range. The dynamic model of the energy harvester with the centrifugal effect is explored, and the corresponding structural parameters are analyzed. The simulation and experimental results show that it can obtain a maximum average power of 23.25 mW at a wind speed of 8 m/s. Furthermore, three different magnet configurations are investigated, and the optimal configuration can effectively decrease the resistance torque by 91.25% compared with the traditional mode. A prototype is manufactured, and the test result shows that it can charge a 2200 mu F supercapacitor to 6.2 V within 120 s, which indicates that it has a great potential to achieve the self-powered low-power sensors. Finally, a deep learning algorithm is applied to detect the stability of the operation, and the average accuracy reached 95.33%, which validates the feasibility of the state monitoring of USVs.

关键词： unmanned surface vessels self-regulation strategy magnetic coupling mechanism piezoelectric and electromagneticeffects deep learning algorithm state monitoring

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A novel intelligent 12-layer convolutional neural network model for gender classification using fingerprint images

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第2期45卷 2685-2706页

作者： Arivalagan, Divya Began, K. Bhoopathy Pushpa, S. Ewins Pon Rajendran, Kiruthiga Anna Univ Madras Inst Technol Dept Elect Engn Chennai Tamil Nadu India Anna Univ Coll Engn Guindy Dept Elect & Commun Engn Chennai Tamil Nadu India

Fingerprints are widely used as effective personal authentication systems, because they constitute unique, robust, and risk-free evidence. Fingerprinting techniques refer to biometric procedures used for identifying individuals based on their physical characteristics. A fingerprint image contains ridges and valleys forming a directionally-oriented pattern. The robustness of the fingerprint authentication technique determines the quality of the fingerprint image. This study proposed an intelligent 12-layered Convolutional Neural Network (CNN) model using deep learning (DL) for gender determination based on fingerprints. Further, the study compared the performance of this model to existing state-of-the-art methods. The primary goal of this study was to reduce the number of comparisons within a large database obtained from automatic fingerprint recognition systems. The classification process was found to be swifter and more accurate when analysis of the DL algorithm was performed. With reference to the criteria of precision, recall, and accuracy evaluation during classification, this proposed 12-layered CNN model outperformed the Residual Neural Network with 50 Layers (ResNet-50) and Dense Convolutional Network with 201 Layers (DenseNet-201) models. The accuracies obtained were 97.0%, 95.8%, 98.0%, and 96.8% for female-left, female-right, male-left, and male-right classes respectively, while achieving an overall accuracy of 94.0%.

关键词： Fingerprint image intelligent system authentication convolutional neural network deep learning algorithm precision recall accuracy DenseNet201 ResNet-50

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Fault detection and classification in hybrid energy-based multi-area grid-connected microgrid clusters using discrete wavelet transform with deep neural networks

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ELECTRICAL ENGINEERING 2024年 1-18页

作者： Rao, S. N. V. Bramareswara Kumar, Y. V. Pavan Amir, Mohammad Muyeen, S. M. Sir CR Reddy Coll Engn Dept Elect & Elect Engn Eluru 534007 Andhra Pradesh India VIT AP Univ Sch Elect Engn Amaravati 522237 Andhra Pradesh India Indian Inst Technol IIT Dept Elect Engn Delhi 110016 India Qatar Univ Dept Elect Engn Doha 2713 Qatar

Microgrid control and operation depend on fault detection and classification because it allows quick fault separation and recovery. Due to their reliance on sizable fault currents, classic fault detection techniques are no longer suitable for microgrids that employ inverter-interfaced distributed generation. Nowadays, deep learning algorithms are essential for ensuring the reliable, safe, and efficient operation of these complex energy systems. They enable quick responses to faults, reduce downtime, enhance energy efficiency, and contribute to the overall sustainability and resilience of microgrids. With this intent, this work proposes a "Discrete Wavelet Transform with deep Neural Network (DWT-DNN)" for detecting and classifying the various faults that occurred in hybrid energy-based multi-area grid-connected microgrid clusters. The proposed DWT-DNN first extracts the input features from the point of common coupling of the cluster system using DWT, and then, these decomposed features are applied as input variables to train the DNN for the detection and classification of various faults. All the investigations are performed in the "MATLAB/Simulink 2022a" environment. To validate the effectiveness of the proposed DWT-DNN, the results are compared with wavelet packet transforms (WPT) in terms of accuracy in detecting and classifying the faults. From the simulation findings and observations, it is evident that the proposed DNN produced fruitful results.

关键词： deep learning algorithm deep neural networks Discrete wavelet transform Hybrid energy sources Microgrids Wavelet transform

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Gated branch neural network for mandatory lane changing suggestion at the on-ramps of highway

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IET INTELLIGENT TRANSPORT SYSTEMS 2019年第1期13卷 48-54页

作者： Dou, Yangliu Fang, Yihao Hu, Chuan Zheng, Rong Yan, Fengjun McMaster Univ Dept Mech Engn 1280 Main St West Hamilton ON L8S 4K1 Canada McMaster Univ Dept Comp & Software 1280 Main St West Hamilton ON L8S 4K1 Canada Univ Waterloo Dept Syst Design Engn Waterloo ON N2L 3G1 Canada

A gated branch neural network (GBNN) is proposed for modelling mandatory lane changing (MLC) behaviour at the on-ramps of highways. It provides a core algorithm for an MLC suggestion system for advanced driver assistance systems (ADAS), where the main challenge is the trade-off between computational speed and prediction accuracy for both non-merge and merge events. The GBNN algorithm employs a gated branch based on correlation analysis, scaled exponential linear units activation function, and adaptive moment estimation optimiser. The algorithm has been evaluated using the real-world dataset of U.S. Highway 101 and Interstate 80 from Federal Highway Administration's Next Generation Simulation (NGSIM). Input features are extracted from NGSIM and pre-processed by standardisation and principal component analysis. TensorFlow framework and Python are used as the development platform. Results show that the proposed GBNN algorithm with the Pearson correlation method has values of 97.7%, 96.3%, and 0.990 for non-merge accuracy, merge accuracy, and receiver operating characteristic score, respectively. It outperforms other traditional binary classifiers for MLC applications, and is more light-weight than a convolutional neural network (AlexNet) of deep learning algorithm. Owing to its compact architecture, the GBNN provides high accuracy and efficiency, demonstrating promising usage as an MLC suggestion system in ADAS.

关键词： driver information systems traffic engineering computing optimisation principal component analysis neural nets behavioural sciences computing learning (artificial intelligence) gated branch neural network GBNN algorithm mandatory lane changing suggestion on-ramps-of-highway mandatory lane changing behaviour modelling MLC behaviour modelling MLC suggestion system advanced driver assistance systems ADAS nonmerge event merge event correlation analysis scaled exponential linear units activation function adaptive moment estimation optimiser real-world dataset U S Highway 101 U S Interstate 80 Federal Highway Administration Next Generation Simulation NGSIM principal component analysis TensorFlow framework Python Pearson correlation method receiver operating characteristic score merge accuracy nonmerge accuracy deep learning algorithm

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