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Power Line Partial Discharge Detection Using Multi-Scale 1D Convolutional Neural networks 5

Power Line Partial Discharge Detection Using Multi-Scale 1D ...

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5th international conference on Power and Energy Technology, ICPET 2023

作者： Jiang, Yuliang Xu, Zhenhuan Fang, Dan Zhang, Guoliang Zhi, Bihen Wang, Bing State Grid Shanghai Electric Power Corporation Training Center Shanghai China Nanjing Haoqing Information Technology Co. Ltd Nanjing China

ISBN: (纸本)9798350339673

In recent years, due to the widespread access of distributed power supply, the topology of electrical network becomes more and more complex such that it is difficult to analyze the fault causes of transmission lines, and partial discharge (PD) is a type of failure that often occurs. An automated transmission lines PD detection system requires automated feature extraction and online identification of PD. Firstly, A PD detection method based on multi-scale one dimensional convolutional neural network (MS-Conv1D) is proposed in this paper, which takes an arbitrary-long time series of fault signals as input. Furthermore, the single-phase and three-phase electrical signals can be dealt with an unified structure of neural network. Secondly, a complete flow of training MS-Conv1D and data processing is given. Finally, simulations on a real PD dataset shows that the detection accuracy of MS-Conv1D reaches 90% with 20MHz sampling rate, and the receiver operating characteristic (ROC) points indicate the robustness of MS-Conv1D at different frequencies. © 2023 IEEE.

关键词： automated feature extraction convolutional neural network fault diagnosis partial discharge

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Enhancing Tone Recognition in Large-Scale Social Media Data with Deep Learning and Big Data processing 5

Enhancing Tone Recognition in Large-Scale Social Media Data ...

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2023 5th international conference on Artificial Intelligence and Computer Applications, ICAICA 2023

作者： Huo, Xingyu School of Theater Film and Television Communication University of China Beijing China

ISBN: (纸本)9798350323313

Huge processing power, big data, and deep learning algorithms have made major technical improvements possible. Technology is developing to analyse, predict, and meet our unmet requirements. Speech is the primary human communication method, and when a microphone sensor is employed, it may be utilised to support human-computer interaction (HCI). A new field of study in human-computer interaction (HCI) is quantifying emotion recognition from speech signals using these sensors. this research has applications in virtual reality, healthcare, emergency call centres, behaviour assessment, and human-reboot interaction. Our main contributions in this study are to: (i) further develop discourse feeling acknowledgment (SER) exactness over the cutting edge;what's more (ii) bring down the figuring cost of the proposed SER model. the method involved with recognizing a speaker's feelings from their discourse signal is known as discourse feeling acknowledgment. the three essential strides in the feeling acknowledgment process are highlight extraction, include determination, and classifier. Improving a framework's capacity to perceive discourse feelings using different element extraction techniques is the essential objective of this work. the work centers around the pre-handling of the sound examples that are gotten, where channels are utilized to wipe out commotion from discourse tests. Worldwide discriminative elements are learned in completely associated layers, though neighborhood stowed away examples are learned in convolutional layers that bring additional consideration to down-example the component maps rather than the pooling layer. Discourse feeling classification is finished utilizing a SoftMax classifier. It shows the worth and viability of the proposed SER approach and exhibits that it is so relevant to functional purposes. © 2023 IEEE.

关键词： Human computer interaction

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A staggered-arrangement EBG decoupling structure for Ku-band vehicle-mounted antennas 5

A staggered-arrangement EBG decoupling structure for Ku-band...

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5th international conference on Circuits and Systems, ICCS 2023

作者： Gao, Feng Li, Qian-Qian Ge, Han He, Guo-Qiang Beijing Zongheng Electromechanical Technology Corporation Limited Beijing China School of Communication and Information Engineering Shanghai University Shanghai China Key Laboratory of Specialty Fiber Optics and Optical Access Networks and the Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai University Shanghai China

ISBN: (纸本)9798350308273

Ku-band (10.7-12.7GHz) vehicle-mounted mobile antenna serves as a link to satellite communication networks, which are widely used in applications such as mobile news, emergency response, and remote work. However, the electromagnetic interference and mutual coupling could disrupt the antenna performance and degrade the communication quality when it integrates multiple antennas on a vehicle platform, which is caused by the excitation and propagation of surface waves between adjacent antennas. A choke slot is commonly employed to enhance the isolation between antennas, but it diverges from the trend toward integrated circuitry and flat design in communication system development. therefore, this paper presents a novel staggered-arrangement electromagnetic band gap (EBG) structure designed on current distribution, which can effectively enhance the overall isolation level to 70 dB within the frequency range of 10.7-12.7 GHz in simulation, and realize the integration of antenna and isolation structure into circuit system. the EBG with high isolation alleviates electromagnetic interference among antennas, ensuring the consistent functionality of vehicle-mounted antennas and improving communication quality. © 2023 IEEE.

关键词： Integrated circuits

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An Optimized DDoS Attack Detection Using Deep Convolutional Generative Adversarial networks 5

An Optimized DDoS Attack Detection Using Deep Convolutional ...

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5th international conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Akana, Chandra Mouli Venkata Srinivas Kumar, Arvind Tiwari, Mohit Yunus, Aslanov Zabit Vijayakumar, E. Singh, Mukesh Bonam Venkata Chalamayya Engineering College Odalarevu India School of Computer Applications Manav Rachna International Institute of Research and Studies India Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering Delhi India Azerbaijan State University of Economics - Unec Baku Azerbaijan KIT-Kalaignar Karunanidhi Institute of Technology Department of Computer Applications Tamilnadu Coimbatore India Graphic Era University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350321425

Attacks known as Distributed Denial-of-Service (DDoS) are rising as a result of recent, dramatic increase in demand for Internet access. When the amount and characteristics of network traffic, which may include harmful DDoS contents, expand dramatically, traditional basic algorithms using machine learning for classifying DDoS attacks frequently fail because it cannot automatically extract high-value characteristics. In order to effectively identify and classify DDoS attacks, a hybrid technique called DFNN-SAE-DCGAN that combines three deep learning-based models is suggested. the Deep Feed-Forward Neural Network (DFNN) and Stacked Auto encoder offers an efficient method for extracting features that identifies the most pertinent feature sets without the assistance of a person. To avoid the operational overhead and presumption associated with processing massive set of features with distortion and redundant characteristic values, the Deep Convolutional Generative Adversarial networks (DCGAN) component of the proposed model classifies the attacks into various DDoS attack types using the restricted and minimized characteristic sets generated by the DFNN-SAE as inputs. the experimental results show a very high and resilient accuracy rate and an F1-score of 98.5%, which is higher than the performance of many similar approaches. these results were acquired by thorough and extensive trials on various performance aspects on the CICDDoS2019 dataset. this demonstrates that the suggested methodology may be utilized to defend against the increasing amount of DDoS attacks. © 2023 IEEE.

关键词： Denial-of-service attack

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Siamese Network Visual Tracking Algorithm Based on GCT Attention and Dual-Template Update 5

Siamese Network Visual Tracking Algorithm Based on GCT Atten...

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5th international conference on Natural Language processing, ICNLP 2023

作者： Ma, Sugang Sun, Siwei Pu, Lei Yang, Xiaobao Xi'an University of Posts and Telecommunications School of Computer Science and Technology Xi'an China Rocket Force Engineering University School of Operational Support Xi'an China Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an China

ISBN: (纸本)9798350302219

To address the problem of insufficient representational capability and lack of online update of the Fully-convolutional Siamese Network (SiamFC) tracker in complex scenes, this paper proposes a siamese network visual tracking algorithm based on GCT attention and dual-template update mechanism. First, the feature extraction network is constructed by replacing AlexNet with the VGG16 network and SoftPool is used to replace the maximum pooling layer. Secondly, the attention module is added after the backbone network to enhance the network's ability to extract object features. Finally, a dual-template update mechanism is designed for response map fusion. Average Peak-to-Correlation Energy (APCE) is used to determine whether to update the dynamic templates, effectively improving the tracking robustness. the proposed algorithm is trained on the Got-10k dataset and tested on the OTB2015 and VOT2018 datasets. the experimental results show that, compared with SiamFC, the success rate and accuracy reach 0.663 and 0.891 on the OTB2015, which improve respectively 7.6% and 11.9%;On the VOT2018 dataset, the tracking accuracy, robustness and EAO are improved respectively by 2.9%, 29% and 14%. the proposed algorithm achieves high tracking accuracy in complex scenes and the tracking speed reaches 52.6 Fps, which meets the real-time tracking requirements. © 2023 IEEE.

关键词： Complex networks

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Analysis and Application of Deep Learning in Novel MEMS Capacitive Pressure sensor 5

Analysis and Application of Deep Learning in Novel MEMS Capa...

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5th IEEE international conference on Knowledge Innovation and Invention, ICKII 2022

作者： Chen, Bin Chen, Shu-Jung National Changhua University of Education Dept. of Mechatronics Engineering Changhua Taiwan

ISBN: (数字)9781665479295

ISBN: (纸本)9781665479295

With the continuous development of MEMS sensor processing and manufacturing technology, MEMS capacitive pressure sensors require computer-aided software to reduce development costs. therefore, simulation with Comsol Multiphysics and building of a deep learning approach s proposed. To build a highly efficient deep neural network (DNN) model with a sufficient dataset from Comsol simulation for MEMS analysis, various parameters of the manufacturing design are needed to understand the design characteristics of the sensor. the designed capacitive pressure sensor has a three-layer structure with a vacuum layer in the middle, the top, and the bottom electrodes depending on the material. the DNN with four hidden layers and 20 neurons for each layer is proposed to fit the relationship between the four input parameters and two outputs of a capacitive pressure sensor with deep learning. the DNN allows the design parameters and characteristics of the capacitive pressure sensor to be understood with a small data set simulated by Comsol Multiphysics, thus reducing the solution time and improving development efficiency. Using the dataset with data augmentation, the DNN was trained and tested. A set of data set samples is obtained at an average of 40 min. If the trained capacitive pressure sensor model is used for prediction, it only consumes an average of 10 s per set of data, and the prediction error is less than 0.3%. the results show a practical and high efficiency and accuracy of deep learning for prediction on capacitive pressure sensors. © 2022 IEEE.

关键词： Deep neural networks

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IICETA 2022 - 5th international conference on Engineering Technology and its Applications

IICETA 2022 - 5th International Conference on Engineering Te...

引用

5th international conference on Engineering Technology and its Applications, IICETA 2022

ISBN: (纸本)9781665472159

the proceedings contain 104 papers. the topics discussed include: enhancing recommendation system using adapted personalized PageRank algorithm;diagnosing bacteria samples using data mining: review study;modeling the performance of wireless body sensor networks;spectrum sensing enhancement based on segmentation of the confused regions;thermal performance of inline and staggered bank of tubes with laminar cross flow;oil spill detection based on machine learning and deep learning: a review;on estimation of hazard, survival and density functions for Weibull Pareto distribution by ranking algorithm;cataract disease detection used deep convolution neural network;design and development of a bracelet to monitor vital signs of COVID-19 patients;design methodology of diagonally reinforced concrete coupling beams;and fire spread monitoring system of a forest based on interpolation and cloud computing.

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Prediction of Plant Leaf Diseases using Drone and Image processing Techniques 5

Prediction of Plant Leaf Diseases using Drone and Image Proc...

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5th international conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Bharathiraja, N. Pradeepa, K. Sheela, I. Jeya Sudhakar, G. Kumar, M. Vinoth Kaur, Gaganpreet Chitkara University Institute of Engineering and Technology Chitakara University Department of Cse Punjab India St. Michael College of Engineering and Technology Department of Cse Kalayarkoil Tamil Nadu Sivaganga630551 India Sri Sai Ranganathan Engineering College Department of Cse Tamil Nadu Coimbatore India Dmi-St.Eugene University Dept. of Cse Great North Road Chibombo District Zambia

ISBN: (纸本)9781665474672

In Asia, there are many countries where paddy serves as the primary agricultural product. Diseases on paddy plants have the potential to reduce paddy production and damage its quality. Detecting the disease earlier will help to prevent serious infection of the paddy and also minimize crop loss. Drones have recently been utilized for agricultural surveillance, along with cameras and GPS sensors. It is a different instrument for swiftly and autonomously obtaining information in a large area. this study describes a drone implementation system that depends upon the Internet of things (IoT) architecture that uses information in actual instances, information collection, and examination of information strategies to increase paddy productivity. By using a GPS sensor to specify the location in the actual instances, the system is capable of mapping the location of diseased paddy plants on paddy fields along with data analysis. With the installation of the IoT architecture, this framework was created and also put forth as an initial system to support the systems earlier and to detect disease in actual time. © 2023 IEEE.

关键词： Internet of things

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Software and Hardware Cooperative Implementation of the Rafflesia Optimization Algorithm 5th

Software and Hardware Cooperative Implementation of the R...

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5th international conference on Smart Vehicular Technology, Transportation, Communication and Applications, VTCA 2022

作者： Fu, Zonglin Pan, Jeng-Shyang Guo, Yundong Snášel, Václav College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Faculty of Electrical Engineering and Computer Science VŠB-Technical University of Ostrava Ostrava70032 Czech Republic

ISBN: (纸本)9789819908479

With the development of edge technology in the fields of transportation, wireless sensor networks, and the internet of things, more and more intelligent optimization algorithms are implemented in hardware structures. the Rafflesia optimization algorithm (ROA) is a novel intelligent optimization algorithm proposed recently. To observe its performance on hardware, this paper implements the ROA algorithm in a cooperative way of software and hardware, referred to as the FROA algorithm. To convenient testing and accelerated computing, the initialization module, fitness module, and update module of the FROA algorithm are deployed on the advanced RISC machine (ARM) platform and the field programmable gate array (FPGA) platform, respectively. In the experimental part, we test the FROA algorithm on nine different benchmark functions. the results are compared with those implemented in software. the experimental results demonstrate the effectiveness and superiority of the FROA algorithm. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Wireless sensor networks

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SyncMesh: Improving Data Locality for Function-as-a-Service in Meshed Edge networks 5

SyncMesh: Improving Data Locality for Function-as-a-Service ...

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5th international Workshop on Edge Systems, Analytics and Networking (EdgeSys)

作者： Habenicht, Daniel Kreutz, Kevin Becker, Soeren Bader, Jonathan thamsen, Lauritz Kao, Odej Tech Univ Berlin Berlin Germany Univ Glasgow Glasgow Lanark Scotland

ISBN: (纸本)9781450392532

the increasing use of Internet of things devices coincides with more communication and data movement in networks, which can exceed existing network capabilities. these devices often process sensor or user information, where data privacy and latency are a major concern. therefore, traditional approaches like cloud computing do not fit well, yet new architectures such as edge computing address this gap. In addition, the Function-as-a-Service (FaaS) paradigm gains in prevalence as a workload execution platform, however the decoupling of storage results in further challenges for highly distributed edge environments. To address this, we propose SyncMesh, a system to manage, query, and transform data in a scalable and stateless manner by leveraging the capabilities of Function-as-a-Service and at the same time enabling data locality. Furthermore, we provide a prototypical implementation and evaluate it against established centralized and decentralized systems in regard to traffic usage and request times. the preliminary results indicate that SyncMesh is able to exonerate the network layer and accelerate the transmission of data to clients, while simultaneously improving local data processing.

关键词： mesh network edge computing fog computing data locality data management function-as-a-service

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