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检索条件"任意字段=5th International Conference on Informational Processing in Sensor Networks"

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Utilizing Deep Belief networks for Power System State Estimation and Anomaly Detection 5

Utilizing Deep Belief Networks for Power System State Estima...

引用

5th IEEE international conference on Power, Electronics and Computer Applications, ICPECA 2025

作者： Liu, Jiali Qiao, Meng Du, Lanna Zhang, Wei Wang, Mingming Jin, Kun Inner Mongolia Electric Power Economics and Technology Research Institute Inner Mongolia Hohhot China

ISBN: (纸本)9798331533694

the state estimation and anomaly detection of power systems highly rely on accurate and comprehensive real-Time data. However, many power systems face data quality issues such as sensor failures, data loss, or noise pollution. therefore, this study proposes a power system fault detection and recovery time prediction model based on deep belief network (DBN). Firstly, various fault feature extraction methods have been proposed for power system operation data, including electrical quantity features (such as voltage and current), time-domain features (such as power fluctuations, frequency fluctuations, etc.), and normalization methods have been used to preprocess the raw data to eliminate outliers, missing values, and redundant data. then, deep belief networks are used for pre training of fault features, and unsupervised learning is used to train each layer of restricted Boltzmann machines (RBM) to extract potential features of power system data. this process optimizes the weights by comparing the divergence (CD) algorithm, and further trains the backpropagation algorithm during the fine-Tuning stage to improve the accuracy of fault detection. In terms of recovery time prediction, by establishing the relationship between recovery time and fault type, combined with the feature extraction ability of DBN, the recovery time of different fault types can be predicted. the experimental results show that the model exhibits good performance in fault detection and recovery time prediction, which can effectively improve the stability and fault response speed of the power system. © 2025 IEEE.

关键词： deep belief network fault detection feature extraction power system recovery time prediction

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Potato Disease Degree Recognition Model Based on Image processing and Contrast Learning 5

Potato Disease Degree Recognition Model Based on Image Proce...

引用

5th international conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI 2023

作者： Jia, Liang Changchun University of Science and Technology ChangChun130022 China

ISBN: (纸本)9798350357950

With the development of computer vision technology and machine learning, some achievements have been made in identifying potato diseases by computer models. Potato disease degree recognition is essentially an image classification task. However, unlike the usual plant disease classification, the potato disease image studied in this paper has a complex background under natural conditions, so it is more difficult to identify accurately. In this paper, the image recognition rate of the improved model reaches 97.53%, which is higher than before the improvement. the research of this paper plays a positive role in plant disease control. © 2023 IEEE.

关键词： Artificial intelligence convolutional neural networks Deep learning Image processing potato disease

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Performance Analysis of Clustering Technique using LEACH in Wireless sensor networks 5

Performance Analysis of Clustering Technique using LEACH in ...

引用

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

作者： Guruprasath, R. Nagarajan, R. Kannadhasan, S. Gnanamani College of Technology Department of Electronics and Communication Engineering Tamilnadu Namakkal India Gnanamani College of Technology Department of Electrical and Electronics Engineering Tamilnadu Namakkal India Study World College of Engineering Department O!f Electronics and Communication Engineering Tamilnadu Namakkal India

ISBN: (纸本)9781665474672

this study discusses about various approaches available for achieving energy-efficient data collection in wireless sensor networks. In wireless sensor networks, movable sink (MULE-Mobile Ubiquitous LAN Extensions) moving at a constant pace over a constrained path may increase energy efficiency. the mobile sink may randomly visit each sensor node and gather data in a short amount of time. these methods provide several hurdles in terms of data collection and power usage. To overcome this issue, this study has created the Maximum Amount Shortest Path (MASP) data gathering approach, which boosts performance. the MASP system is implemented using a twophase communication mechanism. Simulations in NS2 are used to test the suggested algorithms and protocols. the wireless sensor network has gotten a lot of interest in the academic community in the past several years. Many factors, such as resource limits on energy and power needed for computing, have been extensively researched. Because sensor nodes are battery-operated devices, the fundamental challenge in Wireless sensor networks i s reducing the amount of energy spent by the nodes so that the network's lifetime may be extended. WSN also has privacy problems. Taking these two factors into consideration, this study examines the energy consumption of sensor node components and highlight the key possibilities for energy conservation in WSN in this work. Different battery management and security strategies that are currently available in WSN are also analyzed. Finally, this research study has come up with some energy saving ideas for future WSN research. © 2023 IEEE.

关键词： sensor nodes

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Automatic Modulation Classification in Deep Learning 5

Automatic Modulation Classification in Deep Learning

引用

5th international conference on Communications, Signal processing, and their Applications (ICCSPA)

作者： Alnajjar, Khawla A. Ghunaim, Sara Ansari, Sam Univ Sharjah Dept Elect Engn Sharjah U Arab Emirates Univ Sharjah Dept Comp Engn Sharjah U Arab Emirates

ISBN: (纸本)9781665482370

Due to the evolution and availability of vast amounts of data for transferring, receiving, and detection, the field of signal recognition and modulation classification has become vital in various fields and applications. Additionally, the classical approaches to machine learning (ML) no more can satisfy the current needs. Hence, this urged researchers to apply deep learning (DL) algorithms that have a very strong ability to train, learn, and automatically classify types of modulation categories. this paper focuses on three vital DL network algorithms, which are deep neural networks (DNN), convolutional neural networks (CNN), and deep belief networks (DBN). the mentioned algorithms are widely used in many applications for automatic modulation classification/recognition (AMC/AMR). Additionally, an empirical study is performed in this paper to compare a large number of different methods for the performance and recognition percentage of each considered technique.

关键词： Automatic modulation classification convolutional neural networks deep belief neural networks deep learning deep neural networks machine learning

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Neuromorphic Computing Architectures for Enhanced Signal processing in Autonomous Systems 5

Neuromorphic Computing Architectures for Enhanced Signal Pro...

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5th international conference on Emerging Systems and Intelligent Computing, ESIC 2025

作者： Chendhuran, R. Arun Jayanthi, T. Shasidhar, Rampalli Yamsani, Nagendar Chandrasekhar, Sankepally Athiraja, A. Bannari Amman Institute of Technology Department of Eee Sathyangalam Erode India Dr. N.G.P Institute of Technology Department of Ece Tamilnadu Coimbatore India Military College of Electronics and Mechanical Engineering Department of Mechanical Engineering Telangana Secunderabad India Sr University School of Computer Science and Artificial Intelligence Warangal India Department of Mechanical Engineering Medchal-Malkajigiri Telangana Maisammaguda India Telangana Hyderabad India

ISBN: (纸本)9798331522100

Innovative computing techniques that strike a balance between speed, efficiency, and adaptability are required due to the growing complexity of signal processing in autonomous systems. Neuromorphic computing, which draws inspiration from the structure of the human brain, offers a workable method for enhancing signal processing in real-time analysis and decision-making devices, including robotic systems, drones, and driverless cars. the integration of neuromorphic architectures for sophisticated signal processing in robotic systems, drones, and autonomous cars is examined in this research. the application of event-driven processing and spiking neural networks (SNNs) for real-time analysis and decision-making is the main emphasis of the research. the approach involves developing a scalable neuromorphic framework that makes use of cutting-edge hardware, including neuromorphic chips, and evaluating its reaction time, energy efficiency, and processing speed in comparison to traditional von Neumann architectures. Neuromorphic systems demonstrate their potential for continuous real-time data handling by reducing latency by 30% and improving energy efficiency by up to 50%. the system's resilience in handling intricate environmental signals and adjusting to changing inputs is highlighted by important findings. According to the paper's conclusion, incorporating neuromorphic designs greatly improves autonomous systems' efficiency and real-time processing capabilities, opening the door to more responsive and energy-efficient operations. Improving hardware compatibility and broadening applications across various autonomous technologies are two areas of future research. © 2025 IEEE.

关键词： Sorting

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Unlocking Carbide Vacancy Energy in Molybdenum Carbide: Predictive Insights from Numerical Descriptors and Neural networks 5

Unlocking Carbide Vacancy Energy in Molybdenum Carbide: Pred...

引用

5th IEEE international conference on BioInspired processing, BIP 2023

作者： Tovar, Karen Coto-Jimenez, Marvin Gutierrez, Mauricio Borge, Ignacio School of Electrical Engineering University of Costa Rica San Jose Costa Rica School of Chemistry University of Costa Rica San Jose Costa Rica School of Chemistry Bar-Ilan University Ramat Gan Israel

ISBN: (纸本)9798350330052

Carbides, as exemplified by Mo2C, have various valuable properties that affect various industries. Understanding the influence of carbon vacancy patterns on these properties is an essential yet challenging task. this work presents a two-stage approach. First, novel numerical descriptors linked to molecular structure are predicted using Neural networks. Subsequently, using these descriptors alone, carbide vacancy energy is accurately forecasted through Neural networks and an Attention mechanism. the correlation unveiled hints at a numerical relationship between these descriptors and energy, opening avenues for interpreting their significance. this research contributes to computational materials science, offering insights into carbide intricacies and inspiring innovative applications. © 2023 IEEE.

关键词： Convolutional Neural networks Descriptor Prediction Graph Neural networks (GNN) Machine Learning Molybdenum Carbide Vacancy Energy

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Analysis for Predicting English Premier League Matches Using Clifford-Steerable Convolutional networks 5

Analysis for Predicting English Premier League Matches Using...

引用

5th IEEE international conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Mohideen AbdulKader, M. Vaithieswaran, S. Advay Rao, Tanuj School of Computing Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Tamilnadu Chengalpattu India

ISBN: (纸本)9798350356236

the most widely watched football league globally is the Barclays English Premier League (EPL), the future performance of which attracts attention from both scholars and practitioners in terms of outcome prediction. It is still challenging to provide accurate predictions per match result because of the many attempts tried in the past due to complicated interference of many factors that entice game dynamics. In order to predict the EPL matches, this paper proposed a Cubature Kalman Filter Haar Wavelet Transform Clifford-Steerable Convolutional Neural Network with Mud Ring Algorithm (CKFHWTCSCNNet-MRA). the data was downloaded from Kaggle and the OCRCKF was used for data pre-processing. For feature selection the Q-DHWT was used, which stands for Quantized Discrete Haar Wavelet Transform. CS-CNNs were then applied to predict match results while the Mud Ring Algorithm (MRA) was applied to the analysis of hyperparameters. the rationale and validation of the proposed model is established based on the performance measures employed in predicting wins/loses of the match which yielded an outstanding accuracy, specificity, sensitivity and recall of 99%, F1-Score of 99% and error of 0.25%. © 2025 IEEE.

关键词： Image reconstruction

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Effectual Side Channel Attack Detection Using Convolutional Neural Network Optimized CMP-COA 5

Effectual Side Channel Attack Detection Using Convolutional ...

引用

5th IEEE international conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Vijayan, Prasath Sudalaimuthu, T. Department of Cse Chennai India

ISBN: (纸本)9798350356236

Smart phones are more vulnerable to malicious assaults because of technological advancements. An attack known as a side channel attack (SCA) in which attacker exploits the physical implementation of a cryptosystem to obtain confidential information. there are several existing works focused on side channel attack but lacks an accuracy and timely detection through important features. To resolve this issue, this paper proposes the novel technique intends to detect the side channel attack that includes phases of pre-processing, feature extraction and classification. Initially, the input data is pre-processed under data normalization and the statistical and higher order statistical features are extracted and are subjected to SCA classification using Optimal Convolutional Neural Network (OCNN). To make the prediction more precise and accurate, the weight of Optimal Convolutional Neural Network are optimized with hybrid Coronavirus Mask Protection and Coati Optimization Algorithm to improve the accuracy in detection of SCA. the proposed technique is implemented in python and the performance metrics such as accuracy, error rate, and execution time are evaluated. the proposed model shows that it has accuracy of 96% and the lowest execution time and error rate of 2.5 sec and 0.04% which is far better when compared to the existing methods. © 2025 IEEE.

关键词： Convolutional neural networks

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A Review on IoT Based Wireless sensor Network and Security 1

引用

5th international conference on Information Systems and Management Science, ISMS 2022

作者： Shabnam Pandey, Manju National Institute of Technology NIT Raipur India

ISBN: (数字)9783031311536

ISBN: (纸本)9783031311529

Wireless sensor networks are very important for realization of Internet of things (IoT) system. IoT is giant network with connected devices. these devices gather data through the environment. Wireless sensor networks are used to track an object, measure temperature, medical purpose for healthcare and smart cities, which have become a very popular and large number of different applications of the wireless sensor network. A wireless sensor network (WSN) consists of a collection of different sensor nodes and these sensor nodes are communicated from one node to another sensor node. WSN this paper reviews the literature to reduce the problem in security and battery-powered sensor networks using some method and algorithm approaches by the researcher and developers. In WSN, a big challenge is a security, power consumption, loss communication, and low memory. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Internet of things

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A 66.67kHz 4.953 nJ/Conv-Step 5b Fully Integrated Asynchronous SAR ADC Using 4um LTPS TFTs 5

A 66.67kHz 4.953 nJ/Conv-Step 5b Fully Integrated Asynchrono...

引用

5th IEEE international Flexible Electronics Technology conference, IFETC 2023

作者： Li, Hongtian Liu, Jialong Tang, Wenjun Yang, Huazhong Jiang, Chen Zhang, Sheng Li, Xueqing Shenzhen International Graduate School Tsinghua University Shenzhen China Tsinghua University BNRist EE Beijing China

ISBN: (纸本)9798350332094

Flexible smart sensor is a promising application, such as biometric detection. To realize a flexible smart sensor, TFT ADC connecting sensing and data processing is a critical unit. this paper presents a 5-bit asynchronous successive approximation (SAR) ADC using 4-um low-Temperature polysilicon thin-film transistor (LTPS TFT) technology. the ADC achieves a Figure of Merit (FoM) of 4.953 nJ/conv-step at a sampling frequency of 66.67 kHz and a 10 V supply. © 2023 IEEE.

关键词： Smart sensors

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