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2024 IEEE Green Technologies Conference, GreenTech 2024

作者： He, Zhiyao Qu, Yanfeng Chen, Gong Raj, Reuben Samson Lin, Hui Jin, Dong University of Arkansas Electrical Engineering and Computer Science FayettevilleAR United States Illinois Institute of Technology Computer Science ChicagoIL United States University of Rhode Island Electrical Computer and Biomedical Engineering KingstonRI United States

ISBN: (纸本)9798350308587

Phasor Measurement Units (PMUs) enable high-speed and high-precision power quality measurements, but their vulnerability to cyber-attacks poses substantial risks to the stability and reliability of power systems. This paper explores the application of programmable networks, specifically P4 switches, to enhance the cybersecurity of PMU systems. These switches can be programmed to enable the creation of custom forwarding and processing logic at the data plane level. We analyze PMU-specific attacks through attack trees, propose effective mitigation strategies using programmable switches, and present a case study demonstrating the recovery of missing synchrophasor data by implementing a matrix completion algorithm on the switch data plane. The recovery scheme is evaluated in a container-based P4 network emulator using real PMU data from a campus microgrid. For instance, in the 10% missing data scenario, the recovery scheme can achieve a mean absolute percentage error of 0.038% for voltage magnitude and a phase angle error discrepancy of around 0.08 degrees. © 2024 IEEE.

关键词： Phasor measurement units

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CNN Framework for Accurate Brain Tumour Segmentation from Enhanced MRI Slices

CNN Framework for Accurate Brain Tumour Segmentation from En...

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2023 Winter Summit on Smart Computing and Networks, WiSSCoN 2023

作者： Mohan, Ramya Hasson, Feras N Fadhil, Hilal A. Arunmozhi, Rama Rajinikanth, Venkatesan Department of Computer Science Engineering Tamil Nadu Chennai602105 India National University of Science and Technology Department of Electrical and Computer Engineering Oman Sohar University Department of Electrical and Computer Engineering Oman

ISBN: (纸本)9798350334050

The brain tumor (BT) is a severe condition caused by abnormal cell growth. If left untreated, the BT may result in a variety of harsh conditions, including death. As a consequence of the significance of automatic BT detection, several schemes have been developed and implemented in the literature to accurately assess the BT. We propose a method for accurately segmenting brain images from MRI slices in this study. This proposal includes a number of phases, including;(i) collecting and resizing images, (ii) enhancing the image using a selected scheme, (iii) segmenting using ResUnet, and (iv) evaluating and validating the performance. This study examines several brain MRi slices with or without the skull region, and the results are evaluated separately. Based on these outcomes, it is concluded that the proposed ResUnet together with CLAHE provides a significant improvement over other methods concerning Jaccard (>92%), Dice (>95%), and accuracy (>98%). © 2023 IEEE.

关键词： Tumors

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Wafer-Scale Fabrication of Linear Variable Filter-based Reconstructive Micro-Spectrometer

Wafer-Scale Fabrication of Linear Variable Filter-based Reco...

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Kim, Serim Kim, Min Seok Song, Young Min School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology Gwangju61005 Korea Republic of

We present a compact reconstructive micro-spectrometer based on a linear variable filter consisting of multi-layer stacks with random thicknesses in the spatial domain. A simple gradient deposition/rotation method ena... 详细信息

ISBN: (纸本)9781957171258

关键词： Cryptography

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Efficient Classification of Rice Varieties Using Optimized Convolutional Neural Networks 8

Efficient Classification of Rice Varieties Using Optimized C...

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8th IEEE International Conference on Information and Communication technology, CICT 2024

作者： Pathak, Abhishek Kumar Singh, Ankit Kumar Bhatia, Vimal Singh, Puneet Indian Institute of Technology Department of Electrical Engineering Indore India Motihari College of Engineering Department of Computer Science and Engineering India

ISBN: (纸本)9798331531539

Rice type classification is a crucial task in agri-cultural automation, aimed at improving quality control and ensuring market standards. This study presents a deep learning-based approach using a optimized Convolutional Neural Network (CNN) model implemented with PyTorch to classify five rice varieties: Arborio, Basmati, Ipsala, Jasmine, and Karacadag. Our proposed model diverges from conventional methods that rely heavily on complex machine learning algorithms or pretrained CNN architectures like MobileNetV2, VGG16, EfficientNetB0 etc., which require a large number of parameters and layers, re-sulting in increased computational complexity. Through proposed approach, we achieved a remarkable 99.81% validation accuracy and a minimal validation loss of 0.01 % with a significantly reduced model size 30.76 MB. The simplicity of the proposed CNN architecture not only enhances computational efficiency but also maintains high accuracy, making it a viable solution for rice type classification in practical applications. This study demonstrates that a fine-tuned CNN model can outperform more complex models, providing an efficient and effective alternative for agricultural classification tasks. © 2024 IEEE.

关键词： Convolutional neural networks

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Hybrid Xception-ResNext BiLSTM Model with Attention and Visual Synchronization for Robust Deepfake Detection

Hybrid Xception-ResNext BiLSTM Model with Attention and Visu...

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Suresh, M. Raghavan, J.Vijaya Roy, Abhisek Das, Swet Snigdha Srm Institute of Science and Technology Department of Computing Technologies Chennai India Manakula Vinayagar Institute of Technology Department of Electrical and Electronics Engineering Pondicherry India Computer Science Engineering Srm Institute of Science and Technology Chennai India

ISBN: (纸本)9798331510022

Deepfake technology has become a significant problem since it allows for the creationof compelling manipulated videos. This research presents a novel hybrid deepfake detection system that combines the Xception and ResNext networks with a Bidirectional Long Short-Term Memory (BiLSTM) model and Convolutional Block Attention Module (CBAM). Apart from focusing on the temporal and spatial anomalies of deepfakes, our model also includes visual synchronization analysis and physiological signal detection. Extensive experiments are conducted across several datasets, exhibiting cutting-edge performance across various manipulation scenarios. The proposed method achieves good generalization, as they are in real-world circumstances. © 2024 IEEE.

关键词： Gallium nitride

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The rapid growth of the internet and its corresponding data

The rapid growth of the internet and its corresponding data

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2024 IEEE International Conference on Signal Processing, Information, Communication and Systems, SPICSCON 2024

作者： Jafor, Md. Shakiul Paul, Liton Chandra Alam, Md. Nur Islam, Sheikh Shafaet Mimi, Afsana Hossain, Md. Sadik Al Mamun, Md. Shajol Rahman, Md. Hasibur Department of Electrical and Electronic Engineering Rajshahi Bangladesh Department of Electrical Electronic and Communication Engineering Rajshahi Bangladesh Department of Computer Science & Engineering Rajshahi Bangladesh Rajshahi University of Engineering & Technology Rajshahi Bangladesh Pundra University of Science & Technology Bogura Bangladesh Pabna University of Science & Technology Pabna Bangladesh

ISBN: (纸本)9798331510213

A wideband, small-size inset-fed slotted rectangular THz antenna with a polyamide substrate has been designed and analyzed for 6G applications. The substrate measures 27.809×40×10 µm3, which is significantly smaller than known antennas currently in use. The antenna has an operating bandwidth of 1060 GHz, which spans 4.46 THz to 5.52 THz, and it resonates at 5.01 THz. The radiation efficiency, return loss, and maximum gain of the proposed THz antenna are 6.29 dB, 97.6%, and -61.97 dB, respectively. In terms of bandwidth, maximum gain, radiation efficiency, and return loss, the proposed THz antenna performs better. A high-frequency structure simulator (HFSS) platform is used to simulate the proposed model and estimate its performance for 6G communication. © 2024 IEEE.

关键词： Bandwidth

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Artificial vision systems inspired by the eyes of aquatic animals

Artificial vision systems inspired by the eyes of aquatic an...

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Song, Young Min School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology Gwangju61005 Korea Republic of

We present our recent advances in the building of artificial vision systems inspired by the eyes of aquatic animals, including fishes, cephalopods, and crabs. The complete set of bioinspired eyes shows exceptional ima... 详细信息

ISBN: (纸本)9781957171258

关键词： Animals

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EELMCDA: Combining evolutionary ensemble learning with matrix feature decomposition for predicting circRNA-disease associations

EELMCDA: Combining evolutionary ensemble learning with matri...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Wang, Zheng Wang, Lei You, Zhu-Hong Wang, Lei Li, Yang Wang, Zhenyu Xi'an University of Technology School of Computer Science and Engineering Xi'an710048 China Northwestern Polytechnical University School of Computer Science Xi'an710048 China China University of Mining and Technology School of Computer Science and Technology Xuzhou221116 China Hefei University of Technology School of Computer Science and Information Engineering Hefei230601 China Lanzhou University of Technology School of Electrical Engineering and Information Engineering Lanzhou730050 China

ISBN: (纸本)9798350386226

Recent studies have indicated that circular RNAs (circRNAs) play a significant role in the diagnosis and treatment of disease. However, the prediction of associations between circRNAs and diseases using conventional biological methods is constrained by numerous factors. In this study, we proposed a novel computational model called EELMCDA that combines evolutionary ensemble learning (EEL) approach and matrix feature decomposition method to predict potential circRNA-disease associations. The model firstly integrates circRNA function information, disease semantic information, and circRNA and disease gaussian interaction profile kernel (GIPK) information into an integrated matrix and constructed the corresponding feature matrix, then uses the matrix feature decomposition algorithm to obtain its important feature, and finally adopted evolutionary ensemble learning module to predict circRNA-disease associations. The average accuracy of the EELMCDA model by 5-fold cross-validation on CircR2Disease, CircAtlasv2.0, Circ2Disease, and CircRNADisease datasets were 92.40%, 92.90%, 88.91%, and 90.74%, respectively. Moreover, in case studies, the 21 of the top 30 circRNA-disease pairs with the highest EELMCDA scores were validated in recent literatures. These results further demonstrate the effectiveness of EELMCDA in predicting circRNA-disease associations. © 2024 IEEE.

关键词： Contrastive Learning

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System of System Architecture for Cybersecurity of Power Systems Operations 11

System of System Architecture for Cybersecurity of Power Sys...

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11th International Conference on electrical and Electronics engineering, ICEEE 2024

作者： Ur Rahman, Ehab Ashiku, Lirim Dagli, Cihan Bo, Rui Missouri University of Science and Technology Department of Electrical and Computer Engineering RollaMO United States Missouri Universtity of Science and Technology Engineering Management and Systems Engineering RollaMO United States

ISBN: (纸本)9798350362541

Smart grids are at risk of cyber-attacks due to more connected devices being introduced. Different kinds of attacks may incur different consequences in the operation of smart grids. Attackers have identified different vulnerable points in smart grid operations and have developed malicious stealthy attacks to disrupt the operation of physical systems. In this paper we have formulated the power system cyber security problem as a five-dimensional optimization problem. A general framework is developed using the system of system explorer tool providing a defensive solution to the vulnerable points of a smart grid prone to malicious attacks. An optimal meta-architecture composed of defensive system constituents is achieved using Genetic algorithm (GA) and fuzzy inference system providing a solution to ensure cybersecurity of power system operations. © 2024 IEEE.

关键词： Cyber attacks

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WCM-WTrA: A Cross-Project Defect Prediction Method Based on Feature Selection and Distance-Weight Transfer Learning

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Chinese Journal of Electronics 2022年第2期31卷 354-366页

作者： LEI Tianwei XUE Jingfeng WANG Yong NIU Zequn SHI Zhiwei ZHANG Yu School of Computer Science and Technology Beijing Institute of Technology China Information Technology Security Evaluation Center School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture

Cross-project defect prediction is a hot topic in the field of defect prediction. How to reduce the difference between projects and make the model have better accuracy is the core problem. This paper starts from two perspectives: feature selection and distance-weight instance transfer. We reduce the differences between projects from the perspective of feature engineering and introduce the transfer learning technology to construct a cross-project defect prediction model WCM-WTrA and multi-source model Multi-WCM-WTrA. We have tested on AEEEM and ReLink datasets, and the results show that our method has an average improvement of 23%compared with TCA+ algorithm on AEEEM datasets,and an average improvement of 5% on ReLink datasets.

关键词： feature engineering cross-project defect prediction software defect prediction project management feature selection learning (artificial intelligence) software maintenance WCM-WTrA distance-weight transfer learning software quality distance-weight instance transfer multisource model MultiWCM-WTrA

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