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3rd IEEE International Conference of Electron Devices Society Kolkata Chapter, EDKCON 2024

作者： Behera, Sriyanka Dash, Gananath Pattanaik, Satya R. Electron Devices Group The School of Physics Sambalpur University Jyoti Vihar Sambalpur768019 India Gandhi Institute For Technology Department of Computer Science and Engineering Odisha Bhubaneswar752054 India

ISBN: (纸本)9798350374643

A general-purpose frame-work is proposed to explore the potentials of a lateral p-n junction. Based on the motivation, we have augmented the existing knowledge with some new ideas to develop the said frame-work with a view to compute the resistance characteristics of a lateral GPNJ. The results obtained from our formulation are not only supported by experimental trends but also by other theoretical findings. © 2024 IEEE.

关键词： Ambipolar Channel length Channel resistance Graphene Modelling

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An Improved Dual Stream DGCNN Method for Fine-Grained Positive Emotion Classification 11

An Improved Dual Stream DGCNN Method for Fine-Grained Positi...

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11th IEEE International Conference on Behavioural and Social Computing, BESC 2024

作者： Hu, Shuming Yu, Zhiwen Xu, En Wang, Yao Zhang, Ying Guo, Bin School of Computer Science Northwestern Polytechnical University Xian China Harbin Engineering University Harbin China Hong Kong Baptist University Department of Physics Hong Kong Hong Kong

ISBN: (纸本)9798331531904

Recent advances in EEG-based emotion computation have garnered significant interest from the fields of neuroscience and computer science. Despite this, the bulk of EEG-based emotion classification research has concentrated on the dimensions of arousal and valence, with scant attention to the classification of positive emotions as experienced in daily life. Addressing this gap, our study focuses on the classification of four specific positive emotions using the open-source FACED dataset. We processed EEG data by segmenting it, extracting features, and then designing and training the DualStreamDGCNN deep learning model, which achieved the best accuracy of 60.88%. Furthermore, we visualized the adjacency matrices of the four emotions from our model, offering insights from a neuroscientific perspective. This research is pivotal for advancing our understanding of the types and dynamics of positive emotions in practical applications, such as enhancing educational environments and boosting employee efficiency. © 2024 IEEE.

关键词： Electroencephalography

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A Chain Prediction Model Based on Men's Tennis Competition 5

A Chain Prediction Model Based on Men's Tennis Competition

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5th International Conference on Artificial Intelligence and Electromechanical Automation, AIEA 2024

作者： Lei, Huashuo Su, Juncha Guo, Jinfeng Li, Qiuying Jia, Zhongxuan Ke, Wenhan Harbin Institute of Technology College of Ocean Engineering WeiHai China Harbin Institute of Technology College of Information Science and Engineering WeiHai China Xi'an Jiaotong-Li Verpool University School of Mathematics and Physics SuZhou China Harbin Institute of Technology School of Computer Science and Technology WeiHai China

ISBN: (纸本)9798350366174

Traditional statistical analysis techniques for tennis matches often fail to capture subtle changes and psychological dynamics that may revolutionize the course of a match. As a result, this paper proposes a chain prediction model based on a total of 20 features from different perspectives in Player Ability and Competition Status, and trains the model with SVM algorithm. In order to verify the validity of the model, this paper uses the last round of the 2023 Wimbledon Championship for simulation verification. The results show that the proposed model can well represent the winning probability of both sides' dynamics during the match. © 2024 IEEE.

关键词： Prediction models

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Identification of coexistence of biological and non-biological aerosol particles with DAPI (4′,6-diamidino-2-phenylindole) stain

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Particuology 2023年第1期72卷 49-57页

作者： Ting Liu Jiaquan Zhang Junji Cao Han Zheng Changlin Zhan Hongxia Liu Lili Zhang Kai Xiao Shan Liu Dong Xiang Daizhou Zhang School of Environmental Science and Engineering Hubei Polytechnic UniversityHubei Key Laboratory of Mine Environmental Pollution Control and RemediationHuangshi 435003China Institute of Atmospheric Physics Chinese Academy of SciencesBeijing 100029China School of Computer Science and Artificial Intelligence Wuhan University of TechnologyWuhan 430063China Faculty of Environmental and Symbiotic Sciences Prefectural University of KumamotoKumamoto 862-8502Japan

The fluorescent dye 4′,6-diamidino-2-phenylindole (DAPI) has been widely used to stain microorganisms in various environment media. We applied DAPI fluorescence enumeration to airborne microorganisms and found that non-biological particles, including organic compounds, minerals, and soot, were also visible upon exposure to UV excitation under fluorescence microscope. Using laboratory-prepared biological particles as the control, we investigated the feasibility of identifying both biological and non-biological particles in the same sample with DAPI staining. We prepared biological (bacterial, fungi, and plant detritus) and non-biological (biochar, soot, mineral, metal, fly ash, salt) particles in the laboratory and enumerated the particles and their mixture with DAPI. We found that mineral particles were transparent, and biochar, soot, metals and fly ash particles were black under a filter set at excitation 350/50 nm and emission 460/50 nm bandpass (DAPI-BP), while biological particles were blue, as expected. Particles of the water-soluble salts NaCl and (NH_(4))_(2)SO_(4) were yellow under a filter set at excitation 340–380 nm and emission 425 nm long pass (DAPI-LP). Case studies with samples of dustfall, atmospheric aerosols and surface soils could allow for the quantification of the relative number of different types of particles and particles with organic matter or salt coating as well. Fluorescence enumeration with DAPI stain is thus able to identify the co-existence of biological and non-biological particles in the air, at least to the extent of those examined in this study.

关键词： 4',6-diamidino-2-phenylindole(DAPI) Biological particles Nonbiological particles Atmospheric aerosols

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Bidirectional Gated Recurrent Unit Based-Grey Wolf Optimizer for Interval Prediction of Voltage Margin Stability Index in Power Systems

Bidirectional Gated Recurrent Unit Based-Grey Wolf Optimizer...

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2023 IEEE Texas Power and Energy Conference, TPEC 2023

作者： Massaoudi, Mohamed Refaat, Shady S. Ghrayeb, Ali Abu-Rub, Haitham Texas A&M University at Qatar Department of Electrical and Computer Engineering Doha Qatar Texas A&M University Department of Electrical and Computer Engineering College Station United States University of Hertfordshire School of Physics Engineering and Computer Science Hatfield United Kingdom

ISBN: (纸本)9781665490719

Despite the importance of Photovoltaic (PV) generation systems for smart grids, the uncertainty of PV power outputs due to weather variations can cause power stability problems leading up to voltage collapse in power systems. Governed by load dynamics, data-driven short-term Voltage Stability Margin (VSM) assessment is a critical technique for power system design and operation. This paper addresses improving the VSM accuracy and quantifying the uncertainties associated with the predicted values using deep learning. Interval estimation of VSM is significant for monitoring short-term voltage stability in real-time against fast voltage collapse and sustained low voltage without recovery. The proposed solution lies in the use of a Bidirectional Gated Recurrent Unit (GRU) optimized by a multi-objective Grey Wolf optimizer (GWO) algorithm. Time domain simulation results are conducted using IEEE59 and IEEE Nordic-44 bus systems to provide the training samples and maintain a high level of grid observability for the grid stability evaluation. The generated N-l contingency test cases data were conducted using Power System Simulator for engineering (PSS/E)-based time domain simulations. The generated features from fault-induced voltage events include the post-disturbance voltage magnitude, angles, frequencies, and active and reactive power trajectories of the system buses. The obtained results show that the proposed method can enable timely stability assessment with higher effectiveness. © 2023 IEEE.

关键词： Smart power grids

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Short-Term Dynamic Voltage Stability Status Estimation Using Multilayer Neural Networks

Short-Term Dynamic Voltage Stability Status Estimation Using...

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2023 IEEE Texas Power and Energy Conference, TPEC 2023

ISBN: (纸本)9781665490719

The power grid stability is significantly impacted by the exponentially growing electrical demand and the complex electrical systems modernization projects. This intensifies the urgent need and yet challenging Dynamic Security Assessment (DSA) to withstand high-probability severe contingencies. This paper proposes an effective machine-learning solution for Short-Term Voltage Stability (STVS) detection and classification. This work also addresses fault detection and classification into line faults or bus faults under different operating conditions as a supplementary warning system to boost power system protection and resiliency with fast remedial actions. The proposed approach combines three necessary steps for high accuracy: feature subset selection, hyperparameter optimization, and critical bus identification. The efficiency of the proposed forecasting model is assessed using the IEEE New England 39-bus test case with the CLOD composite model. The generated N-1 contingency test cases data from dynamic Power System Simulator/engineering (PSS/E) time domain simulations for fault-induced voltage events include the measured post-disturbance voltage magnitude, angle, frequency, and active and reactive power trajectories of the system buses. Numerical results from the proposed classifier confirm a high classification accuracy of 94.24% in identifying the post-disturbance stability state. The proposed method will be outperforming traditional shallow learning-based approaches. Further, the robustness of classifiers is demonstrated by evaluating the computational time, accuracy, precision, recall, and F-measure. © 2023 IEEE.

关键词： Machine learning

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MEGNet: A MEG-Based Deep Learning Model for Cognitive and Motor Imagery Classification

MEGNet: A MEG-Based Deep Learning Model for Cognitive and Mo...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Sarma, Minerva Bond, Charles Nara, Sanjeev Raza, Haider University of Essex School of Computer Science and Electronics Engineering Colchester United Kingdom Justus-Liebig-Universität Gießen Physics Geography Mathematics Institute Department of Mathematics and Computer Science Gießen Germany

ISBN: (纸本)9798350337488

Decoding complex patterns associated with task-specific activities embedded within magnetoencephalography (MEG) signals is pivotal for understanding brain functions and developing applications such as brain-computer interfacing. It is widely recognized that machine learning algorithms rely on feature extraction before undertaking decoding tasks. In this work, we introduce MEGNet, aiming to enhance the single-trial decoding framework of a compact deep neural network inspired by EEGNet, a model widely utilized in electroencephalography (EEG) studies. MEGNet accepts raw MEG signals, evoked responses and frequency spectrum as input. For validation, the MEG dataset containing motor and cognitive imagery tasks was used for classification. We performed pair-wise decoding of cognitive and motor tasks. Classification accuracy was evaluated using metric scores and benchmarked against ShallowConvNet and DeepConvNet. Our findings demonstrate that MEGNet can successfully decode between cognitive and mental imagery tasks. This MEGNet model surpasses existing feature extraction techniques, exhibiting consistent and stable mean accuracy of 64.76%±3% across tasks and subjects. All codes are available at our GitHub repository: https://***/Charliebond125/***. © 2023 IEEE.

关键词： Cognitive and Motor Imagery Convolutional Neural Network Deep Learning Magnetoencephalography

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A Text Detection Method Based on Multi-Scale Selective Fusion Feature Pyramid and Multi-Semantic Spatial Network for Visual IoT

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IEEE Internet of Things Journal 2025年第11期12卷 15997-16008页

作者： Wang, Manli Dou, Zeya Chen, Wei Wang, Zehua Yu, Fei Richard Leung, Victor C. M. Henan Polytechnic University School of Physics and Electronic Information Henan Jiaozuo454003 China China University of Mining and Technology School of Computer Science and Technology Jiangsu Xuzhou221116 China China University of Mining and Technology School of Mechanical Electronic and Information Engineering Beijing100083 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada Carleton University School of Information Technology OttawaONK1S 5B6 Canada

With the rapid development of Visual Internet of Things (VIoT) and text detection technology, they have been widely combined and applied to many industrial production sites, such as label text detection, achieving impressive results. However, there are still many shortcomings in the text detection technology: 1) The existing VIoT system has very limited detection precision for text with large scale changes, especially for some small-scale text detection;2) The existing text detection algorithms cannot meet the actual situation, as the labels often contain handwritten texts, and the text to be detected is arbitrary shape;3) In the actual detection, there are many creases or defects on the text label. To solve the above problems, this paper designs a text detection method based on a multi-scale selection fusion feature pyramid and multi-semantic spatial network to assist the VIoT system in detecting label text. Firstly, a multi-scale selective fusion feature pyramid is designed, which not only uses the texture extraction module to effectively improve the text texture feature and multi-scale feature extraction ability, but also uses the cross-scale selective fusion block to selectively fuse the features of different stages to reduce the influence of pollution on detection. In addition, a multi-semantic spatial network is designed to capture the multi-semantic spatial information of each feature channel by using the multi-scale deep shared one-dimensional convolution, which effectively integrates global context dependence and multi-semantic spatial prior. Experimental results show that the comprehensive index F-measure on the public datasets ICDAR2015, Total-Text and CTW1500 is increased by 5.7%, 3.3%and 3.8% respectively. Furthermore, the precision, recall, and F-measure on the dataset Label-Text are 94.6%, 90.7%and 92.6% respectively. The label text detection VIoT system we designed has been deployed in the field and achieved excellent performance. The code of our pro

关键词： Semantics

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A Robust Random Forest Model for Classifying the Severity of Partial Discharges in Dielectrics 4

A Robust Random Forest Model for Classifying the Severity of...

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4th International Conference on Smart Grid and Renewable Energy, SGRE 2024

作者： Kameli, Sayed Mohammad Abuelrub, Abdelaziz Saleh, Mohammad Alshaikh Refaat, Shady S. Olesz, Marek Guzinski, Jaroslaw Texas A&M University Department of Electrical and Computer Engineering College Station United States Texas A&M University at Qatar Department of Electrical and Computer Engineering Doha Qatar University of Hertfordshire School of Physics Engineering and Computer Science Department of Engineering United Kingdom Gdańsk University of Technology Department of Mechatronics and High Voltage Engineering Gdańsk Poland

ISBN: (纸本)9798350306262

Partial Discharges (PDs) are a common source of degradation in electrical assets. It is essential that the extent of the deterioration level of insulating medium is correctly identified, to optimize maintenance schedules and prevent abrupt power outages. Temporal PD signals received from damaged insulation, collected through the IEC-60270 method is the gold standard for PD detection. Temporal signals may be transformed to the frequency domain, introducing new spectral features that may be beneficial in certain circumstances. Consequently, time delays are introduced, due to the high utilization of computational resources within the signal processing pipeline. Moreover, some microprocessors struggle with the excess computational burden demanded by resource-heavy mathematical transformations. To rectify these issues, an alternative approach is utilized, where Machine learning (ML) algorithms are directly used for the classification of PD severity. Cylindrically-shaped air cavities with lengths ranging from 1mm-6mm are introduced to a resin-based polyethylene terephthalate (PET) insulation material. The cavities are partitioned based on size, to obtain different classes of PD severity. A comparative analysis is performed on various ML algorithms, to determine which algorithm correctly determined the severity of PDs, with highest efficacy. Random Forest was determined to be the most performant, with an accuracy of 98.33%. The high performance illustrates the model's potential success in accurately determining the hazard level of PDs in real-time, based on merely time-domain signals. © 2024 IEEE.

关键词： Machine learning

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Negative Price Forecasting in Australian Energy Markets using gradient-boosted Machines: Predictive and Probabilistic Analysis 14

Negative Price Forecasting in Australian Energy Markets usin...

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14th IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2023

作者： Kaur, Devinder Aujla, Gagangeet Singh Mahmud, Md. Apel Deakin University School of Engineering Australia Durham University Department of Computer Science Durham United Kingdom Northumbria University Department of Mathematics Physics and Electrical Engineering United Kingdom

ISBN: (纸本)9781665455541

With the integration of distributed energy resources such as roof-top solar panels and wind turbines into the grid, power generation can surpass demand-generation and thus, giving rise to the negative pricing, especially in the summer months. In this regard, a scientific case study is conducted in this paper to analyse and predict the increasing instances of negative energy prices against demand-generation in Australian energy markets (AEMs) using real-time energy data from the Hornsdale power reserve, South Australia. A robust machine learning method, Light gradient boosting machine (LightGBM) is utilised to detect and predict negative prices at different quantiles to quantity the outliers in the pricing data. The implementation results demonstrate that predicting the prices at different quantiles can tackle outliers (negative prices) effectively with the help of extracted upper and lower bounds using quantile regression-based approach. The case study is further extended to learn the complex statistical relationships between different data features using Naive-Bayes Tree Augmented (NB-TAN) algorithm considering 'price' as the dependent feature against the independent features such as demand-generation, battery charging/discharging, and frequency control ancillary services. © 2023 IEEE.

关键词： Costs

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