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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

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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Dynamic Language Refinement for Graceful Degradation in Scene Text Recognition 6

Dynamic Language Refinement for Graceful Degradation in Scen...

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6th IEEE International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2023

作者： Tian, Shaocong Jin, Rize Paik, Joon-Young Fu, Chen Tiangong University School of Software Tianjin China Tiangong University School of Computer Science &technology Tianjin China Shenzhen University College of Physics and Optoelectronic Engineering Shenzhen China

ISBN: (纸本)9798350325485

Scene text recognition is a mature technology which can get a near-perfect result in regular scenes. However, there are still troubles in recognizing scene texts with image blur, characters missing, and occlusion. Recent end-To-end frameworks have investigated the importance of linguistic knowledge, utilizing either implicit or explicit language models. However, joint training of language and vision models tends to interfere the tunings of each other. In this paper, we present a novel gradient isolation method to train language and vision models separately, yet in an end-To-end architecture. In addition, we propose a dynamic iterative training procedure for graceful refining the language model. Extensive experiments confirm that the proposed method has superiority on regular and irregular scene text images and achieves state-of-The-Art results. © 2023 IEEE.

关键词： Iterative methods

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CPMFA: A Character Pair-Based Method for Chinese Nested Named Entity Recognition 9th

CPMFA: A Character Pair-Based Method for Chinese Nested Nam...

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19th International Conference on Advanced Data Mining and Applications, ADMA 2023

作者： Ji, Xiayan Chen, Lina Shen, Fangyao Guo, Hongjie Gao, Hong College of Physics and Electronic Information Engineering Zhejiang Normal University Jinhua China School of Computer Science and Technology Zhejiang Normal University Jinhua China

ISBN: (纸本)9783031466601

Chinese Nested Named Entity Recognition (CNNER) faces several challenges due to the language diversity phenomena, the complexity of the language, and the imbalanced distribution of entity types in Chinese text. To address these challenges in CNNER, we propose a new method called CPMFA (Character Pair-based method with Multi-feature representation and Attention mechanism). The CPMFA method predicts the predefined relations of character pairs in a sentence, and identifies nested named entities based on these relations. First, our method utilizes the pre-trained language model LERT (Linguistically-motivated Bidirectional Encoder Representation from Transformer), and Bidirectional Long Short-Term Memory (BiLSTM) to generate comprehensive and precise character representations. Second, our method uses multi-feature representation to capture complex semantic information within the text, and employs the Pyramid Squeeze Attention (PSA) module to emphasize key features. Finally, to overcome the challenge of the imbalanced distribution of entity types, PolyLoss function is integrated into our model training process. Results of experiments show that the proposed CPMFA method achieves an F1 score of 83.79%. Compared to other mainstream span-based methods, the proposed CPMFA method has excellent performance in CNNER. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Semantics

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NEHATE: Large-Scale Annotated Data Shedding Light on Hate Speech in Nepali Local Election Discourse 26

NEHATE: Large-Scale Annotated Data Shedding Light on Hate Sp...

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26th European Conference on Artificial Intelligence, ECAI 2023

作者： Thapa, Surendrabikram Rauniyar, Kritesh Shiwakoti, Shuvam Poudel, Sweta Naseem, Usman Nasim, Mehwish Department of Computer Science Virginia Tech United States Department of Computer Science Delhi Technological University India Kathmandu Engineering College Tribhuvan University Nepal College of Science and Engineering James Cook University Australia School of Physics Mathematics and Computing The University of Western Australia Australia College of Science and Engineering Flinders University Australia

ISBN: (纸本)9781643684369

The use of social media during election campaigns has become increasingly popular. However, the unbridled nature of online discourse can lead to the propagation of hate speech, which has far-reaching implications for the democratic process. Natural Language Processing (NLP) techniques are being used to counteract the spread of hate speech and promote healthy online discourse. Despite the increasing need for NLP techniques to combat hate speech, research on low-resource languages such as Nepali is limited, posing a challenge to the realization of the United Nations' Leave No One Behind principle, which calls for inclusive development that benefits all individuals and communities, regardless of their backgrounds or circumstances. To bridge this gap, we introduce NEHATE, a large-scale manually annotated dataset of hate speech and its targets in Nepali local election discourse. The dataset comprises 13,505 tweets, annotated for hate speech with further sub-categorization of hate speech into targets such as community, individual, and organization. Benchmarking of the dataset with various algorithms has shown potential for performance improvement. We have made the dataset publicly available at https://***/shucoll/NEHate to promote further research and development, while also contributing to the UN SDGs aimed at fostering peaceful, inclusive societies, and justice and strong institutions. © 2023 The Authors.

关键词： Benchmarking

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Production Capacity Negotiation for Blockchain-enabled Collaborative Manufacturing: A Stackelberg Game Approach

Production Capacity Negotiation for Blockchain-enabled Colla...

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2023 IEEE International Conference on Metaverse Computing, Networking and Applications, MetaCom 2023

作者： Chen, Ying Lin, Feilong Jia, Riheng Chen, Zhongyu Tang, Changbing Li, Minglu Zhejiang Normal University School of Computer Science and Technology Jinhua China Zhejiang Normal University College of Physics and Electronic Information Engineering Jinhua China

ISBN: (纸本)9798350333336

Blockchain, as a decentralized but trustworthy network technology, facilitates efficient and secure peer-to-peer collaboration and has promoted decentralization reforms in various industrial fields. Recently, industrial manufacturing calls upon mass personalization through extensive collaborative manufacturing among enterprises. To adapt to the new trend, a blockchain-enabled collaborative manufacturing (BCM) framework is proposed in this work. First, a digital model that describes the industrial production capacities is established, which enables the credible sharing of production capacities over the blockchain network. Second, a production capacity negotiation problem is constructed for manufacturing collaboration with diverse production needs and different timeliness requirements. Third, the dynamic negotiation process is formulated into a two-stage multileader and multi-follower Stackelberg game. The unique existence of Stackelberg equilibrium is proven. The equilibrium point indicating optimal price and quantity of capacity negotiation can be solved by the developed distributed ADMM-based algorithm with low complexity. Finally, a Fabric-based prototype system is implemented to verify the feasibility of the proposed BCM framework. Simulation results show that the proposed distributed ADMM-based production capacity negotiation algorithm guarantees production timeliness and outperforms baseline methods regarding stable social welfare and negotiation fairness. © 2023 IEEE.

关键词： Blockchain

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Joint Pricing for Carbon and Electricity Trading in Blockchain-based Smart Grids: A Two-stage Game Approach 6

Joint Pricing for Carbon and Electricity Trading in Blockcha...

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6th IEEE International Conference on Blockchain, Blockchain 2023

作者： Xu, Wang Lin, Feilong Jia, Riheng Tang, Changbing Li, Minglu Zhejiang Normal University School of Computer Science and Technology Jinhua China Zhejiang Normal University College of Physics and Electronic Information Engineering Jinhua China

ISBN: (纸本)9798350319293

Reducing carbon emissions and integrating renewable energy sources (RES) are two critical concerns in smart grids. Considering the distributed nature of carbon emission rights (CER) and RES trading, this paper presents a two-stage carbon and electricity trading framework based on blockchain. In particular, a two-stage game-based joint pricing scheme is proposed. In the first stage, the CER pricing for peer-to-peer (P2P) trading is formulated with the Bayesian game, which determines the optimal CER price for both sides without leaking bidding privacy. Then, in the second stage, a Stackelberg game model is used to establish different electricity pricing relationships between conventional energy source users and RES users. RES users can obtain more competitive electricity prices, thereby promoting the utilization of RES. Both stages ensure the accurate execution of pricing strategies through the smart contract, guaranteeing fairness and transparency. Finally, a prototype system based on the Fabric blockchain is implemented, and extensive simulation results demonstrate the effectiveness of the proposed scheme in terms of emission reduction and improvement of user utility. © 2023 IEEE.

关键词： Game theory

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