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3rd International Conference on Emerging Electrical Energy, Electronics and Computing Technologies 2021, ICE4CT 2021

作者： Ismail, Noraini Hossain, Mohammad Asif Md.noor, Rafidah Wahab, Ainuddin Wahid Abdul Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur50603 Malaysia Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur50603 Malaysia Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Terengganu Darul Iman Kuala Nerus21030 Malaysia

Vehicular Ad Hoc Networks (VANETs) is an origination of Mobile Ad-Hoc Network (MANET), where road vehicles will distribute messages and provide safety alerts to notice hazardous circumstances to the drivers. One of its greatest challenges is ensuring Quality of Service (QoS) owing to channel sharing, high traffic and topology changes in VANETs. These challenges cause of degradation of network performance. Congestion control should be properly considered to improve the network performance and transmission message over VANETs. However, there seem significant limitations to most of the other current congestion control mechanisms. In this paper, an enhanced congestion control model based on prioritization and scheduling-based strategy is proposed. This strategy assigns priorities of the safety messages and non-safety messages based of the message type and network condition. Further, safety message can be classified into event-driven messages and beacon messages. At that point, all messages transmit into the appropriate queue based on their priority and schedule all messages in each queue. However, when new message enqueue, reschedule in each queue may cause additional unwanted delay to be processed cause of inefficient scheduling technique. Thus, an improved dynamic scheduling algorithm also proposed to schedule the messages in this circumstance. © Published under licence by IOP Publishing Ltd.

关键词： Vehicular ad hoc networks

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Natural Language Processing for Subjective Exam Grading in Programming Explanations in Thai Language

Natural Language Processing for Subjective Exam Grading in P...

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International Electrical Engineering Congress (iEECON)

作者： Waris Rattanimit Thanyarat Nomponkrang Electrical Technology and Automation System Faculty of Engineering and Technology Siam Technology College Bangkok Thailand Department of Computer Education King Mongkut’s University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798331543952

ISBN: (纸本)9798331543969

In the digital era, learning programming has become an essential skill, and assessing students' understanding in this area plays a crucial role in enhancing educational quality. However, evaluating subjective programming exam responses, especially those involving conceptual explanations or procedural descriptions, remains a challenging task due to the diversity and complexity of answers. Manual grading can lead to delays and inconsistencies in scoring. This research aims to develop an automated system for evaluating Thai subjective programming exams using Natural Language Processing (NLP) techniques to assess the correctness of responses. The system is implemented in Python, utilizing the ThaiNLP library for word tokenization, alongside Machine Learning algorithms such as Naive Bayes, Decision Tree, and Support Vector Machine (SVM) for classification and prediction of answers. The experimental dataset includes responses from 120 students who answered subjective questions about Python commands. Expert testing indicated high user satisfaction with an average score of 4.05. The findings show that SVM achieved the highest accuracy at 85.33%. This system can enhance efficiency and consistency in exam evaluation, reducing grading time and contributing to standardized scoring.

关键词： Support vector machines Electrical engineering Machine learning algorithms Manuals Machine learning Tokenization Libraries Programming profession Python Testing

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Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Learning 22

Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Lea...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Cao, Weipeng Yao, Xuyang Xu, Zhiwu Pan, Yinghui Sun, Yixuan Li, Dachuan Qiu, Bohua Wei, Muheng Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Stony Brook University New York United States Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China ZhenDui Industry Artificial Intelligence Co. Ltd Shenzhen China Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (纸本)9798331527471

Realizing Generalized Zero-Shot Learning (GZSL) based on large models is emerging as a prevailing trend. However, most existing methods merely regard large models as black boxes, solely leveraging the features output by the final layer while disregarding potential performance enhancements from other layers. Indeed, numerous researchers have visually depicted variations in the features learned across different layers of neural networks. Motivated by this observation, we propose a Vision Transformer (ViT)-based GZSL method named Depth-Aware Multi-Modal ViT (DAM2ViT), which exploits multi-level features of ViT. DAM2ViT incorporates a multi-modal interaction block to align semantic information of categories across multiple layers, thereby augmenting the model’s capacity to learn associations between visual and semantic spaces. Extensive experiments conducted on three benchmark datasets (i.e., CUB, SUN, AWA2) have showcased that DAM2ViT achieves competitive results compared to state-of-the-art methods. © 2024 IEEE.

关键词： Zero-shot learning

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The Effect of Covid—19 Pandemic on the Use of Vehicle Rental technology (Ojek) Applications 6th

The Effect of Covid—19 Pandemic on the Use of Vehicle Renta...

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6th International Conference on Intelligent Sustainable systems, ICISS 2023

作者： Efendy, Yulius Syaputra, Muhammad Heru Honggiarto, Kevin Gaol, Ford Lumban Matsuo, Tokuro Bina Nusantara University Alam Sutra School of Information System Kota Tangerang13960 Indonesia Computer Science Department Bina Nusantara University Jakarta11480 Indonesia Advanced Institute of Industrial Technology Tokyo Japan

ISBN: (纸本)9789819917259

This research aims to analyze and determine the impact of the Covid-19 pandemic on people’s behavior in Indonesia by using the Ojek online application during the Covid-19 pandemic. The research method that used in this research is descriptive method with qualitative approach method and questionnaire based on secondary data from literature references and the data collection from Google Form. This research was conducted in December 2020. The Covid-19 pandemic has had quite an unsettling impact on the community in their activities. This impact forces people to stay at home and if there is something to be done outside, they have to use a mask and still follow the health protocols that have been implemented. PSBB (Large-Scale Social Restrictions) regulations in several regions have been enforced and regulations related to online motorcycle taxi transportation have also been issued by the government. And it turns out that during this pandemic, there are peoples that keep using the Ojek online application during this pandemic, even if only stay at home. Although there has been a decline in the passenger shuttle sector. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Taxicabs

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Non-Line-of-Sight Multipath Classification Method for BDS Using Convolutional Sparse Autoencoder with LSTM

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Tsinghua Science and technology 2025年第1期30卷 68-86页

作者： Yahang Qin Zhenni Li Shengli Xie Bo Li Ming Liu Victor Kuzin School of Automation Guangdong University of Technologyand also with Guangdong-Hong Kong-Macao Joint Laboratory for Smart Discrete ManufacturingGuangzhou 510006China School of Automation Guangdong University of TechnologyGuangdong Key Laboratory of IoT Information TechnologyGuangzhou 510006China 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Key Laboratory of Intelligent Information Processing and System Integration of IoTMinistry of EducationGuangzhou 510006China Department of Electronic and Computer Engineering Hong Kong University of Science and TechnologHong Kong 999077China Academician of Russian Engineering Academy Moscow Moscow 125009Russia

Multipath signal recognition is crucial to the ability to provide high-precision absolute-position services by the BeiDou Navigation Satellite system(BDS).However,most existing approaches to this issue involve supervised machine learning(ML)methods,and it is difficult to move to unsupervised multipath signal recognition because of the limitations in signal *** by an autoencoder with powerful unsupervised feature extraction,we propose a new deep learning(DL)model for BDS signal recognition that places a long short-term memory(LSTM)module in series with a convolutional sparse autoencoder to create a new autoencoder ***,we propose to capture the temporal correlations in long-duration BeiDou satellite time-series signals by using the LSTM module to mine the temporal change patterns in the time ***,we develop a convolutional sparse autoencoder method that learns a compressed representation of the input data,which then enables downscaled and unsupervised feature extraction from long-duration BeiDou satellite series ***,we add an l_(1/2) regularizer to the objective function of our DL model to remove redundant neurons from the neural network while ensuring recognition *** tested our proposed approach on a real urban canyon dataset,and the results demonstrated that our algorithm could achieve better classification performance than two ML-based methods(e.g.,11%better than a support vector machine)and two existing DL-based methods(e.g.,7.26%better than convolutional neural networks).

关键词： convolutional sparse autoencoder BeiDou Navigation Satellite system(BDS) long short-term memory(LSTM) multipath classification

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A Comprehensive Analysis of Hate Speech Detection and Recognition Utilising Advanced Machine Learning Algorithms and Techniques

A Comprehensive Analysis of Hate Speech Detection and Recogn...

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Automation and Computation (AUTOCOM), International Conference on

作者： Praveen Kumar Mannepalli Saurabh Sharma Neha Rajput Sanjay Kalamdhad Department of Computer Science & Engineering Chandigarh University Mohali Department of Computer Science and Engineering Chandigarh University Mohali India Department of Computer Science and Engineering Chandigarh University Punjab Department of Computer Science & Business System St. Vincent Pallotti College of Engineering and Technology Nagpur

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Social networking sites have come into demand as a way of sharing ideas and opinions with other people. But unfortunately, people tend to misuse social media to write nasty comments. Victims' lives might be negatively impacted by behaviours that are abusive and humiliating. Social networks give people a great tool to share information on ideas on multiple topics and opinions. However, these platforms are often abused to announce toxic and offensive comments, which eventually negatively affect victims and demographics. This study offers a thorough method for identifying hate speech (HS) on social media by utilising cutting-edge ML algorithms. A publicly available Twitter dataset containing 159,571 tweets classified into six categories, including toxic and identity hatred, was utilised. The methodology comprises data collection, conditioning, and categorisation that employs BiLSTM, which is particularly appropriate for the tasks demanding contextual sensitivity. These operations include symbol removing, stemming and stop-word eliminating, feature extraction using TF-IDF, and using the Bag-of-Words model. For classification of the data between hate and non-hate, the dataset is then split into training and testing datasets and the BiLSTM model used to provide classifications for the text. The evaluation of the BiLSTM model demonstrates superior performance, achieving an accuracy86%, a precision90%, a recall86%, and an F1-score88% • A comparison between the model and other models such as SVM and CNN reveals the strength of the BiLSTM in contextual classification tasks. The results demonstrate that BiLSTM can tackle problems with hate speech detection, providing a strong answer to the problem of harmful content on social media and helping to make the internet a better place for everyone.

关键词： Training Support vector machines Social networking (online) Text recognition Hate speech Bidirectional long short term memory Symbols Speech recognition Context modeling Testing

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CONDITION ASSESSMENT OF 150 KV OUTDOOR COMPOSITE INSULATORS FOLLOWING A VEGETATION FIRE EVENT 23

CONDITION ASSESSMENT OF 150 KV OUTDOOR COMPOSITE INSULATORS ...

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23rd International Symposium on High Voltage Engineering, ISH 2023

作者： Mavrikakis, Nikolaos C. Siderakis, Kiriakos Mouratis, Kiriakos Koudoumas, Emmanouel Section of Crete-Rhodes Transmission System Islands Network Operation Department Hellenic Electricity Distribution Network Operator S.A. Heraklion Greece High Voltage Laboratory Department of Electrical and Computer Engineering Hellenic Mediterranean University Heraklion Greece Center of Materials Technology and Photonics Department of Electrical and Computer Engineering Hellenic Mediterranean University Heraklion Greece

ISBN: (纸本)9781839539954

In electric power transmission and distribution systems, the use of overhead lines instead of underground cables is preferred by power utilities mainly due to the lower construction cost and the easier maintenance access. On the other hand the equipment used in overhead lines is stressed by several factors, such as weather conditions, lightning, vegetation fires, etc. In particular, vegetation fires under the overhead transmission lines could have catastrophic results for the installed equipment and can cause long-lasting electricity interruptions. In this study, condition assessment results are presented for eighteen 150 kV composite insulators exposed to a forest fire event, occurred in the August 2021, in the island of Rhodes, Greece. The evaluation of the fire effects on composite insulators was performed by employing several macroscopic and microscopic diagnostic techniques including visual inspection, hydrophobicity classification, Fourier Transform Infrared spectroscopy (FTIR) and optical microscopy analysis. © The Institution of Engineering & technology 2023.

关键词： Construction equipment

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Global mitigation efforts cannot neglect emerging emitters

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National Science Review 2022年第12期9卷 82-89页

作者： Can Cui Dabo Guan Daoping Wang Jing Meng Vicky Chemutai Paul Brenton Shaohui Zhang Yuli Shan Qiang Zhang Steven J.Davis Department of Earth System Science Tsinghua University The Bartlett School of Sustainable Construction University College London Department of Computer Science and Technology University of Cambridge Centre for Nature and Climate World Economic Forum School of Urban and Regional Science Shanghai University of Finance and Economics The World Bank School of Economics and Management Beihang University International Institute for Applied Systems Analysis School of Geography Earth and Environmental Sciences University of Birmingham Department of Earth System Science University of California Irvine Department of Civil and Environmental Engineering University of California Irvine

International efforts to avoid dangerous climate change have historically focused on reducing energy-related CO2emissions from countries with either the largest economies（*** EU and the USA）and/or the largest populations（*** and India）.However,in recent years,emissions have surged among a different and much less-examined group of countries,raising concerns that a next generation of high-emitting economies will obviate current mitigation ***,we analyse the trends and drivers of emissions in each of the 59 countries where emissions in 2010-2018 grew faster than the global average（excluding China and India）,project their emissions under a range of longer-term energy scenarios and estimate the costs of decarbonization *** emissions from these 'emerging emitters' reach as much as 7.5 GtCO2/year in the baseline 2.5° scenari—substantially greater than the emissions from these regions in previously published scenarios that would limit warming to 1.5℃ or even 2℃.Such unanticipated emissions would in turn require non-emitting energy deployment from all sectors within these emerging emitters,and faster and deeper reductions in emissions from other countries to meet international climate ***,the annual costs of keeping emissions at the low level are in many cases 0.2%-4.1% of countries' gross domestic production,pointing to potential trade-offs with poverty-reduction goals and/or the need for economic support and low-carbon technology transfer from historically high-emitting *** results thus highlight the critical importance of ramping up mitigation efforts in countries that to this point have been largely ignored.

关键词： emerging emiters developing countries climate change mitigation CO_2 emissions net zero

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Accurate Identification of Closing State of Horizontal Rotary Disconnectors in a Substation Based on Mask R-CNN 7

Accurate Identification of Closing State of Horizontal Rotar...

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7th International Conference on Computational Intelligence and Applications, ICCIA 2022

作者： Cao, Renwei Li, Wenliang Huang, Yijun Lu, Deling Lin, Qiuying Guangzhou Power Supply Co. Ltd. System Operation Department Guangzhou China South China University of Technology Department of Computer Science and Engineering Guangzhou China

ISBN: (纸本)9781665495844

Disconnectors are extremely important equipments in a substation. Long-term use may lead to abnormal structure, resulting in inadequate closing, which means a serious accident. Therefore, the judgment of disconnector closing in place has become a very important problem. Judging whether the closing is in place requires very accurate state identification, which is difficult to judge by the current method. Firstly, this paper used mask R-CNN network for target recognition, and obtained a model with high recognition accuracy. Then, this paper used HED method to extract the edge, and proposed an algorithm to detect the closing state. Experiments show that this method can effectively identify the closing state of a horizontal rotary disconnector. © 2022 IEEE.

关键词： Image recognition

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Improved Sparrow Search Algorithm with Support Vector Machine for Wind Turbine Fault Detection 2

Improved Sparrow Search Algorithm with Support Vector Machin...

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2nd International Conference on Data Science and Information system, ICDSIS 2024

作者： Nagendar, Y. Alabdeli, Haider Shruthi, B.S. Kamesh, M.R. Sudha, N. Sr University School Of Computer Science And Engineering Telangana Warangal India The Islamic University Department Of Computers Techniques Engineering College Of Technical Engineering Najaf Iraq Nitte Meenakshi Institute Of Technology Department Of Civil Engineering Bengaluru India Dayananda Sagar College Of Engineering Department Of Mechanical Engineering Bengaluru India Angel College Of Engineering And Technology Embedded System Technologies Tamil Nadu India

ISBN: (纸本)9798350381665

Wind turbines are widely used across the globe as a primary means of renewable energy by using the power of wind, it converts kinetic energy into electrical energy, contributing to generating electricity. However, turbines are positioned far from control centers, operating in environments that continuously change and have uncontrollable factors and conditions lead to wind turbine failures. The proposed method using an Improved Sparrow Search Algorithm (ISSA) optimizer along with also used a Support Vector Machine (SVM) classifier helps to detect the faults in turbines. This method uses the Supervisory Control and Data Acquisition (SCADA) dataset for wind turbine fault detection, the optimization using ISSA which is improved by Uniformity Diversity Orientation Strategy (UDOS) to optimize model's internal parameters. This helps to learn pattern during training period and helps to classify the normal and fault patterns. Then in the SVM classification process using the parameters this compares the obtained values with the current fitness value to classify patterns. The performance of the ISSA is analyzed by using accuracy, precision, f1-score, and recall. This shows that the proposed method outperforms existing methods such as K Nearest Neighbors (KNN), stacking classifier, and Feature Simplification Random Forest (FS-RF) in terms of accuracy 98.78%, precision 98.97%, recall 98.24%, and f1-score 98.00%. © 2024 IEEE.

关键词： Wind turbines

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