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A DYC system for 4WID Electric Vehicles via Fuzzy-Nonsingular Fast Terminal Sliding Mode Control

A DYC system for 4WID Electric Vehicles via Fuzzy-Nonsingula...

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2025 IEEE International Conference on Mechatronics, ICM 2025

作者： Eun Lee, Jung Woo Kim, Byeong Electronic and Computer Engineering University of Ulsan City of Ulsan Dept. of Electrical Korea Republic of

ISBN: (纸本)9798331533892

In this paper, we introduce a novel Direct Yaw Moment Control (DYC) system designed to bolster the stability of four-wheel independent drive (4WID) electric vehicles in diverse extreme driving conditions. Our proposed system is developed based on the design of Non-singular Fast Terminal Sliding Mode Control (NFTSMC) methods to calculate two optimal additional yaw moments, each targeting specific control objectives: the desired yaw rate and the desired sideslip angle. These calculated values are then intelligently weighted in real time using a Fuzzy Logic system to determine the final additional yaw moment applied to the vehicle. The stability of the NFTSMC methods is rigorously ensured through the Lyapunov theory. Simulation results underscore the efficacy of our proposed DYC system, particularly in navigating through extreme driving scenarios. Comparisons with no control and fixed weight-based SMC strategies demonstrated significant improvements in both yaw rate and sideslip angle tracking performance. The proposed method not only amplifies control effectiveness, ensuring swift convergence and high precision, but also substantially mitigates chattering phenomena, as evidenced by yaw moment and torque observations. © 2025 IEEE.

关键词： Lyapunov methods

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Churn Buster: Uncovering Patterns and Predicting Churn in OTT Platforms 6

Churn Buster: Uncovering Patterns and Predicting Churn in OT...

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6th Parul International Conference on engineering and Technology, PICET 2024

作者： Anand, Akshat Sarika, D. Malviya, Harshit Vatsa, Dipti Yadwad, Sunita Adarsh Dholariya, Shreya M. Dept. Computer Science Engineering Parul Institute of Engineering and Technology Vadodara India

ISBN: (纸本)9798350369748

Churn, a common concern among business leaders, poses a significant challenge in today's fast-paced world where consumer attention spans are short. This research focuses on churn analysis within the OTT streaming platform sector, essential for retaining subscribers and ensuring business longevity. Utilising data preprocessing methods and machine learning algorithms, this study aims to identify factors influencing churn and provide insights for targeted retention strategies. The dataset includes 16 variables, with churn serving as the binary-dependent variable. Data preprocessing involved addressing missing values and converting categorical data into numerical format. Four machine learning models-Random Forest, XGBoost, K-Nearest Neighbors, and Support Vector Machine-were applied, with Random Forest achieving the highest accuracy of 89%. Evaluation metrics like confusion matrices, ROC curves, and Precision-Recall curves were used to evaluate model performance, revealing Random Forest's effectiveness in churn prediction. Principal Component Analysis (PCA) was employed to handle the complexity of user data, aiding in the identification of influential churn factors. This study emphasises the importance of understanding customer behaviour and utilising machine learning to forecast subscription choices. The insights gained provide valuable guidance to OTT companies for enhancing customer satisfaction and retention strategies, ensuring competitiveness in a dynamic industry landscape. © 2024 IEEE.

关键词： Sales

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Sentimental Analysis of Various TV Serials in Society 15

Sentimental Analysis of Various TV Serials in Society

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15th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2023

作者： Sarkar, Ripa Sarkar, Ratna R. BRAC University Dept. of Computer Science and Engineering Dhaka Bangladesh Jahangirnagar University Dept. of Computer Science and Engineering Savar Bangladesh

ISBN: (纸本)9798350324433

Sentiment analysis which involves identifying emotions in the text is a widely studied topic of natural language processing. Many researchers focus on social media content like posts, tweets and reviews for their studies. In this paper, a large number of data from comments about serials on social media and internet forums in Banglish is collected and analyzed. We use NLP techniques to do the sentiment analysis of the comments. This study explores how different kinds of serials have impacted society and concentrates on how they have influenced the creation of Banglish comments. This study aims to conduct a sentiment analysis of Banglish comments generated in response to different genres of serials and to analyze their potential societal implications. Along with Long Short-Term Memory (LSTM), we use several machine-learning algorithms such as Logistic Regression (LR), Multinomial Naive Bayes (MNB), Gaussian Naive Bayes, Decision Tree (DT), AdaBoost, Random Forest (RF) and Support Vector Machine (SVM) to create this model. The SVM gives the highest accuracy among all of the machine learning algorithms. © 2023 IEEE.

关键词： Sentiment analysis

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Line Graph is the Key: An Exploration of Line Graph on Link Prediction with Social Networks 11

Line Graph is the Key: An Exploration of Line Graph on Link ...

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

作者： Xing, Chen Makrehchi, Masoud Ontario Tech University Dept. of Electrical Computer and Software Engineering Oshawa Canada

ISBN: (纸本)9798331531904

Network data plays a crucial role in various real-world applications, as connections between entities can be represented and analyzed through graphs. These include various types, such as social, information, and technical networks. However, the complex topologies of these networks present challenges in converting graph data into machine-readable vector formats. Existing models, such as Graph Neural Networks, Graph Attention Networks, and node2vec, have made strides in graph embeddings. For edge-related tasks, models such as node2vec typically use indirect methods like concatenating node vectors to represent edges. This approach is useful for tasks like link prediction. In this paper, we present LineDi2vec, a novel approach that improves the Node2vec embedding method by utilizing a line graph. The proposed LineDi2vec not only generalizes the original graphs, transforming the relationships between edges and nodes but also maintains the original graphs' topological integrity for effective node embedding by Node2vec. We evaluated LineDi2vec on four real-world datasets, focusing on link prediction. The results demonstrate that LineDi2vec outperforms traditional node concatenation methods. © 2024 IEEE.

关键词： Graph embeddings

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HGP-NLP at BioLaySumm: Leveraging LoRA for Lay Summarization of Biomedical Research Articles using Seq2Seq Transformers 23

HGP-NLP at BioLaySumm: Leveraging LoRA for Lay Summarization...

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23rd Meeting of the ACL Special Interest Group on Biomedical Natural Language Processing, BioNLP 2024

作者： Malik, Hemang Pradeep, Gaurav Seth, Pratinav Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Dept. of Data Science & Computer Applications India Dept. of Computer Science & Engineering India

ISBN: (纸本)9798891761308

Lay summarization aims to generate summaries of technical articles for non-experts, enabling easy comprehension for a general audience. The technical language used in research often hinders effective communication of scientific knowledge, making it difficult for non-experts to understand. Automatic lay summarization can enhance access to scientific literature, promoting interdisciplinary knowledge sharing and public understanding. This has become especially important for biomedical articles, given the current global need for clear medical information. Large Language Models (LLMs) (Yao et al., 2024), with their remarkable language understanding capabilities, are ideal for abstractive summarization, helping to make complex information accessible to the public. This paper details our submissions to the BioLaySumm 2024 Shared Task: Lay Summarization of Biomedical Research Articles (Goldsack et al., 2024). We fine-tune and evaluate sequence-to-sequence models like T5 across various training dataset settings and optimization methods such as LoRA (Hu et al., 2021b) for lay summarization. Our submission achieved the 53rd position overall.. ©2024 Association for Computational Linguistics.

关键词： Computational linguistics

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A Conformal Microwave Measurement System for Imaging Buried Anomalies

A Conformal Microwave Measurement System for Imaging Buried ...

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2025 United States National Committee of URSI National Radio science Meeting, USNC-URSI NRSM 2025

作者： Islam, M. Shifatul Islam, M. Asiful Kiourti, Asimina Dept. of Electrical and Computer Engineering The Ohio State University ColumbusOH43212 United States Dept. of Electrical and Electronic Engineering Bangladesh University of Engineering and Technology Dhaka Bangladesh

ISBN: (纸本)9781946815200

Microwave imaging has long been reported for locating buried anomalies inside a bulk object as relying upon external measurements of microwave data. The approach is noninvasive, low-cost, portable, and the use of non-ionizing radiation makes it popular for biomedical applications. In this work, we explore the experimental feasibility of a conformal microwave imaging system, where the antennas are mounted on or very close to the imaging object. Our proof of concept setup entails 12 antennas surrounding a plastic cylindrical frame in free space with two dielectric cylinders used as anomalies. S-parameters were measured using a network analyzer and a 12-port switching matrix. For imaging, we used the Born Iterative Method, where the data equation was replaced with the S-parameter sensitivity formula. Simulation and measurement results show good quality image reconstruction, asserting the feasibility of the proposed conformal imaging setup. Future work will incorporate tissue-emulating materials to ultimately develop a stable and wearable imaging system. © 2025 USNC-URSI.

关键词： Cylinders (shapes)

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Investigating the Efficacy of Diverse Machine Learning Classifiers for Parkinson's Disease Detection 15

Investigating the Efficacy of Diverse Machine Learning Class...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Roy, Sourabarna Debbarma, Tina Pal, Tannistha Duraisamy, Prakash National Institute of Technology Agartala Dept. of Computer Science and Engineering Agartala India University of Wisconsin- Green Bay Dept. of Computer Science and Engineering WI54311 United States

ISBN: (纸本)9798350370249

To detect Parkinson's disease, we compare the effectiveness of K-Nearest Neighbors (KNN), Logistic Regression (LR), Support Vector Machines (SVM), and Random Forest (RF) algorithms. Utilizing a dataset with clinical and biomedical features, we preprocess the data to handle missing values and standardize the features. Subsequently, we train each algorithm with the preprocessed data and evaluate their performance using metrics like accuracy, precision, recall, and F1-score. Our results indicate that all four algorithms achieve excellent accuracy in diagnosing Parkinson's disease, with KNN slightly outperforming the others. However, the selection of the algorithm may depend on specific needs such as interpretability and computational efficiency. Additionally, we conduct a feature importance analysis to identify the most relevant features for Parkinson's disease identification, offering insights that can aid in early diagnosis and disease management. © 2024 IEEE.

关键词： Logistic regression

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Detection of Cardiovascular Disease from ECG Image Using Optimized Weighted Average Ensemble Machine Learning Technique 26

Detection of Cardiovascular Disease from ECG Image Using Opt...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Hasan, Md. Nahid Hossain, Md. Ali Akash, Ahsan Sadek Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Bangladesh Pabna University of Science & Technology Dept. of Computer Science & Engineering Bangladesh

ISBN: (纸本)9798350359015

cardiovascular diseases (CVD) stand as a prominent global cause of death in worldwide. Timely identification and intervention are paramount in dealing with heart disease, as its failure to detect and treated promptly can lead to life-threatening outcomes. The Electrocardiogram (ECG) Signals are mainly used as diagnostic tools that routinely assess the electrical and muscular function of the heart. Until recently, ECG records were predominantly maintained in paper form;thus, manually examining and re-examining the ECG paper records are often proved time-consuming and laborious tasks. Therefore, this research work utilizes, the power of machine learning and deep learning in predicting four different cardiac abnormalities named Abnormal Heartbeats (AHB), History of Myocardial Infarction (HMI), Myocardial Infarction (MI), and Normal Heartbeats (NHB) from paper-based ECG images. Different image pre-processing methods have been employed to improve the quality of individual ECG images and a novel lightweight deep neural model is proposed for extracting prominent features from each ECG images. Finally, we used an optimized weighted average ensemble technique for the final prediction. The grid search technique is used to find the optimal parameters and weights of each machine-learning model. Our proposed model shows state-of-the-art results in accuracy, precision, recall, and f1-score of 99.29%, outperforming the existing work. The model has attained an Area Under Curve (AUC) score of 99.96%. © 2023 IEEE.

关键词： Cardiovascular disease convolution neural network (CNN) deep learning ECG image feature extraction image classification machine learning weighted average ensemble

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Gallium Nitride-based 48V-to-1V Point-of-Load (PoL) Converter for Aerospace Telecommunications and Computing Applications 39

Gallium Nitride-based 48V-to-1V Point-of-Load (PoL) Converte...

引用

39th Annual IEEE Applied Power Electronics Conference and Exposition, APEC 2024

作者： Ellis, Nathan M. Zhu, Yicheng Pilawa-Podgurski, Robert C.N. University of California Dept. of Electrical Engineering and Computer Sciences Berkeley United States

ISBN: (纸本)9798350316643

This paper presents a regulating Hybrid Switched Capacitor Converter (HSCC) topology suitable for high conversion ratio Point-of-Load (PoL) applications, specifically targeting 48 V to 1 V power delivery in space-based high-performance computing systems, where size and weight are important considerations, along with device de-rating for radiation tolerance. The proposed topology merges an initial 2:1 switched capacitor conversion stage with a recently developed symmetric dual inductor hybrid (SDIH) conversion stage, reaping benefits of both. The SDIH stage offers reduced component count and an excellent switch stress figure of merit, while the initial 2:1 voltage reduction stage significantly reduces the volume of flying capacitors, enabling compact size and low weight. A high density gate drive solution is also presented, using a minimal number of driver ICs without compromising on driving capability. A preliminary hardware prototype demonstrates this topology's feasibility while employing de-rated gallium nitride (GaN) switches to safeguard against radiation total ionizing dose (TID) and single event effects (SEE). This prototype achieves a very high measured power density of 2,020 W/inch3 (123.3 kW/liter) while performing 48 V to 1 V conversion and switching at 1 MHz. © 2024 IEEE.

关键词： Gallium nitride

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Identification of Affective States from Malayalam Audios Based on Valence Arousal Prediction

Identification of Affective States from Malayalam Audios Bas...

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2024 International Conference on Brain computer Interface and Healthcare Technologies, iCon-BCIHT 2024

作者： George, Talit Sara George, Jini Pournami, P.N. National Institute of Technology Dept. of Computer Science and Engg. Calicut India Providence College of Engineering Dept. of Computer Science and Engg. Chengannur India

ISBN: (纸本)9798331540067

Affective state plays a key role in emotion recognition, influencing various applications from mental health evaluation to human-machine interaction. These states can be described using two primary aspects: valence, which indicates the pleasantness of the speech, and arousal, which relates to the intensity of the emotion. This study aims to automate the prediction of valence and arousal values from speech data, addressing the limitations of manual annotation and human error. The IIITSAINT-EMOMDB dataset, created from Malayalam films, uses the Circumplex Model of Affect to map emotions such as happy, sad, angry, and calm. The approach leverages machine learning models, including Support Vector Regression (SVR), Artificial Neural Networks (ANN), and Convolutional Neural Networks (CNN), for prediction. Experimental results demonstrate that the traditional models like SVR and ANN are outperformed by the CNN model, which achieved a mean squared error (MSE) of 0.3 for valence and 0.1 for arousal. These findings highlight the power of automated emotion recognition in large-scale audio datasets, offering promising applications in affective computing and psychological analysis. © 2024 IEEE.

关键词： Convolutional neural networks

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