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检索条件"机构=dept. of Computer Computer Systems Engineering"

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127 Level Reduced Switch Asymmetric Cascaded H-Bridge Multilevel Inverter

127 Level Reduced Switch Asymmetric Cascaded H-Bridge Multil...

引用

2023 IEEE International Conference on Emerging Trends in engineering, Sciences and Technology, ICES and T 2023

作者： Zulfiqar, Ismaiel Syed, Shahzada Sufyan Syed, S. Subhan Malik, Yousaf Jadoon, Wasid Ali COMSATS University Islamabad Dept. of Electrical and Computer Engineering Abbottabad Campus Abbottabad Pakistan Allied Engineering and Services Limited Regional Manager dept. of Alternative Energy Solutions Lahore Pakistan

ISBN: (纸本)9781665455602

Depending upon the uses of multilevel inverter in different applications such as Uninterruptible power supply (UPS), high voltage DC transmission (HVDC) and Variable frequency drives (VFD), there is huge research which is carried on them. This paper describes the working of 127 level Reduce Switch Asymmetrical Cascaded H Bridge multilevel inverter (RS-ACHBMLI), the main objective is to produce a pure sinusoidal waveform without the use of any filter element and to reduce total harmonic distortion (THD) up to 0.80Asymmetric topology is used in which DC sources are of different magnitudesfuels cells are the best to use as a DC source in our proposed technique. Sine wave is compared with 63DCoffsets to trigger the Insulated gate bipolar junction transistors (IGBTs) in a specific manner, six dc sources and six IGBT's are used to produce required output waveform. Low frequency PWM technique is used in level generating circuitry to produce required output. © 2023 IEEE.

关键词： Pulse width modulation

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Power Quality Control and Comparison of a Micro Grid System Using Fuzzy MPPT Technique 5

Power Quality Control and Comparison of a Micro Grid System ...

引用

5th International Conference on Energy, Power, and Environment, ICEPE 2023

作者： Sahoo, Satyabrata Thokchom, Surmila Puhan, Pratap Sekhar Panda, Gayadhar NallaMallaReaddy Engineering College Dept. of Electrical and Electronics Engineering Hyderabad India National Institute of Technology Meghalaya Dept. of Computer Science and Engineering Meghalaya India Sreenidhi Institute of Science and Technology Dept. of Electrical and Electronics Engineering Hyderabad India National Institute of Technology Meghalaya Dept. of Electrical Engineering Meghalaya India

ISBN: (纸本)9798350313123

This paper presents a power quality control in a microgrid system using different Maximum Power Point Tracking (MPPT) techniques. The main aim of the study is to evaluate the performance of the microgrid system in terms of power quality parameters, such as harmonic distortion and voltage stability, when different MPPT techniques are employed. The microgrid system incorporates a hybrid power generation system consisting of solar panels, wind turbines, and energy storage systems, which are integrated and controlled using different MPPT techniques, including the Perturb & Observation (P&O) method and the Fuzzy MPPT technique. A single-phase sinusoidal pulse width modulation (SPWM) inverter, based on the PQ control strategy, is used to supply power to the grid and maintain a constant DC link voltage. Additionally, a fuel cell is employed to substantially reduce power fluctuations. A comparison of the power quality performance of the micro grid system using these MPPT techniques is carried out through simulation studies using MATLAB/Simulink platform. The results are analyzed and discussed in detail, and recommendations are provided for improving the power quality control of the microgrid system. © 2023 IEEE.

关键词： Wind turbines

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State-Space Average Modeling Based PID Control for Three Novel Topologies of DC-DC Boost Converter

State-Space Average Modeling Based PID Control for Three Nov...

引用

2022 IEEE International Women in engineering (WIE) Conference on Electrical and computer engineering, WIECON-ECE 2022

作者： Zaman, Meem Tasfia Fu, Xingang Challoo, Rajab North South Univeristy Dept. of Electrical and Computer Engineering Dhaka Bangladesh Texas A&m University-Kingsville Dept. of Electrical Engineering and Computer Science Kingsville United States

ISBN: (纸本)9798350311563

The objectives of this paper are to obtain state-space average models of the three new topologies of the DC-DC boost converter and apply Proportional Integral Derivative (PID) controller on them to analyze their performance. One of the most important applications of DC-DC boost converter in modern world is in Photovoltaic (PV) systems. It boosts up the low DC voltage coming from the PV panels to a high DC bus voltage and it drives the DC-AC inverter. Thus, the efficiency and the control of it play very important role in this entire process. Recently, some new topologies have been proposed to improve the efficiency of this converter. In order to inspect these topologies, proper mathematical modeling is required, and the state-space average model is one of the efficient techniques to do that. Among various control techniques, PID controller is widely used to get the best-desired output voltage of this converter. To get the expected result, MATLAB Simulink models and MATLAB m-file code will be used in this paper. © 2022 IEEE.

关键词： Three term control systems

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Interpretable Machine Learning Model for Breast Cancer Prediction Using LIME and SHAP 9

Interpretable Machine Learning Model for Breast Cancer Predi...

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Maheswari, B Uma Aaditi, A. Avvaru, Ananya Tandon, Aryan De Prado, R. Pérez Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science And Engineering Bengaluru India University of Jaén Dept. Telecommunication Engineering Jaén23700 Spain

ISBN: (纸本)9798350394474

Breast cancer, which occurs in both men and women, causes approximately 10 lakh deaths globally and has no specific risk factors. The time frame of the treatment is a long-drawn process based on the person, the type of cancer, and its level of spread. It is imperative to detect this cancer early on in order to prevent mortality. Given the prediction's significance, an accurate breast cancer prediction model must be developed. This study explores the Breast Cancer Prediction dataset, applies SMOTE (Synthetic Minority Over-sampling Technique) to balance the dataset, and proposes an effective Machine Learning (ML) model fused with Explainable AI to provide health professionals with explanations. ML algorithms are analyzed before and after applying Principal Component Analysis (PCA), and visualization is performed using t-SNE. ML algorithms, Support Vector Machine (SVM), k-Nearest Neighbor (kNN), Random Forest (RF), Stochastic gradient Descent, XGBoost, Gradient Boosting, Decision Tree (DT), and Naïve Bayes are trained on the Breast Cancer dataset. It is seen that the RF model outperforms other models considered with 95.9% accuracy. To understand the weightage of features considered by the best breast cancer prediction models and to provide trust to the doctors, the Explainable AI (XAI) packages LIME (Local Interpretable Model-agnostic Explanations), and SHAP (SHapley Additive exPlanations) are used. The effective ML model with XAI techniques empowers clinicians with actionable insights for more informed breast cancer diagnosis and treatment decision-making. © 2024 IEEE.

关键词： Principal component analysis

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PV systems Generation Prediction Considering Cloud Cover Using Deep Learning Techniques

PV Systems Generation Prediction Considering Cloud Cover Usi...

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2024 IEEE Global Energy Conference, GEC 2024

作者： Wadi, Mohammed Salemdeeb, Mohammed Jouda, Mohammed Tur, Mehmet Rida Ayachi, Bilel Husain, Nour Electrical & Electronics Engineering Dept. Istanbul Sabahattin Zaim University Istanbul Turkey Electrical Engineering Dept. Istanbul Sabahattin Zaim University Istanbul Turkey Dept. of Electrical and Electronic Engineering Batman University Batman Turkey Electrical and Electronic Eng. University 20 August 1955 Skikda Algeria Computer Engineering Dept. Al-Azhar University Gaza Gaza Palestine

ISBN: (纸本)9798331532611

Numerous environmental elements, including sun irradiation, temperature, shade, wind speed, and others, can significantly influence the output of photovoltaic (PV) systems. Solar generation forecasts across various time ranges are required to meet demand and grid restrictions. Short-term predictions are essential for energy trading, real-time control, and grid balancing. This paper presents a concise overview of shading due to cloud cover on the generation of PV systems. Furthermore, the convolutional neural network (CNN) deep learning model predicts PV generation by considering cloud movement over a specific area. The results demonstrate that cloud cover significantly impacts the generation of PV systems. Consequently, PV power generation forecasting is essential for enhancing the stability, quality, and reliability of smart grids and modern power systems. The suggested deep learning model occupies 12% of the memory size of the state-of-the-art model. The suggested model surpasses these models in the MSE, RMSE, and MAE test set metrics, yielding values of 1 KW, 2.37 KW, and 1.35 KW, respectively. ©2024 IEEE.

关键词： Cloud Cover CNN Deep Learning Impact of Cloud Shade PV Generation

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Fingerprint generation and authentication though Adaptive convolution generative adversarial network (ADCGAN) 7

Fingerprint generation and authentication though Adaptive co...

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7th International Multi-Topic ICT Conference: IMTIC 2023

作者： Mustafa, Syed Muhammad Nabeel Zehra, Syeda Sundus Baber, Alina Siddiqui, Maria Andleeb Ned University of Engineering and Technology Dept. of Computer Science & Information Technology Karachi Pakistan Sir Syed University of Engineering and Technology Dept of Software Engineering Karachi Pakistan

ISBN: (纸本)9798350338461

Fingerprints are crucial in identification of humans. The uniqueness of finger prints makes it an interesting subject. Fingerprints are termed as a technique used to define, assess, and quantify a person's physical and behavioral property. Deep learning has made its application in all the major fields such as natural language processing, computer vision and speech processing. Deep learning has also found its application in the important subject of fingerprint synthesis and biometric. The ever-growing complexity of fingerprint authentication issues, from cellphone authentication to airport security systems, seems to be best handled by these models. In recent years, deep learning-based models have been used more and more to raise the accuracy of various fingerprint recognition systems. The persuasive capacity of Generative Adversarial Networks (GANs) to generate believable instances can be credibly taken from an existing distribution of samples. GAN exhibits exceptional performance on data generation-based tasks and also encourages study in privacy and security. In this work, using Adaptive Deep Convolution Generative Adversarial Networks (ADCGAN), we develop a model that generates and authenticate the fingerprints. A Socofing dataset was trained on ADGAN model. The model gave 92% accuracy. The conduct of fingerprint research has been made possible due to ADGAN, without restrictions related to the confidential nature of biometric data. © 2023 IEEE.

关键词： Generative adversarial networks

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Offensive Message Spam Detection in Social Media Using Long Short-Term Memory 3

Offensive Message Spam Detection in Social Media Using Long ...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Sivakumar, M. Abishek, S.A. Karthik, N. Vanitha, J. National Institute of Technology Dept. of Electrical and Electronics Engineering Puducherry India National Institute of Technology Dept. of Computer Science Engineering Puducherry India National Institute of Technology Puducherry India E.G.S.Pillay Engineering College Nagapattinam India

ISBN: (纸本)9798331518592

In this world, online communication has become an essential part in our daily lives. Almost everyone uses social media platforms to communicate with the whole world. But this also leads to a major problem: cyberbullying. This paper proposes a system to detect users who spam offensive messages repeatedly and ban them using natural language processing and machine learning. The system would be beneficial to service providers, social media platforms, and individuals, allowing us to strive for a world of harmony. The amalgamation of NLP, Big Data, and ML approaches presents a robust and efficient solution for offensive message spam detection. The findings of this project will shed light on the potential of these technologies to curb the proliferation of offensive content, thereby fostering a healthier and more respectful online communication ecosystem. This paper delves on offensive spam detection using the LSTM model, and the obtained MSE of this model is 12.58. © 2024 IEEE.

关键词： Social networking (online)

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Customer Segmentation Using Hierarchical Clustering 9

Customer Segmentation Using Hierarchical Clustering

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Afzal, Areeba Khan, Laiba Hussain, Muhammad Zunnurain Zulkifl Hasan, Muhammad Mustafa, Muzzamil Khalid, Aqsa Awan, Rimsha Ashraf, Farhan Khan, Zohaib Ahmed Javaid, Arslan Information Technology University Lahore Department of Computer Engineering Punjab Pakistan Bahria University Lahore Campus Dept. of Computer Science Pakistan University of Central Punjab Lahore Faculty of Information Technology Department of Computer Science Pakistan National College of Business Administration And Economics Department of Computer Science Lahore Pakistan Information Technology University Lahore Pakistan

ISBN: (纸本)9798350394474

In the dynamic landscape of retail, understanding customer behavior is paramount for businesses seeking to optimize marketing strategies and enhance the shopping experience. This study explores the utilization of hierarchical clustering techniques for mall customer segmentation, with a focus on the paper titled 'MALL CUSTOMER SEGMENTATION USING MACHINE LEARNING' as the benchmark. Our dataset encompasses a diverse range of mall customers, spanning demographics and behavioral attributes. Hierarchical clustering systematically groups customers into clusters, revealing distinct segments within the mall's customer base. A comprehensive analysis of these clusters unveils profound insights into customer tendencies, preferences, and purchasing habits. These insights form a solid foundation for tailored marketing campaigns, personalized recommendations, and resource allocation within the mall. The study contributes significantly to customer analytics, providing retailers with a powerful tool to gain a competitive edge in the retail sector. By leveraging hierarchical clustering for mall customer segmentation, businesses can enhance customer satisfaction, drive sales, and foster lasting customer relationships. This paper underscores the importance of data-driven methodologies in understanding customer behavior and offers a practical framework for businesses to harness the potential of hierarchical clustering for strategic decision-making in the retail industry. © 2024 IEEE.

关键词： Consumer behavior

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Indoor Navigation and Mapping for Wheelchair People in KOE 9

Indoor Navigation and Mapping for Wheelchair People in KOE

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9th International Conference on computer and Communication engineering, ICCCE 2023

作者： Hisham, Nur Najihah Nor Mustapha, Nurul Arfah Che Ibrahim, Azhar Mohd International Islamic University Malaysia Dept. Electrical and Computer Engineering Kulliyyah of Engineering Gombak Kuala Lumpur Malaysia International Islamic University Malaysia Dept. of Mechatronics Engineering Kulliyyah of Engineering Gombak Kuala Lumpur Malaysia

ISBN: (纸本)9798350325218

In recent years, the need for effective indoor navigation systems has grown significantly, especially for individuals with mobility challenges such as wheelchair users. Existing navigation systems primarily cater to outdoor environments, leaving wheelchair users without proper guidance and support when navigating within buildings. This limitation results in difficulties and inconveniences for wheelchair users who require accurate and obstacle-free routes to reach their desired destinations within the facility. This project focuses on developing an indoor navigation and mapping system specifically designed for wheelchair users in the Kulliyyah of engineering, International Islamic University Malaysia (IIUM) by creating a 3D indoor navigation map using Unity, considering the specific needs of wheelchair users. The project also involves studying the Dijkstra algorithm for calculating the shortest path route. One of the novel features implemented includes the ability to identify the best routes free of potential hazards such as obstacles and ramps. Results show that the proposed solution supports safe indoor navigation for wheelchair users by providing the shortest path with approximately 66.48% compared to longest path. © 2023 IEEE.

关键词： Wheelchairs

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Mask Generation with Meta-Learning Classifier Weight Transformer Network for Few-Shot Image Segmentation

Mask Generation with Meta-Learning Classifier Weight Transfo...

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Jhou, Fong-Ci Liang, Kai-Wen Lo, Chung-Hsun Wang, Chien-Yao Chen, Yung-Fang Wang, Jia-Ching Chang, Pao-Chi National Central University Dept. Communication Engineering Taiwan National Central University Dept. Computer Science and Information Engineering Taiwan Academia Sinica Institute of Information Science Taipei Taiwan

ISBN: (纸本)9798350324174

This paper proposes a meta-learning classification weight transfer network to generate masks as a few-shot image segmentation architecture. It generates good prior masks via a pretrained classification weight transfer architecture, and uses a pretrained feature extraction architecture on query images and support images. The network architecture exploits a top-down path in a feature augmentation module to adaptively transfer information from fine to coarse features for extracting features from query images. Finally, the classification module predicts the segmentation of the query image. The experimental results show that using the mean intersection of joints (mIOU) as the evaluation mechanism, the accuracy of the 1-shot experimental results is 1.7% higher than that of the baseline. In the 5-shot experimental results, the accuracy is also improved by 2.6%. Therefore, compared with the baseline, it clearly shows that the mask generated by the meta-learning classification weight transfer network can effectively help improve the performance of few-shot image segmentation system. © 2023 IEEE.

关键词： Semantic Segmentation

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