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Optimizing Photovoltaic System Efficiency: A Comparative Study of RNN-based Controller and Incremental Conductance in MPPT 52

Optimizing Photovoltaic System Efficiency: A Comparative Stu...

引用

52nd IEEE Photovoltaic Specialist Conference, PVSC 2024

作者： Soyoye, Babatunde D. Bhattacharya, Indranil Vinolisha, Mary Banik, Trapa Tennessee Tech University Electrical and Computer Engineering dept. CookevilleTN38505 United States Tennessee Tech University Antony Dhason Electrical and Computer Engineering dept. CookevilleTN38505 United States

ISBN: (纸本)9781665464260

In this study, a Recurrent Neural Network (RNN)- based PV controller is thoroughly examined and compared with conventional Incremental Conductance techniques for Maximum Power Point Tracking (MPPT). The study emphasizes the improved responsiveness and efficiency of the RNN controller and focuses on the dynamic adaptation of PV systems to changing environmental conditions. In this study, the better performance of the RNN controller in monitoring the maximum power point, its fast convergence time, and its stability under various environmental conditions are demonstrated by comprehensive simulations and comparative studies. According to the results, using cutting-edge machine learning methods, such as RNNs, may greatly increase PV systems' operational effectiveness, highlighting their potential to optimize renewable energy systems. This study offers insights that advance the realm of renewable energy technologies. © 2024 IEEE.

关键词： DC-DC Boost converter Incremental conductance Machine learning Maximum Power Point Tracking (MPPT) Photovoltaic systems Recurrent Neural Network

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Text-Conditioned Image Synthesis - A Review

Text-Conditioned Image Synthesis - A Review

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2023 IEEE Silchar Subsection Conference, SILCON 2023

作者： Saha, Piyush Ghosh, Sumon Bhandari, Dinabandhu Calcutta Institute of Engineering and Management Dept. of Electrical Engineering Kolkata India Heritage Institute of Technology Dept. of Computer Applications Kolkata India Heritage Institute of Technology Dept. of Computer Science Kolkata India

ISBN: (纸本)9798350314144

With the advent of Generative Adversarial Networks and diffusion models, image synthesis conditioned on text description has been an active area of research. Generative Adversarial Networks are a flexible and intuitive way of conditional image synthesis and significant progress has been made in the last few years regarding visual realism, diversity, and semantic alignment. In recent years, Diffusion probabilistic models have been shown to perform better in the task of image synthesis and have been used extensively for the task of text-to-image synthesis, significantly improving visual realism, semantic alignment, and generation of high-resolution images. However, the field still faces many challenges such as the generation of high-resolution images with multiple objects and the development of suitable and reliable evaluation metrics. In this review, we contextualize the state-of-the-art text-conditioned image generation models, and critically examine current evaluation strategies, architectures, model training, and datasets. This review complements previous surveys on text-conditioned image synthesis which we believe will help researchers to further advance this field. © 2023 IEEE.

关键词： Generative adversarial networks

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Predicting Mental illness (Depression) with the help of NLP Transformers 2

Predicting Mental illness (Depression) with the help of NLP ...

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2nd International Conference on Data science and Information System, ICDSIS 2024

作者： Kokane, Vedant Abhyankar, Ajit Shrirao, Nikhil Khadkikar, Prajakta Pune Institute Of Computer Technology Dept. Of Computer Engineering Pune India

ISBN: (纸本)9798350381665

In today's rapidly developing world, mental health is very important because it directly impacts the human thought process, productivity, relationships, etc. Being one of the most important aspects of a human's life, it is often neglected. Poor mental health can lead to anxiety, hopelessness, struggling to manage emotions, a lack of concentration, and loneliness. A mentally ill person can also have suicidal thoughts. In today's world, almost every person uses social media to share their lives and thoughts. With social media serving as a ubiquitous platform for self-expression and interaction, leveraging artificial intelligence (AI) and natural language processing (NLP) techniques becomes feasible for detecting mental health statuses based on users' social media posts. In this research paper, we proposed a methodology using NLP Transformers to study human behaviour as well as symptoms of depression. We analysed the status of mental health by examining the text of posts posted by different people on Twitter and Reddit. In our proposed methodology, we applied 4 different NLP Transformers (BERT, XLNet, RoBERTa, and DistilBERT) Transformers on two different datasets. Our proposed model concluded that DistilBert was the most accurate Transformer among the four with accuracy with 91% accuracy on twitter dataset and 84% accuracy on reddit dataset. Our proposed methodology will be effective in detecting mental illness in a person. © 2024 IEEE.

关键词： Diseases

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Design and Implementation of A WiFi Password Cracking Tool Based on the Evil Twin Method 4

Design and Implementation of A WiFi Password Cracking Tool B...

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4th International Conference on electrical engineering and computer science, ICECOS 2024

作者： Syahrial, Syahrial Melinda, Melinda Miftahujjannah, Rizka Yunidar, Yunidar Engineering Faculty Universitas Syiah Kuala Dept. of Electrical Engineering and Computer Banda Aceh Indonesia

ISBN: (纸本)9798350368253

In the current technological era, along with the increasingly rapid development of the times, there are many changes in wireless technology, and there are more and more data theft processes via wireless networks, such as stealing WiFi passwords. This research will implement a WiFi password hack tool with a Wemos D1 Pro Esp8266 microcontroller and an external antenna of 3 dBi to provide a slight delay, a maximum distance of 20 meters, and a good signal quality level. To find the WiFi password, the Evil Twin WiFi method is used by duplicating the target SSID, and then the client enters the password to obtain the password. This test was conducted at the electrical and computer engineering Building, Universitas Syiah Kuala, Kopelma Darussalam, Banda Aceh City, Aceh. This research obtained four range distances: 5, 10, 15, and 20 meters, which produce different signal qualities: excellent (less than-60 dBm), good (between-60 and-70 dBm), relatively poor (between-871 and-80 dBm). Even though the Huawei Hg8245h AP with a distance of 20 meters failed to get the password, this happened because of the distance and specifications of the AP. © 2024 IEEE.

关键词： Wi-Fi

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Steganographic Encryption and Decryption Using Duality Preserving GANs 22

Steganographic Encryption and Decryption Using Duality Prese...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Shah, Aaryak Kamal, Alok Kumar Neeraj Dept. of Information Technology ABV-IIITM Gwalior Gwalior India Dept. of Electrical/Electronics ABV-IIITM Gwalior Gwalior India Dept. of Computer Science and Engineering Mahindra University Hyderabad India

ISBN: (纸本)9798331527471

Image steganography has been a significant research area in the security sphere. The core idea revolves around embedding secret messages in existing images (covers) such that the resultant image displays no noticeable detectable artifacts of the secret while also being statistically similar to the original cover. Various steganographic techniques have been developed and tested. However, these traditional techniques often exploit some specific property of the image and are often easy to detect with the right tools. In recent times, generative adversarial networks, or GANs for short, have shown massive promise in their capability to modify and synthesize data. They are now ubiquitous in image synthesis and image translation tasks. With various recent advancements in this technology allowing these models to generate, in many cases, human-imperceptible data, GAN-based steganographic systems have become a significant focus of security research. This report analyzes and compares some existing models and attempts to provide a new solution that performs better on multiple well-defined metrics. A DualGAN-based architecture is proposed that trains both the encoder and decoder processes simultaneously while maintaining the underlying dual relationship of these tasks. The proposed scheme reports better results on the selected metrics (SBR, DER, PSNR) at three values of embedding capacity set at 0.3, 0.6, and 0.9. © 2024 IEEE.

关键词： Generative adversarial networks

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Enhancing VANET Efficiency with ACRR Technique for Improved Traffic Management and Network Scalability

Enhancing VANET Efficiency with ACRR Technique for Improved ...

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2024 International Conference on science, Technology, engineering and Management, ICSTEM 2024

作者： Karthick, K. Talari, Praveen Nishadevi, V. Krishna Prakash, R. Asokan, R. Velusamy, A. Dept. of Computer Science and Engineering Kongunadu College of Engineering and Technology Tamilnadu Trichy India Dept. of Computer Science and Engineering Vignan Bharathi institute of technology Telangana Hyderabad India Dept. of Computer Science and Engineering Mahendra College of Engineering Tamilnadu Salem India Dept. of Information Technology Sona College of Technology Salem India Dept. of Electronics and Communication Engineering Kongunadu College of Engineering and Technology Tamilnadu Trichy India Dept. of Information Technology Sona College of Technology Tamil Nadu Salem India

ISBN: (纸本)9798350376913

The role of Vehicular Ad Hoc Networks (VANETs) is crucial in enabling Intelligent Transportation System (ITS) technologies such as safe financial transactions, media applications, and effective traffic control. As traffic increases, the topology of vehicular networks is in constant flux, and the sparse distribution of vehicles, particularly on highways, presents challenges for network scalability. This situation makes it difficult for cars to keep consistent routes inside the network, which affects the stability of the network. To address these challenges, the developed Adaptive-ant Colony based Randomized Recommendation (ACRR) technique emerges as a unique solution for enhancing VANETs by reducing travel time. In instances of high traffic density on busy roads, the ACRR algorithm is effectively utilized to group vehicles. Leveraging data collected from these densely populated road segments, the system identifies congestion-prone areas and formulates optimal vehicle routes based on customized vehicle groupings. The framework's performance evaluation encompasses various parameters, including packet loss, message transmission rate, energy consumption, and average cluster growth. The proposed VANET framework, empowered by the ACRR algorithm, achieves an impressive message transmission rate of approximately 80%. In comparison, alternative methods like Re-RouTE exhibit a limited transmission rate of 70%, while others such as Net Run Rate (NRR), DIVERT 30, and DIVERT-60 demonstrate rates below 20%. Furthermore, the framework's parcel loss is significantly reduced to only 33% of that observed in the standard VANET framework. As a result, the ACRR algorithm integrated into the VANET framework demonstrates notable efficiency when compared to other approaches. It is crucial to recognize that, even with a refined technique, managing traffic congestion remains challenging if drivers disregard the recommended routing suggestions. Overall, this research offers insights into the p

关键词： Traffic congestion

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Prediction and Modelling of Suicidal Mental Tendencies 8

Prediction and Modelling of Suicidal Mental Tendencies

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8th IEEE International Conference on Distributed Computing, VLSI, electrical Circuits and Robotics, DISCOVER 2024

作者： Guruprasad, Sunitha Rao, Arnav G. Shreeraksha Shivani, K. Varsha, S.N. Yashoda, K. St Joseph Engineering College Dept. of Computer Science & Engineering Mangaluru India Visvesvaraya Technological University Karnataka Belagavi India Ramaiah Institute of Technology Dept. of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350350593

Suicide remains a pressing public health concern worldwide, necessitating effective predictive and preventive strategies. Predicting suicidal tendencies is a complex endeavor, as it involves multifaceted interactions between biological, psychological, and environmental factors. Advances in computational modeling and machine learning have provided promising avenues for understanding and forecasting these tendencies. Such models often integrate clinical data, including psychiatric history, symptom severity, and demographic information, with biological markers and psychosocial variables. The objective of our work is to identify patterns and risk factors that may indicate heightened suicidal risk. Ongoing research seeks to refine predictive models, incorporate real-time monitoring, and develop targeted interventions, aiming to reduce the incidence of suicide and provide timely support to those at risk. The application of suicide text analysis extends to user-friendly interfaces, allowing individuals to input text, receive predictions, and access relevant support. The analysis involves preprocessing techniques such as text normalization, stop-word removal, and stemming to enhance the quality of input data. Various machine learning algorithms, ranging from Naive Bayes to sophisticated ensemble methods, are explored to determine their effectiveness in predicting suicidal ideation. © 2024 IEEE.

关键词： Prediction models

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Optimization of New Radio Physical Uplink Shared Channel Resources Allocation with Efficient Demodulated Referenced Signals and PT-RE Deployment

Optimization of New Radio Physical Uplink Shared Channel Res...

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2024 International Conference on electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Upadhyay, Deepak Gupta, Mridul Sharma, Kunj Bihari Upadhyay, Abhay Yadav, Satya Prakash Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to be University Dept. of Electronics and Communication Engineering Dehradun India Graphic Era Deemed to be University Dept. of Computer Science and Engineering Dehradun India Bennett University School of Computer Science Engineering and Technology Greater Noida India

ISBN: (纸本)9798350378092

This paper explores the resource allocation optimisation of New Radio Physical Uplink Shared Channels with an emphasis on the effective implementation of Phase Tracking Reference Signals and Demodulation Reference Signals in 5G networks. Understanding how important these reference signals are to dependable communication, we created a Python framework to model and display their distribution on a time-frequency grid. By carefully modifying important factors including mapping type, PRB allocation, and symbol allocation, our method greatly increased resource efficiency, decreased overhead, and improved signal quality. The results show how sophisticated resource allocation techniques may be used to maximise system performance, providing important information for the creation of more reliable and effective 5G NR communication systems © 2024 IEEE.

关键词： 5G mobile communication systems

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Reinforcement Learning for Adaptive Traffic Signal Control Using Deep Q-Networks 1

Reinforcement Learning for Adaptive Traffic Signal Control U...

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1st International Conference on Sustainable Energy Technologies and Computational Intelligence, SETCOM 2025

作者： Bohra, Asha Rani Selvi, Thamarai Vishal, V. Sunkara, Pawan Sai Sunku, Balaji Jignesh, Bhavsar Nitya New Horizon College of Engineering Dept. of Computer Science and Engineering Karnataka Bengaluru India New Horizon College of Engineering Dept. of Information Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798331520540

Traffic congestion is a critical issue in urban areas, contributing to increased travel time, fuel consumption, and environmental pollution. Traditional traffic signal control methods, such as fixed-time systems, cannot adapt to real-time changes in traffic conditions. This paper presents a novel approach using Deep Q-Networks (DQN) to control traffic signals based on realtime traffic data dynamically. Our simulation environment models a four-way intersection, where vehicle densities and wait times are continuously monitored. The DQN agent optimizes green light durations by minimizing vehicle wait times and maximizing traffic throughput. Extensive simulation results demonstrate that our system significantly reduces congestion and improves traffic flow compared to traditional fixed-time systems. Furthermore, the approach shows potential for real-world applications in urban traffic control systems, particularly in smart cities equipped with Internet of Things (IoT) devices. © 2025 IEEE.

关键词： Traffic congestion

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The Role of Medical Expert Systems in Diagnosis of Chronic Kidney Diseases 2

The Role of Medical Expert Systems in Diagnosis of Chronic K...

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2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Kumar, Kuchipudi Prasanth Bharati, K.F. Jawaharlal Nehru Technological University Dept. of Computer Science and Engineering Andhra Pradesh Ananthapuramu515002 India Jntua College of Engineering Dept. of Computer Science and Engineering Andhra Pradesh Ananthapuramu515002 India

ISBN: (纸本)9798350370188

The ability to forecast kidney diseases in real-time is critical for preserving patients' lives. Early detection of these diseases not only extends the patient's longevity but also reduces treatment costs. It is very advised to develop a reliable expertise system for detecting these problems before they worsen. These smart solutions have the potential to save many lives. This review study assesses the Dice score efficacy characteristics obtained from various methodologies utilized in previous studies for detecting renal abnormalities or chronic kidney diseases. It gives a complete overview of medical expert systems in proposed methodology and emphasizes the most effective methods for identifying chronic kidney disease (CKD). © 2024 IEEE.

关键词： Diagnosis

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