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Improving Power Factor using Deep Learning algorithms 3

Improving Power Factor using Deep Learning Algorithms

引用

3rd international conference on Electronics and Renewable systems, ICEARS 2025

作者： Tholkappiyan, B. Kaviarasan, R. Parthasarathy, T.R. Dhanapal, M. Gopalakrishnan, R. K.S.Rangasamy College of Technology Electrical and Electronics Engineering Tiruchengode637215 India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509675

In today's power systems, efficient energy efficiency has gained importance due to the increasing demand and integration of electrical (DER) devices such as solar and wind. Unhealthy power problems, including power sags, swells, harmonic, and transients, can lead to inefficiencies and equipment damage. This research looks into the ability of deep learning algorithms to improve energy efficiency by identifying and mitigating these effects in real time. Other methods depend on statistical models and rule-based procedures. Deep learning algorithms, on the other hand, can learn directly from raw data. Examples include recurrent neural networks (RNN) and convolutional neural networks (CNN). This provides a more detailed understanding of negative energy levels and enables faster, more accurate detection. Deep learning models can also continuously adjust to grid changes, which is crucial for controlling a variety of integrated energy resources, sensors, and further devices for monitoring. This research addresses these issues by using deep learning in the learning architecture optimizing it for efficient power consumption. The deep learning model also demonstrates the flexibility to switch to different lines of the grid, which is important for maintaining power and flexibility. As the grid continues to evolve with the integration of renewable energy and distribution, deep learning holds promise for managing energy efficiency. © 2025 IEEE.

关键词： Deep learning Renewable energy sources Recurrent neural networks Active filters Energy efficiency Real-time systems Sensors Artificial intelligence Voltage control Transient analysis

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Wireless Charging Technologies for Electric Vehicles: State of the Art and Future Outlook

Wireless Charging Technologies for Electric Vehicles: State ...

引用

2025 IEEE international Students' conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Sarathkumar, D. Parrthipan, B.K. Manivel, Murugesan Jayakumar, T. Rao, R Subramani Sambooranalaxmi, S. Department of Electrical and Electronics Engineering Tamil Nadu 638060 India Kamaraj College of Engineering and Technology Department of Mechanical Engineering Tamil Nadu Vellakulam625701 India Karpagam Institute of Technology Department of Electrical and Electronics Engineering Tamilnadu Coimbatore641021 India Nandha Engineering College Department of Electrical and Electronics Engineering Tamilnadu 638052 India V.S.B Engineering College Department of Electrical and Electronics Engineering Karur India Psr Engineering College Department of Electronics and Communication Engineering Tamil Nadu Sivakasi626140 India

ISBN: (纸本)9798331529833

Wireless charging technology for electric vehicles (EVs) represents a significant advancement in the transition to sustainable transportation, offering a convenient and efficient alternative to traditional plug-in charging systems. This paper provides a comprehensive review of the state-of-the-art wireless charging techniques, with a focus on inductive power transfer (IPT), resonant inductive coupling, and emerging capacitive and magnetic resonance methods. Key components of wireless charging systems, including power electronics, coil design, alignment techniques, and communication protocols, are discussed. We also address the challenges related to efficiency, safety, standardization, and scalability, particularly in dynamic charging systems designed for highway applications. The paper further explores the integration of wireless charging into smart grids, considering the potential for vehicle-to-grid (V2G) applications and renewable energy sources. A comparative analysis of existing technologies and their readiness for widespread adoption is provided, alongside an outlook on future trends, including high-power charging, autonomous charging infrastructure, and advances in materials and control algorithms. The paper concludes by identifying research gaps and potential directions for innovation in wireless charging for EVs. © 2025 IEEE.

关键词： Wireless charging

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Water Leakage Control Device in Metro Water Pipeline

Water Leakage Control Device in Metro Water Pipeline

引用

2025 international conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Nivishna, S. Kalaivani, C. Hemamalini, S. Arul Arumugam, R. M. Kumarasamy college of Engineering Department of Electronics and communication Engineering Tamilnadu Karur India Krishnaswamy college of Engineering and Technology Department of Electrical and Electronics Engineering Tamilnadu Cuddalore India Anna University Tamilnadu Chennai India Annapoorana Engineering College Department of Electronics and communication Engineering Tamilnadu Salem India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509828

The need for effective water management systems has grown as a result of the population expansion and increasing urbanization of metropolitan areas. Water leaks in metro water pipelines, however, present a serious problem since they cause financial losses, structural damage, and water loss. As a result, creating a Smart Water Leakage Controller (SWLC) becomes essential for immediately identifying and addressing leaks. The main elements and features of a SWLC system created especially for metro water pipelines are described in this abstract. In order to continually monitor the pipeline network, the SWLC system integrates cutting-edge sensor technology, including acoustic, pressure, and flow sensors. These sensors pick up on irregularities that could be signs of leaks, like sharp reductions in pressure or very high flow rates. The sensors send real-time data to a central control unit that has sophisticated algorithms for data processing and decision-making. The SWLC system takes rapid action to minimize water loss and stop more damage after detecting a leakage occurrence. To ensure system stability, these steps can entail alerting maintenance staff, isolating the impacted pipeline portion, and modifying water flow. © 2025 IEEE.

关键词： Decision making

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Leveraging Instruction-based Learning for Effective UAV Traffic Management in Dynamic Environment

Leveraging Instruction-based Learning for Effective UAV Traf...

引用

2025 international conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Ramya, R.S. Daniel, Keren Lois Rajkumar, S. Madhavan, Sruthi S. Akshya, J. Choudhry, Mani Deepak Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India KGiSL Institute of Technology Department of Computer Science and Business Systems Coimbatore India Sona College of Technology Department of Information Technology Salem India Nehru Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798331509828

With the popularization of UAVs (Unmanned Aerial Vehicles) in surveillance, delivery services, environmental monitoring, and increasingly other operational aspects, besides the need to take off and land, efficient traffic management becomes integral for assuring and securing further development and exploitation. Most of the existing traffic management systems for UAVs depend on traditional rule-based algorithms or simpler machine learning techniques, such as DQN or PPO. However, these systems also have enormous challenges: dynamic environments, high-density traffic, and robust decision-making over unpredictable weather and network conditions. In addition, the above traditional models usually experience difficulties in balancing exploration and exploitation, leading to suboptimal routing and enlarged collision risks in complicated urban airspaces. These challenges have driven us to develop a new proposal: a UA V traffic management framework based on Double Deep Q-Network (DDQN) combined with Instruction-Based Learning. In this approach, advanced learning for DDQN optimizes processes at decision-making points for UAVs. The model proposed herein, along with comprehensive simulations and quantitative analyses, demonstrated higher performance in terms of leadership and cumulative rewards while keeping lower training loss compared to the classical algorithms such as DQN, A3C, and PPO. At the same time, the DDQN model presented a better success rate (95%), as also reflected in the balanced precision-recall curve showing an area under the curve of 0.90, over others in the on-point prediction of UAVstate and safety margin of collision. Our experimental results demonstrate that the proposed DDQN-based framework significantly enhances UA V traffic management by optimizing energy consumption in complex airspaces and reducing the risk of collision. © 2025 IEEE.

关键词： Decision making

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A Method for detecting the environmental adaptability of spectral temperature brightness characteristics of polyurethane functional materials 3

A Method for detecting the environmental adaptability of spe...

引用

3rd international conference on algorithms, Network, and Communication Technology, ICANCT 2024

作者： Zhu, Fan Deng, Xianming Zhang, Tiancai Wang, Lin Zhang, Yixiang Wu, Xinrui Southwest Institute of Technology and Engineering Chongqing400039 China Laboratory of Intelligent Unmanned Systems School of Automation Chongqing University Chongqing400044 China

ISBN: (数字)9781510688902

ISBN: (纸本)9781510688896

A method for detecting the environmental adaptability of spectral temperature brightness characteristics of polyurethane functional materials is proposed. This method fully combines the advantages of local preserving projection algorithm, morphological dual window constrained detection model, feature fusion method of BEMD and W transform, deep fusion neural network model, multi evidence combination D-S inference model, multi-dimensional ROC fusion evaluation model, weighted joint regression network model, and random forest model based on forward distribution and Adaboost correction. It also constructs a degradation law model of the image fusion degree between polyurethane functional materials and natural environment, and a model of the influence of natural environmental factors in typical regions on the fusion characteristics of polyurethane functional materials. The classification index of environmental adaptability level of fusion features of polyurethane functional materials is formulated. The experimental results indicate that the environmental adaptability levels of polyurethane functional materials in natural environments in Chongqing and Lhasa are first level adaptation and second level adaptation, respectively. © 2025 SPIE.

关键词： Luminance

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Comparative Analysis of Traditional and Ensemble Learning Models for Network Security System

Comparative Analysis of Traditional and Ensemble Learning Mo...

引用

2025 IEEE international conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Verma, Anand Rathod, Maya Oriental University Department of Computer Science and Engineering Indore India

ISBN: (纸本)9798331512965

In this paper use CIC-IDS2017 dataset to illustrate a comparative analysis of traditional and proposed models for intrusion detection in network security systems. The comparison includes DT, RF, ET and XGBoost classifiers. Normalize class, SMOTE Data preprocessing to compensate for missing values and of imbalance classes From the results of evaluation metrics, we conclude that proposed methods show a significant improvement on its model performances in detecting network intrusions as compared to traditional models XGboost specifically this work. Out of all the algorithms, the accuracy for XGBoost stands out, as it is mostly known to cater to complex attack patterns. The results indicate that ensemble base techniques offer the best solutions which can prove to be both effective and efficient as an intrusion detection systems for current network technologies. © 2025 IEEE.

关键词： Network intrusion

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Spectrum Sensing and Management in Cognitive Radio Networks using Genetic Algorithm based Soft Decision Fusion 1

Spectrum Sensing and Management in Cognitive Radio Networks ...

引用

1st international conference on Intelligent systems and Computational Networks, ICISCN 2025

作者： Rupavath, Rana Veer Samara Sihman Brahmatej Suganya, E. Obidhusin, Saef Gupta, Payal Soni Anitha, R. Indiana Wesleyan University Marion United States Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Dhanalakshmi Srinivasan College of Engineering and Technology Department of Electronics and Communication Engineering Mamallapuram India New Prince Shri Bhavani College of Engineering and Technology Department of Information Technology Chennai India

ISBN: (纸本)9798331529246

In recent years, the advancements of wireless technologies have led to a significant demand in radio frequency spectrum. Cognitive Radio Networks (CRN) are used among the intelligent wireless communication systems that enhanced spectrum efficiency. However, CRN with their help in enhancing the spectrum but possesses various challenges such as noise, dynamic environments that complicate the process of sensing. Therefore, in this research Genetic Algorithm based Soft Decision Fusion (GASDF) model is proposed for an effective Spectrum Sensing and Management in CRN. Primarily, the input data is collected through radio network signals, then the signals are preprocessed using the Z-score normalization to reduce the noise from the signals. Further, the relevant features are extracted using Continuous Wavelet Transform (CWT). Then, spectrum allocation is done using Differential Evolution (DE) and the spectrum sensing is done through GASDF. From the results, proposed model attained better outcomes compared to existing Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) with 2.45% Probability of false alarm (Pf) rate. © 2025 IEEE.

关键词： Genetic algorithms

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Exploring Hand Gesture Recognition: Trends, Technologies, and Application 3

Exploring Hand Gesture Recognition: Trends, Technologies, an...

引用

3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Pohekar, Ayesha Pundge, Ajitkumar Shirsath, Vishal School of Engineering Ajeenkya DY Patil University Maharashtra Pune India

ISBN: (纸本)9798331527549

Hand Gesture Recognition (HGR) systems have garnered significant attention due to their applications in human-computer interaction, virtual reality, and assistive technologies. However, achieving adaptable, accurate, and user-friendly HGR solutions remains a challenge due to issues such as environmental variability and user adaptability. This survey explores state-of-the-art HGR techniques, encompassing traditional methods and advanced deep learning-based methodologies. Classification techniques are analyzed based on sensory-based, vision-based, and hybrid input approaches, highlighting their respective strengths and limitations. The impact of feature extraction methods, dataset quality, and real-time processing on the performance of classification algorithms is discussed. By addressing these challenges, this work identifies potential pathways for advancing HGR systems to enhance their usability and reliability across diverse applications. © 2025 IEEE.

关键词： Palmprint recognition

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AI-Enhanced Cyber Security Proactive Threat Detection and Response systems 4

AI-Enhanced Cyber Security Proactive Threat Detection and Re...

引用

4th international conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Nallapareddy, Venkata Sai Swaroop Reddy Katta, Siva Koteswara Rao Sony Interactive Entertainment Department of Software Engineering Austin United States KIETW Department of Mechanical Engineering India

ISBN: (纸本)9798331523923

The introduction of AI-Enhanced Cyber Threat Detection, which aids in improving organizational security through sophisticated algorithms, has caused a paradigm change in cybersecurity. The proactive posture of AI-driven detection systems that autonomously sift through massive data sets to identify patterns suggestive of cyber intrusions and attacks is explored in this research study. These tools relieve human analysts of routine tasks like threat identification and incident response, freeing them to focus on more strategic matters like making decisions and developing mitigation measures. Artificial intelligence (AI) based detection also makes monitoring network activities easier in real-time, increasing operational efficiency and decreasing dwell time. Another approach to accomplishing this in cybersecurity is combining different technologies, including threat intelligence platforms, with AI or SOAR systems. Further research and improved collaboration are needed to strengthen AI-enabled cyber threat identification in the face of obstacles like adversarial attacks and ethical concerns. Given the prevalence of modern e-crimes, this paper stresses the need for joint defence strategies and proactive cybersecurity measures to safeguard digital assets. © 2025 IEEE.

关键词： Cyber attacks

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Distributed learning with partial information sharing 17

Distributed learning with partial information sharing

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17th international conference on COMmunication systems and NETworkS, COMSNETS 2025

作者： Rao, P. Raghavendra Vyavahare, Pooja IIT Tirupati Department of Electrical Engineering India

ISBN: (纸本)9798331531195

This work studies the distributed learning process on a network of agents. Agents make partial observations about an unknown hypothesis and iteratively share their beliefs over a set of possible hypotheses with their neighbors to learn the true hypothesis. We present and analyze a distributed learning algorithm in which agents share belief on only one randomly chosen hypothesis at a time. Agents estimate the beliefs on missed hypotheses using previously shared beliefs. We show that agents learn the true hypothesis almost surely under standard network connectivity and observation model assumptions if belief on each hypothesis is shared with positive probability at every time. We also present a memory-efficient variant of the learning algorithm with partial belief sharing and present simulation results to compare rate of convergence of full and partial information sharing algorithms. © 2025 IEEE.

关键词： Adversarial machine learning

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