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Intelligent Predetermination of Generator Tripping Scheme: Knowledge Fusion-based Deep Reinforcement Learning Framework

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CSEE Journal of Power and Energy Systems 2024年第1期10卷 66-75页

作者： Lingkang Zeng Wei Yao Ze Hu Hang Shuai Zhouping Li Jinyu Wen Shijie Cheng State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronics EngineeringHuazhong University of Science and TechnologyWuhan 430074China Dispatching and Control Center Central China Branch of State Grid Corporation of ChinaWuhan 430077China. Department of Electrical Engineering and Computer Science University of TennesseeKnoxvilleTN 37996USA

Generator tripping scheme(GTS)is the most commonly used scheme to prevent power systems from losing safety and ***,GTS is composed of offline predetermination and real-time scenario ***,it is extremely time-consuming and labor-intensive for manual predetermination for a large-scale modern power *** improve efficiency of predetermination,this paper proposes a framework of knowledge fusion-based deep reinforcement learning(KF-DRL)for intelligent predetermination of ***,the Markov Decision Process(MDP)for GTS problem is formulated based on transient instability ***,linear action space is developed to reduce dimensionality of action space for multiple controllable ***,KF-DRL leverages domain knowledge about GTS to mask invalid actions during the decision-making *** can enhance the efficiency and learning ***,the graph convolutional network(GCN)is introduced to the policy network for enhanced learning *** simulation results obtained on New England power system demonstrate superiority of the proposed KF-DRL framework for GTS over the purely data-driven DRL method.

关键词： Deep reinforcement learning generator tripping scheme graph convolutional network invalid action masking knowledgefusion

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Implementation of Secure and Verifiable Access Control Procedures using the NTRU Cryptosystem to Store Big Data in the Cloud Environment

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Recent Patents on engineering 2025年第2期19卷 57-70页

作者： Babu B, Ravindra Saxena, Swati Widjaja, Gunawan Vinodha D, Vedha Omarov, Batyrkhan Ramaiah, Gurumurthy B. Vats, Prashant Adama Ethiopia Department of Computer Science KCG College of Technology Chennai India Universitas Krisnadwipayana Jawa Barat 13077 Indonesia Department of Electronics and Communication Engineering JCT College Of Engineering and Technology Pichanur Coimbatore India International University of Tourism and Hospitality Turkistan Kazakhstan Technical and Vocational Training Institute Addis Ababa Ethiopia Department of Computer Science and Engineering Faculty of Engineering and Technology SGT University Haryana Gurugram India

Background: Due to their complexity and size, deploying ciphertexts for clouds is considered the most useful approach to accessing large data stores. Methods: However, access to a user's access legitimacy and improving a decrypted text on the cloud depending on an improved access policy (AP) specified by the data owner are the key challenges for making large data storage realistic and effective in clouds. The traditional ways either totally remove the problem of AP development or offer renewal to arbiter power, but in real-time, enhancing the AP is essential to maximising security and handling agility. Results: In this patent paper, a safe and verifiable access control program characterised by the NTRU cryptographic system for large storage of data in the clouds is proposed. Primarily, an improved NTRU decryption protocol to deal with the decryption failures of the prime NTRU is established, and in addition, the program is analysed for its security strength and computational performance. When a new AP is specified by the data owner, the cloud server allows the program to improve ciphertext and allows the owner to verify the upgrade to oppose the cloud's fraudulent behaviour. Conclusion: It enables (i) checking the user's legitimacy to access the data owner and qualified users, and (ii) allowing the user to verify the data provided by other users for the recovery of the right user. Strong analysis can prevent and block delinquency from various attacks, namely the collusion attack that could potentially target fraud users. © 2025 Bentham Science Publishers.

关键词： Cloud security

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A robust framework for Alzheimer’s disease detection and staging: incorporating multi-feature integration, MRMR feature selection, and Random Forest classification

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Multimedia Tools and Applications 2024年 1-29页

作者： HK, Ravikiran R, Deepak H A, Deepak M S, Prapulla Kumar S, Sharath G H, Yogeesh Department of Electronics and Communication Engineering Navkis College of Engineering Karnataka Hassan573217 India Department of Electronics and Communication Engineering BGS Institute of Technology Adichunchanagiri University B.G Nagar Karnataka Mandya571448 India Department of Computer Science Malnad College of Engineering Karnataka Hassan573202 India Department of Electronics and Communication Engineering Government Engineering College KarnatakaChamarajanagar 571313 India

In advancing Alzheimer’s disease detection, this study presents a potent hybrid approach that leverages sophisticated features. Commencing with the TVL1 norm, the method reduces noise and enhances relevant information within neuroimaging data. Further cartoon features from the TVL1 norm are extracted as texture features. GLSZM features unveil critical texture pattern variations, an early hallmark of Alzheimer’s, by meticulously analyzing the size and distribution of homogeneous zones in the brain. Simultaneously, level-set segmented image features provide invaluable structural insights into the brain’s morphology. Integrating a Convolutional Neural Network (CNN) empowers the system to decipher intricate spatial patterns. At the same time, minimum redundancy and maximum relevance (MRMR) feature selection meticulously curates the most salient and non-redundant features. Finally, a Random Forest classifier seamlessly harmonizes these diverse feature sources, culminating in a robust model that can discern subtle signatures of Alzheimer’s disease. The novel approach not merely presents a rapid and precise module for Alzheimer’s disease (AD) diagnosis. Still, it also provides the capacity for both comprehensive global classification ("Normal vs. Mild Cognitive Impairment (MCI) vs. AD") and precise local classification. The latter facet addresses the intricate undertaking of differentiating MCI into three categories: Very Mild Dementia (VMD), mild dementia (MD), and Moderate Dementia (MoD) as early AD stages. Moreover, the study undertakes a comparative analysis of the proposed methodology against previous techniques in the field. The reported results demonstrate exceptional accuracy and robustness, with an overall testing accuracy of 93.11%, showcasing the method’s effectiveness in AD diagnosis and staging. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Convolutional neural networks

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Faster R-CNN based Framework For Bone Fracture Detection in Telesurgery Systems in Healthcare 4.0 5

Faster R-CNN based Framework For Bone Fracture Detection in ...

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5th IEEE Global Conference for Advancement in technology, GCAT 2024

作者： Patel, Yogi Patel, Dron Patel, Het Gupta, Rajesh Tanwar, Sudeep Nirma University Institute of Technology Department of Computer Science and Engineering India Nirma University Institute of Technology Department of Electronics and Communication Engineering India

ISBN: (纸本)9798350376685

This paper introduces an ideal structure for the detection and treatment of bone fractures utilizing advanced deep learning and telesurgery techniques. The approach begins with the collection of medical imaging data from the X-Ray Layer, which is subsequently divided into training, validation, and testing datasets. The proposed model, ResNet50, is trained on training dataset and validated with the validation dataset to ensure robust performance. Once the model is trained, it accurately detects bone fractures in X-ray images and identifies the region of the fracture using an array of four points representing bounding box defined by four corners. Upon detecting a fracture, telesurgery is employed to treat the patient through a remote arm controlled by a doctor, providing minimally invasive procedures that reduce surgical risks. In case of no fracture, the patient is advised on precautionary measures and follow-up care. This comprehensive framework demonstrates a method for improving the diagnosis and treatment of bone fractures, offering efficient, precise, error-free, and patient-centric solutions. The combination of deep learning with telesurgery shows advancement in medical technology, potentially reducing need for in-person consultations also improving specialized care. Future advancement can include model improvement by using more diverse datasets, integrating additional imaging modalities such as CT and MRI, and expanding real-time capabilities in emergency situations. © 2024 IEEE.

关键词： Medical imaging

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Design of Clustering Techniques in Cognitive Radio Sensor Networks

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computer Systems Science & engineering 2023年第1期44卷 441-456页

作者： R.Ganesh Babu D.Hemanand V.Amudha S.Sugumaran Department of Electronics and Communication Engineering SRM TRP Engineering College621105Tamil NaduIndia Department of Computer Science and Engineering Sriram Engineering College602024Tamil NaduIndia Department of Electronics and Communication Engineering Saveetha School of Engineering602105Tamil NaduIndia Department of Electronics and Communication Engineering Vishnu Institute of TechnologyBhimavaram534202Andhra PradeshIndia

In recent decades,several optimization algorithms have been developed for selecting the most energy efficient clusters in order to save power during trans-mission to a shorter distance while restricting the Primary Users(PUs)*** Cognitive Radio(CR)system is based on the Adaptive Swarm Distributed Intelligent based Clustering algorithm(ASDIC)that shows better spectrum sensing among group of multiusers in terms of sensing error,power sav-ing,and convergence *** this research paper,the proposed ASDIC algorithm develops better energy efficient distributed cluster based sensing with the optimal number of clusters on their *** this research,multiple random Sec-ondary Users(SUs),and PUs are considered for ***,the pro-posed ASDIC algorithm improved the convergence speed by combining the multi-users clustered communication compared to the existing optimization *** results showed that the proposed ASDIC algorithm reduced the node power of 9.646%compared to the existing ***,ASDIC algorithm reduced 24.23%of SUs average node power compared to the existing *** of detection is higher by reducing the Signal-to-Noise Ratio(SNR)to 2 dB *** proposed ASDIC delivers low false alarm rate compared to other existing optimization algorithms in the primary *** results showed that the proposed ASDIC algorithm effectively solves the multimodal optimization problems and maximizes the performance of net-work capacity.

关键词： Adaptive swarm distributed clustering cognitive radio clustering algorithm distributed swarm intelligent energy efficient distributed cluster-based sensing multi modal optimization

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Detecting potholes with a machine learning algorithm for road safety 3

Detecting potholes with a machine learning algorithm for roa...

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3rd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2024

作者： Singh, Yuvraj Roy, Sritama Vellore Institute of Technology School of Computer Science and Engineering Chennai India Vellore Institute of Technology School of Electronics Engineering Chennai600127 India

ISBN: (纸本)9798350364569

An important part of a country's infrastructure are its roads, which provide a vast transportation network necessary for the movement of people and goods. Road transport is the most cost-effective means of transport in India because it is widely available in densely populated areas. On the other hand, potholes continue to be a major problem that cause delays in transit and, regrettably, fatalities. A system and corresponding algorithms have been developed to detect the probability of potholes in order to tackle this problem. With the least amount of work, Single Shot Detection (SSD) technology is essential to accomplishing the goal. SSD's fast object identification and localisation in photos, along with its real-time processing capabilities, make it possible to detect potholes on road surfaces with efficiency. By utilising SSD technology, the proposed system offers a dependable means of reducing the detrimental effects of potholes on traffic safety and the efficiency of transportation. © 2024 IEEE.

关键词： Motor transportation

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Smart IoT-Based Emotion Sensing Wearable: Revolutionizing Emotional Well-Being 2

Smart IoT-Based Emotion Sensing Wearable: Revolutionizing Em...

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2nd International Conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Kannapiran, Pritika Roy, Sritama School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Electronics Engineering Vellore Institute of Technology Chennai600127 India

ISBN: (纸本)9798350366570

In the fast-paced world of today, mental health has become a critical issue, with emotional stability being a key component of general health. Traditional methods of detecting and managing emotions often fall short due to their reliance on verbal and facial cues, leaving individuals with conditions such as Alexithymia or Autism Spectrum Disorder (ASD) at a disadvantage. This paper introduces a novel solution: the Smart IoT-Based Emotion Sensing Wearable, is designed to revolutionize emotional sensing and management. The primary goal of the research is to create a wearable gadget that can track vital signs and instantly identify different emotions. Using the Internet of Things (IoT), machine learning, and cloud computing, this wearable device bridges the gap for people with special needs and improves emotional understanding for all users by providing real-time emotion recognition and targeted help. © 2024 IEEE.

关键词： Emotion Recognition

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Modeling and Simulation of Virtual Reality Assistive Tool for Children with Autism Spectral Disorder 2nd

Modeling and Simulation of Virtual Reality Assistive Tool fo...

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2nd International Conference on Deep Sciences for Computing and Communications, IconDeepCom 2023

作者： NirmalaPriya, G. Shree Aravindh, S.S. Tharun, D. Deepak, M. Hermalin, Cynthiya S. Department of Electronics and Communication Engineering Rajalakshmi Institute of Technology Chennai India Department of Computer and Communication Engineering Rajalakshmi Institute of Technology Chennai India

ISBN: (纸本)9783031689079

Autism spectrum disorder (ASD) is a prevalent issue contributing to deprivation among people in terms of their capability to maintain social relationships and interact normally with fellow members of society. Teens with ASD battle to exercise proper sociable behavior as well as mingle with others sharing the same age range. It affects all sorts of races, ethnicities and budgets alike. Despite its long- term nature, ASD has therapies and treatments that can help address issues associated with it such as restlessness, aggression, repetitive behavior, focus difficulties, worry and depression through the use of medicine. The use of virtual reality-based training, in addition to medical intervention, may be used to address various behavioral tradeoffs through repeated conditioning and learning. A variety of learning scenarios that would be unsafe, challenging, or time-consuming to set up or frequently recreate in the real world can now be simulated using virtual reality (VR) technology. In this paper, a virtual reality simulator for with the aid of social educators, achieve this goal. This study used the gaming program Social Activity Trainer to simulate and construct a virtual reality (VR)-based assistive aid (SAT). The assistive tool was created to help youngsters with ASD interact and use social skills. Based on three key skills—facial expression recognition, reading comprehension, and task delivery—seven tasks were created for the VR assistive tool. There are seven modules in a virtual reality scenario, including one for music therapy. The EEG signals are looked at to assess the degree of autism in 10 ASD children who participated in the test. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Diseases

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Circuit Design using Quantum Dot technology 1

Circuit Design using Quantum Dot Technology

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Manjunath, T.C. Kuravatti, Prakash Rajarajeswari College of Engineering Department of Computer Science & Engineering Bangalore India Rajeev Institute of Technology Department of Electronics and Communication Engineering Hassan India

ISBN: (纸本)9798331529246

In this paper, the harnessing QCA for High-Performance, Low-Power Arithmetic Circuits w.r.t. the focusing on Multipliers and Square Circuits is presented. The background in relation to this work is presented next. Quantum-dot Cellular Automata (QCA) is an emerging technology in nano-electronics that offers promising advantages over traditional CMOS (Complementary Metal-Oxide-Semiconductor) technology, including low power consumption, high operational speeds, and exceptional integration capabilities. These features position QCA as a potential successor to CMOS in various applications. Multipliers, critical components in digital signal processing, and square circuits, essential for mathematical computations, significantly impact system throughput and power efficiency. This research focuses on designing QCA-based multiplier and square circuits to leverage these advantages. Methods, Results, and Concluding Remarks w.r.t. the work is given here. The QCA-based multiplier was constructed using binary adders and AND gates, while a comparative analysis was conducted between QCA and CMOS technologies to evaluate performance. The evaluation considered parameters such as cell count, area, and energy dissipation, utilizing the QCA Designer tool for validation. Results demonstrate that QCA designs offer significant improvements in terms of reduced power dissipation and area requirements compared to CMOS. These findings highlight the potential of QCA technology to enhance the efficiency and integration of critical components in digital systems, marking it as a transformative approach in nano-electronics. © 2025 IEEE.

关键词： CMOS integrated circuits

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An Approach to Detect and Classify Potentially Suspicious Activity from Real-Time Log Data using Anomaly Detection Methods 3

An Approach to Detect and Classify Potentially Suspicious Ac...

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3rd International Conference for Innovation in technology, INOCON 2024

作者： Sengupta, Arnab Kundu, Akash Mukhopadhyay, Aishi Kolkata India Heritage Institute of Technology Computer Science & Engineering Kolkata India Heritage Institute of Technology Electronics & Communication Engineering Kolkata India

ISBN: (纸本)9798350381931

This paper aims to propose an intricate yet streamlined approach to incorporate Machine Learning into Cybersecurity by employing anomaly detection techniques to screen real-time log data and identify as well as classify suspicious activity. Our research focuses on avoiding misclassification with particular emphasis on minimising permissivity to ensure that threats with greater risk are not misclassified to a lesser priority. The paper explores a two-tiered strategy comprising an initial model dedicated to the identification of potentially suspicious log data through anomaly detection methods, serving as a preliminary filter. Subsequently, a secondary model is tasked with the responsibility of classifying the identified threats into distinct priority levels based on anomalous features. This approach sets groundwork for a robust framework that prioritises minimising vulnerability while maximising accuracy. © 2024 IEEE.

关键词： Machine learning

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