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arXiv 2025年

作者： Wang, Yue Wang, Qizhou Liu, Feng Huang, Wei Du, Yali Du, Xiaojiang Han, Bo TMLR Group Department of Computer Science Hong Kong Baptist University Hong Kong The University of Melbourne Australia RIKEN Center for Advanced Intelligence Project Japan King’s College London United Kingdom Department of Electrical and Computer Engineering Stevens Institute of Technology United States

Large language model (LLM) unlearning has demonstrated its essential role in removing privacy and copyright-related responses, crucial for their legal and safe applications. However, the pursuit of complete unlearning often comes with substantial costs due to its compromises in their general functionality, leading to a notorious trade-off between unlearning and retention. In examining the update process for unlearning dynamically, we find gradients hold essential information for revealing this trade-off. In particular, we look at the varying relationship between retention performance and directional disparities between gradients during unlearning. It motivates the sculpting of an update mechanism derived from gradients from two sources, i.e., harmful for retention and useful for unlearning. Accordingly, we propose Gradient Rectified Unlearning (GRU), an enhanced unlearning framework controlling the updating gradients in a geometry-focused and optimization-driven manner such that their side impacts on other, unrelated responses can be minimized. Specifically, GRU derives a closed-form solution to project the unlearning gradient onto the orthogonal space of that gradient harmful for retention, ensuring minimal deviation from its original direction under the condition that overall performance is retained. Comprehensive experiments are conducted to demonstrate that GRU, as a general framework, is straightforward to implement and efficiently enhances a range of baseline methods through its adaptable and compatible characteristics. Additionally, experimental results show its broad effectiveness across a diverse set of benchmarks for LLM unlearning. Copyright © 2025, The Authors. All rights reserved.

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IOT based Electric Vehicles - Implementation

IOT based Electric Vehicles - Implementation

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2022 International Conference on Innovative Computing, Intelligent Communication and Smart electrical Systems, ICSES 2022

作者： Raffik, R. Singh, Virendra Kumar, Sumit Alanya-Beltran, Joel Jethabhai, Patel Yogeshkumar Singh, Rajesh Kumaraguru College of Technology Department of Mechatronics Engineering Tamilnadu Coimbatore India Babasahab Bhimrao Ambedkar University Department of Computer Engineering UIET Uttar Pradesh Lucknow India RGPV Bhopal/Technocrats Institute of Technology Departement of Electrical Engineering Bhopal India Universidad Tecnológica Del Perú Electronic Department Peru Shri C.J. Patel College of Computer Studies Sankalchand Patel University Faculty of Computer Science Gujarat India Uttaranchal University Uttaranchal Institute of Technology India

ISBN: (数字)9781665474139

ISBN: (纸本)9781665474139

In today's world, autonomous automation is inspiring researchers, and growing human needs are driving students and scientists to build new automation devices. This paper will help you understand the changing trends in self-driving car technology. Advances and upgrades in technology have improved their performance and usage. The most important requirement for an automated device or vehicle is the Communication between them. In addition, the Government of India attaches great importance to military progress. In this regard, this paper focuses on a perfect connection system between remote controlled bots, as long as the connection is not an obstacle. To achieve this goal, the relay device (with Raspberry Pi) is configured as a repeater of the connection network that connects the remote control to the vehicle (bot). In this project, the Wi-Fi adapter will act as a beacon or extender to extend the connectivity and range of remote vehicles. This connection allows the bot to easily capture live feeds and send data for face recognition on the receiving side. This project leverages advanced research on network configurations and intercommunication schemes for IoT devices. The range of remote connections is Extended by the relay chain method. This further improves the connection with the bot through subsequent relay bots, making data transmissions untraceable and encrypted. By using existing algorithms, obstacles and Suspicious activity can be easily detected and sent to military bases. © 2022 IEEE.

关键词： Face recognition

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Cyclic Sensing: An Orbital Spectrum Sensing Method for Uplink NOMA IoT Systems

Cyclic Sensing: An Orbital Spectrum Sensing Method for Uplin...

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IEEE International Conference on Communications (ICC)

作者： Tianheng Xu Yinjun Xu Jingyi Wu Haijun Zhang Ting Zhou Honglin Hu Victor C.M. Leung Shanghai Advanced Research Institute Chinese Academy of Sciences Shanghai China Shanghai Frontier lnnovation Research Institute Shanghai China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China School of Microelectronics Shanghai University Shanghai China Department of Electrical and Computer Engineering The University of British Columbia Canada College of Computer and Software Engineering Shenzhen University China

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

The upcoming sixth-generation (6G) of ubiquitous connectivity communications systems is driven by the next-generation of Internet of Things (IoT) technology. Accordingly, the demand for spectrum resources is growing exponentially. Spectrum sensing, which dynamically explores spectrum holes, is expected to be crucial in the 6G era. In the meantime, Non-Orthogonal Multi-Access (NOMA), an efficient means of reusing resources, can enable multiple users to share the same frequency band stably. The combination of both technologies holds promise for more effective use of spectrum resources in future commu-nications. In this paper, we present a spectrum sensing method for the uplink communication scenario in NOMA with multi-user interference, which aims to make effective use of both static and dynamic gain on spectrum efficiency. Firstly, the sensing criterion and workflow are outlined. The closed-form solution for the sensing threshold configuration is derived thereafter. The simulation results demonstrate the feasibility of the proposed approach, which can increase the system throughput up to 19.7%, when compared to NOMA without spectrum sensing.

关键词： 6G mobile communication NOMA Closed-form solutions Simulation Throughput Orbits Sensors

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Deep Learning PNN Based Fault Monitoring System for Three Phase Industrial Drive System

Deep Learning PNN Based Fault Monitoring System for Three Ph...

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International Conference on Circuit, Power and Computing Technologies (ICCPCT)

作者： Chempavathy B K. David Raju P. K. Mani S. Vijayalakshmi N. Janaki D. Karthikeyan Department of Computer Science and Engineering New Horizon College of Engineering Bangalore India Department of Electrical and Electronics Engineering Malla Reddy Engineering College For Women Hyderabad India Department of Electrical and Electronics Engineering Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai India Department of Electrical and Electronics Engineering K. Ramakrishnan College of Technology Samayapuram Tiruchirappalli Tamil Nadu Department of Electrical and Electronics Engineering Vels Institute of Science Technology and Advanced Studies Chennai Tamilnadu India Department of Electrical and Electronics Engineering SRM Institute of Science and Technology Chennai

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372823

Asynchronous motors are widely used in a variety of sectors. Even though induction motors are strong and dependable, they are susceptible to a variety of problems. Faults in induction motors can result in horrible events such as operator injuries, manufacturing disruption, and raw material loss. As a result, defect identification has become more crucial in induction motor management. Among the different problems that can occur in the motor, bearing failure is a severe issue that can cause catastrophic damage to the machine if not detected at an early stage of the fault. As consequently, the functioning of bushings in induction machinery must be checked on a regular basis. This research proposes a novel method for analyzing speed using discrete wavelet transform (DWT) and identifying bearing difficulties using Probabilistic Neural Network (PNN) optimized by improved salp swarm algorithm. The stator electricity is examined and classified when an induction motor is operating under varied loading conditions with good and troublesome bearings. Scale-Invariant Feature Transform (SIFT) is a feature extraction method that condenses visual content into a set of points that may be used to find similar patterns in other images. The proposed PNN classifier can classify different types of bearing faults, and the empirical findings validate the created approach. PNN-based motor bearing recognizing defects and diagnosis outperforms SVM and ANN classifiers in this regard. The Shortest path accuracy of the PNN Classifier is 98.5% respectively.

关键词： Support vector machines Visualization Induction motors Accuracy Neural networks Transforms Feature extraction

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Hybrid BiGRU-CNN Model for Load Forecasting in Smart Grids with High Renewable Energy Integration

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IET Generation, Transmission and Distribution 2025年第1期19卷

作者： Ullah, Kaleem Shakir, Daniyal Abid, Usama Alahmari, Saad Aslam, Sheraz Ullah, Zahid US-Pakistan Centre for Advanced Studies in Energy University of Engineering and Technology Peshawar Peshawar Pakistan Sr. Business Analyst S&P Global Islamabad Islamabad Pakistan Department of Computer Science and Information Technology School of Computing Engineering and Mathematical Sciences La Trobe University MelbourneVIC Australia Department of Computer Science Applied College Northern Border University Arar Saudi Arabia Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus Department of Computer Science CTL eurocolege Limassol Cyprus Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Lombardia Italy

Integrating renewable energy sources into smart grids increases supply and demand management because renewable energy sources are intermittent and variable. To overcome this type of challenge, short-term load forecasting (STLF) is essential for managing energy, demand-side flexibility, and the stability of smart grids with renewable energy integration. This paper presents a new model called BiGRU-CNN to improve the operation of STLF in smart grids. The BiGRU-CNN model integrates bidirectional gated recurrent units (BiGRUs) to model temporal dependencies and convolutional neural networks (CNNs) to extract spatial patterns from energy consumption data. The newly developed BiGRU captures past and future contexts through bidirectional processing, and the CNN component extracts high-level features to enhance the accuracy of load demand prediction. The BiGRU-CNN model is compared with two other hybrid models, CNN-LSTM and CNN-GRU, on real-world data from American electric power (AEP) and ISONE datasets. Simulation results show that the proposed BiGRU-CNN outperforms other models, with single-step forecasting yielding root mean square error (RMSE) of 121.43 (AEP) and 123.57 (ISONE), mean absolute error (MAE) of 90.95 (AEP) and 62.97 (ISONE), and mean absolute percentage error (MAPE) of 0.61% (AEP) and 0.41% (ISONE). For multi-step forecasting, the model yields RMSE of 680.02 (AEP) and 581.12 (ISONE), MAE of 481.12 (AEP) and 411.20 (ISONE), and MAPE of 3.27% (AEP) and 2.91% (ISONE). The results show that the BiGRU-CNN model can generate accurate and reliable STLF, which is useful in managing massive renewable energy-integrated smart grids. © 2025 The Author(s). IET Generation, Transmission & Distribution published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： Convolutional neural networks

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Microwave-Induced Cooling in Double Quantum Dots: Achieving Millikelvin Temperatures to Reduce Thermal Noise around Spin Qubits

arXiv

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arXiv 2024年

作者： Vashaee, Daryoosh Abouie, Jahanfar Department of Materials Science & Engineering North Carolina State University RaleighNC27606 United States Department of Electrical & Computer Engineering North Carolina State University RaleighNC27606 United States Department of Physics Institute for Advanced Studies in Basic Sciences Zanjan45137-66731 Iran

Spin qubits in gate-defined quantum dots are emerging as a leading technology due to their scalability and long coherence times. However, maintaining these qubits at ultra-low temperatures typically requires complex cryogenic systems. This paper proposes a novel gate-defined double quantum dot (DQD) cooling system using microwave-induced state depopulation and phonon filtering to achieve local temperatures below 10 mK at a bath temperature of 1 K. The system utilizes microwave-induced state depopulation and phonon filtering, combined with fast cyclic detuning of the quantum dot on-site energies and Rabi oscillations, to efficiently transfer thermal populations to the ground state, thereby surpassing natural thermal transition rates. The cooling cycle involves adjusting gate potentials to drive the system through adiabatic and diabatic transitions, complemented by microwave pulses resonant with specific energy level differences. This mechanism continuously pumps population from excited states into the ground state, effectively reducing the system's temperature. Numerical calculations demonstrate the feasibility of achieving these low local temperatures, with detailed analysis showing the sensitivity of cooling performance to detuning energy, magnetic field strength, and diabatic return time. Copyright © 2024, The Authors. All rights reserved.

关键词： Semiconductor quantum dots

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A Logical Cyber Security Enabled Methodology Design for Identifying Distributed Denial of Service Attacks Using Enhanced Learning Principles

A Logical Cyber Security Enabled Methodology Design for Iden...

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Karthiga, R. Praveen Kumar, E. Kumari, S. Kala Priyadarshini, G. Sureka, V. Pandi, V. Samuthira R.M.K Engineering College Department of It Tamil Nadu Kavaraipettai India Tamil Nadu Coimbatore India Panimalar Engineering College Department of Information Technology Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India S.A.Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Chennai Institute of Technology Centre for Advanced Wireless Integrated Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350313987

The Distributed Denial of Service (DDoS) attack is a common type of cyber-attack that targets computer networks. When a hostile user seeks to prohibit access to a service by overwhelming the system with data that is not intended, this is an example of the distributed denial of service attack, also known as a DDoS attack. Because of this, the provision of services to legitimate users is slowed down. DDoS attacks are launched when a large number of sources, such as a Botnet that controls the devices remotely for malicious reasons, simultaneously launch attacks on a target. DDoS refers to 'distributed denial of service' attacks. In order to detect and prevent Distributed Denial of Service attacks, classification algorithms have been widely employed in research. Attacks using a Distributed Denial of Service may be easily carried out by using the weaknesses of networks as well as making application service demands. The relevance of this strategy cannot be overstated due to the fact that DDoS attacks are difficult to detect and prevent in real time and can have devastating effects. This study led to the development of a deep learning model called the Learning Model to Detect Cyber Attacks (LMDCA). Its purpose is to anticipate application layer DDoS attacks in real time. It is the responsibility of the availability component to guarantee that authorized users will always have access to the system. During the course of this study, we modeled both a denial-of-service attack as well as normal network traffic. Following that, we utilized packet-level analysis in an effort to differentiate between the aforementioned two usage patterns. Then, in order to detect and classify the various DOS attacks, we make use of the LMDCA-based classifier logic that was recommended. © 2023 IEEE.

关键词： Cyber Attack Cyber Security DDoS Deep Learning Distributed Denial of Service Learning Principle LMDCA

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Insulating materials for realising carbon neutrality:Opportunities,remaining issues and challenges

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High Voltage 2022年第4期7卷 610-632页

作者： Chuanyang Li Yang Yang Guoqiang Xu Yao Zhou Mengshuo Jia Shaolong Zhong Yu Gao Chanyeop Park Qiang Liu Yalin Wang Shakeel Akram Xiaoliang Zeng Yi Li Fangwei Liang Bin Cui Junpeng Fang Lingling Tang Yulin Zeng Xingtao Hu Jiachen Gao Giovanni Mazzanti Jinliang He Jianxiao Wang Davide Fabiani Gilbert Teyssedre Yang Cao Feipeng Wang Yunlong Zi The State Key Laboratory of Power System Department of Electrical EngineeringTsinghua UniversityBeijingChina Department of Mechanical and Automation Engineering The Chinese University of Hong KongHong KongChina Simpson Querrey Institute Northwestern UniversityEvanstonIllinoisUSA Department of Materials Science and Engineering The Pennsylvania State UniversityUniversity ParkPennsylvaniaUSA Department of Information Technology and Electrical Engineering ETH ZürichZürichSwitzerland School of Electrical and Information Engineering Tianjin UniversityTianjinChina Department of Electrical and Computer Engineering Mississippi State UniversityMississippi StateMississippiUSA Electrical and Electronic Engineering The University of ManchesterManchesterUK Department of Electrical Engineering School of Electronic Information and Electrical EngineeringShanghai Jiao Tong UniversityShanghaiChina Key Laboratory of Control of Power Transmission and Conversion(SJTU) Ministry of EducationShanghaiChina College of Electrical Engineering Sichuan UniversityChengduChina Shenzhen Institute of Advanced Technology Chinese Academy of Sciences ShenzhenShenzhenChina School of Electrical Engineering Wuhan UniversityWuhanChina School of Integrated Circuits Tsinghua UniversityBeijingChina Jiangsu Jinxin Electric Appliance Co.Ltd. YangzhouChina Tai'an Tiancheng Safety Evaluation Co. Ltd.Tai'anChina Department Electrical Electronic and Information EngineeringUniversity of BolognaBolognaItaly National Engineering Laboratory for Big Data Analysis and Applications Peking UniversityBeijingChina Laplace Paul Sabatier Universityand CNRSToulouseFrance Electrical and Computer Engineering University of ConnecticutStorrsConnecticutUSA State Key Laboratory of Power Transmission Equipment&System Security and New Technology School of Electrical EngineeringChongqing UniversityChongqingChina Thrust of Sustainable Energy and Environment The Hong Kong University of Science and Technology(Guangzhou)GuangzhouChina De

The 2050 carbon-neutral vision spawns a novel energy structure revolution,and the construction of the future energy structure is based on equipment *** material,as the core of electrical power equipment and electrified transportation asset,faces unprecedented challenges and *** goal of carbon neutral and the urgent need for innovation in electric power equipment and electrification assets are first *** engineering challenges constrained by the insulation system in future electric power equipment/devices and electrified transportation assets are *** materials,including intelligent insulating material,high thermal conductivity insulating material,high energy storage density insulating material,extreme environment resistant insulating material,and environmental-friendly insulating material,are cat-egorised with their scientific issues,opportunities and challenges under the goal of carbon neutrality being *** the context of carbon neutrality,not only improves the understanding of the insulation problems from a macro level,that is,electrical power equipment and electrified transportation asset,but also offers opportunities,remaining issues and challenges from the insulating material *** is hoped that this paper en-visions the challenges regarding design and reliability of insulations in electrical equipment and electric vehicles in the context of policies towards carbon neutrality *** authors also hope that this paper can be helpful in future development and research of novel insulating materials,which promote the realisation of the carbon-neutral vision.

关键词： neutral insulation extreme

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A BCLC Staging System for Hepatocellular Carcinoma using Swin Transformer and CT Imaging 46

A BCLC Staging System for Hepatocellular Carcinoma using Swi...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Chang, Shun-Cheng Wang, Pochuang Wang, Weichung Su, Tung-Hung Kao, Jia-Horng Lin, Che Taipei10617 Taiwan Department of Computer Science and Information Engineering Ntu Taipei10617 Taiwan Institute of Applied Mathematical Sciences Ntu Taipei10617 Taiwan Division of Gastroenterology and Hepatology Department of Internal Medicine National Taiwan University Hospital Taipei10048 Taiwan Department of Electrical Engineering Ntu Taipei10617 Taiwan Center for Advanced Computing and Imaging in Biomedicine Ntu Taipei10617 Taiwan Smart Medicine and Health Informatics Program Ntu Taipei10617 Taiwan

ISBN: (纸本)9798350371499

The Barcelona Clinic Liver Cancer (BCLC) staging system plays a crucial role in clinical planning, offering valuable insights for effectively managing hepatocellular carcinoma. Accurate prediction of BCLC stages can significantly ease the workload on radiologists. However, few datasets are explicitly designed for discerning BCLC stages. Despite the common practice of appending BCLC labels to clinical data within datasets, the inherent imbalance in BCLC distribution is further amplified by the diverse purposes for which datasets are curated. In this study, we aim to develop a BCLC staging system using the advanced Swin Transformer model. Additionally, we explore the integration of two datasets, each originally intended for separate objectives, highlighting the critical challenge of preserving class distribution in practical study designs. This exploration is pivotal for ensuring the applicability of our developed staging system in the designed clinical settings. Our resulting BCLC staging system demonstrates an accuracy of 55.81% (±7.8%), contributing to advancing medical image-based research for predicting BCLC stages. © 2024 IEEE.

关键词： Distribution transformers

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ReViT: A Hybrid Approach for BCLC Staging of Hepatocellular Carcinoma Using 3D CT with Multiple Instance Learning

ReViT: A Hybrid Approach for BCLC Staging of Hepatocellular ...

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2024 IEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2024

作者： Chang, Shun-Cheng Yu, Hsin-Pei Hsieh, Yi-Hsien Wang, Pochuang Wang, Weichung Su, Tung-Hung Kao, Jia-Horng Lin, Che Taipei10617 Taiwan Department of Computer Science and Information Engineering Ntu Taipei10617 Taiwan Institute of Applied Mathematical Sciences Ntu Taipei10617 Taiwan National Taiwan University Hospital Division of Gastroenterology and Hepatology Department of Internal Medicine Taipei10048 Taiwan Department of Electrical Engineering Ntu Taipei10617 Taiwan Center for Advanced Computing and Imaging in Biomedicine Ntu Taipei10617 Taiwan Smart Medicine and Health Informatics Program Ntu Taipei10617 Taiwan

ISBN: (纸本)9798350351552

Deep learning has revolutionized medical imaging, offering advanced methods for accurate diagnosis and treatment planning. The BCLC staging system is crucial for staging Hepatocellular Carcinoma (HCC), a high-mortality cancer. An automated BCLC staging system could significantly enhance diagnosis and treatment planning efficiency. However, we found that BCLC staging, which is directly related to the size and number of liver tumors, aligns well with the principles of the Multiple Instance Learning (MIL) framework. To effectively achieve this, we proposed a new preprocessing technique called Masked Cropping and Padding(MCP), which addresses the variability in liver volumes and ensures consistent input sizes. This technique preserves the structural integrity of the liver, facilitating more effective learning. Furthermore, we introduced Re ViT, a novel hybrid model that integrates the local feature extraction capabilities of Convolutional Neural Networks (CNNs) with the global context modeling of Vision Transformers (ViTs). Re ViT leverages the strengths of both architectures within the MIL framework, enabling a robust and accurate approach for BCLC staging. We will further explore the trade-off between performance and interpretability by employing TopK Pooling strategies, as our model focuses on the most informative instances within each bag. © 2024 IEEE.

关键词： Convolutional neural networks

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