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eLight 2025年第1期5卷 1-3页

作者： Hang, Zhi Hong Guo, Chenzi Qiu, Cheng-Wei School of Physical Science and Technology and Institute for Advanced Study Soochow University Suzhou China Light Publishing Group Changchun Institute of Optics Fine Mechanics and Physics Chinese Academy of Sciences Changchun China Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore

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Boundary-Recovering Network for Temporal Action Detection

arXiv

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arXiv 2024年

作者： Kim, Jihwan Choi, Jaehyun Jeon, Yerim Heo, Jae-Pil Department of Artificial Intelligence Sungkyunkwan University Suwon16419 Korea Republic of Department of Computer Science and Engineering Sungkyunkwan University Suwon16419 Korea Republic of Department of Electrical Engineering Korea Advanced Institute of Science and Technology Daejeon34141 Korea Republic of

Temporal action detection (TAD) is challenging, yet fundamental for real-world video applications. Large temporal scale variation of actions is one of the most primary difficulties in TAD. Naturally, multi-scale features have potential in localizing actions of diverse lengths as widely used in object detection. Nevertheless, unlike objects in images, actions have more ambiguity in their boundaries. That is, small neighboring objects are not considered as a large one while short adjoining actions can be misunderstood as a long one. In the coarse-to-fine feature pyramid via pooling, these vague action boundaries can fade out, which we call 'vanishing boundary problem'. To this end, we propose Boundary-Recovering Network (BRN) to address the vanishing boundary problem. BRN constructs scale-time features by introducing a new axis called scale dimension by interpolating multi-scale features to the same temporal length. On top of scale-time features, scale-time blocks learn to exchange features across scale levels, which can effectively settle down the issue. Our extensive experiments demonstrate that our model outperforms the state-of-the-art on the two challenging benchmarks, ActivityNet-v1.3 and THUMOS14, with remarkably reduced degree of the vanishing boundary problem. © 2024, CC BY.

关键词： computer system recovery

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Joint Optimization of Sensing and Communications in Vehicular Networks: A Graph Neural Network-Based Approach

Joint Optimization of Sensing and Communications in Vehicula...

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IEEE International Conference on Communications (ICC)

作者： Xuefei Li Mingzhe Chen Zhilong Zhang Danpu Liu Yuchen Liu Shiwen Mao Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables FL USA Department of Computer Science North Carolina State University Raleigh NC USA Department of Electrical and Computer Engineering Auburn University Auburn AL USA

In this paper, the problem of joint sensing and communications is studied over terahertz (THz) vehicular networks. In the studied model, a set of service provider vehicles provide either communication service or sensing service to communication target vehicles or sensing target vehicles, respectively. Therefore, it is necessary to determine the service mode (i.e., providing sensing or communication service) for each service provider vehicle and the subset of target vehicles that each service provider vehicle will serve. The problem is formulated as an optimization problem aiming to maximize the sum of the data rates of all communication target vehicles while satisfying the sensing service requirements of all sensing target vehicles by determining the service mode and the user association for each service provider vehicle. To solve this problem, a graph neural network (GNN) based algorithm with a heterogeneous graph representation is proposed. The proposed algorithm enables the central controller to extract each vehicle's graph information related to its location, connection, and communication interference. Using the extracted graph information, the joint service mode selection and user association strategy will be determined. Simulation results show that the proposed GNN-based scheme can achieve 94% of the sum rate produced by the optimal solution, and yield up to 3.95% and 36.16% improvements in sum rate, respectively, compared to a homogeneous GNN-based algorithm and the conventional optimization algorithm without using GNNs.

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Enhanced Memory Performance in Ferroelectric NAND Applications: The Role of Tunnel Dielectric Position for Robust 10-Year Retention

Enhanced Memory Performance in Ferroelectric NAND Applicatio...

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2025 IEEE International Reliability Physics Symposium, IRPS 2025

作者： Venkatesan, Prasanna Padovani, Andrea Fernandes, Lance Ravikumar, Priyankka Park, Chinsung Tran, Huy Wang, Zekai Jayasankar, Hari Garlapati, Amrit Song, Taeyoung Chen, Hang Chern, Winston Wang, Zheng Kim, Kijoon Woog, Jongho Lim, Suhwan Kim, Kwangsoo Kim, Wanki Ha, Daewon Yu, Shimeng Dattal, Suman Larcher, Luca Thareja, Gaurav Khanl, Asif School of Electrical and Computer Engineering Georgia Institute of Technology GA United States University of Modena and Reggio Emilia Engineering Department 'Enzo Ferrari' Italy Institute of Electronics and Nanotechnology Georgia Institute of Technology GA United States Micron Technology United States Samsung Electronics Advanced Device Research Laboratory Semiconductor Research Center Korea Republic of School of Material Science and Engineering Georgia Institute of Technology GA United States Applied Materials United States

ISBN: (纸本)9798331504779

We report a comprehensive study on memory window (MW) enhancement and retention loss (RL) in ferroelectric (FE) field effect transistors for NAND applications with a dielectric layer inserted within the FE film (next to the gate GBL devices or in the middle of the FE layer TDL devices). Using a temperature-dependent modeling framework, we show that MW and RL properties are controlled respectively by carriers trapping into defects at the dielectric-FE interface and by its fragile balance with FE depolarization. While MW enhancement is achieved whenever the inserted dielectric layer is present, a robust retention is obtained only in TDL FEFETs, thanks to a stabilization of the trapped charge preventing FE depolarization. Simulation results clearly demonstrate that the fragile balance between above phenomena is ultimately controlled by the position of the dielectric layer, which determines i) the field redistribution within the stack, ii) the magnitude of charge de-trapping, and iii) the magnitude of the depolarization field. Our results provide critical guidelines for the improvement of FE 3D-NAND reliability. © 2025 IEEE.

关键词： Ferroelectric films

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Estimating Remaining Useful Life of Cutting Tools in Machining Using an Extended Kalman Filter

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IFAC-PapersOnLine 2024年第19期58卷 1264-1269页

作者： Qian Yang Debasish Mishra Krishna R. Pattipati George M. Bollas Department of Electrical and Computer Engineering University of Connecticut Storrs CT 06269 USA Data Science & Information Systems Krea University 5655 Central Expressway Sri City 517646 India Pratt & Whitney Institute of Advanced Systems Engineering University of Connecticut Storrs CT 06269 USA

Monitoring the condition of the cutting tool and forecasting the evolution of its wear during machining are vital to ensure workpiece quality and the safety of machine elements. The accurate prediction of tool wear is crucial to advance predictive maintenance in machining. This article presents a methodology to estimate the remaining useful life (RUL) of cutting tools. The methodology involves extracting features from vibration signals using discrete wavelet transform (DWT) and computing the concomitant indicators of tool health by applying distance metrics to the features. These indicators have a high correlation with tool wear measurements. The RUL is estimated by fusing these indicators in a support vector regression (SVR) algorithm. The SVR outputs then serve as inputs to an extended Kalman filter (EKF) that employs a rectified linear activation unit (ReLU) function for state evolution, enabling real-time estimation of the RUL. The proposed methodology is demonstrated on the IEEE PHM 2010 dataset, showcasing its reliability and effectiveness in accurately estimating the RUL for cutting tools.

关键词： Remaining useful life (RUL) Health indicator Distance metrics Support vector regression Extended Kalman filter (EKF) Cutting tool Machining

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Advancements in Binary Number Processing: Analyzing a CNTFET-Based Digital Circuit for 3-Bit Up and Down Counting with Challenges in Artificial Intelligence

Advancements in Binary Number Processing: Analyzing a CNTFET...

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

作者： Khan, Imran Ahmed Shah, Owais Ahmad Vats, Satvik Arora, Sujata Mahmood, Md Rashid Whig, Pawan Jamia Millia Islamia University Department of Electronics & Communication Engineering New Delhi India School of Engineering & Technology K. R. Mangalam University Giurugram India Graphic Era Hill University Department of Computer Science & Engineering Dehradun India Noida International University Department of Electrical & Electronics Engineering Gautam Budh Nagar India Guru Nanak Institutions Technical Campus Department of Electronics & Communication Engineering Hyderabad India Vivekananda Institute of Professional Studies Department of Electronics & Communication Engineering New Delhi India

ISBN: (纸本)9798350313987

Carbon Nanotube Field- Effect Transistors (CNTFETs) have emerged as a promising alternative to traditional silicon-based transistors, offering enhanced electrical properties and potential integration with existing silicon technology. This research study presents a design and performance analysis of a digital circuit based on CNTFET technology, specifically tailored for precise computation involving 3-bit up and down binary numbers. The circuit's ability to accurately compute additions and subtractions is pivotal for various computational applications. Additionally, this study explores the challenges and implications of integrating Artificial Intelligence (AI) algorithms within the context of this emerging semiconductor technology. The findings shed light on the prospects of utilizing CNTFETs in arithmetic operations, providing insights into the intersection of advanced semiconductor technologies and the field of AI. The comprehensive analysis presented herein establishes a foundation for future research and advancements at the convergence of CNTFET -based digital circuits and AI methodologies. © 2023 IEEE.

关键词： Artificial Intelligence CMOS digital circuit CNTFET Counter

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Design of an Optimized Asymmetric Multilevel Inverter with Reduced Components Using Newton-Raphson Method and Particle Swarm Optimization

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Mathematical Problems in engineering 2023年第1期2023卷

作者： Gireesh Kumar, Devineni Venkata Sireesha, Nagineni Ganesh, Aman Kotb, Hossam Aboras, Kareem M. Zeinoddini-Meymand, Hamed Kamel, Salah Department of Electrical and Electronics Engineering B v Raju Institute of Technology Telangana Narsapur502313 India Department of Information Technology Institute of Aeronautical Engineering Hyderabad Telangana Dundigal500043 India Ambala Mullana133203 India Department of Electrical Power and Machines Faculty of Engineering Alexandria University Alexandria21544 Egypt Department of Electrical and Computer Engineering Graduate University of Advanced Technology Kerman Iran Electrical Engineering Department Faculty of Engineering Aswan University Aswan81542 Egypt

Multilevel inverters have great scope in current developments of grid-connected solar PV systems. Two-level inverters are the simplest kind of multilevel inverter available (MLI). As the number of output levels is raised, the total harmonic distortion decreases. In classic MLI topologies, more electronic components are utilized to get higher-level outputs, which raise the cost, complexity, and volume of typical MLI installations. By reducing the design components, the cost of the system will be reduced. Furthermore, the two- and three-level inverters produce constant dv/dt output, which increases the stress on the power switches. This research proposes an asymmetric MLI topology that is suitable for PV applications and utilizes less number of DC sources and switches. The proposed inverter is controlled by selective harmonic elimination-based pulse width modulation (SHEPWM) to eliminate the lower-order dominant harmonics. The nonlinear equations produced by the SHEPWM are solved for the switching angles of the proposed inverter using the Newton-Raphson (NR) method and particle swarm optimization (PSO) method for various modulation indexes. The performance of the proposed inverter is analyzed based on the total harmonic distortion (THD) of the output for different operating levels of the inverter by comparing similar topologies in the literature. The THD obtained by the NR method is 7.3% and by using PSO is 4.23% at 0.9 modulation index. © 2023 Devineni Gireesh Kumar et al.

关键词： Newton-Raphson method

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AI for Disaster Response: Developing Intelligent Systems for Real-Time Emergency Management

AI for Disaster Response: Developing Intelligent Systems for...

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Next Generation Communication & Information Processing (INCIP), International Conference on

作者： T.K. Lakshmi Naresh Kumar Mangalapu Mary Fathima Anuja Joseph Swaminathan S. Chandravadhana R. Pari T. Ranjana Department of Computer Science and Engineering Malla Reddy University Hyderabad Telangana India Department of Orthodontics & Dentofacial Orthopedics Kamineni Institute of Dental Sciences Narketpally Nalgonda Telangana Department of Computer Science Prince Shri Venketeshwara Arts and Science College Chennai Tamil Nadu India Department of Mechatronics Engineering Chennai Institute of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering VELS Institute of Science Technology and Advanced Studies Chennai Tamil Nadu India Master of Business Administration S.A. Engineering College Thiruverkadu Tamil Nadu India

ISBN: (数字)9798331528140

ISBN: (纸本)9798331528157

The research introduces the design of an AI-based disaster response system that subscribes to enhance real-time emergency management. Manual processing, slow communication and siloed data sources are common downsides of traditional-approach systems, which ultimately translate into slower response times or insufficient resource spend. The system deals with them by exploiting ML, predictive analytics, and automatic decision-making so that these can consolidate pre-existing real-time information from social media, satellite images or IoT sensors. Several experiments show better-than-previous: response times decrease from an average of 11 hours to 3.6 and prediction accuracy increased from 62.5% up to cover %85 Average efficiency also improved including +23% in resource allocation. The innovative solution shifts disaster response from being entirely reactive to proactive, creating faster and more informed responses during times of crisis, thereby assuring public safety for all in the face of calamities.

关键词： Training Accuracy Disasters Emergency services Real-time systems Public security Time factors Resource management Predictive analytics Intelligent systems

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A Study of Lung Cancer Prediction Using Machine Learning Algorithms 3

A Study of Lung Cancer Prediction Using Machine Learning Alg...

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3rd International Conference on Electronic and electrical engineering and Intelligent System, ICE3IS 2023

作者： Modak, Chinmoy Shahriyar, Md Ahnaf Taluckder, Md Siam Haque, Md Sabbirul Sayed, Md Abu Florida Polytechnic University Department of Computer Science LakelandFL United States College of Technology and Engineering Westcliff University IrvineCA United States Lamar University Phillip M. Drayer Department of Electrical Engineering BeaumontTX United States Institute of Electrical and Electronics Engineers Ieee Professional Community New YorkNY United States New Jersey City University Department of Professional Security Studies Jersey CityNJ United States

ISBN: (纸本)9798350327762

Lung cancer is the leading cause of death in the USA It is caused for various reasons, such as malignant tumors that grow spontaneously in the lung and spread to other parts of the body, where they can be devastating. Other than that, there are so many external reasons why smoking is the primary factor that gradually creates lung problems that lead to lung cancer. However, we can prevent this deadly cancer by detecting it at an early stage. A machine learning-based detection model has the potential to detect lung cancer, which helps to predict the early stage of cancer. So, we aim to build some powerful machine learning algorithms to predict this cancer that can help physicians make decisions about the diagnosis process of a particular patient, whether the patient needs a high or a regular diagnosis level. This can reduce the treatment cost of a patient because the physician knows from the result how many doses are needed for a patient, so it can avoid unnecessary treatment, which saves money and brings an efficient diagnosis system. We aimed to build a sustainable model that could predict the disease accurately, and finally, our models, such as XGboost, achieved the highest value of other models. According to the accuracy level, XGboost performed up to 95.92%. Decision Tree, Random Forest, Logistic Regression, and Naïve Bayes achieved 91.50%, 93.32%, 57.41%, and 90.32% accuracy, respectively. © 2023 IEEE.

关键词： Diagnosis

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Flicker Visibility With Different Spectra of White Light

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IEEE Transactions on Industry Applications 2025年

作者： Kukacka, Leos Hergesel, Jan Necasek, Jakub Bilek, Petr Vik, Michal Stiegler, Robert Pourarab, Morteza Meyer, Jan Drapela, Jiri Technical University of Liberec Faculty of Mechatronics Informatics and Interdisciplinary Studies Institute-of Mechatronics and Computer Engineering Liberec Czech Republic Technical University of Liberec Faculty of Textile Engineering Department of Material Engineering Czech Republic Technische Universität Dresden Institute of Electrical Power Systems and High Voltage Engineering Chair of Electrical Power Supply Dresden01069 Germany Brno University of Technology Faculty of Electrical Engineering and Communications Brno61600 Czech Republic

The standard flickermeter (voltage flickermeter) algorithm commonly used for the evaluation of flicker severity in current electromagnetic compatibility standards originates from the 1980 s. It was developed to mimic irritation of an average human observer by flickering incandescent lamp as a reference light source and optimized to voltage fluctuations caused by arc furnaces. However, the algorithm is questionable as the situation in the grid changed since the 1980 s;many modern devices are being connected to the grid, creating voltage distortion patterns which are not properly recognized by the voltage flickermeter. In addition, incandescent lamps have been largely replaced by discharge lamps and later by LEDs. Transitioning the algorithm from incandescent lamp light to LED light requires detailed revision of every part of the algorithm. This paper is concerned with verifying the frequency weighting function of the average human observer which is one of the bases of the voltage flickermeter, as well as of the derived light flickermeter. Flicker sensitivity of 34 human observers is measured with two different light sources (LED warm and neutral white, incandescent lamp) and compared with the frequency weighting function used in the current specification of the flickermeters in standards. Finally, an optimization of the weighting function is proposed based on the findings. © 1972-2012 IEEE.

关键词： Flickering

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