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Laser‑Induced and MOF‑Derived Metal Oxide/Carbon Composite for Synergistically Improved Ethanol Sensing at Room temperature

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Nano-Micro Letters 2024年第6期16卷 210-220页

作者： Hyeongtae Lim Hyeokjin Kwon Hongki Kang Jae Eun Jang Hyuk‑Jun Kwon Department of Electrical Engineering and Computer Science DGISTDaegu 42988South Korea Convergence Research Advanced Centre for Olfaction DGISTDaegu 42988South Korea

Advancements in sensor technology have significantly enhanced atmospheric ***,metal oxide and carbon(MO_(x)/C)hybrids have gained attention for their exceptional sensitivity and room-temperature sensing ***,previous methods of synthesizing MO_(x)/C composites suffer from problems,including inhomogeneity,aggregation,and challenges in ***,we introduce a refined method that employs a metal–organic framework(MOF)as a precursor combined with direct laser *** inherent structure of MOFs ensures a uniform distribution of metal ions and organic linkers,yielding homogeneous MO_(x)/C *** laser processing facilitates precise micropatterning(<2μm,comparable to typical photolithography)of the MO_(x)/C *** optimized MOF-derived MO_(x)/C sensor rapidly detected ethanol gas even at room temperature(105 and 18 s for response and recovery,respectively),with a broad range of sensing performance from 170 to 3,400 ppm and a high response value of up to 3,500%.Additionally,this sensor exhibited enhanced stability and thermal resilience compared to previous MOF-based *** research opens up promising avenues for practical applications in MOF-derived sensing devices.

关键词： Metal-organic frameworks Metal oxide Carbon composite Laser Gas sensor

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A Hyperbolic Micromagnet for Multiple Spin Qubits with Fast Rabi Oscillations and High Addressability

IEEE Electron Device Letters

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IEEE Electron Device Letters 2024年第4期45卷 526-529页

作者： Chang, Che-Hao Li, Yu-Cheng Wu, Yu-Jui Liao, Chen-Yao Lin, Min-Jui Tsao, Hung-Yu Li, Jiun-Yun Graduate Institute of Electronics Engineering National Taiwan University Taipei10617 Taiwan Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science CambridgeMA02139 United States National Taiwan University Department of Electrical Engineering Graduate Institute of Electronics Engineering Graduate School of Advanced Technology Taipei10617 Taiwan Taiwan Semiconductor Research Institute Hsinchu30078 Taiwan

We demonstrate a hyperbolic micromagnet to increase the manipulation speed of electron spins in silicon quantum dots using electric dipole spin resonance (EDSR). For single qubits, the hyperbolic magnet provides a large gradient of transverse magnetic fields of 4.9 mT/nm with the corresponding Rabi frequency ( 69 MHz). We then demonstrate that this hyperbolic micromagnet can drive a linear array of qubits with Rabi frequencies of 19 69 MHz and a high addressability (minimum difference of Larmor frequencies) up to 590 MHz to suppress the crosstalk between different qubits. We show that 15 qubits can be effectively addressed for high fidelity by this hyperbolic micromagnet. © 1980-2012 IEEE.

关键词： Magnetic fields

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Attack Detection for Spoofed Synchrophasor Measurements Using Segmentation Network

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CSEE Journal of Power and Energy Systems 2022年第5期8卷 1327-1337页

作者： Wei Qiu Chengcheng Li Qiu Tang Kaiqi Sun Yilu Liu Wenxuan Yao College of Electrical and Information Engineering Hunan UniversityChangsha 410082China Department of Electrical Engineering and Computer Science University of TennesseeKnoxvilleTN37996USA Oak Ridge National Laboratory Oak RidgeTN37831USA

Synchrophasor measurements are essential to realtime situational awareness of the smart grid but vulnerable to cyber-attacks during the process of transmission and *** ensure data security and mitigate the impact of spoofed synchrophasor measurements,this work proposes a novel object detection method using a Weight-based One-dimensional Convolutional Segmentation Network(WOCSN)with the ability of attack behavior identification and time *** WOCSN,automatic data feature extraction can be achieved by onedimensional convolution from the input signal,thereby reducing the impact of handcrafted features.A weight loss function is designed to distribute the contribution for normal and attack ***,attack time is located via the proposed binary method based on pixel ***,the actual synchrophasor data collected from four locations are used for the performance evaluation of the ***,combined with designed evaluation metrics,the time localization ability of WOCSN is validated in the scenarios of composite attacks with different spoofed intensities and time-sensitivities.

关键词： Data security spoofed synchrophasor measurements weight-based one-dimensional convolutional segmentation network(WOCSN)

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UCF-Crime-DVS: A Novel Event-Based Dataset for Video Anomaly Detection with Spiking Neural Networks 39

UCF-Crime-DVS: A Novel Event-Based Dataset for Video Anomaly...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Qian, Yuanbin Ye, Shuhan Wang, Chong Cai, Xiaojie Qian, Jiangbo Wu, Jiafei Faculty of Electrical Engineering and Computer Science Ningbo University China Merchants’ Guild Economics and Cultural Intelligent Computing Laboratory Ningbo University China Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong

ISBN: (纸本)157735897X

Video anomaly detection plays a significant role in intelligent surveillance systems. To enhance model’s anomaly recognition ability, previous works have typically involved RGB, optical flow, and text features. Recently, dynamic vision sensors (DVS) have emerged as a promising technology, which capture visual information as discrete events with a very high dynamic range and temporal resolution. It reduces data redundancy and enhances the capture capacity of moving objects compared to conventional camera. To introduce this rich dynamic information into the surveillance field, we created the first DVS video anomaly detection benchmark, namely UCF-Crime-DVS. To fully utilize this new data modality, a multi-scale spiking fusion network (MSF) is designed based on spiking neural networks (SNNs). This work explores the potential application of dynamic information from event data in video anomaly detection. Our experiments demonstrate the effectiveness of our framework on UCF-Crime-DVS and its superior performance compared to other models, establishing a new baseline for SNN-based weakly supervised video anomaly detection. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Development of an e-Learning Motivation Index 15th

Development of an e-Learning Motivation Index

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15th International Conference on computer science and its Applications, CSA 2023 and 17th KIPS International Conference on Ubiquitous Information Technologies and Applications, CUTE 2023

作者： Park, JungHyun Park, Ji Su Shon, Jin Gon Advanced Convergence Technology Laboratory Korea National Open University Seoul Korea Republic of Department of Computer Science and Engineering Jeonju University Jeonju-si Korea Republic of Department of Computer Science Graduate School National Open University Seoul Korea Republic of

ISBN: (纸本)9789819724468

Motivation is the driving force to initiate and sustain learner participation in e-Learning environment. Since identifying the extent of motivation is important, this study aims to develop an e-Learning Motivation Index that quantitatively measures motivation concisely and intuitively, capturing the different impact of factors on learner participation. Index development starts with the General Function Establishment. Grounded in the Expectancy-Value Theory and the Self-Regulated Learning Theory, learning motivation is defined as value, cost, cognitive regulation, and scheduling. These act as motivational factors impacting on learner participation, defined in the General Function. Measures for independent and dependent variables are designed to utilize a survey and an experiment on MoodleCloud. After an experiment, analysis using SPSS provides the result that value and scheduling are two factors that significantly impact learner participation. The more learners value learning and have scheduling skills, the more they participate in e-Learning. These results were expressed in a regression function and normalized to derive an e-Learning Motivation Index. This index can assist learners in building learning strategies and educational providers in providing tailored strategies for learners’ characteristics to promote learner participation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Contrastive Learning

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Data-Driven Graph Construction of Power Flow Graphs for Electric Power Transmission Networks 23

Data-Driven Graph Construction of Power Flow Graphs for Elec...

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23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Poole, Benjamin Ratnakumar, Rajan Madurasinghe, Dulip Tharaka Kummerle, Christian Venayagamoorthy, Ganesh Kumar Lee, Minwoo University of North Carolina Department of Computer Science Charlotte United States University of Pretoria Department of Electrical Electronic and Computer Engineering South Africa Clemson University Real-Time Power and Intelligent Systems Laboratory United States

ISBN: (纸本)9798350374889

A comprehensive understanding of the topology of the electric power transmission network (EPTN) is essential for reliable and robust control of power systems. While existing research primarily relies on domain-specific methods, it lacks data-driven approaches that have proven effective in modeling the topology of complex systems. To address this gap, this paper explores the potential of data-driven methods for more accurate and adaptive solutions to uncover the true underlying topology of EPTNs. First, this paper examines Gaussian Graphical Models (GGM) to create an EPTN network graph (i.e., undirected simple graph). Second, to further refine and validate this estimated network graph, a physics-based, domain-specific refinement algorithm is proposed to prune false edges and construct the corresponding electric power flow network graph (i.e., directed multi-graph). The proposed method is tested using a synchrophasor dataset collected from a two-area, four-machine power system simulated on the real-time digital simulator (RTDS) platform. Experimental results show both the network and flow graphs can be reconstructed using various operating conditions and topologies with limited failure cases. © 2024 IEEE.

关键词： Undirected graphs

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Optical Ciphering Scheme for Cancellable Speaker Identification System

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computer Systems science & engineering 2023年第4期45卷 563-578页

作者： Walid El-Shafai Marwa A.Elsayed Mohsen A.Rashwan Moawad I.Dessouky Adel S.El-Fishawy Naglaa F.Soliman Amel A.Alhussan Fathi EAbd El-Samie Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electronics and Communications Faculty of EngineeringCairo UniversityCairoEgypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia

Most current security and authentication systems are based on personal *** security problem is a major issue in the field of biometric *** is due to the use in databases of the original *** biometrics will forever be lost if these databases are *** privacy is the most important goal of cancelable *** order to protect privacy,therefore,cancelable biometrics should be non-invertible in such a way that no information can be inverted from the cancelable biometric templates stored in personal identification/verification *** methodology to achieve non-invertibility is the employment of non-invertible *** work suggests an encryption process for cancellable speaker identification using a hybrid encryption *** system includes the 3D Jigsaw transforms and Fractional Fourier Transform(FrFT).The proposed scheme is compared with the optical Double Random Phase Encoding(DRPE)encryption *** evaluation of simulation results of cancellable biometrics shows that the algorithm proposed is secure,authoritative,and *** encryption and cancelability effects are good and reveal good ***,it introduces recommended security and robustness levels for its utilization for achieving efficient cancellable biometrics systems.

关键词： Cancellable biometrics jigsaw transform FrFT DRPE speaker identification

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EPM: Evolutionary Perception Method for Anomaly Detection in Noisy Dynamic Graphs

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IEEE Transactions on Knowledge and Data engineering 2025年第7期37卷 4035-4048页

作者： Wang, Huan Chen, Junyang Wu, Yirui Leung, Victor C. M. Wang, Di Huazhong Agricultural University Key Laboratory of Smart Farming for Agricultural Animals Engineering Research Center of Intelligent Technology for Agriculture Ministry of Education College of Informatics Wuhan430072 China King Abdullah University of Science and Technology Division of Computer Electrical and Mathematical Sciences and Engineering Thuwal23955 Saudi Arabia Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China Hohai University College of Computer and Information Nanjing210098 China Shenzhen MSU-BIT University Artificial Intelligence Research Institute Shenzhen518115 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

With the rapid expansion of interactions across various domains such as social networks, transaction networks, and IP-IP networks, anomaly detection in dynamic graphs has become increasingly critical for mitigating potential risks. However, existing anomaly detection methods often assume noise-free dynamic graphs, overlooking the prevalence of noisy dynamic graphs in real-world applications. Specifically, noisy dynamic graphs affected by structural noises—such as spurious and missing nodes and edges—struggle to consistently provide reliable structural evidence for anomaly detection. To tackle this challenge, we propose an Evolutionary Perception Method (EPM) for identifying anomalous nodes in noisy dynamic graphs by resisting the interference of structural noises. EPM primarily consists of two components: a dynamic fitter and a filtering reviser. The dynamic fitter characterizes the interaction dynamics of nodes that removes and generates links at each period as a multiple superposition state, utilizing various link prediction algorithms to fit evolutionary mechanisms. Additionally, the filtering reviser designs evolutional entropies to quantify the evolutional uncertainty in multiple superposition states, further reconstructing the Kalman filter to optimize these entropies. Extensive experiments have proved that our proposed EPM outperforms state-of-the-art methods in discovering anomalous nodes in noisy dynamic graphs. © 1989-2012 IEEE.

关键词： Kalman filters

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Reinforcement Learning-Based Time-Synchronized Optimized Control for Affine Systems

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第10期5卷 5216-5231页

作者： Zhang, Yuxiang Liang, Xiaoling Li, Dongyu Ge, Shuzhi Sam Gao, Bingzhao Chen, Hong Lee, Tong Heng National University of Singapore Department of Electrical and Computer Engineering 117576 Singapore Beihang University School of Cyber Science and Technology Beijing100191 China Tianmushan Laboratory Hangzhou310023 China Tongji University Clean Energy Automotive Engineering Center Shanghai201804 China

The approach of (fixed-) time-synchronized control (FTSC) aims at attaining the outcome where all the system state-variables converge to the origin simultaneously/synchronously. This type of outcome can be the highly essential performance desired in various real-world high-precision control applications. Toward this objective, this article proposes and investigates the development of a time-synchronized reinforcement learning algorithm (TSRL) applicable to a particular class of first- and second-order affine nonlinear systems. The approach developed here appropriately incorporates the norm-normalized sign function into the optimal system control design, leveraging on the special properties of this norm-normalized sign function in attaining time-synchronized stability and control. Concurrently, the actor–critic framework in reinforcement learning (RL) is invoked, and the dual quantities of system control and gradient term of the cost function are decomposed with appropriate time-synchronized control items and unknown actor/critic part and to be learned independently. By additionally employing the adaptive dynamic programming technique, the solution of the Hamilton–Jacobi–Bellman equation is iteratively approximated under this actor–critic framework. As an outcome, the proposed TSRL method optimizes the system control while attaining the notable time-synchronized convergence property. The performance and effectiveness of the proposed method are demonstrated to be effectively applicable via detailed numerical studies and on an autonomous vehicle nonlinear system motion control problem. © 2020 IEEE.

关键词： Synchronization

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Exploring the Synergy of Artificial and Computational Intelligence for Technological Advancements 1

Exploring the Synergy of Artificial and Computational Intell...

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1st International Conference on Data, Computation and Communication, ICDCC 2024

作者： Palarimath, Suresh Maran, Pyingkodi Valsalan, Prajoona Wilfred Blessing, N.R. Kavitha, S.J. Steffi, S. College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Oman Kongu Engineering College Department of Computer Applications Erode India College of Engineering Dhofar Univesity Electrical and Computer Engineering Department Salalah Oman College of Computing and Information Sciences Univesity of Technology and Applied Sciences Ibri Oman Vels Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Computer Science and Engineering Chennai India

ISBN: (纸本)9798331532956

Due to the ongoing advancements in science and technology, it seems that artificial intelligence and computers have encountered a limitation in their progress. Contemporary scientific research requires more sophisticated interconnection technology to ensure the continued progress of science and technology. Amidst this setting, the technology of Internet of Things (IoT) has progressively been visible to individuals. This technology will significantly impact human lifestyle and quality of life, and the Internet of Things has seen substantial progress. Information technology is intricately intertwined with the lives of citizens. The integration of information technology with the IoT is aimed at maximizing the technological advancement of the Internet of Things, with Internet technology serving as the central component. The use of artificial intelligence and computer intelligence in Internet technology is versatile, and issues pertaining to artificial intelligence and computing intelligence are progressively emerging. Artificial intelligence and computational intelligence play a significant role in the Internet of Things. However, their application process is challenging and lacks creativity, necessitating ongoing improvements at the application level. This study examines the use of artificial intelligence and computational intelligence in the Internet of Things. © 2024 IEEE.

关键词： Artificial intelligence computational intelligence Internet of things

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