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Wafer-Scale Synthesis of WS_(2)Films with In Situ Controllable p-Type Doping by Atomic Layer Deposition

Research 2021年第1期2021卷 1424-1432页

作者： Hanjie Yang Yang Wang Xingli Zou Rongxu Bai Zecheng Wu Sheng Han Tao Chen Shen Hu Hao Zhu Lin Chen David W.Zhang Jack C.Lee Xionggang Lu Peng Zhou Qingqing Sun Edward T.Yu Deji Akinwande Li Ji State Key Laboratory of ASIC and System School of MicroelectronicsFudan UniversityShanghai 200433China State Key Laboratory of Advanced Special Steel School of Materials Science and EngineeringShanghai UniversityShanghai 200444China Microelectronics Research Center Department of Electrical and Computer EngineeringThe University of Texas at AustinAustin78758 TexasUSA

Wafer-scale synthesis of p-type TMD films is critical for its commercialization in next-generation electro/*** this work,wafer-scale intrinsic n-type WS_(2)films and in situ Nb-doped p-type WS_(2)films were synthesized through atomic layer deposition(ALD)on 8-inchα-Al_(2)O_(3)/Si wafers,2-inch sapphire,and 1 cm^(2)GaN substrate *** Nb doping concentration was precisely controlled by altering cycle number of Nb precursor and activated by ***_(2)n-FETs and Nb-doped p-FETs with different Nb concentrations have been fabricated using CMOS-compatible processes.X-ray photoelectron spectroscopy,Raman spectroscopy,and Hall measurements confirmed the effective substitutional doping with *** on/off ratio and electron mobility of WS_(2)n-FET are as high as 105 and 6.85 cm^(2)V^(-1)s^(-1),*** WS_(2)p-FET with 15-cycle Nb doping,the on/off ratio and hole mobility are 10 and 0.016 cm^(2)V^(-1)s^(-1),*** p-n structure based on n-and p-type WS_(2)films was proved with a 10^(4)rectifying *** realization of controllable in situ Nb-doped WS_(2)films paved a way for fabricating wafer-scale complementary WS_(2)FETs.

关键词： sapphire doping realization

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Transfer Task-Averaged Natural Gradient for Efficient Many-Task Optimization

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IEEE Transactions on Evolutionary Computation 2024年

作者： Li, Yanchi Gong, Wenyin Gu, Qiong China University of Geosciences School of Computer Wuhan430074 China Hubei University of Arts and Science School of Computer Engineering Xiangyang441053 China Hubei University of Arts and Science Hubei Key Laboratory of Power System Design and Test for Electrical Vehicle Xiangyang441053 China

With the increasing requirement for computational efficiency in evolutionary algorithms when tackling many optimization tasks concurrently, many-task optimization (MaTO) has gained much attention in recent years. It involves extracting and transferring the knowledge from successful search experiences across many tasks. As the number of tasks and variable scale grows in MaTO, the need for efficient knowledge transfer in evolutionary algorithms becomes indispensable. In this paper, we present a task-averaged natural gradient-assisted natural evolution strategy (TNG-NES) to deal with MaTO efficiently. The task-averaged natural gradient captures how a group of task distributions evolves, considering their overall trends of mean and covariance. This can accelerate the optimization process across all tasks by leveraging the evolutionary similarities among multiple task search distributions. Notably, TNG-NES exhibits linear computational complexity concerning the number of tasks for knowledge transfer. Additionally, to adaptively utilize task-averaged natural gradient for distribution evolution and mitigate negative transfer, we introduce a transfer adaptive control mechanism for TNG-NES. We conducted extensive experiments on CEC19-MaTO, WCCI22-MaTO, our proposed large-scale MaTO benchmark suite, and real-world applications. The results validate the effectiveness of TNG-NES, outperforming state-of-the-art MaTO algorithms. © 1997-2012 IEEE.

关键词： Knowledge transfer

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Positioning Control for Voice Coil Motor Using Unbalanced Multilevel Inverter Topology

Positioning Control for Voice Coil Motor Using Unbalanced Mu...

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International Conference on Mechatronics (ICM)

作者： Gaku Takei Hidemine Obara Ryo Haneda Takahiro Nozaki Graduate School of Science and Technology Keio University Yokohama Japan Department of Physics Electrical and Computer Engineering Yokohama National University Yokohama Japan Department of System Design Engineering Keio University Yokohama Japan

ISBN: (数字)9798331533892

ISBN: (纸本)9798331533908

This article proposes a new multi-level inverter (MLI) based on flying capacitor inverter (FCI), with focused on high-precision positioning control for a voice coil motor (VCM). Multi-level inverters (MLIs) have the characteristic that increasing the number of output voltage levels reduces the output voltage error. However, as the number of levels increases, the number of circuit elements increases, and the circuit becomes more complex. In addition, the output voltage amplitude for each level is constant in conventional inverters. As a result, such as high-precision positioning control of VCM, designing an appropriate inverter for a reference wave that contains a large amount of low-voltage components increases the number of circuit elements. Therefore, this paper proposes a MLI with two unbalanced dc-input voltages, referred to as unbalanced MLIs (UMLIs) hereinafter. This topology uses two different dc-input voltages, and it is possible to reduce the output voltage level in extremely low-voltage range compared to other voltage levels. Simulation results when the high-precision positioning control of the VCM confirm that the proposed UMLIs improves the control performance compared to the conventional FCIs with equal number of output voltage levels and uniform level widths.

关键词： Coils Low voltage Simulation Switches Multilevel inverters Motors Topology Sensors Voltage control Motion control

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Trust-based Authentication Aided Blockchain for Distributed Learning in UAV Swarms: Challenges and Solutions

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IEEE Network 2024年

作者： He, Daojing Song, Yuxiao Dai, Minghui Chan, Sammy Chen, Lei Guizani, Mohsen School Of Computer Science And Technology Shenzhen China Donghua University School Of Computer Science And Technology Shanghai China City University Of Hong Kong Department Of Electrical Engineering Hong Kong Hong Kong GuangDong Engineering Technology Research Center Of Cryptographic Product And System Evaluation Shenzhen China United Arab Emirates

The remarkable characteristics of unmanned aerial vehicles (UAVs), such as high flexibility and real-time data acquisition, make them a promising platform for providing services and applications like data collection and aerial computing. Furthermore, by integrating UAVs with decentralized learning, emerging machine learning (ML)-assisted services (e.g., urban sensing and natural resource exploration) can be enhanced. However, the limited computation and communication resources of UAVs and the potential security risks pose challenges to the reliability and security for distributed learning in UAV swarms. This necessitates suitable learning policy design and continuous supervision throughout the process of decentralized learning. In this article, motivated by the merits of blockchain, we propose a trust-based authentication aided blockchain for distributed learning in UAV swarms, along with a corresponding layered architecture to ensure effectiveness and security. This architecture includes a perception layer, an aerial layer, and a blockchain layer. Moreover, the trust values of UAVs are derived from their behaviors during the decentralized learning process. These values, combined with smart contracts, enable dynamic and robust authentication for UAV swarms. Based on this framework, we present its advantages and exemplified applications. Moreover, the potential challenges related to security and efficiency when applying the proposed framework in wireless networks are also discussed. Additionally, we present a case study on trust-based authentication aided blockchain for distributed learning in UAV swarms. This includes a detailed procedure and solutions to the associated challenges, demonstrating its performance in terms of learning efficiency and security. We finally discuss some open research directions regarding the blockchain-assisted decentralized learning for UAV swarms. © 1986-2012 IEEE.

关键词： Blockchain

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A Flexible Wideband Microstrip Antenna on TPU Substrate Using Inset Slot Feed and Partial Ground Plane

A Flexible Wideband Microstrip Antenna on TPU Substrate Usin...

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IEEE Symposium on Wireless Technology and Applications (ISWTA)

作者： Aiman Hakimi Rahimi Sarah Yasmin Mohamad Norun Farihah Abdul Malek Md Rafiqul Islam Nur Shahida Midi Nurain Izzati Shuhaimi Department of Electrical and Computer Engineering Microwave Communication and Information System Engineering Research Group (MCISE) Kulliyyah of Engineering International Islamic University Malaysia Gombak Selangor Darul Ehsan School of Electrical Engineering College of Engineering Universiti Teknologi MARA Shah Alam Selangor Darul Ehsan

ISBN: (数字)9798350351354

ISBN: (纸本)9798350351361

This paper aims to present a flexible and wideband microstrip antenna on a Thermoplastic Polyurethane (TPU) substrate. TPU is used as the substrate in designing the flexible antenna due to its high flexibility, elasticity, and strength properties. The characteristic of the antenna is further enhanced by inserting inset slot feed and using a partial ground plane to improve the resonant frequency and realize a wide bandwidth, respectively. The performance of the proposed antenna is analyzed and compared with a common antenna design method which typically produces a narrower bandwidth. The antenna shows promising results resonating from 7.5 to 10.2 GHz with a percentage increase of 17.4% in terms of bandwidth, and greater return loss along the operating frequency range.

关键词： Antenna measurements Slot antennas Patch antennas Resonant frequency Microstrip antennas Antenna feeds Microstrip components

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Modified Aquila Optimizer Feature Selection Approach and Support Vector Machine Classifier for Intrusion Detection system

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Multimedia Tools and Applications 2024年第21期83卷 59887-59913页

作者： Abualigah, Laith Ahmed, Saba Hussein Almomani, Mohammad H. Zitar, Raed Abu Alsoud, Anas Ratib Abuhaija, Belal Hanandeh, Essam Said Jia, Heming Elminaam, Diaa Salama Abd Elaziz, Mohamed Abd Computer Science Department Al Al-Bayt University Mafraq25113 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan MEU Research Unit Middle East University Amman11831 Jordan Applied Science Research Center Applied Science Private University Amman11931 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia Department of Mathematics Facility of Science The Hashemite University P.O box 330127 Zarqa13133 Jordan Sorbonne Center of Artificial Intelligence Sorbonne University-Abu Dhabi Abu Dhabi United Arab Emirates Department of Computer Science Wenzhou-Kean University Wenzhou China Department of Computer Information System Zarqa University P.O. Box 13132 Zarqa Jordan School of Information Engineering Sanming University Sanming365004 China Information Systems Department Faculty of Computers and Artificial Intelligence Benha University Benha12311 Egypt Computer Science Department Faculty of Computer Science Misr International University Cairo12585 Egypt Faculty of Computer Science & Engineering Galala University Suze435611 Egypt Ajman University Ajman346 United Arab Emirates Department of Mathematics Faculty of Science Zagazig University Zagazig44519 Egypt

With the ever-expanding ubiquity of the Internet, wireless networks have permeated every facet of modern life, escalating concerns surrounding network security for users. Consequently, the demand for a robust Intrusion Detection system (IDS) has surged. The IDS serves as a critical bastion within the security framework, a significance further magnified in wireless networks where intrusions may stem from the deluge of sensor data. This influx of data, however, inevitably taxes the efficiency and computational speed of IDS. To address these limitations, numerous strategies for enhancing IDS performance have been posited by researchers. This paper introduces a novel feature selection method grounded in Support Vector Machine (SVM) and harnessing the innovative modified Aquila Optimizer (mAO) for Intrusion Detection systems in Wireless Sensor Networks. To evaluate the efficacy of our approach, we employed the KDD'99 dataset for testing and benchmarking against established methods. Multiple performance metrics, including accuracy, detection rate, false alarm rate, feature count, and execution time, were utilized for assessment. Our comparative analysis reveals the superiority of the proposed method, with standout results in terms of feature reduction, detection accuracy, and false alarm mitigation, yielding significant improvements of 11%, 98.76%, and 0.02%, respectively. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.

关键词： Network security

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A Design and Implementation of Water Heater Model Plant with Temperature Estimation Model for IOT Studies 7

A Design and Implementation of Water Heater Model Plant with...

引用

7th International Conference on Vocational Education and electrical engineering, ICVEE 2024

作者： Pradhana, Anak Agung Surya Funabiki, Nobuo Kotama, I Nyoman Darma Desnanjaya, I Gusti Made Ngurah Radhitya, Made Leo Mahajaya, Nyoman Sarasuartha Okayama University Graduate School of Environmental Life Natural Science and Technology Okayama Japan Okayama University Department of Electrical and Communication Engineering Okayama Japan Institute of Business and Technology Indonesia Department of Computer System Engineering Denpasar Indonesia Institute of Business and Technology Indonesia Department of Informatic Technology Denpasar Indonesia Institut Teknologi Dan Bisnis Stikom Departement of Magister Information Systems Denpasar Indonesia

ISBN: (纸本)9798331505103

This study presents the development of an Internet of Things (IoT) system for a water heater model, focusing on enhancing reliability during sensor malfunctions that could disrupt operations. Using the SEMAR IoT platform, the system efficiently collects and analyzes data, while the integration of the Holt-Winter algorithm ensures accurate temperature estimates, even with unreliable or missing sensor data. The algorithm successfully auto-calibrates sensors, predicts temperature at specific time intervals based on real sensor behavior, and fills in missing data during sensor failures. Tests show strong correlations between estimated and actual temperatures, allowing uninterrupted operation. This solution offers a flexible, cost-effective alternative to traditional redundancy, ensuring system reliability during sensor failures. Future research will explore alternative predictive models and conduct real-world testing to validate the system's resilience under complex conditions. © 2024 IEEE.

关键词： Temperature measurement

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Enabling Quadruple-D Compensation Coil Integration for Efficient Power Transfer to Receiver in Wireless Power Transfer systems for EV Charging

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IEEE Journal of Emerging and Selected Topics in Power Electronics 2025年

作者： Erel, Mehmet Zahid Imtiaz, Tabish Elsanabary, Ahmed Mubin, Marizan Soon, Tey Kok Mekhilef, Saad Ankara Yildirim Beyazit University Department of Energy Systems Engineering Ankara Turkey Universiti Malaya Faculty of Engineering Department of Electrical Engineering Kuala Lumpur Malaysia Port Said University Faculty of Engineering Department of Electrical Engineering Port Said Egypt Universiti Malaya Department of Computer System and Technology Faculty of Computer Science and Information Technology Kuala Lumpur Malaysia Swinburne University of Technology School of Science Computing and Engineering Technologies Hawthorn Australia Presidency University Department of Electrical and Electronics Engineering Karnataka Bengaluru India

Wireless power transfer (WPT) presents a promising approach for charging electric vehicles (EVs), offering increased convenience, reliability, and safety for EV customers. Recently, the incorporation of multiple coil structures in wireless charging systems has addressed various operational challenges, enhanced power transfer, and provided a scalable solution for EVs. However, cross-coupling issues and space limitation problems arise when multiple coils are introduced to the WPT system. In this paper, a novel integrated compact coupler is proposed, including two naturally decoupled coils on the transmitter side, while both are coupled with the receiver coil to improve the power transfer capability of the system. ANSYS Maxwell is employed to simulate the proposed coupler and evaluate its performance under misalignment operations. The proposed wireless charging system inherently detects misalignment direction based on the variation of output voltage with respect to x-misalignment. As a result, the experimental prototype of the designed wireless WPT system presents a peak efficiency of 97.08% at 1.147-kW output power for an air gap of 120 mm under fully-aligned condition. Additionally, the designed compensation coil can transfer power of 243.76-W under fully-aligned condition and increases reliability in power transfer during misalignment operations. © 2013 IEEE.

关键词： Inductive power transmission

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Operating-Envelopes-Aware Decentralized Welfare Maximization for Energy Communities

Operating-Envelopes-Aware Decentralized Welfare Maximization...

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Annual Allerton Conference on Communication, Control, and Computing

作者： Ahmed S. Alahmed Guido Cavraro Andrey Bernstein Lang Tong School of Electrical and Computer Engineering Cornell University Ithaca NY USA Power System Engineering Center National Renewable Energy Laboratory Golden CO USA

We propose an operating-envelope-aware, prosumer-centric, and efficient energy community that aggregates individual and shared community distributed energy resources and transacts with a regulated distribution system operator (DSO) under a generalized net energy metering tariff design. To ensure safe network operation, the DSO imposes dynamic export and import limits, known as dynamic operating envelopes, on end-users’ revenue meters. Given the operating envelopes, we propose an incentive-aligned community pricing mechanism under which the decentralized optimization of community members’ benefit implies the optimization of overall community welfare. The proposed pricing mechanism satisfies the cost-causation principle and ensures the stability of the energy community in a coalition game setting. Numerical examples provide insights into the characteristics of the proposed pricing mechanism and quantitative measures of its performance.

关键词：

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Enhancing Deep Knowledge Tracing via Diffusion Models for Personalized Adaptive Learning

arXiv

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arXiv 2024年

作者： Kuo, Ming Sarker, Shouvon Qian, Lijun Fu, Yujian Li, Xiangfang Dong, Xishuang The Department of Electrical and Computer Engineering Prairie View A&M University Texas A&M University System Prairie ViewTX77446 United States Department of Electrical Engineering and Computer Science School of Engineering Technology & Physical Sciences Alabama A&M University MadisonAL United States

In contrast to pedagogies like evidence-based teaching, personalized adaptive learning (PAL) distinguishes itself by closely monitoring the progress of individual students and tailoring the learning path to their unique knowledge and requirements. A crucial technique for effective PAL implementation is knowledge tracing, which models students' evolving knowledge to predict their future performance. Based on these predictions, personalized recommendations for resources and learning paths can be made to meet individual needs. Recent advancements in deep learning have successfully enhanced knowledge tracking through Deep Knowledge Tracing (DKT). This paper introduces generative AI models to further enhance DKT. Generative AI models, rooted in deep learning, are trained to generate synthetic data, addressing data scarcity challenges in various applications across fields such as natural language processing (NLP) and computer vision (CV). This study aims to tackle data shortage issues in student learning records to enhance DKT performance for PAL. Specifically, it employs TabDDPM, a diffusion model, to generate synthetic educational records to augment training data for enhancing DKT. The proposed method's effectiveness is validated through extensive experiments on ASSISTments datasets. The experimental results demonstrate that the AI-generated data by TabDDPM significantly improves DKT performance, particularly in scenarios with small data for training and large data for testing. Copyright © 2024, The Authors. All rights reserved.

关键词： Students

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