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System-technology Co-Optimization of Multimetal Gated AlGaN/GaN HEMT for Improved rF Linearity

IEEE Journal of the Electron devices Society 2024年

作者： Hossain, Toiyob Hossain, Tanvir Alam, A.K.M. Anindya Sikder, Bejoy Xie, Qingyun Yuan, Mengyang Yagyu, Eiji Teo, Koon Hoo Palacios, Tomás Chowdhury, Nadim the Department of Electrical and Electronic Engineering Bangladesh University of Engineering and Technology Dhaka1205 Bangladesh Microsystems Technology Laboratories Massachusetts Institute of Technology CambridgeMA02139 United States Apple Inc. CupertinoCA95014 United States Mitsubishi Electric Corporation Advanced Technology R&D Center 8-1-1 Tsukaguchi-honmachi Amagasaki City661-8661 Japan Mitsubishi Electric Research Labs CambridgeMA02139 United States

—In this work, a system-technology co-optimization (STCO) of the AlGaN/GaN multimetal gated (MMG) HEMT architecture for third-order transconductance (gm3) engineering and linearity improvement in the presence of fermi-level pinning (FLP) is reported. Through technology computer-aided design (TCAd), compact modeling, load-pull simulations and modulated signal simulations, it is shown that despite incorporating FLP, employing MMG scheme improves device level gm3 - suppression, leading to an improvement in output-referred third-order intercept point per unit dc power (OIP3/PdC) and third order intermodulation distortion (IMd3). remarkably, OIP3/PdC of 18.9 dB is obtained considering an FLP factor of 0.43, which is a 10.7 dB improvement over the conventional HEMT. MMG HEMT exhibits an output-referred 1-dB compression point (P1-dB) of 3.60 W/mm, compared to 0.60 W/mm for the standard/conventional case. A comparative analysis on output power back-off (OBO) for conventional and MMG HEMT with different FLP factors establishes MMG as a robust architecture to FLP. Simulations involving 5G Fr1 signals demonstrate that the adjacent channel power ratio (ACPr) is sustained below −40 dBc up to an output power of 20 dBm. 2.6% lower error vector magnitude (EVM) than baseline case is achieved by MMG HEMT at 5 GHz, under 100 MHz 64-QAM OFdM signals. © 2024 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Aluminum gallium nitride

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A Study on the Vapor-Liquid Equilibria of Vinyl Acrylate-Acetic Acid and Vinyl Acetate-Acetic Acid with Gemc Method

SSRN

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SSrN 2023年

作者： Zhang, Minhua Wang, Yuankang Liu, Shuhan Zhang, Yan Yu, Yingzhe Key Laboratory for Green Chemical Technology The Ministry of Education Tianjin University R&D Center for Petrochemical Technology Tianjin300072 China Zhejiang Institute of Tianjin University Zhejiang Ningbo315201 China Collaborative Innovation Center of Chemical Science and Engineering Tianjin300072 China

Vinyl acetate (VAc) is one of the widely utilized industrial organic raw materials. In the acetoxylation of ethylene to vinyl acetate, there is a by-product vinyl acrylate. However, the vapor-liquid phase equilibrium data of this component with acetic acid and vinyl acetate have not been reported. Here, by density Functional Theory calculations, we fitted the TraPPE-UA force field parameters of vinyl acrylate. The vapor-liquid phase equilibrium data of the vinyl acrylate-acetate and vinyl acrylate-vinyl acetate binary systems were calculated using the Gibbs Ensemble Monte Carlo (GEMC) method. The calculated phase equilibrium data were utilized to fit the binary interaction parameters of the NrTL equations for the binary systems which were used for the separation process simulation of the vinyl acrylate, acetic acid and vinyl acetate systems. It was verified that the TraPPE-UA force field parameters fitted in this paper have high accuracy. The results clarify the concentration distribution of vinyl acrylate gas-liquid phase components in the distillation column, which builds the foundation for the development of the separation process. © 2023, The Authors. All rights reserved.

关键词： Acetic acid

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Predictive model of frailty onset using Bayesian network

Predictive model of frailty onset using Bayesian network

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2022 IEEE International Conference on Big data, Big data 2022

作者： Kawai, Yujiro Sakurai, Eiichi Sugawara, Yuki Okada, Yukihiko Tanabe, Kai Tsukao, Akiko Kuno, Shinya National Institute of Advanced Industrial Science and Technology Artificial Intelligence Research Center Tokyo Japan University of Tsukuba Tsukuba Japan Center for Artificial Intelligence Research University of Tsukuba Institute of Systems and Information Engineering Tsukuba Japan University of Tsukuba R&d Center for Smart Wellness City Policies Tsukuba Japan Tsukuba Wellness Research Co. Ltd Chiba Japan

ISBN: (纸本)9781665480451

In this study, we constructed a model for predicting the future frailty status of individuals based on information regarding their current lifestyle habits. In recent years, as the global population ages, the number of people certified for long-term care has increased. In an effort to prevent the need for long-term care, prediction of the onset of frailty, which is positioned as a preliminary stage of the caregiving state, is gaining attention. However, the onset of frailty takes a long time, and the incidence of frailty differs according to one's health literacy. Therefore, a prediction is extremely difficult to achieve. In this study, we constructed a prediction model using a probabilistic latent semantic analysis and a Bayesian network. First, the probabilistic latent semantic analysis was conducted to classify individuals according to their health literacy. Second, we constructed a future frailty prediction model using a Bayesian network for each health literacy segment. As a result, we clarified the following: It is more appropriate to predict a future frailty by predicting the future lifestyle using information on the current lifestyle. In addition, more accurate prediction models can be constructed when individuals are divided based on their health literacy. © 2022 IEEE.

关键词： Bayesian networks

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Experimental demonstration of multiparty reference-frame-independent QKd

Experimental demonstration of multiparty reference-frame-ind...

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Conference on Lasers and Electro-Optics (CLEO)

作者： donghwa Lee Kyujin Shin Yosep Kim Hyang-Tag Lim Yong-Su Kim Center for Quantum Information Korea Institute of Science and Technology (KIST) Seoul Republic of Korea Division of Nano & Information Technology KIST School Korea University of Science and Technology Seoul Republic of Korea Materials Research & Engineering Center R&D Division Hyundai Motor Company Uiwang Republic of Korea

We report that rFI-QKd protocol can be extended to multiparty system using Greenberger-Horne-Zeilinger (GHZ) state. We derive the asymptotic key rate and perform proof-of-principle experiment to verify the feasibility... 详细信息

ISBN: (纸本)9781957171258

关键词： Optical fibers Photonic entanglement Quantum communications Quantum key distribution Quantum networks Single photon detectors

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Ionisation coefficients in HFO-1336mzz(E) and HFO-1336mzz(E)/synthetic air mixtures

Ionisation coefficients in HFO-1336mzz(E) and HFO-1336mzz(E)...

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23rd International Symposium on High Voltage engineering (ISH 2023)

作者： Yuki Hasukawa Sho Yoshita Satoru Kawaguchi Kazuhiro Takahashi Kohki Satoh ryoko Sasahara Manabu Yoshimura Division of Information & Electronic Engineering Graduate School of Muroran Institute of Technology 27-1 Mizumoto Muroran Hokkaido Japan Advanced Technology R&D Center Mitsubishi Electric Corporation 8-1-1 Tsukaguchihonmachi Amagasaki Hyogo Japan

ISBN: (数字)9781839539923

Prebreakdown ionisation current I ( d ) in (E)-1,1,1,4,4,4-hexafluoro-2-butene (HFO-1336mzz(E) vapour and HFO-1336mzz(E) vapour/synthetic air mixtures is measured by the Steady-State Townsend (SST) method, and the ionisation and the electron attachment, effective ionisation coefficients are deduced. It is found that the value of the limiting reduced-electric field in HFO-1336mzz(E) vapour is at approximately 410 Td. It is also found that the value of the limiting reduced-electric field in the mixtures exceed that in SF 6 for the mixtures the ratio of which is over 70% HFO-1336mzz(E) content.

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Introduction of a Trusted Whitelist Mechanism for ddOS Attack Early Warning Optimization

Introduction of a Trusted Whitelist Mechanism for DDOS Attac...

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International Conference on Wavelet Active Media technology and Information Processing (ICWAMTIP)

作者： Luo Haoran HU Shuisong Wang Wenyong Miao Yu deng Jun LI Yubo Sichuan Cloud Network Integration Engineering Laboratory University of Electronic Science and Technology of China CHENGDU SICHUAN CHINA NSFOCUS R&D Center Building C5 of the XINCHUAN Information Technology Incubation Park CHENGDU SICHUAN CHINA

distributed denial of Service (ddoS) attacks pose a significant threat to modern networks, leading to substantial computational resource consumption and economic losses. Many mature defense technologies exist to block malicious traffic and deploy honeypots for traffic monitoring, enabling early detection and mitigation of attacks. However, these defenses can disrupt normal client services, especially during peak network usage, due to over-blocking and inaccurate attack situation assessments. This paper introduces an optimized mechanism involving a trusted whitelist, which filters legitimate clients in advance during the ddoS attack Early warning stage, addressing potential misjudgments during peak network times. Experimental results show that this mechanism can more effectively and accurately discern ddoS situations, reducing the likelihood of false positives.

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data-driven analysis of the process, organization and properties of large-size complex thin-walled die-casting aluminium alloys

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engineering Applications of Artificial Intelligence 2025年 156卷

作者： Yang, Jian Liu, Bo Shu, dongwei Yang, Qin Zeng, Yunbo Huang, Jun School of Mechanical Engineering University of Science and Technology Beijing Beijing100083 China Beijing Key Laboratory of Lightweight Metal Forming University of Science and Technology Beijing Beijing100083 China School of Mechanical and Aerospace Engineering Nanyang Technological University Singapore 639798 Singapore Global R&D Center Chongqing Changan Automobile Co. Ltd Chongqing 401120 China Chenzhi Science and Technology Co. Ltd Chongqing 402760 China

High pressure die casting technology, as an advanced precision forming process, is widely used in the manufacture of different size parts. Whereas in the production of large-size parts, the spatial heterogeneity of mechanical properties caused by local thermal field inhomogeneity becomes a key technical bottleneck. This paper presents a data-driven framework that effectively leverages the thermal history throughout the casting process, enabling precise predictions of part mechanical properties at various spatial locations. A physical simulation model is used to simulate the casting and molding process of front nacelle, with sensors embedded to provide temperature data at different locations. Subsequently, the effects of process and location on solidification rate, microstructure, and mechanical properties are discussed. relying solely on the solidification rate to predict mechanical properties is inadequate, necessitating the consideration of additional factors and material characteristics. Finally, the thermal history data of the whole casting process is provided to the one-dimensional convolutional neural network model, which is used to extract global features, not just local information, to predict the mechanical properties at unknown locations. To gain deeper insight into the intricate relationship between the variables, each convolutional layer is visualized to elucidate the correlation between thermal history and mechanical properties. This research is an extension of die-casting technology in the field of artificial intelligence and a new application in the field of automotive lightweighting. © 2025 Elsevier Ltd

关键词： Aluminum alloys

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Anti-osteoporosis properties of phosphorylated collagen peptidemagnesium chelates in osteoblast MC3T3-E1 cells and ovariectomized mice

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Chinese Medical Journal 2024年第14期137卷 1762-1764页

作者： Chao Zhang Bowei du Guiya deng Shuai Zhang Tengfei Yu Yuepeng Guan Yaqin Huang College of Materials Science and Engineering Beijing University of Chemical TechnologyBeijing 100029China School of Pharmacy Queen’s University BelfastBelfastBT97BLUK Department of Ultrasound Beijing Tian Tan HospitalCapital Medical UniversityBeijing 100160China Beijing Key Laboratory of Clothing Materials R&D and Assessment Beijing Engineering Research Center of Textile Nano FiberBeijing Institute of Fashion TechnologyBeijing 100029China

To the Editor:Postmenopausal osteoporosis is a typical bone metabolic disruption disease caused by estrogen deficiency,characterized by deteriorated bone structure,reduced bone mineral density(BMd),and thinned sclerotin,which has troubled millions of menopausal women worldwide.[1]Additionally,estrogen deficiency is accompanied by oxidative stress and inflammation,thus causing the occurrence and deterioration of osteoporosis.[2]Conventional treatment options have limited efficacy and cannot meet the requirements of long-term treatment.[3]Therefore,in order to alleviate the problem of osteoporosis in human health,innovative strategies should explore the possibilities and alternatives to current *** this study.

关键词： osteoporosis inflammation magnesium

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Vegetation Shading Fault diagnosis in Photovoltaic Power Stations Based on SVM

Vegetation Shading Fault Diagnosis in Photovoltaic Power Sta...

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Power Electronics and Application Symposium (PEAS), IEEE International

作者： Zeao Wang Mingyao Ma Wenting Ma Qian Xu rui Zhang National and Local Joint Engineering Laboratory for Renewable Energy Access to Grid Technology Hefei University of Technology Hefei China National and Local Joint Engineering Laboratory for Renewable Energy Access to Grid Technology Hefei China R&D Center Sungrow Smart Maintenance Technology Co. Ltd. Hefei China

Aiming at vegetation shading faults in photovoltaic (PV) power stations, a fault diagnosis model based on support vector machine (SVM) is proposed. According to the seasonal variation characteristics of PV string currents, the characteristics of vegetation shadings are extracted, and the characteristic matrix is established. Through the field operation data, the model was established for training and verification. The diagnostic results of SVM with different kernel functions are compared through experiments, and the diagnostic results of different machine learning are compared. The results show that the SVM algorithm can effectively identify shading faults of PV power stations, and the fault diagnosis accuracy is high.

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A deep reinforcement Learning Method for Orthogonal Turn-Milling Parameters Optimization

A Deep Reinforcement Learning Method for Orthogonal Turn-Mil...

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robotics & Intelligent Manufacturing technology (ISrIMT), International Symposium on

作者： Qing Xia Xiushang duan Chao Yin Xiaorong Gong Chuan Yang College of Mechanical and Vehicle Engineering Chongqing University Chongqing China R&D Center Chongqing CEPREI Industrial Technology Co. Ltd Chongqing China College of Mechanical Engineering Chongqing University of Technology Chongqing China CEPREI Southwestern Branch China Electronic Product Reliability and Environmental Testing Research Institute (CEPREI) Guanzhou China

ISBN: (数字)9798331528942

ISBN: (纸本)9798331528959

Turn-milling is an advanced manufacturing technology which has been widely applied to various industries due to its high-speed and interrupted cutting. However, there are few literatures studying on the multi-objective optimization of turn-milling, which can effectively improve overall performance of turn-milling to realize profit maximization. In this paper, we presented a multi-objective optimization method based on deep reinforcement learning (drL) to solve the multi-objective optimization problem of orthogonal turn-milling parameters. This method adopts a decomposition strategy to convert the optimization problem into multiple subproblems of single objective. Then all subproblems are modelled by neural networks, and collaboratively optimized by means of a drL algorithm and a neighborhood-based parameter transfer strategy. Finally, the Pareto optimal solutions are obtained from these optimized neural networks by means of the greedy strategy. Extensive experimental results show the effectiveness and competitiveness of the proposed method for solving the multi-objective optimization problem.

关键词： Industries Neural networks Pareto optimization deep reinforcement learning Manufacturing

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