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2022 International Conference on Biomedical and Intelligent Systems, IC-BIS 2022

作者： Che, U. Kin Liu, Xin Yu, Yuanyu Wang, Jiujiang Pun, Sio Hang Chen, Fei Mak, Peng Un Vai, Mang I. Institude of Mircroelectronic University of Macau Avenida da Universidade Taipa China Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau Avenida da Universidade Taipa China Department of Electrical and Electronic Engineering Collage of Engineering Southern University of Science and Technology Shenzhen China

ISBN: (纸本)9781510660212

Capacitive Micromachined Ultrasonic Transducer (CMUT) is a type of ultrasonic transducer that can be applied in many fields like chemical and mechanical sensing, physical imaging, and bio-imaging. Sacrificial layer release is one of the fabrication methods and cavity releasing is the key part of this method. The stress of the sacrificial material is one of the causes making membranes break in this procedure. The previous studies had a phospho-silicide-glass (PSG) layer deposited between polysilicon sacrificial layer and silicon nitride membrane with LPCVD partially solved the problem. However, its processing temperature is relatively high, which makes it not compatible with CMOS integrated circuits (ICs). This work fabricated CMUTs in the sacrificial layer release process, with polysilicon as the main sacrificial layer. Utilizing chemical reaction of polysilicon in ashing process, a thin oxide was formed between the sacrificial layer and membrane while removing the remaining photoresist with ashing. The result showed that it could also help produce large membranes with a lower processing temperature, which can be more compatible with CMOS ICs. After fabrication, the impedance of an element with 100 µm diameter cells was measured to be several hundred ohms in air with 30 V bias voltage and the resonance frequency was 4.7 MHz. © 2022 SPIE. All rights reserved.

关键词： Polysilicon

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Probabilistic Load Flow Calculation of Power System Integrated with Wind Farm Based on Kriging Model

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Energy engineering 2021年第3期118卷 565-580页

作者： Lu Li Yuzhen Fan Xinglang Su Gefei Qiu Faculty of Land Resource Engineering Kunming University of Science and TechnologyKunming650500China School of Control and Computer Engineering North China Electric Power UniversityBeijing102206China

Because of the randomness and uncertainty,integration of large-scale wind farms in a power system will exert significant influences on the distribution of power *** paper uses polynomial normal transformation method to deal with non-normal random variable correlation,and solves probabilistic load flow based on Kriging *** method is a kind of smallest unbiased variance estimation method which estimates unknown information via employing a point within the confidence scope of weighted linear *** with traditional approaches which need a greater number of calculation times,long simulation time,and large memory space,Kriging method can rapidly estimate node state variables and branch current power distribution *** one of the generator nodes in the western Yunnan power grid,a certain wind farm is chosen for empirical *** are used to compare with those by Monte Carlo-based accurate solution,which proves the validity and veracity of the model in wind farm power modeling as output of the actual turbine through PSD-BPA.

关键词： Probabilistic load flow Kriging model wind turbine clusters polynomial normal transformation correlation

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Development of an Executive Design of a Station for Measuring the Curvature of Metallurgical Charge for Rolling 21st

Development of an Executive Design of a Station for Measurin...

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21st Polish control Conference, PCC 2023

作者： Placzek, Marek Krauze, Piotr Ryfa, Arkadiusz Jablonka, Tomasz Balys, Karol Helmich, Magdalena Badora, Dawid Florkiewicz, Bartosz Polak, Kamil Slonka, Piotr Trybulowski, Mateusz Faculty of Mechanical Engineering Silesian University of Technology Konarskiego 18A Gliwice44-100 Poland Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 16 Gliwice44-100 Poland Faculty of Energy and Environmental Engineering Silesian University of Technology Konarskiego 18 Gliwice44-100 Poland MOSTOSTAL ZABRZE Biprohut S.A Dubois 16 Gliwice44-100 Poland

ISBN: (纸本)9783031351723

The work is a report on the implementation of interdisciplinary group project classes conducted in accordance with the Project Based Learning approach. The aim of the project was to develop an automated station for measuring the curvature of metallurgical charge and to create its technical documentation. The station is intended to work in cooperation with devices of an automated line for spreading and transporting metallurgical charges, which is a part of the technological line for hot rolling. The task of the station is to identify the curvature value, so that a charge with exceeded parameters does not proceed to the next stage of the process (the goal is significant in terms of eliminating defects). The final outcome of the project was to be a complete technical documentation of the designed station along with the measurement algorithm. Additionally, a laboratory test station and a system design for use in real industrial conditions were developed. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Hot rolling

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Two-Stage Robust Optimization Under Decision Dependent Uncertainty

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IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1295-1306页

作者： Yunfan Zhang Feng Liu Yifan Su Yue Chen Zhaojian Wang João P.S.Catalão State Key Laboratory of Power System and Generation Equipment the Department of Electrical EngineeringTsinghua UniversityBeijing 100084China Department of Mechanical and Automation Engineering the Chinese University of Hong KongHong Kong SARChina Ministry of Education Key Laboratory of System Control and Information Processing the Department of AutomationShanghai Jiao Tong Universityand also with Shanghai Engineering Research Center of Intelligent Control and ManagementShanghai 200240China Faculty of Engineering of the University of Porto and Institute for Systems and Computer Engineering Technology and Science(INESC TEC)Porto 4200-465Portugal IEEE

In the conventional robust optimization(RO)context,the uncertainty is regarded as residing in a predetermined and fixed uncertainty *** many applications,however,uncertainties are affected by decisions,making the current RO framework *** paper investigates a class of two-stage RO problems that involve decision-dependent *** introduce a class of polyhedral uncertainty sets whose right-hand-side vector has a dependency on the here-and-now decisions and seek to derive the exact optimal wait-and-see decisions for the second-stage problem.A novel iterative algorithm based on the Benders dual decomposition is proposed where advanced optimality cuts and feasibility cuts are designed to incorporate the uncertainty-decision *** computational tractability,robust feasibility and optimality,and convergence performance of the proposed algorithm are guaranteed with theoretical *** motivating application examples that feature the decision-dependent uncertainties are ***,the proposed solution methodology is verified by conducting case studies on the pre-disaster highway investment problem.

关键词： Benders decomposition decision-dependent uncertainty endogenous uncertainty robust optimization(RO)

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MetaCon: Revitalizing Internet Congestion control with Meta-Reinforcement Learning

MetaCon: Revitalizing Internet Congestion Control with Meta-...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： He Bai Hui Li Jianming Que Minglong Zhang Peter Han Joo Chong Kalupahana Liyanage Kushan Sudheera Xinyuan Pei School of Electronic and Computer Engineering Peking University Shenzhen China School of Computer Science Fuyao University of Science and Technology Fuzhou China Department of Electrical and Computer Engineering Mississippi State University Starkville USA School of Engineering Computer and Mathematical Sciences Auckland University of Technology Auckland New Zealand Department of Electrical and Information Engineering Faculty of Engineering University of Ruhuna Sri Lanka

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Effective congestion control algorithms (CCAs) are crucial for the smooth operation of Internet communication infrastructure. CCAs adjust transmission rates based on congestion signals, optimizing resource utilization and user experience. However, existing studies, both rule-based and learning-based CCAs, often struggle with generalization and underperform when deployed in real-world environments. When applied to unseen network conditions, hand-crafted schemes or pre-trained models may experience significant performance degradation. To address this challenge, we propose MetaCon, a novel adaptive Internet congestion control approach based on meta-reinforcement learning. MetaCon leverages knowledge learned from prior scenarios to quickly adapt to new environments. Experimental results show that MetaCon outperforms existing algorithms by exhibiting superior generalization and achieving better transmission performance across a wide variety of network conditions.

关键词： Degradation Adaptation models Signal processing algorithms Signal processing User experience Acoustics Internet Resource management Speech processing

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Impact of Parameters for the In-situ Measurement of the Electric Field Strength of the SSB Signal in a 5G NR System Using the Frequency-Selective Method in Zero-Span Mode

Impact of Parameters for the In-situ Measurement of the Elec...

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International Conference on Smart Systems and Technologies (SST)

作者： Vanja Mandrić Slavko Rupčić Bogdan Pavković Josip Ilakovac Department of Communication Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia Department of Computing and Control Engineering Faculty of Technical Sciences University of Novi Sad Novi Sad Serbia Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia

ISBN: (数字)9798350386394

ISBN: (纸本)9798350386400

Measuring 5G systems is a major challenge for measurement engineers, who need to be aware of all the limitations of the new generation The parameters of the frequency-selective measurement system (spectrum analyzer - ROHDE & SCHWARZ FSH8) were investigated in the measurement of 5G NR SSB signals (3.4 – 3.8 GHz). The focus was on the setting parameters which, according to the literature, have an influence on the measurement: RBW, VBW, SWEEP TIME and the duration of the measurement. All measurements were carried out in zero-span mode. In the second part of the measurement, these SSB synchronization signal measurement results were compared with the 5G signal measurement results at the same location using the NARDA SRM-3006 device equipped with a decoder for the 5G NR system (FR1) to determine the optimal method for measuring these signals with a spectrum analyzer (SA).The measurement results show that the resolution bandwidth (RBW) has the largest variation in the measured electric field (EF) strength values of the SSB 5G NR signal of 17.20 dB (MH/RMS) and 17.70 (RMS/AGV10). It is therefore necessary to pay particular attention to the setting of this measurement parameter. In addition, the video resolution (VBW) shows a significantly lower field strength variation of 2.50 dB, but also the same deviation from the reference value of 2.50 dB (RMS/AGV10). Other analyzed parameters have a negligible influence on the measurement of the EF *** post-processing was performed in the work to determine the final (maximum) value of the EF strength at the points of assumed highest exposure, which is used to assess the impact and compliance with the permissible values according to the relevant standards.

关键词： Time-frequency analysis 5G mobile communication Measurement uncertainty Electric variables measurement Amplitude modulation Time measurement Frequency measurement Decoding Electric fields Signal resolution

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Robust Length of Stay Prediction Model for Indoor Patients

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computers, Materials & Continua 2022年第3期70卷 5519-5536页

作者： Ayesha Siddiqa Syed Abbas Zilqurnain Naqvi Muhammad Ahsan Allah Ditta Hani Alquhayz M.A.Khan Muhammad Adnan Khan Department of Mechatronics and Control Engineering University of Engineering and TechnologyLahore54000Pakistan Department of Information Sciences Division of Science and TechnologyUniversity of EducationLahore54000Pakistan Department of Computer Science and Information College of Science in ZulfiMajmaah UniversityAl-Majmaah11952Saudi Arabia Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnam13557Korea

Due to unforeseen climate change,complicated chronic diseases,and mutation of viruses’hospital administration’s top challenge is to know about the Length of stay(LOS)of different diseased patients in the *** management does not exactly know when the existing patient leaves the hospital;this information could be crucial for hospital *** could allow them to take more patients for *** a result,hospitals face many problems managing available resources and new patients in getting entries for their prompt ***,a robust model needs to be designed to help hospital administration predict patients’LOS to resolve these *** this purpose,a very large-sized data(more than 2.3 million patients’data)related to New-York Hospitals patients and containing information about a wide range of diseases including Bone-Marrow,Tuberculosis,Intestinal Transplant,Mental illness,Leukaemia,Spinal cord injury,Trauma,Rehabilitation,Kidney and Alcoholic Patients,HIV Patients,Malignant Breast disorder,Asthma,Respiratory distress syndrome,*** been analyzed to predict the *** selected six Machine learning(ML)models named:Multiple linear regression(MLR),Lasso regression(LR),Ridge regression(RR),Decision tree regression(DTR),Extreme gradient boosting regression(XGBR),and Random Forest regression(RFR).The selected models’predictive performance was checked using R square andMean square error(MSE)as the performance evaluation *** results revealed the superior predictive performance of the RFRmodel,both in terms of RS score(92%)and MSE score(5),among all selected *** Exploratory data analysis(EDA),we conclude that maximumstay was between 0 to 5 days with the meantime of each patient 5.3 days and more than 50 years old patients spent more days in the *** on the average LOS,results revealed that the patients with diagnoses related to birth complications spent more days in the hospital than other *** finding coul

关键词： Length of stay machine learning robust model random forest regression

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High Efficiency Dual-Active-Bridge Converter with Triple-Phase-Shift control for Battery Charger of Electric Vehicles

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Energies 2024年第2期17卷

作者： Kuo, Shih-Hao Chiu, Huang-Jen Chiang, Che-Wei Huang, Ta-Wei Chang, Yu-Chen Bachman, Serafin Piasecki, Szymon Jasinski, Marek Turzyński, Marek Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei106 Taiwan Institute of Control and Industrial Electronics Warsaw University of Technology Warsaw00-662 Poland Faculty of Electrical and Control Engineering Gdańsk University of Technology Gdansk80-233 Poland

An optimal modulation scheme with triple-phase-shift (TPS) control could increase the efficiency in the entire load range for a dual-active-bridge (DAB) converter under wide output voltage range conditions. Therefore, this study proposes a convergent approach to TPS mode selection, coupled with an optimal modulation scheme, ensuring the circuit’s efficiency over the entire range in the realm of a high-power and high-efficiency battery charger for electric vehicles. The convergent approach to TPS mode selection also reduces the numerous cases for small-signal analysis through general average modeling. After verifying the small-signal models under various voltage transfer ratios and load conditions to verify the stability, a converter prototype with a rated power of 15 kW is built and tested. Thus, a peak efficiency of 97.7% can be achieved. © 2024 by the authors.

关键词： Charging (batteries)

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A Power Split Strategy for a Vehicular Hybrid Energy Storage System Using Adaptive Neuro Fuzzy Inference System 8

A Power Split Strategy for a Vehicular Hybrid Energy Storage...

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8th IEEE International Conference on advanced Robotics and Mechatronics, ICARM 2023

作者： Li, Jiao Zhang, Lijun Yang, Longzhi Wang, Bo School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Huazhong University of Science and Technology Belt and Road Joint Laboratory on Measurement and Control Technology Wuhan430074 China Northumbria University Faculty of Engineering and Environment Department of Computer Science and Digital Technologies Newcastle upon TyneNE1 8ST United Kingdom

ISBN: (纸本)9798350300178

This paper presents a real-time power split strategy for a battery-supercapacitor hybrid energy storage system. The objective of the proposed strategy is to alleviate battery degradation through effective supercapacitor utilization. The proposed strategy employs an adaptive neural fuzzy inference system, which is capable of learning a power split strategy from offline optimization results and distributing power in real-time. The performance of the strategy proposed and a low pass filtering technique is compared through simulations. The results demonstrate that the proposed method can effectively reduce battery capacity loss and perform well under unknown driving cycles in real time. © 2023 IEEE.

关键词： Supercapacitor

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Power Allocation Strategy for Secret Key Generation Method in Wireless Communications

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computers, Materials & Continua 2021年第8期68卷 2179-2188页

作者： Bin Zhang Muhammad Waqas Shanshan Tu Syed Mudassir Hussain Sadaqat Ur Rehman School of Electronics and Information Engineering Hunan University of Science and EngineeringYongzhou425199China Engineering Research Center of Intelligent Perception and Autonomous Control Faculty of Information TechnologyBeijing University of TechnologyBeijing100124China Faculty of Computer Science and Engineering Ghulam Ishaq Khan(GIK)Institute of Engineering Sciences and TechnologyTopi23460Pakistan Department of Electronics Engineering FICTBalochistan University of Information TechnologyEngineering and Management SciencesQuetta87300Pakistan Department of Computer Science Namal InstituteMianwali42200Pakistan

Secret key generation(SKG)is an emerging technology to secure wireless communication from ***,the SKG at the physical layer is an alternate solution over traditional cryptographic methods due to wireless channels’***,the physical layer secret key generation(PHY-SKG)depends on two fundamental parameters,i.e.,coherence time and power *** coherence time for PHY-SKG is not applicable to secure wireless *** is because coherence time is for a certain period of ***,legitimate users generate the secret keys(SKs)with a shorter key length in ***,an attacker can quickly get information about the ***,the attacker can easily get valuable information from authentic ***,we considered the scheme of power allocation to enhance the secret key generation rate(SKGR)between legitimate ***,we propose an alternative method,i.e.,a power allocation,to improve the *** results show 72%higher SKGR in bits/sec by increasing power *** addition,the power transmission is based on two important parameters,i.e.,epsilon and power loss factor,as given in power transmission *** found out that a higher value of epsilon impacts power transmission and subsequently impacts the *** SKGR is approximately 40.7%greater at 250 from 50 mW at epsilon=*** value of SKGR is reduced to 18.5%at 250 mW when epsilonis ***,the transmission power is also measured against the different power loss factor values,i.e.,3.5,3,and 2.5,respectively,at epsilon=***,it is concluded that the value of epsilon and power loss factor impacts power transmission and,consequently,impacts the SKGR.

关键词： Secret key generation rate power allocation physical layer wireless communication

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