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Cognitive Computing and Complex Data (ICCD), International Conference on the

作者： Zhong Zhang Guanyu Yang Yang Chen Tianjiao Ji Hongtu Liu Wenbo Xu School of Computer Science And Engineering Southeast University Nanjing China National Institute For Viral Desease Control And Prevention Beijing China

Hand-foot-mouth disease (HFMD) has gradually become prevalent in mainland China since the first official report in 2008. The incidence of HFMD is increasing among children mainly aged zero to five. Thus how to monitor and prevent HFMD is becoming an important public health challenge. This study aims to build a forecasting framework of HFMD for two Chinese cities, Chengdu in Sichuan Province and Guangdong in Guangzhou Province based on LSTM, by utilizing clinical data of HFMD, meteorological factors, and Baidu index data. Our method achieves significant results

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Practical Realization of Only Active Floating Lossless Inductance Simulator

Practical Realization of Only Active Floating Lossless Induc...

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International Conference on engineering, Applied Sciences, and Technology (ICEAST)

作者： Natchanai Roongmuanpha Sumalee Unhavanich Tattaya Pukkalanun Worapong Tangsrirat Department of Computer Engineering School of Engineering King Mongkut’s Institute of Technology Ladkrabang (KMITL) Bangkok Thailand Faculty of Digital Technology Chitralada Technology Institute (CDTI) Bangkok Thailand Department of Instrumentation and Control Engineering School of Engineering King Mongkut’s Institute of Technology Ladkrabang (KMITL) Bangkok Thailand

ISBN: (数字)9798350385946

ISBN: (纸本)9798350385953

This paper describes an electronically controllable floating lossless inductance simulator circuit implemented with only off-the-shelf available integrated circuits, namely LT1228 and operational amplifier (OA). The proposed design consists solely of two LT1228s and a single OA. By using an active element named LT1228, the equivalent inductance can be tuned electronically with depended on the externally supplied current applied to LT1228. The proposed simulator imposed just one component matching restriction that required the equality of two transconductances. It is easily attainable with a simple current mirror. The worthiness of the proposed floating inductance simulator has been proven and applied to the construction of the 2nd-order RLC highpass/bandpass filter as an application. PSPICE simulations are used to investigate the characteristics of the proposed simulator and its filter realization.

关键词： Operational amplifiers Inductance Matched filters Current mirrors RLC circuits SPICE Integrated circuit modeling

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Synthesizing Robust Walking Gaits via Discrete-Time Barrier Functions with Application to Multi-Contact Exoskeleton Locomotion

Synthesizing Robust Walking Gaits via Discrete-Time Barrier ...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Maegan Tucker Kejun Li Aaron D. Ames School of Electrical and Computer Engineering and the Woodruff School of Mechanical Engineering Georgia Institute of Technology Atlanta GA Department of Computation and Neural Systems California Institute of Technology Pasadena CA Department of Mechanical and Civil Engineering and the Department of Control and Dynamical Systems California Institute of Technology Pasadena CA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Successfully achieving bipedal locomotion remains challenging due to real-world factors such as model uncertainty, random disturbances, and imperfect state estimation. In this work, we propose a novel metric for locomotive robustness – the estimated size of the hybrid forward invariant set associated with the step-to-step dynamics. Here, the forward invariant set can be loosely interpreted as the region of attraction for the discrete-time dynamics. We illustrate the use of this metric towards synthesizing nominal walking gaits using a simulation-in-the-loop learning approach. Further, we leverage discrete-time barrier functions and a sampling-based approach to approximate sets that are maximally forward invariant. Lastly, we experimentally demonstrate that this approach results in successful locomotion for both flat-foot walking and multi-contact walking on the Atalante lower-body exoskeleton.

关键词： Legged locomotion Measurement Uncertainty Exoskeletons Robustness State estimation Robots

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ACIGS: An automated large-scale crops image generation system based on large visual language multi-modal models

ACIGS: An automated large-scale crops image generation syste...

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IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks (SECON)

作者： Bolong Liu Hao Zhang Jie Liu Qiang Wang Department of Control Science and Engineering Harbin Institute of Technology Harbin China Department of Computer Science and Technology Harbin Institute of Technology (ShenZhen) ShenZhen China

Smart agriculture requires an extensive convergence of information technology and agriculture. Attaining intelligence mandates an enormous amount of data to train models. However, it is challenging to acquire a large number of crop image data, limiting the application and growth of computer vision technology in agriculture. To address this problem, we designed a crop image generation system that combines a large language model with visual language multi-modal large models to augment the scale, variety, and resolution of crop image data. First, the system inputs existing real crop images into the visual language multimodal model to extract features and represent crop images in text form. Then, the system passes the crop text representation to the language model for cleaning and processing, which generates prompts to create crop images. The prompts are input into the visual language multi-modal model to generate crop images based on text representation of crops. The resulting crop images undergo image quality evaluation in the visual language multimodal model, and high-quality crop images are saved to the crop image dataset based on the quality evaluation. These steps lead to the formation of the final generated crop image dataset. The experimental results indicate that the crop images generated using the proposed system are similar to but different from the example images. This characteristic enables the expansion of crop data while circumventing redundancy and allowing for resolution control, which is crucial for dense segmentation tasks. Using this method, the existing data can be enlarged up to 7.5 times.

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Adaptive Active Fault-Tolerant Attitude control for Spacecraft with Adaptive Prescribed Performance

Adaptive Active Fault-Tolerant Attitude Control for Spacecra...

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International Symposium on Autonomous Systems (ISAS)

作者： Ze Yang Baoqing Yang Ruihang Ji Jie Ma Control and Simulation Center Harbin Institute of Technology Harbin China Department of Electrical and Computer Engineering National University of Singapore Singapore

For spacecraft attitude control affected by environmental disturbance, parameter uncertainty and actuator fault, a novel composite active fault-tolerant scheme, combining a strong tracking Cubature Kalman filter (STCKF) with adaptive prescribed performance control (APPC), is investigated in this paper. The proposed STCKF is capable of estimating lumped fault rapidly but accurately, and it is robust to model uncertainty. An adaptive finite-time prescribed performance function (APPF) whose boundaries can be flexibly adjusted in the case of actuator faults is proposed. Then an active fault tolerant controller is designed using nonsingular terminal sliding mode control (NTSMC) in conjunction with APPF. Simulation experiments and comparisons show that the proposed strategy exhibits better fault tolerance, lower conservatism and better steady-state performance.

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High Precision Soft-Switching Converter with Low Voltage Stress and Fast Response Characteristic Based on SiC Power Devices

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IEEE Transactions on Power Electronics 2025年

作者： Wei, Jian Qiu, Guangjuan Kou, Baoquan Yang, Xiaobao Zhao, Xiaokun Wang, Weinan Zhang, Jiawei Northeast Forestry University college of computer and control engineering Harbin150040 China Harbin Institute of Technology School of Electrical Engineering and Automation Harbin150001 China School of Sichuan University Sichuan 610065 China

Due to the characteristic of no blanking time, dual buck topology is attractive in high precision applications and the bias current is injected to eliminate zero crossing distortion. However, with the extending of bus voltage, it is difficult to ensure the current response speed and accuracy due to the limitation of bias current operation and the impact of parasitic parameters of switching devices. Thus, this paper proposes a low voltage stress resonant dual buck soft-switching topology combined with SiC power device for achieving fast response and high precision. At first, the principle of the bias current and soft-switching topology is introduced. Then combined with the bias current decoupling and the realization of zero current switching (ZCS) and zero voltage switching (ZVS), the control strategy is analyzed. Moreover, based on the resonant circuit considering parasitic parameters, the factors that affect the voltage and current stress are presented. Finally, a prototype of 500 V bus voltage with 100 kHz switching frequency is built to verify the theory analysis © 1986-2012 IEEE.

关键词： Silicon carbide

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Green and Low Carbon Load Side Carbon Emission Measurement in Power Systems Based on Opportunity Constraints

Green and Low Carbon Load Side Carbon Emission Measurement i...

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China International Conference on Electricity Distribution, CICED

作者： Zhiyuan Xu Liang Cheng School of Control and Computer Engineering North China Electric Power University Beijing China TCA Lab Institute of Software Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350368345

ISBN: (纸本)9798350368352

In the context of global warming, traditional carbon measurement methods have failed to completely capture the carbon emission dynamics of the power system under various operating states, resulting in biased carbon emission data. Moreover, there is still significant research space for measurement models on the power load side. To improve the accuracy and real-time performance of carbon emission measurement, this study designs a power system load side carbon emission measurement model based on opportunity constraints. The proposed model is built on opportunity constraints and fairness principles to allocate carbon emission responsibilities on the load side. On this basis, considering the dynamic characteristics of the power system, a two-stage carbon emission measurement model is raised, which uses dynamic carbon emission measurement method and node carbon potential method to calculate carbon emissions in the first and second stages, respectively. The outcomes validated that the carbon emissions of Unit 1 in the fixed carbon emission factor model were 620.07 tons, while they were reduced to 470.11 tons in the proposed model. After implementing demand response measures, the total carbon emissions of the system during peak electricity usage decreased by 3130 kg. The fixed carbon emission factor econometric model, traditional carbon market model, and the proposed model resulted in a total carbon emissions of 18306.61 tons, 16939.79 tons, and 19045.21 tons, respectively. Therefore, the proposed model performs better in regulating demand, can more accurately measure carbon emissions on the load side of the power system, and contribute to promoting green and low-carbon development of the energy system.

关键词： Power measurement Biological system modeling Power system dynamics Energy measurement Carbon dioxide Extraterrestrial measurements Time measurement Real-time systems Global warming Load modeling

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A production scheduling and control system for CAR T cell manufacturing

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Procedia CIRP 2024年 125卷 36-41页

作者： Tamás Kis Simon Hort Péter Györgyi Evelin Szögi Robert H. Schmitt HUN-REN Institute for Computer Science and Control Kende str 13-17 Budapest 1111 Hungary Fraunhofer Institute for Production Technology IPT Aachen Germany Laboratory for Machine Tools and Production Engineering (WZL) of RWTH Aachen University Aachen Germany

This paper addresses some of the new challenges posed by scheduling and controlling the production of chimeric antigen receptor (CAR) T cells for personalized therapies. In this therapy, the patient’s T cells are genetically modified by adding a gene for a receptor to help T cells attach to the cancer cell antigens. Then, the modified cells are grown and multiplied (called cell expansion) and finally returned to the patient. The production takes place in a manufacturing system that comprises several devices, which are capable of processing cells from multiple patients at the same time. The efficient operation of such a complex system requires a sophisticated control system accompanied by a scheduler to optimize the production of CAR T cells for several patients simultaneously. We propose a scheduling algorithm, which can handle the uncertain duration of cell expansion, and the maximum time lags between consecutive processes simultaneously, a combination hardly studied in the literature. We also describe the main features of an adaptive control system including service-oriented device connections as well as centralized control and monitoring. Finally, we describe how the overall system works, including a JSON based communication between scheduler and control system.

关键词： Scheduling Production control CAR T cell manufacturing

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Robustness challenges in Reinforcement Learning based time-critical cloud resource scheduling: A Meta-Learning based solution

Robustness challenges in Reinforcement Learning based time-c...

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作者： Liu, Hongyun Chen, Peng Ouyang, Xue Gao, Hui Yan, Bing Grosso, Paola Zhao, Zhiming Informatics Institute University of Amsterdam Amsterdam1098 XH Netherlands Graduate School of Informatics University of Amsterdam Amsterdam1098 XH Netherlands School of Computer and Software Engineering Xihua University Chengdu610039 China School of Computer Science National University of Defense Technology Changsha410073 China College of Electrical and Control Engineering Shaanxi University of Science and Technology Xi'an710021 China School of Electrical and Electronic Engineering University of Adelaide Adelaide South Australia5005 Australia

Cloud computing attracts increasing attention in processing dynamic computing tasks and automating the software development and operation pipeline. In many cases, the computing tasks have strict deadlines. The cloud resource manager (e.g., orchestrator) effectively manages the resources and provides tasks Quality of Service (QoS). Cloud task scheduling is tricky due to the dynamic nature of task workload and resource availability. Reinforcement Learning (RL) has attracted lots of research attention in scheduling. However, those RL-based approaches suffer from low scheduling performance robustness when the task workload and resource availability change, particularly when handling time-critical tasks. This paper focuses on both challenges of robustness and deadline guarantee among such RL, specifically Deep RL (DRL)-based scheduling approaches. We quantify the robustness measurements as the retraining time and investigate how to improve both robustness and deadline guarantee of DRL-based scheduling. We propose MLR-TC-DRLS, a practical, robust Meta Deep Reinforcement Learning-based scheduling solution to provide time-critical tasks deadline guarantee and fast adaptation under highly dynamic situations. We comprehensively evaluate MLR-TC-DRLS performance against RL-based and RL advanced variants-based scheduling approaches using real-world and synthetic data. The evaluations validate that our proposed approach improves the scheduling performance robustness of typical DRL variants scheduling approaches with 97%–98.5% deadline guarantees and 200%–500% faster adaptation. © 2023 The Author(s)

关键词： Reinforcement learning

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An Optimized Ensemble Model for Prediction the Bandwidth of Metamaterial Antenna

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computers, Materials & Continua 2022年第4期71卷 199-213页

作者： Abdelhameed Ibrahim Hattan F.Abutarboush Ali Wagdy Mohamed Mohamad Fouad El-Sayed M.El-kenawy Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Electrical Engineering Department College of EngineeringTaibah UniversityMedina42353Saudi Arabia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Wireless Intelligent Networks Center(WINC) School of Engineering and Applied SciencesNile UniversityGizaEgypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyEgypt

Metamaterial Antenna is a special class of antennas that uses metamaterial to enhance their *** size affects the quality factor and the radiation loss of the *** antennas can overcome the limitation of bandwidth for small *** learning(ML)model is recently applied to predict antenna *** can be used as an alternative approach to the trial-and-error process of finding proper parameters of the simulated *** accuracy of the prediction depends mainly on the selected *** models combine two or more base models to produce a better-enhanced *** this paper,a weighted average ensemble model is proposed to predict the bandwidth of the Metamaterial *** base models are used namely:Multilayer Perceptron(MLP)and Support Vector Machines(SVM).To calculate the weights for each model,an optimization algorithm is used to find the optimal weights of the *** Group-Based Cooperative Optimizer(DGCO)is employed to search for optimal weight for the base *** proposed model is compared with three based models and the average ensemble *** results show that the proposed model is better than other models and can predict antenna bandwidth efficiently.

关键词： Metamaterial antenna machine learning ensemble model multilayer perceptron support vector machines

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