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Science China Life Sciences 2025年第5期68卷 1536-1540页

作者： Hao Xu Shibo Zhou Zefeng Zhu Vincenzo Vitelli Liangyi Chen Ziwei Dai Ning Yang Luhua Lai Shengyong Yang Sergey Ovchinnikov Zhuoran Qiao Sirui Liu Chen Song Jianfeng Pei Han Wen Jianfeng Feng Yaoyao Zhang Zhengwei Xie Yang-Yu Liu Zhiyuan Li Fulai Jin Hao Li Mohammad Lotfollahi Xuegong Zhang Ge Yang Shihua Zhang Ge Gao Pulin Li Qi Liu Jing-Dong Jackie Han Peking-Tsinghua Center for Life Sciences (CLS) Academy for Advanced Interdisciplinary StudiesPeking University Center for Quantitative Biology (CQB) Academy for Advanced Interdisciplinary StudiesPeking University Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program Academy for Advanced Interdisciplinary StudiesPeking University Department of Physics University of Chicago School of Life Sciences Southern University of Science and Technology Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies College of Chemistry and Molecular Engineering Peking University Department of Biotherapy Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan University Department of Biology Massachusetts Institute of Technology Lambic Therapeutics Inc. Changping Laboratory Al for Science Institute Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Department of Obstetrics and Gynecology West China Second University HospitalSichuan University Peking University International Cancer Institute and Peking University-Yunnan Baiyao International Medical Institute and State Key Laboratory of Natural and Biomimetic Drugs Department of Molecular and Cellular PharmacologySchool of Pharmaceutical SciencesPeking University Health Science CenterPeking University Channing Division of Network Medicine Department of MedicineBrigham and Women's Hospital and Harvard Medical School Center for Artificial Intelligence and Modeling the Carl R.Woese Institute for Genomic BiologyUniversity of Illinois Urbana-Champaign Department of Genetics and Genome Sciences School of Medicine and Department of Computer and Data Sciences and Department of Population and Quantitative Health SciencesCase Western Reserve University Department of Biochemistry and Biophysics University of California Sanger Institute Department of Automation Tsinghua University State Key Laboratory of Multimodal Artificial Intelligence Systems I

The development of artificial intelligence(AI) and the mining of biomedical data complement each other. From the direct use of computer vision results to analyze medical images for disease screening, to now integrating biological knowledge into models and even accelerating the development of new AI based on biological discoveries, the boundaries of both are constantly expanding, and their connections are becoming closer.

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Monotonic Controller for Battery Energy Storage System Using Neural Network

Monotonic Controller for Battery Energy Storage System Using...

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Renewable Energy and Hydrogen Technologies (GlobConHT), IEEE IAS Global Conference on

作者： Hassan I. Alhammad Waleed M. Hamanah A. Hussein Mohammad A. Abido Department of Electrical Engineering College of Engineering and Physics King Fahd University of Petroleum and Minerals Dhahran KSA Interdisciplinary Research Center of Renewable Energy and Power Systems (IRC-REPS) Research Institute King Fahd University of Petroleum and Minerals Dhahran KSA

Demand-generation mismatch introduces extensive technical as well as economic challenges to the power system network. Furthermore, with the increasing global interest in integrating variable renewable generation systems, it becomes more challenging to eliminate demand-generation mismatch. Therefore, the elimination of demand-generation mismatch will define the efficacy and quantify the economics of future renewable integrated smart grid systems. Incorporating batteries to provide energy buffering is among the most feasible, but most expensive, solutions to deal with system discrepancies. The battery energy storage system (BESS) has a limited life cycle and inherently lacks economic significance due to frequent charging and discharge. Therefore, in this paper, a monotonic operation of BESS is coordinated and kept between maximum and minimum State-of-Charge (SoC). The classical controller has been designed and validated with sets of generation and load profiles with two batteries. Accordingly, a neural network system based on pattern recognition is trained to operate the BESS under monotonic operation.

关键词： Economics Training Renewable energy sources Neural networks Control systems Batteries Smart grids

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Reviewing Photovoltaics and Electric Vehicles: Synergy and End of Life Management 26th

Reviewing Photovoltaics and Electric Vehicles: Synergy and E...

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26th International Joint Conference on Industrial engineering and Operations Management, IJCIEOM 2021

作者： Marcuzzo, Rafael Campos, Lucila M. S. Maldonado, Mauricio Uriona Vaz, Caroline Rodrigues Graduate Program in Production Engineering Universidade Federal de Santa Catarina Florianópolis Brazil Department of Production Engineering and Systems Universidade Federal de Santa Catarina Florianópolis Brazil Department of Textile Engineering Universidade Federal de Santa Catarina Blumenau Brazil

ISBN: (纸本)9783030785697

Energy supply and mobility are necessary functions of economic development and have become global latent themes concerning sustainability. In the electricity sector renewable energy technologies, such as solar photovoltaic (PV), have grown significantly over its competitors in terms of investment in the past 20 years. Still, adoption of the technology has not grown quickly enough to decarbonize the sector. In the mobility sector, however, combustion engines have been replaced with electric vehicles (EV), illustrating a greater potential to mitigate emission of polluting gases. Still, the production of EV technology can yield high emissions. Instead of either technology working separately, both PV and EV may be able to cooperate in a manner where PV systems power clean electricity to produce and charge EV batteries until they return to a stationary storing system (i.e., second life) for PV systems. The following study investigates the use of EV batteries for PV systems, assessing the possible toxic effects on the environment from manufacturing, using, and disposing the technology. A review of peer-reviewed articles shed light on possible hazards of clean technologies in the energy and mobility sectors. Findings suggest the diffusion of clean technologies must be accompanied by policies that guarantee responsible disposal of them. The study concludes that advanced technological mechanisms for renewable energy may have significantly negative and sometimes obscured environmental impacts, which must be considered in future policies advocating for those technologies. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Environmental technology

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NOMAD: nonlinear manifold decoders for operator learning 22

NOMAD: nonlinear manifold decoders for operator learning

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Proceedings of the 36th International Conference on Neural Information Processing systems

作者： Jacob H. Seidman Georgios Kissas Paris Perdikaris George J. Pappas Graduate Program in Applied Mathematics and Computational Science University of Pennsylvania Department of Mechanical Engineering and Applied Mechanics University of Pennsylvania Department of Electrical and Systems Engineering University of Pennsylvania

ISBN: (纸本)9781713871088

Supervised learning in function spaces is an emerging area of machine learning research with applications to the prediction of complex physical systems such as fluid flows, solid mechanics, and climate modeling. By directly learning maps (operators) between infinite dimensional function spaces, these models are able to learn discretization invariant representations of target functions. A common approach is to represent such target functions as linear combinations of basis elements learned from data. However, there are simple scenarios where, even though the target functions form a low dimensional submanifold, a very large number of basis elements is needed for an accurate linear representation. Here we present NOMAD, a novel operator learning framework with a nonlinear decoder map capable of learning finite dimensional representations of nonlinear submanifolds in function spaces. We show this method is able to accurately learn low dimensional representations of solution manifolds to partial differential equations while outperforming linear models of larger size. Additionally, we compare to state-of-the-art operator learning methods on a complex fluid dynamics benchmark and achieve competitive performance with a significantly smaller model size and training cost.

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Performance Evaluation of a Gan-HEMT-Based Converter for Concentrated Solar Tower Under Harsh Weather Conditions

Performance Evaluation of a Gan-HEMT-Based Converter for Con...

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International Conference on Environment and Electrical engineering (EEEIC)

作者： Waleed M. Hamanah Mohammad A. Abido Interdisciplinary Research Center of Renewable Energy and Power Systems (IRC-REPS) Research Institute King Fahd University of Petroleum and Minerals Dhahran KSA Department of Electrical engineering College of Engineering and Physics King Fahd University of Petroleum and Minerals Dhahran KSA

This research paper explores the practicality of using Gallium Nitride with high-electron-mobility transistors (GaN-HEMTs) based converter in solar power tower (SPT) tracking system. As a part of the SPT heliostat unit, the class-E converter is used to feed the DC motor, and the unit is tested under different loads. SPT drive using this technique reduces switching losses that affect the efficiency of the drive system without affecting the operation of the drive. This makes the SPT drive system more efficient, with less energy consumption and reduced operational costs. The performance of the drive system is demonstrated under different scenarios using MATLAB simulation. Experiments have been conducted on the proposed converters using hardware setup. Simulation results show accurate performance of the drive system and closely match the results of the hardware experiments. This demonstrates the validity of the proposed drive system for use in different applications. Comparing the proposed GaN E-HEMT converters with an identical Silicon (Si-IGBT) has been carried out. The comparison shows that it is a viable option for solar energy harvesting in the KSA hot weather conditions. This converter was found to be much more reliable than the traditional converter, making it an ideal choice for applications that require high temperature operation. Its impressive power-saving capabilities make it an even more attractive option.

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CompdVision: Combining Near-Field 3D Visual and Tactile Sensing Using a Compact Compound-Eye Imaging System

CompdVision: Combining Near-Field 3D Visual and Tactile Sens...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Lifan Luo Boyang Zhang Zhijie Peng Yik Kin Cheung Guanlan Zhang Zhigang Li Michael Yu Wang Hongyu Yu Department of Mechanical and Aerospace Engineering The Hong Kong University of Science and Technology Hong Kong China Department of Individualized Interdisciplinary Program (ROAS) The Hong Kong University of Science and Technology Hong Kong China School of Engineering Great Bay University Dongguan China HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute Shenzhen China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

As automation technologies advance, the need for compact and multi-modal sensors in robotic applications is growing. To address this demand, we introduce CompdVision, a novel sensor that employs a compound-eye imaging system to combine near-field 3D visual and tactile sensing within a compact form factor. CompdVision utilizes two types of vision units to address diverse sensing needs, eliminating the need for complex modality conversion. Stereo units with far-focus lenses can see through the transparent elastomer for depth estimation beyond the contact surface. Simultaneously, tactile units with near-focus lenses track the movement of markers embedded in the elastomer to obtain contact deformation. Experimental results validate the sensor’s superior performance in 3D visual and tactile sensing, proving its capability for reliable external object depth estimation and precise measurement of tangential and normal contact forces. The dual modalities and compact design make the sensor a versatile tool for robotic manipulation.

关键词： Visualization Three-dimensional displays Tracking Depth measurement Multimodal sensors Imaging Elastomers Robot sensing systems Reliability Lenses

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Analyses of Photovoltaic systems Performance with Fuzzy Inference systems and Bayesian Networks

Analyses of Photovoltaic Systems Performance with Fuzzy Infe...

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Power Electronics and Control (SEPOC), Seminar on

作者： Nicole Polityto Cremasco Myriam Regattieri De Biase da Silva Delgado Jair Urbanetz Junior Luiz Ledo Mota Melo Jorge Assade Leludak Keiko Verônica Ono Fonseca Program of Post-Graduation of Electrical Engineering and Industrial Informatics(CPGEI) of Federal University of Technology of Paraná(UTFPR) Program of Post-Graduation of Electrical Engineering and Industrial Informatics(CPGEI) of the Federal University of Technology of Paraná(UTFPR) Program of Post-Graduation in Energy Systems of Federal University of Technology of Paraná(UTFPR Department of Electrical Engineering (DAELT)) Federal University of Technology of Paraná(UTFPR) Curitiba Brazil

Renewable energy sources have been increasingly targeted as an alternative for reducing fossil fuel emissions. This scenario requires a better understanding of energy systems' performance and limitations, notably using photovoltaic setups under diverse meteorological conditions and determining device measurement uncertainties. Accordingly, this study conducts a performance evaluation of a photovoltaic system of copper, indium, and gallium (di)selenide technology. It compares the results provided by a reference normalized equation with two interpretable artificial intelligence techniques, Fuzzy Inference System and Bayesian Networks, when temperature is also considered. In the experiments, the study found significant percentage differences between the normalization procedure and the fuzzy system on days with lower irradiation values. On the other hand, the results provided by Bayesian networks presented lower difference percentages in relation to the normalization equation values.

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Rethinking Blockchain and Decentralized Learning: Position Paper 1

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11th International Conference on Computer Science and its Applications, CSA 2019 and 14th KIPS International Conference on Ubiquitous Information Technologies and Applications, CUTE 2019

作者： Rahmadika, Sandi Rhee, Kyung-Hyune Interdisciplinary Program of Information Security Graduate School Pukyong National University Busan Korea Republic of Department of IT Convergence and Application Engineering Pukyong National University Busan Korea Republic of

ISBN: (数字)9789811593437

ISBN: (纸本)9789811593420

Blockchain technology and decentralized learning are attracting growing attention. Most existing methods of machine learning is in the centralized form which relies upon the third party in terms of the raw datasets and mining resources. Blockchain solves world centralization problems that keep the system secure through complex mathematical computations puzzle solved by blockchain miners. Concurrently, decentralized learning such as federated model allows the user to collaboratively access the updated prediction model without revealing the training data to the public. By doing so, it provides less power consumption, lower latency that respects the user’s privacy concern. Therefore, we study the extent to which these two technologies can be applied in the real world for faster convergence without compromising user’s security. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Blockchain

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Fine inductive VAR control with high power quality using thyristor controlled reactors and discontinuous phase control switching in power systems

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e-Prime - Advances in Electrical engineering, Electronics and Energy 2024年 10卷

作者： Amrr, Syed Muhammad Asghar, M.S. Jamil Control & Instrumentation Engineering Department King Fahd University of Petroleum and Minerals Dhahran 31261 Saudi Arabia Interdisciplinary Research Center for Sustainable Energy Systems (IRC-SES) KFUPM Dharan 31261 Saudi Arabia Department of Electrical Engineering Aligarh Muslim University Aligarh 202002 India

The thyristor-controlled reactors (TCR) are widely employed to regulate the power system voltage and improve stability. However, TCR injects higher-order harmonics in the line current. For instance, in conventional TCR configurations, the total harmonic distortion (THD) is 78.5% at 10% of the fundamental current, and even it is 10% at 80% of the fundamental current. A previously reported control approach uses two reactors with a half-wave control design, which was only effective for the upper half of the control range. This paper proposes four new circuit configurations for reactive power regulation using discontinuous phase control (DPC). The new reactor design is also proposed for high-density magnetic flux perform well with double windings and three-inductor setups under the proposed approach. The proposed configuration of a multistage reactor with varying magnitudes provides extremely low total harmonic distortion (THD) across a near-complete control range. Even, the proposed design has achieved virtually zero THD for the reactor current. For THD to be less than 10%, a specific reactor pair is switched such that the control is only in the top half of the control range. The following configuration is based on reactors with the same magnitude. Like integral cycle control, the on-off control is employed for coarse action, whereas phase control is applied for fine control. All the proposed configurations are practically realized, and the performance is experimentally verified satisfactorily. © 2024 The Author(s)

关键词： Discontinuous phase control (DPC) FACTS devices Power quality control Static VAR control (SVC) Thyristor controlled reactors (TCR) Volt-ampere reactive (VAR)

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Response of Unsteady Thermocapillary Convection to Local Heating at Specific Depths in a Thin Liquid Film

SSRN

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SSRN 2024年

作者： Kudo, Masaki Igarashi, Keita Mechanical Systems Engineering Program Monozukuri Engineering Department Tokyo Metropolitan College of Industrial Technology 10-40 Higashi-oi 1-chome Shinagawa City Tokyo140-0011 Japan University of Tsukuba Tennodai 1-1-1 Tsukuba305-8573 Japan

A hydrothermal wave (HTW) refers to a flow pattern occurring in unsteady thermocapillary convection, adversely impacting the purity of single crystals during zone melting. This study employs active control through local heating to mitigate the amplitude of HTW. The effectiveness of this method was explored both experimentally and numerically by varying the heating depth in a thin liquid film. Silicone oil served as the test fluid, and the liquid film took on a rectangular shape. A line-shaped heater was used to heat the liquid layer, maintaining a constant maximum heat input throughout the trial, with the input period synchronized with that of the HTW. From the experiments, it was deduced that the HTW amplitude ratio significantly relies on the heating depth of the liquid layer. In the numerical simulation, the reduction of HTW was attributed to a substantial change in the streamwise temperature gradient on the free surface, as opposed to the local cancellation of the temperature disturbance. In contrast to the outcomes of control with single-pulse heating, the amplitude ratio exhibited strong dependence on whether the heat input extensively influenced the streamwise thermal and flow fields. © 2024, The Authors. All rights reserved.

关键词： Flow patterns

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