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Recent Advancements in the Optimization Capacity Configuration and Coordination Operation Strategy of Wind-Solar Hybrid Storage system

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Energy Engineering 2025年第1期122卷 285-306页

作者： Hongliang Hao Caifeng Wen Feifei Xue Hao Qiu Ning Yang Yuwen Zhang Chaoyu Wang Edwin E.Nyakilla Department of New Energy Storage Peking University Ordos Research Institute of EnergyOrdos017010China School of Energy and Power Engineering Inner Mongolia University of TechnologyHohhot010080China Key Laboratory of Wind Energy and Solar Energy Utilization technology Inner Mongolia University of TechnologyHohhot010051China

Present of wind power is sporadically and cannot be utilized as the only fundamental load of energy *** paper proposes a wind-solar hybrid energy storage system(HESS)to ensure a stable supply grid for a longer period.A multi-objective genetic algorithm(MOGA)and state of charge(SOC)region division for the batteries are introduced to solve the objective function and configuration of the system capacity,***/Simulink was used for simulation *** optimization results show that for a 0.5 MW wind power and 0.5 MW photovoltaic system,with a combination of a 300 Ah lithium battery,a 200 Ah lead-acid battery,and a water storage tank,the proposed strategy reduces the system construction cost by approximately 18,000 ***,the cycle count of the electrochemical energy storage systemincreases from4515 to 4660,while the depth of discharge decreases from 55.37%to 53.65%,achieving shallow charging and discharging,thereby extending battery life and reducing grid voltage fluctuations *** proposed strategy is a guide for stabilizing the grid connection of wind and solar power generation,capability allocation,and energy management of energy conservation systems.

关键词： Electric-thermal hybrid storage modal decomposition multi-objective genetic algorithm capacity optimization allocation operation strategy

Dynamic Behavior and Deformation Analysis of the Fish Cage system Using Mass-Spring Model

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China Ocean Engineering 2015年第3期29卷 311-324页

作者： Chun Woo LEE Jihoon LEE Subong PARK division of Marine Production system Management Pukyong National University division of Marine technology Chonnam National University Department of Fisheries Physics Pukyong National University

Fish cage systems are influenced by various oceanic conditions, and the movements and deformation of the system by the external forces can affect the safety of the system itself, as well as the species of fish being cultivated. Structural durability of the system against environmental factors has been major concern for the marine aquaculture system. In this research, a mathematical model and a simulation method were presented for analyzing the performance of the large-scale fish cage system influenced by current and waves. The cage system consisted of netting, mooring ropes, floats, sinkers and floating collar. All the elements were modeled by use of the mass-spring model. The structures were divided into finite elements and mass points were placed at the mid-point of each element, and mass points were connected by springs without mass. Each mass point was applied to external and internal forces, and total force was calculated in every integration step. The computation method was applied to the dynamic simulation of the actual fish cage systems rigged with synthetic fiber and copper wire simultaneously influenced by current and waves. Here, we also tried to find a relevant ratio between buoyancy and sinking force of the fish cages. The simulation results provide improved understanding of the behavior of the structure and valuable information concerning optimum ratio of the buoyancy to sinking force according to current speeds.

关键词： fish cage mathematical model aquaculture computer simulation mass-spring model

Modeling compositional heterogeneity resolves deep phylogeny of flowering plants

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Plant Diversity 2025年第1期47卷 13-20页

作者： Yongli Wang Yan-Da Li Shuo Wang Erik Tihelka Michael S.Engel Chenyang Cai Biofuels Institute School of Environmental and Safety EngineeringJiangsu UniversityZhenjiang 212013China Key Laboratory of Palaeobiology and Petroleum Stratigraphy Nanjing Institute of Geology and PalaeontologyChinese Academy of SciencesNanjing 210008China School of Earth Sciences University of BristolLife Sciences BuildingTyndall AvenueBristol BS81TQUnited Kingdom College of Marine Science and Biological Engineering Qingdao University of Science and TechnologyQingdao 266042China division of Invertebrate Zoology American Museum of Natural HistoryNew YorkNY 10024-5192USA Facultad de Ciencias Biologicas Universidad Nacional Mayor de San MarcosLima 15081Peru Departamento de Entomología Museo de Historia NaturalUniversidad Nacional Mayor de San MarcosLima 15072Peru

Angiosperms experienced one of the most remarkable radiations of land plants and are now the dominant autotrophs in terrestrial *** phylogenomic studies based on large-scale data from plastid,mitochondrial,or nuclear transcriptomes/genomes and increased taxon sampling have provided unprecedent resolution into the phylogeny of flowering ***,owing to ancient rapid radiations,the interrelationships among the five lineages of Mesangiospermae,the vast majority of angiosperms,remain *** we show that,although plastid and mitochondrial genomes lack sufficient phylogenetic signal for resolving deeper phylogeny,the relationships among five mesangiosperm lineages can be confidently resolved under better-fitting models using genome-scale *** to our Bayesian cross-validation and model test in a maximum likelihood framework,siteheterogeneous models(e.g.,CAT-GTR+G4,LG+C20+F+G)outperform site-homogeneous or partition models often used in previous *** site-heterogeneous models,the approximately unbiased test favored our preferred tree recovered from various datasets:Ceratophyllales(coontails)are robustly recovered as sister to monocots,and they together are sister to the clade comprising magnoliids,Chloranthales,and *** phylogenomic analyses resolve the last enigma of the deeper phylogeny of angiosperms and emphasize the efficacy of modeling compositional heterogeneity in resolving rapid radiations of plants.

关键词： Angiosperms Ceratophyllales Phylogenomics Radiation Methodological factors CAT model

Validation of virtual sensor-assisted Bayesian inference-based in-situ sensor calibration strategy for building HVAC systems

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building Simulation 2023年第2期16卷 185-203页

作者： Guannan Li Jiahao Xiong Shaobo Sun Jian Chen School of urban Construction Wuhan University of Science and TechnologyWuhan 430065China Department of building Environment and Energy Engineering The Hong Kong Polytechnic UniversityHong KongChina

For building heating,ventilation and air-conditioning systems(HVACs),sensor faults significantly affect the operation and *** with accurate and reliable measurements are critical for ensuring the precise indoor thermal *** to its high calibration accuracy and in-situ effectiveness,a virtual sensor(VS)-assisted Bayesian inference(VS-BI)sensor calibration strategy has been applied for ***,the application feasibility of this strategy for wider ranges of different sensor types(within-control-loop and out-of-control-loop)with various sensor bias fault amplitudes,and influencing factors that affect the practical in-situ calibration performance are still remained to be ***,to further validate its in-situ calibration performance and analyze the influencing factors,this study applied the VS-BI strategy in a HVAC system including a chiller plant with air handle unit(AHU)*** target sensors including air supply(SAT),chilled water supply(CHS)and cooling water return(CWR)temperatures are investigated using introduced sensor bias faults with eight different amplitudes of[−2℃,+2℃]with a 0.5℃ *** performance is evaluated by considering three influencing factors:(1)performance of different data-driven VSs,(2)the influence of prior standard deviationsσon in-situ sensor calibration and(3)the influence of data quality on in-situ sensor calibration from the perspective of energy conservation and data *** comparison,a long short term memory(LSTM)is adopted for VS construction with determination coefficient R-squared of *** indicate thatσhas almost no impact on calibration accuracy of CHS but scanty impact on that of SAT and *** potential of using a prior standard deviationσto improve the calibration accuracy is limited,only 8.61%on *** system within-control-loop sensors like SAT and CHS,VS-BI obtains relatively high in-situ sensor calibration accuracy if the data quality is relatively hig

关键词： heating,ventilation and air-conditioning(HVAC) in-situ sensor calibration Bayesian inference(BI) virtual sensor(VS) influencing factor energy conservation(EC)

Security Strategy of Digital Medical Contents Based on Blockchain in Generative AI Model

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Computers, Materials & Continua 2025年第1期82卷 259-278页

作者： Hoon Ko Marek R.Ogiela division of Computer Science and Engineering Sunmoon UniversityAsan31460Republic of Korea AGH University of Krakow Cryptography and Cognitive Informatics LaboratoryKrakow30-059Poland

This study presents an innovative approach to enhancing the security of visual medical data in the generative AI environment through the integration of blockchain *** combining the strengths of blockchain and generative AI,the research team aimed to address the timely challenge of safeguarding visual medical *** participating researchers conducted a comprehensive analysis,examining the vulnerabilities of medical AI services,personal information protection issues,and overall security *** multi faceted exploration led to an indepth evaluation of the model’s performance and ***,the correlation between accuracy,detection rate,and error rate was *** analysis revealed insights into the model’s strengths and limitations,while the consideration of standard deviation shed light on the model’s stability and performance *** study proposed practical improvements,emphasizing the reduction of false negatives to enhance detection rate and leveraging blockchain technology to ensure visual data integrity in medical *** blockchain to generative AI-created medical content addresses key personal information protection *** utilizing the distributed ledger system of blockchain,the research team aimed to protect the privacy and integrity of medical data especially medical *** approach not only enhances security but also enables transparent and tamperproof ***,the use of generative AI models ensures the creation of novel medical content without compromising personal information,further safeguarding patient *** conclusion,this study showcases the potential of blockchain-based solutions in the medical field,particularly in securing sensitive medical data and protecting patient *** proposed approach,combining blockchain and generative AI,offers a promising direction toward more robust and secure medical content *** research and advancements

关键词： Digitalmedical content medical diagnostic visualization security analysis generativeAI blockchain vulnerability pattern recognition

A novel deep generative modeling-based data augmentation strategy for improving short-term building energy predictions

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building Simulation 2022年第2期15卷 197-211页

作者： Cheng Fan Meiling Chen Rui Tang Jiayuan Wang Key Laboratory for Resilient Infrastructures of Coastal Cities(Shenzhen University) Ministry of EducationChina Sino-Australia Joint Research Center in BIM and Smart Construction Shenzhen UniversityShenzhenChina building technology&urban systems division Lawrence Berkeley National LaboratoryUSA

Short-term building energy predictions serve as one of the fundamental tasks in building operation *** large numbers of studies have explored the value of various supervised machine learning techniques in energy predictions,few studies have addressed the potential data shortage problem in developing data-driven *** promising solution is data augmentation,which aims to enrich existing building data resources for reliable predictive *** study proposes a deep generative modeling-based data augmentation strategy for improving short-term building energy *** types of conditional variational autoencoders have been designed for synthetic energy data generation using fully connected and one-dimensional convolutional layers *** experiments have been designed to evaluate the value of data augmentation using actual measurements from 52 *** results indicate that conditional variational autoencoders are capable of generating high-quality synthetic data samples,which in turns helps to enhance the accuracy in short-term building energy *** average performance enhancement ratios in terms of CV-RMSE range between 12%and 18%.Practical guidelines have been obtained to ensure the validity and quality of synthetic building energy *** research outcomes are valuable for enhancing the robustness and reliability of data-driven models for smart building operation management.

关键词： building energy predictions data augmentation data-driven models generative modeling variational autoencoders

Deterministic quantum state and gate teleportation between distant superconducting chips

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Science Bulletin 2025年第3期70卷 351-358页

作者： Jiawei Qiu Yang Liu Ling Hu Yukai Wu Jingjing Niu Libo Zhang Wenhui Huang Yuanzhen Chen Jian Li Song Liu Youpeng Zhong Luming Duan Dapeng Yu Shenzhen Institute for Quantum Science and Engineering Southern University of Science and TechnologyShenzhen 518055China International Quantum Academy Shenzhen 518048China Guangdong Provincial Key Laboratory of Quantum Science and Engineering Southern University of Science and TechnologyShenzhen 518055China Center for Quantum Information Institute for Interdisciplinary Information SciencesTsinghua UniversityBeijing 100084China Department of Physics Southern University of Science and TechnologyShenzhen 518055China

Quantum teleportation is of both fundamental interest and great practical importance in quantum information *** date,quantum teleportation has been implemented in various physical systems,among which superconducting qubits are of particular practical significance as they emerge as a leading system to realize large-scale quantum ***,scaling up the number of superconducting qubits on a single chip becomes increasing challenging because of some emergent technical *** of quantum teleportation and remote computation over qubits on distant superconducting chips is a key quantum communication technology to scaling up the system through a distributed quantum computational ***,this goal has not been realized yet in experiments due to the technical challenges including making a quantum interconnect between distant superconducting chips and the inefficient transfer of flying microwave photons over the lossy *** we demonstrate deterministic teleportation of quantum states and entangling gates between distant superconducting chips connected by a 64-m-long cable bus featuring an ultralow loss of 0.32 dB/km at cryogenic temperatures,where high fidelity remote entanglement is generated via flying microwave *** work demonstrates a prime building block for distributed quantum computation with superconducting qubits,and opens up a new avenue for waveguide quantum electrodynamics and quantum photonics at microwave frequencies.

关键词： Superconducting circuits Quantum network Quantum computing Flying microwave photon

Three-Dimensional Numerical Modeling of an Ar-N_2 Plasma Arc Inside a Non-Transferred Torch

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Plasma Science and technology 2009年第6期11卷 679-687页

作者： B.SELVAN K.RAMACHANDRAN K.P.SREEKUMAR T.K.THIYAGARAJAN P.V.ANANTHAPADMANABHAN School of Mechanical and building Sciences Vellore Institute of Technology UniversityVellore 632014India Laser and Plasma technology division Bhabha Atomic Research CentreMumbai 400085India

A three-dimensional numerical model is developed to study the behaviour of an argon-nitrogen plasma arc inside a non-transferred torch. In this model, both the entire cathode and anode nozzle are considered to simulate the plasma arc. The argon-nitrogen plasma arc is simulated for different arc currents and gas flow rates of argon. Various combinations of arc core radius and arc length, which correspond to a given torch power, are predicted. A most feasible combination of the same, which corresponds to an actual physical situation of the arc inside the torch, is identified using the thermodynamic principle of minimum entropy production for a particular torch power. The effect of the arc current and gas flow rate on the plasma arc characteristics and torch efficiency is explained. The effect of the nitrogen content in the plasma gas on the torch power and efficiency is clearly detected. Predicted torch efficiencies are comparable to the measured ones and the effect of the arc current and gas flow rate on predicted and measured efficiencies is almost similar. The efficiency of the torch, cathode and anode losses and core temperature and velocity at the nozzle exit are reported for five different cases.

关键词： plasma arc numerical modeling plasma torch minimum entropy production electro-thermal efficiency

An innovative heterogeneous transfer learning framework to enhance the scalability of deep reinforcement learning controllers in buildings with integrated energy systems

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building Simulation 2024年第5期17卷 739-770页

作者： Davide Coraci Silvio Brandi Tianzhen Hong Alfonso Capozzoli Politecnico di Torino Department of EnergyTEBE research groupBAEDA Lab.Corso Duca degli Abruzzi 24Torino10129Italy building technology and urban systems division Lawrence Berkeley National LaboratoryOne Cyclotron RoadBerkeleyCA 94720USA

Deep Reinforcement Learning(DRL)-based control shows enhanced performance in the management of integrated energy systems when compared with Rule-Based Controllers(RBCs),but it still lacks scalability and generalisation due to the necessity of using tailored models for the training *** Learning(TL)is a potential solution to address this ***,existing TL applications in building control have been mostly tested among buildings with similar features,not addressing the need to scale up advanced control in real-world scenarios with diverse energy *** paper assesses the performance of an online heterogeneous TL strategy,comparing it with RBC and offline and online DRL controllers in a simulation setup using EnergyPlus and *** study tests the transfer in both transductive and inductive settings of a DRL policy designed to manage a chiller coupled with a Thermal Energy Storage(TES).The control policy is pre-trained on a source building and transferred to various target buildings characterised by an integrated energy system including photovoltaic and battery energy storage systems,different building envelope features,occupancy schedule and boundary conditions(e.g.,weather and price signal).The TL approach incorporates model slicing,imitation learning and fine-tuning to handle diverse state spaces and reward functions between source and target *** show that the proposed methodology leads to a reduction of 10% in electricity cost and between 10% and 40% in the mean value of the daily average temperature violation rate compared to RBC and online DRL ***,online TL maximises self-sufficiency and self-consumption by 9% and 11% with respect to ***,online TL achieves worse performance compared to offline DRL in either transductive or inductive ***,offline Deep Reinforcement Learning(DRL)agents should be trained at least for 15 episodes to reach the same level of performance as the online

关键词： transfer learning reinforcement learning building control building energy management