检索结果-南通市图书馆

National Science Open 2024年第3期3卷 88-109页

作者： cheng fan Yutian Lei Jinhan Mo Huilong Wang Qiuting Wu Jiena Cai Key Laboratory for Resilient Infrastructures of Coastal Cities Ministry of EducationShenzhen University Sino-Australia Joint Research Center in BIM and Smart Construction Shenzhen University College of Civil and Transportation Engineering Shenzhen University

The increasing availability of building operational data has greatly encouraged the development of advanced data-driven technologies for smart building operations.Building operational data typically suffer from data quality problems,such as insufficient labeled and imbalanced data,making them incompatible with conventional machine learning algorithms.Recent advances in data science have provided novel machine learning paradigms to tackle such data challenges for practical applications,such as transfer learning,semi-supervised learning,and generative learning.This review aims to present the progress and perspectives on the effective utilization of novel machine learning paradigms for three major building energy management tasks,i.e.,building energy predictions,fault detection and diagnosis,and control optimizations.In-depth discussions have been provided to demonstrate the pros and cons of different learning approaches in terms of data compatibility,modeling difficulties,and possible application scenarios,which could be helpful for enhancing the feasibility of data-driven technologies for smart building operations.

关键词： smart building operations building energy management transfer learning semi-supervised learning generative learning

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Cavity continuum

Photonics Research 2024年第3期12卷 391-398页

作者： fan cheng VLADIMIR SHUVAYEV MARK DOUVIDZON LEV DEYCH TAL CARMON School of Electrical Engineering Tel Aviv UniversityTel Aviv 6997801Israel Physics Department Queens College of CUNYFlushingQueensNew York 11367USA Solid State Institute Technion-Israel Institute of TechnologyHaifa 3200003Israel The Graduate Center of CUNY New YorkNew York 10016USA

We experimentally demonstrate and numerically analyze large arrays of whispering gallery resonators.Using fluorescent mapping,we measure the spatial distribution of the cavity ensemble’s resonances,revealing that light reaches distant resonators in various ways,including while passing through dark gaps,resonator groups,or resonator lines.Energy spatially decays exponentially in the cavities.Our practically infinite periodic array of resonators,with a quality factor(Q)exceeding 107,might impact a new type of photonic ensembles for nonlinear optics and lasers using our cavity continuum that is distributed,while having high-Q resonators as unit cells.

关键词： resonator cavity ensemble

The BEL1-like transcription factor GhBLH5-A05 participates in cotton response to drought stress

在线全文

学校读者我要写书评

暂无评论

The Crop Journal 2024年第1期12卷 177-187页

作者： Jing-Bo Zhang Yao Wang Shi-Peng Zhang fan cheng Yong Zheng Yang Li Xue-Bao Li Hubei Key Laboratory of Genetic Regulation and Integrative Biology School of Life SciencesCentral China Normal UniversityWuhan 430079HubeiChina College of Life Sciences Xinjiang Normal UniversityUrumqi 830017XinjiangChina

Drought stress impairs crop growth and development.BEL1-like family transcription factors may be involved in plant response to drought stress,but little is known of the molecular mechanism by which these proteins regulate plant response and defense to drought stress.Here we show that the BEL1-like transcription factor GhBLH5-A05 functions in cotton(Gossypium hirsutum)response and defense to drought stress.Expression of GhBLH5-A05 in cotton was induced by drought stress.Overexpression of GhBLH5-A05 in both Arabidopsis and cotton increased drought tolerance,whereas silencing GhBLH5-A05 in cotton resulted in elevated sensitivity to drought stress.GhBLH5-A05 binds to cis elements in the promoters of GhRD20-A09 and GhDREB2C-D05 to activate the expression of these genes.GhBLH5-A05 interacted with the KNOX transcription factor GhKNAT6-A03.Co-expression of GhBLH5-A05 and GhKNAT6-A03 increased the transcription of GhRD20-A09 and GhDREB2C-D05.We conclude that GhBLH5-A05 acts as a regulatory factor with GhKNAT6-A03 functioning in cotton response to drought stress by activating the expression of the drought-responsive genes GhRD20-A09 and GhDREB2C-D05.

关键词： Cotton(Gossypium hirsutum) BEL1-like transcription factor Drought stress Transcriptional regulation Drought tolerance

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

在线全文

学校读者我要写书评

暂无评论

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 management.While 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 models.One promising solution is data augmentation,which aims to enrich existing building data resources for reliable predictive modeling.This study proposes a deep generative modeling-based data augmentation strategy for improving short-term building energy predictions.Two types of conditional variational autoencoders have been designed for synthetic energy data generation using fully connected and one-dimensional convolutional layers respectively.Data experiments have been designed to evaluate the value of data augmentation using actual measurements from 52 buildings.The 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 predictions.The 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 data.The 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

Data-driven model predictive control for power demand management and fast demand response of commercial buildings using support vector regression

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Building Simulation 2022年第3期15卷 317-331页

作者： Rui Tang cheng fan fanzhe Zeng Wei Feng Institute for Environmental Design and Engineering University College LondonLondonUK 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 School of Municipal and Environmental Engineering Shenyang Jianzhu UniversityShenyangChina China Energy Group Lawrence Berkeley National LaboratoryBerkeleyCAUSA

Demand response(DR)of commercial buildings by directly shutting down part of operating chillers could provide an immediate power reduction for power grids.In this special fast DR event,effective control needs to guarantee expected power reduction and ensure an acceptable indoor environment.This study,therefore,developed a data-driven model predictive control(MPC)using support vector regression(SVR)for fast DR events.According to the characteristics of fast DR events,the optimized hyperparameters of SVR and shortened searching range of genetic algorithm are used to improve the control performance.Meanwhile,a comprehensive comparison with RC-based MPC is conducted based on three scenarios of power demand controls.Test results show that the proposed SVR-based MPC could fulfill the control objectives of power demand and indoor temperature simultaneously.Compared with RC-based MPC,the SVR-based MPC could alleviate the time/labor cost of model development without sacrificing the control performance of fast DR events.

关键词： demand response support vector regression machine learning building peak demand model predictive control smart grid

Improved time-optimal B-spline feedrate scheduling for NURBS tool paths in CNC machining

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Advances in Manufacturing 2023年第1期11卷 111-129页

作者： Yang Li Fu-Sheng Liang Lei Lu cheng fan School of Mechanic Engineering Northeast Electric Power UniversityJilin132012JilinPeople’s Republic of China Jiangsu Provincial Key Laboratory of Advanced Robotics and Collaborative Innovation Center of Suzhou Nano Science and Technology College of Mechanical and Electrical EngineeringSoochow UniversitySuzhou215021JiangsuPeople’s Republic of China

Feedrate scheduling in computer numerical control(CNC)machining is of great importance to fully develop the capabilities of machine tools while maintaining the motion stability of each actuator.Smooth and time-optimal feedrate scheduling plays a critical role in improving the machining efficiency and precision of complex surfaces considering the irregular curvature characteristics of tool paths and the limited drive capacities of machine tools.This study develops a general feedrate scheduling method for non-uniform rational B-splines(NURBS)tool paths in CNC machining aiming at minimizing the total machining time without sacrificing the smoothness of feed motion.The feedrate profile is represented by a B-spline curve to flexibly adapt to the frequent acceleration and deceleration requirements of machining along complex tool paths.The time-optimal B-spline feedrate is produced by continuously increasing the control points sequentially from zero positions in the bidirectional scanning and sampling processes.The required number of knots for the time-optimal B-spline feedrate can be determined using a progressive knot insertion method.To improve the computational efficiency,the B-spline feedrate profile is divided into a series of independent segments and the computation in each segment can be performed concurrently.The proposed feedrate scheduling method is capable of dealing with not only the geometry constraints but also high-order drive constraints for any complex tool path with little computational overhead.Simulations and machining experiments are conducted to verify the effectiveness and superiorities of the proposed method.

关键词： B-spline feedrate Non-uniform rational B-splines(NURBS)tool path Knot insertion Bidirectional scanning Computer numerical control(CNC)machining

Recent advances in Ir/Ru-based perovskite electrocatalysts for oxygen evolution reaction

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Rare Metals 2024年第7期43卷 2891-2912页

作者： Zhi-Qi Jiang cheng-Zhen fan Jun-Yu Pan Li Shao Hao Chen Erum Pervaiz Yan Dong Tong-Zhou Wang Xue-Rong Zheng Ji-Hong Li Yi-Da Deng School of Materials Science and Engineering Hainan University Department of Chemical Engineering School of Chemical and Materials Engineering (SCME)National University of Sciences & Technology (NUST) Key Laboratory of Advanced Energy Materials Chemistry (Ministry of Education) Nankai University

Oxygen evolution reaction(OER) is a kinetically harsh four-electron anode reaction that requires a large overpotential to provide current and is of great importance in renewable electrochemical technique.Ir/Rubased perovskite oxides hold great significance for application as OER electrocatalysts,due to that their multimetal-oxide forms can reduce the use of noble metals,and their compositional tunability can modulate the electronic structure and optimize OER performance.However,high operating potentials and corrosive environments pose a serious challenge to the development of durable Ir-based and Ru-based perovskite electrocatalysts.Tremendous efforts have been dedicated to improving the Ir/Ru-based perovskite activity to enhance the efficiency;however,progress in improving the durability of Ir/Ru-based perovskite electrocatalysts has been rather limited.In this review,the recent research progress of Ir/Ru-based perovskites is reviewed from the perspective of heteroatom doping,structural modulation,and formation of heterostructures.The dissolution mechanism studies of Ir/Ru and experimental attempts to improve the durability of Ir/Ru-based perovskite electrocatalysts are discussed.Challenges and outlooks for further developing Ru-and Irbased perovskite oxygen electrocatalysts are also presented.

关键词： Oxygen evolution reaction Ir/Ru-based perovskites Stability Electrocatalysts

Leveraging graph convolutional networks for semi-supervised fault diagnosis of HVAC systems in data-scarce contexts

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

建筑模拟(英文) 2023年第8期16卷 1499-1517页

作者： cheng fan Yiwen Lin Marco Savino Piscitelli Roberto Chiosa Huilong Wang Alfonso Capozzoli Yuanyuan Ma Key Laboratory for Resilient Infrastructures of Coastal Cities Ministry of EducationShenzhen UniversityShenzhenChina Sino-Australia Joint Research Center in BIM and Smart Construction Shenzhen UniversityShenzhenChina College of Civil and Transportation Engineering Shenzhen UniversityShenzhenChina Department of Energy TEBE research groupBAEDA LabPolitecnico di TorinoTorinoItaly

The continuous accumulation of operational data has provided an ideal platform to devise and implement customized data analytics for smart HVAC fault detection and diagnosis.In practice,the potentials of advanced supervised learning algorithms have not been fully realized due to the lack of sufficient labeled data.To tackle such data challenges,this study proposes a graph neural network-based approach to effectively utilizing both labeled and unlabeled operational data for optimum decision-makings.More specifically,a graph generation method is proposed to transform tabular building operational data into association graphs,based on which graph convolutions are performed to derive useful insights for fault classifications.Data experiments have been designed to evaluate the values of the methods proposed.Three datasets on HVAC air-side operations have been used to ensure the generalizability of results obtained.Different data scenarios,which vary in training data amounts and imbalance ratios,have been created to comprehensively quantify behavioral patterns of representative graph convolution networks and their architectures.The research results indicate that graph neural networks can effectively leverage associations among labeled and unlabeled data samples to achieve an increase of 2.86%–7.30%in fault classification accuracies,providing a novel and promising solution for smart building management.

关键词： fault detection and diagnosis graph convolutional networks semi-supervised learning HVAC systems machine learning

A homogeneous and mechanically stable artificial diffusion layer using rigid-flexible hybrid polymer for high-performance lithium metal batteries

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Energy Chemistry 2023年第1期76卷 631-638,I0015页

作者： Zhenkang Lin Yuyan Ma Wei Wang Yu He Menghao Wang Jun Tang cheng fan Kening Sun Beijing Key Laboratory for Chemical Power Source and Green Catalysis School of Chemistry and Chemical EngineeringBeijing Institute of TechnologyBeijing 100081China

Artificial solid electrolyte interphase(SEI) is promising to inhibit uncontrollable lithium dendrites and enable long cycling stability for lithium metal batteries. However, the essential mechanical stability is limited since organic layers generally have low modulus whereas intrinsic brittleness for inorganic ones remains a great concern. Polymer-based SEIs with rigid and flexible chains in adequate mechanical properties are supposed to address this issue. Herein, a homogeneous and mechanically stable diffusion layer is achieved by blending rigid chains of polyphenylene sulfone(PPSU) with flexible chains of poly(vinylidene fluoride)(PVDF) in a hybrid membrane, enabling uniform diffusion and stabilizing the lithium metal anode. The Li||Cu cell with the protected electrode exhibits a long lifetime more than 450 cycles(0.5 m A cm^(-2), 1.0 m A h cm^(-2))(fourfold longer than the control group) with higher average Coulombic efficiency of 98.7%. Enhanced performances are also observed at Li||Li and full cell configurations. The improved performances are attributed to the controlled morphology and stable interphase, according to scanning electron microscopy(SEM) and electrochemical impedance. This research advances the idea of uniform lithium plating and provides a new insight on how to create a homogeneous and mechanically stable diffusion layer using rigid-flexible polymers.

关键词： Lithium metal battery Lithium dendrite Uniform diffusion Rigid-flexible artificial layer Electrochemical impedance

Interlayer and intralayer co-modified flexible V_(2)CT_(X) MXene@SWCNT films for high-power Li-ion capacitors

在线全文

学校读者我要写书评

暂无评论

Journal of Energy Chemistry 2023年第4期79卷 101-109页

作者： Wanli Wang Min Feng Enze Hu Zhaowei Hu cheng fan Huifang Li Peng Wang Xiaojun Wang Zhiming Liu College of Electromechanical Engineering Shandong Engineering Laboratory for Preparation and Application of High-performance Carbon-MaterialsQingdao University of Science&TechnologyQingdao 266061ShandongChina Qingdao Industrial Energy Storage Research Institute Qingdao Institute of Bioenergy and Bioprocess Technology Chinese Academy of Sciences Qingdao 266101ShandongChina Key Laboratory of Advanced Energy Materials Chemistry(Ministry of Education) College of ChemistryNankai UniversityTianjin 300071China

As an emerging member of the two-dimensional(2D)material family,V_(2)CT_(X)MXene shows great potential in the application of lithium-ion capacitors(LICs)due to its unique structure and excellent electrical conductivity.However,severe nanosheets stacking and intra-layer transport barriers have limited the further development of V_(2)CT_(X)MXene-based materials.Herein,we prepared Kions and–O functional group co-modified V_(2)CT_(X)MXene(VCT-K)and further incorporated it with single-walled carbon nanotube(SWCNT),obtaining freestanding V_(2)CT_(X)composite films(VCT-K@C)with the 3D conductive network.Significantly,K+ions were introduced into V_(2)CT_(X)MXene to stabilize the interlayer structure and prevent the aggregation of nanosheets,the terminal group of–O was controllably modified on the surface of MXene to improve the Li+ions storage reversible capacities and the SWCNT acted as the bridge between MXene nanosheets to opens up the channels for ion/electron transportation in the longitudinal direction.Benefited from the synergistic effect of VCT-K and SWCNT,the VCT-K@C exhibits superior reversible specific capacities of 671.8 mA h g^(-1)at 0.1 A g^(-1)and 318 mA h g^(-1)at 1.0 A g^(-1).Furthermore,the assembled LICs with VCT-K@C anode coupling activated carbon(AC)cathode deliver an outstanding power density of 19.0 kW kg^(-1)at 67.4 Wh kg^(-1),a high energy density of 140.5 Wh kg^(-1)at 94.8 W kg^(-1)and a stable capacitance retention of 86%after 6000 cycles at 10 A g^(-1).Such unique structures and excellent electrochemical properties are expected to pave the way for the large-scale application in LICs of MXene-based materials.

关键词： Interlayer and intralayer co-modification MXene SWCNT Flexible self-supported anodes Lithium-ion capacitors