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Relationship Between Oct agonal Quasicrystal and β-Mn Type Crystal in Cut Description

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Chinese Physics Letters 1996年第3期13卷 199-202页

作者： LI fang-hua cheng Yi-fan Institute of Physics Chinese Academy of SciencesBeijing 1000801 Max-Planck-Institut fur Metallforschung Institut fur Werkstoff-WeissenschaftSeestrasse 9270147 StuttgartGermany

The transformation from the octagonal lattice to a square lattice is demonstrated straightforward under the cut description by introducing a peculiar linear phason *** calculated lattice parameter of the ft-Mn structure from the edge length of squares and rhombi forming the octagonal quasilattice is in agreement with experimental results.

关键词： strain. agonal lattice

Single neuron network PI control of high reliability linear induction motor for Maglev

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2007年第3期8卷 408-411页

作者： fanG You-tong fan cheng-zhi College of Electrical Engineering Zhejiang University Hangzhou 310027 China

The paper deals with a new model of linear induction motor (LIM) to improve the reliability of the system. Based on the normal equation circuit of LIM considering the dynamic end effect, an equivalent circuit model with compensation of large end effect is constructed when the end effect force at synchronism is of braking character. The equivalent circuit model is used for secondary-flux oriented control of LIM. Single neuron network PI unit for LIM servo-drive is also discussed. The effectiveness of mathematical model for drive control is verified by simulations.

关键词： Linear induction motor (LIM) Field-oriented control End effect

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

Development of an ANN-based building energy model for information-poor buildings using transfer learning

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Building Simulation 2021年第1期14卷 89-101页

作者： Ao Li Fu Xiao cheng fan Maomao Hu Department of Building Services Engineering The Hong Kong Polytechnic UniversityHong KongChina Department of Construction Management and Real Estate College of Civil EngineeringShenzhen UniversityShenzhenChina Oxford e-Research Centre Department of Engineering Science University of OxfordOxfordUK

Accurate building energy prediction is vital to develop optimal control strategies to enhance building energy efficiency and energy *** recent years,the data-driven approach based on machine learning algorithms has been widely adopted for building energy prediction due to the availability of massive data in building automation systems(BASs),which automatically collect and store real-time building operational *** new buildings and most existing buildings without installing advanced BASs,there is a lack of sufficient data to train data-driven predictive *** learning is a promising method to develop accurate and reliable data-driven building energy prediction models with limited training data by taking advantage of the rich data/knowledge obtained from other *** studies focused on the influences of source building datasets,pre-training data volume,and training data volume on the performance of the transfer learning *** present study aims to develop a transfer learning-based ANN model for one-hour ahead building energy prediction to fill this research *** 400 non-residential buildings’data from the open-source Building Genome Project are used to test the proposed *** analysis demonstrates that transfer learning can effectively improve the accuracy of BPNN-based building energy models for information-poor buildings with very limited training *** most influential building features which influence the effectiveness of transfer learning are found to be building usage and *** research outcomes can provide guidance for implementation of transfer learning,especially in selecting appropriate source buildings and datasets for developing accurate building energy prediction models.

关键词： building energy prediction data-driven approach transfer learning neural network information poor buildings

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

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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 *** this special fast DR event,effective control needs to guarantee expected power reduction and ensure an acceptable indoor *** study,therefore,developed a data-driven model predictive control(MPC)using support vector regression(SVR)for fast DR *** 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 ***,a comprehensive comparison with RC-based MPC is conducted based on three scenarios of power demand *** results show that the proposed SVR-based MPC could fulfill the control objectives of power demand and indoor temperature *** 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

Optical complex integration-transform for deriving complex fractional squeezing operator

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Chinese Physics B 2020年第3期29卷 101-104页

作者： Ke Zhang cheng-Yu fan Hong-Yi fan Key Laboratory of Atmospheric Optics Anhui Institute of Optics and Fine MechanicsChinese Academy of SciencesHefei 230031China University of Science and Technology of China Hefei 230031China Huainan Normal University Huainan 232038China

We find a new complex integration-transform which can establish a new relationship between a two-mode operator's matrix element in the entangled state representation and its Wigner function. This integration keeps modulus invariant and therefore invertible. Based on this and the Weyl–Wigner correspondence theory, we find a two-mode operator which is responsible for complex fractional squeezing transformation. The entangled state representation and the Weyl ordering form of the two-mode Wigner operator are fully used in our derivation which brings convenience.

关键词： integration-transform two-mode entangled state Weyl–Wigner correspondence theory

Advanced data analytics for building energy modeling and management

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Building Simulation 2021年第1期14卷 1-2页

作者： cheng fan Fu Xiao Da Yan Sino-Australia Joint Research Center in BIM and Smart Construction College of Civil and Transportation EngineeringShenzhen UniversityShenzhenChina Department of Building Services Engineering The Hong Kong Polytechnic UniversityHong KongChina Building Energy Research Center School of ArchitectureTsinghua UniversityBeijingChina

The data-driven approach has been widely adopted in fault detection and diagnosis and optimal control of building energy systems for improving building performance since the 1980s,owing to the increasing demands for c... 详细信息

关键词： energy convenient analytic

Degree-of-Node Task Scheduling of Fine-Grained Parallel Programs on Heterogeneous Systems

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Journal of Computer Science & Technology 2019年第5期34卷 1096-1108页

作者： Han Lin Ming-fan Li cheng-fan Jia Jun-Nan Liu Hong An School of Computer Science and Technology University of Science and Technology of ChinaHefei 230026China

Processor specialization has become the development trend of modern processor industry. It is quite possible that this will still be the main-stream in the next decades of semiconductor era. As the diversity of heterogeneous systems grows, organizing computation efficiently on systems with multiple kinds of heterogeneous processors is a challenging problem and will be a normality. In this paper, we analyze some state-of-the-art task scheduling algorithms of heterogeneous computing systems and propose a Degree of Node First (DONF) algorithm for task scheduling of fine-grained parallel programs on heterogeneous systems. The major innovations of DONF include: 1) simplifying task priority calculation for directed acyclic graph (DAG) based fine-grained parallel programs which not only reduces the complexity of task selection but also enables the algorithm to solve the scheduling problem for dynamic DAGs;2) building a novel communication model in the processor selection phase that makes the task scheduling much more efficient. They are achieved by exploring finegrained parallelism via a dataflow program execution model, and validated through experimental results with a selected set of benchmarks. The results on synthesized and real-world application DAGs show a very good performance. The proposed DONF algorithm significantly outperforms all the evaluated state-of-the-art heuristic algorithms in terms of scheduling length ratio (SLR) and efficiency.

关键词： task scheduling heterogeneous system performance parallel program

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

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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 *** 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 *** 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 *** feedrate profile is represented by a B-spline curve to flexibly adapt to the frequent acceleration and deceleration requirements of machining along complex tool *** time-optimal B-spline feedrate is produced by continuously increasing the control points sequentially from zero positions in the bidirectional scanning and sampling *** required number of knots for the time-optimal B-spline feedrate can be determined using a progressive knot insertion *** 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 *** 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 *** 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