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model-based method for testing, adjusting and balancing of HVAC duct system

ENERGY AND BUILDINGS 2016年 126卷 498-507页

作者： Chen, Haoran Cai, Wenjian Chen, Can Nanyang Technol Univ Sch Elect & Elect Engn Ctr E City EXQUISITUS Nanyang Ave Singapore 639798 Singapore

In this paper, an efficient method for testing, adjusting and balancing (TAB) of HVAC air duct system is proposed. The proposed method consists of three steps: 1) Establish the mathematical model for duct network;2) Take pressure and flow rate measurements in the duct system to identify model parameters;3) Calculate the balancing damper positions to achieve desired flow rates. The novel measuring procedure collects data efficiently, which makes this method practical. In case of no accurate damper position indicator, sequential damper adjustment according to airflow measurements is also proposed. Simulations and experiments have been conducted to validate the proposed method, where the final flow rates in all terminals match with the desired values with no more than 4.7% relative error in experiments. Comparing with existing TAB methods, the proposed method is 1) faster and more efficient by its non-iterative nature, 2) more accurate in balancing results, 3) easier and simpler to be performed, and 4) using flow meters and pressure sensors only. With these advantages, it is expected that the method will be accepted by HVAC industries. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Testing, adjusting and balancing TAB Ventilation Air system balancing model-based method model identification

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Direct diffusion tensor estimation using a model-based method with spatial and parametric constraints

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MEDICAL PHYSICS 2017年第2期44卷 570-580页

作者： Zhu, Yanjie Peng, Xi Wu, Yin Wu, Ed X. Ying, Leslie Liu, Xin Zheng, Hairong Liang, Dong Shenzhen Inst Adv Technol Paul C Lauterbur Res Ctr Biomed Imaging Shenzhen Peoples R China Univ Hong Kong Dept Elect & Elect Engn Pokfulam Hong Kong Peoples R China SUNY Buffalo Dept Biomed Engn Dept Elect Engn Buffalo NY 14260 USA

Purpose: To develop a new model-based method with spatial and parametric constraints (MB-SPC) aimed at accelerating diffusion tensor imaging (DTI) by directly estimating the diffusion tensor from highly undersampled k-space data. methods: The MB-SPC method effectively incorporates the prior information on the joint sparsity of different diffusion-weighted images using an L1-L2 norm and the smoothness of the diffusion tensor using a total variation seminorm. The undersampled k-space datasets were obtained from fully sampled DTI datasets of a simulated phantom and an ex-vivo experimental rat heart with acceleration factors ranging from 2 to 4. The diffusion tensor was directly reconstructed by solving a minimization problem with a nonlinear conjugate gradient descent algorithm. The reconstruction performance was quantitatively assessed using the normalized root mean square error (nRMSE) of the DTI indices. Results: The MB-SPC method achieves acceptable DTI measures at an acceleration factor up to 4. Experimental results demonstrate that the proposed method can estimate the diffusion tensor more accurately than most existing methods operating at higher net acceleration factors. Conclusion: The proposed method can significantly reduce artifact, particularly at higher acceleration factors or lower SNRs. This method can easily be adapted to MR relaxometry parameter mapping and is thus useful in the characterization of biological tissue such as nerves, muscle, and heart tissue. (C) 2016 American Association of Physicists in Medicine

关键词： diffusion tensor imaging distributed compressed sensing joint sparsity constraint model-based method smoothing constraint

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Thermal decomposition kinetics analysis of the oil sludge using model-based method and model-free method

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PROCESS SAFETY AND ENVIRONMENTAL PROTECTION 2020年 141卷 167-177页

作者： Liu, Hui Hong, Rui Xiang, Chenglang Wang, Haining Li, Yanqiang Xu, Guang Chang, Ping Zhu, Kai China Jiliang Univ Coll Qual & Safety Engn 258 Xueyuan St Hangzhou 310018 Zhejiang Peoples R China China Coal Informat Inst Beijing 100029 Peoples R China Missouri Univ Sci & Technol Dept Min & Nucl Engn Rolla MO 65401 USA Curtin Univ WA Sch Mines Minerals Energy & Chem Engn Kalgoorlie WA 6430 Australia

Oil sludge (OS) is one of the main waste in the petrochemical industry, and serious consequences can result from the improper disposal of this waste. Pyrolysis is an effective method to dispose of OS and to recover the high heating value combustibles from it. The pyrolysis of three OS samples was carried out in a nitrogen atmosphere at heating rates of 5 degrees C/min, 10 degrees C/min, and 20 degrees C/min, respectively. The TG-DTG/DSC-DDSC curves show that the thermal decomposition of OS has been divided into 5 stages, and each stage has unique thermal decomposition characteristics. Two model-free methods were used to calculate the apparent activation energy at a conversion rate from 0.05 to 0.95, and based on 10 solid-state mechanisms of thermal decomposition, the model-based method was used to fit the thermal decomposition mechanisms exposed to the three heating rates. The results show that the high heating rate is beneficial for the pyrolysis of OS. The apparent activation energy varies irregularly with the conversion rate, and the apparent activation energy error calculated by the FWO method is smaller. For the three samples, OS-1, OS-2 and OS-3, the average apparent activation energies calculated by the FWO method are 40.39 kJ/mol, 38.01 kJ/mol, and 85.53 kJ/mol, respectively. The mechanism function of OS thermal decomposition changed, and the process of thermal decomposition should be described by a three-step thermal decomposition model. During 140.48-331.88 degrees C, the fitting correlation coefficients of the two-dimensional phase interfacial reaction mechanism under the three heating rates were all above 0.99. During 400.00-556.57 degrees C, the fitting result of the three-dimensional nucleation and growth mechanism is better than fitting results of other models. During 583.82-676.60 degrees C, the two-dimensional diffusion mechanism has a good fitting regression. Significantly, model-based method and model-free method provide effective and reliable the

关键词： Oil sludge Pyrolysis model-based method model-free method Thermal decomposition kinetics

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Elimination of liftoff effect using a model-based method for eddy current characterization of a plate

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NDT & E INTERNATIONAL 2015年 74卷 66-71页

作者： Fan, Mengbao Cao, Binghua Yang, Panpan Li, Wei Tian, Guiyun China Univ Min & Technol Sch Mech & Elect Engn Xuzhou 221116 Peoples R China China Univ Min & Technol Jiangsu Key Lab Mine Mech & Elect Equipment Xuzhou 221116 Peoples R China China Univ Min & Technol Sch Informat & Elect Engn Xuzhou 221116 Peoples R China Newcastle Univ Sch Elect & Elect Engn Newcastle Upon Tyne NE1 7RU Tyne & Wear England

model-based inversion method has been studied extensively for characterization of a metal plate in eddy current testing. However, few reports cover liftoff elimination. In this work, model-based inversion method is evaluated in terms of liftoff reduction. For better inversion accuracy, a complex yet accurate procedure is presented to do calibrations of coil parameters before use. The results from simulations and experiments demonstrate that model-based inversion method has an exceptional ability to compensate influence of large liftoff variations by considering it as an unknown variable to be determined. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Eddy current Characterization Metal plate Liftoff effect model-based method

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A bio-optical model based method of estimating total suspended matter of Lake Taihu from near-infrared remote sensing reflectance

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ENVIRONMENTAL MONITORING AND ASSESSMENT 2008年第1-3期145卷 339-347页

作者： Zhang, B. Li, J. Shen, Q. Chen, D. Chinese Acad Sci State Key Lab Remote Sensing Sci Inst Remote Sensing Applicat Beijing Peoples R China Queens Univ Dept Geog Kingston ON K7L 3N6 Canada

Total suspended matter is an important water quality parameter, and plays a key role in water quality evaluation, especially of inland waters. Many different methods have been developed to estimate TSM from remote sensing data, in which empirical methods and model-based methods are two types of commonly used methods. Compared with empirical methods, model-based methods have the advantages of definite physical meanings, high robustness and retrieval accuracy. In model-based methods, matrix inversion method is commonly used in monitoring water qualities of inland waters. However, matrix inversion method has to predetermine some optical parameters by empirical values or simplified optical model, which may introduce some errors in retrieved water quality parameters. In order to overcome the shortcomings of matrix inversion method and increase the estimating accuracy of total suspended mater, in this paper, a bio-optical model based method is developed, which estimates total suspended mater by using remote sensing reflectance of two near-infrared bands. This method is validated by in-situ experiment data measured in Lake Taihu, a big turbid lake in eastern China. The results show that this method has better performance than matrix inversion method. The average relative error of the estimated total suspended matter by this method is only 13.0%, which is much smaller than the errors by matrix inversion method (32.7%). This method has the advantages of definite physical meaning, easiness to carry out, and high estimating accuracy. However, the applicable scope of this method has limitations: it can only be applied to optically deep waters with high concentrations of total suspended matter.

关键词： bio-optical model total suspended matter TSM Lake Taihu remote sensing reflectance empirical method model-based method matrix inversion method

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System Identification With Fourier Transformation for Long-Term Time Series Forecasting

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IEEE TRANSACTIONS ON BIG DATA 2025年第2期11卷 474-484页

作者： Liu, Xiaoyi Chen, Duxin Wei, Wenjia Zhu, Xia Shi, Hao Yu, Wenwu Southeast Univ Sch Math Jiangsu Key Lab Networked Collect Intelligence Nanjing 210096 Peoples R China Huawei Technol Co Ltd Network Lab Cent Res Inst Lab 2012 Shenzhen 518129 Peoples R China Huawei Technol Co Ltd Dijkstra Lab Data Commun Prod Line ICT Prod Solut Dongguan 523820 Peoples R China

Time-series prediction has drawn considerable attention during the past decades fueled by the emerging advances of deep learning methods. However, most neural network based methods fail in extracting the hidden mechanism of the targeted physical system. To overcome these shortcomings, an interpretable sparse system identification method without any prior knowledge is proposed in this study. This method adopts the Fourier transform to reduces the irrelevant items in the dictionary matrix, instead of indiscriminate usage of polynomial functions in most system identification methods. It shows an visible system representation and greatly reduces computing cost. With the adoption of l(1) norm in regularizing the parameter matrix, a sparse description of the system model can be achieved. Moreover, three data sets including the water conservancy data, global temperature data and financial data are used to test the performance of the proposed method. Although no prior knowledge was known about the physical background, experimental results show that our method can achieve long-term prediction regardless of the noise and incompleteness in the original data more accurately than the widely-used baseline data-driven methods. This study may provide some insight into time-series prediction investigations, and suggests that a white-box system identification method may extract the easily overlooked yet inherent periodical features and may beat neural-network based black-box methods on long-term prediction tasks.

关键词： Time series analysis System identification Mathematical models Predictive models Sparse matrices Computational modeling Task analysis Time-series prediction long-term prediction sparse identification model-based method machine learning

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A model-based air balancing method of a ventilation system

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ENERGY AND BUILDINGS 2018年 174卷 506-512页

作者： Jing, Gang Cai, Wenjian Zhai, Deqing Liu, Shuai Cui, Can Shandong Jiaotong Univ Sch Tansportat & Logist Engn Jinan 250023 Shandong Peoples R China Nanyang Technol Univ EXQUISITUS Ctr E City Sch Elect & Elect Engn 50 Nanyang Ave Singapore 639798 Singapore Shandong Univ Sch Control Sci & Engn Jinan 250061 Shandong Peoples R China

In this paper we propose a novel and easily implementable model-based air balancing method for a ventilation system. The proposed method provides a well-defined form of balancing for a ventilation system, which can be effectively solved. The method follows three procedures: (i) mathematical modeling for duct system which is based on steady-state pressure balance;(ii) model parameters identification by supervised machine learning;(iii) determination of damper positions based on the model. The performance of this method is validated through testing in a duct testbed with five terminals. The results show that the final flow rates in all terminals match with the desired values with no more than 6.8% relative error. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Ventilation system Air balancing model-based method Parameter identification Optimization Experiment

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A Hybrid Direct Search and model-based Derivative-Free Optimization method with Dynamic Decision Processing and Application in Solid-Tank Design

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ALGORITHMS 2023年第2期16卷 92-92页

作者： Huang, Zhongda Ogilvy, Andy Collins, Steve Hare, Warren Hilts, Michelle Jirasek, Andrew Univ British Columbia Okanagan Dept Math Kelowna BC V1V 1V7 Canada Univ British Columbia Okanagan Dept Phys Kelowna BC V1V 1V7 Canada BC Canc Med Phys Kelowna BC V1Y 5L3 Canada

A derivative-free optimization (DFO) method is an optimization method that does not make use of derivative information in order to find the optimal solution. It is advantageous for solving real-world problems in which the only information available about the objective function is the output for a specific input. In this paper, we develop the framework for a DFO method called the DQL method. It is designed to be a versatile hybrid method capable of performing direct search, quadratic-model search, and line search all in the same method. We develop and test a series of different strategies within this framework. The benchmark results indicate that each of these strategies has distinct advantages and that there is no clear winner in the overall performance among efficiency and robustness. We develop the Smart DQL method by allowing the method to determine the optimal search strategies in various circumstances. The Smart DQL method is applied to a problem of solid-tank design for 3D radiation dosimetry provided by the UBCO (University of British Columbia-Okanagan) 3D Radiation Dosimetry Research Group. Given the limited evaluation budget, the Smart DQL method produces high-quality solutions.

关键词： derivative-free optimization black-box optimization local optimization direct search method model-based method 3D radiation dosimetry

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The connectivity domain: Analyzing resting state fMRI data using feature-based data-driven and model-based methods

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NEUROIMAGE 2016年 134卷 494-507页

作者： Iraji, Armin Calhoun, Vince D. Wiseman, Natalie M. Davoodi-Bojd, Esmaeil Avanaki, Mohammad R. N. Haacke, E. Mark Kou, Zhifeng Wayne State Univ Dept Biomed Engn Detroit MI 48201 USA Mind Res Network Albuquerque NM USA LBERI Albuquerque NM USA Univ New Mexico Dept Elect & Comp Engn Albuquerque NM 87131 USA Wayne State Univ Dept Psychiat & Behav Neurosci Detroit MI 48201 USA Henry Ford Hlth Syst Radiol & Res Adm Dept Detroit MI USA Wayne State Univ Dept Neurol Detroit MI 48201 USA Wayne State Univ Dept Radiol Detroit MI 48201 USA

Spontaneous fluctuations of resting state functional MRI (rsfMRI) have been widely used to understand the macro-connectome of the human brain. However, these fluctuations are not synchronized among subjects, which leads to limitations and makes utilization of first-level model-based methods challenging. Considering this limitation of rsfMRI data in the time domain, we propose to transfer the spatiotemporal information of the rsfMRI data to another domain, the connectivity domain, in which each value represents the same effect across subjects. Using a set of seed networks and a connectivity index to calculate the functional connectivity for each seed network, we trans form data into the connectivity domain by generating connectivity weights for each subject. Comparison of the two domains using a data-driven method suggests several advantages in analyzing data using data-driven methods in the connectivity domain over the time domain. We also demonstrate the feasibility of applying model-based methods in the connectivity domain, which offers a new pathway for the use of first-level model-based methods on rsfMRI data. The connectivity domain, furthermore, demonstrates a unique opportunity to perform first-level feature-based data-driven and model-based analyses. The connectivity domain can be constructed from any technique that identifies sets of features that are similar across subjects and can greatly help researchers in the study of macro-connectome brain function by enabling us to perform a wide range of model-based and data-driven approaches on rsfMRI data, decreasing susceptibility of analysis techniques to parameters that are not related to brain connectivity information, and evaluating both static and dynamic functional connectivity of the brain from a new perspective. (C) 2016 The Authors. Published by Elsevier Inc.

关键词： Resting state functional MRI (rsfMRI) Connectivity domain model-based method General linear model (GLM) Independent component analysis (ICA) Feature-based analysis

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An air balancing method using support vector machine for a ventilation system

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BUILDING AND ENVIRONMENT 2018年第Oct.期143卷 487-495页

作者： Jing, Gang Cai, Wenjian Chen, Haoran Zhai, Deqing Cui, Can Yin, Xiaohong Shandong Jiaotong Univ Sch Tansportat & Logist Engn Jinan 250023 Shandong Peoples R China Nanyang Technol Univ EXQUISITUS Ctr E City Sch Elect & Elect Engn 50 Nanyang Ave Singapore 639798 Singapore Qingdao Univ Sci & Technol Sch Automat & Elect Engn Qingdao 266042 Peoples R China ASTAR Inst Microelect 2 Fusionopolis Way Innovis 138634 Singapore

In this paper, support vector machine method is introduced to solve air balancing problem for ventilation systems and a novel non-iterative air balancing method is presented. The proposed method provides a well-formulated form of air balancing problem for air ventilation system by i) building a mathematical model for duct system based on steady-state pressure balance;ii) constructing a pressure prediction model based on the system model and theory of support vector machine;iii) identifying the significant model parameters by support vector machine regression supervised machine lsseaming;and iv) determining the damper positions based on the formulated model. The performance of the method is validated experimentally in a duct testbed with five terminals. The results demonstrate that the method can achieve the desired values within 4.6% relative error and is a promising approach for air balancing.

关键词： Ventilation system Air balancing Support vector machine (SVM) Machine learning model-based method Parameter identification Optimization Experiment

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