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A numerical study on influences of building envelope heat gain on operating performances of a bed-based task/ambient air conditioning (TAC) system in energy saving and thermal comfort

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APPLIED ENERGY 2017年 192卷 213-221页

作者： Mao Ning Pan Dongmei Li Zhao Xu Yingjie Song Mengjie Deng Shiming China Univ Petr East China Coll Pipeline & Civil Engn Qingdao Shandong Peoples R China South China Univ Technol Sch Mech & Automot Engn Guangzhou Guangdong Peoples R China Univ Shanghai Sci & Technol Sch Environm & Architecture 516 Jungong Rd Shanghai Peoples R China Zhejiang Univ Technol Inst Proc Equipment & Control Engn Hangzhou 310014 Zhejiang Peoples R China Guangdong Univ Technol Sch Mat & Energy Dept Energy Engn Guangdong Prov Key Lab Funct Soft Condensed Matte Guangzhou Guangdong Peoples R China Univ Tokyo Grad Sch Frontier Sci Dept Human & Engn Environm Studies Chiba Japan Hong Kong Polytech Univ Dept Bldg Serv Engn Kowloon Hong Kong Peoples R China

There has been an increasing concern on thermal comfort in sleeping environments and its associated energy use in the past few years. To improve the thermal environment and to reduce energy use of air conditioning in bedrooms, applications of task/ambient air conditioning (TAC) systems were proposed and studied previously. It's indicated that the TAC system can be well integrated with a bed. Due to the variation of the envelope heat gain in a bedroom during night, it is necessary to study the thermal environment inside a bedroom and the energy use of a TAC system for the bedroom at varying envelope thermal loads. Therefore, this paper reports on a numerical study on a TAC system applied to a bedroom with different envelope heat gains. The influences of envelope heat gain on energy consumption of the TAC system and the indoor thermal comfort were studied in this paper. The research results show that at supply air flow rate (Qs) of 50 energy consumption was increased from 47.78 W to 213.11 W, and the PMV value was increased from 1.69 to 1.29 with the increase in envelope heat gain from 3.11 W to 155.6 W. To make PMV equal to zero under different envelope heat gains, linear regression models between supply air parameters and PMV were built and solved. The obtained values form curves where PMV = 0 and give a guide for operation of the TAC system. It was found that the calculation of energy consumption on these curves depends only on envelope heat gain. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Thermal comfort Energy consumption Task/ambient air conditioning (TAC) systems Envelope heat gain linear regression model PMV

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Decentralized estimation of regression coefficients in sensor networks

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DIGITAL SIGNAL PROCESSING 2017年 68卷 16-23页

作者： Gispan, Liran Leshem, Amir Be'ery, Yair Tel Aviv Univ Sch Elect Engn Tel Aviv Israel Bar Ilan Univ Fac Engn Ramat Gan Israel

Consider a wireless sensor network with a fusion center deployed to estimate a common non-random parameter vector. Each sensor obtains a noisy observation vector of the non-random parameter vector according to a linear regression model. The observation noise is correlated across the sensors. Due to power, bandwidth and complexity limitations, each sensor linearly compresses its data. The compressed data from the sensors are transmitted to the fusion center, which linearly estimates the non-random parameter vector. The goal is to design the compression matrices at the sensors and the linear unbiased estimator at the fusion center such that the total variance of the estimation error is minimized. In this paper, we provide necessary and sufficient conditions for achieving the performance of the centralized best linear unbiased estimator. We also provide the optimal compression matrices and the optimal linear unbiased estimator when these conditions are satisfied. When these conditions are not satisfied, we propose a sub-optimal algorithm to determine the compression matrices and the linear unbiased estimator. Simulation results are provided to illustrate the effectiveness of the proposed algorithm. (C) 2017 Elsevier Inc. All rights reserved.

关键词： Decentralized estimation Dimensionality reduction linear regression model Wireless sensor networks

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Comment on a generalized stochastic restricted ridge regression estimator

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COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 2017年第10期46卷 4957-4960页

作者： Kaciranlar, Selahattin Dawoud, Issam Cukurova Univ Fac Sci & Letters Dept Stat Adana Turkey

In this note, we make some comments about the paper of Alheety and Kibria (2014) and correct the wrongly proved Theorems in that paper.

关键词： linear regression model Multicollinearity Generalized stochastic restricted ridge regression estimator

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On the ridge regression estimator with sub-space restriction

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COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 2017年第23期46卷 11854-11865页

作者： Fallah, R. Arashi, M. Tabatabaey, S. M. M. Ferdowsi Univ Mashhad Int Campus Dept Stat Mashhad Iran Shahrood Univ Technol Dept Stat Shahrood Iran Ferdowsi Univ Mashhad Dept Stat Mashhad Iran

In the linear regression model with elliptical errors, a shrinkage ridge estimator is proposed. In this regard, the restricted ridge regression estimator under sub-space restriction is improved by incorporating a general function which satisfies Taylor's series expansion. Approximate quadratic risk function of the proposed shrinkage ridge estimator is evaluated in the elliptical regression model. A Monte Carlo simulation study and analysis based on a real data example are considered for performance analysis. It is evident from the numerical results that the shrinkage ridge estimator performs better than both unrestricted and restricted estimators in the multivariate t-regression model, for some specific cases.

关键词： Approximate risk elliptically contoured distribution linear regression model restricted estimator shrinkage estimator ridge estimator

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Correlating cooling energy use with urban microclimate data for projecting future peak cooling energy demands: Residential neighbourhoods in Seoul

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SUSTAINABLE CITIES AND SOCIETY 2017年 35卷 645-659页

作者： Yi, Choo Yoon Peng, Chengzhi Univ Sheffield Sch Architecture Western Bank Arts Tower Sheffield S10 2TN S Yorkshire England

The paper presents a relational study of correlating cooling energy use with local weather station and apartment price data in Seoul. The overall analysis at a macro-level shows monthly variations in the correlation coefficients of cooling energy use and local weather station data during summer months. A further analysis at a micro-level shows temporal and spatial variations in the correlation. As the August correlation appears the strongest across all city districts, up to r = 0.972, a simple bivariate regression (SBR) model is derived to predict peak cooling energy use for each district. Given the latest climate change projections for Seoul, we use the SBR models to estimate increases of cooling energy use for each city district in August 2050s. The largest predicted increase rate (IR) is 96.1% in one city district (from 124.5% in 2012-220.6% in 2047). The smallest IR is 6.0% in another city district (from 51.5% to 57.5%). In 2047, the city district with the highest predicted IR is up to 292.8%, while the lowest is up to 57.5%. We discuss the implications of the projected future peak cooling energy demands for sustainable resilience as well as citizen's health and wellbeing.

关键词： Residential cooling energy use Urban microclimate Property price Relational study linear regression model Climate change projections

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Non-destructive estimation of leaf area of durian (Durio zibethinus) - An artificial neural network approach

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SCIENTIA HORTICULTURAE 2017年 219卷 319-325页

作者： Kumar, M. Kishor Kumar, R. Senthil Sankar, V. Sakthivel, T. Karunakaran, G. Tripathi, P. C. IIHR Cent Hort Expt Stn Bhubaneswar 751019 Odisha India IIHR Cent Hort Expt Stn Chettalli 571248 Kodagu India IIHR Cent Hort Expt Stn Hirehalli 572168 Karnataka India Indian Inst Hort Res ICAR Hessarghatta 560089 Karnataka India

Non destructive estimation of leaf area is necessary for taking successive observations on the same leaf. Usually, a common method for developing leaf area estimation models was to execute regression analysis by taking leaf area as dependant variable and leaf parameters as independent variables. However, the regression analysis applicable only when there is a relation between dependent and independent variables. Therefore, the objective of this work was to develop an artificial neural network (ANN) model including leaf length and leaf width as inputs and to make comparison with regression models. Leaf area (LA) of Durain (Durio zibethinus) was estimated by ANN and regression models. Among the linear regression models (LRM), the equation LA = 0.888 (L*W) - 4.961 (R-2 = 0.91, MSE = 5.17) was found to be the best fitting for durian. In ANN modelling best fitting results were obtained with two input nodes (leaf length and leaf width), one hidden layer, and one output layer (leaf area) as 2-1-1. Based on the values of root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and coefficient determination (R-2), ANN model was found to be more accurate both in terms of training (R-2 = 0.96, RMSE = 3.77, MAE = 2.41 and MAPE = 7.77) and testing phases (R-2 = 0.94, RMSE = 4.81, MAE = 2.91 and MAPE = 0.05) than the best fitting regression model. Hence ANN method may be used as an alternative or supportive model in the estimation of leaf area of durian. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Artificial neural network linear regression model Non destructive Leaf length Leaf width Leaf area

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Design and development of electrochemical biosensor for the simultaneous detection of melamine and urea in adulterated milk samples

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SENSORS AND ACTUATORS B-CHEMICAL 2017年 238卷 1283-1292页

作者： Ezhilan, Madeswari Gumpu, Manju Bhargavi Ramachandra, Bhat Lakshmishri Nesakumar, Noel Babu, K. Jayanth Krishnan, Uma Maheswari Rayappan, John Bosco Balaguru SASTRA Univ Nanosensors Lab Thanjavur 613401 Tamil Nadu India SASTRA Univ Sch Elect & Elect Engn Thanjavur 613401 Tamil Nadu India SASTRA Univ Ctr Nanotechnol & Adv Biomat Thanjavur 613401 Tamil Nadu India SASTRA Univ Sch Chem & Biotechnol Thanjavur 613401 Tamil Nadu India

The consumption of melamine and urea contaminated cow milk causes indigestion, acidity, ulcers and kidney stones in humans. In this context, a highly sensitive acetylcholinesterase cyclic voltammetric biosensor based on zinc oxide nanospheres modified Pt electrode has been successfully developed for the simultaneous determination of melamine and urea in cow milk sample. The fabricated bioelectrode showed 100% permeability to the binary mixture of melamine and urea, which in-turn enhanced selectivity. In addition, linear regression models for the estimation of binary mixture of melamine and urea in cow milk were formulated by keeping added melamine and urea as dependent variables and the estimated electrochemical paremeters as independent variables. The prediction performance of linear regression models was validated using %recovery, relative prediction error and root mean square error for cross validation. The developed Pt/ZnO/AChE/Chitosan bioelectrode detected melamine and urea over a range of 1-20 nM with a limit of detection of 3 pM and 1 pM respectively. The proposed sensor exhibited good recovery (99.96-102.22%), thus providing a promising tool for analysis of melamine and urea in cow milk samples. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Electrochemical biosensor Cyclic voltammetry Melamine Urea Milk linear regression model

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Stratification of telehealthcare for patients with chronic obstructive pulmonary disease using a predictive algorithm as decision support: A pilot study

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JOURNAL OF TELEMEDICINE AND TELECARE 2017年第3期23卷 410-415页

作者： Stausholm, Mads N. Egmose, Andreas Dahl, Simon C. Lilholt, Pernille H. Cichosz, Simon L. Hejlesen, Ole K. Aalborg Univ Dept Hlth Sci & Technol Fredrik Bajers Vej 7 C1-217 DK-9220 Aalborg Denmark

Introduction: The number of patients needing care who suffer from chronic obstructive pulmonary disease (COPD) is expected to increase in the future. The consequences thereof will increase the socio-economic burden for both patients and society. Telehealthcare technologies have shown potential in reducing hospitalisation-related costs and in improving health-related quality of life (HRQOL) for some COPD patients, but not all. The aim of this study was to investigate the potential of predictive algorithms for helping the general practitioner to stratify telehealthcare for COPD patients in a way that maximises HRQOL and minimises COPD-related costs. Methods: Data from 553 COPD patients based in the North Denmark Region were analysed and used as predictors for four multiple linear regression models. The models were trained and evaluated for their abilities to predict individual patient's future health- and cost-related developments, with and without telehealthcare. Results: The average root-mean-square error (RMSE) of the health and cost models was 5.265 HRQOL scores and US dollars (US$)5430.49, respectively. The accuracy regarding the polarity of the predicted changes ranged from 61-65% for the health models and 74-75% for the cost models. While differences in the magnitude of predictions with and without telehealthcare were statistically significant (p<0.01), the polarity of predictions was similar across models in 82.05% of all cases. Discussion: Our results indicate that it may be possible to predict the magnitude and polarity of a COPD patient's future health- and cost-related developments with and without telehealthcare. Predictive algorithms may provide a useful decision support tool in stratifying telehealthcare for COPD patients.

关键词： Chronic obstructive pulmonary disease TeleCare North trial Short Form-36 Health Survey telemedicine linear regression model

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Design and control of a 3-chambered fiber reinforced soft actuator with off-the-shelf stretch sensors

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INTERNATIONAL JOURNAL OF INTELLIGENT ROBOTICS AND APPLICATIONS 2017年第3期1卷 342-351页

作者： Nguyen, Pham Huy Sridar, Saivimal Zhang, Wenlong Polygerinos, Panagiotis Arizona State Univ Polytech Sch Ira A Fulton Sch Engn Mesa AZ 85212 USA

The necessity for efficient control of soft actuators has recently resulted in the development of complex physical models that are actuator geometry and material type dependent. In this paper, we address the ability to do closed-loop control with a simplified model independent of material properties and material characteristics of a soft robotic system. We demonstrate this by using a P-controller on a new 3-chambered, fiber-reinforced elastomeric actuator (3CA) that utilizes off-the-shelf stretch sensors. A motion capture system is used to calibrate and generate two different quasi-static models (a general linear regression model and an input dependent model) that map the varying chamber pressure readings with the actuator's end effector position, effectively represented by the stretch sensor curvature. Simulations using a closed-loop controller, with both models, provide further insight on the quality of the models and corresponding control performance. We use this information in an experimental study that yields comparable performances to the simulated results. Moreover, we demonstrate that the input-dependent model based controller can provide better results than that of the general model based controller. Finally, we demonstrate that our soft actuator can be closed-loop controlled with off-the-shelf stretch sensors with repeatable results. This opens a way to design new control concepts for multi-chambered actuators that can produce more complex motions in the future.

关键词： Soft actuator Soft robot Soft sensor linear regression model Input dependent model Control

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Predictive modeling for Advanced Virtual Metrology: A Tree-Based Approach

Predictive Modeling for Advanced Virtual Metrology: A Tree-B...

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IEEE International Conference on Emerging Technologies and Factory Automation

作者： Yang Liu Xin Li Department of Electrical and Computer Engineering Duke University Durham NC USA

The rapid development of industry 4.0 has promoted the extensive adoption of big data analytics for manufacturing industry. In this domain, virtual metrology is a critical technique that is able to reduce manufacturing cost over a large amount of practical applications. In this paper, we propose a novel tree-based approach for simultaneous feature selection and predictive modeling to facilitate efficient virtual metrology. The proposed method accurately identifies multiple feature sets and then chooses the best candidate to minimize modeling error. As demonstrated by the experimental results based on two industrial examples, the proposed method can achieve higher modeling accuracy and find a more complete feature set than the conventional approach implemented with orthogonal matching pursuit (OMP).

关键词： Predictive modeling Feature Selection Advanced Manufacturing Virtual Metrology linear regression model

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