赣南地区低温地板辐射采暖效果研究

赣南地区属于夏热冬冷地区, 被列为非采暖区, 冬季室内热舒适性较差.由于各方面的原因, 人们对居住环境的要求越来越高, 迫切需要改善室内热舒适性.结合赣南地区冬季气候及地域特点, 研究低温地板辐射采暖的效果, 并与空调采暖进行对比.利用CFD理论分别对该采暖室在2种采暖方式下的室内热环境进行数值模拟, 分析室内空气温度场、湿度场, 并用PMV-PPD评价指标对室内热环境进行评定.研究表明, 与空调采暖相比低温地板辐射采暖:室内热环境更加舒适; 室内设计温度可降低2~3℃, 能节约15%的能量, 有利于节能环保.      

Optimization of back propagation algorithm and GAS-assisted ANN models for hot metal desulphurization

Adaptive neural net (ANN) model of hot metal desulphurization is first optimized by various search methods including the golden section search and Davies-Swann-Campey methods. Logarithmic preprocessing of input data leads to a further improvement in generalization ability of the net. Genetic adaptive search (GAS) method is used to optimize the mathematical model for desulphurization and when the input data are preprocessed with this optimized model and fed into an artificial neural net, the generalization ability of the net becomes even better. Best results are obtained when using GAS to optimize the interconnection weights during the training phase, while training data are preprocessed through a mathematical model already optimized by GAS. For every process several options presented by a combination of ANN and GAS must be systematically investigated before choosing the ultimate model for predictions on shop floor.     

Pilot Study for the Performance of a New Demand Control Ventilation System in Hong Kong

There are different ventilation control methods for outdoor air quantity in air-conditioned spaces to reduce the energy consumed in cooling the outdoor air. Demand controlled ventilation (DCV) is an important strategy to control the outdoor air quantity. However, the current practice in DCV systems creates several problems for air-conditioned office buildings. Although metabolic carbon dioxide (CO2) concentration is mainly used as a surrogate indicator for ventilation adequacy, the conventional DCV system does not seriously consider the placement location of the CO2 sensor, and it does not take into account the adverse effects of the consequential increase in pollutant concentrations in the indoor space when the fresh airflow rate is reduced. In this study, a long period of subjective and objective measurements were conducted in an occupied office to examine its suitability for conversion to a new demand controlled ventilation system (nDCV). After the feasibility investigation, an nDCV system was developed to optimize the energy consumed for outdoor air ventilation while providing the desirable thermal comfort and indoor air quality. The true optimization of this nDCV is achieved by a new concept of the optimized indoor air quality window which determines the most representative locations for the indicator sensors. A minimum ventilation rate is determined by a calibration procedure for the pollutant inventory of a building, taking into account the infiltration characteristics. Radon gas, which is a notorious carcinogenic indoor pollutant, is used as a reference to determine the minimum fresh airflow rate. The findings show that this nDCV system reduced 16% of fresh air cooling energy consumption without jeopardizing the thermal comfort and indoor air quality.     

民用住宅地板低温辐射供暖的节能分析

利用热舒适理论讨论了地板辐射供暖方式与其他方式在房间内人体热舒适方面的差异,并通过对其综合节能效果的定量估计,建议在采用地板辐射供暖的节能建筑中相应降低室内供暖温度。     

Integrating Robot Mapping and Augmented Building Simulation

This paper discusses a framework for integrated augmented reality (AR) architecture for indoor thermal performance data visualization that utilizes a mobile robot to generate environment maps. It consists of three modules: robot mapping, computational fluid dynamics (CFD) simulation, and AR visualization. The robot mapping module enables the modeling of spatial geometry using a mobile robot. In order to generate steady approximations to scanned three-dimensional data sets, the paper presents a novel “split-and-merge expectation-maximization patch fitting” (SMEMPF) planar approximation method. It allows for precise adjustment of patches independent from the initial model. The final result is a set of patches identifying planar macrostructures that consist of a collection of supported tiles. These patches are used to model the spatial geometry under investigation. The CFD simulation module facilitates the prediction of building performance databased on the spatial data generated using the SMEMPF method. The AR visualization module assists in interactive and immersive visualization of CFD simulation results. Such an integrated AR architecture will facilitate rapid multiroom mobile AR visualizations.     

Predicting Cost Deviation in Reconstruction Projects: Artificial Neural Networks versus Regression

This paper investigates the challenging environment of reconstruction projects and describes the development of a predictive model of cost deviation in such high-risk projects. Based on a survey of construction professionals, information was obtained on the reasons behind cost overruns and poor quality from 50 reconstruction projects. For each project, the specific techniques used for project control were reported along with the actual cost deviation from planned values. Two indicators of cost deviation are used in this study: cost overrun to the owner, and the cost of rework to the contractor. Based on the information obtained, 36 factors were identified as having direct impact on the cost performance of reconstruction projects. Two techniques were then used to develop models for predicting cost deviation: statistical analysis, and artificial neural networks (ANNs). While both models had similar accuracy, the ANN model is more sensitive to a larger number of variables. Overall, this study contributes to a better understanding of the reasons for cost deviation in reconstruction projects and provides a decision support tool to quantify that deviation.     

Estimating Actual Evapotranspiration from Limited Climatic Data Using Neural Computing Technique

This paper examines the potential of artificial neural networks (ANN) in estimating the actual crop evapotranspiration (ET) from limited climatic data. The study employed radial-basis function (RBF) type ANN for computing the daily values of ET for rice crop. Six RBF networks, each using varied input combinations of climatic variables, have been trained and tested. The model estimates are compared with measured lysimeter ET. The results of the study clearly demonstrate the proficiency of the ANN method in estimating the ET. The analyses suggest that the crop ET could be computed from air temperature using the ANN approach. However, the present study used a single crop data for a limited period, therefore further studies using more crops as well as weather conditions may be required to strengthen these conclusions.     

井下紧急避险设施内人体舒适度预测模型

长时间处在不舒适的环境中,将对人体健康产生严重影响。井下紧急避险设施中,影响避险人员舒适性感觉的因素众多,本文选取氧气含量、二氧化碳含量、温度和相对湿度四个关键因素为研究对象,采用分级评分法,进行了系列真人模拟生存试验,获得了单一环境变量和多个环境变量条件下总计169组有效人体舒适度投票数据.经统计分析,分别得出了密闭舱室内单一环境变量与人体舒适度感觉之间的函数关系以及避险人员舒适度预测模型,分析了氧气含量、二氧化碳含量、温度和相对湿度的允许范围及调控原则.      

基于减法聚类ANFIS的链-回-环球团热状态控制模型

 链箅机-回转窑氧化球团热工制度是影响球团矿产质量指标及生产能耗的关键因素,但由于其工艺特点,热工过程状态参数和操作参数多、设备的耦合性强、操作变量与被控变量关系复杂,难以采用精确的机理模型进行控制。在分析链箅机-回转窑氧化球团热工过程物料流及热风流特点的基础上,建立了基于自适应神经模糊推理系统（ANFIS）的热状态控制模型。采用减法聚类划分模型输入空间,采用最小二乘法及梯度下降法对T-S模型进行辨识。结合VC++和MATLAB混合编程的方法,开发了链箅机-回转窑氧化球团热状态控制指导系统,实现了模型的在线计算,以及操作变量的实时控制指导。采用国内某球团厂的生产数据对模型进行了仿真,模型的平均相对误差均小于5%。     

Intelligent Multivariable Modeling of Blast Furnace Molten Iron Quality Based on Dynamic AGA-ANN and PCA

Blast furnace(BF)ironmaking process has complex and nonlinear dynamic characteristics.The molten iron temperature(MIT)as well as Si,P and S contents of molten iron is difficult to be directly measured online,and large-time delay exists in offline analysis through laboratory sampling.A nonlinear multivariate intelligent modeling method was proposed for molten iron quality(MIQ)based on principal component analysis(PCA)and dynamic genetic neural network.The modeling method used the practical data processed by PCA dimension reduction as inputs of the dynamic artificial neural network(ANN).A dynamic feedback link was introduced to produce a dynamic neural network on the basis of traditional back propagation ANN.The proposed model improved the dynamic adaptability of networks and solved the strong fluctuation and resistance problem in a nonlinear dynamic system.Moreover,a new hybrid training method was presented where adaptive genetic algorithms(AGA)and ANN were integrated,which could improve network convergence speed and avoid network into local minima.The proposed method made it easier for operators to understand the inside status of blast furnace and offered real-time and reliable feedback information for realizing close-loop control for MIQ.Industrial experiments were made through the proposed model based on data collected from a practical steel company.The accuracy could meet the requirements of actual operation.     

Investigation of Optimal Megnetic Properties in NdFeB Magnets by Artificial Neural Network

In order to study the effect of alloy component on magnetic properties of NdFeB magnets, the experiment schemes are carried out by the uniform design theory, and the relationship between the component and the magnetic properties is established by artificial neural network(ANN) predicting model.The element contents of alloys are optimized by the ANN model.Meanwhile, the influences of mono-factor or multi-factor interaction on alloy magnetic properties are respectively discussed according to the curves ploted by ANN model.Simulation result shows that the predicted and measured results are in good agreement.The relative error is every low, the error is not more than 1.68% for remanence Br, 1.56% for maximal energy product (BH)m, and 7.73% for coercivity Hcj.Hcj can be obviously improved and Br can be reduced by increasing Nd or Zr content.Co and B have advantageous effects on increasing Br and disadvantageous effects on increasing Hcj.Influence of alloying elements on Hcj and Br are inverse, and the interaction among the alloying elements play an important role in the magnetic properties of NdFeB magnets.The ANN prediction model presents a new approach to investigate the nonlinear relationship between the component and the magnetic properties of NdFeB alloys.     

Decision Support for Natural Ventilation of Nonresidential Buildings

This paper describes a decision-support framework assisting the design of nonresidential buildings with natural ventilation. The framework is composed of decision modules with input, analysis algorithms and output of natural ventilation design. The framework covers ventilation with natural driving force and mechanical-assisted ventilation. The proposed framework has two major assessment levels: feasibility assessment and comparison of alternative natural ventilation approaches. The feasibility assessment modules assess the potential of the site with the design proposition for natural ventilation in terms of wind, temperature, humidity, noise, and pollution conditions. All of the possible natural ventilation approaches and system designs are assessed by first applying constraint functions to each of the alternatives. Then the comparison of alternative approaches to natural ventilation continues by assessing the critical performance mandates that include energy savings, thermal comfort, acoustic control, indoor air quality, and cost. Approaches are finally ranked based on their performance.     

Predicting Longitudinal Dispersion Coefficient in Natural Streams by Artificial Neural Network

An artificial neural network (ANN) model was developed to predict the longitudinal dispersion coefficient in natural streams and rivers. The hydraulic variables [flow discharge (Q), flow depth (H), flow velocity (U), shear velocity (u*), and relative shear velocity (U/u*)] and geometric characteristics [channel width (B), channel sinuosity (σ), and channel shape parameter (β)] constituted inputs to the ANN model, whereas the dispersion coefficient (Kx) was the target model output. The model was trained and tested using 71 data sets of hydraulic and geometric parameters and dispersion coefficients measured on 29 streams and rivers in the United States. The training of the ANN model was accomplished with an explained variance of 90% of the dispersion coefficient. The dispersion coefficient values predicted by the ANN model satisfactorily compared with the measured values corresponding to different hydraulic and geometric characteristics. The predicted values were also compared with those predicted using several equations that have been suggested in the literature and it was found that the ANN model was superior in predicting the dispersion coefficient. The results of sensitivity analysis indicated that the Q data alone would be sufficient for predicting more frequently occurring low values of the dispersion coefficient (Kx<100?m2/s). For narrower channels (B/H<50) using only U/u* data would be sufficient to predict the coefficient. If β and σ were used along with the flow variables, the prediction capability of the ANN model would be significantly improved.     

Response Analysis of Field-Scale Fully Grouted Standard Cable Bolts Using a Coupled ANN–FDM Approach

This paper presents a coupled approach using an artificial neural network (ANN) and the finite difference method (FDM) that has been developed to predict the distribution of axial load along fully grouted standard cable bolts in the field using laboratory pullout test data. A back-propagation training algorithm was used in ANN to determine axial loads in the cables tested in the laboratory. The ANN component of the computational model was trained using two different types of data sets. At first, the ANN was trained to predict the axial loads in a series of short cables grouted with Portland cement at a specific water-to-cement ratio and subjected to different radial confining stiffness values. Next, the ANN model was trained for an expanded case to include the influence of lateral confining stress on the distribution of axial load in the cable reinforcement. Finally, the ANN model was implemented into a widely used, FDM-based geotechnical software (FLAC). The accuracy of the ANN–FDM model is demonstrated in this paper against measured data from laboratory and field tests. The analysis approach introduced in this study is a valuable computational tool that can be used to determine the axial load distribution in long standard cable bolts, which are commonly installed to stabilize rock masses in various geotechnical, transportation, and mining applications.     

Neural Networks for Profiling Stress History of Clays from PCPT Data

This paper evaluates the feasibility of using artificial neural network (ANN) models for estimating the overconsolidation ratio (OCR) of clays from piezocone penetration tests (PCPT). Three feed-forward, back-propagation ANN models are developed, and trained using actual PCPT records from test sites around the world. The soil deposits range from soft, normally consolidated intact clays to very stiff, heavily overconsolidated fissured clays. ANN model 1 is a general model applicable for both intact and fissured clays. ANN model 2 is suited for intact clays, and ANN model 3 is applicable to fissured clays only. The models are validated using new PCPT data (not used for training), and by comparing model predictions with reference OCR values obtained from oedometer tests. For intact clays, ANN model 2 gives better OCR estimates compared to ANN model 1. For fissured clays, ANN model 3 gives better estimates compared to ANN model 1. Some of the existing interpretation methods are reviewed. Compared to the existing methods, ANN models 2 and 3 give very good estimates of OCR.     

Neural Network Modeling of Resilient Modulus Using Routine Subgrade Soil Properties

Artificial neural network (ANN) models are developed in this study to correlate resilient modulus with routine properties of subgrade soils and state of stress for pavement design application. A database is developed containing grain size distribution, Atterberg limits, standard Proctor, unconfined compression, and resilient modulus results for 97 soils from 16 different counties in Oklahoma. Of these, 63 soils (development data set) are used in training, and the remaining 34 soils (evaluation data set) from two different counties are used in the evaluation of the developed models. A commercial software, STATISTICA 7.1, is used to develop four different feedforward-type ANN models: linear network, general regression neural network, radial basis function network, and multilayer perceptrons network (MLPN). In each of these models, the input layer consists of seven nodes, one node for each of the independent variables, namely moisture content (w), dry density (γd), plasticity index (PI), percent passing sieve No. 200 (P200), unconfined compressive strength (Uc), deviatoric stress (σd), and bulk stress (θ). The output layer consists of only one node—resilient modulus (MR). After the architecture is set, the development data set is fed into the model for training. The strengths and weaknesses of the developed models are examined by comparing the predicted MR values with the experimental values with respect to the R2 values. Overall, the MLPN model with two hidden layers was found to be the best model for the present development and evaluation data sets. This model as well as the other models could be refined using an enriched database.     

Online prediction and monitoring of mechanical properties of industrial galvanised steel coils using neural networks

In galvanising line of cold rolling mill, mechanical properties, i.e. yield strength (YS) and ultimate tensile strength (UTS), are achieved by controlling the key process parameters within specified limits. In this paper, a feed-forward back-propagation artificial neural network (ANN) is proposed to predict the mechanical properties of a coil from its chemical composition, thickness, width and key galvanising process parameters. Principal component analysis is used to avoid redundancy and collinearity effects in input variables for the ANN. The model predicted the YS and UTS with an accuracy of ±10?megapascal (MPa) for 90% of the data. The model was implemented in the continuous galvanising line of Tata Steel, India. An online quality monitoring system was developed to monitor the predicted mechanical properties and process parameters of a galvanised coil. This system helps quality team in decision making.     

Planning macroscopic aspects of manual control: end-state comfort and point-of-change effects

A recent emphasis in motor control research is the planning of macroscopic features and how variables such as efficiency and comfort influence the planning process. This paper extends the work by Rosenbaum and Jorgensen (1992) by further studying the end-state comfort effect. In the first experiment, participants picked up a dowel using an underhand or overhand grip and touched one end to a numbered target on the wall. The height of the #9 target was set at the height of participants' right shoulder. The second experiment involved awkwardness ratings. Participants touched the 14 targets with the dowel as well as with a small dumbbell and the comfort of the end position was rated on a seven-point scale. In the third experiment, participants moved a dumbbell to the targets in the same procedure as the first experiment. Overall, the probability analyses indicated that as the end-state comfort effect was magnified, the sequential effect vanished and a distinct point-of-change effect appeared. Optimization theory and the knowledge model readily explained the phenomena of the end-state comfort effect, the sequential effect, and the point-of-change effect. The present findings indicate that comfort has a powerful influence on the planning of motor performance.     

Subsurface Characterization Using Artificial Neural Network and GIS

A method for characterizing the subsurface is developed using an artificial neural network (ANN) and geographic information system (GIS). Data on the distribution of aquifer materials from monitoring well lithologic logs are used to train a multilayer perceptron using the back-propagation algorithm. The trained ANN predicts using an appropriate prediction scale, the subsurface formation materials at each point on a discretized grid of the model area. GIS is then used to develop subsurface profiles from the data generated using the ANN. These subsurface profiles are then compared with available geological sections to check the accuracy of the ANN-GIS generated profiles. This methodology is applied to determine the aquifer extent and calculate aquifer parameters for input to ground-water models for the multiaquifer system underlying the city of Bangkok, Thailand. A selected portion of the model domain is used for illustration. The integrated approach of ANN and GIS is shown to be a powerful tool for characterizing complex aquifer geometry, and for calculating aquifer parameters for ground-water flow modeling.