scholarly journals Application of Multilayer Perceptron Method on Heat Flow Meter Results for Reducing the Measurement Time

2020 ◽  
Vol 2 (1) ◽  
pp. 29
Author(s):  
Sanjin Gumbarević ◽  
Bojan Milovanović ◽  
Mergim Gaši ◽  
Marina Bagarić
Keyword(s):  
Neural Network ◽  
Heat Flux ◽  
Multilayer Perceptron ◽  
Heat Losses ◽  
Measurement Time ◽  
Flux Rate ◽  
U Value ◽  
The Impact ◽  
Building Elements

To reduce the impact on climate change, many countries have developed strategies for the building sector with a goal to reduce the energy demands and carbon emission of buildings. As most buildings that exist today will very likely exist in foreseeable future, many buildings will need to undergo major renovations. One of the most important parameters in determining the transmission heat losses through the building envelope is the U-value, i.e., thermal transmittance, and it is simply the rate of heat transfer per unit temperature. Since the U-value is one of the most important parameters regarding building energy performance, envelope elements that do not perform well in terms of transmission heat losses must undergo a renovation processes. The in-situ U-value of building elements is usually determined by the Heat Flux Method (HFM). One of the issues of current application of the HFM is the measurement duration. This paper explores the possibilities of reducing the measurement time by predicting the heat flux rate using a multilayer perceptron (MLP), a class of artificial neural network. The MLP uses two input layers that are the interior and exterior air temperatures, and the output layer that is the predicted heat flux rate. The predicted value is trained by comparing the predicted heat flux rates with the measured values, and by rearranging the neural network parameters (weights and biases) in corresponding neurons by minimizing the mean squared error defined for trained values (measured versus predicted heat flux rates). The use of MLP shows promising results for predicting the heat flux rates for the analyzed cases (4 days HFM results) when the training is performed on 2/3 or 1/2 of the overall measurement time. The application of the MLP could be in reducing the in-situ measurement time when determining heat losses through building elements in shorter time periods.

scholarly journals SMOS Neural Network Soil Moisture Data Assimilation in a Land Surface Model and Atmospheric Impact

2019 ◽  
Author(s):  
Nemesio Rodriguez-Fernandez ◽  
Patricia de Rosnay ◽  
Clement Albergel ◽  
Philippe Richaume ◽  
Filipe Aires ◽  
...  
Keyword(s):  
Neural Network ◽  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Positive Impact ◽  
Land Surface Model ◽  
Surface Model ◽  
Northern Asia ◽  
The Impact

The assimilation of Soil Moisture and Ocean Salinity (SMOS) data into the ECMWF (European Centre for Medium Range Weather Forecasts) H-TESSEL (Hydrology revised - Tiled ECMWF Scheme for Surface Exchanges over Land) model is presented. SMOS soil moisture (SM) estimates have been produced specifically by training a neural network with SMOS brightness temperatures as input and H-TESSEL model SM simulations as reference. This can help the assimilation of SMOS information in several ways: (1) the neural network soil moisture (NNSM) data have a similar climatology to the model, (2) no global bias is present with respect to the model even if regional differences can exist. Experiments performing joint data assimilation (DA) of NNSM, 2 metre air temperature and relative humidity or NNSM-only DA are discussed. The resulting SM was evaluated against a large number of in situ measurements of SM obtaining similar results to those of the model with no assimilation, even if significant differences were found from site to site. In addition, atmospheric forecasts initialized with H-TESSEL runs (without DA) or with the analysed SM were compared to measure of the impact of the satellite information. Although, NNSM DA has an overall neutral impact in the forecast in the Tropics, a significant positive impact was found in other areas and periods, especially in regions with limited in situ information. The joint NNSM, T2m and RH2m DA improves the forecast for all the seasons in the Southern Hemisphere. The impact is mostly due to T2m and RH2m, but SMOS NN DA alone also improves the forecast in July- September. In the Northern Hemisphere, the joint NNSM, T2m and RH2m DA improves the forecast in April-September, while NNSM alone has a significant positive effect in July-September. Furthermore, forecasting skill maps show that SMOS NNSM improves the forecast in North America and in Northern Asia for up to 72 hours lead time.

New insights into nutrients dynamics and the carbonate system using a neural network approach in the Mediterranean Sea

2020 ◽  
Author(s):  
Marine Fourrier ◽  
Laurent Coppola ◽  
Fabrizio D'Ortenzio
Keyword(s):  
Neural Network ◽  
Mediterranean Sea ◽  
Dissolved Inorganic Carbon ◽  
In Situ Measurements ◽  
Total Alkalinity ◽  
Anthropogenic Pressure ◽  
Carbonate System ◽  
The Mediterranean ◽  
The Impact

<p>The semi-enclosed nature of the Mediterranean Sea, together with its small inertia which is due to the relatively short residence time of its water masses, make it highly reactive to external forcings and anthropogenic pressure. In this context, several rapid changes have been observed in physical and biogeochemical processes in recent decades, partly masked by episodic events and high regional variability. To better understand the underlying processes driving the Mediterranean evolution and, anticipate changes, the measurement, and integration of many biogeochemical variables are mandatory.</p><p>The development of new BGC sensors implemented on <em>in situ</em> autonomous platforms allows to increase the acquisition of essential biogeochemical variables. However, the measurements carried out by<em> in situ</em> autonomous platforms (e.g. profiling floats, gliders, moorings) are not exhaustive.</p><p>Recently, deep learning techniques and in particular neural networks have been developed. The CANYON-MED (for Carbonate system and Nutrients concentration from hYdrological properties and Oxygen using a Neural-network in the MEDiterranean Sea) neural network-based method provides estimations of nutrients (i.e. nitrates, phosphates, and silicates) and carbonate system variables (i.e. total alkalinity, dissolved inorganic carbon, pH<sub>T</sub>) from systematically measured oceanographic variables such as in situ measurements of pressure, temperature, salinity, and oxygen together with geolocation and date of sampling.</p><p>This regional approach, therefore, using quality-controlled in situ measurements from more than 35 cruises. CANYON-MED obtains satisfactory results: accuracies of 0.73, 0.045, and 0.70 µmol.kg<sup>-1</sup> for the nitrates, phosphates and silicates concentrations respectively, and 0.016, 11 µmol.kg<sup>-1</sup> and 10 µmol.kg<sup>-1</sup> for pH<sub>T</sub>, total alkalinity and dissolved organic carbon respectively. CANYON-MED thus generates “virtual” data of parameters not yet measured by autonomous platforms, while ably reproducing the data already sampled, emphasizing its ability to fill the gaps in time-series.</p><p>Hence, by applying it to the large and growing network of autonomous platforms in the Mediterranean Sea, this method allows us to gain new insights into nutrients and carbonate system dynamics in targeted areas. In particular, in the northwestern Mediterranean Sea, the impact of deep convection on biogeochemistry (e.g., nutrient replenishment and pH<sub>T</sub> variability) is highly variable over time and poorly covered by observing networks. In this case, CANYON-MED would improve our observations and understanding of the dynamic and coupled system.</p>

scholarly journals Comparison of Infrared Thermography and Heat Flux Method for Dynamic Thermal Transmittance Determination

Buildings ◽  
2019 ◽  
Vol 9 (5) ◽  
pp. 132 ◽  
Author(s):  
Mergim Gaši ◽  
Bojan Milovanović ◽  
Sanjin Gumbarević
Keyword(s):  
Heat Flux ◽  
Infrared Thermography ◽  
Dynamic Method ◽  
Flux Method ◽  
Microsoft Excel ◽  
Experimental Procedure ◽  
Thermal Transmittance ◽  
U Value ◽  
Stationary Heat Transfer

This paper proposes an alternative experimental procedure that uses infrared thermography (IRT) for measuring the surface temperature of building elements, through which it is possible to approximate the thermal transmittance or the U-value. The literature review showed that all authors used similar procedures that require semi-stationary heat transfer conditions, which, in most cases, could not be achieved. The dynamic and the average methods that are given in ISO 9869 were also used with the IRT and the heat flux method (HFM). The dynamic method (DYNM) shows a higher level of accuracy compared to the average method (AVGM). Since the algorithm of the DYNM is more complicated than that of the AVGM, Microsoft Excel VBA was used to implement the algorithm of the DYNM. Using the procedure given in this paper, the U-value could be approximated within 0–30% of the design U-value. The use of IRT, in combination with the DYNM, could be used in-situ since the DYNM does not require stable boundary conditions. Furthermore, the procedure given in this paper could be used for relatively fast and inexpensive U-value approximation without the use of expensive equipment (e.g., heat flux sensors).

scholarly journals The Impact of Internal Insulation on Heat Transport through the Wall: Case Study

2020 ◽  
Vol 10 (21) ◽  
pp. 7484 ◽  
Author(s):  
Paweł Krause ◽  
Artur Nowoświat ◽  
Krzysztof Pawłowski
Keyword(s):  
Heat Flux ◽  
Energy Efficiency ◽  
Numerical Modeling ◽  
Thermal Insulation ◽  
Flux Measurement ◽  
Heat Losses ◽  
Infrared Temperature Measurement ◽  
The Impact ◽  
Wall System

This paper presents a case study on how to improve the energy efficiency of an institutional building of significant heritage value through retrofitting the external wall system. This building is located in Upper Silesia, Poland. Due to the architectural value of the facade, thermal insulation had to be applied from the inside. As part of this publication, basing on the measurements and simulations, the authors present the results involving the improvement of energy efficiency of the insulated wall. On this basis, they also demonstrate the impact of insulation from the inside on the change of humidity inside the room. The tests were carried out both quantitatively by means of heat flux measurement and qualitatively by means of infrared temperature measurement. The research was supported by numerical modeling. The obtained results indicate that the thermal insulation used in the form of mineral insulation boards applied from the inside improves thermal insulation of the wall. Thus, heat losses through the examined envelope were limited. Computer simulations indicated that no condensation may occur under the condition considered.

scholarly journals The challenges of an in situ validation of a nonequilibrium model of soil heat and moisture dynamics during fires

2021 ◽  
Vol 25 (2) ◽  
pp. 685-709 ◽  
Author(s):  
William J. Massman
Keyword(s):  
Boundary Condition ◽  
Heat Flux ◽  
Soil Surface ◽  
Vapor Source ◽  
Nonequilibrium Model ◽  
Apparent Thermal Conductivity ◽  
The Impact ◽  
Energy Balance Approach ◽  
Upper Boundary

Abstract. With the increasing frequency and severity of fire, there is an increasing desire to better manage fuels and minimize, as much as possible, the impacts of fire on soils and other natural resources. Piling and/or burning slash is one method of managing fuels and reducing the risk and consequences of wildfire, but the repercussions to the soil, although very localized, can be significant and often irreversible. In an effort to provide a tool to better understand the impact of fire on soils, this study outlines the improvements to and the in situ validation of a nonequilibrium model for simulating the coupled interactions and transport of heat, moisture and water vapor during fires. Improvements to the model eliminate the following two important (but heretofore universally overlooked) inconsistencies: one that describes the relationship between evaporation and condensation in the parameterization of the nonequilibrium vapor source term, and the other that is the incorrect use of the apparent thermal conductivity in the soil heat flow equation. The first of these made a small enhancement in the stability and performance of the model. The second is an important improvement in the physics underpinning the model but had less of an impact on the model's performance and stability than the first. This study also (a) develops a general heating function that describes the energy input to the soil surface by the fire and (b) discusses the complexities and difficulties of formulating the upper boundary condition from a surface energy balance approach. The model validation uses (in situ temperature, soil moisture and heat flux) data obtained in a 2004 experimental slash pile burn. Important temperature-dependent corrections to the instruments used for measuring soil heat flux and moisture are also discussed and assessed. Despite any possible ambiguities in the calibration of the sensors or the simplicity of the parameterization of the surface heating function, the difficulties and complexities of formulating the upper boundary condition and the obvious complexities of the dynamic response of the soil's temperature and heat flux, the model produced at least a very credible, if not surprisingly good, simulation of the observed data. This study then continues with a discussion and sensitivity analysis of some important feedbacks (some of which are well known and others that are more hypothetical) that are not included in the present (or any extant) model, but that undoubtedly are dynamically influencing the physical properties of the soil in situ during the fire and, thereby, modulating the behavior of the soil temperature and moisture. This paper concludes with a list of possible future observational and modeling studies and how they would advance the research and findings discussed here.

scholarly journals The challenges of an in situ validation of a non-equilibrium model of soil heat and moisture dynamics during fires

2020 ◽  
Author(s):  
William J. Massman
Keyword(s):  
Heat Flux ◽  
Equilibrium Model ◽  
Soil Heat Flux ◽  
Flow Equation ◽  
Vapor Source ◽  
Apparent Thermal Conductivity ◽  
And Performance ◽  
The Impact ◽  
Non Equilibrium

Abstract. With the increasing frequency and severity of fire there is an increasing desire to better manage fuels and minimize, as much as possible, the impacts of fire on soils and other natural resources. Piling and/or burning slash is one method of managing fuels and reducing the risk and consequences of wildfire, but the repercussions to the soil, although very localized, can be significant and often irreversible. In an effort to provide a tool to better understand the impact of fire on soils, this study outlines the improvements to and the in-situ validation of a non-equilibrium model for simulating the coupled interactions and transport of heat, moisture and water vapor during fires. Improvements to the model eliminate two important (but heretofore universally overlooked) inconsistencies: one that describes the relationship between evaporation and condensation in the parameterization of the non-equilibrium vapor source term and the other, is the incorrect use of the apparent thermal conductivity in the soil heat flow equation. The first of these enhanced the stability and performance of the model. The second is an important improvement in the model's physical realism, but had less of an impact on the model's performance and stability than the first. The model validation uses (in-situ temperature, soil moisture, and heat flux) data obtained in a 2004 experimental slash pile burn. Important temperature dependent corrections to the instruments used for measuring soil heat flux and moisture are also discussed and assessed. Despite any possible ambiguities in the calibration the sensors or the simplicity of the parameterization of the surface heating function, the difficulties and complexities of formulating the upper boundary condition, and the obvious complexities of the dynamic response of the soil's temperature and heat flux, the model produced at least a very credible, if not surprisingly good, simulation of the observed data. This study then continues with a discussion and sensitivity analysis of some important feedbacks (some of which are well known and others that are more hypothetical) that are not included in the present (or any extant) model, but undoubtedly are dynamically influencing the physical properties of the soil in-situ during the fire and thereby modulating the behavior of the soil temperature and moisture. This manuscript concludes with a list of possible future observational and modeling studies and how they would advance the research and findings discussed here.

scholarly journals SMOS Neural Network Soil Moisture Data Assimilation in a Land Surface Model and Atmospheric Impact

2019 ◽  
Vol 11 (11) ◽  
pp. 1334 ◽  
Author(s):  
Nemesio Rodríguez-Fernández ◽  
Patricia de Rosnay ◽  
Clement Albergel ◽  
Philippe Richaume ◽  
Filipe Aires ◽  
...  
Keyword(s):  
Neural Network ◽  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Positive Impact ◽  
Land Surface Model ◽  
Surface Model ◽  
Northern Asia ◽  
The Impact

The assimilation of Soil Moisture and Ocean Salinity (SMOS) data into the ECMWF (European Centre for Medium Range Weather Forecasts) H-TESSEL (Hydrology revised-Tiled ECMWF Scheme for Surface Exchanges over Land) model is presented. SMOS soil moisture (SM) estimates have been produced specifically by training a neural network with SMOS brightness temperatures as input and H-TESSEL model SM simulations as reference. This can help the assimilation of SMOS information in several ways: (1) the neural network soil moisture (NNSM) data have a similar climatology to the model, (2) no global bias is present with respect to the model even if local biases can remain. Experiments performing joint data assimilation (DA) of NNSM, 2 m air temperature and relative humidity or NNSM-only DA are discussed. The resulting SM was evaluated against a large number of in situ measurements of SM obtaining similar results to those of the model with no assimilation, even if significant differences were found from site to site. In addition, atmospheric forecasts initialized with H-TESSEL runs (without DA) or with the analysed SM were compared to measure of the impact of the satellite information. Although NNSM DA has an overall neutral impact in the forecast in the Tropics, a significant positive impact was found in other areas and periods, especially in regions with limited in situ information. The joint NNSM, T2m and RH2m DA improves the forecast for all the seasons in the Southern Hemisphere. The impact is mostly due to T2m and RH2m but SMOS NN DA alone also improves the forecast in July- September. In the Northern Hemisphere, the joint NNSM, T2m and RH2m DA improves the forecast in April–September, while NNSM alone has a significant positive effect in July–September. Furthermore, forecasting skill maps show that SMOS NNSM improves the forecast in North America and in Northern Asia for up to 72 h lead time.

scholarly journals The viability of neural network for modeling the impact of individual job satisfiers on work commitment in Indian manufacturing unit

2015 ◽  
Vol 16 (3) ◽  
pp. 326-333 ◽  
Author(s):  
Therasa Chandrasekar ◽  
Vijayabanu Chidambaram ◽  
Srinivasakumar Venkatraman ◽  
Vijayanand Venugopal
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Multilayer Perceptron ◽  
Simple Random Sampling ◽  
Primary Data ◽  
Work Commitment ◽  
Classification Rate ◽  
Data Set ◽  
The Impact

This paper provides an exposition about application of neural networks in the context of research to find out the contribution of individual job satisfiers towards work commitment. The purpose of the current study is to build a predictive model to estimate the normalized importance of individual job satisfiers towards work commitment of employees working in TVS Group, an Indian automobile company. The study is based on the tool developed by Spector (1985) and Sue Hayday (2003).The input variable of the study consists of nine independent individual job satisfiers which includes Pay, Promotion, Supervision, Benefits, Rewards, Operating procedures, Co-workers, Work-itself and Communication of Spector (1985) and dependent variable as work commitment of Sue Hayday (2003).The primary data has been collected using a closed-ended questionnaire based on simple random sampling approach. This study employed the multilayer Perceptron neural network model to envisage the level of job satisfiers towards work commitment. The result from the multilayer Perceptron neural network model displayed with four hidden layer with correct classification rate of 70% and 30% for training and testing data set. The normalized importance shows high value for coworkers, superior satisfaction and communication and which acts as most significant attributes of job satisfiers that predicts the overall work commitment of employees.

Research on behavior recognition based on feature fusion of automatic coder and recurrent neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8927-8935
Author(s):  
Bing Zheng ◽  
Dawei Yun ◽  
Yan Liang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Behavior Pattern ◽  
Rapid Development ◽  
Video Data ◽  
Support Vector ◽  
Behavior Recognition ◽  
Learning Methods ◽  
The Impact ◽  
Internet Of Things Technology

Under the impact of COVID-19, research on behavior recognition are highly needed. In this paper, we combine the algorithm of self-adaptive coder and recurrent neural network to realize the research of behavior pattern recognition. At present, most of the research of human behavior recognition is focused on the video data, which is based on the video number. At the same time, due to the complexity of video image data, it is easy to violate personal privacy. With the rapid development of Internet of things technology, it has attracted the attention of a large number of experts and scholars. Researchers have tried to use many machine learning methods, such as random forest, support vector machine and other shallow learning methods, which perform well in the laboratory environment, but there is still a long way to go from practical application. In this paper, a recursive neural network algorithm based on long and short term memory (LSTM) is proposed to realize the recognition of behavior patterns, so as to improve the accuracy of human activity behavior recognition.

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