scholarly journals Wind-Induced Pressure Prediction on Tall Buildings Using Generative Adversarial Imputation Network

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2515
Author(s):  
Bubryur Kim ◽  
N. Yuvaraj ◽  
K. R. Sri Preethaa ◽  
Gang Hu ◽  
Dong-Eun Lee
Keyword(s):  
Wind Tunnel ◽  
Mean Squared Error ◽  
Pressure Sensors ◽  
Tall Buildings ◽  
Wind Pressure ◽  
Data Imputation ◽  
K Nearest Neighbor ◽  
Time Intervals ◽  
Squared Error ◽  
Proposed Model

Wind tunnel testing techniques are the main research tools for evaluating the wind loadings of buildings. They are significant in designing structurally safe and comfortable buildings. The wind tunnel pressure measurement technique using pressure sensors is significant for assessing the cladding pressures of buildings. However, some pressure sensors usually fail and cause loss of data, which are difficult to restore. In the literature, numerous techniques are implemented for imputing the single instance data values and data imputation for multiple instantaneous time intervals with accurate predictions needs to be addressed. Thus, the data imputation capacity of machine learning models is used to predict the missing wind pressure data for tall buildings in this study. A generative adversarial imputation network (GAIN) is proposed to predict the pressure coefficients at various instantaneous time intervals on tall buildings. The proposed model is validated by comparing the performance of GAIN with that of the K-nearest neighbor and multiple imputations by chained equation models. The experimental results show that the GAIN model provides the best fit, achieving more accurate predictions with the minimum average variance and minimum average standard deviation. The average mean-squared error for all four sides of the building was the minimum (0.016), and the average R-squared error was the maximum (0.961). The proposed model can ensure the health and prolonged existence of a structure based on wind environment.

scholarly journals Recommender System for Term Deposit Likelihood Prediction Using Cross-Validated Neural Network

2020 ◽  
Author(s):  
Shawni Dutta ◽  
Samir Kumar Bandyopadhyay
Keyword(s):  
Neural Network ◽  
Nearest Neighbor ◽  
Mean Squared Error ◽  
Automated System ◽  
K Nearest Neighbor ◽  
Decision Tree Classifier ◽  
Squared Error ◽  
Proposed Model ◽  
Tree Classifier ◽  
Customer Perspective

For enhancing the maximized profit from bank as well as customer perspective, term deposit can accelerate finance fields. This paper focuses on likelihood of term deposit subscription taken by the customers. Bank campaign efforts and customer details are influential while considering possibilities of taking term deposit subscription. An automated system is provided in this paper that approaches towards prediction of term deposit investment possibilities in advance. Neural network(NN) along with stratified 10-fold cross-validation methodology is proposed as predictive model which is later compared with other benchmark classifiers such as k-Nearest Neighbor (k-NN), Decision tree classifier (DT), and Multi-layer perceptron classifier (MLP). Experimental study concluded that proposed model provides significant prediction results over other baseline models with an accuracy of 88.32% and Mean Squared Error (MSE) of 0.1168.

scholarly journals Map-Reduce based Distance Weighted k-Nearest Neighbor Machine Learning Algorithm for Big Data Applications

2021 ◽  
Author(s):  
Gothai E ◽  
Usha Moorthy ◽  
Sathishkumar V E ◽  
Abeer Ali Alnuaim ◽  
Wesam Atef Hatamleh ◽  
...  
Keyword(s):  
Big Data ◽  
Nearest Neighbor ◽  
Mean Squared Error ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
K Nearest Neighbor ◽  
Squared Error ◽  
Big Data Applications ◽  
Distance Weighted ◽  
Classification Tasks

Abstract With the evolution of Internet standards and advancements in various Internet and mobile technologies, especially since web 4.0, more and more web and mobile applications emerge such as e-commerce, social networks, online gaming applications and Internet of Things based applications. Due to the deployment and concurrent access of these applications on the Internet and mobile devices, the amount of data and the kind of data generated increases exponentially and the new era of Big Data has come into existence. Presently available data structures and data analyzing algorithms are not capable to handle such Big Data. Hence, there is a need for scalable, flexible, parallel and intelligent data analyzing algorithms to handle and analyze the complex massive data. In this article, we have proposed a novel distributed supervised machine learning algorithm based on the MapReduce programming model and Distance Weighted k-Nearest Neighbor algorithm called MR-DWkNN to process and analyze the Big Data in the Hadoop cluster environment. The proposed distributed algorithm is based on supervised learning performs both regression tasks as well as classification tasks on large-volume of Big Data applications. Three performance metrics, such as Root Mean Squared Error (RMSE), Determination coefficient (R2) for regression task, and Accuracy for classification tasks are utilized for the performance measure of the proposed MR-DWkNN algorithm. The extensive experimental results shows that there is an average increase of 3–4.5% prediction and classification performances as compared to standard distributed k-NN algorithm and a considerable decrease of Root Mean Squared Error (RMSE) with good parallelism characteristics of scalability and speedup thus, proves its effectiveness in Big Data predictive and classification applications.

scholarly journals Deep Learned Quantization-Based Codec for 3D Airborne LiDAR Point Cloud Images

2021 ◽  
Vol 8 ◽  
Author(s):  
A. Christoper Tamilmathi ◽  
P. L. Chithra
Keyword(s):  
Point Cloud ◽  
Gradient Descent ◽  
Mean Squared Error ◽  
Airborne Lidar ◽  
Stochastic Gradient Descent ◽  
Squared Error ◽  
Proposed Model ◽  
Optimization Function ◽  
Execution Speed

This paper introduces a novel deep learned quantization-based coding for 3D Airborne LiDAR (Light detection and ranging) point cloud (pcd) image (DLQCPCD). The raw pcd signals are sampled and transformed by applying the Nyquist signal sampling and Min-max signal transformation techniques, respectively for improving the efficiency of the training process. Then, the transformed signals are feed into the deep learned quantization module for compressing the data. To the best of our knowledge, this proposed DLQCPCD is the first deep learning-based model for 3D airborne LiDAR pcd compression. The functions of Mean Squared Error and Stochastic Gradient Descent optimization function enhance the quality of the decompressed image by 67.01 percent on average, compared to other functions. The model’s efficiency has been validated with established well-known compression techniques such as the 7-Zip, WinRAR, and tensor tucker decomposition algorithm on the three inconsistent airborne datasets. The experimental results show that the proposed model compresses every pcd image into constant 16 Number of Neurons of data and decompresses the image with approximately 160 dB of PSNR value, 174.46 s execution time with 0.6 s execution speed per instruction, and proved that it outperforms the other existing algorithms regarding space and time.

scholarly journals Deep Learning-Based Optimal Smart Shoes Sensor Selection for Energy Expenditure and Heart Rate Estimation

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7058
Author(s):  
Heesang Eom ◽  
Jongryun Roh ◽  
Yuli Sun Hariyani ◽  
Suwhan Baek ◽  
Sukho Lee ◽  
...  
Keyword(s):  
Heart Rate ◽  
Deep Learning ◽  
Energy Expenditure ◽  
Mean Squared Error ◽  
Pressure Sensors ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Proposed Model ◽  
Point Pressure

Wearable technologies are known to improve our quality of life. Among the various wearable devices, shoes are non-intrusive, lightweight, and can be used for outdoor activities. In this study, we estimated the energy consumption and heart rate in an environment (i.e., running on a treadmill) using smart shoes equipped with triaxial acceleration, triaxial gyroscope, and four-point pressure sensors. The proposed model uses the latest deep learning architecture which does not require any separate preprocessing. Moreover, it is possible to select the optimal sensor using a channel-wise attention mechanism to weigh the sensors depending on their contributions to the estimation of energy expenditure (EE) and heart rate (HR). The performance of the proposed model was evaluated using the root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). Moreover, the RMSE was 1.05 ± 0.15, MAE 0.83 ± 0.12 and R2 0.922 ± 0.005 in EE estimation. On the other hand, and RMSE was 7.87 ± 1.12, MAE 6.21 ± 0.86, and R2 0.897 ± 0.017 in HR estimation. In both estimations, the most effective sensor was the z axis of the accelerometer and gyroscope sensors. Through these results, it is demonstrated that the proposed model could contribute to the improvement of the performance of both EE and HR estimations by effectively selecting the optimal sensors during the active movements of participants.

scholarly journals Forecasting Brazilian Ethanol Spot Prices Using LSTM

Energies ◽  
2021 ◽  
Vol 14 (23) ◽  
pp. 7987
Author(s):  
Gustavo Carvalho Santos ◽  
Flavio Barboza ◽  
Antônio Cláudio Paschoarelli Veiga ◽  
Mateus Ferreira Silva
Keyword(s):  
Short Term Memory ◽  
Mean Squared Error ◽  
Percentage Error ◽  
Spot Prices ◽  
Statistical Measures ◽  
Squared Error ◽  
Proposed Model ◽  
Price Trends ◽  
Long Short Term Memory ◽  
Crisis Scenarios

Ethanol is one of the most used fuels in Brazil, which is the second-largest producer of this biofuel in the world. The uncertainty of price direction in the future increases the risk for agents operating in this market and can affect a dependent price chain, such as food and gasoline. This paper uses the architecture of recurrent neural networks—Long short-term memory (LSTM)—to predict Brazilian ethanol spot prices for three horizon-times (12, 6 and 3 months ahead). The proposed model is compared to three benchmark algorithms: Random Forest, SVM Linear and RBF. We evaluate statistical measures such as MSE (Mean Squared Error), MAPE (Mean Absolute Percentage Error), and accuracy to assess the algorithm robustness. Our findings suggest LSTM outperforms the other techniques in regression, considering both MSE and MAPE but SVM Linear is better to identify price trends. Concerning predictions per se, all errors increase during the pandemic period, reinforcing the challenge to identify patterns in crisis scenarios.

scholarly journals A Hybrid Recommender System Based-on Link Prediction for Movie Baskets Analysis

2020 ◽  
Author(s):  
Mohammadsadegh Vahidi Farashah ◽  
Akbar Etebarian ◽  
Reza Azmi ◽  
Reza Ebrahimzadeh Dastjerdi
Keyword(s):  
Decision Tree ◽  
Link Prediction ◽  
Mean Squared Error ◽  
Randomized Algorithm ◽  
Similarity Criteria ◽  
Squared Error ◽  
Proposed Model ◽  
The Mean ◽  
Hybrid Recommender System ◽  
Hybrid Recommender

Abstract The proposed method in this paper consists of three steps: initial clustering of all users and assigning new user to appropriate clusters, assigning appropriate weights to users' characteristics, and identifying new user’s adjacent users using hybrid similarity criteria and adjacency matrix of adjacent users’ rating to the movie services and calculating new user’s rating to each movie considering adjacent users’ rating and the similarity level of each adjacent user to the new user. The results show that the mean squared error of the proposed model has decreased respectively 8.59%, 8.67%, 8.45% and 8.15% compared to the basic models such as Naive Bayes, multi-attribute decision tree and randomized algorithm. Also, MAE of the proposed method decreased by 4.5% compared to SVD and approximately 4.4% compared to ApproSVD and RMSE of the proposed method decreased by 6.05% compared to SVD and approximately 6.02% compared to ApproSVD.

scholarly journals Comparison of Semi-Empirical Single Point Wall Pressure Spectrum Models with Experimental Data

Fluids ◽  
2021 ◽  
Vol 6 (8) ◽  
pp. 270
Author(s):  
Nicholas Thomson ◽  
Joana Rocha
Keyword(s):  
Wind Tunnel ◽  
Spectral Density ◽  
Power Spectral Density ◽  
Mean Squared Error ◽  
Single Point ◽  
Flight Test ◽  
Wall Pressure ◽  
Squared Error ◽  
Power Spectral ◽  
Semi Empirical

This study presents an evaluation of semi-empirical single-point wall pressure spectrum models by comparing model predictions with wind tunnel and flight test data. The mean squared error was used to compare the power spectral density of the wall pressure fluctuations predicted by semi-empirical models with a large amount of experimental data. Results show that the models proposed by Goody and Smol’yakov have the lowest mean squared error when predicting the power spectral density for wind tunnel experiments and the Rackl and Weston model has the lowest mean squared error when predicting the power spectral density for flight test data. In addition, although current studies of the power spectra obtained in the wind tunnel are similar, they are not generally an accurate representation of flight test experiments.

Wind Tunnel Test of Tall Buildings with Irregularities of Elevation

2014 ◽  
Vol 578-579 ◽  
pp. 1208-1211
Author(s):  
Jian Guo Zhang ◽  
Hui Min Zhuang
Keyword(s):  
Wind Tunnel ◽  
Wind Tunnel Test ◽  
Tall Buildings ◽  
Wind Pressure ◽  
High Rise ◽  
Pressure Distributions ◽  
Building Models ◽  
The Mean ◽  
Wind Pressures ◽  
Fluctuating Wind

In this paper, 2 high-rise building models with ladder and cascade irregularities of elevation were tested in a wind tunnel respectively to measure the mean and fluctuating wind pressure distributions. The mean and RMS (root-mean-square) coefficients of the drag, lift and torsion moment on the measuring layer were obtained from the wind pressures. In the direction which the buildings were positive in the wind, the variation of these above mentioned coefficients with height and the power spectrum densities of the fluctuating wind loads on sudden changed positions were analyzed in detail. Compared with the elevation regular tall building, the wind load characteristics of irregular ones were more complicated.

scholarly journals Prediction of Hotspots in Riau Province, Indonesia Using the Autoregressive Integrated Moving Average (ARIMA) Model

2020 ◽  
pp. 101-110
Author(s):  
Evizal Abdul Kadir ◽  
Nur Ezzati Dayana ◽  
Sri Listia Rosa ◽  
Mahmod Othman ◽  
Rizauddin Saian
Keyword(s):  
Time Series ◽  
Forest Fires ◽  
Mean Squared Error ◽  
Moving Average ◽  
Arima Model ◽  
Forest Damage ◽  
Autoregressive Integrated Moving Average ◽  
Squared Error ◽  
Proposed Model ◽  
Forecasting System

Various forms of disasters occur worldwide, one of which is fire. Indonesia has been suffering from frequent land and forest fires. These events are not a new phenomenon and seem to be an annual tradition, especially in the dry season. This nation was most affected by an excessively disastrous forest fire in 2015. The misfortunes suffered were massive and resulted in land and forest damage that may have great economic and environmental costs. One solution to reduce the impacts of such events is to predict the emergence of hotspots. Therefore, in this work, a modeling method using time series produced by the Box-Jenkins' Autoregressive Integrated Moving Average (ARIMA) model was used to predict the appearance of hotspots. Since the forecasting system does not expect any detailed form to be predicted in terms of the time series of historical data, the data demonstrated in the proposed model were different from data from other models used for prediction. The study was conducted based on monthly hotspot occurrence data from January 2014 through June 2019 in Riau Province, Indonesia. The data were downloaded from the collection of the "LAPAN-MODIS-Catalog". Based on the results shown, the Autoregressive Integrated Moving Average (ARIMA) model (2,1,2) produced good predictions based on its lowest value of Mean Squared Error (MSE), 9540.088. Moreover, the proposed model has produced highly accurate forecasts of hotspots for time periods of up to five months using the fitting model of ARIMA (2,1,2), and the values forecasted for 5 months ahead were 25, 31, 26, 30 and 27.

scholarly journals Soft Computing Techniques for Weather Change Predictions in Delhi

2019 ◽  
Vol 8 (4) ◽  
pp. 793-800
Keyword(s):  
Soft Computing ◽  
Mean Squared Error ◽  
Weather Forecasting ◽  
Weather Prediction ◽  
Research Work ◽  
Absolute Error ◽  
Support Vector ◽  
Squared Error ◽  
Proposed Model ◽  
Soft Computing Techniques

Weather forecasting and warning is the application of science and technology to predict the state of the weather for a future time of a given location. The emergence of adverse effects of weather has endangered the life of general public in previous years. The unpredicted flood and super cyclone in many places have created havoc. The government and private agencies are working on its behaviours but still it is challenging and incomplete. But, the application of soft computing techniques in weather prediction has made a significant perfomance now a days. This research work presents the comparative study of soft computing techniques like MultiLayer Perceptron(MLP), Support Vector Machine(SVM) and J48 Decision Tree for forecasting the weather of Delhi with ten years data comprising of temperature, dew, humidity, air pressure, wind speed and visibility. This paper tries to describe the comparison among above models using four different error values like Relative Absolute Error(RAE), Mean Absolute Error(MAE), Root Mean Squared Error(RMSE) and Root Relative Squared Error(R2 ) with a proposed model by defining new algorithm. Further the performance can be enhanced if textmining will be applied in this proposed model.

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