scholarly journals Behavior Modeling for a Beacon-Based Indoor Location System

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4839
Author(s):  
Aritz Bilbao-Jayo ◽  
Aitor Almeida ◽  
Ilaria Sergi ◽  
Teodoro Montanaro ◽  
Luca Fasano ◽  
...  
Keyword(s):  
Behavior Modeling ◽  
Percentage Error ◽  
Location Prediction ◽  
Indoor Environments ◽  
Prediction System ◽  
Indoor Location ◽  
The Mean ◽  
Locating System ◽  
Monitoring Devices ◽  
Do So

In this work we performed a comparison between two different approaches to track a person in indoor environments using a locating system based on BLE technology with a smartphone and a smartwatch as monitoring devices. To do so, we provide the system architecture we designed and describe how the different elements of the proposed system interact with each other. Moreover, we have evaluated the system’s performance by computing the mean percentage error in the detection of the indoor position. Finally, we present a novel location prediction system based on neural embeddings, and a soft-attention mechanism, which is able to predict user’s next location with 67% accuracy.

Bayesian Nonparametric Approach to Average Annual Daily Traffic Estimation for Bridges

2021 ◽  
pp. 036119812199459
Author(s):  
Grace Ashley ◽  
Nii Attoh-Okine
Keyword(s):  
Performance Indicators ◽  
Federal Funding ◽  
Percentage Error ◽  
Bayesian Nonparametric ◽  
Transportation Services ◽  
Nonparametric Approach ◽  
Nonparametric Models ◽  
Bayesian Nonparametric Models ◽  
Average Annual Daily Traffic ◽  
The Mean

Every year, the U.S. government provides several billions of dollars in the form of federal funding for transportation services in the U.S.A. Decision making with regard to the use of these funds largely depends on performance indicators like average annual daily traffic (AADT). In this paper, Bayesian nonparametric models are developed through machine learning for the estimation of AADT on bridges. The effect of hyperparameter choice on the accuracy of estimations produced by Bayesian nonparametric models is also assessed. The predictions produced using the Bayesian nonparametric approach are then compared with predictions from a popular Frequentist approach for the selected bridges. Evaluation metrics like the mean absolute percentage error are subsequently employed in model evaluation. Based on the results, the best methods for AADT forecasting for the selected bridges are recommended.

scholarly journals Distance variable improvement of time-series big data stream evaluation

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Ari Wibisono ◽  
Petrus Mursanto ◽  
Jihan Adibah ◽  
Wendy D. W. T. Bayu ◽  
May Iffah Rizki ◽  
...  
Keyword(s):  
Time Series ◽  
Standard Deviation ◽  
Standard Method ◽  
Time Series Data ◽  
Series Data ◽  
Percentage Error ◽  
Traffic Demand ◽  
Incremental Model ◽  
Chernoff Bound ◽  
The Mean

Abstract Real-time information mining of a big dataset consisting of time series data is a very challenging task. For this purpose, we propose using the mean distance and the standard deviation to enhance the accuracy of the existing fast incremental model tree with the drift detection (FIMT-DD) algorithm. The standard FIMT-DD algorithm uses the Hoeffding bound as its splitting criterion. We propose the further use of the mean distance and standard deviation, which are used to split a tree more accurately than the standard method. We verify our proposed method using the large Traffic Demand Dataset, which consists of 4,000,000 instances; Tennet’s big wind power plant dataset, which consists of 435,268 instances; and a road weather dataset, which consists of 30,000,000 instances. The results show that our proposed FIMT-DD algorithm improves the accuracy compared to the standard method and Chernoff bound approach. The measured errors demonstrate that our approach results in a lower Mean Absolute Percentage Error (MAPE) in every stage of learning by approximately 2.49% compared with the Chernoff Bound method and 19.65% compared with the standard method.

scholarly journals Agrometeorological models for estimating sweet cassava yield

2018 ◽  
Vol 48 (1) ◽  
pp. 43-51
Author(s):  
Victor Brunini Moreto ◽  
Lucas Eduardo de Oliveira Aparecido ◽  
Glauco de Souza Rolim ◽  
José Reinaldo da Silva Cabral de Moraes
Keyword(s):  
Great Influence ◽  
Soil Water Storage ◽  
Percentage Error ◽  
P Value ◽  
Climate Conditions ◽  
Paulo State ◽  
State Water ◽  
The Mean ◽  
Sweet Cassava ◽  
Main Factor

ABSTRACT Brazil is the fourth largest producer of cassava in the world, with climate conditions being the main factor regulating its production. This study aimed to develop agrometeorological models to estimate the sweet cassava yield for the São Paulo state, as well as to identify which climatic variables have more influence on yield. The models were built with multiple linear regression and classified by the following statistical indexes: lower mean absolute percentage error, higher adjusted determination coefficient and significance (p-value < 0.05). It was observed that the mean air temperature has a great influence on the sweet cassava yield during the whole cycle for all regions in the state. Water deficit and soil water storage were the most influential variables at the beginning and final stages. The models accuracy ranged in 3.11 %, 6.40 %, 6.77 % and 7.15 %, respectively for Registro, Mogi Mirim, Assis and Jaboticabal.

scholarly journals Uncertainty visualization of gaze estimation to support operator-controlled calibration

2018 ◽  
Vol 10 (5) ◽  
Author(s):  
Almoctar Hassoumi ◽  
Vsevolod Peysakhovich ◽  
Christophe Hurter
Keyword(s):  
Qualitative Evaluation ◽  
Point Of View ◽  
Angular Error ◽  
Qualitative Assessment ◽  
Gaze Estimation ◽  
Uncertainty Visualization ◽  
Estimation Uncertainty ◽  
The Mean ◽  
Gaze Position ◽  
Do So

      In this paper, we investigate how visualization assets can support the qualitative evaluation of gaze estimation uncertainty. Although eye tracking data are commonly available, little has been done to visually investigate the uncertainty of recorded gaze information. This paper tries to fill this gap by using innovative uncertainty computation and visualization. Given a gaze processing pipeline, we estimate the location of this gaze position in the world camera. To do so we developed our own gaze data processing which give us access to every stage of the data transformation and thus the uncertainty computation. To validate our gaze estimation pipeline, we designed an experiment with 12 participants and showed that the correction methods we proposed reduced the Mean Angular Error by about 1.32 cm, aggregating all 12 participants’ results. The Mean Angular Error is 0.25° (SD=0.15°) after correction of the estimated gaze. Next, to support the qualitative assessment of this data, we provide a map which codes the actual uncertainty in the user point of view. 

scholarly journals Novel Spatiotemporal Feature Extraction Parallel Deep Neural Network for Forecasting Confirmed Cases of Coronavirus Disease 2019

2020 ◽  
Author(s):  
Chiou-Jye Huang ◽  
Yamin Shen ◽  
Ping-Huan Kuo ◽  
Yung-Hsiang Chen
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Absolute Error ◽  
Mean Absolute Percentage Error ◽  
The Other ◽  
Percentage Error ◽  
Model Assessment ◽  
Absolute Percentage Error ◽  
Spatiotemporal Feature ◽  
The Mean

AbstractThe coronavirus disease 2019 pandemic continues as of March 26 and spread to Europe on approximately February 24. A report from April 29 revealed 1.26 million confirmed cases and 125 928 deaths in Europe. This study proposed a novel deep neural network framework, COVID-19Net, which parallelly combines a convolutional neural network (CNN) and bidirectional gated recurrent units (GRUs). Three European countries with severe outbreaks were studied—Germany, Italy, and Spain—to extract spatiotemporal feature and predict the number of confirmed cases. The prediction results acquired from COVID-19Net were compared to those obtained using a CNN, GRU, and CNN-GRU. The mean absolute error, mean absolute percentage error, and root mean square error, which are commonly used model assessment indices, were used to compare the accuracy of the models. The results verified that COVID-19Net was notably more accurate than the other models. The mean absolute percentage error generated by COVID-19Net was 1.447 for Germany, 1.801 for Italy, and 2.828 for Spain, which were considerably lower than those of the other models. This indicated that the proposed framework can accurately predict the accumulated number of confirmed cases in the three countries and serve as a crucial reference for devising public health strategies.

scholarly journals OPTIMIZATION OF REFLUX EXTRACTION FOR CAESALPINIA SAPPAN LINN. WOOD BY USING RESPONSE SURFACE METHODOLOGY

2020 ◽  
Vol 83 (1) ◽  
pp. 85-92
Author(s):  
Mohd Azahar Mohd Ariff ◽  
Muhammad Syafiq Abd Jalil ◽  
Noor ‘Aina Abdul Razak ◽  
Jefri Jaapar
Keyword(s):  
Particle Size ◽  
Response Surface Methodology ◽  
Drinking Water ◽  
Response Surface ◽  
Extraction Time ◽  
Percentage Error ◽  
Optimum Conditions ◽  
Caesalpinia Sappan ◽  
The Mean ◽  
Repeated Runs

Caesalpinia sappan linn. (CSL) is a plant which is also known as Sepang tree contains various medicinal values such as to treat diarrhea, skin rashes, syphilis, jaundice, drinking water for blood purifying, diabetes, and to improve skin complexion. The aim of this study is to obtain the most optimum condition in terms of the ratio of sample to solvent, particle size, and extraction time to get the highest amount of concentration of the CSL extract. In this study, the ranges of each parameters used were: ratio sample to solvent: 1.0:20, 1.5:20, 2.0:20, 2.5:20, 3.0:20, particle size: 1 mm, 500 um, 250 um, 125 um, 63 um, and extraction time: 1 hr, 2 hr, 3 hr, 4 hr, 5 hr. The concentration was analyzed using a UV-vis spectrophotometer. The optimum conditions were obtained by response surface methodology. From the design, 20 samples were run throughout this experiment. The optimized value from the RSM were 2.0:20 for ratio sample to solvent, 125 µm of particle size and 2.48 hours with the concentration of 37.1184 ppm. The accuracy of the predictive model was validated with 2 repeated runs and the mean percentage error was less than 3%. This confirmed the model’s capability for optimizing the conditions for the reflux extraction of CSL’s wood.

scholarly journals Kajian Adaptive Neuro-Fuzzy Inference System (ANFIS) Dalam Memprediksi Penerimaan Mahasiswa Baru Pada Universitas Buana Perjuangan Karawang

2021 ◽  
Vol 6 (1) ◽  
pp. 44-54
Author(s):  
Tatang Rohana Cucu
Keyword(s):  
Error Rate ◽  
Fuzzy Inference ◽  
Percentage Error ◽  
Hybrid Technique ◽  
Mean Absolute Deviation ◽  
Absolute Deviation ◽  
Inference System ◽  
Neuro Fuzzy ◽  
New Students ◽  
The Mean

Abstract - The process of admitting new students is an annual routine activity that occurs in a university. This activity is the starting point of the process of searching for prospective new students who meet the criteria expected by the college. One of the colleges that holds new student admissions every year is Buana Perjuangan University, Karawang. There have been several studies that have been conducted on predictions of new students by other researchers, but the results have not been very satisfying, especially problems with the level of accuracy and error. Research on ANFIS studies to predict new students as a solution to the problem of accuracy. This study uses two ANFIS models, namely Backpropagation and Hybrid techniques. The application of the Adaptive Neuro-Fuzzy Inference System (ANFIS) model in the predictions of new students at Buana Perjuangan University, Karawang was successful. Based on the results of training, the Backpropagation technique has an error rate of 0.0394 and the Hybrid technique has an error rate of 0.0662. Based on the predictive accuracy value that has been done, the Backpropagation technique has an accuracy of 4.8 for the value of Mean Absolute Deviation (MAD) and 0.156364623 for the value of Mean Absolute Percentage Error (MAPE). Meanwhile, based on the Mean Absolute Deviation (MAD) value, the Backpropagation technique has a value of 0.5 and 0.09516671 for the Mean Absolute Percentage Error (MAPE) value. So it can be concluded that the Hybrid technique has a better level of accuracy than the Backpropation technique in predicting the number of new students at the University of Buana Perjuangan Karawang.   Keywords: ANFIS, Backpropagation, Hybrid, Prediction

scholarly journals Indoor path visualization method based on the spatial characteristics of indoor environment

2019 ◽  
Vol 1 ◽  
pp. 1-2
Author(s):  
Jiafeng Shi ◽  
Jie Shen ◽  
Zdeněk Stachoň ◽  
Yawei Chen
Keyword(s):  
Indoor Environment ◽  
Design Method ◽  
Three Dimensional ◽  
Indoor Environments ◽  
Route Guidance ◽  
Visualization Method ◽  
Continuous Space ◽  
Indoor Location ◽  
Indoor Space ◽  
Map Design

<p><strong>Abstract.</strong> With the increasing number of large buildings and more frequent indoor activities, indoor location-based service has expanded. Due to the complicated internal passages of large public buildings and the three-dimensional interlacing, it is difficult for users to quickly reach the destination, the demand of indoor paths visualization increases. Isikdag (2013), Zhang Shaoping (2017), Huang Kejia (2018) provided navigation services for users based on path planning algorithm. In terms of indoor path visualization, Nossum (2011) proposed a “Tubes” map design method, which superimposed the channel information of different floors on the same plane by simplifying the indoor corridor and the room. Lorenz et al (2013) focused on map perspective (2D/3D) and landmarks, developed and investigated cartographic methods for effective route guidance in indoor environments. Holscher et al (2007) emphasized using the landmark objects at the important decision points of the route in indoor map design. The existing studies mainly focused on two-dimensional plane to visualize the indoor path, lacking the analysis of three-dimensional connectivity in indoor space, which makes the intuitiveness and interactivity of path visualization greatly compromised. Therefore, it is difficult to satisfy the wayfinding requirements of the indoor multi-layer continuous space. In order to solve this problem, this paper aims to study the characteristics of the indoor environment and propose a path visualization method. The following questions are addressed in this study: 1) What are the key characteristics of the indoor environment compared to the outdoor space? 2) How to visualize the indoor paths to satisfy the users’ wayfinding needs?</p>

scholarly journals Validation of a Mexican food photograph album as a tool to visually estimate food amounts in adolescents

2012 ◽  
Vol 109 (5) ◽  
pp. 944-952 ◽  
Author(s):  
M. Fernanda Bernal-Orozco ◽  
Barbara Vizmanos-Lamotte ◽  
Norma P. Rodríguez-Rocha ◽  
Gabriela Macedo-Ojeda ◽  
María Orozco-Valerio ◽  
...  
Keyword(s):  
High Schools ◽  
Error Estimation ◽  
Error Rate ◽  
Lower Percentage ◽  
Percentage Error ◽  
Public High Schools ◽  
Estimation Errors ◽  
Portion Sizes ◽  
The Mean ◽  
Almost All

The aim of the present study was to validate a food photograph album (FPA) as a tool to visually estimate food amounts, and to compare this estimation with that attained through the use of measuring cups (MC) and food models (FM). We tested 163 foods over fifteen sessions (thirty subjects/session; 10–12 foods presented in two portion sizes, 20–24 plates/session). In each session, subjects estimated food amounts with the assistance of FPA, MC and FM. We compared (by portion and method) the mean estimated weight and the mean real weight. We also compared the percentage error estimation for each portion, and the mean food percentage error estimation between methods. In addition, we determined the percentage error estimation of each method. We included 463 adolescents from three public high schools (mean age 17·1 (sd1·2) years, 61·8 % females). All foods were assessed using FPA, 53·4 % of foods were assessed using MC, and FM was used for 18·4 % of foods. The mean estimated weight with all methods was statistically different compared with the mean real weight for almost all foods. However, a lower percentage error estimation was observed using FPA (2·3v. 56·9 % for MC and 325 % for FM,P< 0·001). Also, when analysing error rate ranges between methods, there were more observations (P< 0·001) with estimation errors higher than 40 % with the MC (56·1 %), than with the FPA (27·5 %) and FM (44·9 %). In conclusion, although differences between estimated and real weight were statistically significant for almost all foods, comparisons between methods showed FPA to be the most accurate tool for estimating food amounts.

scholarly journals LINK OF MOMENTS BEFORE AND AFTER TRANSFORMATIONS, WITH AN APPLICATION TO RESAMPLING FROM FAT-TAILED DISTRIBUTIONS

2018 ◽  
Vol 35 (03) ◽  
pp. 630-652 ◽  
Author(s):  
Karim M. Abadir ◽  
Adriana Cornea-Madeira
Keyword(s):  
Finite Number ◽  
Confidence Intervals ◽  
Characteristic Functions ◽  
Infinite Variance ◽  
Fundamental Relation ◽  
Bootstrap Confidence Intervals ◽  
Finite Samples ◽  
Before And After ◽  
The Mean ◽  
Do So

Let x be a transformation of y, whose distribution is unknown. We derive an expansion formulating the expectations of x in terms of the expectations of y. Apart from the intrinsic interest in such a fundamental relation, our results can be applied to calculating E(x) by the low-order moments of a transformation which can be chosen to give a good approximation for E(x). To do so, we generalize the approach of bounding the terms in expansions of characteristic functions, and use our result to derive an explicit and accurate bound for the remainder when a finite number of terms is taken. We illustrate one of the implications of our method by providing accurate naive bootstrap confidence intervals for the mean of any fat-tailed distribution with an infinite variance, in which case currently available bootstrap methods are asymptotically invalid or unreliable in finite samples.

Sign in / Sign up

Export Citation Format

Share Document