scholarly journals A DNN-Based UVI Calculation Method Using Representative Color Information of Sun Object Images

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7766
Author(s):  
Deog-Hyeon Ga ◽  
Seung-Taek Oh ◽  
Jae-Hyun Lim
Keyword(s):  
Uv Radiation ◽  
Calculation Method ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
The Sun ◽  
Atmospheric Conditions ◽  
Color Information ◽  
The Cost ◽  
Deep Learning Model ◽  
Ultraviolet Index

As outdoor activities are necessary for maintaining our health, research interest in environmental conditions such as the weather, atmosphere, and ultraviolet (UV) radiation is increasing. In particular, UV radiation, which can benefit or harm the human body depending on the degree of exposure, is recognized as an essential environmental factor that needs to be identified. However, unlike the weather and atmospheric conditions, which can be identified to some extent by the naked eye, UV radiation corresponds to wavelength bands that humans cannot recognize; hence, the intensity of UV radiation cannot be measured. Recently, although devices and sensors that can measure UV radiation have been launched, it is very difficult for ordinary users to acquire ambient UV radiation information directly because of the cost and inconvenience caused by operating separate devices. Herein, a deep neural network (DNN)-based ultraviolet index (UVI) calculation method is proposed using representative color information of sun object images. First, Mask-region-based convolutional neural networks (R-CNN) are applied to sky images to extract sun object regions and then detect the representative color of the sun object regions. Then, a deep learning model is constructed to calculate the UVI by inputting RGB color values, which are representative colors detected later along with the altitude angle and azimuth of the sun at that time. After selecting each day of spring and autumn, the performance of the proposed method was tested, and it was confirmed that accurate UVI could be calculated within a range of mean absolute error of 0.3.

scholarly journals AJUSTE DA EQUAÇÃO DE HAZEN-WILLIANS PARA DETERMINAÇÃO DA PERDA DE CARGA CONTÍNUA EM TUBULAÇÕES DE PVC

Irriga ◽  
2019 ◽  
Vol 1 (1) ◽  
pp. 94-100
Author(s):  
EUGENIO PACELI MIRANDA ◽  
TATIANA BELO DE SOUSA CUSTODIO ◽  
FRANCISCO UCHOA de LIMA ◽  
TAIANE ALMEIDA Pereira ◽  
ANDRE LUIZ RIBEIRO BICUDO
Keyword(s):  
Mean Absolute Error ◽  
Absolute Error ◽  
Head Loss ◽  
Fundamental Parameter ◽  
Pumping System ◽  
Pvc Pipe ◽  
The Cost ◽  
E Mail ◽  
Selection Of

AJUSTE DA EQUAÇÃO DE HAZEN-WILLIANS PARA DETERMINAÇÃO DA PERDA DE CARGA CONTÍNUA EM TUBULAÇÕES DE PVC     EUGENIO PACELI MIRANDA1; TATIANA BELO DE SOUSA CUSTODIO2; FRANCISCO UCHOA DE LIMA3; TAIANE ALMENDA PEREIRA4 E ANDRE LUIZ RIBEIRO BICUDO5   1Professor, Doutor IFCE, Campus Iguatu/CE, Rua Plácido Almino Uchoa, 60, Iguatu/Ceará. CEP: 63.500-000, Brasil, email:[email protected]; 2 Tecnólogo em Irrigação e Drenagem, IFCE, Campus Iguatu, Rodovia Iguatu-Várzea Alegre km 05, CEP: 63.500-000, Brasil, email: [email protected]; 3Graduando em Tecnologia em Irrigação e Drenagem, IFCE, Campus Iguatu, Rodovia Iguatu-Várzea Alegre km 05, CEP: 63.500-000, Brasil, e-mail: [email protected]; 4Graduandos em Tecnologia em Irrigação e Drenagem, IFCE, Campus Iguatu, Rodovia Iguatu-Várzea Alegre km 05, CEP: 63.500-000, Brasil, e-mail: [email protected]; 5 Prof. do Colégio Técnico Industrial "Prof. Isaac Portal Roldan", Unesp, Campus Bauru. Av. Eng. Luis Edmundo Carrijo Coube, 14-01, CEP: 17.033-360, Brasil, e-mail: [email protected]     1 RESUMO   A perda de carga é um parâmetro fundamental para o dimensionamento das tubulações, estando diretamente relacionada ao custo dessas tubulações, a seleção do sistema de bombeamento e o custo com o consumo de energia elétrica. Nesse estudo ajustou-se a equação de Hazen-Willians e comparou com a equação de Darcy-Weisbach. O desempenho da equação ajustada foi determinada pelo índice de concordância (c), Erro Padrão Estatístico (EPE) e o Erro Absoluto Médio (EAM). Os resultados mostram que todos os índices usados para verificar o desempenho da equação de Hazen-Willians ajustada melhoraram. A maior diferença entre os valores da perda de carga contínua entre o método de Darcy-Weisbach e a equação de Hazen-Willians ajustada ficou em torno de 0,08%, valor que pode ser considerado extremamente baixo.   Palavras-chave: Perda de carga, Darcy- Weisbach, diâmetro.     MIRANDA, E. P; CUSTODIO, T. B. S; LIMA, F. U; PEREIRA, T. A.; BICUDO, A. L. R. ADJUSTMENT OF THE HAZEN-WILLIANS EQUATION FOR DETERMINATION OF CONTINUOUS PRESSURE DROP IN PVC PIPE     2 ABSTRACT   The head loss is a fundamental parameter for the dimensioning of the pipes, being directly related to the cost of these pipes, the selection of the pumping system and the energy cost of electricity. That study fitted the Hazen-Williams equation and compared it with the Darcy-Weisbach equation. The performance of the adjusted equation was determined by the concordance index (c), Statistical Standard Error (EPE) and Mean Absolute Error (EAM). The results show that all gridded indices to verify the performance of the adjusted Hazen-Willians equation have improved. The largest difference between the values of continuous load loss between the Darcy-Weisbach method and the adjusted Hazen-Williams equation was about 0.08%, which can be considered extremely low   Keywords: Head loss, Darcy-Weisbach, diameter.

Contactless Core-Temperature Monitoring by Infrared Thermal Sensor using Mean Absolute Error Analysis

2020 ◽  
Vol 15 ◽  
Author(s):  
Fahad Layth Malallah ◽  
Baraa T. Shareef ◽  
Mustafah Ghanem Saeed ◽  
Khaled N. Yasen
Keyword(s):  
Body Temperature ◽  
Embedded System ◽  
Core Temperature ◽  
Integrated Circuit ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Thermal Sensor ◽  
Human Face ◽  
Body Contact ◽  
Arduino Microcontroller

Aims: Normally, the temperature increase of individuals leads to the possibility of getting a type of disease, which might be risky to other people such as coronavirus. Traditional techniques for tracking core-temperature require body contact either by oral, rectum, axillary, or tympanic, which are unfortunately considered intrusive in nature as well as causes of contagion. Therefore, sensing human core-temperature non-intrusively and remotely is the objective of this research. Background: Nowadays, increasing level of medical sectors is a necessary targets for the research operations, especially with the development of the integrated circuit, sensors and cameras that made the normal life easier. Methods: The solution is by proposing an embedded system consisting of the Arduino microcontroller, which is trained with a model of Mean Absolute Error (MAE) analysis for predicting Contactless Core-Temperature (CCT), which is the real body temperature. Results: The Arduino is connected to an Infrared-Thermal sensor named MLX90614 as input signal, and connected to the LCD to display the CCT. To evaluate the proposed system, experiments are conducted by participating 31-subject sensing contactless temperature from the three face sub-regions: forehead, nose, and cheek. Conclusion: Experimental results approved that CCT can be measured remotely depending on the human face, in which the forehead region is better to be dependent, rather than nose and cheek regions for CCT measurement due to the smallest

Design of Electrolytic Strengthening Machine for Cutting Edge of Cutter

2016 ◽  
Vol 693 ◽  
pp. 1585-1590
Author(s):  
Yi Zhuo Guo ◽  
Xian Guo Yan ◽  
Shu Juan Li ◽  
Hong Guo
Keyword(s):  
Service Life ◽  
Calculation Method ◽  
High Speed ◽  
High Speed Steel ◽  
Cutting Edge ◽  
The Cost ◽  
Electric Quantity

Many studies have proved the service life of cutter can be prolonged by electrolytic strengthening. Based on the theory of electrolytic strengthening technology, this paper introduced and developed prototype equipment for strengthening cutting edge of rotary cutter and put forward a calculation method of total electric quantity consumption during the electrolysis suitable for microcontroller. The M8 high-speed steel tap is taken as a strengthening example. After finished the strengthening process that it clearly see the results of the surface of tap was obviously polished by observing the micrograph. This equipment improves the reliability of electrolytic strengthening and the cost is relatively cheap.

scholarly journals Towards Tracking of Deep Brain Stimulation Electrodes Using an Integrated Magnetometer

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2670
Author(s):  
Thomas Quirin ◽  
Corentin Féry ◽  
Dorian Vogel ◽  
Céline Vergne ◽  
Mathieu Sarracanie ◽  
...  
Keyword(s):  
Deep Brain Stimulation ◽  
Brain Stimulation ◽  
Mean Absolute Error ◽  
Tracking System ◽  
Three Dimensional ◽  
Absolute Error ◽  
Magnetic Tracking ◽  
Magnetic Camera ◽  
Magnetic Resonance Imaging Mri ◽  
Deep Brain

This paper presents a tracking system using magnetometers, possibly integrable in a deep brain stimulation (DBS) electrode. DBS is a treatment for movement disorders where the position of the implant is of prime importance. Positioning challenges during the surgery could be addressed thanks to a magnetic tracking. The system proposed in this paper, complementary to existing procedures, has been designed to bridge preoperative clinical imaging with DBS surgery, allowing the surgeon to increase his/her control on the implantation trajectory. Here the magnetic source required for tracking consists of three coils, and is experimentally mapped. This mapping has been performed with an in-house three-dimensional magnetic camera. The system demonstrates how magnetometers integrated directly at the tip of a DBS electrode, might improve treatment by monitoring the position during and after the surgery. The three-dimensional operation without line of sight has been demonstrated using a reference obtained with magnetic resonance imaging (MRI) of a simplified brain model. We observed experimentally a mean absolute error of 1.35 mm and an Euclidean error of 3.07 mm. Several areas of improvement to target errors below 1 mm are also discussed.

scholarly journals A Review of Deep Learning-Based Contactless Heart Rate Measurement Methods

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3719
Author(s):  
Aoxin Ni ◽  
Arian Azarang ◽  
Nasser Kehtarnavaz
Keyword(s):  
Heart Rate ◽  
Deep Learning ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Video Camera ◽  
Measurement Methods ◽  
Rate Measurement ◽  
Learning Methods ◽  
The Public ◽  
Heart Rate Measurement

The interest in contactless or remote heart rate measurement has been steadily growing in healthcare and sports applications. Contactless methods involve the utilization of a video camera and image processing algorithms. Recently, deep learning methods have been used to improve the performance of conventional contactless methods for heart rate measurement. After providing a review of the related literature, a comparison of the deep learning methods whose codes are publicly available is conducted in this paper. The public domain UBFC dataset is used to compare the performance of these deep learning methods for heart rate measurement. The results obtained show that the deep learning method PhysNet generates the best heart rate measurement outcome among these methods, with a mean absolute error value of 2.57 beats per minute and a mean square error value of 7.56 beats per minute.

scholarly journals Non-invasive cuff-less blood pressure estimation using a hybrid deep learning model

2021 ◽  
Vol 53 (2) ◽  
Author(s):  
Sen Yang ◽  
Yaping Zhang ◽  
Siu-Yeung Cho ◽  
Ricardo Correia ◽  
Stephen P. Morgan
Keyword(s):  
Machine Learning ◽  
Blood Pressure ◽  
Deep Learning ◽  
Absolute Error ◽  
Learning Model ◽  
Physiological Measurement ◽  
British Hypertension Society ◽  
Non Invasive ◽  
Blood Pressure Estimation ◽  
Deep Learning Model

AbstractConventional blood pressure (BP) measurement methods have different drawbacks such as being invasive, cuff-based or requiring manual operations. There is significant interest in the development of non-invasive, cuff-less and continual BP measurement based on physiological measurement. However, in these methods, extracting features from signals is challenging in the presence of noise or signal distortion. When using machine learning, errors in feature extraction result in errors in BP estimation, therefore, this study explores the use of raw signals as a direct input to a deep learning model. To enable comparison with the traditional machine learning models which use features from the photoplethysmogram and electrocardiogram, a hybrid deep learning model that utilises both raw signals and physical characteristics (age, height, weight and gender) is developed. This hybrid model performs best in terms of both diastolic BP (DBP) and systolic BP (SBP) with the mean absolute error being 3.23 ± 4.75 mmHg and 4.43 ± 6.09 mmHg respectively. DBP and SBP meet the Grade A and Grade B performance requirements of the British Hypertension Society respectively.

scholarly journals Detection and Severity Evaluation of Combined Rail Defects Using Deep Learning

Vibration ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 341-356
Author(s):  
Jessada Sresakoolchai ◽  
Sakdirat Kaewunruen
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Machine Learning Techniques ◽  
Rolling Stock ◽  
Raw Data ◽  
Learning Techniques ◽  
Combined Defects

Various techniques have been developed to detect railway defects. One of the popular techniques is machine learning. This unprecedented study applies deep learning, which is a branch of machine learning techniques, to detect and evaluate the severity of rail combined defects. The combined defects in the study are settlement and dipped joint. Features used to detect and evaluate the severity of combined defects are axle box accelerations simulated using a verified rolling stock dynamic behavior simulation called D-Track. A total of 1650 simulations are run to generate numerical data. Deep learning techniques used in the study are deep neural network (DNN), convolutional neural network (CNN), and recurrent neural network (RNN). Simulated data are used in two ways: simplified data and raw data. Simplified data are used to develop the DNN model, while raw data are used to develop the CNN and RNN model. For simplified data, features are extracted from raw data, which are the weight of rolling stock, the speed of rolling stock, and three peak and bottom accelerations from two wheels of rolling stock. In total, there are 14 features used as simplified data for developing the DNN model. For raw data, time-domain accelerations are used directly to develop the CNN and RNN models without processing and data extraction. Hyperparameter tuning is performed to ensure that the performance of each model is optimized. Grid search is used for performing hyperparameter tuning. To detect the combined defects, the study proposes two approaches. The first approach uses one model to detect settlement and dipped joint, and the second approach uses two models to detect settlement and dipped joint separately. The results show that the CNN models of both approaches provide the same accuracy of 99%, so one model is good enough to detect settlement and dipped joint. To evaluate the severity of the combined defects, the study applies classification and regression concepts. Classification is used to evaluate the severity by categorizing defects into light, medium, and severe classes, and regression is used to estimate the size of defects. From the study, the CNN model is suitable for evaluating dipped joint severity with an accuracy of 84% and mean absolute error (MAE) of 1.25 mm, and the RNN model is suitable for evaluating settlement severity with an accuracy of 99% and mean absolute error (MAE) of 1.58 mm.

Trust and fuzzy inference based cross domain serendipitous item recommendations (TFCDSRS)

2021 ◽  
pp. 1-13
Author(s):  
Richa ◽  
Punam Bedi
Keyword(s):  
Fuzzy Inference ◽  
Mean Absolute Error ◽  
Information Filtering ◽  
Absolute Error ◽  
User Acceptance ◽  
Decision Making Process ◽  
End User ◽  
Cross Domain ◽  
F Measure ◽  
Filtering Approach

Recommender System (RS) is an information filtering approach that helps the overburdened user with information in his decision making process and suggests items which might be interesting to him. While presenting recommendation to the user, accuracy of the presented list is always a concern for the researchers. However, in recent years, the focus has now shifted to include the unexpectedness and novel items in the list along with accuracy of the recommended items. To increase the user acceptance, it is important to provide potentially interesting items which are not so obvious and different from the items that the end user has rated. In this work, we have proposed a model that generates serendipitous item recommendation and also takes care of accuracy as well as the sparsity issues. Literature suggests that there are various components that help to achieve the objective of serendipitous recommendations. In this paper, fuzzy inference based approach is used for the serendipity computation because the definitions of the components overlap. Moreover, to improve the accuracy and sparsity issues in the recommendation process, cross domain and trust based approaches are incorporated. A prototype of the system is developed for the tourism domain and the performance is measured using mean absolute error (MAE), root mean square error (RMSE), unexpectedness, precision, recall and F-measure.

Explanation Plus Prediction—The Logical Focus of Project Management Research

2021 ◽  
pp. 875697282199994
Author(s):  
Joseph F. Hair ◽  
Marko Sarstedt
Keyword(s):  
Project Management ◽  
Statistical Models ◽  
Predictive Power ◽  
Mean Absolute Error ◽  
Explanatory Power ◽  
Absolute Error ◽  
Model Parameters ◽  
Mean Square ◽  
Management Research ◽  
The Mean

Most project management research focuses almost exclusively on explanatory analyses. Evaluation of the explanatory power of statistical models is generally based on F-type statistics and the R 2 metric, followed by an assessment of the model parameters (e.g., beta coefficients) in terms of their significance, size, and direction. However, these measures are not indicative of a model’s predictive power, which is central for deriving managerial recommendations. We recommend that project management researchers routinely use additional metrics, such as the mean absolute error or the root mean square error, to accurately quantify their statistical models’ predictive power.

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