Comparison of Deep-Learning Training Techniques for Person’s Social Convention Estimation Model

The knock phenomenon is one of the major hindrances for enhancing the thermal efficiency in spark-ignited engines. Due to the stochastic behavior of knocking combustion, analytical cycle studies are required. However, there are many problems to be addressed with regard to the individual cycle analysis of in-cylinder pressure data. This study thus proposes novel, comprehensive and efficient methodologies for evaluating the knocking combustion in the internal combustion engine. The proposed methodologies include a filtering method for the in-cylinder pressure, the determination of the knock onset, and the calculation of the residual gas fraction. Consequently, a smart knock onset model with high accuracy could be developed using a supervised deep learning that was not available in the past. Moreover, an improved zero-dimensional (0D) estimation model for the residual gas fraction was developed to obtain better accuracy for closed system analysis. Finally, based on a cyclic analysis, a knock prediction model is suggested; the model uses 0D ignition delay correlation under various experimental conditions including aggressive cam phase shifting by a dual variable valve timing (VVT) system. Using the proposed analysis method, insight into stochastic knocking combustion can be obtained, and a faster combustion speed can lead to a higher knock intensity in a steady-state operation.

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Deep Learning Approach of Energy Estimation Model of Remote Laser Welding

Energies ◽

10.3390/en12091799 ◽

2019 ◽

Vol 12 (9) ◽

pp. 1799 ◽

Cited By ~ 2

Author(s):

Jumyung Um ◽

Ian Anthony Stroud ◽

Yong-keun Park

Keyword(s):

Deep Learning ◽

Laser Welding ◽

Process Parameters ◽

Energy Use ◽

Technical Specification ◽

Average Energy ◽

Welding Process ◽

Learning Approach ◽

Estimation Model ◽

Remote Laser Welding

Due to concerns about energy use in production systems, energy-efficient processes have received much interest from the automotive industry recently. Remote laser welding is an innovative assembly process, but has a critical issue with the energy consumption. Robot companies provide only the average energy use in the technical specification, but process parameters such as robot movement, laser use, and welding path also affect the energy use. Existing literature focuses on measuring energy in standardized conditions in which the welding process is most frequently operated or on modularizing unified blocks in which energy can be estimated using simple calculations. In this paper, the authors propose an integrated approach considering both process variation and machine specification and multiple methods’ comparison. A deep learning approach is used for building the neural network integrated with the effects of process parameters and machine specification. The training dataset used is experimental data measured from a remote laser welding robot producing a car back door assembly. The proposed estimation model is compared with a linear regression approach and shows higher accuracy than other methods.

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Development of Vehicle Queue Length Estimation Model Using Deep Learning

The Journal of The Korea Institute of Intelligent Transport Systems ◽

10.12815/kits.2018.17.2.39 ◽

2018 ◽

Vol 17 (2) ◽

pp. 39-57

Author(s):

Lee Yong-Ju ◽

◽

Hwang Jae-Seong ◽

Kim Soo-Hee ◽

Lee Choul-Ki

Keyword(s):

Deep Learning ◽

Queue Length ◽

Estimation Model ◽

Length Estimation

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Estimation of Tool Wear and Surface Roughness Development Using Deep Learning and Sensors Fusion

Sensors ◽

10.3390/s21165338 ◽

2021 ◽

Vol 21 (16) ◽

pp. 5338

Author(s):

Pao-Ming Huang ◽

Ching-Hung Lee

Keyword(s):

Surface Roughness ◽

Deep Learning ◽

Tool Wear ◽

Sensor Fusion ◽

Sensor Selection ◽

Numerical Control ◽

Estimation Accuracy ◽

Machining Parameters ◽

Estimation Model ◽

Selection Analysis

This paper proposes an estimation approach for tool wear and surface roughness using deep learning and sensor fusion. The one-dimensional convolutional neural network (1D-CNN) is utilized as the estimation model with X- and Y-coordinate vibration signals and sound signal fusion using sensor influence analysis. First, machining experiments with computer numerical control (CNC) parameters are designed using a uniform experimental design (UED) method to guarantee the variety of collected data. The vibration, sound, and spindle current signals are collected and labeled according to the machining parameters. To speed up the degree of tool wear, an accelerated experiment is designed, and the corresponding tool wear and surface roughness are measured. An influential sensor selection analysis is proposed to preserve the estimation accuracy and to minimize the number of sensors. After sensor selection analysis, the sensor signals with better estimation capability are selected and combined using the sensor fusion method. The proposed estimation system combined with sensor selection analysis performs well in terms of accuracy and computational effort. Finally, the proposed approach is applied for on-line monitoring of tool wear with an alarm, which demonstrates the effectiveness of our approach.

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Improving multi-class Boosting-based object detection

Integrated Computer-Aided Engineering ◽

10.3233/ica-200636 ◽

2020 ◽

Vol 28 (1) ◽

pp. 81-96

Author(s):

José Miguel Buenaposada ◽

Luis Baumela

Keyword(s):

Deep Learning ◽

Object Detection ◽

Data Augmentation ◽

Detection Performance ◽

Significant Progress ◽

Training Techniques ◽

Multi Scale ◽

Bounding Box ◽

Open Issue ◽

The Impact

In recent years we have witnessed significant progress in the performance of object detection in images. This advance stems from the use of rich discriminative features produced by deep models and the adoption of new training techniques. Although these techniques have been extensively used in the mainstream deep learning-based models, it is still an open issue to analyze their impact in alternative, and computationally more efficient, ensemble-based approaches. In this paper we evaluate the impact of the adoption of data augmentation, bounding box refinement and multi-scale processing in the context of multi-class Boosting-based object detection. In our experiments we show that use of these training advancements significantly improves the object detection performance.

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Feasibility of Combining Deep Learning and RGB Images Obtained by Unmanned Aerial Vehicle for Leaf Area Index Estimation in Rice

Remote Sensing ◽

10.3390/rs13010084 ◽

2020 ◽

Vol 13 (1) ◽

pp. 84

Author(s):

Tomoaki Yamaguchi ◽

Yukie Tanaka ◽

Yuto Imachi ◽

Megumi Yamashita ◽

Keisuke Katsura

Keyword(s):

Deep Learning ◽

Leaf Area Index ◽

Leaf Area ◽

Unmanned Aerial Vehicle ◽

Regression Models ◽

Estimation Model ◽

Area Index ◽

Aerial Vehicle ◽

Rgb Images ◽

Multispectral Camera

Leaf area index (LAI) is a vital parameter for predicting rice yield. Unmanned aerial vehicle (UAV) surveillance with an RGB camera has been shown to have potential as a low-cost and efficient tool for monitoring crop growth. Simultaneously, deep learning (DL) algorithms have attracted attention as a promising tool for the task of image recognition. The principal aim of this research was to evaluate the feasibility of combining DL and RGB images obtained by a UAV for rice LAI estimation. In the present study, an LAI estimation model developed by DL with RGB images was compared to three other practical methods: a plant canopy analyzer (PCA); regression models based on color indices (CIs) obtained from an RGB camera; and vegetation indices (VIs) obtained from a multispectral camera. The results showed that the estimation accuracy of the model developed by DL with RGB images (R2 = 0.963 and RMSE = 0.334) was higher than those of the PCA (R2 = 0.934 and RMSE = 0.555) and the regression models based on CIs (R2 = 0.802-0.947 and RMSE = 0.401–1.13), and comparable to that of the regression models based on VIs (R2 = 0.917–0.976 and RMSE = 0.332–0.644). Therefore, our results demonstrated that the estimation model using DL with an RGB camera on a UAV could be an alternative to the methods using PCA and a multispectral camera for rice LAI estimation.

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Multi-Regional Modeling of Cumulative COVID-19 Cases Integrated with Environmental Forest Knowledge Estimation: A Deep Learning Ensemble Approach

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph19020738 ◽

2022 ◽

Vol 19 (2) ◽

pp. 738

Author(s):

Abdelgader Alamrouni ◽

Fidan Aslanova ◽

Sagiru Mati ◽

Hamza Sabo Maccido ◽

Afaf. A. Jibril ◽

...

Keyword(s):

Deep Learning ◽

Autocorrelation Function ◽

Short Term Memory ◽

Least Squares Method ◽

Arima Model ◽

Information Criterion ◽

Generalized Least Squares ◽

Performance Criteria ◽

Regional Modeling ◽

Estimation Model

Reliable modeling of novel commutative cases of COVID-19 (CCC) is essential for determining hospitalization needs and providing the benchmark for health-related policies. The current study proposes multi-regional modeling of CCC cases for the first scenario using autoregressive integrated moving average (ARIMA) based on automatic routines (AUTOARIMA), ARIMA with maximum likelihood (ARIMAML), and ARIMA with generalized least squares method (ARIMAGLS) and ensembled (ARIMAML-ARIMAGLS). Subsequently, different deep learning (DL) models viz: long short-term memory (LSTM), random forest (RF), and ensemble learning (EML) were applied to the second scenario to predict the effect of forest knowledge (FK) during the COVID-19 pandemic. For this purpose, augmented Dickey–Fuller (ADF) and Phillips–Perron (PP) unit root tests, autocorrelation function (ACF), partial autocorrelation function (PACF), Schwarz information criterion (SIC), and residual diagnostics were considered in determining the best ARIMA model for cumulative COVID-19 cases (CCC) across multi-region countries. Seven different performance criteria were used to evaluate the accuracy of the models. The obtained results justified both types of ARIMA model, with ARIMAGLS and ensemble ARIMA demonstrating superiority to the other models. Among the DL models analyzed, LSTM-M1 emerged as the best and most reliable estimation model, with both RF and LSTM attaining more than 80% prediction accuracy. While the EML of the DL proved merit with 96% accuracy. The outcomes of the two scenarios indicate the superiority of ARIMA time series and DL models in further decision making for FK.

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