• Korean Journal of Agricultural Science
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• v.50 no.4
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• pp.675-696
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• 2023

Terpenoids, also referred to as terpenes, are a large family of naturally occurring chemical compounds present in the essential oils extracted from medicinal plants. In this study, a nondestructive methodology was created by combining ATR-FT-IR (attenuated total reflectance-Fourier transform infrared), and Raman spectroscopy for the terpenoids assessment in medicinal plants essential oils from ten different geographical locations. Partial least squares regression (PLSR) and support vector regression (SVR) were used as machine learning methodologies. However, a deep learning based model called as one-dimensional convolutional neural network (1D CNN) were also developed for models comparison. With a correlation coefficient (R²) of 0.999 and a lowest RMSEP (root mean squared error of prediction) of 0.006% for the prediction datasets, the SVR model created for FT-IR spectral data outperformed both the PLSR and 1 D CNN models. On the other hand, for the classification of essential oils derived from plants collected from various geographical regions, the created SVM (support vector machine) classification model for Raman spectroscopic data obtained an overall classification accuracy of 0.997% which was superior than the FT-IR (0.986%) data. Based on the results we propose that FT-IR spectroscopy, when coupled with the SVR model, has a significant potential for the non-destructive identification of terpenoids in essential oils compared with destructive chemical analysis methods.

• Computers and Concrete
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• v.33 no.1
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• pp.77-90
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• 2024

Waste foundry sand (WFS) is the waste product that cause environmental hazards. WFS can be used as a partial replacement of cement or fine aggregates in concrete. A database comprising 234 compressive strength tests of concrete fabricated with WFS is used. To construct the machine learning-based prediction models, the water-to-cement ratio, WFS replacement percentage, WFS-to-cement content ratio, and fineness modulus of WFS were considered as the model's inputs, and the compressive strength of concrete is set as the model's output. A base extreme gradient boosting (XGBoost) model together with two hybrid XGBoost models mixed with the tunicate swarm algorithm (TSA) and the salp swarm algorithm (SSA) were applied. The role of TSA and SSA is to identify the optimum values of XGBoost hyperparameters to obtain the higher performance. The results of these hybrid techniques were compared with the results of the base XGBoost model in order to investigate and justify the implementation of optimisation algorithms. The results showed that the hybrid XGBoost models are faster and more accurate compared to the base XGBoost technique. The XGBoost-SSA model shows superior performance compared to previously published works in the literature, offering a reduced system error rate. Although the WFS-to-cement ratio is significant, the WFS replacement percentage has a smaller influence on the compressive strength of concrete. To improve the compressive strength of concrete fabricated with WFS, the simultaneous consideration of the water-to-cement ratio and fineness modulus of WFS is recommended.

• Journal of Distribution Science
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• v.22 no.4
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• pp.11-22
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• 2024

Purpose: This research delves into the various factors that influence the performance of restaurant businesses on social commerce platforms in Bangkok, Thailand. The study considers both internal and external factors, including but not limited to business characteristics and location. Moreover, this research also analyzes the effects of employing multiple social commerce platforms on business efficiency and explores the underlying reasons for such effects. Research design, data, and methodology: Restaurants can be classified into different price ranges: low, medium, and high. To further investigate, we employed natural language processing AI to analyze online reviews and evaluate algorithm performance using machine learning techniques. We aimed to develop a model to gauge customer satisfaction with restaurants across different price categories effectively. Results: According to the research findings, several factors significantly impact restaurant groups in the low and mid-price ranges. Among these factors are population density and the number of seats at the restaurant. On the other hand, in the mid-and high-price ranges, the price levels of the food and drinks offered by the restaurant play a crucial role in determining customer satisfaction. Furthermore, the correlation between different social commerce platforms can significantly affect the business performance of high-price range restaurant groups. Finally, the level of online review sentiment has been found to influence customer decision-making across all restaurant types significantly. Conclusions: The study emphasizes that restaurants' characteristics based on their price level differ significantly, and social commerce platforms have the potential to affect one another. It is worth noting that the sentiment expressed in online reviews has a more significant impact on customer decision-making than any other factor, regardless of the type of restaurant in question.

• Advanced Industrial SCIence
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• v.3 no.1
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• pp.17-22
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• 2024

History, which began with the emergence of mankind, has a means of recording. Today, we can check the past through data. Generated data may only be generated and stored at a certain moment, but it is not only continuously generated over a certain time interval from the past to the present, but also occurs in the future, so making predictions using it is an important task. In order to find out trends in the use of time series data among numerous data, this paper analyzes the concept of time series data, analyzes Recurrent Neural Network and Long-Short Term Memory, which are mainly used for time series data analysis in the machine learning field, and analyzes the use of these models. Through case studies, it was confirmed that it is being used in various fields such as medical diagnosis, stock price analysis, and climate prediction, and is showing high predictive results. Based on this, we will explore ways to utilize it in the future.

• Knowledge Management Research
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• v.25 no.1
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• pp.217-233
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• 2024

The demand for two-wheelers has increased in recent years, driven by the growing delivery culture, which has also led to a rise in the number of two-wheelers. Although two-wheelers are economically efficient in congested traffic conditions, reckless driving and ambiguous traffic laws for two-wheelers have turned two-wheeler accidents into a significant social issue. Given the high fatality rate associated with two-wheelers, the severity and risk of two-wheeler accidents are considerable. It is, therefore, crucial to thoroughly understand the characteristics of two-wheeler accidents by analyzing their attributes. In this study, the characteristics of two-wheeled vehicle accidents were categorized using the K-prototypes algorithm, based on data from two-wheeled vehicle accidents. As a result, the accidents were divided into four clusters according to their characteristics. Each cluster showed distinct traits in terms of the roads where accidents occurred, the major laws violated, the types of accidents, and the times of accident occurrences. By tailoring enforcement methods and regulations to the specific characteristics of each type of accident, we can reduce the incidence of accidents involving two-wheelers in metropolitan areas, thereby enhancing road safety. Furthermore, by applying machine learning techniques to urban transportation and safety, this study adds to the body of related literature.

• Structural Engineering and Mechanics
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• v.90 no.1
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• pp.27-40
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• 2024

The prediction of VIV amplitude is essential for the design and fatigue life estimation of steel tubes in tubular transmission towers. Limited to costly and time-consuming traditional experimental and computational fluid dynamics (CFD) methods, a machine learning (ML)-based method is proposed to efficiently predict the VIV amplitude of steel tubes in transmission towers. Firstly, by introducing the first-order mode shape to the two-dimensional CFD method, a simplified response analysis method (SRAM) is presented to calculate the VIV amplitude of steel tubes in transmission towers, which enables to build a dataset for training ML models. Then, by taking mass ratio M^*, damping ratio ξ, and reduced velocity U^* as the input variables, a Kriging-based prediction method (KPM) is further proposed to estimate the VIV amplitude of steel tubes in transmission towers by combining the SRAM with the Kriging-based ML model. Finally, the feasibility and effectiveness of the proposed methods are demonstrated by using three full-scale steel tubes with C-shaped, Cross-shaped, and Flange-plate joints, respectively. The results show that the SRAM can reasonably calculate the VIV amplitude, in which the relative errors of VIV maximum amplitude in three examples are less than 6%. Meanwhile, the KPM can well predict the VIV amplitude of steel tubes in transmission towers within the studied range of M^*, ξ and U^*. Particularly, the KPM presents an excellent capability in estimating the VIV maximum amplitude by using the reduced damping parameter S_G.

• Geophysics and Geophysical Exploration
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• v.23 no.2
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• pp.72-88
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• 2020

Recent rapid advances in computer hardware performance have led to relatively low computational costs, increasing the number of applications of machine-learning techniques to geophysical problems. In particular, deep-learning techniques are gaining in popularity as the number of cases successfully solving complex and nonlinear problems has gradually increased. In this paper, applications of seismic data denoising methods using deep-learning techniques are introduced and investigated. Depending on the type of attenuated noise, these studies are grouped into denoising applications of coherent noise, random noise, and the combination of these two types of noise. Then, we investigate the deep-learning techniques used to remove the corresponding noise. Unlike conventional methods used to attenuate seismic noise, deep neural networks, a typical deep-learning technique, learn the characteristics of the noise independently and then automatically optimize the parameters. Therefore, such methods are less sensitive to generalized problems than conventional methods and can reduce labor costs. Several studies have also demonstrated that deep-learning techniques perform well in terms of computational cost and denoising performance. Based on the results of the applications covered in this paper, the pros and cons of the deep-learning techniques used to remove seismic noise are analyzed and discussed.

• Journal of Practical Engineering Education
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• v.15 no.2
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• pp.529-538
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• 2023

At the present time when the need for universal artificial intelligence education is expanding and job changes are being made, research and discussion on artificial intelligence liberal arts education for non-majors in universities who experience artificial intelligence as part of their job is insufficient. Although artificial intelligence education courses for non-majors are being operated, they are mainly operated as theory-oriented education on the concepts and principles of artificial intelligence. In order to understand the general concept of artificial intelligence for non-majors, it is necessary to proceed with experiential learning in parallel. Therefore, this study designs artificial intelligence experiential education learning contents of difficulty that can reduce the burden of artificial intelligence classes with interest in learning by considering the characteristics of non-majors. After, we will examine the learning effect of experiential education using App Inventor and the Orange artificial intelligence platform. As a result of analysis based on the learning-related data and survey data collected through the creation of AI-related projects by teams, positive changes in the perception of the need for AI education were found, and AI literacy skills improved. It is expected that it will serve as an opportunity for instructors to lay the groundwork for designing a learning model for artificial intelligence experiential education learning.

• Journal of IKEEE
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• v.25 no.3
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• pp.493-500
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• 2021

In this paper, we propose the development of deep learning structure to improve quality of polygonal containers. The deep learning structure consists of a convolution layer, a bottleneck layer, a fully connect layer, and a softmax layer. The convolution layer is a layer that obtains a feature image by performing a convolution 3x3 operation on the input image or the feature image of the previous layer with several feature filters. The bottleneck layer selects only the optimal features among the features on the feature image extracted through the convolution layer, reduces the channel to a convolution 1x1 ReLU, and performs a convolution 3x3 ReLU. The global average pooling operation performed after going through the bottleneck layer reduces the size of the feature image by selecting only the optimal features among the features of the feature image extracted through the convolution layer. The fully connect layer outputs the output data through 6 fully connect layers. The softmax layer multiplies and multiplies the value between the value of the input layer node and the target node to be calculated, and converts it into a value between 0 and 1 through an activation function. After the learning is completed, the recognition process classifies non-circular glass bottles by performing image acquisition using a camera, measuring position detection, and non-circular glass bottle classification using deep learning as in the learning process. In order to evaluate the performance of the deep learning structure to improve quality of polygonal containers, as a result of an experiment at an authorized testing institute, it was calculated to be at the same level as the world's highest level with 99% good/defective discrimination accuracy. Inspection time averaged 1.7 seconds, which was calculated within the operating time standards of production processes using non-circular machine vision systems. Therefore, the effectiveness of the performance of the deep learning structure to improve quality of polygonal containers proposed in this paper was proven.

• Korean Journal of Remote Sensing
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• v.32 no.3
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• pp.221-234
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• 2016

In Korea, most industrial parks and major cities are located in coastal areas, which results in serious environmental problems in both coastal land and ocean. In order to effectively manage such problems especially in coastal ocean, water quality should be monitored. As there are many factors that influence water quality, the Korean Government proposed an integrated Water Quality Index (WQI) based on in situmeasurements of ocean parameters(bottom dissolved oxygen, chlorophyll-a concentration, secchi disk depth, dissolved inorganic nitrogen, and dissolved inorganic phosphorus) by ocean division identified based on their ecological characteristics. Field-measured WQI, however, does not provide spatial continuity over vast areas. Satellite remote sensing can be an alternative for identifying WQI for surface water. In this study, two schemes were examined to estimate coastal WQI around Korea peninsula using in situ measurements data and Geostationary Ocean Color Imager (GOCI) satellite imagery from 2011 to 2013 based on machine learning approaches. Scheme 1 calculates WQI using estimated water quality-related factors using GOCI reflectance data, and scheme 2 estimates WQI using GOCI band reflectance data and basic products(chlorophyll-a, suspended sediment, colored dissolved organic matter). Three machine learning approaches including Random Forest (RF), Support Vector Regression (SVR), and a modified regression tree(Cubist) were used. Results show that estimation of secchi disk depth produced the highest accuracy among the ocean parameters, and RF performed best regardless of water quality-related factors. However, the accuracy of WQI from scheme 1 was lower than that from scheme 2 due to the estimation errors inherent from water quality-related factors and the uncertainty of bottom dissolved oxygen. In overall, scheme 2 appears more appropriate for estimating WQI for surface water in coastal areas and chlorophyll-a concentration was identified the most contributing factor to the estimation of WQI.