• Journal of the Semiconductor & Display Technology
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• v.20 no.3
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• pp.77-82
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• 2021

This numerical study focuses on the analysis of fume particle dispersion characteristics over the surface of target workpiece in laser micro-hole machining process. The effects of oblique stagnation flow over fume generating machining point are examined by carrying out a series of three-dimensional random particle simulations along with probabilistic particle generation model and particle drag correlation of low Reynolds number. Present computational model of fume particle dispersion is found to be capable of assessing and quantifying the fume particle contamination in precision hole machining which may influenced by different types of air flow patterns and their flow intensity. The particle size dependence on dispersion distance of fume particles from laser machining point is significant and the effects of increasing flow oblique angle are shown quite differently when slot blowing or slot suction flows are applied in micro-hole machining.

• Journal of applied mathematics & informatics
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• v.39 no.1_2
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• pp.239-249
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• 2021

This study attempts to predict the price of gold futures, a real financial product, using ARIMA and LSTM. The wavelet analysis was applied to the data to predict the price of gold futures through LSTM and ARIMA. As results, it is confirmed that the prediction performance of the existing model of predict was improved. the case of predict of price of gold futures, we confirmed that the use of a deep learning model that is not affected by the non-stationary series data is suitable and the possibility of improving the accuracy of prediction through wavelet analysis.

• International journal of advanced smart convergence
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• v.10 no.1
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• pp.62-74
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• 2021

We compare Over-the-Tops (OTTs), Netflix and Amazon Prime Video (APV) with five success factors of innovation. Firstly, Netflix offers better personalized service than APV, because APV has collaborative filtering algorithms to recommend safe bets, not the customers really want. Secondly, APV' user interface is undercooked to lock the members in, even if it has more content and better price offer than Netflix retaining its loyal customers despite the price increase. Thirdly, Netflix has simple subscription model with three tiering, but APV has complicated pricing model having annual and monthly, APV and Prime Video (AV) app, Amazon subscription and extra payment of Amazon Prime Channels (APCs). Fourthly, Amazon has fewer partnership than Netflix especially when it comes to local TV series. Instead, Amazon has live TV channel collaboration including sports content. Lastly, both have strategic and operational agility in their organization well.

• New & Renewable Energy
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• v.18 no.2
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• pp.18-25
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• 2022

In this study, we propose a wind power generation prediction system that applies machine learning and data mining to predict wind power generation. This system increases the utilization rate of new and renewable energy sources. For time-series data, the data set was established by measuring wind speed, wind generation, and environmental factors influencing the wind speed. The data set was pre-processed so that it could be applied appropriately to the model. The prediction system applied the CNN (Convolutional Neural Network) to the data mining process and then used the LSTM (Long Short-Term Memory) to learn and make predictions. The preciseness of the proposed system is verified by comparing the prediction data with the actual data, according to the presence or absence of data mining in the model of the prediction system.

• Communications for Statistical Applications and Methods
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• v.29 no.4
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• pp.471-486
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• 2022

Autoregressive moving average (ARMA) models involve nonlinearity in the model coefficients because of unobserved lagged errors, which complicates the likelihood function and makes the posterior density analytically intractable. In order to overcome this problem of posterior analysis, some approximation methods have been proposed in literature. In this paper we first review the main analytic approximations proposed to approximate the posterior density of ARMA models to be analytically tractable, which include Newbold, Zellner-Reynolds, and Broemeling-Shaarawy approximations. We then use the Kullback-Leibler divergence to study the relation between these three analytic approximations and to measure the distance between their derived approximate posteriors for ARMA models. In addition, we evaluate the impact of the approximate posteriors distance in Bayesian estimates of mean and precision of the model coefficients by generating a large number of Monte Carlo simulations from the approximate posteriors. Simulation study results show that the approximate posteriors of Newbold and Zellner-Reynolds are very close to each other, and their estimates have higher precision compared to those of Broemeling-Shaarawy approximation. Same results are obtained from the application to real-world time series datasets.

• Smart Structures and Systems
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• v.28 no.6
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• pp.779-789
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• 2021

Cracks in railway sleeper are an inevitable condition and has a significant influence on the safety of railway system. Although the technology of railway sleeper condition monitoring using machine learning (ML) models has been widely applied, the crack recognition accuracy is still in need of improvement. In this paper, a two-stage method using edge detection and convolutional neural network (CNN) is proposed to reduce the burden of computing for detecting cracks in railway sleepers with high accuracy. In the first stage, the edge detection is carried out by using the 3×3 neighborhood range algorithm to find out the possible crack areas, and a series of mathematical morphology operations are further used to eliminate the influence of noise targets to the edge detection results. In the second stage, a CNN model is employed to classify the results of edge detection. Through the analysis of abundant images of sleepers with cracks, it is proved that the cracks detected by the neighborhood range algorithm are superior to those detected by Sobel and Canny algorithms, which can be classified by proposed CNN model with high accuracy.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.10
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• pp.112-122
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• 1996

In order to monitior machine tool condition and diagnose alarm states due to electrical and mechanical faults, and expert system using diagnostic parameters of NC machine tools was developed. A model-based knowledge base was constructed via searching and comparing procedures of diagnostic parameters and state parameters of the machine tool. Diagnostic monitoring results generate through a successive type inference engine were graphically displayed on the screen of the console. The validity and reliability of the expert system was rcrified on a vertical machining center equipped with FANUC OMC through a series of experiments.

• Journal of Microbiology and Biotechnology
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• v.33 no.5
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• pp.680-686
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• 2023

Aging is a complex series of multi-organ processes that occur in various organisms. As such, an in vivo study using an animal model of aging is necessary to define its exact mechanisms and identify anti-aging substances. Using Drosophila as an in vivo model system, we identified Crataegus pinnatifida extract (CPE) as a novel anti-aging substance. Regardless of sex, Drosophila treated with CPE showed a significantly increased lifespan compared to those without CPE. In this study, we also evaluated the involvement of CPE in aging-related biochemical pathways, including TOR, stem cell generation, and antioxidative effects, and found that the representative genes of each pathway were induced by CPE administration. CPE administration did not result in significant differences in fecundity, locomotion, feeding amount, or TAG level. These conclusions suggest that CPE is a good candidate as an anti-aging food substance capable of promoting a healthy lifespan.

• Geomechanics and Engineering
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• v.34 no.4
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• pp.425-435
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• 2023

Granite and marble are widely produced and utilized in the construction industry, resulting in significant waste production. It is essential to manage this waste appropriately and repurpose it in recycling processes to ensure sustainability. The utilization of waste materials such as marble and granite waste (MGW) has become increasingly important in geotechnical engineering to improve the physical and mechanical properties of weak soils. This study investigated the applicability of utilizing MGW and cement (C)-MGW mixtures to improve clayey soil. A series of model plate loading tests were carried out in a specialized circular test tank to assess the influence of MGW and C-MGW mixing ratios on clayey soil samples. The samples were prepared by blending MGW and C-MGW in predetermined proportions. It is found that the bearing capacity of clay soil increased by approximately 71% when using MGW and C additives. Moreover, the consolidated settlement values of the clay soil decreased up to 6 times compared to the additive-free case.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.463-467
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• 2015

Mega-shock means a sporadic event such as the earning shock, which occurred by sudden market changes, and it can cause serious problems of profit loss of international construction projects. Therefore, the early response and prevention by analyzing and predicting the Mega-shock is critical for successful project delivery. This research is preliminary study to develop a prediction model that supports market condition analysis and Mega-shock forecasting. To avoid disadvantages of classic statistical approaches that assume the market factors are linear and independent and thus have limitations to explain complex interrelationship among a range of international market factors, the research team explored the Fractal Theory that can explain self-similarity and recursiveness of construction market changes. The research first found out correlation of the major market factors by statistically analyzing time-series data. The research then conducted a base of the Fractal analysis to distinguish features of fractal from data. The outcome will have potential to contribute to building up a foundation of the early shock warning system for the strategic international project management.