• Journal of the Korean Society for Precision Engineering
• /
• v.15 no.3
• /
• pp.24-30
• /
• 1998

Strength and displacement of jig body in index machine utilized for multiprocess machining such as drilling, boring and tapping, etc, at the same time were analyzed by the use of finite element analysis soft ware ANSYS 5.2A. The whole geometry was constructed by 4048 elements and 7016 nodes employing 8 node brick element. The analyses were carried out on five loading cases combining vertical and horizontal machining to simulate the case occurring large displacement and the one occurring small displacement one and provided following conclusions. (1) Jig body had sufficient strength because its safety factor was 6.95 even in the most severe loading case. (2) The largest displacement in Z direction was 549 m and that in radial direction was 43.7 m. (3) In order to reduce the displacement, vertical machining rather than horizontal or two or three processes should be adopted in the same station. (4) Alternate change of horizontal machining direction at consecutive stations can reduce the displace ment. (5) The dimension of the slider should be increased to reduce the displacement by the tolerance in the sliding part. (6) A bypass idle piston head needs to be installed to give a counterpart supporting load from opposite direction for a single horizontal machining case.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.35 no.4
• /
• pp.433-438
• /
• 2011

A machine that makes paper cups has many parts, including a barrel cam, an index, and a turret. When the barrel cam, which is the main operating part of the machine, rotates, it pushes the roller fixed on the index, and paper cups are formed as the turret connected to the index rotates. Therefore, the performance of the machine is affected by the barrel cam. In this study, the program for designing barrel cam, which creates the profile of the cam is developed using MATLAB. This profile is used to develop a 3D CAD model by using a 3D CAD program. Dynamic models containing the barrel cam are created on the basis of the profile and 3D laser scan of the barrel cam. Further, the rotation angle of the index in the machine is measured using a high-speed camera. The rotation angles of the dynamics models are compared to verify the effectiveness of the program.

• Journal of the Korean Data and Information Science Society
• /
• v.22 no.5
• /
• pp.921-929
• /
• 2011

In Information age, the academic liberal art Computer education course set up goals for promoting computer literacy and for developing the ability to cope actively with in Information Society and for improving productivity and competition among nations. In this paper, we analyze on discovering of decisive property and satisfaction index to have a influence on computer education on university students. As a preprocessing method, the proposed method select optimum property using correlation feature selection of machine learning tool based on Java and then we use multiclass least square support vector machine based on statistical learning theory. After applying that compare with multiclass support vector machine and multiclass least square support vector machine, we can see the fact that the proposed method have a excellent result like multiclass support vector machine in analysis of the academic liberal art computer education satisfaction index data.

• Geomechanics and Engineering
• /
• v.29 no.3
• /
• pp.219-228
• /
• 2022

The most widely used parameter to represent rock abrasiveness is the Cerchar abrasivity index (CAI). The CAI value can be applied to predict wear in TBM cutters. It has been extensively demonstrated that the CAI is affected significantly by cementation degree, strength, and amount of abrasive minerals, i.e., the quartz content or equivalent quartz content in rocks. The relationship between the properties of rocks and the CAI is investigated in this study. A database comprising 223 observations that includes rock types, uniaxial compressive strengths, Brazilian tensile strengths, equivalent quartz contents, quartz contents, brittleness indices, and CAIs is constructed. A linear model is developed by selecting independent variables while considering multicollinearity after performing multiple regression analyses. Machine learning-based regression methods including support vector regression, regression tree regression, k-nearest neighbors regression, random forest regression, and artificial neural network regression are used in addition to multiple linear regression. The results of the random forest regression model show that it yields the best prediction performance.

• Journal of Intelligence and Information Systems
• /
• v.22 no.4
• /
• pp.177-192
• /
• 2016

Machine learning is a field of artificial intelligence. It refers to an area of computer science related to providing machines the ability to perform their own data analysis, decision making and forecasting. For example, one of the representative machine learning models is artificial neural network, which is a statistical learning algorithm inspired by the neural network structure of biology. In addition, there are other machine learning models such as decision tree model, naive bayes model and SVM(support vector machine) model. Among the machine learning models, we use SVM model in this study because it is mainly used for classification and regression analysis that fits well to our study. The core principle of SVM is to find a reasonable hyperplane that distinguishes different group in the data space. Given information about the data in any two groups, the SVM model judges to which group the new data belongs based on the hyperplane obtained from the given data set. Thus, the more the amount of meaningful data, the better the machine learning ability. In recent years, many financial experts have focused on machine learning, seeing the possibility of combining with machine learning and the financial field where vast amounts of financial data exist. Machine learning techniques have been proved to be powerful in describing the non-stationary and chaotic stock price dynamics. A lot of researches have been successfully conducted on forecasting of stock prices using machine learning algorithms. Recently, financial companies have begun to provide Robo-Advisor service, a compound word of Robot and Advisor, which can perform various financial tasks through advanced algorithms using rapidly changing huge amount of data. Robo-Adviser's main task is to advise the investors about the investor's personal investment propensity and to provide the service to manage the portfolio automatically. In this study, we propose a method of forecasting the Korean volatility index, VKOSPI, using the SVM model, which is one of the machine learning methods, and applying it to real option trading to increase the trading performance. VKOSPI is a measure of the future volatility of the KOSPI 200 index based on KOSPI 200 index option prices. VKOSPI is similar to the VIX index, which is based on S&P 500 option price in the United States. The Korea Exchange(KRX) calculates and announce the real-time VKOSPI index. VKOSPI is the same as the usual volatility and affects the option prices. The direction of VKOSPI and option prices show positive relation regardless of the option type (call and put options with various striking prices). If the volatility increases, all of the call and put option premium increases because the probability of the option's exercise possibility increases. The investor can know the rising value of the option price with respect to the volatility rising value in real time through Vega, a Black-Scholes's measurement index of an option's sensitivity to changes in the volatility. Therefore, accurate forecasting of VKOSPI movements is one of the important factors that can generate profit in option trading. In this study, we verified through real option data that the accurate forecast of VKOSPI is able to make a big profit in real option trading. To the best of our knowledge, there have been no studies on the idea of predicting the direction of VKOSPI based on machine learning and introducing the idea of applying it to actual option trading. In this study predicted daily VKOSPI changes through SVM model and then made intraday option strangle position, which gives profit as option prices reduce, only when VKOSPI is expected to decline during daytime. We analyzed the results and tested whether it is applicable to real option trading based on SVM's prediction. The results showed the prediction accuracy of VKOSPI was 57.83% on average, and the number of position entry times was 43.2 times, which is less than half of the benchmark (100 times). A small number of trading is an indicator of trading efficiency. In addition, the experiment proved that the trading performance was significantly higher than the benchmark.

• Journal of the Korean Geosynthetics Society
• /
• v.19 no.1
• /
• pp.45-53
• /
• 2020

There is an increasing tendency to try to make predictive analysis using measurement data based on machine learning techniques in the railway industries. In this paper, it was predicted that Track quality index (TQI) using vehicle acceleration data based on the machine learning method. The XGB (XGBoost) was the most accurate with 85% in the all data sets. Unlike the SVM model with a single algorithm, the RF and XGB model with a ensemble system were considered to be good at the prediction performance. In the case of the Surface TQI, it is shown that the acceleration of the z axis is highly related to the vertical direction and is in good agreement with the previous studies. Therefore, it is appropriate to apply the model with the ensemble algorithm to predict the track quality index using the vehicle vibration acceleration data because the accuracy may vary depending on the applied model in the machine learning methods.

• Journal of the Korean Society for information Management
• /
• v.25 no.2
• /
• pp.199-221
• /
• 2008

The purpose of this study is to investigate the characteristics of indexes by human and machine, and differences between them in terms of term identification in a fulltext environment. A back-of-book index and two indexes produced by two term identifiers (LinkIt and Termer) as pseudo-indexing systems for a whole body of a monograph are examined. In the investigation, the traditional contrast between manual and automatic indexing is confirmed in fulltext environment, manual index is for browsing and human use, and automatic index is for searching and machine use. The border between them, however, becomes vague. Some considerations for the use of the term identifiers for browsing and for searching are discussed, and further research for the use of the term identifier is suggested.

• Journal of Welding and Joining
• /
• v.21 no.6
• /
• pp.26-32
• /
• 2003

Arc welding process has indicated that it suffers from many flaws. It's because requirement of products is diverse and factors which affects the quality is also various. Therefore, in order to stabilize the welding process, it is important to choose a proper welding machine for the each process, and to evaluate the welding process capability of each machine. In this study, rational and simple index to evaluate the welding machine was set the coefficient of resistance variation through the arc stability examination such as spatter generation weight and bead configuration uniformity etc. And the method to evaluate the process capability index was developed by application of 6$\sigma$.

• Journal of the Korean Society of Manufacturing Technology Engineers
• /
• v.19 no.5
• /
• pp.678-683
• /
• 2010

The drum cam with rotational follower is used to apply the ATC and index table of machine tools and it has the merit of minimizing the backlash. In general, to machine the drum cam with rotational follower, 5-axis CNC machine must be used and its kinematic principle must be included in modeling on CAM. So, the commercialized CAM software can't be applied to this machining of drum cam. Though some special software for machining drum cam was developed, it could be applied to special 5-axis CNC machine tools and the finish machining module was not applied. To solve this problem, this study includes the induction of the post processing algorithm for the rough machining of drum cam on several 5-axis CNC machine tools, type AC, AB and Be. The finish machining software will be treated in next study. A sample drum cam was machined on 5-axis CNC machine tool of AC type. The designed geometric profile of drum cam consist to the measured profile after machining well. This post processing algorithm for rough machining of the drum cam was clearly verified.

• Journal of Internet Computing and Services
• /
• v.23 no.3
• /
• pp.125-135
• /
• 2022

This study aims to explore variables using machine learning and provide analysis techniques suitable for predicting pharmacy sales whether government statistical indicators built to create an industrial ecosystem based on data, network, and artificial intelligence affect pharmacy sales. Therefore, this study explored predictive variables and performance through machine learning techniques such as Random Forest, XGBoost, LightGBM, and CatBoost using analysis data from January 2016 to December 2021 for 28 government statistical indicators and pharmacies in the retail sector. As a result of the analysis, economic sentiment index, economic accompanying index circulation change, and consumer sentiment index, which are economic indicators, were found to be important variables affecting pharmacy sales. As a result of examining the indicators MAE, MSE, and RMSE for regression performance, random forests showed the best performance than XGBoost, LightGBM, and CatBoost. Therefore, this study presented variables and optimal machine learning techniques that affect pharmacy sales based on machine learning results, and proposed several implications and follow-up studies.