• Journal of the Korean Society for Precision Engineering
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• v.18 no.7
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• pp.98-105
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• 2001

The machining accuracy is affected by geometric, volumetric errors of the machine tools. To improve the product quality, we need to enhance the machining accuracy of the machine tools. To this point of view, measurement and inspection of finished part as error analysis of machine tools ahas been studied for last several decades. This paper suggests the enhancement method of machining accuracy for precision machining of high quality metal reflection mirror or optics lens, etc. In this paper, we study 1) the compensation of linear pitch error with NC controller compensation function using laser interferometer measurement, 2) the method for enhancing the accuracy of NC milling machining by modeling and compensation of volumetric error, 3) the spherical surface manufacturing by modeling and compensation of volumetric error of the machine tool, 4) the system development of OMM without detaching work piece from a bed of machine tool after working, 5) the generation of the finished part profile by OMM. Furthermore, the output of OMM is compared with that of CMM, and verified the feasibility of the measurement system.

• Journal of Internet Computing and Services
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• v.6 no.3
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• pp.159-170
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• 2005

OC-SVM(One Class Support Vector Machine) avoids solving a full density estimation problem, and instead focuses on a simpler task, estimating quantiles of a data distribution, i.e. its support. OC-SVM seeks to estimate regions where most of data resides and represents the regions as a function of the support vectors, Although OC-SVM is powerful method for data description, it is difficult to incorporate human subjective importance into its estimation process, In order to integrate the importance of each point into the OC-SVM process, we propose a fuzzy version of OC-SVM. In FOC-SVM (Fuzzy One-Class Support Vector Machine), we do not equally treat data points and instead weight data points according to the importance measure of the corresponding objects. That is, we scale the kernel feature vector according to the importance measure of the object so that a kernel feature vector of a less important object should contribute less to the detection process of OC-SVM. We demonstrate the performance of our algorithm on several synthesized data sets, Experimental results showed the promising results.

• The Journal of Information Systems
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• v.29 no.3
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• pp.35-52
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• 2020

Purpose Local governments in each region actively hold local festivals for the purpose of promoting the region and revitalizing the local economy. Existing studies related to local festivals have been actively conducted in tourism and related academic fields. Empirical studies to understand the effects of latent variables on local festivals and studies to analyze the regional economic impacts of festivals occupy a large proportion. Despite of practical need, since few researches have been conducted to predict the number of visitors, one of the criteria for evaluating the performance of local festivals, this study developed a model for predicting the number of visitors through various observed variables using a machine learning algorithm and derived its implications. Design/methodology/approach For a total of 593 festivals held in 2018, 6 variables related to the region considering population size, administrative division, and accessibility, and 15 variables related to the festival such as the degree of publicity and word of mouth, invitation singer, weather and budget were set for the training data in machine learning algorithm. Since the number of visitors is a continuous numerical data, random forest, Adaboost, and linear regression that can perform regression analysis among the machine learning algorithms were used. Findings This study confirmed that a prediction of the number of visitors to local festivals is possible using a machine learning algorithm, and the possibility of using machine learning in research in the tourism and related academic fields, including the study of local festivals, was captured. From a practical point of view, the model developed in this study is used to predict the number of visitors to the festival to be held in the future, so that the festival can be evaluated in advance and the demand for related facilities, etc. can be utilized. In addition, the RReliefF rank result can be used. Considering this, it will be possible to improve the existing local festivals or refer to the planning of a new festival.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1996.03a
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• pp.67-71
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• 1996

The dynamic equation is built by the mathematical modelling. The modelling is composed of various components used for the automatic transmission of tracked vehicles. When the transmission is shifting, the shock occurs in the drivetrain. The transient torques affect the durability and reliability of the vehicle. The factor and design point are analyzed for the transmission.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.20 no.8
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• pp.742-752
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• 2010

In case of the working machine that using in the loud-noisy workplace, as it generates the loud-noise, it is influencing a physical, mental bad effect to those workers. Accordingly, though the noise countermeasure for the loud-noisy workplace is acutely requiring, until now, those methods that wearing the soundproof-protection tool, or restriction the working hours, and minimize the noise exposure volume, were mainly used. However, such noise countermeasures occur many problem points. On such point of view, using the acoustic simulation technique, let the workers to choose the workplace where suffering many damages due to the noise of working machine, and after grasp the physical property of working machine and indoor acoustic characteristic, this Study has attempted to grasp the reduction degree of noise level at before-improvement?after-improvement, through the sound-absorption measure. Passing through such preceding step, using auralizational technique based on the noise of working machine of before-improvement after-improvement, and by conduct psycho-acoustics evaluation, this study intended to investigate the change degree of subject reaction. As the result of evaluation, it is considering that the noise-reduction countermeasure method for the loud-noisy workplace could be much effective, through the sound-absorption measure.

• Journal of Multimedia Information System
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• v.5 no.3
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• pp.195-200
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• 2018

Recently, as the sensor and big data analysis technology have been developed, there have been a lot of researches that analyze the purchase-related data such as the trajectory information and the stay time. Such purchase-related data is usefully used for the purchase pattern prediction and the purchase time prediction. Because it is difficult to find periodic patterns in large-scale human data, it is necessary to look at actual data sets, find various feature patterns, and then apply a machine learning algorithm appropriate to the pattern and purpose. Although existing papers have been used to analyze data using various machine learning methods, there is a lack of statistical analysis such as finding feature patterns before applying the machine learning algorithm. Therefore, we analyze the purchasing data of Songjeong Maeil Market, which is a data gathering place, and finds some characteristic patterns through statistical data analysis. Based on the results of 1, we derive meaningful conclusions by applying the machine learning algorithm and present future research directions. Through the data analysis, it was confirmed that the number of visits was different according to the regional characteristics around Songjeong Maeil Market, and the distribution of time spent by consumers could be grasped.

• Journal of Industrial Technology
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• v.33 no.A
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• pp.31-39
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• 2013

There are two methods to make a distinction of deterioration of high-speed railway track. One is that an administrator checks for each attribute value of track induction data represented in graph and determines whether maintenance is needed or not. The other is that an administrator checks for monthly trend of attribute value of the corresponding section and determines whether maintenance is needed or not. But these methods have a weak point that it takes longer times to make decisions as the amount of track induction data increases. As a field of artificial intelligence, the method that a computer makes a distinction of deterioration of high-speed railway track automatically is based on machine learning. Types of machine learning algorism are classified into four type: supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. This research uses supervised learning that analogizes a separating function form training data. The method suggested in this research uses SVM classifier which is a main type of supervised learning and shows higher efficiency binary classification problem. and it grasps the difference between two groups of data and makes a distinction of deterioration of high-speed railway track.

• Journal of Biosystems Engineering
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• v.34 no.4
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• pp.220-227
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• 2009

In the process of oyster mushroom production, loading work of culture medium needs the most intensive labor power. Therefore, development of culture medium machine causes to reduce the manpower and cost. The main objective of this study is to develop the culture medium loading machine and investigate the optimal operation conditions and to evaluate the economic value of the machine. The results are summarized as follows: 1. Optimum transporting velocity of the conveyor was 0.61 m/s 2. Optimum speed of blower was 3183 rpm at the transporting velocity of 0.61 m/s with the loading quantity of 3.41 t/hr 3. Recommendable opening area ratio of pressure controller was 1/2 at the blower speed of 3183 rpm and the transporting velocity of 0.61 m/s 4. The break even point resulted in $240\;m^2$ of cultivating area compared to the method of with portable workbench, and $350\;m^2$ of cultivating area compared to the method of with a tractor and a truck.

• Journal of the Korean Society for Precision Engineering
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• v.31 no.3
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• pp.189-197
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• 2014

Diffractive optical elements (DOEs) - in general a complex pattern of micro- and nano-scale structures - can modulate and transform light in a predetermined way. Their importance is being increased nowadays because they can be designed to handle a number of simultaneous tasks. In view point of machining DOEs, it is a big challenge to fabricate micro- and nano-scale structures on a free-form surfaces. In this paper, the state-of-the-art of the ultra-precision machine tools is reviewed. Also some technical issues which determine the machine tool accuracy are discussed.

• Proceedings of the KSME Conference
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• 2000.04a
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• pp.831-836
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• 2000

During machining, since more than 50% compliance of the cutting point in machine tool structures comes from headstocks, with the remainder coming from beds, slides and structural joints, the structural analysis of the headstock is very important to improve the static and dynamic performances. Especially, in case of machining hard and brittle materials such as glasses and ceramics with the grinding machine, the reinforced headstock with the high damping material is demanded. Since the fiber reinforced composite materials have excellent properties for structures, owing to its high specific modulus, high damping and low thermal expansion, it is expected that the dynamic and thermal characteristics of the headstock will be improved if they are employed as the materials fur headstock. In this paper, the design and the manufacturing methods as well as the static and dynamic characteristics of a steel-composite hybrid headstock were investigated analytically and experimentally to improve the performance of the grinding machine system.