• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.24 no.1
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• pp.7-14
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• 2015

The machine tools builders are trying to improve the efficiency and performance of the machine tools. The electro-mechanical driven broaching machine has many advantages such as lower noisy operating, higher energy efficiency, and smaller space of installation. This paper presents the structural and mechanical development of an electro-mechanical driven broaching machine that is replaced for traditional hydraulic one. The servo motor, ball screw and roller linear guide are used instead of hydraulic cylinder and translation frictional sliding guides. The simulation method based on FEM was applied to analyze the stress, deformation of the machine for static analysis. The dynamic analysis was carried out for verifying and assessing the mechanical behavior of the developed broaching machine. This work helps broaching machine developer make a better product at the early design stage with lower cost and development time.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.22 no.1
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• pp.15-28
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• 2018

Recently Machine Learning algorithms are widely used to process Big Data in various applications and a lot of these applications are executed in run time. Therefore the speed of Machine Learning algorithms is a critical issue in these applications. However the most of modern iteration Machine Learning algorithms use a successive iteration technique well-known in Numerical Linear Algebra. But this technique has a very low convergence, needs a lot of iterations to get solution of considering problems and therefore a lot of time for processing even on modern multi-core computers and clusters. Tchebychev iteration technique is well-known in Numerical Linear Algebra as an attractive candidate to decrease the number of iterations in Machine Learning iteration algorithms and also to decrease the running time of these algorithms those is very important especially in run time applications. In this paper we consider the usage of Tchebychev iterations for acceleration of well-known K-Means and SVM (Support Vector Machine) clustering algorithms in Machine Leaning. Some examples of usage of our approach on modern multi-core computers under Apache Spark framework will be considered and discussed.

• Proceedings of the KSME Conference
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• 2003.11a
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• pp.1858-1863
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• 2003

The properties of a machine tool greatly affect machining quality since a machine tool has large variance in its features. Machine tool makers want to find best machining condition with the one that they have built. Machine builders need to develop test specimen since it helps finding characteristics of machine tools when the machining properties of the specimen are analyzed. This paper develops test specimen to identify features of the main spindle, the feeding device, and the frame of a machine tool. The specimen is machined with a high speed machine and the features of the machine are analyzed with test items. They are surface roughness, overshoot in axial movement, errors in circular movement, feeding with small movement, and compensational error. This work can improve usability for a machine tool in machining practice.

• Journal of the Korean Society of Safety
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• v.30 no.5
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• pp.108-113
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• 2015

Plastic injection moulding machine is widely used for many industrial field. It is classified into mandatory safety certification machinery in Industrial Safety and Health Act because of its high hazard. In order to prevent industrial accidents by plastic injection moulding machine, it is necessary for designer to identify hazardous factors and assess the failure modes to mitigate them. This study tabulates the failure modes of main parts of plastic injection moulding machine and how their failure has affect on the machine being considered. Failure Mode & Effect Analysis(FMEA) method has been used to assess the hazard on plastic injection moulding machine. Risk and risk priority number(RPN) has been calculated in order to estimate the hazard of failures using severity, probability and detection. Accidents caused by plastic injection moulding machine is compared with the RPN which was estimated by main regions such as injection unit, clamping unit, hydraulic and system units to find out the most dangerous region. As the results, the order of RPN is injection unit, clamping unit, hydraulic unit and system units. Barrel is the most dangerous part in the plastic injection moulding machine.

• The Pure and Applied Mathematics
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• v.24 no.4
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• pp.211-226
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• 2017

In this paper, machine learning models employed in various fields are discussed and applied to KOSPI200 stock index return forecasting. The results of hyperparameter analysis of the machine learning models are also reported and practical methods for each model are presented. As a result of the analysis, Support Vector Machine and Artificial Neural Network showed a better performance than k-Nearest Neighbor and Random Forest.

• Transactions of the Korean Society of Mechanical Engineers
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• v.19 no.1
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• pp.1-13
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• 1995

In order to investigate the effect of liquid balancer in washing machine, we identify the vibration characteristics of suspension system of washing machine and formulate the 4 D. O. F. system dynamic equations. As the washing machine rotates higher speed, it is emphasized to reduce the ecentric force due to unbalanced mass. Nowadays, the most effective cancelling method of eccentric force is known as the usage of liquid balancer. To determine the liquid distribution in liquid balancer, the fluid statics is considered. The system dynamic equations are solved by Runge-Kutta method and represent the good characteristics of real washing machine in X-Y plane. The accuracy of the numerical solution was examined by experiments. The simulation results show that the unbalanced mass has so much influence on vibration magnitude and the rotating shape of spin-basket. But the effect of mass reduction due to the dehydration of the spin-basket has little influence on transient vibration.

• Journal of Biosystems Engineering
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• v.40 no.2
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• pp.137-144
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• 2015

Purpose: The high energy requirement of extraction of oil from jatropha seed and reduction of loss in oil content between whole seed and kernel of jatropha necessitate seed shelling. The purpose of this study is to develop and evaluate the performance of a jatropha seed shelling machine based on seed moisture content. Methods: A shelling machine was designed and constructed for jatropha seed. The components are frame, hopper, shelling chamber, concave, and blower with discharge units. The performance evaluation of the machine was carried out by determining parameters such as percentage of whole kernel recovered, percentage of broken kernel recovered, percentage of partially shelled seed, percentage of unshelled seed, machine capacity, machine efficiency, and shelling efficiency. All of the parameters were evaluated at five different moisture levels: 8.00%, 9.37%, 10.77%, 12.21%, and 13.68% w.b.). Results: The shelling efficiency of the machine increased with increase in seed moisture content; the percentage of whole kernel recovered and percentage of partially shelled seed decreased with increase in moisture content; and percentage of broken kernel, machine efficiency, and percentage of unshelled seed followed a sinusoidal trend with moisture content variation. Conclusion: The best operating condition for the shelling machine was at a moisture content of 8.00% w.b., at which the maximum percentage of whole kernel recovered was 23.23% at a shelling efficiency of 73.95%.

• Korean Journal of Artificial Intelligence
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• v.11 no.2
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• pp.19-27
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• 2023

In recent times, an exponential increase in Internet traffic has been observed as a result of advancing development of the Internet of Things, mobile networks with sensors, and communication functions within various devices. Further, the COVID-19 pandemic has inevitably led to an explosion of social network traffic. Within this context, considerable attention has been drawn to research on network traffic analysis based on machine learning. In this paper, we design and develop a new machine learning framework for network traffic analysis whereby normal and abnormal traffic is distinguished from one another. To achieve this, we combine together well-known machine learning algorithms and network traffic analysis techniques. Using one of the most widely used datasets KDD CUP'99 in the Weka and Apache Spark environments, we compare and investigate results obtained from time series type analysis of various aspects including malicious codes, feature extraction, data formalization, network traffic measurement tool implementation. Experimental analysis showed that while both the logistic regression and the support vector machine algorithm were excellent for performance evaluation, among these, the logistic regression algorithm performs better. The quantitative analysis results of our proposed machine learning framework show that this approach is reliable and practical, and the performance of the proposed system and another paper is compared and analyzed. In addition, we determined that the framework developed in the Apache Spark environment exhibits a much faster processing speed in the Spark environment than in Weka as there are more datasets used to create and classify machine learning models.

• Journal of the Korea Society of Computer and Information
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• v.27 no.5
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• pp.11-19
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• 2022

In this paper, we propose a fault tolerant embedded system using memory redundancy on the full-virtualization based virtual machine monitor. The proposed virtual machine monitor first virtualizes main memory of embedded system utilizing efficient shadow page table scheme so that the embedded system runs as a virtual machine on the virtual machine monitor. The virtual machine monitor makes the backup of the embedded system run as another virtual machine by copying memory contents of the embedded system into memory space of backup system according to predefined schedules. When an error occurs in the target virtual machine, the corresponding standby virtual machine takes the role of target virtual machine and continues its operation. Performance evaluation studies show that such backups and switches of virtual machines are performed with minor performance degradation.

• Journal of the Korean Society for Precision Engineering
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• v.9 no.2
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• pp.61-68
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• 1992

To meet the requirements for accuracy, productivity and reliability of machine tools, it is necessary to evaluate the chatter-free machining performance and to improve the dynamic performance of machine tools. In order to perform dynamic design of machine tools reasonably and effectively, the joint parts must be modelled accurately because their characteristics affect significantly on the total characteristics of machine tool. In this paper, an approach which identifies the effect of joint parts on the performance of total machine tool structure was proposed. That uses the experimental modal analysis, the finite element method and the sensitivity analysis method. The effectiveness of this approach was confirmed by applying it to structures with bearing joints. And as a result of the application, the change of dynamic characteristics of bearing joints was indentified.