• Korean Journal of Computational Design and Engineering
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• v.10 no.4
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• pp.254-261
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• 2005

The purpose of this research is to develop a method for cutting non-circular holes on a bent thick plate. Generally in order to cut the holes on the large plates, a special-purpose 5-axis machine is needed. However, such a machine is unavailable in most of the machine shops. This paper provides a description of such a method that utilizes a general-purpose 5-axis water-jet machine in place of the special-purpose machine: First, the bent piece is transformed into a flat plate, where the shape of the holes is reconstructed by considering deformation during bending. Then, after a 5-axis NC data is generated, the holes on the flat plate are cut using the 5-axis water-jet machine. The final step is to return to its, original shape by bending the plate with its newly-cut holes. The proposed methodology is implemented as a dedicated system by customizing a commercial CAD/CAM system. Some illustrations are provided throughout the paper in order to show the validity of the proposed methods and the developed system.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.8
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• pp.1098-1103
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• 2014

This study deals with the development of an electrofusion welding machine that is capable of joining plastic pipes using a recently developed electrofusion fitting. This fitting has built-in conductive plastics that are used to weld the joint together as a heating element. In order to explain the mechanism of the new machine, 1) the resistance characteristics of the heating element were explained, 2) the method of electric welding that uses the electrofusion fitting was described, and 3) the method of power supply based on controlling the firing angle was explained. A control system for an intelligent electrofusion welding machine was proposed. This system has the ability to recognize the diameter of an electrofusion fitting using a lookup-table based on the difference of resistance curves according to fitting types, and it is able to weld the fittings regardless of the ambient temperature. A new algorithm was developed to control the power of electric welding through the recognition of feature points from the resistance curve of the heating element. In order to evaluate the performance of the developed welding machine, tests involving the welding of 16 mm- and 20 mm-type fittings were carried out. Examining the welding results, we concluded that the proposed welding machine will offer high productivity and reliability in the field of electrofusion welding.

• Journal of IKEEE
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• v.22 no.3
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• pp.759-766
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• 2018

This paper presents a high-speed axial-flux machine which utilizes the idea of sinusoidal shaped pole combined with a consequent iron-pole. The target of the proposed machine is the cost reduction of the relatively expensive Samarium-Cobalt (SmCo) permanent magnet (PM) material and the torque per PM volume improvement by using sinusoidal consequent-pole rotor. The effectiveness of the proposed machine is validated by comparing it with conventional consequent-pole and with conventional PM machines using 3-D finite element method (FEM) simulations. The comparison and analysis is done in terms of back electro-motive force (back-EMF) harmonic contents, torque per PM volume and torque ripple characteristics. The simulation results show that the proposed machine is suitable and cost-effective for high-speed and high torque per PM volume applications. Furthermore, due to the consequent pole, the magnetic flux saturation and the overload current torque-capability are also presented and discussed in the paper.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.4
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• pp.383-390
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• 2016

Remote sensing data has been widely used in the estimation of crop yields by employing statistical methods such as regression model. Machine learning, which is an efficient empirical method for classification and prediction, is another approach to crop yield estimation. This paper described the corn yield estimation in Iowa State using four machine learning approaches such as SVM (Support Vector Machine), RF (Random Forest), ERT (Extremely Randomized Trees) and DL (Deep Learning). Also, comparisons of the validation statistics among them were presented. To examine the seasonal sensitivities of the corn yields, three period groups were set up: (1) MJJAS (May to September), (2) JA (July and August) and (3) OC (optimal combination of month). In overall, the DL method showed the highest accuracies in terms of the correlation coefficient for the three period groups. The accuracies were relatively favorable in the OC group, which indicates the optimal combination of month can be significant in statistical modeling of crop yields. The differences between our predictions and USDA (United States Department of Agriculture) statistics were about 6-8 %, which shows the machine learning approaches can be a viable option for crop yield modeling. In particular, the DL showed more stable results by overcoming the overfitting problem of generic machine learning methods.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.5
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• pp.1601-1607
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• 2010

This paper presents FMEA (Failure Mode Effect Analysis) for maintenance of mail sorting machine which is for automatic sorting of mail. We suggest the update method of regular diagnosis item and period for maintenance of mail sorting machine using the risk priority number which is calculated by severity, occurrence, and detection of failure mode of mail sorting machine, and shows FMEA adoption example of letter sorting machine. This paper also describes the current maintenance system and status of mail sorting machine in the domestic postal logistics environment, and FMEA adoption step. The proposed maintenance using FMEA will be adapted for more easy and efficiency maintenance of mail sorting machine.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.3
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• pp.121-126
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• 2019

Recently IoT named Internet of Things technology is widely used throughout the industry. In this paper, a smart vending machine system is proposed to solve inconvenience of vending machine which is commonly seen. This allows users to purchase goods without cash and saves management costs by allowing vending machine managers to stock and sell conveniently. For the production of this smart vending machine, Analysis example using UML which is object-oriented analysis method and flowchart and screen design applying it are presented. Also this presented method is shown to being applied usefully for a guideline of building smart vending machine commercialization system based on IoT.

• Journal of radiological science and technology
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• v.42 no.1
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• pp.9-17
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• 2019

Although digital mammography is a representative method for breast cancer detection. It has a limitation in detecting and classifying breast tumor due to superimposed structures. Machine learning, which is a part of artificial intelligence fields, is a method for analysing a large amount of data using complex algorithms, recognizing patterns and making prediction. In this study, we proposed a technique to improve the diagnostic accuracy of energy-selective mammography by training data using the machine learning algorithm and using dual-energy measurements. A dual-energy images obtained from a photon-counting detector were used for the input data of machine learning algorithms, and we analyzed the accuracy of predicted tumor thickness for verifying the machine learning algorithms. The results showed that the classification accuracy of tumor thickness was above 95% and was improved with an increase of imput data. Therefore, we expect that the diagnostic accuracy of energy-selective mammography can be improved by using machine learning.

• Journal of the Korean Society of Industry Convergence
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• v.24 no.4_2
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• pp.481-490
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• 2021

In the ultrasonic welding machine, when the load fluctuates, the L and C of the piezo element in the oscillation part change. As a result, the resonant frequency is changed, so it is necessary to match the operating frequency of the ultrasonic welding machine to the new resonant frequency. That is, in order to maximize the output of the oscillation unit of the ultrasonic welding machine, it is inevitable to follow the resonance frequency. Accordingly, many methods for following the resonant frequency are being actively studied. In addition, in order to check the effect of external inductance on the operation of the ultrasonic welding machine, The equivalent circuit of the piezo element was analyzed by including the external inductance for resonance in the equivalent circuit of the piezo element, and the method of selecting an appropriate inductance was described. In this paper, we propose a new system that allows the switching frequency of the inverter to tracking the resonance frequency even if the resonance frequency is changed due to the load of the ultrasonic welding machine.

• Tribology and Lubricants
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• v.36 no.6
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• pp.297-306
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• 2020

This paper reviews studies on the types and functions of oil sensors used for machine condition monitoring. Machine condition monitoring is essential for maintaining the reliability of machines and can help avoid catastrophic failures while ensuring the safety and longevity of operation. Machine condition monitoring involves several components, such as compliance monitoring, structural monitoring, thermography, non-destructive testing, and noise and vibration monitoring. Real-time monitoring with oil analysis is also utilized in various industries, such as manufacturing, aerospace, and power plants. The three main methods of oil analysis are off-line, in-line, and on-line techniques. The on-line method is the most popular among these three because it reduces human error during oil sampling, prevents incipient machine failure, reduces the total maintenance cost, and does not need complicated setup or skilled analysts. This method has two advantages over the other two monitoring methods. First, fault conditions can be noticed at the early stages via detection of wear particles using wear particle sensors; therefore, it provides early warning in the failure process. Second, it is convenient and effective for diagnosing data regardless of the measurement time. Real-time condition monitoring with oil analysis uses various oil sensors to diagnose the machine and oil statuses; further, integrated oil sensors can be used to measure several properties simultaneously.

• Proceedings of the Safety Management and Science Conference
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• 2005.11a
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• pp.199-202
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• 2005

All machines and systems in a firm should be managed for optimizing its productivity. Actually, however, it is hard to manage the machines of a firm in collective methods because the company making each machine varies and the way maintaining each machine is various. As a machine Is out of order, the machine is fixed by the repair person from the company making the machine. Every time comes repair person to fix a machine firm having the machine would experience time loss and cost loss. Therefore, a firm needs to manage blueprint of their machine to reduce loss in time and cost and to maintain their facilities in TPM(Total Productive Management) to make operators manage directly. In this study, we suggest the method to manage a blueprint of a machine with TPM.