• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.22 no.6
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• pp.26-33
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• 2008

This paper presents a new full-bridge soft switching PWM DC-DC converter circuit topology that adding one switcher, one lossless snubber quasi-resonance capacity to power source for general welding machine This full-bridge soft switching DC-DC convoter· topology can applicable 600[V] switching device (IGBT)incase of AC 400[V] common power source because the voltage of active switcher is 1/2 of DC bus line voltage. And low voltage hight current out)ut that first coil current is smaller than second coil current in high frequency transformer can be obtained with decreasing path loss in conventional DC bus line switcher. As it operate ZCS/ZVS in full range, high frequency, high efficiency and high output are implemented at low voltage and high DC current switching power supplies. All of this items are got from simulation and the result of experiment. If make up for the weak points of this proposed circuit, it will be used more easily for next generation TIG, MIG and MAG type of arc-welding machine.

• Journal of KIISE:Computer Systems and Theory
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• v.26 no.7
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• pp.752-765
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• 1999

대부분 병렬처리 시스템에서 성능 파라미터는 복잡하고 프로그램의 수행 시 예견할 수 없게 변하기 때문에 컴파일러가 프로그램 수행에 대한 최적의 성능 파라미터들을 컴파일 시에 결정하기가 힘들다. 본 논문은 병렬 처리 시스템의 프로그램 수행 시, 변화하는 시스템 성능 상태에 따라 전체 성능이 최적화로 적응하는 적응 수행 방식을 제안한다. 본 논문에서는 이 적응 수행 방식 중에 적응 프로그램 수행을 위한 이론적인 방법론 및 구현 방법에 대해 제안하고 적응 제어 수행을 위해 프로그램의 데이타 공유 단위에 대한 적응방식(적응 입도 방식)을 사용한다. 적응 프로그램 수행 방식은 프로그램 수행 시 하드웨어와 컴파일러의 도움으로 프로그램 자신이 최적의 성능을 얻을 수 있도록 적응하는 방식이다. 적응 제어 수행을 위해 수행 시에 병렬 분산 공유 메모리 시스템에서 프로세서 간 공유될 수 있은 데이타의 공유 상태에 따라 공유 데이타의 크기를 변화시키는 적응 입도 방식을 적용했다. 적응 입도 방식은 기존의 공유 메모리 시스템의 공유 데이타 단위의 통신 방식에 대단위 데이타의 전송 방식을 사용자의 입장에 투명하게 통합한 방식이다. 시뮬레이션 결과에 의하면 적응 입도 방식에 의해서 하드웨어 분산 공유 메모리 시스템보다 43%까지 성능이 개선되었다. Abstract On parallel machines, in which performance parameters change dynamically in complex and unpredictable ways, it is difficult for compilers to predict the optimal values of the parameters at compile time. Furthermore, these optimal values may change as the program executes. This paper addresses this problem by proposing adaptive execution that makes the program or control execution adapt in response to changes in machine conditions. Adaptive program execution makes it possible for programs to adapt themselves through the collaboration of the hardware and the compiler. For adaptive control execution, we applied the adaptive scheme to the granularity of sharing adaptive granularity. Adaptive granularity is a communication scheme that effectively and transparently integrates bulk transfer into the shared memory paradigm, with a varying granularity depending on the sharing behavior. Simulation results show that adaptive granularity improves performance up to 43% over the hardware implementation of distributed shared memory systems.

• Journal of Sensor Science and Technology
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• v.28 no.5
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• pp.311-317
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• 2019

Rotating devices are commonly installed in power plants and factories. This study proposes a self-powered sensor node that is powered by converting the vibration energy of a rotating device into electrical energy. The self-powered sensor consists of a piezoelectric harvester for self-power generation, a rectifier circuit to rectify the AC signal, a sensor unit for measuring the vibration frequency, and a circuit to control the light emitting diode (LED) lighting. The frequency of the vibration source was measured using a piezoelectric-cantilever-type vibration frequency sensor. A green LED was illuminated when the measured frequency was within the normal range. The power generated by the piezoelectric harvester was determined, and the LED operation was assessed in terms of the vibration frequency. The piezoelectric harvester was found to generate a power of 3.061 mW or greater at a vibration acceleration of 1.2 g ($1g=9.8m/s^2$) and vibration frequencies between 117 and 123 Hz. Notably, the power generated was 4.099 mW at 122 Hz. As such, our self-powered sensor node can be used as a module for monitoring rotating devices, because it can convert vibration energy into electrical energy when installed on rotating devices such as air compressors.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.2
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• pp.8-13
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• 2021

The study for soil has been conducted separately by several areas such as soil mechanics and soil chemistry. Soil is important in terms of prediction of how the plant grow with nutrient requirement. Also, soil is important for machines to work on to solve labor shortage and save farmers from harsh environment during farm work. To meet diverse needs related to soil in agriculture, the soil related study needs to be conducted synthetically. Thus, we tried to obtain the data related to soil chemistry including pH and Electrical Conductivity (EC) with data related to soil mechanics including Cone Index (CI), moisture content, soil classification. Specifically, the condition of the field was set to be cultivated at least for five years continuously at a first step. The soil was taken from 30 sites. CI was obtained using the soil penetrometer and soil classification was conducted using sieve analysis with eight kinds of sieve. The soil was taken on December when is during winter in Korea. There was variation of data including moisture content and CI.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.1
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• pp.163-169
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• 2020

Recently, research on automation and unmanned operation of machines in the industrial field has been conducted with the advent of AI, Big data, and the IoT, which are the core technologies of the Fourth Industrial Revolution. The machines for these automation processes are controlled based on the data collected from the sensors attached to them, and further, the processes are managed. Conventionally, the abnormalities of sensors are periodically checked and managed. However, due to various environmental factors and situations in the industrial field, there are cases where the inspection due to the failure is not missed or failures are not detected to prevent damage due to sensor failure. In addition, even if a failure occurs, it is not immediately detected, which worsens the process loss. Therefore, in order to prevent damage caused by such a sudden sensor failure, it is necessary to identify the failure of the sensor in an embedded system in real-time and to diagnose the failure and determine the type for a quick response. In this paper, a deep neural network-based fault diagnosis system is designed and implemented using Raspberry Pi to classify typical sensor fault types such as erratic fault, hard-over fault, spike fault, and stuck fault. In order to diagnose sensor failure, the network is constructed using Google's proposed Inverted residual block structure of MobilieNetV2. The proposed scheme reduces memory usage and improves the performance of the conventional CNN technique to classify sensor faults.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.2
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• pp.185-195
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• 2018

As machines have been automated in the field of industries in recent years, it is a paramount importance to manage and maintain the automation machines. When a fault occurs in sensors attached to the machine, the machine may malfunction and further, a huge damage will be caused in the process line. To prevent the situation, the fault of sensors should be monitored, diagnosed and classified in a proper way. In the paper, we propose a sensor fault detection scheme based on SVM and CNN to detect and classify typical sensor errors such as erratic, drift, hard-over, spike, and stuck faults. Time-domain statistical features are utilized for the learning and testing in the proposed scheme, and the genetic algorithm is utilized to select the subset of optimal features. To classify multiple sensor faults, a multi-layer SVM is utilized, and ensemble technique is used for CNN. As a result, the SVM that utilizes a subset of features selected by the genetic algorithm provides better performance than the SVM that utilizes all the features. However, the performance of CNN is superior to that of the SVM.

• Journal of Biomedical Engineering Research
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• v.32 no.2
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• pp.151-157
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• 2011

Tattooing is a performance for decorative and cosmetic marking by placing permanent ink into the skin. As the cultural meaning of tattoo in Korea is changing, the tattoo machines are widely spread n permanent cosmetic market. Though the use of the tattoo machine was increased, the evaluation standards of tattoo machine were not existed. Korea Food and Drug Association regulated the electrical and mechanical safety standards which were founded on the IEC 601-1 second edition. Also they regulated he biological safety standards which were derived from the ISO 10993 series, however, these general valuations of common medical device were insufficient for evaluating tattoo machine. We developed the standards of tattoo machine for safety and performance evaluation for tattoo machine by preliminary hazard analysis in ISO 14971. The evaluation criteria of tattoo machines are focused on the mechanical invasion. We suggested the additional evaluation items of the needle speed, length, vibration with general valuation criteria of common medical device. We anticipate that this research may be a primary stage to figure a standard regulation and evaluation for tattoo machine.

• Journal of the Korean Institute of Gas
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• v.2 no.4
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• pp.79-84
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• 1998

This paper describes the minimum ignition limit voltages for LPG-Air 5.25[Vol$\%$] mixture gas by discharge sparks in radio-frequency limits using RF power supply and IEC type ignition spark apparatus. As a result, the minimum ignition limit voltages is increased in proportional to the rate of increasing of frequency in LPG-Air mixture gas. Especially, the minimum ignition limit voltages increase remarkably between 3[KHz] and 10[KHz]. It is considered that ignition is caused by one discharge until 3[KHz] and, beyond 3[KHz] ignitiof is caused by more than two discharges. The reason is analyzed that energy loss is caused by existing pause interval between discharges. It is considered that the result can be used for not only data for researches and development of intrinsically safe explosion-proof RF machines which are applied tole-equipments and detectors used in dangerous areas but also for datum for its equipment tests.

• Journal of Korean Tunnelling and Underground Space Association
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• v.16 no.6
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• pp.599-614
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• 2014

When tunnelling with TBM (Tunnel Boring Machine), accessibility to tunnel face is very limited because tunnel face is mostly occupied by a bunch of machines. Existing techniques that can predict ground condition ahead of TBM tunnel are extremely limited. In this study, the TBM Resistivity Prediction (TRP) system has been developed for predicting anomalous zone ahead of tunnel face utilizing electrical resistivity. The applicability and prediction accuracy of the developed system has been verified by performing field tests at subway tunnel construction site in which an EPB (Earth Pressure Balanced) shield TBM was used for tunnelling work. The TRP system is able to predicts the location, thickness and electrical properties of anomalous zone by performing inverse analysis using measured resistivity of the ground. To make field tests possible, an apparatus was devised to attach electrode to tunnel face through the chamber. The electrode can be advanced from the chamber to the tunnel face to fully touch the ground in front of the tunnel face. In the 1st field test, none of the anomalous zone was predicted, because the rock around the tunnel face has the same resistivity and permittivity with the rock ahead of tunnel face. In the 2nd field test, 5 m thick anomalous zone was predicted with lower permittivity than that of the rock around the tunnel face. The test results match well with the ground condition predicted, respectively, from geophysical exploration, or directly obtained either from drilling boreholes or from daily observed muck condition.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.6
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• pp.433-438
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• 2016

This paper proposes a grade prediction method to measure meat quality in Hanwoo (Korean Native Cattle) using classification and feature extraction algorithms. The applied classification algorithm is an AdaBoost and the texture features of the given ultrasound images are extracted using SFTA. In this paper, as an initial phase, we selected ultrasound images of Hanwoo for verifying experimental results; however, we ultimately aimed to develop a diagnostic decision support system for human body scan using ultrasound images. The advantages of using ultrasound images of Hanwoo are: accurate grade prediction without butchery, optimizing shipping and feeding schedule and economic benefits. Researches on grade prediction using biometric data such as ultrasound images have been studied in countries like USA, Japan, and Korea. Studies have been based on accurate prediction method of different images obtained from different machines. However, the prediction accuracy is low. Therefore, we proposed a prediction method of meat quality. From the experimental results compared with that of the real grades, the experimental results demonstrated that the proposed method is superior to the other methods.