• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.6
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• pp.988-994
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• 1997

The demands for robotic and automatic system are continually increasing in manufacturing fields. There have been many studies to monitor and predict the system, but they have mainly focused upon measuring cutting force, and current of motor spindle, and upon using acoustic sensor, etc. In this study, digital image of time series sequence was acquired by taking advantage of optical technique. Mean square error was obtained from it and was available for useful observation data. The parameter was estimated using PAA(parameter adaptation algorithm) from observation data. AR(auto regressive) model was selected for system model and fifth order was decided according to parameter estimation. Uncorrelation test was also carried out to verify convergence of parameter. Through the proceedings, it was found that there was a system stability.

• Journal of the Optical Society of Korea
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• v.17 no.5
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• pp.423-427
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• 2013

We present the preparation and characteristics of liquid-phase sensors based on nano-porous silicon multilayer structures for determination of organic content in gasoline. The principle of the sensor is a determination of the cavity-resonant wavelength shift caused by refractive index change of the nano-porous silicon multilayer cavity due to the interaction with liquids. We use the transfer matrix method (TMM) for the design and prediction of characteristics of microcavity sensors based on nano-porous silicon multilayer structures. The preparation process of the nano-porous silicon microcavity is based on electrochemical etching of single-crystal silicon substrates, which can exactly control the porosity and thickness of the porous silicon layers. The basic characteristics of sensors obtained by experimental measurements of the different liquids with known refractive indices are in good agreement with simulation calculations. The reversibility of liquid-phase sensors is confirmed by fast complete evaporation of organic solvents using a low vacuum pump. The nano-porous silicon microcavity sensors can be used to determine different kinds of organic fuel mixtures such as bio-fuel (E5), A92 added ethanol and methanol of different concentrations up to 15%.

• Transactions of the Korean Society of Automotive Engineers
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• v.10 no.5
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• pp.9-14
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• 2002

The durability prediction of emission control components, especially 02 sensor and catalytic converter, is getting more important as emission regulation is getting stricter and vehicle durability mileage requirement is also extended from 80,000 ㎞ to 160,000 km in Korean market. And the duration of vehicle mileage accumulation to get vehicle exhaust emission deterioration factor for certification is required to be shorter in order to reduce the vehicle development time. Since most of the vehicle emission development tests are done on chassis dynamometer and aging bench by using vehicle aging modes, real road condition and in-use driving patterns must be reflected into them to predict the vehicle emission level and to meet emission regulation especially at high mileage. In order to get the frequent driving pattern of vehicle and the aging characteristic of emission components, a vehicle was tested by changing drivers and driving roads around Seoul. Real road driving patterns were analyzed and compared with those of the certification modes which are well known in automotive industry.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.4
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• pp.751-756
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• 2018

In smart factories and industrial IoT, all facilities in a factory are monitored over the Internet, thereby facility can reduce the downtime and increase the availiability by preventive maintenance before it breaks down. The abnormal conditions of the major facilities in the plant are caused by abnormal temperature rise, vibration, and variations in noise. Consequently, it is critical to develop a very small smart device that is easily installed in a small space to enable real-time monitoring of the vibration status of the facility. In this study, smart devices were developed for smart factory fault prediction and robustness management using ultra small micro-controllers with WiFi capabilities and MEMS acceleration sensors.

• Journal of the Korea Institute of Military Science and Technology
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• v.22 no.6
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• pp.735-744
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• 2019

One of the key factors for recognition performance in the automatic target recognition for synthetic aperture radar imagery(SAR-ATR) system is reliability of the SAR target database. To achieve optimal performance, the database should be constructed using the images obtained under the same operating condition as the SAR sensor. However, it is impractical to have the extensive set of real-world SAR images, and thus those from the electro magnetic prediction tool with 3-D CAD models are suggested as an alternative where their reliability can be always questionable. In this paper, a method for similarity assessment between target SAR images is presented inspired by the fact that a target SAR image is mainly characterized by the features of scattering centers. The method is demonstrated using a variety of examples and quantitatively measures the similarity related to reliability. Its assessment performance is further compared with that of the existing metric, structural similarity(SSIM).

• KIPS Transactions on Computer and Communication Systems
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• v.5 no.3
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• pp.71-78
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• 2016

Recently, The interest about underwater acoustic communication is increase such as marine resources, disaster prevention, weather prediction, and so on. Because the underwater acoustic communication uses a water as media, the underwater acoustic communication has a lot of restrictions. Although the underwater acoustic communication is hard, it is important to consider the security. In this paper, we estimate the performance of cryptographic algorithms(AES, ARIA, and LEA) on a various devices, available in underwater acoustic communication, and analysis the results. This result will be provide effective data confidentiality for underwater communication.

• Annual Conference of KIPS
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• 2019.05a
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• pp.552-555
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• 2019

디지털 트윈이란 현실 세계의 물리적인 사물을 컴퓨터 상에 동일하게 가상화 시키는 기술을 의미하는 것으로, 물리적 사물이나 시스템을 모델링하거나 IoT 기술에 접목되어 활용되고 있는 기술이다. 디지털 트윈 기술은 가상의 모델을 무한정 시뮬레이션을 통해 동작을 튜닝하고 환경변화에 대한 대응을 미리 실험하여 리스크를 최소화할 수 있는 장점을 지닌다. 최근 인공지능이나 기계학습에 관련된 기술들이 주목받기 시작하면서, 이와 같은 물리적인 사물의 모델링 작업을 데이터 기반으로 수행하려는 시도가 증가하고 있다. 특히, 산업현장에서 많이 활용되는 인더스트리 4.0 공장 자동화의 핵심인 협력 로봇의 디지털 트윈을 구축하기 위해서는 로봇의 동작을 인지하는 과정이 필수적으로 요구된다. 그러나 현재 협력 로봇의 동작을 인지하기 위한 시도는 미비하며, 센서 데이터를 기반으로 동작을 역으로 예측하는 기술은 더욱 그렇다. 따라서 본 논문에서는 로봇의 동작을 인지하기 위해 가정용 협력 로봇에서 전류 및 관성 데이터를 수집하기 위한 실험 환경을 구축하고, 수집한 센서 데이터를 기반으로 한 동작 예측 모델을 제안하고자 한다. 제안하는 방식은 로봇의 동작 명령어를 조인트 위치 기반으로 분류하고 전류와 위치 센서 값을 사용하여 학습을 통해 예측하는 방식이다. SVM 을 이용하여 학습한 결과, 모델의 성능은 평균적으로 정확도, 정밀도, 및 재현율이 모두 96%로 평가되었다.

• Journal of the Society of Naval Architects of Korea
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• v.54 no.4
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• pp.294-300
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• 2017

The offshore installations and ships are the structures most likely to be exposed to hazards such as hydrocarbon fire and/or explosion. Developing proactive measures to prevent the escalation of such events thus requires detailed knowledge of the related phenomena and their consequences. $CO_2$ extinguishing systems are extensively used for fire accidents of on-and offshore installations because of outstanding performance and low cost. There is, however, the risk of carbon dioxide system which enumerates many of the fatalities by suffocation associated with industrial fire protection requirements. Therefore, the aim of this study is to perform the prediction of fire suppression characteristics of the carbon dioxide system in realistic enclosed compartment area of ships and propose $CO_2$ extinguish fire fighting system for preventing suffocation accidents during fire fighting. According to CFD calculations, it can be observed and assessed that various fire profiles with $CO_2$ and $O_2$ mole fraction in the target enclosed compartment area are applicable within the proposed system. Additionally, the design of fire safety system of ships and offshore installations can utilize ventilation system and/or layout arrangement through the proposed system.

• IEMEK Journal of Embedded Systems and Applications
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• v.6 no.1
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• pp.16-24
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• 2011

Automotive Radar Systems are currently under development for various applications to increase accuracy and reliability. The target tracking is most important in single or multiple target environments for accuracy. The tracking algorithm provides smoothed and predicted data for target position and velocity(Doppler). To this end, the fixed gain filter(${\alpha}{\beta}$ filter, ${\alpha}{\beta}{\gamma}$ filter) and dynamic filter(Kalman filter, Singer-Kalman filter, etc) are commonly used. Gating is used to decide whether an observation is assigned to an existing track or new track. Gating algorithms are normally based on computing a statistical error distance between an observation and prediction. The data association takes the observation-to-track pairings that satisfied gating and determines which observation-to-track assignment will actually be made. For data association, NNPDA(Nearest Neighbor Probabilistic Data Association) algorithm is proposed. In this paper, we designed a target tracking system developed for an Automotive Radar System. We show the experimental results of the 77GHz FMCW radar sensor on the roads. Four tracking algorithms(${\alpha}{\beta}$ filter, ${\alpha}{\beta}{\gamma}$ filter, 2nd order Kalman filter, Singer-Kalman filter) have been compared and analyzed to evaluate the performance in test scenario.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.9
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• pp.28-34
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• 2021

The Fourth Industrial Revolution has led to the development of drones for commercial and private applications. Therefore, the malfunction of drones has become a prominent problem. Failure mode and effect analysis was used in this study to analyze the primary cause of drone failure, and blade breakage was observed to have the highest frequency of failure. This was tested using a vibration sensor placed on drones along the breakage length of the blades. The data exhibited a significant increase in vibration within the drone body for blade fracture length. Principal component analysis was used to reduce the data dimension and classify the state with machine learning algorithms such as support vector machine, k-nearest neighbor, Gaussian naive Bayes, and random forest. The performance of machine learning was higher than 0.95 for the four algorithms in terms of accuracy, precision, recall, and f1-score. A follow-up study on failure prediction will be conducted based on the results of fault diagnosis.