• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.37 no.8
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• pp.737-743
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• 2009

Design optimization process and results of Loop Heat Pipe(LHP) using Tabu Search have been presented in this study. An objective of optimization is to reduce a mass of the LHP with satisfying operating temperature of a Lithium Ion battery onboard an aircraft. The battery is assumed to be used as power supply of air borne high energy laser system because of its high specific energy. The analytical models are based on a steady state mathematical model and the design optimization is performed using a Meta Model and Tabu Search. As an optimization results, the Tabu search algorithm guarantees global optimum with small computation time. Due to searching by random numbers, initial value is dominant factor to search global optimum. The optimization process could reduce the mass of the LHP which express the same performance as an published LHP.

• Journal of Korea Society of Industrial Information Systems
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• v.25 no.2
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• pp.129-136
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• 2020

Recognizing finger movements have been used as a intuitive way of human-computer interaction. In this study, we implement an wearable device for finger motion recognition and evaluate the accuracy of several ML (Machine learning) techniques. Not only HMM (Hidden markov model) and DTW (Dynamic time warping) techniques that have been traditionally used as time series data analysis, but also NN (Neural network) technique are applied to compare and analyze the accuracy of each technique. In order to minimize the computational requirement, we also apply the pre-processing to each ML techniques. Our extensive evaluations demonstrate that the NN-based gesture recognition system achieves 99.1% recognition accuracy while the HMM and DTW achieve 96.6% and 95.9% recognition accuracy, respectively.

• Proceedings of the Korean Society of Computer Information Conference
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• 2011.06a
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• pp.175-177
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• 2011

본 논문의 Live Site 개념을 도입한 웹사이트 저작도구의 실시간 자동화 모델에 관한 연구에서는 MVC(Model-View-Controller) 패턴의 시스템으로 사용자가 요구 사항을 전달하면 jsp에서 jsp 간의 호출을 통해 서버로 변경 사항을 넘기고 그 결과물을 다시 사용자에게 보여주는 형식으로 진행된다. 본 시스템 개발에 사용한 Jquery는 자바스크립트와 HTML 사이의 상호작용을 강조하는 경량화된 web application framework로 일반적 웹 스크립팅에 폭넓게 사용 될 수 있는 추상적 계층을 제공하여 스크립팅에서 필요로 하는 거의 모든 상황에 사용 할 수 있다. 본 논문에서는 추상화된 데이터를 제공하여 일상적인 작업들을 일반화 하고 코드의 크기를 줄이며 극도로 단순하게 개발이 가능한 jquery를 사용하여 거의 모든 브라우저에 호환이 가능한, 사용자 각 개인의 경향에 맞춘 웹사이트 저작 도구를 개발하였다. 본 논문에서는 추상화된 데이터를 제공하므로 일상적인 작업들을 일반화 하고 코드의 크기를 줄이며 극도로 단순하게 개발이 가능한 jquery를 사용하여 거의 모든 브라우저에 호환이 가능한, 사용자 각 개인의 경향에 맞춘 웹 저작 도구를 연구하였다. 전 세계적으로 웹 시장이 대두 되는 이 시점에 본 프로그램은 다양한 웹 제작 공급에 대한 새로운 시장을 형성해 주며, 새로운 콘텐츠 제작 방식의 도입으로 인한 활발한 인터넷 시장이 형성 되리라 기대한다. 현재 일부 생소한 Live Site 개념 즉, '사용자가 직접 보고 느끼며 원하는 대로 만드는 웹' 이란 개념의 가지고 고객 만족 커뮤니티라는 목적에 중점을 둔 본 프로그램 개발은 최근 웹 경향에 따른 이상적인 시스템이라 할 수 있다.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.4
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• pp.95-105
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• 2021

Road surface damage detection is essential for a comfortable driving environment and the prevention of safety accidents. Road management institutes are using automated technology-based inspection equipment and systems. As one of these automation technologies, a sensor to detect road surface damage plays an important role. For this purpose, several studies on sensors using deep learning have been conducted in recent years. Road images and label images are needed to develop such deep learning algorithms. On the other hand, considerable time and labor will be needed to secure label images. In this paper, the adversarial learning method, one of the semi-supervised learning techniques, was proposed to solve this problem. For its implementation, a lightweight deep neural network model was trained using 5,327 road images and 1,327 label images. After experimenting with 400 road images, a model with a mean intersection over a union of 80.54% and an F1 score of 77.85% was developed. Through this, a technology that can improve recognition performance by adding only road images was developed to learning without label images and is expected to be used as a technology for road surface management in the future.

• Composites Research
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• v.34 no.6
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• pp.337-344
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• 2021

In this study, the progressive failure behavior of the composite fan blade dovetail element under tensile loading is numerically investigated through finite element(FE) simulation. The accuracy of prediction by FE simulation is verified through tensile testing. The dovetail element is one of the joints for coupling the fan blade with the disk in a turbofan engine. The dovetail element is usually made of a metal material such as titanium, but the application of composite material is being studied for weight reduction reasons. However, manufacturing defects such as drop-off ply and resin pocket inevitably occur in realizing complex shapes of the fan blade made by composite materials. To investigate the effect of these manufacturing defects on the composite fan blade dovetail element, we performed numerical simulation with FE model to compare the prediction of the FE model and the tensile test results. At this time, the cohesive zone model is used to simulate the delamination behavior. Finally, we found that FE simulation results agree with test results when considering thermal residual stress and through-thickness compression enhancement effect.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.1
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• pp.77-89
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• 2024

This paper presents an unmanned store management system that can provide inventory management and theft prevention for displayed products using a small camera that can monitor the shelves of sold products in small and medium-sized stores. This system is a service solution that integrates object recognition, real-time communication, security management, access management, and mobile authentication. The proposed system uses a custom YOLOv5-x model to recognize objects on the display, measure quantities in real time, and support real-time data communication with servers through Raspberry Pie. In addition, the number of objects in the database and the object recognition results are compared to detect suspected theft situations and provide burial images at the time of theft. The proposed unmanned store solution is expected to improve the efficiency of small and medium-sized unmanned store operations and contribute to responding to theft.

• Journal of the Korea Society of Computer and Information
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• v.29 no.3
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• pp.75-81
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• 2024

In this paper, we proposes a method for real-time processing of inter-floor noise problems by embedding TinyML, which includes a deep learning model, into ultra-low-power systems. The reason this method is feasible is because of lightweight deep learning model technology, which allows even systems with small computing resources to perform inference autonomously. The conventional method proposed to solve inter-floor noise problems was to send data collected from sensors to a server for analysis and processing. However, this centralized processing method has issues with high costs, complexity, and difficulty in real-time processing. In this paper, we address these limitations by employing On-Sensor AI using TinyML. The method presented in this paper is simple to install, cost-effective, and capable of processing problems in real-time.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.5
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• pp.364-371
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• 2017

TPMS is a vehicle electric system that measures the air pressure of a tire, and informs the driver of current tire states. The TPMS sensor typically uses unidirectional communication for small size, light weight, and low power. The transmission period of the sensor indicates the service quality of monitoring the tire. In order to determine the optimal transmission period, frame collision probability and the life time of the sensor should be analyzed. In this paper, collision probability model using Venn diagram is designed in low power TPMS with the normal and warning mode. And the life time and a collision probability were analyzed with the ratio(n) of the normal mode to warning mode transmission period. As a result, $T_{nP}=31sec$ and $T_{wP}=2.4sec$ at 5 years, and $T_{nP}=71sec$ and $T_{wP}=2.5sec$ at 7 years.

• Journal of KIISE:Computer Systems and Theory
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• v.36 no.5
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• pp.351-359
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• 2009

Recently with increase of data in the Internet, platform technologies that can process huge data effectively such as Google platform and Hadoop are regarded as worthy of notice. In this kind of platform, there exist network I/O overheads to send task outputs due to the MapReduce operation which is a programming model to support parallel computation in the large cluster system. In this paper, we suggest applying of TIPC (Transparent Inter-Process Communication) protocol for reducing network I/O overheads and increasing network performance in the distributed computing environments. TIPC has a lightweight protocol stack and it spends relatively less CPU time than TCP because of its simple connection establishment and logical addressing. In this paper, we analyze main features of the Hadoop-based distributed computing system, and we build an experimental model which can be used for experiments to compare the performance of various protocols. In the experimental result, TIPC has a higher bandwidth and lower CPU overheads than other protocols.

• The Journal of the Korea institute of electronic communication sciences
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• v.5 no.3
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• pp.262-268
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• 2010

In this paper, this study restores the distorted image to its original image by compensating for the distortion of image from a fixed-focus camera lens. The various developments and applications of the imaging devices and the image sensors used in a wide range of industries and expanded use, but due to the needs of the small size and light weight of the camera, the distortion from acquiring images of the distorted curvature of the lens tends to affect many. In particular, the three-dimensional imaging camera, each different distortion of left and right lens cause the degradation of three-dimensional sensitivity and left-right image distortion ratio. we approached the way of generalizing the approximate equations to restore each part of left-right camera images to the coordinators of the original images. The adaptive Neuro-Fuzzy Inference System is configured for it. This system is divided from each membership function and is inferred by 1st order Sugeno Fuzzy model. The result is that the compensated images close to the left, right original images. Using low-cost and compact imaging lens by which also determine the exact three-dimensional image-sensing capabilities and will be able to expect from this study.