• 반도체디스플레이기술학회지
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• 제16권3호
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• pp.78-81
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• 2017

Augmented Reality(AR) is the core technology of the future knowledge service industry. It is expected to be used in various fields such as medical, education, entertainment etc. Briefly, augmented reality technology is a technique in which a mapped virtual object is augmented when a real-world object is viewed through a device after mapping a real-world object and a virtual object. In this paper, we implemented object detection and tracking system, which is a key technology of augmented reality. To speed up the object tracking, the ORB algorithm, which is a lightweight algorithm compared to the detection algorithm, is applied. In addition, KNN classifier, which is a machine learning algorithm, was applied to detect a single object by learning multiple images.

• 한국기계가공학회지
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• 제20권3호
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• pp.1-7
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• 2021

With the advent of industry 4.0, big data and machine learning techniques are being widely adopted in the maintenance domain. Inverters are widely used in many engineering applications. However, overloading and complex operation conditions may lead to various failures in inverters. In this study, failure mode effect analysis was performed on inverters and voltages collected to investigate the over-voltage effect on capacitors. Several features were extracted from the collected sensor data, which indicated the health state of the inverter. Based on this correlation, the best features were selected for classification. Moreover, random forest classifiers were used to classify the healthy and faulty states of inverters. Different performance metrics were computed, and the classifiers' performance was evaluated in terms of various health features.

• 반도체디스플레이기술학회지
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• 제14권3호
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• pp.73-77
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• 2015

This paper proposes an object tracking system according to reliability assessment of learning in mobile environments. The proposed system is based on markerless tracking, and there are four modules which are recognition, tracking, detecting and learning module. Recognition module detects and identifies an object to be matched on current frame correspond to the database using LSH through SURF, and then this module generates a standard object information that has the best reliability of learning. The standard object information is used for evaluating and learning the object that is successful tracking in tracking module. Detecting module finds out the object based on having the best possible knowledge available among the learned objects information, when the system fails to track. The experimental results show that the proposed system is able to recognize and track the reliable objects with reliability assessment of learning for the use of mobile platform.

• International journal of advanced smart convergence
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• 제12권1호
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• pp.92-100
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• 2023

Wireless communication technology is becoming increasingly prevalent in smart factories, but the rise in the number of wireless devices can lead to interference in the ISM band and obstacles like metal blocks within the factory can weaken communication signals, creating radio shadow areas that impede information exchange. Consequently, accurately determining the radio communication coverage range is crucial. To address this issue, a Radio Environment Map (REM) can be used to provide information about the radio environment in a specific area. In this paper, a technique for estimating an indoor REM usinga mobile robot and machine learning methods is introduced. The mobile robot first collects and processes data, including the Received Signal Strength Indicator (RSSI) and location estimation. This data is then used to implement the REM through machine learning regression algorithms such as Extra Tree Regressor, Random Forest Regressor, and Decision Tree Regressor. Furthermore, the numerical and visual performance of REM for each model can be assessed in terms of R2 and Root Mean Square Error (RMSE).

• 한국전자통신학회논문지
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• 제10권3호
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• pp.327-332
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• 2015

교육용 로봇은 과학의 기초 원리를 배우고 과학적 창의성을 키우는 것에 효과적인 교육 도구로 소개되면서 방과 후 학습에 적용되어 과학 교육을 위한 교육용 키트의 구성이 바뀌고 있다. 중소기업을 중심으로 과학적 창의성을 키우는 도구로서 다양한 교육용 로봇 기술과 학생들을 위한 다양한 범주의 게임로봇기술이 개발되고 있고, 기존의 mp3 음악이나 e-Learning 콘텐츠의 제공과 같이 로봇용 전문콘텐츠를 다운받아 학습에 활용하는 기술을 개발 중에 있으나 아직 전문 커뮤니티 형성이 미흡한 편이다. 현재는 사용자가 손쉽게 제어가 가능한 인터페이스 모듈용 모터가 개발이 되어있지 않아 1포인트 드래그 연동 스마트터치 제어용 다이나믹 모터 개발이 필요하다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 ITC-CSCC -3
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• pp.1889-1892
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• 2002

In this paper, we propose the self-autonomous algorithm for mobile robot system (MRS). The proposed mobile robot system which is learned by learning with the neural network can trace the target at the same distances. The mobile robot can use ultrasonic sensors and calculate the distance between target and mobile robot. By teaming the setup distance, current distance and command velocity, the robot can do intelligent self-autonomous drive. We use the neural network and back-propagation algorithm as a tool of learning. As a result, we confirm the ability of tracing the target with proposed mobile robot.

• 반도체디스플레이기술학회지
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• 제19권4호
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• pp.65-70
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• 2020

CNN's technologies that represent emotional detection include primitive CNN algorithms, deployment normalization, and drop-off. We present the methods and data of the three experiments in this paper. The training database and the test database are set up differently. The first experiment is to extract emotions using Batch Normalization, which complemented the shortcomings of distribution. The second experiment is to extract emotions using Dropout, which is used for rapid computation. The third experiment uses CNN using convolution and maxpooling. All three results show a low detection rate, To supplement these problems, We will develop a deep learning algorithm using feature extraction method specialized in image processing field.

• 한국정보전자통신기술학회논문지
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• 제12권1호
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• pp.92-98
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• 2019

본 논문에서는 몰포러지 연산을 기본으로 하는 몰포러지 신경망(MNN: Morphological Neural Network) 기반 딥러닝 시스템을 제안하였다. 딥러닝에 사용되는 레이어는 몰포러지 레이어, 풀링 레이어, ReLU 레이어, Fully connected 레이어 등이다. 몰포러지 레이어에서 사용되는 연산은 에로전, 다이레이션, 에지검출 등이다. 본 논문에서 새롭게 제안한 MNN은 기존의 CNN(Convolutional Neural Network)을 이용한 딥러닝 시스템과는 달리 히든 레이어의 수와 각 레이어에 적용되는 커널 수가 제한적이다. 레이어 단위 처리시간이 감소하고, VLSI 칩 설계가 용이하다는 장점이 있으므로 모바일 임베디드 시스템에 딥러닝을 다양하게 적용할 수 있다. MNN에서는 제한된 수의 커널로 에지와 형상검출 등의 연산을 수행하기 때문이다. 데이터베이스 영상을 대상으로 행한 실험을 통해 MNN의 성능 및 딥러닝 시스템으로의 활용 가능성을 확인하였다.

• Investigative Magnetic Resonance Imaging
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• 제23권2호
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• pp.81-99
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• 2019

Recently, deep learning methods have shown great potential in various tasks that involve handling large amounts of digital data. In the field of MR imaging research, deep learning methods are also rapidly being applied in a wide range of areas to complement or replace traditional model-based methods. Deep learning methods have shown remarkable improvements in several MR image processing areas such as image reconstruction, image quality improvement, parameter mapping, image contrast conversion, and image segmentation. With the current rapid development of deep learning technologies, the importance of the role of deep learning in MR imaging research appears to be growing. In this article, we introduce the basic concepts of deep learning and review recent studies on various MR image processing applications.

• 전자공학회논문지 IE
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• 제48권2호
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• pp.71-78
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• 2011

본 연구에서는 전자매체를 활용한 사이버수업에서 자기조절학습능력, 사회적 실재감, 학습몰입, 만족도 간의 구조적 관계를 검증하는 것을 목적으로 하였다. 이를 위해 W사이버대학의 2010년 2학기 사회복지조사론학과 304명을 대상으로 연구를 수행하였다. 그 결과 첫째, 자기조절학습능력과 사회적 실재감이 학습몰입에 영향을 미치는지 검증한 결과, 자기조절학습능력만이 학습몰입에 유의한 영향(${\beta}$=.69, p<.05)을 미치고 있었다. 둘째, 자기조절학습능력과 사회적 실재감이 만족도에 영향을 미치는지 검증한 결과, 역시 자기조절학습능력만이 만족도에 유의한 영향(${\beta}$ = .38, p<.05)을 미치고 있었다. 또한 학습몰입은 자기조절학습능력과 만족도를 매개하는 변수임을 확인하였다. 본 연구결과를 바탕으로 전자매체를 활용한 사이버수업의 성공적인 학습전략을 제공하는 운영전략을 제언하였다.