• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.376-378
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• 2022

Artificial intelligence technology is widely used in various fields such as artificial intelligence speakers, artificial intelligence chatbots, and autonomous vehicles. Among these AI application fields, the image processing field shows various uses such as detecting objects or recognizing objects using artificial intelligence. In this paper, data synthesized by a virtual human is used as a method to analyze images taken in a specific space.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.2
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• pp.141-146
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• 2017

New infectious diseases such as MERS have been in need of many measures such as initial discovery, isolation, and crisis response. In addition, the culture of hospitals is changing, such as the general public 's visiting and Nursing Care Integration Services. However, as the qualifications and regulations of medical personnel in hospitals become rigid, overseas such as linens, wastes movements are replacing possible works with robots. we have developed a hospital logistics robot that can carry out various goods delivery within a hospital, and can move various kinds of objects safely to a desired location. In this thesis, we have studied a hospital logistics robot that can carry out various kinds of goods delivery within the hospital, and can move various kinds of objects such as waste, and linen safely to a desired location. The movement of a robot in a hospital may cause a collision between a person and an object, so that the collision must be prevented. In order to prevent collision, it is necessary to recognize whether or not an object exists in the movement path of the robot. And if there is an object, it should recognize whether it moves or not. In order to recognize human beings and objects, we recognize the person with face/body recognition technology and generate the context awareness of the object using 3D Vision image segmentation technology. We use the generated information to create a map that considers objects and person in the robot moving range. Thus, the robot can be operated safely and efficiently.

• Journal of Internet Computing and Services
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• v.13 no.2
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• pp.87-98
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• 2012

Understanding agent's intent is an essential component of the human-computer interaction of ubiquitous computing. Because correct inference of subject's intention in ubiquitous computing system helps particularly to understand situations that involve collaboration among multiple agents or detection of situations that can pose a particular activity. This paper, inspired by people have a mechanism for interpreting one another's actions and for inferring the intentions and goals that underlie action, proposes an approach that allows a computing system to quickly recognize the intent of agents based on experience data acquired through prior capabilities of activities recognition. To proceed intention recognition, proposed method uses formulations of Hidden Markov Models (HMM) to model a system's prior experience and agents' action change, then makes for system infer intents in advance before the agent's actions are finalized while taking the perspective of the agent whose intent should be recognized. Quantitative validation of experimental results, while presenting an accurate rate, an early detection rate and a correct duration rate with detecting the intent of several people performing various activities, shows that proposed research contributes to implement effective intent recognition system.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.178-179
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• 2018

Recently, methods of using machine learning in artificial intelligence such as autonomous navigation and speech recognition have been actively studied. Classical image processing methods such as classical boundary detection and pattern recognition have many limitations in order to recognize a specific object or area in a digital image. However, when a machine learning method such as deep-learning is used, Can be obtained. However, basically, a large amount of learning data must be secured for machine learning such as deep-learning. Therefore, it is difficult to apply the machine learning for area classification when the amount of data is very small, such as aerial photographs for environmental analysis. In this study, we apply a transfer-learning technique that can be used when the dataset size of the input image is small and the shape of the input image is not included in the category of the training dataset.

• Proceedings of the Korean Vacuum Society Conference
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• 2013.08a
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• pp.236-236
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• 2013

비냉각 적외선 검출기는 산업용 군사용으로 최근 각광을 받고 있다. 이는 주야간 빛이 없는 곳에서도 사물의 열을 감지할 수 있어 인체감지 및 보안감시, 에너지 절감 등에 응용될 수 있는 핵심부품이다. 비냉각 적외선 검출기로는 재료의 저항의 변화를 감지하는 마이크로볼로미터형이 가장 많이 사용된다. 감지재료로는 비정질 실리콘(a-Si)과 산화바나듐(VOx)이 가장 많이 사용된다. VOx 박막은 일반적으로 RF sputtering 방법으로 증착이 되며, 저항이 낮고, 저항의 온도변화 계수(TCR)가 크며 신호 대 잡음 특성이 우수한 반면 산소(oxygen) phase가 다양하여 갓 증착된 상태의 박막은 재현성이 떨어지는 단점이 있다. 본 연구에서는 기존의 V 타겟을 사용한 VOx 박막을 증착하는 방법을 개선하여 ZnO 나노박막을 중간에 삽입하여 저항 특성을 조절할 뿐만 아니라 열처리에 의해 TCR 값을 향상시키고, VO2 phase 가 주로 나타나는 박막 증착 및 공정 방법을 소개한다. RF sputtering 장비를 이용하여 산소와 아르곤 가스의 혼합비를 4.5로 하였으며, VOx 증착 시 플라즈마 Power는 150 W 로 하여 상온에서 증착하였다. 갓 증착된 VOx 다층박막의 XRD 스펙트럼은 V2O5 피크가 주된 상을 이루고 있었으며, 산소열처리에 의해 VO2 상이 주로 나타남을 알 수 있었다. TCR 값은 갓 증착된 샘플에서 -0.13%/K의 값을 얻었으며, $300^{\circ}C$에서 50분간 열처리 후 -3.37%/K 으로 급격히 향상됨을 알 수 있었다. 저항은 열처리 후 약 100 kohm으로 낮아져 검출소자를 위한 조건에 적합한 특성을 얻을 수 있었다. 또한 산소열처리의 온도 및 시간에 따라 TCR 및 표면 거칠기 특성을 조사하였으며, 최적의 열처리 조건을 얻고자 하였다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.7
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• pp.962-970
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• 2021

The cause of the majority of vehicle accidents is a safety issue due to the driver's inattention, such as drowsy driving. A forward collision warning system (FCWS) can significantly reduce the number and severity of accidents by detecting the risk of collision with vehicles in front and providing an advanced warning signal to the driver. This paper describes a low power embedded system based FCWS for safety. The algorithm computes time to collision (TTC) through detection, tracking, distance calculation for the vehicle ahead and current vehicle speed information with a single camera. Additionally, in order to operate in real time even in a low-performance embedded system, an optimization technique in the program with high and low levels will be introduced. The system has been tested through the driving video of the vehicle in the embedded system. As a result of using the optimization technique, the execution time was about 170 times faster than that when using the previous non-optimized process.

• Proceedings of the Korea Information Processing Society Conference
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• 2003.05a
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• pp.659-662
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• 2003

본 논문에서는 2차원 영상 사이에 움직임 변위를 검출하여 영상내의 원근 깊이를 생성하였으며 양 시차(positive) parallax처리를 하여 입체 영상을 생성한 방법을 제안했다. 이 방식은 2 차원 영상내의 사물의 운동 방향과 속도에 관계없이 3차원 효과를 느낄 수 있다. 제안한 방법은 다양한 영상원에 대해서 실시간으로 입체 영상 변환이 가능하며 LCD shutter goggle 방식의 입체 영상 장치를 통해 실제 시각적인 검증을 수행하였다.

• Proceedings of the KAIS Fall Conference
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• 2010.05a
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• pp.225-227
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• 2010

최근 증강현실(Augmented Reality, AR) 기술의 중요성이 인식되면서 다양한 분야의 서비스에 기술 도입사례가 등장하고 있다. AR에 있어 마커 검출 기술은 가장 기본이 되는 중요한 기술이다. 하지만 마커 기반의 증강현실 시스템은 그 오차가 매우 크고 이로 인해 센서정보와 같은 다양한 보조적 정보를 요구하게 된다. 따라서 본 연구에서는 마커사용의 한계를 극복하기 위한 마커리스 트래킹(Markerless Tracking Technology)기술을 연구 하여 실시간 영상에서 목표로 하는 사물을 추적하여 증강현실 서비스로 도입하기 위한 시스템은 설계하였다. 본 시스템은 기존에 도입된 명함 분석, 자동차 번호판 인식 등 제한된 서비스의 한계를 극복하고 보다 다양한 연구 분야에 활용될 것이다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.01a
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• pp.257-258
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• 2019

영상처리에 관한 다양한 오픈소스의 등장으로 현실의 사물을 인식하고 그에 따른 처리가 가능해졌다. 이에 따라 본 논문에서는 허프 변환 알고리즘을 이용하여 인식된 영상에서 효과적으로 차선을 검출하여 차량이 차선과의 거리를 일정하게 유지한 상태로 목적지까지 이동할 수 있게 하고, RFID를 이용하여 도착지점 알려주는 기술로 차선인식 자율주행 카를 개발하였다.

• Journal of Digital Contents Society
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• v.19 no.9
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• pp.1819-1824
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• 2018

This paper proposes a control method of ASMR(autonomous sensory meridian response) contents to relieve user's stress and improve his attention. The proposed method measures EEG(electroencephalography), attention, meditation, and eyeblink data from an EEG device and sends them to an oneM2M-compliant IoT(internet of things) server platform through an Android IoT Application. Then a SVM(support vector machine) model is built to classify user's mental health status by using EEG, attention and meditation data collected in the server platform. The ASMR contents are controlled by the mental health status classified by a SVM model and the eyeblink data. When comparing the SVM models according to types of data used, the SVM model with attention and meditation data showed accuracy of 85.7%. It was verified that the proposed control algorithm of ASMR contents properly worked as the mental health status from the SVM model and the eyeblink data changed.