• Information and Communications Magazine
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• v.28 no.4
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• pp.41-50
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• 2011

미래 전에서는 정보의 중요성이 더욱 부각되고 있으며 정보의 수집 및 전파, 정확한 지휘결심 및 전파, 기동타격체계의 통제 등으로 이어지는 지휘통제의 전 과정을 유기적으로 연결하여, 그 성능을 극대화시키는 것이 네트워크 중심전(NCW: Net-Centric Warfare)의 개념이다. NCW 실현을 위해 개발중인 여러 체계 중 주목 받고 있는 것이 무인기(UAV: Unmanned Aerial Vehicle) 이다. 무인기는 감시정찰, 고정밀 타격, 그리고 전투피해평가 기능 등을 수행하며, 전술적인 상황인식이 가능하게 한다. 무인기가 이러한 임무를 수행하기 위해서는 비행체의 상태정보, 비행체의 조종통제정보, 그리고 임무 탑재체가 획득한 정보의 간단없고 정확한 전달이 요구된다. 이러한 정보를 전송하기 위한 비행체와 지상체간의 제반 통신을 데이터 링크(Data Link)라 하며, NCW 구현에 있어서 가장 핵심이 되는 요소이다. 세계 각국은 영상정보 수집자산으로서 무인기와 데이터링크의 개발에 박차를 가하고 있는 실정이며, 우리 군도 전력화가 계획된 각 제대별 무인기의 통합운용을 위한 영상정보 공용데이터 링크 (MPI-CDL: Multi-Platform Image and Intelligence Commom Data Link)를 개발중에 있으며 지속적인 영상정보 수집자산의 소요증가에 따른 주파수 획득문제와 사업별 독자적인 데이터 링크의 개발을 지양하고 기존체계와의 상호운용 및 단절없는 통신을 보장을 위해 개발과 동시에 국가적 차원에서의 기술구조 표준화가 추진되어야 한다. 이러한 시점에서 본고에서는 먼저 선진국의 (CDL : Commom Data Link) 표준화 동향을 알아보고, 상호운용성과 연동을 위한 한국형 MPI-CDL 기술 표준화방향을 제시하고자 한다.

• The Journal of Bigdata
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• v.7 no.1
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• pp.99-111
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• 2022

A simulation technique can be used to analyze performance measures and support decision makings in manufacturing systems considering operational uncertainty and complexity. The simulation requires an input modeling procedure to reflect the target system's characteristics. However, data collection to build a simulation is quite limited when a target system includes manual productions with a lot of operational time such as construction equipment assembly lines. This study proposes a procedure for simulation input modeling using video data when it is difficult to collect enough input data to fit a probability distribution. We conducted a video-data analysis and specify input distributions for the simulation. Based on the proposed procedure, simulation experiments were conducted to evaluate key performance measures of the target system. We also expect that the proposed procedure may help simulation-based decision makings when obtaining input data for a simulation modeling is quite challenging.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2018.06a
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• pp.244-247
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• 2018

본 논문에서는 시니어를 위한 라이프 데이터 수집 및 행동분석 프레임 워크를 설명하고, 이의 부분적 구현을 자세히 설명한다. 본 연구는 시니어를 위한 라이프 데이터를 바탕으로 보호자가 없는 시니어를 보살핌과 동시에, 보호자가 미처 인지하지 못하는 시니어의 비정상적인 상태를 분석하여 판단하는 시스템을 연구한다. 먼저, 시니어가 시간을 많이 소요하는 TV 앞 상황을 가정하고, 방영되는 TV 콘텐츠와 TV 카메라를 이용한 시니어의 영상/음성 정보로 이상상태와 감정상태, TV 콘텐츠에 대한 반응과 반응속도를 체크한다. 구체적으로는 딥 러닝 기반의 API 와 멀티미디어 데이터 분석에서 사용되는 오픈 패키지를 바탕으로, 영상/음성의 키 프레임을 추출하여 감정 및 분위기를 분석하고 시니어의 얼굴 표정 인식, 행동 인식, 음성 인식을 수행한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.281-283
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• 2021

The representative companies mentioned in the recommendation service in the domestic OTT(Over-the-top media service) market are YouTube and Netflix. YouTube, through various methods, started personalized recommendations in earnest by introducing an algorithm to machine learning that records and uses users' viewing time from 2016. Netflix categorizes users by collecting information such as the user's selected video, viewing time zone, and video viewing device, and groups people with similar viewing patterns into the same group. It records and uses the information collected from the user and the tag information attached to the video. In this paper, we propose a method to improve video media recommendation by automatically generating metadata of video media that was written by hand.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.6
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• pp.96-106
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• 2020

To solve the limitation of traffic monitoring that occur from embedded sensor such as loop and piezo sensors, the thermal imaging camera was installed on the roadside. As the length of Class 1(passenger car) is getting longer, it is becoming difficult to classify from Class 3(2-axle truck) by using an embedded sensor. The collected images were labeled to generate training data. A total of 17,536 vehicle images (640x480 pixels) training data were produced. CNN (Convolutional Neural Network) was used to achieve vehicle classification based on thermal image. Based on the limited data volume and quality, a classification accuracy of 97.7% was achieved. It shows the possibility of traffic monitoring system based on AI. If more learning data is collected in the future, 12-class classification will be possible. Also, AI-based traffic monitoring will be able to classify not only 12-class, but also new various class such as eco-friendly vehicles, vehicle in violation, motorcycles, etc. Which can be used as statistical data for national policy, research, and industry.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.1
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• pp.55-58
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• 2024

In this paper, we propose a design and implementation of big data-based fine dust anomaly detection machine learning system. The proposed is system that classifies the fine dust air quality index through meteorological information composed of fine dust and big data. This system classifies fine dust through the design of an anomaly detection algorithm according to the outliers for each air quality index classification categories based on machine learning. Depth data of the image collected from the camera collects images according to the level of fine dust, and then creates a fine dust visibility mask. And, with a learning-based fingerprinting technique through a mono depth estimation algorithm, the fine dust level is derived by inferring the visibility distance of fine dust collected from the monoscope camera. For experimentation and analysis of this method, after creating learning data by matching the fine dust level data and CCTV image data by region and time, a model is created and tested in a real environment.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1935-1941
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• 2021

In order to develop an algorithm using deep learning, which has been recently applied to various fields, it is necessary to have rich, high-quality learning data. In this paper, we propose an acquisition system for biological signals that simultaneously collects bio-signal data such as optical videos, thermal videos, and voices, which are mainly used in developing deep learning algorithms and useful in derivation of information, and transmit them to the server. To increase the portability of the collector, it was made based on Raspberry Pi, and the collected data is transmitted to the server through the wireless Internet. To enable simultaneous data collection from multiple collectors, an ID for login was assigned to each subject, and this was reflected in the database to facilitate data management. By presenting an example of biological data collection for fatigue measurement, we prove the application of the proposed acquisition system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.13 no.6
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• pp.490-500
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• 1988

In recent years the acquisition, processing and interpretation of three dimensional seisimic data, for the purpose of locating gas and reservoirs, have become practical. This paper exlores one way in which the volume data can be searched and visualized, which may aid the interpreter. The illusion of looking at a three dimensional volume can be obrained by fusing a stereoscopic pair of pictures. Each picture can be made by projecting each data point of the volume into a plane from a point where the eye is placed. The data valuse along any projection line can be summed to form the picture, or only a segment along the line can be selected. By selective projection, the volume can be searched and obscuring layers removed. The stereoscopic pictures show the physical models in there ture spatial positions. Projection of the envelope function of the seismic traces is shown to give improved depth perception compared with projection of the position amplitudes.

• The Journal of Society for e-Business Studies
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• v.27 no.2
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• pp.205-218
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• 2022

Recently, digital transformation in manufacturing has been accelerating. It results in that the data collection technologies from the shop-floor is becoming important. These approaches focus primarily on obtaining specific manufacturing data using various sensors and communication technologies. In order to expand the channel of field data collection, this study proposes a method to automatically collect manufacturing data based on vision-based artificial intelligence. This is to analyze real-time image information with the object detection and tracking technologies and to obtain manufacturing data. The research team collects object motion information for each frame by applying YOLO (You Only Look Once) and DeepSORT as object detection and tracking algorithms. Thereafter, the motion information is converted into two pieces of manufacturing data (production performance and time) through post-processing. A dynamically moving factory model is created to obtain training data for deep learning. In addition, operating scenarios are proposed to reproduce the shop-floor situation in the real world. The operating scenario assumes a flow-shop consisting of six facilities. As a result of collecting manufacturing data according to the operating scenarios, the accuracy was 96.3%.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.614-617
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• 2022

인공지능의 발전으로 의료영상 분야에서 딥러닝 기반 질병 진단 연구가 활발하다. 그러나 모델 개발 시 학습 데이터의 개수와 품질은 매우 중요한데, 의료 분야 특성상 접근 가능한 데이터셋이 적으며 오픈 데이터셋은 서로 다른 기관에서 배포되거나 웹상에서 수집된 것으로 진단에 적합한 품질을 기대하기 어렵다. 또한, 기존 연구는 데이터셋이 학습에 적합한지에 대한 품질검증 없이 사용한다. 따라서 본 논문에서는 임상에서 사용하는 화질 평가 요소에 근거를 두고 영역별 화소값 분석을 통한 흉부 X선 영상 품질 평가 기법을 제안한다. 오픈 데이터셋 JSRT, Chest14와 국내 A 병원 데이터셋 AUH에 제안한 기법을 적용한 결과 민감도 91.5%, 특이도 96.1%의 우수한 성능을 확인하였다.