• 한국철도학회:학술대회논문집
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• 한국철도학회 2000년도 춘계학술대회 논문집
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• pp.284-291
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• 2000

Interlocking software used in the electronic interlocking has been implemented through the structured approach until now. But there was the demerit that interlocking software has some problem in the standardization and the maintenance because of the limit in structured approach. Object-Oriented method overcoming this demerit was specified in the design step and the analysis step. There were object model, dynamic model and functional model in the analysis step and there were also two steps which were system design and object design in the design step. In this paper, Interlocking software was designed using Object-Oriented method to improve the standardization and the maintenance of the electronic interlocking. The electronic interlocking was analyzed with object modeling, dynamic modeling and functional modeling.

• 한국철도학회:학술대회논문집
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• 한국철도학회 2003년도 추계학술대회 논문집(III)
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• pp.587-593
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• 2003

There has been much interest in the Bluetooth technology since it could enable users to connect to a wide range of computing and telecommunication devices without the need to carry or connect cables. It delivers opportunities for rapid, ad-hoc connections, and in the future, could possibly enable automatic, unconscious, connections between devices. In this paper, we discussed a scatternet formation protocol for Bluetooth-based ad hoc networks and proposed the identification system of an object using Bluetooth Scatternets. Over the past few years, several studies have been made on the identification of an object by several RF(Radio Frequency) systems. But there are still some problems, the interference of signals as an example, for the identification of an object. The critical question is how to identify an object precisely without the interference of signals. This paper is an investigation of the Scatternet formation protocol for Bluetooth-based ad hoc networks and the identification system of an object using Bluetooth Scatternets composed of multiple Piconets.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2007년도 하계종합학술대회 논문집
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• pp.311-312
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• 2007

This dissertation proposes a global search and a local search method to track the object in real-time. The global search recognizes a target object among the candidate objects through the entire image search, and the local search recognizes and track only the target object through the block search. This dissertation uses the object color and feature information to achieve fast object recognition. Finally we conducted an experiment for the object tracking system based on a pan/tilt structure.

• 한국군사과학기술학회지
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• 제13권5호
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• pp.734-738
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• 2010

Object Generator is a software to provide simulation object data(aircraft, ship, submarine, missile, torpedo) for sumulators in LBTS(Land Based Test System). but there is a burden to the system, because Object generator needs to send many object's data, display objects in a tactical screen, show object's information in a list in 1 second. This paper suggests a parallel software structure taking a few factors(deadlock, dependency) into consideration. At last, the paper shows the performance of the parallel structure's software compared with the former structure's software.

• 한국수학교육학회지시리즈A:수학교육
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• 제51권4호
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• pp.377-393
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• 2012

The understanding demands the different degree of the understanding according to student's learning situation. In this paper, we investigate what is the foundation for the complete understanding for the generalization in the generalization-process and justification of some concepts or some theories, through a case. We discovered that the completeness of the understanding in the generalization-process and justification requires 'the meaningful-mental object' which can give the meaning about the concept or theory to students. Students can do the generalization-process through the construction of 'the meaningful-mental object' and confirm the validity of generalization through 'the meaningful-mental object' which is constructed by them. And we can judge the whether students construct the completeness of the understanding or not, by 'the meaningful-mental object' of the student. Hence 'the meaningful-mental object' are vital condition for the generalization-process and justification.

• 한국콘텐츠학회:학술대회논문집
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• 한국콘텐츠학회 2004년도 춘계 종합학술대회 논문집
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• pp.396-400
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• 2004

본 연구에서는 단일 이동 객체를 추적할 수 있는 추적 시스템을 제안하였다. 추적 시스템은 차영상 기법을 이용하여 객체의 움직임을 추정하고, 카메라의 Pan/Tilt를 제어함으로서 이동 객체를 추적할 수 있도록 하였다. 구현된 시스템은 영상획득 및 전처리 단계, 움직임 추정 단계, 객체 추적 단계로 구성되었다. 시간 간격을 두고 획득된 두 영상에 있어 움직임 추정은 기본적으로 차영상 기법을 이용하였다. 차영상의 이진화 작업에 있어 임계값의 결정은 배경과 객체의 변화에 적응적으로 동작할 수 있는 기법을 사용하였다. 또한 객체 영역의 그룹화에 있어 블록 단위의 재귀적 레이블링 방법을 사용하여 연결성을 향상시켰다. 실험 결과 이동 객체의 움직임을 추정할 수 있었고, 추적 과정에서도 객체를 잃어버리지 않고 정상적으로 추적할 수 있었다.

• 한국지능시스템학회논문지
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• 제17권3호
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• pp.368-373
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• 2007

최근에 지능형 로봇분야에서 주위 카메라를 기반으로 실시간으로 환경인식 및 물체 추적 등 다양한 분야에서 연구가 활발히 진행되고 있다. 환경인식 및 물체 추적은 결국 배경과 관심물체를 분리하는 것이라고 볼 수 있는 데, 차 연산을 이용하여 물체의 움직임만을 배경으로 분리하는 방법과 물체인식을 통해 배경으로부터 분리하여 추적하는 방법에 대한 연구가 지속적으로 이루어지고 있다. 본 논문에서는 배경과 물체 사이에서 변화하는 색상의 변화를 퍼지기법을 이용하여 물체를 배경과 분리하여 실시간으로 물체를 추적하고자 한다. 실시간 물체 추적을 위해 전체영상에 대한 전역적 탐색을 통해 여러 후보 물체 중 관심물체를 배경에서 추출 후, 추출된 물체의 크기에 따른 지역탐색을 통하여 물체를 추적하는 방법이다. 그리고 본 논문에서는 ARM 프로세서를 이용한 카메라시스템을 제작하여 실시간으로 영상분활을 실험하였다.

• 대한원격탐사학회지
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• 제23권3호
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• pp.153-160
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• 2007

Forest cover density studies using high resolution satellite data and object oriented classification are limited in India. This article focuses on the potential use of QuickBird satellite data and object oriented classification in forest density mapping. In this study, the high-resolution satellite data was classified based on NDVI/pixel based and object oriented classification methods and results were compared. The QuickBird satellite data was found to be suitable in forest density mapping. Object oriented classification was superior than the NDVI/pixel based classification. The Object oriented classification method classified all the density classes of forest (dense, open, degraded and bare soil) with higher producer and user accuracies and with more kappa statistics value compared to pixel based method. The overall classification accuracy and Kappa statistics values of the object oriented classification were 83.33% and 0.77 respectively, which were higher than the pixel based classification (68%, 0.56 respectively). According to the Z statistics, the results of these two classifications were significantly different at 95% confidence level.

• 한국멀티미디어학회논문지
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• 제21권12호
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• pp.1493-1503
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• 2018

We propose an object recognition system and experiential game contents using Kinect to maximize object recognition rate by utilizing underwater robots. we implement an ice hockey game based on object-aware interactive contents to validate the excellence of the proposed system. The object recognition system, which is a preprocessor module, is composed based on Kinect and OpenCV. Network sockets are utilized for object recognition communications between C/S. The problem of existing research, degradation of object recognition at long distance, is solved by combining the system development method suggested in the study. As a result of the performance evaluation, the underwater robot object recognized all target objects (90.49%) with 80% of accuracy from a 2m distance, revealing 42.46% of F-Measure. From a 2.5m distance, it recognized 82.87% of the target objects with 60.5% of accuracy, showing 34.96% of F-Measure. Finally, it recognized 98.50% of target objects with 59.4% of accuracy from a 3m distance, showing 37.04% of F-measure.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제27권4호
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• pp.311-323
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• 2023

This paper introduces the industrial problem, the solution, and the results of the research conducted with Define Inc. The client company wanted to improve the performance of an object detection model on the floor plan dataset. To solve the problem, we analyzed the operational principles, advantages, and disadvantages of the existing object detection model, identified the characteristics of the floor plan dataset, and proposed to use of YOLO v5 as an appropriate object detection model for training the dataset. We compared the performance of the existing model and the proposed model using mAP@60, and verified the object detection results with real test data, and found that the performance increase of mAP@60 was 0.08 higher with a 25% shorter inference time. We also found that the training time of the proposed YOLO v5 was 71% shorter than the existing model because it has a simpler structure. In this paper, we have shown that the object detection model for the floor plan dataset can achieve better performance while reducing the training time. We expect that it will be useful for solving other industrial problems related to object detection in the future. We also believe that this result can be extended to study object recognition in 3D floor plan dataset.