• 전자공학회논문지B
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• 제33B권11호
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• pp.11-21
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• 1996

Robot manipulators constituing multi-robot system must exert the desired motion force on an object to preserve tghe fine motion of it. The forces exerte on an object by the end-effectors of multi-inducing force and the internal force. Here, motion-inducing force effects the motion of an object, but internal force as lies in the null space of an object coordinate can't effect it. The motion of an object can't track exactly the desired motion by the effect of an object, but internal force as lies in the null space of the effect of internal force component, therefore internal force component must be considered. In this paper, first, under assumption that we can estimate exactly the parameter of dynamics, we constitute paper, first, under assumption that we can estimate exactly the parameter of dynamics, we constitute the controller concerning internal force. And we obtain the internal force as projecting force sensor readings onto the space spanned by null basis set of jacobian matrix. Using the resolved acceleration control method and the fact that internal force lies in the null space of jacobian matrix, we construct the robust control law to preserve the robustness with respect to the uncertainty of mainpulator parameters.

• 한국정보과학회논문지:소프트웨어및응용
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• 제31권8호
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• pp.969-979
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• 2004

최근 대부분의 대규모 기업정보시스템은 기능재활용성, 다종의 시스템 리소스, 다중 플랫폼 등을 지원하기 위해 다층구조의 미들웨어 또는 프레임워크를 기반으로 하고 있다. 그러나 이러한 다층 및 다중 플랫폼 분산 구조는 미들웨어간의 컴포넌트 및 메타정보에 대한 상호운용성 문제를 제기한다. 본 논문은 추상화 프로그래밍 스타일과 XML-SOAP에 기반한 컴포넌트 보존 방법을 통해서, 다종의 리소스를 지원하고 플랫폼에 독립적인 컴포넌트 기반 개발을 가능케 하는 객체지향프레임워크 SOAF (Simple Object Application Framework)을 제시하고 그 아키텍쳐 및 주요 특징에 대해 소개한다.

• 수산해양기술연구
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• 제48권2호
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• pp.155-164
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• 2012

The CSR rule was defined by IACS as the unified rule for a commercial ship like a bulk carrier and a tanker. It have been required more strict conditions for various parts like loading conditions, the local and girder strength, fatigue strength, FEM for the ship rule. It was changed in many parts of the ship rules. In this paper, the mid-parts of 17.5K DWT bulk carrier were optimized by the CSR rule. On the other hand, the modified artificial life algorithms with multi-object functions were developed for optimizing the scantling. It is possible to find multi-global optimum solutions in the multi-object functions. And it is faster and efficient than the artificial life algorithm. First, to be optimizing the scantling and the weight by CSR rule, that is calculated by the CSR rule. The next, the result is re-calculated by the modified artificial life algorithm with multi-object functions. The optimized results which are satisfied with the CSR rule like the minimum size and the thickness of stiffener and the minimum cost have been searched by the optimizing algorithm. And the results have been compared with the non-optimizing results.

• Journal of Animal Science and Technology
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• 제65권3호
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• pp.519-534
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• 2023

Rumination in cattle is closely related to their health, which makes the automatic monitoring of rumination an important part of smart pasture operations. However, manual monitoring of cattle rumination is laborious and wearable sensors are often harmful to animals. Thus, we propose a computer vision-based method to automatically identify multi-object cattle rumination, and to calculate the rumination time and number of chews for each cow. The heads of the cattle in the video were initially tracked with a multi-object tracking algorithm, which combined the You Only Look Once (YOLO) algorithm with the kernelized correlation filter (KCF). Images of the head of each cow were saved at a fixed size, and numbered. Then, a rumination recognition algorithm was constructed with parameters obtained using the frame difference method, and rumination time and number of chews were calculated. The rumination recognition algorithm was used to analyze the head image of each cow to automatically detect multi-object cattle rumination. To verify the feasibility of this method, the algorithm was tested on multi-object cattle rumination videos, and the results were compared with the results produced by human observation. The experimental results showed that the average error in rumination time was 5.902% and the average error in the number of chews was 8.126%. The rumination identification and calculation of rumination information only need to be performed by computers automatically with no manual intervention. It could provide a new contactless rumination identification method for multi-cattle, which provided technical support for smart pasture.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2011년도 춘계학술대회
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• pp.816-819
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• 2011

터널 내에서의 사고 유발 요소는 CCTV 카메라를 이용하여 검출하여 조기에 대응함으로써 차량의 정체뿐만 아니라 인적 물적 피해를 최소화하기 위하여 영상인식시스템이 도입되고 있다. 본 논문에서는 터널 내에서 여러 차량을 추적하는 알고리즘을 제안한다. 제안하는 알고리즘은 Adaboost 알고리즘을 이용하여 차량을 검출하고 검출된 차량(객체)에 대하여 템플릿 매칭 기법을 이용하여 차량을 추적한다. 컴퓨터 시뮬레이션을 통하여 제안하는 알고리즘이 여러 차량을 추적하는데 유용한 것을 확인 하였다.

• 스마트미디어저널
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• 제4권1호
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• pp.16-24
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• 2015

본 논문에서는 멀티터치 추적 및 제스처 인식을 위하여 MRF기반 입자필터와 제스처 우도 측정 방법을 제안한다. 멀티터치 추적에서 자주 발생하는 문제 중 하나는 강탈 문제이며 터치 객체 추적기가 이웃 터치 객체에게 빼앗기는 현상을 가리킨다. 강탈 문제의 원인은 입자필터의 예측 입자들이 이웃 터치 객체에 가까이 갈 경우 입자의 가중치(우도)가 낮아야 하지만 이웃 객체 영향으로 높게 계산되는 오류 때문이다. 따라서 MRF를 기반으로 이웃 객체에 가까운 입자의 가중치를 낮추는 벌점함수를 정의한다. MRF가 멀티터치를 노드로 정의하고 거리가 가까운 이웃 멀티터치들을 에지로 표현한 그래프정보이므로 이웃 멀티터치들에 대한 데이터구조로 활용되기 쉽다. 또한 MRF 그래프 정보를 바탕으로 멀티터치 제스처 분석이 가능하다. 본 논문에서는 MRF를 기반으로 다양한 제스처 우도를 정의할 수 있는 방법을 서술한다. 실험 결과에서는 제안 방법이 효과적으로 강탈 현상을 회피하고 멀티터치 제스처 우도를 정확히 측정할 수 있음을 확인할 수 있다.

• 한국멀티미디어학회논문지
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• 제23권3호
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• pp.447-459
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• 2020

The Internet of Things(IoT) paradigm is flourishing strenuously for the last two decades. Researchers around the globe have their dreams to transmute every real-world object to the virtual object. Consequently, IoT devices are escalating exponentially. The abrupt evolution of these IoT devices has caused a major challenge i.e. object classification. In order to classify devices comprehensively and accurately, this paper proposes a context-aware based multi-classification model for devices, which classifies the smart devices according to people's contexts. However, the classification features of contextual data of different contexts are difficult to extract. The deep learning algorithm has the capability to solve this problem. This paper proposes a context-aware based multi-classification model of devices, which classifies the smart devices according to people's contexts.

• 연구논문집
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• 통권28호
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• pp.239-246
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• 1998

Today’s information technology departments face a dilemma to create a competitive advantage for the organization by developing. deploying, and managing distributed applications that scale across the LAN, WAN, and Internet, while preserving investments in systems. Applications, information. IT organizations are today being asked to build the future, without breaking systems that maintain the current business. A possible answer to this dilemma is the implementation of a multi-tier distributed computing architecture. Multi-tier architecture has the potential to provide better, more timely information across the enterprise at a lower cost than the current combination of PC LAN, two-tier client/server, or mainframe applications that have been developed in most organizations. In this paper, we implement management information system based on distributed object middleware using multi-tier distributed computing architecture. This system uses Microsoft's implementation of DCOM/ActiveX and provides easily accessible web interface to the system users.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.363-365
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• 2003

This paper presents an object oriented approach(OOA) for classification of multi-channel and multi-polarization NASA/JPL POLSAR images. Some test results in Taiwan are also given and analyzed. It is concluded that this approach can utilize as more information of both low- and high-levels involved in all images as possible for image classification and thus provides a better classification accuracy. For instance, the OOA has a better overall classification accuracy(98.27%) than the nearest-neighbor classifier(91.31%) and minimum-distance classifier(80.52%).

• 스마트미디어저널
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• 제12권9호
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• pp.45-59
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• 2023

Weakly supervised object localization (WSOL) is a task of localizing an object in an image using only image-level labels. Previous studies have followed the conventional class activation mapping (CAM) pipeline. However, we reveal the current CAM approach suffers from problems which cause original CAM could not capture the complete defects features. This work utilizes a convolutional neural network (CNN) pretrained on image-level labels to generate class activation maps in a multi-scale manner to highlight discriminative regions. Additionally, a vision transformer (ViT) pretrained was treated to produce multi-head attention maps as an auxiliary detector. By integrating the CNN-based CAMs and attention maps, our approach localizes defective regions without requiring bounding box or pixel-level supervision during training. We evaluate our approach on a dataset of apple images with only image-level labels of defect categories. Experiments demonstrate our proposed method aligns with several Object Detection models performance, hold a promise for improving localization.