• 한국항행학회논문지
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• 제14권6호
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• pp.900-908
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• 2010

최근 소프트웨어 개발에 적용하기 시작한 객체지향 방법(OOM:Object-Oriented Method)은 독립적인 소프트웨어의 재사용을 통한 개발 비용과 시간의 단축을 강조하고 있다. 그러나 개발 기술에 대한 지식 부족과 확장성 및 성능을 배제한 설계로 많은 문제점이 나타나고 있다. 따라서 본 논문에서는 소프트웨어 개발 생명주기에서 소프트웨어 생산성 향상을 위한 효율적인 객체지향 모델링 방법을 제안하고자 한다. 제안 방법은 Use Case 모델링, 분석 모델링, 그리고 설계 모델링 방법들을 포함하고 있다.

• Journal of Information Processing Systems
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• 제14권6호
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• pp.1307-1317
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• 2018

To deal with the problems of occlusion, pose variations and illumination changes in the object tracking system, a regression model weighted multi-templates mean-shift (MS) algorithm is proposed in this paper. Target templates and occlusion templates are extracted to compose a multi-templates set. Then, the MS algorithm is applied to the multi-templates set for obtaining the candidate areas. Moreover, a regression model is trained to estimate the Bhattacharyya coefficients between the templates and candidate areas. Finally, the geometric center of the tracked areas is considered as the object's position. The proposed algorithm is evaluated on several classical videos. The experimental results show that the regression model weighted multi-templates MS algorithm can track an object accurately in terms of occlusion, illumination changes and pose variations.

• Ocean Systems Engineering
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• 제9권2호
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• pp.135-155
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• 2019

One of the safest and the most economical methods to transfer oil and gas is pipeline system. Prediction and prevention of pipeline failures during its assessed lifecycle has considerable importance. The dropped object is one of the accidental scenarios in the failure of the submarine pipelines. In this paper, using Monte Carlo Sampling, the probability of damage to a submarine pipeline due to a box-shaped dropped object has been calculated in terms of dropped object impact frequency and energy transfer according to the DNV-RP-F107. Finally, Reliability sensitivity analysis considering random variables is carried out to determine the effect intensity of each parameter on damage probability. It is concluded that impact area and drag coefficient have the highest sensitivity and mass and add mass coefficient have the lowest sensitivity on probability of failure.

• 반도체디스플레이기술학회지
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• 제20권3호
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• pp.117-119
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• 2021

With the development of the industry, the use of drones in specific mission flight is being actively studied. These drones fly a specified path and perform repetitive tasks. if the drone system will detect objects in real time, the performance of these mission flight will increase. In this paper, we implement object detection system and mount GPU acceleration to maximize the efficiency of limited device resources with drone video using Tensorflow Lite which enables in-device inference from a mobile device and Mobile SDK of DJI, a drone manufacture. For performance comparison, the average processing time per frame was measured when object detection was performed using only the CPU and when object detection was performed using the CPU and GPU at the same time.

• 한국멀티미디어학회논문지
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• 제24권3호
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• pp.345-354
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• 2021

Recently, deep learning-based image recognition systems have been adopted to various surveillance environments, but most of them are still picture-type object recognition methods, which are insufficient for the long term temporal analysis and high-dimensional situation management. Therefore, we propose a method recognizing the specific dangerous situation generated by human in real-time, and utilizing deep learning-based object analysis techniques. The proposed method uses deep learning-based object detection and tracking algorithms in order to recognize the situations such as 'trespassing', 'loitering', and so on. In addition, human's joint pose data are extracted and analyzed for the emergent awareness function such as 'falling down' to notify not only in the security but also in the emergency environmental utilizations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권8호
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• pp.2067-2081
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• 2024

Recently, image analysis research has been actively conducted due to the accumulation of big image data and the development of deep learning. Image analytics research has different characteristics from other data such as data size, real-time, image quality diversity, structural complexity, and security issues. In addition, a large amount of data is required to effectively analyze images with deep-learning models. However, in many fields, the data that can be collected is limited, so there is a need for meta learning based image analysis technology that can effectively train models with a small amount of data. This paper presents a comprehensive survey of meta-learning-based object-tracking techniques. This approach comprehensively explores object tracking methods and research that can achieve high performance in data-limited situations, including key challenges and future directions. It provides useful information for researchers in the field and can provide insights into future research directions.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 제37회 하계학술대회 논문집 D
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• pp.1929-1930
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• 2006

Human like robot arm posture for grasping by considering the shape of the target object is quite a challenge in the field of robotics. In this paper, an optimized grasp posture with respect to the shape of the object considering the wrist joint angle and elbow elevation angle, in order to verify that the grasp posture is human like has been proposed. Given a target object, the candidates for grasp are computed by the method described in this paper. For each candidate, the closed loop inverse kinematics has been solved for the corresponding hand position and orientation. From the obtained joint angles through inverse kinematics, the elbow elevation angle has been computed and compared with the elbow elevation angle obtained through human movement data by the characteristic equation. After considering all the candidates, the hand position and orientation with minimum wrist joint and difference in elbow elevation angles has been utilized as the optimized grasp posture. Simulation results are presented.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.25.2-25
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• 2001

This paper describes a mobile robot intended for being employed in a multi-agent system. We have already proposed a multi-agent system which realizes patient-aid by helping a lying patient take a distant object on the table. In this paper, a mobile robot agent is developed and is included in the system. An effective man-machine communication strategy is proposed by use of a vision agent settled on the ceiling. If a human (assumed to be a patient) wishes to take an object distant on the floor, he points to the object. The vision agent detects the direction of his arm by image processing and guesses which object he intends to take. The vision agent asks him if it is what he wants and, if yes, the mobile robot runs to take and bring it to him. The system is overviewed with the explanation of a mobile robot. Some experimental results are shown with discussion.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 2009년도 IWAIT
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• pp.71-75
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• 2009

In this paper, we present a real-time method to detect moving objects in a rotating and zooming camera. It is useful for camera surveillance of fixed but rotating camera, camera on moving car, and so on. We first compensate the global motion, and then exploit the displaced frame difference (DFD) to find the block-wise boundary. For robust detection, we propose a kind of image to combine the detections from consecutive frames. We use the block-wise detection to achieve the real-time speed, except the pixel-wise DFD. In addition, a fast block-matching algorithm is proposed to obtain local motions and then global affine motion. In the experimental results, we demonstrate that our proposed algorithm can handle the real-time detection of common object, small object, multiple objects, the objects in low-contrast environment, and the object in zooming camera.

• 반도체디스플레이기술학회지
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• 제18권3호
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• pp.110-114
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• 2019

Recently, video monitoring system technology has been rapidly developed to monitor and respond quickly to various situations. In particular, computer vision and related research are being actively carried out to track objects in the video. This paper proposes an efficient multiple objects detection method based on convolutional neural network (CNN) for multiple objects tracking. The results of the experiment show that multiple objects can be detected and tracked in the video in the proposed method, and that our method is also good performance in complex environments.