• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2001년도 추계종합학술대회
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• pp.503-509
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• 2001

분산환경에서 효율적인 분산 서비스를 제공하기 위해 TINA 콘소시엄과 OMG CORBA에서는 객체지향기술을 적용한 분산 객체 플랫폼의 제안과 다양한 서비스의 요구사항들 정립하고 있다. 그러나, 어플리케이션의 규모가 점점 커지고 분산화 됨에 따라 객체들간의 서비스 및 관리 인터페이스가 매우 복잡해지고 있다. 이러한 문제들을 해결하기 위해서 새로운 객체그룹 모델의 제안과 객체그룹하에서 도입될 수 있는 객체 관리 및 서비스를 위한 요구사항들이 필요하다. 본 논문에서는 TINA에서 제안한 그룹객체 정의를 도입하여 현재 분산 시스템의 표준으로 사용하는 CORBA 기반에서 분산된 객체들을 효율적으로 관리할 수 있는 시스템을 제안한다.

• 대한임베디드공학회논문지
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• 제16권6호
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• pp.323-330
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• 2021

Visual object tracking is a challenging area of study in the field of computer vision due to many difficult problems, including a fast variation of target shape, occlusion, and arbitrary ground truth object designation. In this paper, we focus on the reinforced feature of the dynamic search area to get better performance than conventional discriminative model prediction trackers on the condition when the accuracy deteriorates since low feature discrimination. We propose a reinforced input feature method shown like the spotlight effect on the dynamic search area of the target tracking. This method can be used to improve performances for deep learning based discriminative model prediction tracker, also various types of trackers which are used to infer the center of the target based on the visual object tracking. The proposed method shows the improved tracking performance than the baseline trackers, achieving a relative gain of 38% quantitative improvement from 0.433 to 0.601 F-score at the visual object tracking evaluation.

• 한국멀티미디어학회논문지
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• 제25권2호
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• pp.145-156
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• 2022

Nowadays, the complexity of object tracking models among hardware applications has become a more in-demand duty to complete in various indeterminable environment tracking situations with multifunctional algorithm skills. In this paper, we propose a virtual city environment using AirSim (Aerial Informatics and Robotics Simulation - AirSim, CityEnvironment) and use the DQN (Deep Q-Learning) model of deep reinforcement learning model in the virtual environment. The proposed object tracking DQN network observes the environment using a deep reinforcement learning model that receives continuous images taken by a virtual environment simulation system as input to control the operation of a virtual drone. The deep reinforcement learning model is pre-trained using various existing continuous image sets. Since the existing various continuous image sets are image data of real environments and objects, it is implemented in 3D to track virtual environments and moving objects in them.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권1호
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• pp.364-380
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• 2016

The most popular approach in object classification is based on the bag of visual-words model, which has several fundamental problems that restricting the performance of this method, such as low time efficiency, the synonym and polysemy of visual words, and the lack of spatial information between visual words. In view of this, an object classification based on weakly supervised E2LSH and saliency map weighting is proposed. Firstly, E2LSH (Exact Euclidean Locality Sensitive Hashing) is employed to generate a group of weakly randomized visual dictionary by clustering SIFT features of the training dataset, and the selecting process of hash functions is effectively supervised inspired by the random forest ideas to reduce the randomcity of E2LSH. Secondly, graph-based visual saliency (GBVS) algorithm is applied to detect the saliency map of different images and weight the visual words according to the saliency prior. Finally, saliency map weighted visual language model is carried out to accomplish object classification. Experimental results datasets of Pascal 2007 and Caltech-256 indicate that the distinguishability of objects is effectively improved and our method is superior to the state-of-the-art object classification methods.

• 한국멀티미디어학회논문지
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• 제5권5호
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• pp.599-607
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• 2002

8진트리 모델은 3차원 물체를 계층적으로 모델링할 수 있는 기법으로 임의의 시각 방향에서 투영영상을 생성할 수 있으므로 3차원 물체인식 등 다양한 분야에서 효율적인 데이터 베이스로 사용될 수 있다. 본 논문에서는 8진트리 모델을 사용해 투영 영상을 만들어 보고 Multi level boundary search 알고리즘을 사용해 표면 영상을 생성해 본다. 또한 2D 영상과 3D 영상의 특징점을 구하는 방법과 2D 특징점, 3D 특징점의 기하학적 변환을 통하여 유사 특징점을 찾는 방법에 대하여 언급한다. 이 방법들은 3D 물체 모델링을 위한 효율적인 데이터 베이스 구축과 물체 특징점 응용을 위한 기본 자료로 활용될 수 있다.

• 대한임베디드공학회논문지
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• 제17권2호
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• pp.77-83
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• 2022

In this paper, the two-stage object detection approach is proposed to implement a deep learning-based label inspection system on edge computing environments. Since the label printed on the products during the production process contains important information related to the product, it is significantly to check the label information is correct. The proposed system uses the lightweight deep learning model that able to employ in the low-performance edge computing devices, and the two-stage object detection approach is applied to compensate for the low accuracy relatively. The proposed Two-Stage object detection approach consists of two object detection networks, Label Area Detection Network and Character Detection Network. Label Area Detection Network finds the label area in the product image, and Character Detection Network detects the words in the label area. Using this approach, we can detect characters precise even with a lightweight deep learning models. The SF-YOLO model applied in the proposed system is the YOLO-based lightweight object detection network designed for edge computing devices. This model showed up to 2 times faster processing time and a considerable improvement in accuracy, compared to other YOLO-based lightweight models such as YOLOv3-tiny and YOLOv4-tiny. Also since the amount of computation is low, it can be easily applied in edge computing environments.

• 드라이브 ㆍ 컨트롤
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• 제19권3호
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• pp.23-31
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• 2022

Traffic safety issues have recently been seriously magnified, due to child deaths in apartment complexes and parking lots. Accordingly, traffic safety technologies are being developed to recognize dangerous situations on living roads and to provide warning services. In this study, a collision detection algorithm was developed to prevent collision accidents between moving objects, by using object type and location information provided from CCTV monitoring devices. To determine the exact collision between moving objects, an object movement model was developed to predict the range of movement by considering the moving characteristics of the object, and a collision detection algorithm was developed to efficiently analyze the presence and location of the collision. The developed object movement model as well as the collision detection algorithm were simulated, in a virtual space of an actual living road to verify performance and derive supplementary matters.

• 정보처리학회논문지D
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• 제12D권3호
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• pp.375-384
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• 2005

실세계의 많은 객체들은 시간적 정보와 공간적 정보를 함께 가지고 있고, 이러한 정보를 다루기 위한 시공간 데이터 모델링의 목적 중에 하나는 시공간 객체를 위한 응용프로그램을 지원할 수 있게 하는 것이다. 객체지향 시공간 모델은 객체단위로 시공간 데이터를 관리 할 수 있기 때문에 효율적으로 시공간 데이터를 관리할 수 있어야 한다. 따라서, 이 논문에서는 시공간 데이터 관리를 객체지향 방식으로 지원할 수 있도록 개방형 지리정보 시스템에서 제안한 2차원 공간모델을 실세계 정보에 맞게 차원적으로 확장하고, 시간 정보를 포함하도록 확장한 모델을 제안한다. 그리고, 제안된 모델의 타당성을 검증하기 위해, 모델을 위한 시공간 데이터 제공자 컴포넌트인 data source 컴포넌트, session 컴포넌트, command 컴포넌트와 rowset 컴포넌트를 구현하고 수행 예를 보인다. 제안된 시공간 데이터 모델과 데이터 제공자는 서로 다르게 저장된 시공간 데이터를 객체지향 모델링을 사용하여 저장, 검색 및 관리할 수 있게 한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권8호
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• pp.2688-2703
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• 2022

Research on the advanced detection of harmful objects in airport cargo for passenger safety against terrorism has increased recently. However, because associated studies are primarily focused on the detection of relatively large objects, research on the detection of small objects is lacking, and the detection performance for small objects has remained considerably low. Here, we verified the limitations of existing research on object detection and developed a new model called the Small Hazardous Object detection enhanced and reconstructed Model based on the You Only Look Once version 5 (YOLOv5) algorithm to overcome these limitations. We also examined the performance of the proposed model through different experiments based on YOLOv5, a recently launched object detection model. The detection performance of our model was found to be enhanced by 0.3 in terms of the mean average precision (mAP) index and 1.1 in terms of mAP (.5:.95) with respect to the YOLOv5 model. The proposed model is especially useful for the detection of small objects of different types in overlapping environments where objects of different sizes are densely packed. The contributions of the study are reconstructed layers for the Small Hazardous Object detection enhanced and reconstructed Model based on YOLOv5 and the non-requirement of data preprocessing for immediate industrial application without any performance degradation.

• 경영과학
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• 제11권2호
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• pp.43-63
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• 1994

A new model management framework is proposed to accommodate wide-spreading algebraic modeling languages (AMLs), and to facilitate a full range of model manipulation functions. To incorporate different modeling conventions of the leading AMLs (AMPL, GAMS, and SML) homogeneously, generic model concepts are introduced as a conceptual basis and are embodied by the structural and operational constructs of an Object-Oriented Database Management System(ODBMS), enabling the framework to consolidate components of DSSs(database, modelbase, and associated solvers) in a single formalism effectively. Empowered by a database query language, the new model management framework can provide uniform model management commands to models represented in different AMLs, and effectively facilitate integration of the DSS components. A prototype system of the framework has been developed on a commercial ODBMS, ObjectStore, and a C++ programming language.