• Journal of KIISE:Computer Systems and Theory
• /
• v.31 no.8
• /
• pp.432-444
• /
• 2004

In this paper, we construct the TMO-based object group model on distributed environment, and analyze and evaluate the executability for distributed real-time service of our object group model by developing the distributed real-time application simulator applying the model. The Time-triggered Message-triggered Object(TMO) is a real-time server object having real-time property itself. The TMO-based object group is defined as a set of objects which logically reconfigured the physically distributed one or more TMOs on network by a given distributed application. For supporting group management of the server objects, the TMO-based object group we suggested provides the functions which register and withdraw the solver objects as a group member to an arbitrary object group, and also provides the functions which insert and delete the access rights of server objects from clients. Also, our model was designed and implemented to support the appropriate object selection and dynamic binding service for a single TMO as well as the duplicated TMOs, and to support the real-time scheduling service for the clients which are requesting the service. Finally, we developed the Defence System against Invading Enemy Planes(DSIEP) simulator as a practical example of distributed real-time application by applying our model, and evaluated the adaptability of distributed service strategies for the group components and the executability of real-time services that the TMO-based object group model provides.

• Spatial Information Research
• /
• v.3 no.2
• /
• pp.191-204
• /
• 1995

The data model is a system model which abstracts the spatial and nonspatial fea¬tures of the real world. A system defines through its data model a framework for the inner rep¬resentation of and connections with the outside world. The spatial query language is one of the most efficent framework for defining connection with outside world in the GIS. Existing GIS uses a spatial data model based on relational data model. Therefore, it has some difficulties in data abstraction and representing complex objects through inheritance. In this paper, we pro-pose an object oriented data model-Topological Object Model(TOM). TOM combines object model in ODMG and the planer topological object. Based on this model, we present an object-oriented spatial query language-OQL/Geo. OQL/Geo extends OQL in ODMG and represents TOM effectively. It also provides several operators such as geometric, topological and visible ope-rators. Moreover, it represents with diverse flexivility the request for complex spatial analysis and presentation of query results.

• Journal of Institute of Control, Robotics and Systems
• /
• v.14 no.1
• /
• pp.54-61
• /
• 2008

This paper proposes a new algorithm for detecting and recognizing overlapped objects among a stack of arbitrarily located objects using a signature representation scheme. The proposed algorithm consists of two processes of detecting overlap of objects and of determining the boundary between overlapping objects. To determine overlap of objects, in the first step, the edge image of object region is extracted and those areas in the object region are considered as the object areas if an area is surrounded by a closed edge. For each object, its signature image is constructed by measuring the distances of those edge points from the center of the object, along the angle axis, which are located at every angle with reference to the center of the object. When an object is not overlapped, its features which consist of the positions and angles of outstanding points in the signature are searched in the database to find its corresponding model. When an object is overlapped, its features are partially matched with those object models among which the best matching model is selected as the corresponding model. The boundary among the overlapping objects is determined by projecting the signature to the original image. The performance of the proposed algorithm has been tested with the task of picking the top or non-overlapped object from a stack of arbitrarily located objects. In the experiment, a recognition rate of 98% has been achieved.

• The Transactions of the Korea Information Processing Society
• /
• v.6 no.5
• /
• pp.1241-1252
• /
• 1999

It is proposed in TINA, the open information telecommunication network architecture, that the definition of object group which is collection of objects provides a decrease of complex networking and a facility of object managing by service executing of application on distributed computing environment. Based on a new distributed object group model[13] we have been researched according to TINA specification, this paper proposed the object group model with the scheduler object and objects management mechanisms that can support real-time services on CORBA. To do this, we described the definition of object grouping and the requirements to suggest the object group model supporting real-time service, designed the object group structure and functional components containing in an object group using James Rumbaugh's modelling[12], and showed a class diagram of components in an object group. This paper designed IDLs of an object group manager and scheduler among the components, and finally showed the procedures of management and service interconnections between objects in the different object groups vi ETD.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.27 no.5
• /
• pp.697-704
• /
• 2003

In this paper, the visual tracking system for arbitrary shaped moving object is proposed. The established tracking system can be divided into model based method that needs previous model for target object and image based method that uses image feature. In the model based method, the reliable tracking is possible, but simplification of the shape is necessary and the application is restricted to definite target mod el. On the other hand, in the image based method, the process speed can be increased, but the shape information is lost and the tracking system is sensitive to image noise. The proposed tracking system is composed of the extraction process that recognizes the existence of moving object and tracking process that extracts dynamic characteristics and shape information of the target objects. Specially, active contour model is used to effectively track the object that is undergoing shape change. In initializatio n process of the contour model, the semi-automatic operation can be avoided and the convergence speed of the contour can be increased by the proposed effective initialization method. Also, for the efficient solution of the correspondence problem in multiple objects tracking, the variation function that uses the variation of position structure in image frame and snake energy level is proposed. In order to verify the validity and effectiveness of the proposed tracking system, real time tracking experiment for multiple moving objects is implemented.

• Journal of Drive and Control
• /
• v.16 no.2
• /
• pp.22-29
• /
• 2019

This study presented a model predictive tracking control algorithm of autonomous truck based on object state estimation using extended Kalman filter. To design the model, the 1-layer laser scanner was used to estimate position and velocity of the object using extended Kalman filter. Based on these estimations, the desired linear path for object tracking was computed. The lateral and yaw angle errors were computed using the computed linear path and relative positions of the truck. The computed errors were used in the model predictive control algorithm to compute the optimal steering angle for object tracking. The performance evaluation was conducted on Matlab/Simulink environments using planar truck model and actual point data obtained from laser scanner. The evaluation results showed that the tracking control algorithm developed in this study can track the object reasonably based on the model predictive control algorithm based on the estimated states.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.13 no.1
• /
• pp.125-133
• /
• 2017

Color in an image is determined by illuminant and surface reflectance. So, to recover unique color of object, estimation of exact illuminant is needed. In this study, the illumination models suggested to get the object color constancy with the physical illumination model based on physical phenomena. Their characteristics and application limits are presented and the necessity of an extended illumination model is suggested to get more appropriate object colors recovered. The extended illumination model should contain an additional term for the ambient light in order to account for spatial variance of illumination in object images. Its necessity is verified through an experiment under simple lighting environment in this study. Finally, a reconstruction method for recovering input images under standard white light illumination is experimented and an useful method for computing object color reflectivity is suggested and experimented which can be induced from combination of the existing illumination models.

• Computational Structural Engineering
• /
• v.8 no.3
• /
• pp.123-133
• /
• 1995

This study presents an application of object-oriented methodology for structural design process. A prototype of integrated structural design system is developed by introducing a structural analysis object model(SAOM) and structural design object model (SDOM). This SAOM module, which models structural member, performs structural analysis using FEM approach and the SDOM module checks structural members based on Korea steel design standard. The abstraction, encapsulation and reusability properties of the proposed models are in establishing the integrated structural design system.

• ETRI Journal
• /
• v.34 no.3
• /
• pp.399-409
• /
• 2012

In this paper, we propose a new color histogram model for object tracking. The proposed model incorporates the color arrangement of the target that encodes the relative spatial distribution of the colors inside the object. Using the color arrangement, we can determine which color bin is more reliable for tracking. Based on the proposed color histogram model, we derive a mean shift framework using a modified Bhattacharyya distance. In addition, we present a method of updating an object scale and a target model to cope with changes in the target appearance. Unlike conventional mean shift based methods, our algorithm produces satisfactory results even when the object being tracked shares similar colors with the background.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.17 no.5
• /
• pp.289-296
• /
• 2022

In this paper, we developed a deep learning-based recyclable object detection model. The model is developed based on YOLOv5 that is a one-stage detector. The deep learning model detects and classifies the recyclable object into 7 categories: paper, carton, can, glass, pet, plastic, and vinyl. We propose two methods for recyclable object detection models to solve problems during training. Bounding Box CutMix solved the no-objects training images problem of Mosaic, a data augmentation used in YOLOv5. Standardized Distance-based IoU replaced DIoU using a normalization factor that is not affected by the center point distance of the bounding boxes. The recyclable object detection model showed a final mAP performance of 0.91978 with Bounding Box CutMix and 0.91149 with Standardized Distance-based IoU.