• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.723-726
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• 2000

In this paper, we present an abstract object-oriented plat-form model .suitable for the real-time distributed telecommunication system. The proposed platform is constructed upon the extended version of the real-time, distributed operating system, SROS(Scalable Real-time Operating System), that is developed at ETRI and successfully operated in the ATM switching system for several years. The object-oriented software development and maintenance methodology will resolve the current software crisis in the area of telecommunication and switching systems due to the everlasting maintenance about the huge amount of the existing software and the ever increasing needs for the better and new communication services. In general, an object-oriented software platform realizes the object-oriented methodology and possesses many good aspects like high productivity, better reusability, easy maintenance, et at. The platform is also designed to present the distributed multimedia service in addition to real-time event delivery. Recently, we have been implementing a couple of prototype bated on the proposed platform. Reflecting the evaluation results from these prototypes, the final platform specification will be determined.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.1
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• pp.395-405
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• 2012

A joint template matching algorithm is proposed in this paper to reduce the high rate of miss-detection and false-alarm caused by the traditional template matching algorithm during the process of multi-object detection. The proposed algorithm can reduce the influence on each object by matching all objects together according to the correlation information among different objects. Moreover, the rate of miss-detection and false-alarm in the process of single-template matching is also reduced based on the algorithm. In this paper, firstly, joint template is created from the information of relative positions among different objects. Then, matching criterion according to normalized cross correlation is generated for multi-object matching. Finally, the proposed algorithm is applied to the detection of watermarks in bill. The experiments show that the proposed algorithm has lower miss-detection and false-alarm rate comparing to the traditional NCC algorithm during the process of multi-object detection.

• Journal of Drive and Control
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• v.19 no.3
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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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.505-507
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• 2021

Image recognition technology is a technology that recognizes an image object by using the generated feature descriptor and generates object feature points and feature descriptors that can compensate for the shape of the object to be recognized based on artificial intelligence technology, environmental changes around the object, and the deterioration of recognition ability by object rotation. The purpose of the present invention is to implement a power management framework required to increase profits and minimize damage to livestock farmers by preventing accidents that may occur due to the improvement of efficiency of the use of livestock house power and overloading of electricity by integrating and managing a power fire management device installed for analyzing a complex environment of power consumption and fire occurrence in a smart safety livestock house, and to develop and disseminate a safe and optimized intelligent smart safety livestock house.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.3
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• pp.13-18
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• 2015

This paper proposes an optimum vision system analysis and a reliable high-precision positioning system that converges a GNSS and a vision system in order to resolve position error and outdoor shaded areas two disadvantages of GNSS. For location determination of the object, it should receive signal from at least four GNSS. However, in urban areas, exact location determination is difficult due to factors like high buildings, obstacles, and reflected waves. In order to deal with the above problem, a vision system was employed. First, determine an exact position value of a target object in urban areas whose environment is poor for a GNSS. Then, identify such target object by a vision system and its position error is corrected using such target object. A vehicle can identify such target object using a vision system while moving, make location data values, and revise location calculations, thereby resulting in reliable high precision location determination.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.8
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• pp.1117-1123
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• 2021

Object recognition is to identify the location and class of a specific object by analyzing the given image when a specific image is input. One of the fields in which object recognition technology is actively applied in recent years is autonomous vehicles, and this paper describes the trend of image-based object recognition artificial intelligence technology in autonomous vehicles. The image-based object detection algorithm has recently been narrowed down to two methods (a single-step detection method and a two-step detection method), and we will analyze and organize them around this. The advantages and disadvantages of the two detection methods are analyzed and presented, and the YOLO/SSD algorithm belonging to the single-step detection method and the R-CNN/Faster R-CNN algorithm belonging to the two-step detection method are analyzed and described. This will allow the algorithms suitable for each object recognition application required for autonomous driving to be selectively selected and R&D.

• The Journal of Information Technology and Database
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• v.6 no.1
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• pp.1-13
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• 1999

The modeling components in the object-oriented paradigm are based on the object, not the structured function or procedure. That is, in the past, when one wanted to solve problems, he would describe the solution procedure. However, the object-oriented paradigm includes the concepts that solve problems through interaction between objects. The object-oriented model is constructed by describing the relationship between object to represent the real world. As in object-oriented model the relationships between objects increase, the control of objects caused by their insertions, deletions, and modifications comes to be very complex and difficult. Because the loss of the referential integrity happens and the object reusability is reduced. For these reasons, the necessity of the control of objects and the visualization of the relationships between them is required. In order that we design a database necessary to implement Object Browser that has functionalities to visualize Java objects and to perform the query processing in Java object modeling, in this paper we show the processes for EER modeling on Java object and its transformation into relational database schema. In addition we implement Java Object Parser that parses Java object and inserts the parsed results into the implemented database.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2008.04a
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• pp.71-74
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• 2008

The main object of this paper is concentrated on distance control of two robot arms of a humanoid using Fuzzy Logic Controller (FLC) for handling a common object. Serial Link Robot arms are widely used in most significantly in Humanoids serving for older people and also in various industrial applications. A method is proposed here that separates the interconnections between two robot arms so that the resulting model of two arms is decomposed into fuzzy logic based controller. The distance between two end effectors is always kept equal to that of the diameter of an object to be handled, so that the object would not fall down. Mathematical model of this system was obtained to simulate the behavior of serial robotic arms in close loop control before using fuzzy logic controller. Lagrangian equation of motion has been used to obtain the appropriate mathematical model of Robotic arms. The results are shown to provide some improvement over those obtained by more conventional means.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.9
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• pp.3204-3220
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• 2021

We perform the task of video object segmentation by incorporating a conditional random field (CRF) and convolutional neural networks (CNNs). Most methods employ a CRF to refine a coarse output from fully convolutional networks. Others treat the inference process of the CRF as a recurrent neural network and then combine CNNs and the CRF into an end-to-end model for video object segmentation. In contrast to these methods, we propose a novel higher-order CRF model to solve the problem of video object segmentation. Specifically, we use CNNs to establish a higher-order dependence among pixels, and this dependence can provide critical global information for a segmentation model to enhance the global consistency of segmentation. In general, the optimization of the higher-order energy is extremely difficult. To make the problem tractable, we decompose the higher-order energy into two parts by utilizing auxiliary variables and then solve it by using an iterative process. We conduct quantitative and qualitative analyses on multiple datasets, and the proposed method achieves competitive results.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.3
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• pp.486-489
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• 2021

Recently, the demand for the use of personal mobility vehicles, such as an electric kickboard, is increasing explosively because of its high portability and usability. However, the number of traffic accidents caused by personal mobility vehicles has also increased rapidly in recent years. To address the issues regarding the driver's safety, we propose a novel approach that can monitor context information around personal mobility vehicles using deep learning-based object detection and smartphone captured videos. In the proposed framework, a smartphone is attached to a personal mobility device and a front or rear view is recorded to detect an approaching object that may affect the driver's safety. Through the detection results using YOLOv5 model, we report the preliminary results and validated the feasibility of the proposed approach.