• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.9B
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• pp.1092-1097
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• 2011

An anti-collision protocol which tries to minimize the collision probability and identification time is the most important factor in all identification technologies. This paper focuses on methods to improve the efficiency of tag's process in identification systems. Our scheme, Binary Slotted Query Tree (BSQT) algorithm, is a memoryless protocol that identifies an object's ID more efficiently by removing the unnecessary prefixes of the traditional Query Tree (QT) algorithm. With enhanced QT algorithm, the reader will broadcast 1 bit and wait the response from the tags but the difference in this scheme is the reader will listen in 2 slots (slot 1 is for 0 bit Tags and slot 2 is for 1 bit Tags). Base on the responses the reader will decide next broadcasted bit. This will help for the reader to remove some unnecessary broadcasted bits which no tags will response. Numerical and simulation results show that the proposed scheme decreases the tag identification time by reducing the overall number of request.

• Journal of Internet Computing and Services
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• v.24 no.5
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• pp.29-35
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• 2023

CCTV is used for various purposes such as crime prevention, public safety reinforcement, and traffic management. However, as the range and resolution of the camera improve, there is a risk of exposing personal information in the video. Therefore, there is a need for new technologies that can identify individuals while protecting personal information in images. In this paper, we propose histogram-based singular value decomposition for object identification and tracking. The proposed method distinguishes different objects present in the image using color information of the object. For object recognition, YOLO and DeepSORT are used to detect and extract people present in the image. Color values are extracted with a black-and-white histogram using location information of the detected person. Singular value decomposition is used to extract and use only meaningful information among the extracted color values. When using singular value decomposition, the accuracy of object color extraction is increased by using the average of the upper singular value in the result. Color information extracted using singular value decomposition is compared with colors present in other images, and the same person present in different images is detected. Euclidean distance is used for color information comparison, and Top-N is used for accuracy evaluation. As a result of the evaluation, when detecting the same person using a black-and-white histogram and singular value decomposition, it recorded a maximum of 100% to a minimum of 74%.

• Journal of the Semiconductor & Display Technology
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• v.12 no.1
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• pp.1-7
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• 2013

Grey-level statistical models have been widely used in many applications for object location and identification. However, conventional models yield some problems in model refinement when training images are not properly aligned, and have difficulties for real-time recognition of arbitrarily rotated models. This paper presents improved grey-level statistical models that align training images using image or feature matching to overcome problems in model refinement of conventional models, and that enable real-time recognition of arbitrarily rotated objects using efficient hierarchical search methods. Edges or features extracted from a mean training image are used for accurate alignment of models in the search image. On the aligned position and orientation, fitness measure based on grey-level statistical models is computed for object recognition. It is demonstrated in various experiments in PCB inspection that proposed methods are superior to conventional methods in recognition accuracy and speed.

• Journal of Information Technology Services
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• v.8 no.3
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• pp.145-157
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• 2009

As the software development cycles is getting shorter, the software reusability is emphasized accordingly. Specifically, the design reusability is being recognized as one of the most important factor to increase the software quality and productivity and make the maintenance cost down. Two essential abilities are needed to improve the design reusability. One is the identification of the reusable objects, and the other is the organization of the relationships among the objects. However, the existing methods using such as a grammatical analysis, a scenario matching and a unit of design problems(design pattern) have not been proposed proper principles to identify the reusable objects on the basis of the abstraction which is the core of the object-oriented concept. In this paper, we will offer the Two-Pass abstraction principle based in the abstraction concept.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2021.05a
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• pp.40-41
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• 2021

The construction industry has the highest occupational accidents/injuries among all industries. Korean government installed surveillance camera systems at construction sites to reduce occupational accident rates. Construction safety managers are monitoring potential hazards at the sites through surveillance system; however, the human capability of monitoring surveillance system with their own eyes has critical issues. Therefore, this study proposed to build a deep learning-based safety monitoring system that can obtain information on the recognition, location, identification of workers and heavy equipment in the construction sites by applying multiple-object tracking with instance segmentation. To evaluate the system's performance, we utilized the MS COCO and MOT challenge metrics. These results present that it is optimal for efficiently automating monitoring surveillance system task at construction sites.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.45-46
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• 2023

In order to improve and promote immersive learning experiences for English as a Second Language (ESL) students, the deployment of a YOLOv5 model for object identification in videos is proposed. The procedure includes collecting annotated datasets, preparing the data, and then fine-tuning a model using the YOLOv5 framework. The study's major objective is to integrate a well-trained model into ESL instruction in order to analyze the effectiveness of AI application in the field.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.19 no.38
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• pp.169-179
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• 1996

The object of this paper is to give an overview of one method of modeling and analysing situations involving inter-identification. Hypergame analysis is used as this approach. Hypergame analysis is one of what we call soft system approaches. In this paper, We first reformulate concept of the simple hypergame and define the hypergames with inter-identification as a system consisting of simple hypergames connected by the mutual identification. We propose a definition of solutions for them. Based on the formulation, We derives existence conditions of the solutions. We then apply the framework to a realistic problem. i. e, America-North korea conference problem to demonstrate the validity of the derived theoretical results as well as In obtain some suggestions to improve the problematic situation.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.737-739
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• 1996

Abnormal conditions and disturbances in distribution system cause an immediate influence to the customers. Conventional detection schemes for the distribution abnormalities have been applied in limited extents mainly because of their low reliability. In this paper, we developed a disturbance identification system which monitors the load level after a transient, checks the harmonic behavior of the load, and finally makes decision on the cause of the disturbance. This system identifies and discriminates overcurrent faults, arcing ground faults, recloser activities, and foreign object or tree contacts. In the implementation of the identification system, we applied fuzzy logic to better represent some variables whose Quantities are expressed only in non-numerical terms.

• Smart Structures and Systems
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• v.23 no.2
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• pp.123-139
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• 2019

Impact forces induced by external object collisions can cause serious damages to civil engineering structures. While accurate and prompt identification of such impact forces is a critical task in structural health monitoring, it is not readily feasible for civil structures because the force measurement is extremely challenging and the force location is unpredictable for full-scale field structures. This study proposes a novel approach for identification of impact force including its location and time history using a small number of multi-metric observations. The method combines an augmented Kalman filter (AKF) and Genetic algorithm for accurate identification of impact force. The location of impact force is statistically determined in the way to minimize the AKF response estimate error at measured locations and then time history of the impact force is accurately constructed by optimizing the error co-variances of AKF using Genetic algorithm. The efficacy of proposed approach is numerically demonstrated using a truss and a plate model considering the presence of modelling error and measurement noises.

• Journal of the Semiconductor & Display Technology
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• v.19 no.4
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• pp.112-115
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• 2020

Trump cards are used in variable games in households such as poker and blackjack. In many cases, it is able to be helpful to algorithmically identify the playing cards from camera views. In this paper, we provide an approach that detects and identifies the playing card using template matching scheme, and evaluate the results of the provided implementation. For ideal cases, the implemented system provides a 100% success rate for card identification correct. However, non-ideal case of perspective distortion is estimated with 70% success ratio. This work aims to evaluate the effectiveness of augmented reality user interface for an entertainment application like playing card games.