• The Transactions of the Korean Institute of Electrical Engineers A
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• v.54 no.11
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• pp.525-532
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• 2005

This paper presents a new two-terminal numerical algorithm for fault location estimation and for faults recognition using the synchronized phaser in time-domain. The proposed algorithm is also based on the synchronized voltage and current phasor measured from the assumed PMUs(Phasor Measurement Units) installed at both ends of the transmission lines. Also the arc voltage wave shape is modeled numerically on the basis of a great number of arc voltage records obtained by transient recorder. From the calculated arc voltage amplitude it can make a decision whether the fault is permanent or transient. In this paper the algorithm is given and estimated using DFT(discrete Fourier Transform) and the LES(Least Error Squares Method). The algorithm uses a very short data window and enables fast fault detection and classification for real-time transmission line protection. To test the validity of the proposed algorithm, the Electro-Magnetic Transient Program(EMTP/ATP) is used.

• International Journal of Internet, Broadcasting and Communication
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• v.10 no.3
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• pp.42-50
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• 2018

The majority of existing falling recognition techniques provide service by recognizing only that the falling occurred. However, it is important to recognize not only the occurrence of falling but also the situation before and after the falling, as well as the location of the falling. In this paper, we design and propose the falling notification service system to recognize and provide service. This system uses the acceleration sensor of the smartphone to recognize the occurrence of a falling and the situation before and after the falling. In order to check the location of falling, GPS sensor data is used in the Google Map API to map to the map. Also, a crosswalk map converted into grid-based coordinates based on the longitude and latitude of the crosswalk is stored, and the locations before and after falling are mapped. In order to reduce the connection speed and server overload for real-time data processing, fog computing and cloud computing are designed to be distributed processing.

• Journal of the Korea Society of Computer and Information
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• v.10 no.4 s.36
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• pp.17-27
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• 2005

In this study, it proposes the neural network system for character recognition and restoration. Proposes system composed by recognition part and restoration part. In the recognition part. it proposes model of effective pattern recognition to improve ART Neural Network's performance by restricting the unnecessary top-down frame generation and transition. Also the location feature extraction algorithm which applies with Hangeul's structural feature can apply the recognition. In the restoration part, it composes model of inputted image's restoration by Hopfield neural network. We make part experiments to check system's performance, respectively. As a result of experiment, we see improve of recognition rate and possibility of restoration.

• Journal of Digital Convergence
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• v.11 no.1
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• pp.211-223
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• 2013

In Korea, the location of yard tractors is figured out in real time by using RFID system in container terminals. However, even though the location recognition of RFID system works fine when transfer crane is in yard operation, there are some problems when container crane is in ship operation. That is because yard tractors come one by one to each transfer crane in an order, but yard tractors come in 4 lanes to the container crane, which makes the system impossible to recognize each yard tractor separately. Therefore, we developed the infrared-ray communication system which can recognize yard tractors accurately in not only in the yard operation of transfer crane but also in the ship operation of container crane in same way in this study. The result in this study showed constant number of recognition, and the range of recognition measures 5.7m in 25m distance. The range of recognition shown in this study is enough to recognize each yard tractor passing under container crane separately.

• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.465-468
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• 2022

The incidence of traffic accidents is increasing every year, and Korea is among the top OECD countries. In order to improve this, various road traffic laws are being implemented, and various traffic control methods using equipment such as unmanned speed cameras and traffic control cameras are being applied. However, as drivers avoid crackdowns by detecting the location of traffic control cameras in advance through navigation, a mobile crackdown system that can be cracked down is needed, and research is needed to increase the recognition rate of vehicle license plates on the side of the road for accurate crackdown. This paper proposes a method to improve the vehicle number recognition rate on the road side by applying a gradient correction algorithm using image processing. In addition, custom data learning was conducted using a CNN-based YOLO algorithm to improve character recognition accuracy. It is expected that the algorithm can be used for mobile traffic control cameras without restrictions on the installation location.

• Magazine of the Korea Concrete Institute
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• v.24 no.3
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• pp.20-24
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• 2012

• The KIPS Transactions:PartA
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• v.16A no.3
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• pp.209-216
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• 2009

The ubiquitous smart home is the home of the future that takes advantage of context information from the human and the home environment and provides an automatic home service for the human. Human location and motion are the most important contexts in the ubiquitous smart home. We present a real-time human tracker that predicts human location and motion for the ubiquitous smart home. We used four network cameras for real-time human tracking. This paper explains the real-time human tracker's architecture, and presents an algorithm with the details of two functions (prediction of human location and motion) in the real-time human tracker. The human location uses three kinds of background images (IMAGE1: empty room image, IMAGE2: image with furniture and home appliances in the home, IMAGE3: image with IMAGE2 and the human). The real-time human tracker decides whether the human is included with which furniture (or home appliance) through an analysis of three images, and predicts human motion using a support vector machine. A performance experiment of the human's location, which uses three images, took an average of 0.037 seconds. The SVM's feature of human's motion recognition is decided from pixel number by array line of the moving object. We evaluated each motion 1000 times. The average accuracy of all the motions was found to be 86.5%.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.4
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• pp.109-117
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• 2010

With the advent of new technologies such as HSDPA, WiBro(Wireless Broadband) and personal devices, we can access various contents and services anytime and anywhere. A location based service(LBS) is essential for providing personalized services with individual location information in ubiquitous computing environment. In this paper, we propose mapping algorithm for error compensation of indoor localization system. Also we explain filter and indoor localization system. we have developed mapping algorithms composed of a map recognition method and a position compensation method. The map recognition method achieves physical space recognition and map element relation extraction. We improved the accuracy of position searching. In addition, we reduced position errors using a dynamic scale factor.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1836-1839
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• 1997

This paper presents machine vision technique with a camera modeling that accounts for major sources of camera distortion, namely, radial, decentering, and thin prism distortion. Radial distortion causes an inward or outward displacement of a given image point from its ideal location. Actual optical systems are subject to various degrees of decentering, that is, the optical centers of lens elements are not strictly collinear. It is our purpose to develop the vision system for the pattern recognition and the automatic test of parts and to apply the line of manufacturing.