• Proceedings of the Acoustical Society of Korea Conference
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• 1987.11a
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• pp.39-39
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• 1987

자동차에서 발생되는 소음은 여러 가지 발생원으로부터의 복합적인 것으로서 차량의 속도가 고속화하면서 타이어 소음의 기여도가 매우 커지는 것으로 알려져 있다. 타이어 소음은 근본적으로 노면과 타이어의 상호작용에 의하여 발생되는 데 타이어/노면의 상호작용에 영향을 미치는 인자로는 마모상태, 차량속도, 하중, 공기압, carcass 구조, 타이어 온도등 여러 가지가 있으나 tread 모양과 노면의 상태에 가장 크게 영향을 받는다. 본 연구에서는 승용차용 155SR13 radial 타이어를 대상으로 하여 실내에 설치된 dynamometer를 이용하여 groove 의 개수, groove 길이, groove 폭, groove 깊이, groove 방향 등 트레드 패턴 인자가 발생소음에 미치는 기여도를 실험적으로 측정하고 트레드 패턴형상에 따른 소음도를 예측할 수 있는 실험식을 구했다. 또한 단일 groove 내에서 발생되는 소음의 시간신호를 측정하여 모델화하고 차량속도와 groove 사이의 간격에 따른 시간신호를 합성하고 이 신호로부터 FFT 알고리듬을 통해 소음 spectrum을 구하는 소음 예측 프로그램을 개발하였다. 비교적 단순한 tread 패턴에 대해 이를 적용한 결과 실험적으로 구한 spectrum과 상당히 유사함을 볼 수 있었다.

• Journal of Korea Multimedia Society
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• v.9 no.9
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• pp.1118-1130
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• 2006

Existed pattern analysis algorithms in data streams environment have researched performance improvement and effective memory usage. But when new data streams come, existed pattern analysis algorithms have to analyze patterns again and have to generate pattern tree again. This approach needs many calculations in real situation that needs real time pattern analysis. This paper proposes a method that continuously analyzes patterns of incoming data streams in real time. This method analyzes patterns fast, and thereafter obtains real time patterns by updating previously analyzed patterns. The incoming data streams are divided into several sequences based on time based window. Informations of the sequences are inputted into a hash table. When the number of the sequences are over predefined bound, patterns are analyzed from the hash table. The patterns form a pattern tree, and later created new patterns update the pattern tree. In this way, real time patterns are always maintained in the pattern tree. During pattern analysis, suffixes of both new pattern and existed pattern in the tree can be same. Then a pointer is created from the new pattern to the existed pattern. This method reduce calculation time during duplicated pattern analysis. And old patterns in the tree are deleted easily by FIFO method. The advantage of our algorithm is proved by performance comparison with existed method, MILE, in a condition that pattern is changed continuously. And we look around performance variation by changing several variable in the algorithm.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.2
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• pp.91-96
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• 2022

At the current camera-based technology level, sensor-based basic life pattern recognition technology has to suffer inconvenience to obtain accurate data, and commercial band products are difficult to collect accurate data, and cannot take into account the motive, cause, and psychological effect of behavior. the current situation. In this paper, radar technology for life pattern recognition is a technology that measures the distance, speed, and angle with an object by transmitting a waveform designed to detect nearby people or objects in daily life and processing the reflected received signal. It was designed to supplement issues such as privacy protection in the existing image-based service by applying it. For the implementation of the proposed system, based on TI IWR1642 chip, RF chipset control for 60GHz band millimeter wave FMCW transmission/reception, module development for distance/speed/angle detection, and technology including signal processing software were implemented. It is expected that analysis of individual life patterns will be possible by calculating self-management and behavior sequences by extracting personalized life patterns through quantitative analysis of life patterns as meta-analysis of living information in security and safe guards application.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10b
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• pp.712-714
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• 2003

서로 다른 형태와 크기를 가지는 탐색패턴과 움직임 벡터의 분포는 블록 정합 기법에서 탐색 속도와 화질을 좌우하는 중요한 요소이다. 본 논문에서는 납작한 육각패턴을 이용한 새로운 고속 블록 정합 알고리즘을 제안한다. 이 방법은 작은 육각패턴을 이용하여 적은 탐색점으로 움직임이 적은 벡터를 우선 찾은 다음에 움직임이 큰 벡터에 대해서는 납작한 육각패턴을 이용하여 고속으로 움직임 벡터를 찾게 하였다. 실험결과, 제안된 알고리즘은 육각패턴 탐색기법에 비하여 움직임 벡터 예측의 속도에 있어서 약 11~51% 이상의 높은 성능 향상을 보였으며 화질 또한 PSNR 기준으로 약 0.05~0.74dB 의 향상을 보였다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2001.10a
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• pp.600-602
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• 2001

본 논문은 웨이블릿 필터를 이용하여 홍채영상의 에지를 검출하고 패턴매칭 기법을 적용하여 홍채의 결함조직에 대한 위치를 추정하는 연구이다. 필터는 웨이블릿 변환을 이용한 2차원 주파수 영역의 고역통과 필터를 사용하여 홍채영상의 에지를 검출하고, 이를 표준진단패턴과 오버랩 매칭으로 결함조직을 검출한다. 실험결과 처리속도가 기존의 에지검출기법에 비해 처리속도향상과 에지검출영상의 PSNR 증가에 따라 오버랩 패턴매칭기법에 의한 인식률에서 92%로 홍채결함조직을 자동 진단시스템에 응용 가능하다.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.5
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• pp.7-13
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• 2004

간섭성 광원인 레이저를 이용한 계측 및 검사기법 중 대표적인 것이 홀로그래피를 이용한 간섭법(Holographic Interferometry, HI)이다. HI는 레이저 파장을 단위로 하기 때문에 물체변형에 대해 측정감도가 좋고 비파괴 비접촉의 계측이 가능하다. 또한 삼차원 정보 추출이 가능해서 주어진 간섭무늬로부터 전 영역의 변형을 구할 수 있다는 커다란 장점을 가지고 있다. 그러나 일반적으로 홀로그래피용 필름은 기록 및 현상방법이 성가시고 그 속도 또한 느릴 뿐만 아니라 간섭무늬 패턴도 매우 복잡해서 산업현장 등 실제의 응용에는 현실적인 어려움이 있다.(중략)

• Journal of Korean Society of Transportation
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• v.32 no.5
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• pp.522-530
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• 2014

Spatio-temporal congestion evolution pattern can be reproduced using the VDS(Vehicle Detection System) historic speed dataset in the TMC(Traffic Management Center)s. Such dataset provides a pool of spatio-temporally experienced traffic conditions. Traffic flow pattern is known as spatio-temporally recurred, and even non-recurrent congestion caused by incidents has patterns according to the incident conditions. These imply that the information should be useful for traffic prediction and traffic management. Traffic flow predictions are generally performed using black-box approaches such as neural network, genetic algorithm, and etc. Black-box approaches are not designed to provide an explanation of their modeling and reasoning process and not to estimate the benefits and the risks of the implementation of such a solution. TMCs are reluctant to employ the black-box approaches even though there are numerous valuable articles. This research proposes a more readily understandable and intuitively appealing data-driven approach and developes an algorithm for identifying congestion patterns for recurrent and non-recurrent congestion management and information provision.

• The KIPS Transactions:PartD
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• v.18D no.2
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• pp.81-88
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• 2011

Sequential pattern mining that determines frequent patterns appearing in a given set of sequences is an important data mining problem with broad applications. For example, sequential pattern mining can find the web access patterns, customer's purchase patterns and DNA sequences related with specific disease. In this paper, we develop the sequential pattern mining algorithms using MapReduce framework. Our algorithms distribute input data to several machines and find frequent sequential patterns in parallel. With synthetic data sets, we did a comprehensive performance study with varying various parameters. Our experimental results show that linear speed up can be achieved through our algorithms with increasing the number of used machines.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.10 no.4
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• pp.11-23
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• 2011

The objective of this paper is to develop a daily pattern clustering algorithm using historical traffic data that can reliably detect under various traffic flow conditions in urban streets. The developed algorithm in this paper is categorized into two major parts, that is to say a macroscopic and a microscopic points of view. First of all, a macroscopic analysis process deduces a daily peak/non-peak hour and emphasis analysis time zones based on the speed time-series. A microscopic analysis process clusters a daily pattern compared with a similarity between individuals or between individual and group. The name of the developed algorithm in microscopic analysis process is called "Two-step speed clustering (TSC) algorithm". TSC algorithm improves the accuracy of a daily pattern clustering based on the time-series speed variation data. The experiments of the algorithm have been conducted with point detector data, installed at a Ansan city, and verified through comparison with a clustering techniques using SPSS. Our efforts in this study are expected to contribute to developing pattern-based information processing, operations management of daily recurrent congestion, improvement of daily signal optimization based on TOD plans.

• Journal of the Korea Society of Computer and Information
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• v.13 no.7
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• pp.99-108
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• 2008

For video compression, we have to consider two performance factors that are the search speed and coded video's quality. In this paper, we propose an enhanced fast block matching algorithm using the spatial correlation of the video sequence and the center-biased characteristic of motion vectors(MV). The proposed algorithm first finds a predicted motion vector from the adjacent macro blocks of the current frame and determines an exact motion vector using the cross pattern and a flat hexagon search pattern. From the performance evaluations, we can see that our algorithm outperforms both the hexagon-based search(HEXBS) and the cross-hexagon search(CHS) algorithms in terms of the search speed and coded video's quality. Using our algorithm, we can improve the search speed by up to 31%, and also increase the PSNR(Peak Signal Noise Ratio) by at most 0.5 dB, thereby improving the video quality.