• Journal of Biosystems Engineering
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• 제21권3호
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• pp.315-324
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• 1996

This study was one of a series of studies on application of machine vision and image processing to extract the geometrical features of plants and to analyze plant growth. Several algorithms were developed to measure morphological properties of plants and describing the growth development of in-situ lettuce(Lactuca sativa L.). Canopy, centroid, leaf density and fractal dimension of plant were measured from a top viewed binary image. It was capable of identifying plants by a thinning top viewed image. Overlapping the thinning side viewed image with a side viewed binary image of plant was very effective to auto-detect meaningful nodes associated with canopy components such as stem, branch, petiole and leaf. And, plant height, stem diameter, number and angle of branches, and internode length and so on were analyzed by using meaningful nodes extracted from overlapped side viewed images. Canopy, leaf density and fractal dimension showed high relation with fresh weight or growth pattern of in-situ lettuces. It was concluded that machine vision system and image processing techniques are very useful in extracting geometrical features and monitoring plant growth, although interactive methods, for some applications, were required.

• Journal of Information Processing Systems
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• 제13권2호
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• pp.285-304
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• 2017

Big data information and pattern analysis have applications in many industrial sectors. To reduce energy consumption effectively, the eco-driving method that reduces the fuel consumption of vehicles has recently come under scrutiny. Using big data on commercial vehicles obtained from digital tachographs (DTGs), it is possible not only to aid traffic safety but also improve eco-driving. In this study, we estimate fuel consumption efficiency by processing and analyzing DTG big data for commercial vehicles using parallel processing with the MapReduce mechanism. Compared to the conventional measurement of fuel consumption using the On-Board Diagnostics II (OBD-II) device, in this paper, we use actual DTG data and OBD-II fuel consumption data to identify meaningful relationships to calculate fuel efficiency rates. Based on the driving pattern extracted from DTG data, estimating fuel consumption is possible by analyzing driving patterns obtained only from DTG big data.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 2005년도 추계학술대회 및 정기총회
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• pp.175-181
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• 2005

This paper describes the development of on-line computer vision laboratories to teach the detailed image processing and pattern recognition techniques. The computer vision laboratories include distant image acquisition method, basic image processing and pattern recognition methods, lens and light, and communication. This study introduces a case study that teaches computer vision in distance learning environment. It shows a schematic of a distant loaming workstation and contents of laboratories with image processing examples. The study focus more on the contents of the vision Labs rather than internet application method. The study proposes the ways to improve the on-line computer vision laboratories and includes the further research perspectives

• 한국컴퓨터정보학회논문지
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• 제10권5호
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• pp.95-102
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• 2005

본 논문에서는 MLP신경망의 패턴 학습과정을 위하여 시공간 병렬성을 고려한 병렬처리모델을 제시한다. 시간 병렬성을 위한 학습집합 분할과 공간 병렬성을 위한 네트워크 분할을 동시 적용하여 융통성있는 병렬처리모델을 설계하고자 하였다. 성능평가모델로부터 해석적으로 구한 결과, 대규모 과제라고 해도 패턴 크기와 패턴 갯수 중 어느 쪽이 지배적이냐에 따라 분할병렬처리 방법이 절충되어야 할 것으로 본다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1987년도 전기.전자공학 학술대회 논문집(I)
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• pp.260-264
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• 1987

Algorithms to detect in real-time both position and orientation of rectangular type electronic components are developed for industrial vision. In order to conduct detection in real-time, parallel processing algorithm of image date which uses several control processor is proposed. Image processing area is divided into several regions which can be processed by each cpu. As a result, processing time is improved when two control processors are used and real-time pattern recognition of not-well-aligned components is accomplished.

• Journal of Information Processing Systems
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• 제18권1호
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• pp.26-47
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• 2022

Chatbots were an important research subject in the past. A chatbot is a computer program or an artificial intelligence program that participates in a conversation via auditory or textual methods. As the research on chatbots progressed, some important issues regarding them changed over time. Therefore, it is necessary to review the technology with a focus on recent advancements and core research technologies. In this paper, we introduce five different chatbot technologies: natural language processing, pattern matching, semantic web, data mining, and context-aware computer. We also introduce the latest technology for the chatbot researchers to recognize the present situation and channelize it in the right direction.

• 인터넷정보학회논문지
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• 제17권6호
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• pp.53-59
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• 2016

최근 무선 센서 네트워크, 사물 인터넷, 소셜 네트워크 서비스와 같은 다양한 응용 분야에서 대용량 스트림 데이터가 실시간으로 생성되고 있으며, 효율적인 기법을 통해 처리 및 분석하여 유용한 정보를 찾아내고, 이를 의사 결정을 위해 사용할 수 있도록 하는 것은 중요한 이슈 중에 하나이다. 스트림 데이터는 끊임없이 빠른 속도로 생성되므로 최소한의 접근을 통해 처리해야 하며, 신속한 저전력 처리를 필요로 하는 자원이 제한된 환경에서 분석될 수 있도록 적합한 기법이 요구된다. 이러한 문제를 해결하기 위해, 슬라이딩 윈도우 개념이 제안되어 연구되고 있다. 한편, 대용량 데이터로부터 의미 있는 정보를 찾아내기 위한 데이터 마이닝 기법 중에 하나인 패턴 마이닝은 중요 정보를 패턴 형태로 추출한다. 전통적인 빈발 패턴 마이닝은 이진 데이터베이스를 대상으로 하고 모든 아이템을 동일한 중요도로 고려함으로써 데이터 마이닝 분야에서 중요한 역할을 수행해 왔지만, 실제 데이터 특성을 반영하지 못하는 단점을 지닌다. 하이 유틸리티 패턴 마이닝은 비 이진 데이터베이스로부터 상대적인 아이템 중요도를 반영하여 더욱 의미 있는 정보를 찾아내기 위해 제안되었다. 정적 데이터를 대상으로 하는 하이 유틸리티 패턴 마이닝 기법은 그러나 스트림 데이터 처리에 적합하지 못하다. 제한된 환경에서 스트림 데이터의 특성을 반영하고 효율적으로 처리하여 중요한 정보를 찾아내기 위해 슬라이딩 윈도우 기반의 접근법이 제안되었다. 본 논문은 슬라이딩 윈도우 기반 하이 유틸리티 패턴 마이닝 기법들의 성능을 평가하고 분석하여 해당 기법들의 특성 및 발전 방향을 고찰한다.

• 한국레이저가공학회:학술대회논문집
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• 한국레이저가공학회 1998년도 춘계학술발표대회 초록개요집
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• pp.33-34
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• 1998

• 한국레이저가공학회:학술대회논문집
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• 한국레이저가공학회 1999년도 추계학술발표대회 논문개요집
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• pp.50-52
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• 1999

• 산업기술연구
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• 제19권
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• pp.179-187
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• 1999

The computer pattern sewing machine is an automatic sewing machine that is controlled by an input pattern. Even a novice can run this machine for various tasks fast and reliably such as sewing a button, a belt ring and an airbag, etc. The pattern processing software, which is the main software of this machine, is for editing and modifying pattern data by online teaching or off-line editing, setting up parameters, and calculate a moving distance of working area on the x-y axes. In this paper we propose an algorithm to generate pattern data for sewing by simplifying image data. The pattern data are composed of outline data like dot, line, circle, arc, curve, etc. We need converting this data into sewing data which involve sewing parameter, moving distance of working are an the x-y axes, thread, spindle speed.