• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.2 s.308
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• pp.82-86
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• 2006

The design of a pattern recognition system generally involves the three aspects: preprocessing, feature extraction, and decision making. Among them, a feature extraction method determines an appropriate subspace of dimensionality in the original feature space of dimensionality so that it can reduce the complexity of the system and help to improve successful recognition rates. Linear transforms, such as principal component analysis, factor analysis, and linear discriminant analysis have been widely used in pattern recognition for feature extraction. This paper shows that singular value decomposition (SVD) can be applied usefully in feature extraction stage of pattern recognition. As an application, a remote sensing problem is applied to verify the usefulness of SVD. The experimental result indicates that the feature extraction using SVD can improve the recognition rate about 25% compared with that of PCA.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2474-2476
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• 2003

An algorithm was embodied to automatic pump operation Pattern for remote control located in 60 km far. This automation pattern included least cost operation, peak load time response, pump operation time balancing etc. It was programmed four kind of operation mode as following; normal operation nude, before peak load time nude, peak load time operation, after peak load time operation.

• Journal of Intelligence and Information Systems
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• v.16 no.2
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• pp.95-108
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• 2010

In this paper, we present a hybrid neural network model for dynamic hand gesture recognition. The model consists of two modules, feature extraction module and pattern classification module. We first propose a modified CNN(convolutional Neural Network) a pattern recognition model for the feature extraction module. Then we introduce a weighted fuzzy min-max(WFMM) neural network for the pattern classification module. The data representation proposed in this research is a spatiotemporal template which is based on the motion information of the target object. To minimize the influence caused by the spatial and temporal variation of the feature points, we extend the receptive field of the CNN model to a three-dimensional structure. We discuss the learning capability of the WFMM neural networks in which the weight concept is added to represent the frequency factor in training pattern set. The model can overcome the performance degradation which may be caused by the hyperbox contraction process of conventional FMM neural networks. From the experimental results of human action recognition and dynamic hand gesture recognition for remote-control electric home appliances, the validity of the proposed models is discussed.

• Korean Journal of Remote Sensing
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• v.32 no.3
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• pp.331-337
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• 2016

Water vapor is main absorption factor of outgoing longwave radiation. So, it is essential to monitoring the changes in the amount of water vapor and to understanding the causes of such changes. In this study, we monitor temporal variability of Total Precipitable Water (TPW) which observed by satellite. Among climate variables, precipitation play an important part to analyze temporal variability of water vapor because it is produced by water vapor. And El $Ni{\tilde{n}}o$ is one of climate variables which appear regularly in comparison with the others. Through them, we analyze relationship between temporal variability of TPW and climate variable. In this study, we analyzed long-term change of TPW from Moderate-Resolution Imaging Spectroadiometer (MODIS) data and change of precipitation in middle area of Korea peninsula quantitatively. After these analysis, we compared relation of TPW and precipitation with El $Ni{\tilde{n}}o$. The aim of study is to research El $Ni{\tilde{n}}o$ has an impact on TPW and precipitation change in middle area of Korea peninsula. First of all, we calculated TPW and precipitation from time series analysis quantitatively, and anomaly analysis is performed to analyze their correlation. As a result, TPW and precipitation has correlation mostly but the part had inverse correlation was found. This was compared with El $Ni{\tilde{n}}o$ of anomaly results. As a result, TPW and precipitation had inverse correlation after El $Ni{\tilde{n}}o$ occurred. It was found that El $Ni{\tilde{n}}o$ have a decisive effect on change of TPW and precipitation.

• Korean Journal of Remote Sensing
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• v.30 no.4
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• pp.493-503
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• 2014

A hierarchical classification scheme, which can reduce the spectral ambiguity and also reflect crop cultivation patterns from past land-cover maps, is presented for the purpose of the early production of crop classification maps in large-scale crop areas. Specifically, the effects of mixed pixels are minimized not only by applying a hierarchical classification approach based on different spectral characteristics from crop growth cycles, but also by considering temporal contextual information derived from past crop cultivation patterns. The applicability of the presented classification scheme was evaluated by a case study of Iowa State in USA with time-series MODIS 250 m Normalized Difference Vegetation Index(NDVI) data sets and past Cropland Data Layers(CDLs). Corn and soybean, which are major crop types in the study area and also display spectral similarity, could be properly classified by applying different classification stages and accounting for past crop cultivation patterns. The classification result by the presented scheme showed increases of minimum 7.68%p and maximum 20.96%p in overall accuracy, compared with one based on purely spectral information. In addition, the combination of temporal contextual information during classification was less affected by the number of NDVI data sets and the best overall accuracy of 86.63% was achieved. Thus, it is expected that this classification scheme can be effectively used for the early production of large-area crop classification maps in major feed-grain importing countries.

• Korean Journal of Remote Sensing
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• v.12 no.2
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• pp.111-125
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• 1996

A forecast technique using GMS-4(Geostationary Meteorological Satellite) infrared images and its $T_{BB}$ (Brightness Temperature) data to determine the tropical cyclone center and to analyze the tropical cyclone intensity has been developed. First, the determination of typhoon center using $T_{BB}$ distribution pattern is practiced by understanding a special feature of central cloud pattern and cloud band which is analyzed with the method of pseudo coloring. Then, to forecast the intensity of tropical cyclone, a relationship between the central pressure (or maximum wind speed) of tropical cyclone and $T_{BB}$ measured by GMS near the tropical cyclone center was investigated. The results showed a correlation with a high lag relationship between central pressures and $T_{BB}$. The mean Tee in the ring of 200~300km apart from the tropical cyclone center showed the best correlation to central pressure of the tropical cyclone after 24hour. From this relationship, a regression equation to forecast the central pressure (or maximum wind speed) was derived.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.1964_1965
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• 2009

최근 경제성장과 함께 생활수준의 향상으로 인하여 홈 게이트웨이는 디지털 홈 가전기기에 초고속 통신망 접속 기능을 제공하며 각종 기기들을 하나로 네트워크화 하는 중요한 기능을 담당하게 된다. 전력선 통신은 전력선을 매체로 하기 때문에 신규선로의 포설 없이 가전기기 및 정보화 서비스 모뎀들의 네트워크화에 용이할 뿐만 아니라 커버리지 확장에도 뛰어나서 디지털 가전, 원격검침, 전력설비 감시제어, 국가 재난 감시 시스템 등의 기본 통신 방식으로 가장 유력한 기술로써 디지털 가전을 포함한 유비쿼터스 전기설비 네트워크 구성에서 필수적 기술로 채택되고 있기 때문에 지능형 홈 네트워크, 전력IT 부가 서비스, 설비감시 네트워크, 유비쿼터스 네트워크 관련 기술에 대한 파급 효과가 매우 크며, 디지털 가전의 기본 통신 방식으로 가장 유력한 기술로써 디지털 가전 구성에서 필수적 기술로 채택되고 있다. 본 연구에서는 전력선통신모뎀을 이용하여 가전기기의 전력 소비를 감지할 수 있는 센서를 내장한 전력선 통신기반의 전력 감시 모듈을 개발하여 실시간 원격 모니터링을 통해 소비전력 패턴을 작성한다. 그리고 전력감시 모듈에 연결된 가전기기의 소비전력 패턴 분석을 통해 전력소비 이상 유무를 감지할 수 있는 알고리즘을 개발, 탑재하여 이상 유무를 판단하고 전력소비가 급증할 시 자동으로 전력을 차단하여 화재나 누전의 위험을 방지한다. 이에 본 연구는 전력감시 및 이상전력 차단기능을 갖는 저전력 전력선통신 모뎀 개발에 관한 것이다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.1
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• pp.351-356
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• 2011

In this paper, we design a pet location tracking and remote monitoring system that uses ultrasonic, temperature, humidity and illumination sensors to study behavioral patterns and habits. Using ultrasonic waves to calculate distances, a WSN(Wireless Sensor Network) was constructed to transmit data at pet's location, such as temperature, humidity and illumination, to a sink mote. Data received by the system are stored in the database in real time to trace pet's location. Interference among transmitting motes was eliminated by sequentially transmitting RF beacons using sink mote's beacon as the reference signal. Experiments were performed with the laboratory prototype of a pet animal monitoring system implemented for this study. The system analyzes locations of a pet and displays movement patterns, areas of movement, temperature, humidity and illumination using a GUI (graphical user interface).

• Korean Journal of Remote Sensing
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• v.29 no.4
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• pp.351-360
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• 2013

Harmonic analysis enables to characterize patterns of variation in MODIS NDVI time series data and track changes in ground vegetation cover. In harmonic analysis, a periodic phenomenon of time series data is decomposed into the sum of a series of sinusoidal waves and an additive term. Each wave is defined by an amplitude and a phase angle and accounts for the portion of variance of complex curve. In this study, harmonic analysis was explored to tract ground vegetation variation through time for land-cover vegetation change detection. The process also enables to reconstruct observed time series data including various noise components. Harmonic model was tested with simulation data to validate its performance. Then, the suggested change detection method was applied to MODIS NDVI time series data over the study period (2006-2012) for a selected test area located in the northern plateau of Korean peninsula. The results show that the proposed approach is potentially an effective way to understand the pattern of NDVI variation and detect the change for long-term monitoring of land cover.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.279-280
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• 2009

최근 경세성장과 함께 생활수준의 향상으로 인하여 에너지 수요는 매년 증가하고 있으며 그 중에서 하나인 전력수요도 역시 급격히 늘어나고 있는 추세이다. 이와 더불어 투자자원 및 입지확보의 어려움, 환경제약의 문제로 전력 공급의 어려움은 날로 증가되고 있다. 따라서 수요증가를 전력공급 능력의 증진뿐만 아니라 수요관리 측면에서도 필요성이 대두되고 있다. 전력선 통신은 전력선을 매체로 하기 때문에 신규선로의 포설 없이 가전기기 및 정보화 서비스 모뎀들의 네트워크화에 용이할 뿐만 아니라 커버리지 확장에도 뛰어나서 디지털 가전, 원격검침, 전력설비 감시제어, 국가 재난 감시 시스템 등의 기본 통신 방식으로 가장 유력한 기술로써 디지털 가전을 포함한 유비쿼터스 전기설비 네트워크 구성에서 필수적 기술로 채택되고 있기 때문에 지능형 홈 네트워크, 전력IT 부가서비스, 설비감시 네트워크, 유비쿼터스 네트워크 관련 기술에 대한 파급 효과가 매우 크며, 디지털 가전의 기본 통신 방식으로 가장 유력한 기술로써 디지털 가전 구성에서 필수적 기술로 채택되고 있기 때문에 지능형 홈 네트워크 관련 기술에 대한 파급 효과가 매우 크다. 본 연구에서는 전력선통신모뎀을 이용하여 가전기기의 전력 소비를 감지할 수 있는 센서를 내장한 전력선 통신기반의 전력 감시 모듈을 개발하여 실시간 원격 모니터링을 통해 소비전력 패턴을 작성한다. 그리고 전력감시 모듈에 연결된 가전기기의 소비전력 패턴 분석을 통해 전력소비 이상 유무를 감지할 수 있는 알고리즘을 개발, 탑재하여 이상유무를 판단하고 전력소비가 급증할 시 자동으로 전력을 차단하여 화재나 누전의 위험을 방지한다. 이에 본 연구는 전력선통신을 이용하여 전력소비감시 및 이상전력차단에 관한 연구에 관한 것이다.