• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 신호처리소사이어티 추계학술대회 논문집
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• pp.405-408
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• 2003

This paper proposes a new algorithm for cloudy area detection using K-Means and GHA (Generalized Hebbian Algorithm). K-Means is one of simple classification algorithm, and GHA is unsupervised neural network for data compression and pattern classification. Proposed algorithm is based on block based image processing that size is l6$\times$l6. Experimental results shows good performance of cloudy area detection except blur cloudy areas.

• 대한전기학회논문지:시스템및제어부문D
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• 제51권9호
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• pp.389-401
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• 2002

To complement the conventional fusion methodologies of state fusion and measurement fusion, a time-propagated measurement fusion methodology is proposed. Various aspects of common process noise are investigated regarding information preservation. Based on time-propagated measurement fusion methodology, four compression filters are derived. The derived compression filters are efficient in asynchronous sensor fusion and fault detection since they maintain correct statistical information. A new batch Kalman recursion is proposed to show the optimality under the time-propagated measurement fusion methodology. A simple simulation result evaluates estimation efficiency and characteristic.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.76.2-76
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• 2001

In this paper, a new fusion architecture consisting of a host filter and a do-correlated compression filter is proposed based on propagated measurement fusion. In the proposed architecture, the host filter estimates the system states in long-term sense based on the measurements from the beginning to the current time. The de-correlated compression filter assists the host filter by providing fusion results in short-term sense based on the measurements within a block of time. The proposed de-correlated compression filter alleviates computational burden of the host filter by reducing the maximum amount of instantaneous computation, and provides an efficient environment for real-time fault detection and estimation.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 심포지엄 논문집 정보 및 제어부문
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• pp.6-8
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• 2006

Using enhanced ordering scheme of graylevel in an image, we can apply it to lossless image compression in this paper. The proposed method is ordering scheme to replace an original grayscale image with a particular ordered image without additional information. From the simulation, it is verified that the proposed method reduces the bit rates than plain ordering scheme. And it can be applied in various fields of lossless compression, water marking and edge detection.

• 전기학회논문지P
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• 제56권1호
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• pp.28-36
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• 2007

Sensor networks are usually composed of tens or thousands of tiny devices with limited resources. Because of their limited resources, many researchers have studied on the energy management in the WSNs(Wireless Sensor Networks), especially taking into account communications efficiency. For effective data transmission and sensor fault detection in sensor network environment, a new remote monitoring system based on PCA(Principle Component Analysis) and AANN(Auto Associative Neural Network) is proposed. PCA and AANN have emerged as a useful tool for data compression and identification of abnormal data. Proposed system can be effectively applied to sensor network working in LEA2C(Low Energy Adaptive Connectionist Clustering) routing algorithms. To verify its applicability, some simulation studies on the data obtained from real WSNs are executed.

• 한국멀티미디어학회논문지
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• 제21권11호
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• pp.1279-1287
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• 2018

In this paper, a new type of color coordinate conversion is proposed as modified CIEXYZ from RGB to compress the color gamut. The proposed segmentation includes principal component analysis for the optimal projection of a feature vector into a one-dimensional feature. The final step adopted for lip segmentation is Otsu's threshold for a two-class problem. The performance of the proposed method was better than that of conventional methods, especially for the chromatic feature.

• 한국재료학회지
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• 제28권1호
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• pp.1-5
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• 2018

Gas detection is necessary for various reasons, including the prevention of gas leakages and the creation of necessary environmental conditions. Among the gas detection methods, leakage of gas can be confirmed using materials that undergo color changes that are easily distinguished by the naked eye. Metal nanoparticles (NPs) experience variations in their absorption wavelengths under the localized surface plasmon effect (LSPR) with mechanical stresses, which change the distance between NPs. In this study, we attempted to detect the presence of gas utilizing the LSPR-related color change of a chain of Au NPs. The assembly of Au NPs, arranged in a chain shape, experienced a color change from dark blue to purple with a change in the distance between the NPs by applying a physical force, i.e., compression, stretching, and gas pressure. As the force of compression and the degree of stretching increased, the absorption wavelength shifted from doublet peaks at 650 and 550 nm to a singlet peak at 550 nm. Further, applying gas pressure caused an identical color change. With this result, we propose a method that could be applied to all gases that require detection based on gas pressure.

• Asia pacific journal of information systems
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• 제31권3호
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• pp.335-357
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• 2021

As reproduction of images can be done with ease, copy detection has increasingly become important. In the duplication process, image modifications are likely to occur and some alterations are deliberate and can be viewed as attacks. A wide range of copy detection techniques has been proposed. In our study, content-based copy detection, which basically applies DCT-based features for images, namely, pixel values, edges, texture information and frequency-domain component distribution, is employed. Experiments are carried out to evaluate robustness and sensitivity of DCT-based features from attacks. As different types of DCT-based features hold different pieces of information, how features and attacks are related can be shown in their robustness and sensitivity. Rather than searching for proper features, use of robustness and sensitivity is proposed here to realize how the attacked features have changed when an image attack occurs. The experiments show that, out of ten attacks, the neural networks are able to detect seven attacks namely, Gaussian noise, S&P noise, Gamma correction (high), blurring, resizing (big), compression and rotation with mostly related to their sensitive features.

• 융복합기술연구소 논문집
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• 제9권1호
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• pp.13-19
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• 2019

The purpose of this study is to suggest a new method to determine a combustion phase (start of combustion and end of combustion) using a combustion pressure data. Unlike previous research method that used heat release amount, the difference between the combustion pressure measured in the combustion chamber and the motoring pressure was used to determine the combustion phase. This research was conducted using a single-cylinder diesel engine with a compression ratio of 17.7. The test was conducted under various injection timing. The newly proposed method showed high accuracy in combustion mode with early injection, as well as the conventional combustion mode. It is expected that this method will be used to study new combustion strategies such as HCCI (homogeneous charge compression ignition) and RCCI (reactivity controlled compression ignition) that are applying early injection strategies as well as existing combustion modes.

• 한국통신학회논문지
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• 제28권2C호
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• pp.147-161
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• 2003

비디오 객체 분할(Video Object Segmentation)은 MPEG-4 부호화의 핵심기술로 실시간 요구사항을 위해 빠르고 정확하여야 한다. 그러나 대부분의 존재하는 알고리즘은 계산량이 많으며 실시간 응용을 위해 적합하지 않다. 또한 이전 MPEG-4 VM(Verification Model) 기본 모델은 MPEG-4 부호화 처리를 위한 기본 알고리즘을 제공하였으나 실시간 요구사항을 위한 카메라 입력 시스템, 실용적인 소프트웨어 개발, 비디오 객체 분할 그리고 압축효율에 많은 제한이 있다. 이에 본 논문은 기본 MPEG-4 VM모델에 내용 기반 비디오 코딩의 핵심인 VOP 추출알고리즘, 실시간 카메라 입력 시스템, 압축율을 높일 수 있는 움직임 감지 알고리즘을 추가하여 최대 180:1의 압축율을 보여주는 실시간 고압축 MPEG-4 전처리 시스템을 개발하였다.