• Atmosphere
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• v.24 no.2
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• pp.141-150
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• 2014

This study investigates the characteristics of the Gross Moist Stability (GMS) over the tropics. The GMS summarizes the relationship between large-scale entropy forcing due to radiation and surface fluxes and the response of smaller-scale convection. The GMS is able to explain both to where moist entropy is advected by the atmospheric circulation and how deep the moisture flux convergence is in the tropical region. In the deep convective region, positive GMS appears over the warm pool region due to the strong column-integrated moisture convergence and the ensuing export of moist entropy to the environment. The vertical advection of moist entropy dominates over the horizontal advection in this region. Meanwhile, over the eastern tropical ITCZ region, which is characterized by shallow convective area, import of moist entropy by horizontal winds is dominant compared to the vertical moist entropy advection. Future changes in the GMS are also examined using the 22 CMIP5 model simulations. A decrease in the GMS appears widely across the tropics, but its increase occurs over the western-central equatorial Pacific. It is evident that the increased GMS region corresponds to an increased region of precipitation, implying that strengthened convection in the future due to increased entropy forcing exports the enhanced moist energy to stabilize the environment.

• ETRI Journal
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• v.37 no.5
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• pp.1023-1031
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• 2015

In infrared (IR) images, near targets have a transient distribution at the boundary region, as opposed to a steady one at the inner region. Based on this fact, this paper proposes a novel IR target extraction method that uses both a weighted information entropy (WIE) and an adaptive opening filter to extract near finely shaped targets in IR images. Firstly, the boundary region of a target is detected using a local variance WIE of an original image. Next, a coarse target region is estimated via a labeling process used on the boundary region of the target. From the estimated coarse target region, a fine target shape is extracted by means of an opening filter having an adaptive structure element. The size of the structure element is decided in accordance with the width information of the target boundary and mean WIE values of windows of varying size. Our experimental results show that the proposed method obtains a better extraction performance than existing algorithms.

• Proceedings of KOSOMES biannual meeting
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• 2017.04a
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• pp.104-104
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• 2017

• Proceedings of KOSOMES biannual meeting
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• 2018.06a
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• pp.22-22
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• 2018

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.4
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• pp.333-337
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• 2002

A content-based image retrieval method using color, texture, and shape features is proposed in this paper. A region segmentation technique using PIM(Picture Information Measure) entropy is used for similarity indexing. For segmentation, a color image is first transformed to a gray image and it is divided into n$\times$n non-overlapping blocks. Entropy using PIM is obtained from each block. Adequate variance to perform good segmentation of images in the database is obtained heuristically. As variance increases up to some bound, objects within the image can be easily segmented from the background. Therefore, variance is a good indication for adequate image segmentation. For high variance image, the image is segmented into two regions-high and low entropy regions. In high entropy region, hue-saturation-intensity and canny edge histograms are used for image similarity calculation. For image having lower variance is well represented by global texture information. Experiments show that the proposed method displayed similar images at the average of 4th rank for top-10 retrieval case.

• Journal of the Korean Society of Mechanical Technology
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• v.20 no.6
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• pp.872-878
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• 2018

Steam tables including superheated, saturated and compressed region were simultaneously modeled using the neural networks. Pressure and temperature were used as two inputs for superheated and compressed region. On the other hand Pressure and dryness fraction were two inputs for saturated region. The outputs were specific volume, specific enthalpy and specific entropy. The neural network model were compared with the linear interpolation model in terms of the percentage relative errors. The criterion of judgement was selected with the percentage relative error of 1%. In conclusion the neural networks showed better results than the interpolation method for all data of superheated and compressed region and specific volume of saturated region, but similar for specific enthalpy and entropy of saturated region.

• Journal of Advanced Navigation Technology
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• v.20 no.4
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• pp.380-385
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• 2016

Shadows are common phenomena observed in natural scenes and often bring a major problem that is affected negatively in colour image analysis. It is important to detect the shadow areas and should be considered in the pre-processing of computer vision. In this paper, the method of shadow detection is proposed using cross entropy and intensity image, and is performed in single image based on the satellite images. After converting the color image to a gray level image, the shadow candidate region has been estimated the optimal threshold value by cross entropy, and then the final shadow region has been detected using intensity image. For the validity of the proposed method, the satellite images is used to experiment. Some experiments are conducted so as to verify the proposed method, and as a result, shadow detection is well performed.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1999.06a
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• pp.83-88
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• 1999

In this paper, a novel gray-scale lossless image coder combining context-based minimum mean squared error (MMSE) prediction and entropy coding is proposed. To obtain context of prediction, this paper first defines directional difference according to sharpness of edge and gradients of localities of image data. Classification of 4 directional differences forms“geometry context”model which characterizes two-dimensional general image behaviors such as directional edge region, smooth region or texture. Based on this context model, adaptive DPCM prediction coefficients are calculated in MMSE sense and the prediction is performed. The MMSE method on context-by-context basis is more in accord with minimum entropy condition, which is one of the major objectives of the predictive coding. In entropy coding stage, context modeling method also gives useful performance. To reduce the statistical redundancy of the residual image, many contexts are preset to take full advantage of conditional probability in entropy coding and merged into small number of context in efficient way for complexity reduction. The proposed lossless coding scheme slightly outperforms the CALIC, which is the state-of-the-art, in compression ratio.

• IEIE Transactions on Smart Processing and Computing
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• v.5 no.2
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• pp.85-93
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• 2016

The existence of correlation characteristics brings significant potential advantages to the development of efficient routing protocols in wireless sensor networks. This research proposes a new simple method of clustering sensor nodes into correlation groups in multiple-correlation areas. At first, the evaluation of joint entropy for multiple-sensed data is considered. Based on the evaluation, the definition of correlation region, based on entropy theory, is proposed. Following that, a correlation clustering scheme with less computation is developed. The results are validated with a real data set.

• Journal of Advanced Navigation Technology
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• v.18 no.4
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• pp.387-392
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• 2014

Recently, various research for intelligent video surveillance system have been proposed, but the existing monitoring systems are inefficient because all of situational awareness is judged by the human. In this paper, shadow removal based moving object tracking method is proposed using the chromaticity and entropy image. The background subtraction model, effective in the context awareness environment, has been applied for moving object detection. After detecting the region of moving object, the shadow candidate region has been estimated and removed by RGB based chromaticity and minimum cross entropy images. For the validity of the proposed method, the highway video is used to experiment. Some experiments are conducted so as to verify the proposed method, and as a result, shadow removal and moving object tracking are well performed.