• Journal of Korean Society of Forest Science
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• v.81 no.4
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• pp.363-371
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• 1992

This study was aimed at developing a computer model to determine rational road networks in mountainous forests. The computer model is composed of two major subroutines for digital terrain analyses and route selection. The digital terrain model(DTM) provides various information on topographic and vegetative characteristics of forest stands. The DTM also evaluates the effectiveness of road construction based on slope gradients. Using the results of digital terrain analyses, the route selection subroutine, heuristically, determines the optimal road layout satisfying the predefined road densities. The route selection subroutine uses the area-partitioning method in order to fully of roads. This method leads to unbiased road layouts in forest areas. The size of the unit partitiones area can be calculated as a function of the predefined road density. In addition, the user-defined road density of the area-partitioning method provides flexibility in applying the model to real situations. The rational road network can be easily achived for varying road densities, which would be an essential element for network design of forest roads. The optimality conditions are evaluated in conjuction with longitudinal gradients, investment efficiency earthwork quantity or the mixed criteria of these three. The performance of the model was measured and, then, compared with those of conventional ones in terns of average skidding distance, accessibility of stands, development index and circulated road network index. The results of the performance analysis indicate that selection of roading routes for network design using the digital terrain analysis and the area-partitioning method improves performance of the network design medel.

• Journal of KIISE
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• v.44 no.4
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• pp.383-391
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• 2017

As we enter a new era of Big Data, the amount of semantic data has rapidly increased. In order to derive meaningful information from this large semantic data, studies that utilize the SWRL(Semantic Web Rule Language) are being actively conducted. SWRL rules are based on data extracted from a user's empirical knowledge. However, conventional reasoning systems developed on single machines cannot process large scale data. Similarly, multi-node based reasoning systems have performance degradation problems due to network shuffling. Therefore, this paper overcomes the limitations of existing systems and proposes more efficient distributed inference methods. It also introduces data partitioning strategies to minimize network shuffling. In addition, it describes a method for optimizing the incremental reasoning process through data selection and determining the rule order. In order to evaluate the proposed methods, the experiments were conducted using WiseKB consisting of 200 million triples with 83 user defined rules and the overall reasoning task was completed in 32.7 minutes. Also, the experiment results using LUBM bench datasets showed that our approach could perform reasoning twice as fast as MapReduce based reasoning systems.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.4B
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• pp.725-733
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• 2000

In this paper, we propose a modified boundary block merging technique for the texture coding of MPEG-4. We propose an ORP(Optimized Region Partitioning) method that partition the VOP-based reference position to minimize the number of coding blocks. The merging possibility is improved by adding +90。and -90。 Rotation merging. We propose a MRM(Multiple Rotation Merging) method which applies the rotation merging in the order of 180。, +90。and -90。. If a pair of boundary blocks has low correlation, existing BBM's padding technique is not efficient. Our padding after merging method gives better result even if it has low correlation. The proposed method showed 5 ~8(%) coding bit reduction at the same PSNR values compared to BBM method.

• The Transactions of the Korea Information Processing Society
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• v.2 no.4
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• pp.524-534
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• 1995

Image segmentation is a process of partitioning of an image into different regions that have similar properties. It is an old and difficult problem. They have developed many image segmentation systems and have studied to evaluate the existing segmentation systems. However, evaluation of image segmentation systems is very difficult in nature. In this paper, we propose the evaluation criteria that evaluate image segmentation systems automatically. In order to overcome the drawbacks of using only a single evaluation criterion, we have incorporated four evaluation criteria, that is, boundary difference, boundary consistency, region uniformity, and region difference. As the experimental results show, our evaluation criteria performs very well.

• Journal of the Korean Chemical Society
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• v.56 no.6
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• pp.720-724
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• 2012

Under pseudo-first order conditions, the monomeric species of Cr(VI) was found to be kinetically active in the absence of phenanthroline (phen) whereas in the phen-promoted path, the Cr(VI)-phen complex undergoes a nucleophilic attack by etane-1,2-diol to form a ternary complex which subsequently experience a redox decomposition leading to hydroxy ethanal and Cr(III)-phen complex. The effect of the cationic surfactant (CPC), anionic surfactant (SDS) and neutral surfactant (TX-100) on the unpromoted and phen-promoted path have been studied. Micellar effects have been explained by considering the preferential partitioning of reactants between the micellar and aqueous phase. Combination of TX-100 and phenanthroline will be the ideal for chromic acid oxidation of ethane-1,2-diol in aqueous media.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.2059-2064
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• 2004

Texture analysis is an important role for automatic visual insfection. This paper presents an application of wavelet extension and Gray level co-occurrence matrix (GLCM) for detection of defect encountered in textured images. Texture characteristic in low quality images is not to easy task to perform caused by noise, low frequency and small dimension. In order to solve this problem, we have developed a procedure called wavelet image extension. Wavelet extension procedure is used to determine the frequency bands carrying the most information about the texture by decomposing images into multiple frequency bands and to form an image approximation with higher resolution. Thus, wavelet extension procedure offers the ability to robust feature extraction in images. Then the features are extracted from the co-occurrence matrices computed from the sub-bands which performed by partitioning the texture image into sub-window. In the detection part, Mahalanobis distance classifier is used to decide whether the test image is defective or non defective.

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.204-207
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• 2002

The current paper proposes a new multispectral image data compression algorithm that can efficiently reduce spatial and spectral redundancies by applying classified prediction, a Karhunen-Loeve transform (KLT), and the three-dimensional set partitioning in hierarchical trees (3-D SPIHT) algorithm In the wavelet transform (WT) domain. The classification is performed in the WT domain to exploit the interband classified dependency, while the resulting class information is used for the interband prediction. The residual image data on the prediction errors between the original image data and the predicted image data is decorrelated by a KLT. Finally, the 3D-SPIHT algorithm is used to encode the transformed coefficients listed in a descending order spatially and spectrally as a result of the WT and KLT. Simulation results showed that the reconstructed images after using the proposed algorithm exhibited a better quality and higher compression ratio than those using conventional algorithms.

• Journal of Information Processing Systems
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• v.12 no.1
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• pp.46-56
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• 2016

Dynamic textures are videos that exhibit a stationary property with respect to time (i.e., they have patterns that repeat themselves over a large number of frames). These patterns can easily be tracked by a linear dynamic system. In this paper, a model that identifies the underlying linear dynamic system using wavelet coefficients, rather than a raw sequence, is proposed. Content based threshold filtering based on Set Partitioning in a Hierarchical Tree (SPIHT) helps to get another representation of the same frames that only have low frequency components. The main idea of this paper is to apply SPIHT based threshold filtering on different bands of wavelet transform so as to have more significant information in fewer parameters for singular value decomposition (SVD). In this case, more flexibility is given for the component selection, as SVD is independently applied to the different bands of frames of a dynamic texture. To minimize the time complexity, the proposed model is implemented on a graphics processing unit (GPU). Test results show that the proposed dynamic system, along with a discrete wavelet and SPIHT, achieve a highly compact model with better visual quality, than the available LDS, Fourier descriptor model, and higher-order SVD (HOSVD).

• Journal of Korea Multimedia Society
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• v.23 no.8
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• pp.965-976
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• 2020

We present in this work a segmentation method of E. coli bacterial images generated via phase contrast microscopy using a deep learning based hybrid feature generation. Unlike conventional machine learning methods that use the hand-crafted features, we adopt the denoising autoencoder in order to generate a precise and accurate representation of the pixels. We first construct a hybrid vector that combines original image, difference of Gaussians and image gradients. The created hybrid features are then given to a deep autoencoder that learns the pixels' internal dependencies and the cells' shape and boundary information. The latent representations learned by the autoencoder are used as the inputs of a softmax classification layer and the direct outputs from the classifier represent the coarse segmentation mask. Finally, the classifier's outputs are used as prior information for a graph partitioning based fine segmentation. We demonstrate that the proposed hybrid vector representation manages to preserve the global shape and boundary information of the cells, allowing to retrieve the majority of the cellular patterns without the need of any post-processing.

• Korean Journal of Applied Biomechanics
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• v.12 no.1
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• pp.125-133
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• 2002

The purpose of this study was to develop a method for estimating 3-D coordinates of lower trunk muscles using orientation angles during a motion. Traditional 3-D motion analysis system with DLT technique was used to track down the locations of eight reference markers which were attached on the back of the subject. In order to estimate the orientations of individual lumbar vertebrae and musculoskeletal parameters of the lower trunk muscle, the rotation matrix of the middle trunk reference frame relative to the lower trunk reference frame was determined and the angular locations of individual lumbar vertebrae were estimated by partitioning the orientation angles (Cardan angles) that represent the relative angles between the rotations of the middle and lower trunks. When the orientation angles of individual intervertebral joints were known at a given instant, the instantaneous coordinates of the origin and insertion for all selected muscles relative to the L5 local reference frame were obtained by applying the transformation matrix to the original coordinates which were relative to a local reference frame (S1, L4, L3, L2, or L1) in a rotation sequence about the Z-, X- and Y-axes. The multiplication of transformation matrices was performed to estimate the geometry and kinematics of all selected muscles. The time histories of the 3-D coordinates of the origin and insertion of all selected muscles relative to the center of the L4-L5 motion segment were determined for each trial.