• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.11
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• pp.153-162
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• 1995

When vector quantization is used in low rate image coding (e.g., R<0.5), the primary problem is the tremendous computational complexity which is required to search the whole codebook to find the closest codevector to an input vector. Since the number of code vectors in a vector quantizer is given by an exponential function of the dimension. i.e., L=2$^{nR}$ where Rn. To alleviate this problem, a multiple shell structure of hypercube feature maps (MSSHFM) is proposed. A binary HFM of k-dimension is composed of nodes at hypercube vertices and a multiple shell architecture is constructed by surrounding the k-dimensional hfm with a (k+1)-dimensional HFM. Such a multiple shell construction of nodes inherently has a complete tree structure in it and an efficient partial search scheme can be applied with drastically reduced computational complexity, computer simulations of still image coding were conducted and the validity of the proposed method has been verified.

• Communications for Statistical Applications and Methods
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• v.29 no.2
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• pp.193-202
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• 2022

Semi-supervised learning has gained significant attention in recent applications. In this article, we provide a selective overview of popular semi-supervised methods and then propose a simple but effective algorithm for semi-supervised classification using support vector machines (SVM), one of the most popular binary classifiers in a machine learning community. The idea is simple as follows. First, we apply the dimension reduction to the unlabeled observations and cluster them to assign labels on the reduced space. SVM is then employed to the combined set of labeled and unlabeled observations to construct a classification rule. The use of SVM enables us to extend it to the nonlinear counterpart via kernel trick. Our numerical experiments under various scenarios demonstrate that the proposed method is promising in semi-supervised classification.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.2C
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• pp.100-109
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• 2003

A differential index assignment scheme is proposed for the image encoding system in which a variable-length tree-structured vector quantizer is adopted. Each source vector is quantized into a terminal node of VLTSVQ and each terminal node is represented as a unique binary vector. The proposed index assignment scheme utilizes the correlation between interblocks of the image to increase the compression ratio with the image quality maintained. Simulation results show that the proposed scheme achieves a much higher compression ratio than the conventional one does and that the amount of the bit rate reduction of the proposed scheme becomes large as the correlation of the image becomes large. The proposed encoding scheme can be effectively used to encode R images whose pixel values we, in general, highly correlated with those of the neighbor pixels.

• Horticultural Science & Technology
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• v.35 no.3
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• pp.323-332
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• 2017

Homology-specific transcriptional and post-transcriptional silencing, an intrinsic mechanism of gene regulation in most eukaryotes, can be induced by anti-sense, co-suppression, or hairpin-based double-stranded RNA. Hairpin-based RNA interference (RNAi) has been applied to analyze gene function and genetically modify crops. However, RNAi vector construction usually requires high-cost cloning steps and large amounts of time, or involves methods that are protected by intellectual property rights. We describe a more effective method for generating intron-spliced RNAi constructs. To produce intron-spliced hairpin RNA, an RNAi cassette was ligated with the first intron and splicing sequences of the Brassica rapa ssp. pekinensis histone deacetylase 1 gene. This method requires a single ligation of the PCR-amplified target gene to SpeI-NcoI and SacI-BglII enzyme sites to create a gene-specific silencing construct. We named the resulting binary vector system pKHi and verified its functionality by constructing a vector to silence DIHYDROFLAVONOL 4-REDUCTASE (DFR), transforming it into tobacco plants, and confirming DFR gene-silencing via PCR, RT-qPCR, and analysis of the accumulation of small interfering RNAs. Reduction of anthocyanin biosynthesis was also confirmed by analyzing flower color of the transgenic tobacco plants. This study demonstrates that small interfering RNAs generated through the pKHi vector system can efficiently silence target genes and could be used in developing genetically modified crops.

• Smart Media Journal
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• v.7 no.4
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• pp.24-29
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• 2018

Text analysis technique for natural language processing in deep learning represents words in vector form through word embedding. In this paper, we propose a method of constructing a document vector and classifying it into spam and normal text message, using word embedding and deep learning method. Automatic spacing applied in the preprocessing process ensures that words with similar context are adjacently represented in vector space. Additionally, the intentional word formation errors with non-alphabetic or extraordinary characters are designed to avoid being blocked by spam message filter. Two embedding algorithms, CBOW and skip grams, are used to produce the sentence vector and the performance and the accuracy of deep learning based spam filter model are measured by comparing to those of SVM Light.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.6
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• pp.1191-1198
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• 2018

The wind profiler radar provides a standing profile of the wind vector and the atmospheric physical signal for the fixed point. Since the wind vector is calculated by the manufacturer's data processing program, the quality control of the date is limited. Therefore, understanding and exploiting the raw spectrum data need to improve the quality of the wind vector. The raw data of the wind vector is the power spectral density stored in binary form. In this study, an algorithm was completed to transform the raw data into the real spectral density, and the use of raw data was evaluated by retrieving zero-order and first-order moments of the spectral based on the spectrum quality control.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.5
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• pp.857-864
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• 2006

This paper describes a design of low-power/small-area arithmetic circuits which are vector processing unit powering nit, divider unit and square-root unit for mobile 3D graphic accelerator. To achieve area-efficient and low-power implementation that is an essential consideration for mobile environment, the fixed-point f[mat of 16.16 is adopted instead of conventional floating-point format. The vector processing unit is designed using redundant binary(RB) arithmetic. As a result, it can operate 30% faster and obtained gate count reduction of 10%, compared to the conventional methods which consist of four multipliers and three adders. The powering nit, divider unit and square-root nit are based on logarithm number system. The binary-to-logarithm converter is designed using combinational logic based on six-region approximation method. So, the powering mit, divider unit and square-root unit reduce gate count when compared with lookup table implementation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.4
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• pp.1807-1823
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• 2016

Local Binary Pattern (LBP) and its variants have powerful discriminative capabilities but most of them just consider each LBP code independently. In this paper, we propose sub oriented histograms of LBP for smoke detection and image classification. We first extract LBP codes from an image, compute the gradient of LBP codes, and then calculate sub oriented histograms to capture spatial relations of LBP codes. Since an LBP code is just a label without any numerical meaning, we use Hamming distance to estimate the gradient of LBP codes instead of Euclidean distance. We propose to use two coordinates systems to compute two orientations, which are quantized into discrete bins. For each pair of the two discrete orientations, we generate a sub LBP code map from the original LBP code map, and compute sub oriented histograms for all sub LBP code maps. Finally, all the sub oriented histograms are concatenated together to form a robust feature vector, which is input into SVM for training and classifying. Experiments show that our approach not only has better performance than existing methods in smoke detection, but also has good performance in texture classification.

• The KIPS Transactions:PartB
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• v.11B no.3
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• pp.317-326
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• 2004

This paper presents a new method for the detection of multiple pedestrians and tracking of a specific pedestrian using color information from a moving camera. We first extract motion vector on the input image using BMA. Next, a difference image is calculated on the basis of the motion vector. The difference image is converted to a binary image. The binary image has an unnecessary noise. So, it is removed by means of the proposed noise deletion method. Then, we detect pedestrians through the projection algorithm. But, if pedestrians are very adjacent to each other, we separate them using RGB color information. And we track a specific pedestrian using RGB color information in center region of it. The experimental results on our test sequences demonstrated the high efficiency of our approach as it had shown detection success ratio of 97％ and detection failure ratio of 3％ and excellent tracking.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.6
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• pp.665-670
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• 2014

A SVM is a kind of binary classifier in order to find optimal hyperplane which separates training data into two groups. Due to its remarkable performance, the SVM is applied in various fields such as inductive inference, binary classification or making predictions. Also it is a representative black box model; there are plenty of actively discussed researches about analyzing trained SVM classifier. This paper conducts a study on a method that is automatically detecting the line-shaped echoes, sun strobe echo and radial interference echo, using the SVM algorithm because the line-shaped echoes appear relatively often and disturb weather forecasting process. Using a spatial clustering method and corrected reflectivity data in the weather radar, the training data is made up with mean reflectivity, size, appearance, centroid altitude and so forth. With actual occurrence cases of the line-shaped echoes, the trained SVM classifier is verified, and analyzed its characteristics using the decision tree method.