• Journal of KIISE:Software and Applications
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• v.32 no.8
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• pp.742-751
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• 2005

This paper proposes a novel text image watermarking algorithm based on word classification. The words are classified into K classes using simple features. Several adjacent words are grouped into a segment. and the segments are also classified using the word class information. The same amount of information is inserted into each of the segment classes. The signal is encoded by modifying some inter-word spaces statistics of segment classes. Subjective comparisons with conventional word-shift algorithms are presented under several criteria.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.1
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• pp.139-146
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• 2017

The ISDB-T(Integrated Service Digital Broadcasting Terrestrial) can be used in the multipath and impulsive noise, also it provide good performance over mobile reception environment since it use the OFDM(Orthogonal Frequency Division Multiplexing) based transmission technology and time interleaving technology. One segment and full segment are divided according to the number of the assigned segment. And one-segment broadcasting receiver can design and implement without high levels of technology than the full-segment broadcasting receiver using 64QAM(64 Quadrature Amplitude Modulation) since it uses QPSK(Quadrature Phase Shift Keying) modulation/demodulation. However, it has a constraint in the display size and resolution due to data-rate limits. In this paper, we design and implementation of full-segment ISDB-T receiver module which support HD resolution for set-top box, digital TV, navigation. In experimental results, the implemented full-segment ISDB-T receiver module was satisfactory for all of the desired functions.

• Radiation Oncology Journal
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• v.26 no.4
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• pp.280-288
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• 2008

Purpose: To identify the inter-fractional shift pattern and to assess an adequate treatment margin in the radiotherapy of a liver tumor using mega-voltage computed tomography (MVCT) of a tomotherapy unit. Materials and Methods: Twenty-six patients were treated for liver tumors by tomotherapy from April 2006 to August 2007. The MVCT images of each patient were analyzed from the $1^{st}$ to the $10^{th}$ fraction for the assessment of the daily liver shift by four groups based on Couinard's proposal. Daily setup errors were corrected by bony landmarks as a prerequisite. Subsequently, the anterior-, posterior-, right-, and left shifts of the liver edges were measured by maximum linear discrepancies between the kilo-voltage computed tomography (KVCT) image and MVCT image. All data were set in the 2-dimensional right angle coordinate system of the transverse section of each patient's body. Results: The liver boundary shift had different patterns for each group. In group II (segment 2, 3, and 4), the anterior mean shift was $2.80{\pm}1.73\;mm$ outwards, while the left mean shift was $2.23{\pm}1.37\;mm$ inwards. In group IV (segment 7 and 8), the anterior-, posterior-, right-, and left mean shifts were $0.15{\pm}3.93\;mm$ inwards, $3.15{\pm}6.58\;mm$ inwards, $0.60{\pm}3.58\;mm$ inwards, and $4.50{\pm}5.35\;mm$ inwards, respectively. The reduced volume in group II after MVCT reassessment might be a consequence of stomach toxicity. Conclusion: Inter-fractional liver shifts of each group based on Couinard's proposal were somewhat systematic despite certain variations observed in each patient. The geometrical deformation of the liver by respiratory movement can cause shrinkage in the left margins of liver. We recommend a more sophisticated approach in free-breathing mode when irradiating the left lobe of liver in order to avoid stomach toxicity.

• Journal of Korean Society for Atmospheric Environment
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• v.32 no.3
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• pp.280-288
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• 2016

Greenhouse gas (GHG) emissions have given rise to climate change which is one of the most serious environmental challenges that the world faces today. In response, Republic of Korea has proposed "Low Carbon, Green Growth" as a new economic paradigm accompanying with the ultimate aim of building a sense of responsibility for the environment. Korean government has set the ambitious national GHG emission reduction target which aims 37% reduction in the business-as-usual (BAU) level of 2030. The transportation sector plays a key role in this target. In the transportation sector, the GHG reduction target of 34.3% in the BAU level by 2020 has been allocated in order to consider the industrial specificity. Furthermore, it is known that the GHG reduction in the transportation sector has relatively minimal side effects compared to those of other sectors. In order to meet this national GHG reduction target, Korean government has set $CO_2$ emission regulation of vehicle for 2020. The purpose of this study is to evaluate the reduction effects by the average GHG regulation of vehicles. $CO_2$ emissions, between 2009 and 2013 were analysed by reduction measure such as technology improvement, light-weight, segment shift, diesel vehicle sales. During this period, $CO_2$ of vehicle was reduced every year by 19.9 g/km (i.e., 3.3% reduction per year). $CO_2$ reduction of imported vehicle is greater than domestic vehicle because of segment shift toward small size vehicle and higher diesel vehicle sales.

• The Mathematical Education
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• v.50 no.1
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• pp.27-40
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• 2011

The purpose of this paper is to analyze and discuss the viewpoint dealing with the fundamental figures-point, line segment, and angle-of elementary school teachers. In fact, our main subjects in this article are as follows; how do elementary school teachers deal with the fundamental figures?, what is the general notion about the fundamental figures of elementary school teachers? Our such subjects come from the survey results about the 'fundamental figures in J. A. Ko(2009); the elementary school students have a tendency to regard the fundamental figures as not mathematical figures. In this article, we discuss mainly the meta-cognitive shift in the transform of notion, for example, from 'congruent' concept to 'equal' concept, about the fundamental figures.

• Journal of Broadcast Engineering
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• v.27 no.6
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• pp.923-935
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• 2022

This paper deals with detection and classification of leaf diseases for phenomics systems. As the smart farm systems of plants are increased, It is important to determine quickly the abnormal growth of plants without supervisors. This paper considers the color distribution and shape information of leaf diseases, and designs two deep leaning networks in training the leaf diseases. In the first step, color distribution of input image is analyzed for possible diseases. In the second step, the image is first partitioned into small segments using mean shift clustering, and the color information of each segment is inspected by the proposed Color Network. When a segment is determined as disease, the shape parameters of the segment are extracted and inspected by proposed Shape Network to classify the leaf disease types in the third step. According to the experiments with two types of diseases (frogeye/rust and tipburn) for apple leaves and iceberg, the leaf diseases are detected with 92.3% recall for a segment and with 99.3% recall for an input image where there are usually more than two disease segments. The proposed method is useful for detecting leaf diseases quickly in the smart farm environment, and is extendible to various types of new plants and leaf diseases without additional learning.

• Journal of Broadcast Engineering
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• v.20 no.5
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• pp.728-737
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• 2015

Stitching algorithm obtain a descriptor of the feature points extracted from multiple images, and create a single image through the matching process between the each of the feature points. In this paper, a feature extraction and matching techniques for the creation of a high-speed panorama using video input is proposed. Features from Accelerated Segment Test(FAST) is used for the feature extraction at high speed. A new feature point matching process, different from the conventional method is proposed. In the matching process, by tracking region containing the feature point through the Mean shift vector required for matching is obtained. Obtained vector is used to match the extracted feature points. In order to remove the outlier, the RANdom Sample Consensus(RANSAC) method is used. By obtaining a homography transformation matrix of the two input images, a single panoramic image is generated. Through experimental results, we show that the proposed algorithm improve of speed panoramic image generation compared to than the existing method.

• Bulletin of the Korean Chemical Society
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• v.32 no.7
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• pp.2332-2338
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• 2011

In this study, we proposed a new promising idea of utilizing moving window principal component analysis (MWPCA) as a sensitive diagnostic tool to detect the presence of peak position shift. In this approach, the moving window is constructed from a small data segment along the wavenumber axis. For each window bound by a narrow wavenumber region, separate PCA analysis was applied. Simulated spectra with complex spectral feature variations were analyzed to explore the possibility of MWPCA technique. This MWPCA-based detection of the peak shift, potentially coupled with 2D correlation analysis to provide additional verification, may offer an attractive solution.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.2
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• pp.262-266
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• 2010

Recognizing a license plate of a vehicle has widely been issued. In this thesis, firstly, mean shift algorithm is used to filter and segment a color vehicle image in order to get candidate regions. These candidate regions are then analyzed and classified in order to decide whether a candidate region contains a license plate. We then present an approach to recognize a vehicle's license plate using the Fuzzy ARTMAP neural network, a relatively new architecture of the neural network family. We show that the proposed system is well to recognize the license plate and shows some compute simulations.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.9 no.3
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• pp.166-171
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• 2009

In recent years, video detection systems have been implemented in various infrastructures such as airport, public transportation, power generation system, water dam and so on. Recognizing moving objects in video sequence is an important problem in computer vision, with applications in several fields, such as video surveillance and target tracking. Segmentation and tracking of multiple vehicles in crowded situations is made difficult by inter-object occlusion. In the system described in this paper, the mean shift algorithm is firstly used to filter and segment a color vehicle image in order to get candidate regions. These candidate regions are then analyzed and classified in order to decide whether a candidate region contains a license plate or not. And then some characters in the license plate is recognized by using the fuzzy ARTMAP neural network, which is a relatively new architecture of the neural network family and has the capability to learn incrementally unlike the conventional BP network. We finally design a license plate recognition system using the mean shift algorithm and fuzzy ARTMAP neural network and show its performance via some computer simulations.