• Journal of the Speleological Society of Korea
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• no.68
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• pp.75-98
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• 2005

Korea physiographic province is divided into two provinces which is northern Chugaryung graben zone and southern Chugraryung graben zone. Northern Chugraryung is also divided in to Gema block and Kohan block, and southern Chugraryung dividedinto Han block, Yongnam basin and Honam plain. The above mentioned macro geomorophic units is divided, mainly on the geotectonics. The meso geomorphic units is divided, based upon the regional distribution of topographic characteristic that is plateaus, mountains, mountain range, basins and great plain etc. Micro geomorphic units id into a mountain, a hill, a plain, and a lowland, and then it is formed by selfreliant topographic unit. And micro topographic (fan, peneplain, delta, etc) dealt with a characteristics unit. In this article has a disregarded amallest scale that is included flood plait natural levee, back marsh and oxbow lake etc. Accordingly, it shows macro units are meso units are 5, meso units are 53, micro units are 299. A study method of physiographic provincs prefered to aufsteigende and abstergands methoy. How to organically combine topographic factors can be seen in regional distribution of the peculiar topographic characteristics, for charage teristic of topographic makes a study on the topographic of micro unit such understanding as aufsteigende method. At the same time, since it can be studied systematically from marco unit to micro unit like the absteigende methods, I used both methods. And this establishment of physiographic province based on the scientific method depend on the base map of climate classification. Geology, Soil, Biology. I feel confident that it will be used the basic map for land use map, land classification map, study of geomorphology of Korea. And will be used for study of a topographic standard data.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.11C
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• pp.662-672
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• 2011

This paper presents a new edge-preserving algorithm and its VLSI architecture for block artifact reduction. Unlike previous approaches using block classification, our algorithm utilizes pixel classification to categorize each pixel into one of two classes, namely smooth region and edge region, which are described by the edge-preserving maps. Based on these maps, a two-step adaptive filter which includes offset filtering and edge-preserving filtering is used to remove block artifacts. A pipelined VLSI architecture of the proposed deblocking algorithm for HD video processing is also presented in this paper. A memory-reduced architecture for a block buffer is used to optimize memory usage. The architecture of the proposed deblocking filter is prototyped on FPGA Cyclone II, and then we estimated performance when the filter is synthesized on ANAM 0.25 ${\mu}m$ CMOS cell library using Synopsys Design Compiler. Our experimental results show that our proposed algorithm effectively reduces block artifacts while preserving the details.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.12B
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• pp.1574-1585
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• 2011

This paper proposes a real-time algorithm for speed limit sign recognition for advanced safety vehicle system. The proposed algorithm uses Block-DCT in extracting features from a given ROI(Region Of Interest) instead of using entire pixel values as in previous works. The proposed algorithm chooses parts of the DCT coefficients according to the proposed discriminant factor, uses correlation coefficients and variances among ROIs from training samples to reduce amount of arithmetic operations without performance degradation in classification process. The algorithm recognizes the speed limit signs using the information obtained during training process by calculating LDA and Mahalanobis Distance. To increase the hit rate of recognition, it uses accumulated classification results computed for a sequence of frames. Experimental results show that the hit rate of recognition for sequential frames reaches up to 100 %. When compared with previous works, numbers of multiply and add operations are reduced by 69.3 % and 67.9 %, respectively. Start after striking space key 2 times.

• Transactions on Semiconductor Engineering
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• v.2 no.1
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• pp.17-20
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• 2024

This paper presents a novel lightweight object detection model tailored for low-powered edge devices, addressing the limitations of traditional resource-intensive computer vision models. Our proposed detector, inspired by the Single Shot Detector (SSD), employs a compact yet robust network design. Crucially, it integrates an 'enhancer block' that significantly boosts its efficiency in detecting smaller objects. The model comprises two primary components: the Light_Block for efficient feature extraction using Depth-wise and Pointwise Convolution layers, and the Enhancer_Block for enhanced detection of tiny objects. Trained from scratch on the Udacity Annotated Dataset with image dimensions of 300x480, our model eschews the need for pre-trained classification weights. Weighing only 5.5MB with approximately 0.43M parameters, our detector achieved a mean average precision (mAP) of 27.7% and processed at 140 FPS, outperforming conventional models in both precision and efficiency. This research underscores the potential of lightweight designs in advancing object detection for edge devices without compromising accuracy.

• The Korean Journal of Pain
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• v.5 no.2
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• pp.273-278
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• 1992

With the medical progress that has given spinal cord injured(SCI) individuals greater longevity and better overall health, chronic pain is emerged as major challenge in treating this population. According to past reports, estimates of prevalance of severe/disabling chronic pain in SCI patients have ranged from 18% to 63%. In etiologies of chronic pain in SCI patients, psychic or psychogenic pain categories should be included and more recent data have demonstrated that the persistant pain is directly related to higher levels of psychosocial distress and impairment. Recently, neurophysiological classification of the SCI pain syndrome into three etiologic groups(a; mechanical pain, b; radicular pain, c; deafferentation pain) is more frequently adopted for the classification of chronic SCI pain syndrome. The deafferentation pain is most common of the pain syndromes associated with SCI. After cervical epidural anesthesia for the surgical intervention of decubitus ulcer on the hip of two SCI patients, there were much reduction of existing chronic deafferentation character pain.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.903-904
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• 2008

This paper suggests a simple scheme of block motion estimation in which the search pattern selection is based on the classification of motion content available in the spatio temporal neighboring blocks. The search area is divided into eight sectors and the search pattern selection is also based on the direction of predicted motion vector. Experimental results show that the proposed algorithm has achieved good predicted image quality measured in terms of PSNR and has very less computational complexity.

• Journal of the Korea Society of Computer and Information
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• v.21 no.9
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• pp.19-25
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• 2016

This paper is to recognize and play tab chords from guitar musical sheets. The musical chord area of an input image is segmented by changing the image in saturation and applying the Grabcut algorithm. Based on a template matching, our approach detects tab starting sections on a segmented musical area. The virtual block method is introduced to search blanks over chord lines and extract tab fret segments, which doesn't cause the computation loss to remove tab lines. In the experimental tests, the prototype based classification outperforms Bayesian method and the nearest neighbor rule with the whole set of training data and its performance is similar to that of the support vector machine. The experimental result shows that the prediction rate is about 99.0% and the number of selected prototypes is below 3.0%.

• Journal of KIBIM
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• v.12 no.1
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• pp.72-80
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• 2022

It has become mandatory to designs by BIM in construction. It is urgent to make accurate decisions through the linkage between complex and various types of data in projects. In particular, block-chain based data sharing process (using BIM files, general construction submitted files) is essential to support reliable decision making in complex data flood systems. Prior to developing data sharing system based on block-chain, in this paper, a data linkage method is proposed so that practitioners can simultaneously utilize existing construction information and BIM data. Examples are shown based on the construction classification system and file expression, and incentive strategies are explored through a survey so that heterogeneous information can be used at the same time in overall projects.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.493-494
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• 2022

The development of livestock faces intensive farming results in a rising need for recognition of individual animals such as cows and pigs is related to high traceability. In this paper, we present a non-invasive biometrics systematic approach based on the deep-learning classification model to pig face identification. Firstly, in our systematic method, we build a ROS data collection system block to collect 10 pig face data images. Secondly, we proposed a preprocessing block in that we utilize the SSIM method to filter some images of collected images that have high similarity. Thirdly, we employ the improved image classification model of CNN (ViT), which uses the finetuning and pretraining technique to recognize the individual pig face. Finally, our proposed method achieves the accuracy about 98.66%.

• Journal of Fashion Business
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• v.9 no.3
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• pp.64-87
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• 2005

The purpose at this study is to develop the appropriate bodice model for Chinese women in order to contribute to the improvement of fitness at clothing products that are exported to China. The sample group was the subject of 149 persons with the standard body shape at 19-24 years old women who reside in Beijing and Shanghai, China. The data analysis is processed for statistics using SPSSWIN 10.0 PROGRAM, and the used analysis methods are technical statistics analysis, factor analysis, group analysis, and one-way ANOVA. The outcome of this study is summarized as follows. 1. Prior to develop the tight-fitting shape of bodice model, the body classification approach by the posture and type of bending and stretching is selected to use 6 index items, and the body types are classified into bent body, right body, and pull-back body. 2. The average body size of standard body shape had 3 times of wearing experiment based on the tight-fitting shape of ESMOD bodice block drawing, and the system was corrected and supplemented to present the final bodice block drawing. 3. Comparisons have been made based on the center front line, center back line and chest circumference for each of existing bodice block for Chinese women, existing bodice block for Korean women and the combination of the bodice block under this study.