• Imaging Science in Dentistry
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• v.33 no.3
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• pp.151-159
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• 2003

Purpose: The purpose of this study is to evaluate the effectiveness and usefulness of newly developed personal computer-based software to eliminate the linear artifacts by the metal restorations. Materials and Methods: A 3D CT image was conventionally reconstructed using ADVANTAGE WINDOWS 2.0 3D Analysis software (GE Medical System, Milwaukee, USA) and eliminated the linear artifacts manually. Next, a 3D CT image was reconstructed using V-works 4.0/sup TM/(Cybermed Inc., Seoul, Korea) and the linear artifacts eliminated manually in the axial images by a skillful operator using a personal computer. A 3D CT image was reconstructed using V-works 4.0/sup TM/(Cybermed Inc., Seoul, Korea) and the linear artifacts were removed using a simplified algorithm program to eliminate the linear artifacts automatically in the axial images using a personal computer, abbreviating the manual editing procedure. Finally, the automatically edited reconstructed 3D images were compared to the manually edited images. Results and Conclusion: We effectively eliminated the linear artifacts automatically by this algorithm, not by the manual editing procedures, in some degree. But programs based on more complicated and accurate algorithms may lead to a nearly flawless elimination of these linear artifacts automatically.

• Journal of Acupuncture Research
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• v.23 no.6
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• pp.199-205
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• 2006

Objectives : The present study was conducted to evaluate the analgesic effects of automatically controlled heating acupuncture(ACHA) using 2 different pain models(acute pain and neuropathic pain) and 2 different stimulation conditions (heating $41.5^{\cdot}C$ and heating $44.5^{\cdot}C$) in Sprague-Dawley rats. Methods : Tail flick latency(TFL) to a noxious radiant heat stimulus in lightly anesthetized rats was measured before and after ACHA stimulation for 5-min at the Zusanli(ST36) acupoint. For the neuropathic surgery, the right superior caudal trunk was resected at the level between S1 and S2 spinal nerves innervating the tail. Two weeks after the nerve injury, ACHA stimulation($41.5^{\cdot}C$ or $44.5^{\cdot}C$) was delivered to Zusanli(ST36) for 5 min. The behavioral signs of warm allodynia were evaluated by the tail immersion test (i.e. immersing the tail in warm $water(40^{\cdot}C)$ and measuring the latency to an abrupt tail movement) before and after the ACHA stimulation. Results : In the TFL test, ACHA stimulations under both the conditions above produced more potent analgesic effects than plain acupuncture(PA, acupuncture needle insertion only) and control(no treatment). In the tail immersion test, ACHA stimulations under all of the conditions had markedly relieved the warm allodynia signs. Conclusion : Automatically controlled heating acupul1cture produced analgesic effecs in acute and neuropathic pains.

• Journal of the Korea Society of Computer and Information
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• v.24 no.12
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• pp.17-24
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• 2019

The biggest problem in the AI field, which has become a hot topic in recent years, is how to deal with the lack of training data. Since manual data construction takes a lot of time and efforts, it is non-trivial for an individual to easily build the necessary data. On the other hand, automatic data construction needs to handle data quality issue. In this paper, we introduce a method to automatically extract the data required to develop Korean speech command recognizer from the web and to automatically select the data that can be used for training data. In particular, we propose a modified ResNet model that shows modest performance for the automatically constructed Korean speech command data. We conducted an experiment to show the applicability of the command set of the health and daily life domain. In a series of experiments using only automatically constructed data, the accuracy of the health domain was 89.5% in ResNet15 and 82% in ResNet8 in the daily lives domain, respectively.

• Journal of Korea Society of Industrial Information Systems
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• v.11 no.3
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• pp.47-58
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• 2006

Address Auto-configuration capability is one of important advantages of IPv6 protocol This function enables the IPv6 hosts to configure IPv6 networks automatically, while IPv6 routers still have to be configured manually. To solve this problem, we propose RACP(Router Auto-Configuration Protocol), a new address auto-configuration protocol which configures all routers of a small network consisting of several routers and sub-networks automatically. The RACP protocol can automatically create and deliver IPv6 prefixes and routing informations of all routers on the network by using the network's prefix assigned by ISP. The proposed RACP can be used to set up network automatically for a small IPv6 site such as a small office network, a home network without the assistance of network administrator.

• Journal of Korean Society for Geospatial Information Science
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• v.10 no.3 s.21
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• pp.41-51
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• 2002

Recently, many studies extracting the geography information using digital aerial image have been implemented. But it is very difficult that automatically recognizing objects using edge detection method on the aerial image, and so that work have practiced manually or semi-automatically. Therefore, in this study, we have removed impedimental elements for recognition using the image which overlapped the significant information bands of brightness-sliced aerial images, then have developed the algorithm which can automatically recognize and extract road information and we will try to apply that method when we develope a system. For this, first of all, we have developed the 'template conformal-transformation moving operator' for automatically recognizing crosswalk area from crosswalk band image and the 'window normal search algorithm' which is able to track road area based on long-side length of crosswalk, so that we have proposed the method that can extract directly the road information from the aerial image.

• Journal of Information Processing Systems
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• v.16 no.1
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• pp.6-29
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• 2020

Biometrics identification using multiple modalities has attracted the attention of many researchers as it produces more robust and trustworthy results than single modality biometrics. In this paper, we present a novel multimodal recognition system that trains a deep learning network to automatically learn features after extracting multiple biometric modalities from a single data source, i.e., facial video clips. Utilizing different modalities, i.e., left ear, left profile face, frontal face, right profile face, and right ear, present in the facial video clips, we train supervised denoising auto-encoders to automatically extract robust and non-redundant features. The automatically learned features are then used to train modality specific sparse classifiers to perform the multimodal recognition. Moreover, the proposed technique has proven robust when some of the above modalities were missing during the testing. The proposed system has three main components that are responsible for detection, which consists of modality specific detectors to automatically detect images of different modalities present in facial video clips; feature selection, which uses supervised denoising sparse auto-encoders network to capture discriminative representations that are robust to the illumination and pose variations; and classification, which consists of a set of modality specific sparse representation classifiers for unimodal recognition, followed by score level fusion of the recognition results of the available modalities. Experiments conducted on the constrained facial video dataset (WVU) and the unconstrained facial video dataset (HONDA/UCSD), resulted in a 99.17% and 97.14% Rank-1 recognition rates, respectively. The multimodal recognition accuracy demonstrates the superiority and robustness of the proposed approach irrespective of the illumination, non-planar movement, and pose variations present in the video clips even in the situation of missing modalities.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.1-17
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• 2012

The Smartphone, one of essential mobile devices widely used recently, can be very effectively applied to capture knowledge on the spot by jointly applying the pervasive functionality of cloud computing. The process of knowledge capturing can be also effectively automated if the topic of knowledge is automatically identified. Therefore, this paper suggests an interdisciplinary approach to automatically acquire knowledge on the spot by combining technologies of text mining-based topic identification and cloud computing-based Smartphone. The Smartphone is used not only as the recorder to record knowledge possessor's dialogue which plays the role of the knowledge source, but also as the sensor to collect knowledge possessor's context data which characterize specific situations surrounding him or her. The support vector machine, one of well-known outperforming text mining algorithms, is applied to extract the topic of knowledge. By relating the topic and context data, a business rule can be formulated, and by aggregating the rule, the topic, context data, and the dictated dialogue, a set of knowledge is automatically acquired.

• KIISE Transactions on Computing Practices
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• v.21 no.11
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• pp.699-705
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• 2015

Thousands of glyphs are necessary for Hangul font generation. It is mandatory to generate the required glyphs before producing Hangul font. This paper, entitled "Multiple Master Glyph Algorithm", presents an process that generates a target number of glyphs automatically from a very small number of glyphs by using a combination rule setting and a glyph interpolation method. A font editor, which is able to generate Hangul glyphs or fonts, is developed based on this algorithm. The editor generates a target number of fundamental glyphs automatically by using a combination rule setting and four master glyphs, which can be set up by a user. The automatically generated glyphs can be used to generate a target font by combining KSX1001 standard Hangul 2350 characters or Unicode standard Hangul 11172 characters automatically. The efficiency of the proposed Hangul editor is analyzed quantitatively in this paper through application to several commercial typefaces.

• Geomechanics and Engineering
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• v.13 no.6
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• pp.893-906
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• 2017

Automatic large scale soil model generation is very critical stage for earthquake hazard simulation of urban areas. Manual model development may cause some data losses and may not be effective when there are too many data from different soil observations in a wide area. Geographic information systems (GIS) for storing and analyzing spatial data help scientists to generate better models automatically. Although the original soil observations were limited to soil profile data, the recent developments in mapping technology, interpolation methods, and remote sensing have provided advanced soil model developments. Together with advanced computational technology, it is possible to handle much larger volumes of data. The scientists may solve difficult problems of describing the spatial variation of soil. In this study, an algorithm is proposed for automatic three dimensional soil and velocity model development of southern part of the European side of Istanbul next to Sea of Marmara based on GIS data. In the proposed algorithm, firstly bedrock surface is generated from integration of geological and geophysical measurements. Then, layer surface contacts are integrated with data gathered in vertical borings, and interpolations are interpreted on sections between the borings automatically. Three dimensional underground geology model is prepared using boring data, geologic cross sections and formation base contours drawn in the light of these data. During the preparation of the model, classification studies are made based on formation models. Then, 3D velocity models are developed by using geophysical measurements such as refraction-microtremor, array microtremor and PS logging. The soil and velocity models are integrated and final soil model is obtained. All stages of this algorithm are carried out automatically in the selected urban area. The system directly reads the GIS soil data in the selected part of urban area and 3D soil model is automatically developed for large scale earthquake hazard simulation studies.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.6
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• pp.525-534
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• 2018

Camera calibration is the process of determining the parameters such as the focal length of a camera, the position of a principal point, and lens distortions. For this purpose, images of checkerboard have been mainly used. When targets were automatically recognized in checkerboard image, the existing studies had limitations in that the user should have a good understanding of the input parameters for recognizing the target or that all checkerboard should appear in the image. In this study, a methodology for automatic target recognition was proposed. In this method, even if only a part of the checkerboard image was captured using rectangles including eight blobs, four each at the central portion and the outer portion of the checkerboard, the index of the target can be automatically assigned. In addition, there is no need for input parameters. In this study, three conditions were used to automatically extract the center point of the checkerboard target: the distortion of black and white pattern, the frequency of edge change, and the ratio of black and white pixels. Also, the direction and numbering of the checkerboard targets were made with blobs. Through experiments on two types of checkerboards, it was possible to automatically recognize checkerboard targets within a minute for 36 images.