• Journal of the Korean Society of Radiology
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• v.15 no.4
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• pp.547-554
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• 2021

In order to quickly and accurately diagnose pneumonia on a chest X-ray image, different batch sizes of 4, 8, 16, and 32 were applied to the same Xception deep learning model, and modeling was performed 3 times, respectively. As a result of the performance evaluation of deep learning modeling, in the case of modeling to which batch size 32 was applied, the results of accuracy, loss function value, mean square error, and learning time per epoch showed the best results. And in the accuracy evaluation of the Test Metric, the modeling applied with batch size 8 showed the best results, and the precision evaluation showed excellent results in all batch sizes. In the recall evaluation, modeling applied with batch size 16 showed the best results, and for F1-score, modeling applied with batch size 16 showed the best results. And the AUC score evaluation was the same for all batch sizes. Based on these results, deep learning modeling with batch size 32 showed high accuracy, stable artificial neural network learning, and excellent speed. It is thought that accurate and rapid lesion detection will be possible if a batch size of 32 is applied in an automatic diagnosis study for feature extraction and classification of pneumonia in chest X-ray images using deep learning in the future.

• Korean Journal of Construction Engineering and Management
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• v.23 no.4
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• pp.15-25
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• 2022

As the incidence and mortality of serious disasters in the construction industry are the highest, various efforts are being made in Korea to reduce them. Among them, risk assessment is used as data for disaster reduction measures and evaluation of risk factors at the construction stage. However, the existing risk assessment involves the subjectivity of the performer and is vulnerable to the domestic construction site. This study established a DB classification system for risk assessment with the aim of early identification and pre-removal of risks by quantitatively deriving risk factors using BIM in the risk assessment field and presents a methodology for risk assessment using BIM. Through this, prior removal of risks increases the safety of construction workers and reduces additional costs in the field of safety management. In addition, since it can be applied to new construction methods, it improves the understanding of project participants and becomes a tool for communication. This study proposes a framework for deriving quantitative risks based on BIM, and will be used as a base technology in the field of risk assessment using BIM in the future.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.3D
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• pp.445-450
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• 2008

Recently, environmental and ecological approaches is in progress in urban stream, especially the guarantee of instream flow becomes very important. In this paper, it is suggested that water volume estimation method utilizing the topographical shape data obtained by field investigation and satellite image to manage the urban stream efficiently. The data obtained at Gap River is the study area are analysed and those results are as belows. First, surveying to investigate topographic shape characteristics of urban stream is carried out. In details, the gradient characteristics from water surface to bottom in case of sand area and in case of grass area are 0.013 and 0.065 respectively. In conclusion, the gradient characteristic of grass area is five times bigger than that of sand area. Besides, IKONOS image is classified by spectrum analysis and Minimum Distance Method and the sand area extraction method by the generalization method as Median filter is suggested to calculate water volume. Finally, mapping process on the sand area extracted from the topographical shape field data in river and satellite images is carried out by the GIS spatial analysis. And on the assumption that the water level was 1m at that time when satellite image was taken, the water volume was $225,258m^3$. It is clarified that the effect of water volume improvement was about 10.5% in comparison with water volume that had no consideration on the gradient characteristics of sand-bar.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.11
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• pp.114-122
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• 2016

EIS (Electro interstitial scan) is a non-invasive and simple method to find the physio-pathological information inferred by electric current response with respect to low direct current applied between remote sites of the body. Although a few EIS-based devices for diagnosing diabetes were commercialized, they were not successful in offering clinical validity nor in confirming diagnostic principle. In this study, we measured the voltage responses of diabetic patients and normal subjects with a commercialized EIS device to test the usefulness of EIS in screening diabetes. For this purpose, voltage was measured between pairs of electrodes contacted at both palm, both soles of the feet and left and right forehead above both eyes. After feature extraction of voltage signals, the AUC (area under the curve) between the two groups was calculated and we found that seven variables were appropriately shown above 60% of accuracy. In addition, we applied the k-NN (k-nearest neighbors) method and found that the accuracy of classification between the two groups reached the accuracy of 76.2%. This result implies that the voltage response analysis based on EIS has potential as a diabetics screening method.

• Korean Journal of Food Science and Technology
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• v.26 no.6
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• pp.750-758
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• 1994

Flavour components of seven Korean folk sojues, five Chinese kaoliangchiews and Japanese shochu were determined by GC and GC-MS after solid phase extraction with polydivinyl benzene. Less volatile ethyl succinate and ethyl pelargonate were present in Korean folk sojues while volatile ethyl acetate and ethyl butyrate in Chinese kaoliangchiews. In the case of alcohols, the amount of isopentyl alcohol was relatively higher than that of isobutyl alcohol or n-propyl alcohol in Korean folk sojues. On the contrary, less volatile n-propyl alcohol was present more than isopentyl alcohol in Chinese kaoliangchiews. Multivariate statistical analyses involving principal components analysis (PCA) and discriminant analysis (DA) were applied to the GC data. The results of PCA clearly demonstrate that the first principal scores of Korean folk sojues were similar but the second principal scores were different from each other. Classification of Korean sojues and Chinese kaoliangchiews into two groups could be conducted by DA. These results suggested that the common charateristics and identities as a distilled liquors was found in Korean folk sojues.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.27 no.6
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• pp.521-530
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• 2016

Weather radar is observation equipment that transmits electromagnetic waves and receives backscattered signals from the targets. The weather radar systems of the Korea Meteorological Administration have a doppler mode that can extract the target's radial velocity. However, the radial velocity over the maximum unambiguous velocity(${\nu}_m$) for which is in a trade-off relationship with the maximum unambiguous range is folded. Therefore, a dual PRF mode of which transmits and receives signals using two different PRFs(high and low) must be used to extend the vm while maintaining the maximum unambiguous range. Using a dual PRF mode, vm can be extended to the amount of lowest common denominator of two observed vm from high and low PRF. For this extension, we have developed a velocity unfolding algorithm of which uses several criteria for classification considering observed velocity differences between high and low PRF and their error boundary. Then, correction factors are calculated for each class and are applied to unfold radial velocity. The developed algorithm was applied to the Yong-In Testbed(YIT) radar and the generated better performance of radial velocity extraction than those of the previous system.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.2
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• pp.91-108
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• 2022

In most cases, optical images have been used as training data of DL (Deep Learning) models for object detection, recognition, identification, classification, semantic segmentation, and instance segmentation. However, properties of 3D objects in the real-world could not be fully explored with 2D images. One of the major sources of the 3D geospatial information is DSM (Digital Surface Model). In this matter, characteristic information derived from DSM would be effective to analyze 3D terrain features. Especially, man-made objects such as buildings having geometrically unique shape could be described by geometric elements that are obtained from 3D geospatial data. The background and motivation of this paper were drawn from concept of the intrinsic image that is involved in high-level visual information processing. This paper aims to extract buildings after classifying terrain features by training DL model with DSM-derived information including slope, aspect, and SRI (Shaded Relief Image). The experiments were carried out using DSM and label dataset provided by ISPRS (International Society for Photogrammetry and Remote Sensing) for CNN-based SegNet model. In particular, experiments focus on combining multi-source information to improve training performance and synergistic effect of the DL model. The results demonstrate that buildings were effectively classified and extracted by the proposed approach.

• Journal of Cadastre & Land InformatiX
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• v.47 no.1
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• pp.53-79
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• 2017

The main purpose of this study was to detect and classify public perceptions toward smog disasters on Twitter using topic modeling. To help achieve these objectives and to identify gaps in the literature, this research carried out a literature review on public opinions toward smog disasters and topic modeling. The literature review indicated that there are huge gaps in the related literature. In this research, this author formed five research questions to fill the gaps in the literature. And then this study performed research steps such as data extraction, word cloud analysis on the cleaned data, building the network of terms, correlation analysis, hierarchical cluster analysis, topic modeling with the LDA, and stream graphs to answer those research questions. The results of this research revealed that there exist huge differences in the most frequent terms, the shapes of terms network, types of correlation, and smog-related topics changing patterns between New York and London. Therefore, this author could find positive answers to the four of the five research questions and a partially positive answer to Research question 4. Finally, on the basis of the results, this author suggested policy implications and recommendations for future study.

• Korean Journal of Remote Sensing
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• v.14 no.4
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• pp.376-391
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• 1998

Images from satellites will have 1 to 3 meter ground resolution and will be very useful for analyzing current status of earth surface. An image processing system named GeoWatch with more intelligent image processing algorithms has been designed and implemented to support the detailed analysis of the land surface using high-resolution satellite imagery. The GeoWatch is a valuable tool for satellite image processing such as digitizing, geometric correction using ground control points, interactive enhancement, various transforms, arithmetic operations, calculating vegetation indices. It can be used for investigating various facts such as the change detection, land cover classification, capacity estimation of the industrial complex, urban information extraction, etc. using more intelligent analysis method with a variety of visual techniques. The strong points of this system are flexible algorithm-save-method for efficient handling of large size images (e.g. full scenes), automatic menu generation and powerful visual programming environment. Most of the existing image processing systems use general graphic user interfaces. In this paper we adopted visual program language for remotely sensed image processing for its powerful programmability and ease of use. This system is an integrated raster/vector analysis system and equipped with many useful functions such as vector overlay, flight simulation, 3D display, and object modeling techniques, etc. In addition to the modules for image and digital signal processing, the system provides many other utilities such as a toolbox and an interactive image editor. This paper also presents several cases of image analysis methods with AI (Artificial Intelligent) technique and design concept for visual programming environment.

• Journal of Internet Computing and Services
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• v.22 no.5
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• pp.47-55
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• 2021

Analysis of research trends in specific subject areas is performed by examining related topics and subject changes by using topic modeling techniques through keyword extraction for most of the literature information (paper, patents, etc.). Unlike existing research methods, this paper extracts topics related to the research topic using the LDA topic modeling technique for the project information of national R&D projects provided by the National Science and Technology Knowledge Information Service (NTIS) in the field of artificial intelligence. By analyzing these topics, this study aims to analyze research topics and investment directions for national R&D projects. NTIS provides a vast amount of national R&D information, from information on tasks carried out through national R&D projects to research results (thesis, patents, etc.) generated through research. In this paper, the search results were confirmed by performing artificial intelligence keywords and related classification searches in NTIS integrated search, and basic data was constructed by downloading the latest three-year project information. Using the LDA topic modeling library provided by Python, related topics and keywords were extracted and analyzed for basic data (research goals, research content, expected effects, keywords, etc.) to derive insights on the direction of research investment.