• Journal of the Korea Society of Computer and Information
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• v.18 no.6
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• pp.21-28
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• 2013

This paper proposes an effective fire detection approach that includes the following multiple heterogeneous algorithms: moving region detection using grey level histograms, color segmentation using fuzzy c-means clustering (FCM), feature extraction using a grey level co-occurrence matrix (GLCM), and fire classification using support vector machine (SVM). The proposed approach determines the optimal threshold values based on grey level histograms in order to detect moving regions, and then performs color segmentation in the CIE LAB color space by applying the FCM. These steps help to specify candidate regions of fire. We then extract features of fire using the GLCM and these features are used as inputs of SVM to classify fire or non-fire. We evaluate the proposed approach by comparing it with two state-of-the-art fire detection algorithms in terms of the fire detection rate (or percentages of true positive, PTP) and the false fire detection rate (or percentages of true negative, PTN). Experimental results indicated that the proposed approach outperformed conventional fire detection algorithms by yielding 97.94% for PTP and 4.63% for PTN, respectively.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.5
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• pp.177-187
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• 2014

This paper proposes a fast subspace tracking methods, which is called GVFF FAPI, based on FAPI (Fast Approximated Power Iteration) method and GVFF RLS (Gradient-based Variable Forgetting Factor Recursive Lease Squares). Since the conventional FAPI uses a constant forgetting factor for estimating covariance matrix of source signals, it has difficulty in applying to non-stationary environments such as continuously changing DOAs of source signals. To overcome the drawback of conventioanl FAPI method, the GVFF FAPI uses the gradient-based variable forgetting factor derived from an improved means square error (MSE) analysis of RLS. In order to achieve the decreased subspace error in non-stationary environments, the GVFF-FAPI algorithm used an improved forgetting factor updating equation that can produce a fast decreasing forgetting factor when the gradient is positive and a slowly increasing forgetting factor when the gradient is negative. Our numerical simulations show that GVFF-FAPI algorithm offers lower subspace error and RMSE (Root Mean Square Error) of tracked DOAs of source signals than conventional FAPI based MUSIC (MUltiple SIgnal Classification).

• Investigative Magnetic Resonance Imaging
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• v.1 no.1
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• pp.94-102
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• 1997

Purpose: There have been some efforts to diagnose intracranial aneurysm through a non-invasive method using MRA, although the process may be difficult when the lesion is less than 3mm. The present study prospectively compares the results of high resolution, fast speed slice interpolation MRA and DSA thereby examing the potentiality of primary non-invasive screening test. Materials and Methods: A total of 26 cerebral aneurysm lesions from 14 patients with subarachnoid hemorrhage from ruptured aneurysm (RA) and 5 patients with unruptured aneurysm(UA). In all subjects, MRA was taken to confirm the vessel of origin, definition of aneurysm neck and the relationship of the aneurysm to nearby small vessels, and the results were compared with the results of DSA. The images were obtained with 1.5T superconductive machine (Vision, Siemens, Erlangen, Germany) on 4 slabs of MRA using slice interpolation. The settings include TR/TE/FA=30/6.4/25, matrix $160{\times}512$, FOV $150{\times}200$, 7minutes 42 seconds of scan time, effective thickness of 0.7 mm and an entire thickness of 102. 2mm. The images included structures from foramen magnum to A3 portion of anterior cerebral artery. MIP was used for the image analysis, and multiplanar reconstruction (MPR) technique was used in cases of intracranial aneurysm. Results: A total of 26 intracranial aneurysm lesions from 19 patients with 2 patients having 3 lesion, 3 patients having 2 lesions and the rest of 14 patients having 1 lesion each were examined. Among those, 14 were RA and 12 were UA. Eight lesions were less than 2mm in size, 9 lesions were 3-5mm, 7 were 6-9mm and 2 were larger than IOmm. On initial exams, 25 out of 26 aneurysm lesions were detected in either MRA or DSA showing 96% sensitivity. Specificity cannot be estimated since there was no true negative of false positive findings. When MRA and MPR were used concurrently for the confirmation of size and shape, the results were equivalent to those of DSA, while in the confirmation of aneurysm neck and parent vessels, the concurrent use of MRA and MPR was far superior to the sole use of either MRA or DSA. Conclusion: High resolution MRA using slice interpolation technique showed equal results as those of DSA for the detection of intracranial aneurysm, and may be used as a primary non-invasive screening test in the future.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.47-73
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• 2020

KTX rolling stocks are a system consisting of several machines, electrical devices, and components. The maintenance of the rolling stocks requires considerable expertise and experience of maintenance workers. In the event of a rolling stock failure, the knowledge and experience of the maintainer will result in a difference in the quality of the time and work to solve the problem. So, the resulting availability of the vehicle will vary. Although problem solving is generally based on fault manuals, experienced and skilled professionals can quickly diagnose and take actions by applying personal know-how. Since this knowledge exists in a tacit form, it is difficult to pass it on completely to a successor, and there have been studies that have developed a case-based rolling stock expert system to turn it into a data-driven one. Nonetheless, research on the most commonly used KTX rolling stock on the main-line or the development of a system that extracts text meanings and searches for similar cases is still lacking. Therefore, this study proposes an intelligence supporting system that provides an action guide for emerging failures by using the know-how of these rolling stocks maintenance experts as an example of problem solving. For this purpose, the case base was constructed by collecting the rolling stocks failure data generated from 2015 to 2017, and the integrated dictionary was constructed separately through the case base to include the essential terminology and failure codes in consideration of the specialty of the railway rolling stock sector. Based on a deployed case base, a new failure was retrieved from past cases and the top three most similar failure cases were extracted to propose the actual actions of these cases as a diagnostic guide. In this study, various dimensionality reduction measures were applied to calculate similarity by taking into account the meaningful relationship of failure details in order to compensate for the limitations of the method of searching cases by keyword matching in rolling stock failure expert system studies using case-based reasoning in the precedent case-based expert system studies, and their usefulness was verified through experiments. Among the various dimensionality reduction techniques, similar cases were retrieved by applying three algorithms: Non-negative Matrix Factorization(NMF), Latent Semantic Analysis(LSA), and Doc2Vec to extract the characteristics of the failure and measure the cosine distance between the vectors. The precision, recall, and F-measure methods were used to assess the performance of the proposed actions. To compare the performance of dimensionality reduction techniques, the analysis of variance confirmed that the performance differences of the five algorithms were statistically significant, with a comparison between the algorithm that randomly extracts failure cases with identical failure codes and the algorithm that applies cosine similarity directly based on words. In addition, optimal techniques were derived for practical application by verifying differences in performance depending on the number of dimensions for dimensionality reduction. The analysis showed that the performance of the cosine similarity was higher than that of the dimension using Non-negative Matrix Factorization(NMF) and Latent Semantic Analysis(LSA) and the performance of algorithm using Doc2Vec was the highest. Furthermore, in terms of dimensionality reduction techniques, the larger the number of dimensions at the appropriate level, the better the performance was found. Through this study, we confirmed the usefulness of effective methods of extracting characteristics of data and converting unstructured data when applying case-based reasoning based on which most of the attributes are texted in the special field of KTX rolling stock. Text mining is a trend where studies are being conducted for use in many areas, but studies using such text data are still lacking in an environment where there are a number of specialized terms and limited access to data, such as the one we want to use in this study. In this regard, it is significant that the study first presented an intelligent diagnostic system that suggested action by searching for a case by applying text mining techniques to extract the characteristics of the failure to complement keyword-based case searches. It is expected that this will provide implications as basic study for developing diagnostic systems that can be used immediately on the site.

• Journal of the Korea Society for Simulation
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• v.32 no.1
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• pp.35-44
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• 2023

In recent, there are many research cases that analyze trends or research trends with text analysis. When collecting documents by searching for keywords in abbreviations for data analysis, it is necessary to disambiguate abbreviations. In many studies, documents are classified by hand-work reading the data one by one to find the data necessary for the study. Most of the studies to disambiguate abbreviations are studies that clarify the meaning of words and use supervised learning. The previous method to disambiguate abbreviation is not suitable for classification studies of documents looking for research data from abbreviation search documents, and related studies are also insufficient. This paper proposes a method of semi-automatically classifying documents collected by abbreviations by going topic modeling with Non-Negative Matrix Factorization, an unsupervised learning method, in the data pre-processing step. To verify the proposed method, papers were collected from academic DB with the abbreviation 'MSA'. The proposed method found 316 papers related to Micro Services Architecture in 1,401 papers. The document classification accuracy of the proposed method was measured at 92.36%. It is expected that the proposed method can reduce the researcher's time and cost due to hand work.

• Journal of Korea Port Economic Association
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• v.39 no.2
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• pp.59-76
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• 2023

In this study, we tried to find out port safety from various perspectives through news data that can be easily accessed by the general public and domestic academic journal data that reflects the insights of port researchers. Non-negative Matrix Factorization(NMF) based topic modeling was conducted using Python to derive the main topics for each data, and then semantic analysis was conducted for each topic. The news data mainly derived natural and environmental factors among port safety risk factors, and the academic journal data derived security factors, mechanical factors, human factors, environmental factors, and natural factors. Through this, the need for strategies to strengthen the safety of domestic ports, such as strengthening the resilience of port safety, improve safety awareness to broaden the public's view of port safety, and conduct research to develop the port industry environment into a safe and specialized mature port. As a result, this study identified the main factors to be improved and provided basic data to develop into a mature port with a port safety culture.

• The korean journal of orthodontics
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• v.28 no.5 s.70
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• pp.699-716
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• 1998

This study was designed to evaluate the expression of non-collagenous protein in periodontal tissue during the experimental movement of rat incisors, by LSAB(labelled streptavidine biotin) immunohistochemical staining for osteonectin and osteocalcin. Twenty seven Sprague-Dawley rats were divided into a control group(3 rats) and 6 experimental groups(24 rats) where 75g of force was applied from helical springs across the maxillary incisors. Rats of experimental groups were sacrificed at 12 hours, 1, 4, 7, 14 and 28 days after force application, respectively. And the tissues of a control group and experimental groups were studied immunohistochemically and histologically. The results were as follows : 1. Until 28 days after force application, periodontal fibers had been strectched on tension side and compressed in pressure side of all the experimental groups, and the arrangement of periodontal fibers had not been recovered yet. 2. The expression of osteonectin in control group was rare in dentin, cementum and osteocyte, and was mild in odontoblasts and matrix of alveolar bone. 3. The expression of osteocalcin in control group was negative in gingiva, osteoblasts, osteocyte and cementum, and was rare in predentin, capillaries in pulp and periodontal ligament and the matrix of alveolar bone. 4. There was no difference in the expression of osteocalcin or osteonectin in dentin, cementum, pulp, odontoblasts, between of control and of experimental groups. 5. The expression of osteonectin in intermaxillary suture got the peak in 7-day and was declined after 14-day. The expression of osteocalcin remained in a same degree since it became mild in 14-day. 6. The expression of osteonectin in pressure side of periodontal ligament of experimental group was rare, which was similar to control group. But in tension side, it was increased until 14-day aftrer which it was declined. 7. The expression of osteocalcin in periodntal ligament was rare in 12-hour to 14-day, but became severe in 28-day, which was greater in tension side than in pressure side, and in the periodontal fiber next to alveolar bone than to tooth surface. 8. The expression of osteocalcin in alveolar bone was rare until 14-day in pressure side, but became moderate in 28-day. The expression of osteonectin was increased from 7-day by time dependency, which was greater in tension side than in pressure side.

• Tuberculosis and Respiratory Diseases
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• v.59 no.3
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• pp.286-297
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• 2005

Background : Non-small cell lung cancer (NSCLC) is a common cause of cancer-related death in North America and Korea, with an overall 5-year survival rate of between 4 and 14%. The TNM staging system is the best prognostic index for operable NSCLC . However, epidermal growth factor receptor (EGFR), matrix metalloproteinase-9(MMP-9), and C-erbB-2 have all been implicated in the pathogenesis of NSCLC and might provide prognostic information. Methods : Immunohistochemical staining of 81 specimens from a resected primary non-small cell lung cancer was evaluated in order to determine the role of the biological markers on NSCLC . Immunohistochemical staining for EGFR, MMP-9, and C-erbB-2 was performed on paraffin-embedded tissue sections to observe the expression pattern according to the pathologic type and surgical staging. The correlations between the expression of each biological marker and the survival time was determined. Results : When positive immunohistochemical staining was defined as the extent area>20%(more than Grade 2), the positive rates for EGFR, MMP-9, and C-erbB-2 staining were 71.6%, 44.3%, and 24.1% of the 81 patients, respectively. The positive rates of EGFR and MMP-9 stain for NSCLC according to the surgical stages I, II, and IIIa were 75.0% and 41.7%, 66.7% and 47.6%, and 76.9% and 46.2%, respectively. The median survival time of the EGFR(-) group, 71.8 months, was significantly longer than that of the EGFR(+) group, 33.5 months.(p=0.018, Kaplan-Meier Method, log-rank test).. The MMP-9(+) group had a shorter median survival time than the MMP-9(-) group, 35.0 and 65.3 months, respectively (p=0.2). The co-expression of EGFR and MMP-9 was associated with a worse prognosis with a median survival time of 26.9 months, when compared with the 77 months for both negative-expression groups (p=0.0023). There were no significant differences between the C-erbB-2(+) and C-erbB-2 (-) groups. Conclusion : In NSCLC, the expression of EGFR might be a prognostic factor, and the co-expression of EGFR and MMP-9 was found to be associated with a poor prognosis. However, C-erbB-2 expression had no prognostic significance.