• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.2
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• pp.117-126
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• 2019

Legacy study for detecting arrhythmia have mostly used nonlinear method to increase classification accuracy. Most methods are complex to process and manipulate data and have difficulties in classifying various arrhythmias. Therefore it is necessary to classify various arrhythmia based on short-term data. In this study, we propose a feature extraction based on auto regressive modeling and an premature contraction arrhythmia classification method using SVM., For this purpose, the R-wave is detected in the ECG signal from which noise has been removed, QRS and RR interval segment is modelled. Also, we classified Normal, PVC, PAC through SVM in realtime by extracting four optimal segment length and AR order. The detection and classification rate of R wave and PVC is evaluated through MIT-BIH arrhythmia database. The performance results indicate the average of 99.77% in R wave detection and 99.23%, 97.28%, 96.62% in Normal, PVC, PAC classification.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.1
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• pp.1-8
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• 2019

Among the methods for extracting the most appropriate information from a large amount of log data, there is a method using inductive inference. In this paper, we use SVM (Support Vector Machine), which is an excellent classification method for inductive inference, in order to determine the ranking of intrusion logs in digital forensic analysis. For this purpose, the logs of the training log set are classified into intrusion logs and normal logs. The associated words are extracted from each classified set to generate a related word dictionary, and each log is expressed as a vector based on the generated dictionary. Next, the logs are learned using the SVM. We classify test logs into normal logs and intrusion logs by using the log set extracted through learning. Finally, the recommendation orders of intrusion logs are determined to recommend intrusion logs to the forensic analyst.

• The Journal of the Korea Contents Association
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• v.19 no.5
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• pp.417-425
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• 2019

Storage snapshot technology allows to preserve data at a specific point in time, and recover and access data at a desired point in time. It is an essential technology for storage protection application. Existing snapshot methods have some problems in that they dependent on storage hardware vendor, file system or virtual block device. In this paper, we propose a new snapshot method for solving the problems and creating snapshots on-line. The proposed snapshot method uses a method of extracting the log records of update operations at the virtual file system layer to enable the snapshot method to operate independently on file systems, virtual block devices, and storage hardwares. In addition, the proposed snapshot mehod creates and manages snapshots for directories and files without interruption to the storage service. Finally, through experiments we measure the snapshot creation time and the performance degradation caused by the snapshot.

• Database Research
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• v.34 no.3
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• pp.45-57
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• 2018

The drowsiness that occurs in the driving is a very dangerous driver condition that can be directly linked to a major accident. In order to prevent drowsiness, there are traditional drowsiness detection methods to grasp the driver's condition, but there is a limit to the generalized driver's condition recognition that reflects the individual characteristics of drivers. In recent years, deep learning based state recognition studies have been proposed to recognize drivers' condition. Deep learning has the advantage of extracting features from a non-human machine and deriving a more generalized recognition model. In this study, we propose a more accurate state recognition model than the existing deep learning method by learning image and PPG at the same time to grasp driver's condition. This paper confirms the effect of driver's image and PPG data on drowsiness detection and experiment to see if it improves the performance of learning model when used together. We confirmed the accuracy improvement of around 3% when using image and PPG together than using image alone. In addition, the multimodal deep learning based model that classifies the driver's condition into three categories showed a classification accuracy of 96%.

• Journal of the Society of Disaster Information
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• v.17 no.3
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• pp.556-567
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• 2021

Purpose: In this study, various methods of deep learning-based automatic damage analysis technology were reviewed based on images taken through Unmanned Aerial Vehicle to more efficiently and reliably inspect the exterior inspection and inspection of railway bridges using Unmanned Aerial Vehicle. Method: A deep learning analysis model was created by defining damage items based on the acquired images and extracting deep learning data. In addition, the model that learned the damage images for cracks, concrete and paint scaling·spalling, leakage, and Reinforcement exposure among damage of railway bridges was applied and tested with the results of automatic damage analysis. Result: As a result of the analysis, a method with an average detection recall of 95% or more was confirmed. This analysis technology enables more objective and accurate damage detection compared to the existing visual inspection results. Conclusion: through the developed technology in this study, it is expected that it will be possible to analysis more accurate results, shorter time and reduce costs by using the automatic damage analysis technology using Unmanned Aerial Vehicle in railway maintenance.

• The Korea Journal of Herbology
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• v.36 no.5
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• pp.93-99
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• 2021

Objective : Caryophylli Flos has been used in Korean medicine to relieve vomiting and pains caused by chills that make fluid circulation difficult. This study was designed to investigate the protective effect of ethanol extract of Caryophylli Flos (CF) in hydrogen peroxide (H₂O₂)-induced apoptotic cell death in human keratinocyte HaCaT cells. Methods : CF was prepared by extracting 200 g of Caryophylli Flos in 2 L of ethanol for 48 h. Cell viability was measured by MTT assay, and the protein expression was monitored by Western blot analysis. Apoptosis was determined by terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) assay. Reactive oxygen species (ROS) was measured using fluorescent dye, and reduced glutathione (GSH) was determined with a colorimetric commercial kit. Results : CF protected HaCaT cells from cell death caused by oxidative stress after H₂O₂ treatment. H₂O₂ amplified generation of ROS and induced depletion of GSH, whereas these changes in ROS and GSH were inhibited by GF treatment. In addition, H₂O₂ resulted in apoptosis as assessed by TUNEL assay and the expression of apoptosis regulator proteins. However, cells treated with CF showed a decrease in TUNEL-positive cells and restored the reduced expression of procaspase-9, -3 and PARP. Conclusion : This study showed cytoprotective effects of CF by anti-apoptotic activity while exerting antioxidative activity in H₂O₂-treated HaCaT cells. These results suggest that CF could be beneficial in skin damage caused by oxidative stress.

• The Journal of Korean Academic Society of Nursing Education
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• v.27 no.2
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• pp.175-185
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• 2021

Purpose: The aim of this study was to identify core keywords and topic groups in the 'Gestational diabetes mellitus (GDM) and Breastfeeding' field of research for better understanding research trends in the past 20 years. Methods: This was a text-mining and topic modeling study composed of four steps: 1) collecting abstracts, 2) extracting and cleaning semantic morphemes, 3) building a co-occurrence matrix, and 4) analyzing network features and clustering topic groups. Results: A total of 635 papers published between 2001 and 2020 were found in databases (Web of Science, CINAHL, RISS, DBPIA, RISS, KISS). Among them, 3,639 words extracted from 366 articles selected according to the conditions were analyzed by text network analysis and topic modeling. The most important keywords were 'exposure', 'fetus', 'hypoglycemia', 'prevention' and 'program'. Six topic groups were identified through topic modeling. The main topics of the study were 'cardiovascular disease' and 'obesity'. Through the topic modeling analysis, six themes were derived: 'cardiovascular disease', 'obesity', 'complication prevention strategy', 'support of breastfeeding', 'educational program' and 'management of GDM'. Conclusion: This study showed that over the past 20 years many studies have been conducted on complications such as cardiovascular diseases and obesity related to gestational diabetes and breastfeeding. In order to prevent complications of gestational diabetes and promote breastfeeding, various nursing interventions, including gestational diabetes management and educational programs for GDM pregnancies, should be developed in nursing fields.

• Journal of Digital Convergence
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• v.17 no.11
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• pp.233-240
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• 2019

In order to predict and respond to cyber attacks, a number of security companies quickly identify the methods, types and characteristics of attack techniques and are publishing Security Intelligence Reports(SIRs) on them. However, the SIRs distributed by each company are huge and unstructured. In this paper, we propose a framework that uses five analytic techniques to formulate a report and extract key information in order to reduce the time required to extract information on large unstructured SIRs efficiently. Since the SIRs data do not have the correct answer label, we propose four analysis techniques, Keyword Extraction, Topic Modeling, Summarization, and Document Similarity, through Unsupervised Learning. Finally, has built the data to extract threat information from SIRs, analysis applies to the Named Entity Recognition (NER) technology to recognize the words belonging to the IP, Domain/URL, Hash, Malware and determine if the word belongs to which type We propose a framework that applies a total of five analysis techniques, including technology.

• Journal of Korean Society for Quality Management
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• v.49 no.1
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• pp.81-95
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• 2021

Purpose: The purpose of this study was to analysis pattern of nonconforming farmers who is one of the factors of unconformity in residual pesticides. Methods: Pattern analysis of nonconforming farmers were analyzed through convergence of safety data and farmer's DB data. Exploratory data analysis and association rule analysis were used for extracting factors related to unconformity. Results: The results of this study are as follows; regarding the exploratory data analysis, it was found that factors of farmers influencing unconformity in residual pesticides by total 9 factors; sampling time, gender, age, cultivation region, farming career, agricultural start form, type of agriculture, cultivation area, classification of agricultural products. Regarding the association rule analysis, non-conformity association rules were found over the past three years. There was a difference in the pattern of nonconforming farmers depending on the cultivation period. Conclusion: Exploratory data analysis and association rule analysis will be useful tools to establish more efficient and economical safety management plan for agricultural products.

• The Korea Journal of Herbology
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• v.36 no.1
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• pp.41-49
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• 2021

Objective : Citrus unshiu peel (Citri Unshius Pericarpium) has been prescribed to suppress coughing and phlegm in Korean medicine. In this study, the effect of ethanol extract of Citrus unshiu peel (CEE) on apoptosis was investigated using cadmium chloride (CdCl2) treated HepG2 cells. Methods : CEE was prepared by extracting 300 g of Citri Unshius Pericarpium in 3 L of ethanol for 72 h. Apoptosis was determined by the TUNEL assay. The mitochondrial membrane potential (MMP) was monitored using the membrane-permeable fluorescent dye Rh123. The expression level of each protein was monitored by Western blot analysis. Results : CEE protected HepG2 cells from apoptosis as determined by the TUNEL assay. A decrease in MMP was observed in cells exposed to cadmium, indicating that mitochondria are involved in the induction of apoptosis. However, CEE recovered the reduction in MMP caused by cadmium. In addition, decreased expression of B-cell lymphoma 2 (Bcl-2), procaspase, and poly(ADP-ribose) polymerase (PARP) by cadmium was increased by CEE. The anti-apoptotic effect of CEE was found to be associated with inhibition of JNK and p38 phosphorylation when examining the expression of phosphorylated MAPK by Western blot. Conclusion : This study showed that CEE exerted anti-apoptotic effects in cadmium-induced HepG2 cells by inhibiting the reduction of MMP and changes in the expression level of apoptotic proteins. These results suggest the potential for CEE to be used for heavy metal-induced liver damage.