• Journal of Preventive Medicine and Public Health
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• v.55 no.4
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• pp.360-370
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• 2022

Objectives: This study evaluated the response in Daegu, Korea to the first wave of the coronavirus disease 2019 (COVID-19) pandemic according to a public health emergency response model. Methods: After an examination of the official data reported by the city of Daegu and the Korea Centers for Disease Control and Prevention, as well as a literature review and advisory meetings, we chose a response model. Daegu's responses were organized into 4 phases and evaluated by applying the response model. Results: In phase 1, efforts were made to block further transmission of the virus through preemptive testing of a religious group. In phase 2, efforts were concentrated on responding to mass infections in high-risk facilities. Phase 3 involved a transition from a high-intensity social distancing campaign to a citizen participation-based quarantine system. The evaluation using the response model revealed insufficient systematic preparation for a medical surge. In addition, an incorporated health-related management system and protection measures for responders were absent. Nevertheless, the city encouraged the participation of private hospitals and developed a severity classification system. Citizens also played active roles in the pandemic response by practicing social distancing. Conclusions: This study employed the response model to evaluate the early response in Daegu to the COVID-19 pandemic and revealed areas in need of improvement or maintenance. Based on the study results, creation of a systematic model is necessary to prepare for and respond to future public health emergencies like the COVID-19 pandemic.

• Journal of Internet of Things and Convergence
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• v.8 no.5
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• pp.33-39
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• 2022

A storage device used in the mobile computing field should have low power, light weight, durability, etc., and should be able to effectively store and manage large-capacity data generated by users. NAND flash memory is mainly used as a storage device in the field of mobile computing. Due to the structural characteristics of NAND flash memory, it is impossible to overwrite in place when a data update request is made, so it can be solved by accurately separating requests that frequently request data update and requests that do not, and storing and managing them in each block. The classification method for such a data update request is called a hot data identification method, and various studies have been conducted at present. This paper continuously records the occurrence of data update requests using a counting filter for more accurate hot data validation, and also verifies hot data by considering how often the requested update requests occur during a specific time.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.35 no.5
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• pp.259-266
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• 2022

In this study, we developed a deep learning and vision sensor-based assembly performance evaluation method isfor prefabricated steel structures. The assembly parts were segmented using a modified version of the receptive field block convolution module inspired by the eccentric function of the human visual system. The quality of the assembly was evaluated by detecting the bolt holes in the segmented assembly part and calculating the bolt hole positions. To validate the performance of the evaluation, models of standard and defective assembly parts were produced using a 3D printer. The assembly part segmentation network was trained based on the 3D model images captured from a vision sensor. The sbolt hole positions in the segmented assembly image were calculated using image processing techniques, and the assembly performance evaluation using the k-nearest neighbor algorithm was verified. The experimental results show that the assembly parts were segmented with high precision, and the assembly performance based on the positions of the bolt holes in the detected assembly part was evaluated with a classification error of less than 5%.

• Computers and Concrete
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• v.31 no.3
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• pp.185-195
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• 2023

Antalya is located south part of minor Asia, one of the biggest cities in Türkiye. As a result of population growth and vast migration to Antalya, many parts of the city that were not suitable for construction due to its geological conditions have become urban areas, and most of these urban areas are full of poorly engineered buildings. Poor engineering has been combined with unplanned urbanization, that causes utter vulnerability to disasters in Antalya. When an earthquake-prone city, Antalya faces with an earthquake risk, fear arises in society. To overcome this problem, it has become necessary to investigate the building stock, expressed in hundreds of thousands, in a fast and reliable way and then perform an urban transformation to create the perception of structural safety. However, the excessive building stock, labor, and economic problems made the implementation stage challenging and revealed the necessity of finding alternative solutions in the field. The present study presents a novel approach for assessment and model based on a rapid visual inspection method to transform areas under earthquake risk in Türkiye. The approach aimed to rank the interventions for decision-making mechanisms by making comparisons in the scale hierarchy. In the present study, to investigate the proposed approach, over 26,000 buildings were examined in Antalya, which is the fifth largest city in Türkiye that has a population of over 2.5 Million. In the results of the study, the risk classification was defined in the framework of building, block, street, neighborhood, and district scales.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.5
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• pp.1431-1445
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• 2022

We construct a deep neural network model named ECGResNet. This model can diagnosis diseases based on 12-lead ECG data of eight common cardiovascular diseases with a high accuracy. We chose the 16 Blocks of ResNet50 as the main body of the model and added the Squeeze-and-Excitation module to learn the data information between channels adaptively. We modified the first convolutional layer of ResNet50 which has a convolutional kernel of 7 to a superposition of convolutional kernels of 8 and 16 as our feature extraction method. This way allows the model to focus on the overall trend of the ECG signal while also noticing subtle changes. The model further improves the accuracy of cardiovascular and cerebrovascular disease classification by using a fully connected layer that integrates factors such as gender and age. The ECGResNet model adds Dropout layers to both the residual block and SE module of ResNet50, further avoiding the phenomenon of model overfitting. The model was eventually trained using a five-fold cross-validation and Flooding training method, with an accuracy of 95% on the test set and an F1-score of 0.841.We design a new deep neural network, innovate a multi-scale feature extraction method, and apply the SE module to extract features of ECG data.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.99-109
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• 2023

The darknet is frequently referred to as the hub of illicit online activity. In order to keep track of real-time applications and activities taking place on Darknet, traffic on that network must be analysed. It is without a doubt important to recognise network traffic tied to an unused Internet address in order to spot and investigate malicious online activity. Any observed network traffic is the result of mis-configuration from faked source addresses and another methods that monitor the unused space address because there are no genuine devices or hosts in an unused address block. Digital systems can now detect and identify darknet activity on their own thanks to recent advances in artificial intelligence. In this paper, offer a generalised method for deep learning-based detection and classification of darknet traffic. Furthermore, analyse a cutting-edge complicated dataset that contains a lot of information about darknet traffic. Next, examine various feature selection strategies to choose a best attribute for detecting and classifying darknet traffic. For the purpose of identifying threats using network properties acquired from darknet traffic, devised a hybrid deep learning (DL) approach that combines Recurrent Neural Network (RNN) and Bidirectional LSTM (BiLSTM). This probing technique can tell malicious traffic from legitimate traffic. The results show that the suggested strategy works better than the existing ways by producing the highest level of accuracy for categorising darknet traffic using the Black widow optimization algorithm as a feature selection approach and RNN-BiLSTM as a recognition model.

• The Korean Journal of Physiology and Pharmacology
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• v.12 no.2
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• pp.83-87
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• 2008

The fyn-related kinase (FRK) belongs to the tyrosine kinase family of protein kinases. Recent studies have shown that Frk affects pancreatic beta cell number during embryogenesis and promotes beta cell cytotoxic signals in response to streptozotocin. To investigate the genetic association between FRK polymorphisms and the risk of obesity in Korean population, single nucleotide polymorphisms (SNPs) in the FRK gene region were selected and analyzed. The body mass index (BMI) was calculated, and biochemical data (systolic blood pressure, diastolic blood pressure, hemoglobin A1C, triglyceride, total cholesterol, high density lipoprotein, and low density lipoprotein) of blood sample from each subject were also measured. One hundred fifty five healthy control and 204 overweight/obesity subjects were recruited. Genotype frequencies of six SNPs [rs6568920 (+8391G>A), rs3756772 (+56780A>G), rs3798234 (+75687C>T), rs9384970 (+68506G>A), rs1933739 (+72978G>A), and rs9400883 (+75809A>G)] in the FRK gene were determined by Affymetrix Targeted Genotyping Chip data. According to the classification of Korean Society for the Study of Obesity, control (BMI 18 to < 23) and overweight/obesity (BMI$\geq$23) subjects were recruited. For the analysis of genetic data, EM algorithm, SNPStats, Haploview, HapAnalyzer, SNPAnalyzer, and Helixtree programs were used. Multiple logistic regression analysis (codominant, dominant, and recessive models) was performed. Age and gender as covariates were adjusted. For biochemical data, Student's t test was used. The mean value of BMI in the control and overweigh/obesity groups was 21.1${\pm}$1.2 (mean${\pm}$SD) and 25.6${\pm}$2.0, respectively. All biochemical data of the overweight/obesity group were statistically significance, compared with the control group. Among six SNPs, two linkage disequilibrium (LD) blocks were discovered. One block consisted of rs1933739 and rs9400883, and the other comprised rs3756772 and rs3798234. One SNP (rs9384970, +68506G>A) showed an association with overweight/obesity in the codominant model (p=0.03). Interestingly, the AA genotype distribution in the overweight/obesity group (n=7, 3.5%) was higher than those in the control group (n=1, 0.6%), which is not found in either Japanese or Chinese subjects. Therefore, the AA genotype of rs9384970 may be a risk factor for development of obesity in Korean population. The results suggest that FRK may be associated with overweight/obesity in Korean population.

• The Journal of the Korea Contents Association
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• v.16 no.5
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• pp.351-360
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• 2016

Purpose: The purpose of this study was to identify the nursing needs of elderly patients who got a surgical operation under regional anesthesia(spinal, epidural, nerve block). Methods: The participants were 126 elderly patients who completed a questionnaire. The data were collected October 1 and October 31, 2012, and analyzed using descriptive statistics, t-test and ANOVA, $Scheff{\acute{e}}$ test. Results: The mean score for the nursing needs of elderly patients administered regional anesthesia during surgery was $3.08{\pm}0.38$, The scores for specific nursing needs were as follows: educational needs ($3.47{\pm}0.50$), spiritual needs ($3.37{\pm}0.78$), physical needs ($3.31{\pm}0.46$), emotional needs ($2.72{\pm}0.50$), and environmental needs ($2.51{\pm}0.47$). There were significant differences in nursing needs relative to gender, religion, and spouse status. Additionally, there were significant differences in nursing needs according to surgery department, the length of time the surgery, and the ASA(American Society of Anesthesiologists) physical classification. of the operation-related characteristics. Conclusion: When caring for elderly patients during the surgery, nurses must provide adequate information about the surgery and anesthesia. Further studies are needed to develop and evaluate nursing interventions to provide quality surgical care for the elderly patients.

• The Plant Pathology Journal
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• v.34 no.5
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• pp.381-392
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• 2018

Clavibacter michiganensis subsp. michiganesis (Cmm) is a quarantine-worthy pest in $M{\acute{e}}xico$. The implementation and validation of new technologies is necessary to reduce the time for bacterial detection in laboratory conditions and Raman spectroscopy is an ambitious technology that has all of the features needed to characterize and identify bacteria. Under controlled conditions a contagion process was induced with Cmm, the disease epidemiology was monitored. Micro-Raman spectroscopy ($532nm\;{\lambda}$ laser) technique was evaluated its performance at assisting on Cmm detection through its characteristic Raman spectrum fingerprint. Our experiment was conducted with tomato plants in a completely randomized block experimental design (13 plants ${\times}$ 4 rows). The Cmm infection was confirmed by 16S rDNA and plants showed symptoms from 48 to 72 h after inoculation, the evolution of the incidence and severity on plant population varied over time and it kept an aggregated spatial pattern. The contagion process reached 79% just 24 days after the epidemic was induced. Micro-Raman spectroscopy proved its speed, efficiency and usefulness as a non-destructive method for the preliminary detection of Cmm. Carotenoid specific bands with wavelengths at 1146 and $1510cm^{-1}$ were the distinguishable markers. Chemometric analyses showed the best performance by the implementation of PCA-LDA supervised classification algorithms applied over Raman spectrum data with 100% of performance in metrics of classifiers (sensitivity, specificity, accuracy, negative and positive predictive value) that allowed us to differentiate Cmm from other endophytic bacteria (Bacillus and Pantoea). The unsupervised KMeans algorithm showed good performance (100, 96, 98, 91 y 100%, respectively).

• Journal of the Korean association of regional geographers
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• v.13 no.3
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• pp.301-320
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• 2007

In Korean academic societies, old maps has not yet been properly investigated in terms of their genealogy, classification, detailed place names, historical backgrounds and the other aspects. With publication of the bibliographies and papers on old maps reserved in museum and library, the scope of research enlarged gradually its scope from 1970s. In 1980s, with the development of theoretical geography, scientific analysis were applied to investigate the projection method of Daedongyeo-jido. The 1990s proved a prominent decade for researches. The photo-copies of old maps enabled researchers to investigate the in-depth comparative study. The more important thing is that old maps became to be powerful instrument in the research of historical geography, such as territorial disputes and marine name(東海). And county old maps compiled by region became to be regional-cultural contents of local areas. Important issues in old map research in Korean academic societies are about Cheonha-do which is unique old world map in Korea, grid-system projection in old county maps and the genealogy of Daedongyeo-jido(manuscript and block print edition). This study shows that bibliography of all old maps preserved in each library and museum should be standardized. This could enable the exchange of information of old maps between institutes. The more important thing is that conciliation of human, social and natural sciences should be applied in the research of old maps.