• Journal of Preventive Medicine and Public Health
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• 제55권4호
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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.

• 사물인터넷융복합논문지
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• 제8권5호
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• pp.33-39
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• 2022

모바일 컴퓨팅 분야에서 사용되는 저장장치는 저전력, 경량화, 내구성 등을 갖추어야 하며 사용자에 의해 생성되는 대용량 데이터를 효과적으로 저장 및 관리할 수 있어야 한다. 낸드 플래시 메모리는 모바일 컴퓨팅 분야에서 저장장치로 주로 사용되고 있다. 낸드 플래시 메모리는 구조적 특징 때문에 데이터 갱신요청 시 제자리 덮어쓰기가 불가능하여 데이터 갱신요청이 자주 발생하는 요청과 그렇지 않은 요청을 정확히 구분하여 각 블록에 저장 및 관리함으로써 해결할 수 있다. 이러한 데이터 갱신요청에 분류기법을 핫 데이터 식별 기법이라고 하며 현재 다양한 연구가 진행되었다. 본 논문은 더 정확한 핫 데이터 검증을 위해 카운팅 필터를 사용하여 데이터 갱신요청 발생을 연속적으로 기록하고 또한 특정 시간 동안 요청된 갱신요청이 얼마나 자주 발생하는지를 고려하여 핫 데이터를 검증한다.

• 한국전산구조공학회논문집
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• 제35권5호
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• pp.259-266
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• 2022

본 논문에서는 프리팹 구조물의 품질관리를 위한 딥러닝 및 비전센서 기반의 조립 성능 평가 모델을 개발하였다. 조립부 검출을 위해 인코더-디코더 형식의 네트워크와 수용 영역 블록 합성곱 모듈을 적용한 딥러닝 모델을 사용하였다. 검출된 조립부 영역 내의 볼트홀을 검출하고, 볼트홀의 위치 값을 산정하여 k-근접 이웃 기반 모델을 사용하여 조립 품질을 평가하였다. 제안된 기법의 성능을 검증하기 위해 조립부 모형을 3D 프린팅을 이용하여 제작하여 조립부 검출 및 조립 성능 예측 모델의 성능을 검증하였다. 성능 검증 결과 높은 정밀도로 조립부를 검출하였으며, 검출된 조립부내의 볼트홀의 위치를 바탕으로 프리팹 구조물의 조립 성능을 5% 이하의 판별 오차로 평가할 수 있음을 확인하였다.

• Computers and Concrete
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• 제31권3호
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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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• 제16권5호
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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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• 제23권11호
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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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• 제12권2호
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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.

• 한국콘텐츠학회논문지
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• 제16권5호
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• pp.351-360
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• 2016

본 연구는 부위마취로 수술을 받는 노인환자의 간호요구를 파악하기 위한 서술적 조사연구이다. 연구대상자는 일개 종합병원과 일개 준 종합병원에 입원하고 있는 60세 이상의 척추마취, 경막외마취, 신경차단하에 수술을 받은 126명으로 구조화된 설문지를 이용하여 2012년 10월 1일부터 10월 30일까지 자료를 수집하였다. 수집된 자료는 기술통계, t-test와 ANOVA, $Scheff{\acute{e}}$ test로 분석하였으며 연구결과는 다음과 같다. 노인환자의 부위마취 수술 중 간호요구의 평균은 $3.08{\pm}0.38$점이었고, 영역별로는 교육적($3.47{\pm}0.50$점), 영적($3.37{\pm}0.78$점), 신체적($3.31{\pm}0.46$점), 정서적($2.72{\pm}0.50$점), 환경적($2.51{\pm}0.47$점) 간호요구의 순으로 나타났다. 일반적인 특성에 따른 간호 요구는 성별, 종교, 배우자 유무에서 유의한 차이를 보였고, 수술관련 특성에 따른 간호 요구는 수술과, 수술시간, ASA 신체분류에서 유의한 차이를 보였다. 본 연구결과 부위마취 수술 노인 환자에게 수술과 마취에 대한 교육을 제공하는 것이 필요하며, 연구결과를 토대로 부위마취 수술 노인환자의 간호요구를 충족시키기 위한 교육지침 및 중재프로그램을 개발하고 평가하는 연구를 제언한다.

• The Plant Pathology Journal
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• 제34권5호
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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).

• 한국지역지리학회지
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• 제13권3호
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• pp.301-320
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• 2007

본 연구는 일제 강점기 이후 우리나라 고지도 연구 동향을 시기적으로 분석하고 주요 주제를 대상으로 연구 쟁점을 소개하였다. 우리나라 초기 연구는 "대동여지도"를 중심으로 신비주의에서 벗어나지 못하였으나 1960년과 1970년대에 규장각 소장 자료가 공개되면서 연구 지평은 크게 확대되었다. 1980년대 들어 목록집이 완성되고 군현지도책이 소개되었다. "대동여지도"에 대한 도법뿐만 아니라 지리지와의 관계가 연구되었으며 지역을 단위로 하는 정리 사업이 시작되었다. 1990년대 이후 전통지리사상, 경관의 복원, 영토 문제, 장소의 의미가 중요시되면서 새롭게 접근되기 시작하였다. 지방에서는 지역 정체성 회복의 중요한 수단으로 인식되기 시작하여 도록뿐만 아니라 이와 관련된 논문과 저서들이 발표되었다. 특히 2000년대 이후에는 각 기관에 소장된 자료에 대한 비교 연구가 가능해지면서 계열에 대한 연구가 활성화되기 시작하였다. 이와 같은 연구의 흐름 속에서 (1) "천하도"의 제작 기원 (2) 방안식 군현지도 (3) 필사본 "대동여지도"를 중심으로 논쟁이 형성되었다. 고지도 연구가 활성화되기 위해서는 (1) 각 기관에 소장된 자료들이 동일한 서지 정보를 바탕으로 상세 목록집의 간행 (2) 소장 기관간의 교류 사업이 필요하며 이를 위해 교류 협의체의 설립이 요구된다. (3) 또한 연구 인력이 매우 부족한 현실로 볼 때, 학계와 소장 기관간의 원활한 교류뿐만 아니라 학계 내에서도 학문간 경계를 넘는 소통과 협력이 필요함을 보여준다.