• 대한임베디드공학회논문지
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• 제17권4호
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• pp.249-255
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• 2022

The COVID-19 virus appeared in 2019 and is extremely contagious. Because it is very infectious and has a huge impact on people's mobility. In this paper, multiple linear regression and random forest models are used to predict the number of COVID-19 cases using COVID-19 infection status data (open source data provided by the Ministry of health and welfare) and Google Mobility Data, which can check the liquidity of various categories. The data has been divided into two sets. The first dataset is COVID-19 infection status data and all six variables of Google Mobility Data. The second dataset is COVID-19 infection status data and only two variables of Google Mobility Data: (1) Retail stores and leisure facilities (2) Grocery stores and pharmacies. The models' performance has been compared using the mean absolute error indicator. We also a correlation analysis of the random forest model and the multiple linear regression model.

• The Journal of Asian Finance, Economics and Business
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• 제7권12호
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• pp.115-121
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• 2020

This study aims to investigate the effects of restrictions in economic activity on the spread of COVID-19 in the Philippines. This research employs daily time-series data of confirmed new COVID-19 cases, Apple mobility trends (i.e., use of public transport to destinations, volume of people driving, and amount of walking to destinations) and Google community mobility (i.e., visits to transit stations, visits to workplaces, and staying-at-home) indicators covering the period February 17 to September 11, 2020. The analysis starts by establishing the correlation pattern of new confirmed COVID-19 daily infections to each independent variable. The results show negative linear correlation of the number of new COVID-19 daily infections with less visit to transit station, increase stay-at-home, less use of public transport, and less amount of walking to destinations. Interestingly, the number of new COVID-19 daily infections indicates some form of positive linear correlation with visits to workplaces and volume of people driving. Moreover, employing robust least square regression via the method of MM-estimation, major findings reveal that across mobility measures, staying-at-home has the highest impact on reducing the spread of COVID-19, followed by visiting transit stations less, less use of public transport, less amount of walking, and less workplace visits.

• Journal of Preventive Medicine and Public Health
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• 제53권6호
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• pp.397-404
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• 2020

Objectives: The objective of this study was to demonstrate the effects of community-based social distancing interventions after the first coronavirus disease 2019 (COVID-19) case in Turkey on the course of the pandemic and to determine the number of prevented cases. Methods: In this ecological study, the interventions implemented in response to the first COVID-19 cases in Turkey were evaluated and the effect of the interventions was demonstrated by calculating the effective reproduction number (R_t) of severe acute respiratory syndrome coro navirus 2 (SARS-CoV-2) when people complied with community-based social distancing rules. Results: Google mobility scores decreased by an average of 36.33±22.41 points (range, 2.60 to 84.80) and a median of 43.80 points (interquartile range [IQR], 24.90 to 50.25). The interventions caused the calculated R_t to decrease to 1.88 (95% confidence interval, 1.87 to 1.89). The median growth rate was 19.90% (IQR, 10.90 to 53.90). A positive correlation was found between Google mobility data and R_t (r=0.783; p<0.001). The expected number of cases if the growth rate had not changed was predicted according to Google mobility categories, and it was estimated to be 1 381 922 in total. Thus, community-based interventions were estimated to have prevented 1 299 593 people from being infected. Conclusions: Community-based social distancing interventions significantly decreased the R_t of COVID-19 by reducing human mobility, and thereby prevented many people from becoming infected. Another important result of this study is that it shows health policymakers that data on human mobility in the community obtained via mobile phones can be a guide for measures to be taken.

• Journal of Preventive Medicine and Public Health
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• 제55권2호
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• pp.134-143
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• 2022

Objectives: Many governments have imposed-and are still imposing-mobility restrictions to contain the coronavirus disease 2019 (COVID-19) pandemic. However, there is no consensus on whether policy-induced reductions of human mobility effectively reduce the effective reproduction number (Rt) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Several studies based on country-restricted data reported conflicting trends in the change of the SARS-CoV-2 Rt following mobility restrictions. The objective of this study was to examine, at the global scale, the existence of regional specificities in the correlations between Rt and human mobility. Methods: We computed the Rt of SARS-CoV-2 using data on worldwide infection cases reported by the Johns Hopkins University, and analyzed the correlation between Rt and mobility indicators from the Google COVID-19 Community Mobility Reports in 125 countries, as well as states/regions within the United States, using the Pearson correlation test, linear modeling, and quadratic modeling. Results: The correlation analysis identified countries where Rt negatively correlated with residential mobility, as expected by policymakers, but also countries where Rt positively correlated with residential mobility and countries with more complex correlation patterns. The correlations between Rt and residential mobility were non-linear in many countries, indicating an optimal level above which increasing residential mobility is counterproductive. Conclusions: Our results indicate that, in order to effectively reduce viral circulation, mobility restriction measures must be tailored by region, considering local cultural determinants and social behaviors. We believe that our results have the potential to guide differential refinement of mobility restriction policies at a country/regional resolution.

• 한국정보교육학회:학술대회논문집
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• 한국정보교육학회 2011년도 동계학술대회
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• pp.153-158
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• 2011

최근 교육현장에서 학습에 모바일을 사용하고자 하는 아이디어는 모바일의 이동성, 현장성, 휴대성, 시성, 학습정보 접근의 용이성이라는 장점에서부터 시작된다. 특히 안드로이드폰에서 구글맵 API와 GPS를 기반으로 현재 위치의 정보(지리 및 문화관련 사진과 텍스트 등)를 공유할 수 있는 본 연구의 애플리케이션은 이러한 장점들을 적극적으로 활용한 것이며 교실밖에서, 또 정규수업 이후에도 활용할 수 있어서 지속적인 학습 자원의 관리와 자기 주도적 학습의 가능성을 열어두고 있기에 더욱 효과적이라고 할 수 있다.

• 한국조경학회지
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• 제52권4호
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• pp.45-55
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• 2024

본 연구는 보행 활동을 일상보행(utilitarian walking)과 여가보행(leisure walking)으로 구분하고, 보행 유형과 보행 환경 간의 상관관계를 분석하고자 한다. 연구 대상지는 서울특별시 용산구로, 보행환경 측정을 위해 구글 스트리트뷰(Google Street View: GSV)와 의미론적 분할(semantic segmentation) 딥러닝 기법을 활용하여 보행자가 실제로 체감하는 도시 보행환경 요소들을 정량적으로 산출하였다. 일상보행과 여가보행, 인지적 보행환경 만족도를 측정하기 위해 설문조사를 실시하여 192명의 유효 응답을 수집하였고, 설문 응답 데이터를 바탕으로 일상보행, 여가보행, 보행환경 만족도를 시각화하고, 보행친화도 값 간의 상관관계를 분석하였다. 연구 결과, 여가보행과 보행친화도는 유의미한 양의 상관관계를 보였으나(Pearson's r= 0.121, p-value= 0.012), 일상보행과 보행친화도 간에는 유의미한 상관관계가 없었다(Pearson's r= 0.093, p-value= 0.055). 이는 사람들이 일상보행에서는 보행환경보다 이동 효율성을 더 중요하게 고려하지만, 여가보행에서는 보행환경의 질을 고려하여 보행 빈도가 결정된다는 결과를 보여준다. 이러한 결과를 바탕으로 본 연구는 여가보행을 증진시키기 위해 주거지 주변의 보행환경을 개선하는 방안으로, 좁은 보행로에 수직 정원이나 다양한 형태의 입체적 정원을 조성하고, 보도 디자인을 개선하는 등의 구체적인 전략이 필요함을 제시한다. 본 연구 결과는 보행 친화적인 환경 조성을 통해 여가보행을 활성화하고, 궁극적으로 서울시의 지속 가능성과 주민들의 삶의 질을 향상시키는 데 기여할 수 있을 것이다.

• 한국정보시스템학회지:정보시스템연구
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• 제31권1호
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• pp.175-195
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• 2022

Purpose The purpose of this study is to examine news articles and analyze trends and key agendas related to artificial intelligence(AI). In particular, this study tried to compare the reporting behaviors of Korea and the United States, which is considered to be a leader in the field of AI. Design/methodology/approach This study analyzed news articles using a big data method. Specifically, main agendas of the two countries were derived and compared through the keyword frequency analysis, topic modeling, and language network analysis. Findings As a result of the keyword analysis, the introduction of AI and related services were reported importantly in Korea. In the US, the war of hegemony led by giant IT companies were widely covered in the media. The main topics in Korean media were 'Strategy in the 4th Industrial Revolution Era', 'Building a Digital Platform', 'Cultivating Future human resources', 'Building AI applications', 'Introduction of Chatbot Services', 'Launching AI Speaker', and 'Alphago Match'. The main topics of US media coverage were 'The Bright and Dark Sides of Future Technology', 'The War of Technology Hegemony', 'The Future of Mobility', 'AI and Daily Life', 'Social Media and Fake News', and 'The Emergence of Robots and the Future of Jobs'. The keywords with high centrality in Korea were 'release', 'service', 'base', 'robot', 'era', and 'Baduk or Go'. In the US, they were 'Google', 'Amazon', 'Facebook', 'China', 'Car', and 'Robot'.

• Journal of Preventive Medicine and Public Health
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• 제56권1호
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• pp.21-30
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• 2023

Objectives: To prevent the spread of coronavirus disease 2019 (COVID-19), behaviors such as mask-wearing, social distancing, decreasing mobility, and avoiding crowds have been suggested, especially in high-risk countries such as Indonesia. Unfortunately, the level of compliance with those practices has been low. This study was conducted to determine the predisposing, enabling, and reinforcing factors of COVID-19 prevention behavior in Indonesia. Methods: This cross-sectional study used a mixed-methods approach. The participants were 264 adults from 21 provinces in Indonesia recruited through convenience sampling. Data were collected using a Google Form and in-depth interviews. Statistical analysis included univariate, bivariate, and multivariate logistic regression. Furthermore, qualitative data analysis was done through content analysis and qualitative data management using Atlas.ti software. Results: Overall, 44.32% of respondents were non-compliant with recommended COVID-19 prevention behaviors. In multivariate logistic regression analysis, low-to-medium education level, poor attitude, insufficient involvement of leaders, and insufficient regulation were also associated with decreased community compliance. Based on in-depth interviews with informants, the negligence of the Indonesian government in the initial stages of the COVID-19 pandemic may have contributed to the unpreparedness of the community to face the pandemic, as people were not aware of the importance of preventive practices. Conclusions: Education level is not the only factor influencing community compliance with recommended COVID-19 prevention behaviors. Changing attitudes through health promotion to increase public awareness and encouraging voluntary community participation through active risk communication are necessary. Regulations and role leaders are also required to improve COVID-19 prevention behavior.

• 지능정보연구
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• 제24권3호
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• pp.1-19
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• 2018

최근 딥러닝은 오디오, 텍스트 및 이미지 데이터와 같은 비 체계적인 데이터를 대상으로 다양한 추정, 분류 및 예측 문제에 사용 및 적용되고 있다. 특히, 의류산업에 적용될 경우 딥러닝 기법을 활용한 의류 인식, 의류 검색, 자동 제품 추천 등의 심층 학습을 기반으로 한 응용이 가능하다. 이 때의 핵심모형은 합성곱 신경망을 사용한 이미지 분류이다. 합성곱 신경망은 입력이 전달되고 출력에 도달하는 과정에서 가중치와 같은 매개 변수를 학습하는 뉴런으로 구성되고, 영상 분류에 가장 적합한 방법론으로 사용된다. 기존의 의류 이미지 분류 작업에서 대부분의 분류 모형은 의류 이미지 자체 또는 전문모델 착용 의류와 같이 통제된 상황에서 촬영되는 온라인 제품 이미지를 사용하여 학습을 수행한다. 하지만 본 연구에서는 통제되지 않은 상황에서 촬영되고 사람들의 움직임과 다양한 포즈가 포함된 스트릿 패션 이미지 또는 런웨이 이미지를 분류하려는 상황을 고려하여 분류 모형을 훈련시키는 효과적인 방법을 제안한다. 이동성을 포착하는 런웨이 의류 이미지로 모형을 학습시킴으로써 분류 모형의 다양한 쿼리 이미지에 대한 적응력을 높일 수 있다. 모형 학습 시 먼저 ImageNet 데이터셋을 사용하여 pre-training 과정을 거치고 본 연구를 위해 수집된 32 개 주요 패션 브랜드의 2426개 런웨이 이미지로 구성된 데이터셋을 사용하여 fine-tuning을 수행한다. 학습 과정의 일반화를 고려해 10번의 실험을 수행하고 제안된 모형은 최종 테스트에서 67.2 %의 정확도를 기록했다. 본 연구 모형은 쿼리 이미지가 런웨이 이미지, 제품 이미지 또는 스트릿 패션 이미지가 될 수 있는 다양한 분류 환경에 적용될 수 있다. 구체적으로는 패션 위크에서 모바일 어플리케이션 서비스를 통해 브랜드 검색을 용이하게 하는 서비스를 제공하거나, 패션 잡지사의 편집 작업에 사용되어 브랜드나 스타일을 분류하고 라벨을 붙일 수 있으며, 온라인 쇼핑몰에서 아이템 정보를 제공하거나 유사한 아이템을 추천하는 등의 다양한 목적에 적용될 수 있다.