• Journal of People, Plants, and Environment
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• v.22 no.6
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• pp.563-573
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• 2019

The North Korean government conducted its first nuclear test in 2006 and more recently the sixth nuclear test on September 3, 2017. In order to identify how North Korea's nuclear tests have affected the environment, a scientific approach is required. Although North Korea's nuclear tests and their environmental destruction are not a severe threat to the environment of the Korean Peninsula at this time, identifying environmental damage and taking countermeasures in advance are essential to minimize their potential threats to the environments. The purpose of this study is to study the environmental impact of North Korea's nuclear tests using Google Earth image analysis. As a method of the study, we compare Google Earth images taken before and after each nuclear test was conducted in North Korea. To overcome limitations of the suggested comparison method, we cross-checked our results with those of previous scientific research. After the 1st-3rd nuclear tests, green spaces were found to be considerably reduced. In particular, when comparing the Google Earth images before and after the second nuclear test, some ground subsidences were observed. Such subsidences can cause tunnels on the mountainsides and cracks in rocks around the mountains, leading to the release of radioactive materials and contaminating groundwater. Besides, after the 4th-6th nuclear tests, decay and deforestation were observed not in the nuclear test sites, but in their surrounding areas. Especially after the 5th and 6th nuclear tests, the topography and the forests of the surrounding areas were severely damaged. In relation to North Korea's nuclear tests and their impact on the natural environment, we need to prepare various policy measures to reduce North Korea's environmental pollution and natural environment destruction. Those policy measures include the establishment of various cooperative governance between the Korean government, the private sector, the academia, NGOs, and international organizations.

• Journal of the Korea Society of Computer and Information
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• v.29 no.1
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• pp.41-49
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• 2024

Speech recognition technology is continuously advancing and widely used in various fields. In this study, we aimed to investigate the impact of speech data quality on speech recognition models by dividing the dataset into the entire dataset and the top 70% based on Signal-to-Noise Ratio (SNR). Utilizing Seamless M4T and Google Cloud Speech-to-Text, we examined the text transformation results for each model and evaluated them using the Levenshtein Distance. Experimental results revealed that Seamless M4T scored 13.6 in models using data with high SNR, which is lower than the score of 16.6 for the entire dataset. However, Google Cloud Speech-to-Text scored 8.3 on the entire dataset, indicating lower performance than data with high SNR. This suggests that using data with high SNR during the training of a new speech recognition model can have an impact, and Levenshtein Distance can serve as a metric for evaluating speech recognition models.

• Korean Journal of Remote Sensing
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• v.40 no.3
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• pp.285-293
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• 2024

This study explores the use of Sentinel-1 Synthetic Aperture Radar (SAR), processed through Google Earth Engine (GEE), to monitor changes in the areas of Antarctic ice shelves. Focusing on the Campbell Glacier Tongue (CGT) and Drygalski Ice Tongue (DIT),the research utilizes GEE's cloud computing capabilities to handle and analyze large datasets. The study employs Otsu's method for image binarization to distinguish ice shelves from the ocean and mitigates detection errors by averaging monthly images and extracting main regions. Results indicate that the CGT area decreased by approximately 26% from January 2016 to January 2024, primarily due to calving events,while DIT showed a slight increase overall,with notable reduction in recent years. Validation against Sentinel-2 optical images demonstrates high accuracy,underscoring the effectiveness of SAR and GEE for continuous, long-term monitoring of Antarctic ice shelves.

• Journal of the Korean Institute of Landscape Architecture
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• v.49 no.4
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• pp.15-29
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• 2021

The study aims to grasp the perception and appraisal of urban park users through text analysis. This study used Google review data provided by Google Maps. Google Maps Review is an online review platform that provides information evaluating locations through social media and provides an understanding of locations from the perspective of general reviewers and regional guides who are registered as members of Google Maps. The study determined if the Google Maps Reviews were useful for extracting meaningful information about the user perceptions and appraisals for parks management plans. The study chose three urban parks in Seoul, South Korea; Seoul Forest, Boramae Park, and Olympic Park. Review data for each of these three parks were collected via web crawling using Python. Through text analysis, the keywords and network structure characteristics for each park were analyzed. The text was analyzed, as were park ratings, and the analysis compared the reviews of residents and foreign tourists. The common keywords found in the review comments for the three parks were "walking", "bicycle", "rest" and "picnic" for activities, "family", "child" and "dogs" for accompanying types, and "playground" and "walking trail" for park facilities. Looking at the characteristics of each park, Seoul Forest shows many outdoor activities based on nature, while the lack of parking spaces and congestion on weekends negatively impacted users. Boramae Park has the appearance of a city park, with various facilities providing numerous activities, but reviewers often cited the park's complexity and the negative aspects in terms of dog walking groups. At Olympic Park, large-scale complex facilities and cultural events were frequently mentioned, emphasizing its entertainment functions. Google Maps Review can function as useful data to identify parks' overall users' experiences and general feelings. Compared to data from other social media sites, Google Maps Review's data provides ratings and understanding factors, including user satisfaction and dissatisfaction.

• 한국공간정보시스템학회:학술대회논문집
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• 2007.06a
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• pp.430-435
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• 2007

지금까지 우리나라의 GIS는 국가 중심의 GIS로, 사용자들이 적극 참여하거나 민간 산업 부분으로 확대되기에는 기술력, 비용 등의 한계를 갖고 있었다. 그래서 우리는 Open Source를 이용하여 종래 GIS의 한계를 극복하고 Google Map, Google Earth, Sketch Up을 연동한 Web Portal Site 구축을 통하여, 사용자들이 의사결정을 하는데 도움이 되는 효율적인 정보를 제공하며 이들이 곧 정보 창출의 주체가 되는 쌍방향적인 참여를 이끌어내고자 한다.

• Genomics & Informatics
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• v.5 no.2
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• pp.88-91
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• 2007

Repetitive sequences such as SINE, LINE, and LTR elements form a major part of eukaryotic genomes. A literature search tool that summarizes the information contained within repeat elements would provide biologists in the field of genomics with a useful tool for analyzing genomic sequence features. We developed a java program designed to make literature access easier by using two search engines simultaneously. RepWeb is a web-based search system that provides a user friendly interface for searching the reference data and journals for information related to repeat elements by using the search engines, Google Scholar and PubMed, simultaneously. It provides an interface that displays the repeat element- related biological information, and includes useful functions such as the production of a repeat tree, clickable links to PubMed and Google Scholar, exporting, and sorting a field into date, author, journal and title.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2010.06a
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• pp.6-8
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• 2010

불투수면의 증가는 물, 대기의 순환 과정 교란뿐만 아니라, 자연 생태계 전반에 심각한 문제를 야기하기 때문에 도심의 자연 환경의 질을 평가 할 수 있는 중요한 척도가 되고 있다. 본 연구는 Google Earth와 수치지도를 이용하여 경북대학교 캠퍼스의 불투수율 변화추세를 정량적으로 비교 평가하는 방안을 제안한다. 경북대 캠퍼스 전체 면적에 대한 2003년과 2009년 각각의 불투수면적은 25%에서 42%로 증가하였고, 투수면적은 약 74%에서 57%로 감소하였다. 이러한 경과는 캠퍼스 개발과정에 있어 필요한 개선점을 지침화 할 수 있고, 캠퍼스 자연환경의 보전과정에서 발생할 수 있는 문제점과 시행착오 등을 사전에 점검할 수 있는 중요한 기초자료를 확보할 수 있을 것이다.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2009.04a
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• pp.265-266
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• 2009

구글 어스(Google Earth)란 도구는 인공위성에서 찍은 사진들을 바탕을 3차원 지구 타원체에 바탕에 입힌 도구이다. 이 도구는 3차원 공간에서 OpenAPI등을 이용하여 3차원객체를 지구 타원체상에 구현 할 수 있다. 이러한 점들 때문에 많은 사람들이 주요 건축물을 구현하거나 실외 이동객체(자동차, 비행기 등)를 구현하여 더욱더 지구와 가까운 모형으로 구현을 해 왔다. 하지만 공간 정보란 것은 실외 공간정보만이 아닌 건물의 내부 구조나 쇼핑몰상의 유동 인구의 움직임등과 같은 정보도 존재한다. 이 연구는 구글 어스 상에서 내부 구조를 가진 건물을 구축하고 이곳을 탐색할 수 있게 해 주며 또한 실내 이동 객체들의 실시간 움직임을 구현한다.

• Journal of Korean Library and Information Science Society
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• v.31 no.4
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• pp.191-212
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• 2000

This study is intlded to ampre the effectiveness of the Northemlight and Google, which are based on Datamining kdmique with a Metacrawler, one of metasearch engines. As a result, searches responding to queries in the Northemlight and Google produced a higher precision and recall as comparrd with searches nspcdhg to queries in the metacrawler. The results show that the Datamining techniques can help improve information retrieval effectinveness.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.3
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• pp.363-370
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• 2009

In order to populate ontology, this paper presents a generalization method of ontology instances, extracted from texts and web pages, by using unsupervised learning techniques for word sense disambiguation, which uses open APIs and lexical resources such as Google and WordNet. According to the experimental results, our method achieved a 15.8% improvement over the previous research.