• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.302-302
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• 2021

지하차도의 경우 국지 및 돌발홍수가 발생할 경우 대부분 침수됨에도 불구하고 2020년 7월 23일 부산 지역에 밤사이 시간당 80mm가 넘는 폭우가 발생하면서 순식간에 지하차도 천장까지 물이 차면서 선제적인 차량 통제가 우선적으로 수행되지 못하여 미처 대피하지 못한 3명의 운전자 인명사고가 발생하였다. 수재해를 비롯한 재난 관리를 빠르게 수행하기 위해서는 기존의 정부 및 관주도 중심의 단방향의 재난 대응에서 벗어나 정형 데이터와 비정형 데이터를 총칭하는 빅데이터의 통합적 수집 및 분석을 수행이 필요하다. 본 연구에서는 부산지역의 지하차도와 인접한 지하터널 CCTV 자료(센서)를 통한 재난 발생 시 인명피해를 최소화 정보 제공을 위한 Object Detection(객체 탐지)연구를 수행하였다. 지하터널 침수가 발생한 부산지역의 CCTV 영상을 사용하였으며, 영상편집에 사용되는 CCTV 자료의 음성자료를 제거하는 인코딩을 통하여 불러오는 영상파일 용량파일 감소 효과를 볼 수 있었다. 지하차도에 진입하는 물체를 탐지하는 방법으로 YOLO(You Only Look Once)를 사용하였으며, YOLO는 가장 빠른 객체 탐지 알고리즘 중 하나이며 최신 GPU에서 초당 170프레임의 속도로 실행될 수 있는 YOLOv3 방법을 적용하였으며, 분류작업에서 보다 높은 Classification을 가지는 Darknet-53을 적용하였다. YOLOv3 방법은 기존 객체탐지 모델 보다 좀 더 빠르고 정확한 물체 탐지가 가능하며 또한 모델의 크기를 변경하기만 하면 다시 학습시키지 않아도 속도와 정확도를 쉽게 변경가능한 장점이 있다. CCTV에서 오전(일반), 오후(침수발생) 시점을 나눈 후 Car, Bus, Truck, 사람을 분류하는 YOLO 알고리즘을 적용하여 지하터널 인근 Object Detection을 실제 수행 하였으며, CCTV자료를 이용하여 실제 물체 탐지의 정확도가 높은 것을 확인하였다.

• The Journal of the Society of Stroke on Korean Medicine
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• v.24 no.1
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• pp.13-24
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• 2023

■Objectives This case study is to report the effects of Korean medicine on parkinsonism patient's Gait Disturbance. ■Methods During 12 days of hospitalization, the patient was treated by acupuncture, pharmaco-acupuncture, moxibustion, herbal medicine, especially Cheongsimyeonjatang-gamibang. In order to assess the change of symptoms, we used a 3-Dimensional(3D) gait analysis system, Unified Parkinson's Disease Rating Scale(UPDRS), analysis of gait video and self-evaluation of discomfort. ■Results After treatment, The improvements of walking pattern were observed in both objective analysis results of gait analysis system and subjective video analysis. And the UPDRS score decreased, especially Part III score decreased more than minimal clinically important difference(MCID). In addition, There was improvement in self assessment of the patient. ■Conclusion This study suggests that Korean medical treatment might be effective in motor disorder of parkinsonism patient.

• The Journal of Korean Medical History
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• v.35 no.2
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• pp.45-67
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• 2022

Currently, there are about 20,000 or so known ancient medical texts from the East Asian medical traditions. Although the most famous texts are widely known, many texts still exist only as original manuscripts. We are interested exploring these texts to uncover the potential benefits of their therapeutic knowledge. This study aims to develop a database program that automatically converts the treatment skills described in the text version into a more structured version. In the previous study, our team analyzed patterns in the way that treatment skills are described and then tried to design a database program algorithm that identified every meaningful keyword used to describe treatment skills and put that word in the right cell of a structured table. This study continues the development of this program. East Asian medical herbal treatment information is broken down into 4 elements: the first one is the name or title of treatment skills, and the second is the symptoms to which the treatment is applied, the third is ingredients used, the fourth is how information is processed and the indications taken. This study presents the algorithm's principles on how to analyze and structure the fourth element, the processing of information and taking of indications, which is described in a form of ancient natural language.

• Journal of the Society of Disaster Information
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• v.19 no.4
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• pp.968-975
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• 2023

Purpose: We would like to confirm that the false positive rate of flames/smoke is high when detecting fires. Propose a method and dataset to recognize and classify fire situations to reduce the false detection rate. Method: Using the video as learning data, the characteristics of the fire situation were extracted and applied to the classification model. For evaluation, the model performance of Yolov8 and Slowfast were compared and analyzed using the fire dataset conducted by the National Information Society Agency (NIA). Result: YOLO's detection performance varies sensitively depending on the influence of the background, and it was unable to properly detect fires even when the fire scale was too large or too small. Since SlowFast learns the time axis of the video, we confirmed that detects fire excellently even in situations where the shape of an atypical object cannot be clearly inferred because the surrounding area is blurry or bright. Conclusion: It was confirmed that the fire detection rate was more appropriate when using a video-based artificial intelligence detection model rather than using image data.

• Journal of the Earthquake Engineering Society of Korea
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• v.28 no.3
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• pp.149-158
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• 2024

Piloti-type structures with vertical irregularity are vulnerable to earthquakes due to the soft structure of the first story. Structural characteristics of buildings can significantly affect the seismic loss function, calculated based on seismic fragility, and therefore need to be considered. This study investigated the effects of the number of stories and core locations on the seismic loss function of piloti-type buildings in Korea. Twelve analytical models were developed considering two variations: three stories (4-story, 5-story, and 6-story) and four core locations (center core, x-eccentric core, y-eccentric core, and xy-eccentric core). The interstory drift ratio and peak floor acceleration were assessed through incremental dynamic analysis using 44 earthquake records, and seismic fragility was derived. Seismic loss functions were calculated and compared using the derived seismic fragility and repair cost ratio of each component. The results indicate that the seismic loss function increases with more stories and when the core is eccentrically located in the piloti-type structure model. Therefore, the uncertainty due to the number of stories and core location should be considered when deriving the seismic loss function of piloti-type structures.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.1-13
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• 2015

As opinion mining in big data applications has been highlighted, a lot of research on unstructured data has made. Lots of social media on the Internet generate unstructured or semi-structured data every second and they are often made by natural or human languages we use in daily life. Many words in human languages have multiple meanings or senses. In this result, it is very difficult for computers to extract useful information from these datasets. Traditional web search engines are usually based on keyword search, resulting in incorrect search results which are far from users' intentions. Even though a lot of progress in enhancing the performance of search engines has made over the last years in order to provide users with appropriate results, there is still so much to improve it. Word sense disambiguation can play a very important role in dealing with natural language processing and is considered as one of the most difficult problems in this area. Major approaches to word sense disambiguation can be classified as knowledge-base, supervised corpus-based, and unsupervised corpus-based approaches. This paper presents a method which automatically generates a corpus for word sense disambiguation by taking advantage of examples in existing dictionaries and avoids expensive sense tagging processes. It experiments the effectiveness of the method based on Naïve Bayes Model, which is one of supervised learning algorithms, by using Korean standard unabridged dictionary and Sejong Corpus. Korean standard unabridged dictionary has approximately 57,000 sentences. Sejong Corpus has about 790,000 sentences tagged with part-of-speech and senses all together. For the experiment of this study, Korean standard unabridged dictionary and Sejong Corpus were experimented as a combination and separate entities using cross validation. Only nouns, target subjects in word sense disambiguation, were selected. 93,522 word senses among 265,655 nouns and 56,914 sentences from related proverbs and examples were additionally combined in the corpus. Sejong Corpus was easily merged with Korean standard unabridged dictionary because Sejong Corpus was tagged based on sense indices defined by Korean standard unabridged dictionary. Sense vectors were formed after the merged corpus was created. Terms used in creating sense vectors were added in the named entity dictionary of Korean morphological analyzer. By using the extended named entity dictionary, term vectors were extracted from the input sentences and then term vectors for the sentences were created. Given the extracted term vector and the sense vector model made during the pre-processing stage, the sense-tagged terms were determined by the vector space model based word sense disambiguation. In addition, this study shows the effectiveness of merged corpus from examples in Korean standard unabridged dictionary and Sejong Corpus. The experiment shows the better results in precision and recall are found with the merged corpus. This study suggests it can practically enhance the performance of internet search engines and help us to understand more accurate meaning of a sentence in natural language processing pertinent to search engines, opinion mining, and text mining. Naïve Bayes classifier used in this study represents a supervised learning algorithm and uses Bayes theorem. Naïve Bayes classifier has an assumption that all senses are independent. Even though the assumption of Naïve Bayes classifier is not realistic and ignores the correlation between attributes, Naïve Bayes classifier is widely used because of its simplicity and in practice it is known to be very effective in many applications such as text classification and medical diagnosis. However, further research need to be carried out to consider all possible combinations and/or partial combinations of all senses in a sentence. Also, the effectiveness of word sense disambiguation may be improved if rhetorical structures or morphological dependencies between words are analyzed through syntactic analysis.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.11
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• pp.670-681
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• 2016

Recently, based on Internet and mobile environments, the Fintech industry that fuses finance and IT together has been rapidly growing and Fintech services armed with simplicity and convenience have been leading the conversion of all financial services into online and mobile services. However, despite the rapid growth of the Fintech industry, few studies have classified Fintech technologies into detailed technologies, analyzed the technology development trends of major market countries, and supported technology planning. In this respect, using Fintech technological data in the form of unstructured data, the present study extracts and defines detailed Fintech technologies through the topic modeling technique. Thereafter, hot and cold topics of the derived detailed Fintech technologies are identified to determine the trend of Fintech technologies. In addition, the trends of technology development in the USA, South Korea, and China, which are major market countries for major Fintech industrial technologies, are analyzed. Finally, through the analyses of networks between detailed Fintech technologies, linkages between the technologies are examined. The trends of Fintech industrial technologies identified in the present study are expected to be effectively utilized for the establishment of policies in the area of the Fintech industry and Fintech related enterprises' establishment of technology strategies.

• Journal of Bio-Environment Control
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• v.22 no.2
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• pp.116-122
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• 2013

We investigated characteristics and self-incompatibility genotypes of 11 crabapple cultivars to introduce a new pollinizer of 'Fuji' apple tree in Korea. Flowering dates of eleven crabapples were two to seven days earlier than that of 'Fuji'. The rate of pollen germination in vitro was ranged from 85.6% to 98.0% except 'Virginia'. Controlled pollination treatment with each crabapples to 'Fuji' increased fruit set rate about 20.4% to 34.4%, the number of seed per fruit about 13.8% to 42.3% and fruit weight about 7.4% to 16.7% compared to open pollination. Tested crabapples were resistant to peach fruit moth, brown leaf spot and sooty blotch in general. A PCR amplification method using S-RNase primers was carry out in eleven crabapples. S-alleles, $S_3$, $S_5$, $S_9$, $S_{10}$, $S_{20}$, $S_{26} from six crabapples were determinated. Through sequencing analysis, $S_5$ ('Manchurian', 'Virginia') and $S_9$ ('Yantaishagou') showed 100% homologous to previous result. Based on our results, it was recommended that 'Manchurian', 'Hopa A', 'Hanyaehanakaidou', 'Spectabilis' could be promising pollinzers for 'Fuji' apple cultivar.

• The Korean Journal of Applied Statistics
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• v.32 no.6
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• pp.795-835
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• 2019

Big data has been generated in various fields. Many companies have now tried to make profits by building a system capable of analyzing big data based on artificial intelligence (AI) techniques. Integrating AI technology has made analyzing and utilizing vast amounts of data increasingly valuable. In particular, demand forecasting with maximum accuracy is critical to government and business management in various fields such as finance, procurement, production and marketing. In this case, it is important to apply an appropriate model that considers the demand pattern for each field. It is possible to analyze complex patterns of real data that can also be enlarged by a traditional time series model or regression model. However, choosing the right model among the various models is difficult without prior knowledge. Many studies based on AI techniques such as machine learning and deep learning have been proven to overcome these problems. In addition, demand forecasting through the analysis of stereotyped data and unstructured data of images or texts has also shown high accuracy. This paper introduces important areas where demand forecasts are relatively active as well as introduces machine learning and deep learning techniques that consider the characteristics of each field.

• Journal of the Korea Society of Computer and Information
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• v.25 no.6
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• pp.219-224
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• 2020

The purpose of this study is mainly to provide theoretical basis of private security industry by analyzing the perception and flow of private security from the press-released materials according to periodic classification and duties through 'Big Kinds', a website of analyzing news big data. The research method has been changed to structured data to allow an analysis of various scattered unstructured data, and the keywords trend and related words by duties of private security were analyzed in growth period of private security. The perception of private security based on the results of the study was exposed a lot by the media through various crimes, accidents and incidents, and the issues related permanent position. Also, it tended to be perceived as a simple security guard, not recognized as the area of private security, and judging from the high correlation between private security and police, it was recognized not only as a role to assist the police force, but also as a common agent in charge of the public peace. Therefore, it should objectively judge the perception of private security, and through this, it is believed that it should be a foundation for recognizing private security as a main agent responsible for the safety of the nation and maintaining social orders.