• 한국도서관정보학회지
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• 제49권4호
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• pp.265-294
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• 2018

최근 정보의 폭발적인 증가로 인해 사용자에게 적합한 정보를 제공하기 위한 정보의 필터링이 매우 중요시 되고 있다. 한국과학기술정보연구원에서 운영하고 있는 학술정보서비스인 NDSL은 방대한 자료를 보유함에도 불구하고 사용자들은 검색 외에 자료 획득이 쉽지가 않다. 본 논문은 사용자에게 적합한 정보를 제공하기 위하여 키워드 특성을 활용한 서비스인 PIN(Profiling service In NDSL)을 제안한다. PIN은 키워드만을 가지고 검색하는 것이 아닌 사용자 본인 및 유사 사용자가 등록한 관심 키워드, 동시이용 키워드, 검색 키워드로 분석된 워드 클라우드를 제공하고 이를 통하여 사용자에게 맞춤형 논문, 보고서, 특허, 동향의 콘텐츠를 추천한다. 또한 콘텐츠를 보다 쉽게 접근하기 위하여 중복분류가 가능한 학술연구분류체계 기반 분류를 제공한다. 이를 검증하기 위해 NDSL의 축적된 2016년도의 국내논문의 데이터를 기반으로 분류별로 키워드를 추출하고 이를 통해 매칭 기반의 분류 모델을 만든 후 트레이닝 및 테스트를 거쳐 결과를 도출한다.

• 대한전자공학회논문지SP
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• 제40권3호
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• pp.203-216
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• 2003

In this paper a new feature extraction and target classification method is proposed for the recognition part of FLIR(Forwar Looking Infrared)-image-based ATR system. Proposed feature extraction method is "cluster(=set of classes)-based"version of previous fisherfaces method that is known by its robustness to illumination changes in face recognition. Expecially introduced class clustering and cluster-based projection method maximizes the performance of fisherfaces method. Proposed target image classification method is based on the mixture of experts model which consists of RBF-type experts and MLP-type gating networks. Mixture of experts model is well-suited with ATR system because it should recognizee various targets in complexed feature space by variously mixed conditions. In proposed classification method, one expert takes charge of one cluster and the separated structure with experts reduces the complexity of feature space and achieves more accurate local discrimination between classes. Proposed feature extraction and classification method showed distinguished performances in recognition test with customized. FLIR-vehicle-image database. Expecially robustness to pixelwise sensor noise and un-wanted intensity variations was verified by simulation.

• 한국전자통신학회논문지
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• 제12권1호
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• pp.129-138
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• 2017

본 연구에서는 지역자치단체의 관점에서 정부 정책, 지역 역량, 산업 유망성 등을 모두 고려한 지역 맞춤형 미래 유망 산업 발굴 방법을 제시하고자 한다. 이를 위하여 정부의 '5개년 지역산업발전 종합계획(2014)'에 명시된 지역별 주력/협력산업 자료를 활용하였으며, 해당 산업군의 특허 데이터를 분석하여 지역별 과학기술 역량을 측정하였다. 또한 산업별로 연결된 IPC(: International Patent Classification)를 토대로 지식스톡(Knowledge Stock')과 연구활동지수(Activity Index) 등을 산출하여 세부산업별 유망성을 확인하였다. 부산 지역을 대상으로 사례분석을 실시한 결과, 한국표준산업분류 세세분류(5자리)를 기준으로 부산의 유망산업 예비 후보군 94개 분야 및 유망산업 핵심 산업군 7개 세세분야를 도출하였다. 본 연구에서 제시하는 방법론은 지방자치단체가 근거기반의 효율적이고 미래지향적인 지역 개발 로드맵을 수립하는데 기여할 수 있을 것이다.

• 한국안전학회지
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• 제33권1호
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• pp.148-161
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• 2018

A traffic fatality by young people marked average annual decrease of 4.5% since 2011. Meanwhile, a traffic fatality by senior over 65 years old marked average annual increase of 7.9% for the last five years which means that the annual increase of traffic fatality by senior will be a serious problem. This study started questioning that senior drivers over 65 years old did not retain the same causal factor of fatal traffic accidents and thus extensively analyzed a risk of it by age group quantitatively, dividing the senior driver group into the early, middle and latter stages. Depending on the aging level, the risk of traffic fatality showed a wide difference in seven different types of traffic accidents generally, and happened to increase with latter and middle parts of the senior driver more than the early part. Therefore, this study proposes four policy suggestions: 1) The senior driver need to be offered customized driving educations and the improvement of road environment is also recommended. 2) Political assistance is needed to support and guide a safety related technology installation for the new or existing car. 3) Renewal of driving license and an aptitude test(physical examination, cognitive test) for drivers over 75 years old should take in a less than 3 years and an additional road test is needed as occasion demands. 4) Like the United States and Europe, development and extension of customized treatment guidebook for medical teams who examine senior drivers is needed and establishment of education and administration system that a supervisor of driving license renewal can impose safety restriction and American anonymity reporting system is considered to institutionalize in the medium to longer term.

• Journal of Oral Medicine and Pain
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• 제48권1호
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• pp.3-10
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• 2023

This review aimed to update our knowledge of the classification, pathophysiology, prognosis, and treatment of trigeminal neuralgia (TN), with the intention of establishing better treatment protocols. The latest version of the International Classification of Headache Disorders uses an etiology-based approach to characterize TN patients, potentially contributing to the development of targeted treatment measures. Noticeable changes in the recent European Academy of Neurology guidelines for the management of TN include the use of magnetic resonance imaging for exclusion of secondary TN and differentiation of idiopathic and classical TN. Additionally, the use of botulinum toxin type A as an addon therapy for mid-term treatment of TN has also been included. Though there has been limited recent progress in the treatment of TN, previous studies emphasize the importance of customized, multidisciplinary management protocols that include drug therapy optimization; provision of continuous education and support; and timely referral of medically refractory patients for surgery in order to achieve favorable prognosis. Furthermore, slow but growing evidence on gene mutations will help elucidate the pathophysiology of TN and contribute to the development of targeted drugs that are effective and safe.

• 한국정보시스템학회지:정보시스템연구
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• 제32권3호
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• pp.133-150
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• 2023

Purpose This study aims to develop classification models using a decision tree algorithm to identify core keywords and rules influencing online consumer review evaluations for the robot vacuum cleaner on Amazon.com. The difference from previous studies is that we analyze core keywords that affect the evaluation results by dividing the subjects that evaluate online consumer reviews into self-evaluation (star ratings) and peer evaluation (helpfulness votes). We investigate whether the core keywords influencing star ratings and helpfulness votes vary across different products and whether there is a similarity in the core keywords related to star ratings or helpfulness votes across all products. Design/methodology/approach We used random under-sampling to balance the dataset. We progressively removed independent variables based on decreasing importance through backwards elimination to evaluate the classification model's performance. As a result, we identified classification models that best predict star ratings and helpfulness votes for each product's online consumer reviews. Findings We have identified that the core keywords influencing self-evaluation and peer evaluation vary across different products, and even for the same model or features, the core keywords are not consistent. Therefore, companies' producers and marketing managers need to analyze the core keywords of each product to highlight the advantages and prepare customized strategies that compensate for the shortcomings.

• 센서학회지
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• 제33권3호
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• pp.139-146
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• 2024

Surface-enhanced Raman scattering (SERS) enables the detection of various types of π-conjugated biological and chemical molecules owing to its exceptional sensitivity in obtaining unique spectra, offering nondestructive classification capabilities for target analytes. Herein, we demonstrate an innovative strategy that provides significant machine learning (ML)-enabled predictive SERS platforms through surface-engineered graphene via complementary hybridization with Au nanoparticles (NPs). The hybridized Au NPs/graphene SERS platforms showed exceptional sensitivity (10^-7 M) due to the collaborative strong correlation between the localized electromagnetic effect and the enhanced chemical bonding reactivity. The chemical and physical properties of the demonstrated SERS platform were systematically investigated using microscopy and spectroscopic analysis. Furthermore, an innovative strategy employing ML is proposed to predict various analytes based on a featured Raman spectral database. Using a customized data-preprocessing algorithm, the feature data for ML were extracted from the Raman peak characteristic information, such as intensity, position, and width, from the SERS spectrum data. Additionally, sophisticated evaluations of various types of ML classification models were conducted using k-fold cross-validation (k = 5), showing 99% prediction accuracy.

• 한국산업융합학회 논문집
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• 제25권6_2호
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• pp.979-986
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• 2022

Chemical accidents are increasing day by day as the industry develops. To prevent such chemical accidents, Korea enacted the Chemicals Control Act. Through these laws, systematic management of chemical substances began. There are various positions in the companies. hazardous chemical supervisors, equipment and technical human, operators and employees. Chemicals-related education for each position should be provided. As a result of the survey, hazardous chemical supervisors and equipment and technical human liked the overall content of the Chemicals Control Act and the education subject on safety management standards for facilities. Conversely, the operators liked the course on how to wear personal protective equipment. The employees preferred subjects such as classification of chemical substances and prevention of chemical accidents. Currently, various modular textbooks are widely available. Rather than general education, it is necessary to select and provide customized subjects that are preferred and interested according to the position. Then it will be more effective in understanding harzardous chemical substances and in preventing chemical accidents.

• 디지털융복합연구
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• 제12권5호
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• pp.395-401
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• 2014

소셜 네트워크 서비스가 보편화되면서 사용자들은 소셜 커머스를 통해 상품을 저렴하게 구입할 수 있는 할인 쿠폰서비스를 많이 이용하고 있다. 현재 소셜 커머스에서 제공되는 쿠폰의 양은 크게 증가하고 있으나, 사용자의 선호도를 반영한 맞춤형 쿠폰 서비스는 이루어지지 않고 있다. 본 논문에서는 소셜 커머스를 위한 맞춤형 쿠폰 서비스를 제공하기 위하여 음식 쿠폰을 대상으로 사용자의 주관적 성향을 반영한 쿠폰 서비스 방법을 제안한다. 이를 위하여 음식 종류, 가격, 할인율, 구매자수 등과 같은 쿠폰을 선택하는 기준이 되는 요소를 계층화하고, 주관적 성향을 반영한 의사결정 지원 방법인 Fuzzy-AHP를 이용하여 쿠폰을 분류하고 추출하여 제공하였다. 추출된 쿠폰에 대한 사용자의 만족도를 조사한 결과, 매우 만족은 45%, 만족 33%, 보통 22%로 대체적으로 만족스러웠으며 불만족하는 실험자는 없었다.

• Annals of Occupational and Environmental Medicine
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• 제35권
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• pp.26.1-26.15
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• 2023

Background: The objective of this study is to investigate the differences in incidence rates of targeted diseases by classification of occupations among construction workers in Korea. Methods: In a subject-based cohort of the Korean Construction Worker's Cohort, we surveyed a total of 1,027 construction workers. As occupational exposure, the classification of occupations was developed using two axes: construction business and job type. To analyze disease incidence, we linked survey data with National Health Insurance Service data. Eleven target disease categories with high prevalence or estimated work-relatedness among construction workers were evaluated in our study. The average incidence rates were calculated as cases per 1,000 person-years (PY). Results: Injury, poisoning, and certain other consequences of external causes had the highest incidence rate of 344.08 per 1,000 PY, followed by disease of the musculoskeletal system and connective tissue for 208.64 and diseases of the skin and subcutaneous tissue for 197.87 in our cohort. We especially found that chronic obstructive pulmonary disease was more common in construction painters, civil engineering welders, and civil engineering frame mold carpenters, asthma in construction painters, landscape, and construction water proofers, interstitial lung diseases in construction water proofers. Conclusions: This is the first study to systematically classify complex construction occupations in order to analyze occupational diseases in Korean construction workers. There were differences in disease incidences among construction workers based on the classification of occupations. It is necessary to develop customized occupational safety and health policies for high-risk occupations for each disease in the construction industry.