• 보건행정학회지
• /
• 제32권2호
• /
• pp.125-136
• /
• 2022

The costliness index (CI) is an index that is used in various ways to improve the quality of medical care and the management of appropriate treatment in medical institutions. However, the current calculation method for CI has a limitation in reflecting the actual medical cost of the patient unit because the outpatient and inpatient costs are evaluated separately. It is desirable to calculate the CI by integrating the medical cost into the episode unit. We developed an episode-based CI method using the episode classification system of the Centers for Medicare and Medicaid Services to the National Inpatient Sample data in Korea, which can integrate the admission and ambulatory care cost to episode unit. Additionally, we compared our new method with the previous method. In some episodes, the correlation between previous and episode-based CI was low, and the proportion of outpatient treatment costs in total cost and readmission rates are high. As a result of regression analysis, it is possible that the level of total medical costs of the patient unit in low volume medical institute and rural area has been underestimated. High proportion of outpatient treatment cost in total medical cost means that some medical institutions may have provided medical services in the ambulatory care that are ancillary to inpatient treatment. In addition, a high readmission rate indicates insufficient treatment service for inpatients, which means that previous CI may not accurately reflect actual patient-based treatment costs. Therefore, an integrated patient-unit classification system which can be used as a more effective CI indicator is needed.

• 한국IT서비스학회지
• /
• 제19권1호
• /
• pp.103-112
• /
• 2020

Job seekers are making various efforts to find a good company and companies attempt to recruit good people. Job search activities through self-introduction essay are nowadays one of the most active processes. Companies spend time and cost to reviewing all of the numerous self-introduction essays of job seekers. Job seekers are also worried about the possibility of acceptance of their self-introduction essays by companies. This research builds a classification model and conducted an experiments to classify self-introduction essays into pass or fail using deep learning and decision tree techniques. Real world data were classified using stratified sampling to alleviate the data imbalance problem between passed self-introduction essays and failed essays. Documents were embedded using Doc2Vec method developed from existing Word2Vec, and they were classified using logistic regression analysis. The decision tree model was chosen as a benchmark model, and K-fold cross-validation was conducted for the performance evaluation. As a result of several experiments, the area under curve (AUC) value of PV-DM results better than that of other models of Doc2Vec, i.e., PV-DBOW and Concatenate. Furthmore PV-DM classifies passed essays as well as failed essays, while PV_DBOW can not classify passed essays even though it classifies well failed essays. In addition, the classification performance of the logistic regression model embedded using the PV-DM model is better than the decision tree-based classification model. The implication of the experimental results is that company can reduce the cost of recruiting good d job seekers. In addition, our suggested model can help job candidates for pre-evaluating their self-introduction essays.

• 한국멀티미디어학회논문지
• /
• 제13권8호
• /
• pp.1115-1127
• /
• 2010

TFT-LCD 생산 과정에서 발생하는 결함을 정확하게 분류하여 결함 유형에 따라 폐기, 사용가능 등의 의사결정을 적절하게 내리는 것은 수율 증가 및 생산성 향상에 필수적인 요소이다. 본 논문에서는 TFT-LCD 생산 라인에서 획득한 결함 영상에 대하여 명암도 분포(intensity distribution) 및 결함 영상의 형태 특징(shape feature)을 분석하여 효과적으로 필름 결함 유형을 분류하는 기법을 제시한다. 본 연구에서는 먼저 필름 결함 영상을 결함 영역과 결함이 아닌 배경 영역으로 이진화하고, 결함 영역에서 결함의 선형성(linearity), 명암도 분포를 고려한 형태 특징 등의 여러 가지 특징을 분석하여 기준 영상(referential image) 데이터베이스를 구축하였으며, 분류하고자 하는 결함 영상과 데이터베이스에 저장된 기준 영상과의 매칭 비용 함수(matching cost function)를 정의하여 적절히 매칭시킴으로써 결함의 유형을 결정하였다. 제시한 기법의 성능을 검증하기 위하여 실제 TFT-LCD 생산 라인에서 획득한 결함 영상들을 대상으로 분류 실험을 수행하였으며, 실험 결과 생산 라인에서 이용할 수 있을 정도의 상당한 수준의 분류 정확도를 달성하였음을 보여주었다.

• Smart Structures and Systems
• /
• 제24권6호
• /
• pp.723-732
• /
• 2019

Globally road transport networks are subjected to continuous levels of stress from increasing loading and environmental effects. As the most popular mean of transport in the UK the condition of this civil infrastructure is a key indicator of economic growth and productivity. Structural Health Monitoring (SHM) systems can provide a valuable insight to the true condition of our aging infrastructure. In particular, monitoring of the displacement of a bridge structure under live loading can provide an accurate descriptor of bridge condition. In the past B-WIM systems have been used to collect traffic data and hence provide an indicator of bridge condition, however the use of such systems can be restricted by bridge type, assess issues and cost limitations. This research provides a non-contact low cost AI based solution for vehicle classification and associated bridge displacement using computer vision methods. Convolutional neural networks (CNNs) have been adapted to develop the QUBYOLO vehicle classification method from recorded traffic images. This vehicle classification was then accurately related to the corresponding bridge response obtained under live loading using non-contact methods. The successful identification of multiple vehicle types during field testing has shown that QUBYOLO is suitable for the fine-grained vehicle classification required to identify applied load to a bridge structure. The process of displacement analysis and vehicle classification for the purposes of load identification which was used in this research adds to the body of knowledge on the monitoring of existing bridge structures, particularly long span bridges, and establishes the significant potential of computer vision and Deep Learning to provide dependable results on the real response of our infrastructure to existing and potential increased loading.

• 한국콘텐츠학회논문지
• /
• 제8권1호
• /
• pp.290-299
• /
• 2008

본 논문에서는 하이브리드 공간 DBMS에서 질의 분류를 이용한 최적화 기법을 제안한다. 제안 기법은 질의에 이용되는 데이터의 위치에 따라 메모리 질의, 디스크 질의, 하이브리드 질의로 분류하여 처리한다. 특히, 하이브리드 질의의 경우에는 실체화 뷰의 사용률을 높이기 위해 실체화 뷰 생성 조건과 사용자 질의 조건을 비교하여 술어를 분할하는 메커니즘을 적용한다. 또한 질의를 최적화하기 위해 분류된 질의의 비용 계산 결과를 이용하여 최소 비용의 데이터 접근 경로를 선택할 수 있는 데이터 접근 경로 선택 알고리즘을 제안한다. 제안 기법은 대용량 데이터 관리와 빠른 응답 속도를 동시에 만족하는 하이브리드 공간 DBMS의 성능을 기존의 디스크 기반 공간 DBMS보다 최소 20%에서 최대 50%의 성능 향상을 보인다.

• 한국지반공학회지:지반
• /
• 제9권3호
• /
• pp.61-66
• /
• 1993

본 논문에서는 암반 분류를 위해 물리탐사 결과나 그동안 축적된 시공경험 등의 정성적 자료의 사용을 고려하였다. 터널 설계를 위한 요소(parameter)들이 공간적 상관관계를 갖기 때문에 지구 통계학(Geostatistics)을 이용하였으며, 특히, 비모수적 (non-parametric)방법 중의 하나인 지시 크리깅(indicator kriging) 기법을 사용했다. 최적 분류를 위한 선택 기준으로는 오차에 대응하는 비용(the cost of errors)을 사용했으며, 암반분류는 이분적 분류에 한정하였다. 앞으로, 정량적 데이타가 절대적으로 부족한 터널공사등에서 비교적 많은 양이 존재하는 정성적 데이타의 이용은 절실하며, 이러한 점에서 본 연구가 가지는 의미는 크다.

• 한국소음진동공학회:학술대회논문집
• /
• 한국소음진동공학회 2012년도 추계학술대회 논문집
• /
• pp.394-399
• /
• 2012

최근 MEMS 센서는 기계상태감시에 있어서 전력소모, 크기, 비용, 이동성, 응용 등에 있어서 각광을 받고 있다. 특히, MEMS 센서는 스마트센서와 통합가능하고, 대량생산이 가능하여 가격이 저렴하다는 장점이 있다. 이와 관련한 기계상태감시를 위한 많은 실험적 연구가 수행되고 있다. 이 논문은 MEMS 센서들을 3 가지 인공지능 분류기 성능평가를 위한 비교연구에 대해 설명하고 있다. 회전기계에 MEMS 가속도와 전류센서들을 부착하여 데이터를 취득했고, 특징추출과 파라미터 최적화를 위해 Cross validation 기법을 사용하였다. MEMS 센서를 이용한 결함분류기 적용은 적합하다고 판단된다.

• 한국군사과학기술학회지
• /
• 제25권6호
• /
• pp.572-579
• /
• 2022

The analysis of foreign aircraft appearing suddenly in air defense identification zones requires a lot of cost and time. This study aims to develop a pre-trained model that can identify neighboring military aircraft based on aircraft photographs available on the web and present a model that can determine which aircraft corresponds to based on aerial photographs taken by allies. The advantages of this model are to reduce the cost and time required for model classification by proposing a pre-trained model and to improve the performance of the classifier by data augmentation of edge-detected images, cropping, flipping and so on.

• 동아시아경상학회지
• /
• 제10권3호
• /
• pp.51-61
• /
• 2022

Purpose - The purpose of this study is to establish the government's logistics policy by calculating the logistics cost of the company and grasping the management status, to reduce the logistics cost of the related companies and to provide basic statistical data necessary for the management strategy. This work examines some associations between reasons for reducing corporate logistics costs (RCLC) and corporate classification such as industry and sales size. Research design, data, and methodology - The survey was conducted in 2018 for 2,000 companies based on the business of mining, manufacturing and wholesale and retail industries since 2010. The survey population is 94,976, of which 92,708 are small and medium enterprises and 2,268 are large corporations. The association among factors may be statistically and visually explored by using chi-squared test and correspondence analysis. Result - This study reveals the association between reasons for RCLC and corporate classification and properties and closeness that exist between the categories of each factor can be mined. Conclusion - As a task to reduce logistics costs of industrial products, expansion and operation of joint logistics business, establishment of cooperative logistics network, and establishment of ordinance on support for smart distribution logistics can be proposed.

• 한국건축시공학회:학술대회논문집
• /
• 한국건축시공학회 2016년도 춘계 학술논문 발표대회
• /
• pp.235-236
• /
• 2016

The importance of construction management has been increasing due to the fact that complex construction projects blend several different industries depending on the traits of the construction. This research was conducted to search for a method to enhance efficiency in cost management of construction project and meet the need for reusability of accumulated construction information. The process of detailed estimation and methodology for using standard unit price information has been developed to strengthen the interoperability in cost information by utilizing a standard classification system. The concept of ontology is proposed as a method of connecting construction information based on a standard breakdown structure to increasing the connectivity of the cost information in the construction project. Therefore, construction information knowledge framework is developed in order to improve the efficiency of the detailed estimation work process.