• 정보처리학회논문지B
• /
• 제11B권4호
• /
• pp.465-470
• /
• 2004

기존의 웹 정보 예측 시스템은 예측에 필요한 정보를 얻기 위하여 사용자 프로파일과 사용자로부터의 명시적 피드백 정보를 필요로 하는 단점이 존재한다. 본 논문에서는 이러한 단점을 극복하고자 웹 사이트에 접속한 고객의 행동을 나타내는 클릭 스트림 데이터와 이를 기반으로 한 사용자의 암시적 피드백 정보를 이용하여 각 사용자가 가장 필요로 하는 웹 정보를 예측한다. 이를 이용하여 관련 정보를 제공할 수 있는 앙상블 SVM을 이용한 동적 웹 정보 예측 시스템을 설계하고 구현하며, 기존의 웹 정보 예측 시스템과 성능 비교를 수행한 결과, 제안된 방법의 우수함이 입증되었다.

• 한국지능시스템학회논문지
• /
• 제16권4호
• /
• pp.416-422
• /
• 2006

소프트웨어 개발 인력 프로파일에 대한 현존하는 모든 통계적 모델들은 소프트웨어 사용과 개발 프로세스의 가정에 기반을 두고 있어 일반적으로 적용 가능한 추정과 예측 모델이 없는 실정이다. 본 논문은 예측필터를 적용하여 소프트웨어 개발 투입 인력 프로파일을 예측하였다. 먼저 소프트웨어 개발 인력분포를 살펴보고, 예측필터를 적용하기 위해 모델의 입력 -출력, 모수를 결정하는 방법을 제시하였다. 이어서 제안된 모델의 유용성은 실제 개발된 소프트웨어 프로젝트로부터 획득된 데이터 분석으로 경험적으로 검증되었다. 평균 상대오차와 Pred(0.25)에 기반하여 제안된 예측필터는 잘 알려진 통계적 추정 모델들과 비교되었다. 검증 결과 예측필터는 단순한 구조를 갖고 있으면서도 소프트웨어 인력분포를 적절히 표현하는 결과를 보였다.

• 보건행정학회지
• /
• 제9권2호
• /
• pp.1-20
• /
• 1999

This study was conducted to investigate the predictors of hospital bankruptcy in Korea and to examine the predictive power for 3 types of statistical models of hospital bankruptcy. Data on 17 financial and 4 non-financial indicators of 30 bankrupt and 30 profitable hospitals in 1. 2, and 3 years before bankruptcy were obtained from the hospital performance databank of Korea Institute of Health Services Management. Significant variables were identified through mean comparison of each indicator between bankrupt and profitable hospitals, and the predictive power of statistical models of hospital bankruptcy were compared. The major findings are as follows. 1. Nine out of 21 indicators - fixed ratio, quick ratio, operating profit to total assets, operating profit to gross revenue, normal profit to total assets,normal profit to gross revenue, net profit to gross revenue, inventories turnrounds, and added value per adjusted patient - were found to be significantly predictitive variables in Logit and Probit models. 2. The predicdtive power of discriminant model of hospital bankruptcy in 1. 2, and 3 years before bankruptcy were 85.4, 79.0, and 83.8% respectively. With regard to the predictive power of the Logit model of hospital bankruptcy, they were 82.3, 75.8, and 80.6% respectively, and of the Probit model. 87.1. 80.6, and 88.7% respectively. 3. The predictive power of the Probit model of hospital bankruptcy is better than the other two predictive models.

• Human Ecology Research
• /
• 제52권3호
• /
• pp.275-284
• /
• 2014

This study was designed to examine the relationships between empowerment and child care teachers' intention of teaching, the reason for teaching intent. The subjects were 181 child care teachers from Chungcheongnamdo and the city of Daejeon, Korea. This study was conducted using questionnaires. The results were as follows: first, there were significant relationships between empowerment and child care teachers' intention of teaching and, the reason for teaching intent. There were positive relationships between decision making, professional growth, status, self-efficacy, autonomy, impact empowerment and child care teacher' intention of teaching and, the reason for teaching intent. Second, child care teachers' intention of teaching and the reason for teaching intent were affected by empowerment. Status and professional growth empowerment were the most predictive variables for the child care teachers' intention of teaching. The impact and self-efficacy empowerment were the most predictive variables for enjoy working with children, impact and professional growth empowerment were the most predictive variables for finding meaning in teaching, impact and status empowerment were the most predictive variables for opportunities to face ongoing challenges, and achievement motive. Status empowerment were the most predictive variable for reasonable pay and working environment, stability and skill. Therefore, status and impact empowerment were the most predictive variable for the reason for teaching intent.

• 제어로봇시스템학회논문지
• /
• 제21권7호
• /
• pp.662-668
• /
• 2015

This paper presents an improved predictive control of an induction machine fed by a matrix converter using N-switching vectors as the control action during a complete sampling period of the controller. The conventional model predictive control scheme based matrix converter uses a single switching vector over the same period which introduces high torque ripple. The proposed switching scheme for a matrix converter based model predictive control of an induction machine drive selects the appropriate switching vectors for control of electromagnetic torque with small variations of the stator flux. The proposed method can reduce the ripple of the electrical variables by selecting the switching state as well as the method used in the space vector modulation techniques. Simulation results are presented to verify the effectiveness of the improved predictive control strategy for induction machine fed by a matrix converter.

• 전기학회논문지
• /
• 제65권2호
• /
• pp.311-318
• /
• 2016

This paper is a study on the neutral point voltage balancing of the three-phase 3-level T-type inverter using the predictive control techniques. Recently, multi-level inverter has been attracting attention as the advantages such as efficiency improving and harmonic reduction. Especially, the T-type inverter topology is advantageous in low DC-link voltage. However, in case of the prediction control, it takes a lot of time, because there exist 27 voltage vectors and it has to be calculated according to the respective voltage vectors. Therefore, in this paper, we propose a method to implement predictive control techniques while reducing the operation time. In order to reduce the operation time, the predictive control is implemented by using the minimum voltage vector except for the unnecessary voltage vector. The result of the implemented predictive control is added to the SPWM by using the offset voltage. It was verified through simulation and experimental results.

• International journal of advanced smart convergence
• /
• 제12권3호
• /
• pp.221-232
• /
• 2023

Recently, due to the expansion of the logistics industry, demand for logistics automation equipment is increasing. The modern logistics industry is a high-tech industry that combines various technologies. In general, as various technologies are grafted, the complexity of the system increases, and the occurrence rate of defects and failures also increases. As such, it is time for a predictive maintenance model specialized for logistics automation equipment. In this paper, in order to secure the operational reliability of the ICT/Cold-Chain Unmanned Storage, a predictive maintenance system was implemented based on the LSTM model. In this paper, a server for data management, such as collection and monitoring, and an analysis server that notifies the monitoring server through data-based failure and defect analysis are separately distinguished. The predictive maintenance platform presented in this paper works by collecting data and receiving data based on RabbitMQ, loading data in an InMemory method using Redis, and managing snapshot data DB in real time. The predictive maintenance platform can contribute to securing reliability by identifying potential failures and defects that may occur in the operation of the ICT/Cold-Chain Unmanned Storage in the future.

• Journal of the Korean Society for Industrial and Applied Mathematics
• /
• 제26권1호
• /
• pp.23-48
• /
• 2022

In this paper, we provide predictive models for the market price of fruits, and analyze the performance of each fruit price predictive model. The data used to create the predictive models are fruit price data, weather data, and Korea composite stock price index (KOSPI) data. We collect these data through Open-API for 10 years period from year 2011 to year 2020. Six types of fruit price predictive models are constructed using the LSTM algorithm, a special form of deep learning RNN algorithm, and the performance is measured using the root mean square error. For each model, the data from year 2011 to year 2018 are trained to predict the fruit price in year 2019, and the data from year 2011 to year 2019 are trained to predict the fruit price in year 2020. By comparing the fruit price predictive models of year 2019 and those models of year 2020, the model with excellent efficiency is identified and the best model to provide the service is selected. The model we made will be available in other countries and regions as well.

• International Journal of Computer Science & Network Security
• /
• 제24권1호
• /
• pp.215-225
• /
• 2024

Predictive maintenance has been considered fundamental in the industrial applications in the last few years. It contributes to improve reliability, availability, and maintainability of the systems and to avoid breakdowns. These breakdowns could potentially lead to system shutdowns and to decrease the production efficiency of the manufacturing plants. The present article aims to study how predictive maintenance could be planed into the production scheduling, through a systematic review of literature. . The review includes the research articles published in international journals indexed in the Scopus database. 165 research articles were included in the search using #predictive maintenance# AND #production scheduling#. Press articles, conference and non-English papers are not considered in this study. After careful evaluation of each study for its purpose and scope, 50 research articles are selected for this review by following the 2020 Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA) statement. A benchmarking of predictive maintenance methods was used to understand the parameters that contributed to improve the production scheduling. The results of the comparative analysis highlight that artificial intelligence is a promising tool to anticipate breakdowns. An additional impression of this study is that each equipment has its own parameters that have to be collected, monitored and analyzed.

• 대한상부위장관⦁헬리코박터학회지
• /
• 제18권3호
• /
• pp.186-197
• /
• 2018

Background/Aims: To investigate the predictive factors for improvement of atrophic gastritis (AG) and intestinal metaplasia (IM). Materials and Methods: A total of 778 subjects were prospectively enrolled and followed up for 10 years. Histological analysis of AG and IM was performed by using the updated Sydney system. To find the predictive factors for reversibility of AG and IM, 24 factors including genetic polymorphisms and bacterial and environmental factors were analyzed. Results: In all subjects, the predictive factor by multivariate analysis for improvement of both antral and corpus AG was successful eradication. The predictive factors for improvement of antral IM were age and successful eradication. The predictive factor for improvement of corpus IM was successful eradication. In patients with Helicobacter pylori infection, age and cagA were predictive factors for improvement of AG and IM. In patients with H. pylori eradication, monthly income and cagA were predictive factors for improvement of AG and IM. Conclusions: H. pylori eradication is an important predictive factor of regression of AG and IM and would be beneficial for the prevention of intestinal-type gastric cancer. Young age, high income, and cagA are additional predictive factors for improving AG and IM status. Thus, various factors affect the improvement of AG and IM.