• Journal of Information Science Theory and Practice
• /
• 제8권2호
• /
• pp.68-78
• /
• 2020

This study is a preliminary study to develop an evaluation framework necessary for evaluating the DataON platform. The first objective is to examine expert perceptions of the level of DataON platform construction. The second objective is to evaluate the importance, stability, and usability of DataON platform features over OpenAIRE features. The third objective is to derive weights from the evaluation perspective for future DataON platform evaluation. The fourth objective is to examine the preferences of experts in each evaluation perspective and to derive unbiased evaluation criteria. This study used a survey method for potential stakeholders of the DataON platform. The survey included 12 professionals with at least 10 years of experience in the field. The 57 overall functions and services were measured at 3.1 out of 5 for importance. Stability was -0.07 point and usability was measured as -0.05 point. The 42 features and services scored 3.04 points in importance. Stability was -0.58 points and usability was -0.51 points. In particular, the stability and usability scores of the 42 functions and services provided as of 2018 were higher than the total functions were, which is attributed to the stable and user-friendly improvement after development. In terms of the weight of the evaluation point, the collection quality has the highest weight of 27%. Interface usability is then weighted 22%. Subsequently, service quality is weighted 19%, and finally system performance efficiency and user feedback solicitation are equally weighted 16%.

• Journal of Computing Science and Engineering
• /
• 제6권3호
• /
• pp.193-206
• /
• 2012

Keys play a fundamental role in all data models. They allow database systems to uniquely identify data items, and therefore, promote efficient data processing in many applications. Due to this, support is required to discover keys. These include keys that are semantically meaningful for the application domain, or are satisfied by a given database. We study the discovery of keys from SQL tables. We investigate the structural and computational properties of Armstrong tables for sets of SQL keys. Inspections of Armstrong tables enable data engineers to consolidate their understanding of semantically meaningful keys, and to communicate this understanding to other stake-holders. The stake-holders may want to make changes to the tables or provide entirely different tables to communicate their views to the data engineers. For such a purpose, we propose data mining algorithms that discover keys from a given SQL table. We combine the key mining algorithms with Armstrong table computations to generate informative Armstrong tables, that is, key-preserving semantic samples of existing SQL tables. Finally, we define formal measures to assess the distance between sets of SQL keys. The measures can be applied to validate the usefulness of Armstrong tables, and to automate the marking and feedback of non-multiple choice questions in database courses.

• 제어로봇시스템학회논문지
• /
• 제10권12호
• /
• pp.1295-1304
• /
• 2004

In this paper, the Bayesian recurrent neural network is proposed to predict time series data. A neural network predictor requests proper learning strategy to adjust the network weights, and one needs to prepare for non-linear and non-stationary evolution of network weights. The Bayesian neural network in this paper estimates not the single set of weights but the probability distributions of weights. In other words, the weights vector is set as a state vector of state space method, and its probability distributions are estimated in accordance with the particle filtering process. This approach makes it possible to obtain more exact estimation of the weights. In the aspect of network architecture, it is known that the recurrent feedback structure is superior to the feedforward structure for the problem of time series prediction. Therefore, the recurrent neural network with Bayesian inference, what we call Bayesian recurrent neural network (BRNN), is expected to show higher performance than the normal neural network. To verify the proposed method, the time series data are numerically generated and various kinds of neural network predictor are applied on it in order to be compared. As a result, feedback structure and Bayesian learning are better than feedforward structure and backpropagation learning, respectively. Consequently, it is verified that the Bayesian reccurent neural network shows better a prediction result than the common Bayesian neural network.

• 한국지능시스템학회논문지
• /
• 제13권4호
• /
• pp.433-438
• /
• 2003

최근, 인터넷 쇼핑몰과 같은 웹 사이트를 대상으로 각 사용자에게 가장 필요한 정보를 제공하기 위한 웹 정보 추천 시스템에 대한 연구가 활발히 진행되고 있다. 사용자 프로파일과 명시적 피드백에 의존하는 대부분의 웹 정보 추천 시스템의 경우 사용자의 다양하고 정확한 정보를 필요로 하지만 실세계의 문제에 있어 이러한 연관 정보를 얻기란 쉽지 않다. 본 논문에서는 사용자의 명시적 피드백과 프로파일에 의존하지 않는 웹 정보 서비스를 위한 정보 예측 기법을 제안한다. 이를 위해 앙상블 Support Vector Machine과 하이브리드 SOM을 설계하고 적용하여 웹 로그 데이터의 희소성 문제를 해결하면서 대용량 웹 데이터로부터 사용자에게 꼭 필요하고 유용한 정보를 추천할 수 있는 동적 웹 정보 예측 시스템을 설계하고 구현한다.

• 한국항해항만학회지
• /
• 제32권5호
• /
• pp.379-385
• /
• 2008

컨테이너 크레인의 수학적 모델 정확도는 모델 내부 파라미터 값의 정확도에 의해 결정되나, 기술적 혹은 환경적인 문제로 내부 파라미터의 정확한 값을 알지 못하는 경우가 발생하기도 한다. 이 경우에는 시스템의 입 출력 데이터에 근거하여 모델의 파라미터를 추정해야 하는데, 본 논문에서는 입 출력 데이터와 RCGA가 결합된 모델조정기법을 이용하여 모형 컨테이너 크레인 선형모델의 파라미터를 추정하는 방법을 보인다. 또한, 이렇게 추정한 모델에 또 다른 RCGA를 적용하여 제어에 필요한 이득행렬을 탐색한다. 제안하는 파라미터 추정법과 제어기법은 컨테이너 크레인의 모형실험장치에 적용하고, 실험을 실시하여 그 유효성을 검증한다.

• 한국기계가공학회지
• /
• 제16권2호
• /
• pp.88-93
• /
• 2017

Due to the requirement of the development of the precision manufacturing industry, the accuracy of machine tools has become a key issue in this field. A critical factor that affects the accuracy of machine tools is the feed system, which is generally driven by a ball screw. Basically, to improve the performance of the feed drive system, which will be thermally extended lengthwise by continuous usage, a thermal error compensation system that is highly dependent on the feedback temperature or positioning data is employed in the machine tool system. Due to the overdependence on measuring technology, the cost of the compensation system and low productivity level are inevitable problems in the machine tool industry. This paper presents a novel feed drive thermal error compensation system method that could compensate for thermal error without positioning or temperature feedback. Regarding this thermal error compensation system, the heat generation of components, principal of compensation, thermal model, mathematic model, and calculation method are discussed. As a result, the test data confirm the correctness of the developed feed drive thermal error compensation system very well.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제5권11호
• /
• pp.1959-1975
• /
• 2011

Adaptive Bit Loading (ABL) in Multiple-Input Multiple-Output Orthogonal Frequency-Division Multiplexing (MIMO-OFDM) is often used to achieve the desired Bit Error Rate (BER) performance in wireless systems. In this paper, we discuss some of the bit loading algorithms, compare them in terms of the BER performance, and present an effective and concise Adaptive Grouped Loading (AGL) algorithm. Furthermore, we propose a "weight factor" for loading algorithm to converge rapidly to the final solution for various data rate with variable Signal to Noise Ratio (SNR) gaps. In particular, we consider the bit loading in near optimal Singular Value Decomposition (SVD) based MIMO-OFDM system. While using SVD based system, the system requires perfect Channel State Information (CSI) of channel transfer function at the transmitter. This scenario of SVD based system is taken as an ideal case for the comparison of loading algorithms and to show the actual enhancement achievable by our AGL algorithm. Irrespective of the CSI requirement imposed by the mode of the system itself, ABL demands high level of feedback. Grouped Loading (GL) would reduce the feedback requirement depending upon the group size. However, this also leads to considerable degradation in BER performance. In our AGL algorithm, groups are formed with a number of consecutive sub-channels belonging to the same transmit antenna, with individual gains satisfying predefined criteria. Simulation results show that the proposed "weight factor" leads a loading algorithm to rapid convergence for various data rates with variable SNR gap values and AGL requires much lesser CSI compared to GL for the same BER performance.

• 대한물리치료과학회지
• /
• 제10권2호
• /
• pp.145-152
• /
• 2003

Recruiting and maintaining capable physical therapists in hospitals is crucial to provide patients with better quality's physical therapy and to achieve hospital business objectives. This study is to provide basic data for effective personnel management of physical therapists in the hospital. For this, this study was conducted to confirm the relationship between turnover intention and job characteristics(task importance, job autonomy, job feedback, task identity, skill diversity), and to find out factors affecting the turnover intention of hospital physical therapists in Korea. The sample used in this study consisted of 173 physical therapists who were working in hospitals located in Seoul from June 18 to 29. The data for analysis were collected by questionnaire survey. The dependent variable of this study was turnover intention and the independent variables were job characteristics(task importance, job autonomy, job feedback, task identity, skill diversity), demographic characteristics of respondents(sex, age, education level, marital status, working hospital, working career). The major statistical methods used for the analysis were factor analysis, t-test, ANOVA, correlation, and hierarchical multiple regression analysis. Turnover intention according to demographic characteristics of respondents was significantly high in the graduate school above group. As the result of hierarchical multiple regression analysis of turnover intention, R2 of job characteristics was much more powerful than demographic characteristics. The factors had significant negative effect on turnover intention were job autonomy, task identity, and skill diversity. And in demographic characteristics factors, there were not significant factors on turnover intention. In considering above findings, for decreasing turnover intention of physical therapists, hospitals need to develop strategies for enhancing job satisfaction by improving job autonomy, task identity, and skill diversity from the adequate job environment.

• 한국콘텐츠학회논문지
• /
• 제13권8호
• /
• pp.482-489
• /
• 2013

본 연구는 일개 대학에서 e-Learning 수업을 듣고 있는 간호학과 학생 239명을 대상으로 학습전이 인식수준과 만족도에 영향을 미치는 e-Learning 교수설계 변인을 밝히고자 시행된 서술적 조사연구이다. 그 결과, 대상자의 학습전이 인식수준에 영향을 미치는 도입의 하부영역은 학습상황 및 방향제시, 학습자 초기 동기화로 나타났으며, 이는 41%의 설명력이 있었다(F=81.16, p<.001). 대상자의 학습전이 인식수준에 영향을 미치는 학습객체의 하부영역은 동기화, 학습목적 일치, 접근성, 피드백 및 적합으로 나타났으며, 이는 46%의 설명력이 있었다(F=50.69, p<.001). 대상자의 만족도에 영향을 미치는 도입의 하부영역은 학습상황 및 방향제시, 학습자 초기 동기화로 나타났으며, 이는 33%의 설명력이 있었다(F=59.32, p<.001). 대상자의 만족도에 영향을 미치는 학습객체의 하부영역은 동기화, 표현설계, 상호작용 유용성, 피드백 및 적합, 학습목적 일치, 콘텐츠 품질로 나타났으며, 이는 52%의 설명력이 있었다(F=43.93, p<.001). 이를 통해 대학 e-Learning 프로그램의 교수설계 요인이 학습자의 학습전이와 만족도에 영향을 미치고 있다는 것을 알 수 있었다. 이는 e-Learning 프로그램 교수설계 전략을 개발하기 위한 기초자료로서 활용될 수 있을 것이다.

• 한국콘텐츠학회:학술대회논문집
• /
• 한국콘텐츠학회 2007년도 추계 종합학술대회 논문집
• /
• pp.60-64
• /
• 2007

VoIP, 영상회의, DMB, IPTV 등과 같은 멀티미디어 서비스의 활용이 급증하여 네트워크 트래픽이 증가되므로 흐름제어에 관한 실시간 트래픽 메커니즘이 요구되고 있다. 이에 따라 실시간 멀티미디어 데이터를 전송하기 위해 주기적으로 피드백되는 RTP/RTCP 패킷을 통해 수신측의 패킷 손실률과 패킷 지연시간으로 측정되는 네트워크 상태정보를 이용하여 전송률을 조절할 수 있다. 본 연구에서는 멀티캐스트에 효율적인 흐름제어와 RTCP를 기반으로 피드백 전송지연의 오차율을 줄이고, 네트워크의 실시간 트래픽에 대한 동적 변화에 적응할 수 있는 기법을 제안한다. 본 연구의 시뮬레이션 결과, 네트워크 상태에 적응적으로 전송률을 조절하며 대역폭의 최대 활용과 패킷 손실의 최소화를 이룰 수 있다.