• Journal of Computing Science and Engineering
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• v.6 no.3
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• pp.193-206
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• 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.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.12
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• pp.1295-1304
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• 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.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.4
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• pp.433-438
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• 2003

Recently, some studies of a web-based information recommendation technique which provides users with the most necessary information through websites like a web-based shopping mall have been conducted vigorously. In most cases of web information recommendation techniques which rely on a user profile and a specific feedback from users, they require accurate and diverse profile information of users. However, in reality, it is quite difficult to acquire this related information. This paper is aimed to suggest an information prediction technique for a web information service without depending on the users'specific feedback and profile. To achieve this goal, this study is to design and implement a Dynamic Web Information Prediction System which can recommend the most useful and necessary information to users from a large volume of web data by designing and embodying Ensemble Support Vector Machine and hybrid SOM algorithm and eliminating the scarcity problem of web log data.

• Journal of Navigation and Port Research
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• v.32 no.5
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• pp.379-385
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• 2008

In this paper, we presents a scheme for the parameter estimation and optimal control scheme for apparatus of container crane system. For parameter estimation, first, we construct the open loop of the container crane system and estimate its parameters based on input-output data, a real-coded genetic algorithm(RCGA) and the model adjustment technique. The RCGA plays an important role in parameter estimation as an adaptive mechanism. For controller design, state feedback gain matrix is searched by another RCGA and the estimated model. The performance of the proposed methods are demonstrated through a set of simulation and experiments of the experimental apparatus.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.16 no.2
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• pp.88-93
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• 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)
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• v.5 no.11
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• pp.1959-1975
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• 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.

• Journal of Korean Physical Therapy Science
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• v.10 no.2
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• pp.145-152
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• 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.

• The Journal of the Korea Contents Association
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• v.13 no.8
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• pp.482-489
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• 2013

The purpose of this study was to identify the relationship of instructional design, perceived learning transfer, and satisfaction. The data were collected using questionnaire from the sample of 239 nursing students. The level of learning transfer was explained by introduction with learning context & providing guidance and initial attention. The level of learning transfer was explained by learning object with motivation, learning goal alignment, accessibility and feedback & adaptation. The level of program satisfaction was explained by introduction with learning context & providing guidance and initial attention. The level of program satisfaction was explained by learning object with motivation, presentation design, interaction availability, feedback & adaptation, learning goal alignment and contents quality. The findings serve as basic data to design e-Learning program to improve learning transfer and satisfaction.

• Proceedings of the Korea Contents Association Conference
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• 2007.11a
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• pp.60-64
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• 2007

Increasing using multimedia services as VoIP, Video conference, DMB, IPTV, etc, it is necessary to increase network traffics and develop the mechanism about a flow control for real time traffic transmission. In order to transfer realtime multimedia data, the transfer rate can be control on network state data measuring packet losses of a receiver and delay time of packets through getting periodical feedback RTP/RTCP packet. This paper describes using efficiant flow control on multicast that can reduce errors according to getting feedback tranfer delay and proposes the mechanism that can adapt dynamic change of network. In simulation, the transfer rate can efficiently be control on dynamic change of network and it makes the maximum of the use of a bandwidth and the minimum of packet losses.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.7A
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• pp.529-536
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• 2003

In this Paper, a new DCT HLMS DFE(Discrete Cosine Transform Hierarchical Least Mean Square Decision Feedback Equalizer) algorithm is proposed to improve the convergence speed and MSE(Mean Square Error) performance of a receive channel equalizer in ATSC(Advanced Television System Committee) 8VSB(Vestigial Side Band) digital terrestrial TV system. The proposed algorithm reduces the eigenvalue range of input data autocorrelation by transforming LMS (Least Mean Square) DFE into the subfilter of hierarchical structure. Moreover, the use of DCT and power estimation algorithm makes it possible to reduce the eigenvalue deviation of input data which results from distortion and delay of the receive signal in the miulti-path environment. Simulation results show that proposed DCT HLMS DFE has SNR improvement of approximately 3.8dB, 5dB and 2dB as compared to LMS DFE when the equalized symbol error rate is 0.2 in ATTC defined digital terrestrial TV broadcasting channels A, B and F, respectively.