• Journal of the Korean Operations Research and Management Science Society
• /
• v.27 no.3
• /
• pp.1-19
• /
• 2002

Recently, increasing attention has been paid to building a successful software process in Information System(IS) implementation. This study establishes software process model as a key predictor of organizational performance. We propose a theoretical framework for capability maturity model derived from the Software Engineering Institute(SEI). This paper identify the process-related variables, financial performance and non-financial performance from the relevant literature and clarify the concept of software process by distinguishing between its component and determinants. We then examine the impact of software process on organizational performance. Hypotheses on software process were tested for 36 enterprises including 118 organizational units. Results indicate that software process capability may serve as a key predictor of organizational performance. Software process maturity found to be positively influenced on the financial and non-financial performance, while investment of information technology as a mediating variable not significantly affected to the performance.

• Asia pacific journal of information systems
• /
• v.21 no.2
• /
• pp.43-58
• /
• 2011

Support vector machines (SVMs), a machine learning technique, has been applied to not only binary classification problems such as bankruptcy prediction but also multi-class problems such as corporate credit ratings. However, in general, the performance of SVMs can be easily worse than the best alternative model to SVMs according to the selection of predictors, even though SVMs has the distinguishing feature of successfully classifying and predicting in a lot of dichotomous or multi-class problems. For overcoming the weakness of SVMs, this study has proposed an approach for selecting features for multi-class SVMs that utilize the impurity measures of classification trees. For the selection of the input features, we employed the C4.5 and CART algorithms, including the stepwise method of discriminant analysis, which is a well-known method for selecting features. We have built a multi-class SVMs model for credit rating using the above method and presented experimental results with data regarding S&P 500 companies.

• Nuclear Engineering and Technology
• /
• v.56 no.9
• /
• pp.3594-3601
• /
• 2024

It is difficult to detect a small-scale leakage in a nuclear power plant (NPP) quickly and take appropriate action. Delaying these procedures can have adverse effects on NPPs. In this paper, we propose leak flow rate prediction using the bidirectional gated recurrent unit (Bi-GRU) method to detect leakage quickly and accurately in small-scale leakage situations because large-scale leak rates are known to be predicted accurately. The data were acquired by simulating small loss-of-coolant accidents (LOCA) or small-scale leakage situations using the modular accident analysis program (MAAP) code. In addition, to improve prediction performance, data were collected by distinguishing the break sizes in more detail. In addition, the prediction accuracy was improved by performing both LOCA diagnosis and leak flow rate prediction in small LOCA situations. The prediction model developed using the Bi-GRU showed a superior prediction performance compared with other artificial intelligence methods. Accordingly, the accurate and effective prediction model for small-scale leakage situations proposed herein is expected to support operators in decision-making and taking actions.

• Journal of Korean Society of Transportation
• /
• v.9 no.1
• /
• pp.29-46
• /
• 1991

The purpose of this study is to develop a parking behavior model in prior step for solv-ing parking problems in Pusan C. B. D. The results of this study are as follows; In the C. B. D of Pusan the peak parking time is between 2 and 3 o'clock P. M., and the average parking duration is 237 minutes. It means the use of parking lots is very ineffi-cient. Hence in order to shorten the parking duration, it is very urgent for drivers to chan-ge parking attitude. The walking distance from the parking lots to his destination is below 300∼500m, so the establishment of parking areas and the arrangement of parking lots in C. B. D should be planned on the base of the above walking distance. The model distinguishing between legal and illegal parking behaviors is derived from the binary decision model. The selected model has the correlation rate, η2=0.505 which is relatively high value This result shows that the detetminating judgement on the legal and illegal parking behavior is influenced mutually such factors as driver's occupation parking purpose monthly income distance to his destination averaged parking duration and age.

• Journal of Internet Computing and Services
• /
• v.22 no.3
• /
• pp.9-16
• /
• 2021

This study aims to develop a human activity recognition (HAR) system as a Deep-Learning (DL) classification model, distinguishing various human activities. We solely rely on the signals from a wristband accelerometer worn by a person for the user's convenience. 3-axis sequential acceleration signal data are gathered within a predefined time-window-slice, and they are used as input to the classification system. We are particularly interested in developing a Deep-Learning model that can outperform conventional machine learning classification performance. A total of 13 activities based on the laboratory experiments' data are used for the initial performance comparison. We have improved classification performance using the Convolutional Neural Network (CNN) combined with an auto-encoder feature reduction and parameter tuning. With various publically available HAR datasets, we could also achieve significant improvement in HAR classification. Our CNN model is also compared against Recurrent-Neural-Network(RNN) with Long Short-Term Memory(LSTM) to demonstrate its superiority. Noticeably, our model could distinguish both general activities and near-identical activities such as sitting down on the chair and floor, with almost perfect classification accuracy.

• Journal of rehabilitation welfare engineering & assistive technology
• /
• v.11 no.3
• /
• pp.261-270
• /
• 2017

Bad sitting postures are known to cause for a variety of diseases or physical deformation. However, it is not easy to fit right sitting posture for long periods of time. Therefore, methods of distinguishing and inducing good sitting posture have been constantly proposed. Proposed methods were image processing, using pressure sensor attached to the chair, and using the IMU (Internal Measurement Unit). The method of using IMU has advantages of simple hardware configuration and free of various constraints in measurement. In this paper, we researched on distinguishing sitting postures with a small amount of data using just one IMU. Feature extraction method was used to find data which contribution is the least for classification. Machine learning algorithms were used to find the best position to classify and we found best machine learning algorithm. Used feature extraction method was PCA(Principal Component Analysis). Used Machine learning models were five : SVM(Support Vector Machine), KNN(K Nearest Neighbor), K-means (K-means Algorithm) GMM (Gaussian Mixture Model), and HMM (Hidden Marcov Model). As a result of research, back neck is suitable position for classification because classification rate of it was highest in every model. It was confirmed that Yaw data which is one of the IMU data has the smallest contribution to classification rate using PCA and there was no changes in classification rate after removal it. SVM, KNN are suitable for classification because their classification rate are higher than the others.

• Journal of the Korea Society of Computer and Information
• /
• v.21 no.3
• /
• pp.65-71
• /
• 2016

To reduce the expenses for development a novel drug, systems biology has been studied actively. Target prediction, a part of systems biology, contributes to finding a new purpose for FDA(Food and Drug Administration) approved drugs and development novel drugs. In this paper, we propose a classification model for predicting novel target genes based on relation between target genes and disease related genes. After collecting known target genes from TTD(Therapeutic Target Database) and disease related genes from OMIM(Online Mendelian Inheritance in Man), we analyzed the effect of target genes on disease related genes based on PPI(Protein-Protein Interactions) network. We focused on the distinguishing characteristics between known target genes and random target genes, and used the characteristics as features for building a classifier. Because our model is constructed using information about only a disease and its known targets, the model can be applied to unusual diseases without similar drugs and diseases, while existing models for finding new drug-disease associations are based on drug-drug similarity and disease-disease similarity. We validated accuracy of the model using LOOCV of ten times and the AUCs were 0.74 on Alzheimer's disease and 0.71 on Breast cancer.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.29 no.2B
• /
• pp.163-171
• /
• 2009

Classical linear models have been generally used to analyze and forecast hydrologic time series. However, there is growing evidence of nonlinear structure in natural phenomena and hydrologic time series associated with their patterns and fluctuations. Therefore, the classical linear techniques for time series analysis and forecasting may not be appropriate for nonlinear processes. In recent, the BDS (Brock-Dechert-Scheinkman) statistic instead of conventional techniques has been used for detecting nonlinearity of time series. The BDS statistic was derived from the statistical properties of the correlation integral which is used to analyze chaotic system and has been effectively used for distinguishing nonlinear structure in dynamic system from random structures. DVS (Deterministic Versus Stochastic) algorithm has been used for detecting chaos and stochastic systems and for forecasting of chaotic system. This study showed the DVS algorithm can be also used for detecting nonlinearity of the time series. In this study, the stochastic and hydrologic time series are analyzed to detect their nonlinearity. The linear and nonlinear stochastic time series generated from ARMA and TAR (Threshold Auto Regressive) models, a daily streamflow at St. Johns river near Cocoa, Florida, USA and Great Salt Lake Volume (GSL) data, Utah, USA are analyzed, daily inflow series of Soyang dam and the results are compared. The results showed the BDS statistic is a powerful tool for distinguishing between linearity and nonlinearity of the time series and DVS plot can be also effectively used for distinguishing the nonlinearity of the time series.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.10 no.4
• /
• pp.325-332
• /
• 2017

In this paper, a survey of test sequence generation methods for testing the conformance of a protocol implementation to its specification is presented. The best known methods proposed in the literature are called transition tour, distinguishing sequence, characterizing sequence, and unique input/output sequence. Also, several variants of the above methods are introduced. Applications of these methods to the finite state machine model are discussed. Then, comparative analysis of the methods is made in terms of test sequence length. Finally, conclusions are given as follows. The T-method produces the shortest test sequence, but it has the worst fault coverage. The W-method tends to produce excessively long test sequences even though its fault coverage is complete. The problem with the DS-method is that a distinguishing sequence may not exist. The UIO-method is more widely applicable, but it does not provide the same fault coverage as the DS-method.

• Phonetics and Speech Sciences
• /
• v.6 no.4
• /
• pp.195-203
• /
• 2014

The aim of this study was to investigate the effects of phonation types ([+/- aspirated], [+/- fortis]) on aerodynamic measures with Korean bilabial stops. Sixty-three healthy young adults (30 males, 33 females) participated to evaluate the VOEF (Voicing Efficiency) tasks with bilabial stop consonants /$p^h$/, /p/, /p'/ using Phonatory Aerodynamic System (PAS) Model 6600 (Kay PENTAX Corp, Lincoln Park, NJ). All VOEF measures were significantly influenced by phonation types except RANP(pitch range)(p <.01). For sound pressure, maximum SPL, mean SPL, and Mean SPL during Voicing have been shown to be significantly greatest in fortis stop /p'/ than aspirated /$p^h$/ and lenis stop /p/ (p<.001). On the other hand, mean pitch after lenis stop was significantly lower than after aspirated and fortis stops (p<.001). Peak expiratory airflow, Target airflow, and FVC (Expiratory volume) were significantly lowest in fortis stop /p'/ which might be associated with higher aerodynamic resistance while peak air pressure and mean peak air pressure during closure were significantly lower in lenis stop /p/. Additionally, AEFF (Aerodynamic efficiency) was significantly higher in fortis stop /p'/ than lenis stop /p/ as well as aspirated stop /$p^h$/ (p<.001). Thus, sound pressure, airflow parameters, and aerodynamic resistance made crucial roles in distinguishing fortis /p'/ from lenis stop /p/ and aspirated. Additionally, pitch and subglottal air pressure parameters were important aerodynamic characteristics in distinguishing lenis /p/ from fortis /p'/ and aspirated /$p^h$/. Therefore, accurate aspirated /p/ stop consonant should be elicited when collecting the airflow, intraoral pressure related data with patients with voice disorders in order to enhance the reliability and relevance or validity of aerodynamic measures using PAS.