• 한국경영정보학회:학술대회논문집
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• 2007.06a
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• pp.608-613
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• 2007

Prediction of corporate bankruptcies has long been an important topic and has been studied extensively in the finance and management literature because it is an essential basis for the risk management of financial institutions. Recently, support vector machines (SVMs) are becoming popular as a tool for bankruptcy prediction because they use a risk function consisting of the empirical error and a regularized term which is derived from the structural risk minimization principle. In addition, they don't require huge training samples and have little possibility of overfitting. However. in order to Use SVM, a user should determine several factors such as the parameters ofa kernel function, appropriate feature subset, and proper instance subset by heuristics, which hinders accurate prediction results when using SVM In this study, we propose a novel hybrid SVM classifier with simultaneous optimization of feature subsets, instance subsets, and kernel parameters. This study introduces genetic algorithms (GAs) to optimize the feature selection, instance selection, and kernel parameters simultaneously. Our study applies the proposed model to the real-world case for bankruptcy prediction. Experimental results show that the prediction accuracy of conventional SVM may be improved significantly by using our model.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 1996.04a
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• pp.59-76
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• 1996

Vessels in the world merchant fleet generally operate in either liner or bulk trade. The supply and the demand trend of general cargo ship are both on the ebb however those trend of tankers and containers are ins light ascension. Oil tankers are so far the largest single vessel type in the world fleet and the tanker market is often cited as a texbook example of perfect competition. Some shipping statistics in recent years show that there has been a radical fluctuation in spot charter rate under easy charter's market. This implies that the proper scheduling of tankers under spot market fluctuation has the great potential of improving the owner's profit and economic performance of shipping. This paper aims at developing the TS-DSS(Decision Support System for Tanker Scheduling) in the context of the importance of scheduling decisions. TS-DSS is defined as a DSS based on the optimization models for tanker scheduling. The system has been developed through the life cycle of systems analysis design and implementation to be user-friendly system. The performance of the system has been tested and examined by using the data edited under several tanker scheduling has been tested and examined by using the data edited under several tanker scheduling scenarios and thereby the effectiveness of TS-DSS is validated satisfactorily. The authors conclude the paper with the comments of the need of appropriate support environment such as data-based DSS and network system for successful implementatio of the TS-DSS.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.14 no.2
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• pp.119-127
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• 2004

Butterfly valves are popularly used in service in the industrial and water works pipeline systems with large diameter because of its lightweight, simple structure and the rapidity of its manipulation. Sometimes cavitation can occur. resulting in noise, vibration and rapid deterioration of the valve trim, and do not allow further operation. Thus, the monitoring of cavitation is of economic interest and is very importance in industry. This paper proposes a condition monitoring scheme using statistical feature evaluation and support vector machine (SVM) to detect the cavitation conditions of butterfly valve which used as a flow control valve at the pumping stations. The stationary features of vibration signals are extracted from statistical moments. The SVMs are trained, and then classify normal and cavitation conditions of control valves. The SVMs with the reorganized feature vectors can distinguish the class of the untrained and untested data. The classification validity of this method is examined by various signals that are acquired from butterfly valves in the pumping stations and compared the classification success rate with those of self-organizing feature map neural network.

• Korean Journal of Clinical Pharmacy
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• v.30 no.3
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• pp.143-148
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• 2020

Objective: This study aimed to investigate pharmaceutical care for critically ill neonates and suggest targeted strategies compatible with the Korean health-system pharmacy. Methods: Articles that reported pharmacy practices for critically ill neonates were reviewed. Pharmaceutical care practices and roles of neonatal pharmacists were identified, and criteria were developed for neonates in need of specialized care by clinical pharmacists. Results: Neonatal pharmacists play many roles in the overall medication management pathway. For clinical decision support, multidisciplinary ward rounds, clinical pharmacokinetic services, and consultation for pharmacotherapy and nutrition support were conducted. Prevention and resolution of drug-related problems through review of medication charts contributed to medication safety. Pharmaceutical optimization of intravenous medication played an important role in safe and effective therapy. Information on the use of off-label medicine, recommended dosage and dosing schedules, and stability of intravenous medicine was provided to other health professionals. Most clinical practices for neonates in Korea included therapeutic drug monitoring and nutrition support services. Reduction in medication errors and adverse drug reactions, shortening the duration of weaning medicines, decreasing the use and cost of antimicrobials, and improvement in nutrition status were reported as the outcomes of pharmacist-led interventions. The essential criteria of pharmaceutical care, including for patients with potential high-risk factors for drug-related problems, was developed. Conclusion: Pharmaceutical care for critically ill neonates varies widely. Development and provision of standardized pharmaceutical care for Korean neonates and a stepwise strategy for the expansion of clinical pharmacy services are required.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2006.06a
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• pp.167-175
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• 2006

Building accurate corporate bankruptcy prediction models has been one of the most important research issues in finance. Recently, support vector machines (SVMs) are popularly applied to bankruptcy prediction because of its many strong points. However, in order to use SVM, a modeler should determine several factors by heuristics, which hinders from obtaining accurate prediction results by using SVM. As a result, some researchers have tried to optimize these factors, especially the feature subset and kernel parameters of SVM But, there have been no studies that have attempted to determine appropriate instance subset of SVM, although it may improve the performance by eliminating distorted cases. Thus in the study, we propose the simultaneous optimization of the instance selection as well as the parameters of a kernel function of SVM by using genetic algorithms (GAs). Experimental results show that our model outperforms not only conventional SVM, but also prior approaches for optimizing SVM.

• Journal of the Korean Institute of Navigation
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• v.20 no.1
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• pp.27-46
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• 1996

Vessles in the world merchant fleet generally operate in either liner or bulk trade. The supply and the demand trend of general cargo ship are both on the ebb, however, those trend of tankers and containers are in slight ascension. Oil tankers are so far the largest single vessel type in the world fleet and the tanker market is often cited as a textbook example of perfect competition. Some shipping statistics in recent years show that there has been a radical fluctuation in spot charter rate under easy charterer's market. This implys that the proper scheduling of tankers under spot market fluctuation has the great potential of improving the owner's profit and economic performance of shipping. This paper aims at developing the TS-DSS(Decision Support System for Tanker Scheduling) in the context of the importance of scheduling decisions. The TS-DSS is defined as the DSS based on the optimization models for tanker scheduling. The system has been developed through the life cycle of systems analysis, design, and implementation to be user-friendly system. The performance of the system has been tested and examined by using the data edited under several tanker scheduling scenarios and thereby the effectiveness of TS-DSS is validated satifactorily. The authors conclude the paper with the comments on the need of appropriate support environment such as data-based DSS and network system for succesful implementation of the TS-DSS.

• ETRI Journal
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• v.39 no.5
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• pp.621-631
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• 2017

Abnormal samples are usually difficult to obtain in production systems, resulting in imbalanced training sample sets. Namely, the number of positive samples is far less than the number of negative samples. Traditional Support Vector Machine (SVM)-based anomaly detection algorithms perform poorly for highly imbalanced datasets: the learned classification hyperplane skews toward the positive samples, resulting in a high false-negative rate. This article proposes a new imbalanced SVM (termed ImSVM)-based anomaly detection algorithm, which assigns a different weight for each positive support vector in the decision function. ImSVM adjusts the learned classification hyperplane to make the decision function achieve a maximum GMean measure value on the dataset. The above problem is converted into an unconstrained optimization problem to search the optimal weight vector. Experiments are carried out on both Cloud datasets and Knowledge Discovery and Data Mining datasets to evaluate ImSVM. Highly imbalanced training sample sets are constructed. The experimental results show that ImSVM outperforms over-sampling techniques and several existing imbalanced SVM-based techniques.

• Journal of the Korea Society of Computer and Information
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• v.15 no.7
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• pp.11-17
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• 2010

This paper proposes an improved SMO solving a quadratic optmization problem for class imbalanced learning. The SMO algorithm is aproporiate for solving the optimization problem of a support vector machine that assigns the different regularization values to the two classes, and the prosoposed SMO learning algorithm iterates the learning steps to find the current optimal solutions of only two Lagrange variables selected per class. The proposed algorithm is tested with the UCI benchmarking problems and compared to the experimental results of the SMO algorithm with the g-mean measure that considers class imbalanced distribution for gerneralization performance. In comparison to the SMO algorithm, the proposed algorithm is effective to improve the prediction rate of the minority class data and could shorthen the training time.

• Nuclear Engineering and Technology
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• v.50 no.8
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• pp.1306-1313
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• 2018

The randomness and incipient nature of certain faults in reactor systems warrant a robust and dynamic detection mechanism. Existing models and methods for fault diagnosis using different mathematical/statistical inferences lack incipient and novel faults detection capability. To this end, we propose a fault diagnosis method that utilizes the flexibility of data-driven Support Vector Machine (SVM) for component-level fault diagnosis. The technique integrates separately-built, separately-trained, specialized SVM modules capable of component-level fault diagnosis into a coherent intelligent system, with each SVM module monitoring sub-units of the reactor coolant system. To evaluate the model, marginal faults selected from the failure mode and effect analysis (FMEA) are simulated in the steam generator and pressure boundary of the Chinese CNP300 PWR (Qinshan I NPP) reactor coolant system, using a best-estimate thermal-hydraulic code, RELAP5/SCDAP Mod4.0. Multiclass SVM model is trained with component level parameters that represent the steady state and selected faults in the components. For optimization purposes, we considered and compared the performances of different multiclass models in MATLAB, using different coding matrices, as well as different kernel functions on the representative data derived from the simulation of Qinshan I NPP. An optimum predictive model - the Error Correcting Output Code (ECOC) with TenaryComplete coding matrix - was obtained from experiments, and utilized to diagnose the incipient faults. Some of the important diagnostic results and heuristic model evaluation methods are presented in this paper.

• Journal of the Korean Society of Industry Convergence
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• v.23 no.6_2
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• pp.1025-1032
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• 2020

In this paper, we propose a method to classify road weather conditions into rain, fog, and sun using a SVM (Support Vector Machine) classifier after extracting weather features from images acquired in real time using an optical sensor installed on a roadside post. A multi-dimensional weather feature vector consisting of factors such as image sharpeness, image entropy, Michelson contrast, MSCN (Mean Subtraction and Contrast Normalization), dark channel prior, image colorfulness, and local binary pattern as global features of weather-related images was extracted from road images, and then a road weather classifier was created by performing machine learning on 700 sun images, 2,000 rain images, and 1,000 fog images. Finally, the classification performance was tested for 140 sun images, 510 rain images, and 240 fog images. Overall classification performance is assessed to be applicable in real road services and can be enhanced further with optimization along with year-round data collection and training.