• Proceedings of the Korea Inteligent Information System Society Conference
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• 2003.05a
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• pp.416-425
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• 2003

Recommender systems are a personalized information filtering technology to help customers find the products they would like to purchase. Collaborative filtering (CF) has been known to be the most successful recommendation technology. However its widespread use in e-commerce has exposed two research issues, sparsity and scalability. In this paper, we propose several hybrid recommender procedures based on web usage mining, clustering techniques and collaborative filtering to address these issues. Experimental evaluation of suggested procedures on real e-commerce data shows interesting relation between characteristics of procedures and diverse situations.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.192-192
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• 2016

Nowadays, stormwaters have been affected by urbanization and climate change. These transition can cause many problems for hydrologic cycle by increasing runoff volume like flood and low water quality. As with other metropolises and peninsulas, Busan has involved with these problems too. Therefore, it is really vital to do some arrangements to solve them by low impact development (LID) technology. In fact, LID has been introduced to reduce runoff by applying some techniques such as green infrastructure (GI). In order to deal with the aforementioned issues in Busan, this study attempts to design and construct a hybrid green roof-planter box system at Pusan National University GI/LID Facility based on local weather. For this purpose, we used experiment and modeling method on some planter boxes and optimized them by trial and error method.

• Wind and Structures
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• v.16 no.6
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• pp.629-660
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• 2013

This paper reviews the current state-of-the-art in the numerical evaluation of wind loads on buildings. Important aspects of numerical modeling including (i) turbulence modeling, (ii) inflow boundary conditions, (iii) ground surface roughness, (iv) near wall treatments, and (vi) quantification of wind loads using the techniques of computational fluid dynamics (CFD) are summarized. Relative advantages of Large Eddy Simulation (LES) over Reynolds Averaged Navier-Stokes (RANS) and hybrid RANS-LES over LES are discussed based on physical realism and ease of application for wind load evaluation. Overall LES based simulations seem suitable for wind load evaluation. A need for computational wind load validations in comparison with experimental or field data is emphasized. A comparative study among numerical and experimental wind load evaluation on buildings demonstrated generally good agreements on the mean values, but more work is imperative for accurate peak design wind load evaluations. Particularly more research is needed on transient inlet boundaries and near wall modeling related issues.

• International journal of advanced smart convergence
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• v.8 no.3
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• pp.61-68
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• 2019

Spatial modulation (SM) is the first proposed space modulation technique. By further utilizing the quadrature spatial dimension, quadrature spatial modulation (QSM) has been developed as an amendment to SM system to enhance the overall spectral efficiency. Both techniques are capable of entirely eliminating interchannel interference (ICI) at the receiver. In this paper, we propose a simple adaptive hybrid switching transmission scheme to obtain better system performance than SM and QSM systems under a fixed transmission date rate. The presented modulator selection criterion for switching between spatial modulator and quadrature spatial modulator is based on the larger received minimum distance of spatial modulator and quadrature spatial modulator to exploit the spatial channel freedom. It is shown through Monte Carlo simulations that the proposed hybrid SM and QSM switching system yields lower error performance than the conventional SM and QSM systems under the same fixed data rate and thus can provide signal to noise ratio (SNR) gain.

• Journal of Korea Society of Digital Industry and Information Management
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• v.15 no.3
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• pp.197-206
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• 2019

Telemarketing has been used in finance due to the reduction of offline channels. In order to select telemarketing target customers, various machine learning techniques have emerged to maximize the effect of minimum cost. However, there are problems that the class imbalance, which the number of marketing success customers is smaller than the number of failed customers, and the recall rate is lower than accuracy. In this paper, we propose a method that solve the imbalanced class problem and increase the recall rate to improve the efficiency. The hybrid sampling method is applied to balance the data in the class, and the stacked deep network is applied to improve the recall and precision as well as the accuracy. The proposed method is applied to actual bank telemarketing data. As a result of the comparison experiment, the accuracy, the recall, and the precision is improved higher than that of the conventional methods.

• Journal of Hydrogen and New Energy
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• v.35 no.4
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• pp.437-450
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• 2024

Sustainable shipping depends on eco-friendly energy solutions. This paper reviews methods for predicting marine fuel cell performance, including empirical approaches, physical modeling, data-driven techniques, and hybrid methods. Accurate prediction models tailored to the marine environment's unique conditions are crucial for operational efficiency. By evaluating the strengths and weaknesses of each method, this study provides a comprehensive analysis of effective strategies for forecasting polymer electrolyte membrane fuel cell and solid oxide fuel cell performance in marine applications. These insights contribute to the advancement of eco-friendly shipping technologies and enhance fuel cell performance in challenging marine environments.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.125-140
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• 2013

We call a data set in which the number of records belonging to a certain class far outnumbers the number of records belonging to the other class, 'imbalanced data set'. Most of the classification techniques perform poorly on imbalanced data sets. When we evaluate the performance of a certain classification technique, we need to measure not only 'accuracy' but also 'sensitivity' and 'specificity'. In a customer churn prediction problem, 'retention' records account for the majority class, and 'churn' records account for the minority class. Sensitivity measures the proportion of actual retentions which are correctly identified as such. Specificity measures the proportion of churns which are correctly identified as such. The poor performance of the classification techniques on imbalanced data sets is due to the low value of specificity. Many previous researches on imbalanced data sets employed 'oversampling' technique where members of the minority class are sampled more than those of the majority class in order to make a relatively balanced data set. When a classification model is constructed using this oversampled balanced data set, specificity can be improved but sensitivity will be decreased. In this research, we developed a hybrid model of support vector machine (SVM), artificial neural network (ANN) and decision tree, that improves specificity while maintaining sensitivity. We named this hybrid model 'hybrid SVM model.' The process of construction and prediction of our hybrid SVM model is as follows. By oversampling from the original imbalanced data set, a balanced data set is prepared. SVM_I model and ANN_I model are constructed using the imbalanced data set, and SVM_B model is constructed using the balanced data set. SVM_I model is superior in sensitivity and SVM_B model is superior in specificity. For a record on which both SVM_I model and SVM_B model make the same prediction, that prediction becomes the final solution. If they make different prediction, the final solution is determined by the discrimination rules obtained by ANN and decision tree. For a record on which SVM_I model and SVM_B model make different predictions, a decision tree model is constructed using ANN_I output value as input and actual retention or churn as target. We obtained the following two discrimination rules: 'IF ANN_I output value <0.285, THEN Final Solution = Retention' and 'IF ANN_I output value ${\geq}0.285$, THEN Final Solution = Churn.' The threshold 0.285 is the value optimized for the data used in this research. The result we present in this research is the structure or framework of our hybrid SVM model, not a specific threshold value such as 0.285. Therefore, the threshold value in the above discrimination rules can be changed to any value depending on the data. In order to evaluate the performance of our hybrid SVM model, we used the 'churn data set' in UCI Machine Learning Repository, that consists of 85% retention customers and 15% churn customers. Accuracy of the hybrid SVM model is 91.08% that is better than that of SVM_I model or SVM_B model. The points worth noticing here are its sensitivity, 95.02%, and specificity, 69.24%. The sensitivity of SVM_I model is 94.65%, and the specificity of SVM_B model is 67.00%. Therefore the hybrid SVM model developed in this research improves the specificity of SVM_B model while maintaining the sensitivity of SVM_I model.

• Journal of Intelligence and Information Systems
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• v.9 no.1
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• pp.71-89
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• 2003

Although many studies demonstrate that one technique outperforms the others for a given data set, there is often no way to tell a priori which of these techniques will be most effective in the classification problems. Alternatively, it has been suggested that a better approach to classification problem might be to integrate several different forecasting techniques. This study proposes the linearly combining methodology of different classification techniques. The methodology is developed to find the optimal combining weight and compute the weighted-average of different techniques' outputs. The proposed methodology is represented as the form of mixed integer programming. The objective function of proposed combining methodology is to minimize total misclassification cost which is the weighted-sum of two types of misclassification. To simplify the problem solving process, cutoff value is fixed and threshold function is removed. The form of mixed integer programming is solved with the branch and bound methods. The result showed that proposed methodology classified more accurately than any of techniques individually did. It is confirmed that Proposed methodology Predicts significantly better than individual techniques and the other combining methods.

• International Journal of Industrial Entomology and Biomaterials
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• v.29 no.2
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• pp.198-206
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• 2014

The olive fly Bactrocera oleae (Rossi) is the key pest for olive cultivation worldwide. Substantial effort has been invested in the development of the sterile insect technique (SIT) to control this pest. One of the limitations to develop SIT technology for olive fruit fly is the low ability of wild females to lay eggs in other medium than olive fruits, and their slow adaptation to oviposition in artificial substrates. In the present study, fruit grapes were used as an alternative egg collection medium to harvest eggs and young larvae from freshly colonized wild strains originating from France, Italy, Spain and Croatia. The larvae were allowed to develop into the fruits until the second instar, before they were extracted out and further reared on a standard artificial diet. Furthermore, F1 to F4 female flies were alternatively offered wax bottles to oviposit. Finally, the performance of hybrid strains created from crosses between wild and long colonised flies was assessed. The results showed that females of all 4 wild strains readily oviposited eggs in grapes and from the F2 generation onward, females from all strains were adapted to laying eggs in wax bottles. No difference was observed in eggs and pupae production among all strains tested. The findings are discussed for their implications on SIT application against olive fruit fly.

• Journal of Internet Computing and Services
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• v.22 no.3
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• pp.27-35
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• 2021

With the development of the Internet, various IT technologies such as IoT, Cloud, etc. have been developed, and various systems have been built in countries and companies. Because these systems generate and share vast amounts of data, they needed a variety of systems that could detect threats to protect the critical data contained in the system, which has been actively studied to date. Typical techniques include anomaly detection and misuse detection, and these techniques detect threats that are known or exhibit behavior different from normal. However, as IT technology advances, so do technologies that threaten systems, and these methods of detection. Advanced Persistent Threat (APT) attacks national or companies systems to steal important information and perform attacks such as system down. These threats apply previously unknown malware and attack technologies. Therefore, in this paper, we propose a hybrid intrusion detection system that combines anomaly detection and misuse detection to detect unknown threats. Two detection techniques have been applied to enable the detection of known and unknown threats, and by applying machine learning, more accurate threat detection is possible. In misuse detection, we applied Classification based on Association Rule(CBA) to generate rules for known threats, and in anomaly detection, we used One-Class SVM(OCSVM) to detect unknown threats. Experiments show that unknown threat detection accuracy is about 94%, and we confirm that unknown threats can be detected.