• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.13 no.2
• /
• pp.817-821
• /
• 2012

In this paper we applied the optimal mixture number estimation technique in GMM(Gaussian mixture model) using BIC(Bayesian information criterion) and MDL(minimum description length) as a model selection criterion for environmental sounds recognition. In the experiment, we extracted 12 MFCC(mel-frequency cepstral coefficients) features from 9 kinds of environmental sounds which amounts to 27747 data and classified them with GMM. As mentioned above, BIC and MDL is applied to estimate the optimal number of mixtures in each environmental sounds class. According to the experimental results, while the recognition performances are maintained, the computational complexity decreases by 17.8% with BIC and 31.7% with MDL. It shows that the computational complexity reduction by BIC and MDL is effective for environmental sounds recognition using GMM.

• The KIPS Transactions:PartD
• /
• v.11D no.1
• /
• pp.153-162
• /
• 2004

Software development methodology using object-oriented analysis and design techniques for improving productivity and maintainability has acquired a substantial acceptance in both academia and industry as a fundamental paradigm. Enterprise Java Beans (EJB) is widely used in both academia and industry because it provides high unability and rapid application development. EJB supports object-oriented paradigm, but there are several things to consider when designing detail model of object-oriented model or implementing object-oriented model with EJB model. One of them is inheritance problem. In this paper, we classify the types of class inheritance which is shown upon in object-oriented model into three types and identify the problems which can happen when implementing the inheritance mechanism with EJB model. And this paper proposes three patterns for realizing the inheritance in EJB. Moreover, applicable patterns and guidelines for each object-oriented inheritance types for the proposed patterns are suggested.

• Transactions of the Korean Society for Noise and Vibration Engineering
• /
• v.21 no.11
• /
• pp.997-1004
• /
• 2011

A fault detection algorithm of a charge and discharge system to ensure the safe use of hybrid electric vehicle is proposed in this paper. This algorithm can be used as a complementary way to existing fault detection technique for a charge and discharge system. The proposed algorithm uses a SVDD technique, which additionally utilizes two methods for learning a large amount of data; one is to incrementally learn a large amount of data, the other one is to remove the data that does not affect the next learning using a new data reduction technique. Removal of data is selected by using lines connecting support vectors. In the proposed method, the data processing speed is drastically improved and the storage space used is remarkably reduced than the conventional methods using the SVDD technique only. A battery data and speed data of a commercial hybrid electrical vehicle are utilized in this study. A fault boundary is produced via SVDD techniques using the input and output in normal operation of the system without using mathematical modeling. A fault detection simulation is performed using both an artificial fault data and the obtained fault boundary via SVDD techniques. In the fault detection simulation, fault detection time via proposed algorithm is compared with that of the peak-peak method. Also the proposed algorithm is revealed to detect fault in the region where conventional peak-peak method is never able to do.

• Journal of the Korean Data and Information Science Society
• /
• v.24 no.1
• /
• pp.13-22
• /
• 2013

Smartphone addiction has become more serious than internet addiction since people can download and run numerous applications with smartphones even without internet connection. However, smartphone addiction is not sufficiently dealt with in current studies. The S-scale method developed by Korea National Information Society Agency involves so many questions that respondents are likely to avoid the diagnosis itself. Moreover, since S-scale is determined by the total score of responded items without taking into account of demographic variables, it is difficult to get an accurate result. Therefore, in this paper, we have extracted important factors from all data, which affect smartphone addiction, including demographic variables. Then we classified the selected items with a neural network. The result of a comparative analysis with backpropagation learning algorithm and multiclass support vector machine shows that learning rate is slightly higher in multiclass SVM. Since multiclass SVM suggested in this paper is highly adaptable to rapid changes of data, we expect that it will lead to a more accurate self-diagnosis of smartphone addiction.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.24 no.7
• /
• pp.857-865
• /
• 2020

This paper presents a method for code generation of UML sequence diagrams based on message patterns. In the sequence diagrams, it is shown that messages are some types of forms typically. This paper classifies according to type as three patterns, and construct meta-information for code generation analysing structural infomation for each patterns. The meta-message of structural information (MetaMessage) is stored in the MetaMessage datastore and the meta-method information from the MetaMessage is stored in the MetaMethod datastore. And then, the structural information of MetaClass and MetaObject is constructed in each datastore too. For each pattern, this paper presents a method for code generation based on the meta information of message patterns and the syntax of target progamming language. Also, branching and looping that has been seldom handled integratedly in the previous works are handled as same as the basic patterns by classifying the branching pattern and the looping pattern for code generation integratedly.

• Information Systems Review
• /
• v.22 no.1
• /
• pp.113-124
• /
• 2020

In the operation of farms, it is very important to evaluate the quality of harvested crops and to classify defective products. However, farmers have difficulty coping with the cost and time required for quality assessment due to insufficient capital and manpower. This study thus aims to detect defects by analyzing the epidermis of fruit using deep learning algorithm. We developed a model that can analyze the epidermis by applying YOLOv3 algorithm based on Region Convolutional Neural Network to video images of peach. A total of four classes were selected and trained. Through 97,600 epochs, a high performance detection model was obtained. The crop failure detection model proposed in this study can be used to automate the process of data collection, quality evaluation through analyzed data, and defect detection. In particular, we have developed an analytical model for peach, which is the most vulnerable to external wounds among crops, so it is expected to be applicable to other crops in farming.

• Journal of the Korea Society of Computer and Information
• /
• v.29 no.1
• /
• pp.31-40
• /
• 2024

In the realm of domestic online fashion platform industry the manual registration of product information by individual business owners leads to inconvenience and reliability issues, especially when dealing with simultaneous registrations of numerous product groups. Moreover, bias is significantly heightened due to the low quality of product images and an imbalance in data quantity. Therefore, this study proposes a ResNet50 model aimed at minimizing data bias through oversampling techniques and conducting multiple classifications for 13 fashion categories. Transfer learning is employed to optimize resource utilization and reduce prolonged learning times. The results indicate improved discrimination of up to 33.4% for data augmentation in classes with insufficient data compared to the basic convolution neural network (CNN) model. The reliability of all outcomes is underscored by precision and affirmed by the recall curve. This study is suggested to advance the development of the domestic online fashion platform industry to a higher echelon.

• Journal of Korea Multimedia Society
• /
• v.13 no.5
• /
• pp.722-729
• /
• 2010

Prediction problems are widely used in medical domains. For example, computer aided diagnosis or prognosis is a key component in a CDSS (Clinical Decision Support System). SVMs with nonlinear kernels like RBF kernels, have shown superior accuracy in prediction problems. However, they are not preferred by physicians for medical prediction problems because nonlinear SVMs are difficult to visualize, thus it is hard to provide intuitive interpretation of prediction results to physicians. Nomogram was proposed to visualize SVM classification models. However, it cannot visualize nonlinear SVM models. Localized Radial Basis Function (LRBF) was proposed which shows comparable accuracy as the RBF kernel while the LRBF kernel is easier to interpret since it can be linearly decomposed. This paper presents a new tool named VRIFA, which integrates the nomogram and LRBF kernel to provide users with an interactive visualization of nonlinear SVM models, VRIFA visualizes the internal structure of nonlinear SVM models showing the effect of each feature, the magnitude of the effect, and the change at the prediction output. VRIFA also performs nomogram-based feature selection while training a model in order to remove noise or redundant features and improve the prediction accuracy. The area under the ROC curve (AUC) can be used to evaluate the prediction result when the data set is highly imbalanced. The tool can be used by biomedical researchers for computer-aided diagnosis and risk factor analysis for diseases.

• Journal of KIISE:Software and Applications
• /
• v.31 no.12
• /
• pp.1637-1648
• /
• 2004

When digital libraries are developed by the traditional client/sever system using a single agent on the distributed environment, several problems occur. First, as the search method is one dimensional, the search results have little relationship to each other. Second, the results do not reflect the user's preference. Third, whenever a client connects to the server, users have to receive the certification. Therefore, the retrieval of documents is less efficient causing dissatisfaction with the system. I propose a new platform of mobile multiagents for a personal digital library to overcome these problems. To develop this new platform I combine the existing DECAF multiagents platform with the Voyager mobile ORB and propose a new negotiation algorithm and scheduling algorithm. Although there has been some research for a personal digital library, I believe there have been few studies on their integration and systemization. For searches of related information, the proposed platform could increase the relationship of search results by subdividing the related documents, which are classified by a supervised neural network. For the user's preference, as some modular clients are applied to a neural network, the search results are optimized. By combining a mobile and multiagents platform a new mobile, multiagents platform is developed in order to decrease a network burden. Furthermore, a new negotiation algorithm and a scheduling algorithm are activated for the effectiveness of PDS. The results of the simulation demonstrate that as the number of servers and agents are increased, the search time for PDS decreases while the degree of the user's satisfaction is four times greater than with the C/S model.

• Particle and aerosol research
• /
• v.14 no.4
• /
• pp.153-169
• /
• 2018

To identify a variety of organic compounds in the ambient aerosols, the two-dimensional gas chromatography-time of flight mass spectrometry (GCxGC) system (2DGC) has been applied. While 2DGC provides more peaks, the amount of the generated data becomes huge. A two-step approach has been proposed to efficiently interpret the organic aerosol analysis data. The two-dimensional 2DGC data were divided into 6 chemical groups depending on their volatility and polarity. Using these classification standards, all the peaks were subject to both qualitative and quantitative analyses and then classified into 8 classes. The aerosol samples collected in Seoul in summer 2013 and winter 2014 were used as the test case. It was found that some chemical classes such as furanone showed seasonal variation in the high polarity-volatile organic compounds (HP-VOC) group. Also, for some chemical classes, qualitative and quantitative analyses showed different trends. Limitations of the proposed method are discussed.