• Journal of the Korean Wood Science and Technology
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• v.47 no.3
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• pp.265-276
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• 2019

In our previous study, the LeNet3 model successfully classified images from the transverse surfaces of five Korean softwood species (cedar, cypress, Korean pine, Korean red pine, and larch). However, a practical limitation exists in our system stemming from the nature of the training images obtained from the transverse plane of the wood species. In real-world applications, it is necessary to utilize images from the longitudinal surfaces of lumber. Thus, we improved our model by training it with images from the longitudinal and transverse surfaces of lumber. Because the longitudinal surface has complex but less distinguishable features than the transverse surface, the classification performance of the LeNet3 model decreases when we include images from the longitudinal surfaces of the five Korean softwood species. To remedy this situation, we adopt ensemble methods that can enhance the classification performance. Herein, we investigated the use of ensemble models from the LeNet and MiniVGGNet models to automatically classify the transverse and longitudinal surfaces of the five Korean softwoods. Experimentally, the best classification performance was achieved via an ensemble model comprising the LeNet2, LeNet3, and MiniVGGNet4 models trained using input images of $128{\times}128{\times}3pixels$ via the averaging method. The ensemble model showed an F1 score greater than 0.98. The classification performance for the longitudinal surfaces of Korean pine and Korean red pine was significantly improved by the ensemble model compared to individual convolutional neural network models such as LeNet3.

• Journal of applied mathematics & informatics
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• v.1 no.1
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• pp.1-12
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• 1994

We consider a single channel CSMA/CD system with D homogeneous stations and impeded buffer of infinite size. We find a sufficient condition for the model to be stable under the threshold control policy and derive the limiting distri-bution of the number of messages in the system at the moment of service completion. We also derive the limiting distributing of the number of messages in the system size at arbitrary time by using Markov regenerative processes. Some numerical examples and special cases are also treated.

• Asia pacific journal of information systems
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• v.12 no.2
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• pp.179-195
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• 2002

In this research, we developed an efficient Hangul document classification system for text mining. We mean 'efficient' by maintaining an acceptable classification performance while taking shorter computing time. In our system, given a query document, k documents are first retrieved from the document case base using the k-nearest neighbor technique, which is the main algorithm of case-based reasoning. Then, TFIDF method, which is the traditional vector model in information retrieval technique, is applied to the query document and the k retrieved documents to classify the query document. We call this procedure 'CB_TFIDF' method. The result of our research showed that the classification accuracy of CB_TFIDF was similar to that of traditional TFIDF method. However, the average time for classifying one document decreased remarkably.

• Health Policy and Management
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• v.15 no.4
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• pp.46-64
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• 2005

This study was conducted to verify the current criteria and classification system used to determine specialized general hospitals status. In this study, we proposed a new classification system which Is simpler and more convenient than the current one. In the new classification system clinical procedure was chosen as the unit of analysis in order to reflect all the resource consumption and the complexities and degree of medical technologies in determining specialized general hospitals. We developed a statistical model and applied this model to 117 general hospitals which claim their national insurance through electronic data interchange(EDI). Analysis based on 984 clinical procedures and medical facilities' characteristic variable discriminated specialized general hospital in present without misclassification. It means that we can determine specialized general hospital's permission In new way without using the current complicated criteria. This study discriminated specialized general hospital by the new proposed model based on clinical procedures provided by each hospital. For clustering the same types of medical facilities using 984 clinical procedures, we executed multidimensional scale analysis and divided 117 hospitals into 4 groups by two axises : a variety of procedure and the Proportion of high technology Procedure. Therefore, we divided 117 hospitals into 4 groups and one of them was considered as specialized general hospital. In discriminating analysis, we abstracted proportion of 16 clinical procedures which effect on discriminating the specialized general hospital in statistical system also we identify discriminating function which include these variables. As a result, we identify 2 discriminating functions, one is for current discriminating system and the other two is for new discriminating system of specialized general hospital.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.1
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• pp.105-115
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• 2016

Ensemble classification involves combining individually trained classifiers to yield more accurate prediction, compared with individual models. Ensemble techniques are very useful for improving the generalization ability of classifiers. The random subspace ensemble technique is a simple but effective method for constructing ensemble classifiers; it involves randomly drawing some of the features from each classifier in the ensemble. The instance selection technique involves selecting critical instances while deleting and removing irrelevant and noisy instances from the original dataset. The instance selection and random subspace methods are both well known in the field of data mining and have proven to be very effective in many applications. However, few studies have focused on integrating the instance selection and random subspace methods. Therefore, this study proposed a new hybrid ensemble model that integrates instance selection and random subspace techniques using genetic algorithms (GAs) to improve the performance of a random subspace ensemble model. GAs are used to select optimal (or near optimal) instances, which are used as input data for the random subspace ensemble model. The proposed model was applied to both Kaggle credit data and corporate credit data, and the results were compared with those of other models to investigate performance in terms of classification accuracy, levels of diversity, and average classification rates of base classifiers in the ensemble. The experimental results demonstrated that the proposed model outperformed other models including the single model, the instance selection model, and the original random subspace ensemble model.

• Nuclear Engineering and Technology
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• v.55 no.2
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• pp.493-505
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• 2023

Comprehensive condition monitoring of large industry systems such as nuclear power plants (NPPs) is essential for safety and maintenance. In this study, we developed novel system-scale diagnostic technology based on deep-learning and IR thermography that can efficiently and cost-effectively classify system conditions using compact Raspberry Pi and IR sensors. This diagnostic technology can identify the presence of an abnormality or accident in whole system, and when an accident occurs, the type of accident and the location of the abnormality can be identified in real-time. For technology development, the experiment for the thermal image measurement and performance validation of major components at each accident condition of NPPs was conducted using a thermal-hydraulic integral effect test facility with compact infrared sensor modules. These thermal images were used for training of deep-learning model, convolutional neural networks (CNN), which is effective for image processing. As a result, a proposed novel diagnostic was developed that can perform diagnosis of components, whole system and accident classification using thermal images. The optimal model was derived based on the modern CNN model and performed prompt and accurate condition monitoring of component and whole system diagnosis, and accident classification. This diagnostic technology is expected to be applied to comprehensive condition monitoring of nuclear power plants for safety.

• Korean Journal of Computational Design and Engineering
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• v.12 no.3
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• pp.191-199
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• 2007

In order to construct a data warehouse of process plant equipment, a classification structure should be defined first, identifying not only the equipment categories but also attributes of an each equipment to represent the specifications of equipment. ISO 15926 Process Plants is an international standard dealing with the life-cycle data of process plant facilities. From the viewpoints of defining classification structure, Part 2 data model and Reference Data Library (RDL) of ISO 15926 are seen to respectively provide standard syntactic structure and semantic vocabulary, facilitating the exchange and sharing of plant equipment's life-cycle data. Therefore, the equipment data warehouse with an ISO 15926-based classification structure has the advantage of easy integration among different engineering systems. This paper introduces ISO 15926 and then discusses how to define a classification structure with ISO 15926 Part 2 data model and RDL. Finally, we describe the development result of an ISO 15926-based classification structure for a variety of equipment consisting in the reactor coolant system (RCS) of APR 1400 nuclear plant.

• Proceedings of the KSR Conference
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• 2005.11a
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• pp.1-6
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• 2005

To improve safety management of railway and cope with the factors to threat technical and social safety, we need to establish railway safety management system based on analysis of hazards and assessment of risk for railway system. So we have to conduct PHA(Preliminary Hazard Analysis) first to understand weak points and factors to possibly threat safety using analysis of related data such as past accident/incident data and safety regulation and classification standards of hazards/causes of railway accidents. Therefore in this research, we led types/dangerous events/causes of risks/factors of risks from hazard log developed based on railway accident classification and hazards of railway accident. PHA model for domestic railway system will be used in risk analysis and risk assessment of railway accident.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.57 no.4
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• pp.479-488
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• 2024

In recent years, changes in the fishing ground environment have led to reduced catches by fishermen at traditional fishing spots and increased operational costs related to vessel exploration, fuel, and labor. In this study, we developed a deep learning model to classify the fishing activities of drift gillnet fishing boats using AIS (automatic identification system) trajectory data. The proposed model integrates long short-term memory and 1-dimensional convolutional neural network layers to effectively distinguish between fishing (throwing and hauling) and non-fishing operations. Training on a dataset derived from AIS and validation against a subset of CCTV footage, the model achieved high accuracy, with a classification accuracy of 90% for fishing events. These results show that the model can be used effectively to monitor and manage fishing activities in coastal waters in real time.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2005.11a
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• pp.516-525
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• 2005

Case-based reasoning (CBR) has been widely used in various areas due to its convenience and strength in complex problem solving. Generally, in order to obtain successful results from CBR, effective retrieval of useful prior cases for the given problem is essential. However, designing a good matching and retrieval mechanism for CBR systems is still a controversial research issue. Most prior studies have tried to optimize the weights of the features or selection process of appropriate instances. But, these approaches have been performed independently until now. Simultaneous optimization of these components may lead to better performance than in naive models. In particular, there have been few attempts to simultaneously optimize the weight of the features and selection of the instances for CBR. Here we suggest a simultaneous optimization model of these components using a genetic algorithm (GA). We apply it to a customer classification model which utilizes demographic characteristics of customers as inputs to predict their buying behavior for a specific product. Experimental results show that simultaneously optimized CBR may improve the classification accuracy and outperform various optimized models of CBR as well as other classification models including logistic regression, multiple discriminant analysis, artificial neural networks and support vector machines.