• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.594-597
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• 2007

We present in this paper a novel mid and long term power load prediction method using temporal pattern mining from AMR (Automatic Meter Reading) data. Since the power load patterns have time-varying characteristic and very different patterns according to the hour, time, day and week and so on, it gives rise to the uninformative results if only traditional data mining is used. Also, research on data mining for analyzing electric load patterns focused on cluster analysis and classification methods. However despite the usefulness of rules that include temporal dimension and the fact that the AMR data has temporal attribute, the above methods were limited in static pattern extraction and did not consider temporal attributes. Therefore, we propose a new classification method for predicting power load patterns. The main tasks include clustering method and temporal classification method. Cluster analysis is used to create load pattern classes and the representative load profiles for each class. Next, the classification method uses representative load profiles to build a classifier able to assign different load patterns to the existing classes. The proposed classification method is the Calendar-based temporal mining and it discovers electric load patterns in multiple time granularities. Lastly, we show that the proposed method used AMR data and discovered more interest patterns.

• Journal of Sasang Constitutional Medicine
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• v.13 no.2
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• pp.156-164
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• 2001

This study started from the curiasty that Sasang Constitution spirit of Lee ]e-ma has something to do with MBTI based on classification of character of C.G. Jung. This reports made from the information of 368 people who got the Sasang Constitution therapy and showed the good result in health in my hospital and they take part in self-report from of MBTI which are made as statistics and research in relationship between Sasang Constitution spitrit of Lee ]e-ma and classification of character of C.G. Jung This is the statistical result of the research. There is not exactly statistical result which support Sasang Constitution spirit of Lee Je-ma relate to the classification of character of C.G. Jung. The identification of statement of Sasang Constitution spirit with partispart is average 41 which is low while the identification of classification of character with partispart is average 76 which is high. In this reseult, It is hard to get general agreement that the statement of Sasang Constitution of spirit relate to the classification of character because of the difference of identification. so more studies are needed in this part.

• Journal of the Society of Disaster Information
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• v.9 no.1
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• pp.65-77
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• 2013

There are many institutes which manage the disaster protection resources in their system. The system of the institutes is not mutually compatible, because there is no standard framework of the classification and identification for the disaster management resources. NIMS of FEMA defines the classification and identification framework for the incident resources. All incidents management system of USA including IRIS and webEOC follows the standard resources framework. The aim of the classification and identification of the resources provides the resources list for the disaster and supports to find the resources information efficiently. In this study, we defined the classification and identification of the resources considering the compatibility with the international standard and the field requirements.

• Korean Journal of Materials Research
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• v.16 no.10
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• pp.633-638
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• 2006

Silica powders with spherical shape and narrow size distribution were prepared by large-scale ultrasonic spray pyrolysis applying the droplet classification apparatus. On the other hand, silica powders prepared by large-scale ultrasonic spray pyrolysis without droplet classification apparatus had broad size distribution. Droplet classification apparatus used in this paper applied the principles of cyclone and dispersion plate with small holes. The droplets formed from the ultrasonic spray generator applying the droplet classification apparatus had narrow size distribution. The droplets with fine and large sizes were eliminated by droplet classification apparatus. The optimum flow rate of the carrier gas and diameter of the hole of the dispersion plate were studied to reduce the size distribution of the silica powders prepared by large-scale ultrasonic spray pyrolysis. The size distribution of the silica powders prepared by large-scale ultrasonic spray pyrolysis at the optimum preparation conditions was 0.76.

• Journal of Korean Society for Quality Management
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• v.48 no.4
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• pp.553-566
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• 2020

Purpose: The purpose of this study was to design a framework for generating one-class classification algorithm based on Hyper-Rectangle(H-RTGL) in a distributed environment connected by network. Methods: At first, we devised one-class classifier based on H-RTGL which can be performed by distributed computing nodes considering model and data parallelism. Then, we also designed facilitating components for execution of distributed processing. In the end, we validate both effectiveness and efficiency of the classifier obtained from the proposed framework by a numerical experiment using data set obtained from UCI machine learning repository. Results: We designed distributed processing framework capable of one-class classification based on H-RTGL in distributed environment consisting of physically separated computing nodes. It includes components for implementation of model and data parallelism, which enables distributed generation of classifier. From a numerical experiment, we could observe that there was no significant change of classification performance assessed by statistical test and elapsed time was reduced due to application of distributed processing in dataset with considerable size. Conclusion: Based on such result, we can conclude that application of distributed processing for generating classifier can preserve classification performance and it can improve the efficiency of classification algorithms. In addition, we suggested an idea for future research directions of this paper as well as limitation of our work.

• Journal of Software Assessment and Valuation
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• v.14 no.1
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• pp.9-17
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• 2018

In many respects, the use and importance of open source software in companies and individuals are increasing as the days pass. However, software evaluation for users, software classification of filtering fundamentals research can not deal flexibly according to the characteristics of open source software. They are using a fixed classification system. In this research, we provide a classification model of open source software that can flexibly deal with the classification of open source software and the software category of new open source software.

• Journal of the Korea Institute of Military Science and Technology
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• v.27 no.3
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• pp.319-328
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• 2024

Semi-supervised learning is a good way to train a classification model using a small number of labeled and large number of unlabeled data. We applied semi-supervised learning to a synthetic aperture radar(SAR) image classification model with a limited number of datasets that are difficult to create. To address the previous difficulties, semi-supervised learning uses a model trained with a small amount of labeled data to generate and learn pseudo labels. Besides, a lot of number of papers use a single fixed threshold to create pseudo labels. In this paper, we present a semi-supervised synthetic aperture radar(SAR) image classification method that applies different thresholds for each class instead of all classes sharing a fixed threshold to improve SAR classification performance with a small number of labeled datasets.

• Journal of the Korea Institute of Military Science and Technology
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• v.20 no.1
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• pp.40-47
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• 2017

To classify the vehicle targets automatically using thermal images, there are usually two main categories of feature extraction method, local and shape feature extraction methods. Since thermal images have less texture information than color images, the shape feature extraction method is useful when the segmentation results are correct. However, if there are some errors in target segmentation, the shape feature may contain some errors, then the classification accuracy can be decreased. To overcome these problems, in this paper, we propose the segmentation reliability estimation method for target classification. The segmentation reliability can be estimated by using the difference information of average intensities and edge energies between the target and the background area. The estimated segmentation reliability is applied in the decision level fusion method of classification results using both shape and local features. Experiment results using the thermal images of the vehicle targets (main battle tank, armored personnel carrier, military truck, and an estate car) show that the proposed classification method and the segmentation reliability estimation method have a good performance in classification accuracy.

• Journal of KIBIM
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• v.13 no.4
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• pp.96-105
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• 2023

This study focuses on developing a building object recognition technology for efficient use in the remodeling of buildings constructed without drawings. In the era of the 4th industrial revolution, smart technologies are being developed. This research contributes to the architectural field by introducing a deep learning-based method for automatic object classification and recognition, utilizing point cloud data. We use a TD3D network with voxels, optimizing its performance through adjustments in voxel size and number of blocks. This technology enables the classification of building objects such as walls, floors, and roofs from 3D scanning data, labeling them in polygonal forms to minimize boundary ambiguities. However, challenges in object boundary classifications were observed. The model facilitates the automatic classification of non-building objects, thereby reducing manual effort in data matching processes. It also distinguishes between elements to be demolished or retained during remodeling. The study minimized data set loss space by labeling using the extremities of the x, y, and z coordinates. The research aims to enhance the efficiency of building object classification and improve the quality of architectural plans by reducing manpower and time during remodeling. The study aligns with its goal of developing an efficient classification technology. Future work can extend to creating classified objects using parametric tools with polygon-labeled datasets, offering meaningful numerical analysis for remodeling processes. Continued research in this direction is anticipated to significantly advance the efficiency of building remodeling techniques.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2001.10a
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• pp.265-268
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• 2001

As growing the electronic commerce there are significant changes in the products/services catalog into the on-line environment. Advertent of e-catalog business opportunity for their own product/services enlarges the market volume and there are diverse methods for the presentation of its product/services. A method for the presentation of product/services features one uses identification and classification system. This study constructs a classification system and database layout for the product/services classification system as a part of e-catalog system. We consider the specific method for the GDAS-based dataset and UNSPSC classification system in the distribution industry.