• Proceedings of the KIEE Conference
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• 2001.11b
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• pp.371-373
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• 2001

In this paper, we present 2-step load cycle for daily load curve of up to and including 100kVA distribution transformer in domestic. Daily load patterns are classified by two methods dependent upon possession information. In case we possess daily load profiles make use of K-mean algorithm and in case we have not daily load profiles, make use of customer information of KEPCO. As the parameters of the load pattern classification, we use are daily load profiles and customer information of each distribution transformers. Data management system is used for NT oracle. We can present peak load magnitude, initial load magnitude and peak load duration for daily load patterns by 2-step load cycle for daily load curve of up to and including 100kVA distribution transformer in domestic. We think that this paper contributes to enhancing the distribution transformer overload criterion.

• The Mathematical Education
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• v.42 no.2
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• pp.111-119
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• 2003

This research discussed a general concept on the qualitative research methods in mathematics education. It provided a classification of research methods in mathematics education. It also described research trends in mathematics education. It addressed how research design facilitates formulating a research problem, selecting a research design, choosing who and what to study, deciding how to approach Participants, selecting means to collect data choosing how to analyzing data, and interpreting data and applying the analysis. This study addressed the issues involved in choosing relevant populations and in selecting and sampling qualitative data. It described how populations are conceptualized and distinguished between probability sampling and criterion based selection. It discussed not only data arrangement such as, cross-sectional and categorical indexing, non-cross- sectional data organization, but also diagram flow chart matrix, cognitive map, family tree to facilitate analyzing data.

• Journal of Integrative Natural Science
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• v.13 no.1
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• pp.1-7
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• 2020

This paper proposes a novel fractional frequency reuse(FFR) based on dynamic user distribution. In the FFR, a macro cell is divided into two regions, i.e., the inner region(IR) and outer region(OR). The criterion for dividing the IR and OR is the distance ratio of the radius. However, these distance-based criteria are uncertain in measuring user performance. This is because there are various attenuation phenomena such as shadowing and wall penetration as well as path loss. Therefore, we propose a novel FFR based on dynamic user classification with signal to interference plus noise ratio(SINR) of macro users and classify the FFR into two regions newly. Simulation results show that the proposed scheme has better performance than the conventional FFR in terms of SINR and throughput of macro cell users.

• Journal of Positioning, Navigation, and Timing
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• v.5 no.1
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• pp.37-45
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• 2016

In this paper, design and implementation issues for a network-oriented RF emitter localization system with array antenna are discussed. For hardware, the problem of array mismatch and RF/IF channel mismatch are introduced and the calibration schemes for solving those problems are also provided. For software, it is explained how to overcome the drawback of conventional MUltiple Signal Identification and Classification (MUSIC) algorithm in a point of identifying the number of received signals and problems such as Data Association Problem and Ghost Node Problem in regard to multiple emitter localization are presented with some approaches for getting around those problems. Finally, for implementation, a criterion for arranging each of sensors and a requirement for alignment of array antenna' orientation are also given.

• Speech Sciences
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• v.11 no.4
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• pp.43-52
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• 2004

Model parameters in HMM based speech recognition systems are normally estimated using Maximum Likelihood Estimation(MLE). The MLE method is based mainly on the principle of statistical data fitting in terms of increasing the HMM likelihood. The optimality of this training criterion is conditioned on the availability of infinite amount of training data and the correct choice of model. However, in practice, neither of these conditions is satisfied. In this paper, we propose a training algorithm, MCE(Minimum Classification Error), to improve the performance of a speech recognizer detecting mispronunciation of a foreign language. During the conventional MLE(Maximum Likelihood Estimation) training, the model parameters are adjusted to increase the likelihood of the word strings corresponding to the training utterances without taking account of the probability of other possible word strings. In contrast to MLE, the MCE training scheme takes account of possible competing word hypotheses and tries to reduce the probability of incorrect hypotheses. The discriminant training method using MCE shows better recognition results than the MLE method does.

• IEMEK Journal of Embedded Systems and Applications
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• v.6 no.5
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• pp.295-302
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• 2011

This paper proposes facial feature tracking based on modified ART2 neural networks. And we also suggest new measurement devices such as 'Persistence Exponent' and 'Moving Space Exponent' for the criterion of input vector which consists features. The proposed methods have been applied to classify 48 students by 2-class (ADHD positive, ADHD negative). The results of the experiment have shown that the proposed methods are effective for ADHD Behavior Pattern Classification based on the Image Processing.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.10
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• pp.3498-3512
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• 2021

This article describes how events that make up text stories can be represented and extracted. We also address the results from our simple experiment on extracting and clustering events in terms of emotions, under the assumption that different emotional events can be associated with the classified clusters. Each emotion cluster is based on Plutchik's eight basic emotion model, and the attributes of the NLTK-VADER are used for the classification criterion. While comparisons of the results with human raters show less accuracy for certain emotion types, emotion types such as joy and sadness show relatively high accuracy. The evaluation results with NRC Word Emotion Association Lexicon (aka EmoLex) show high accuracy values (more than 90% accuracy in anger, disgust, fear, and surprise), though precision and recall values are relatively low.

• ETRI Journal
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• v.41 no.5
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• pp.560-573
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• 2019

Two supervised learning algorithms, a basic neural network and a long short-term memory recurrent neural network, are applied to traffic including DDoS attacks. The joint effects of preprocessing methods and hyperparameters for machine learning on performance are investigated. Values representing attack characteristics are extracted from datasets and preprocessed by two methods. Binary classification and two optimizers are used. Some hyperparameters are obtained exhaustively for fast and accurate detection, while others are fixed with constants to account for performance and data characteristics. An experiment is performed via TensorFlow on three traffic datasets. Three scenarios are considered to investigate the effects of learning former traffic on sequential traffic analysis and the effects of learning one dataset on application to another dataset, and determine whether the algorithms can be used for recent attack traffic. Experimental results show that the used preprocessing methods, neural network architectures and hyperparameters, and the optimizers are appropriate for DDoS attack detection. The obtained results provide a criterion for the detection accuracy of attacks.

• Journal of Broadcast Engineering
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• v.13 no.6
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• pp.828-837
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• 2008

We propose a method to select representative views of single objects and classes of objects for 3D object retrieval and classification. Our method is based on projected 2D shapes, or views, of the 3D objects, where the representative views are selected by applying affinity propagation to cluster uniformly sampled views. Affinity propagation assigns prototypes to each cluster during the clustering process, thereby providing a natural criterion to select views. We recursively apply affinity propagation to the selected views of objects classified as single classes to obtain representative views of classes of objects. By enabling classification as well as retrieval, effective management of large scale databases for retrieval can be enhanced, since we can avoid exhaustive search over all objects by first classifying the object. We demonstrate the effectiveness of the proposed method for both retrieval and classification by experimental results based on the Princeton benchmark database [16].

• 한국노년학
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• v.34 no.2
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• pp.369-386
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• 2014

The purpose of this study was to develop the classification of home-based long-term care activities and to test its validity. In this study, the taxonomy of long-term care activities was structured according to the service domain and process. Two expert groups participated in making a draft of the taxonomy that was composed of 7 service domains, 22 care needs, 22 service objectives, and 114 activities. Reliability and validity of the taxonomy was tested in a sample of 152 elderly subjects who used the home-based long-term care services. Based on the factor analysis of 114 activities, 21 factors were extracted from 114 activities. Internal consistency of the factors was high. Content validity was confirmed by the CVI. Long-term care insurance grade was used to assess the criterion validity. Among 21 care needs, 12 cares needs were significantly different from their grade. The classification of home-based long-term care activities demonstrated reliability and validity. In conclusion, the use of this classification is recommended while communicating with the elderly subjects, service providers, and the 3rd party payers.