• Journal of Digital Contents Society
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• v.18 no.3
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• pp.457-463
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• 2017

VOD service provides an additional revenue model for digital broadcasting companies in addition to the existing subscription fees and advertisement-based revenue models. Therefore, each digital broadcasting company develops its own VOD service and performs frequent improvement work. In this circumstance, the developer is seeking to improve the efficiency of the VOD service development. To address the needs of such developers, this study conducted a basic study to apply the feature-oriented analysis model to the development of VOD services. The feature-oriented analysis model is recognized (through a number of case studies) as an effective tool for analyzing the requirements of softwares with the functions that are interconnected organically. In this paper, we developed a feature model of VOD service and designed the primary functions of each feature and the test-cases that can test the these functions, laying the foundation for developing VOD services based on feature-oriented analysis model.

• Journal of the Korea Society of Computer and Information
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• v.13 no.5
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• pp.53-67
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• 2008

At present, for reuse of similar domains between feature model and class model. researches of transformation at the model level and of transformation using ontology between two models are being made. but consistent transformation through metamodel is not made. And the factors of modeling transformation targets are not sufficient, and especially, automatic transformation algorithm and supporting tools are not provided so reuse of domains between models is not activated. This paper proposes a method of transformation from feature model to class model using ontology on the metamodel. For this, it re-establishes the metamodel of feature model, class model, and ontology, and it defines the properties of modelling factors for each metamodel. Based on the properties, it defines the profiles of transformation rules between feature mndel and ontology, and between ontology and class model, using set theory and propositional calculus. For automation of the transformation, it creates transformation algorithm and supporting tools. Using the proposed transformation rules and tools, real application is made through Electronic Approval System. Through this, it is possible to transform from the existing constructed feature model to the class model and to use it again for a different development method. Especially, it is Possible to remove ambiguity of semantic transformation using ontology, and automation of transformation maintains consistence between models.

• Korean Journal of Remote Sensing
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• v.23 no.5
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• pp.401-408
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• 2007

Animated anaglyph is the most cost-effective method for 3D stereo visualization of virtual or actual 3D geo-based data model. Unlike 3D anaglyph scene generation using paired epipolar images, the main data sets of this study is the multi-typed 3D feature model containing 3D shaped objects, DEM and satellite imagery. For this purpose, a prototype implementation for 3D animated anaglyph using OpenGL API is carried out, and virtual 3D feature modeling is performed to demonstrate the applicability of this anaglyph approach. Although 3D features are not real objects in this stage, these can be substituted with actual 3D feature model with full texture images along all facades. Currently, it is regarded as the special viewing effect within 3D GIS application domains, because just stereo 3D viewing is a part of lots of GIS functionalities or remote sensing image processing modules. Animated anaglyph process can be linked with real-time manipulation process of 3D feature model and its database attributes in real world problem. As well, this approach of feature-based 3D animated anaglyph scheme is a bridging technology to further image-based 3D animated anaglyph rendering system, portable mobile 3D stereo viewing system or auto-stereo viewing system without glasses for multi-viewers.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.1
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• pp.121-127
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• 2009

The Multistage Feature-based Classification Model(MFCM) is proposed in this paper. MFCM does not use whole feature vectors extracted from the original data at once to classify each data, but use only groups related to each feature vector to classify separately. In the training stage, the contribution rate calculated from each feature vector group is drew throughout the accuracy of each feature vector group and then, in the testing stage, the final classification result is obtained by applying weights corresponding to the contribution rate of each feature vector group. In this paper, the proposed MFCM algorithm is applied to the problem of music genre classification. The results demonstrate that the proposed MFCM outperforms conventional algorithms by 7% - 13% on average in terms of classification accuracy.

• Journal of information and communication convergence engineering
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• v.20 no.1
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• pp.31-40
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• 2022

Coronary heart disease (CHD) is a comorbidity of COVID-19; therefore, routine early diagnosis is crucial. A large number of examination attributes in the context of diagnosing CHD is a distinct obstacle during the pandemic when the number of health service users is significant. The development of a precise machine learning model for diagnosis with a minimum number of examination attributes can allow examinations and healthcare actions to be undertaken quickly. This study proposes a CHD diagnosis model based on feature selection, data balancing, and ensemble-based classification methods. In the feature selection stage, a hybrid SVM-GA combined with fast correlation-based filter (FCBF) is used. The proposed system achieved an accuracy of 94.60% and area under the curve (AUC) of 97.5% when tested on the z-Alizadeh Sani dataset and used only 8 of 54 inspection attributes. In terms of performance, the proposed model can be placed in the very good category.

• ETRI Journal
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• v.41 no.2
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• pp.235-241
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• 2019

In this paper, we propose a rank-weighted reconstruction feature to improve the robustness of a feed-forward deep neural network (FFDNN)-based acoustic model. In the FFDNN-based acoustic model, an input feature is constructed by vectorizing a submatrix that is created by slicing the feature vectors of frames within a context window. In this type of feature construction, the appropriate context window size is important because it determines the amount of trivial or discriminative information, such as redundancy, or temporal context of the input features. However, we ascertained whether a single parameter is sufficiently able to control the quantity of information. Therefore, we investigated the input feature construction from the perspectives of rank and nullity, and proposed a rank-weighted reconstruction feature herein, that allows for the retention of speech information components and the reduction in trivial components. The proposed method was evaluated in the TIMIT phone recognition and Wall Street Journal (WSJ) domains. The proposed method reduced the phone error rate of the TIMIT domain from 18.4% to 18.0%, and the word error rate of the WSJ domain from 4.70% to 4.43%.

• Proceedings of the Korean Society of Computer Information Conference
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• 2017.07a
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• pp.281-284
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• 2017

최근 임베디드 시스템 개발이 Model Driven Engineering 방식으로 변화하면서 요구사항과 모델 간의 추적성을 보장하는 것이 매우 중요해졌다. 이 논문에서는 기존의 FDD(Feature Driven Development)와 FOSE(Feature Oriented Software Engineering) 방법론에 적용된 feature 개념을 재정의하여 이를 AUTOSAR platform에 적용하는 방법을 제시하며 요구사항부터 model, code까지 추적성을 검증한다.

• ETRI Journal
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• v.44 no.3
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• pp.462-475
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• 2022

Although researchers have proposed numerous techniques for speech emotion recognition, its performance remains unsatisfactory in many application scenarios. In this study, we propose a speech emotion recognition model based on a genetic algorithm (GA)-decision tree (DT) fusion of deep and acoustic features. To more comprehensively express speech emotional information, first, frame-level deep and acoustic features are extracted from a speech signal. Next, five kinds of statistic variables of these features are calculated to obtain utterance-level features. The Fisher feature selection criterion is employed to select high-performance features, removing redundant information. In the feature fusion stage, the GA is is used to adaptively search for the best feature fusion weight. Finally, using the fused feature, the proposed speech emotion recognition model based on a DT support vector machine model is realized. Experimental results on the Berlin speech emotion database and the Chinese emotion speech database indicate that the proposed model outperforms an average weight fusion method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2787-2800
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• 2022

The aging society increases emergency situations of the elderly living alone and a variety of social crimes. In order to prevent them, techniques to detect emergency situations through voice are actively researched. This study proposes CutPaste-based anomaly detection model using multi-scale feature extraction in time series streaming data. In the proposed method, an audio file is converted into a spectrogram. In this way, it is possible to use an algorithm for image data, such as CNN. After that, mutli-scale feature extraction is applied. Three images drawn from Adaptive Pooling layer that has different-sized kernels are merged. In consideration of various types of anomaly, including point anomaly, contextual anomaly, and collective anomaly, the limitations of a conventional anomaly model are improved. Finally, CutPaste-based anomaly detection is conducted. Since the model is trained through self-supervised learning, it is possible to detect a diversity of emergency situations as anomaly without labeling. Therefore, the proposed model overcomes the limitations of a conventional model that classifies only labelled emergency situations. Also, the proposed model is evaluated to have better performance than a conventional anomaly detection model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.4028-4042
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• 2021

Aiming at the problem of software defect prediction difficulty caused by insufficient software defect marker samples and unbalanced classification, a semi-supervised software defect prediction model based on a tri-training algorithm was proposed by combining feature normalization, over-sampling technology, and a Tri-training algorithm. First, the feature normalization method is used to smooth the feature data to eliminate the influence of too large or too small feature values on the model's classification performance. Secondly, the oversampling method is used to expand and sample the data, which solves the unbalanced classification of labelled samples. Finally, the Tri-training algorithm performs machine learning on the training samples and establishes a defect prediction model. The novelty of this model is that it can effectively combine feature normalization, oversampling techniques, and the Tri-training algorithm to solve both the under-labelled sample and class imbalance problems. Simulation experiments using the NASA software defect prediction dataset show that the proposed method outperforms four existing supervised and semi-supervised learning in terms of Precision, Recall, and F-Measure values.