• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2013년도 춘계학술발표대회
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• pp.204-207
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• 2013

Music genre classification is an essential component for music information retrieval system. There are two important components to be considered for better genre classification, which are audio feature extraction and classifier. This paper incorporates two different kinds of features for genre classification, timbral texture and rhythmic content features. Timbral texture contains several spectral and Mel-frequency Cepstral Coefficient (MFCC) features. Before choosing a timbral feature we explore which feature contributes less significant role on genre discrimination. This facilitates the reduction of feature dimension. For the timbral features up to the 4-th order central moments and the covariance components of mutual features are considered to improve the overall classification result. For the rhythmic content the features extracted from beat histogram are selected. In the paper Extreme Learning Machine (ELM) with bagging is used as classifier for classifying the genres. Based on the proposed feature sets and classifier, experiment is performed with well-known datasets: GTZAN databases with ten different music genres, respectively. The proposed method acquires the better classification accuracy than the existing approaches.

• International journal of advanced smart convergence
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• 제12권3호
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• pp.13-18
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• 2023

This paper aims to investigate data features for neighbor path selection (NPS) in computer network with regional failures. It is necessary to find an available alternate communication path in advance when regional failures due to earthquakes or forest fires occur simultaneously. We describe previous general heuristics and simulation heuristic to solve the NPS problem in the regional fault network. The data features of general heuristics using proximity and sharing factor and the data features of simulation heuristic using machine learning are explained through examples. Simulation heuristic may be better than general heuristics in terms of communication success. However, additional data features are necessary in order to apply the simulation heuristic to the real environment. We propose novel data features for NPS in computer network with regional failures and Keras modeling for computing the communication success probability of candidate neighbor path.

• 한국재료학회지
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• 제34권7호
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• pp.363-369
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• 2024

In this study, the magnetocaloric effect and transition temperature of bulk metallic glass, an amorphous material, were predicted through machine learning based on the composition features. From the Python module 'Matminer', 174 compositional features were obtained, and prediction performance was compared while reducing the composition features to prevent overfitting. After optimization using RandomForest, an ensemble model, changes in prediction performance were analyzed according to the number of compositional features. The R2 score was used as a performance metric in the regression prediction, and the best prediction performance was found using only 90 features predicting transition temperature, and 20 features predicting magnetocaloric effects. The most important feature when predicting magnetocaloric effects was the 'Fe' compositional ratio. The feature importance method provided by 'scikit-learn' was applied to sort compositional features. The feature importance method was found to be appropriate by comparing the prediction performance of the Fe-contained dataset with the full dataset.

• Journal of Mechanical Science and Technology
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• 제18권12호
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• pp.2095-2103
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• 2004

A macro-level CAPP system is proposed to plan the complicated mechanical prismatic parts efficiently. The system creates the efficient machining sequence of the features in a part by analyzing the feature information. Because the planning with the individual features is very complicated, feature groups are formed for effective planning using the nested relations of the features of a part, and special feature groups are determined for sequencing. The process plan is generated based on the sequences of the feature groups and features. When multiple machines are required, efficient machine assignment is performed. A series of heuristic rules are developed to accomplish it.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1994년도 추계학술대회 논문집
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• pp.737-742
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• 1994

In this paper , we propose an incremental approach for recognizing a class of machining features from a featurebased design model as a part design proceeds, utilizing various information such as nominal geometry, design intents, and design feature characteristics. The proposed apptroach can handle complex intersecting features and protrusion features designed on oblique faces. The class of recognized volumetric machining features can be expressed as Material Removal Shape Element Volumes (MRSEVs), a PDES/STEP-based library of machining features.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.700-702
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• 2003

A new method for automatic detection of absorption features is proposed. This method is based on the modulus maximum of the scale-space image calculated by continuous wavelet transform. This method is computationally efficient as compared to traditional methods. The continuum removal algorithm is than implemented on the detected absorption features to reduce some additive factors caused by other absorbing of materials. The results show that the chlorophyll absorption features are detected exactly.

• 대한음성학회지:말소리
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• 제44호
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• pp.1-14
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• 2002

Because the accuracy and the fluency are the essence in English speaking, both of them are very important in English trencher training and in-service English training programs. To get the accuracy and the fluency, the causes and the phenomena of the unnatural pronunciation have to be diagnosed. Consequently, the problematic and unnatural pronunciation of Korean elementary and secondary English teachers should be analyzed with using Acoustic Analyzing tools like CSL, Multi-speech and Praat. In addition, an attempt to Pinpoint what the causes of unnatural pronunciation was executed. Next a procedure and steps were proposed for in-service training programs that would cultivate the fluency and the accuracy. In case of elementary teachers, the unnatural pronunciation of segmental features and suprasegmental features were found much. therefore segmental features should be emphasized in the begging of pronunciation training courses and then suprasegmental features have to be emphasized. In case of secondary teachers, the unnatural pronunciation of suprasegmental features were found much. Therefore segmental and suprasegmental features have to be focused at the same time. In other words, features in word level should be focused first for elementary English teacher, and features in word level and beyond word level should be trained at the same time for secondary English teachers.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제24권1호
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• pp.23-37
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• 2020

The quadratic error metric (QEM) algorithm has been frequently used for simplification of triangular surface models that utilize the vertex-pair algorithm. Simplified models obtained using such algorithms present the advantage of smaller storage capacity requirement compared to the original models. However, a number of cases exist where significant features are lost geometrically, and these features can generally be preserved by utilizing the advantages of the curvature-weighted algorithm. Based on the vertex-based geometric features, a method capable of preserving the geometric features better than the previous algorithms is proposed in this work. To validate the effectiveness of the proposed method, a simplification experiment is conducted using several models. The results of the experiment indicate that the geometrically important features are preserved well when a local feature is present and that the error is similar to those of the previous algorithms when no local features are present.

• 한국멀티미디어학회논문지
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• 제16권7호
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• pp.820-828
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• 2013

영상에서 임의의 점에 대한 고유한 특징을 계산하는 알고리즘은 파노라마 영상의 제작, 스테레오 영상의 획득, 물체 인식, 이미지 분석 등에 다양하게 사용되는 중요한 요소이다. 일반적으로 어떤 점의 특징은 스칼라 형태가 아닌 벡터형태로 나타나게 되는데, 무수히 많은 특징 점들을 서로 비교하는 작업은 매우 많은 계산량을 요구한다. 본 연구에서는 영상의 특징점 계산에 SURF(speeded up robust features)를 이용하였고, 이미지로부터 추출된 특징을 PCA(principal component analysis)기법을 이용하여 벡터의 차원을 축소하여 연결리스트 자료구조에 정렬한 다음 특징을 비교하는 기법을 제안한다. 제안된 특징의 비교 방법을 적용할 경우 기존 방법의 매칭 정확도는 유지한 상태에서 계산시간을 줄일 수 있는 것을 실험을 통하여 확인하였다.

• 한국통신학회논문지
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• 제17권12호
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• pp.1465-1474
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• 1992

본 논문은 확장된 지역특징을 이용하여 서로 중첩된 물체를 인식하는 새로운 방법에 대해 기술한다. 먼저 모델물체의 화상으로부터 코너, 아크, 평행선 및 코너-아크로 구성된 지역특징을 추출하고 지식베이스를 구축한다. 물체의 정합을 위해, 입력화상으로 부터 지역특징을 추출한 다음, 지식베이스의 특징과의 유사성을 조사하여 유사한 특징 set으로부터 기하변환을 구한다. 기하변환이 클러스터를 형성하면, 그 클러스터의 중심으로 가설을 설정하고 역기하변환으로 정합을 검증한다. 실험을 통해, 제안된 물체인식방법이 기존의 방법에 비해 인식율과 정확도를 높인다는 것을 확인하였다.