• Journal of the Korean Data and Information Science Society
• /
• v.28 no.2
• /
• pp.461-471
• /
• 2017

In this paper we propose a feature selection method identifying important features in the semivarying coefficient model. One important issue in semivarying coefficient model is how to estimate the parametric and nonparametric components. Another issue is how to identify important features in the varying and the constant effects. We propose a feature selection method able to address this issue using generalized cross validation functions of the varying coefficient least squares support vector regression (LS-SVR) and the linear LS-SVR. Numerical studies indicate that the proposed method is quite effective in identifying important features in the varying and the constant effects in the semivarying coefficient model.

• Journal of Information Processing Systems
• /
• v.15 no.1
• /
• pp.91-99
• /
• 2019

An innovative tone modeling framework based on deep neural networks in tone recognition was proposed in this paper. In the framework, both the prosodic features and the articulatory features were firstly extracted as the raw input data. Then, a 5-layer-deep deep belief network was presented to obtain high-level tone features. Finally, support vector machine was trained to recognize tones. The 863-data corpus had been applied in experiments, and the results show that the proposed method helped improve the recognition accuracy significantly for all tone patterns. Meanwhile, the average tone recognition rate reached 83.03%, which is 8.61% higher than that of the original method.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.16 no.1
• /
• pp.17-27
• /
• 2021

In this paper, we develop a galaxy image classification method based on hand-crafted features and machine learning techniques. Additionally, we provide an empirical analysis to reveal which combination of the techniques is effective for galaxy image classification. To achieve this, we developed a framework which consists of four modules such as preprocessing, feature extraction, feature post-processing, and classification. Finally, we found that the best technique for galaxy image classification is a method to use a median filter, ORB vector features and a voting classifier based on RBF SVM, random forest and logistic regression. The final method is efficient so we believe that it is applicable to embedded environments.

• Journal of Information Processing Systems
• /
• v.16 no.6
• /
• pp.1250-1260
• /
• 2020

This study proposes a novel deep neural network model that can accurately detect objects and their relationships in an image and represent them as a scene graph. The proposed model utilizes several multimodal features, including linguistic features and visual context features, to accurately detect objects and relationships. In addition, in the proposed model, context features are embedded using graph neural networks to depict the dependencies between two related objects in the context feature vector. This study demonstrates the effectiveness of the proposed model through comparative experiments using the Visual Genome benchmark dataset.

• 한국HCI학회:학술대회논문집
• /
• 2009.02a
• /
• pp.263-266
• /
• 2009

Comments are short and not use spacing words or comma more than general document. We convert the 7-gram into 3-gram and select key features using topic signature. Topic signature is widely used for selecting features in document classification and summarization. We use the SVM(Support Vector Machines) as a classifier. From the result of experiments, we can see that the proposed method is outstanding over the previous methods. The proposed system can also apply to other languages.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.30 no.6_2
• /
• pp.665-672
• /
• 2012

Amid rapidly increasing imagery inputs and their volume in a remote sensing imagery database, Content-Based Image Retrieval (CBIR) is an effective tool to search for an image feature or image content of interest a user wants to retrieve. It seeks to capture salient features from a 'query' image, and then to locate other instances of image region having similar features elsewhere in the image database. For a CBIR approach that uses texture as a primary feature primitive, designing a texture descriptor to better represent image contents is a key to improve CBIR results. For this purpose, an extended feature vector combining the Gabor filter and co-occurrence histogram method is suggested and evaluated for quantitywise and qualitywise retrieval performance criterion. For the better CBIR performance, assessing similarity between high dimensional feature vectors is also a challenging issue. Therefore a number of distance metrics (i.e. L1 and L2 norm) is tried to measure closeness between two feature vectors, and its impact on retrieval result is analyzed. In this paper, experimental results are presented with several CBIR samples. The current results show that 1) the overall retrieval quantity and quality is improved by combining two types of feature vectors, 2) some feature is better retrieved by a specific feature vector, and 3) retrieval result quality (i.e. ranking of retrieved image tiles) is sensitive to an adopted similarity metric when the extended feature vector is employed.

• Proceedings of the IEEK Conference
• /
• 1998.10a
• /
• pp.1263-1266
• /
• 1998

In this paper, we propose an optimal feature extraction method for normally distributed multiclass data. We search the whole feature space to find a set of features that give the smallest classification error for the Gaussian ML classifier. Initially, we start with an arbitrary feature vector. Assuming that the feature vector is used for classification, we compute the classification error. Then we move the feature vector slightly and compute the classification error with this vector. Finally we update the feature vector such that the classification error decreases most rapidly. This procedure is done by taking gradient. Alternatively, the initial vector can be those found by conventional feature extraction algorithms. We propose two search methods, sequential search and global search. Experiment results show that the proposed method compares favorably with the conventional feature extraction methods.

• Proceedings of the KIPE Conference
• /
• 1998.11a
• /
• pp.5-8
• /
• 1998

The adaptation to vector control theory is so generalized that it is widely used for implementing the high-performance of AC machine. Nowadays, One-Chip microprocessors or DSP chips are being well-used to implement Vector Control algorithm. DSP Chip have less flexibility for memory decoding and I/O rather than One-Chip microprocessor so that is requires more additional circuit and high cost. And the past One-Chip micro processors have difficult of implementation the complex algorithm because of small memory capacity and low arithmetic performance. Therefore we implemented the vector control algorithm of PMSM(Permanent Magnetic Synchronous Motors) using 80296SA form intel , which have many features as 6M memory space, 500MHz clock frequency, including memory decoding circuit and general I/O, Special I/O(EPA, Interrupt controller, Timer/Count, PWM generator) which is proper controller for the complex algorithm or operation program requiring so much memory capacity, So in this paper we fully digitized the vector control of PMSM included SVPWM Voltage controller using the intel 80296SA

• Journal of Power Electronics
• /
• v.11 no.5
• /
• pp.688-696
• /
• 2011

This paper proposes a simple space vector PWM (SVPWM) scheme including overmodulation operation for 3-level NPC (Neutral Point Clamped) Inverters. The proposed scheme features a simple decision and calculation procedure for determining switching times in the overmodulation range by utilizing the duty calculation method used in 2-level inverters and the minimum phase error projection method widely employed in motor drive systems. The proposed scheme does not need to detect the angle of the reference vector or calculate trigonometric functions to determine the magnitude of the voltage vector. The magnitude of the angle of the new reference voltage vector is decided in advance with the help of the Fourier Series Expansion to extend the linearity of the output voltage of 3-level inverters in the overmodulation region. Experimental results demonstrate the validity of the proposed SVPWM scheme including overmodulation operation for 3-level NPC inverters.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.18 no.5
• /
• pp.718-723
• /
• 2008

Bearing is one of the important mechanical elements used in various industrial equipments. Most of failures occurred during the equipment operation result from bearing defects and breakages. Therefore, monitoring of bearings is essential in preventing equipment breakdowns and reducing unexpected loss. The purpose of this paper is to present an online monitoring method to predict bearing states using vibration signals. Bearing vibrations, which are collected as a form of profile signal, are first analyzed by a discrete wavelet transform. Next, some statistical features are obtained from the resultant wavelet coefficients. In order to select significant ones among them, analysis of variance (ANOVA) is employed in this paper. Statistical features screened in this way are used as input variables to support vector machine (SVM). An hierarchical SVM tree is proposed for dealing with multi-class problems. The result of numerical experiments shows that the proposed SVM tree has a competent performance for classifying bearing fault states.