• Korean Journal of Computational Design and Engineering
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• v.9 no.2
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• pp.122-132
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• 2004

Progressive mesh representation and generation have become one of the most important issues in network-based computer graphics. However, current researches are mostly focused on triangular mesh models. On the other hand, solid models are widely used in industry and are applied to advanced applications such as product design and virtual assembly. Moreover, as the demand to share and transmit these solid models over the network is emerging, the generation and the transmission of progressive solid models depending on specific engineering needs and purpose are essential. In this paper, we present a Cellular Topology-based approach to generating and transmitting progressive solid models from a feature-based solid model for internet-based design and collaboration. The proposed approach introduces a new scheme for storing and transmitting solid models over the network. The Cellular Topology (CT) approach makes it possible to effectively generate progressive solid models and to efficiently transmit the models over the network with compact model size. Thus, an arbitrary solid model SM designed by a set of design features is stored as a much coarser solid model SM/sup 0/ together with a sequence of n detail records that indicate how to incrementally refine SM/sup 0/ exactly back into the original solid model SM = SM/sup 0/.

• Journal of Information Processing Systems
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• v.9 no.1
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• pp.173-188
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• 2013

In Facial Expression Recognition Systems (FERS), only particular regions of the face are utilized for discrimination. The areas of the eyes, eyebrows, nose, and mouth are the most important features in any FERS. Applying facial features descriptors such as the local binary pattern (LBP) on such areas results in an effective and efficient FERS. In this paper, we propose an automatic facial expression recognition system. Unlike other systems, it detects and extracts the informative and discriminant regions of the face (i.e., eyes, nose, and mouth areas) using Haar-feature based cascade classifiers and these region-based features are stored into separate image files as a preprocessing step. Then, LBP is applied to these image files for facial texture representation and a feature-vector per subject is obtained by concatenating the resulting LBP histograms of the decomposed region-based features. The one-vs.-rest SVM, which is a popular multi-classification method, is employed with the Radial Basis Function (RBF) for facial expression classification. Experimental results show that this approach yields good performance for both frontal and near-frontal facial images in terms of accuracy and time complexity. Cohn-Kanade and JAFFE, which are benchmark facial expression datasets, are used to evaluate this approach.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.5
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• pp.1766-1784
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• 2014

Finger vein images contain rich oriented features. Local line binary pattern (LLBP) is a good oriented feature representation method extended from local binary pattern (LBP), but it is limited in that it can only extract horizontal and vertical line patterns, so effective information in an image may not be exploited and fully utilized. In this paper, an orientation-selectable LLBP method, called generalized local line binary pattern (GLLBP), is proposed for finger vein recognition. GLLBP extends LLBP for line pattern extraction into any orientation. To effectually improve the matching accuracy, the soft power metric is employed to calculate the matching score. Furthermore, to fully utilize the oriented features in an image, the matching scores from the line patterns with the best discriminative ability are fused using the Hamacher rule to achieve the final matching score for the last recognition. Experimental results on our database, MMCBNU_6000, show that the proposed method performs much better than state-of-the-art algorithms that use the oriented features and local features, such as LBP, LLBP, Gabor filter, steerable filter and local direction code (LDC).

• Annual Conference on Human and Language Technology
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• 1999.10e
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• pp.297-303
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• 1999

오늘날 전산망을 통해 대량의 다양한 언어 정보가 일상 언어로 교환되고 있다. 따라서 대량의 이러한 정보를 효율적으로 처리할 수 있는 언어 정보 처리 시스템이 필요하다. Hausser (1999)와 이기용(1999)는 그러한 언어 정보 처리 시스템으로 데이터베이스 의미론을 주장하였다. 이 의미론의 특징은 자연언어의 정보 처리 시스템 구축에 상업용 데이터베이스 관리 시스템을 활용한다는 점이다. 이때 야기되는 문제 중의 하나가 표상(representation)의 문제이다. 그 이유는 언어학의 표상 방법이 데이터베이스 관리 시스템의 표상 방법과 다르기 때문이다. 특히, 관계형 데이터베이스 관리 시스템(RDBMS)에서는 테이블 (table) 형식으로 각종 정보를 표시한다. 따라서, 이 논문의 주안점(主眼点)은 언어학에서 흔히 쓰이는 표상 방법, 즉 문장의 통사 구조를 표시하는 수형(tree)이나 의미 구조를 표시하는 논리 형태(logical form), 또는 단어나 구의 특성을 나타내는 자질 구조(feature structure)를 테이블 형식으로 대체하는 방법을 모색하는 것이다. 더욱이 관계형 데이터베이스 관리 시스템에서는 테이블에 대한 각종 연산, 특히 두 테이블을 연결(link)하는 작업이 가능하고 이런 연산 과정을 통해 정보를 통합하거나 여과할 수 있기 때문에 관련 정보를 하나의 테이블에 표상하거나 정보 자료의 분산 저장과 자료의 순수성을 유지하는 것이 용이하다. 이 논문은 곧 이러한 점을 가급적 간단한 예를 들어 설명하는 데 그 목적이 있다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.12C
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• pp.1162-1166
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• 2005

A new low dimensional feature representation technique is presented in this paper. Linear discriminant analysis is a popular feature extraction method. However, in the case of high dimensional data, the computational difficulty and the small sample size problem are often encountered. In order to solve these problems, we propose two dimensional direct LDA algorithm, which directly extracts the image scatter matrix from 2D image and uses Direct LDA algorithm for face recognition. The ORL face database is used to evaluate the performance of the proposed method. The experimental results indicate that the performance of the proposed method is superior to DLDA.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2013.10a
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• pp.338-345
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• 2013

Induction motors play an increasing importance in industrial manufacturing. Therefore, the state monitoring systems also have been considering as the key in dealing with their negative effect by absorbing faulty symptoms in motors. There are numerous proposed systems in literature, in which, several kinds of signals are utilized as the input. To solve the multiple faults problem of induction motors, like the proposed system, the vibration signals is good candidate. In this study, a new signal processing scheme was utilized, which transforms the time domain vibration signal into the spatial domain as an image. Then the spatial features of converted image then have been extracted by applying the dominant neighbourhood structure (DNS) algorithm. In addition, these feature vectors were evaluated to obtain the fruitful dimensions, which support to discriminate between states of motors. Because of reliability, the conventional one-against-all (OAA) multi-class support vector machines (MCSVM) have been utilized in the proposed system as classifier module. Even though examined in severity levels of signal-to-noise ratio (SNR), up to 15dB, the proposed system still reliable in term of two criteria: true positive (TF) and false positive (FP). Furthermore, it also offers better performance than five state-of-the-art systems.

• ETRI Journal
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• v.44 no.2
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• pp.286-299
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• 2022

Human activity recognition in real time is a challenging task. Recently, a plethora of studies has been proposed using deep learning architectures. The implementation of these architectures requires the high computing power of the machine and a massive database. However, handcrafted features-based machine learning models need less computing power and very accurate where features are effectively extracted. In this study, we propose a handcrafted model based on three-dimensional sequential skeleton data. The human body skeleton movement over a frame is computed through joint positions in a frame. The joints of these skeletal frames are projected into two-dimensional space, forming a "movement polygon." These polygons are further transformed into a one-dimensional space by computing amplitudes at different angles from the centroid of polygons. The feature vector is formed by the sampling of these amplitudes at different angles. The performance of the algorithm is evaluated using a support vector machine on four public datasets: MSR Action3D, Berkeley MHAD, TST Fall Detection, and NTU-RGB+D, and the highest accuracies achieved on these datasets are 94.13%, 93.34%, 95.7%, and 86.8%, respectively. These accuracies are compared with similar state-of-the-art and show superior performance.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.47-54
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• 2023

For Bengali music emotion classification, deep learning models, particularly CNN and RNN are frequently used. But previous researches had the flaws of low accuracy and overfitting problem. In this research, attention-based Conv1D and BiGRU model is designed for music emotion classification and comparative experimentation shows that the proposed model is classifying emotions more accurate. We have proposed a Conv1D and Bi-GRU with the attention-based model for emotion classification of our Bengali music dataset. The model integrates attention-based. Wav preprocessing makes use of MFCCs. To reduce the dimensionality of the feature space, contextual features were extracted from two Conv1D layers. In order to solve the overfitting problems, dropouts are utilized. Two bidirectional GRUs networks are used to update previous and future emotion representation of the output from the Conv1D layers. Two BiGRU layers are conntected to an attention mechanism to give various MFCC feature vectors more attention. Moreover, the attention mechanism has increased the accuracy of the proposed classification model. The vector is finally classified into four emotion classes: Angry, Happy, Relax, Sad; using a dense, fully connected layer with softmax activation. The proposed Conv1D+BiGRU+Attention model is efficient at classifying emotions in the Bengali music dataset than baseline methods. For our Bengali music dataset, the performance of our proposed model is 95%.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.11a
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• pp.29-32
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• 2020

Due to the lack of improper image acquisition process, noise induction is an inevitable step. As a result, objective image quality assessment (IQA) plays an important role in estimating the visual quality of noisy image. Plenty of IQA methods have been proposed including traditional signal processing based methods as well as current deep learning based methods where the later one shows promising performance due to their complex representation ability. The deep learning based methods consists of several convolution layers and down sampling layers for feature extraction and fully connected layers for regression. Usually, the down sampling is performed by using max-pooling layer after each convolutional block. We reveal that this max-pooling causes information loss despite of knowing their importance. Consequently, we propose a better IQA method that replaces the max-pooling layers with strided convolutions to down sample the feature space and since the strided convolution layers have learnable parameters, they preserve optimal features and discard redundant information, thereby improve the prediction accuracy. The experimental results verify the effectiveness of the proposed method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.5
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• pp.1755-1777
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• 2022

The development of wireless communication technology has led to the underutilization of radio spectra. To address this limitation, an intelligent cognitive radio network was developed. Specific emitter identification (SEI) is a key technology in this network. However, in realistic non-cooperative scenarios, the system may detect signal classes beyond those in the training database, and only a few labeled signal samples are available for network training, both of which deteriorate identification performance. To overcome these challenges, a meta-learning-based open-set identification system is proposed for SEI. First, the received signals were pre-processed using bi-spectral analysis and a Radon transform to obtain signal representation vectors, which were then fed into an open-set SEI network. This network consisted of a deep feature extractor and an intrinsic feature memorizer that can detect signals of unknown classes and classify signals of different known classes. The training loss functions and the procedures of the open-set SEI network were then designed for parameter optimization. Considering the few-shot problems of open-set SEI, meta-training loss functions and meta-training procedures that require only a few labeled signal samples were further developed for open-set SEI network training. The experimental results demonstrate that this approach outperforms other state-of-the-art SEI methods in open-set scenarios. In addition, excellent open-set SEI performance was achieved using at least 50 training signal samples, and effective operation in low signal-to-noise ratio (SNR) environments was demonstrated.