• KIPS Transactions on Software and Data Engineering
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• v.5 no.2
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• pp.89-94
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• 2016

This paper proposed a facial expression recognition approach using SIFT feature and SVM classifier. The SIFT was generally employed as feature descriptor at key-points in object recognition fields. However, this paper applied the SIFT descriptor as feature vector for facial expression recognition. In this paper, the facial feature was extracted by applying SIFT descriptor at each sub-block image without key-point detection procedure, and the facial expression recognition was performed using SVM classifier. The performance evaluation was carried out through comparison with binary pattern feature-based approaches such as LBP and LDP, and the CK facial expression database and the JAFFE facial expression database were used in the experiments. From the experimental results, the proposed method using SIFT descriptor showed performance improvements of 6.06% and 3.87% compared to previous approaches for CK database and JAFFE database, respectively.

• The Journal of the Acoustical Society of Korea
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• v.24 no.4E
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• pp.144-151
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• 2005

We evaluate the performance of emotion recognition via speech signals when a plain speaker talks to an entertainment robot. For each frame of a speech utterance, we extract the frame-based features: pitch, energy, formant, band energies, mel frequency cepstral coefficients (MFCCs), and velocity/acceleration of pitch and MFCCs. For discriminative classifiers, a fixed-length utterance-based feature vector is computed from the statistics of the frame-based features. Using a speaker-independent database, we evaluate the performance of two promising classifiers: support vector machine (SVM) and hidden Markov model (HMM). For angry/bored/happy/neutral/sad emotion classification, the SVM and HMM classifiers yield $42.3\%\;and\;40.8\%$ accuracy, respectively. We show that the accuracy is significant compared to the performance by foreign human listeners.

• Steel and Composite Structures
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• v.29 no.3
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• pp.309-318
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• 2018

Steel girders are the structural members often used for passing long spans. Mostly being subjected to patch loading, or concentrated loading, steel girders are likely to face sudden deformation or damage e.g., web breathing. Horizontal or vertical stiffeners are employed to overcome this phenomenon. This study aims at assessing the feasibility of a machine learning method, namely the support vector machines (SVM) in predicting the patch loading resistance of longitudinally stiffened webs. A database consisting of 162 test data is utilized to develop SVM models and the model with best performance is selected for further inspection. Existing formulations proposed by other researchers are also investigated for comparison. BS5400 and other existing models (model I, model II and model III) appear to yield underestimated predictions with a large scatter; i.e., mean experimental-to-predicted ratios of 1.517, 1.092, 1.155 and 1.256, respectively; whereas the selected SVM model has high prediction accuracy with significantly less scatter. Robust nature and accurate predictions of SVM confirms its feasibility of potential use in solving complex engineering problems.

• Speech Sciences
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• v.11 no.1
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• pp.7-20
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• 2004

In this paper, we propose a two-step noise compensation algorithm in feature extraction for achieving robust speech recognition. The proposed method frees us from requiring a priori information on noisy environments and is simple to implement. First, in frequency domain, the Harmonics-based Spectral Subtraction (HSS) is applied so that it reduces the additive background noise and makes the shape of harmonics in speech spectrum more pronounced. We then apply a judiciously weighted variance Feature Vector Normalization (FVN) to compensate for both the channel distortion and additive noise. The weighted variance FVN compensates for the variance mismatch in both the speech and the non-speech regions respectively. Representative performance evaluation using Aurora 2 database shows that the proposed method yields 27.18% relative improvement in accuracy under a multi-noise training task and 57.94% relative improvement under a clean training task.

• Journal of Korea Society of Industrial Information Systems
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• v.4 no.4
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• pp.143-148
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• 1999

This paper presents a method which enables raster maps to be used like vector maps in the wed environment and develops a raster map viewer which can be executed in the wed browser. Through the preprocessing process the coordinates attached to each object in the raster map can be used as vector information. The raster map viewer makes the spatial analysis possible using the attribute database connected to the coordinates of each object, This map viewer makes the Web GIS can be constructed at the lower cost because the viewer uses the characteristics of raster map. And the map viewer has merit point of easily developing a component for spatial analysis.

• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.557-560
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• 2003

Voice is one of the promising biometrics because it is one of the most convenient ways human would distinguish someone from others. The target of speaker verification is to divide the client from imposters. Support Vector Machine(SVM) is in the limelight as a binary classifier, so it can work well in speaker verification. In this paper, we combined SVM with genetic algorithm(GA) to reduce the dimensionality of input feature. Experiments were conducted with Korean connected digit database using different feature dimensions. The verification accuracy of SVM with GA is slightly lower than that of SVM, but the proposed algorithm has greater strength in the memory limited systems.

• 한국전산유체공학회:학술대회논문집
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• 1999.05a
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• pp.113-123
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• 1999

In this study a pure vector potential method (PVPM) for a three dimensional, unsteady, incompressible flow is proposed. A simplified version for a two dimensional problem is described in detail, and a method to prescribe appropriate boundary conditions is also presented. The resulting numerical algorithm is applied to the cavity flow driven by an impulsively started wall and also to the Stokes' first problem. Some important unsteady/steady features are captured for these two flows, and quantitative agreements of flow variables with available reference database are good.

• KIEE International Transaction on Systems and Control
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• v.12D no.1
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• pp.7-11
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• 2002

In this study, fur the shape-based image retrieval, a method using local differential invariants is proposed. This method calculates the differential invariant feature vector at every feature point extracted by Harris comer point detector. Then through vector quantization using LBG algorithm, all feature vectors are represented by a codebook index. All images are indexed by the histogram of codebook index, and by comparing the histograms the similarity between images is obtained. The proposed method is compared with the existing method by performing experiments for image database including various 1100 trademarks.

• Proceedings of the KSRS Conference
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• v.2
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• pp.635-638
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• 2006

Regression of conventional prediction techniques in data mining uses the model which is generated from the training step. This model is applied to new input data without any change. If this model is applied directly to time series, the rate of prediction accuracy will be decreased. This paper proposes an incremental regression for time series prediction like typhoon track prediction. This technique considers the characteristic of time series which may be changed over time. It is composed of two steps. The first step executes a fractional process for applying input data to the regression model. The second step updates the model by using its information as new data. Additionally, the model is maintained by only recent data in a queue. This approach has the following two advantages. It maintains the minimum information of the model by using a matrix, so space complexity is reduced. Moreover, it prevents the increment of error rate by updating the model over time. Accuracy rate of the proposed method is measured by RME(Relative Mean Error) and RMSE(Root Mean Square Error). The results of typhoon track prediction experiment are performed by the proposed technique IMLR(Incremental Multiple Linear Regression) is more efficient than those of MLR(Multiple Linear Regression) and SVR(Support Vector Regression).

• Korean Management Science Review
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• v.13 no.2
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• pp.127-146
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• 1996

Integrating database views is an important step in the conceptual database design process. This paper develops a view integration support system by using a semantic object model. In order to determine the order of the integration, affinities among views and objects are analyzed by employing the vector space theory. Semantic conflicts such as naming and structural conflicts are then resolved. The resolution results are stored in a view repository. Objects and views are integrated and stored in this view repository until all views are considered. A prototype for the system is built and can be used in a client/server environment.