• Wind and Structures
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• v.35 no.6
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• pp.369-383
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• 2022

Wind tunnel experiment was carried out to study the cross-wind layer forces on a square cross-section building model using a synchronous multi-pressure sensing system. The stationarity of measured wind loadings are firstly examined, revealing the non-stationary feature of cross-wind forces. By converting the measured non-stationary wind forces into an energetically equivalent stationary process, the characteristics of local wind forces are studied, such as power spectrum density and spanwise coherence function. Mathematical models to describe properties of cross-wind forces at different layers are thus established. Then, a conditional simulation method, which is able to ex-tend pressure measurements starting from experimentally measured points, is proposed for the cross-wind loading. The method can reproduce the non-stationary cross-wind force by simulating a stationary process and the corresponding time varying amplitudes independently; in this way the non-stationary wind forces can finally be obtained by combining the two parts together. The feasibility and reliability of the proposed method is highlighted by an ex-ample of across wind loading simulation, based on the experimental results analyzed in the first part of the paper.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.9
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• pp.77-84
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• 2013

There are lots of tries to make a combination between the technology development which is fast arisen and cultural phenomenon which imply in it. We called this research area as the cultural computing or cultural modeling. In this paper, we examine the cultural user interface design, especially cultural design structure based on the contents considering the research trend of the cultural modeling. To design of the contents based on the culture, there is a need to draw a structure of the cultural feature for the contents. To do this, we combine Hofstede's cultural dimensions model with the data of contents and then we suggest cultural index of content(CiCo). Furthermore, we draw national index of cultural content(NiCC), through conjoining CiCo with preference pattern of content consumption for the nations. Suggested CiCo and NiCC are based on Hofstede's model, however they are improved approximately 10% of the explanatory of model than the Hofstede's.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.27 no.2
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• pp.71-86
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• 2022

The water quality index (WQI) has been widely used to evaluate marine water quality. The WQI in Korea is categorized into five classes by marine environmental standards. But, the WQI calculation on huge datasets is a very complex and time-consuming process. In this regard, the current study proposed machine learning (ML) based models to predict WQI class by using water quality datasets. Sihwa Lake, one of specially-managed coastal zone, was selected as a modeling site. In this study, adaptive boosting (AdaBoost) and tree-based pipeline optimization (TPOT) algorithms were used to train models and each model performance was evaluated by metrics (accuracy, precision, F1, and Log loss) on classification. Before training, the feature importance and sensitivity analysis were conducted to find out the best input combination for each algorithm. The results proved that the bottom dissolved oxygen (DO_Bot) was the most important variable affecting model performance. Conversely, surface dissolved inorganic nitrogen (DIN_Sur) and dissolved inorganic phosphorus (DIP_Sur) had weaker effects on the prediction of WQI class. In addition, the performance varied over features including stations, seasons, and WQI classes by comparing spatio-temporal and class sensitivities of each best model. In conclusion, the modeling results showed that the TPOT algorithm has better performance rather than the AdaBoost algorithm without considering feature selection. Moreover, the WQI class for unknown water quality datasets could be surely predicted using the TPOT model trained with satisfactory training datasets.

• Journal of KIISE
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• v.41 no.11
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• pp.921-926
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• 2014

Face detection refers to the process extracting facial regions in an input image, which can improve speed and accuracy of recognition or authorization system, and has diverse applicability. Since conventional works have tried to detect faces based on the whole shape of faces, its detection performance can be degraded by occlusion made with accessories or parts of body. In this paper we propose a method combining local feature descriptors and probability modeling in order to detect partially occluded face effectively. In training stage, we represent an image as a set of local feature descriptors and estimate a statistical model for normal faces. When the test image is given, we find a region that is most similar to face using our face model constructed in training stage. According to experimental results with benchmark data set, we confirmed the effect of proposed method on detecting partially occluded face.

• Journal of the Korean Society for Nondestructive Testing
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• v.27 no.5
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• pp.383-392
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• 2007

In this paper, we study the prediction of depth and width of a defect in steam generator tube in nuclear power plant using neural network. To this end, we first generate eddy current testing (ECT) signals for 4 defect patterns of SG tube: I-In type, I-Out type, V-In type, and V-Out type. In particular, we generate 400 ECT signals for various widths and depths for each defect type by the numerical analysis program based on finite element modeling. From those generated ECT signals, we extract new feature vectors for the prediction of defect size, which include the angle between the two points where the maximum impedance and half the maximum impedance are achieved. Using the extracted feature vector, multi-layer perceptron with one hidden layer is used to predict the size of defects. Through the computer simulation study, it is shown that the proposed method achieves decent prediction performance in terms of maximum error and mean absolute percentage error (MAPE).

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.5
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• pp.129-136
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• 2011

This paper presents the coding method that have multi-mode and efficiency of audio codecs using the feature of audio signal. Recently, the developed extension super-wideband codec based on G.718 wideband divides two mode between Generic and Sinusiodal. So codec efficently encode audio signal exist in super-wideband. But the codec is not as efficent coding for harmonic component of wind instrument and string instrument and individual-Line component of percussion instrument. The proposed method are modeling and encoding multiple pitch and individual-line feature using multi mode coding. For the performance evaluation, we used SNR in MDCT domain for objective test and MUSHRA test for subjective test. As a result, the performance of SNR and MUSHRA test of the proposed method have better performance than the G.718 super-wideband codec.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2393-2398
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• 2003

In this paper, we proposes a new fuzzy inference system for modeling nonlinear systems given input and output data. In the suggested fuzzy inference system, the number of fuzzy rules and parameter values of membership functions are automatically decided by using the kernel-based method. The kernel-based method individually performs linear transformation and kernel mapping. Linear transformation projects input space into linearly transformed input space. Kernel mapping projects linearly transformed input space into high dimensional feature space. The structure of the proposed fuzzy inference system is equal to a Takagi-Sugeno fuzzy model whose input variables are weighted linear combinations of input variables. In addition, the number of fuzzy rules can be reduced under the condition of optimizing a given criterion by adjusting linear transformation matrix and parameter values of kernel functions using the gradient descent method. Once a structure is selected, coefficients in consequent part are determined by the least square method. Simulated result illustrates the effectiveness of the proposed technique.

• The Transactions of the Korea Information Processing Society
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• v.7 no.3
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• pp.891-900
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• 2000

A Visual development framework for embedded system is presented based on virtual prototyping. Embedded systems often are used in life critical situation, where reliability is very important. Time_to_market, correctness, user_friendly_design are another features required for embedded system design. However, embedded systems are today designed with an ad hoc approach that is heavily based on earlier experience with similar products. We believe that new design paradigm is needed and it should be based on the use of formal model and visual system to describe the behavior of the system at a high level abstraction. Virtual prototyping has all the required features. It has the following advantages; correct design, clear interface definition, idea experimentation, increased communication. In this paper, we describe the design and implementation of VIP/Sim(Virtual Prototyping Simulator), a visionary development framework for embedded system design. New feature such as state polymorphism is augmented to the de_facto standard formal language, statechart, for enhanced dynamic modeling. Actual design experience with VIP/Sim is also discussed.

• The KIPS Transactions:PartB
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• v.12B no.4 s.100
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• pp.437-442
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• 2005

For the purpose of speaker normalization in speaker independent speech recognition systems, experiments are conducted on a method based on Gaussian mixture model(GMM). The method, which is an improvement of the previous study based on vector quantizer, consists of modeling the probability distribution of canonical feature vectors by a GMM with an appropriate number of clusters, and of estimating the warp factor of a test speaker by making use of the obtained probabilistic model. The purpose of this study is twofold: improving the existing ML based methods, and comparing the performance of what is called 'soft decision' method with that of the previous study based on vector quantizer. The effectiveness of the proposed method is investigated by recognition experiments on the TIMIT corpus. The experimental results showed that a little improvement could be obtained tv adjusting the number of clusters in GMM appropriately.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1999.06a
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• pp.133-140
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• 1999

In this paper, we propose a method of image-based three-dimensional modeling for realistic facial expression. In the proposed method, real human facial images are used to deform a generic three-dimensional mesh model and the deformed model is animated to generate facial expression animation. First, we take several pictures of the same person from several view angles. Then we project a three-dimensional face model onto the plane of each facial image and match the projected model with each image. The results are combined to generate a deformed three-dimensional model. We use the feature-based image metamorphosis to match the projected models with images. We then create a synthetic image from the two-dimensional images of a specific person's face. This synthetic image is texture-mapped to the cylindrical projection of the three-dimensional model. We also propose a muscle-based animation technique to generate realistic facial expression animations. This method facilitates the control of the animation. lastly, we show the animation results of the six represenative facial expressions.