• Structural Engineering and Mechanics
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• v.88 no.3
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• pp.273-285
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• 2023

This study examines how the interaction between soil and a wind turbine's supporting system affects the optimal design. The supporting system resting on an elastic soil foundation consists of a steel conical tower and a concrete circular raft foundation, and it is subjected to wind loads. The material cost of the supporting system is aimed to be minimized employing various metaheuristic optimization algorithms including teaching-learning based optimization (TLBO). To include the influence of the soil in the optimization process, modified Vlasov and Gazetas elastic soil models are integrated into the optimization algorithms using the application programing interface (API) feature of the structural analysis program providing two-way data flow. As far as the optimal designs are considered, the best minimum cost design is achieved for the TLBO algorithm, and the modified Vlasov model makes the design economical compared with the simple Gazetas and infinitely rigid soil models. Especially, the optimum design dimensions of the raft foundation extremely reduce when the Vlasov realistic soil reactions are included in the optimum analysis. Additionally, as the designated design wind speed is decreased, the beneficial impact of soil interaction on the optimum material cost diminishes.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.15 no.4
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• pp.277-282
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• 2015

Braincomputer interface (BCI) technology is making advances in the field of humancomputer interaction (HCI). To improve the BCI technology, we study the changes in the electroencephalogram (EEG) signals for six levels of grip strength: 10%, 20%, 40%, 50%, 70%, and 80% of the maximum voluntary contraction (MVC). The measured EEG data are categorized into three classes: Weak, Medium, and Strong. Features are then extracted using power spectrum analysis and multiclass-common spatial pattern (multiclass-CSP). Feature datasets are classified using a support vector machine (SVM). The accuracy rate is higher for the Strong class than the other classes.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.12
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• pp.2314-2318
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• 2010

For human-robot interaction, a robot should recognize the meaning of human behavior. In the case of static behavior such as face expression and sign language, the information contained in a single image is sufficient to deliver the meaning to the robot. In the case of dynamic behavior such as gestures, however, the information of sequential images is required. This paper proposes behavior classification by using fuzzy classifier to deliver the meaning of dynamic behavior to the robot. The proposed method extracts feature points from input images by a skeleton model, generates a vector space from a differential image of the extracted feature points, and uses this information as the learning data for fuzzy classifier. Finally, we show the effectiveness and the feasibility of the proposed method through experiments.

• Korean Institute of Interior Design Journal
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• v.18 no.5
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• pp.31-39
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• 2009

Today, 'Emotion' has come to stay as a powerful culture code. Though there has been not a few research results based on the recognition of the importance of emotion, the concept of 'Emotion' still differs according not only to researchers but also to the fields. There firstly lies the aim of this study to research the moaning and feature of 'Emotion' through John Dewey's Theory of Experience. Secondly, this study tries to categorize the strata of emotion in space, and further tries to present the methodology for the study of emotion shown in the space. Emotion is the impulse corresponding to the root force drawing out indefinite situations into the context of the problem, and is also the force which integrates all the elements in the process of reflective correlation. Emotion can be referred to as the activating and combining force which makes it possible for the separate elements to be related to the whole as one, and as the feature forming the completion of the transaction between organisms and environment.

• Industrial Engineering and Management Systems
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• v.15 no.2
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• pp.148-155
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• 2016

Existing power plants may consume significant amounts of fuel and require high operating costs, partly because of poor electrical power output estimates. This paper suggests a continuous conditional random field (C-CRF) model to predict more precisely the full-load electrical power output of a base load operated combined cycle power plant. We introduce three feature functions to model association potential and one feature function to model interaction potential. Together, these functions compose the C-CRF model, and the model is transformed into a multivariate Gaussian distribution with which the operation parameters can be modeled more efficiently. The performance of our model in estimating power output was evaluated by means of a real dataset and our model outperformed existing methods. Moreover, our model can be used to estimate confidence intervals of the predicted output and calculate several probabilities.

• Interaction and multiscale mechanics
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• v.3 no.4
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• pp.373-387
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• 2010

This study is intended to assess low frequency sound radiated from a viaduct under normal traffic. The bridge comprises steel box girders and wide cantilever decks on which vehicles pass. The low frequency sound and the acceleration response of the bridge under normal traffic are measured to investigate how bridge vibrations affect the low frequency sound observed near the bridge. Observations demonstrate that strong relationships exist between frequency characteristic of bridge's acceleration response and the sound pressure level of low frequency sound. A noteworthy point is that the dynamic feature of the sound pressure level is mostly affected by dynamic feature of the span locating near the observation point.

• Structural Engineering and Mechanics
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• v.57 no.3
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• pp.529-541
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• 2016

Under the interaction between dam body, dam foundation and external environment, the dam structural behavior presents the time-varying nonlinear characteristics. According to the prototypical observations, the correct identification on above nonlinear characteristics is very important for dam safety control. It is difficult to implement the description, analysis and diagnosis for dam structural behavior by use of any linear method. Based on the rescaled range analysis approach, the algorithm is proposed to identify and extract the fractal feature on observed dam structural behavior. The displacement behavior of one actual dam is taken as an example. The fractal long-range correlation for observed displacement behavior is analyzed and revealed. The feasibility and validity of the proposed method is verified. It is indicated that the mechanism evidence can be provided for the prediction and diagnosis of dam structural behavior by using the fractal identification method. The proposed approach has a high potential for other similar applications.

• Proceedings of the Acoustical Society of Korea Conference
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• 1998.08a
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• pp.250-253
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• 1998

In digital communicatioin networks, speech recognition systems conventionally reconstruct speech followed by extracting feature [parameters. In this paper, we consider a useful approach by incorporating speech coding parameters into the speech recognizer. Most speech coders employed in the networks represent line spectral pairs as spectral parameters. In order to improve the recognition performance of the LSP-based speech recognizer, we introduce two different ways: one is to devise weighed distance measures of LSPs and the other is to transform LSPs into a new feature set, named a pseudo-cepstrum. Experiments on speaker-independent connected-digit recognition showed that the weighted distance measures significantly improved the recognition accuracy than the unweighted one of LSPs. Especially we could obtain more improved performance by using PCEP. Compared to the conventional methods employing mel-frequency cepstral coefficients, the proposed methods achieved higher performance in recognition accuracies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.3
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• pp.1188-1203
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• 2020

The human expression contains a lot of information that can be used to detect complex conditions such as pain and fatigue. After deep learning became the mainstream method, the traditional feature extraction method no longer has advantages. However, in order to achieve higher accuracy, researchers continue to stack the number of layers of the neural network, which makes the real-time performance of the model weak. Therefore, this paper proposed an expression recognition framework based on densely concatenated convolutional neural networks to balance accuracy and latency and apply it to humanoid robots. The techniques of feature reuse and parameter compression in the framework improved the learning ability of the model and greatly reduced the parameters. Experiments showed that the proposed model can reduce tens of times the parameters at the expense of little accuracy.

• The Bulletin of The Korean Astronomical Society
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• v.35 no.2
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• pp.69.2-69.2
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• 2010

We investigate the southeastern region of the Vela supernova remnant (SNR) in the multi-wavelength domains. This region is quite interesting because it includes the bullet feature D/D´ and another SNR (the Vela Jr.). The C IV $\lambda\lambda1548$, 1551 emission-line morphologies obtained from the FIMS/SPEAR data show that there are several local peaks of C IV on the bullet D/D´ and the Vela Jr. SNR. This may provide clues to direct interaction between both SNRs. Also, we found that the southeastern side of the Vela is in direct contact with an H-alpha ring feature whose central source seems to be a B-type star, HD 76161. The C IV emission peaks along this contact boundary. We investigate this interacting region in detail.