• Smart Structures and Systems
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• v.19 no.1
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• pp.11-20
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• 2017

For structural damage detection of shear buildings, this paper proposes a new concept using structural element mass-stiffness vector (SEMV) based on special mass and stiffness distribution characteristics. A corresponding damage identification method is developed combining the SEMV with the cross-model cross-mode (CMCM) model updating algorithm. For a shear building, a model is assumed at the beginning based on the building's distribution characteristics. The model is updated into two models corresponding to the healthy and damaged conditions, respectively, using the CMCM method according to the modal parameters of actual structure identified from the measured acceleration signals. Subsequently, the structural SEMV for each condition can be calculated from the updated model using the corresponding stiffness and mass correction factors, and then is utilized to form a new feature vector in which each element is calculated by dividing one element of SEMV in health condition by the corresponding element of SEMV in damage condition. Thus this vector can be viewed as a damage detection feature for its ability to identify the mass or stiffness variation between the healthy and damaged conditions. Finally, a numerical simulation and the laboratory experimental data from a test-bed structure at the Los Alamos National Laboratory were analyzed to verify the effectiveness and reliability of the proposed method. Both simulated and experimental results show that the proposed approach is able to detect the presence of structural mass and stiffness variation and to quantify the level of such changes.

• Journal of Digital Convergence
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• v.16 no.11
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• pp.335-340
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• 2018

Commercialized speech recognition systems that have an accuracy recognition rates are used a learning model from a type of speaker dependent isolated data. However, it has a problem that shows a decrease in the speech recognition performance according to the quantity of data in noise environments. In this paper, we proposed the vector quantization based speech recognition performance improvement using maximum log likelihood in Gaussian distribution. The proposed method is the best learning model configuration method for increasing the accuracy of speech recognition for similar speech using the vector quantization and Maximum Log Likelihood with speech characteristic extraction method. It is used a method of extracting a speech feature based on the hidden markov model. It can improve the accuracy of inaccurate speech model for speech models been produced at the existing system with the use of the proposed system may constitute a robust model for speech recognition. The proposed method shows the improved recognition accuracy in a speech recognition system.

• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.987-990
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• 2005

An important issue in the field of face recognitions and man-machine interfaces is an automatic detection of faces in visual scenes. it should be computationally fast enough to allow an online detection. In this paper we describe our ongoing work on face detection that models the face appearance by edge orientation and color distribution. We show that edge orientation is a powerful feature to describe objects like faces. We present a method for face region detection using edge orientation and a method for face feature detection using local color information. We demonstrate the capability of our detection method on an image database of 1877 images taken from more than 700 people. The variations in head size, lighting and background are considerable, and all images are taken using low-end cameras. Experimental results show that the proposed scheme achieves 94% detection rate with a resonable amount of computation time.

• Journal of Computational Design and Engineering
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• v.3 no.4
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• pp.370-384
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• 2016

Computer-aided materials design requires new modeling approaches to characterize and represent fine-grained geometric structures and material compositions at multiple scales. Recently, a dual-Rep approach was developed to model materials microstructures based on a new basis function, called surfacelet. As a combination of implicit surface and wavelets, surfacelets can efficiently identify and represent planar, cylindrical, and ellipsoidal geometries in material microstructures and describe the distribution of compositions and properties. In this paper, these primitive surfacelets are extended and composite surfacelets are proposed to model more complex geometries. Composite surfacelets are constructed by Boolean operations on the primitives. The surfacelet transform is applied to match geometric features in three-dimensional images. The composition of the material near the identified features can then be modeled. A cubic surfacelet and a v-joint surfacelet are developed to demonstrate the reverse engineering process of retrieving material compositions from material images.

• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.619-622
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• 2001

In this paper, an efficient approach to classification of transient and harmonic disturbances in power systems is proposed. First, the Stop-and-Go CA CFAR Detector is utilized to detect a disturbance from the power signals which are mixed with other disturbances and noise. Then, (i) Wigner Distribution, SVD(Singular Value Decomposition) and Fisher´s Criterion (ii) DWT and Fisher´s Criterion, are applied to extract an efficient feature vector. For the classification procedure, a combined neural network classifier is proposed to classify each corresponding disturbance class. Finally, the 10 class data simulated by Matlab power system blockset are used to demonstrate the performance of the proposed classification system.

• Journal of Information Processing Systems
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• v.4 no.2
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• pp.77-86
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• 2008

This research proposes a new neural network for text categorization which uses alternative representations of documents to numerical vectors. Since the proposed neural network is intended originally only for text categorization, it is called NTC (Neural Text Categorizer) in this research. Numerical vectors representing documents for tasks of text mining have inherently two main problems: huge dimensionality and sparse distribution. Although many various feature selection methods are developed to address the first problem, the reduced dimension remains still large. If the dimension is reduced excessively by a feature selection method, robustness of text categorization is degraded. Even if SVM (Support Vector Machine) is tolerable to huge dimensionality, it is not so to the second problem. The goal of this research is to address the two problems at same time by proposing a new representation of documents and a new neural network using the representation for its input vector.

• 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.

• The KIPS Transactions:PartC
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• v.15C no.6
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• pp.447-454
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• 2008

In this paper, we propose new technique which uses the fingerprint features in order to generate one time passwords(OTPs). Fingerprint is considered to be one of the powerful personal authentication factors and it can be used for generating variable passwords for one time use. Also we performed a simulation of homomorphic graph variable of fingerprint feature point using dendrogram and distribution of fingerprint feature points for proposed password generation method.

• Journal of Korea Multimedia Society
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• v.14 no.10
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• pp.1243-1251
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• 2011

This paper proposes a new vehicle model recognition using scale invariant feature transform to car's headlights image. Proposed vehicle model recognition raises the accuracy using "homogeneity" calculated from the distribution of features. In the experiment with 400 test images taken from 54 different vehicles, proposed method has 90% recognition rate and 16.45 homogeneity.

• Journal of the Microelectronics and Packaging Society
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• v.18 no.2
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• pp.1-9
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• 2011

Rare metals, called as vitamins of industry, are defined as the elements which are very few in the earth and are difficult to extract from ores. The requirements for total amounts of rare metals have increased as the global economics grows and the function of electronic components varies considerably. The rare metals have been maldistributed to America, CIS(Commonwealth of Independent States), China, Australia, Canada as about 80% of total natural resources, which also lead to unequal materials flow or distribution. Therefore, it was needed to investigate the feature of rare metals in terms of industry as well as technology. We could identify the industrial issues associated with the supply of critical rare elements in electronic components.