• Smart Structures and Systems
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• v.16 no.4
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• pp.641-665
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• 2015

In this paper, a simple and efficient phenomenological macroscopic one-dimensional model is proposed which is able to simulate main features of shape memory alloys (SMAs) particularly ferro-elasticity effect. The constitutive model is developed within the framework of thermodynamics of irreversible processes to simulate the one-dimensional behavior of SMAs under uniaxial simple tension-compression as well as pure torsion+/- loadings. Various functions including linear, cosine and exponential functions are introduced in a unified framework for the martensite transformation kinetics and an analytical description of constitutive equations is presented. The presented model can be used to reproduce primary aspects of SMAs including transformation/orientation of martensite phase, shape memory effect, pseudo-elasticity and in particular ferro-elasticity. Experimental results available in the open literature for uniaxial tension, torsion and bending tests are simulated to validate the present SMA model in capturing the main mechanical characteristics. Due to simplicity and accuracy, it is expected the present SMA model will be instrumental toward an accurate analysis of SMA components in various engineering structures particularly when the ferro-elasticity is obvious.

• The Korean Journal of Applied Statistics
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• v.33 no.1
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• pp.1-10
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• 2020

We propose a unified approach to variable selection, transformation and outliers in the linear model. The procedure includes a sequential method for outlier detection and a least trimmed squares estimator for variable transformation. It uses all possible subsets regressions for model selection. Some real data analyses and the simulation results are provided to show the efficiency of the methods in the context of the correct variable selection and the fitness of the estimated model.

• Journal of KIISE
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• v.43 no.9
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• pp.953-962
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• 2016

Model-based testing is a technique for performing the test by using a model that represents the behavior of the system as a system specification. Industrial domains such as automotive, military/aerospace, medical, railway and nuclear power generation require model-based testing and code coverage-based testing to improve the quality of software. Despite the fact that both model-based testing and code coverage-based testing are required, difficulty in achieving a high coverage using model-based testing caused by the abstraction level difference between the test model and the source code, results in the need for performing model-based testing separately. In this study, to overcome the limitations of the existing model-based testing, we proposed the state machine transformation method to effectively improve the code coverage using the protocol state machine, one of the typical modeling methods is used as the test model in model-based testing, as the test model. In addition, we performed a case study of both systems and analyzed the effectiveness of the proposed method.

• Speech Sciences
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• v.11 no.4
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• pp.75-88
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• 2004

In this paper, we propose a new approach to sequential linear regression adaptation of continuous density hidden Markov models (CDHMMs) based on transformation space model (TSM). The proposed TSM which characterizes the a priori knowledge of the training speakers associated with maximum likelihood linear regression (MLLR) matrix parameters is effectively described in terms of the latent variable models. The TSM provides various sources of information such as the correlation information, the prior distribution, and the prior knowledge of the regression parameters that are very useful for rapid adaptation. The quasi-Bayes (QB) estimation algorithm is formulated to incrementally update the hyperparameters of the TSM and regression matrices simultaneously. Experimental results showed that the proposed TSM approach is better than that of the conventional quasi-Bayes linear regression (QBLR) algorithm for a small amount of adaptation data.

• Journal of Plant Biotechnology
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• v.39 no.1
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• pp.1-12
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• 2012

With the increasing advances in Brassicaceae genetics and genomics, considerable progress has been made in the transformation of Brassicaceae. Transformation technologies are now being exploited routinely to determine the gene function and contribute to the development of novel enhanced crops. $Agrobacterium$-mediated transformation remains the most widely used approach for the introduction of transgenes into Brassicaceae. In $Brassica$, the transformation relies mainly on $in$ $vitro$ transformation methods. Nevertheless, despite the significant progress made towards enhancing the transformation efficiencies, some genotypes remain recalcitrant to transformation. Advances in our understanding of the genetics behind various transformations have enabled researchers to identify more readily transformable genotypes for use in routine high-throughput systems. These developments have opened up exciting new avenues to exploit model $Brassica$ genotypes as resources for understanding the gene function in complex genomes. Although many other Brassicaceae have served as model species for improving plant transformation systems, this paper summarizes on the recent technologies employed in the transformation of both $Arabidopsis$ and $Brassica$. The use of transformation technologies for the introduction of desirable traits and a comparative analysis of these as well as their future prospects are also important parts of the current research that is reviewed.

• The Journal of the Acoustical Society of Korea
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• v.31 no.1
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• pp.39-50
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• 2012

A voice transformation (VT) method that can make the utterance of a source speaker mimic that of a target speaker is described. Speaker individuality transformation is achieved by altering three feature parameters, which include the LPC cepstrum, pitch period and gain. The main objective of this study involves construction of an optimal sequence of features selected from a target speaker's database, to maximize both the correlation probabilities between the transformed and the source features and the likelihood of the transformed features with respect to the target model. A set of two-pass conversion rules is proposed, where the feature parameters are first selected from a database then the optimal sequence of the feature parameters is then constructed in the second pass. The conversion rules were developed using a statistical approach that employed a maximum likelihood criterion. In constructing an optimal sequence of the features, a hidden Markov model (HMM) was employed to find the most likely combination of the features with respect to the target speaker's model. The effectiveness of the proposed transformation method was evaluated using objective tests and informal listening tests. We confirmed that the proposed method leads to perceptually more preferred results, compared with the conventional methods.

• Journal of the Korea Society of Computer and Information
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• v.21 no.5
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• pp.57-64
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• 2016

In this paper, we propose the design of railway safety common data model to provide common transformation method for collecting data from railway facility fields to Real-time railway safety monitoring and control system. This common data model is divided into five abstract sub-models according to the characteristics of data such as 'StateInfoMessage', 'ControlMessage', 'RequestMessage', 'ResponseMessage' and 'ExtendedXXXMessage'. This kind of model structure allows diverse heterogeneous data acquisitions and its common conversion method to DDS (Data Distribution Service) format to share data to the sub-systems of Real-time railway safety monitoring and control system. This paper contains the design of common data model and its DDS Topic expression for DDS communication, and presents two kinds of data transformation case studied for verification of the model design.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.885-890
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• 2002

As Geographic Information Systems represents three-dimensional topological Information, the systems provide accurate and delicate services for user. In order to execute three-dimensional topological operations, a dimensional transformation and heterogeneous spatial models should be used. However, the existing systems that use the dimensional transformation and the heterogeneous models, are not only difficult to operate the spatial operators, but also happened to support non- interoperability. Therefore, in order to support the spatial operation as well as interoperability between dimensions, we propose three-dimensional spatial operators for the proposed models. We defined the three-dimensional spatial operators prior to designing the proposed model. We also implemented the operators of proposed model and evaluated the implemented model on the component environment. Finally, the proposed model is able to not only support interoperability among systems but also execute spatial queries efficiently on three-dimensional spatial objects.

• Communications for Statistical Applications and Methods
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• v.7 no.1
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• pp.97-106
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• 2000

In time series analysis we generally use Box-Cox power transformation for variance stabilization. In this paper we show that order estimator and one step ahead forecast of transformed AR(1) model are approximately invariant to those of the original model under some assumptions. A small Monte-Carlo simulation is performed to support the results.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.149-155
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• 2013

In these days, Entity-Relationship Model is the most popular modeling tool for designing databases, and XML is a de facto standard language for representing and exchanging data. But, because of many commercial products supporting Entity-Relationship Model use their's own representation formats, and thus it gives rise to difficulties the inter-operability between these products. In this paper, we propose an ERX, a generation tool of XML Schema from Entity-Relationship Model. ERX receives an Entity-Relationship Diagram as an input, transforms it based on transformation rules, and generates a XML Schema Definition as an output. Transformation rules contain entity set, relationship set, mapping cardinalities, and generalization.