• Structural Engineering and Mechanics
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• v.89 no.4
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• pp.375-381
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• 2024

In this paper, Eringen's nonlocal thermoelasticity is constructed to study wave propagation in a rotating two-temperature thermoelastic half-space. The problem is applied in the context of the dual-phase-lag (Dual) model, coupled theory (CD), and Lord-Shulman (L-S) theory. Using suitable non-dimensional fields, the harmonic wave analysis is used to solve the problem. Comparisons are carried with the numerical values predicted in the absence and presence of the gravity field, a nonlocal parameter as well as rotation. The present study is valuable for the analysis of nonlocal thermoelastic problems under the influence of the gravity field, mechanical force, and rotation.

• Advances in nano research
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• v.16 no.4
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• pp.395-412
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• 2024

This article develops a nonclassical size dependent nanoplate model to study the dynamic response of functionally graded carbon nanotube (FGCNT) nanoplates under a moving load. Both nonlocal and microstructure effects are incorporated through the nonlocal strain gradient elasticity theory. To investigate the effect of reinforcement orientation of CNT, four different configurations are studied and analysed. The FGM gradation thorough the thickness direction is simulated using the power law. In the context of the first order shear deformation theory, the dynamic equations of motion and the associated boundary conditions are derived by Hamilton's principle. An analytical solution of the dynamic equations of motion is derived based on the Navier methodology. The proposed model is verified and compared with the available results in the literature and good agreement is found. The numerical results show that the dynamic performance of FGCNT nanoplates could be governed by the reinforcement pattern and volume fraction in addition to the non-classical parameters and the moving load dimensionless parameter. Obtained results are reassuring in design and analysis of nanoplates reinforced with CNTs.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.1069-1074
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• 2009

Public displays are evolving from a one-way display to an interactive medium which contains dynamic transition of various media. This study focuses on the interaction between human and digital technology-based art on public display from a HCI point of view, while several viewpoints exist on interactive public displays. We present a new interaction model which suggests different interactions depending on the viewer's distance and direction so that public display could offer rich media experiences. We have also and built an installation art to examine the efficacy of our interaction model. As such, we introduced two methodologies from HCI to derive our final interaction model. First of all, we analyze previous distance-dependent interaction models for public display in terms of context analytic approach, and propose an effective model for human-digital art interaction. Second, we introduce the concept of aura in HCI as user analytic approach to redefine interaction depending on the viewer's direction of attention. Thus, this study aims to suggest a new interaction model based on the previous two analyses to improve interaction between human and digital technology-based art on public display.

• Coupled systems mechanics
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• v.5 no.3
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• pp.235-254
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• 2016

A numerical model for fluid-structure interactions (abbr. FSI) is presented in the context of sloshing effects in movable, partially filled tanks to improve understanding of interactions between the fluid and the dynamics of a tank flexibly attached to a vehicle. The purpose of this model is to counteract the penalizing impact of the added mass effect on classical partitioned FSI coupling scheme: the proposed investigation is based on an added mass corrected version of the classical strongly coupled partitioned scheme presented in (Song et al. 2013). Results show that this corrected version systematically allows convergence to the coupled solution. In the rare cases where convergence is already obtained, the corrected version significantly reduces the number of iterations required. Finally, it is shown that the convergence limit imposed by added mass effect for the non-corrected coupling scheme, is directly dependent on the aspect ratio of the fluid domain and highly related to the precision order of the temporal discretization scheme.

• Bulletin of the Korean Chemical Society
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• v.30 no.11
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• pp.2717-2722
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• 2009

The use of the classification and regression tree (CART) methodology was studied in a quantitative structure-activity relationship (QSAR) context on a data set consisting of the binding affinities of 39 imidazobenzodiazepines for the α1 benzodiazepine receptor. The 3-D structures of these compounds were optimized using HyperChem software with semiempirical AM1 optimization method. After optimization a set of 1481 zero-to three-dimentional descriptors was calculated for each molecule in the data set. The response (dependent variable) in the tree model consisted of the binding affinities of drugs. Three descriptors (two topological and one 3D-Morse descriptors) were applied in the final tree structure to describe the binding affinities. The mean relative error percent for the data set is 3.20%, compared with a previous model with mean relative error percent of 6.63%. To evaluate the predictive power of CART cross validation method was also performed.

• Nuclear Engineering and Technology
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• v.54 no.5
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• pp.1635-1643
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• 2022

STPA (System-Theoretic Process Analysis) is a widely used safety analysis technique to identify UCAs (Unsafe Control Actions) resulting in potential losses. It is totally dependent on the experience and ability of analysts to construct an information model called Control Structures, upon which analysts try to identify unsafe controls between system components. This paper proposes a formal approach to support the manual identification of UCAs, effectively and systematically. It allows analysts to mechanically extract Process Model, an important element that makes up the Control Structures, from a formal requirements specification for a software controller. It then concisely constructs the contents of Context Tables, from which analysts can identify all relevant UCAs effectively, using a software fault tree analysis technique. The case study with a preliminary version of a Korean nuclear reactor protections system shows the proposed approach's effectiveness and applicability.

• The Journal of the Acoustical Society of Korea
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• v.18 no.5
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• pp.44-51
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• 1999

This paper presents a massively parallel computational model for the efficient integration of speech and natural language understanding. The phoneme model is based on continuous Hidden Markov Model with context dependent phonemes, and the language model is based on a knowledge base approach. To construct the knowledge base, we adopt a hierarchically-structured semantic network and a memory-based parsing technique that employs parallel marker-passing as an inference mechanism. Our parallel speech recognition algorithm is implemented in a multi-Transputer system using distributed-memory MIMD multiprocessors. Experimental results show that the parallel speech recognition system performs better in recognition accuracy than a word network-based speech recognition system. The recognition accuracy is further improved by applying code-phoneme statistics. Besides, speedup experiments demonstrate the possibility of constructing a realtime parallel speech recognition system.

• Korean Management Science Review
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• v.23 no.1
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• pp.21-41
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• 2006

Given the growing deployment of mobile offices, we need to understand the fectors which affect their successful use and implementation. We in this paper present a parsimonious model which integrates the IS success model and the TAM. The main feature of the model is that it allows one to explore the role of mobility of mobile office systems in determining the level of system usage and dependence. Although the mobility is the key property of any mobile system, there is little research effort exerted to understand how mobility affects the traditional IS factors such as perceived ease of use and usefulness. In addition to the system usage, this model also uses dependence as the dependent variable. This is expected to provide richer understanding of the study context. We collected survey data from 1,614 field service workers of a major domestic electronic company. They use PDAs to retrieve information regarding the specifics of the customer service requests and to report what has been done for each service job. The results from SEM analysis show that mobility improves the perceived ease of use which then leads the field service workers to depend more on the PDA systems. Interestingly enough, however, the workers' satisfaction does not cause more frequent use of the system.

• Proceedings of the KSPS conference
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• 2007.05a
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• pp.217-220
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• 2007

This study outlines a small-sized dialog style ETRI Korean TTS system which applies a HMM based speech synthesis techniques. In order to build the VoiceFont, dialog-style 500 sentences were used in training HMM. And the context information about phonemes, syllables, words, phrases and sentence were extracted fully automatically to build context-dependent HMM. In training the acoustic model, acoustic features such as Mel-cepstrums, logF0 and its delta, delta-delta were used. The size of the VoiceFont which was built through the training is 0.93Mb. The developed HMM-based TTS system were installed on the ARM720T processor which operates 60MHz clocks/second. To reduce computation time, the MLSA inverse filtering module is implemented with Assembly language. The speed of the fully implemented system is the 1.73 times faster than real time.

• Proceedings of the KOR-KST Conference
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• 2000.02a
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• pp.25-47
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• 2000

In route guidance systems fastest-path routing has typically been adopted because of its simplicity. However, empirical studies on route choice behavior have shown that drivers use numerous criteria in choosing a route. The objective of this study is to develop computationally efficient algorithms for identifying a manageable subset of the nondominated (i.e. Pareto optimal) paths for real-time vehicle routing which reflect the drivers' preferences and route choice behaviors. We propose two pruning algorithms that reduce the search area based on a context-dependent linear utility function and thus reduce the computation time. The basic notion of the proposed approach is that ⅰ) enumerating all nondominated paths is computationally too expensive, ⅱ) obtaining a stable mathematical representation of the drivers' utility function is theoretically difficult and impractical, and ⅲ) obtaining optimal path given a nonlinear utility function is a NP-hard problem. Consequently, a heuristic two-stage strategy which identifies multiple routes and then select the near-optimal path may be effective and practical. As the first stage, we utilize the relaxation based pruning technique based on an entropy model to recognize and discard most of the nondominated paths that do not reflect the drivers' preference and/or the context-dependency of the preference. In addition, to make sure that paths identified are dissimilar in terms of links used, the number of shared links between routes is limited. We test the proposed algorithms in a large real-life traffic network and show that the algorithms reduce CPU time significantly compared with conventional multi-criteria shortest path algorithms while the attributes of the routes identified reflect drivers' preferences and generic route choice behaviors well.