• Smart Structures and Systems
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• v.32 no.6
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• pp.349-358
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• 2023

Real-Time Hybrid Testing (RTHT) requires the numerical substructure calculations to be completed within the defined integration time interval due to its real-time loading demands. For solving the problem, A Real-Time Hybrid Testing based on Restart-Loading Technology (RTHT-RLT) is proposed in this paper. In the proposed method, in case of the numerical substructure calculations cannot be completed within the defined integration time interval, the experimental substructure was returned back to the initial state statically. When the newest loading commands were calculated by the numerical substructure, the experimental substructure was restarted loading from the initial state to the newest loading commands so as to precisely disclosing the dynamic performance of the experimental substructure. Firstly, the methodology of the RTHT-RLT is proposed. Furthermore, the numerical simulations and experimental tests on one frame structure with a viscous damper are conducted for evaluating the feasibility and effectiveness of the proposed RTHT-RLT. It is shown that the proposed RTHT-RLT innovatively renders the nonreal-time refined calculation of the numerical substructure feasible for the RTHT. The numerical and experimental results show that the proposed RTHT-RLT exhibits excellent performance in terms of stability and accuracy. The proposed RTHT-RLT may have broad application prospects for precisely investigating the dynamic behavior of large and complex engineering structures with specific experimental substructure where a restarting procedure does not affect the relevant hysteretic response.

• Restorative Dentistry and Endodontics
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• v.32 no.6
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• pp.530-541
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• 2007

Currently, various Nickel-Titanium rotary files are used in endodontic treatment, but there is no one perfect system that can be applied to any clinical situation. Therefore, the combined uses of various file systems which can emphasize the advantages of each system are introduced as hybrid instrumentation. The ProTaper system is efficient in body shaping and apical pre-enlargement but is reported to have more possibility of transportation and produce more aberrations and deformation in more or less severe curved canals. Recently, new ProTaper system(ProTaper Universal) with different configuration and cross-sectional design to overcome the week points of ProTaper have been marketed. The purpose of this study was to compare and evaluate the shaping abilities of ProTaper, ProTaper Universal system, and two hybrid methods using S-series of ProTaper Universal and Hero Shaper or ProFile. The time lapses for instrumentation were measured and the used files were inspected for distortion. The pre- and post-instrumented root canals were scanned and superimposed to evaluate the aberrations and reduction of root canal curvature and change of radius of canal curvature. The increased canal width and apical centering ratio were calculated at 1, 2, 3, 4 and 5 mm levels from apical foramen. Under the conditions of this study, the ProTaper Universal seems to have better shaping ability than ProTaper in terms of instrumented width and instrumentation time. It may be suggested that the ProTaper Universal system is efficient as much as hybrid instrumentation using ProTaper and other constant-tapered NiTi file systems in highly experienced operators.

• Journal of Hydrogen and New Energy
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• v.22 no.2
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• pp.152-160
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• 2011

The electromagnetic characteristics of FCEVs (fuel cell electric vehicles) are much different from the existing combustion engine cars as well as hybrid, plug-in-hybrid, and pure electric vehicles due to the high voltage/current generated by a fuel cell stack which uses a compressed hydrogen gas reacted with oxygen. To operate fuel cell stack efficiently, BOP (Balance of Plant) which is consisted of many motors in water pump, air blower, and hydrogen recycling pump as well as inverters for these motors is essential. Furthermore, there are also electric systems for entertainment, information, and vehicle control such as navigation, broadcasting, vehicle dynamic control systems, and so on. Since these systems are connected by high voltage or general cables, EMC (Electromagnetic compatibility) analysis for high voltage and general cable of FCEV is the most important element to prevent the possible electric functional safety errors. In this paper, electromagnetic fields by high voltage and general cables for FCEVs is studied. From numerical analysis results, total time harmonic electromagnetic field strength from high voltage and general cables have difference of 13~16 dB due to ground effect by impedance matching. The EMI results of FECV at 10 m distance shows difference of 41 dB at 30 MHz and 54 dB at 230 MHz compared with only general cable routing.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.1
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• pp.1-8
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• 2016

The Nuclear Power Plants(: NPP) are being protected as national infrastructure, and instrumentation and control(: I&C) systems are one of the principle facilities of the NPP, which perform the protection, control, and monitoring function. The I&C systems are being evolved into digitalization based on computer and network technology from analog system. In addition, the I&C systems are mostly employ the specialized logic controllers which are dedicated for the NPP, but the usage of generalized IT resources are steadily increased. The cyber security issues for the NPP are being emerged due to cyber incidents by Stuxnet and various accidents in the NPP. In this paper, hybrid access control model is proposed which are applicable to I&C system by analyzing the access control requirements specified in regulatory guides. The safety of in-service and under construction of NPP are effectively increased by applying proposed hybrid model.

• Smart Structures and Systems
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• v.26 no.4
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• pp.495-506
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• 2020

Monitoring of complex processes faces several challenges mainly due to the lack of relevant sensory information or insufficient elaborated decision-making strategies. These challenges motivate researchers to adopt complex data processing and analysis in order to improve the process representation. This paper presents the development and implementation of quality monitoring framework based on a model-driven approach using embedded artificial intelligence strategies. In this work, the strategies are applied to the supervision of a microfabrication process aiming at showing the great performance of the framework in a very complex system in the manufacturing sector. The procedure involves two methods for modelling a representative quality variable, such as surface roughness. Firstly, the hybrid incremental modelling strategy is applied. Secondly, a generalized fuzzy clustering c-means method is developed. Finally, a comparative study of the behavior of the two models for predicting a quality indicator, represented by surface roughness of manufactured components, is presented for specific manufacturing process. The manufactured part used in this study is a critical structural aerospace component. In addition, the validation and testing are performed at laboratory and industrial levels, demonstrating proper real-time operation for non-linear processes with relatively fast dynamics. The results of this study are very promising in terms of computational efficiency and transfer of knowledge to manufacturing industry.

• Proceedings of the Korean Vacuum Society Conference
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• 2012.02a
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• pp.556-556
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• 2012

Transition metal-based organometallic complexes have shown great talents as a catalyst in various reactions. Designing organic molecules and coordinating them to such active centers have been a promising route to control the catalytic natures. Metallocene, which has transition metal atoms sandwiched by aromatic rings, is one of the representative systems for organometallic catalysts. Group 4-based metallocene catalysts have been most commonly used for the production of polyolefins, which have great world-wide markets in the real life. Graphenes and carbon nanotubes (CNTs) were composed of extended $sp^2$ carbon networks, showing high electron mobility as well as have extremely large steric bulkiness relative to metal centers. We were inspired by these characteristics of such carbon-based nano-materials and assumed that they could intimately interact with active centers of metallocene catalysts. We examined this hypothesis and, recently, reported that CNTs dramatically changed catalytic natures of group 4-based catalysts when they formed hybrid systems with such catalysts. In conclusion, we produced hybrid materials composed of group-4 based metallocenes, $Cp_2ZrCl_2$ and $Cp_2TiCl_2$, and carbon-based nano-materials such as RGO and MWCNT. Such hybrids were generated via simple adsorption between Cp rings of metallocenes and graphitic surfaces of graphene/CNT. The hybrids showed interesting catalytic behaviors for ethylene polymerizations. Resulting PEs had significantly increased Mw relative to those produced from free metallocene-based catalytic systems, which are not adsorbed on carbon-based nano-materials. UHMWPEs with extremely high Mw were obtained at low Tp.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.6A
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• pp.544-549
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• 2007

Carrier-to-noise and interference ratio (CNIR) measurement is important in providing accurate channel quality measurements for orthogonal frequency division multiplexing (OFDM) systems in different bit loading environments. Interference and noise power estimation for CNIR measurement nay be carried out either with known data or without any prior knowledge about the transmitted data. With prior knowledge on transmitted data, better estimation can be achieved. However, this results in huge increase in system load. As for estimation methods without any prior knowledge, poor system performance results under certain circumstances. In this paper, we investigate the effect due to different interference and noise power on decision-directed (DD) CNIR estimation for OFDM systems and propose a new CNIR estimation scheme that provides optimal trade off between the performance and system load.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.3
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• pp.273-278
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• 2004

In this paper, the recurrent neural networks (RNN) is applied to compensate for HMM recognition algorithm, which is commonly used as main recognizer. Among these recurrent neural networks, the multi-layer recurrent neural prediction model (MRNPM), which allows operating in real-time, is used to implement learning and recognition, and HMM and MRNPM are used to design a hybrid-type main recognizer. After testing the designed speech recognition algorithm with Korean number pronunciations (13 words), which are hardly distinct, for its speech-independent recognition ratio, about 5% improvement was obtained comparing with existing HMM recognizers. Based on this result, only optimal (recognition) codes were extracted in the actual DSP (TMS320C6711) environment, and the embedded speech recognition system was implemented. Similarly, the implementation result of the embedded system showed more improved recognition system implementation than existing solid HMM recognition systems.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.120-123
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• 1997

This paper presents models of fuzzy inference systems that can be built from a set of input-output training data pairs through hybrid structure-parameter learning. Fuzzy inference systems has the difficulty of parameter learning. Here we develop a coding format to determine a fuzzy neural network(FNN) model by chromosome in a genetic algorithm(GA) and present systematic approach to identify the parameters and structure of FNN. The proposed FNN can automatically identify the fuzzy rules and tune the membership functions by modifying the connection weights of the networks using the GA and the back-propagation learning algorithm. In order to show effectiveness of it we simulate and compare with conventional methods.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1865-1869
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• 2004

A new model for the propagation prediction for mobile communication network inside building is presented in this paper. The model is based on the determination of the dominant paths between the transmitter and the receiver. The field strength is predicted with adaptive neuro - fuzzy inference systems (ANFIS), trained with measurements. The advantage of the ANFIS with hybrid least squares and gradient descent algorithms is fast convergence compared with original neural network. The K-means algorithm for selection of training patterns is also used. Comparison of our predicted results to measurements indicate that improvements in accuracy over conventional empirical model are achieved.