• Structural Engineering and Mechanics
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• 제3권4호
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• pp.391-410
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• 1995

The application of adaptive finite element method to dynamic problems is investigated. Both the kinetic and strain energy errors induced by space and time discretization were estimated in a consistent manner and controlled by the simultaneous use of the adaptive mesh generation and the automatic time stepping. Also an optimal ratio of spatial discretization error to temporal discretization error was discussed. In this study it was found that the best performance can be obtained when the specified spatial and temporal discretization errors have the same value. Numerical examples are carried out to verify the performance of the procedure.

• Interaction and multiscale mechanics
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• 제6권2호
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• pp.137-156
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• 2013

In this paper, a meshfree shell adaptive procedure is developed for the applications in the sheet metal forming simulation. The meshfree shell formulation is based on the first-order shear deformable shell theory and utilizes the degenerated continuum and updated Lagrangian approach for the nonlinear analysis. For the sheet metal forming simulation, an h-type adaptivity based on the meshfree background cells is considered and a geometric error indicator is adopted. The enriched nodes in adaptivity are added to the centroids of the adaptive cells and their shape functions are computed using a first-order generalized meshfree (GMF) convex approximation. The GMF convex approximation provides a smooth and non-negative shape function that vanishes at the boundary, thus the enriched nodes have no influence outside the adapted cells and only the shape functions within the adaptive cells need to be re-computed. Based on this concept, a multi-level refinement procedure is developed which does not require the constraint equations to enforce the compatibility. With this approach the adaptive solution maintains the order of meshfree approximation with least computational cost. Two numerical examples are presented to demonstrate the performance of the proposed method in the adaptive shell analysis.

• 4차산업연구
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• 제3권1호
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• pp.13-20
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• 2023

Purpose - By designing a PEF(Personalized Education Feedback) system for real-time prediction of learning achievement and motivation through real-time EEG analysis of learners, this system provides some modules of a personalized adaptive learning system. By applying these modules to e-learning and offline learning, they motivate learners and improve the quality of learning progress and effective learning outcomes can be achieved for immersive self-directed learning Research design, data, and methodology - EEG data were collected simultaneously as the English test was given to the experimenters, and the correlation between the correct answer result and the EEG data was learned with a machine learning algorithm and the predictive model was evaluated.. Result - In model performance evaluation, both artificial neural networks(ANNs) and support vector machines(SVMs) showed high accuracy of more than 91%. Conclusion - This research provides some modules of personalized adaptive learning systems that can more efficiently complete by designing a PEF system for real-time learning achievement prediction and learning motivation through an adaptive learning system based on real-time EEG analysis of learners. The implication of this initial research is to verify hypothetical situations for the development of an adaptive learning system through EEG analysis-based learning achievement prediction.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1993년도 한국자동제어학술회의논문집(국제학술편); Seoul National University, Seoul; 20-22 Oct. 1993
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• pp.378-384
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• 1993

In this paper, an intelligent adaptive controller is proposed for the process with unmodelled dynamics. The intelligent adaptive controller consists of the numeric adaptive controller and the intelligent tuning part. The continuous scheme is used for the numeric adaptive controller to avoid the problems occurred in the discrete time schemes. The adaptive controller is adopted to the process with time delay. It is an implicit adaptive algorithm based on GMV using the emulator. The tuning part changes the design parameters in the control algorithm. It is a multilayer neural network trained by robustness analysis data. The proposed method can improve the robustness of the adaptive control system because the design parameters are tuned according to the operating points of the process. Through the simulation, robustnesses are shown for intelligent adaptive controller. Finally, the proposed algorithms are implemented on the electric furnace temperature control system. The effectiveness of the proposed algorithm is shown from experiments.

• 한국음향학회지
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• 제12권1호
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• pp.37-46
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• 1993

The adaptive algorithm for the Volterra filter is considered. Owing to its simplicity, the LMS algorithm for adaptive Volterra filter(AVF) is widely used as in linear adaptive filters. However, the convergence speed is unsatisfactory. For improving the convergence speed, the frequency domain LMS second order adaptive Volterra filter(FLMS-AVF) is proposed and analyzed. We show that the time and frequency domain LMS AVF's have the same steady state performance under approprate conditons. Moreover, it can be shown that this algorithm can improve the convergence speed significantly by applying self-orthogonalizing method.

• 대한기계학회논문집A
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• 제20권7호
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• pp.2067-2077
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• 1996

In adaptive finite element analysis, r- and h-methods are generally used on the basis of a discretization error estimator. In this paper, an rh-method is proposed as a new adaptive method which can improve the adaptivity performance by using both of them. This suggested rh-method moves nodal coordinates of initially given model to adjust element discretization errors and thereafter performes the h-method tdo obtain the specified accuracy of finite element solutions. Numerical experiments for various plane problems were performed using 4-noded isoparametric quadrilateral elements. As a result, the rh-method has been shown to be an accurate and efficient adaptive analysis method to obtain as improved solution.

• 국토계획
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• 제53권7호
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• pp.87-107
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• 2018

With more than 10 million inhabitants, in particular, Seoul, the capital of Korea, has already experienced a number of severe heat wave. To alleviate the potential impacts of heat wave and the vulnerability to heat wave, policy-makers have generally considered the option of heat wave strategies containing adaptation elements. From the perspective of sustainable planning for adaptation to heat wave, the objective of this study is to identify the elements of vulnerability and assess heat wave-vulnerability at the dong level. This study also performs an exploratory investigation of the spatial pattern of vulnerable areas in Seoul to heat wave by applying exploratory spatial data analysis. Then this study attempts to select areas with the relatively highest and lowest level of adaptive capacity to heat wave based on an framework of climate change vulnerability assessment. In our analysis, the adaptive capacity is the relatively highest for Seongsan-2-dong in Mapo and the relatively lowest for Changsin-3-dong in Jongno. This study sheds additional light on the spatial patterns of heat wave-vulnerability and the relationship between adaptive capacity and heat wave.

• 응용통계연구
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• 제23권6호
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• pp.1093-1102
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• 2010

비모수적 판별분류방법인 k-Nearest Neighbors Classification(KNNC) 방법은 널리 사용되고 있지만 고정된 이웃의 개수를 사용하며 또한 집단변수의 정보를 활용하지 않음으로서 자료의 국소적 특징을 반영하지 못하는 단점이 있다. Adaptive Nearest Neighbors Classification(ANNC) 방법과 Modified k-Nearest Neighbors Classification(MKNNC) 방법은 각각 이러한 단점들을 보완하기 위해 제안된 방법이다. 본 연구에서는 ANNC 방법과 MKNNC 방법의 장점을 결합한 Modified Adaptive Nearest Neighbors Classification(MANNC) 방법을 제안하였다. 나아가, 제안된 방법의 활용 가능성을 살펴보고자 실제자료에 대한 분석과 모의실험을 통해 기존의 방법들과 비교하였다.

• 한국항공운항학회지
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• 제27권3호
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• pp.30-44
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• 2019

This study utilized the HEXACO model developed by Lee et al. to investigate the effect of personal level personality variables on adaptive performance of new pilots engaged in domestic airlines. As a result of the analysis, it was found out that extroversion had a statistically significant effect on adaptive performance, while openness to experience and conscientiousness did not affect the adaptive performance statistically. In the analysis of interaction between personality variables and demographic variables, there was a statistically significant interaction effect between the origin and extroversion. Second, it was confirmed that the extroversion variable had an influence on the adaptive performance, suggesting that personality variables should be reflected in the selection of new pilots. Third, when the extroversion level was low, the adaptive performance of the civilian was relatively lower than that of the military.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권10호
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• pp.4865-4885
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• 2019

We propose a FPGA based design that performs real-time power-efficient analysis of heterogeneous sensor data using adaptive ANN on edge gateway of smart military wearables. In this work, four independent ANN classifiers are developed with optimum topologies. Out of which human activity, BP and toxic gas classifier are multiclass and ECG classifier is binary. These classifiers are later integrated into a single adaptive ANN hardware with a select line(s) that switches the hardware architecture as per the sensor type. Five versions of adaptive ANN with different precisions have been synthesized into IP cores. These IP cores are implemented and tested on Xilinx Artix-7 FPGA using Microblaze test system and LabVIEW based sensor simulators. The hardware analysis shows that the adaptive ANN even with 8-bit precision is the most efficient IP core in terms of hardware resource utilization and power consumption without compromising much on classification accuracy. This IP core requires only 31 microseconds for classification by consuming only 12 milliwatts of power. The proposed adaptive ANN design saves 61% to 97% of different FPGA resources and 44% of power as compared with the independent implementations. In addition, 96.87% to 98.75% of data throughput reduction is achieved by this edge gateway.