• 한국정보통신학회논문지
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• 제14권12호
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• pp.2675-2680
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• 2010

3D 그래픽을 처리하는 연산 과정에는 고정적인 연산만을 수행하는 영역과 Shader 등과 같은 명령어에 의한 프로그래밍이 요구되는 영역이 구분되어 있다. 이러한 3D 파이프라인의 특성을 고려하여 fixed 구조로 설계한 graphics hardware와 명령어 기반의 programmable hardware를 혼합한 구조로 설계하면 효율적인 그래픽 처리가 가능하다. 본 논문에서는 이러한 혼합 구조에 적합한 OpenGL ES(Open Graphics Library Embedded System) 2.0을 지원하는 Fragment Shader를 설계하였다. fixed hardware와 Shader간 데이터 입출력으로 인해 발생할 수 있는 전체 파이프라인의 지연을 줄일 수 있도록 내부 인터페이스를 최적화하였으며 Shader 내부 레지스터 그룹을 interleaved 구조로 설계하여 레지스터 면적과 처리 속도를 개선하였다.

• 정보처리학회논문지C
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• 제13C권3호
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• pp.275-282
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• 2006

다양한 응용분야를 지원하기 위해 여러 개의 독립적인 애플릿이 카드에 탑재되는 자바카드에서는 각 애플릿이 응용분야에 따라 데이터를 처리 및 관리하게 된다. 본 논문에서는 각 애플릿이 데이터를 효율적으로 처리 및 관리하기 위해 자바카드에서 지원하는 기본 API(Application Program Interface)와 스마트카드 국제 표준인 ISO 7816-4에 정의되어 있는 파일 시스템을 이용하여 자바카드용 파일 시스템 API를 설계 및 구현하였다. 제안된 파일 시스템 API를 이용하여 각 애플릿에서 메소드 호출로 동일한 코드를 줄일 수 있다. 이를 통하여 처리 시간과 메모리 사용을 감소시키며, 개발 시간과 비용을 줄일 수 있을 것으로 기대된다.

• ETRI Journal
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• 제21권1호
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• pp.28-39
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• 1999

Koblitz has suggested to use "anomalous" elliptic curves defined over ${\mathbb{F}}_2$, which are non-supersingular and allow or efficient multiplication of a point by and integer, For these curves, Meier and Staffelbach gave a method to find a polynomial of the Frobenius map corresponding to a given multiplier. Muller generalized their method to arbitrary non-supersingular elliptic curves defined over a small field of characteristic 2. in this paper, we propose an algorithm to speed up scalar multiplication on an elliptic curve defined over a small field. The proposed algorithm uses the same field. The proposed algorithm uses the same technique as Muller's to get an expansion by the Frobenius map, but its expansion length is half of Muller's due to the reduction step (Algorithm 1). Also, it uses a more efficient algorithm (Algorithm 3) to perform multiplication using the Frobenius expansion. Consequently, the proposed algorithm is two times faster than Muller's. Moreover, it can be applied to an elliptic curve defined over a finite field with odd characteristic and does not require any precomputation or additional memory.

• Nuclear Engineering and Technology
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• 제43권6호
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• pp.531-546
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• 2011

The backward differentiation formula (BDF) method is applied to a three-dimensional reactor kinetics calculation for efficient yet accurate transient analysis with adaptive time step control. The coarse mesh finite difference (CMFD) formulation is used for an efficient implementation of the BDF method that does not require excessive memory to store old information from previous time steps. An iterative scheme to update the nodal coupling coefficients through higher order local nodal solutions is established in order to make it possible to store only node average fluxes of the previous five time points. An adaptive time step control method is derived using two order solutions, the fifth and the fourth order BDF solutions, which provide an estimate of the solution error at the current time point. The performance of the BDF- and CMFD-based spatial kinetics calculation and the adaptive time step control scheme is examined with the NEACRP control rod ejection and rod withdrawal benchmark problems. The accuracy is first assessed by comparing the BDF-based results with those of the Crank-Nicholson method with an exponential transform. The effectiveness of the adaptive time step control is then assessed in terms of the possible computing time reduction in producing sufficiently accurate solutions that meet the desired solution fidelity.

• 대한기계학회논문집A
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• 제26권4호
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• pp.667-676
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• 2002

Many studies on the finite element modeling for bolted joints have proceeded, but the structures with bolted joints are complicated in shape and it is difficult to find out the characteristics according to joint condition. Usually, experimental methods have been used for bolted joint analysis. A reliable and practical finite element modeling technique for structure with bolted joints is very important for engineers in industry. In this study, three kinds of model are presented; a detailed model, a practical model and a simple model. The detailed model is modeled by using 3-D solid element and gap element, and the practical model is modeled by using shell element (a portion of bolt head) and beam element (a portion of bolt body), the simple model is modeled by simplifying practical model without using gap elements. Among these models, the simple model has the least degree of freedom and show the effect of memory reduction of 59%, when compared with the detailed model.

• International Journal of Contents
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• 제15권3호
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• pp.32-38
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• 2019

We usually suffer from difficulties in treating or managing Big Data generated from various digital media and/or sensors using traditional mining techniques. Additionally, there are many problems relative to the lack of memory and the burden of the learning curve, etc. in an increasing capacity of large volumes of text when new data are continuously accumulated because we ineffectively analyze total data including data previously analyzed and collected. In this paper, we propose a general-purpose classifier and its structure to solve these problems. We depart from the current feature-reduction methods and introduce a new scheme that only adopts changed elements when new features are partially accumulated in this free-style learning environment. The incremental learning module built from a gradually progressive formation learns only changed parts of data without any re-processing of current accumulations while traditional methods re-learn total data for every adding or changing of data. Additionally, users can freely merge new data with previous data throughout the resource management procedure whenever re-learning is needed. At the end of this paper, we confirm a good performance of this method in data processing based on the Big Data environment throughout an analysis because of its learning efficiency. Also, comparing this algorithm with those of NB and SVM, we can achieve an accuracy of approximately 95% in all three models. We expect that our method will be a viable substitute for high performance and accuracy relative to large computing systems for Big Data analysis using a PC cluster environment.

• 품질경영학회지
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• 제49권2호
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• pp.201-211
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• 2021

Purpose: This paper proposes a density adaptive grid algorithm for the k-NN regression model to reduce the computation time for large datasets without significant prediction accuracy loss. Methods: The proposed method utilizes the concept of the grid with centroid to reduce the number of reference data points so that the required computation time is much reduced. Since the grid generation process in this paper is based on quantiles of original variables, the proposed method can fully reflect the density information of the original reference data set. Results: Using five real-life datasets, the proposed k-NN regression model is compared with the original k-NN regression model. The results show that the proposed density adaptive grid-based k-NN regression model is superior to the original k-NN regression in terms of data reduction ratio and time efficiency ratio, and provides a similar prediction error if the appropriate number of grids is selected. Conclusion: The proposed density adaptive grid algorithm for the k-NN regression model is a simple and effective model which can help avoid a large loss of prediction accuracy with faster execution speed and fewer memory requirements during the testing phase.

• International journal of advanced smart convergence
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• 제12권2호
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• pp.159-166
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• 2023

Due to the aging workforce in the construction industry in South Korea, the accident rate has been increasing. The cognitive abilities of older workers are closely related to both safety incidents and labor productivity. Therefore, there is a need to improve cognitive abilities through personalized training based on cognitive assessment results, using cognitive training content, in order to enable safe performance in labor-intensive environments. The provided cognitive training content includes concentration, memory, oreintation, attention, and executive functions. Difficulty levels were applied to each content to enhance user engagement and interest. To stimulate interest and encourage active participation of the participants, the difficulty level was automatically adjusted based on feedback from the MMSE-DS results and content measurement data. Based on the accumulated data, individual training scenarios have been set differently to intensively improve insufficient cognitive skills, and cognitive training programs will be developed to reduce safety accidents at construction sites through measured data and research. Through such simple cognitive training, it is expected that the reduction of accidents in the aging construction workforce can lead to a decrease in the social costs associated with prolonged construction periods caused by accidents.

• Structural Engineering and Mechanics
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• 제84권3호
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• pp.323-335
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• 2022

The main idea of the framework is to seamlessly combine a reasonably accurate and fast surrogate model with the importance sampling strategy. Developing a surrogate model for predicting structures' dynamic responses is challenging because it involves high-dimensional inputs and outputs. For this purpose, a novel surrogate model based on cutting-edge deep learning architectures specialized for capturing temporal relationships within time-series data, namely Long-Short term memory layer and Transformer layer, is designed. After being properly trained, the surrogate model could be utilized in place of the finite element method to evaluate structures' responses without requiring any specialized software. On the other hand, the importance sampling is adopted to reduce the number of calculations required when computing the failure probability by drawing more relevant samples near critical areas. Thanks to the portability of the trained surrogate model, one can integrate the latter with the Importance sampling in a straightforward fashion, forming an efficient framework called TTIS, which represents double advantages: less number of calculations is needed, and the computational time of each calculation is significantly reduced. The proposed approach's applicability and efficiency are demonstrated through three examples with increasing complexity, involving a 1D beam, a 2D frame, and a 3D building structure. The results show that compared to the conventional Monte Carlo simulation, the proposed method can provide highly similar reliability results with a reduction of up to four orders of magnitudes in time complexity.

• ETRI Journal
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• 제45권4호
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• pp.650-665
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• 2023

A novel smart metering technique capable of anomaly detection was proposed for real-time home power management system. Smart meter data generated in real-time were obtained from 900 households of single apartments. To detect outliers and missing values in smart meter data, a deep learning model, the autoencoder, consisting of a graph convolutional network and bidirectional long short-term memory network, was applied to the smart metering technique. Power management based on the smart metering technique was executed by multi-objective optimization in the presence of a battery storage system and an electric vehicle. The results of the power management employing the proposed smart metering technique indicate a reduction in electricity cost and amount of power supplied by the grid compared to the results of power management without anomaly detection.