• Journal of the Korean Society for Precision Engineering
• /
• v.12 no.9
• /
• pp.137-147
• /
• 1995

The unbalance response analysis is one of the essential area in the forced vibration analysis of rotor-bearing systems. Local bearing parameters in rotor-bearing systems are the major sources which give rise to a difficulty in unbalance response computation due to the complicated dynamic properties such as rotational speed dependency and anisotropy. In the present paper, an efficient method for unbalance responses is proposed so as to easily take into account bearing parameters in computation. An exact matrix condensation procedure is proposed which enables the present method to compute unbalance responses by dealing with condensed, small matrices. The proposed method causes no errors even though the computation procedure is based on the small matrices condensed from the full matrices. The present method is illustrated through a numerical example and compared with the conventional method.

• The KIPS Transactions:PartD
• /
• v.14D no.6
• /
• pp.597-604
• /
• 2007

Stream data is a continuous flow of information that mostly arrives as the form of an infinite rapid stream. Recently researchers show a great deal of interests in analyzing such data to obtain value added information. Here, we propose an efficient cube computation algorithm for multidimensional analysis of stream data. The fact that stream data arrives in an unsorted fashion and aggregation results can only be obtained after the last data item has been read. cube computation requires a tremendous amount of memory. In order to resolve such difficulties, we compute user selected aggregation fables only, and use a combination of an way and AVL trees as a temporary storage for aggregation tables. The proposed cube computation algorithm works even when main memory is not large enough to store all the aggregation tables during the computation. We showed that the proposed algorithm is practically fast enough by theoretical analysis and performance evaluation.

• Structural Engineering and Mechanics
• /
• v.27 no.6
• /
• pp.713-739
• /
• 2007

Nowadays computers can perform symbolic computations in addition to mere number crunching operations for which they were originally designed. Symbolic computation opens up exciting possibilities in Structural Mechanics and engineering. Classical areas have been increasingly neglected due to the advent of computers as well as general purpose finite element software. But now, classical analysis has reemerged as an attractive computer option due to the capabilities of symbolic computation. The repetitive cycles of simultaneous - equation sets required by the finite element technique can be eliminated by solving a single set in symbolic form, thus generating a truly closed-form solution. This consequently saves in data preparation, storage and execution time. The power of Symbolic computation is demonstrated by six examples by applying symbolic computation 1) to solve coupled shear wall 2) to generate beam element matrices 3) to find the natural frequency of a shear frame using transfer matrix method 4) to find the stresses of a plate subjected to in-plane loading using Levy's approach 5) to draw the influence surface for deflection of an isotropic plate simply supported on all sides 6) to get dynamic equilibrium equations from Lagrange equation. This paper also presents yet another computationally efficient and accurate numerical method which is based on the concept of derivative of a function expressed as a weighted linear sum of the function values at all the mesh points. Again this method is applied to solve the problems of 1) coupled shear wall 2) lateral buckling of thin-walled beams due to moment gradient 3) buckling of a column and 4) static and buckling analysis of circular plates of uniform or non-uniform thickness. The numerical results obtained are compared with those available in existing literature in order to verify their accuracy.

• Journal of Information Technology and Architecture
• /
• v.9 no.4
• /
• pp.455-464
• /
• 2012

Due to the recent data explosion, methods which can meet the requirement of large data analysis has been studying. This paper proposes MRIterativeBUC algorithm which enables efficient computation of large data cube by distributed parallel processing with MapReduce framework. MRIterativeBUC algorithm is developed for efficient iterative operation of the BUC method with MapReduce, and overcomes the limitations about the storage size and processing ability caused by large data cube computation. It employs the idea from the iceberg cube which computes only the interesting aspect of analysts and the distributed parallel process of cube computation by partitioning and sorting. Thus, it reduces data emission so that it can reduce network overload, processing amount on each node, and eventually the cube computation cost. The bottom-up cube computation and iterative algorithm using MapReduce, proposed in this paper, can be expanded in various way, and will make full use of many applications.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.27 no.3
• /
• pp.477-487
• /
• 2017

Multiparty computation (MPC) is a computation technique where many participants provide their data and jointly compute operations to get a computation result. Earlier MPC protocols were mostly depended on communication between the users. Several schemes have been presented that mainly work by delegating operations to two non-colluding servers. Peter et al. propose a protocol that perfectly eliminates the need of users' participation during the whole computation process. However, the drawback of their scheme is the excessive dependence on the server communication. To cater this issue, we propose a protocol that reduce server communication overhead using the proxy re-encryption (PRE). Recently, some authors have put forward their efforts based on the PRE. However, these schemes do not achieve the desired goals and suffer from attacks that are based on the collusion between users and server. This paper, first presents a comprehensive analysis of the existing schemes and then proposes a secure and efficient MPC protocol. The proposed protocol completely eliminates the need of users' participation, incurs less communication overhead and does not need to solve the discrete logarithm problem (DLP) in order to get the computation results.

• Proceedings of the Korea Multimedia Society Conference
• /
• 2003.11b
• /
• pp.630-633
• /
• 2003

최근에 조명의 표현 성능을 향상시키기 위한 몇 가지 기술이 발전되었다. Homomorphic Factorization(HF) 기반의 새로운 기술은 BRDF에 근사화 하지는 않는다. 그러나 전체조명 장면에서의 isotropic BRDF의 full lighting computation을 대신할 수 있다. 이 방법으로 현재 그래픽 하드웨어의 일반적인 성능을 이용하여 최소 두개의 2D Texture로 Isotropic illumination 환경을 시뮬레이션 할 수 있었다.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2008.06a
• /
• pp.837-838
• /
• 2008

• 제어로봇시스템학회:학술대회논문집
• /
• 1986.10a
• /
• pp.295-297
• /
• 1986

In this study, the strap down INS on an earth satellite launch vehicle is simulated with different computing cycles between processing the IMU signals and the navigation computation. At the time of separation of booster, error are discussed. The acceptable computing cycles can be determined by simulation.

• 한국전산유체공학회:학술대회논문집
• /
• 2006.05a
• /
• pp.66-68
• /
• 2006

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.35 no.5
• /
• pp.249-257
• /
• 2022

This paper presents an improved computational framework for the direct-solution-based finite element tearing and interconnecting (FETI) algorithm. The FETI-local algorithm is further improved herein, and localized Lagrange multipliers are used to define the interface among its subdomains. Selective inverse entry computation, using a property of the Boolean matrix, is employed for the computation of the subdomain interface stiffness and load, in which the original FETI-local algorithm requires a full matrix inverse computation of a high computational cost. In the global interface computation step, the original serial computation is replaced by a parallel multi-frontal method. The performance of the improved FETI-local algorithm was evaluated using a numerical example with 64 million degrees of freedom (DOFs). The computational time was reduced by up to 97.8% compared to that of the original algorithm. In addition, further stable and improved scalability was obtained in terms of a speed-up indicator. Furthermore, a performance comparison was conducted to evaluate the differences between the proposed algorithm and commercial software ANSYS using a large-scale computation with 432 million DOFs. Although ANSYS is superior in terms of computational time, the proposed algorithm has an advantage in terms of the speed-up increase per processor increase.