• Proceedings of the Computational Structural Engineering Institute Conference
• /
• 2000.04b
• /
• pp.466-473
• /
• 2000

Optimality Criteria algorithm based on the derivation of reciprocal approximations has been applied to structural optimization of large-scale structures. However, required computational cost for the serial analysis algorithm of large-scale structures consisting of a large number of degrees of freedom and members is too high to be adopted in the solution process of O.C. algorithm Thus, parallel version of O.C. algorithm on the network of personal computers is presented in this Paper. Parallelism in O.C. algorithm may be classified into two regions such as analysis and optimizer part As the first step of development of parallel algorithm, parallel structural analysis algorithm is developed and used in O.C. algorithm The algorithm is applied to optimal design of a 54-story plane frame structure

• Journal of information and communication convergence engineering
• /
• v.8 no.3
• /
• pp.307-311
• /
• 2010

EPCglobal Class-1 Gen-2 protocol has been proposed for UHF-band RFID systems. In Gen-2 standard, Q-algorithm was proposed to select a frame size for the next query round without estimating the number of tags. Therefore, the Q-algorithm has advantage that the reader's algorithm is simpler than other algorithms. However, it is impossible to allocate the optimized frame size. Also, the original Q-algorithm did not define an optimized parameter C for adjusting the frame size. In this paper, we propose an adaptive Q-algorithm with the different parameter $C_c$ and $C_i$ in accordance with the status of reply slot. Simulation results show that the proposed adaptive Q-algorithm outperforms the original Gen-2 Q-algorithm.

• Computational Structural Engineering
• /
• v.2 no.2
• /
• pp.85-92
• /
• 1989

A modified skyline algorithm suitable for C language in this paper. The modified algorithm improves the computational efficiency and the structure of the program. Substantial reduction of execution time is achieved by simplifying assemblage and decomposition of the stiffness matrix. A source program is also provided for use in future development of finite element softwares.

• Proceedings of the Korea Institute of Convergence Signal Processing
• /
• 2001.06a
• /
• pp.113-116
• /
• 2001

The conventional K-means algorithm is widely used in vector quantizer design and clustering analysis. Recently modified K-means algorithm has been proposed where the codevector updating step is as fallows: new codevector = current codevector + scale factor (new centroid - current codevector). This algorithm uses a fixed value for the scale factor. In this paper, we propose a new algorithm for the enhancement of learning time in fuzzy c-means a1gorithm. Experimental results show that the proposed method produces codebooks about 5 to 6 times faster than the conventional K-means algorithm with almost the same Performance.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.19 no.39
• /
• pp.47-61
• /
• 1996

We propose a parallel neural network model in which patterns are clustered and patterns in a cluster are studied in a parallel neural network. The learning algorithm used in this paper is based on LVQ algorithm of Kohonen(1990) for clustering and ADALINE(Adaptive Linear Neuron) network of Widrow and Hoff(1990) for parallel learning. The proposed algorithm consists of two parts. First, N patterns to be learned are categorized into C clusters by LVQ clustering algorithm. Second, C patterns that was selected from each cluster of C are learned as input pattern of ADALINE(Adaptive Linear Neuron). Data used in this paper consists of 250 patterns of ASCII characters normalized into $8\times16$ and 1124. The proposed algorithm consists of two parts. First, N patterns to be learned are categorized into C clusters by LVQ clustering algorithm. Second, C patterns that was selected from each cluster of C are learned as input pattern of ADALINE(Adaptive Linear Neuron). Data used in this paper consists 250 patterns of ASCII characters normalized into $8\times16$ and 1124 samples acquired from signals generated from 9 car models that passed Inductive Loop Detector(ILD) at 10 points. In ASCII character experiment, 191(179) out of 250 patterns are recognized with 3%(5%) noise and with 1124 car model data. 807 car models were recognized showing 71.8% recognition ratio. This result is 10.2% improvement over backpropagation algorithm.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.14 no.5
• /
• pp.517-524
• /
• 2004

The fuzzy set theory has been wide used in clustering of machine learning with data mining since fuzzy theory has been introduced in 1960s. In particular, fuzzy C-means algorithm is a popular fuzzy clustering algorithm up to date. An element is assigned to any cluster with each membership value using fuzzy C-means algorithm. This algorithm is affected from the location of initial cluster center and the proper cluster size like a general clustering algorithm as K-means algorithm. This setting up for initial clustering is subjective. So, we get improper results according to circumstances. In this paper, we propose a cluster merging using enhanced density based fuzzy C-means clustering algorithm for solving this problem. Our algorithm determines initial cluster size and center using the properties of training data. Proposed algorithm uses grid for deciding initial cluster center and size. For experiments, objective machine learning data are used for performance comparison between our algorithm and others.

• ETRI Journal
• /
• v.28 no.4
• /
• pp.475-485
• /
• 2006

Network-on-chip (NoC) is an emerging design paradigm intended to cope with future systems-on-chips (SoCs) containing numerous built-in cores. Since NoCs have some outstanding features regarding design complexity, timing, scalability, power dissipation and so on, widespread interest in this novel paradigm is likely to grow. The test strategy is a significant factor in the practicality and feasibility of NoC-based SoCs. Among the existing test issues for NoC-based SoCs, test access mechanism architecture and test scheduling particularly dominate the overall test performance. In this paper, we propose an efficient NoC-based SoC test scheduling algorithm based on a rectangle packing approach used for current SoC tests. In order to adopt the rectangle packing solution, we designed specific methods and configurations for testing NoC-based SoCs, such as test packet routing, test pattern generation, and absorption. Furthermore, we extended and improved the proposed algorithm using multiple test clocks. Experimental results using some ITC'02 benchmark circuits show that the proposed algorithm can reduce the overall test time by up to 55%, and 20% on average compared with previous works. In addition, the computation time of the algorithm is less than one second in most cases. Consequently, we expect the proposed scheduling algorithm to be a promising and competitive method for testing NoC-based SoCs.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.21 no.4
• /
• pp.876-883
• /
• 1996

Adaptive FIR filters are widely used in the echo canceller. But, most of practical systems have the transfer function composed of poles and zeros. In that case, adaptive IIR filters may be more efficient rather than FIR fiters. In this paper, a recently developed C-HARF algorithm is used to implement an adaptive IIR echo canceller. The proposed convergence of the algorithm make it attractive for this application. Extensive computer simulations show that C-HARF algorithm performs better than the NLMS algorithm after convergence, although C-HARF algorithm converges more slowly.

• Journal of computational fluids engineering
• /
• v.1 no.1
• /
• pp.26-34
• /
• 1996

The performance of the SIMPLE, SIMPLE-C and PISO algorithms for the treatment of the pressure-velocity coupling in fluid flow problems were examined by comparing the computational effort required to obtain the same level of the convergence. Example problems are circular duct and 90-degree bent square-duct. For circular duct case, laminar and turbulent flow were computed. For 90-degree bent square-duct case, laminar flow was simulated by the time-marching method as well as the iterative method. The convergence speed of the other two algorithms are not always superior to SIMPLE algorithm. SIMPLE algorithm is faster than SIMPLE-C algorithm in the simple laminar flow calculations. The application of the PISO algorithm in three dimensional general coordinates is not so effective as in two-dimensional ones. Since computational time of PISO algorithm is increased at each time step(or iterative step) in three dimension, the total convergence speed is not decreased. But PISO algorithm is stable for large time step by using time marching method,.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.17 no.5
• /
• pp.297-307
• /
• 2022

The simple procedural segment selection algorithm commonly used in Dynamic Adaptive Streaming over HTTP (DASH) reveals severe weakness to provide high-quality streaming services in the integrated mobile networks of various wired and wireless links. A major issue could be how to properly cope with dynamically changing underlying network conditions. The key to meet it should be to make the segment selection algorithm much more adaptive to fluctuation of network traffics. This paper presents a system architecture that replaces the existing procedural segment selection algorithm with a deep reinforcement learning algorithm based on the Asynchronous Advantage Actor-Critic (A3C). The distributed A3C-based deep learning server is designed and implemented to allow multiple clients in different network conditions to stream videos simultaneously, collect learning data quickly, and learn asynchronously, resulting in greatly improved learning speed as the number of video clients increases. The performance analysis shows that the proposed algorithm outperforms both the conventional DASH algorithm and the Deep Q-Network algorithm in terms of the user's quality of experience and the speed of deep learning.