• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.408-410
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• 2003

In this paper, an improved gradient direction assisted linking algorithm is proposed. This algorithm begins with initial seeds satisfying some local criteria. Then it will search along the direction provided by the initial point. A window will be generated in the gradient direction of the current point. Instead of the conventional method which only considers the value of the local salient structure, an improved mathematical model is proposed to describe the desired linear features. This model not only considers the value of the salient structure but also the direction of it. Furthermore, the linking problem under this model can be efficiently solved by dynamic programming method. This algorithm is tested for linear features detection in IKONOS images. The result demonstrates this algorithm is quite promising.

• ETRI Journal
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• v.24 no.5
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• pp.360-372
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• 2002

In this paper, we propose the modified dynamic weighted round robin (MDWRR) cell scheduling algorithm, which guarantees the delay property of real-time traffic and also efficiently transmits non-real-time traffic. The proposed scheduling algorithm is a variation of the dynamic weighted round robin (DWRR) algorithm and guarantees the delay property of real-time traffic by adding a cell transmission procedure based on delay priority. It also uses a threshold to prevent the cell loss of non-real-time traffic that is due to the cell transmission procedure based on delay priority. Though the MDWRR scheduling algorithm may be more complex than the conventional DWRR scheme, considering delay priority minimizes cell delay and decreases the required size of the temporary buffer. The results of our performance study show that the proposed scheduling algorithm has better performance than the conventional DWRR scheme because of the delay guarantee of real-time traffic.

• ETRI Journal
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• v.40 no.4
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• pp.531-536
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• 2018

The weighted K-nearest neighbor (WKNN) algorithm is used to reduce positioning accuracy, as it uses a fixed number of neighbors to estimate the position. In this paper, we propose a dynamic threshold location algorithm (DH-KNN) to improve positioning accuracy. The proposed algorithm is designed based on a dynamic threshold to determine the number of neighbors and filter out singular reference points (RPs). We compare its performance with the WKNN and Enhanced K-Nearest Neighbor (EKNN) algorithms in test spaces of networks with dimensions of $20m{\times}20m$, $30m{\times}30m$, $40m{\times}40m$ and $50m{\times}50m$. Simulation results show that the maximum position accuracy of DH-KNN improves by 31.1%, and its maximum position error decreases by 23.5%. The results demonstrate that our proposed method achieves better performance than other well-known algorithms.

• Journal of information and communication convergence engineering
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• v.10 no.1
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• pp.78-84
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• 2012

This paper presents a branch prediction algorithm and a 4-way set-associative cache for performance improvement of an embedded RISC core and a clock-gating algorithm with observability don’t care (ODC) operation to reduce the power consumption of the core. The branch prediction algorithm has a structure using a branch target buffer (BTB) and 4-way set associative cache that has a lower miss rate than a direct-mapped cache. Pseudo-least recently used (LRU) policy is used for reducing the number of LRU bits. The clock-gating algorithm reduces dynamic power consumption. As a result of estimation of the performance and the dynamic power, the performance of the OpenRISC core applied to the proposed architecture is improved about 29% and the dynamic power of the core with the Chartered 0.18 ${\mu}m$ technology library is reduced by 16%.

• Journal of Advanced Marine Engineering and Technology
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• v.33 no.3
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• pp.403-410
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• 2009

The aim of this research is to develop the Bees Algorithm named 'the dynamic allocated Bees Algorithm' for multi-objective problem, especially in order to be suit for Pareto optimality. In addition two new neighbourhood search methods have been developed to produce enhanced solutions for a multi-objective problem named 'random selection neighbourhood search' and 'weighted sum neighbourhood search' and they were compared with the basic neighbourhood search in the dynamic allocated Bees Algorithm. They were successfully applied to an Environmental/Economic (electric power) dispatch (EED) problem and simulation results presented for the standard IEEE 30-bus system and they were compared to those obtained using other approaches. The comparison shows the superiority of the proposed dynamic allocated Bees Algorithms and confirms its suitability for solving the multi-objective EED problem.

• Computational Structural Engineering
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• v.9 no.3
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• pp.187-197
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• 1996

An explicit direct time integration method based solution algorithm is presented to predict dynamic buckling response of Euler column. On the basis of large deflection beam theory, a plane frame finite element is formulated and implemented into the solution algorithm. The element formulation takes into account geometrical nonlinearity and overall buckling of steel structural frames. The solution algorithm employs the central difference method. Using the computer program developed by the author, dynamic instability behavior of Euler column under impact loading is investigated by considering the time variation of load, load magnitude, and load duration. The free vibration of Euler column caused by a short duration impact load is also studied. The validity and efficiency of the present formulation and solution algorithm are verified through illustrative numerical examples.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.11a
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• pp.575-580
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• 2000

Recently it is increased by degrees to construct complex and large lattice structure such as bridge, tower and crane structures. It is very important problem to know dynamic properties of such structures. Authors presented new dynamic response analysis algorithm for rectilinear structure already. This analysis algorithm is combined transfer stiffness coefficient method with Newmark method. Presented method improves the computational accuracy remarkably owing to advantage of the transfer stiffness coefficient method. This paper formulates dynamic response analysis algorithm for plane lattice structure expanding rectilinear structures.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.693-694
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• 2014

This paper proposes a new pipelined dynamic bandwidth allocation algorithm for XG-PON (10-Gbps-capable passive optical network) passive optical networks) system. The pipelined algorithm is used when a dynamic bandwidth allocation algorithm cannot finished in an unit time. In the proposed mechanism, the request is immediately transferred to each pipeline block to improve performance.

• Journal of the Korean Society for Precision Engineering
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• v.24 no.4 s.193
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• pp.84-92
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• 2007

Dynamic process planning requires not only more flexible capabilities of a CAPP system but also higher utility of the generated process plans. In order to meet the requirements, this paper develops an algorithm that can select machines for the machining operations by calculating the machine loads. The developed algorithm is based on the multi-objective genetic algorithm that gives rise to a set of optimal solutions (in general, known as the Pareto-optimal solutions). The objective is to satisfy both the minimization number of part movements and the maximization of machine utilization. The algorithm is characterized by a new and efficient method for nondominated sorting through K-means algorithm, which can speed up the running time, as well as a method of two stages for genetic operations, which can maintain a diverse set of solutions. The performance of the algorithm is evaluated by comparing with another multiple objective genetic algorithm, called NSGA-II and branch and bound algorithm.

• The Transactions of the Korea Information Processing Society
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• v.4 no.11
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• pp.2769-2779
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• 1997

This Paper Proposes an efficient method for improving the training performance of the neural networks using a hybrid of conjugate gradient backpropagation algorithm and dynamic tunneling backpropagation algorithm The conjugate gradient backpropagation algorithm, which is the fast gradient algorithm, is applied for high speed optimization. The dynamic tunneling backpropagation algorithm, which is the deterministic method with tunneling phenomenon, is applied for global optimization. Conversing to the local minima by using the conjugate gradient backpropagation algorithm, the new initial point for escaping the local minima is estimated by dynamic tunneling backpropagation algorithm. The proposed method has been applied to the parity check and the pattern classification. The simulation results show that the performance of proposed method is superior to those of gradient descent backpropagtion algorithm and a hybrid of gradient descent and dynamic tunneling backpropagation algorithm, and the new algorithm converges more often to the global minima than gradient descent backpropagation algorithm.