• Journal of KIISE:Computer Systems and Theory
• /
• v.36 no.6
• /
• pp.511-520
• /
• 2009

In this paper, we present a new iso-density surface reconstruction scheme based on a hierarchy on the input volume data and the output mesh data. From the input volume data, we construct a hierarchy of volumes, called a volume pyramid, based on a 3D dilation filter. After constructing the volume pyramid, we extract a coarse base mesh from the coarsest resolution of the pyramid with the Cell-boundary representation scheme. We iteratively fit this mesh to the iso-points extracted from the volume data under O(3)-adjacency constraint. For the surface fitting, the shrinking process and the smoothing process are adopted as in the SWIS (Shrink-wrapped isosurface) algorithm[6], and we subdivide the mesh to be able to reconstruct fine detail of the isosurface. The advantage of our method is that it generates a mesh which can be utilized by several multiresolution algorithms such as compression and progressive transmission.

• Journal of the Korea Society of Computer and Information
• /
• v.16 no.6
• /
• pp.179-186
• /
• 2011

This paper suggests the minimum number of vertex guards algorithm. Given n rooms, the exact number of minimum vertex guards is proposed. However, only approximation algorithms are presented about the maximum number of vertex guards for polygon and orthogonal polygon without or with holes. Fisk suggests the maximum number of vertex guards for polygon with n vertices as follows. Firstly, you can triangulate with n-2 triangles. Secondly, 3-chromatic vertex coloring of every triangulation of a polygon. Thirdly, place guards at the vertices which have the minority color. This paper presents the minimum number of vertex guards using dominating set. Firstly, you can obtain the visibility graph which is connected all edges if two vertices can be visible each other. Secondly, you can obtain dominating set from visibility graph or visibility matrix. This algorithm applies various art galley problems. As a results, the proposed algorithm is simple and can be obtain the minimum number of vertex guards.

• Journal of the Korea Society for Simulation
• /
• v.24 no.4
• /
• pp.77-88
• /
• 2015

In this paper, we consider a two-agent single-machine scheduling problem with exponential job-dependent position-based learning effects. The objective is to minimize the total weighted completion time of one agent with the restriction that the makespan of the other agent cannot exceed an upper bound. First, we propose a branch-and-bound algorithm by developing some dominance /feasibility properties and a lower bound to find an optimal solution. Second, we design an efficient simulated annealing (SA) algorithm to search a near optimal solution by considering six different SAs to generate initial solutions. We show the performance superiority of the suggested SA using a numerical experiment. Specifically, we verify that there is no significant difference in the performance of %errors between different considered SAs using the paired t-test. Furthermore, we testify that random generation method is better than the others for agent A, whereas the initial solution method for agent B did not affect the performance of %errors.

• The KIPS Transactions:PartB
• /
• v.18B no.4
• /
• pp.213-220
• /
• 2011

This paper proposes an effective, four-stage smoke detection method based on video that provides emergency response in the event of unexpected hazards in early fire-alarming systems. In the first phase, an approximate median method is used to segment moving regions in the present frame of video. In the second phase, a color segmentation of smoke is performed to select candidate smoke regions from these moving regions. In the third phase, a feature extraction algorithm is used to extract five feature parameters of smoke by analyzing characteristics of the candidate smoke regions such as area randomness and motion of smoke. In the fourth phase, extracted five parameters of smoke are used as an input for a K-nearest neighbor (KNN) algorithm to identify whether the candidate smoke regions are smoke or non-smoke. Experimental results indicate that the proposed four-stage smoke detection method outperforms other algorithms in terms of smoke detection, providing a low false alarm rate and high reliability in open and large spaces.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.39 no.2
• /
• pp.50-55
• /
• 2002

The standard approach of image resampling is to fit the original image with continuous model and resample the function at a desired rate. We used the B-spline function as the continuous model because it oscillates less than the others. The main purpose of this paper is the derivation of a nonuniform optimal resampling algorithm. To derive it, needing approximation can be computed in three steps: 1) determining the I-spline coefficients by matrix inverse process, 2) obtaining the transformed-spline coefficients by the optimal resampling algorithm derived from the orthogonal projection theorem, 3) converting of the result back into the signal domain by indirect B-spline transformation. With these methods, we can use B-spline in the non-uniform resampling, which is proved to be a good kernel in uniform resampling, and can also verify the applicability from our experiments.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.34 no.1C
• /
• pp.98-104
• /
• 2009

Tone injection scheme has been known as one of PAPR(Peak to Average Power Ratio) reduction methods deployable to multi-carrier system like OFDM(Orthogonal Frequency Division Multiplexing). The basic idea in tone injection scheme is to enforce the constellation size larger so that each of original constellation points is mapped into the preassigned distinct points. Along the accomplishment of tone injection, it needs great amount of computations to search out not only an appropriate frequency but a phase. Although there is no loss of transmission rate is expected because of no need to send the overhead, the tone injection scheme has not been preferable due to its enormous computations. To alleviate the amount of complexity, this paper proposes the GA(Genetic Algorithm) based tone injection scheme such that its complexity is reduced comparing with that of the conventional method. The simulation results show that the proposed GA based tone injection scheme approaches the PAPR performance associated with the conventional exhaustive search method at the expense of low computations.

• Journal of KIISE:Computer Systems and Theory
• /
• v.31 no.5_6
• /
• pp.288-296
• /
• 2004

This paper proposes a new mesh simplification algorithm using differential error metric. Many simplification algorithms make use of a distance error metric, but it is hard to measure an accurate geometric error for the high-curvature region even though it has a small distance error measured in distance error metric. This paper proposes a new differential error metric that results in unifying a distance metric and its first and second order differentials, which become tangent vector and curvature metric. Since discrete surfaces may be considered as piecewise linear approximation of unknown smooth surfaces, theses differentials can be estimated and we can construct new concept of differential error metric for discrete surfaces with them. For our simplification algorithm based on iterative edge collapses, this differential error metric can assign the new vertex position maintaining the geometry of an original appearance. In this paper, we clearly show that our simplified results have better quality and smaller geometry error than others.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.23 no.1
• /
• pp.47-54
• /
• 2019

An improved method of spectrum detection algorithm for mass spectrum analysis system is proposed. In the conventional spectrum detection algorithm that utilizes the results of the linear approximation and quadratic curve fitting on the ion signal block of each mass index, it is possible to reduce the detection error in the mass spectrum detection by further improving the condition of eliminating the invalid ion signals. Also, the proposed method can reduce the estimation error of the peak value of the mass spectrum by using the result of quadratic curve fitting for the effective ion signal block in which the peak position error is corrected. To evaluate the effectiveness of the proposed method, computer simulations were carried out step by step using the measured ion signal. Also, by comparing the rate of false detection for several inputs, the proposed method showed better detection performance than the conventional method.

• The Journal of Bigdata
• /
• v.7 no.1
• /
• pp.1-14
• /
• 2022

The proof-of-work consensus algorithm used by most blockchains is causing a massive waste of computing resources in the form of mining. A useful proof-of-work consensus algorithm has been studied to reduce the waste of computing resources in proof-of-work, but there are still resource waste and mining centralization problems when creating blocks. In this paper, the problem of resource waste in block generation was solved by replacing the relatively inefficient computation process for block generation with distributed artificial intelligence model learning. In addition, by providing fair rewards to nodes participating in the learning process, nodes with weak computing power were motivated to participate, and performance similar to the existing centralized AI learning method was maintained. To show the validity of the proposed methodology, we implemented a blockchain network capable of distributed AI learning and experimented with reward distribution through resource verification, and compared the results of the existing centralized learning method and the blockchain distributed AI learning method. In addition, as a future study, the thesis was concluded by suggesting problems and development directions that may occur when expanding the blockchain main network and artificial intelligence model.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.41 no.2
• /
• pp.77-86
• /
• 2004

In general, small-sized neural networks, even though they show good generalization performance, tend to fail to team the training data within a given error bound, whereas large-sized ones learn the training data easily but yield poor generalization. Therefore, a way of achieving good generalization is to find the smallest network that can learn the data, called the optimal-sized neural network. This paper proposes a new scheme for network pruning with ‘impact factor’ which is defined as a multiplication of the variance of a neuron output and the square of its outgoing weight. Simulation results of function approximation problems show that the proposed method is effective in regression.