• Journal of Multimedia Information System
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• v.8 no.4
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• pp.203-210
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• 2021

Deep leaning convolutional neural networks (CNN) have successfully been applied to image super-resolution (SR). Despite their great performances, SR techniques tend to focus on a certain upscale factor when training a particular model. Algorithms for single model multi-scale networks can easily be constructed if images are upscaled prior to input, but sub-pixel convolution upsampling works differently for each scale factor. Recent SR methods employ multi-scale and multi-path learning as a solution. However, this causes unshared parameters and unbalanced parameter distribution across various scale factors. We present a multi-scale single-path upsample module as a solution by exploiting the advantages of sub-pixel convolution and interpolation algorithms. The proposed model employs sub-pixel convolution for the highest scale factor among the learning upscale factors, and then utilize 1-dimension interpolation, compressing the learned features on the channel axis to match the desired output image size. Experiments are performed for the single-path upsample module, and compared to the multi-path upsample module. Based on the experimental results, the proposed algorithm reduces the upsample module's parameters by 24% and presents slightly to better performance compared to the previous algorithm.

• Journal of the Korea Institute of Military Science and Technology
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• v.9 no.4
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• pp.24-31
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• 2006

To reduce the interferences between the radio equipments which are operated in frequency hopping mode, the frequency resource should be assigned to each equipment without overlapping when several groups of radio equipments operate in the same area. If the radio equipments are in a different area, the partial frequency overlaying can be permitted. From the isotropic multi-dimensional array, several frequency assignment tables can be extracted for a same area. Also several tables can be extracted for different areas. Since there can be no overlapped frequencies between the tables for the same area, no interference between the radio equipments in an area is guaranteed. The frequencies overlapped between 2 tables for 2 different areas are pre-planed as required. The interference performance in frequency hopping radio can be controlled as desired using the proposed Frequency Resource Assignment Algorithm using Isotropic multi-dimensional Array.

• Proceedings of the Korean Geotechical Society Conference
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• 2000.11a
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• pp.665-672
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• 2000

The problems of discontinuous layer interface are very important in the algorithm and programming for the analysis of multi-layered consolidation using a numerical analysis, finite difference method(F.D,M.). Better results can be obtained by the process for discontinuous layer interface, since it can help consolidation analysis to model the actual ground Explicit method is simple for analysis algorithm and convenient for use except for applying the operator Crank-Nicolson method represents implicit method, which have different analysis method according to weighting factor. This method uses different algorithm according to dimension. And, this paper uses alternative direction implicit method. The purpose of this paper provides an efficient computer algorithm based on numerical analysis using finite difference method which account for multi-layered soils with confined aquifer to determine the degree of consolidation and excess pore pressures relative to time and positions more realistically.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.9
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• pp.4320-4333
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• 2017

Energy-efficient virtual resource allocation algorithm has become a hot research topic in cloud computing. However, most of the existing allocation schemes cannot ensure each type of resource be fully utilized. To solve the problem, this paper proposes a virtual machine (VM) allocation algorithm on the basis of multi-dimensional resource, considering the diversity of user's requests. First, we analyze the usage of each dimension resource of physical machines (PMs) and build a D-dimensional resource state model. Second, we introduce an energy-resource state metric (PAR) and then propose an energy-aware multi-dimensional resource allocation algorithm called MRBEA to allocate resources according to the resource state and energy consumption of PMs. Third, we validate the effectiveness of the proposed algorithm by real-world datasets. Experimental results show that MRBEA has a better performance in terms of energy consumption, SLA violations and the number of VM migrations.

• Interaction and multiscale mechanics
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• v.6 no.2
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• pp.83-105
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• 2013

In this paper, a multi-scale meshfree-enriched finite element formulation is presented for the analysis of acoustic wave propagation problem. The scale splitting in this formulation is based on the Variational Multi-scale (VMS) method. While the standard finite element polynomials are used to represent the coarse scales, the approximation of fine-scale solution is defined globally using the meshfree enrichments generated from the Generalized Meshfree (GMF) approximation. The resultant fine-scale approximations satisfy the homogenous Dirichlet boundary conditions and behave as the "global residual-free" bubbles for the enrichments in the oscillatory type of Helmholtz solutions. Numerical examples in one dimension and two dimensional cases are analyzed to demonstrate the accuracy of the present formulation and comparison is made to the analytical and two finite element solutions.

• MALSORI
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• no.60
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• pp.191-201
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• 2006

Discrimination between speech and music is important in many multimedia applications. Previously we proposed a new parameter for speech/music discrimination, the mean of minimum cepstral distances (MMCD), and it outperformed the conventional parameters. One weakness of MMCD is that its performance depends on range of candidate frames to compute the minimum cepstral distance, which requires the optimal selection of the range experimentally. In this paper, to alleviate the problem, we propose a multi-dimensional MMCD parameter which consists of multiple MMCDS with combination of different candidate frame ranges. Experimental results show that the multi-dimensional MMCD parameter yields an error rate reduction of 22.5% compared with the optimally chosen one-dimensional MMCD parameter.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.10
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• pp.2211-2218
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• 2002

In this study, an optimal design study has been made to determine dimensions of die and multi stress rings for extrusion process. For this purpose, a thermo-rigid-viscoplastic finite element program, CAMPform, was used fur forming analysis of extrusion process and a developed elastic finite element program fur elastic stress analysis of the die set including stress rings. And an optimization program, DOT, was employed for the optimization analysis. From this investigation, it was found out that the amount of shrink fitting incurred by the order of assembly of the die set should be taken into account for optimization when the multi stress rings are used in practice. In addition, it is construed that the proposed design method can be beneficial fur improving the tool life of cold extrusion die set.

• The Journal of the Korea Contents Association
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• v.15 no.1
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• pp.123-131
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• 2015

The purpose of this study was to find out the reason why the integration policy of local government in 2010~2014 has failed in the light of multi-dimensional model including normative, structural, constituentive, and technical dimension. Central government pushed integration policy of local government focused on increasing economic efficiency based on the theory of economy of scale on 16 regions and 36 local governments. Only one region(Cheong-Ju and Cheong-Won), however, completed integration procedure in 2014. Although most regions don't have common value on integration, and cultural, political ties in normative and structural dimension, central government pushed the integration policy. Futhermore central government failed to coordinate various interests of the participants and design incentive system including demands of local residents in constituentive and technical dimension. Based on this study central government should consider these policy implications when it propel the integration policy of local government in the future.

• Journal of Korea Multimedia Society
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• v.21 no.8
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• pp.952-959
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• 2018

Clustering is one of the most fundamental algorithms in machine learning. The performance of clustering is affected by the distribution of data, and when there are more data or more dimensions, the performance is degraded. For this reason, we use a stacked auto encoder, one of the deep learning algorithms, to reduce the dimension of data which generate a feature vector that best represents the input data. We use k-means, which is a famous algorithm, as a clustering. Sine the feature vector which reduced dimensions are also multi dimensional, we use the Euclidean distance as well as the cosine similarity to increase the performance which calculating the similarity between the center of the cluster and the data as a vector. A deep clustering networks combining a stacked auto encoder and k-means re-trains the networks when the k-means result changes. When re-training the networks, the loss function of the stacked auto encoder and the loss function of the k-means are combined to improve the performance and the stability of the network. Experiments of benchmark image ad document dataset empirically validated the power of the proposed algorithm.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.39 no.4
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• pp.363-369
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• 2002

In this paper, it could improved on the arbitrary nonlinear function learning approximation which have the wavelet neural network based on Adaptive Neuro-Fuzzy Inference System(ANFIS) and the multi-resolution Analysis(MRA) of the wavelet transform. ANFIS structure is composed of a bell type fuzzy membership function, and the wavelet neural network structure become composed of the forward algorithm and the backpropagation neural network algorithm. This wavelet composition has a single size, and it is used the backpropagation algorithm for learning of the wavelet neural network based on ANFIS. It is confirmed to be improved the wavelet base number decrease and the convergence speed performances of the wavelet neural network based on ANFIS Model which is using the wavelet translation parameter learning and bell type membership function of ANFIS than the conventional algorithm from 1 dimension and 2 dimension functions.