• Journal of Multimedia Information System
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• 제8권4호
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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.

• 한국군사과학기술학회지
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• 제9권4호
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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.

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2000년도 가을 학술발표회 논문집
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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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• 제11권9호
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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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• 제6권2호
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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.

• 대한음성학회지:말소리
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• 제60호
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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.

• 대한기계학회논문집A
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• 제26권10호
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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.

• 한국콘텐츠학회논문지
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• 제15권1호
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• pp.123-131
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• 2015

본 연구는 2010~2014년 사이에 중앙정부가 추진한 시 군구 통합정책을 다차원분석모형에 기초하여 분석하고 있다. 2010년 이후 중앙정부는 환경변화로 생활권과 행정구역이 일치하지 않게 되었거나, 인구고령화 등으로 인구가 급격히 감소한 지방자치단체를 대상으로 통합을 추진하여 효율성을 증진시키려 하였다. 그러나 중앙정부의 의도와는 달리, 지역주민의 공감대 부족, 정치적 반대 등 복잡한 요인의 영향으로 최종적으로 중앙정부가 선정한 16개 지역 36개 시 군 구 중에서 시 군 구 통합에 성공한 지역은 단 1개(청주 청원)에 그쳤다. 본 연구는 다차원분석모형에 따라 이명박정부가 추진한 시 군 구 통합정책을 규범적, 구조적, 구성적, 기술적 측면에서 분석 평가한 결과, 다음과 같은 이유로 정책이 성공적으로 추진되지 못하였다는 결론을 도출하였다. 규범적 측면에서 통합 대상 지역 주민들 사이에 통합의 가치에 대한 공감대를 형성하지 못했고, 구조적 측면에서 기존에 형성되어 있는 지역의 문화 정치적 정서 차이를 극복하지 못한 상태에서 정책이 중앙정부 주도로 추진되었다. 구성적 측면에서 통합 대상 지역의 이익을 대표하는 구성원들의 이해관계를 통합과정에 충실히 조정하지 못하였으며, 기술적 측면에서도 통합의 유인제도 설계에 지역주민의 수요가 적절히 반영되지 못하였다. 이러한 결과는 향후에 이루어질 시 군 구 통합정책이 규범적, 구조적, 구성적, 기술적 측면에서 통합 대상 지역주민의 의견을 충실히 반영하는 방향으로 설계되고, 충분한 시간을 두고 자율적인 참여를 통해 이루어질 수 있도록 유도하는 방향으로 추진되어야 한다는 정책적 함의를 주고 있다.

• 한국멀티미디어학회논문지
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• 제21권8호
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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.

• 대한전자공학회논문지TE
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• 제39권4호
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• pp.363-369
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• 2002

본 논문은 적응성 뉴로-퍼지 인터페이스 시스템(Adaptive Neuro-Fuzzy Inference System : ANFIS)과 웨이브렛 변환 다중해상도 분해(multi-resolution Analysis : MRA)을 기반으로 한 웨이브렛 신경망을 가지고 임의의 비선형 함수 학습 근사화를 개선하는 것이다. ANFIS 구조는 벨형 퍼지 소속 함수로 구성이 되었으며, 웨이브렛 신경망은 전파 알고리즘과 역전파 신경망 알고리즘으로 구성되었다. 이 웨이브렛 구성은 단일 크기이고, ANFIS 기반 웨이브렛 신경망의 학습을 위해 역전파 알고리즘을 사용하였다. 1차원과 2차원 함수에서 웨이브렛 전달 파라미터 학습과 ANFIS의 벨형 소속 함수를 이용한 ANFIS 모델 기반 웨이브렛 신경망의 웨이브렛 기저 수 감소와 수렴 속도 성능이 기존의 알고리즘 보다 개선되었음을 확인하였다.