• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송∙미디어공학회 2020년도 추계학술대회
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• pp.252-255
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• 2020

Unsupervised deep learning methods have shown impressive results for the challenging monocular depth estimation task, a field of study that has gained attention in recent years. A common approach for this task is to train a deep convolutional neural network (DCNN) via an image synthesis sub-task, where additional views are utilized during training to minimize a photometric reconstruction error. Previous unsupervised depth estimation networks are trained within a fixed depth estimation range, irrespective of its possible range for a given image, leading to suboptimal estimates. To overcome this suboptimal limitation, we first propose an unsupervised adaptive depth estimation method guided by minimum and maximum (min-max) depth priors for a given input image. The incorporation of min-max depth priors can drastically reduce the depth estimation complexity and produce depth estimates with higher accuracy. Moreover, we propose a novel network architecture for adaptive depth estimation, called the AdaMM-DepthNet, which adopts the min-max depth estimation in its front side. Intensive experimental results demonstrate that the adaptive depth estimation can significantly boost up the accuracy with a fewer number of parameters over the conventional approaches with a fixed minimum and maximum depth range.

• 한국통신학회논문지
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• 제35권2A호
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• pp.196-201
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• 2010

Measurement of Quality of Service (QoS) parameters and its statistical analysis becomes a key issue for Mobile WiMAX service providers to manage the converged network efficiently and to support end-to-end QoS. In this paper, we investigate the population distribution of end-to-end one-way delay which is the most important QoS parameter in Mobile WiMAX networks. The samples are analyzed with Chi-Square Goodness-of-Fit test, Kolmogorov-Smirnov (K-S), and Anderson-Darling (A-D) test to verify the distribution of parent population. The relation with confidence level and the minimum number of sample size is also performed for logistic distribution. The statistical analysis is a promising approach for measuring the performance Mobile WiMAX networks.

• Journal of Computing Science and Engineering
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• 제7권1호
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• pp.44-52
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• 2013

This paper addresses the problem of resource scheduling in a grid computing environment. One of the main goals of grid computing is to share system resources among geographically dispersed users, and schedule resource requests in an efficient manner. Grid computing resources are distributed, heterogeneous, dynamic, and autonomous, which makes resource scheduling a complex problem. This paper proposes a new approach to resource scheduling in grid computing environments, the hierarchical stochastic Petri net (HSPN). The HSPN optimizes grid resource sharing, by categorizing resource requests in three layers, where each layer has special functions for receiving subtasks from, and delivering data to, the layer above or below. We compare the HSPN performance with the Min-min and Max-min resource scheduling algorithms. Our results show that the HSPN performs better than Max-min, but slightly underperforms Min-min.

• 정보처리학회논문지C
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• 제16C권1호
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• pp.57-64
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• 2009

모바일 와이맥스에서 IP 기반의 무선 광대역 서비스를 효율적으로 제공하기 위해, 제한적인 무선링크의 대역폭을 보다 효율적으로 활용하는 방안이 필요하다. 모바일 와이맥스 표준에서 정의된 헤더 압축 기법인 PHS(Payload Header Suppression) 대신에 ROHC(RObust Header Compression) O(Bi-directional Optimistic) 모드를 사용할 경우, 압축효율성과 견고성 면에서 성능을 향상시킬 수 있으나[1, 2], 모바일 와이맥스의 음영서비스 지역이나 비트 에러로 인한 수신단에서의 연속적인 패킷의 손실이 발생할 경우 컴프레서와 디컴프레서가 유지하는 압축 정보가 비동기화되어 O 모드의 패킷 복구 실패율이 증가하게 되므로 이를 보완할 방안이 필요하다. 이에 본 논문에서는, 모바일 와이맥스 환경에서 패킷 손실에 대한 ROHC O 모드의 성능 향상을 위해 O 모드의 압축률과 견고성에 모두 영향을 미치는 Optimistic 파라미터의 값을 동적으로 조절하는 방안을 제안하고, OPNET 시뮬레이터를 사용하여 제안한 방안의 성능 분석을 하였다.

• 한국도로학회논문집
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• 제4권4호
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• pp.23-39
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• 2002

본 논문은 아스팔트 포장의 소성변형 저항성 추정을 보다 쉽게 하기 위하여 새로운 시험법 및 장비를 개발하기 위한 연구이다. 본 연구에서는 다양한 종류의 아스팔트 혼합물을 사용하여 소성변형 저항성과의 상관성이 높게 나타나는 변형강도를 개발하기 위하여 하중봉의 직경(D) 3, 4cm, 각 봉의 하단을 다양하게 원형절삭(r)한 김 테스터를 개발하였다. 이를 이용하여 마샬시험과 같이 공시체를 $60^{\circ}C$에 수침처리하고 같은 속도의 정하중을 공시체에 다짐방향으로 가하여 최대하중 ($P_{max}$)과 이때의 수직 변위 (y)를 구하였다. 변형강도는 개발된 식 $K_D = 4P_{max}/{\pi}(D-2(r-\sqrt{2ry-y^2}))^2$으로 구하였으며 소성변형 특성치와 평균 $R^2$이 0.77이상의 높은 상관성을 보였다. 따라서 이 시험방법은 마샬안정도보다 훨씬 소성변형과의 상관관계가 큰 측정치를 얻을 수 있는 방법임을 알 수 있었으며, 특히 기존의 마샬시험기를 그대로 사용하고 공시체도 기존의 방법대로 제조 및 처리하도록 하여 적용이 빠를 것으로 판단된다. 향후 다양한 골재입도, 최대치수, r 등에서도 타당성과 상관성 검증을 통해 시험법을 표준화한다면 실용화 가능성이 매우 클 것이다.

• International Journal of Control, Automation, and Systems
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• 제6권6호
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• pp.928-938
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• 2008

This study presents an approach for approximation an unknown function from a numerical data set based on the synthesis of a neuro-fuzzy model. An adaptive input data space parting method, which is used for building hyperbox-shaped clusters in the input data space, is proposed. Each data cluster is implemented here as a fuzzy set using a membership function MF with a hyperbox core that is constructed from a min vertex and a max vertex. The focus of interest in proposed approach is to increase degree of fit between characteristics of the given numerical data set and the established fuzzy sets used to approximate it. A new cutting procedure, named NCP, is proposed. The NCP is an adaptive cutting procedure using a pure function $\Psi$ and a penalty function $\tau$ for direction the input data space parting process. New algorithms named CSHL, HLM1 and HLM2 are presented. The first new algorithm, CSHL, built based on the cutting procedure NCP, is used to create hyperbox-shaped data clusters. The second and the third algorithm are used to establish adaptive neuro- fuzzy inference systems. A series of numerical experiments are performed to assess the efficiency of the proposed approach.

• 한국생산제조학회지
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• 제19권4호
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• pp.469-475
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• 2010

The objective of this study is to integrate reliability analysis into shape optimization problem using the evolutionary structural optimization (ESO) in the application example. Reliability-based shape optimization is formulated as volume minimization problem with probabilistic stress constraint under minimization max. von Mises stress and allow stress. Young's modulus, external load and thickness are considered as uncertain variables. In order to compute reliability index, four methods, i.e., reliability index approach (RIA), performance measure approach (PMA), single-loop singlevector (SLSV) and adaptive-loop (ADL), are used. Reliability-based shape optimization design process is conducted to obtain optimal shape satisfying max. von Mises stress and reliability index constraints with the above four methods, and then each result is compared with respect to numerical stability and computing time.

• 전기학회논문지
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• 제56권7호
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• pp.1309-1314
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• 2007

The assignment of cells to reporting or non-reporting cells is an NP-hard problem having an exponential complexity in the Reporting Cell Location Management (RCLM) system. Frequent location update may result in degradation of quality of service due to interference. Miss on the location of a mobile terminal will necessitate a search operation on the network when a call comes in. The number of reporting cells and which cell must be reporting cell should be determined to balance the registration (location update) and search (paging) operations to minimize the cost of RCLM system. T1is paper compares Max-Min ant system (MMAS), rank-based ant system (RAS) and hybrid method of MMAS and RAS that generally used to solve combinatorial optimization problems. Experimental results demonstrate that hybrid method of MMAS and RAS is an effective and competitive approach in fairly satisfactory results with respect to solution quality and execution time for the optimal design of location management system.

• Journal of applied mathematics & informatics
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• 제17권1_2_3호
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• pp.121-144
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• 2005

In this paper, we propose unconstrained and constrained posynomial Geometric Programming (GP) problem with negative or positive integral degree of difficulty. Conventional GP approach has been modified to solve some special type of GP problems. In specific case, when the degree of difficulty is negative, the normality and the orthogonality conditions of the dual program give a system of linear equations. No general solution vector exists for this system of linear equations. But an approximate solution can be determined by the least square and also max-min method. Here, modified form of geometric programming method has been demonstrated and for that purpose necessary theorems have been derived. Finally, these are illustrated by numerical examples and applications.

• 전자공학회논문지B
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• 제30B권10호
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• pp.77-85
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• 1993

In this paper, we present a method to solve a convexly combined fuzzy relational equation with generalized connectives. For this, we propose a neural network whose structure represents the fuzzy relational equation. Then we derive a learning algorithm by using the concept of back-propagation learning. Since the proposed method can be used for a general form of fuzzy relational equations, such fuzzy max-min or min-max relational equations can be treated as its special cases. Moreover, the relational structure adopted in the proposed neurocomputational approach can work in a highly parallel manner so that real-time applications of fuzzy sets are possibles as in fuzzy logic controllers, knowledge-based systems, and pattern recognition systems.