• 대한전자공학회논문지SP
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• 제38권4호
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• pp.371-380
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• 2001

본 논문에서는 균일한 색 분포를 위한 밝기 사상과 최대 색도 재현을 위한 색역 사상 방법을 제안한다. 기존의 밝기 사상시 발생하는 두 색역에서의 평균 밝기의 차이의 증가 및 어두운 영역과 밝은 영역에서의 색 변화의 차이가 증가하는 문제를 해결하기 위하여 각 색상의 정점에서의 밝기 차이를 최소화하고 밝은 영역과 어두운 영역에서의 균일한 색 분포를 가지는 밝기 사상 방법을 제안하였다. 또한, 최대 채도 유지 및 균일 색 분포를 위한 가변 닻점과 고정 닻점을 혼합으로 사용하는 색역 사상 방법을 제안하였다. 따라서 저가의 칼라 출력장치에서 고화질의 칼라를 재현할 수 있게 한다.

• Journal of Mechanical Science and Technology
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• 제18권1호
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• pp.92-105
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• 2004

A new algorithm for planning a collision-free path based on algebraic curve is developed and the concept of collision-free Path Space (PS) is introduced. This paper presents a Geometry Mapping (GM) based on two straight curves in which the intermediate connection point is organized in elliptic locus ($\delta$, $\theta$). The GM produces two-dimensional PS that is used to create the shortest collision-free path. The elliptic locus of intermediate connection point has a special property in that the total distance between the focus points through a point on ellipse is the same regardless of the location of the intermediate connection point on the ellipse. Since the radial distance, a, represents the total length of the path, the collision-free path can be found as the GM proceeds from $\delta$=0 (the direct path) to $\delta$=$\delta$$\_$max/(the longest path) resulting in the minimum time search. The GM of elliptic workspace (EWS) requires calculation of interference in circumferential direction only. The procedure for GM includes categorization of obstacles to .educe necessary calculation. A GM based on rectangular workspace (RWS) using Cartesian coordinate is also considered to show yet another possible GM. The transformations of PS among Circular Workspace Geometry Mapping (CWS GM) , Elliptic Workspace Geometry Mapping (EWS GM) , and Rectangular Workspace Geometry Mapping (RWS GM), are also considered. The simulations for the EWS GM on various computer systems are carried out to measure performance of algorithm and the results are presented.

• 대한전자공학회논문지SD
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• 제39권8호
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• pp.84-91
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• 2002

In this paper, we proposed a CLB-based CPLD technology mapping algorithm for power minimization under time constraint in combinational circuit. The main idea of our algorithm is to exploit the "cut enumeration and feasible cluster" technique to generate possible mapping solutions for the sub-circuit rooted at each node. In our technology mapping algorithm conducted a low power by calculating TD and EP of each node and decomposing them on the circuit composed of DAG. It also takes the number of input, output, and OR-term into account on condition that mapping can be done up to the base of CLB, and so it generates the feasible clusters to meet the condition of time constraint. Of the feasible clusters, we should first be mapping the one that h3s the least output for technology mapping of power minimization and choose to map the other to meet the condition of time constraint afterwards. To demonstrate the efficiency of our approach, we applied our algorithm to MCNC benchmarks and compared the results with those of the exiting algorithms. The experimental results show that our approach is shown a decrease of 46.79% compared with DDMAP and that of 24.38% for TEMPLA in the power consumption.

• ETRI Journal
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• 제36권6호
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• pp.913-923
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• 2014

This paper proposes a global mapping algorithm for multiple robots from an omnidirectional-vision simultaneous localization and mapping (SLAM) approach based on an object extraction method using Lucas-Kanade optical flow motion detection and images obtained through fisheye lenses mounted on robots. The multi-robot mapping algorithm draws a global map by using map data obtained from all of the individual robots. Global mapping takes a long time to process because it exchanges map data from individual robots while searching all areas. An omnidirectional image sensor has many advantages for object detection and mapping because it can measure all information around a robot simultaneously. The process calculations of the correction algorithm are improved over existing methods by correcting only the object's feature points. The proposed algorithm has two steps: first, a local map is created based on an omnidirectional-vision SLAM approach for individual robots. Second, a global map is generated by merging individual maps from multiple robots. The reliability of the proposed mapping algorithm is verified through a comparison of maps based on the proposed algorithm and real maps.

• 한국통신학회논문지
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• 제37권5A호
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• pp.311-316
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• 2012

논문에서 부분 체이스 결합을 위한 LDPC 부호의 비트 매핑 기법을 제안한다. 정보 비트를 항상 신뢰도가 높은 채널에 할당하는 기존의 비트 매핑 기법에 비해, 제안된 비트 매핑 기법은 LDPC 부호와 채널 특성을 동시에 고려하여 비균일 LDPC 부호의 부호어 비트를 최적의 채널에 할당한다. 또한, 밀도 진화 기법을 사용하여 부분 체이스 결합을 위한 순환 방정식을 유도하여 주어진 환경에서 다양한 비트 매핑 기법 중에서 최적이 성능을 갖는 매핑을 얻고, 모의실험을 통해 이를 검증한다.

• 대한원격탐사학회지
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• 제23권1호
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• pp.21-32
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• 2007

Fire fuel map is one of the most critical factors for planning and managing the fire hazard and risk. However, fuel mapping is extremely difficult because fuel properties vary at spatial scales, change depending on the seasonal situations and are affected by the surrounding environment. Remote sensing has potential to reduce the uncertainty in mapping fuels and offers the best approach for improving our abilities. Especially, Hyperspectral sensor have a great potential for mapping vegetation properties because of their high spectral resolution. The objective of this paper is to evaluate the potential of mapping fuel properties using Hyperion hyperspectral remote sensing data acquired in April, 2002. Fuel properties are divided into four broad categories: 1) fuel moisture, 2) fuel green live biomass, 3) fuel condition and 4) fuel types. Fuel moisture and fuel green biomass were assessed using canopy moisture, derived from the expression of liquid water in the reflectance spectrum of plants. Fuel condition was assessed using endmember fractions from spectral mixture analysis (SMA). Fuel types were classified by fuel models based on the results of SMA. Although Hyperion imagery included a lot of sensor noise and poor performance in liquid water band, the overall results showed that Hyperion imagery have good potential for wildfire fuel mapping.

• International journal of advanced smart convergence
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• 제10권1호
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• pp.142-150
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• 2021

Open-channel SSD is a new type of Solid-State Disk (SSD) that improves the garbage collection overhead and write amplification due to physical constraints of NAND flash memory by exposing the internal structure of the SSD to the host. However, the host-level Flash Translation Layer (FTL) provided for open-channel SSDs in the current Linux kernel consumes host memory excessively because it use page-level mapping table to translate logical address to physical address. Therefore, in this paper, we implemente a selective mapping table loading scheme that loads only a currently required part of the mapping table to the mapping table cache from SSD instead of entire mapping table. In addition, to increase the hit ratio of the mapping table cache, filesystem information and mapping table access history are utilized for cache replacement policy. The proposed scheme is implemented in the host-level FTL of the Linux kernel and evaluated using open-channel SSD emulator. According to the evaluation results, we can achieve 80% of I/O performance using the only 32% of memory usage compared to the previous host-level FTL.

• Investigative Magnetic Resonance Imaging
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• 제25권4호
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• pp.300-312
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• 2021

Compressed sensing (CS) has been investigated in magnetic resonance (MR) parametric mapping to reduce scan time. However, the relatively long reconstruction time restricts its widespread applications in the clinic. Recently, deep learning-based methods have shown great potential in accelerating reconstruction time and improving imaging quality in fast MR imaging, although their adaptation to parametric mapping is still in an early stage. In this paper, we proposed a novel deep learning-based framework DEMO for fast and robust MR parametric mapping. Different from current deep learning-based methods, DEMO trains the network in an unsupervised way, which is more practical given that it is difficult to acquire large fully sampled training data of parametric-weighted images. Specifically, a CS-based loss function is used in DEMO to avoid the necessity of using fully sampled k-space data as the label, thus making it an unsupervised learning approach. DEMO reconstructs parametric weighted images and generates a parametric map simultaneously by unrolling an interaction approach in conventional fast MR parametric mapping, which enables multi-tasking learning. Experimental results showed promising performance of the proposed DEMO framework in quantitative MR T1ρ mapping.

• JSTS:Journal of Semiconductor Technology and Science
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• 제9권4호
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• pp.209-215
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• 2009

Reconfigurable array architecture is recently attracting much attention. It is a flexible hardware architecture, which can dynamically change its configuration to execute various functions while maintainning high performance. However, pursuing flexibility and performance at the same time leads to complexity, thereby makes the mapping of applications a difficult process. There have been attempts to use compiler or high level synthesis techniques to solve the problem. In this paper, we propose yet another method, which uses libraries for the mapping to provide an abstracttion of the internal structure and at the same time to reduce the development time and efforts through the automated process. We have selected a JPEG decoder as an example to apply the proposed method. As a result, we obtained about 20% less performance compared to manual mapping but development time is dramatically reduced to less than 1%.

• 한국컴퓨터그래픽스학회논문지
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• 제2권1호
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• pp.15-23
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• 1996

범프 맵핑(bump mapping)은 나무껍질과 같은 울퉁불퉁한 물체를 표현하기 위한 방법으로서 텍스쳐 맵핑(texture mapping)보다 더욱 사실적인 영상을 얻을 수 있는 렌더링(rendering) 기법이다. 본 논문에서는 기존의 범프 맵핑 알고리즘의 단점을 보완한 새로운 알고리즘을 제시하며, 또한 이 알고리즘을 실시간으로 처리하기 위한 하드웨어 구조를 제시한다. 이는 기존의 구조에 비해 구현하기가 더욱 간단한 것이다.