• 대한전자공학회논문지SD
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• 제44권6호
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• pp.28-33
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• 2007

BD-R/RE/ROM을 2배속으로 기록하고 재생할 수 있는 전단 시스템을 개발하였다. 시스템의 재생능력은 PR(a,b,c,d,e)형 5탭 PRML을 채택함으로써 향상되었다. 제안된 PRML덕분에 2배속 25GB 디스크에서 ${\mp}0.6{\circ}$ 이상의 라디알 및 탄젠셜 틸드 마진을 가지고도 $2{\times}10^{-4}$이하의 BER을 얻을 수 있었다. 최적파워레벨이 다른 다양한 BD-R/RE의 안정적인 기록을 위해 OPC 방법에 대해서도 제안하였다. 개발한 시스템은 $0.18-{\mu}m$ CMOS공정으로 $60mm^2$ 면적에 1,400만 트랜지스터를 칩에 집적하였다.

• 대한전자공학회논문지TC
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• 제38권3호
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• pp.1-8
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• 2001

DS/CDMA방식의 이동통신 시스템에서 다중접속 및 다중경로 페이딩 간섭을 제거하기 위하여 비교적 구조가 간단한 병렬 간섭제거기가 이용된다. 다단계로 구성되는 병렬 간섭제거방식에서 다중접속 간섭의 제거 성능을 향상시키기 위하여 간섭의 정확한 추정이 필수적이다. 본 논문에서는 제거성능의 향상을 위해 NLMS(Normalized Least Mean Square)알고리즘으로 가중치(weight)를 계산하는 적응 제거방식과 시험 검파기로서 HT(Hyperbolic Tangent)검파방식에 NZ(Null Zone)을 이용하는 새로운 검파방식을 제안하고 모의실험을 통하여 그 성능을 분석하였다. 모의실험 결과 제안하는 방식이 비트오율 $2{\times}10^{-2}$에서 기존의 제거기에 비해 사용자수가 약 48%정도 증가하는 개선된 성능을 얻었다.

• 한국컴퓨터정보학회논문지
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• 제23권7호
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• pp.81-90
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• 2018

In this paper, reducing lense Vignetting effect and adaptive learning rate method are proposed to complement Scribner's neural network for nuc algorithm which is the effective algorithm in statistic SBNUC algorithm. Proposed reducing vignetting effect method is updated weight and bias each differently using different cost function. Proposed adaptive learning rate for updating weight and bias is using sobel edge detection method, which has good result for boundary condition of image. The ordinary statistic SBNUC algorithm has problem to compensate lense vignetting effect, because statistic algorithm is updated weight and bias by using gradient descent method, so it should not be effective for global weight problem same like, lense vignetting effect. We employ the proposed methods to Scribner's neural network method(NNM) and Torres's reducing ghosting correction for neural network nuc algorithm(improved NNM), and apply it to real-infrared detector image stream. The result of proposed algorithm shows that it has 10dB higher PSNR and 1.5 times faster convergence speed then the improved NNM Algorithm.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 2009년도 IWAIT
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• pp.808-811
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• 2009

Robovie-R2 [1], developed by ATR, is a 110cm high, 60kg weight, two wheel drive, human like robot. It has two arms with dynamic fingers. It also has a position sensitive detector sensor and two cameras as eyes on his head for recognizing his surrounding environment. Recent years, we have carried out a project to integrate new functions into Robovie-R2 so as to make it possible to be used in a dining room in healthcare center for helping serving meal for elderly. As a new function, we have developed software system for adaptive movement control of Robovie-R2 that is primary important since a robot that cannot autonomously control its movement would be a dangerous object to the people in dining room. We used the cameras on Robovie-R2's head to catch environment images, applied our original algorithm for recognizing obstacles such as furniture or people, so as to control Roboie-R2's movement. In this paper, we will focus our algorithm and its results.

• 천문학회보
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• 제36권2호
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• pp.132.2-132.2
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• 2011

We are developing Adaptive Optics (AO) system for astronomical use. The He-Ne laser works as an artificial light source. The tip-tilt correction servo is added to our AO system. The tip-tilt term, among the Zernike terms, is the biggest contributor of wavefront deformation caused by atmospheric turbulence at small telescopes. The tip-tilt correction servo consists of a Piezo tip-tilt platform with a mirror, a quadrant photodiode as a tip-tilt sensor, and controllers. The Shack-Hartmann wavefront sensor measures the residual wavefront errors and they are corrected by the MEMS (Micro Electro Mechanical System) deformable mirror. The MEMS deformable mirror allows the compact size at low cost compare to adaptive secondary mirror and other deformable mirrors. As the frame rates of the MEMS deformable mirror is about tens of kHz, the frame rates of the detector in wavefront sensor is the bottleneck of the wavefront correction speed. For faster performance, we replaced a CCD which provides frame rates only 70 Hz with a CMOS with frame rates up to 450 Hz.

• Journal of Communications and Networks
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• 제5권1호
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• pp.33-42
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• 2003

In this paper a two-stage maximum-likelihood (ML) detection structure for group detection in DS/CDMA systems is presented. The first stage of the receiver is a linear filter, aimed at suppressing the effect of the unwanted (i.e., out-of-grout) users' signals, while the second stage is a non-linear block, implementing a ML detection rule on the set of desired users signals. As to the linear stage, we consider both the decorrelating and the minimum mean square error approaches. Interestingly, the proposed detection structure turns out to be a generalization of Varanasi's group detector, to which it reduces when the system is synchronous, the signatures are linerly independent and the first stage of the receiver is a decorrelator. The issue of blind adaptive receiver implementation is also considered, and implementations of the proposed receiver based on the LMS algorithm, the RLS algorithm and subspace-tracking algorithms are presented. These adaptive receivers do not rely on any knowledge on the out-of group users' signals, and are thus particularly suited for rejection of out-of-cell interference in the base station. Simulation results confirm that the proposed structure achieves very satisfactory performance in comparison with previously derived receivers, as well as that the proposed blind adaptive algorithms achieve satisfactory performance.

• 산업경영시스템학회지
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• 제19권39호
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• pp.47-61
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• 1996

We propose a parallel neural network model in which patterns are clustered and patterns in a cluster are studied in a parallel neural network. The learning algorithm used in this paper is based on LVQ algorithm of Kohonen(1990) for clustering and ADALINE(Adaptive Linear Neuron) network of Widrow and Hoff(1990) for parallel learning. The proposed algorithm consists of two parts. First, N patterns to be learned are categorized into C clusters by LVQ clustering algorithm. Second, C patterns that was selected from each cluster of C are learned as input pattern of ADALINE(Adaptive Linear Neuron). Data used in this paper consists of 250 patterns of ASCII characters normalized into $8\times16$ and 1124. The proposed algorithm consists of two parts. First, N patterns to be learned are categorized into C clusters by LVQ clustering algorithm. Second, C patterns that was selected from each cluster of C are learned as input pattern of ADALINE(Adaptive Linear Neuron). Data used in this paper consists 250 patterns of ASCII characters normalized into $8\times16$ and 1124 samples acquired from signals generated from 9 car models that passed Inductive Loop Detector(ILD) at 10 points. In ASCII character experiment, 191(179) out of 250 patterns are recognized with 3%(5%) noise and with 1124 car model data. 807 car models were recognized showing 71.8% recognition ratio. This result is 10.2% improvement over backpropagation algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권12호
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• pp.4776-4794
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• 2020

We propose a rapid adaptive learning framework for streaming object detection, called EER-ASSL. The method combines the expected error reduction (EER) dependent rollback learning and the active semi-supervised learning (ASSL) for a rapid adaptive CNN detector. Most CNN object detectors are built on the assumption of static data distribution. However, images are often noisy and biased, and the data distribution is imbalanced in a real world environment. The proposed method consists of collaborative sampling and EER-ASSL. The EER-ASSL utilizes the active learning (AL) and rollback based semi-supervised learning (SSL). The AL allows us to select more informative and representative samples measuring uncertainty and diversity. The SSL divides the selected streaming image samples into the bins and each bin repeatedly transfers the discriminative knowledge of the EER and CNN models to the next bin until convergence and incorporation with the EER rollback learning algorithm is achieved. The EER models provide a rapid short-term myopic adaptation and the CNN models an incremental long-term performance improvement. EER-ASSL can overcome noisy and biased labels in varying data distribution. Extensive experiments shows that EER-ASSL obtained 70.9 mAP compared to state-of-the-art technology such as Faster RCNN, SSD300, and YOLOv2.

• 한국통신학회논문지
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• 제29권11A
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• pp.1244-1252
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• 2004

본 논문에서는 Alamouti의 시공간블록부호화를 적용한 다중화 시스템에서 선형 검출기 (zero forcing 또는 minimum mean square error)의 특성을 유도하고 이를 이용하여 수신기의 복잡도를 줄일 수 있는 방안을 제시한다. MMSE 검출기를 적응형으로 설계할 경우 계산해야 하는 가중치 벡터들의 수는 공간 다중화하여 전송한 심볼들의 수만큼 필요하지만 유도한 특성을 이용하면 STBC블록들의 수로 줄어든다. 적응형 알고리즘으로 RLS 알고리즘을 적용해 보았고 복잡도를 50%이상 줄일 수 있었다. 또한 V-Blast검출방법의 복잡도를 줄이기 위해 제안된 정렬QR분해 검출기를 본 시스템에 적용할 때 동일한 특성이 유니타리 행렬 Q와 상위삼각행렬 R에 나타나는 것을 확인하였고, 이 경우에도 성능의 저하 없이 복잡도를 50%까지 줄일 수 있었다.

• 전기전자학회논문지
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• 제20권3호
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• pp.299-302
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• 2016

본 논문에서는 성능이 향상된 Stack Monitoring System을 설계한다. Stack Monitoring System의 증폭기(AMP)에 들어오는 펄스성 잡음을 차단하기 위하여, 차폐 및 전원부 임피던스를 낮추고 전원회로를 분리하여 노이즈를 차단한다. 신틸레이션 검출기 특성을 최대한 장치에 매칭하기 위한 가변 고전압, 이득(Gain), 상쇄(Offset), 한계(Threshold) 등을 설정 할 수 있는 제어부를 설계한다. 또한 300 ~ 1,500V의 가변 고전압 전원회로를 구성하여 다양한 신틸레이션 검출기에 적용가능 한 가변 전압 공급 장치를 설계한다. 성능이 향상된 Stack Monitoring System은 다종의 신틸레이션 검출기가 각각의 특성을 고려하여 동작하게 함으로서 효율적이고 높은 신뢰성을 보장한다. 개발된 Stack Monitoring System의 측정 불확도에 대하여 공인 시험기관의 장비를 사용하여 실험한 결과 우수한 성능을 나타내었다.