• 한국멀티미디어학회논문지
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• 제6권1호
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• pp.40-47
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• 2003

본 논문에서는 저해상도 대역에서의 움직임 벡터를 고려한 고해상도 대역의 영역에서 움직임 탐색을 행함으로써 밀-소 다중해상도 움직임 추정의 과도한 계산량을 줄일 수 있는 새로운 방법을 제안하였다. 또한 정확한 움직임을 찾아서 복원 영상의 화질을 높이기 위해 반화소 단위 움직임 추정을 사용하는데, 많은 계산량이 필요하게 된다. 따라서 본 논문에서는 인접 화소들간의 상관관계를 이용해 오차가 최소가 되는 방향을 예측하여 선택적 보간을 이용하여 계산량을 줄일 수 있었다.

• International Journal of Aeronautical and Space Sciences
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• 제9권1호
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• pp.147-161
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• 2008

In this paper, fine attitude estimation using low-cost sensors for attitude pointing missions of spacecraft is addressed. Attitude kinematics and gyro models including bias models are in general utilized to estimate spacecraft attitude and angular rate. However, a linearized model and a transition matrix are derived in this paper from nonlinear spacecraft dynamics with external disturbances. A Kalman filtering technique is applied and offers relatively high estimation accuracy under dynamic uncertainties. The proposed approach is demonstrated using numerical simulations.

• 제어로봇시스템학회논문지
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• 제17권5호
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• pp.499-504
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• 2011

In this paper, we suggest a method for discretized frequency modulations of ultrasonic signals. A continuous sweep of frequency modulation signals can be modelled with fine levels of discretization. If the ultrasound signals are modulated with monotonically decreasing frequencies, then the cross-correlation between an emitted signal and received signal can be used to identify the distance of multiple target objects. For the discretized frequency synthesis, CF ultrasounds with different frequencies are serially ordered. The auto-correlation test with the signal shows effective results for distance estimation. The discretized frequency syntheses have better distance resolution than CF ultrasound signals and the resolution depends on the number of the combined ultrasound frequencies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제6권4호
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• pp.1063-1075
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• 2012

To locate an object accurately in the wireless sensor networks, the distance measure based on time-delay plays an important role. In this paper, we propose a maximum likelihood (ML) time-delay estimation algorithm in multi-path wireless propagation channel. We get the joint probability density function after sampling the frequency domain response of the multi-path channel, which could be obtained by the vector network analyzer. Based on the ML criterion, the time-delay values of different paths are estimated. Considering the ML function is non-linear with respect to the multi-path time-delays, we first obtain the coarse values of different paths using the subspace fitting algorithm, then take them as an initial point, and finally get the ML time-delay estimation values with the pattern searching optimization method. The simulation results show that although the ML estimation variance could not reach the Cramer-Rao lower bounds (CRLB), its performance is superior to that of subspace fitting algorithm, and could be seen as a fine algorithm.

• 천문학회보
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• 제37권2호
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• pp.157.2-157.2
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• 2012

Direct-conversion receivers often suffer from a DC-offset that is a by-product of the direct conversion process to baseband. In general, a basic approach to reduce the DC-offset is to do simple average of the baseband signal and remove the DC by subtracting the average. However, this gives rise to a residual DC offset which degrades the performance when the receiver adopts the coding schemes with high coding rates such as 8-PSK. Therefore, more advanced methods should be additionally required for better performance. While the training sequences are basically designed to have good auto-correlation properties to facilitate the channel estimation, they may be not good for the simultaneous estimation of the channel response and the DC-offset. Also the DC offset compensation under a bad condition does not give good results due to the estimation error. Correspondingly, the proposed scheme employs the two important points. First, the training sequence codes are divided into two groups by MSE(Mean Squared Errors) for estimating the channel taps and then SNR calculated from each group is compared to predefined threshold to do fine DC-offset estimation. Next, ON/OFF module is applied for preventing performance degradation by large estimation error under severe channel conditions. The simulation results of the proposed scheme shows good performances compared to the existing algorithm. As a result, this scheme is surely applicable to the receiver design in many communications systems.

• 한국통신학회논문지
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• 제32권11A호
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• pp.1086-1095
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• 2007

본 논문에서는 다수의 피코넷들이 공존 (Simultaneously Operating Piconets; SOP) 하는 환경하에서 다중 상관처리를 이용한 새로운 Non-coherent TOA 추정 기법을 제안한다. SOP 환경에서 각 피코넷들을 구분하기 위해서 Gold 시퀀스를 이용한 DSSS (Direct Sequence Spread Spectrum) 기반의 IR-UWB (Impulse Radio-Ultra WideBand) 신호를 사용하였으며 상관 특성을 개선시키기 위해서 다중 마스트 처리 (Multiple Mask Operation; MMO) 방안을 제시하였다. 직접 LOS 경로에 대한 도착 시간 (Time Of Arrival; TOA) 정보는 2단계의 대략적인(Coarse)/ 정밀 (Fine) 타이밍 검출 과정을 통해 획득된다. 제안된 기법에 대한 성능을 검증하기 위해서 IEEE 802.15.4a TG (Task Group)에서 제시한 두 채널 모델을 적용하였고 시뮬레이션 결과로부터 제안된 기법이 다수개의 피코넷이 공존하는 다중 경로 환경에서 일반적인 단일 상관 처리 기법보다 성능이 개선됨을 확인하였다.

• 한국작물학회:학술대회논문집
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• 한국작물학회 2022년도 추계학술대회
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• pp.80-80
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• 2022

Estimation of heading date of paddy rice is laborious and time consuming. Therefore, automatic estimation of heading date of paddy rice is highly essential. In this experiment, deep learning classification models were used to classify two difference categories of rice (vegetative and reproductive stage) based on the panicle initiation of paddy field. Specifically, the dataset includes 444 slanted view images belonging to two categories and was then expanded to include 1,497 images via IMGAUG data augmentation technique. We adopt two transfer learning strategies: (First, used transferring model weights already trained on ImageNet to six classification network models: VGGNet, ResNet, DenseNet, InceptionV3, Xception and MobileNet, Second, fine-tuned some layers of the network according to our dataset). After training the CNN model, we used several evaluation metrics commonly used for classification tasks, including Accuracy, Precision, Recall, and F1-score. In addition, GradCAM was used to generate visual explanations for each image patch. Experimental results showed that the InceptionV3 is the best performing model in terms of the accuracy, average recall, precision, and F1-score. The fine-tuned InceptionV3 model achieved an overall classification accuracy of 0.95 with a high F1-score of 0.95. Our CNN model also represented the change of rice heading date under different date of transplanting. This study demonstrated that image based deep learning model can reliably be used as an automatic monitoring system to detect the heading date of rice crops using CCTV camera.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.1098-1101
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• 2004

The ordinary stereo image of an object consists of data of left and right views. Therefore, the left and right image pairs have to be transmitted simultaneously in order to display 3-dimentional video at the remote site. However, due to the twice data in comparing with a monoscopic image of the same object, it needs to be compressed for fast transmission and resource saving. Hence, it needs an effective coding algorithm for compressing stereo image. It was found previously that compressing left and right frames independently will achieve the compression ratio lower than compressing by utilizing the spatial redundancy between both frames. Therefore, in this paper, we study the stereo image compression technique based on the multiresolution wavelet transform using varied disparity-block size for estimation and compensation. The size of disparity-block in the stereo pair subbands are scaling on a coarse-to-fine wavelet coefficients strategy. Finally, the reference left image and residual right image after disparity estimation and compensation are coded by using SPIHT coding. The considered method demonstrates good performance in both PSNR measures and visual quality for stereo image.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 2000년도 가을 학술발표회 논문집(II)
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• pp.961-966
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• 2000

It is difficult to change or remedy concrete structure after hardened. It is usual to evaluate the quality of hardened concrete using several test method. This study was performed to make fundamental data that could be used to evaluate the quality of hardened concrete. This study is to estimate mix proportion of hardened concrete. Each elements of concrete needed different estimation methods. First, the cement that handled by the most important compounds measured by XRF(X-ray fluorecence) machine with scanning Ca-K${\alpha}$. Second, the coarse aggregate that divided by maximum size measured by the area comparison method that starts from the assumption of uniform distribution. Third, the fine aggregate measured by the weight comparison method that needs several prerequsite constants which concerned cement hydration reaction. Fourth, the water content would be estimated by expert system that has data base of design data, the contents of above estimation results, the characteristics of concrete strength. As the result of the above research, some conclusions are as follows. The cement estimation method resulted by reliability of mean 96.7%, standard deviation 3.92. The area comparison method resulted by reliability of mean 95.3%, standard deviation 2.08. The weight comparison method resulted by reliability of mean 93.3%, standard deviation 3.35.

• ETRI Journal
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• 제43권1호
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• pp.120-132
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• 2021

A frequency diverse array (FDA) multiple-input multiple-output (MIMO) radar employs a small frequency increment across transmit elements to produce an angle-range-dependent beampattern for target angle and range detection. The joint angle and range estimation problem is a trilinear model. The traditional trilinear alternating least square (TALS) algorithm involves high computational load due to excessive iterations. We propose a fast-convergence trilinear decomposition (FC-TD) algorithm to jointly estimate FDA-MIMO radar target angle and range. We first use a propagator method to obtain coarse angle and range estimates in the data domain. Next, the coarse estimates are used as initialized parameters instead of the traditional TALS algorithm random initialization to reduce iterations and accelerate convergence. Finally, fine angle and range estimates are derived and automatically paired. Compared to the traditional TALS algorithm, the proposed FC-TD algorithm has lower computational complexity with no estimation performance degradation. Moreover, Cramer-Rao bounds are presented and simulation results are provided to validate the proposed FC-TD algorithm effectiveness.