• Nuclear Engineering and Technology
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• 제53권3호
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• pp.888-893
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• 2021

The International Atomic Energy Agency has developed a tomographic imaging system for accomplishing the total fuel rod-by-rod verification time of fuel assemblies within the order of 1-2 h, however, there are still limitations for some fuel types. The aim of this study is to develop a deep learning-based denoising process resulting in increasing the tomographic image acquisition speed of fuel assembly compared to the conventional techniques. Convolutional AutoEncoder (CAE) was employed for denoising the low-quality images reconstructed by filtered back-projection (FBP) algorithm. The image data set was constructed by the Monte Carlo method with the FBP and ground truth (GT) images for 511 patterns of missing fuel rods. The de-noising performance of the CAE model was evaluated by comparing the pixel-by-pixel subtracted images between the GT and FBP images and the GT and CAE images; the average differences of the pixel values for the sample image 1, 2, and 3 were 7.7%, 28.0% and 44.7% for the FBP images, and 0.5%, 1.4% and 1.9% for the predicted image, respectively. Even for the FBP images not discriminable the source patterns, the CAE model could successfully estimate the patterns similarly with the GT image.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권3호
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• pp.653-669
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• 2024

Dynamic scene deblurring is a complex computer vision problem owing to its difficulty to model mathematically. In this paper, we present a novel approach for image deblurring with the help of the sharp reference image, which utilizes the reference image for high-quality and high-frequency detail results. To better utilize the clear reference image, we develop an encoder-decoder network and two novel modules are designed to guide the network for better image restoration. The proposed Reference Extraction and Aggregation Module can effectively establish the correspondence between blurry image and reference image and explore the most relevant features for better blur removal and the proposed Spatial Feature Fusion Module enables the encoder to perceive blur information at different spatial scales. In the final, the multi-scale feature maps from the encoder and cascaded Reference Extraction and Aggregation Modules are integrated into the decoder for a global fusion and representation. Extensive quantitative and qualitative experimental results from the different benchmarks show the effectiveness of our proposed method.

• 한국전자파학회논문지
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• 제16권6호
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• pp.556-562
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• 2005

본 논문에서는 무선 페이딩 환경에 적합한 인터리브된 초직교 길쌈 부호화 초광대역 임펄스 라디오 (Interleaved Super-Orthogonal Convolutional Coded Ultra Wide-Band Impulse Radio: ISOC-UWB-IR) 시스템을 제안하고, 그 시스템의 성능을 변환함수 상한(transfer function bounding)을 이용하여 분석하였다. ISOC-UWB-IR 시스템에 사용되는 인터리빙 기법은 복잡도와 성능에 따라 펄스 단위, 펄스 부그룹 단위, 펄스 그룹 단위 중에서 선택될 수 있다. 성능 분석 결과 레이리 페이딩 환경에서 ISOC-UWB-IR 시스템의 성능은 초직교 길쌈 부호(Super-Orthogonal Convolutional Code: SOC)부호기의 부호율보다 인터리빙 방식의 종류에 더 많은 영향을 받는 것으로 나타났다.

• 한국통신학회논문지
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• 제27권3B호
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• pp.251-258
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• 2002

이 논문에서는 여러 반송파 직접수열 부호분할 다중접속 시스템에서 적응 부호율 길쌈부호화 방법을 살펴본다. 여러 가지 부호율을 쉽게 다를 수 있고 부호기와 복호기 얼개가 간단하도록 부호율 호환 구멍 뚫은 길쌈부호를(rate compatible punctured convolutional code: RCPC code) 쓴다. 데이터 처리량이 가장 많아지도록, 신호 대간섭과 잡음비 추정을 바탕으로 하는 적응 부호율 시스템을 제안한다. 제안한 적응 부호율 여러 반송파 직접수열부호분할 다중접속 시스템을 쓰면 주파수 대역 효율을 높이고 주파수 다양성을 얻을 수 있음을 보인다.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제25권2호
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• pp.26-38
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• 2021

Image coloration refers to adding plausible colors to a grayscale image or video. Image coloration has been used in many modern fields, including restoring old photographs, as well as reducing the time spent painting cartoons. In this paper, a method is proposed for colorizing grayscale images using a convolutional neural network. We propose an encoder-decoder model, adapting FusionNet to our purpose. A proper loss function is defined instead of the MSE loss function to suit the purpose of coloring. The proposed model was verified using the ImageNet dataset. We quantitatively compared several colorization models with ours, using the peak signal-to-noise ratio (PSNR) metric. In addition, to qualitatively evaluate the results, our model was applied to images in the test dataset and compared to images applied to various other models. Finally, we applied our model to a selection of old black and white photographs.

• 대한전자공학회논문지
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• 제23권5호
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• pp.591-596
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• 1986

A binary continuous phase frequency shift keying (CPFSK), whose phase is a continuous function of time and instantaneous frequency is constant, is a bandwidth efficient constant envelope signalling scheme. A transmitting signal is formed by combined coding of a convolutional encoder and a binary CPFSK modulator. The signal is transmitted throuth additive white Gaussian noise(AWGN) channel. If the received signal is detected by a coherent maximum likelihood(ML) receiver, error probability can be expressed approximately in terms of minimum Euclidean distance. We propose rate 2/4 codes for the improvement of error performance without increating the data rate per bandwidth and the receiver complexity. Its minimum Euclidean distances are compared with those of rate \ulcornercodes as a function of modulation index and observation interval.

• ETRI Journal
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• 제46권2호
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• pp.307-322
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• 2024

Most object detection methods use a horizontal bounding box that causes problems between adjacent objects with arbitrary directions, resulting in misaligned detection. Hence, the horizontal anchor should be replaced by a rotating anchor to determine oriented bounding boxes. A two-stage process of delineating a horizontal bounding box and then converting it into an oriented bounding box is inefficient. To improve detection, a box-boundary-aware vector can be estimated based on a convolutional neural network. Specifically, we propose a ResNeXt101 encoder to overcome the weaknesses of the conventional ResNet, which is less effective as the network depth and complexity increase. Owing to the cardinality of using a homogeneous design and multi-branch architecture with few hyperparameters, ResNeXt captures better information than ResNet. Experimental results demonstrate more accurate and faster oriented object detection of our proposal compared with a baseline, achieving a mean average precision of 89.41% and inference rate of 23.67 fps.

• 한국통신학회논문지
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• 제21권4호
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• pp.984-995
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• 1996

Data errors according to the various noises caused in the satellite communication links are corrected by the Viterbi decoding algorithm which has extreme error correcting capability. In this paper, we designed and implemented a convolutional encoder and Viterbi decoder ASIC which is used to encode the input data at the transmit side and correct the errors of the received data at the receive side for use in the VSAT communication system. And this chip may be used in any BPSK, QPSK, or OQPSK transmission system. The ambiguity resolver corrects PSK modem ambiguities by delaying, interting, and/or exchanging code symbol to restore their original sequence and polarity. In case of previous decoding system, ambiguity state(AS) of data is resolved by external control logic and extra redundancy data are needed to resolve AS. But, by adopting decoder proposed in this paper, As of data is resolved automatically by internal logic of decoder in case of continuous mode, and by external As line withoug extra redudancy data in burst mode case. So, decoding parts are simple in continuous mode and transmission efficiency is increased in bust mode. The features of this chip are full duplex operation with independent transmit and receive control and clocks, start/stop inputs for use in burst mode systems, loopback function to verify encoder and decoder, and internal or external control to resolve ambinguity state. For verification of the function and performance of a fabricated ASIC chip, we equiped this chip in the Central and Remote Earth Station of VSAT system, and did the performance test using the commerical INTELSAT VII under the real satellite link environmens. The results of test were demonstrated the superiority of performance.

• 한국가시화정보학회지
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• 제20권3호
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• pp.94-101
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• 2022

The evaluation of the drag and lift as the aerodynamic performance of airfoils is essential. In addition, the analysis of the velocity and pressure fields is needed to support the physical mechanism of the force coefficients of the airfoil. Thus, the present study aims at establishing two different deep learning models to predict force coefficients and flow fields of the airfoil. One is the convolutional neural network (CNN) model to predict drag and lift coefficients of airfoil. Another is the Encoder-Decoder (ED) model to predict pressure distribution and velocity vector field. The images of airfoil section are applied as the input data of both models. Thus, the computational fluid dynamics (CFD) is adopted to form the dataset to training and test of both CNN models. The models are established by the convergence performance for the various hyperparameters. The prediction capability of the established CNN model and ED model is evaluated for the various NACA sections by comparing the true results obtained by the CFD, resulting in the high accurate prediction. It is noted that the predicted results near the leading edge, where the velocity has sharp gradient, reveal relatively lower accuracies. Therefore, the more and high resolved dataset are required to improve the highly nonlinear flow fields.

• 정보처리학회논문지A
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• 제11A권1호
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• pp.81-88
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• 2004

본 논문에서는 차세대 디지털 방송규격인 지상파 DM용 Outer 인코더/디코더를 설계하고 ALTERA의 FPGA를 이용하여 구현하고 검증하였다. 인코더 부분에서는 입력되는 MPEG-2 TS 패킷(188바이트)으로부터 비트 시리얼 알고리즘을 이용한 RS(Reed-Solomon) 인코더를 이용해 패리티 바이트(16바이트)를 생성하고 군집에러를 효과적으로 수정하기 위해 콘볼루션 인터리버를 구현해 데이터를 분산 출력 시켰다. 디코더 부분에서는 인코더에서 송신된 데이터에서 DMB에 적합한 동기 바이트 검출하는 알고리즘을 제시하였으며, RS디코더는 수정된 유클리드 알고리즘을 적용하여 회로구성을 간략화 하였다. 본 시스템은 하나의 패킷에서 최대 8바이트의 에러를 수정할 수 있고, C언어를 이용하여 알고리즘을 검증하고 VHDL로 작성하였으며, FPGA 칩 상에서 회로를 검증하였다.