• 정보처리학회논문지B
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• 제10B권5호
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• pp.553-560
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• 2003

웨이블릿 변환(Wavelet Transform)된 비디오는 주파수와 해상도가 다근 부대역으로 분해되므로 전송 오류가 발생한 패킷의 위치에 따라 복원된 프레임 간 화질 편차가 크게 된다. 복원된 프레임의 화질 변화가 클수록 사용자가 느끼는 비디오의 화질은 떨어진다. 특히, 움직임 예측을 이용한 웨이블릿 비디오의 경우, 특정 부대역에서 발생한 오류는 같은 프레임의 다른 부대역 뿐 아니라 이후 프레임의 화질에도 지속적인 영향을 미치게 된다. 본 논문에서는 웨이블릿 기반 비디오를 네트워크로 전송하기 위해 패킷화론 수행할 때, 오류발생 패킷의 위치에 관계없이 일정한 화질을 유지하며 오류 은닉이 쉬운 블록기반 패킷화 기법인 BDP(Block based Dispersive Packetization)를 제안한다. 본 논문은 MRME(Multi-Resolution Motion Estimation)글 적용하여 압축된 비디오와 무선 네트워크에서의 오류 발생 모델을 이용하여 성능평가를 수행하였다. 실험결과 제안된 기법은 프레임을 일정한 블록으로 분할하여 순차적으로 패킷화하는 BP나 픽셀단위로 분산하는 DP기법에 비해 주ㆍ객관적인 성능 모두 뛰어남을 알 수 있었다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
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• pp.113-117
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• 1998

We propose a way to show the inherent learning complexity for the multilayer perceptron. We display the solution space and the error surfaces on the input space of a single neuron with two inputs. The evolution of its weights will follow one of the two error surfaces. We observe that when we use the back-propagation(BP) learning algorithm (1), the wight cam not jump to the lower error surface due to the implicit continuity constraint on the changes of weight. The self-relaxation approach is to explicity find out the best combination of all neurons' two error surfaces. The time complexity of training a multilayer perceptron by self-relaxationis exponential to the number of neurons.

• 한국통신학회논문지
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• 제16권11호
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• pp.1186-1193
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• 1991

본 논문에서는 신경회로망을 사용한 로보트 매니퓰레이터의 궤적 제어 방법을 제안하였다. 매니퓰레이터에 가해지는 토크는 신경회로망이 출력인 feedforward 토크와 보조제어기로 사용되는 비례 미분 제어기PD 제어기의 출력인 feedback 토크의 합이다. 제안된 전경 회로망은 다층 신경회로로서 시간 지연 요소를 가지며 PD 제어기의 오차 토크를 사용하여 매니퓰레이터 이동력학 모델을 학습한다. errror backpropagation(BP) 학습 신경회로 제어기를 사용해보므로서 매니퓰레이터 동특성에 대한 정보를 미리 필요로 하지 않으며, 연결 가중치 값에 그러한 정보가 저장된다. 확인될 신경회로망의 특성을 컴퓨터 시뮬레이션을 통하여 입증한다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1995년도 하계학술대회 논문집 B
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• pp.705-709
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• 1995

The objective of this study is to design a multi-layer neural network which controls the position of excavator's attachment. In this paper, a dynamic controller has been developed based on an error back-propagation(BP) neural network. Since the neural network can model an arbitrary nonlinear mapping, it was used as a commanded feedforward input generator. A PD feedback controller is used in parallel with the feedforward neural network to train the system. The neural network was trained by the current state of the excavator as well as the PD feedback error. By using the BP network as a feedforward controller, no a priori knowledge on system dynamics is need. Computer simulation results demonstrate such powerful characteristics of the proposed controller as adaptation to changing environment, robustness to disturbancen and performance improvement with the on-line learning in the position control of excavator attachment.

• 한국위성정보통신학회논문지
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• 제9권4호
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• pp.104-110
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• 2014

본 논문에서는 무인항공기인 방송용 멀티콥터를 이용한 Full-HD급 이상 화질의 이미지를 효율적으로 전송하기 위해 이미지 압축 센싱 기법을 적용하고, Sparse 신호의 효율적 복원을 위해 Turbo 알고리즘과 Markov chain Monte Carlo (MCMC) 알고리즘의 복원 성능을 모의실험을 통해 비교 분석하였다. 제안된 복원 기법은 압축 센싱에 기반하여 데이터 용량을 줄이고 빠르고 오류 없는 원신호 복원에 중점을 두었다. 다수의 이미지 파일로 모의실험을 진행한 결과 Loopy belief propagation(BP) 기반의 Turbo 복원 알고리즘이 Gibbs sampling기반 알고리즘을 수행하는 MCMC 알고리즘 보다 평균 복원 연산 시간, NMSE 값에서 우수하여 보다 효율적인 복원 방법으로 생각된다.

• ETRI Journal
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• 제46권3호
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• pp.485-500
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• 2024

A hardware architecture is presented to decode (N, K) polar codes based on a low-density parity-check code-like decoding method. By applying suitable pruning techniques to the dense graph of the polar code, the decoder architectures are optimized using fewer check nodes (CN) and variable nodes (VN). Pipelining is introduced in the CN and VN architectures, reducing the critical path delay. Latency is reduced further by a fully parallelized, single-stage architecture compared with the log N stages in the conventional belief propagation (BP) decoder. The designed decoder for short-to-intermediate code lengths was implemented using the Virtex-7 field-programmable gate array (FPGA). It achieved a throughput of 2.44 Gbps, which is four times and 1.4 times higher than those of the fast-simplified successive cancellation and combinational decoders, respectively. The proposed decoder for the (1024, 512) polar code yielded a negligible bit error rate of 10^-4 at 2.7 E_b/N_o (dB). It converged faster than the BP decoding scheme on a dense parity-check matrix. Moreover, the proposed decoder is also implemented using the Xilinx ultra-scale FPGA and verified with the fifth generation new radio physical downlink control channel specification. The superior error-correcting performance and better hardware efficiency makes our decoder a suitable alternative to the successive cancellation list decoders used in 5G wireless communication.

• ETRI Journal
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• 제30권6호
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• pp.807-817
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• 2008

We present a low-density parity-check (LDPC)-based, threaded layered space-time-frequency system with emphasis on the iterative receiver design. First, the unbiased minimum mean-squared-error iterative-tree-search (U-MMSE-ITS) detector, which is known to be one of the most efficient multi-input multi-output (MIMO) detectors available, is improved by augmentation of the partial-length paths and by the addition of one-bit complement sequences. Compared with the U-MMSE-ITS detector, the improved detector provides better detection performance with lower complexity. Furthermore, the improved detector is robust to arbitrary MIMO channels and to any antenna configurations. Second, based on the structure of the iterative receiver, we present a low-complexity belief-propagation (BP) decoding algorithm for LDPC-codes. This BP decoder not only has low computing complexity but also converges very fast (5 iterations is sufficient). With the efficient receiver employing the improved detector and the low-complexity BP decoder, the proposed system is a promising solution to high-data-rate transmission over selective-fading channels.

• Computers and Concrete
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• 제16권3호
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• pp.343-356
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• 2015

1078 sets of mixtures in total that include fly ash, slag, and/or silica fume have been collected for prediction on concrete properties. A new database platform (Compos) has been developed, by which the stepwise multiple linear regression (SMLR) and BP artificial neural networks (BP ANNs) programs have been applied respectively to identify correlations between the concrete properties (strength, workability, and durability) and the dosage and/or quality of raw materials'. The results showed obvious nonlinear relations so that forecasting by using nonlinear method has clearly higher accuracy than using linear method. The forecasting accuracy rises along with the increasing of age and the prediction on cubic compressive strength have the best results, because the minimum average relative error (MARE) for 60-day cubic compressive strength was less than 8%. The precision for forecasting of concrete workability takes the second place in which the MARE is less than 15%. Forecasting on concrete durability has the lowest accuracy as its MARE has even reached 30%. These conclusions have been certified in a ready-mixed concrete plant that the synthesized MARE of 7-day/28-day strength and initial slump is less than 8%. The parameters of BP ANNs and its conformation have been discussed as well in this study.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2006년도 International Symposium on GPS/GNSS Vol.1
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• pp.491-494
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• 2006

Kalman filtering (KF) is hard to be applied to the SINS (Strap-down Inertial Navigation System)/RDSS (Radio Determination Satellite Service) integrated navigation system directly because the time delay of RDSS positioning in active mode is random. BP (Back-Propagation) Neuron computing as a powerful technology of Artificial Neural Network (ANN), is appropriate to solve nonlinear problems such as the random time delay of RDSS without prior knowledge about the mathematical process involved. The new algorithm betakes a BP neural network (BPNN) and velocity feedback to aid KF in order to overcome the time delay of RDSS positioning. Once the BP neural network was trained and converged, the new approach will work well for SINS/RDSS integrated navigation. Dynamic vehicle experiments were performed to evaluate the performance of the system. The experiment results demonstrate that the horizontal positioning accuracy of the new approach is 40.62 m (1 ${\sigma}$), which is better than velocity-feedback-based KF. The experimental results also show that the horizontal positioning error of the navigation system is almost linear to the positioning interval of RDSS within 5 minutes. The approach and its anti-jamming analysis will be helpful to the applications of SINS/RDSS integrated systems.

• 한국멀티미디어학회논문지
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• 제22권7호
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• pp.790-803
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• 2019

The effects of different heat treatments on the sensory characteristics of abalones are studied in this study. In this paper, the sensory evaluation of abalone samples under different heat treatment conditions is carried out, and the evaluation results are analyzed. The three-dimensional (3D) scanning and reverse engineering are used in tooth modeling of the sensory evaluation of abalone samples under different heat treatment conditions. Besides, the chewing movement models are simplified into three modes, including the cutting mode, compressing mode and grinding mode, which are simulated using finite element simulation. The elastic modulus of the abalone samples is obtained through the compression testing using a texture analyzer to distinguish their material properties under different heat treatments and to obtain simulated mechanical parameters. Finally, taking the mechanical parameters of the finite element simulation of abalone chewing as input and sensory evaluation parameters as the output, BP neural network is established in which the sensory texture evaluation model of abalone samples is obtained. Through verification, the neural network prediction model can meet the requirements of food texture evaluation, with an average error of 9.12%.