• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1954-1955
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• 2007

This paper presents a modeling method of speed controller for DC motor drive system by using the Embedded Target for TI C2000 DSP in MATLAB/SIMULINK. Speed controller is easily designed and implemented by using the MATLAB/SIMULINK program, and speed control response and stability of the DC motor can be improved. Feedback of motor speed is processed through C28x QEP(Quadrature Encoder Pulse) from encoder pulse. The controller is designed as PI speed controller. Simulation program is drawn using SIMULINK. Then a real-time program for speed control of the DC motor is downloaded into the eZdsp F2811 control board. Speed control response is verified through simulations and experiments.

• Journal of the Korean Society of Industry Convergence
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• v.20 no.2
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• pp.89-95
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• 2017

In this paper, we suggest the method for a mobile robot to move safely from an initial position to a goal position in the wide environment like a building. There is a problem using odometry encoder sensor to estimate the position of a mobile robot in the wide environment like a building. Because of the phenomenon of wheel's slipping, a encoder sensor has the accumulated error of a sensor measurement as time. Therefore the error must be compensated with using other sensor. A vision sensor is used to compensate the position of a mobile robot as using the regularly attached light's panel on a building's ceiling. The method to create global path planning for a mobile robot model a building's map as a graph data type. Consequently, we can apply floyd's shortest path algorithm to find the path planning. The effectiveness of the method is verified through simulations and experiments.

• Journal of Power Electronics
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• v.15 no.6
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• pp.1547-1558
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• 2015

A predictive functional control (PFC) scheme for permanent magnet synchronous motor (PMSM) servo systems is proposed in this paper. The PFC-based method is first introduced in the control design of speed loop. Since the accuracy of the PFC model is influenced by external disturbances and speed detection quantization errors of the low distinguishability optical encoder in servo systems, it is noted that the standard PFC method does not achieve satisfactory results in the presence of strong disturbances. This paper adopted the Kalman filter to observe the load torque, the rotor position and the rotor angular velocity under the condition of a limited precision encoder. The observations are then fed back into PFC model to rebuild it when considering the influence of perturbation. Therefore, an improved PFC method, called the PFC+Kalman filter method, is presented, and a high performance PMSM servo system was achieved. The validity of the proposed controller was tested via experiments. Excellent results were obtained with respect to the speed trajectory tracking, stability, and disturbance rejection.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.7
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• pp.1506-1511
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• 2004

The error control of digital transmission system is a very important subject because of the noise effects, which is very sensitive to transmission performance of the digital communication system It employs a modified Euclid's algorithm to compute the error-location polynomial and error-magnitude polynomial of input data. The circuit size is reduced by selecting the Modified Euclid's Algorithm with one Euclid Cell of mutual operation. And the operation speed of Decoder is improved by using ROM and parallel structure. The proposed Encoder and Decoder are simulated with ModelSim and Active-HDL and synthesized with Synopsys. We can see that this chip is implemented on Xilinx Virtex2 XC2V3000. A share of slice is 28%. nut speed of this paper is 45Mhz.

• Nuclear Engineering and Technology
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• v.53 no.3
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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.

• Journal of Animal Science and Technology
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• v.66 no.1
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• pp.167-177
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• 2024

Iris segmentation is an initial step for identifying the biometrics of animals when establishing a traceability system for livestock. In this study, we propose a deep learning framework for pixel-wise segmentation of bovine iris with a minimized use of annotation labels utilizing the BovineAAEyes80 public dataset. The proposed image segmentation framework encompasses data collection, data preparation, data augmentation selection, training of 15 deep neural network (DNN) models with varying encoder backbones and segmentation decoder DNNs, and evaluation of the models using multiple metrics and graphical segmentation results. This framework aims to provide comprehensive and in-depth information on each model's training and testing outcomes to optimize bovine iris segmentation performance. In the experiment, U-Net with a VGG16 backbone was identified as the optimal combination of encoder and decoder models for the dataset, achieving an accuracy and dice coefficient score of 99.50% and 98.35%, respectively. Notably, the selected model accurately segmented even corrupted images without proper annotation data. This study contributes to the advancement of iris segmentation and the establishment of a reliable DNN training framework.

• Journal of the Korea Society of Computer and Information
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• v.26 no.10
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• pp.157-165
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• 2021

Recently, studies using deep learning to analyze a large amount of text are being actively conducted. In particular, a pre-trained language model that applies the learning results of a large amount of text to the analysis of a specific domain text is attracting attention. Among various pre-trained language models, BERT(Bidirectional Encoder Representations from Transformers)-based model is the most widely used. Recently, research to improve the performance of analysis is being conducted through further pre-training using BERT's MLM(Masked Language Model). However, the traditional MLM has difficulties in clearly understands the meaning of sentences containing new words such as newly coined words. Therefore, in this study, we newly propose NTM(Newly coined words Target Masking), which performs masking only on new words. As a result of analyzing about 700,000 movie reviews of portal 'N' by applying the proposed methodology, it was confirmed that the proposed NTM showed superior performance in terms of accuracy of sensitivity analysis compared to the existing random masking.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.6
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• pp.1516-1529
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• 2023

In this paper, a motor gearbox fault-detection system based on a hierarchical flow-based model is proposed. The proposed system is used for the anomaly detection of a motion sound-based actuator module. The proposed flow-based model, which is a generative model, learns by directly modeling a data distribution function. As the objective function is the maximum likelihood value of the input data, the training is stable and simple to use for anomaly detection. The operation sound of a car's side-view mirror motor is converted into a Mel-spectrogram image, consisting of a folding signal and an unfolding signal, and used as training data in this experiment. The proposed system is composed of an encoder and a decoder. The data extracted from the layer of the pretrained feature extractor are used as the decoder input data in the encoder. This information is used in the decoder by performing an interlayer cross-scale convolution operation. The experimental results indicate that the context information of various dimensions extracted from the interlayer hierarchical data improves the defect detection accuracy. This paper is notable because it uses acoustic data and a normalizing flow model to detect outliers based on the features of experimental data.

• Journal of KIISE:Information Networking
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• v.35 no.3
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• pp.235-242
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• 2008

To improve video quality and coding efficiency, H.264/AVC adopted an adaptive rate control. But this method has a problem as it cannot predict an accurate quantization parameter(QP) for the first frame. The first QP is decided among four constant values by using encoder input parameters. It does not consider encoding bits, results in significant fluctuation of the image quality and decreases the average quality of the whole coded sequence. In this paper, we propose a new algorithm for the first frame QP decision in the H.264/AVC encoder. The QP is decided by the existing algorithm and the first frame is encoded. According to the encoded bits, the new initial QP is decided. We can predict optimal value because there is a linear relationship between encoded bits and the new initial QP. Next, we re-encode the first frame using the new initial QP. Experimental results show that the proposed algorithm not only achieves better quality than the state of the art algorithm, but also adopts a rate control forthe sequence that was impossible with the existing algorithm. By reducing fluctuation, subjective quality also improved.

• Journal of Broadcast Engineering
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• v.18 no.6
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• pp.835-849
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• 2013

Joint Collaborative Team on Video Coding (JCT-VC), which have founded ISO/IEC MPEG and ITU-T VCEG, has standardized High Efficiency Video Coding (HEVC). Standardization of HEVC has started with purpose of twice or more coding performance compared to H.264/AVC. However, flexible and hierarchical coding block and recursive coding structure are problems to overcome of HEVC standard. Many fast encoding algorithms for reducing computational complexity of HEVC encoder have been proposed. However, it is hard to implement a real-time HEVC encoder only with those fast encoding algorithms. In this paper, for implementation of software-based real-time HEVC encoder, data-level parallelism using SIMD instructions and CPU/GPU multi-threading methods are proposed. And we also proposed appropriate operations and functional modules to apply the proposed methods on HM 10.0 software. Evaluation of the proposed methods implemented on HM 10.0 software showed 20-30fps for $832{\times}480$ sequences and 5-10fps for $1920{\times}1080$ sequences, respectively.