• Journal of the Institute of Electronics Engineers of Korea SP
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• 제44권6호
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• pp.63-74
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• 2007

Fast Fourier transform(FFT) is powerful, fast computation framework for convolution in many image restoration application. However, an actually observed image acquired with finite aperture of the acquisition device from the infinite background and it lost data outside the cropped region. Because of these the boundary artifacts are produced. This paper reviewed and summarized the up to date the techniques that have been applied to reduce of the boundary artifacts. Moreover, we propose a new block-based fast image restoration using combined extrapolation and edge-tapering without boundary artifacts with reduced computational loads. We apply edgetapering to the inner blocks because they contain outside information of boundary. And outer blocks use half-convolution extrapolation. For this process it is possible that fast image restoration without boundary artifacts.

• The Journal of the Convergence on Culture Technology
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• 제9권1호
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• pp.649-654
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• 2023

This paper proposes the design of a neural network structure search model using graph convolutional neural networks. Deep learning has a problem of not being able to verify whether the designed model has a structure with optimized performance due to the nature of learning as a black box. The neural network structure search model is composed of a recurrent neural network that creates a model and a convolutional neural network that is the generated network. Conventional neural network structure search models use recurrent neural networks, but in this paper, we propose GC-NAS, which uses graph convolutional neural networks instead of recurrent neural networks to create convolutional neural network models. The proposed GC-NAS uses the Layer Extraction Block to explore depth, and the Hyper Parameter Prediction Block to explore spatial and temporal information (hyper parameters) based on depth information in parallel. Therefore, since the depth information is reflected, the search area is wider, and the purpose of the search area of the model is clear by conducting a parallel search with depth information, so it is judged to be superior in theoretical structure compared to GC-NAS. GC-NAS is expected to solve the problem of the high-dimensional time axis and the range of spatial search of recurrent neural networks in the existing neural network structure search model through the graph convolutional neural network block and graph generation algorithm. In addition, we hope that the GC-NAS proposed in this paper will serve as an opportunity for active research on the application of graph convolutional neural networks to neural network structure search.

• Proceedings of the IEEK Conference
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• 대한전자공학회 2003년도 통신소사이어티 추계학술대회논문집
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• pp.200-203
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• 2003

By minimizing the burst error of data and correcting the error, we can define the convolution coding and interleaving in IEEE 802.11a wireless tan system. Two step block interleaver was decided by coded bits per OFDM symbol and due to this it comes to the delay time in IEEE 802.11a. This is the point of the question which we must consider. We try to decrease the delay time by all 48-clock from interleavings, and we have proposed a way carried out the interleaving outputs per symbol. So in comparison with the existing interleaver, we can decrease the delay time in reading and writing data, as well as reduce the delay time of bit re-ordering per symbol. Also this scheme is apply in all x-QAM cases.

• Journal of the Society of Naval Architects of Korea
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• 제60권2호
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• pp.110-119
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• 2023

The prediction of ship resistance performance is typically obtained by Computational Fluid Dynamics (CFD) simulations or model tests in towing tank. However, these methods are both costly and time-consuming, so hull-form designers use statistical methods for a quick feed-back during the early design stage. It is well known that results from statistical methods are often less accurate compared to those from CFD simulations or model tests. To overcome this problem, this study suggests a new approach using a Convolution Neural Network (CNN) with voxelized hull-form data. By converting the original Computer Aided Design (CAD) data into three dimensional voxels, the CNN is able to abstract the hull-form data, focusing only on important features. For the verification, suggested method in this study was compared to a parametric method that uses hull parameters such as length overall and block coefficient as inputs. The results showed that the use of voxelized data significantly improves resistance performance prediction accuracy, compared to the parametric approach.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• 제20권1호
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• pp.163-169
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• 2020

Recently, research on automation and unmanned operation of machines in the industrial field has been conducted with the advent of AI, Big data, and the IoT, which are the core technologies of the Fourth Industrial Revolution. The machines for these automation processes are controlled based on the data collected from the sensors attached to them, and further, the processes are managed. Conventionally, the abnormalities of sensors are periodically checked and managed. However, due to various environmental factors and situations in the industrial field, there are cases where the inspection due to the failure is not missed or failures are not detected to prevent damage due to sensor failure. In addition, even if a failure occurs, it is not immediately detected, which worsens the process loss. Therefore, in order to prevent damage caused by such a sudden sensor failure, it is necessary to identify the failure of the sensor in an embedded system in real-time and to diagnose the failure and determine the type for a quick response. In this paper, a deep neural network-based fault diagnosis system is designed and implemented using Raspberry Pi to classify typical sensor fault types such as erratic fault, hard-over fault, spike fault, and stuck fault. In order to diagnose sensor failure, the network is constructed using Google's proposed Inverted residual block structure of MobilieNetV2. The proposed scheme reduces memory usage and improves the performance of the conventional CNN technique to classify sensor faults.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• 제20권5호
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• pp.421-427
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• 2009

This paper has proposed 1-2-1 coded cooperative communication that is a combination of STBC and ARQ. Coded cooperative communication is a protocol that integrates channel coding with cooperative communication. In this paper consider convolution encoder. ARQ method can increase the spectral efficiency than conventional cooperative communication because if the received signal from source node is satisfied by the destination preferentially, the destination transmits ACK message to both relay node and source node and then recovers the received signal. Where each relay 1, 2 forwards a punctured portion of receive data. When relay transmit to destination apply STBC the reliability to increase. Moreover this protocol can get better BER performance of receiver using simple comparator. We verified BER performance for the proposed protocol through Monte-Carlo simulation over Rayleigh fading plus AWGN.

• Journal of IKEEE
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• 제24권4호
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• pp.1141-1147
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• 2020

The hornet species are so similar in shape that they are difficult for non-experts to classify, and because the size of the objects is small and move fast, it is more difficult to detect and classify the species in real time. In this paper, we developed a system that classifies hornets species in real time based on a deep learning algorithm using a boundary box. In order to minimize the background area included in the bounding box when labeling the training image, we propose a method of selecting only the head and body of the hornet. It also experimentally compares existing boundary box-based object recognition algorithms to find the best algorithms that can detect wasps in real time and classify their species. As a result of the experiment, when the mish function was applied as the activation function of the convolution layer and the hornet images were tested using the YOLOv4 model with the Spatial Attention Module (SAM) applied before the object detection block, the average precision was 97.89% and the average recall was 98.69%.

• The Journal of the Korea institute of electronic communication sciences
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• 제17권3호
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• pp.529-536
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• 2022

In the past two years, Severe Acute Respiratory Syndrome Coronavirus-2(SARS-CoV-2) has been hitting more and more to people. This paper proposes a novel U-Net Convolutional Neural Network to classify and segment COVID-19 lung CT images, which contains Sub Coding Block (SCB), Atrous Spatial Pyramid Pooling(ASPP) and Attention Gate(AG). Three different models such as FCN, U-Net and U-Net-SCB are designed to compare the proposed model and the best optimizer and atrous rate are chosen for the proposed model. The simulation results show that the proposed U-Net-MMFE has the best Dice segmentation coefficient of 94.79% for the COVID-19 CT scan digital image dataset compared with other segmentation models when atrous rate is 12 and the optimizer is Adam.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권1호
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• pp.1-15
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• 2022

The MobileNetV3 is specially designed for mobile devices with limited memory and computing power. To reduce the network parameters and improve the network inference speed, a new lightweight network is proposed based on MobileNetV3. Firstly, to reduce the computation of residual blocks, a partial residual structure is designed by dividing the input feature maps into two parts. The designed partial residual structure is used to replace the residual block in MobileNetV3. Secondly, a dual-path feature extraction structure is designed to further reduce the computation of MobileNetV3. Different convolution kernel sizes are used in the two paths to extract feature maps with different sizes. Besides, a transition layer is also designed for fusing features to reduce the influence of the new structure on accuracy. The CIFAR-100 dataset and Image Net dataset are used to test the performance of the proposed partial residual structure. The ResNet based on the proposed partial residual structure has smaller parameters and FLOPs than the original ResNet. The performance of improved MobileNetV3 is tested on CIFAR-10, CIFAR-100 and ImageNet image classification task dataset. Comparing MobileNetV3, GhostNet and MobileNetV2, the improved MobileNetV3 has smaller parameters and FLOPs. Besides, the improved MobileNetV3 is also tested on CPU and Raspberry Pi. It is faster than other networks

• Journal of the Computational Structural Engineering Institute of Korea
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• 제35권5호
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• pp.259-266
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• 2022

In this study, we developed a deep learning and vision sensor-based assembly performance evaluation method isfor prefabricated steel structures. The assembly parts were segmented using a modified version of the receptive field block convolution module inspired by the eccentric function of the human visual system. The quality of the assembly was evaluated by detecting the bolt holes in the segmented assembly part and calculating the bolt hole positions. To validate the performance of the evaluation, models of standard and defective assembly parts were produced using a 3D printer. The assembly part segmentation network was trained based on the 3D model images captured from a vision sensor. The sbolt hole positions in the segmented assembly image were calculated using image processing techniques, and the assembly performance evaluation using the k-nearest neighbor algorithm was verified. The experimental results show that the assembly parts were segmented with high precision, and the assembly performance based on the positions of the bolt holes in the detected assembly part was evaluated with a classification error of less than 5%.