• Transactions of Materials Processing
• /
• v.31 no.4
• /
• pp.229-239
• /
• 2022

Convolution Neural Network(CNN) is a class of deep learning algorithms and can be used for image analysis. In particular, it has excellent performance in finding the pattern of images. Therefore, CNN is commonly applied for recognizing, learning and classifying images. In this study, the surface defect classification performance of Al 6061 extruded material using CNN-based algorithms were compared and evaluated. First, the data collection criteria were suggested and a total of 2,024 datasets were prepared. And they were randomly classified into 1,417 learning data and 607 evaluation data. After that, the size and quality of the training data set were improved using data augmentation techniques to increase the performance of deep learning. The CNN-based algorithms used in this study were VGGNet-16, VGGNet-19, ResNet-50 and DenseNet-121. The evaluation of the defect classification performance was made by comparing the accuracy, loss, and learning speed using verification data. The DenseNet-121 algorithm showed better performance than other algorithms with an accuracy of 99.13% and a loss value of 0.037. This was due to the structural characteristics of the DenseNet model, and the information loss was reduced by acquiring information from all previous layers for image identification in this algorithm. Based on the above results, the possibility of machine vision application of CNN-based model for the surface defect classification of Al extruded materials was also discussed.

• Journal of Information Processing Systems
• /
• v.18 no.1
• /
• pp.115-129
• /
• 2022

Adopting Internet of Things (IoT)-based technologies in smart homes helps users analyze home appliances electricity consumption for better overall cost monitoring. The IoT application like smart home system (SHS) could suffer from large missing values gaps due to several factors such as security attacks, sensor faults, or connection errors. In this paper, a novel framework has been proposed to predict large gaps of missing values from the SHS home appliances electricity consumption time-series datasets. The framework follows a series of steps to detect, predict and reconstruct the input time-series datasets of missing values. A hybrid convolutional neural network-long short term memory (CNN-LSTM) neural network used to forecast large missing values gaps. A comparative experiment has been conducted to evaluate the performance of hybrid CNN-LSTM with its single variant CNN and LSTM in forecasting missing values. The experimental results indicate a performance superiority of the CNN-LSTM model over the single CNN and LSTM neural networks.

• Smart Structures and Systems
• /
• v.29 no.4
• /
• pp.625-640
• /
• 2022

Maglev rail joints are vital components serving as connections between the adjacent F-type rail sections in maglev guideway. Damage to maglev rail joints such as bolt looseness may result in rough suspension gap fluctuation, failure of suspension control, and even sudden clash between the electromagnets and F-type rail. The condition monitoring of maglev rail joints is therefore highly desirable to maintain safe operation of maglev. In this connection, an online damage detection approach based on three-dimensional (3D) convolutional neural network (CNN) and time-frequency characterization is developed for simultaneous detection of multiple damage of maglev rail joints in this paper. The training and testing data used for condition evaluation of maglev rail joints consist of two months of acceleration recordings, which were acquired in-situ from different rail joints by an integrated online monitoring system during a maglev train running on a test line. Short-time Fourier transform (STFT) method is applied to transform the raw monitoring data into time-frequency spectrograms (TFS). Three CNN architectures, i.e., small-sized CNN (S-CNN), middle-sized CNN (M-CNN), and large-sized CNN (L-CNN), are configured for trial calculation and the M-CNN model with excellent prediction accuracy and high computational efficiency is finally optioned for multiple damage detection of maglev rail joints. Results show that the rail joints in three different conditions (bolt-looseness-caused rail step, misalignment-caused lateral dislocation, and normal condition) are successfully identified by the proposed approach, even when using data collected from rail joints from which no data were used in the CNN training. The capability of the proposed method is further examined by using the data collected after the loosed bolts have been replaced. In addition, by comparison with the results of CNN using frequency spectrum and traditional neural network using TFS, the proposed TFS-CNN framework is proven more accurate and robust for multiple damage detection of maglev rail joints.

• Proceedings of the IEEK Conference
• /
• 1998.10a
• /
• pp.1041-1044
• /
• 1998

In this paper the cellular neural networks with annealing capability is designed. The annealing capability helps the networks escape from the local-minimum points and quickly search for the global-minimum point. A 6$\times$6 CNN chip is designed using a $0.8\mu\textrm{m}$ CMOS technology, and the chip area is 2.89mm$\times$2.89mm. The simulation results for hole filling image processing show that the general CNN has a local-minimum problem, but the annealed CNN finds the global-minimum solutions very efficiently.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2003.05a
• /
• pp.121-124
• /
• 2003

본 논문에서는 SC-CNN의 특성을 이용한 임베딩 구동 카오스 동기화 방법을 소개한다. SC-CNN은 Chua 회로의 미분방정식에서의 세가지 상태변수중 전류성분과 같이 동기화와 신호 합성에 어려운 상태변수를 전압성분으로 분리하여 다룰 수 있는 방법을 제공하고있다. 여기서 지금까지 이용되 오던 결합 동기나 구동동기화의 방법에 새로운 임베딩 구동 동기 방식을 제안한다. 제안하는 Embedding Drive Synchronization(임베딩 구동동기)은 구동동기에서 미분상태변수를 완전히 대체하는 것이 아닌 한 성분만을 대체하여 구성되며 그 타당성을 검증하였다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2018.06a
• /
• pp.195-197
• /
• 2018

본 논문은 초해상도 영상 생성을 위한 CNN 구조를 제안한다. 제안하는 Cross-CNN 은 2 차원 필터의 분리성과 활성화 함수의 비선형성을 바탕으로 VDSR 구조의 시작과 마지막 층을 제외한 중간 층들에 교차하는 1 차원 필터를 적용한다. 제안하는 방법은 기존의 방법보다 적은 가중치를 사용하여 실행 시간을 단축하였다. 실험은 VDSR 실험에 사용된 291 개의 영상과 B100 영상을 이용하였다. 제안하는 방법은 네트워크 중간층에서 기존 방법 대비 약 1/3 의 가중치를 사용하여 20%의 속도 향상을 보였다.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2023.07a
• /
• pp.47-50
• /
• 2023

본 논문에서는 딥러닝 CNN 알고리즘을 이용하여 사람의 얼굴 이미지를 학습한 다음 나이와 성별을 예측하는 시스템을 제안한다. 이 시스템은 개개인 마다 각기 다른 외형적 특성을 고려하여 이를 분석한 다음 이에 맞는 헤어 스타일, 옷차림을 추천할 수 있다. 해당 기술을 활용하여 메타버스 아바타 생성에 사용자의 얼굴과 같은 신체적 특성을 고려할 수 있다. 향후에는 신체 전체를 이미지화하여 보다 더 다양한 정보를 인식할 수 있도록 연구를 진행할 것이다.

• Proceedings of the Korean Society of Disaster Information Conference
• /
• 2023.11a
• /
• pp.349-350
• /
• 2023

본 논문에서는 FMCW(Frequency Modulated Continuous Wave) 레이더를 사용하여 재난 상황에서 누워 있는 사람의 다양한 각도의 자세를 통해 사람의 상태를 파악하거나 위치를 추정하고자하였다. 사람의 세 가지 누운 자세 데이터를 전처리하고 이미지로 변환한 데이터를 CNN(Convolutional Neural Network) 1D 모델로 학습시켜 누운 자세를 다양한 각도에서 구별할 수 있는지 분석하여 확인하고자하였으며, 분석 결과 CNN 1D 모델은 99.27%의 정확도를 보였다.

• Journal of the Institute of Convergence Signal Processing
• /
• v.20 no.4
• /
• pp.238-244
• /
• 2019

In this study, we compared and analyzed the performance of each Convolution Neural Network (CNN) by implementing the CNN that reflected the characteristics of the cerebral structure, in order to analyze the CNN that was used for the prediction of cybersickness, and provided the performance varying depending on characteristics of the brain. Dizziness has many causes, but the most severe symptoms are considered attributable to vestibular dysfunction associated with the brain. Brain waves serve as indicators showing the state of brain activities, and tend to exhibit differences depending on external stimulation and cerebral activities. Changes in brain waves being caused by external stimuli and cerebral activities have been proved by many studies and experiments, including the thesis of Martijn E. Wokke, Tony Ro, published in 2019. Based on such correlation, we analyzed brain wave data collected from dizziness-inducing environments and implemented the dizziness predictive artificial neural network reflecting characteristics of the cerebral structure. The results of this study are expected to provide a basis for achieving optimal performance of the CNN used in the prediction of dizziness, and for predicting and preventing the occurrence of dizziness under various virtual reality (VR) environments.

• Journal of Korea Society of Industrial Information Systems
• /
• v.27 no.2
• /
• pp.71-82
• /
• 2022

In order to replace the sampling tensile test of products produced in the tab welding process, which is one of the automotive battery manufacturing processes, vision inspectors are currently being developed and used. However, the vision inspection has the problem of inspection position error and the cost of improving it. In order to solve these problems, there are recent cases of applying deep learning technology. As one such case, this paper tries to examine the usefulness of applying Faster R-CNN, one of the deep learning technologies, to existing product inspection. The images acquired through the existing vision inspection machine are used as training data and trained using the Faster R-CNN ResNet101 V1 1024x1024 model. The results of the conventional vision test and Faster R-CNN test are compared and analyzed based on the test standards of 0% non-detection and 10% over-detection. The non-detection rate is 34.5% in the conventional vision test and 0% in the Faster R-CNN test. The over-detection rate is 100% in the conventional vision test and 6.9% in Faster R-CNN. From these results, it is confirmed that deep learning technology is very useful for detecting welding error of lead tabs in automobile batteries.