• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2002.11a
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• pp.899-902
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• 2002

In this paper, we introduce a hyper-chaos synchronization method using State-Controlled Cellular Neural Network(SC-CNN). We make a hyper-chaos circuit using SC-CNN with the n-double scroll. A hyper-chaos circuit is created by applying identical n-double scrolls with weak coupled method, to each cell. Hyper-chaos synchronization was achieved using drive response synchronization between the transmitter and receiver about each state variable in the SC-CNN.

• Journal of Information Science Theory and Practice
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• v.7 no.2
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• pp.32-39
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• 2019

A convolutional neural network (CNN) has been widely used in facial expression recognition (FER) because it can automatically learn discriminative appearance features from an expression image. To make full use of its discriminating capability, this paper suggests a simple but effective method for CNN based FER. Specifically, instead of an original expression image that contains facial appearance only, the expression image with facial geometry visualization is used as input to CNN. In this way, geometric and appearance features could be simultaneously learned, making CNN more discriminative for FER. A simple CNN extension is also presented in this paper, aiming to utilize geometric expression change derived from an expression image sequence. Experimental results on two public datasets (CK+ and MMI) show that CNN using facial geometry visualization clearly outperforms the conventional CNN using facial appearance only.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2018.06a
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• pp.63-66
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• 2018

Recently, a convolutional neural network (CNN) models at single image super-resolution have been very successful. Residual learning improves training stability and network performance in CNN. In this paper, we compare four convolutional neural network models for super-resolution (SR) to learn nonlinear mapping from low-resolution (LR) input image to high-resolution (HR) target image. Four models include general CNN model, global residual learning CNN model, local residual learning CNN model, and the CNN model with global and local residual learning. Experiment results show that the results are greatly affected by how skip connections are connected at the basic CNN network, and network trained with only global residual learning generates highest performance among four models at objective and subjective evaluations.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.101-110
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• 2022

Histopathological analysis of biopsy specimens is still used for diagnosis and classifying the brain tumors today. The available procedures are intrusive, time consuming, and inclined to human error. To overcome these disadvantages, need of implementing a fully automated deep learning-based model to classify brain tumor into multiple classes. The proposed CNN model with an accuracy of 92.98 % for categorizing tumors into five classes such as normal tumor, glioma tumor, meningioma tumor, pituitary tumor, and metastatic tumor. Using the grid search optimization approach, all of the critical hyper parameters of suggested CNN framework were instantly assigned. Alex Net, Inception v3, Res Net -50, VGG -16, and Google - Net are all examples of cutting-edge CNN models that are compared to the suggested CNN model. Using huge, publicly available clinical datasets, satisfactory classification results were produced. Physicians and radiologists can use the suggested CNN model to confirm their first screening for brain tumor Multi-classification.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.370-373
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• 2022

Recently, various deep learning framework models such as Tensorflow, Pytorch, Keras, etc. have appeared. In addition, CNN (Convolutional Neural Network) is applied to image recognition using frameworks such as Tensorflow, Pytorch, and Keras, and the optimization model in image classification is mainly used. In this paper, based on the results of training the CNN model with the Paitotchi and tensor flow frameworks most often used in the field of deep learning image recognition, the two frameworks are compared and analyzed for image analysis. Derived an optimized framework.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2018.06a
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• pp.270-271
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• 2018

본 논문에서는 새로이 시작된 비디오 압축 표준인 VVC(Versatile Video Coding)의 인-루프(in-loop) 필터링을 위한 CNN 구조를 제안한다. 제안하는 CNN 구조는 복호화된 영상을 입력으로 하고 원본 영상과 복호화된 영상의 오차를 손실함수로 사용하여 학습을 진행한다. 또한, 비디오 부호화에서의 다양한 크기의 CU(Coding Unit)를 고려한 다양한 크기의 컨볼루션 필터를 사용하여 특징을 추출하는 구조에 기반하고 있다. 실험을 통하여 제안한 CNN 기반의 필터링이 VVC 의 시험모델인 VTM(VVC Test Model)의 인-루프 필터링의 성능을 개선할 수 있음을 확인하였다.

• Journal of the Korea Convergence Society
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• v.10 no.3
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• pp.1-6
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• 2019

In modern society, lifestyle and individuality are important, and personalized lifestyle and patterns are emerging. The number of people with articulation diseases is increasing due to wrong living habits. In addition, as the number of households increases, there is a case where emergency care is not received at the appropriate time. We need information that can be managed by ourselves through accurate analysis according to the individual's condition for health and disease management, and care appropriate to the emergency situation. It is effectively used for classification and prediction of data using CNN in deep learning. CNN differs in accuracy and processing time according to the data features. Therefore, it is necessary to improve processing speed and accuracy for real-time healthcare. In this paper, we propose motion monitoring using Mask R-CNN for articulation disease management. The proposed method uses Mask R-CNN which is superior in accuracy and processing time than CNN. After the user's motion is learned in the neural network, if the user's motion is different from the learned data, the control method can be fed back to the user, the emergency situation can be informed to the guardian, and appropriate methods can be taken according to the situation.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.2
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• pp.128-133
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• 2021

Currently, autonomous vehicle markets are commercializing a third-level autonomous vehicle, but there is a possibility that an accident may occur even during fully autonomous driving due to stability issues. In fact, autonomous vehicles have recorded 81 accidents. This is because, unlike level 3, autonomous vehicles after level 4 have to judge and respond to emergency situations by themselves. Therefore, this paper proposes a vehicle crisis detection system(VCDS) that collects and stores information outside the vehicle through CNN, and uses the stored information and vehicle sensor data to output the crisis situation of the vehicle as a number between 0 and 1. The VCDS consists of two modules. The vehicle external situation collection module collects surrounding vehicle and pedestrian data using a CNN-based neural network model. The vehicle crisis situation determination module detects a crisis situation in the vehicle by using the output of the vehicle external situation collection module and the vehicle internal sensor data. As a result of the experiment, the average operation time of VESCM was 55ms, R-CNN was 74ms, and CNN was 101ms. In particular, R-CNN shows similar computation time to VESCM when the number of pedestrians is small, but it takes more computation time than VESCM as the number of pedestrians increases. On average, VESCM had 25.68% faster computation time than R-CNN and 45.54% faster than CNN, and the accuracy of all three models did not decrease below 80% and showed high accuracy.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.7
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• pp.1064-1068
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• 2002

In this paper, we introduce a hyper-chaos synchronization method using State-Controlled Cellular Neural Network(SC-CNN). We make a hyper-chaos circuit using SC-CNN with the n-double scroll. A hyper-chaos circuit is created by applying identical n-double scrolls with weak coupled method, to each cell. Hyper-chaos synchronization was achieved using drive response synchronization between the transmitter and receiver about each state in the SC-CNN. From the result of the recovery signal through the demodulation method in the receiver, We shown that recovery quality of state variable $$\chi$_3$ is superior to that of ${$\chi$_2}, {$\chi$_1}$ in secure communication.

• Journal of IKEEE
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• v.22 no.3
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• pp.693-699
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• 2018

CNN based object recognitions have reported splendid results. However, the recognition of mobile apps raises an interesting challenge that recognition performance of similar widgets is not consistent. In order to improve the performance, we propose a noble method utilizing relations between input widgets. The recognition process flows from the Faster R-CNN based recognition to enhancement using a relation recognizer. The relations are represented as vector translation between objects in a relation space. Experiments on 323 apps show that our method significantly enhances the Faster R-CNN only approach.