• Nuclear Engineering and Technology
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• 제54권12호
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• pp.4751-4758
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• 2022

Two-phase flow may almost exist in every branch of the energy industry. For the corresponding engineering design, it is very essential and crucial to monitor flow patterns and their transitions accurately. With the high-speed development and success of deep learning based on convolutional neural network (CNN), the study of flow pattern identification recently almost focused on this methodology. Additionally, the photographing technique has attractive implementation features as well, since it is normally considerably less expensive than other techniques. The development of such a two-phase flow pattern online monitoring system is the objective of this work, which seldom studied before. The ongoing preliminary engineering design (including hardware and software) of the system are introduced. The flow pattern identification method based on CNNs and transfer learning was discussed in detail. Several potential CNN candidates such as ALexNet, VggNet16 and ResNets were introduced and compared with each other based on a flow pattern dataset. According to the results, ResNet50 is the most promising CNN network for the system owing to its high precision, fast classification and strong robustness. This work can be a reference for the online monitoring system design in the energy system.

• 한국멀티미디어학회논문지
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• 제23권9호
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• pp.1129-1138
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• 2020

The performance of convolutional deep learning networks is generally determined according to parameters of target dataset, structure of network, convolution kernel, activation function, and optimization algorithm. In this paper, a genetic algorithm is used to select the appropriate deep learning model and parameters for Alzheimer's classification and to compare the learning results with preliminary experiment. We compare and analyze the Alzheimer's disease classification performance of VGG-16, GoogLeNet, and ResNet to select an effective network for detecting AD and MCI. The simulation results show that the network structure is ResNet, the activation function is ReLU, the optimization algorithm is Adam, and the convolution kernel has a 3-dilated convolution filter for the accuracy of dementia medical images.

• 한국멀티미디어학회논문지
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• 제23권6호
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• pp.729-737
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• 2020

Screening with low-dose spiral computed tomography (LDCT) has been shown to reduce lung cancer mortality by about 20% when compared to standard chest radiography. One of the problems arising from screening programs is that large amounts of CT image data must be interpreted by radiologists. To solve this problem, automated detection of pulmonary nodules is necessary; however, this is a challenging task because of the high number of false positive results. Here we demonstrate detection of pulmonary nodules using six off-the-shelf convolutional neural network (CNN) models after modification of the input/output layers and end-to-end training based on publicly databases for comparative evaluation. We used the well-known CNN models, LeNet-5, VGG-16, GoogLeNet Inception V3, ResNet-152, DensNet-201, and NASNet. Most of the CNN models provided superior results to those of obtained using customized CNN models. It is more desirable to modify the proven off-the-shelf network model than to customize the network model to detect the pulmonary nodules.

• Journal of Multimedia Information System
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• 제6권4호
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• pp.209-216
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• 2019

Over the past decade, researchers were able to solve complex medical problems as well as acquire deeper understanding of entire issue due to the availability of machine learning techniques, particularly predictive algorithms and automatic recognition of patterns in medical imaging. In this study, a technique called transfer learning has been utilized to classify Magnetic Resonance (MR) images by a pre-trained Convolutional Neural Network (CNN). Rather than training an entire model from scratch, transfer learning approach uses the CNN model by fine-tuning them, to classify MR images into Alzheimer's disease (AD), mild cognitive impairment (MCI) and normal control (NC). The performance of this method has been evaluated over Alzheimer's Disease Neuroimaging (ADNI) dataset by changing the learning rate of the model. Moreover, in this study, in order to demonstrate the transfer learning approach we utilize different pre-trained deep learning models such as GoogLeNet, VGG-16, AlexNet and ResNet-18, and compare their efficiency to classify AD. The overall classification accuracy resulted by GoogLeNet for training and testing was 99.84% and 98.25% respectively, which was exceptionally more than other models training and testing accuracies.

• Journal of the Korean Wood Science and Technology
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• 제49권5호
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• pp.491-503
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• 2021

Wood identification is regularly performed by observing the wood anatomy, such as colour, texture, fibre direction, and other characteristics. The manual process, however, could be time consuming, especially when identification work is required at high quantity. Considering this condition, a convolutional neural networks (CNN)-based program is applied to improve the image classification results. The research focuses on the algorithm accuracy and efficiency in dealing with the dataset limitations. For this, it is proposed to do the sample selection process or only take a small portion of the existing image. Still, it can be expected to represent the overall picture to maintain and improve the generalisation capabilities of the CNN method in the classification stages. The experiments yielded an incredible F1 score average up to 93.4% for medium sample area sizes (200 × 200 pixels) on each CNN architecture (VGG16, ResNet50, MobileNet, DenseNet121, and Xception based). Whereas DenseNet121-based architecture was found to be the best architecture in maintaining the generalisation of its model for each sample area size (100, 200, and 300 pixels). The experimental results showed that the proposed algorithm can be an accurate and reliable solution.

• International Journal of Computer Science & Network Security
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• 제23권5호
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• pp.73-88
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• 2023

Brain tumors can also be an abnormal collection or accumulation of cells in the brain that can be life-threatening due to their ability to invade and metastasize to nearby tissues. Accurate diagnosis is critical to the success of treatment planning, and resonant imaging is the primary diagnostic imaging method used to diagnose brain tumors and their extent. Deep learning methods for computer vision applications have shown significant improvements in recent years, primarily due to the undeniable fact that there is a large amount of data on the market to teach models. Therefore, improvements within the model architecture perform better approximations in the monitored configuration. Tumor classification using these deep learning techniques has made great strides by providing reliable, annotated open data sets. Reduce computational effort and learn specific spatial and temporal relationships. This white paper describes transfer models such as the MobileNet model, VGG19 model, InceptionResNetV2 model, Inception model, and DenseNet201 model. The model uses three different optimizers, Adam, SGD, and RMSprop. Finally, the pre-trained MobileNet with RMSprop optimizer is the best model in this paper, with 0.995 accuracies, 0.99 sensitivity, and 1.00 specificity, while at the same time having the lowest computational cost.

• 한국음향학회지
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• 제39권3호
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• pp.143-149
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• 2020

본 논문에서는 딥러닝(deep learning)을 이용하여 환경음 분류 시 전처리 단계에서 사용하는 특징 추출 방법이 콘볼루션 신경망의 분류 성능에 미치는 영향에 대해서 다루었다. 이를 위해 환경음 분류 연구에서 많이 사용되는 UrbanSound8K 데이터셋에서 멜 스펙트로그램(mel spectrogram), 로그 멜 스펙트로그램(log mel spectrogram), Mel Frequency Cepstral Coefficient(MFCC), 그리고 delta MFCC를 추출하고 각각을 3가지 분포로 스케일링하였다. 이 데이터를 이용하여 4 종의 콘볼루션 신경망과 이미지넷에서 좋은 성능을 보였던 VGG16과 MobileNetV2 신경망을 학습시킨 다음 오디오 특징과 스케일링 방법에 따른 인식률을 구하였다. 그 결과 인식률은 스케일링하지 않은 로그 멜 스펙트럼을 사용했을 때 가장 우수한 것으로 나타났다. 도출된 결과를 모든 오디오 인식 문제로 일반화하기는 힘들지만, Urbansound8K의 환경음이 포함된 오디오를 분류할 때는 유용하게 적용될 수 있을 것이다.

• 전기전자학회논문지
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• 제24권1호
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• pp.147-153
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• 2020

기존의 차선 검출 방법들은 곡률과 날씨 변화가 큰 도로 환경에서 검출률이 낮다. 확률적 허프 변환을 이용한 방법은 에지와 직선의 각도를 이용해서 차선을 검출함으로 곡선과 악천후일 때 검출률이 낮다. 슬라이딩 윈도우 방법은 윈도우로 이미지를 분할해서 검출하기 때문에 곡선 형태의 차선도 검출하지만 어파인 변환을 사용하기 때문에 도로의 경사율에 영향을 받는다. 본 논문에서는 다양한 외부 환경에서도 차선을 강인하게 검출하고 장애물을 회피하기 위한 딥러닝 기반의 주행 보조 시스템을 제안한다. VGG-16기반의 SegNet으로 입력 영상을 의미론적으로 분할해서 차선을 검출한다. 검출한 차선과의 이격거리를 계산하고 안전범위를 산출해서 차량이 차선의 중앙을 주행하도록 제어한다. 또한, 전방의 미확인 물체와 충돌이 예상되면 운전자에게 경보를 주고 Adaptive-MPC로 차량을 제어해서 충돌을 회피하는 알고리즘도 제안한다. CARLA로 시뮬레이션한 결과 제안한 알고리즘은 곡률이 큰 차선과 다양한 환경에서도 강인하게 차선을 검출하고 전방의 안전범위를 계산하여 충돌을 회피하는 것을 볼 수 있다.

• 한국인터넷방송통신학회논문지
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• 제21권3호
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• pp.81-89
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• 2021

자연 조건에서 Apple 감지에는 가림 문제와 작은 대상 감지 어려움이 있다. 본 논문은 SSD 기반의 개선 된 모델을 제안한다. SSD 백본 네트워크 VGG16은 ResNet50 네트워크 모델로 대체되고 수용 필드 구조 RFB 구조가 도입되었다. RFB 모델은 작은 표적의 특징 정보를 증폭하고 작은 표적의 탐지 정확도를 향상시킨다. 유지해야 하는 정보를 필터링하기 위해 주의 메커니즘 (SE)과 결합하면 감지 대상의 의미 정보가 향상된다. 향상된 SSD 알고리즘은 VOC2007 데이터 세트에 대해 학습된다. SSD에 비해 개선 된 알고리즘은 폐색 및 작은 표적 탐지의 정확도를 3.4 % 및 3.9 % 향상 시켰다. 이 알고리즘은 오 탐지율과 누락된 감지율을 향상 시켰다. 본 논문에서 제안한 개선 된 알고리즘은 더 높은 효율성을 갖는다.

• 한국멀티미디어학회논문지
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• 제25권8호
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• pp.1224-1232
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• 2022

Whole body bone scan is the most frequently performed nuclear medicine imaging to evaluate bone metastasis in cancer patients. We evaluated the performance of a VGG16-based transfer learning classifier for bone scan images in which metastatic bone lesion was present. A total of 1,000 bone scans in 1,000 cancer patients (500 patients with bone metastasis, 500 patients without bone metastasis) were evaluated. Bone scans were labeled with abnormal/normal for bone metastasis using medical reports and image review. Subsequently, gradient-weighted class activation maps (Grad-CAMs) were generated for explainable AI. The proposed model showed AUROC 0.96 and F1-Score 0.90, indicating that it outperforms to VGG16, ResNet50, Xception, DenseNet121 and InceptionV3. Grad-CAM visualized that the proposed model focuses on hot uptakes, which are indicating active bone lesions, for classification of whole body bone scan images with bone metastases.