• Journal of Broadcast Engineering
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• v.28 no.3
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• pp.293-301
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• 2023

Unstandardized medical data collection and management are still being conducted manually, and studies are being conducted to classify CT data using deep learning to solve this problem. However, most studies are developing models based only on the axial plane, which is a basic CT slice. Because CT images depict only human structures unlike general images, reconstructing CT scans alone can provide richer physical features. This study seeks to find ways to achieve higher performance through various methods of converting CT scan to 2D as well as axial planes. The training used 1042 CT scans from five body parts and collected 179 test sets and 448 with external datasets for model evaluation. To develop a deep learning model, we used InceptionResNetV2 pre-trained with ImageNet as a backbone and re-trained the entire layer of the model. As a result of the experiment, the reconstruction data model achieved 99.33% in body part classification, 1.12% higher than the axial model, and the axial model was higher only in brain and neck in contrast classification. In conclusion, it was possible to achieve more accurate performance when learning with data that shows better anatomical features than when trained with axial slice alone.

• Management & Information Systems Review
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• v.39 no.3
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• pp.1-16
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• 2020

The current tariff return system requires tax officials to calculate tax amount by themselves and pay the tax amount on their own responsibility. In other words, in principle, the duty and responsibility of reporting payment system are imposed only on the taxee who is required to calculate and pay the tax accurately. In case the tax payment system fails to fulfill the duty and responsibility, the additional tax is imposed on the taxee by collecting the tax shortfall and imposing the tax deduction on For this reason, item classifications, together with tariff assessments, are the most difficult and could pose a significant risk to entities if they are misclassified. For this reason, import reports are consigned to customs officials, who are customs experts, while paying a substantial fee. The purpose of this study is to classify HS items to be reported upon import declaration and to indicate HS codes to be recorded on import declaration. HS items were classified using the attached image in the case of item classification based on the case of the classification of items by the Korea Customs Service for classification of HS items. For image classification, CNN was used as a deep learning algorithm commonly used for image recognition and Vgg16, Vgg19, ResNet50 and Inception-V3 models were used among CNN models. To improve classification accuracy, two datasets were created. Dataset1 selected five types with the most HS code images, and Dataset2 was tested by dividing them into five types with 87 Chapter, the most among HS code 2 units. The classification accuracy was highest when HS item classification was performed by learning with dual database2, the corresponding model was Inception-V3, and the ResNet50 had the lowest classification accuracy. The study identified the possibility of HS item classification based on the first item image registered in the item classification determination case, and the second point of this study is that HS item classification, which has not been attempted before, was attempted through the CNN model.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2006.06a
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• pp.519-520
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• 2006

Research was performed to increase the efficiency of a plasma reactor for $H_2$ yield. In the preceding studies $H_2$ was increased by adding Ni as a transitional metal catalyst and $TiO_2$ as a photocatalyst. In these experiments, it was found that distilled water, discharge frequency, and electrode configuration had a significant impact on $H_2$ generation. A substantial amount of hydrogen yield was observed at 2 kHz of discharge frequency and 12 kV of applied voltage. Within this favorable discharge conditions, the weight rate of $TiO_2$ and Ni powders was investigated. Plasma phenomenon was measured by electrical, optical and acoustical devices. It was found that emitted light, electric current and acoustical signals acquired from the discharge demonstrated systematical correlation. Changing the electrode's configuration allowed discharge distribution along the perimeter of the electrode's tip, which increased the density of streamers and plasma energy loadings, as the value of inception voltage for the discharge propagation decreased.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.50 no.6
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• pp.288-295
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• 2001

Predischarges in nonuniform electric field stressed by lightning impulse voltagesin SF(sub)6-$N_2$mixtures are initiated by streamer coronas. Due to field ehnancement at a protrusion point of electrodes new ionization processes occur and a precursor, which leads to a first leader, is created. The leader proceeds step by step to the opposite electrode and the final jump bridges the test gap. It was found that the predischarge is propagated with a leader mechanism of stepwise expansion from the predischarge current waveforms measured by a shunt. The predischarge current is closely related to the amplitude and polarity of applied voltages, the gas pressure and the gap geometry. The time intervals between step leaders for the positive and negative polarities were inversely proportional to V.P$^2$. When the gas pressure increases in the positive polarity, statistical time lag to statistical time lag to streamer corona inception increase slightly, but the formative time lag to flashover is significantly decreased.

• Journal of Internet Computing and Services
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• v.20 no.4
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• pp.91-102
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• 2019

In autonomous driving systems, the ability to classify pedestrians in images captured by cameras is very important for pedestrian safety. In the past, after extracting features of pedestrians with HOG(Histogram of Oriented Gradients) or SIFT(Scale-Invariant Feature Transform), people classified them using SVM(Support Vector Machine). However, extracting pedestrian characteristics in such a handcrafted manner has many limitations. Therefore, this paper proposes a method to classify pedestrians reliably and effectively using CNN's(Convolutional Neural Network) deep features and transfer learning. We have experimented with both the fixed feature extractor and the fine-tuning methods, which are two representative transfer learning techniques. Particularly, in the fine-tuning method, we have added a new scheme, called M-Fine(Modified Fine-tuning), which divideslayers into transferred parts and non-transferred parts in three different sizes, and adjusts weights only for layers belonging to non-transferred parts. Experiments on INRIA Person data set with five CNN models(VGGNet, DenseNet, Inception V3, Xception, and MobileNet) showed that CNN's deep features perform better than handcrafted features such as HOG and SIFT, and that the accuracy of Xception (threshold = 0.5) isthe highest at 99.61%. MobileNet, which achieved similar performance to Xception and learned 80% fewer parameters, was the best in terms of efficiency. Among the three transfer learning schemes tested above, the performance of the fine-tuning method was the best. The performance of the M-Fine method was comparable to or slightly lower than that of the fine-tuningmethod, but higher than that of the fixed feature extractor method.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.29 no.7
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• pp.429-434
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• 2016

This paper deals with the characteristics of partial discharge (PD) for the purpose of a condition based maintenance (CBM) of gas insulated switchgears (GIS) in power equipment. Four types of electrode systems such as a protrusion on enclosure (POE), a particle on spacer (POS), a free particle (FP) and a Floating were designed and fabricated. PD pulses were measured using UHF sensor with a frequency range of 300 MHz~1.4 GHz and a DAQ with a sampling rate of 250 MS/s. Discharge inception voltage (DIV), discharge extinction voltage (DEV), and phase resolved partial discharge (PRPD) were analyzed depending on electrode systems. The average DIV in the POS was 28.8 kV. It was about 1.7 times higher than that in the FP, which was the lowest value of 17.2 kV. The FP shuffled and jumped at the applied voltage of 23.5 kV. Over 95% of PD pulses in the POE were generated in the negative polarity ($181^{\circ}{\sim}360^{\circ}$) of applied voltage. The results showed the phase (${\Phi}$)-magnitude (dBm) of PD pulses by UHF sensor, a cluster was formed separately depending on electrode systems.

• Journal of Biomedical Engineering Research
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• v.44 no.3
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• pp.182-189
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• 2023

CT is a medical device that acquires medical images based on Attenuation coefficient of human organs related to X-rays. In addition, using this theory, it can acquire sagittal and coronal planes and 3D images of the human body. Then, CT is essential device for universal diagnostic test. But Exposure of CT scan is so high that it is regulated and managed with special medical equipment. As the special medical equipment, CT must implement quality control. In detail of quality control, Spatial resolution of existing phantom imaging tests, Contrast resolution and clinical image evaluation are qualitative tests. These tests are not objective, so the reliability of the CT undermine trust. Therefore, by applying an artificial intelligence classification model, we wanted to confirm the possibility of quantitative evaluation of the qualitative evaluation part of the phantom test. We used intelligence classification models (VGG19, DenseNet201, EfficientNet B2, inception_resnet_v2, ResNet50V2, and Xception). And the fine-tuning process used for learning was additionally performed. As a result, in all classification models, the accuracy of spatial resolution was 0.9562 or higher, the precision was 0.9535, the recall was 1, the loss value was 0.1774, and the learning time was from a maximum of 14 minutes to a minimum of 8 minutes and 10 seconds. Through the experimental results, it was concluded that the artificial intelligence model can be applied to CT implements quality control in spatial resolution and contrast resolution.

• Journal of the Korean Vacuum Society
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• v.17 no.2
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• pp.96-101
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• 2008

The magnesium oxide (MgO) protective layer plays an important role in plasma display panels (PDPs). Our previous work demonstrated that the properties of MgO thin film could be improved, which were deposited by Ion-Beam-Assisted Deposition (IBAD). However arc discharge always occurs during the IBAD process. To avoid this problem, Oxygen-Neutral-Beam-Assisted Deposition (NBAD) is used to deposit MgO thin films in this paper. The energy of the oxygen neutral beam was used as the parameter to control the deposition. The experimental results showed that the oxygen neutral beam energy was effective in determining in structural and discharge characteristics. The lowest firing inception voltage, the highest brightness and the highest luminous efficiency were obtained when the MgO thin film was deposited with an oxygen neutral beam energy of 300eV. The surface morphology of MgO thin film was also analyzed using AFM (Atomic Force Microscopy) and SEM (Scanning Electron Microscopy).

• Journal of Animal Science and Technology
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• v.65 no.2
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• pp.365-376
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• 2023

Pig breeding management directly contributes to the profitability of pig farms, and pregnancy diagnosis is an important factor in breeding management. Therefore, the need to diagnose pregnancy in sows is emphasized, and various studies have been conducted in this area. We propose a computer-aided diagnosis system to assist livestock farmers to diagnose sow pregnancy through ultrasound. Methods for diagnosing pregnancy in sows through ultrasound include the Doppler method, which measures the heart rate and pulse status, and the echo method, which diagnoses by amplitude depth technique. We propose a method that uses deep learning algorithms on ultrasonography, which is part of the echo method. As deep learning-based classification algorithms, Inception-v4, Xception, and EfficientNetV2 were used and compared to find the optimal algorithm for pregnancy diagnosis in sows. Gaussian and speckle noises were added to the ultrasound images according to the characteristics of the ultrasonography, which is easily affected by noise from the surrounding environments. Both the original and noise added ultrasound images of sows were tested together to determine the suitability of the proposed method on farms. The pregnancy diagnosis performance on the original ultrasound images achieved 0.99 in accuracy in the highest case and on the ultrasound images with noises, the performance achieved 0.98 in accuracy. The diagnosis performance achieved 0.96 in accuracy even when the intensity of noise was strong, proving its robustness against noise.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.1
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• pp.126-146
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• 2024

Caladium bicolor is one of the most popular plants in Thailand. The original species of Caladium bicolor was found a hundred years ago. Until now, there are more than 500 species through multiplication. The classification of Caladium bicolor can be done by using its color and shape. This study aims to develop a model to classify Caladium bicolor using a transfer learning technique. This work also presents a proof of concept, GUI design, and web application deployment using the user-design-center method. We also evaluated the performance of the following pre-trained models in this work, and the results are as follow: 87.29% for AlexNet, 90.68% for GoogleNet, 93.59% for XceptionNet, 93.22% for MobileNetV2, 89.83% for RestNet18, 88.98% for RestNet50, 97.46% for RestNet101, and 94.92% for InceptionResNetV2. This work was implemented using MATLAB R2023a.