• Korean Journal of Radiology
• /
• v.22 no.6
• /
• pp.983-993
• /
• 2021

Objective: To investigate the image quality of ultralow-dose CT (ULDCT) of the chest reconstructed using a cycle-consistent generative adversarial network (CycleGAN)-based deep learning method in the evaluation of pulmonary tuberculosis. Materials and Methods: Between June 2019 and November 2019, 103 patients (mean age, 40.8 ± 13.6 years; 61 men and 42 women) with pulmonary tuberculosis were prospectively enrolled to undergo standard-dose CT (120 kVp with automated exposure control), followed immediately by ULDCT (80 kVp and 10 mAs). The images of the two successive scans were used to train the CycleGAN framework for image-to-image translation. The denoising efficacy of the CycleGAN algorithm was compared with that of hybrid and model-based iterative reconstruction. Repeated-measures analysis of variance and Wilcoxon signed-rank test were performed to compare the objective measurements and the subjective image quality scores, respectively. Results: With the optimized CycleGAN denoising model, using the ULDCT images as input, the peak signal-to-noise ratio and structural similarity index improved by 2.0 dB and 0.21, respectively. The CycleGAN-generated denoised ULDCT images typically provided satisfactory image quality for optimal visibility of anatomic structures and pathological findings, with a lower level of image noise (mean ± standard deviation [SD], 19.5 ± 3.0 Hounsfield unit [HU]) than that of the hybrid (66.3 ± 10.5 HU, p < 0.001) and a similar noise level to model-based iterative reconstruction (19.6 ± 2.6 HU, p > 0.908). The CycleGAN-generated images showed the highest contrast-to-noise ratios for the pulmonary lesions, followed by the model-based and hybrid iterative reconstruction. The mean effective radiation dose of ULDCT was 0.12 mSv with a mean 93.9% reduction compared to standard-dose CT. Conclusion: The optimized CycleGAN technique may allow the synthesis of diagnostically acceptable images from ULDCT of the chest for the evaluation of pulmonary tuberculosis.

• The Journal of the Korea Contents Association
• /
• v.22 no.3
• /
• pp.71-80
• /
• 2022

In the past, fish diseases were bacterial in aqua farms, but in recent years, the frequency of fish diseases has increased as they have become viral and mixed. Viral diseases in an enclosed space called a aqua farm have a high spread rate, so it is very likely to lead to mass death. Fast identification of fish diseases is important to prevent group death. However, diagnosis of fish diseases requires a high level of expertise and it is difficult to visually check the condition of fish every time. In order to prevent the spread of the disease, an automatic identification system of diseases or fish is needed. In this paper, in order to improve the performance of the disease identification system of Paralichthys olivaceus based on deep learning, the existing pre-processing method is compared and tested. Target diseases were selected from three most frequent diseases such as Scutica, Vibrio, and Lymphocystis in Paralichthys olivaceus. The RGB, HLS, HSV, LAB, LUV, XYZ, and YCRCV were used as image pre-processing methods. As a result of the experiment, HLS was able to get the best results than using general RGB. It is expected that the fish disease identification system can be advanced by improving the recognition rate of diseases in a simple way.

• The Transactions of the Korean Institute of Power Electronics
• /
• v.24 no.6
• /
• pp.411-418
• /
• 2019

Light-triggered thyristors (LTTs) are essential components in high-power applications, such as HVDC transmission and several pulsed-power applications. Generally, LTT fabrication includes a deep diffusion of aluminum as a p-type dopant to form a uniform p-base region, which needs careful concern for contamination and additional facilities in silicon semiconductor manufacturing factories. We fabricated 4-inch 5,000 V LTTs with boron implantation and diffusion process as a p-type dopant. The LTT contains a main cathode region, edge termination designed with a variation of lateral doping, breakover diode, integrated resistor, photosensitive area, and dV/dt protection region. The doping concentration of each region was adjusted with different doses of boron ion implantation. The fabricated LTTs showed good light triggering characteristics for a light pulse of 905 nm and a blocking voltage (V_DRM) of 6,500 V. They drove an average on-state current (I_TAVM) of 2,270 A, peak nonrepetitive surge current (I_TSM) of 61 kA, critical rate of rise of on-state current (di/dt) of 1,010 A/㎲, and limiting load integral (I²T) of 17 MA²s without damage to the device.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
• /
• 2001.06a
• /
• pp.4103-4103
• /
• 2001

The aim of this study was to examine whether near infrared spectroscopy (NIRS) is an acceptable tool to determine cholesterol and collagen in human atherosclerotic plaque without destruction of the analyzed areas and without danger the endothelial cells - three preconditions for the development of a NIR-heart-catheter. The questions were: Can the cholesterol and collagen content of the arterial intima be estimated with acceptable precision in vitro by NIRS despite the matrix inhomogeneity of the plaques and their anatomic variability\ulcorner How deep can such NIR radiation penetrate into arterial tissue without danger for endothelial cells\ulcorner Is this penetration sufficient for information on the lipid and collagen accumulation\ulcorner Using NIRS, cholesterol and collagen can be determined with acceptable precision in model mixtures and human aortic specimens (r=0,896 to 0,957). The chemical reference method was HPLC. The energy dose was 71 mW/$cm^{-2}$ using a fiber optic strand with a length of 1.5m and an optical window of d=4mm. This dose appears to be not dangerous for endothelial cells, It will be attenuated to 50% by a arterial tissue of about 170-$200\mu\textrm{m}$ thickness. The results are also acceptable using a thin coronary catheter-like fiber optic strand (d=1mm).

• 한국지구과학회:학술대회논문집
• /
• 2005.09a
• /
• pp.124-131
• /
• 2005

A GDS (Geomagnetic Depth Sounding) method, one of extremely low-frequency EM methods, has been carried out to examine deep geo-electrical structures of the Korean peninsula. In this study, five additive GDS sites acquired in south-eastern area of the Korea were integrated into twelve previous GDS results. In addition, 3-D MT modeling considering the surrounding seas of the Korean peninsula was performed to evaluate sea effect at each GDS site quantitatively. As a result, Observed real induction arrows was not explained by solely sea effect, two conductive structures that are able to explain differences between observed and calculated induction arrows, was suggested. The first conductive structure is the Imjingang Belt, which is thought as a extension of Quiling-Dabie-sulu continental collision belt. The effects of the Imjingang Belt clearly appear at YIN and ICHN sites. The second one is the HCL (Highly Conductive Layer), which is considered as a conductive anomaly by mantle upwelling generated in back-basin region. The effects of the HCL are also confirmed at KZU, KMT101, 107 sites, in the south-eastern of the Korean peninsula.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
• /
• 2002.10a
• /
• pp.142-148
• /
• 2002

Autonomous underwater vehicles (AUVs) are unmanned underwater vessels to investigate sea environments, oceanography and deep-sea resources autonomously. Docking systems are required to increase the capability of the AUVs to recharge the batteries and to transmit data in real time for specific underwater works, such as repeated jobs at sea bed. This paper presents a visual servo control system for an AUV to dock into an underwater station with a camera mounted at the nose center of the AUV. To make the visual servo control system, this paper derives an optical flow model of a camera, where the projected motions of the image plane are described with the rotational and translational velocities of the AUV. This paper combines the optical flow equation of the camera with the AUVs equation of motion, and derives a state equation for the visual servoing AUV. This paper proposes a discrete-time MIMO controller minimizing a cost function. The control inputs of the AUV are automatically generated with the projected target position on the CCD plane of the camera and with the AUVs motion. To demonstrate the effectiveness of the modeling and the control law of the visual servoing AUV, simulations on docking the AUV to a target station are performed with the 6-dof nonlinear equations of REMUS AUV and a CCD camera.

• Biomaterials Research
• /
• v.22 no.4
• /
• pp.303-310
• /
• 2018

Background: Photodynamic therapy (PDT) is photo-treatment of malignant or benign diseases using photosensitizing agents, light, and oxygen which generates cytotoxic reactive oxygens and induces tumour regressions. Several photodynamic treatments have been extensively studied and the photosensitizers (PS) are key to their biological efficacy, while laser and oxygen allow to appropriate and flexible delivery for treatment of diseases. Introduction: In presence of oxygen and the specific light triggering, PS is activated from its ground state into an excited singlet state, generates reactive oxygen species (ROS) and induces apoptosis of cancer tissues. Those PS can be divided by its specific efficiency of ROS generation, absorption wavelength and chemical structure. Main body: Up to dates, several PS were approved for clinical applications or under clinical trials. $Photofrin^{(R)}$ is the first clinically approved photosensitizer for the treatment of cancer. The second generation of PS, Porfimer sodium ($Photofrin^{(R)}$), Temoporfin ($Foscan^{(R)}$), Motexafin lutetium, Palladium bacteriopheophorbide, $Purlytin^{(R)}$, Verteporfin ($Visudyne{(R)}$), Talaporfin ($Laserphyrin^{(R)}$) are clinically approved or under-clinical trials. Now, third generation of PS, which can dramatically improve cancer-targeting efficiency by chemical modification, nano-delivery system or antibody conjugation, are extensively studied for clinical development. Conclusion: Here, we discuss up-to-date information on FDA-approved photodynamic agents, the clinical benefits of these agents. However, PDT is still dearth for the treatment of diseases in specifically deep tissue cancer. Next generation PS will be addressed in the future for PDT. We also provide clinical unmet need for the design of new photosensitizers.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.18 no.4
• /
• pp.938-958
• /
• 2024

Unsupervised Video Object Segmentation (UVOS) is a highly challenging problem in computer vision as the annotation of the target object in the testing video is unknown at all. The main difficulty is to effectively handle the complicated and changeable motion state of the target object and the confusion of similar background objects in video sequence. In this paper, we propose a novel deep Dual-stream Co-enhanced Network (DC-Net) for UVOS via bidirectional motion cues refinement and multi-level feature aggregation, which can fully take advantage of motion cues and effectively integrate different level features to produce high-quality segmentation mask. DC-Net is a dual-stream architecture where the two streams are co-enhanced by each other. One is a motion stream with a Motion-cues Refine Module (MRM), which learns from bidirectional optical flow images and produces fine-grained and complete distinctive motion saliency map, and the other is an appearance stream with a Multi-level Feature Aggregation Module (MFAM) and a Context Attention Module (CAM) which are designed to integrate the different level features effectively. Specifically, the motion saliency map obtained by the motion stream is fused with each stage of the decoder in the appearance stream to improve the segmentation, and in turn the segmentation loss in the appearance stream feeds back into the motion stream to enhance the motion refinement. Experimental results on three datasets (Davis2016, VideoSD, SegTrack-v2) demonstrate that DC-Net has achieved comparable results with some state-of-the-art methods.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.8
• /
• pp.3567-3582
• /
• 2020

Human activities are often affected by weather conditions. Automatic weather recognition is meaningful to traffic alerting, driving assistance, and intelligent traffic. With the boost of deep learning and AI, deep convolutional neural networks (CNN) are utilized to identify weather situations. In this paper, a three-channel convolutional neural network (3C-CNN) model is proposed on the basis of ResNet50.The model extracts global weather features from the whole image through the ResNet50 branch, and extracts the sky and ground features from the top and bottom regions by two CNN5 branches. Then the global features and the local features are merged by the Concat function. Finally, the weather image is classified by Softmax classifier and the identification result is output. In addition, a medium-scale dataset containing 6,185 outdoor weather images named WeatherDataset-6 is established. 3C-CNN is used to train and test both on the Two-class Weather Images and WeatherDataset-6. The experimental results show that 3C-CNN achieves best on both datasets, with the average recognition accuracy up to 94.35% and 95.81% respectively, which is superior to other classic convolutional neural networks such as AlexNet, VGG16, and ResNet50. It is prospected that our method can also work well for images taken at night with further improvement.

• Smart Structures and Systems
• /
• v.29 no.1
• /
• pp.195-206
• /
• 2022

Crack detection is essential for inspection of existing structures and crack segmentation based on deep learning is a significant solution. However, datasets are usually one of the key issues. When building a new dataset for deep learning, laborious and time-consuming annotation of a large number of crack images is an obstacle. The aim of this study is to develop an approach that can automatically select a small portion of the most informative crack images from a large pool in order to annotate them, not to label all crack images. An active learning method with difficulty learning mechanism for crack segmentation tasks is proposed. Experiments are carried out on a crack image dataset of a steel box girder, which contains 500 images of 320×320 size for training, 100 for validation, and 190 for testing. In active learning experiments, the 500 images for training are acted as unlabeled image. The acquisition function in our method is compared with traditional acquisition functions, i.e., Query-By-Committee (QBC), Entropy, and Core-set. Further, comparisons are made on four common segmentation networks: U-Net, DeepLabV3, Feature Pyramid Network (FPN), and PSPNet. The results show that when training occurs with 200 (40%) of the most informative crack images that are selected by our method, the four segmentation networks can achieve 92%-95% of the obtained performance when training takes place with 500 (100%) crack images. The acquisition function in our method shows more accurate measurements of informativeness for unlabeled crack images compared to the four traditional acquisition functions at most active learning stages. Our method can select the most informative images for annotation from many unlabeled crack images automatically and accurately. Additionally, the dataset built after selecting 40% of all crack images can support crack segmentation networks that perform more than 92% when all the images are used.