• Nuclear Engineering and Technology
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• v.55 no.9
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• pp.3359-3366
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• 2023

In this study, a spiking cortical model (SCM) based n-g discrimination method is proposed. The SCM-based algorithm is compared with three other methods, namely: (i) the pulse-coupled neural network (PCNN), (ii) the charge comparison, and (iii) the zero-crossing. The objective evaluation criteria used for the comparison are the FoM-value and the time consumption of discrimination. Experimental results demonstrated that our proposed method outperforms the other methods significantly with the highest FoM-value. Specifically, the proposed method exhibits a 34.81% improvement compared with the PCNN, a 50.29% improvement compared with the charge comparison, and a 110.02% improvement compared with the zero-crossing. Additionally, the proposed method features the second-fastest discrimination time, where it is 75.67% faster than the PCNN, 70.65% faster than the charge comparison and 38.4% slower than the zero-crossing. Our study also discusses the role and change pattern of each parameter of the SCM to guide the selection process. It concludes that the SCM's outstanding ability to recognize the dynamic information in the pulse signal, improved accuracy when compared to the PCNN, and better computational complexity enables the SCM to exhibit excellent n-γ discrimination performance while consuming less time.

• Molecules and Cells
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• v.38 no.2
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• pp.145-150
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• 2015

Continuous intra- and extracellular stresses induce disorder of $Ca^{2+}$ homeostasis and accumulation of unfolded protein in the endoplasmic reticulum (ER), which results in ER stress. Severe long-term ER stress triggers apoptosis signaling pathways, resulting in cell death. Neural epidermal growth factor-like like protein 2 (NELL2) has been reported to be important in protection of cells from cell death-inducing environments. In this study, we investigated the cytoprotective effect of NELL2 in the context of ER stress induced by thapsigargin, a strong ER stress inducer, in Cos7 cells. Overexpression of NELL2 prevented ER stress-mediated apoptosis by decreasing expression of ER stress-induced C/EBP homologous protein (CHOP) and increasing ER chaperones. In this context, expression of anti-apoptotic Bcl-xL was increased by NELL2, whereas NELL2 decreased expression of pro-apoptotic proteins, such as cleaved caspases 3 and 7. This anti-apoptotic effect of NELL2 is likely mediated by extracellular signal-regulated kinase (ERK) signaling, because its inhibitor, U0126, inhibited effects of NELL2 on the expression of anti- and pro-apoptotic proteins and on the protection from ER stress-induced cell death.

• Journal of Radiation Protection and Research
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• v.47 no.3
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• pp.111-133
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• 2022

The exciting advancement related to the "modeling of digital human" in terms of a computational phantom for radiation dose calculations has to do with the latest hype related to deep learning. The advent of deep learning or artificial intelligence (AI) technology involving convolutional neural networks has brought an unprecedented level of innovation to the field of organ segmentation. In addition, graphics processing units (GPUs) are utilized as boosters for both real-time Monte Carlo simulations and AI-based image segmentation applications. These advancements provide the feasibility of creating three-dimensional (3D) geometric details of the human anatomy from tomographic imaging and performing Monte Carlo radiation transport simulations using increasingly fast and inexpensive computers. This review first introduces the history of three types of computational human phantoms: stylized medical internal radiation dosimetry (MIRD) phantoms, voxelized tomographic phantoms, and boundary representation (BREP) deformable phantoms. Then, the development of a person-specific phantom is demonstrated by introducing AI-based organ autosegmentation technology. Next, a new development in GPU-based Monte Carlo radiation dose calculations is introduced. Examples of applying computational phantoms and a new Monte Carlo code named ARCHER (Accelerated Radiation-transport Computations in Heterogeneous EnviRonments) to problems in radiation protection, imaging, and radiotherapy are presented from research projects performed by students at the Rensselaer Polytechnic Institute (RPI) and University of Science and Technology of China (USTC). Finally, this review discusses challenges and future research opportunities. We found that, owing to the latest computer hardware and AI technology, computational human body models are moving closer to real human anatomy structures for accurate radiation dose calculations.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.468-468
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• 2000

In our laboratory, the control aspects are investigated in the photovoltaic power generation systems (PV systems). The PV system is very good for earth environment, but if it connects to power network system, many problems are raised (protection, voltage, harmonics etc.). In this paper, we present the result of the basic studies for the building of the PV system that amplifies the electric energy obtained from the solar cell. We consider electronic circuits in order to protect the PV system from power surge induced by lightning and also design an electronic circuit in order to detect defaults in the power network system. We would like to integrate these circuits into the PV system by considering its control equipment build by 8-bit microcomputer using various control theory (fuzzy, neural network etc.).

• Journal of the Semiconductor & Display Technology
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• v.22 no.2
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• pp.81-86
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• 2023

Recently, there has been an increased demand for light-emitting diode (LED) due to the growing emphasis on environmental protection. However, the use of GaN-based sapphire in LED manufacturing leads to the generation of defects, such as dislocations caused by lattice mismatch, which ultimately reduces the luminous efficiency of LEDs. Moreover, most inspections for LED semiconductors focus on evaluating the luminous efficiency after packaging. To address these challenges, this paper aims to detect defects at the wafer stage, which could potentially improve the manufacturing process and reduce costs. To achieve this, image processing and deep learning-based defect detection techniques for Sapphire Epi-Wafer used in Green LED manufacturing were developed and compared. Through performance evaluation of each algorithm, it was found that the deep learning approach outperformed the image processing approach in terms of detection accuracy and efficiency.

• Journal of Korea Water Resources Association
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• v.54 no.5
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• pp.301-309
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• 2021

In this study, we tried to improve the performance of the existing U-net-based deep learning rainfall prediction model, which can weaken the meaning of time series order. For this, ConvLSTM2D U-Net structure model considering temporal consistency of data was applied, and we evaluated accuracy of the ConvLSTM2D U-Net model using a RainNet model and an extrapolation-based advection model. In addition, we tried to improve the uncertainty in the model training process by performing learning not only with a single model but also with 10 ensemble models. The trained neural network rainfall prediction model was optimized to generate 10-minute advance prediction data using four consecutive data of the past 30 minutes from the present. The results of deep learning rainfall prediction models are difficult to identify schematically distinct differences, but with ConvLSTM2D U-Net, the magnitude of the prediction error is the smallest and the location of rainfall is relatively accurate. In particular, the ensemble ConvLSTM2D U-Net showed high CSI, low MAE, and a narrow error range, and predicted rainfall more accurately and stable prediction performance than other models. However, the prediction performance for a specific point was very low compared to the prediction performance for the entire area, and the deep learning rainfall prediction model also had limitations. Through this study, it was confirmed that the ConvLSTM2D U-Net neural network structure to account for the change of time could increase the prediction accuracy, but there is still a limitation of the convolution deep neural network model due to spatial smoothing in the strong rainfall region or detailed rainfall prediction.

• Progress in Medical Physics
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• v.20 no.3
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• pp.132-138
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• 2009

Studies on target motion in 4-dimensional radiotherapy are being world-widely conducted to enhance treatment record and protection of normal organs. Prediction of tumor motion might be very useful and/or essential for especially free-breathing system during radiation delivery such as respiratory gating system and tumor tracking system. Neural network is powerful to express a time series with nonlinearity because its prediction algorithm is not governed by statistic formula but finds a rule of data expression. This study intended to assess applicability of neural network method to predict tumor motion in 4-dimensional radiotherapy. Scaled Conjugate Gradient algorithm was employed as a learning algorithm. Considering reparation data for 10 patients, prediction by the neural network algorithms was compared with the measurement by the real-time position management (RPM) system. The results showed that the neural network algorithm has the excellent accuracy of maximum absolute error smaller than 3 mm, except for the cases in which the maximum amplitude of respiration is over the range of respiration used in the learning process of neural network. It indicates the insufficient learning of the neural network for extrapolation. The problem could be solved by acquiring a full range of respiration before learning procedure. Further works are programmed to verify a feasibility of practical application for 4-dimensional treatment system, including prediction performance according to various system latency and irregular patterns of respiration.

• Journal of Distribution Science
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• v.18 no.1
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• pp.15-26
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• 2020

Purpose: This study is to analyze the image classification using Convolution Neural Network and Transfer Learning for Jeju Island and to suggest related implications. As the biggest tourist destination in Korea, Jeju Island encounters environmental issues frequently caused by marine debris along the seaside. The ever-increasing volume of plastic waste requires multidirectional management and protection. Research design, data and methodology: In this study, the deep learning CNN algorithm was used to train a number of images from Jeju clean and polluted beaches. In the process of validating and testing pre-processed images, we attempted to explore their applicability to coastal tourism applications through probabilities of classifying images and predicting clean shores. Results: We transformed and augmented 194 small image dataset into 3,880 image data. The results of the pre-trained test set were 85%, 70% and 86%, and then its accuracy has increased through the process. We finally obtained a rapid convergence of 97.73% and 100% (20/20) in the actual training and validation sets. Conclusions: The tested algorithms are expected to implement in applications for tourism service distribution aimed at reducing coastal waste or in CCTVs as a detector or indicator for residents and tourists to protect clean beaches on Jeju Island.

• Journal of International Academy of Physical Therapy Research
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• v.1 no.2
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• pp.107-112
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• 2010

The majority of strokes are caused by ischemia and result in brain tissue damage, leading to problems of the central nervous system including hemiparesis, dysfunction of language and consciousness, and dysfunction of perception. The purpose of this study was to investigate the effects of Poly(ADP-ribose) polymerase(PARP) on necrosis in neuronal cells that have undergone needle electrode electrical stimulation(NEES) prior to induction of ischemia. Ischemia was induced in male SD rats(body weight 300g) by occlusion of the common carotid artery for 5 min, after which the blood was reperfused. After induction of brain ischemia, NEES was applied to Zusanli(ST 36), at 12, 24 and 48 hours. Protein expression was investigated using immuno-reactive cells, which react to PARP antibodies in cerebral nerve cells, and Western blotting. The results were as follows: In the cerebral cortex, the number of PARP reactive cells after 24 hours significantly decreased(p<.05) in the NEES group compared to the GI group. PARP expression after 24 hours significantly decreased(p<.05) in the NEES group compared to the GI group. As a result, NEES showed the greatest effect on necrosis-related PARP immuno-reactive cells 24 hours after ischemia, indicating necrosis inhibition, blocking of neural cell death, and protection of neural cells. Based on the results of this study, NEES can be an effective method of treating dysfunction and improving function of neuronal cells in brain damage caused by ischemia.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.10
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• pp.454-460
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• 2020

In recent years, the rapid increases in video distribution and viewing over the Internet have increased the risk of personal information exposure. In this paper, a method is proposed to robustly identify areas in images where a person's privacy is compromised and simultaneously blocking the object area by blurring it while rapidly tracking it using a prediction algorithm. With this method, the target object area is accurately identified using artificial neural network-based learning. The detected object area is then tracked using a location prediction algorithm and is continuously blocked by blurring it. Experimental results show that the proposed method effectively blocks private areas in images by blurring them, while at the same time tracking the target objects about 2.5% more accurately than another existing method. The proposed blocking method is expected to be useful in many applications, such as protection of personal information, video security, object tracking, etc.