• Nuclear Engineering and Technology
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• v.53 no.7
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• pp.2229-2236
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• 2021

In nuclear-related facilities, such as nuclear power plants, research reactors, accelerators, and nuclear waste storage sites, radiation detection, and mapping are required to prevent radiation overexposure. Sensor network systems consisting of radiation sensor interfaces and wxireless communication units have become promising tools that can be used for data collection of radiation detection that can in turn be used to draw a radiation map. During data collection, malfunctions in some of the sensors can occasionally occur due to radiation effects, physical damage, network defects, sensor loss, or other reasons. This paper proposes a reproduction strategy for radiation maps using a U-net model to compensate for the loss of radiation detection data. To perform machine learning and verification, 1,561 simulations and 417 measured data of a sensor network were performed. The reproduction results show an accuracy of over 90%. The proposed strategy can offer an effective method that can be used to resolve the data loss problem for conventional sensor network systems and will specifically contribute to making initial responses with preserved data and without the high cost of radiation leak accidents at nuclear facilities.

• Current Optics and Photonics
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• v.5 no.4
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• pp.362-369
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• 2021

Absolute position sensors are widely used in machine tools and precision measuring instruments because measurement errors are not accumulated, and position measurements can be performed without initialization. The laser speckle-based absolute position sensor, in particular, has advantages in terms of simple system configuration and high measurement accuracy. Unlike traditional absolute position sensors, it does not require an expensive physical length scale; instead, it uses a laser speckle image database to measure a moving surface position. However, there is a problem that a huge database is required to store information in all positions on the surface. Conversely, reducing the size of the database also decreases the accuracy of position measurements. Therefore, in this paper, we propose a new method to measure the surface position with high precision while reducing the size of the database. We use image stitching and approximation methods to reduce database size and speed up measurements. The absolute position error of the proposed method was about 0.27 ± 0.18 ㎛, and the average measurement time was 25 ms.

• Advances in concrete construction
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• v.12 no.5
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• pp.411-418
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• 2021

Roller compacted concrete (RCC) used in the island reef airport runway will be subjected to the coupling actions of the fatigue impacts and the salt spray cycles, which will accelerate the deterioration of runway concrete and even threaten the flight safety. A cyclic impact testing machine and a climatic chamber were used to simulate the low-velocity fatigue impact and the salt spray cycles, respectively. The physical properties, the microstructures and the porosity of RCC were investigated. The results show the flexural strength firstly increases and then decreases with the increase of the fatigue impacts and the salt spray cycles. However, the decrease in the flexural strength is significantly earlier than the compressive strength of RCC only subjected to the salt spray cycles. The chlorine, sulfur and magnesium elements significantly increase in the pores of RCC subjected to 30000 fatigue impacts and 300 salt spray cycles, which causes the decrease in the porosity of RCC. The coupling effects of the fatigue impacts and the salt spray cycles in the later period accelerates the deterioration of RCC.

• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.73-78
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• 2022

The aim of this paper is to investigate the prevalence of Deep Learning in the literature on Fire & Rescue Service. It is found that deep learning techniques are only beginning to benefit the firefighters. The popular areas where deep learning techniques are making an impact are situational awareness, decision making, mental stress, injuries, well-being of the firefighter such as his sudden fall, inability to move and breathlessness, path planning by the firefighters while getting to an fire scene, wayfinding, tracking firefighters, firefighter physical fitness, employment, prediction of firefighter intervention, firefighter operations such as object recognition in smoky areas, firefighter efficacy, smart firefighting using edge computing, firefighting in teams, and firefighter clothing and safety. The techniques that were found applied in firefighting were Deep learning, Traditional K-Means clustering with engineered time and frequency domain features, Convolutional autoencoders, Long Short-Term Memory (LSTM), Deep Neural Networks, Simulation, VR, ANN, Deep Q Learning, Deep learning based on conditional generative adversarial networks, Decision Trees, Kalman Filters, Computational models, Partial Least Squares, Logistic Regression, Random Forest, Edge computing, C5 Decision Tree, Restricted Boltzmann Machine, Reinforcement Learning, and Recurrent LSTM. The literature review is centered on Firefighters/firemen not involved in wildland fires. The focus was also not on the fire itself. It must also be noted that several deep learning techniques such as CNN were mostly used in fire behavior, fire imaging and identification as well. Those papers that deal with fire behavior were also not part of this literature review.

• Journal of the Korean Wood Science and Technology
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• v.31 no.3
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• pp.35-41
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• 2003

The mechanical and physical properties of wood are strongly dependent upon the slope of grain. Specially, tensile strength is more severely affected by the slope of grain. Therefore, tension tests were performed on small thin wood samples made from Pinus radiata with varying the slope of grain. Determining the tensile strength for clear thin wood samples the other variabilities associated with material, size, drying, defects, etc were discarded. Slope of grain was measured by the slope of grain indicator and actual slope of grain was also determined by a protractor. Correlation coefficients between machine measured and actual slope of grain for 40 pieces of 2×20 mm, 300 mm long Pinus radiata were 0.84 for wide face measurement. Results also showed that tensile strength and MOE from stress wave tests decreased with increasing the slope of grain. This study did not establish a relationships for tensile strength and MOE from stress wave with slope of grain. However, the trends of MOEs from stress wave test with both slope of grain are agreed well with Hankinson's equation. Predicted tension strength curve by Hankinson's equation was also agreed well with the experimental data over the range from 0 to 13 degrees for slope of grain.

• Tribology and Lubricants
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• v.37 no.6
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• pp.213-223
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• 2021

Electrochemical mechanical planarization (ECMP) was developed to overcome the shortcomings of conventional chemical mechanical planarization (CMP). Because ECMP technology utilizes electrochemical reactions, it can have a higher efficiency than CMP even under low pressure conditions. Therefore, there is an advantage in that it is possible to reduce dicing and erosions, which are physical defects in semiconductor CMP. This paper summarizes the papers on ECMP published from 2003 to 2021 and analyzes research trends in ECMP technology. First, the material removal mechanisms and the configuration of the ECMP machine are dealt with, and then ECMP research trends are reviewed. For ECMP research trends, electrolyte, processing variables and pads, tribology, modeling, and application studies are investigated. In the past, research on ECMP was focused on basic research for the development of electrolytes, but it has recently developed into research on tribology and process variables and on new processing systems and applications. However, there is still a need to increase the processing efficiency, and to this end, the development of a hybrid ECMP processing method using another energy source is required. In addition, ECMP systems that can respond to the developing metal 3D printing technology must be researched, and ECMP equipment technology using CNC and robot technology must be developed.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.690-693
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• 2022

In this study, we used wine data to perform correlation analysis on factors that affect wine quality, and predicted wine quality standards based on the results. The dataset used in this study used data from 1599 red wines and 4898 white wines produced in Vinho verde, Portugal, for a total of 6497. The variable items are 12 kinds of component variables that represent wine components through physical and chemical analysis tests, a total of 1599 observations, and a total of one of the representative wines of the three major wine producing regions in the world (France, Italy, Spain). Added 3 pieces. Analysis was made by applying national climate change data.

• Journal of the Korea Institute of Military Science and Technology
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• v.26 no.1
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• pp.44-53
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• 2023

Adversarial attacks have received great attentions for their capacity to distract state-of-the-art neural networks by modifying objects in physical domain. Patch-based attack especially have got much attention for its optimization effectiveness and feasible adaptation to any objects to attack neural network-based object detectors. However, despite their strong attack performance, generated patches are strongly perceptible for humans, violating the fundamental assumption of adversarial examples. In this paper, we propose a camouflaged adversarial patch optimization method using military camouflage assessment metrics for naturalistic patch attacks. We also investigate camouflaged attack loss functions, applications of various camouflaged patches on army tank images, and validate the proposed approach with extensive experiments attacking Yolov5 detection model. Our methods produce more natural and realistic looking camouflaged patches while achieving competitive performance.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.2
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• pp.124-132
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• 2023

In this paper, we present a simple classification model based on statistical features and demonstrate the successful implementation of a brain-computer interface (BCI) based light on/off control system. This research shows study and development of light on/off control system based on BCI technology, which allows the users to control switching a lamp using electroencephalogram (EEG) signals. The logistic regression algorithm is used for classification of the EEG signal to convert it into light on, light off control commands. Training data were collected using 14-channel BCI system which records the brain signals of participants watching a screen with flickering lights and saves the data into .csv file for future analysis. After extracting a number of features from the data and performing classification using logistic regression, we created commands to switch on a physical lamp and tested it in a real environment. Logistic regression allowed us to quite accurately classify the EEG signals based on the user's mental state and we were able to classify the EEG signals with 82.5% accuracy, producing reliable commands for turning on and off the light.

• Wind and Structures
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• v.36 no.6
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• pp.367-377
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• 2023

This study proposes a few-shot learning model for extrapolating the wind pressure of scaled experiments to full-scale measurements. The proposed ML model can use scaled experimental data and a few full-scale tests to accurately predict the remaining full-scale data points (for new specimens). This model focuses on extrapolating the prediction to different scales while existing approaches are not capable of accurately extrapolating from scaled data to full-scale data in the wind engineering domain. Also, the scaling issue observed in wind tunnel tests can be partially resolved via the proposed approach. The proposed model obtained a low mean-squared error and a high coefficient of determination for the mean and standard deviation wind pressure coefficients of the full-scale dataset. A parametric study is carried out to investigate the influence of the number of selected shots. This technique is the first of its kind as it is the first time an ML model has been used in the wind engineering field to deal with extrapolation in wind performance prediction. With the advantages of the few-shot learning model, physical wind tunnel experiments can be reduced to a great extent. The few-shot learning model yields a robust, efficient, and accurate alternative to extrapolating the prediction performance of structures from various model scales to full-scale.