• Journal of Soil and Groundwater Environment
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• v.24 no.3
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• pp.24-35
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• 2019

It is known that ${\delta}^{13}C_{DIC}$ (carbon-13 isotope of dissolved inorganic carbonate (DIC) ions) of water increases when dissolved $CO_2$ degases. However, ${\delta}^{13}C_{DIC}$ could decrease when the pH of water is lower than 5.5 at the early stage of degassing. Laboratory experiments were performed to observe the changes of ${\delta}^{13}C_{DIC}$ as $CO_2$ degassed from three different artificial $CO_2$-rich waters (ACWs) in which the initial pH was 4.9, 5.4, and 6.4, respectively. The pH, alkalinity and ${\delta}^{13}C_{DIC}$ were measured until 240 hours after degassing began and those data were compared with kinetic isotope fractionation calculations. Furthermore, same experiment was conducted with the natural $CO_2$-rich water (pH 4.9) from Daepyeong, Sejong City. As a result of experiments, we could observe the decrease of DIC and increase of pH as the degassing progressed. ACW with an initial pH of 6.4, ${\delta}^{13}C_{DIC}$ kept increasing but, in cases where the initial pH was lower than 5.5, ${\delta}^{13}C_{DIC}$ decreased until 6 hours. After 6 hours ${\delta}^{13}C_{DIC}$ increased within all cases because the $CO_2$ degassing caused pH increase and subsequently the ratio of $HCO_3{^-}$ in solution. In the early stage of $CO_2$ degassing, the laboratory measurements were well matched with the calculations, but after about 48 hours, the experiment results were deviated from the calculations, probably due to the equilibrium interaction with the atmosphere and precipitation of carbonates. The result of this study may be not applicable to all natural environments because the pressure and $CO_2$ concentration in headspace of reaction vessels was not maintained constant as well as the temperature. Nevertheless, this study provides fundamental knowledge on the ${\delta}^{13}C_{DIC}$ evolution during $CO_2$ degassing, and therefore it can be utilized in the studies about carbonated water with low pH and the monitoring of geologic carbon sequestration.

• Journal of Ocean Engineering and Technology
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• v.32 no.6
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• pp.447-457
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• 2018

In this study, a two-dimensional fully nonlinear transient wave numerical tank was developed using a desingularized indirect boundary integral equation method. The desingularized indirect boundary integral equation method is simpler and faster than the conventional boundary element method because special treatment is not required to compute the boundary integral. Numerical simulations were carried out in the time domain using the fourth order Runge-Kutta method. A mixed Eulerian-Lagrangian approach was adapted to reconstruct the free surface at each time step. A numerical damping zone was used to minimize the reflective wave in the downstream region. The interpolating method of a Gaussian radial basis function-type artificial neural network was used to calculate the gradient of the free surface elevation without element connectivity. The desingularized indirect boundary integral equation using an isolated point source and radial basis function has no need for information about the element connectivity and is a meshless method that is numerically more flexible. In order to validate the accuracy of the numerical wave tank based on the desingularized indirect boundary integral equation method and meshless technique, several numerical simulations were carried out. First, a comparison with numerical results according to the type of desingularized source was carried out and confirmed that continuous line sources can be replaced by simply isolated sources. In addition, a propagation simulation of a $2^{nd}$-order Stokes wave was carried out and compared with an analytical solution. Finally, simulations of propagating waves in shallow water and propagating waves over a submerged bar were also carried and compared with published data.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.12
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• pp.291-306
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• 2020

Nowadays, Data-Network-AI (DNA)-based intelligent services and applications have become a reality to provide a new dimension of services that improve the quality of life and productivity of businesses. Artificial intelligence (AI) can enhance the value of IoT data (data collected by IoT devices). The internet of things (IoT) promotes the learning and intelligence capability of AI. To extract insights from massive volume IoT data in real-time using deep learning, processing capability needs to happen in the IoT end devices where data is generated. However, deep learning requires a significant number of computational resources that may not be available at the IoT end devices. Such problems have been addressed by transporting bulks of data from the IoT end devices to the cloud datacenters for processing. But transferring IoT big data to the cloud incurs prohibitively high transmission delay and privacy issues which are a major concern. Edge computing, where distributed computing nodes are placed close to the IoT end devices, is a viable solution to meet the high computation and low-latency requirements and to preserve the privacy of users. This paper provides a comprehensive review of the current state of leveraging deep learning within edge computing to unleash the potential of IoT big data generated from IoT end devices. We believe that the revision will have a contribution to the development of DNA-based intelligent services and applications. It describes the different distributed training and inference architectures of deep learning models across multiple nodes of the edge computing platform. It also provides the different privacy-preserving approaches of deep learning on the edge computing environment and the various application domains where deep learning on the network edge can be useful. Finally, it discusses open issues and challenges leveraging deep learning within edge computing.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.3
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• pp.187-193
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• 2022

In the Yeongdong region of Gangwon-do, coastal areas are important resources in terms of cultural, social and economic aspects. However, the coast of Gangwon-do is experiencing severe erosion, and it is concerned that its adverse effects will gradually increase. In this study, coastline changes of Yangyang and Gangneung in Gangwon-do were tracked and analyzed over a long period of time. In order to build time series image data, aerial photos from the 1940s to the present were mainly used, and data from CORONA satellite, which operated from the 1960s to the early 1970s, were collected and used together. Using 51cm resolution ortho image and 2m resolution Digital Elevation Model(DEM) as reference, ground control points were selected to perform geometric correction on the aerial photos and CORONA images. Subsequently, Canny edge detector applied to these images to extract the coastlines. As a result of analyzing the extracted and vectorized coastlines by overlaying them in chronological order, erosion and deposition occurring around the artificial structures and on the nearby beaches were observed. In this study, the effect of seasonal variation, tide, and various coastal management including the beach filling were not considered. Because coastal erosion is greatly affected by geographic factors, each local government must find its own solution. Continuous research and local data accumulation are required.

• Journal of the Korean Society of Radiology
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• v.16 no.6
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• pp.799-805
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• 2022

Radiation therapy of human tissues, including bone tissue, is accompanied by side effects on normal tissues. It has a more lethal effect on stem cells, which play an essential role in tissue regeneration, including the basal cells constituting the tissue. In this study, the mouse parietal model, which implemented an artificial osteoradionecrosis model on the parietal region of the mouse, was artificially defected and then the bone regeneration was tested. In order to overcome the implemented osteoradionecrosis, a fibrin scaffold, widely used as a biomaterial, and stromal cell-derived factor-1 (SDF-1), which is used as a long-term treatment for damaged, were mixed to verify the osteoradionecrosis regeneration effect on the parietal of mouse. In order to expect a synergistic effect in the fibrin scaffolds, a fibrin scaffolds was prepared after maintaining the concentration of SDF-1 (1 ㎍/ml) in the fibrinogen solution. In this study, after artificially creating a osteoradionecrosis model in the parietal region of mouse, fibrin scaffolds were incorporated to analyze the effect of bone regeneration within 4 weeks, the initial stage of bone regeneration. In conclusion, the combined use of these two substances did not show a dramatic regenerative effect in inducing the regeneration of osteoradionecrosis in the parietal region of mouse. However, positive results were obtained that can be maintain the bone regeneration effect environment at the initial stage. Therefore, the combined use of the fibrin scaffold and SDF-1 is considered to be a suitable candidate for the effect of overcoming osteoradionecrosis.

• Journal of Science Education
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• v.46 no.1
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• pp.17-29
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• 2022

The purpose of this study is to look qualitatively into how efficiently and reasonably a computer can learn themes related to the Nature of Science (NOS). In this regard, a corpus has been constructed focusing on literature (920 abstracts) related to NOS, and factors of the optimized Word2Vec (CBOW, Skip-gram) were confirmed. According to the four dimensions (Inquiry, Thinking, Knowledge and STS) of NOS, the comparative evaluation on the word-level word embedding was conducted. As a result of the study, according to the previous studies and the pre-evaluation on performance, the CBOW model was determined to be 200 for the dimension, five for the number of threads, ten for the minimum frequency, 100 for the number of repetition and one for the context range. And the Skip-gram model was determined to be 200 for the number of dimension, five for the number of threads, ten for the minimum frequency, 200 for the number of repetition and three for the context range. The Skip-gram had better performance in the dimension of Inquiry in terms of types of words with high similarity by model, which was checked by applying it to the four dimensions of NOS. In the dimensions of Thinking and Knowledge, there was no difference in the embedding performance of both models, but in case of words with high similarity for each model, they are sharing the name of a reciprocal domain so it seems that it is required to apply other models additionally in order to learn properly. It was evaluated that the dimension of STS also had the embedding performance that was not sufficient to look into comprehensive STS elements, while listing words related to solution of problems excessively. It is expected that overall implications on models available for science education and utilization of artificial intelligence could be given by making a computer learn themes related to NOS through this study.

• Journal of Korea Water Resources Association
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• v.55 no.11
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• pp.903-911
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• 2022

Groundwater, one of the resources for supplying water, fluctuates in water level due to various natural factors. Recently, research has been conducted to predict fluctuations in groundwater levels using Artificial Neural Network (ANN). Previously, among operators in ANN, Gradient Descent (GD)-based Optimizers were used as Optimizer that affect learning. GD-based Optimizers have disadvantages of initial correlation dependence and absence of solution comparison and storage structure. This study developed Gradient Descent combined with Harmony Search (GDHS), a new Optimizer that combined GD and Harmony Search (HS) to improve the shortcomings of GD-based Optimizers. To evaluate the performance of GDHS, groundwater level at Icheon Yullhyeon observation station were learned and predicted using Multi Layer Perceptron (MLP). Mean Squared Error (MSE) and Mean Absolute Error (MAE) were used to compare the performance of MLP using GD and GDHS. Comparing the learning results, GDHS had lower maximum, minimum, average and Standard Deviation (SD) of MSE than GD. Comparing the prediction results, GDHS was evaluated to have a lower error in all of the evaluation index than GD.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.1
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• pp.175-183
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• 2022

The collision between a ship and bridge across a waterway may result in extremely serious consequences that may endanger the safety of life and property. Therefore, factors affecting ship bridge collision must be investigated, and the impact force should be discussed based on various collision conditions. In this study, a finite element model of ship bridge collision is established, and the peak impact force of a ship bridge collision based on 50 operating conditions combined with three input parameters, i.e., ship loading condition, ship speed, and ship bridge collision angle, is calculated via numerical simulation. Using neural network models trained with the numerical simulation results, the prediction model of the peak impact force of ship bridge collision involving an extremely short calculation time on the order of milliseconds is established. The neural network models used in this study are the basic backpropagation neural network model and Elman neural network model, which can manage temporal information. The accuracy of the neural network models is verified using 10 test samples based on the operating conditions. Results of a verification test show that the Elman neural network model performs better than the backpropagation neural network model, with a mean relative error of 4.566% and relative errors of less than 5% in 8 among 10 test cases. The trained neural network can yield a reliable ship bridge collision force instantaneously only when the required parameters are specified and a nonlinear finite element solution process is not required. The proposed model can be used to predict whether a catastrophic collision will occur during ship navigation, and thus hence the safety of crew operating the ship.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.107-119
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• 2023

The technology that recognizes a soldier's motion and movement status has recently attracted large attention as a combination of wearable technology and artificial intelligence, which is expected to upend the paradigm of troop management. The accuracy of state determination should be maintained at a high-end level to make sure of the expected vital functions both in a training situation; an evaluation and solution provision for each individual's motion, and in a combat situation; overall enhancement in managing troops. However, when input data is given as a timer series or sequence, existing feedforward networks would show overt limitations in maximizing classification performance. Since human behavior data (3-axis accelerations and 3-axis angular velocities) handled for military motion recognition requires the process of analyzing its time-dependent characteristics, this study proposes a high-performance data-driven classifier which utilizes the long-short term memory to identify the order dependence of acquired data, learning to classify eight representative military operations (Sitting, Standing, Walking, Running, Ascending, Descending, Low Crawl, and High Crawl). Since the accuracy is highly dependent on a network's learning conditions and variables, manual adjustment may neither be cost-effective nor guarantee optimal results during learning. Therefore, in this study, we optimized hyperparameters using Bayesian optimization for maximized generalization performance. As a result, the final architecture could reduce the error rate by 62.56% compared to the existing network with a similar number of learnable parameters, with the final accuracy of 98.39% for various military operations.

• Composites Research
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• v.19 no.5
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• pp.7-11
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• 2006

The improved SWNTs/PANi composite actuator films applicable to an artificial muscle were fabricated successfully using a new process of manufacture that consists of 90% pure single-walled carbon nanotubes (SWNT) and chemical polymerization. PANi is electrically conducting polyaniline polymer. The conductivities of the composite SWNTs/PANi film-type actuators and the pure PANi films fabricated were measured as 56.15 S/cm and 17.38 S/cm, respectively, by the 4-prove method. The conductivity of the composite actuator is 3.2 times higher than the pure PANi film. The fabricated composite actuator showed higher conductivity than any other similar ones. The quality of samples was investigated by an electron scanning microscope (SEM). To measure the actuating strains, a specially designed beam balance apparatus was developed and strains of the composite actuators was measured by a laser displacement sensor subjected to electric currents. During the operation, the sample was soaked in the $NaNO_3$ solution and the sine-wave voltage in the range of $+1V{\sim}-1V$ was applied. The length of the composite actuator changed from $l_0=12.690$ mm to $l_1=12.733$ so that the change of length was l=0.043 mm and the strain was 0.34 %. This is a very high strain for this kind of a composite actuator. Other result reported by Tahhan showed 0.23 % strain, so that the present result is improved by 48%.