• Journal of the Korean Geophysical Society
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• v.7 no.2
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• pp.113-120
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• 2004

Hydrochemical characteristics of deep groundwater at Surak-ri, Nonsan-gun, Chungnam Province was explained by major ion concentration, water type, and phase stability diagram. The area is composed of meta-sedimentary rock and quartz pophyry. The 5 boreholes where deep groundwater was sampled and analyzed are located on the meta-sedimentary rocks and drilling depth range of the wells is from 554 m to 928 m. pH, TDS, Na, and SiO2 values are high in the groundwater from meta-sedimentary area intruded by quartz pophyry, while Ca is high in the groundwater from meta-sedimentary area. K and Mg concentrations are low but F concentration is high both groundwater. The content of major anions is in the order of CO3(HCO3)>Cl>SO4(F) in both geology, while that of major cations shows the order of Na>Ca>K(Mg) in meta-sedimentary area intruded by quartz porphyry and a>Na>Mg>Na in meta-sedimentary area. Based on the phase equilibrium in the systems Na2O-Al2O3-SiO2-H2O and K2O-Al2O3-SiO2-H2O, the groundwater is saturated with respect to Quartz and more evolved compared with the natural mineral water. It is concluded that chemical evolution in the groundwater from meta-sedimentary area intruded by quartz porphyry, is nearly saturated with respect to feldspar, while the groundwater from meta-sedimentary area continue to proceed with increasing pH by reaction of feldspar.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.6
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• pp.123-128
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• 2021

As the spread of DC power distribution systems increases, the occurrence of failures and fire accidents are also increasing. In particular, the ESS fire accident, which is a component of the smart grid, and the fire accident of the solar power system, which is a direct current system, are caused by problems in the electrical connection between system components as the supply of new and renewable energy rapidly increases and old facilities increase. An arc that can cause a direct fire by releasing the induced light and heat has been pointed out as one of the causes of fire. Therefore, the problem of such an arc defect is that it is impossible to block an arc accident in advance with the existing overcurrent circuit breaker and earth leakage circuit breaker. In this paper, we intend to develop a test equipment that satisfies international standardization and to develop a DC arc generator to protect against failure and fire caused by arcing.

• Nuclear Engineering and Technology
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• v.51 no.3
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• pp.807-817
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• 2019

Airborne Gamma Ray Spectrometry (AGRS) with its important applications such as gathering radiation information of ground surface, geochemistry measuring of the abundance of Potassium, Thorium and Uranium in outer earth layer, environmental and nuclear site surveillance has a key role in the field of nuclear science and human life. The Broyden-Fletcher-Goldfarb-Shanno (BFGS), with its advanced numerical unconstrained nonlinear optimization in collaboration with Artificial Neural Networks (ANNs) provides a noteworthy opportunity for modern AGRS. In this study a new AGRS system empowered by ANN-BFGS has been proposed and evaluated on available empirical AGRS data. To that effect different architectures of adaptive ANN-BFGS were implemented for a sort of published experimental AGRS outputs. The selected approach among of various training methods, with its low iteration cost and nondiagonal scaling allocation is a new powerful algorithm for AGRS data due to its inherent stochastic properties. Experiments were performed by different architectures and trainings, the selected scheme achieved the smallest number of epochs, the minimum Mean Square Error (MSE) and the maximum performance in compare with different types of optimization strategies and algorithms. The proposed method is capable to be implemented on a cost effective and minimum electronic equipment to present its real-time process, which will let it to be used on board a light Unmanned Aerial Vehicle (UAV). The advanced adaptation properties and models of neural network, the training of stochastic process and its implementation on DSP outstands an affordable, reliable and low cost AGRS design. The main outcome of the study shows this method increases the quality of curvature information of AGRS data while cost of the algorithm is reduced in each iteration so the proposed ANN-BFGS is a trustworthy appropriate model for Gamma-ray data reconstruction and analysis based on advanced novel artificial intelligence systems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1260-1283
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• 2019

Low earth orbit (LEO) satellite broadband network is a crucial part of the space information network. LEO satellite constellation design is a top-level design, which plays a decisive role in the overall performance of the LEO satellite network. However, the existing works on constellation design mainly focus on the coverage criterion and rarely take network performance into the design process. In this article, we develop a unified framework for constellation optimization design in LEO satellite broadband networks. Several design criteria including network performance and coverage capability are combined into the design process. Firstly, the quality of service (QoS) metrics is presented to evaluate the performance of the LEO satellite broadband network. Also, we propose a network stability model for the rapid change of the satellite network topology. Besides, a mathematical model of constellation optimization design is formulated by considering the network cost-efficiency and stability. Then, an optimization algorithm based on non-dominated sorting genetic algorithm-II (NSGA-II) is provided for the problem of constellation design. Finally, the proposed method is further evaluated through numerical simulations. Simulation results validate the proposed method and show that it is an efficient and effective approach for solving the problem of constellation design in LEO satellite broadband networks.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.1
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• pp.74-89
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• 2021

For higher-level requirements of urban planning and management and the recent development of "digital earth" and "digital city", it is urgent to establish protocols for the construction of three-dimensional digital city models. However, some problems still exist in the digital technology of the three-dimensional city model, such as insufficient precision of the three-dimensional model, not optimizing the scene and not considering the constraints of building codes. In view of those points, a method to interactively simulate a virtual city scene based on building codes is proposed in this paper. Firstly, some constraint functions are set up to restrict the models to adhere to the building codes, and an improved directional bounding box technique is utilized to solve the problem that geometric objects may intersect in a virtual city scene. The three-dimensional model invocation strategy is designed to convert two-dimensional layouts to a three-dimensional urban scene. A Leap Motion hardware device is used to interactively place the 3D models in a virtual scene. Finally, the design and construction of the three-dimensional scene are completed by using Unity3D. The experiment shows that this method can simulate urban virtual scenes that strictly adhere to building codes in a virtual scene of the city environment, but also provide information and decision-making functions for urban planning and management.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.47-53
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• 2021

The article deals with the problems of application of microwave methods in systems of geoecological monitoring of natural environments and resources of the agro-industrial complex. It is noted that the methods of microwave radiometry make it possible, by the power of the measured intrinsic radio-thermal radiation of the atmosphere, when solving inverse problems using empirical and semi-empirical models, to determine such parameters of the atmosphere as thermodynamic temperature, humidity, water content, moisture content, precipitation intensity, and the presence of different fractions of clouds.In addition to assessing the meteorological parameters of the atmosphere and the geophysical parameters of the underlying surface based on the data of microwave radiometric measurements, it is possible to promptly detect and study pollution of both the atmosphere and the earth's surface. A technique has been developed for the analysis of sources of measurement error and their numerical evaluation, because they have a significant effect on the accuracy of solving inverse problems of reconstructing the values of the physical parameters of the probed media.To analyze the degree of influence of the limited spatial selectivity of the antenna of the microwave radiometric system on the measurement error, we calculated the relative measurement error of the ratio of radio brightness contrasts in two angular directions. It has been determined that in the system of geoecological monitoring of natural environments, the effect of background noise is maximal with small changes in the radiobrightness temperature during angular scanning and high sensitivity of the receiving equipment.

• Journal of the Earthquake Engineering Society of Korea
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• v.25 no.2
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• pp.71-81
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• 2021

This paper presents a real-time, false-pick filter based on deep learning to reduce false alarms of an onsite Earthquake Early Warning (EEW) system. Most onsite EEW systems use P-wave to predict S-wave. Therefore, it is essential to properly distinguish P-waves from noises or other seismic phases to avoid false alarms. To reduce false-picks causing false alarms, this study made the EEWNet Part 1 'False-Pick Filter' model based on Convolutional Neural Network (CNN). Specifically, it modified the Pick_FP (Lomax et al.) to generate input data such as the amplitude, velocity, and displacement of three components from 2 seconds ahead and 2 seconds after the P-wave arrival following one-second time steps. This model extracts log-mel power spectrum features from this input data, then classifies P-waves and others using these features. The dataset consisted of 3,189,583 samples: 81,394 samples from event data (727 events in the Korean Peninsula, 103 teleseismic events, and 1,734 events in Taiwan) and 3,108,189 samples from continuous data (recorded by seismic stations in South Korea for 27 months from 2018 to 2020). This model was trained with 1,826,357 samples through balancing, then tested on continuous data samples of the year 2019, filtering more than 99% of strong false-picks that could trigger false alarms. This model was developed as a module for USGS Earthworm and is written in C language to operate with minimal computing resources.

• Geophysics and Geophysical Exploration
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• v.24 no.4
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• pp.149-157
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• 2021

Given that induced polarization (IP) and direct current (DC) resistivity surveys are similar in terms of data acquisition, most DC resistivity systems are equipped with a time-domain IP data acquisition function. In addition, the time-domain IP data include the DC resistivity values. As such, IP and DC resistivity data are intimately linked, and the inversion of IP data is a two-step process based on DC resistivity inversions. Nevertheless, IP surveys are rarely applied, in contrast to DC resistivity surveys, as proper inversion software is unavailable. In this study, through numerical modeling and inversion experiments, we analyze the problems with the conventional inverse mapping technique used to invert time-domain IP data. Furthermore, we propose a modified inverse mapping technique that can effectively suppress inversion artifacts. The performance of the technique is confirmed through inversions applied to synthetic IP data.

• Atmosphere
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• v.33 no.2
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• pp.297-306
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• 2023

Celebrating its 60th anniversary, this study suggests the future vision of the Korean Meteorological Society (KMS) for the next 60 years. The vision is "to advance atmospheric science and technology that contributes to human society as well as protect people from not only climate change risks but also weather, climate, and environmental disasters". Based on the suggestions from its members, this study proposes the KMS future development plan as follows. The first plan is to strengthen in leading the development and growth of atmospheric sciences in Korea, especially to improve weather, climate, and environment forecasts and to reduce uncertainty in future climate projections. The second is to enhance interaction not only among its members in academy, Korea Meteorological Administration and related organizations, meteorological industry, and science communicators but also with other related fields such as energy, water resources, agriculture, fishery, and forestry. The third is to enhance in nurturing young scientists by supporting domestic and international networks and training the state-of-the-art sciences, and to create opportunities for young scientists to advance into a wider field. The last is to expand its international activities for solving the challenges facing mankind, such as climate change risks and weather, climate, and environment disasters. The KMS should also continue the efforts to establish an integrative platform for leading fundamental and interdisciplinary research in weather, climate, and environment.

• Computers and Concrete
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• v.32 no.3
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• pp.327-337
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• 2023

Concrete is the most widely used building material, with various types including high- and ultra-high-strength, reinforced, normal, and lightweight concretes. However, accurately predicting concrete properties is challenging due to the geotechnical design code's requirement for specific characteristics. To overcome this issue, researchers have turned to new technologies like machine learning to develop proper methodologies for concrete specification. In this study, we propose a highly accurate deep learning-based predictive model to investigate the compressive strength (UCS) of lightweight concrete with natural aggregates (pumice). Our model was implemented on a database containing 249 experimental records and revealed that water, cement, water-cement ratio, fine-coarse aggregate, aggregate substitution rate, fine aggregate replacement, and superplasticizer are the most influential covariates on UCS. To validate our model, we trained and tested it on random subsets of the database, and its performance was evaluated using a confusion matrix and receiver operating characteristic (ROC) overall accuracy. The proposed model was compared with widely known machine learning methods such as MLP, SVM, and DT classifiers to assess its capability. In addition, the model was tested on 25 laboratory UCS tests to evaluate its predictability. Our findings showed that the proposed model achieved the highest accuracy (accuracy=0.97, precision=0.97) and the lowest error rate with a high learning rate (R2=0.914), as confirmed by ROC (AUC=0.971), which is higher than other classifiers. Therefore, the proposed method demonstrates a high level of performance and capability for UCS predictions.