• Journal of the Korean earth science society
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• v.41 no.6
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• pp.647-657
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• 2020

Infrasound signals associated with the 29 May 2004 offshore Uljin earthquake (Mw 5.1) were recorded at infrasound arrays of CHNAR (epicentral distance of 321 km) and TJNAR (256 km). Back-azimuths, indicating the directions to source locations, varied more than 28° broadly for the long-lasting signals over several minutes. From the analysis of the back-projecting location method and attenuation correction for infrasound propagation, the infrasound waves were to be generated by the interaction (diffraction) between seismic waves and topography in an area of ~4,600 ㎢ connecting the Samcheok-Uljin-Pohang regions. The maximum sound source pressure (BSP) was estimated to be 11.1 Pa. This result was consistent with the peak sound pressure (PSP) calculated by the Rayleigh integral approximation to the peak ground acceleration (PGA) dataset. In addition, the minimum PGA that was detectable at the two arrays was estimated to be ~3.0 cm s-2. Although the earthquake occurred offshore, diffracted infrasound signals were effectively generated by ground motions when seismic surface waves passed through high-topographic regions in the eastern Korean Peninsula. The relationship between infrasound source pressure and PGA can be applicable to characterize the ground motions in areas with insufficient seismological observatories.

• Journal of Pharmacopuncture
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• v.19 no.4
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• pp.319-328
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• 2016

Objectives: Advancements in nanotechnology have led to nanoparticle (NP) use in various fields of medicine. Although the potential of NPs is promising, the lack of documented evidence on the toxicological effects of NPs is concerning. A few studies have documented that homeopathy uses NPs. Unfortunately, very few sound scientific studies have explored the toxic effects of homeopathic drugs. Citing this lack of high-quality scientific evidence, regulatory agencies have been reluctant to endorse homeopathic treatment as an alternative or adjunct treatment. This study aimed to enhance our insight into the impact of commercially-available homeopathic drugs, to study the presence of NPs in those drugs and any deleterious effects they might have, and to determine the distribution pattern of NPs in zebrafish embryos (Danio rerio). Methods: Homeopathic dilutions were studied using high-resolution transmission electron microscopy with selected area electron diffraction (SAED). For the toxicity assessment on Zebrafish, embryos were exposed to a test solution from 4 - 6 hours post-fertilization, and embryos/larvae were assessed up to 5 days post-fertilization (dpf ) for viability and morphology. Toxicity was recorded in terms of mortality, hatching delay, phenotypic defects and metal accumulation. Around 5 dpf was found to be the optimum developmental stage for evaluation. Results: The present study aimed to conclusively prove the presence of NPs in all high dilutions of homeopathic drugs. Embryonic zebrafish were exposed to three homeopathic drugs with two potencies (30CH, 200CH) during early embryogenesis. The resulting morphological and cellular responses were observed. Exposure to these potencies produced no visibly significant malformations, pericardial edema, and mortality and no necrotic and apoptotic cellular death. Conclusion: Our findings clearly demonstrate that no toxic effects were observed for these three homeopathic drugs at the potencies and exposure times used in this study. The embryonic zebrafish model is recommended as a well-established method for rapidly assessing the toxicity of homeopathic drugs.

• Journal of Environmental Impact Assessment
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• v.26 no.1
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• pp.11-26
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• 2017

Approximate empirical equations obtained by measuring overall noise levels at different distances have been used to evaluate environmental influence of the railway noise though the accurate prediction of noise levels is important. In this paper, a noise prediction model considering the frequency characteristics of noise sources and propagation was suggested to improve the accuracy of noise prediction. The railway noise source was assorted into track, wheel, traction and aerodynamic components and they were characterized with the source strength and speed coefficient at each octave-band frequency. Correction terms for the acoustic roughness and the track/bridge condition were introduced. The sound attenuation from a source to a receiver was calculated taking account of the geometrical divergence, atmospheric absorption, ground effect, diffraction at obstacles and directivity of source by applying ISO 9613-2. For obtaining the source strength and speed coefficients, the results of rolling noise model, numerical analysis and measurements of pass-by noise were analyzed. We compared the predicted and measured noise levels in various vehicles and tracks, and verified the accuracy of the present model. It is found that the present model gives less error than the conventional one, so that it can be applied to make the accurate prediction of railway noise effect and establish its countermeasures efficiently.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.233-247
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• 2023

Recently, research on detecting objects in hidden spaces beyond the direct line-of-sight of observers has received attention. Most studies use optical equipment that utilizes the directional of light, but sound that has both diffraction and directional is also suitable for non-line-of-sight(NLOS) research. In this paper, we propose a novel method of detecting objects in non-line-of-sight (NLOS) areas using acoustic signals in the audible frequency range. We developed a deep learning model that extracts information from the NLOS area by inputting only acoustic signals and predicts the properties and location of hidden objects. Additionally, for the training and evaluation of the deep learning model, we collected data by varying the signal transmission and reception location for a total of 11 objects. We show that the deep learning model demonstrates outstanding performance in detecting objects in the NLOS area using acoustic signals. We observed that the performance decreases as the distance between the signal collection location and the reflecting wall, and the performance improves through the combination of signals collected from multiple locations. Finally, we propose the optimal conditions for detecting objects in the NLOS area using acoustic signals.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.5
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• pp.512-519
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• 2023

Noise pollution poses significant challenges to human well-being and marine ecosystems. It is primarily caused by the flow around ships and marine installations, emphasizing the need for accurate noise evaluation of flow noise to ensure environmental safety. Existing flow noise analysis methods for underwater environments typically use a hybrid method combining computational fluid dynamics and Ffowcs Williams-Hawkings acoustic analogy. However, this approach has limitations, neglecting near-field effects such as reflection, scattering, and diffraction of sound waves. In this study, an alternative using direct method flow noise analysis via the lattice Boltzmann method (LBM) is incorporated. The LBM provides a more accurate representation of the underwater structural boundaries and acoustic wave effects. Despite challenges in underwater environments due to numerical instabilities, a novel DM-TS LBM collision operator has been developed for stable implementations for hydroacoustic applications. This expands the LBM's applicability to underwater structures. Validation through flow noise analysis in pipe orifice demonstrates the feasibility of near-field analysis, with experimental comparisons confirming the method's reliability in identifying main pressure peaks from flow noise. This supports the viability of near-field flow noise analysis using the LBM.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.603-614
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• 2021

Recently, the surface temperature in the seas around Korea has been continuously rising. This temperature rise causes changes in fishery resources and affects leisure activities such as fishing. In particular, high temperatures lead to the occurrence of red tides, causing severe damage to ocean industries such as aquaculture. Meanwhile, changes in sea temperature are closely related to military operation to detect submarines. This is because the degree of diffraction, refraction, or reflection of sound waves used to detect submarines varies depending on the ocean mixed layer. Currently, research on the prediction of changes in sea water temperature is being actively conducted. However, existing research is focused on predicting only the surface temperature of the ocean, so it is difficult to identify fishery resources according to depth and apply them to military operations such as submarine detection. Therefore, in this study, we predicted the temperature of the ocean mixed layer at a depth of 38m by using temperature data for each water depth in the upper mixed layer and meteorological data such as temperature, atmospheric pressure, and sunlight that are related to the surface temperature. The data used are meteorological data and sea temperature data by water depth observed from 2016 to 2020 at the IEODO Ocean Research Station. In order to increase the accuracy and efficiency of prediction, LSTM (Long Short-Term Memory), which is known to be suitable for time series data among deep learning techniques, was used. As a result of the experiment, in the daily prediction, the RMSE (Root Mean Square Error) of the model using temperature, atmospheric pressure, and sunlight data together was 0.473. On the other hand, the RMSE of the model using only the surface temperature was 0.631. These results confirm that the model using meteorological data together shows better performance in predicting the temperature of the upper ocean mixed layer.