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.

본 연구는 탐방객의 이용밀도가 높은 금정산 북쪽사면의 생태적 특성을 평가하여 식생 관리방안의 기초자료로 제시하고자 하였으며, 그 결과는 다음과 같다. 본 조사지역의 생태적 특성을 보면, 조사구1, 2, 3, 6, 7, 8은 산능선으로 해발고는 637 m에서 749m이며, 경사는 5°에서 15°의 신갈나무 군락으로 이루어져 있으며, 조사구4는 산능선으로 해발고 605m, 경사 5°의 상수리-떡갈나무 군락, 조사구5는 산능선으로 해발고 645 m, 경사 5°의 졸참나무 군락, 조사구9는 사면으로 해발고 623 m, 경사 15°의 신갈나무 군락, 조사구10은 사면으로 해발고 380m, 경사 20°의 비목나무 군락을 이루고 있었다. 교목층의 수고는 8-15 m, 아교목층의 수고 2-7 m, 관목층의 수고 0.8-1.5 m, 초본층의 수고 0.1-0.7 m으로 조사되었다. 상대우점치를 분석한 결과, 조사구1은 아교목층에서 소나무와 신갈나무가 우점하여 앞으로 이들 수종이 교목층을 구성할 것으로 생각되며, 조사구2, 조사구3, 조사구4, 조사구8, 조사구9, 조사구10은 교목층에 신갈나무, 상수리나무, 비목나무 등이 우점하고 있으며, 당분간 현재의 식생군락이 유지될 것으로 분석되었다. 조사구5와 조사구6은 현재 소나무가 우점되어 있으나 장기적으로 신갈나무 군락으로 천이 될 것으로 평가되었다. 초본의 우점도 및 군도를 분석한 결과, 애기나리와 족제비고사리가 대부분의 조사구에서 다수 우점하고 있었다. 단위면적(400㎡)당 출현종수는 조사구10에서 24종으로 가장 많이 나타났으며, 조사구2에서 10종으로 가장 적게 나타났다. 이와 더불어 등산객의 이용밀도가 높은 조사지역의 식생은 천이가 진행되지 않을 뿐만 아니라 인공조림수종은 천이를 방해하고, 다층구조 식생을 이루지 못하게 하여 산림을 생태적으로 불안정상태가 되게 한다. 따라서 식생관리방안으로는 본 조사 자료가 기초자료로 마련될 것으로 예상되며, 식생구조의 변화를 정량적으로 분석하여 식생 관리방안을 제시한다면, 탐방객에 의하여 훼손된 도시림의 식생을 합리적으로 관리하는데 기여할 것이다.

Background: Leonurine is a the aerial part of Leonurus japonicus Houttuyn, which has been used as a traditional medicines and is registered in the Korean Pharmacopoeia.Methods and Results:In the present study we performed a heavy metals and thin layer chromatography and measured loss on drying, as well as the contents of total ash, acid-insoluble ash, ethanol soluble compounds, and leonurine, using 15 domestically collected L. japonicus samples. The methods were performed according to the ‘crude drugs test of the general test, processes and apparatus’, published by MFDS, Korea (2014). The purity test (heavy metals) indicated that levels of Pb and Hg were 0.35 - 3.64, and 0.001 ppm, respectively, whereas the levels of As and Cd were undetectable, and stachydrine was identified by thin layer chromatography (Rf : 0.15). We found that 5.93 - 10.62% (average: 8.58 ± 1.8%) of the sample mass was lost during drying, and the contents of total ash, acid-insoluble ash, ethanol soluble compounds, and leonurine were 7.87 - 10.84% (average: 9.62 ± 0.82%), 0.99 - 1.76% (average: 1.38 ± 0.24%), 16.70 - 23.11% (average: 19.49 ± 2.14%) and 0.04 - 0.17% (average: 0.11 ± 0.04%) respectively. In addition, HPLC profiling detected leonurine (5.94 min), rutin (16.43 min) and myricetin (26.78 min).Conclusions:We hope that this the rusult of the present study will contribute to the standardization and quality control of Korean herbal medicines.

In this study, the concentrations of polycyclic aromatic hydrocarbons (PAHs) were investigated in meat process food and for cooking methods (pan-frying and charcoal fire). The methodology involved liquid-liquid extraction, silica gel cartridge clean-up and determination by gas chromatography/mass spectrometry (GC/MS). The recovery of 17 PAHs spiked into these samples ranged from 66.6 % to 98.0% and the coefficient of variation was less than 10%, but that of dibenz(a,h)anthracene was 16.39%. The mean concentration of total PAHs in processed samples was ND∼7.2 ng/g, whereas that pan-fried and charcoal-fired samples were ND∼22.1 ng/g and, 12.7∼367.8 ng/g, respectively. Therefore, the concentrations of total PAHs in cooked samples were higher than in original samples and charcoal-fired samples had the highest total PAH levels.

From the experiments on the effect of chitosan and wood vinegar, which are environment-friendly materials, on the growth of eggplant and leaf lettuce, the following results were obtained. 1. The effect of chitosan and wood vinegar treatment on the growth of eggplant 1) There was no difference in soil component elements before and after treatment with chitosan and wood vinegar. The total number of microorganisms tended to increase after treatment with chitosan and wood vinegar, while the number of bacteria increased among microorganisms. However, there was no consistent tendency in the number of Acinomycetes, fungi, and trichodema between treatments. 2) The growth and the yield of eggplant increased compared with the control plot in both the chitosan-treated plot and the wood vinegar-treated plot. However, there was no significant difference between the treatments. The yield of eggplant per 10a increased in chitosan- and wood vinegar-treated plots compared with the controlled plot. There was no significant difference in treatments, however, the plot treated with 800 times-diluted solution showed a higher growth. 2. The effect of chitosan and wood vinegar treatment on the growth of leaf lettuce 1) There was no difference in soil component elements before and after treatment with chitosan and wood vinegar. The number of bacteria increased among microorganisms. However, there was no consistent tendency in the number of Acinomycetes, fungi, and trichodema between treatments. 2) The growth of leaf lettuce in both chitosan-treated plot and wood vinegartreated plot increased compared with the control plot, however, there was no significant difference between the treatments. On the whole, the plot treated with greater concentration showed a higher growth.