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.

This study was developed to recognize the occurrence of 250[m] of experimental distance, rain and nighttime as an educational and training device without any safety accidents by applying algorithms to optical filters and noise filters to be used in all weather environments and closest to actual shooting training using IR Laser. There are live-fire shooting using live ammunition, screen shooting using beam project and screen, MILES using IR Laser and sensor, BB shooting using compressed gas and BB bullet, and painting shooting using CO2 gas and paint bullet. Among them, the actual shooting training is not efficient in terms of management and operation because it requires considerable risk factors and large costs in preparation for the highest efficiency. Therefore, training that replaces these problems is needed, and various alternative shooting training is being implemented. Therefore, research and development was conducted to solve these problems by using it as one of efficient shooting training and education systems. This study was conducted to develop high-performance and low-cost precision shooting training equipment to contribute to strengthening the defense of the Republic of Korea.

버섯은 대부분 시설에서 재배되기 때문에 안전하게 고품질의 버섯 생산을 위해서는 재배사 내 환경에 대한 정보가 필요하다. 따라서 본 연구에서는 헤파필터 교체에 따른 큰느타리 재배사 대기중 미생물상 변화를 분석하여 헤파필터 적정 교체기간을 설정하고자 연구를 수행하였다. 헤파필터 교체 전 재배단계별 대기중 세균 및 진균 밀도는 배지제조과정에서 세균 169.7 cfu/m 3 , 진균 570 cfu/m 3 , 균 긁기과정에서 세균 126.3 cfu/m 3 , 진균 560cfu/m 3로 부유균 의 밀도가 가장 높았다. 헤파필터 교체 후 세균의 밀도는 배양실에서 가장 낮아졌고, 진균의 밀도는 냉각실에서 가장 낮게 나타났다. 헤파필터 교체전 Cladosporium sp. 등 7속 7종이었고, 교체 후 1개월은 Penicillium sp. 등 6속 6종, 2개월은 Cladosporium cladosporioides 등 4속 7종, 3 개월차는 Mucor plumbeus 등 5속 7종, 4개월에서 6개월까지는 Penicillium brevicompactum 등 각각 5속 12종, 5속 10종, 5속 10종으로 교체 후 기간이 지날수록 종이 다양해지고 증가하였다. 부유균의 밀도는 헤파필터 교체 후 2개월 후 가장 낮았고 차츰 증가하다가 6개월에는 교체전 밀도와 비슷해지거나 높아지는 것을 확인하였다. 따라서 헤파필터는 6개월마다 교체하는 것이 오염저감을 위해 효율적인 것으로 판단된다.

Various underwater studies using underwater sonar sensors are actively in progress. However, unlike the ground, the underwater has a lot of noise. So it is difficult to accurately recognize the underwater environment. The final purpose of this study is to improve the efficiency of the underwater environment recognition using the underwater sonar sensor by developing a filtering algorithm that removes noise and expresses the object from the underwater sonar image captured by the underwater sonar sensor. To develop a filtering algorithm, convolutional calculations were used with three types of filters. This paper is about the case study that conducted to set the parameters of ‘Gabor Filter’ suitable for underwater sonar image during the design process of filtering algorithm. As a result, it was possible to find the most suitable ‘Gabor Filter’ parameters for underwater sonar images. And it showed high accuracy with a binary map of obstacles created by hand using the naked eye. Through this study, it can be utilized not only as a binary map of real-time obstacles, but also as an algorithm for generating object masks in underwater sonar images for deep learning.

본 논문은 버스정류장 미세먼지 저감을 위해 설계된 도로시설물의 성능평가 과정 및 결과를 보고한다. 먼저, 유한요 소해석 프로그램인 LS-DYNA를 이용하여 대상 구조물에 대한 충돌해석이 수행되었다. 해석변수로 차량의 속도와 충돌 위치를 고려한 다양한 조건에서의 구조해석이 진행되었다. 대상 시설물의 성능은 이 결과를 이용하여 구조적 성능과 충돌 후 차량의 안전성능이 평가되었다. 해석결과로부터 충돌속도가 증가하고 충돌위치가 전면으로 갈수록 시설물의 구조성능과 차량의 충돌 후 안전성능이 저하되는 것이 콘크리트의 손상량을 통해 확인되었다. 더불어 충돌 후 차량의 거동에 대한 분석을 통해 시설물에 연속되는 연석이 설치되면 차량의 이탈을 방지해 안전성능을 확보하는 것으로 예측되었다. 최종적으로, 대상 시설물은 설계 시 고려된 목표 충돌속도 25km/h보다 더 큰 40km/h까지 충분한 안전성능을 확보하고 있다는 것이 확인되었다.

This experiment was aimed to identify concentrations of mineral nutrients in leaf lettuce (Lactuca sativa) grown on hydroball aquaponics and in the water for growing fish by conducting two experiments. The experiment I (Expt. I) was conducted with 12 fishes (F12) with and without filter, hydroball and plants (H12 (12 fishes, hydroball), FHP12 (12 fishes, filter, hydroball, 6 plants) and HP12 (12 fishes, hydroball, 6 plants)), and the experiment II (Expt. II) was with and without plants (FH15 (15 fishes, filter, hydroball), FHP15 (15 fishes, filter, hydroball, 6 plants)). The pH level in the water of all the treatments was decreased during the growing period, and the pH and EC of the water were lower in all the treatment with plants than those without plants in both Expt. I and Expt. II. When compared with adequate nutrient concentrations for hydroponics, nitrate nitrogen (NO3-N) and phosphorus (P) concentrations in the fish growing water were higher under the FHP15 treatment in Expt. II; however, potassium (K), calcium (Ca), and magnesium (Mg) were only 16, 49, and 82% of hydroponics, representatively, and iron (Fe) was not detected. The fresh weight of lettuce harvested from the FHP15 treatment was 38 g, only a 30% of marketable lettuce yield. The T-N and P contents of the leaf tissue grown under the FHP15 treatment were close to the optimal level; however, the K, Ca, and iron (Fe) contents were less than the optimal with no deficiency symptom.

In in vitro fermentation studies, feed samples can either be included in the in vitro rumen medium using filter bags or can be directly dispersed. The objective of this study was to investigate the effects of different pore sizes of filter bags on the rumen fermentation characteristics in an in vitro system. Corn, soybean meal, and timothy were ground to pass through a 1.0-mm screen and were formulated in the ratio of 70:7:23 based on DM, respectively. The formulated experimental diet (2g/DM) was put in F57 filter bags and R510 nylon bags (Ankom®) which pore sizes were 25 and 50 μm, respectively. An in vitro study was conducted to determine the rumen fermentation characteristics for 3, 6, 12, 24, and 48 h and rumen microbial community at 48 h of incubation. A significantly higher production of gas was observed in the R510 bags than in F57 at all the incubation times (p<0.01). IVDMD (p<0.01) and IVNDFD (p<0.01) were significantly higher, whereas pH (p<0.01) and NH3-N (p<0.01) were lower when R510 bags were used. In the VFA composition, acetate and butyrate were significantly higher (p<0.01) in R510 bags, and propionate and total VFA concentration did not differ (p=0.55 and 0.25, respectively) between F57 and R510 bags. The log copy numbers of bacteria and protozoa did not differ (p=0.69 and 0.94, respectively) between F57 and R510 bags, whereas those of fungi were significantly higher in R510 than in F57 bags (p<0.01). Therefore, the use of R510 may reflect actual rumen fermentation characteristics more precisely than those of F57 because increased gas production, nutrient digestibility and acetate, butyrate proportion were founded in R510.

PURPOSES: The intensiveness of highway management has increased owing to the growth in the number of vehicles and the rapid climate change. The disadvantages produced by these factors can affect management time and cost. Serious traffic accidents and traffic jam may be experienced when snow fall accumulates on highway surfaces and the friction between tires and pavements is lower than that in the general state, in a non-management condition. Such conditions need intensive management. In this regard, one of the spread methods used for the melting material is pre-wetted salt (PWS), which is the frequently used method in South Korea. In the PWS method, the solid material with CaCl2 is mixed with water in 30% concentration and then finally mixed with NaCl before application to pavements. The chloride-type melting material not only is cheaper, but also has a high melting property than the others. It can shorten the pavement or structure life by deterioration and corrosion. This melting material can affect the flora near the highways; hence, an eco-friendly de-icing agent must be utilized considering the environmental effect. METHODS : The Kalman filter algorithm (KFA) was utilized herein to develop optimization models using the performed test data. The KFA, which was developed from recursive filter algorithms, such as the low- and high-pass filters, applies a weighting filter to the Kalman filter. The algorithm has the property of utilizing the filter and updated estimations. In this regard, melting tests were performed for the real applicative utilization of de-icing agents. The KFA was also applied to reduce the error rates and optimize the relationships between the test data and the predictions. RESULTS: Comparing the measurements performed, the error was reduced by 1.69 g when the KFA was applied. Moreover, the error can be optimized to approximately 91.4% compared to the test errors. The prediction data had over 85% tendency in the test measurement, showing that the KFA application can reduce the error and increase the tendency. By comparison, the agent with CaCl2 showed the best ice melting performance within 10 min without surface temperature. However, the PWS with a 25% concentration indicated the best water melting performance from start to end of the test time, implying that this is a powerful agent in terms of performance. CONCLUSIONS : The melting test is an artificial test method; therefore, it can generate a huge error from the test. The error and the tendency can be controlled by tracking the measurement error and the white noise matrix using the KFA. A further research will be performed to track the measurement error and the white noise matrix. Other optimization methods will also be applied to reduce the experimental error.

바이오 의약품 생산과정의 대부분 공정에서 분리막이 사용되고 있다. 분리막 공정은 다른 공정의 전처리, 공정 자 체의 불순물 분리, 바이러스 제거, 목표 생성물 농도 조절 및 완충 용액 교환 등에 사용된다. 인체에 사용하는 바이오 의약품 의 바이러스 오염은 심각한 임상 결과와 직결되는 민감한 문제이기 때문에 바이러스 필터는 제품의 효능과 안정성을 보장하 기 위해 중요한 역할을 한다. 바이러스 필터는 일반적으로 표면 개질된 PVDF, PES, CRC 등 다양한 고분자로 만들어진 복합 다층 구조를 가지고 있다. 제조업체에 따라 대칭(symmetric) 또는 비대칭(asymmetric) 등 다른 기공 구조와 형태를 가지고 있 으며, 주름막, 평판 시트 또는 중공사 형태로 사용된다. 바이러스 필터는 Asahi Kasei 를 비롯해 Millipore, Pall, Sartorius 등 몇몇 해외 업체들이 독점적으로 국내에 공급하고 있다. 바이러스 필터를 대체하려면 검증작업을 통해 규제기관의 승인을 받 는 등 상당한 시간과 비용이 소요된다. 최근 일본의 수출규제로 국산화가 중요해진 만큼 제거 성능 고도화 등 선제적으로 기 술자립도를 높여가야 한다.