본 논문은 지상파 기반의 측위·항법·시각(PNT, Positioning, Navigation, and Timing) 서비스의 대표격인 eLoran(enhance LOng RAnge Navigation) 시스템의 시각동기 성능 모니터링 시스템의 설계에 관한 것으로서, GNSS(Global Navigation Satellite System)의 신호 취약성에 따른 시각동기시스템의 한계에 대해 설명하고, 이에 대한 백업시스템으로 대표적 지상파항법시스템인 eLoran 시스템의 시각동기 성능모니터링 시스템에 대해 중점적으로 다룬다. eLoran 시스템을 이용한 시각동기 서비스 및 이에 대한 성능감시를 위한 보정기준국(dLoran, differential Loran) 관점에서의 시각동기 성능모니터링 시스템의 구성과 그 요구성능에 대해 설명한다. 또한 eLoran 테스트베드 환경 내 시각동기 모니터링 시스템의 장기 시범운영을 통해서, eLoran 시각동기서비스의 성능을 분석한다. 시각동기 성능모니터링 시스템을 이용한 성능 분석결과 보정 전 43.71 ns, 보정 후 22.52 ns (rms)의 시각정밀도를 나타내었으며, 이를 통해서 정밀시각 동기원으로 eLoran 서비스가 충분히 GPS 백업 시각동기시스템으로 활용이 가능함을 확인할 수 있었다.

This is the research report concerned with the new stability concept and designing the controller of the time periodic systems. The periodic systems which is governed by time is very common in various type of dynamic systems. For example, the satellite is the large scale and the experimental test equipment as pendulum is also time periodic systems. The motion of the systems during the time interval is running repeatedly. While this periodic systems is running, the dynamic stability is important to behave the appropriate motion of the system's function. This research propose the new stability approach based on Lyapunov theorem which is analyzing the system's stability adapting the specific energy function. And also this research introduces the developing numerical procedures to retain the stability of time periodic system. Finally, the result of this research is very useful to judge the stability and design the controller of time periodic system.

본 연구에서는 강한 한파가 발생했던 2018년과 온난 한파가 발생했던 2019년의 기온에 따른 수온의 반응 및 지연시간과 북풍계 열 바람 빈도와의 상관관계를 분석하였다. 사용된 시간 자료는 국립수산과학원에서 제공하는 7개 지점 해역별 수온자료와 수온관측소 인 근 7개 지점 AWS 기온자료를 이용하였다. 관측되지 못한 자료는 내삽법으로 근사값을 계산하였고, FIR Filter를 이용하여 자료의 주기성을 파악하였다. 그 결과, 강한 한파가 발생했던 2018년은 북풍계열 바람을 통해 차가운 공기가 남하하면서 기온을 하강시켜 전 해역에 저수온을 유발한 반면 온난 한파가 발생했던 2019년은 평년 수준의 기온으로 하강하였지만 수온은 크게 변화하지 않았다. 강한 한파가 발생했 던 2018년 기온 하강에 따른 수온의 지연시간은 평균 14시간으로 0.7 이상의 높은 상관성을 나타냈고 온난 한파가 발생했던 2019년은 평균 지연시간이 20시간으로 0.44-0.67 사이의 상관성을 보였다. 본 연구를 통해 해역별로 기온 하강에 따른 표층수온의 반응을 해석하였고 지연시간을 파악함으로써 양식생물의 피해를 최소화하고 한파 피해의 신속한 대응에 기여할 수 것으로 기대한다.

본 시험은 ‘감홍’/M.9 사과나무의 인력 적과 시기가 영양생장, 고두증상, 과실품질 및 이듬해 개화율에 미치는 영향을 조사하고자 실시하였다. 인력 적과 시기는 만개 후 3, 5, 7, 9주였으며, 1차 적과(중심과만 남기는 적과)와 2차 적과(착과수 조절을 위한 적과)는 동시에 이루어졌다. 인력 적과 시기는 영양 생장, 평균 과중, 생산량, 가용성 고형물 함량, 고두증상 발생 률 및 이듬해 개화율과 역의 상관관계를 가졌으나 나무별 중급 과실(과중이 214－299g이면서 고두증상이 발생하지 않은 과실)의 생산량, 잎 내 칼슘 함량 및 과실의 착색 정도와는 정의 상관관계를 가졌다. 과실의 경도, 산함량 및 나무 당 총 조수입은 인력 적과 시기에 영향을 받지 않았다. 결론적으로, ‘감홍’/M.9 사과나무의 인력 적과가 만개 후 9주에 마무리되면 고두증상이 없는 300g 정도의 고품질 과실을 생산할 수 있었다.

유인 방법은 딸기 묘를 생산하는 전통적인 방법이다. 그러나 딸기 묘를 유인하여 번식하는 방법은 오랜 노동시간이 있어야 하는 노동 집약적인 방법이다. 삽목 방법은 상대적으로 유인 방법에 비해 노동 기간과 작업 시간이 짧으므로 유인의 효과적인 대안으로 주목되어왔다. 이 연구는 유인과 삽목 사이의 노동 기간, 작업 시간 및 묘의 생육을 조사하기 위해 수행되었다. ‘매향’ 딸기는 두 개 동의 딸기 육묘용 유인 온실과 삽 목 온실에서 각각 재배되었다. 유인 방법(유인 작업, 모주 제거, 자묘 간 분리) 및 삽목 방법(삽수 채묘, 저장 전 처리, 삽목 작업)에 대한 주요 작업 시간을 측정했으며, 유인은 158일 동 안 6개의 작업이 필요하며, 삽목은 113일 동안 4개의 작업이 필요했다. 유인은 삽목보다 작업 시간이 더 많이 필요했다. 유인과 삽목묘 육묘 방법에 따른 유의적인 차이가 없었다. 이러한 결과는 묘소질의 손실 없이 육묘 동안 유인 방법보다 삽목 방법이 더 많은 노동 기간과 작업 시간을 절약할 수 있음을 의미한다.

목적 : 코로나19 팬데믹 기간 동안 젊은 성인을 대상으로 설문조사를 시행하여 스마트폰 사용 시간과 건성안 유병률 사이의 연관성을 평가하고자 하였다. 방법 : 대학생들을 대상으로 설문조사를 실시하였고 안구건조증은 OSDI 설문지를 이용하여 평가하였다. 안구 건조증은 OSDI 점수에 따라 경도(13~22), 중등도(23~32), 중증(33~100) 건성안으로 분류하였다. 결과 : 완성된 설문지는 총 282개를 받았지만, 기준에 맞지 않는 응답자를 제외하고 157개 설문지를 분석했다. 본 연구에서 코로나19 팬데믹 기간 동안 응답자의 77%가 스마트폰 사용 시간이 증가하였고, OSDI 을 통한 안구 건조 진단결과는 98명(62.4%)이 정상, 30명(19.1%)이 경도, 16명(10.2%)이 중등도, 13명(8.3%)이 중증인 건성안으로 밝혀졌다. OSDI는 스마트폰 사용시간 증가와 유의한 상관관계를 보였다(r=0.241, p=0.012). 결론 : 건성안 유병률은 157명 중 37.6%로 나타났고 안구건조증은 스마트폰 사용 시간의 증가와 관련이 있는 것으로 나타났다.

Pacific herring, Clupea pallasii, a keystone species with significant ecological and commercial importance, is declining globally throughout much of its range. While traditional fishing equipment methods remain limited, new sensitive and rapid detection methods should be developed to monitor fisheries resources. To monitor the presence and quantity of C. pallasii from environmental DNA (eDNA) extracted from seawater samples, a pair of primers and a TaqMan® probe specific to this fish based on mitochondrial cytochrome b (COB) sequences were designed for the real-time PCR (qPCR) assay. The combination of our molecular markers showed high specificity in the qPCR assay, which affirmed the success of presenting a positive signal only in the C. pallasii specimens. The markers also showed a high sensitivity for detecting C. pallasii genomic DNA in the range of 1 pg~100 ng rxn-1 and its DNA plasmid containing COB amplicon in the range of 1~100,000 copies rxn-1, which produced linear standard calibration curves (r2=0.99). We performed a qPCR assay for environmental water samples obtained from 29 sampling stations in the southeastern coastal regions of South Korea using molecular markers. The assay successfully detected the C. pallasii eDNA from 14 stations (48.2%), with the highest mean concentration in Jinhae Bay with a value of 76.09±18.39 pg L-1 (246.20±58.58 copies L-1). Our preliminary application of molecular monitoring of C. pallasii will provide essential information for efficient ecological control and management of this valuable fisheries resource.

This study aimed to develop Lautropia mirabilis -specific quantitative real-time polymerase chain reaction (qPCR) primers based on the sequence of DNA-directed RNA polymerase subunit beta gene. The PrimerSelect program was used in designing of the qPCR primers, RTLam-F4 and RTLam-R3. The specificity of the qPCR primers were performed by conventional PCR with 37 strains of 37 oral bacterial species, including L. mirabilis . The sensitivity of the primers was determined by qPCR with the serial dilution of purified genomic DNA of L. mirabilis KCOM 3484, ranged from 4 ng to 4 fg. The data showed that the qPCR primers could detect only L. mirabilis strains and as little as 40 fg of genome DNA of L. mirabilis KCOM 3484. These results indicate that this qPCR primer pair (RTLam-F4/ RTLam-R3) may be useful for species-specific detection of L. mirabilis in epidemiological studies of oral bacterial infectious diseases such as periodontal disease.

The development of IOT technology and artificial intelligence technology is promoting the smartization of manufacturing system. In this study, data extracted from acceleration sensor and current sensor were obtained through experiments in the cutting process of SKD11, which is widely used as a material for special mold steel, and the amount of tool wear and product surface roughness were measured. SVR (Support Vector Regression) is applied to predict the roughness of the product surface in real time using the obtained data. SVR, a machine learning technique, is widely used for linear and non-linear prediction using the concept of kernel. In particular, by applying GSVQR (Generalized Support Vector Quantile Regression), overestimation, underestimation, and neutral estimation of product surface roughness are performed and compared. Furthermore, surface roughness is predicted using the linear kernel and the RBF kernel. In terms of accuracy, the results of the RBF kernel are better than those of the linear kernel. Since it is difficult to predict the amount of tool wear in real time, the product surface roughness is predicted with acceleration and current data excluding the amount of tool wear. In terms of accuracy, the results of excluding the amount of tool wear were not significantly different from those including the amount of tool wear.

The sensory stimulation of a cosmetic product has been deemed to be an ancillary aspect until a decade ago. That point of view has drastically changed on different levels in just a decade. Nowadays cosmetic formulators should unavoidably meet the needs of consumers who want sensory satisfaction, although they do not have much time for new product development. The selection of new products from candidate products largely depend on the panel of human sensory experts. As new product development cycle time decreases, the formulators wanted to find systematic tools that are required to filter candidate products into a short list. Traditional statistical analysis on most physical property tests for the products including tribology tests and rheology tests, do not give any sound foundation for filtering candidate products. In this paper, we suggest a deep learning-based analysis method to identify hand cream products by raw electric signals from tribological sliding test. We compare the result of the deep learning-based method using raw data as input with the results of several machine learning-based analysis methods using manually extracted features as input. Among them, ResNet that is a deep learning model proved to be the best method to identify hand cream used in the test. According to our search in the scientific reported papers, this is the first attempt for predicting test cosmetic product with only raw time-series friction data without any manual feature extraction. Automatic product identification capability without manually extracted features can be used to narrow down the list of the newly developed candidate products.

As mechatronic systems have various, complex functions and require high performance, automatic fault detection is necessary for secure operation in manufacturing processes. For conducting automatic and real-time fault detection in modern mechatronic systems, multiple sensor signals are collected by internet of things technologies. Since traditional statistical control charts or machine learning approaches show significant results with unified and solid density models under normal operating states but they have limitations with scattered signal models under normal states, many pattern extraction and matching approaches have been paid attention. Signal discretization-based pattern extraction methods are one of popular signal analyses, which reduce the size of the given datasets as much as possible as well as highlight significant and inherent signal behaviors. Since general pattern extraction methods are usually conducted with a fixed size of time segmentation, they can easily cut off significant behaviors, and consequently the performance of the extracted fault patterns will be reduced. In this regard, adjustable time segmentation is proposed to extract much meaningful fault patterns in multiple sensor signals. By considering inflection points of signals, we determine the optimal cut-points of time segments in each sensor signal. In addition, to clarify the inflection points, we apply Savitzky-golay filter to the original datasets. To validate and verify the performance of the proposed segmentation, the dataset collected from an aircraft engine (provided by NASA prognostics center) is used to fault pattern extraction. As a result, the proposed adjustable time segmentation shows better performance in fault pattern extraction.

Recently, there has been growing interest in harmful substances released from household items such as volatile organic compounds (VOCs) and this has increased people’s environmental awareness. In this study, adhesives and manicures were used as samples of indoor household goods and formaldehyde emission and tested over time under temperature conditions of 15oC, 25oC, 35oC, and 45oC. The small chamber method as the indoor air quality process test method was employed and used to evaluate the concentration of formaldehyde emissions. As a result, formaldehyde emissions gradually decreased over time in both tests using adhesives and manicures. The cumulative emission showed a logarithmic function over time, and the formaldehyde can be released for longer periods of time at lower temperature conditions. The logarithmic value and response time showed linear relationships, and it can be inferred that the formaldehyde was released from the sample through the first order reaction. Furthermore, the relationship between temperature and velocity constants which was determined using the Arenius linear equation showed that the reaction rate of formaldehyde can be estimated by a temperature change.

In this study, the purpose of this study was to examine the effect of twisting in the preparation of waterproofing in the process of unfolded donut-type staking method fire hoses in indoor hydrant system. The central pull-out method caused more twisting than the rolling method, and there was no significant difference in the number of twists according to the pull-out method in the case of male and female students. It was found that the time it took to untwist and prepare waterproofing was much shorter for male students. The angle valve and hose are connected, and the time to untwist and prepare for waterproofing after withdrawing the fire hose with the hose and nozzle connected was shorter than the unconnected state. In the rolling method, when a hose connected with two 15 m fire hoses was used and the angle valve-hose was connected, but the hose-nozzle was not connected, the least kinking occurred. The time to untwist and prepare for waterproofing was also the shortest. As a result, in the withdrawal method of the donut-type loaded fire hose in the indoor hydrant system, it is a rolling method rather than a central withdrawal method. With the angle valve and hose connected, unfold the fire hose with the hose and nozzle connected, if a large number of people unwind the twisted hose, the time to prepare for waterproofing can be shortened.

This study developed a scenario to understand the reaction rate and operational time according to RTI value of rate of rise detector in each type in case of fire mattress. In the results of analyzing the reaction rate and operational time of detector in each scenario, in case when installing a single detector, the elevated temperature per minute was raised to 8℃/min ~ 9℃/min. In case when installing two detectors, it was raised to 9℃/min ~ 10℃/min. In case when installing three detectors, it was raised to 10℃/min. The horizontal distance between detector and mattress was 1.8m~2.5m. Whenever the number of detectors was increased, the horizontal distance was decreased. The operational time of detector was within maximum 540 seconds and minimum 420 seconds. As the research tasks in the future, there should be the researches on the effects of reaction rate of detector on the evacuation in case of fire through the result value of RSET by setting up the latency until the detector operates, and the researches on the safety by understanding if the operational time of detector is suitable for the evaluation standard of performancecentered design.