High-temperature molten salts not only demonstrate exceptional thermal and chemical stability but also offer significant advantages in catalyzing chemical reactions. Consequently, they have garnered attention as a promising medium for next-generation nuclear reactors and a wide range of electrochemical processes. Nevertheless, the challenging experimental conditions in molten salts make applying conventional analytical methods to understand reaction mechanisms a formidable task. This underscores the imperative need for more intuitive approaches to investigate molten salt chemistry. One of the simplest yet potent methods involves real-time visual monitoring of the reaction system as chemical reactions progress. In light of this, we have developed an experimental system enabling real-time visual monitoring of the internal dynamics of molten salt media. This system can capture high-resolution videos and images within molten salts, surpassing existing methodologies. We have applied this system in various electrochemical experiments using the molten LiCl-KCl eutectic salt medium. Among them, this study primarily focuses on two challenging experimental scenarios that became comprehensible through our proposed system’s application: (1) the transpassivation of Zr metal and the agglomeration of potassium hexachlorozirconate (K2ZrCl6) solid salt, and (2) the solvation of electrons during the oxidation of Li metal within the molten LiCl-KCl eutectic salt.

원전 구조물의 실시간 모니터링 기술이 요구되고 있지만, 현재 운영 중인 지진 감시계통으로는 동특성 추출 등 시스템 식별이 제한 된다. 전역적인 거동 데이터 및 동특성 추출을 위해서는 다수의 센서를 최적 배치하여야 한다. 최적 센서배치 연구는 많이 진행되어 왔 지만 주로 토목, 기계 구조물이 대상이었으며 원전 구조물 대상으로 수행된 연구는 없었다. 원전 구조물은 미미한 신호대잡음비에도 강건한 신호를 획득하여야 하며, 모드 기여도가 저차 모드에 집중되어 있어 모드별 잡음 영향을 고려해야 하는 등 구조물 특성을 고려 해야 한다. 이에 본 연구에서는 잡음에 대한 강건도와 모드별 영향을 평가할 수 있는 최적 센서배치 방법론을 제시하였다. 활용한 지표 로서 auto MAC(Modal Assurance Criterion), cross MAC, 노드별 모드형상 분포를 분석하였으며, 잡음에 대한 강건도 평가의 적합성을 수치해석으로 검증하였다.

In the case of a die-casting process, defects that are difficult to confirm by visual inspection, such as shrinkage bubbles, may occur due to an error in maintaining a vacuum state. Since these casting defects are discovered during post-processing operations such as heat treatment or finishing work, they cannot be taken in advance at the casting time, which can cause a large number of defects. In this study, we propose an approach that can predict the occurrence of casting defects by defect type using machine learning technology based on casting parameter data collected from equipment in the die casting process in real time. Die-casting parameter data can basically be collected through the casting equipment controller. In order to perform classification analysis for predicting defects by defect type, labeling of casting parameters must be performed. In this study, first, the defective data set is separated by performing the primary clustering based on the total defect rate obtained during the post-processing. Second, the secondary cluster analysis is performed using the defect rate by type for the separated defect data set, and the labeling task is performed by defect type using the cluster analysis result. Finally, a classification learning model is created by collecting the entire labeled data set, and a real-time monitoring system for defect prediction using LabView and Python was implemented. When a defect is predicted, notification is performed so that the operator can cope with it, such as displaying on the monitoring screen and alarm notification.

Recently, an international issue due to the discharge of contaminated water from the Fukushima has been highlighted. Since the Fukushima nuclear power plant accident in japan, marine environmental radioactivity survey has been strengthened with increased sampling frequency and range for seawater in territorial waters. And a stationary underwater radiation monitoring system including floating equipment-based system such as oceanographic buoys, tidal stations have been deployed on-site to detect abnormal radiological events. However, stationary monitoring systems may be insufficient for the early detection of abnormal radioactivity over a wide area, since it is a passive way of waiting for radioactive materials to spread in the ocean. So, our team developed a ship-mounted seawater gammaray monitoring system that can be operated remotely and in real time. In this study, it includes a detailed description of the design, installation, monitoring method, and operation of the system.

최근 농지 및 수자원의 염류화로 인해 염생식물에 대한 작물화와 생육 조건 구명을 위한 연구 필요성이 높아지고 있다. 그러나 재배 조건 변화에 따른 작물의 생육 및 기능성 성분 변화 분석을 위해서는 양액 및 작물 생육부 시료를 채취하여 이를 전처리 및 분석해야 하여 시간적, 인적 자원의 소모가 높다. 본 연구에서는 염생식물의 최적 생육 조건 구명을 효율적으로 수행하기 위해 비파괴적으로 재배 현장에서 실시간으로 작물 생육부와 수경재배 근권부의 염분 농도를 모니터링할 수 있는 시스템을 개발하였다. 개발 시스템을 통해 NaCl 0 mM, 50 mM, 150 mM, 300 mM의 4가지 농도 조건에서 갯방풍을 대상으로 재배실험을 수행한 결과, 재배 중 염분 농도 수치의 변화 및 염분 농도가 높을수록 생체중이 감소하는 것을 확인하였다. 실제 생육 분석 결과에 따르면 개발 시스템을 통한 모니터링 결과에 상응하는 생체중 결과를 확인하여 염생식물의 생육 모니터링 적용이 가능할 것으로 평가되었다. 그러나 단위 생체중 당 기능성물질 함량의 경우, NaCl 농도가 증가할수록 증가하는 것으로 나타나 갯방풍의 상업적 재배 조건의 구명을 위해서는 생체중 외에 기능성 성분의 모니터링을 위한 시스템 보완이 필요하다고 판단되었다.

The aging society are causing complicated problems from chronic diseases such as diabetes, heart disease in aged patients. Recently Covid pandemic swept across global aging societies and put aged people with chronic disease more vulnerable to mobidity. Therefore, from view of preventive and precision medicine, remote monitoring an aged people with chronic disease is an important issue than ever. Smart health care systems as remote monitoring is so called telemetry system which enable doctors or health care providers to manage remotely to consult, diagnose and treat patients with chronic disease and infectious disease as well. The goal of telemetry system is to meet timely medical need in aged patients in distant place through communication technologies. Through technological breakthrough, particularly in real-time health care industry, Telemetry system with realtime mornitoring of heart rhythm and blood glucose level can play an important role in managing healthcare services in aged patients. This report introduces a comprehensive and inclusive review on the usefulness of realtime patiens monitoring with wearable sensors in telemedicine applications. The findings of this report are as follows: (1) The advantage of real-time monitoring and blood glucose level and biofeedback based on visual data (2) The advantage of realtime-monitoring heart rhythm coupled with blood glucose level. (3) There is an additional need to produce a digital imaging from protable diagnostic divices such as X-ray and ultrasound.

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.

PURPOSES : For large-scale construction, such as a concrete pavement, design and construction are not entirely consistent. If the inconsistency between design and construction is very large, construction quality is significantly degraded, affecting performance life span and driving comfort. The quality of pavement construction is managed according to standards. However, it is difficult to improve construction quality as the standard measures construction quality after construction is completed. Therefore, this study developed a system to measure the construction quality of concrete pavement in real-time and presented the corresponding standards. METHODS : A basic module for simultaneously measuring the width, thickness, and roughness of the concrete pavement was designed. Based on the measurement results of the distance measurement sensor, a calibration method is presented that can remove noise. The system process was developed to measure construction quality based on location and distance data, measured in real-time using GPSs and sensors. The field application experiment was conducted and the results were analyzed. RESULTS : The measurement module is properly designed to be used in concrete pavement construction sites. Noise was removed from the distance measurement sensor results according to the presented calibration method, leaving only the wave of pavement surface irregularities. As a result of applying the system process in the field application, a reasonable level of PRI was observed. CONCLUSIONS : In the past, the width, thickness, and roughness were measured after construction was completed and, if the standard was not met, construction quality control was performed via reconstruction or repair. Through this study, it is expected that the width, thickness, and roughness of the concrete pavement can be measured in real-time and, if the standard is not met, construction quality can be immediately controlled during construction to maintain high quality.

본 연구는 양액 내 존재하는 다량 영양소의 농도를 실시간으로 측정하기 위해 이온 선택 전극 (ISE) 으로 구성된 임베디드 시스템의 개발을 보여준다. NO3, K 및 Ca 이온을 감지하기위한 PVC ISE, H2PO4를 감지하기위한 코발트 전극, 기준 전극, 샘플 용액이 담기는 챔버, 펌프 및 밸브를 사용하여 측정하는 시스템으로 구성된다. 양액 샘플양 조절과 데이터 수집을 위해서 데이터 Due 보드가 사용되었고, 각각의 샘플 측정 전에, 측정 중 발생하는 드리프트를 최소화시키기 위해 2 점 정규화 방법을 사용하였다. PVC 멤브레인을 기반으로 한 NO3 및 K 전극의 농도 예측 성능은 표준 분석기의 결과와 근접한 일치 (R2 = 0.99) 나타내며 만족스러운 결과를 나타냈다. 하지만, Ca Ⅱ 이온 투과체 제조된 Ca 전극은 고농도 양액 농도에서 Ca 농도를 55 %로 낮게 측정하였다. 코발트 전극 기반 인산 측정은 반복측정 중에 발생한 코발트 전극의 불안정한 신호로 인해 표준 방법과 비교하여 45 ~ 155 mg / L의 인산 농도 범위에서 24.7 ± 9.26 %의 비교적 높은 오차를 나타냈다. 수경 P 감지의 예측 능력을 향상시키기 위해 코발트 전극의 신호 컨디셔닝에 대한 추가 연구가 필요함으로 판단된다.

The concentrations of volatile organic compounds (VOCs) and odor-inducing substances were measured using selected ion flow tube mass spectrometers (SIFT-MS) and a drone equipped with an air quality monitoring system. SIFT-MS can continuously measure the concentration of VOCs and odor-inducing substances in realtime without any pre-treating steps for the sample. The vehicle with SIFT-MS was used for real-time measurement of VOC concentration at the site boundaries of pollution sources. It is possible to directly analyze VOCs concentration generated at the outlets by capturing air from the pollution sources with a drone. VOCs concentrations of nine spots from Banwol National Industrial Complex were measured by a vehicle equipped with SIFT-MS and were compared with the background concentration measured inside the Metropolitan Air Quality Management Office. In three out of the nine spots, the concentration of toluene, xylene, hydrogen sulfide, and methyl ethyl ketone was shown to be much higher than the background concentration. The VOCs concentrations obtained using drones for high-concentration suspected areas showed similar tendencies as those measured using the vehicle with SIFTMS at the site boundary. We showed that if both the drone and real-time air quality monitoring equipment are used to measure VOCs concentration, it is possible to identify the pollutant sources at the industrial complex quickly and efficiently check sites with high concentrations of VOCs.

In this study, real-time monitoring of air quality using a real-time mobile monitoring system was conducted to identify the emission characteristics and current status of air pollutants and odorous substances that are mainly generated in domestic dyeing industrial areas and to trace the pollutant sources. The concentration of toluene in the industrial area was detected up to 926.4 ppb, which was 3 to 4 times higher than that of other industrial areas. The concentration of methylethylketone was 124.7 ppb and the concentration of dichloromethane was 129.5 ppb. Acrolein concentration was highest at E point at 521.6 ppb, methanol concentration was highest at D point at 208.8 ppb, and acetone concentration was highest at M and N points at 549.3 ppb. The most frequently detected concentration of pollutants in the air quality monitoring results in the industrial area was, in descending order, toluene > methanol > acrolein > dichloromethane > acetone, which was similar to the chemical emissions used in the industrial area by the Pollutant Release and Transfer Register data. The concentration of odorous substances measured in real time was compared with the concentration of minimum detection, and the concentration of hydrogen sulfide was about 10 times higher than the concentration of minimum detection at A point, which was judged to be the main odorous cause of A point. In the future, if the real-time mobile measurement system is constructed to automatically connect wind direction/wind speed, PRTR (Pollutant Release and Transfer Register) data and SEMS (Stack Emission Management System) data, etc., it was judged that more accurate monitoring could be performed.