PURPOSES : The initial smoothness of concrete pavement surfaces must be secured to ensure better driving performance and user comfort. The roughness was measured after hardening the concrete pavement in Korea. When the initial roughness is poor, relatively large-scale repair works, such as milling or reconstruction must be performed. Hence, a method to measure the roughness of the concrete pavements in realtime during construction and immediately correct the abnormal roughness was developed in this study. METHODS : The profile of a concrete pavement section was measured at a construction site using sensors that were attached to the tinning equipment of the paver. The measured data included outliers and noise caused by the sensor and vibration of the paving equipment, respectively, which were further calibrated. Consequently, the calibrated data were input into the ProVAL program to calculate the roughness based on the international roughness index (IRI). Additionally, the profile of the section was re-measured using another method to verify the reliability of the calculated IRI. RESULTS : The profile data measured at the concrete pavement construction site were calibrated using methods, such as overlapped boxplot outlier removal and low-pass filtering. The outlier data from the global positioning system (GPS), which was installed to identify the construction distance, was also calibrated. The IRI was calculated using the ProVAL program by matching the measured profile and GPS data, and applying the moving average method. The calculated IRI was compared to that measured using another method, and the difference was within the tolerance. CONCLUSIONS : A method to measure the roughness of the concrete pavements in real time during construction was developed in this study. Hence, the performance of concrete pavements can be improved by enhancing the roughness of the pavement considerably using the aforementioned method.

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.

PURPOSES : Recently, increasing number of local governments are introducing on-demand public transportation service in real time to improve the management efficiency of public transportation. In preparation for the autonomous driving era, the “autonomous public transportation mobility on-demand service in real time” is being developed in the public transportation sector to introduce on-demand public transportation. For this service to become the public transportation of the future, it must receive publicity. Therefore, in this study, indicators were selected to evaluate the publicity of the autonomous public transportation mobility on-demand service in real time, and a survey was conducted among the general public to examine the validity of the indicators. METHODS : To secure the publicity of on-demand public transportation in real time, the goal of the service was first set. Second, the keywords of the service were analyzed to define the publicity requirements of this service. Based on the analyzed keywords and definition of publicity, service indicator types were classified, and the indicators were defined by type. A user satisfaction survey was conducted on the final selected indicators to improve the degree of completion of indicator development. The user survey was conducted by presenting selected publicity indicators to respondents responding on a five-point scale to determine whether each indicator could satisfactorily evaluate publicity. RESULTS : When examining various previous research cases and the contents required for autonomous public transportation mobility on-demand service in real time, the required items of publicity indicators were found to be “safety” in terms of “autonomous driving” and “convenience” in terms of “demand response.” Publicity indicators were developed according to these three items. Thirty-one indicators were developed, and a satisfaction survey was conducted on the general public for each indicator. In the survey, most of the indicators scored 3.5 points or higher, indicating that the indicators were generally well defined. Users gave the highest score to “fairness” among the three items, and “reasonable fairness” was found to be a necessary item as a publicity indicator. CONCLUSIONS : In this study, evaluation indicators were selected to secure the publicity of autonomous public transportation mobility on-demand service in real time, and a satisfaction survey was conducted for each indicator. Most of the indicators showed a score of 3.5 or higher, indicating that they were generally well defined. However, this study has a limitation in that it surveys the general population. In future, experts should be included in the survey to increase the degree of completeness of the public indicators.

Anomaly detection for each industrial machine is recognized as one of the essential techniques for machine condition monitoring and preventive maintenance. Anomaly detection of industrial machinery relies on various diagonal data from equipped sensors, such as temperature, pressure, electric current, vibration, and sound, to name a few. Among these data, sound data are easy to collect in the factory due to the relatively low installation cost of microphones to existing facilities. We develop a real time anomalous sound detection (ASD) system with the use of Autoencoder (AE) models in the industrial environments. The proposed processing pipeline makes use of the audio features extracted from the streaming audio signal captured by a single-channel microphone. The pipeline trains AE model by the collected normal sound. In real factory applications, the reconstruction error generated by the trained AE model with new input sound streaming is calculated to measure the degree of abnormality of the sound event. The sound is identified as anomalous if the reconstruction error exceeds the preset threshold. In our experiment on the CNC milling machining, the proposed system shows 0.9877 area under curve (AUC) score.

To ensure the peaceful use of nuclear energy, nuclear safeguards are applied in member states of the International Atomic Energy Agency (IAEA) under the Non-Proliferation Treaty. The two major considerations in implementing nuclear safeguards are effectiveness and efficiency. In terms of efficiency, the IAEA has a great interest in using containment and surveillance (C/S) technology to maintain continuity of knowledge. A representative means of C/S technology is a sealing system to detect tampering. The existing sealing systems used by the IAEA are of limited functionality in realtime verification purposes. To address this limitation, the present study develops a real-time verification sealing system. First, we analyzed the design requirements of a sealing system proposed by various institutions including the IAEA, the U.S. Nuclear Regulatory Commission, and a number of national laboratories and companies. Then, we identified the appropriate design requirements of this system for real-time verification. Finally, the prototype system was developed and tested based on the identified design requirements. The validation tests of the prototype system were performed for anticipated environmental conditions, radiation resistance, and safeguards functionality. Additionally, we are developing user-friendly verification software. The software validation is planned to perform for functionality, performance efficiency, and security. The next step is to develop a commercialized realtime verification sealing system based on the results of validation tests. Using this commercialized system, we plan to evaluate the performance in various actual use cases. Such a system is expected to significantly enhance the efficiency of nuclear safeguards.

Fescues, which are widely cultivated as grasses and forages around the world, are often naturally infected with the endophyte, Epichloë. This fungus, transmitted through seeds, imparts resistance to drying and herbivorous insects in its host without causing any external damage, thereby contributing to the adaptation of the host to the environment and maintaining a symbiosis. However, some endophytes, such as E. coenophialum synthesize ergovaline or lolitrem B, which accumulate in the plant and impart anti-mammalian properties. For example, when livestock consume excessive amounts of grass containing toxic endophytes, problems associated with neuromuscular abnormalities, such as convulsions, paralysis, high fever, decreased milk production, reproductive disorders, and even death, can occur. Therefore, pre-inoculation with non-toxic endogenous fungi or management with endophyte-free grass is important in preventing damage to livestock and producing high-quality forage. To date, the diagnosis of endophytes has been mainly performed by observation under a microscope following staining, or by performing an immune blot assay using a monoclonal antibody. Recently, the polymerase chain reaction (PCR)-based molecular diagnostic method is gaining importance in the fields of agriculture, livestock, and healthcare given the method’s advantages. These include faster results, with greater accuracy and sensitivity than those obtained using conventional diagnostic methods. For the diagnosis of endophytes, the nested PCR method is the only available option developed; however, it is limited by the fact that the level of toxic alkaloid synthesis cannot be estimated. Therefore, in this study, we aimed to develop a triplex real-time PCR diagnostic method that can determine the presence or absence of endophyte infection using DNA extracted from seeds within 1 h, while simultaneously detecting easD and LtmC genes, which are related to toxic alkaloid synthesis. This new method was then also applied to real field samples.

The introduction of autonomous underwater gliders (AUGs) specifically addresses the reduction of operational costs that were previously prohibited with conventional autonomous underwater vehicles (AUVs) using a "scaling-down" design philosophy by utilizing the characteristics of autonomous drifters to far extend operation duration and coverage. Long-duration, wide-area missions raise the cost and complexity of in-water testing for novel approaches to autonomous mission planning. As a result, a simulator that supports the rapid design, development, and testing of autonomy solutions across a wide range using software-in-the-loop simulation at faster-than-real-time speeds becomes critical. This paper describes a faster-than-real-time AUG simulator that can support high-resolution bathymetry for a wide variety of ocean environments, including ocean currents, various sensors, and vehicle dynamics. On top of the de facto standard ROS-Gazebo framework and open-sourced underwater vehicle simulation packages, features specific to AUGs for ocean mapping are developed. For vehicle dynamics, the next-generation hybrid autonomous underwater gliders (Hybrid-AUGs) operate with both the buoyancy engine and the thrusters to improve navigation for bathymetry mappings, e.g., line trajectory, are is implemented since because it can also describe conventional AUGs without the thrusters. The simulation results are validated with experiments while operating at 120 times faster than the real-time.

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.

It is important to ensure worker’s safety from radiation hazard in decommissioning site. Real-time tracking of worker’s location is one of the factors necessary to detect radiation hazard in advance. In this study, the integrated algorithm for worker tracking has been developed to ensure the safety of workers. There are three essential techniques needed to track worker’s location, which are object detection, object tracking, and estimating location (stereo vision). Above all, object detection performance is most important factor in this study because the performance of tracking and estimating location is depended on worker detection level. YOLO (You Only Look Once version 5) model capable of real-time object detection was applied for worker detection. Among the various YOLO models, a model specialized for person detection was considered to maximize performance. This model showed good performance for distinguishing and detecting workers in various occlusion situations that are difficult to detect correctly. Deep SORT (Simple Online and Realtime Tracking) algorithm which uses deep learning technique has been considered for object tracking. Deep SORT is an algorithm that supplements the existing SORT method by utilizing the appearance information based on deep learning. It showed good tracking performance in the various occlusion situations. The last step is to estimate worker’s location (x-y-z coordinates). The stereo vision technique has been considered to estimate location. It predicts xyz location using two images obtained from stereo camera like human eyes. Two images are obtained from stereo camera and these images are rectified based on camera calibration information in the integrated algorithm. And then workers are detected from the two rectified images and the Deep SORT tracks workers based on worker’s position and appearance between previous frames and current frames. Two points of workers having same ID in two rectified images give xzy information by calculating depth estimation of stereo vision. The integrated algorithm developed in this study showed sufficient possibility to track workers in real time. It also showed fast speed to enable real-time application, showing about 0.08 sec per two frames to detect workers on a laptop with high-performance GPU (RTX 3080 laptop version). Therefore, it is expected that this algorithm can be sufficiently used to track workers in real decommissioning site by performing additional parameter optimization.

In order to enhance the transparency of nuclear power, the investigation of nuclear activities is important at the right time. And investigation of North Korea, one of safety measures for such a surveillance system seems essential in the near future. However, the inspection period is limited and the possibility of revisiting is unclear, so it seems easy to install and more reliable monitoring devices are needed. The seal is a security device for monitoring forgery and alteration in the IAEA easily but simple. Since the existing seal devices used by IAEA and KINAC can only be checked through onsite inspections whether it is damaged or not, there is a limit to immediate response in the event of an emergency such as the theft of nuclear materials and loss of Continuity of Knowledge. While the North Korea-U.S. talks and inter-Korean talks are slowly approaching the North Korea’s nuclear issue, it is judged that the inspection of North Korea’s nuclear weapons is not in the distant future. Accordingly, it is necessary to develop a customized seal device for managing and monitoring spent nuclear fuel that can be used for inspection of North Korea. The real-time active seal device consists of a seal equipped with a detection sensor, a translator, and a server, and the translator use wireless communication in consideration of ease of installation and management, respectively. The translator and server use wired communication for data sustainability. The seal device is designed to maintain continuity of knowledge (COK) through wireless communication in consideration of security even in the event of equipment damage, malfunction, and loss of power, while enabling location detection in case of theft or abnormality. Therefore, in this study, a technical investigation on remote data communication was conducted accordingly, and a data communication module for a small amount of broadband data communication was manufactured and applied. In addition, several tests were conducted to determine data integrity and location accuracy according to the communication range.

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 prevalence of cancer in companion dogs is growing nowadays with the increasing worldwide population of domestic dogs. Since there is a less established standard of care in veterinary medicine, investigational treatments, such as the development of biomarkers can be considered as a therapeutic intervention for early diagnosis. Despite the enormous efforts that have been invested in the search of biomarkers, still, there is a need for easy detection of significant biological markers for predicting canine cancers at an early stage. In this study, we have analyzed the expression pattern of previously reported 46 canine cancer-associated candidate genes in blood specimens using real-time qPCR. We hypothesized that analysis of gene expression in blood would provide preliminary evidence of local or systemic immunogenic response which further contribute to the easy and early diagnosis of canine cancer from blood specimen as an analytical tool. The datasets included a total of 22 blood samples collected from different breeds of dogs diagnosed with cancer and five from healthy normal dogs. RT-qPCR analysis was performed by employing the SYBR Green PCR mix to assess the expression of these 46 genes in a total of 27 samples. From our result, a total of nine genes (ROS1, C1QA, CD48, IL1b, TLR2, IL2R, CHI3L1, CTSS, and TLR7) were found to be significantly up-regulated (p < 0.05 and p < 0.01) in the cancer samples compared to non-cancer samples. The relative expression level of ROS1, C1QA, CD48, IL1b, TLR2, IL2R, CHI3L1, CTSS, and TLR7 genes was 5.74, 4.78, 3.94, 2.94, 2.57, 2.53, 2.50, 2.04, and 2.57, respectively, in cancer samples compared to non-cancer samples. Thus, our results reveal several highly expressed cancer genes that can be therapeutic target genes for further testing in canine cancers.

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.

PURPOSES : In a previous study, an error was detected in data pertaining to the direction and velocity of a roller. Hence, in this study, the correlation between these two variables and acceleration data is analyzed. Relevant algorithms are developed by adding variables to existing algorithms. METHODS : A tachometer and GPS are used to acquire the velocity, compaction direction of rollers, and number of compactions. Subsequently, data input to an accelerometer are compared and analyzed. RESULTS : Based on FFT analysis, it is discovered that the data are inaccurate when a forward reverse is performed. Using the GPS, the velocity of the roller is differentiated based on the number of pledges, and then added as a variable to the algorithm. Subsequently, it is evaluated and analyzed only with data during forward movement based on changes in the latitude and longitude. CONCLUSIONS : It is discovered that the acceleration data values from both the left and right rollers differ owing to their weight difference, as indicated by the asphalt gradient. Data changes based on asphalt gradients are analyzed using gyro sensors. If the correlation between the two sets of data is high, then the algorithm is advanced by introducing a cross spectrum after calibrating the acceleration value based on the gradient.

본 연구는 실시간 원격수업에서 학생들의 학습 장애 요인을 분석하고, 그러한 장애 요인을 해소 할 수 있는 수업을 설계하였으며, 이를 실제로 적용한 사례 연구이다. 2015 개정 교육과정에서 강조 하고 있는 ‘역량’을 함양할 수 있는 실시간 원격수업을 계획하였다. 이 수업은 개별 한자의 모양·음· 뜻과 필순에 대한 지식 암기가 아니라, 한자 정보를 찾아 활용할 수 있는 능력을 함양하는 데에 있다고 할 수 있다. 이를 위해 학생들이 실시간 원격수업에서 겪은 학습 장애 요인을 조사하였다. 응답 내용은 ‘집중’, ‘기술’, ‘학습’, ‘소통’, ‘피로’, ‘흥미’, ‘기타’ 등으로 분류할 수 있었다. 이러한 학습 장애 요인에 대한 개선 방안은 수업의 내적 요인과 수업의 외적 요인으로 나누어 살펴볼 수 있다. 수업의 내적 요인으로는 ‘학습, 흥미’의 문제, 수업의 외적 요인은 ‘집중, 기술, 소통, 피로’의 문제이다. 학습 부담이나 학습 부진에 관한 문제는 화상회의 플랫폼을 통하여 실시간 쌍방향 원격수업 실시, 학습량 적정화 등으로 개선 방안을 모색하고, 흥미는 대면 수업에서 친구들 과 함께 수업 듣는 분위기 조성 등으로 개선 방안을 찾는다. 집중은 활동 중심의 실시간 원격수업, 기술은 학습 플랫폼 오류에 대비한 대체 수단 마련, 소통은 실시간 쌍방향 수업과 피드백 강화, 피로는 일과 시간의 변경 등을 통해 개선 방안을 찾는다. 이러한 응답 내용을 토대로 학생들이 집중할 수 있는 활동 중심 한문 수업, 수업 시간 동안 해결 할 수 있는 적절한 학습량의 과제 제시를 통해 학습 부담 감소, 교사와 소통할 수 있는 채널 확보, 흥미를 가질 수 있는 요소를 가미한 수업 방안을 강구하였다. 기술적인 문제나 피로는 개별 교과에서 해결할 수 없는 문제이기 때문에 수업 구성의 고려 사항에서 제외하였다. 중학교 1학년의 학년 초에 실시한 수업이기 때문에 한자 영역을 대상으로 하였다. 수업은 ‘학습지 제공 → 신습 한자 선택 → 인터넷 사전 검색 → 필순에 따라 한자 쓰기 → 한자를 선택한 이유 쓰기’ 순서로 진행하였다. 처음 한자를 써보는 학생들이 다수였지만, 대부분 정해진 시간 안에 한자의 모양을 정확하게 작성하 였다. 일부 학생은 정해진 수업 시간 동안 학습지를 완성하지 못하였다. 원격수업에서 수업 환경은 교실에서 대면으로 이루어졌던 학교 수업을 그대로 온라인으로 옮겨가는 것이 아니라, 본질적인 수업 방법의 변화가 있어야 할 것이다.