The purpose of this paper is to understand the key factors for efficient maintenance of rapidly aging facilities. Therefore, the safety inspection/diagnosis reports accumulated in the unstructured data were collected and preprocessed. Then, the analysis was performed using a text mining analysis method. The derived vulnerabilities of tunnel facilities can be used as elements of inspections that take into account the characteristics of individual facilities during regular inspections and daily inspections in the short term. In addition, if detailed specification information and other inspection results(safety, durability, and ease of use) are used for analysis, it provides a stepping stone for supporting preemptive maintenance decision-making in the long term.

An implementation of OSEK NM, a network management protocol for automotive embedded systems, has been defined. In this experiment, the NM ID of each ECU of the vehicle is reverse-engineered when the vehicle is parked with a discharged battery, and the power management status and error status of each ECU and node are identified through NM ID OPCODE analysis, Sleep status, and Wake-up status, LIMP HOME status, were analyzed. After analyzing the NM OPCODE to find the faulty ECU, remove the cause of the failure and analyze the sleep mode entry state.In order to prevent the vehicle battery from being discharged, additional research is needed on how to enter the forced sleep mode when a failure not related to vehicle security occurs.

구조물에 장기적으로 발생하는 노후화를 정량적으로 파악하기 위해 상시진동 데이터를 활용한 일반화된 모니터링 시스템에 관한 연구가 세계적으로 활발히 수행중이다. 본 연구에서는 구조물에서 장기적으로 취득되는 동특성을 앙상블 학습에 활용하여 구조물의 이상을 감지하기 위한 보급형 엣지 컴퓨팅 시스템을 구축하였다. 시스템의 하드웨어는 라즈베리파이와 보급형 가속도계, 기울기센서, GPS RTK 모듈, 로라 모듈로 구성됐다. 실험실 규모의 구조물 모형 진동실험을 통해 동특성을 활용한 앙상블 학습의 구조물 이상 감지를 검증하였으며, 실험을 기반으로 한 실시간 동특성 추출 분산처리 알고리즘을 라즈베리파이에 탑재하였다. 구축된 시스템을 하우징하고 포항시 행정복지센터에 설치하여 데이터를 취득함으로써 개발된 시스템의 현장 적용성을 검증하였다.

In this paper, each controller's unique ID and PICO oscilloscope were used to measure the voltage waveform of each CAN communication line, and compare and analyze the serial decoding results. Using the voltage change level of the CAN communication line, it was possible to check whether the CAN-High line and the Low line were disconnected. And it was possible to infer the circuit disconnection point between the controller and the controller only with the unique ID information of each controller. And when the CAN-High circuit was disconnected, the voltage of the high line was measured at the same voltage level as the Low line.

The present study was conducted in a goat farm for pregnancy diagnosis by using vaginal cytology and B-mode real time ultrasound using 5 MHz probe by transabdominal method. Seventeen pregnant does were used for this study. The objective of the study was to determine the earliest day of pregnancy and describe the chronological characteristics of pregnancy from day 22 to day 40 for vaginal cytology and day 25 to day 60 for ultrasonography of gestation. The differences among the average percentage of cell value in different age of pregnancy were significant (p < 0.05). The average percentage of intermediate cells (81.12%) was also significantly (p < 0.05) higher than superficial (9.41%), keratinized (7.10%) and neutrophil (2.61%) on 22-40th days of pregnancy. In case of real time B-mode ultrasonography, the gestational sac was observed only in three does out of seventeen (17.6 %) at 25-30 days whereas the placentomes and heart beat of the foetus were first detected at 31-35 days in six does (35.3%). The foetal leg buds were first visualized at 36-40 days in four does (23.5%) whereas the foetal vertebral column was first observed at 36-40 days of gestation in only three does (17.6%). In conclusion, vaginal cytology and trans-abdominal ultrasonography can be used for detection of early pregnancy in does.

Industrial Motors diagnostic equipment is highly dependent on the automation system, so if there are defects in the automation equipment, it can only rely on the operator’s intuitive judgment.To help with intuitive judgment, Park’s Vactor Approach(PVA) represents the current signal as a pattern of circles, so it can tell if a fault occurs when the circle is distorted. However, the failure to judge the degree of distortion of the circle pattern is the basis of the fault, so it will face difficulties. In this paper, in order to compare the faults of PVA, the period of d-axis current of PVA pulsation was mastered, so that two phase differences occurred in the same signal source. Through experiments, it is confirmed that this is a 90 degree cross formation of PVA, which is convenient for judging from the vision that there is no fault, thus helping the operator to make intuitive judgment.

Maintenance of power distribution facilities is a significant subject in the power supplies. Fault caused by deterioration in power distribution facilities may damage the entire power distribution system. However, current methods of diagnosing power distribution facilities have been manually diagnosed by the human inspector, resulting in continuous pole accidents. In order to improve the existing diagnostic methods, a thermal image analysis model is proposed in this work. Using a thermal image technique in diagnosis field is emerging in the various engineering field due to its non-contact, safe, and highly reliable energy detection technology. Deep learning object detection algorithms are trained with thermal images of a power distribution facility in order to automatically analyze its irregular energy status, hereby efficiently preventing fault of the system. The detected object is diagnosed through a thermal intensity area analysis. The proposed model in this work resulted 82% of accuracy of detecting an actual distribution system by analyzing more than 16,000 images of its thermal images.

본 연구는 훼손된 하천 수생태계의 훼손원인을 식별하고 그 전이 과정을 규명하기 위해 수생태계 훼손원인 진단체계를 구축하고 정책적 활용 가능성을 보고자 하였다. 수생태계 현황 조사 및 건강성 평가결과는 하천의 훼손에 대한 전반적인 상황을 알 수 있어 하천 복원 및 관리의 판단 근거로 사용된다. 그러나 하천이 훼손된 원인이나 훼손이 발생하게 된 전이 과정을 고려하지 못한다는 한계를 가진다. 따라서 본 연구에서는 과학적이고 체계적인 진단체계를 제안하여 훼손원인과 그 전이 과정을 규명하였다. 미국, 호주, 유럽의 사례를 분석하여 우리나라에 맞는 진단체계를 제안하였다. 총 8단계로 구성되며, 기초자료 조사, 하천 수생태계 훼손 판단, 훼손 구간 설정, 훼손 유형화 및 잠재적 훼손원인 도출, 본 조사, 주 훼손원인 진단 및 최적 훼손 모델 도출, 종합분석 및 하천 복원방안으로 이루어져 있다. 이러한 진단체계는 하천 생태계의 복잡하고 다양한 특성을 고려하여 훼손원인을 파악할 수 있으며 하천 복원 및 관리 방안을 제시하여 효율적인 의사결정을 지원할 수 있다.

EUS는 통상적인 US보다 좋은 영상을 제공하기 때문에 콜레스테롤 용종과 다른 악성 용종과의 감별에 도움을 준다. 그러나 10 mm 이하의 작은 용종성 병변에서는 치료를 결정함에 있어 결정적인 역할을 하는 데는 아직 제한점이 있다. 담낭의 용종성 병변에서 비악성, 악성 그리고 악성화 가능성이 있는 용종성 병변들을 감별하고자 다양한 소견을 통한 점수제 방법과 조영제를 이용한 조영증강 EUS 검사 등의 새로운 검사기법들의 시도가 이루어지고 있어 담낭용종성 병변의 감별과 치료에 도움이 되고 있다.

Objective: To evaluate item-factor structures of the 15 sensory items from the Survey of Pathways to Diagnosis and Services (Pathways) and examine the best fitting model. Methods: The study subjects were 1,968 children aged 6-17 years, who had ever been diagnosed with Autism Spectrum Disorder (ASD) and/or Intellectual Disability (ID). Factor analyses and item response theory models were used to determine the best fitting item-factor structure of the sensory items. The Strengths and Difficulties Questionnaires (SDQ) was used to test the concurrent validity of the sensory severity estimates. Results: A bifactor MIRT model (a general and four sensory factors) was selected as the best fitting model. All items statistically fitted to the bifactor model (p > .01) and showed moderate correlations with all five subscales of the SDQ (r s = .31 ~.51, p < .0001). The general sensory score differentiated the four different diagnostic groups (ASD, ID, ASD with ID, and no current symptoms) [F (3,1961) = 207.4, p < .0001]. Conclusion: The Pathways sensory items yielded reliable estimates of the general and each corresponding sensory factor by applying a bifactor MIRT model. The sensory score can be used as a valid sensory measure in the population survey.

Nowadays, since there are so many big data available everywhere, those big data can be used to find useful information to improve design and operation by using various analysis methods such as data mining. Especially if we have event log data that has execution history data of an organization such as case_id, event_time, event (activity), performer, etc., then we can apply process mining to discover the main process model in the organization. Once we can find the main process from process mining, we can utilize it to improve current working environment. In this paper we developed a new method to find a final diagnosis of a patient, who needs several procedures (medical test and examination) to diagnose disease of the patient by using process mining approach. Some patients can be diagnosed by only one procedure, but there are certainly some patients who are very difficult to diagnose and need to take several procedures to find exact disease name. We used 2 million procedure log data and there are 397 thousands patients who took 2 and more procedures to find a final disease. These multi-procedure patients are not frequent case, but it is very critical to prevent wrong diagnosis. From those multi-procedure taken patients, 4 procedures were discovered to be a main process model in the hospital. Using this main process model, we can understand the sequence of procedures in the hospital and furthermore the relationship between diagnosis and corresponding procedures.