The alarm monitoring technology applied to existing operating ships manages data items such as temperature and pressure with AMS (Alarm Monitoring System) and provides an alarm to the crew should these sensing data exceed the normal level range. In addition, the maintenance of existing ships follows the Planned Maintenance System (PMS). whereby the sensing data measured from the equipment is monitored and if it surpasses the set range, maintenance is performed through an alarm, or the corresponding part is replaced in advance after being used for a certain period of time regardless of whether the target device has a malfunction or not. To secure the reliability and operational safety of ship engine operation, it is necessary to enable advanced diagnosis and prediction based on real-time condition monitoring data. To do so, comprehensive measurement of actual ship data, creation of a database, and implementation of a condition diagnosis monitoring system for condition-based predictive maintenance of auxiliary equipment and piping must take place. Furthermore, the system should enable management of auxiliary equipment and piping status information based on a responsive web, and be optimized for screen and resolution so that it can be accessed and used by various mobile devices such as smartphones as well as for viewing on a PC on board. This update cost is low, and the management method is easy. In this paper, we propose CBM (Condition Based Management) technology, for autonomous ships. This core technology is used to identify abnormal phenomena through state diagnosis and monitoring of pumps and purifiers among ship auxiliary equipment, and seawater and steam pipes among pipes. It is intended to provide performance diagnosis and failure prediction of ship auxiliary equipment and piping for convergence analysis, and to support preventive maintenance decision-making.
The Journal of Korea Institute of Information, Electronics, and Communication Technology
/
v.15
no.2
/
pp.109-116
/
2022
In this paper, we propose a band rejection filter (BRF) with a state variable filter (SVF) structure to effectively remove the influence of 60 Hz line frequency noise introduced into the sensor system. The conventional BRF of the SVF structure uses an additional operational amplifier (OPAMP) to add a low pass filter (LPF) output and a high pass filter (HPF) output or an input signal and a band pass filter. Therefore, the notch frequency and the notch depth that determine the signal attenuation of the BRF greatly depend on the tolerance of the resistors used to obtain the sum or difference of the signals. On the other hand, in the proposed BRF, since the BRF output is formed naturally within the SVF structure, there is no need for a combination between each port. The notch frequency of the proposed BRF is 59.99 Hz, and it can be confirmed that it is not affected at all by the tolerance of the resistor through the Monte Carlo simulation results. The notch depth also has an average of -42.54dB and a standard deviation of 0.63dB, confirming that normal operation as a BRF is possible. Also, with the proposed BRF, noise filtering was applied to the electrocardiogram (ECG) signal that interfered with 60 Hz noise, and it was confirmed that the 60 Hz noise was appropriately suppressed.
Maglev rail joints are vital components serving as connections between the adjacent F-type rail sections in maglev guideway. Damage to maglev rail joints such as bolt looseness may result in rough suspension gap fluctuation, failure of suspension control, and even sudden clash between the electromagnets and F-type rail. The condition monitoring of maglev rail joints is therefore highly desirable to maintain safe operation of maglev. In this connection, an online damage detection approach based on three-dimensional (3D) convolutional neural network (CNN) and time-frequency characterization is developed for simultaneous detection of multiple damage of maglev rail joints in this paper. The training and testing data used for condition evaluation of maglev rail joints consist of two months of acceleration recordings, which were acquired in-situ from different rail joints by an integrated online monitoring system during a maglev train running on a test line. Short-time Fourier transform (STFT) method is applied to transform the raw monitoring data into time-frequency spectrograms (TFS). Three CNN architectures, i.e., small-sized CNN (S-CNN), middle-sized CNN (M-CNN), and large-sized CNN (L-CNN), are configured for trial calculation and the M-CNN model with excellent prediction accuracy and high computational efficiency is finally optioned for multiple damage detection of maglev rail joints. Results show that the rail joints in three different conditions (bolt-looseness-caused rail step, misalignment-caused lateral dislocation, and normal condition) are successfully identified by the proposed approach, even when using data collected from rail joints from which no data were used in the CNN training. The capability of the proposed method is further examined by using the data collected after the loosed bolts have been replaced. In addition, by comparison with the results of CNN using frequency spectrum and traditional neural network using TFS, the proposed TFS-CNN framework is proven more accurate and robust for multiple damage detection of maglev rail joints.
Journal of the Korea Society of Computer and Information
/
v.27
no.12
/
pp.9-16
/
2022
In this paper, we propose a plan to design and utilize the RDBS(Record Block Data file management System) so that data can be recovered when data files in the Combat Management System are mismatched. The CMS(Combat Management System) manages the same files in multiple IPN(Infomation Processing Node) repositories to support multiplexing. However, mismatches in data files can occur due to equipment maintenance or user immaturity. The existing CMS does not manage the history of changes in data files, and when a mismatch occurs, data file were synchronized based on the latest date. But, It is difficult to say that files with the latest date have the highest reliability, and once the file synchronization has progressed, it cannot be recovered with pre-synchronization data. To solve this problem, data was stored and synchronized in units of record blocks using RDBS proposed in this paper, and the Rsync algorithm was used to reduce the overhead of file synchronization due to units of record blocks. SW applied with RDBS was tested for performance in a simulated environment, and it was confirmed that it could be applied to CMS through normal operation confirmation.
Recently, Urban Aircraft Mobility (UAM) has been attracting attention as a transportation system of the future, and small drones also play a role in various industries. The failure of various types of aviation systems can lead to crashes, which can result in significant property damage or loss of life. In the defense industry, where aviation systems are widely used, the failure of aviation systems can lead to mission failure. Therefore, this study proposes an anomaly detection model using deep learning technology to detect anomalies in aviation systems to improve the reliability of development and production, and prevent accidents during operation. As training and evaluating data sets, current data from aviation systems in an extremely low-temperature environment was utilized, and a deep learning network was implemented using the convolutional neural network, which is a deep learning technique that is commonly used for image recognition. In an extremely low-temperature environment, various types of failure occurred in the system's internal sensors and components, and singular points in current data were observed. As a result of training and evaluating the model using current data in the case of system failure and normal, it was confirmed that the abnormality was detected with a recall of 98 % or more.
To predict accurately the thermal hydraulic behavior of light water reactors during normal or abnormal operation, the accurate estimation of the void distribution is required. Up to date, many techniques for predicting void fraction of two-phase flow systems have been suggested. Among these techniques, the drift-flux model is widely used because of its exact calculation ability and simplicity. However, to get more accurate prediction of void fraction using drift-flux model, slip and flow regime effects must be considered more properly In the drift-flux method, these two effects are accounted for by two drift-flux parameters ; $C_{o}$ and (equation omitted). At earlier stage, $C_{o}$ is measured in a circular tube. In this study, $C_{o}$ is experimentally determined by measuring local void fraction and vapor velocity distribution in a rectangular subchannel having 4 heating rods which simulates nuclear subchannels. The measurements are peformed with two-electrical conductivity probes which are known to be adequate for measuring local parameters. The experiments are performed at low flow rate and the system pressure less than 3 atmo spheric pressure. In this experiment, (equation omitted), is not measured, but quoted from well-known empirical correlation to formulate $C_{o}$. Finally, $C_{o}$ is expressed as a function of channel averaged void fraction. fraction.
Korean Journal of Construction Engineering and Management
/
v.24
no.6
/
pp.63-65
/
2023
BIM and AR technologies have been assessed as a means of enhancing productivity within the construction industry, through the provision of effortless access to critical data on site, achieved via the projection of 3D models and associated information onto actual structures. However, most of the previous researches for applying AR technology in construction quality management has been performed for construction projects in general, resulting in only overall on-site management solutions. Also, a few previous researches for the application of AR in the quality management of specific elements like reinforcements focused only on simple projection, so conducting specific quality inspection was impossible. Hence, this study aimed to develop a practically applicable BIM-based AR quality management system targeted for reinforcements. For the development of this system, the reinforcement inspection items on the quality checklist used at railway construction sites were analyzed, and four types of AR functions that can effectively address these items were developed and installed. The validation result of the system for the actual railway bridge showed a degradation of projection stability. This problem was solved through model simplification and enhancement of the AR device's hardware performance, and then the normal operation of the system was validated. Subsequently, the final developed reinforcement quality inspection system was evaluated for practical applicability by on-site quality experts, and the efficiency of inspection would significantly increase when using the AR system compared to the current inspection method for reinforcements.
Asia-Pacific Journal of Business Venturing and Entrepreneurship
/
v.18
no.2
/
pp.67-79
/
2023
The bounce rate is the rate at which a user leaves immediately after visiting, and this study aimed to find out what attributes of a website affect the bounce rate. Web site evaluation items were defined as a total of 4 items and 27 evaluation attributes, including usability, information, service interaction, and technology, so that they can be commonly applied to venture companies in various industries through prior research. As a result of the study, 6 website attributes that affect the bounce rate were verified to be significant by discriminant analysis and decision tree analysis. Suggestions to reduce the bounce rate of venture business websites through this study are as follows. First, the path name of the website is displayed as mandatory and a pull-down menu function is added to facilitate movement to other pages. Second, it is good to expose core content that can attract users' attention in the form of a banner, and place internal link banners in the right place on sub-pages. Third, external links should be linked to a new window so that they do not leave the current page immediately so that they can be re-entered. Lastly, it is recommended to expose the contact information of the person in charge and consultation function as direct information for communication with customers, but if individual response is difficult, at least the consultation function must be added. These suggestions are expected to be of practical help in various fields such as website development, operation, and marketing. However, in special cases, a high bounce rate may be normal, so it should be considered according to the situation.
Sae Rom Chung;Jung Hwan Baek;Young Jun Choi;Tae-Yon Sung;Dong Eun Song;Tae Yong Kim;Jeong Hyun Lee
Korean Journal of Radiology
/
v.21
no.10
/
pp.1187-1195
/
2020
Objective: This study aimed to determine the sonographic features suggestive of extrathyroidal extension (ETE) of thyroid cancers. Materials and Methods: We retrospectively reviewed the sonographic images of 1656 consecutive patients who had undergone thyroidectomy in 2017. The diagnostic performance of sonographic features suggestive of ETE was evaluated using operation and histopathologic reports. Sonographic features for gross ETE to the strap muscle and minor ETE were assessed for thyroid cancer abutting the anterolateral thyroid capsule. Sonographic features for tracheal invasion were assessed according to whether the angle between the tumor and the trachea was an acute, right, or obtuse angle. Sonographic features for recurrent laryngeal nerve (RLN) invasion were assessed based on the association between the tumor and tracheoesophageal groove (TEG) as preserved normal tissue, abutting or protruding into the TEG. Results: ETE was observed in 783 patients (47.3%), including 123 patients with gross ETE (7.4% [strap muscle, n = 97; RLN, n = 24; and trachea, n = 14]) and 660 patients with minor ETE (39.9%). Regarding the diagnosis of gross and minor ETE to the strap muscle, sonographic features of replacement of the strap muscle and capsular disruption showed the highest positive predictive value (75.9% and 58.5%, respectively). Thyroid cancer forming an obtuse angle with the trachea had the highest sensitivity for the diagnosis of tracheal invasion (85.7%), and thyroid cancer protrusion into the TEG showed the highest sensitivity for the diagnosis of RLN (83.3%). Conclusion: Sonography is considered beneficial in the diagnosis of ETE to the strap muscle, trachea, and RLN. Assessment of ETE is important for the accurate staging of thyroid cancer, which in turn determines the extent of surgery or whether active surveillance is appropriate or not.
Dual Energy X-ray Absorptiometry(DEXA) is commonly used to diagnose Osteoporosis. The errors of DEXA bone density operation are caused by operator, bone mineral density meter, blood testing, patient. We focus on operator error then study about how much influence operator's region of intest(ROI) in bone testing result. During from March to July in 2011. 50 patients ware selected respectively from 30, 40, 50, 60, and 70 age groups who came to Korea University Medical Center(KUMC) for their Osteoporosis treatment. A-test was performed with usually ROI and B-test was performed with most widely ROI. Then, We compare A-test and B-test for find maximum difference of T-score error which occurred operator ROI controlling. Standard deviation of T-score of B-test showed 0.1 higher then A-test in femur neck. Standard deviation of B-test showed 0.2 higher then A-test in Ward's area which in Greater trocanter and Inter trocanter. Standard deviation of B-test showed 0,1 lower then A-test in L-1. Bone density testing about Two hundred patients results are as follow. When operator ROI was changed wider than normal ROI, bone density of femur was measured more higher but bone density of L-spine was measured more lower then normal bone density. That means, sometime DEXA bone density testing result is dependent by operator ROI controlling. This is relevant with the patient's medicine and health insurance, thus, tester always keep the size of ROI for to prevent any problem in the patient.
본 웹사이트에 게시된 이메일 주소가 전자우편 수집 프로그램이나
그 밖의 기술적 장치를 이용하여 무단으로 수집되는 것을 거부하며,
이를 위반시 정보통신망법에 의해 형사 처벌됨을 유념하시기 바랍니다.
[게시일 2004년 10월 1일]
이용약관
제 1 장 총칙
제 1 조 (목적)
이 이용약관은 KoreaScience 홈페이지(이하 “당 사이트”)에서 제공하는 인터넷 서비스(이하 '서비스')의 가입조건 및 이용에 관한 제반 사항과 기타 필요한 사항을 구체적으로 규정함을 목적으로 합니다.
제 2 조 (용어의 정의)
① "이용자"라 함은 당 사이트에 접속하여 이 약관에 따라 당 사이트가 제공하는 서비스를 받는 회원 및 비회원을
말합니다.
② "회원"이라 함은 서비스를 이용하기 위하여 당 사이트에 개인정보를 제공하여 아이디(ID)와 비밀번호를 부여
받은 자를 말합니다.
③ "회원 아이디(ID)"라 함은 회원의 식별 및 서비스 이용을 위하여 자신이 선정한 문자 및 숫자의 조합을
말합니다.
④ "비밀번호(패스워드)"라 함은 회원이 자신의 비밀보호를 위하여 선정한 문자 및 숫자의 조합을 말합니다.
제 3 조 (이용약관의 효력 및 변경)
① 이 약관은 당 사이트에 게시하거나 기타의 방법으로 회원에게 공지함으로써 효력이 발생합니다.
② 당 사이트는 이 약관을 개정할 경우에 적용일자 및 개정사유를 명시하여 현행 약관과 함께 당 사이트의
초기화면에 그 적용일자 7일 이전부터 적용일자 전일까지 공지합니다. 다만, 회원에게 불리하게 약관내용을
변경하는 경우에는 최소한 30일 이상의 사전 유예기간을 두고 공지합니다. 이 경우 당 사이트는 개정 전
내용과 개정 후 내용을 명확하게 비교하여 이용자가 알기 쉽도록 표시합니다.
제 4 조(약관 외 준칙)
① 이 약관은 당 사이트가 제공하는 서비스에 관한 이용안내와 함께 적용됩니다.
② 이 약관에 명시되지 아니한 사항은 관계법령의 규정이 적용됩니다.
제 2 장 이용계약의 체결
제 5 조 (이용계약의 성립 등)
① 이용계약은 이용고객이 당 사이트가 정한 약관에 「동의합니다」를 선택하고, 당 사이트가 정한
온라인신청양식을 작성하여 서비스 이용을 신청한 후, 당 사이트가 이를 승낙함으로써 성립합니다.
② 제1항의 승낙은 당 사이트가 제공하는 과학기술정보검색, 맞춤정보, 서지정보 등 다른 서비스의 이용승낙을
포함합니다.
제 6 조 (회원가입)
서비스를 이용하고자 하는 고객은 당 사이트에서 정한 회원가입양식에 개인정보를 기재하여 가입을 하여야 합니다.
제 7 조 (개인정보의 보호 및 사용)
당 사이트는 관계법령이 정하는 바에 따라 회원 등록정보를 포함한 회원의 개인정보를 보호하기 위해 노력합니다. 회원 개인정보의 보호 및 사용에 대해서는 관련법령 및 당 사이트의 개인정보 보호정책이 적용됩니다.
제 8 조 (이용 신청의 승낙과 제한)
① 당 사이트는 제6조의 규정에 의한 이용신청고객에 대하여 서비스 이용을 승낙합니다.
② 당 사이트는 아래사항에 해당하는 경우에 대해서 승낙하지 아니 합니다.
- 이용계약 신청서의 내용을 허위로 기재한 경우
- 기타 규정한 제반사항을 위반하며 신청하는 경우
제 9 조 (회원 ID 부여 및 변경 등)
① 당 사이트는 이용고객에 대하여 약관에 정하는 바에 따라 자신이 선정한 회원 ID를 부여합니다.
② 회원 ID는 원칙적으로 변경이 불가하며 부득이한 사유로 인하여 변경 하고자 하는 경우에는 해당 ID를
해지하고 재가입해야 합니다.
③ 기타 회원 개인정보 관리 및 변경 등에 관한 사항은 서비스별 안내에 정하는 바에 의합니다.
제 3 장 계약 당사자의 의무
제 10 조 (KISTI의 의무)
① 당 사이트는 이용고객이 희망한 서비스 제공 개시일에 특별한 사정이 없는 한 서비스를 이용할 수 있도록
하여야 합니다.
② 당 사이트는 개인정보 보호를 위해 보안시스템을 구축하며 개인정보 보호정책을 공시하고 준수합니다.
③ 당 사이트는 회원으로부터 제기되는 의견이나 불만이 정당하다고 객관적으로 인정될 경우에는 적절한 절차를
거쳐 즉시 처리하여야 합니다. 다만, 즉시 처리가 곤란한 경우는 회원에게 그 사유와 처리일정을 통보하여야
합니다.
제 11 조 (회원의 의무)
① 이용자는 회원가입 신청 또는 회원정보 변경 시 실명으로 모든 사항을 사실에 근거하여 작성하여야 하며,
허위 또는 타인의 정보를 등록할 경우 일체의 권리를 주장할 수 없습니다.
② 당 사이트가 관계법령 및 개인정보 보호정책에 의거하여 그 책임을 지는 경우를 제외하고 회원에게 부여된
ID의 비밀번호 관리소홀, 부정사용에 의하여 발생하는 모든 결과에 대한 책임은 회원에게 있습니다.
③ 회원은 당 사이트 및 제 3자의 지적 재산권을 침해해서는 안 됩니다.
제 4 장 서비스의 이용
제 12 조 (서비스 이용 시간)
① 서비스 이용은 당 사이트의 업무상 또는 기술상 특별한 지장이 없는 한 연중무휴, 1일 24시간 운영을
원칙으로 합니다. 단, 당 사이트는 시스템 정기점검, 증설 및 교체를 위해 당 사이트가 정한 날이나 시간에
서비스를 일시 중단할 수 있으며, 예정되어 있는 작업으로 인한 서비스 일시중단은 당 사이트 홈페이지를
통해 사전에 공지합니다.
② 당 사이트는 서비스를 특정범위로 분할하여 각 범위별로 이용가능시간을 별도로 지정할 수 있습니다. 다만
이 경우 그 내용을 공지합니다.
제 13 조 (홈페이지 저작권)
① NDSL에서 제공하는 모든 저작물의 저작권은 원저작자에게 있으며, KISTI는 복제/배포/전송권을 확보하고
있습니다.
② NDSL에서 제공하는 콘텐츠를 상업적 및 기타 영리목적으로 복제/배포/전송할 경우 사전에 KISTI의 허락을
받아야 합니다.
③ NDSL에서 제공하는 콘텐츠를 보도, 비평, 교육, 연구 등을 위하여 정당한 범위 안에서 공정한 관행에
합치되게 인용할 수 있습니다.
④ NDSL에서 제공하는 콘텐츠를 무단 복제, 전송, 배포 기타 저작권법에 위반되는 방법으로 이용할 경우
저작권법 제136조에 따라 5년 이하의 징역 또는 5천만 원 이하의 벌금에 처해질 수 있습니다.
제 14 조 (유료서비스)
① 당 사이트 및 협력기관이 정한 유료서비스(원문복사 등)는 별도로 정해진 바에 따르며, 변경사항은 시행 전에
당 사이트 홈페이지를 통하여 회원에게 공지합니다.
② 유료서비스를 이용하려는 회원은 정해진 요금체계에 따라 요금을 납부해야 합니다.
제 5 장 계약 해지 및 이용 제한
제 15 조 (계약 해지)
회원이 이용계약을 해지하고자 하는 때에는 [가입해지] 메뉴를 이용해 직접 해지해야 합니다.
제 16 조 (서비스 이용제한)
① 당 사이트는 회원이 서비스 이용내용에 있어서 본 약관 제 11조 내용을 위반하거나, 다음 각 호에 해당하는
경우 서비스 이용을 제한할 수 있습니다.
- 2년 이상 서비스를 이용한 적이 없는 경우
- 기타 정상적인 서비스 운영에 방해가 될 경우
② 상기 이용제한 규정에 따라 서비스를 이용하는 회원에게 서비스 이용에 대하여 별도 공지 없이 서비스 이용의
일시정지, 이용계약 해지 할 수 있습니다.
제 17 조 (전자우편주소 수집 금지)
회원은 전자우편주소 추출기 등을 이용하여 전자우편주소를 수집 또는 제3자에게 제공할 수 없습니다.
제 6 장 손해배상 및 기타사항
제 18 조 (손해배상)
당 사이트는 무료로 제공되는 서비스와 관련하여 회원에게 어떠한 손해가 발생하더라도 당 사이트가 고의 또는 과실로 인한 손해발생을 제외하고는 이에 대하여 책임을 부담하지 아니합니다.
제 19 조 (관할 법원)
서비스 이용으로 발생한 분쟁에 대해 소송이 제기되는 경우 민사 소송법상의 관할 법원에 제기합니다.
[부 칙]
1. (시행일) 이 약관은 2016년 9월 5일부터 적용되며, 종전 약관은 본 약관으로 대체되며, 개정된 약관의 적용일 이전 가입자도 개정된 약관의 적용을 받습니다.