Incident investigation is regarded as a means to improve safety performance. For the prevention of industrial accidents, measures such as providing safety education, enhancing management interest and participation, establishing a safety management system, and conducting inspection of the work site are necessary. In particular, accident investigation activities, which are an important element of safety management, help to prevent similar accidents, thereby minimizing damage and enhancing work safety. They are critical for understanding business-related incidents and the vulnerabilities and opportunities associated with them. Therefore, it is clear that accident investigation activities are important for accident prevention. The primary focus of many incident investigation processes is on identifying the cause of an event. While considerable research has been conducted on potential accident investigation tools there has been little research on including the views and experiences of practitioners in the accident investigation process. In this study, a questionnaire survey was conducted among safety managers in the domestic manufacturing/construction industry to understand the practice of accident investigation. The investigation pertained to companies' accident investigation systems, the competence of investigators, and the identification and recommendations of the cause of accidents. From the analysis results of accident investigations, investigators' competence, the difficulty level of investigations, and the root causes of accidents were identified from the viewpoint of the participants of the accident investigations. In particular, the development of standardized and simple accident investigation methods and their dissemination to companies were found to be necessary for activating the root cause of accidents. Based on this, it can be used as basic data for the development of root cause analysis investigation techniques that are easily applicable to organizations.
Song, Eun Jeong;Kim, Mi Seon;Lee, Joo Hee;Jeon, Mi Yang
Journal of Korean Clinical Nursing Research
/
v.26
no.3
/
pp.344-351
/
2020
Purpose: The purpose of this study was to identify related factors in the organizational socialization of new nurses, focusing on the reality shock of new nurses and social support by education specialist nurses. Methods: Participants were 122 new nurses with clinical experience of 1 year or less as nurses working in a general hospital that is carrying out a pilot project for a new nurse training system. Data were collected using self-report questionnaires which included identification of participants' characteristics, social support of education specialist nurse, reality shock and organizational socialization. Results: The organizational socialization score was 3.07±0.45 points. The results of the stepwise multiple regression showed that factors affecting organizational socialization of new nurses were social support by gender, education specialist nurse and reality shock. These three variables accounted for 72.0% of organizational socialization. Conclusion: These findings suggest that the social support of education specialist nurses be enhanced to increase the organizational socialization of new nurses. There is also a need for a program to decreased the reality shock of new nurses.
Background: Gut microflora contributes to the nutritional metabolism of the host and to strengthen its immune system. However, if the intestinal barrier function of the living body is destroyed by radiation exposure, the intestinal bacteria harm the health of the host and cause sepsis. Therefore, this study aims to trace short-term radiation-induced changes in the mouse gut microflora-dominant bacterial genus, and analyze the degree of intestinal epithelial damage. Materials and Methods: Mice were irradiated with 0, 2, 4, 8 Gy X-rays, and the gut microflora and intestinal epithelial changes were analyzed 72 hours later. Five representative genera of Actinobacteria, Firmicutes, and Bacteroidetes were analyzed in fecal samples, and the intestine was pathologically analyzed by Hematoxylin-Eosin and Alcian blue staining. In addition, DNA fragmentation was evaluated by the TdT-mediated dUTP nick-end labeling (TUNEL) assay. Results and Discussion: The small intestine showed shortened villi and reduced number of goblet cells upon 8 Gy irradiation. The large intestine epithelium showed no significant morphological changes, but the number of goblet cells were reduced in a radiation dose-dependent manner. Moreover, the small intestinal epithelium of 8 Gy-irradiated mice showed significant DNA damaged, whereas the large intestine epithelium was damaged in a dose-dependent manner. Overall, the large intestine epithelium showed less recovery potential upon radiation exposure than the small intestinal epithelium. Analysis of the intestinal flora revealed fluctuations in lactic acid bacteria excretion after irradiation regardless of the morphological changes of intestinal epithelium. Altogether, it became clear that radiation exposure could cause an immediate change of their excretion. Conclusion: This study revealed changes in the intestinal epithelium and intestinal microbiota that may pave the way for the identification of novel biomarkers of radiation-induced gastrointestinal disorders and develop new therapeutic strategies to treat patients with acute radiation syndrome.
Journal of the Korean Society for Aeronautical & Space Sciences
/
v.50
no.2
/
pp.103-110
/
2022
Degradation of handling qualities(HQs) due to bad weather or mechanical failure can pose a fatal risk to pilots unfamiliar with such situation. In particular, icing is an important issue to consider as it is a frequent cause of accidents. Most of the previous research works focuses on aerodynamic performance changes due to icing and the corresponding icing modeling or methods to prevent icing, whereas the present work attempts to actively compensate for HQ degradation due to icing on a full-scale helicopter through flight control law design. To this end, the present work first demonstrates HQ degradation due to icing using CONDUIT software, and subsequently presents a robust control design via the RS-LQR(Robust Servomechanism Linear Quadratic Regulation) procedure to compensate for the HQ degradation. Simulation results show that the proposed robust control maintains Level 1 HQ in the presence of icing.
International Journal of Computer Science & Network Security
/
v.22
no.4
/
pp.420-426
/
2022
Breast cancer is among the cancers that may be healed as the disease diagnosed at early times before it is distributed through all the areas of the body. The Automatic Analysis of Diagnostic Tests (AAT) is an automated assistance for physicians that can deliver reliable findings to analyze the critically endangered diseases. Deep learning, a family of machine learning methods, has grown at an astonishing pace in recent years. It is used to search and render diagnoses in fields from banking to medicine to machine learning. We attempt to create a deep learning algorithm that can reliably diagnose the breast cancer in the mammogram. We want the algorithm to identify it as cancer, or this image is not cancer, allowing use of a full testing dataset of either strong clinical annotations in training data or the cancer status only, in which a few images of either cancers or noncancer were annotated. Even with this technique, the photographs would be annotated with the condition; an optional portion of the annotated image will then act as the mark. The final stage of the suggested system doesn't need any based labels to be accessible during model training. Furthermore, the results of the review process suggest that deep learning approaches have surpassed the extent of the level of state-of-of-the-the-the-art in tumor identification, feature extraction, and classification. in these three ways, the paper explains why learning algorithms were applied: train the network from scratch, transplanting certain deep learning concepts and constraints into a network, and (another way) reducing the amount of parameters in the trained nets, are two functions that help expand the scope of the networks. Researchers in economically developing countries have applied deep learning imaging devices to cancer detection; on the other hand, cancer chances have gone through the roof in Africa. Convolutional Neural Network (CNN) is a sort of deep learning that can aid you with a variety of other activities, such as speech recognition, image recognition, and classification. To accomplish this goal in this article, we will use CNN to categorize and identify breast cancer photographs from the available databases from the US Centers for Disease Control and Prevention.
The effectiveness of system identification, damage detection, condition assessment and other structural analyses relies heavily on the accuracy and reliability of the measured data in structural health monitoring (SHM) systems. However, data anomalies often occur in SHM systems, leading to inaccurate and untrustworthy analysis results. Therefore, anomalies in the raw data should be detected and cleansed before further analysis. Previous studies on data anomaly detection mainly focused on just single type of data anomaly for denoising or removing outliers, meanwhile, the existing methods of detecting multiple data anomalies are usually time consuming. For these reasons, recognising multiple anomaly patterns for real-time alarm and analysis in field monitoring remains a challenge. Aiming to achieve an efficient and accurate detection for multi-type data anomalies for field SHM, this study proposes a pattern-recognition-based data anomaly detection method that mainly consists of three steps: the feature extraction from the long time-series data samples, the training of a pattern recognition neural network (PRNN) using the features and finally the detection of data anomalies. The feature extraction step remarkably reduces the time cost of the network training, making the detection process very fast. The performance of the proposed method is verified on the basis of the SHM data of two practical long-span bridges. Results indicate that the proposed method recognises multiple data anomalies with very high accuracy and low calculation cost, demonstrating its applicability in field monitoring.
Medical AI, which has lately made significant advances, is playing a vital role, such as assisting clinicians with diagnosis and decision-making. The field of chest X-rays, in particular, is attracting a lot of attention since it is important for accessibility and identification of chest diseases, as well as the current COVID-19 pandemic. However, despite the vast amount of data, there remains a limit to developing an effective AI model due to a lack of labeled data. A research that used federated learning on chest X-ray data to lessen this difficulty has emerged, although it still has the following limitations. 1) It does not consider the problems that may occur in the Non-IID environment. 2) Even in the federated learning environment, there is still a shortage of labeled data of clients. We propose a method to solve the above problems by using the self-supervised learning model as a global model of federated learning. To that aim, we investigate a self-supervised learning methods suited for federated learning using chest X-ray data and demonstrate the benefits of adopting the self-supervised learning model for federated learning.
Objectives: To compare Korean medicine (KM) and traditional Chinese medicine (TCM) psychotherapy for anxiety. Methods: Databases including MEDLINE (via PubMed), EMBASE (via Elsevier), Cochrane Central Register of Controlled Trials, China National Knowledge Infrastructure, and Oriental Medicine Advanced Searching Integrated System were comprehensively searched. Prospective clinical studies on KM or TCM psychotherapy for patients with anxiety disorder or individuals with elevated anxiety levels published up to August 3, 2022 were reviewed. Psychotherapy was divided into counselling, art therapy, and meditation according to its characteristics. Results: A total of 12 clinical studies were reviewed, including nine randomized controlled trials. The most common disorder investigated was post-traumatic stress disorder. Ten studies used TCM psychotherapy and two used KM psychotherapy. As for differences between TCM psychotherapy and KM psychotherapy, TCM psychotherapy utilized pattern identification in the procedure more actively than KM psychotherapy. In addition, some TCM studies have attempted to directly converge Western psychotherapy (i.e., hypnosis) and Eastern psychotherapy (i.e., Taoin qigong therapy). In the case of KM psychotherapy, there was an attempt to incorporate psychotherapy with Sasang constitutional medicine. Reported effects of TCM psychotherapy and KM psychotherapy on anxiety were positive. Conclusions: Research status of KM psychotherapy and TCM psychotherapy for anxiety was investigated, revealing some of their characteristics, commonalities, and differences. Findings of this review have the potential to provide a clue to the development of conventional KM psychotherapy and new medical technology for KM psychotherapy.
The Journal of the Korea institute of electronic communication sciences
/
v.18
no.1
/
pp.115-126
/
2023
Due to the fluctuating random and periodical nature of renewable energy generation power quality disturbances occurred more frequently in power generation transformation transmission and distribution. Various power quality disturbances may lead to equipment damage or even power outages. Therefore it is essential to detect and classify different power quality disturbances in real time automatically. The traditional PQD identification method consists of three steps: feature extraction feature selection and classification. However, the handcrafted features are imprecise in the feature selection stage, resulting in low classification accuracy. This paper proposes a deep neural architecture based on Convolution Neural Network and Long Short Term Memory combining the time and frequency domain features to recognize 16 types of Power Quality signals. The frequency-domain data were obtained from the Fast Fourier Transform which could efficiently extract the frequency-domain features. The performance in synthetic data and real 6kV power system data indicate that our proposed method generalizes well compared with other deep learning methods.
Background: Sexual dimorphism is important for sex determination in the field of forensics. However, sexual dimorphism is commonly assessed using cone beam computed tomography (CBCT) rather than three-dimensional (3D) modeling software; therefore, studies using a more accurate measurement approach are necessary. This study assessed the sexual dimorphism of the MS using a 3D modeling program to obtain information that could contribute to the fields of surgery and forensics. Methods: The CBCT data of 60 patients (age, 20~29 y; 30 males and 30 females) admitted to the Department of Orthodontics at the Dankook University School of Dentistry were provided in Digital Imaging and Communications in Medicine (DICOM) format. The left MS and right MS were modeled based on the DICOM files using the Mimics (version 22; Materialise, Leuven, Belgium) 3D program and converted to stereolithography (STL) files used to measure the width, length, and height of the MS, infraorbital foramen (IOF), right MS, and left MS. The average of three repeated measurements was calculated, and a reliability test was performed to ensure data reliability (Cronbach's α=0.618). A canonical discriminant analysis was performed using a standard approach (left: Box's M=0.096; right: Box's M=0.115). Results: Males had greater values for all parameters (MS width, MS length, MS height, IOF, right MS, left MS) than females. The discriminant analysis identified six independent variables (MS width, MS height, MS length, IOF, right MS, left MS) that could identify sex. The left MS and right MS correctly identified the sex of 81.7% and 71.7% of the patients, respectively, with the left MS having higher accuracy. Conclusion: This study confirmed that, for Korean individuals, the left MS has a better ability to identify sex than the right MS. These results may contribute to sex identification in the fields of surgery and forensics.
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.