• 대한전기학회논문지:시스템및제어부문D
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• 제51권9호
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• pp.413-422
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• 2002

In a power plant, disturbance detection and diagnosis are massive and complex problems. Once a disturbance occurs, it can be either persistent, self cleared, cleared by the automatic controllers or propagated into another disturbance until it subsides in a new equilibrium or a stable state. In addition to the Physical complexity of the power plant structure itself, these dynamic behaviors of the disturbances further complicate the fault monitoring and diagnosis tasks. A data structure called a disturbance interrelation analysis graph(DIAG) is proposed in this paper, trying to capture, organize and better utilize the vast and interrelated knowledge required for power plant disturbance detection and diagnosis. The DIAG is a multi-layer directed AND/OR graph composed of 4 layers. Each layer includes vertices that represent components, disturbances, conditions and sensors respectively With the implementation of the DIAG, disturbances and their relationships can be conveniently represented and traced with modularized operations. All the cascaded disturbances following an initial triggering disturbance can be diagnosed in the context of that initial disturbance instead of diagnosing each of them as an individual disturbance. DIAG is applied to a typical cooling water system of a thermal power plant and its effectiveness is also demonstrated.

• 한국조명전기설비학회지:조명전기설비
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• 제9권2호
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• pp.68-78
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• 1995

지금까지 공동주택에서 전력감시반은 지하실에 설치된 그래픽보드를 이용하여 감시를 수행하고 있으나, 공동주택에서의 인력구조와 시스템특성상 적절한 감시방법이라고 할 수 없다. 따라서 본 논문에서는 컴퓨터를 이용한 인텔리전트 감시시스템을 제안하였다. 제안된 감시반은 직렬통신방식을 이용한 집중감시시스템으로 설비를 디자인된 화면으로 실시간 감시하고 사용이력과 고장진단을 위한 지식베이스를 구현하여 감시반 효율성의 향상과 감시반의 공사비 절감을 유도하였으며, 실시간 O.S는 Trend 940을 이용하였다.

• Smart Structures and Systems
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• 제24권6호
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• pp.733-744
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• 2019

Wind speed forecasting is critical for a variety of engineering tasks, such as wind energy harvesting, scheduling of a wind power system, and dynamic control of structures (e.g., wind turbine, bridge, and building). Wind speed, which has characteristics of random, nonlinear and uncertainty, is difficult to forecast. Nowadays, machine learning approaches (generalized regression neural network (GRNN), back propagation neural network (BPNN), and extreme learning machine (ELM)) are widely used for wind speed forecasting. In this study, two schemes are proposed to improve the forecasting performance of machine learning approaches. One is that optimization algorithms, i.e., cross validation (CV), genetic algorithm (GA), and particle swarm optimization (PSO), are used to automatically find the optimal model parameters. The other is that the combination of different machine learning methods is proposed by finite mixture (FM) method. Specifically, CV-GRNN, GA-BPNN, PSO-ELM belong to optimization algorithm-assisted machine learning approaches, and FM is a hybrid machine learning approach consisting of GRNN, BPNN, and ELM. The effectiveness of these machine learning methods in wind speed forecasting are fully investigated by one-year field monitoring data, and their performance is comprehensively compared.

• 대한영양사협회학술지
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• 제21권1호
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• pp.37-56
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• 2015

This study was conducted to develop job standards for clinical dietitian administering clinical nutrition therapy to diabetic patients in hospitals. Based on DACUM (Developing A Curriculum) analysis of 17 members including clinical dietitians, professors majoring in clinical nutrition and researchers, information on duties, tasks and task elements of clinical dietitians for diabetes care were derived and applied to diabetes mellitus-specific clinical nutrition care in hospitals for evaluation. The final developed job standards for clinical dietitians for diabetes care included four duties, 19 tasks and 56 task elements. The duties consisted of nutrition assessment, nutrition diagnosis, nutrition intervention, and nutrition monitoring evaluation. For application of diabetes mellitus-specific job standards in clinical nutrition care, 108 work activities were developed and classified into 90 basic and 18 recommended types. Performance rates of standardized jobs were 80.2% at nutrition assessment, 99.6% at nutrition diagnosis, 78.5% at nutrition intervention, and 32.9% at nutrition monitoring evaluation. These results can be applied as guidelines to implement jobs for diabetes mellitus-specific clinical nutrition services in clinical settings. In addition, they would be useful for education standards in educational institutions for education and training of clinical dietitian.

• 한국안전학회지
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• 제36권5호
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• pp.79-85
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• 2021

A standard engineering model that reflects the current organization system and engineering operation process of domestic nuclear power plants was developed based on the Standard Nuclear Performance Model developed by the American Nuclear Energy Association. The level 0 screen, which is the main screen of the engineering model computer system, consisted of an object tree structure, which provided information that is phased down from a higher structure level to a lower structure level (i.e., level 3). The level 1 screen provided information related to the sub-process of the engineering operation, whereas the Level 2 screen provided information related to each engineering operation activity. In addition, the Level 2 screen provided additional functions, such as linking electronic procedures/guidelines, providing electronic performance forms, and connecting legacy computer systems (such as total equipment reliability monitoring system, configuration management systems, technical information systems, risk monitoring systems, regulatory information, and electronic drawing system). This screen level increased the convenience of user's engineering tasks by implementing them. The computerization of an engineering model that connects the entire engineering tasks of an establishment enables the easy understanding of information related to the engineering process before and after the operation, and builds a foundation for the enhancement of the work efficiency and employee capacity. In addition, KHNP developed an online training module, which operates as an e-learning process, on the overview and utilization of a standard engineering model to expand the understanding of standard engineering models by plant employees and to secure competitiveness.

• 한국물환경학회지
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• 제37권6호
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• pp.493-500
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• 2021

Floating solar photovoltaic (hereinafter PV) power generation is emerging as a proper alternative to overcome various environmental limitations of existing offshore PV generation. However, more government-led policy design and technical and institutional development are still required. Based on the policy agenda setting theory and technological innovation theory, this study contains the research questions concerning the co-evolution of technology and the floating solar PV policy. This study primarily evaluates the technological and institutional development level of floating solar PV policy through a survey of domestic floating solar PV experts. Secondly, we also analyze the kind of policy agenda that should be set a priori. Analyzing the priorities to be considered, the first environmental enhancement needs to be considered from both the technical and institutional aspects. The second candidate task for the policy agenda is residents' conflict and improvement of regulations. Both candidate tasks need to be actively considered in the policy agenda from the institutional point of view. The third is publicity, profit sharing, follow-up monitoring, and cost. Among them, public relations and profit sharing are tasks that need to be considered in the policy agenda from the institutional point of view. On the other hand, the cost of follow-up monitoring should be considered as a policy agenda in terms of technology, system, and common aspects. Finally, there are technical standards. Likewise, technical standards need to be considered in the policy agenda in terms of both technical and institutional commonality.

• 한국보건간호학회지
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• 제35권3호
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• pp.430-447
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• 2021

Purpose: The purpose of this study was to analyze the role and tasks of nurses who were working for the elderly in the visiting health services at the public health centers. Methods: Literature reviews, two rounds of meetings with 5 experts and a two-round Delphi technique with 15 experts were performed in this study. Results: The nurses' role and job analysis revealed 5 roles, 16 duties and, 71 tasks. The nurses' roles, including discovery and registration of households/groups in visiting health service in the community, case manager, administrative management, program planning, operation and evaluation, and development of job competency. Sixteen duties included client registration and management, need assessment and plan establishment, education, consultation and support, seasonal health care, prevention and monitoring of infectious diseases, basic nursing care, chronic disease management, linkage and utilization of resources, team cooperation and coordination, home environment management, monitoring and support for intervention outcomes, evaluation, administrative management, program planning, operation and evaluation, development of professional competency and, adoption of fourth industrial revolution technology. Conclusions: Based on the results, the government should provide sufficient nursing personnel to provide universal preventive health services for the elderly and a job training program to perform these roles well.

• 한국정보과학회논문지:소프트웨어및응용
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• 제37권7호
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• pp.568-572
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• 2010

Mission-critical 시스템의 경우 자가 치유는 신뢰성을 보장하기 위한 기술 중 하나이다. 자가치유는 오류 탐지와 오류 회복으로 이루어져 있으며 오류 탐지는 오류 회복을 가능하게 하는 자가 치유의 중요한 첫 단계이지만 시스템에 과부하를 주는 문제가 있다. 모델 기반의 방법 등으로 오류를 탐지할 수 있는데 시스템의 모든 행위를 통지하고 정상 행위 모델과 통지된 시스템의 행위를 비교하여야 하므로 그양이 많고 부하가 크기 때문이다. 본 논문에서는 모델 기반의 오류 탐지 방법을 보완하는 아키텍처 기반의 다계층적 자가적응형 모니터링 방법을 제안한다. 소프트웨어 아키텍처 상에서 오류 탐지의 중요도는 컴포넌트 마다 다르다. 각 컴포넌트마다 발생하는 오류의 심각도와 빈도가 다르기 때문이다. 모니터링 중요도가 높은 컴포넌트에는 강도가 높고 모니터링 중요도가 낮은 컴포넌트에는 강도가 낮도록 모니터가 적응한다면 오류 탐지의 부하는 줄이고 효율은 유지시킬 수 있다. 또한 소프트웨어의 환경 변화 및 아키텍처상의 변화 등에 따라 오류 발생 빈도가 변화하여 컴포넌트의 오류 탐지 중요도가 변화하기 때문에 학습을 통해 이를 추적하여 자가적응적으로 중요도가 높은 컴포넌트를 집중 모니터링 한다.

• Wind and Structures
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• 제12권6호
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• pp.479-504
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• 2009

Structural health monitoring (SHM) systems have been recently embraced in long span cable-supported bridges, in which buffeting-induced stress monitoring is one of the tasks to ensure the safety of the bridge under strong winds. In line with this task, this paper presents a SHM-oriented finite element model (FEM) for the Tsing Ma suspension bridge in Hong Kong so that stresses/strains in important bridge components can be directly computed and compared with measured ones. A numerical procedure for buffeting induced stress analysis of the bridge based on the established FEM is then presented. Significant improvements of the present procedure are that the effects of the spatial distribution of both buffeting forces and self-excited forces on the bridge deck structure are taken into account and the local structural behaviour linked to strain/stress, which is prone to cause local damage, are estimated directly. The field measurement data including wind, acceleration and stress recorded by the wind and structural health monitoring system (WASHMS) installed on the bridge during Typhoon York are analyzed and compared with the numerical results. The results show that the proposed procedure has advantages over the typical equivalent beam finite element models.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권7호
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• pp.1759-1772
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• 2023

Chinese named entity recognition (NER) is a challenging work that seeks to find, recognize and classify various types of information elements in unstructured text. Due to the Chinese text has no natural boundary like the spaces in the English text, Chinese named entity identification is much more difficult. At present, most deep learning based NER models are developed using a bidirectional long short-term memory network (BiLSTM), yet the performance still has some space to improve. To further improve their performance in Chinese NER tasks, we propose a new NER model, IDCNN-BiLSTM-Highway, which is a combination of the BiLSTM, the iterated dilated convolutional neural network (IDCNN) and the highway network. In our model, IDCNN is used to achieve multiscale context aggregation from a long sequence of words. Highway network is used to effectively connect different layers of networks, allowing information to pass through network layers smoothly without attenuation. Finally, the global optimum tag result is obtained by introducing conditional random field (CRF). The experimental results show that compared with other popular deep learning-based NER models, our model shows superior performance on two Chinese NER data sets: Resume and Yidu-S4k, The F1-scores are 94.98 and 77.59, respectively.