• Nuclear Engineering and Technology
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• 제54권4호
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• pp.1230-1244
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• 2022

This paper proposes an algorithm for signal validation using unsupervised methods in emergency situations at nuclear power plants (NPPs) when signals are rapidly changing. The algorithm aims to determine the stuck failures of signals in real time based on a variational auto-encoder (VAE), which employs unsupervised learning, and long short-term memory (LSTM). The application of unsupervised learning enables the algorithm to detect a wide range of stuck failures, even those that are not trained. First, this paper discusses the potential failure modes of signals in NPPs and reviews previous studies conducted on signal validation. Then, an algorithm for detecting signal failures is proposed by applying LSTM and VAE. To overcome the typical problems of unsupervised learning processes, such as trainability and performance issues, several optimizations are carried out to select the inputs, determine the hyper-parameters of the network, and establish the thresholds to identify signal failures. Finally, the proposed algorithm is validated and demonstrated using a compact nuclear simulator.

• 교육심리연구
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• 제31권4호
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• pp.745-766
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• 2017

학생들의 지적 성장은 학교교육의 오랜 목표이다. 학생들의 지적 성장을 위해 최근에는 학습상황에서 학생들이 경험하는 실패의 교육적 활용가능성에 대해서 연구들이 진행되고 있다. 특히 최근에는 학습실패가 해당 영역에 대한 지식이나 문제해결력 뿐 아니라 학생들의 창의적 역량을 신장하기 위한 중요한 학습도구가 될 수 있다는 문헌들이 제시되고 있다. 본 연구는 학습자가 경험하는 학습실패가 어떻게 창의성 역량의 교육과 연계될 수 있는지 주요 이론과 교수-학습 전략들을 제시하고 있다. 교육 분야에서 학습실패 연구는 Clifford(1988)에 의해서 실패내성에 대한 연구에서부터 시작하여 현재 많은 연구들이 학습의 과정에서 학습실패의 중요성에 대해 주목하고 있다. 특히, Kapur(2008)와 같은 연구자는 학습에서 실패경험이 단순히 지식의 습득을 넘어서서 새로운 대안들을 만들어내고 창의적 문제해결 전략들을 찾아내는데 긍정적인 역할을 할 수 있음을 주장하고 있다. 구체적으로 본 연구에서는 학습과정에서 경험하는 실패를 어떻게 교육장면에서 활용할 수 있는 지 그 전략들에 대해 논의하고 그 과정에서 교수자나 학습자의 입장에서 주의해야 할 점들이 무엇인지 논의하였다. 구체적으로 학습자가 실패경험을 통해서 기존의 생각의 틀로 부터 벗어나기, 주어진 교육과정 영역을 넘어서 자유롭게 교육과정 영역을 탐색하도록 허용하기 등과 같은 전략을 제시하였으며 특히, 학습실패가 가져올 수 있는 부정적인 정서와 실패의 누적으로 인한 학습된 무기력을 극복하기 위한 전략으로서 학습 초기과정에서 실패경험의 부정적인 영향을 고려하거나 최소화하기 위한 전략에 대해 서술하고 있다. 그리고 실패의 경험을 결과로 인식하는 것이 아니라 학습과정의 일부로 인식하는 것이 왜 중요한 지 기술하였다.

• 대한임베디드공학회논문지
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• 제19권1호
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• pp.57-64
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• 2024

In the industrial field, robots are used to increase productivity by replacing labors with dangerous, difficult, and hard tasks. However, failures of individual industrial robots in the entire production process may cause product defects or malfunctions, and may cause dangerous disasters in the case of manufacturing parts used in automobiles and aircrafts. Although requirements for early diagnosis of industrial robot failures are steadily increasing, there are many limitations in early detection. This paper introduces methods for diagnosing robot failures using sound-based data and deep learning. This paper also analyzes, compares, and evaluates the performance of failure diagnosis using various deep learning technologies. Furthermore, in order to improve the performance of the fault diagnosis system using deep learning technology, we propose a method to increase the accuracy of fault diagnosis based on an inference window. When adopting the inference window of deep learning, the accuracy of the failure diagnosis was increased up to 94%.

• 한국컴퓨터정보학회논문지
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• 제16권10호
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• pp.211-218
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• 2011

본 연구에서는 우리나라 재난관리의 실패를 과거의 경험적 지식을 통해 재난의 예방과 대응, 복구가 이루어져야 함에도 유사한 재난의 발생과 되풀이되는 실패를 재난관리 조직들의 학습부재에서 찾고 있다. 즉, 재난관리 조직이 경험적 오차로부터 학습하지 못한다는 것은 조직자체가 환경에 적응하지 못한다는 뜻이며, 미래에도 동일한 재난을 반복할 가능성이 크다는 의미이다. 따라서 본 연구의 목적은 재난관리 실패의 원인을 조직학습 관점에서 분석하여 반복되는 실패의 문제점을 검증하므로써 유사한 실패의 반복을 막고, 우리나라 재난관리 정책에 시사점을 제시하고자 하였다. 사례의 분석대상은 2003년 대구지하철 화재 사고를 선정하였다.

• 대한안전경영과학회지
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• 제24권4호
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• pp.1-8
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• 2022

Elevators are the main means of transport in buildings. A malfunction of an elevator in operation may cause in convenience to users. Furthermore, fatal accidents, such as injuries and death, may occur to the passengers also. Therefore, it is important to prevent failure before accidents happen. In related studies, preventive measures are proposed through analyzing failures, and the lifespan of elevator components. However, these methods are limited to existing an elevator model and its surroundings, including operating conditions and installed environments. Vibration occurs when the elevator is operated. Experts have classified types of faults, which are symptoms for malfunctions (failures), via analyzing vibration. This study proposes an artificial intelligent model for classifying faults automatically with deep learning algorithms through elevator vibration data, hereby preventing failures before they occur. In this study, the vibration data of six elevators are collected. The proposed methodology in this paper removes "the measurement error data" with incorrect measurements and extracts operating sections from the input datasets for proceeding deep learning models. As a result of comparing the performance of training five deep learning models, the maximum performance indicates Accuracy 97% and F1 Score 97%, respectively. This paper presents an artificial intelligent model for detecting elevator fault automatically. The users' safety and convenience may increase by detecting fault prior to the fatal malfunctions. In addition, it is possible to reduce manpower and time by assisting experts who have previously classified faults.

• 한국과학교육학회지
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• 제17권4호
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• pp.415-423
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• 1997

The purposes of this study were to investigate the intercorrelations among various motivational patterns and learning strategies and to examine the differences in motivation and strategy usage in terms of students' science achievement level, gender, and grade. A questionnaire on achievement goal, self-efficacy, self-concept of ability, expectancy, value, causal attributions, and learning strategies was administered to 360 junior high/high school students (178 males, 182 females). Students who adopted performance-oriented goal tended not to be task oriented. Task-oriented students had high levels of self-efficacy, high self-concept of ability, and expectancies for future performance in science. They also valued science and attributed thier failures to the lack of effort. However, performance-oriented students evaluated their ability negatively, did not value science, and attributed thier failures to uncontrollable causes. With respect to learning strategy, task-oriented students tended to use deep-level strategy, whereas performance-oriented students tended to use surface-level strategy and not to use deep-level strategy. High-achieving students, boys, and junior high school students were more task-oriented, evaluated their ability more positively, and valued science more than low-achieving students, girls, and high school students, respectively. High-achieving students and boys also used deep-level strategy more than each of their counterparts. However, no significant difference in learning strategy was found between junior high school students and high school students. Educational implications of these findings are discussed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권6호
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• pp.1545-1559
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• 2023

In the cloud environment, microservices are implemented through Kubernetes, and these services can be expanded or reduced through the autoscaling function under Kubernetes, depending on the service request or resource usage. However, the increase in the number of nodes or distributed microservices in Kubernetes and the unpredictable autoscaling function make it very difficult for system administrators to conduct operations. Artificial Intelligence for IT Operations (AIOps) supports resource management for cloud services through AI and has attracted attention as a solution to these problems. For example, after the AI model learns the metric or log data collected in the microservice units, failures can be inferred by predicting the resources in future data. However, it is difficult to construct data sets for generating learning models because many microservices used for autoscaling generate different metrics or logs in the same timestamp. In this study, we propose a cloud data refining module and structure that collects metric or log data in a microservice environment implemented by Kubernetes; and arranges it into computing resources corresponding to each service so that AI models can learn and analogize service-specific failures. We obtained Kubernetes-based AIOps learning data through this module, and after learning the built dataset through the AI model, we verified the prediction result through the differences between the obtained and actual data.

• Nuclear Engineering and Technology
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• 제56권4호
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• pp.1472-1479
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• 2024

Cyber-Physical Energy Systems (CPESs) integrate cyber and hardware components to ensure a reliable and safe physical power production and supply. Renewable Energy Sources (RESs) add uncertainty to energy demand that can be dealt with flexible operation (e.g., load-following) of CPES; at the same time, scenarios that could result in severe consequences due to both component stochastic failures and aging of the cyber system of CPES (commonly overlooked) must be accounted for Operation & Maintenance (O&M) planning. In this paper, we make use of Deep Reinforcement Learning (DRL) to search for the optimal O&M strategy that, not only considers the actual system hardware components health conditions and their Remaining Useful Life (RUL), but also the possible accident scenarios caused by the failures and the aging of the hardware and the cyber components, respectively. The novelty of the work lies in embedding the cyber aging model into the CPES model of production planning and failure process; this model is used to help the RL agent, trained with Proximal Policy Optimization (PPO) and Imitation Learning (IL), finding the proper rejuvenation timing for the cyber system accounting for the uncertainty of the cyber system aging process. An application is provided, with regards to the Advanced Lead-cooled Fast Reactor European Demonstrator (ALFRED).

• 전자공학회논문지B
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• 제30B권11호
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• pp.74-83
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• 1993

This paper presents methodology which enables the derivation of goal ordering rules from the analysis of problem failures. We examine all the possible ways of taking actions that lead to failures. If there are restrictions imposed by a problem state on possible actions to be taken, the restrictions manifest themselves in the form of a restricted set of possible operator bindings. Our method makes use of this observation to derive general control rules which are guaranteed to be correct. The overhead involved in learning is very low because this methodology needs only small amount of data to learn from namely, the goal stacks from the leaf nodes of a failure search tree. Empirical tests show that the rules derived by our system PAL couperform those derived by other systems such as PRODIGY and STATIC.

• International journal of advanced smart convergence
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• 제12권3호
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• pp.13-18
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• 2023

This paper aims to investigate data features for neighbor path selection (NPS) in computer network with regional failures. It is necessary to find an available alternate communication path in advance when regional failures due to earthquakes or forest fires occur simultaneously. We describe previous general heuristics and simulation heuristic to solve the NPS problem in the regional fault network. The data features of general heuristics using proximity and sharing factor and the data features of simulation heuristic using machine learning are explained through examples. Simulation heuristic may be better than general heuristics in terms of communication success. However, additional data features are necessary in order to apply the simulation heuristic to the real environment. We propose novel data features for NPS in computer network with regional failures and Keras modeling for computing the communication success probability of candidate neighbor path.