• Nuclear Engineering and Technology
• /
• v.54 no.4
• /
• pp.1230-1244
• /
• 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.

• (The) Korean Journal of Educational Psychology
• /
• v.31 no.4
• /
• pp.745-766
• /
• 2017

Students' creative ability has become the one of important educational goals recently. Beliefs that students can grow intellectually is a key principle in creativity education. In recently, researchers have focused on learners' failure as a way for promoting creativity in schools. They start look into the ways in which learning failures are connected to creativity. Recent studies such as Kapur(2008) demonstrated that learners' failure experiences enable students to create novel solutions to solve problems to go beyond memorizing facts or knowledge. This paper discussed strategies that students or teachers can utilize learning failures to produce positive educational outcomes and also suggested some caveats when learning failures are introduced to a classroom. Specifically, learners should avoid any pre-existing frames of thoughts to create new alternatives to solve problems. Second, teachers or students should be allowed to explore content areas freely without having any risks of academic punishment. In addition, this paper also discussed possible negative results of early experiencing learning failures regards to negative emotion. Especially, experiencing continuous failures can bring students to learned helplessness. This paper discussed how to avoid this negative consequences. Related with negative emotional effects of failures, teacher or students should be careful in the earlier stage of learning processes to avoid learning failures. Lastly, this paper also suggested that minimizing fears related with learning failures and promoting failure tolerance so that students have motivation to overcome learning failures.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.19 no.1
• /
• pp.57-64
• /
• 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%.

• Journal of the Korea Society of Computer and Information
• /
• v.16 no.10
• /
• pp.211-218
• /
• 2011

Although the disaster management of Korea such as mitigation of disasters, preparedness for them and recovery from them. It should be considered based on the failures of the disaster management and the past experimental knowledge, it is believed that the repetitive occurrence of similar disasters is caused by absence of learning of disaster management organizational. That is, non-learning of the management organs due to experimental errors indicates that the organization themselves are not able to adjust to environment and the same kinds of disasters may happen in the future. Therefore, this study identifies repetitive failures by analysing reasons of the failures in terms of organizational learning in order to prevent from repetition of similar failures, and presents suggestions on the policy of disaster management. For the purposes, it carries both bibliographical analysis and case analysis. this study targets Daegu Subway Fire in 2003.

• Journal of the Korea Safety Management & Science
• /
• v.24 no.4
• /
• pp.1-8
• /
• 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.

• Journal of The Korean Association For Science Education
• /
• v.17 no.4
• /
• pp.415-423
• /
• 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)
• /
• v.17 no.6
• /
• pp.1545-1559
• /
• 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
• /
• v.56 no.4
• /
• pp.1472-1479
• /
• 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).

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.30B no.11
• /
• pp.74-83
• /
• 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
• /
• v.12 no.3
• /
• pp.13-18
• /
• 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.