• The KIPS Transactions:PartD
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• v.8D no.4
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• pp.387-392
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• 2001

This Many software projects collect grouped failure data (failures in some failure interval or in variable time interval) rather than individual failure times or failure count data during the testing or operational phase. This paper presents the neural network (NN) modeling for grouped failure data that is able to predict cumulative failures in the variable future time. The two variant models of cascade-correlation learning (CasCor) algorithm are presented. Suggested models are compared with other well-known NN models and statistical software reliability growth models (SRGMs). Experimental results show that the suggested models show better predictability.

• International Journal of Precision Engineering and Manufacturing
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• v.3 no.1
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• pp.33-42
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• 2002

Recently, various experiments to apply reinforcement learning method to the self-learning intelligent control of continuous dynamic system have been reported in the machine learning related research community. The reports have produced mixed results of some successes and some failures, and show that the success of reinforcement learning method in application to the intelligent control of continuous control systems depends on the ability to combine proper function approximation method with temporal difference methods such as Q-learning and value iteration. One of the difficulties in using function approximation method in connection with temporal difference method is the absence of guarantee for the convergence of the algorithm. This paper provides a proof of convergence of a particular function approximation method based on \"barycentric interpolator\" which is known to be computationally more efficient than multilinear interpolation .

• International journal of advanced smart convergence
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• v.10 no.1
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• pp.56-61
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• 2021

Neighbor path selection is to pre-select alternate routes in case geographically correlated failures occur simultaneously on the communication network. Conventional heuristic-based algorithms no longer improve solutions because they cannot sufficiently utilize historical failure information. We present a novel solution model for neighbor path selection by using machine learning technique. Our proposed machine learning neighbor path selection (ML-NPS) model is composed of five modules- random graph generation, data set creation, machine learning modeling, neighbor path prediction, and path information acquisition. It is implemented by Python with Keras on Tensorflow and executed on the tiny computer, Raspberry PI 4B. Performance evaluations via numerical simulation show that the neighbor path communication success probability of our model is better than that of the conventional heuristic by 26% on the average.

• Journal of Positioning, Navigation, and Timing
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• v.5 no.4
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• pp.173-180
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• 2016

This paper presents a method to suppress multipath induced by pulses using supervised learning. In modern electronics, pulses have been used for various purposes such as communication or distance measurements. Like other signals, pulses also suffer from multipath. When a pulse and a multipath are overlapped, the original pulse shape is distorted. The distorted pulse could result in communication failures or distance measurement errors. However, a large number of samples available from a pulse can be used to effectively reject multipath by using a supervised learning method. This paper introduces how a supervised learning method can be applied to Distance Measuring Equipment. Simulation results show that multipath induced distance measuring error can be suppressed by 10 ~ 45 percent depending on the allowed pulse shape variation allowed in a standard.

• Journal of Korea Society of Digital Industry and Information Management
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• v.8 no.2
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• pp.1-10
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• 2012

In this study, software products developed in the course of testing, software managers in the process of testing software test and test tools for effective learning effects perspective has been studied using the NHPP software. The finite failure nonhomogeneous Poisson process models presented and the life distribution applied exponential and log shaped type hazard function. Software error detection techniques known in advance, but influencing factors for considering the errors found automatically and learning factors, by prior experience, to find precisely the error factor setting up the testing manager are presented comparing the problem. As a result, the learning factor is greater than autonomous errors-detected factor that is generally efficient model could be confirmed. This paper, a failure data analysis of applying using time between failures and parameter estimation using maximum likelihood estimation method, after the efficiency of the data through trend analysis model selection were efficient using the mean square error and coefficient of determination.

• Journal of Korea Society of Digital Industry and Information Management
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• v.7 no.2
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• pp.1-8
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• 2011

In this study, software products developed in the course of testing, software managers in the process of testing software test and test tools for effective learning effects perspective has been studied using the NHPP software. The finite failure non-homogeneous Poisson process models presented and the life distribution applied extreme distribution which used to find the minimum (or the maximum) of a number of samples of various distributions. Software error detection techniques known in advance, but influencing factors for considering the errors found automatically and learning factors, by prior experience, to find precisely the error factor setting up the testing manager are presented comparing the problem. As a result, the learning factor is greater than automatic error that is generally efficient model could be confirmed. This paper, a numerical example of applying using time between failures and parameter estimation using maximum likelihood estimation method, after the efficiency of the data through trend analysis model selection were efficient using the mean square error.

• Korean Journal of Construction Engineering and Management
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• v.9 no.6
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• pp.257-267
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• 2008

In order to encourage construction practitioners to acknowledge failures and disseminate the information, the failure information must be documented and accumulated with a well-structured format, which contains not only the fact and result but also the circumstance and cause of the failure. In the Korean construction industry, many failures are not explained clearly and often not even reported publicly, partly because due to the lack of understanding positive aspects of failures, which can improve construction practices as a result of learning from failures. The purpose of this study is to develop a web-based construction failure information system using the case-based reasoning techniques, which can systematically accumulate, manage, and share the valuable failure information using a structured failure cases database. It can be utilized for planning proactive solutions on future failures by searching the very similar past failure cases.

• Education of Primary School Mathematics
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• v.18 no.2
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• pp.123-139
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• 2015

The purpose of this study was to investigate the effects of the experiences of productive failures on students' mathematical problem solving abilities and mathematical dispositions. The experiment was conducted with two groups. The treatment group was applied with the productive mathematics failure program, and the comparative group was taught with traditional mathematics lessons. In this study, for quantitative analysis, the students were tested their understanding of mathematical concepts, mathematical reasoning abilities, students' various strategies and mathematical dispositions before and after using the program. For qualitative analysis, the researchers analyzed the discussion processes of the students, students's activity worksheets, and conducted interviews with selected students. The results showed the followings. First, use of productive failures showed students' enhancement in problem solving abilities. Second, the students who experienced productive failures positively affected the changes in students' mathematical dispositions. Along with the more detailed research on productive mathematical failures, the research results should be included in the development of mathematics textbooks and teaching and learning mathematics.

• Journal of Korean Elementary Science Education
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• v.35 no.2
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• pp.253-264
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• 2016

The purpose of this study was to investigate implications of self-directed learning experiments in elementary science education through understanding elementary school students' awareness of their experiences in self-directed learning experiments. Twenty students joined the school science club voluntarily and conducted self-directed learning experiments. We collected data through observation of the experiments, interviews, and questionnaires. The students who participated in the club showed high satisfaction with self-directed learning experiments. The participants were aware that their scientific interest and knowledge, and the confidence in conducting experiments were increased. The students felt positive about the inquiry process of conducting self-directed learning experiments with their own subjects. They also felt a sense of achievement in attempting their experiments in defiance of several failures. The participants realized that the self-directed inquires led to increased declarative and procedural knowledge of science. The students stated that they had some difficulties in coping with the different results contrary to expectations and preparing laboratory materials and instruments. Nonetheless, they showed the promotion of their scientific literacy during overcoming those difficulties. We suggest that self-directed learning experiments can be a more effective way in science learning to make students experience the nature of science than existing school experiments. This can be implemented through a creative experience activities such as science clubs.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.1-11
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• 2021

Recently, the importance of preventive maintenance has been emerging since failures in a complex system are automatically detected due to the development of artificial intelligence techniques and sensor technology. Therefore, prognostic and health management (PHM) is being actively studied, and prediction of the remaining useful life (RUL) of the system is being one of the most important tasks. A lot of researches has been conducted to predict the RUL. Deep learning models have been developed to improve prediction performance, but studies on identifying the importance of features are not carried out. It is very meaningful to extract and interpret features that affect failures while improving the predictive accuracy of RUL is important. In this paper, a total of six popular deep learning models were employed to predict the RUL, and identified important variables for each model through SHAP (Shapley Additive explanations) that one of the explainable artificial intelligence (XAI). Moreover, the fluctuations and trends of prediction performance according to the number of variables were identified. This paper can suggest the possibility of explainability of various deep learning models, and the application of XAI can be demonstrated. Also, through this proposed method, it is expected that the possibility of utilizing SHAP as a feature selection method.