• Journal of Korean Elementary Science Education
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• v.31 no.3
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• pp.298-310
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• 2012

The purpose of this study was to analyze the characteristics of elementary school students' prediction changes by the suggestion types in a multiple anomalous situation. We investigated the responses, the rate and time of changing prediction, and cognitive conflicts of the students when repeated anomalous situation was suggested in experimental or logical way in science classes focused on buoyancy. As the anomalous situation was repeated, the students to change the prediction increased in number and also the rates to choose the correct prediction became higher. The group who was exposed in experimental way changed their prediction more than in logical way. In addition, when we classified the students to change the prediction by types, the group in experimental way showed higher rate of NM, MM type and FFT type. With anomalous situation repeated, cognitive conflicts of the students has been gradually declining in both groups. But it seemed that the group in experimental way experienced higher mental conflicts. In particular, as students changed the prediction more and arrived at the correct answer after changing their prediction, all the more so. It is concluded that the degree of students' changing prediction and experiencing cognitive conflict can be different according to the suggestion types for situation. Therefore the correlation with cognitive conflict factors can be also observed with the types of students' reactions.

• ETRI Journal
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• v.42 no.5
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• pp.799-804
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• 2020

Network security situation prediction is difficult due to its strong uncertainty, but DS evidence theory performs well in solving the problem of uncertainty. Based on DS evidence theory, this study analyzed the prediction of the network security situation, designed a prediction model based on the improved DS evidence theory, and carried out a simulation experiment. The experimental results showed that the improved method could predict accurately in the case of a large conflict, and had strong anti-jamming abilities as compared with the original method. The experimental results prove the effectiveness of the improved method in the prediction of the network security situation and provide some theoretical basis for the further application of DS evidence theory.

• Korean Journal of Culture and Social Issue
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• v.11 no.4
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• pp.83-95
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• 2005

The purpose of the present study was to investigate the relationship between spatial-erceptual ability and several aspects of driving-related situation awareness(in particular, recognition and prediction). Video clips of real driving were used in both recognition and prediction tasks, and the digit calculation task during driving the simulator was required as the integration task of recognition and prediction. The results showed that the subjects of higher spatial-perceptual ability performed better in recognition task, especially in terms of sensitivity measured in d'(as signal detection theory), prediction task, and digits calculation performance than those of lower spatial-perceptual ability.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.3 no.4
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• pp.28-34
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• 2010

In this paper, we proposes a emergency medical information system for predicting emergency situation by using the body's vital signs. Main research of existing emergency system has focused on body sensor networks. The problem of these studies have a delay of the emergency first aid since occurring of an emergency situation send a message of emergency situation to user. In the serious situation, patients of these problem can lead to death. To solve this problem, it need to the prediction of emergency situation for doing quickly the First Aid with identify signs of a pre-emergency situations until an emergency occurs. In this paper, the sensor network technology, the security technology, the internet information retrieval techniques, data mining technology, and medical information are studied for the convergence of medical information systems of the prediction of emergency situations.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2012.10a
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• pp.153-158
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• 2012

Noise maps are produced according to 'The Method of making a Noise Map' in order to noise control efficiently, and prediction model to predict road traffic noise which may apply to Korean situation, include CRTN, RLS 90, NMPB, Nord 2000 and ASJ 2003. Of them, ASJ 2003, Japan's prediction model has not been verified for the application to Korean situation according to the classification of vehicle. In addition, ASJ 2003 was revised to ASJ 2008 recently, a classification for motorcycle was added. This study attempts to check the classification of vehicle in ASJ 2008 and 'The Method of making a Noise Map' to confirm the suitability of the application of them to Korean situation.

• Nuclear Engineering and Technology
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• v.55 no.5
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• pp.1630-1643
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• 2023

The correct situation awareness (SA) of operators is important for managing nuclear power plants (NPPs), particularly in accident-related situations. Among the three levels of SA suggested by Ensley, Level 3 SA (i.e., projection of the future status of the situation) is challenging because of the complexity of NPPs as well as the uncertainty of accidents. Hence, several prediction methods using artificial intelligence techniques have been proposed to assist operators in accident prediction. However, these methods only predict short-term plant status (e.g., the status after a few minutes) and do not provide information regarding the uncertainty associated with the prediction. This paper proposes an algorithm that can predict the multivariate and long-term behavior of plant parameters for 2 h with 120 steps and provide the uncertainty of the prediction. The algorithm applies bidirectional long short-term memory and an attention mechanism, which enable the algorithm to predict the precise long-term trends of the parameters with high prediction accuracy. A conditional variational autoencoder was used to provide uncertainty information about the network prediction. The algorithm was trained, optimized, and validated using a compact nuclear simulator for a Westinghouse 900 MWe NPP.

• Proceedings of the Korean Geotechical Society Conference
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• 2001.10c
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• pp.103-122
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• 2001

For prediction of ground movement per the excavation step, observational results of ground movement during the construction was very different with prediction during the analysis of design. step because of the uncertainty of the numerical analysis modelling, the soil parameter, and the condition of a construction field, etc. however accuratly numerical analysis method was applied. Therefore, the management system through the construction field measurement should be achieved for grasping the situation during the excavation. Until present, the measurement system restricted by ‘Absolute Value Management system’only analyzing the stability of present step was executed. So, it was difficult situation to expect the prediction of ground movement for the next excavation step. In this situation, it was developed that ‘The Management system TOMAS-EXCAV’ consisted of ‘Absolute value management system’ analyzing the stability of present step and ‘Prediction management system’ expecting the ground movement of next excavation step and analyzing the stability of next excavation step by‘Back Analysis’. TOMAS-EXCAV could be applied to all uncertainty of earth retaining structures analysis by connecting ‘Forward analysis program’ and ‘Back analysis program’ and optimizing the main design variables using SQP-MMFD optimization method through measurement results. The application of TOMAS-EXCAV was confirmed that verifed the three earth retaing construction field by back analysis.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.1
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• pp.71-82
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• 2022

Most of the predictions using machine learning are neutral predictions considering the symmetrical situation where the predicted value is not smaller or larger than the actual value. However, in some situations, asymmetric prediction such as over-prediction or under-prediction may be better than neutral prediction, and it can induce better judgment by providing various predictions to decision makers. A method called Asymmetric Twin Support Vector Regression (ATSVR) using TSVR(Twin Support Vector Regression), which has a fast calculation time, was proposed by controlling the asymmetry of the upper and lower widths of the ε-tube and the asymmetry of the penalty with two parameters. In addition, by applying the existing GSVQR and the proposed ATSVR, prediction using the prediction propensities of over-prediction, under-prediction, and neutral prediction was performed. When two parameters were used for both GSVQR and ATSVR, it was possible to predict according to the prediction propensity, and ATSVR was found to be more than twice as fast in terms of calculation time. On the other hand, in terms of accuracy, there was no significant difference between ATSVR and GSVQR, but it was found that GSVQR reflected the prediction propensity better than ATSVR when checking the figures. The accuracy of under-prediction or over-prediction was lower than that of neutral prediction. It seems that using both parameters rather than using one of the two parameters (p_1,p_2) increases the change in the prediction tendency. However, depending on the situation, it may be better to use only one of the two parameters.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.7-10
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• 2003

In the field of financial technology, it is the U.S. initiative, and Japan is obliged to flattery in many respect. Currently Japan is in a too much defenseless situation that the economic structure is based on U.S. theory, In the conventional stochastic theory, it is also face that the prediction sometimes does not hit in the actual problem because it assumes a known probability distribution, none of which illustrates the real situation. A new research and development of management prediction support system is proposed based on fuzzy measures, that deals with the ambiguous, subjective evaluation by the people living in the real world well. Especially, the system will support venture, small and medium companies.

• Journal of Korea Multimedia Society
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• v.23 no.7
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• pp.870-882
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• 2020

It is that failure of equipment at the factory site causes personal injury and property damage. We are required a real-time monitoring and risk forecasting techniques to prevent for equipment failure. In this paper, we proposed a 3-phase smart plug and real-time monitoring system that can be used in factories, and collected environmental information and power information using a smart plug to analyze the data. In order to analyze the correlation between the risk situation and the collected data, we predicted the risk situation using Linear Regression, SVM, and ANN algorithms. As a result, the SVM and ANN algorithms obtained high predictive accuracy and developed a mobile app that could use it to check the risk forecast results.