• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.69-71
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• 2018

Looking at Korea's manufacturing industry, there are many old manufacturing plants. In fact, the manufacturing process of the product inventory management and the unit price of the product are all created by using Excel, and the factory is operated by using it. Also, the operator can not predict the failure of the equipment in order to produce the product at work. Problems related to this may result in the loss of the documents during the instruction and work process between the manager and the worker, and the communication between the manager and the worker can not be properly performed, There is appear a situation in which the operation is continued by using the equipment without recognizing in the failure. In this paper, we propose a method for upgrading the aging manufacturing plant to improve the productivity and productivity of the product by predicting the efficient inventory management, unit price management, production volume, and the operator's failure prediction.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.6
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• pp.1197-1213
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• 2014

Due to the increase in size of the computer network, the network security systems such as a firewall, IDS, IPS generate much more vast amount of information related to network security. So detecting signs of hidden security threats has become more difficult. Security personnels' 'Network Security Situational Awareness(NSSA)' is effectively determining the security situation of overall computer network on the basis of the relation between the security events that occur in the several views. The process of situational awareness is divided into three stages of the 'identification,' 'understanding' and 'prediction'. And 'identification' and 'understanding' are prerequisites for 'predicting' and the following appropriate responses. But 'identification' and 'understanding' in the vast amount of information became more difficult. In this paper, we propose Honeycomb security situational awareness visualization system that is designed to help NSSA in large-scale networks by using visualization techniques known effective to the 'identification' and 'understanding' stages. And we identified the empirical effects of this system on the basis of the 'VAST Challenge 2012' data.

• Journal of Advanced Navigation Technology
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• v.23 no.5
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• pp.473-480
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• 2019

The smart home is a technology that can monitor and control by connecting everything to a communication network in various fields such as home appliances, energy consumers, and security devices. The Smart home is developing not only automatic control but also learning situation and user's taste and providing the result accordingly. This paper proposes a model that can provide a comfortable indoor environment control service for the user's characteristics by detecting the user's behavior as well as the automatic remote control service. The whole system consists of ESP 8266 with sensor and Wi-Fi, Firebase as a real-time database, and a smartphone application. This model is divided into functions such as learning mode when the home appliance is operated, learning control through learning results, and automatic ventilation using indoor and outdoor sensor values. The study used moving averages for temperature and humidity in the control of home appliances such as air conditioners, humidifiers and air purifiers. This system can provide higher quality service by analyzing and predicting user's characteristics through various machine learning and deep learning.

• Journal of the Korea Society for Simulation
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• v.25 no.3
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• pp.65-76
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• 2016

Self-adaptive software is software that adapts by itself to system requirements about the recognized problems without stopping the software cycle. In order to reduce the unnecessary adaptation in the system having the critical points, we propose proactive approach which can predict the future operation after a critical point. In this paper, we predict the future operation after a critical point using a hybrid model to deal with the characteristics of the observed data with the linear and non-linear pattern. The operation of the prediction method is determined on a timing decision indicator based on the prediction accuracy. The two main points of contributions of this paper are to reduce uncertainty about the future operation by predicting the situation after a critical point using hybrid model and to reduce unnecessary adaptation implementation by deciding a timing based on a timing decision indicator.

• Nuclear Engineering and Technology
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• v.53 no.10
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• pp.3275-3285
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• 2021

A Nuclear Power Plant (NPP) is a complex dynamic system-of-systems with highly nonlinear behaviors. In order to control the plant operation under both normal and abnormal conditions, the different systems in NPPs (e.g., the reactor core components, primary and secondary coolant systems) are usually monitored continuously, resulting in very large amounts of data. This situation makes it possible to integrate relevant qualitative and quantitative knowledge with artificial intelligence techniques to provide faster and more accurate behavior predictions, leading to more rapid decisions, based on actual NPP operation data. Data-driven models (DDM) rely on artificial intelligence to learn autonomously based on patterns in data, and they represent alternatives to physics-based models that typically require significant computational resources and might not fully represent the actual operation conditions of an NPP. In this study, a feed-forward backpropagation artificial neural network (ANN) model was trained to simulate the interaction between the reactor core and the primary and secondary coolant systems in a pressurized water reactor. The transients used for model training included perturbations in reactivity, steam valve coefficient, reactor core inlet temperature, and steam generator inlet temperature. Uncertainties of the plant physical parameters and operating conditions were also incorporated in these transients. Eight training functions were adopted during the training stage to develop the most efficient network. The developed ANN model predictions were subsequently tested successfully considering different new transients. Overall, through prompt prediction of NPP behavior under different transients, the study aims at demonstrating the potential of artificial intelligence to empower rapid emergency response planning and risk mitigation strategies.

• Journal of the Korea Institute of Building Construction
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• v.19 no.5
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• pp.439-448
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• 2019

There are various factors affecting the success and failure of an apartment building project. However, after the unit sale price has been determined and the sale has started, the most important factor affecting on the project is the initial sales ratio for one month after the sale. Generally, developers predict an initial sales ratio by various data such as economic situation, the trend of the housing market, and the house price near the business place. However, it is very difficult for these factors to be calculated quantitatively in connection with the initial sales ratio. Therefore, the purpose of this study is to develop a regression model for forecasting the initial sales ratio of apartment building projects. For this study, pre-sales data collection, correlation analysis between influencing factors, and regression model development are performed sequentially. The results of this study are used as basic data for predicting the initial sales ratio in the feasibility analysis of apartment building projects and are used as key data for the development of the risk management model.

• The Journal of the Korea Contents Association
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• v.21 no.5
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• pp.22-35
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• 2021

The unprecedented pandemic caused by the COVID-19 has led to a massive global public health campaign to slow the spread of the virus. Thus, this study examines the importance of individual's prevention behavior intention by adapting health belief model(HBM). In addition, we added social variables to understand the prevention behavior better considering the situation in which collective behaviors are important. The online survey results(N=298) showed that higher level of perceived severity, perceived susceptibility, perceived benefits, perceived peril, perceived social norms and lower level of perceived responsibility led to higher prevention behavior intention. Peril was the most influential factor among all the variables. In addition, perceived severity and social norms followed after that. Additional analysis also implied that socio-HBM model we proposed better explained the prevention behavior intention than traditional HBM.

• Science of Emotion and Sensibility
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• v.25 no.2
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• pp.87-100
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• 2022

People who spend more time alone tend to report higher levels of loneliness. However, whether people experience loneliness in solitude can differ The current research investigated the role of a sense of power as a predictor of loneliness among people who lack social interaction. investigated factors predicting loneliness in people with little social time large-scale survey data. As a result of discriminant analysis, a sense of power was verified as a factor that lonely non-lonely groups. a sense of power As a result, a causal relationship between a sense of power and social loneliness was confirmed. When people feel alone, a high sense of power can work as a buffer against loss of belongingness and the experience of social loneliness. This research focused on psychological rather than situational factors to alleviate loneliness in the current situation where social encounters are limited due to the increase of single-person households and the 19 pandemic.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.2
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• pp.67-71
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• 2023

Recently, there are increasing cases of managing software labs by assigning virtual PCs in the cloud instead of physical PCs to each student. In this paper, we design and implement a Linux-based software practice platform that allows students to efficiently build their environments in the cloud. In our platform, instructors can create and control virtual machine templates for all students at once, and students practice on their own machines as administrators. Instructors can also troubleshoot each machine and restore its state. Meanwhile, the biggest obstacle to implementing this approach is the difficulty of predicting the costs of cloud services instantly. To cope with this situation, we propose a model that can estimate the cost of cloud resources used. By using daemons in each user's virtual machine, we instantly estimate resource usage and costs. Although our model has very low overhead, the predicted results are very close to the actual resource usage measured by cloud service providers. To further validate our model, we used the proposed platform in a Linux practice lecture for a semester and confirmed that the proposed model is very accurate.

• Journal of the Korean Geotechnical Society
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• v.25 no.9
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• pp.5-17
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• 2009

The microcomputer program PileNSF (Pile Negative Skin Friction) is developed by the authors in a graphical user interface (GUI) environment using $MATLAB^{(R)}$ for predicting the bearing capacity of a pile embedded in a consolidating ground by surcharge loading. The proposed method extends the one-dimensional soil-pile model based on the nonlinear load transfer method in OpenSees to perform an advanced one-dimensional consolidation settlement analysis based on finite strain. The developed program has significant features of incorporating Mikasa's finite strain consolidation theory that accounts for reduction in the thickness of the clay layer as well as the change of the soil-pile interface length during the progress of consolidation. In addition, the consolidating situation of the ground by surcharge filling after the time of pile installation can also be considered in the analysis. The program analysis by the presented method has been verified and validated with several case studies of long-term test on single piles subjected to negative skin friction. Predicted results of negative skin friction (downdrag and dragload) as a result of long from consolidation settlement are shown to be in good agreement with measured and observed case data.