• Asia pacific journal of information systems
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• v.18 no.4
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• pp.1-26
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• 2008

Rapid progress of information technology and widespread use of the personal computers have brought various conveniences in our life. But this also provoked a series of problems such as hacking, malicious programs, illegal exposure of personal information etc. Information security threats are becoming more and more serious due to enhanced connectivity of information systems. Nevertheless, users are not much aware of the severity of the problems. Using appropriate password is supposed to bring out security effects such as preventing misuses and banning illegal users. The purpose of this research is to empirically analyze a research model which includes a series of factors influencing the effectiveness of passwords. The research model incorporates the concept of risk based on information systems risk analysis framework as the core element affecting the selection of passwords by users. The perceived risk is a main factor that influences user's attitude on password security, security awareness, and intention of security behavior. To validate the research model this study relied on questionnaire survey targeted on evening class MBA students. The data was analyzed by AMOS 7.0 which is one of popular tools based on covariance-based structural equation modeling. According to the results of this study, while threat is not related to the risk, information assets and vulnerability are related to the user's awareness of risk. The relationships between the risk, users security awareness, password selection and security effectiveness are all significant. Password exposure may lead to intrusion by hackers, data exposure and destruction. The insignificant relationship between security threat and perceived risk can be explained by user's indetermination of risk exposed due to weak passwords. In other words, information systems users do not consider password exposure as a severe security threat as well as indirect loss caused by inappropriate password. Another plausible explanation is that severity of threat perceived by users may be influenced by individual difference of risk propensity. This study confirms that security vulnerability is positively related to security risk which in turn increases risk of information loss. As the security risk increases so does user's security awareness. Security policies also have positive impact on security awareness. Higher security awareness leads to selection of safer passwords. If users are aware of responsibility of security problems and how to respond to password exposure and to solve security problems of computers, users choose better passwords. All these antecedents influence the effectiveness of passwords. Several implications can be derived from this study. First, this study empirically investigated the effect of user's security awareness on security effectiveness from a point of view based on good password selection practice. Second, information security risk analysis framework is used as a core element of the research model in this study. Risk analysis framework has been used very widely in practice, but very few studies incorporated the framework in the research model and empirically investigated. Third, the research model proposed in this study also focuses on impact of security awareness of information systems users on effectiveness of password from cognitive aspect of information systems users.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.32 no.5
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• pp.322-330
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• 2020

Modeling for outliers in time series was carried out using the MSL and extreme high, low tide levels (EHL, HLL) data set in the Busan and Mokpo stations. The time-series model is seasonal ARIMA model including the components of the AO (additive outliers) and LS (level shift). The optimal model was selected based on the AIC value and the model parameters were estimated using the 'tso' function (in 'tsoutliers' package of R). The main results by the model application, i.e.. outliers and level shift detections, are as follows. (1) The two AO are detected in the Busan monthly EHL data and the AO magnitudes were estimated to 65.5 cm (by typhoon MAEMI) and 29.5 cm (by typhoon SANBA), respectively. (2) The one level shift in 1983 is detected in Mokpo monthly MSL data, and the LS magnitude was estimated to 21.2 cm by the Youngsan River tidal estuary barrier construction. On the other hand, the RMS errors are computed about 1.95 cm (MSL), 5.11 cm (EHL), and 6.50 cm (ELL) in Busan station, and about 2.10 cm (MSL), 11.80 cm (EHL), and 9.14 cm (ELL) in Mokpo station, respectively.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.2
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• pp.204-208
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• 2018

In this paper, we proposed and evaluated the time series deep learning prediction model for learning fluctuation pattern of stock price. Recurrent neural networks, which can store previous information in the hidden layer, are suitable for the stock price prediction model, which is time series data. In order to maintain the long - term dependency by solving the gradient vanish problem in the recurrent neural network, we use LSTM with small memory inside the recurrent neural network. Furthermore, we proposed the stock price prediction model using bidirectional LSTM recurrent neural network in which the hidden layer is added in the reverse direction of the data flow for solving the limitation of the tendency of learning only based on the immediately preceding pattern of the recurrent neural network. In this experiment, we used the Tensorflow to learn the proposed stock price prediction model with stock price and trading volume input. In order to evaluate the performance of the stock price prediction, the mean square root error between the real stock price and the predicted stock price was obtained. As a result, the stock price prediction model using bidirectional LSTM recurrent neural network has improved prediction accuracy compared with unidirectional LSTM recurrent neural network.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.6
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• pp.822-831
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• 2021

Although programs have been developed to evaluate the risk of dragging anchors, it is practically difficult for VTS(vessel traffic service) operators to calculate and evaluate these risks by obtaining input factors from anchored ships. Therefore, in this study, the gross tonnage (GT) that could be easily obtained from the ship by the VTS operators was set as an independent variable, and linear and nonlinear regression analyses were performed using the input factors as the dependent variables. From comparing the fit of the polynomial model (linear) and power series model (nonlinear), the power series model was evaluated to be more suitable for all input factors in the case of container ships and bulk carriers. However, in the case of tanker ships, the power supply model was suitable for the LBP(length between perpendiculars), width, and draft, and the polynomial model was evaluated to be more suitable for the front wind pressure area, weight of the anchor, equipment number, and height of the hawse pipe from the bottom of the ship. In addition, all other dependent variables, except for the front wind pressure area factor of the tanker ship, showed high degrees of fit with a coefficient of determination (R-squared value) of 0.7 or more. Therefore, among the input factors of the dragging anchor risk assessment program, all factors except the external force, seabed quality, water depth, and amount of anchor chain let out are automatically applied by the regression analysis model formula when only the GT of the ship is provided.

• Fibers and Polymers
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• v.7 no.4
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• pp.436-441
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• 2006

An integrated platform for garment drape simulation system has been developed. In this system, garment patterns from conventional two-dimensional CAD systems can be assembled into a three-dimensional garment on a parametrically resizable realistic human body model. A fast and robust particle-based physical calculation engine has been developed for garment shape generation. Then a series of geometric and graphical techniques were applied to create realistic impressions on simulated garments. This system can be used as the rapid prototyping tool for garments in the future quick-response system.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2000.10a
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• pp.159-166
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• 2000

Structures with a long span have a higher possibility of experiencing excessive vibration induced by human activities such as walking, running, jumping and dancing. These excessive vibration give occupants annoyance. The general method for the vibration analysis of structures subjected to walking loads is to apply a series of nodal loads with assigned time delays at the nodes. But this method has a limit in representing the walking loads. In this study, the equivalent nodal loads are introduced for an effective analysis of floor vibration induced by walking loads. And, walking loads with difference walking rate are measured and applied to the analytical model for numerical analysis.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1561-1564
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• 1997

In the study, simulation result was studied by connecting PID controller in series to the established Neural Networks Controller. Neural Network model is composed of two layers to evaluate tracking performance improvement. The reqular dynamic characteristics was also studied for the expected error to be minimized by using Widrow-Hoff delta rule. As a result of the study, We identified that tracking performance inprovement was developed more in case of connecting PID than Neural Network Contoller and that tracking plant parameter in 251 sample was approached rapidly case of time variable.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.1
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• pp.34-40
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• 1994

The design and implementation of a fuzzy self-organizing controller for an environmental temperature chamber is discussed. The chamber is a non-linear, time-variant system with delay-time and dead-time. And the parameter tuning is required in PI control when the performance degraded. However the proposed fuzzy-SOC monitors the performance of the process. modifies the data base, and performs the delay-time compensation based on the idealized process model. A series of experiments was performed for the conventional PI and the fuzzy-SOC. These experimental results show the usefulness of the fuzzy-SOC.

• Proceedings of the KIPE Conference
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• 1997.07a
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• pp.299-302
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• 1997

This paper describes a dynamic var compensator to compensate the line reactance for power transmission and distribution system. The compensator consists of a voltage source inverter with dc capacitor, coupling transformers, and control circuit. The operation of compensator was verified by computer simulations with EMPT and experimental works with a scaled hardware model. The advantage of the proposed system is rapid and continuous regulation of the reactive power.

• Journal of the Korean Home Economics Association
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• v.35 no.1
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• pp.59-74
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• 1997

FACES II & III do not capture the high extremes of the dimension and are linear rather than curvilinear measure. FACES IV is the latest revision of FACES series and can capture two extreme dimension of Circumplex Model. The purpose of this study is to examine reliability and validity of reconstructed FACES using by FACES II, III, IV. Factor analysis showed that Cohesion and Adaptability consisted 3 factors (disengaged, connected, emmeshed/rigid, flexble, chaotic) Extremes on each dimension conceptually were opposite and they were uncorrelated with each other. FACES effectively predicted family function. Reliability coefficients of subscales ranged from 61~85 Reconstructed FACES had good internal consistency and construct and criterion related validity.