• The Korean Journal of Financial Management
• /
• v.24 no.2
• /
• pp.183-206
• /
• 2007

The price-dividend ratio is one of the most frequently used financial variables to predict long-horizon stock return. However, the persistency of the price-dividend ratio is found to cause the spuriousness of the stock return prediction regression. The stable relationship between the stock price and the dividend, however, seems to weaken after World War II and to experience structural break. In this paper, we identify a structural change in the cointegrating relationship between the log of the stock price and the log of the dividend. Confirming a structural break in 1962, we subdivide the sample and apply the fully modified estimator to correct for the nonstationarity of the regressor. With the subdivided sample, we exercise the nonparametric bootstrap procedure to derive the empirical distribution of the test statistics and fail to find return predictability in each subsample period.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2001.11a
• /
• pp.409-412
• /
• 2001

Identification of systems operating in closed loop has long been of prime interest in industrial applications. The fundamental problem with closed-loop data is the correlation between the unmeasurable noise and the input. This is the reason why several methods that work in open loop fail when applied to closed-loop data. The prediction error based approaches to the closed-loop system are divided to direct method and indirect method. Both of direct and indirect methods are known to be applied to the closed-loop data without critical modification. But the direct method induces the bias error in the experimental frequency response function and this bias error may deteriorates the parameter estimation performance

• Proceedings of the Korea Concrete Institute Conference
• /
• 2000.04a
• /
• pp.491-496
• /
• 2000

This paper presents the evaluation of behavior and the prediction of tensile capacity of anchors that fail concrete, as the design basis for anchorage. Tests of cast-in-place headed anchors, domestically manufactured and installed in uncracked, unreinforced concrete are performed to investigate the behavior of single anchors and multiple anchors with the consideration of various embedment lengths and edge distances. The failure mode and the load-deformation response of these anchors are discussed and the concrete failure dta are then compared with capacity predictions by the two existing methods : the 45 degree cone method of ACI 349, 318 and the concrete capacity design (CCD) method. Discrepancies between the test results and these two prediction methods, FEM analysis are assessed.

• 한국태양에너지학회:학술대회논문집
• /
• 2009.04a
• /
• pp.184-189
• /
• 2009

In order to improve the operation of energy systems, it is necessary for the urban communities to have reliable optimization routines, both computerized and manual, implemented in their organizations. However, before a production plan for the energy system units can be constructed, a prediction of the energy systems first needs to be determined. So, several methodologies have been proposed for energy demand prediction, but due to uncertainties in urban community, many of them will fail in practice. The main topic of this paper has been the development of a method for energy demand prediction at urban community. Energy demand prediction is important input parameters to plan for the energy planing. This paper presents a energy demand prediction method which estimates heat and electricity for various building categories. The method has been based on artificial neural networks(ANN). The advantage of ANN with respect to the other method is their ability of modeling a multivariable problem given by the complex relationships between the variables. Also, the ANN can extract the relationships among these variables by means of learning with training data. In this paper, the ANN have been applied in oder to correlate weather conditions, calendar data, schedules, etc. Space heating, cooling, hot water and HVAC electricity can be predicted using this method. This method can produce 10% of errors hourly load profile from individual building to urban community.

• Structural Engineering and Mechanics
• /
• v.90 no.2
• /
• pp.117-125
• /
• 2024

This article presents the behaviour and design of cold-formed steel (CFS) web-stiffened lipped channel beams that primarily fail owing to the buckling interaction of distortional and global buckling modes. The incorporation of an intermediate stiffener in the web of the lipped channel improved the buckling performance leads to distortional buckling at intermediate length beams. The prediction of the strength of members that fail in individual buckling modes can be easily determined using the current DSM equations. However, it is difficult to estimate the strength of members undergoing buckling interactions. Special attention is required to predict the strength of the members undergoing strong buckling interactions. In the present study, the geometric dimensions of the web stiffened lipped channel beam sections were chosen such that they have almost equal distortional and global buckling stresses to have strong interactions. A validated numerical model was used to perform a parametric study and obtain design strength data for CFS web-stiffened lipped channel beams. Based on the obtained numerical data, an assessment of the current DSM equations and the equations proposed in the literature (for lipped channel CFS sections) is performed. Suitable modifications were also proposed in this work, which resulted in a higher level of design accuracy to predict the flexural strength of CFS web stiffened lipped channel beams undergoing distortional and global mode interaction. Furthermore, reliability analysis was performed to confirm the reliability of the proposed modification.

• KIISE Transactions on Computing Practices
• /
• v.22 no.9
• /
• pp.455-460
• /
• 2016

Recently, with more severe types felonies such as robbery and sexual violence, the importance of crime prediction and prevention is emphasized. For accurate and prompt crime prediction and prevention, both a classification model of crime with high accuracy based on past criminal records and well-designed system interface are required. However previous studies on the analysis of crime factors have limitations in terms of accuracy due to the difficulty of data preprocessing. In addition, existing crime monitoring systems merely offer a vast amount of crime analysis results, thereby they fail to provide users with functions for more effective monitoring. In this paper, we propose a classification model for types of crime based on random-forest algorithms and system design factors for real-time crime prediction. From our experiments, we proved that our proposed classification model is superior to others that only use criminal records in terms of accuracy. Through the analysis of existing crime monitoring systems, we also designed and developed a system for real-time crime monitoring.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.11 no.3
• /
• pp.219-230
• /
• 2015

It is difficult to make plans about the production schedule and volume of seasonal products due to the huge uncertainty in the prediction of their demands, which is why the amounts of carryover seasonal products increase after the peak season. Traditional models fail to meet the important requirements of production and stock plans related to the enhanced efficiency of logistics system due to the reduced value of carryover products by the disposal based on large discounts and deterioration, which poses considerable difficulties with actual problem solving. This study examined the stages of product storage from the specialized factory warehouses during a low season through the stores and the warehouses of local distribution centers during a high season to stock disposal and carryover product warehouses after a high season. The study developed a model for logistics rationalization plans to minimize carryover products by advance selling new products by subscription during a low season in anticipation of high season demands, increasing the accuracy of demands prediction, and making stable production plans, as well as demonstrated its excellence through numerical analysis.

• Proceedings of the KSME Conference
• /
• 2007.05a
• /
• pp.997-1002
• /
• 2007

A guideway vehicle is used in automobile, semiconductor and LCD manufacturing industries to transport products efficiently. Since the operating speed of the guideway vehicle should be increased for maximum productivity, the weight of the vehicle has to be reduced. This may cause parts in the system to fail before the life of the system. Therefore estimation of the fatigue life of the parts becomes an important problem. In this study, the fatigue life of the driving wheel in the guideway vehicle is estimated using a S-N curve. To obtain the fatigue life of a part, the S-N curve, load time history applied on a driving wheel and material property are required. The S-N curve of the driving wheel is obtained using the fatigue experiment on wheels. Load time history of the wheel is obtained from multibody dynamics analysis. To obtain the material properties of the driving wheel, which is composed of aluminum with urethane coating, a compression hardware testing has been done with the static analysis of the FE model. The fatigue life prediction using computational analysis model guarantees the safety of the vehicle at the design stage of the product.

• 한국전산유체공학회:학술대회논문집
• /
• 2006.10a
• /
• pp.77-82
• /
• 2006

Evaluation of the elliptic blending turbulence model (EBM) together with the two-layer model, shear stress transport (SST) model and elliptic relaxation model (V2-F) is performed for a better prediction of natural convection and thermal stratification. For a natural convection problem the models are applied to the prediction of a natural convection in a rectangular cavity and the computed results are compared with the experimental data. It is shown that the elliptic blending model predicts as good as or better than the existing second moment differential stress and flux model for the mean velocity and turbulent quantities. For thermal stratification problem the models are applied to the thermal stratification in the upper plenum of liquid metal reactor. In this analysis there exist much differences between the turbulence models in predicting the temporal variation of temperature. The V2-F model and EBM better predict the steep gradient of temperature at the interface of thermal stratification, and the V2-F model and EBM predict properly the oscillation of temperature. The two-layer model and SST model fail to predict the temporal oscillation of temperature.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.5
• /
• pp.1909-1928
• /
• 2020

Predicting human mobility has always been an important task in Location-based Social Network. Previous efforts fail to capture spatial dependence effectively, mainly reflected in weakening the location topology information. In this paper, we propose a neural network-based method which can capture spatial-temporal dependence to predict the next location of a person. Specifically, we involve a graph convolutional network (GCN) based on a seq2seq framework to capture the location topology information and temporal dependence, respectively. The encoder of the seq2seq framework first generates the hidden state and cell state of the historical trajectories. The GCN is then used to generate graph embeddings of the location topology graph. Finally, we predict future trajectories by aggregated temporal dependence and graph embeddings in the decoder. For evaluation, we leverage two real-world datasets, Foursquare and Gowalla. The experimental results demonstrate that our model has a better performance than the compared models.