• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.1D
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• pp.1-9
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• 2009

Bus information system(BIS), as a part of the intelligent transportation system(ITS), is one of the most advanced public transportation systems which provide the real-time bus traffic information for the users waiting the buses at the bus stop. However, correct bus information data, such as the present bus location, the user waiting time, the bus arrival time, etc. are not provided for the bus users because the proper bus arrival time predictive models are not used yet in most of the cities operating the bus information system, including the metropolitan City of Ulsan. Thus, the purpose in this study is to investigate real-time bus traffic characteristic data for identifying the bus operation characteristics on the arterial under the study in the metropolitan City of Ulsan, analyze real-time bus traffic characteristic data on the ID locations of the arterial under the study, construct the optimal unit segment models for the unit segments which are the bus stop, node and travel section using the exponential smoothing, weighted smoothing and Kalman Filter methods, respectively, and finally suggest the optimal integrated model for predicting the real-time bus arrival time at the bus stop of the arterial under the study.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.20 no.44
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• pp.243-255
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• 1997

Forecasting the future values of an observed time series is an important problem in many areas, including economics, traffic engineering, production planning, sales forecasting, and stock control. The purpose of this paper is aimed to discover the more efficient forecasting model through the parameter estimation and residual analysis among the quantitative method such as Winters' exponential smoothing model, Box-Jenkins' model, and Kalman filtering model. The mean of the time series is assumed to be a linear combination of known functions. For a parameter estimation and residual analysis, Winters', Box-Jenkins' model use Statgrap and Timeslab software, and Kalman filtering utilizes Fortran language. Therefore, this paper can be used in real fields to obtain the most effective forecasting model.

• Proceedings of the KIEE Conference
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• summer
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• pp.683-685
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• 2004

This paper introduces a basic design for the short-term load forecasting system using a commercial data base. The proposed system uses a hybrid load forecasting method using fuzzy linear regression for forecasting of weekends and Monday and general exponential smoothing for forecasting of weekdays. The temperature sensitive is used to improve the accuracy of the load forecasting during the summer season. MS-SQL Sever has been used a commercial data base for the proposed system and the database is operated by ADO(ActiveX Data Objects) and RDO(Remote Data Object). Database has been constructed by altering the historical load data for the past 38 years. The weather iDormation is included in the database. The developed short-term load forecasting system is developed as a user friendly system based on GUI(Graphical User interface) using MFC(Microsoft Foundation Class). Test results show that the developed system efficiently performs short-term load forecasting.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.12
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• pp.4950-4966
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• 2015

Cloud providers now face the problem of estimating the amount of computing resources required to satisfy a future workload. In this paper, a virtual machine provisioning (VMP) mechanism is designed to adapt workload fluctuation. The arrival rate of forthcoming jobs is predicted for acquiring the proper service rate by adopting an exponential smoothing (ES) method. The proper service rate is estimated to guarantee the service level agreement (SLA) constraints by using a diffusion approximation statistical model. The VMP problem is formulated as a facility location problem. Furthermore, it is characterized as the maximization of submodular function subject to the matroid constraints. A greedy-based VMP algorithm is designed to obtain the optimal virtual machine provision pattern. Simulation results illustrate that the proposed mechanism could increase the average profit efficiently without incurring significant quality of service (QoS) violations.

• KIEE International Transactions on Power Engineering
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• v.4A no.3
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• pp.159-166
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• 2004

Forecasting prices in electricity markets is critical for consumers and producers in planning their operations and managing their price risk. We utilize the generalized autoregressive conditionally heteroskedastic (GARCH) method to forecast the electricity prices in two regions of New York： New York City and Central New York State. We contrast the one-day forecasts of the GARCH against techniques such as dynamic regression, transfer function models, and exponential smoothing. We also examine the effect on our forecasting of omitting some of the extreme values in the electricity prices. We show that accounting for the extreme values and the heteroskedactic variance in the electricity price time-series can significantly improve the accuracy of the forecasting. Additionally, we document the higher volatility in New York City electricity prices. Differences in volatility between regions are important in the pricing of electricity options and for analyzing market performance.

• Transactions of the Korean Society of Mechanical Engineers
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• v.12 no.1
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• pp.163-172
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• 1988

An off-line Geometric Adaptive Control Scheme is applied to the milling machine to identify its guideway errors. In the milling process, the workpiece fixed on the bed travels along the guideway while the tool and spindle system is fixed onto the machine. The scheme is based on the exponential smoothing of post-process measurements of relative machining errors due to the tool, workpiece and bed deflections. The guideway error identification system consists of a gap sensor, a, not necessarily accurate, straightedge, and the numerical control unit. Without a priori knowledge of the variations of the cutting parameters, the time-varying parameters are also estimated by an exponentially weighted recursive least squares method. Experimental results show that the guideway error is well identified within the range of RMS values of geometric error changes between machining passes disregarding the machining conditions.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2008.10a
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• pp.225-228
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• 2008

The electricity supply and demand to be stable to a system link increase of the variance power supply and operation are requested in jeju Island electricity system. A short-term Load forecasting which uses the characteristic of the Load is essential consequently. We use the interrelationship of the electricity Load and change of a summertime temperature and data refining in the paper. We presented a short-term Load forecasting algorithm of jeju Island and used the correlation coefficient to the criteria of the refining. We used each temperature area data to be refined and forecasted a short-term Load to an exponential smoothing method.

• Journal of the Korean Society for Railway
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• v.7 no.1
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• pp.9-13
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• 2004

Railway passenger demand forecasts may be used directly, or as inputs to other optimization models use them to produce estimates of other activities. The optimization models require demand forecasts at the most detailed level. In this environment exponential smoothing forecasting methods such as Holt-Winters are appropriate because it is simple and inexpensive in terms of computation. There are several initialization methods for Holt-Winters Model. The purpose of this paper is to compare the initialization methods for Holt-Winters model.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.27 no.6
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• pp.17-22
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• 2013

Fuzzy linear regression method has been used for short-term load forecasting of the special day in the previous researches. However, considerable load forecasting errors would be occurring if a special day is located on Saturday or Monday. In this paper, a new load forecasting method for the consecutive holidays is proposed with the consideration of the power demand variation rate. In the proposed method, a exponential smoothing model reflecting temperature is used to short-term load forecasting for Sunday during the consecutive holidays and then the loads of the special day during the consecutive holidays is calculated using the hourly power demand variation rate between the previous similar consecutive holidays. The proposed method is tested with 10 cases of the consecutive holidays from 2009 to 2012. Test results show that the average accuracy of the proposed method is improved about 2.96% by comparison with the fuzzy linear regression method.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.10
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• pp.1018-1023
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• 2010

The CAN (Controller Area Network) system is the most dominant protocol for in-vehicle networking system because it provides bounded transmission delay among ECUs (Electronic Control Units) at data rates between 125Kbps and 1Mbps. And, many automotive companies have chosen the CAN protocol for their in-vehicle networking system such as chassis network system because of its excellent communication characteristics. However, the increasing number of ECUs and the need for more intelligent functions such as ADASs (Advanced Driver Assistance Systems) or IVISs (In-Vehicle Information Systems) require a network with more network capacity and the real-time QoS (Quality-of-Service). As one approach to enhancing the network capacity of a CAN system, this paper introduces a CAN system with dual communication channel. And, this paper presents a distributed message allocation method that allocates messages to the more appropriate channel using forecast traffic of each channel. Finally, an experimental testbed using commercial off-the-shelf microcontrollers with two CAN protocol controllers was used to demonstrate the feasibility of the CAN system with dual communication channel using the distributed message allocation method.