• Journal of Advanced Marine Engineering and Technology
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• v.39 no.2
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• pp.179-186
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• 2015

In the indoor location estimation system, which has recently been actively studied, the received signal strength indicator contains a high level of noise when measuring the signal strength in the range between two nodes consisting of a receiver and a transceiver. To minimize the noise level, this paper proposes an improved adaptive smoothing filter that provides different exponential weights to the current value and previous averaged one of the data that were obtained from the nodes, because the characteristic signal attenuation of the received signal strength indicator generally has a log distribution. The proposed method can effectively decrease the noise level by using a feedback filter that can provide different weights according to the noise level of the obtained data and thus increase the accuracy in the distance and location without an additional filter such as the link quality indicator, which can verify the communication quality state to decrease the range errors in the indoor location recognition using ZigBee based on IEEE 802.15.4. For verifying the performance of the proposed improved adaptive smoothing filter, actual experiments are conducted in three indoor locations of different spatial sections. From the experimental results, it is verified that the proposed technique is superior to other techniques in range measurement.

• Proceedings of the KIEE Conference
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• 1993.07a
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• pp.29-32
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• 1993

A technique computing short-term load foadcasting is essential for monitoring and controlling power system operation. This paper shows the use of general exponential smoothing to develop an adaptive forecasting system based on observed value of hourly demand. Forecasts of hourly load with lead times of one to twenty-four hours are computed at hourly intervals throughout the week. Standard error for lead times of one to twenty-four hour range from three to four percent average load. Studies are planned to investigate the use of weather influence to increase forecast accuracy.

• 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 Korea Information Processing Society Conference
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• 2005.05a
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• pp.589-592
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• 2005

웹을 통한 정보량의 폭발적인 증가로 인하여, 사용자에게 적합한 정보만을 제공할 수 있는 개인화 기술에 관심이 증가하고 있다. 정보를 선별하고 추천하는 대표적인 개인화 기술로서 Contentbased Filtering(CBF) 기법과 Collaborative Filtering(CF) 기법이 널리 사용되고 있다. 본 논문에서는 위에서 언급한 CBF 기법과 CF 기법을 혼합하여, 사용자 선호도를 보다 정확하게 반영할 수 있는 새로운 모델을 제시한다. 또한, Demographic Filtering 기법과 전문가의 추천을 고려한 Fusion Model 을 제시한다. 그리고 사용자 선호 모델을 실시간으로 반영하기 위한 업데이트 방법을 Exponential Smoothing 기법을 사용하여 구성하였다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.2
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• pp.284-294
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• 2018

Statistical methods have been widely used to estimate the remaining battery runtime of mobile smart devices, such as smart phones, smart gears, tablets, and etc. However, existing work available in the literature only considers a particular statistical method. Thus, it is difficult to determine whether statistical methods are applicable to estimating thr remaining battery runtime of mobile devices or not. In this paper, we evaluated the performance of statistical methods applicable to estimating the remaining battery runtime of mobile smart devices. The statistical estimation methods evaluated in this paper are as follows: simple and moving average, linear regression, multivariate adaptive regression splines, auto regressive, polynomial curve fitting, and double and triple exponential smoothing methods. Research results presented in this paper give valuable data of insight to IT engineers who are willing to deploy statistical methods on estimating the remaining battery runtime of mobile smart devices.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.2
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• pp.276-283
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• 2018

Climate changes consistently cause coastal accidents such as coastal flooding, so the studies on monitoring the marine environments are progressing to prevent and reduce the damage from coastal accidents. In this paper, we propose a new method to predict the sea level which can be applied to coastal monitoring systems to observe the variation of sea level and warn about the dangers. Existing sea level models are very complicated and need a lot of tidal data, so they are not proper for real-time prediction systems. On the other hand, the proposed algorithm is very simple but precise in short period such as one or two hours since we use the measured data from the sensor. The proposed method uses Kalman filter algorithm for harmonic analysis and double exponential smoothing for additional error correction. It is shown by experimental results that the proposed method is simple but predicts the sea level accurately.