• Journal of Advanced Navigation Technology
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• v.25 no.6
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• pp.441-449
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• 2021

With advances in autonomous vehicles, there is a growing demand for more accurate position estimation. Especially, this is a case for a moving robot for the indoor operation which necessitates the higher accuracy in position estimation when the robot is required to execute the task at a predestined location. Thus, a method for improving the position estimation which is applicable to both the fixed and the moving object is proposed. The proposed method exploits the initial position estimation from Bluetooth beacon signals as observation signals. Then, it estimates the gravitational acceleration applied to each axis in an inertial frame coordinate through computing roll and pitch angles and combining them with magnetometer measurements to compute yaw angle. Finally, it refines the control inputs for an object with motion dynamics by computing acceleration on each axis, which is used for improving the performance of Kalman filter. The experimental assessment of the proposed algorithm shows that it improves the position estimation accuracy in comparison to a conventional Kalman filter in terms of average error distance at both the fixed and moving states.

• Journal of Korea Society of Industrial Information Systems
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• v.18 no.6
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• pp.51-59
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• 2013

The smart device is a personal device and has the ability to use built-in sensors. Therefore, it is possible to provide individually customized services through recognizing the user's context from the sensor information. For these customized services, the user's moving status is the important information that can be utilized in many areas. Because the existing researches on determining the user's moving status assume that the user has a smart device in his/her pocket or bag, if the user is using his/her smart device it is not suitable for exactly distinguishing the user's moving status. In order to solve this problem, this paper proposes an algorithm to determine the user's using and moving status. Our proposed algorithm utilizes smart device's events to distinguish the smart device usage and the accelerometer sensor data to determine the user's movement. As an analyzing result through the real experiment, the accuracy of our algorithm is about 75 percent on average.

• Journal of KIISE:Databases
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• v.33 no.4
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• pp.396-408
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• 2006

Moving object databases manage a large amount of past location data which are accumulated as the time goes. To retrieve fast the past location of moving objects, we need index structures which consider features of moving objects. The KDB-tree has a good performance in processing range queries. Although we use the KDB-tree as an index structure for moving object databases, there has an over-split problem in the spatial domain since the feature of moving object databases is to increase the time domain. Because the over-split problem reduces spatial regions in the MBR of nodes inverse proportion to the number of splits, there has a problem that the cost for processing spatial-temporal range queries is increased. In this paper, we propose the dynamic split strategy of the KDB-tree to process efficiently the spatial-temporal range queries. The dynamic split strategy uses the space priority splitting method for choosing the split domain, the recent time splitting policy for splitting a point page to maximize the space utilization, and the last division policy for splitting a region page. We compare the performance of proposed dynamic split strategy with the 3DR-tree, the MV3R-tree, and the KDB-tree. In our performance study for range queries, the number of node access in the MKDB-tree is average 30% less than compared index structures.

• Proceedings of the IEEK Conference
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• 2003.07c
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• pp.2729-2732
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• 2003

This paper proposes a real-time method to recognize shoulder elevation motions by comparing EMG signals on the Levator scapulae muscles with double threshold values. To achieve real-time, we implement a EMG signal processing hareware embedded band-rejection filter, low-pass filter, full rectifier and moving average circuits. And a high speed microprocessor is used for implementing the double thresholds method. The available shoulder motions for the human-computer interface are elevation of left, right and both shoulders. From experimental results we show that the proposed real-time processing hardware and double thresholds method are useful for the real-time EMG-based human-computer interface.

• Journal of Advanced Marine Engineering and Technology
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• v.21 no.5
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• pp.579-583
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• 1997

A diesel Engine rotates vibratively because of alternative explosion strokes. Traditional measurement of a diesel engine speed is carried out by measuring output voltage of FV convertor from input signals of MPU(magnetic pickup unit) or of DC tachometer. Because these measurement include also vibrative rotation of a diesel engine, an analog filter is often used to eliminate high frequency noises due to periodic explosion stroke. But by this method these high frequency noises do not eliminated effectively because noise frequencies are changed according to diesel engine speed. In this paper, author proposes a new measurement method of a diesel engine revolution which read digital signal directly from MPU and prove the utility of proposed method through the real experiment.

• Korean Management Science Review
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• v.20 no.1
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• pp.103-124
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• 2003

The seasonal adjustment is an essential process in analyzing the time series of economy and business. There are various methods to adjust seasonal effect such as moving average, extrapolation, smoothing and X11. One of the powerful adjustment methods is X11-ARIMA Model which is popularly used in Korea. This method was delivered from Canada. However, this model has been developed to be appropriate for Canadian and American environment. Therefore, we need to review whether the Xl1-ARIMA Model could be used properly in Korea. In this study, we have applied the method to the annual sales of refrigerator sales in A electronic company. We appreciated the adjustment by result analyzing the time series components such as seasonal component, trend-cycle component, and irregular component, with the proposed method.

• Journal of the Korean Professional Engineers Association
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• v.18 no.2
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• pp.1-5
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• 1985

In the flood prediction research, it is pointed out that the difficulty of flood prediction is the frequently experienced overestimation of flood peak. That is caused by the rainfall prediction difficulty and the nonlinearity of hydrological phenomena. Even though the former reason will remain still unsolved, but the latter one can be possibly resolved the method of the AMRA (Auto Regressive Moving Average) model for each runoff component as developed by Dr. Hino and Dr. Hasebe. The principle of the method consists of separating though the numerical filters the total runoff time series into long-term, intermediate and short-term components, or ground water flow, interflow, and surface flow components. As a total system, a hydrological system is a non-linear one. However, once it is separated into two or three subsystems, each subsystem may be treated as a linear system. Also the rainfall components into each subsystem a estimated inversely from the runoff component which is separated from the observed flood. That is why flood prediction can be done without rainfall data. In the prediction of surface flow, the Kalman filter will be applicable but this paper shows only impulse function method.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.5 no.4
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• pp.108-114
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• 1972

The present paper deals with secular analysis of zooplankton biomass by statistical method in the south sea of Korea during the period of nine years from 1963 through 1971. Data were taken from the Annual Report of Oceanographic Observations, Fisheries Research and Development Agency, Korea. The trend value of annual variations in the 108 month period above was calculated by method of 12 month moving average, and a period analysis was made by ore of correlogram method using autocorrelation coefficients. The trend of zooplankton biomass shows periodical fluctuation for the period of 63 months with high interrelationships. The seasonal variation has teen obvious with growth phase twice a year, in April and October.

• Journal of Welding and Joining
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• v.15 no.1
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• pp.81-91
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• 1997

In this study, a magnetic sensor to make use of eddy current was developed to detect the weld seam of butt joint in the sheet metal arc welding. This system consist of the sensor device for detecting the weld line, the servo control device for driving the weld torch movement and the control unit. A signal processing was applied to smooth the output signal of the sensor. The weld joint was determined by using a 1st order differential method. To improve tracking accuracy of the system, moving average method which has an effect of proportional and weighted integral control was applied to a series of the weld joint positions obtained above. The weld line for tracking was generated by using data regeneration algorithm. Based on these results, each servo motor was controlled by pulse generator. From experimental results, it was revealed that this system has excellent detecting ability for weld line and seam tracking ability.

• 한국정보통신설비학회:학술대회논문집
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• 2009.08a
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• pp.127-130
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• 2009

We propose a method for detecting DDoS (Distributed Denial of Service) traffic in real-time inside the backbone network. For this purpose, we borrow the concepts of MACD (Moving Average Convergence Divergence) and RoC (Rate of Change), which are used for technical analysis in the stock market Due to the fact that the method is based on a quantitative, rather than a heuristic, detection level, DDoS traffic can be detected with greater accuracy (by reducing the false alarm ratio). Through simulation results, we show how the detection level is determined and demonstrate how much the accuracy of detection is enhanced.