• Journal of Information Processing Systems
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• v.16 no.5
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• pp.1158-1168
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• 2020

The distance vector-hop wireless sensor node location method is one of typical range-free location methods. In distance vector-hop location method, if a wireless node A can directly communicate with wireless sensor network nodes B and C at its communication range, the hop count from wireless sensor nodes A to B is considered to be the same as that form wireless sensor nodes A to C. However, the real distance between wireless sensor nodes A and B may be dissimilar to that between wireless sensor nodes A and C. Therefore, there may be a discrepancy between the real distance and the estimated hop count distance, and this will affect wireless sensor node location error of distance vector-hop method. To overcome this problem, it proposes a wireless sensor network node location method by modifying the method of distance estimation in the distance vector-hop method. Firstly, we set three different communication powers for each node. Different hop counts correspond to different communication powers; and so this makes the corresponding relationship between the real distance and hop count more accurate, and also reduces the distance error between the real and estimated distance in wireless sensor network. Secondly, distance difference between the estimated distance between wireless sensor network anchor nodes and their corresponding real distance is computed. The average value of distance errors that is computed in the second step is used to modify the estimated distance from the wireless sensor network anchor node to the unknown sensor node. The improved node location method has smaller node location error than the distance vector-hop algorithm and other improved location methods, which is proved by simulations.

• Journal of Korea Port Economic Association
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• v.39 no.2
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• pp.129-141
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• 2023

This study analyzes the dynamic characteristics of daily freight rates of dry bulk and tanker shipping markets and their forecasting accuracy by using the error correction models. In order to calculate the error terms from the co-integrated time series, this study uses the common stochastic trend model (CSTM model) and vector error correction model (VECM model). First, the error correction model using the error term from the CSTM model yields more appropriate results of adjustment speed coefficient than one using the error term from the VECM model. Furthermore, according to the adjusted determination coefficients (adjR²), the error correction model of CSTM-model error term shows more model fitness than that of VECM-model error term. Second, according to the criteria of mean absolute error (MAE) and mean absolute scaled error (MASE) which measure the forecasting accuracy, the results show that the error correction model with CSTM-model error term produces more accurate forecasts than that of VECM-model error term in the 12 cases among the total 15 cases. This study proposes the analysis and forecast tasks 1) using both of the CSTM-model and VECM-model error terms at the same time and 2) incorporating additional data of commodity and energy markets, and 3) differentiating the adjustment speed coefficients based the sign of the error term as the future research topics.

• ETRI Journal
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• v.40 no.2
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• pp.266-274
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• 2018

In error-prone wireless environments, it is difficult to realize video coding systems that are robust to various types of data loss. In this paper, a novel motion-vector refinement approach is presented for video error concealment. A traditional boundary-matching approach is exploited to reduce blocky effects along the block boundary. More specifically, a downhill simplex approach is combined with a boundary-matching approach to fine-tune the motion vectors, reducing the blocky effects along the prediction unit block boundary, and minimizing the computational cost. Extensive simulations are performed, and the results obtained verify the robustness and effectiveness of the proposed approach.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.2
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• pp.327-336
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• 1997

This paper addresses an analytical approach to the mechanical error analysis and tolerance design of a four-bar path generator with lubricated joints. The mobility method is applied to consider lubrication effects and the four-bar path generator is stochastically modeled by using the clearance vector model for methanical error analysis. To show the validity of the proposed method, the mechanical errors obtained by applying the method to a four-bar path generator are compared with those by Monte Carlo simulation. Based on this analytical method, an optimal tolerance design problem is formulated and solved for the four-bar path generator.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.38 no.4
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• pp.261-268
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• 1989

A novel current controlled PWM voltage source type converter and control strategy is proposed that is able to draw nearly sinusoidal current at unity power factor from three phase power lines. Current error vector control scheme is used which has two operating states : low harmonic current content state and quick current response state. The state is changed according to the current error to optimize the steady state and transient state performances. To regulate the dc oupput voltage, the magnitude of the reference current is determined by a controller dc voltage error. The ac input power factor can be controlled with unity, and even leading or lagging by adjusting the relative position of the reference current with respect to the supply voltage.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.10 no.1
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• pp.59-64
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• 2010

To improve system performance, we apply the concept of membership function to Variance Considered Machines (VCMs) which is a modified algorithm of Support Vector Machines (SVMs) proposed in our previous studies. Many classification algorithms separate nonlinear data well. However, existing algorithms have ignored the fact that probabilities of error are very high in the data-mixed area. Therefore, we make our algorithm ignore data which has high error probabilities and consider data importantly which has low error probabilities to generate system output according to the probabilities of error. To get membership function, we calculate sigmoid function from the dataset by considering means and variances. After computation, this membership function is applied to the VCMs.

• Journal of Power Electronics
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• v.16 no.1
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• pp.388-397
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• 2016

An error-compensated pulse width modulator (ECPWM) is proposed to improve the baseband harmonic performance and the switching loss of voltage source inverters (VSIs). Selecting between harmonic distortion and switching loss is a design tradeoff in the conventional space vector pulse width modulation. In this work, an accumulated difference in produced and desired phase voltages is considered to adjust the reference signal. This mechanism can compensate for the voltage error in the previous carrier period. With error compensation every half-carrier period, the proposed ECPWM allows one-half reduction in carrier frequency without scarifying baseband harmonic distortion. The proposed modulator is applied to a three-phase VSI with R-L load and a motor-speed-control system for experiments. The measured efficiency and operating temperature of switches confirm the effectiveness of the proposed scheme.

• Journal of the Korea Institute of Military Science and Technology
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• v.10 no.3
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• pp.129-138
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• 2007

Recently, Direction Finding(DF) System is using switching DF algorithm to reduce system-weight by eliminating RF cable as much as possible. Also, Correlation Vector Direction Finding(CVDF) algorithms is being used for Fast Direction finding in tactical environment. In this paper, I will give you burst error elimination algorithms and compare the performance in case we use switching CVDF algorithm. Although antenna array is not working, we will successfully perform direction finding when we use this burst error elimination algorithms. Also, we will be completely capable of DF mission despite of meeting the unwanted situation that the monitoring signal disappear in case we use Switching Direction Finding algorithms. That situation frequently occurs under the Frequency Hopping signal circumstances.

• Journal of Korean Society for Quality Management
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• v.47 no.4
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• pp.783-793
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• 2019

Purpose: The purpose of this study is to apply the machine and deep learning methodology on error terms which are continuously auto-generated on the sensors with specific time period and prove the improvement effects of power generator prediction diagnosis system by comparing detection ability. Methods: The SVM(Support Vector Machine) and MLP(Multi Layer Perception) learning procedures were applied for predicting the target values and sequentially producing the error terms for confirming the detection improvement effects of suggested application. For checking the effectiveness of suggested procedures, several detection methodologies such as Cusum and EWMA were used for the comparison. Results: The statistical analysis result shows that without noticing the sequential trivial changes on current diagnosis system, suggested approach based on the error term diagnosis is sensing the changes in the very early stages. Conclusion: Using pattern of error terms as a diagnosis tool for the safety control process with SVM and MLP learning procedure, unusual symptoms could be detected earlier than current prediction system. By combining the suggested error term management methodology with current process seems to be meaningful for sustainable safety condition by early detecting the symptoms.

• Communications for Statistical Applications and Methods
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• v.19 no.4
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• pp.517-526
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• 2012

In this paper a support vector method is proposed for use when the sample observations are contaminated by a normally distributed measurement error. The performance of deconvolution density estimators based on the support vector method is explored and compared with kernel density estimators by means of a simulation study. An interesting result was that for the estimation of kurtotic density, the support vector deconvolution estimator with a Gaussian kernel showed a better performance than the classical deconvolution kernel estimator.