• Proceedings of the Acoustical Society of Korea Conference
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• 1991.06a
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• pp.78-81
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• 1991

Digital radio data is composed of groups which are divided into 4 blocks of 26 bits. And each block is made up of information word and check word. Check word of digital radio data that is composed ofcode word and offset word is used for group/block synchronization and error correction. In this paper, we have investigated the group/block synchronizer using offext word and shortened cyclic decoder for correcting error produced during the radio data transimission. Also, we have simulated the decoding process of the proposed decoder. From the simulation results, we have confirmed that the proposed decoder most with the required coding capcbility.

• Journal of Industrial Technology
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• v.20 no.A
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• pp.159-167
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• 2000

In the study rainfall frequency analysis attemped the many specific property data record duration it is differance from occur to error-term and probability ditribution of concern manifest. error-term analysis of method are fact sample data using method in other hand it is not appear to be fault that sample data of number to be small random variates. Therefore, day-rainfall data: to randomicity consider of this study sample data to the Monte Carlo method by randomize after data recode duration of form was choice method which compared an assumed maternal distribution from splitting frequency analysis consequence. In the conclusion, frequency analysis of chuncheon region rainfall appeared samll RMSE to the Gamma II distribution. In the rainfall frequency analysis estimate RMSE using random variates great transform, RMSE is appear that return period increasing little by little RMSE incresed and data number incresing to RMSE decreseing.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.1
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• pp.62-77
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• 2010

Emerging applications with high data rates will need to transport bulk data reliably in wireless sensor networks. ARQ (Automatic Repeat request) or Forward Error Correction (FEC) code schemes can be used to provide reliable transmission in a sensor network. However, the naive ARQ approach drops the whole frame, even though there is a bit error in the frame and the FEC at the bit level scheme may require a highly complex method to adjust the amount of FEC redundancy. We propose a bulk data transmission scheme based on erasure-resilient code in this paper to overcome these inefficiencies. The sender fragments bulk data into many small blocks, encodes the blocks with LT codes and packages several such blocks into a frame. The receiver only drops the corrupted blocks (compared to the entire frame) and the original data can be reconstructed if sufficient error-free blocks are received. An incidental benefit is that the frame error rate (FER) becomes irrelevant to frame size (error recovery). A frame can therefore be sufficiently large to provide high utilization of the wireless channel bandwidth without sacrificing the effectiveness of error recovery. The scheme has been implemented as a new data link layer in TinyOS, and evaluated through experiments in a testbed of Zigbex motes. Results show single hop transmission throughput can be improved by at least 20% under typical wireless channel conditions. It also reduces the transmission time of a reasonable range of size files by more than 30%, compared to a frame ARQ scheme. The total number of bytes sent by all nodes in the multi-hop communication is reduced by more than 60% compared to the frame ARQ scheme.

• Journal of the Korean Data and Information Science Society
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• v.17 no.1
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• pp.77-86
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• 2006

This study is to investigate the error of prediction value with related variables from the recommended system and to examine the error of prediction value with related variables. To decrease the error on the collaborative recommended system on user based, this research explored the effects on the prediction related response pair between raters' demographic variables and Pearson's coefficient and sparsity. The result shows comparative analysis between existing error of prediction value and conditioned one.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.10
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• pp.99-106
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• 2002

This paper proposes an error compensation method that improves accuracy with geometry information of injection molding parts. Geometric information can give an improved accuracy in reverse engineering. Measuring data can not lead to get accurate geometric model, including errors of physical parts and measuring machines. Measuring data include errors which can be classified into two types. One is molding error in product, the other is measuring error. Measuring error includes optical error of laser scanner, deformation by probe forces of CMM and machine error. It is important to compensate these in reverse engineering. Least square method (LSM) provides the cloud data with a geometry compensation, improving accuracy of geometry. Also, the functional shape of a part and design concept can be reconstructed by error compensation using geometry information.

• Atmosphere
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• v.26 no.3
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• pp.483-492
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• 2016

We have examined the random error of eddy covariance (EC) measurements on the basis of two-tower approach during daytime. Two EC towers were placed on the grassland with different vegetation density near Gumi-weir. We calculated the random error using three different methods. The first method (M1) is two-tower method suggested by Hollinger and Richardson (2005) where random error is based on differences between simultaneous flux measurements from two towers in very similar environmental conditions. The second one (M2) is suggested by Kessomkiat et al. (2013), which is extended procedure to estimate random error of EC data for two towers in more heterogeneous environmental conditions. They removed systematic flux difference due to the energy balance deficit and evaporative fraction difference between two sites before determining the random error of fluxes using M1 method. Here, we introduce the third method (M3) where we additionally removed systematic flux difference due to available energy difference between two sites. Compared to M1 and M2 methods, application of M3 method results in more symmetric random error distribution. The magnitude of estimated random error is smallest when using M3 method because application of M3 method results in the least systematic flux difference between two sites among three methods. An empirical formula of random error is developed as a function of flux magnitude, wind speed and measurement height for use in single tower sites near Nakdong River. This study suggests that correcting available energy difference between two sites is also required for calculating the random error of EC data from two towers at heterogeneous site where vegetation density is low.

• Journal of the Korean Data and Information Science Society
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• v.28 no.3
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• pp.709-720
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• 2017

In healthcare and medical research, many important variables have a measurement error such as body mass index and laboratory data. It is also not easy to collect samples of large size because of high cost and long time required to collect the target patient satisfied with inclusion and exclusion criteria. Beside, the demand for solving a complex scientific problem has highly increased so that a semiparametric regression approach could be of substantial value solving this problem. To address the issues of measurement error, small domain and a scientific complexity, we conduct a multivariable Bayesian smoothing under structural measurement error covariate in this article. Specifically we enhance our previous model by incorporating other useful auxiliary covariates free of measurement error. For the regression spline, we use a radial basis functions with fixed knots for the measurement error covariate. We organize a fully Bayesian approach to fit the model and estimate parameters using Markov chain Monte Carlo. Simulation results represent that the method performs well. We illustrate the results using a national survey data for application.

• Journal of Korea Multimedia Society
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• v.25 no.2
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• pp.280-286
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• 2022

In order to efficiently collect data, it is essential to locate the facilities and analyze the movement data. The current technology for location collection can collect data using a GPS sensor, but GPS has a strong straightness and low diffraction and reflectance, making it difficult for indoor positioning. In the case of indoor positioning, the location is determined by using wireless network technologies such as Wifi, but there is a problem with low accuracy as the error range reaches 20 to 30 m. In this paper, using BLE 4.2 built in Raspberry Pi, we implement Bluetooth Smart Ready. In detail, a beacon was produced for Advertise, and an experiment was conducted to support the serial port for data transmission/reception. In addition, advertise mode and connection mode were implemented at the same time, and a 3-count gradual algorithm and a quadrangular positioning algorithm were implemented for Bluetooth RSSI error correction. As a result of the experiment, the average error was improved compared to the first correction, and the error rate was also improved compared to before the correction, confirming that the error rate for position measurement was significantly improved.

• Journal of the Korean Data and Information Science Society
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• v.17 no.2
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• pp.269-278
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• 2006

This paper presents the new hybrid data mining technique using error pattern, modeling of improving classification accuracy. The proposed method improves classification accuracy by combining two different supervised learning methods. The main algorithm generates error pattern modeling between the two supervised learning methods(ex: Neural Networks, Decision Tree, Logistic Regression and so on.) The Proposed modeling method has been applied to the simulation of 10,000 data sets generated by Normal and exponential random distribution. The simulation results show that the performance of proposed method is superior to the existing methods like Logistic regression and Discriminant analysis.

• Journal of the Korean Operations Research and Management Science Society
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• v.30 no.4
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• pp.27-43
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• 2005

This paper presents a new hybrid data mining technique using error pattern modeling to improve classification accuracy when the data type of a target variable is binary. The proposed method increases prediction accuracy by combining two different supervised learning methods. That is, the algorithm extracts a subset of training cases that are predicted inconsistently by both methods, and models error patterns from the cases. Based on the error pattern model, the Predictions of two different methods are merged to generate final prediction. The proposed method has been tested using practical 10 data sets. The analysis results show that the performance of proposed method is superior to the existing methods such as artificial neural networks and decision tree induction.