• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.12A
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• pp.1862-1867
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• 1999

In wireless ATM system, powerful FEC code is required for highly reliable data transmission. In this paper, we propose an adaptive hybrid ARQ scheme using RCPSCCC for WATM system. The code rate of RCPSCCC is adjusted to match channel conditions and data types. By using only the effective free distances of outer and inner encoders, we derive upper bounds of the bit and word error probabilities over Rayleigh and Rician fading channels. By applying RCPSCCC to the adaptive hybrid ARQ protocol, highly reliable data transmission can be achieved.

• Journal of The Korean Society of Agricultural Engineers
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• v.62 no.1
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• pp.61-70
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• 2020

To estimate the ventilation volume of mechanically ventilated swine farms, various regression models were applied, and errors were compared to select the regression model that can best simulate actual data. Linear regression, linear spline, polynomial regression (degrees 2 and 3), logistic curve, generalized additive model (GAM), and gompertz curve were compared. Overfitting models were excluded even when the error rate was small. The evaluation criteria were root mean square error (RMSE) and mean absolute percentage error (MAPE). The evaluation results indicated that degree 3 exhibited the lowest error rate; however, an overestimation contradiction was observed in a certain section. The logistic curve was the most stable and superior to all the models. In the estimation of ventilation volume by all of the models, the estimated ventilation volume of the logistic curve was the smallest except for the model with a large error rate and the overestimated model.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2021.11a
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• pp.30-31
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• 2021

This study aims to build a deep learning model that can predict the value of concrete mixing properties according to a given concrete strength value. A model was created for a total of 1,291 concrete data, including 8 characteristics related to concrete mixing elements and environment, and the compressive strength of concrete. As the deep learning model, DNN-3L-256N, which showed the best performance on the prior study, was used. The average value for each characteristic of the data set was used as the initial input value. In results, in the case of 'curing temperature', which had a narrow range of values in the existing data set, showed the lowest error rate with less than 1% error based on MAE. The highest error rate with an error of 12 to 14% for fly and bfs.

• Journal of the Semiconductor & Display Technology
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• v.19 no.3
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• pp.112-117
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• 2020

NAND flash memory is a non-volatile memory that retains stored data even without power supply. Internal memory used as a data storage device and solid-state drive (SSD) is used in portable devices such as smartphones and digital cameras. However, NAND flash memory carries the risk of electric shock, which can cause errors during read/write operations, so use error correction codes to ensure reliability. It efficiently recovers bad block information, which is a defect in NAND flash memory. BBT (Bad Block Table) is configured to manage data to increase stability, and as a result of experimenting with the error correction code algorithm, the bit error rate per page unit of 4Mbytes memory was on average 0ppm, and 100ppm without error correction code. Through the error correction code algorithm, data stability and reliability can be improved.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.210-212
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• 1996

Random drift rate (i.e., random drift in angle rate) of a gyro represents the major error source of inertial navigation systems that are required to operate over long time intervals. It is uncorrectable and leads to an increase in the error with the passage of time. In this paper a technique is presented for analyzing random process from experimental data and the results are presented. The problem of estimating the a priori statistics of a random process is considered using time averages of experimental data. Time averages are calculated and used in the optimal data-processing techniques to determine the statistics of the random process. Therefore the contribution each component to the gyro drift process can be quantitatively measured by its statistics. The above techniques will be applied to actual gyro drift rate data with satisfactory results.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38A no.6
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• pp.471-478
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• 2013

Powerful error control and increase in the number of bits per symbol should be provided for future high-quality communication systems. Each message bit may have different importance in multimedia data. Hence, UEP(unequal error protection) may be more efficient than EEP(equal error protection) in such cases. And the LDPC(low-density parity-check) code shows near Shannon limit error correcting performance. Therefore, the effect of UEP with LDPC codes is analyzed for high-quality message data in this paper. The relationship among MSE(mean square error), BER(bit error rate) and the number of bits per symbol is analyzed theoretically. Then, total message bits in a symbol are classified into two groups according to importance to prove the relationship by simulation. And the UEP performance is obtained by simulation according to the number of message bits in each group with the constraint of a fixed total code rate and codeword length. As results, the effect of UEP with the LDPC codes is analyzed by MSE according to the number of bits per symbol, the ratio of the message bits, and protection level of the classified groups.

• The KIPS Transactions:PartC
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• v.13C no.2 s.105
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• pp.241-248
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• 2006

Mobile computing has such characteristics as portability, wireless network, mobility, etc. These characteristics cause various problems to mobile terminals like frequent disconnection, high error rate, and varying network status. These problems motivate us to develop an adaptive error control mechanism for supporting multimedia service in mobile computing environment. In this paper, we propose the Adaptive Error Control(AEC) scheme using client's buffer size and current error rate. After categorizing the status into four groups according to client's buffer size and current error rate, this scheme applies an appropriate error control scheme to each status. In this scheme, thresholds of buffer size and error rate are determined by the data transmission time, play rate and average VOP size, and by the probability of error for a sequence of packets. The performance of proposed scheme is evaluated by flaying MPEG-4 files on an experimental client/server environment, respectively. The results show that error correcting rate is similar to other schemes while the time for correcting error reduce a little. In addition, the size of data for correcting error is decreased by 23% compared with FEC and Hybrid FEC, respectively. Theses results demonstrate that the proposed scheme is more suitable in mobile computing environment with small bandwidth and varying environment than existing schemes.

• Proceedings of the KSRS Conference
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• v.2
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• pp.635-638
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• 2006

Regression of conventional prediction techniques in data mining uses the model which is generated from the training step. This model is applied to new input data without any change. If this model is applied directly to time series, the rate of prediction accuracy will be decreased. This paper proposes an incremental regression for time series prediction like typhoon track prediction. This technique considers the characteristic of time series which may be changed over time. It is composed of two steps. The first step executes a fractional process for applying input data to the regression model. The second step updates the model by using its information as new data. Additionally, the model is maintained by only recent data in a queue. This approach has the following two advantages. It maintains the minimum information of the model by using a matrix, so space complexity is reduced. Moreover, it prevents the increment of error rate by updating the model over time. Accuracy rate of the proposed method is measured by RME(Relative Mean Error) and RMSE(Root Mean Square Error). The results of typhoon track prediction experiment are performed by the proposed technique IMLR(Incremental Multiple Linear Regression) is more efficient than those of MLR(Multiple Linear Regression) and SVR(Support Vector Regression).

• Journal of Astronomy and Space Sciences
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• v.37 no.4
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• pp.219-228
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• 2020

A CubeSat platform has become a popular choice due to inexpensive commercial off-the-shelf (COTS) components and low launch cost. However, it requires more power-efficient and higher-data rate downlink capability for space applications related to remote sensing. In addition, the platform is limited by the size, weight and power (SWaP) constraints as well as the regulatory issue of licensing the radio frequency (RF) spectrum. The requirements and limitations have put optical communications on promising alternatives to RF communications for a CubeSat platform, owing to the power efficiency and high data rate as well as the license free spectrum. In this study, we analyzed the performance of optical downlink communications compatible with CubeSat platforms in terms of data rate, bit error rate (BER) and outage probability. Mathematical models of BER and outage probability were derived based on not only the log-normal model of atmospheric turbulence but also a transmitter with a finite extinction ratio. Given the fixed slot width, the optimal guard time and modulation orders were chosen to achieve the target data rate. And the two performance metrics, BER and outage data rate, were analyzed and discussed with respect to beam divergence angle, scintillation index and zenith angle.

• Journal of Electrical Engineering and Technology
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• v.10 no.3
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• pp.1201-1211
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• 2015

WSN acts as an effective tool for tracking the large scale environments. In such environment, the battery life of the sensor networks is limited due to collection of the data, usage of sensing, computation and communication. To resolve this, a mobile robot is presented to identify the data present in the partitioned sensor networks and passed onto the sink. In novel data collection algorithm, the performance of the data collecting operation is reduced because mobile robot can be used only within the limited range. To enhance the data collection in a changing environment, Non Linear Error Identifier (NLEI) algorithm has been developed and presented in this paper to configure the robot by means of error models which are non-linear. Experimental evaluation has been conducted to estimate the performance of the proposed NLEI and it has been observed that the proposed NLEI algorithm increases the error correction rate upto 42% and efficiency upto 60%.