• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.152-155
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• 2002

In this paper, a new approach is proposed for the error detection dedicated to MPEG-4 video coding by using the digital watermarking. In the process of encoding, the proposed scheme abstracts the macroblock features of headers, motion vectors, and Discrete Cosine Transform (DCT) coefficients, which are embedded in the quantized DCT coefficients as the digital watermarks. The decoder performs the accurate error detection through the watermark extraction. Simulation results demonstrate that the error detection ability of the proposed approach can be significantly enhanced, and that digital watermark embedding incurs little degradation to the picture quality.

• ETRI Journal
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• v.45 no.6
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• pp.929-938
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• 2023

The detection of all the symbols transmitted simultaneously in multiuser systems using limited wireless resources is challenging. Traditional model-based methods show high performance with perfect channel state information (CSI); however, severe performance degradation will occur if perfect CSI cannot be acquired. In contrast, data-driven methods perform slightly worse than model-based methods in terms of symbol error ratio performance in perfect CSI states; however, they are also able to overcome extreme performance degradation in imperfect CSI states. This study proposes a novel deep learning-based method by improving a state-of-the-art data-driven technique called deep soft interference cancellation (DSIC). The enhanced DSIC (EDSIC) method detects multiuser symbols in a fully sequential manner and uses an efficient neural network structure to ensure high performance. Additionally, error-propagation mitigation techniques are used to ensure robustness against channel uncertainty. The EDSIC guarantees a performance that is very close to the optimal performance of the existing model-based methods in perfect CSI environments and the best performance in imperfect CSI environments.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.38 no.11
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• pp.1299-1307
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• 2014

In this work, an appropriate analysis model of a precise foot sensor with low detection pressure capability under a low range of variation in the dimensional variables was proposed. With a simple two-dimensional model, it was found that a remarkably high error level sometimes occurred between the analysis and experimental results. In order to overcome the error and improve the performance, a three-dimensional model was introduced, and the detection pressure and sensor characteristics were compared with those of the experimental results, which showed its enhanced performance with less error and higher precision.

• Journal of IKEEE
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• v.21 no.2
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• pp.130-135
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• 2017

Normally, in data transmission, extra parity bits are added to the input message which were derived from its input and a pre-defined algorithm. The same algorithm is used by the receiver to check the consistency of the delivered information, to determine if it is corrupted or not. It recovers and compares the received information, to provide matching and correcting the corrupted transmitted bits if there is any. This paper aims the following objectives: to use an alternative error detection-correction method, to lessens both the fixed number of the required redundancy bits 'r' in cyclic redundancy checking (CRC) because of the required polynomial generator and the overhead of interspersing the r in Hamming code. The experimental results were synthesized using Xilinx Virtex-5 FPGA and showed a significant increase in both the transmission rate and detection of random errors. Moreover, this proposal can be a better option for detecting and correcting errors.

• IEIE Transactions on Smart Processing and Computing
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• v.3 no.2
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• pp.83-87
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• 2014

This paper introduces a refined opportunistic interference alignment (OIA) technique that uses minimum mean square error (MMSE) detection at the receivers in multiple-input multiple-output multi-cell uplink networks. In the OIA scheme under consideration, each user performs the optimal transmit beamforming and power control to minimize the level of interference generated to the other-cell base stations, as in the conventional energy-efficient OIA. The result showed that owing to the enhanced receiver structure, the OIA scheme shows much higher sum-rates than those of the conventional OIA with zero-forcing detection for all signal-to-noise ratio regions.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.17 no.2
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• pp.62-67
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• 2009

LOSA is a flight safety program that analyses human errors in normal operations. Trained pilot observers monitor the normal flights at the observer seat. LOSA is a proactive non jeopardy data collection tool using threat and error management(TEM) as a framework. With the analysis of crew behaviors through LOSA with The LOSA collaborative(TLC), the airlines can identify the behaviors of the crew during normal operations. The major objective of LOSA is to measure how the crew manage threats, errors and undesired aircraft deviations in the cockpit on day to day operations. The airlines are able to set up effective TEM training with practical six generation Crew recourse management(CRM) with data of error from LOSA instead of theoretical CRM courses. The Airlines can use TEM as an integral part of a Safety Management System(SMS) and uses monitoring and cross-checking skills in the flight operations to manage threats and errors effectively when we know the errors we make in the cockpit on daily operation. The result of LOSA indicates that the error detection rate should be enhanced since around the half of the errors went undetected. The areas which should be focused for enhancing the error detection are monitor, cross-check, the management of workload, automation and taxiway/ runway to manage errors effectively.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.101.2-101
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• 2001

This paper proposes a novel face recognition algorithm to recognize human face robustly under various conditions, such as changes of pose, illumination, and expression, etc. at indoor environments. A conventional automatic face recognition system consists of the detection and the recognition part. Generally, the detection part is dominant over the other part in the estimating whole recognition rate. So, in this paper, we suggest the view-specific eigenface method as preprocessor to estimate various poses of the face in the input image. Then, we apply the Enhanced FLD Models (EFM) to the result of it, twice. Because, the EFM recognizes human face, and reduces the error of standardization effectively. To deal with view-varying problem, we build one basis vector set for each view individually. Finally, the dimensionalities of ...

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.3A
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• pp.232-239
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• 2006

In this paper, we analyze the preamble model for Wireless PAN(WPAN) in proposed Ultra WideBand(UWB) Multi-Band OFDM(MB-OFDM) system of IEEE 802.15.3a standard. Besides, we propose effective Carrier Frequency Offset and Symbol Timing Offset Estimation algorithm which offers enhanced performance, and analyze its performance using Detection Probability, False Alarm Probability, Missing Probability, Mean Acquisition Time and MSE(Mean Square Error) through simulation in AWGN and UWB channel environments.

• Journal of Information Processing Systems
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• v.13 no.4
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• pp.1029-1042
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• 2017

A joint channel estimation and data detection technique for a multiple input multiple output (MIMO) wireless communication system is proposed. It combines the least square (LS) training based channel estimation (TBCE) scheme with sphere decoding. In this new approach, channel estimation is enhanced with the help of blind symbols, which are selected based on their correctness. The correctness is determined via sphere decoding. The performance of the new scheme is studied through simulation in terms of the bit error rate (BER). The results show that the proposed channel estimation has comparable performance and better computational complexity over the existing semi-blind channel estimation (SBCE) method.

• Journal of Internet of Things and Convergence
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• v.6 no.3
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• pp.15-20
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• 2020

In this paper, an error tracking platform is designed for enhanced-reliable IOT system. The platform is designed to enhance reliability of IOT system by analysing additional informations(OS, Browser, Device) and by notifying error detection to developers. Especially, in the case of an error in the service which it is difficult for developers to recognize it, The platform also supports notification services through various communication media(Email, Slack, SMS). The common interface is designed to accommodate many languages(typescript, Swift, and Android) in the development process, and the interface allows users to analyze errors that occur on various platforms, including mobile/web/desktop applications. By presenting each error in groups through issues, developers can easily identify issues in the service. The visualizing function is included to recognize various error type by dashboard.