• Journal of the Optical Society of Korea
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• v.14 no.1
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• pp.22-27
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• 2010

Bit error rate (BER) monitoring is the ultimate goal of performance monitoring in all digital transmission systems as well as optical fiber transmission systems. To achieve this goal, optimization of the decision threshold must also be considered because BER is dependent on the level of decision threshold. In this paper, we analyze a pseudo-error counting scheme and propose an algorithm to achieve both BER monitoring and adaptive decision threshold optimization in optical fiber transmission systems. To verify the effectiveness of the proposed algorithm, we conduct computer simulations in both Gaussian and non-Gaussian distribution cases. According to the simulation results, BER and the optimum decision threshold can be estimated with the errors of < 20% and < 10 mV, respectively, within 0.1-s processing time in > 40-Gb/s transmission systems.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.18 no.1
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• pp.11-18
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• 2010

GNSS(Global Navigation Satellite System) Ground Station performs GNSS signal acquisition and processing. This system generates error correction information and distributes them to GNSS users. GNSS Ground Station consists of sensor station which contains receiver and meteorological sensor, monitoring and control subsystem which monitors and controls sensor station, control center which generates error correction information, and uplink station which transmits correction information to navigation satellites. Monitoring and control subsystem acquires and processes navigation data from sensor station. The processed data is transmitted to GNSS control center. Monitoring and control subsystem consists of data acquisition module, data formatting and archiving module, data error correction module, navigation determination module, independent quality monitoring module, and system maintenance and management module. The independent quality monitoring module inspects navigation signal, data, and measurement. This paper introduces independent quality monitoring and performs the analysis using measurement data.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.329-334
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• 1998

This work is concerned with the problem of tracking a maneuvering target. In this paper, an error monitoring and recovery method of perception net is utilized to improve tracking performance for a highly maneuvering tar-get. Many researches have been performed in tracking a maneuvering target. The conventional Interacting Multiple Model (IMM) filter is well known as a suboptimal hybrid filter that has been shown to be one of the most cost-effective hybrid state estimation scheme. The subfilters of IMM can be considered as fusing its initial value with new measurements. This approach is also shown in this paper. Perception net based error monitoring and recovery technique, which is a kind of geometric data fusion, makes it possible to monitor errors and to calibrate possible biases involved in sensed data and extracted features. Both detecting a maneuvering target and compensating the estimated state can be achieved by employing the properly implemented error monitoring and recovery technique. The IMM filter which employing the error monitoring and recovery technique shows good tracking performance for a highly maneuvering target as well as it reduces maximum values of estimation errors when maneuvering starts and finishes. The effectiveness of the pro-posed method is validated through simulation by comparing it with the conventional IMM algorithm.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2003.04a
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• pp.472-477
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• 2003

Spindle state monitoring is getting more and more important according to the technology trend of spindle that is accurate and automated. Spindle state monitoring is to measure the state of rotation vibrations. The spindle rotation error motion detected by sensing device includes rotation object's unbalance, external forced vibrations, shape error of spindle, as well as measuring error of monitoring device. In this paper, we have inspected the runout characteristics. Also, we introduce the way to exclude the runout element that appear while you monitor a spindle state.

• Ocean and Polar Research
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• v.39 no.1
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• pp.13-21
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• 2017

Temporal changes in the number of zooplankton species are important information for understanding basic characteristics and species diversity in marine ecosystems. The aim of the present study was to estimate the optimal monitoring frequency (OMF) to guarantee and predict the minimum number of species occurrences for studies concerning marine ecosystems. The OMF is estimated using the temporal number of zooplankton species through bi-weekly monitoring of zooplankton species data according to operational taxonomic units in the Tongyoung coastal sea. The optimal model comprises two terms, a constant (optimal mean) and a cosine function with a one-year period. The confidence interval (CI) range of the model with monitoring frequency was estimated using a bootstrap method. The CI range was used as a reference to estimate the optimal monitoring frequency. In general, the minimum monitoring frequency (numbers per year) directly depends on the target (acceptable) estimation error. When the acceptable error (range of the CI) increases, the monitoring frequency decreases because the large acceptable error signals a rough estimation. If the acceptable error (unit: number value) of the number of the zooplankton species is set to 3, the minimum monitoring frequency (times per year) is 24. The residual distribution of the model followed a normal distribution. This model can be applied for the estimation of the minimal monitoring frequency that satisfies the target error bounds, as this model provides an estimation of the error of the zooplankton species numbers with monitoring frequencies.

• Smart Structures and Systems
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• v.24 no.5
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• pp.649-657
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• 2019

Recently, an advanced video deflectometer based on the principle of off-axis digital image correlation was presented and advocated for remote and real-time deflection monitoring of large engineering structures. In engineering practice, measurement accuracy is one of the most important technical indicators of the video deflectometer. However, it has been observed in many outdoor experiments that data drift often presents in the measured deflection-time curves, which is caused by the instability of imaging system and the unavoidable influences of ambient interferences (e.g., ambient light changes, ambient temperature variations as well as ambient vibrations) in non-laboratory conditions. The non-ideal unstable imaging conditions seriously deteriorate the measurement accuracy of the video deflectometer. In this work, to perform high-accuracy deflection monitoring, potential sources for the drift error are analyzed, and a drift error model is established by considering these error sources. Based on this model, a simple, easy-to-implement yet effective reference point compensation method is proposed for real-time removal of the drift error in measured deflections. The practicality and effectiveness of the proposed method are demonstrated by in-situ deflection monitoring of railway and highway bridges.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2014.10a
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• pp.739-742
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• 2014

In the process of MLCC manufacturing, MLCC stacking process is the key process of making high quality MLCC. Since MLCC is small components, the entire process of MLCC stacking process is minute and sensitive to micro errors. To prevent micro error, we suggest condition-based monitoring which quantifies error based on feature extraction and quantifying error method. As results, it has been shown that the suggested algorithm has effectiveness of condition based monitoring of MLCC stacker.

• Korean Journal of Remote Sensing
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• v.17 no.2
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• pp.121-129
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• 2001

A DEM(digital elevation model) is generated from a SPOT panchromatic stereo-pair using automated algorithms over a 8 km$\times$10 km region around Mokpo city. The aims are to continue the accuracy assessment over diverse conditions and to examine the applicability of a SPOT DEM for coastal disaster monitoring. The accuracy is assessed with respect to three reference data sets: 10 global positioning system records, 19 leveling data, and 1:50,000 topography map. The planimetric error is 10.6m r.m.s. and the elevation erroer ranges from 12.4m to 14.4m r.m.s.. The DEM accuracy of the flat Mokpo region is consistent with that over a mountainous area, which supports the robustness of the algorithms. It was found that coordinate transformation errors are significant at a few meters when using the data from leveling and topographic maps. The error budget is greater than the requirements for coastal disaster monitoring. Exploiting that a sub-scene is used, the affine transformation improves the accuracy by 50% during the camera modeling.

• Journal of Integrative Natural Science
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• v.10 no.4
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• pp.232-239
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• 2017

The researcher set up multiple sensor units on the road slope such as national highway and highway where there is a possibility of loss, and using the acceleration sensor built into the sensor unit the researcher will sense whether the inclination of the road slope occur in real time, and Based on the sensed data, the researcher tries to implement a system that detects collapse of road slope and dangerous situation. In the experiment of measuring the error between the actual measurement time and the judgment time of the monitoring system when judging the warning of the sensor and falling rock detection by using the acceleration sensor, the error between measurement time and the judgment time at the sensor warning was 0.34 seconds on average, and an error between measurement time and judgment time at falling rock detection was 0.21 seconds on average. The error is relatively small, the accuracy is high, and thus the change of the slope can be clearly judged.

• Journal of Environmental Science International
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• v.26 no.9
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• pp.1073-1080
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• 2017

Manufacturing and technology industries produce large amounts of air pollutants. Ulsan Metropolitan City, South Korea, is well-known for its large industrial complexes; in particular, the concentration of $SO_2$ here is the highest in the country. We assessed $SO_2$ monitoring sites based on conditional and joint entropy, because this is a common method for determining an optimal air monitoring network. Monthly $SO_2$ concentrations from 12 air monitoring sites were collected, and the distribution of spatial locations was determined by kriging. Mean absolute error, Root Mean Squared Error (RMSE), bias and correlation coefficients were employed to evaluate the considered algorithms. An optimal air monitoring network for Ulsan was suggested based on the improvement of RMSE.