• International conference on construction engineering and project management
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• 2022.06a
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• pp.814-822
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• 2022

The deployment of sensors for Structural Health Monitoring requires a complicated network arrangement, ground truthing, and calibration for validating sensor performance periodically. Any conventional sensor on a structural element is also subjected to static and dynamic vertical loadings in conjunction with other environmental factors, such as brightness, noise, temperature, and humidity. A structural model with strain gauges was built and tested to get realistic sensory information. This paper investigates different deep learning architectures and algorithms, including unsupervised, autoencoder, and supervised methods, to benchmark blind drift calibration methods using deep learning. It involves a fully connected neural network (FCNN), a long short-term memory (LSTM), and a gated recurrent unit (GRU) to address the blind drift calibration problem (i.e., performing calibrations of installed sensors when ground truth is not available). The results show that the supervised methods perform much better than unsupervised methods, such as an autoencoder, when ground truths are available. Furthermore, taking advantage of time-series information, the GRU model generates the most precise predictions to remove the drift overall.

• ETRI Journal
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• v.36 no.6
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• pp.894-904
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• 2014

Blind mismatch correction of time-interleaved analog-to-digital converters (TI-ADC) is a challenging task. We present a practical blind calibration technique for low-computation, low-complexity, and high-resolution applications. Its key features are: dramatically reduced computation; simple hardware; guaranteed parameter convergence with an arbitrary number of TI-ADC channels and most real-life input signals, with no bandwidth limitation; multiple Nyquist zone operation; and mixed-domain error correction. The proposed technique is experimentally verified by an M = 4 400 MSPS TI-ADC system. In a single-tone test, the proposed practical blind calibration technique suppressed mismatch spurs by 70 dB to 90 dB below the signal tone across the first two Nyquist zones (10 MHz to 390 MHz). A wideband signal test also confirms the proposed technique.

• Journal of Astronomy and Space Sciences
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• v.17 no.2
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• pp.295-308
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• 2000

OSMI(Ocean Scanning Multi-Spectral Imager) raw image data(Level 0) were acquired and radiometrically corrected. We have applied two methods, using solar & dark calibration data from OSMI sensor and comparing with the SeaWiFS data, to the radiometric correction of OSMI raw image data. First, we could get the values of the gain and the offset for each pixel and each band from comparing the solar & dark calibration data with the solar input radiance values, calculated from the transmittance, BRDF (Bidirectional Reflectance Distribution Function) and the solar incidence angle($\beta$, $\theta$) of OSMI sensor. Applying this calibration data to OSMI raw image data, we got the two odd results, the lower value of the radiometric corrected image data than the expected value, and the Venetian Blind Effect in the radiometric corrected image data. Second, we could get the reasonable results from comparing OSMI raw image data with the SeaWiFS data, and get a new problem of OSMI sensor.

• Journal of the Korean Society for Nondestructive Testing
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• v.10 no.1
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• pp.91-95
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• 1990

Welding residual stress was measured by Barkhausen noise method. The calibration experiment was done for the quantitative analysis. The specimen for the calibration experiment must has the same thermo-mechanical history as the actual material to be tested. The Barkhausen noise were analysed by the pulse-height distribution. The results show that the distribution and magnitude of welding residual stress from Barkhausen noise method are in good agreement with those from blind hole method.

• ETRI Journal
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• v.38 no.3
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• pp.551-559
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• 2016

In this paper, we propose a hybrid sensor calibration scheme for mobile crowdsensing applications. As the number of newly produced mobile devices containing embedded sensors continues to rise, the potential to use mobile devices as a sensor data source increases. However, because mobile device sensors are generally of a lower performance and cost than dedicated sensors, sensor calibration is crucial. To enable more accurate measurements of natural phenomena through the use of mobile device sensors, we propose a hybrid sensor calibration scheme for such sensors; the scheme makes use of mobile device sensors and existing sensing infrastructure, such as weather stations, to obtain dense data. Simulation results show that the proposed scheme supports low mean square errors. As a practical application of our proposed scheme, we built a temperature map of a city using six mobile phone sensors and six reference sensors. Thanks to the mobility of the sensors and the proposed scheme, our map presents more detailed information than infrastructure-based measurements.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1091-1091
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• 2001

Previous reports have shown that Near Infrared Spectroscopy (NIRS) can be used to assess physical and chemical properties of flax fibre and fabric quality. Currently, spinners assess yarn quality mainly based on strength and regularity measurements. There two key characteristics are influenced by quality of raw fibres used, especially the degree of rotting and strength. The aim of this investigation was to evaluate the use of NIRS for assessing quality of weft grade yarn available on the commercial market. In order to develop the NIR calibrations, a range of samples representing poor, medium and good quality weft yarn samples was included in the calibration and validation sample sets. The samples were analysed for physical and chemical parameters including caustic weight loss, fibre fractions, lipid, ash and minerals. A detailed protocol for assessing yarn quality has been developed to maximize the accuracy of the reflectance spectra. The development of partial least squares regression models and validation of the calibration equations using blind samples will be presented and discussed.

• Journal of Information Processing Systems
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• v.13 no.1
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• pp.83-94
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• 2017

Near-field source localization algorithms are very sensitive to sensor gain/phase response errors, and so it is important to calibrate the errors. We took into consideration the uniform linear array and are proposing a blind calibration algorithm that can estimate the directions-of-arrival and range parameters of incident signals and sensor gain/phase responses jointly, without the need for any reference source. They are estimated separately by using an iterative approach, but without the need for good initial guesses. The ambiguities in the estimations of 2-D electric angles and sensor gain/phase responses are also analyzed in this paper. We show that the ambiguities can be remedied by assuming that two sensor phase responses of the array have been previously calibrated. The behavior of the proposed method is illustrated through simulation experiments. The simulation results show that the convergent rate is fast and that the convergent precision is high.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.1
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• pp.96-110
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• 2017

Most of vehicle detection studies using the existing general lens or wide-angle lens have a blind spot in the rear detection situation, the image is vulnerable to noise and a variety of external environments. In this paper, we propose a method that is detection in harsh external environment with noise, blind spots, etc. First, using a fish-eye lens will help minimize blind spots compared to the wide-angle lens. When angle of the lens is growing because nonlinear radial distortion also increase, calibration was used after initializing and optimizing the distortion constant in order to ensure accuracy. In addition, the original image was analyzed along with calibration to remove fog and calibrate brightness and thereby enable detection even when visibility is obstructed due to light and dark adaptations from foggy situations or sudden changes in illumination. Fog removal generally takes a considerably significant amount of time to calculate. Thus in order to reduce the calculation time, remove the fog used the major fog removal algorithm Dark Channel Prior. While Gamma Correction was used to calibrate brightness, a brightness and contrast evaluation was conducted on the image in order to determine the Gamma Value needed for correction. The evaluation used only a part instead of the entirety of the image in order to reduce the time allotted to calculation. When the brightness and contrast values were calculated, those values were used to decided Gamma value and to correct the entire image. The brightness correction and fog removal were processed in parallel, and the images were registered as a single image to minimize the calculation time needed for all the processes. Then the feature extraction method HOG was used to detect the vehicle in the corrected image. As a result, it took 0.064 seconds per frame to detect the vehicle using image correction as proposed herein, which showed a 7.5% improvement in detection rate compared to the existing vehicle detection method.

• Journal of Radiation Industry
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• v.18 no.2
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• pp.101-107
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• 2024

Rapid screening for internal contamination by alpha- and beta-emitting radionuclides is essential in situations involving radiation workers or radiation accidents. This study focused on the use of urine samples and liquid scintillation counting to quickly and accurately assess contamination. Calibration of the alpha and beta detection areas ensured precise measurement results. The major radionuclides recommended for surveillance during accidents were also considered. This study evaluated the effectiveness of the method by examining various parameters, including the limit of detection, linearity, sensitivity, selectivity, accuracy, ruggedness, and blind test sample analysis. The liquid scintillation counting method is an effective tool for screening urinary samples to detect alpha- and beta-emitting radionuclides, particularly during radiation emergencies, despite some limitations in precision.

• Journal of Korea Water Resources Association
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• v.41 no.10
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• pp.969-982
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• 2008

As hydrological models have been progressively developed, they are recognized as appropriate tools to manage water resources. Especially, the need to evaluate the effects of landuse and climate change on hydrological phenomena has been increased, which requires powerful validation methods for the hydrological models to be employed. As measured streamflow data at many locations may not be available, or include significant errors in application of hydrological models, streamflow data simulated by models only might be used to conduct hydrological analysis. In many cases, reducing errors in model simulations requires a powerful model validation method. In this research, we demonstrated a validation methodology of SWAT model using observed flow in two basins with different physical characteristics. First, we selected two basins, Gap-cheon basin and Yongdam basin located in the Guem River Basin, showing different hydrological characteristics. Next, the methodology developed to estimate parameter values for the Gap-cheon basin was applied for estimating those for the Yongdam basin without calibration a priori, and sought for validation of the SWAT. Application result with SWAT for Yongdam basin showed $R_{eff}$ ranging from 0.49 to 0.85, and $R^{2}$ from 0.49 to 0.84. As well, comparison of predicted flow and measured flow in each subbasin showed reasonable agreement. Furthermore, the model reproduced the whole trends of measured total flow and low flow, though peak flows were rather underestimated. The results of this study suggest that SWAT can be applied for predicting effects of future climate and landuse changes on flow variability in river basins. However, additional studies are recommended to further verify the validity of the mixed method in other river basins.