• Journal of Sensor Science and Technology
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• v.25 no.4
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• pp.291-296
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• 2016

The ideal chemical sensor must show the similar result under the same condition for accurate measurement of gases regardless of time. However, the actual responses of chemical sensors have been shown the lacks of repeatability and reproducibility because of the drift which has been caused by aging and pollution of the sensor and the environment change such as temperature and humidity. If the problems are not properly taken into considerations, the stability and reliability of the system using chemical sensors would be decreased. In this paper, we analyzed the sensor's drift and applied the three different compensation methods(DWT( Discrete Wavelets Transform), Baseline Manipulation, Internal Normalization) for reducing the effects of the drift in order to improve the stability and the reliability of short term of the chemical sensors. And in order to compare the results of the methods, the standard deviation was used as a criterion. The sensor drift was analyzed by a trend line graph. We applied the three methods to the successive data measured for three days and compared the results. As a result of comparison, the standard deviation of DWT showed lowest value. (Before compensation: 7.1219, DWT: 1.3644, Baseline Manipulation: 2.5209, Internal Normalization: 3.1425).

• 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.

• Journal of Sensor Science and Technology
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• v.24 no.4
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• pp.270-273
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• 2015

One of the main problems when working the chemical sensor is the lack of repeatability and reproducibility of the sensor response. If the problem is not properly taken into consideration, the stability and reliability of the system using chemical sensors would be decreased. In this paper we analyzed the sensor's drift of short term and proposed a compensation method for reducing the effects of the drift in order to improve the stability and the reliability of the chemical sensor. The sensor drift was analyzed by a trend line graph and CV(coefficient of variation) was used to quantify. And we compensated for the drift by using the internal normalization. As a result it was found that the value of CV was decreased after compensation.

• Journal of Sensor Science and Technology
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• v.24 no.6
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• pp.368-372
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• 2015

In this paper, we proposed a post-processing technique for improving classification performance of electronic nose (E-Nose) system which may be occurred drift signals from sensor array. An adaptive radial basis function network using stochastic gradient (SG) and singular value decomposition (SVD) is applied to process signals from sensor array. Due to drift from sensor's aging and poisoning problems, the final classification results may be showed bias and fluctuations. The predicted classification results with drift are quantized to determine which identification level each class is on. To mitigate sharp fluctuations moving-averaging (MA) technique is applied to quantized identification results. Finally, quantization and some edge correction process are used to decide levels of the fluctuation-smoothed identification results. The proposed technique has been indicated that E-Nose system was shown correct odor identification results even if drift occurred in sensor array. It has been confirmed throughout the experimental works. The enhancements have produced a very robust odor identification capability which can compensate for decision errors induced from drift effects with sensor array in electronic nose system.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.2
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• pp.55.5-56
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• 2021

The optical design of the Linear-Astigmatism-Free Three-Mirror-System (LAF-TMS) for KASI-Deep Rolling Imaging Fast-optics Telescope(K-DRIFT) is presented. LAF-TMS is an all-reflective imaging system consists of three freeform mirrors. Due to its well-corrected aberrations and obstruction-free clear aperture, the LAF-TMS provides a wide field of view with very low scattered lights.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.1
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• pp.101-108
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• 1998

This paper concerns the navigation algorithm of motion reference unit (MRU) for autonomous underwater vehicle (AUV) We apply the strapdown navigation system using middle level inertial sensors. But, because the MRU consists of inertial sensors, the values of AUV motion calculated by navigation computer are increased by drift property of inertial sensors. Therefore, we propose the attitude algorithm using switching method according to the motion of AUV From this algorithm, the drift terms are eliminated effectively for roll and pitch. But, another device is required for yaw angle.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.611-615
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• 1994

In this paper, the sensitivity, linearity and temperature drift characteristics of various capacitive force sensors are evaluated and compared using new experimental methods. In particular, two designs were employed to reduce temperature drift. Both types of sensor use high-sensitivity Al coated PET film, and their externals are miniaturized. The first has a layered design consisting of two dielectric substances with different temperature characteristics. The prototype of this design had a temperature drift of only 0.1% of the sensor's capacity in the 20-80.deg. C range. The second type uses both a dummy sensor ind an active sensor with the same characteristics. The temperature drift of the prototype was one-fifth the temperature drift of a single sensor.

• Journal of the Korean earth science society
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• v.26 no.2
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• pp.129-136
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• 2005

Continued observation of ARGO floats for years(about 4 years) makes the conductivity sensor more vulnerable to fouling by marine life and associated drift in salinity measurements. In this paper, we address this issue by making use of floats deployed in different years. Floats deployed in the East Sea and the Indian Ocean are examined to find out float-to-float match-ups in such a way that an older float pops up simultaneously with a newer deployment (with tolerable space-time difference). A time difference of less than five days and space difference of less than 100km are considered for the match-up data sets. For analysis of the salinity drift under the stable water mass, observations of the floats from deepest water masses have been used. From the cross-check of ARGO floats in the East Sea and the Indian Ocean, it is found that there is a systematic drift in the older float compared to later deployments. All drift results, consistently show negative bias indicating the typical nature of drift from fouled sensors. However, the drift is much less than 0.01, the specified accuracy of ARGO program.

• Journal of Sensor Science and Technology
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• v.17 no.4
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• pp.295-302
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• 2008

$TiO_2,\;ZrO_2$, and $SiO_2$ were added in the concentration of 1 - 3 wt.% to improve long-term stability for the $SnO2$ thick film gas sensor. Short-term sensor resistances up to 90 h were measured to investigate the stabilization time of initial resistance in air. Long-term resistance drifts in air and in gas to 5000 ppm methane for the sensors annealed at $750^{\circ}C$ for 1 h and continuously heated at an operating temperature of $400^{\circ}C$ were also measured up to 90 days at an interval of 1 day. The long-term drifts in methane sensitivity for the three metal oxide-doped $SnO2$ sensors are closely related to methane sensitivity level, catalytic activity, and long-term drift in sensor resistance in air. Those stabilities are mainly discussed in terms of oxidation state and catalytic activity.

• Journal of Sensor Science and Technology
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• v.33 no.5
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• pp.338-343
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• 2024

The measurement of pH is of significant importance in chemistry, life sciences, and environmental monitoring. Unlike conventional pH sensors that utilize glass electrodes, thin-film transistor (TFT)-based pH sensors offer distinct advantages, including enhanced response speed and additional circuit functions. In this study, we developed a pH sensor that incorporates biocompatible parylene-C as both the substrate and sensing layer, thereby enhancing flexibility, transparency, and biological compatibility. We conducted tests to measure the voltage-current characteristics of the pH solutions and assessed their performance in terms of drift and hysteresis. Using InGaZnO (IGZO) as the channel material, our pH sensor demonstrated an average sensitivity of approximately 82 mV/pH, albeit with certain drift limitations. The initial pH measurements exhibited good reversibility over time. IGZO- and parylene-C-based TFT pH sensors are well suited for various applications, including wearable health monitoring, owing to their flexibility and biocompatibility.