• Korean Journal of Materials Research
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• v.24 no.1
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• pp.19-24
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• 2014

We report the nitrogen monoxide (NO) gas sensing properties of p-type CuO-nanorod-based gas sensors. We synthesized the p-type CuO nanorods with breadth of about 30 nm and length of about 330 nm by a hydrothermal method using an as-deposited CuO seed layer prepared on a $Si/SiO_2$ substrate by the sputtering method. We fabricated polycrystalline CuO nanorod arrays at $80^{\circ}C$ under the hydrothermal condition of 1:1 morality ratio between copper nitrate trihydrate [$Cu(NO_2)_2{\cdot}3H_2O$] and hexamethylenetetramine ($C_6H_{12}N_4$). Structural characterizations revealed that we prepared the pure CuO nanorod array of a monoclinic crystalline structure without any obvious formation of secondary phase. It was found from the gas sensing measurements that the p-type CuO nanorod gas sensors exhibited a maximum sensitivity to NO gas in dry air at an operating temperature as low as $200^{\circ}C$. We also found that these CuO nanorod gas sensors showed reversible and reliable electrical response to NO gas at a range of operating temperatures. These results would indicate some potential applications of the p-type semiconductor CuO nanorods as promising sensing materials for gas sensors, including various types of p-n junction gas sensors.

• Journal of Sensor Science and Technology
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• v.32 no.2
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• pp.88-94
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• 2023

Recently, piezoelectric tactile sensors have garnered considerable attention in the field of texture recognition owing to their high sensitivity and high-frequency detection capability. Despite their remarkable potential, improving their mechanical flexibility to attach to complex surfaces remains challenging. In this study, we present a flexible piezoelectric sensor that can be bent to an extremely small radius of up to 2.5 mm and still maintain good electrical performance. The proposed sensor was fabricated by controlling the thickness that induces internal stress under external deformation. The fabricated piezoelectric sensor exhibited a high sensitivity of 9.3 nA/kPa ranging from 0 to 10 kPa and a wide frequency range of up to 1 kHz. To demonstrate real-time texture recognition by rubbing the surface of an object with our sensor, nine sets of fabric plates were prepared to reflect their material properties and surface roughness. To extract features of the objects from the detected sensing data, we converted the analog dataset to short-term Fourier transform images. Subsequently, texture recognition was performed using a convolutional neural network with a classification accuracy of 97%.

• Proceedings of the KSME Conference
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• 2008.11a
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• pp.1723-1726
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• 2008

In recent years, there has been a remarkable progress in the development of the fiber optic sensors for the detection of various chemicals. Fiber optic sensors have the advantages of very small size, flexibility and low cost. The fiber optic sensors employing different optical or spectroscopic phenomena have been reported such as bulk absorption, optical reflectance, fluoresces and energy transfer. In this study, the effect of nanoparticle concentration in liquid upon light absorption and scattering was studied using extrinsic fiber optic method. For the evaluation, we used Red (650 nm) and Blue (430 nm) light sources which are coupled through the standard cuvette using optical fiber to detector. The experiments are carried out with Polystyrene latex (400 - 800 nm), and Silicon (35 - 110 nm) nanoparticles suspended in Isopropanol. Differences in light absorption and scattering depending on nanoparticle concentration and type are discussed. This method may be useful to study nanoparticles properties for various application and research.

• Smart Structures and Systems
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• v.5 no.4
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• pp.469-482
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• 2009

The emerging sensor-based structural health monitoring (SHM) technology has a potential for cost-effective maintenance of aging civil infrastructure systems. The author proposes to integrate continuous and global monitoring using on-structure sensors with targeted local non-destructive evaluation (NDE). Significant technical challenges arise, however, from the lack of cost-effective sensors for monitoring spatially large structures, as well as reliable methods for interpreting sensor data into structural health conditions. This paper reviews recent efforts and advances made in addressing these challenges, with example sensor hardware and health monitoring software developed in the author's research center. The hardware includes a novel fiber optic accelerometer, a vision-based displacement sensor, a distributed strain sensor, and a microwave imaging NDE device. The health monitoring software includes a number of system identification methods such as the neural networks, extended Kalman filter, and nonlinear damping identificaiton based on structural dynamic response measurement. These methods have been experimentally validated through seismic shaking table tests of a realistic bridge model and tested in a number of instrumented bridges and buildings.

• Journal of Sensor Science and Technology
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• v.32 no.6
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• pp.378-385
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• 2023

Human biosignals provide essential information for diagnosing diseases such as dementia and Parkinson's disease. Owing to the shortcomings of current clinical assessments, noninvasive solutions are required. Machine learning (ML) on wearable sensor data is a promising method for the real-time monitoring and early detection of abnormalities. ML facilitates disease identification, severity measurement, and remote rehabilitation by providing continuous feedback. In the context of wearable sensor technology, ML involves training on observed data for tasks such as classification and regression with applications in clinical metrics. Although supervised ML presents challenges in clinical settings, unsupervised learning, which focuses on tasks such as cluster identification and anomaly detection, has emerged as a useful alternative. This review examines and discusses a variety of ML algorithms such as Support Vector Machines (SVM), Random Forests (RF), Decision Trees (DT), Neural Networks (NN), and Deep Learning for the analysis of complex clinical data.

• Journal of Sensor Science and Technology
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• v.31 no.3
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• pp.168-174
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• 2022

Hydrogen sulfide(H₂S) gas is a high-risk gas that can cause suffocation or death in severe cases, depending on the concentration of exposure. Various studies to detect this gas are still in progress. In this study, we demonstrate a colorimetric sensor that can detect H₂S gas using its direct color change. The proposed nanofiber sensor containing a dye material named Lead(II) acetate, which changes its color according to H₂S gas reaction, is fabricated by electrospinning. The performance of this sensor is evaluated by measuring RGB changes, ΔE value, and gas selectivity. It has a ΔE value of 5.75 × 10^-3 ΔE/s·ppm, showing improved sensitivity up to 1.4 times that of the existing H₂S color change detection sensor, which is a result of the large surface area of the nanofibers. The selectivity for H₂S gas is confirmed to be an excellent value of almost 70 %.

• Journal of Sensor Science and Technology
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• v.10 no.1
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• pp.16-23
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• 2001

Comparing with general optical fiber sensors performing localized measurement, distributed optical fiber sensors can measure along an optical fiber, and they have large measuring range. The surface-mounting method with epoxy adhesive is general in attaching optical fiber sensors to structures, This is also appliable to the structural integrity monitoring with Brillouin-scattering distributed optical fiber sensors. In this paper, Brillouin-scattering distributed optical fiber sensors, which are attached to the surface of a structure with epoxy adhesive, was verified with the finite element method. From the analysis results of strain transfer through the structure, optical fiber coating, cladding and core, the strain transfer rates were calculated. And the influence of the epoxy free-end was also studied.

• Korean Journal of Materials Research
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• v.29 no.5
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• pp.297-303
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• 2019

A triple-layered $PMMA/Ni_{64}Zr_{36}/PDMS$ hydrogen gas sensor using hydrogen permeable alloy and flexible polymer layers is fabricated through spin coating and DC-magnetron sputtering. PDMS(polydimethylsiloxane) is used as a flexible substrate and PMMA(polymethylmethacrylate) thin film is deposited onto the $Ni_{64}Zr_{36}$ alloy layer to give a high hydrogen-selectivity to the sensor. The measured hydrogen sensing ability and response time of the fabricated sensor at high hydrogen concentration of 99.9 % show a 20 % change in electrical resistance, which is superior to conventional Pd-based hydrogen sensors, which are difficult to use in high hydrogen concentration environments. At a hydrogen concentration of 5 %, the resistance of electricity is about 1.4 %, which is an electrical resistance similar to that of the $Pd_{77}Ag_{23}$ sensor. Despite using low cost $Ni_{64}Zr_{36}$ alloy as the main sensing element, performance similar to that of existing Pd sensors is obtained in a highly concentrated hydrogen atmosphere. By improving the sensitivity of the hydrogen detection through optimization including of the thickness of each layer and the composition of Ni-Zr alloy thin film, the proposed Ni-Zr-based hydrogen sensor can replace Pd-based hydrogen sensors.

• Electronics and Telecommunications Trends
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• v.33 no.1
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• pp.123-130
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• 2018

Tactile sensors, which are commonly referred to as pressure and strain sensors, have been extensively investigated to meet the demands for attachable and wearable electronics for monitoring the health status or activity of human users. For this purpose, the introduction of two-dimensional (2D) materials such as graphene and transition metal dichalcogenides (TMDs) with high mechanical strength at the atomic scale is very suitable for tactile sensors applicable for use in human-friendly devices. In this paper, we examine a descriptive summary of a tactile sensor and review state-of- the-art research trends of 2D material-based tactile sensors in terms of the material and architecture. Finally, we propose a roadmap for future studies into advanced tactile sensors based on our ongoing research.

• Proceedings of the Korea Concrete Institute Conference
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• 2005.05a
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• pp.47-50
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• 2005

Fiber Bragg Grating (FBG) Sensors as advanced measuring system are introduced and actively being applied to establish a smart monitoring system for bridge maintenance. This study develops FBG sensors and suggests a smart monitoring system. As for its first step, to verify the reliability of FBG sensors that developed, a specimen is made FBG sensors and electric sensor are attached. Then, Static test is conducted on the specimen on the specimens to check reliability. In addition, this study estimates the optimum deflection curve that converts strain curve data measured by FBG sensors into deflection.