• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.9
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• pp.104-109
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• 2021

Tactile sensors and integrated circuits that detect external stimuli have been developed for use in various industries. Most tactile sensors have been developed using the MEMS(micro electro-mechanical systems) process in which metal electrodes and strain sensors are applied to a silicon substrate. However, tactile sensors made of highly brittle silicon lack flexibility and are prone to damage by external forces. Flexible tactile sensors based on polydimethylsiloxane and using a multi-walled carbon nano-tube mixture as a pressure-sensitive material are currently being developed as an alternative to overcome these limitations. In this study, a manufacturing process of pressure-sensitive materials with low initial electrical resistance is developed and applied to the fabrication of flexible tactile sensors. In addition, flexible tactile sensors are developed with pressure-sensitive materials dispensed on a substrate with flexible mechanical properties. Finally, a study is conducted on the change in electrical resistance of pressure-sensitive materials according to the modulus of elasticity of the substrate.

• Journal of Sensor Science and Technology
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• v.30 no.6
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• pp.381-383
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• 2021

Light-activated metal oxide gas sensors have been investigated in recent decades. Light illumination enhances the sensing attributes, including the operational temperature, sensitivity, and selectivity. Unfortunately, high operating temperature is a major problem for gas sensors because of the huge energy consumption. Therefore, the importance of light-activated room-temperature sensing has increased. This paper reviews recent light-activated sensors and their sensing mechanisms with a specific focus on metal oxide gas sensors. Studies use the outstanding ZnO and SnO₂ sensors to research photoactivation when illuminated by various sources such as ultraviolet (UV), halogen lamp, or monochromatic light. Photon induction generates electron-hole pairs that increase the number of adsorption sites of gas molecules and ions improving the sensor's sensing properties.

• Journal of Sensor Science and Technology
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• v.28 no.3
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• pp.139-145
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• 2019

Precautionary detection of chemical warfare agents (CWAs) has been an important global issue mainly owing to their toxicity. To achieve proper detection, many studies have been conducted to develop sensitive gas sensors for CWAs. In particular, metal-oxide semi-conductors (MOS) have been investigated as promising sensing materials owing to their abundance in nature and excellent sensitivity. In this review, we mainly focus on various MOS-based gas sensors that have been fabricated for the detection of two specific CWA simulants, 2-chloroethyl ethyl sulfide (2-CEES) and dimethyl methyl phosphonate (DMMP), which are simulants of sulfur mustard and sarin, respectively. In the case of 2-CEES, we mainly discuss $CdSnO_3-$ and ZnO-based sensors and their reaction mechanisms. In addition, a method to improve the selectivity of ZnO-based sensors is mentioned. Various sensors and their sensing mechanisms have been introduced for the detection of DMMP. As the reaction with DMMP may directly affect the sensing properties of MOS, this paper includes previous studies on its poisoning effect. Finally, promising sensing materials for both gases are proposed.

• Tribology and Lubricants
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• v.36 no.6
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• pp.307-314
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• 2020

In this paper, studies on application cases of machine condition monitoring using oil sensors are reviewed. Owing to rapid industrial advancements, maintenance strategies play a crucial role in reducing the cost of downtime and improving system reliability. Consequently, machine condition monitoring plays an important role in maintaining operation stability and extending the period of usage for various machines. Machine condition monitoring through oil analysis is an effective method for assessing a machine's condition and providing early warnings regarding a machine's breakdown or failure. Among the three prevalent methods, the online analysis method is predominantly employed because this method incorporates oil sensors in real-time and has several advantages (such as prevention of human errors). Wear debris sensors are widely employed for implementing machine condition monitoring through oil sensors. Furthermore, various types of oil sensors are used in different machines and systems. Integrated oil sensors that can measure various oil attributes by incorporating a single sensor are becoming popular. By monitoring wear debris, machine condition monitoring using oil sensors is implemented for engines, automotive transmission, tanks, armored vehicles, and construction equipment. Additionally, such monitoring systems are incorporated in aircrafts such as passenger airplanes, fighter airplanes, and helicopters. Such monitoring systems are also employed in chemical plants and power plants for managing overall safety. Furthermore, widespread application of oil condition diagnosis requires the development of diagnostic programs.

• Journal of Sensor Science and Technology
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• v.32 no.6
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• pp.386-390
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• 2023

Stiffness-engineered stretchable substrate technology has been widely used to produce stretchable displays, transistors, and integrated circuits because it is compatible with various flexible electronics technologies. However, the stiffness-engineering technology has never been applied to transistor-based stretchable strain sensors. In this study, we developed thin-film transistor-based strain sensors on stiffness-engineered stretchable substrates. We designed and fabricated strain-sensitive stretchable resistors capable of inducing changes in drain currents of transistors when subjected to stretching forces. The resistors and source electrodes of the transistors were connected in series to integrate the developed stretchable resistors with thin-film transistors on stretchable substrates by printing the resistors after fabricating transistors. The thin-film transistor-based stretchable strain sensors demonstrate feasibility as strain sensors operating under strains of 0%-5%. This strain range can be extended with further investigations. The proposed stiffness-engineering approach will expand the potential for the advancement and manufacturing of innovative stretchable strain sensors.

• Composites Research
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• v.14 no.6
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• pp.47-58
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• 2001

This paper describes applications of cure monitoring techniques by using embedded fiber optic strain sensors, which are extrinsic Fabry-Perot interoferometric (EFPI) and/or fiber Bra99 grating (FBG) sensors, to three kinds of molding methods of autoclave, FW and RTM molding methods. In these applications, internal strain of high-temperature curing resin was monitored by EFPI sensors. From theme experimental results, it was shown that strain caused by thermal shrink at cooling stage could be measured well. In addition, several specific matters to these molding methods were considered. As thor an autoclave molding of unidirectional FRP laminates, it was confirmed that off-axis strain of unidirectional FRP could be monitored by EFPI sensors. As for FW molding using room-temperature (RT) cured resin, it was found that the strain outputs from EFPI sensors represented curing shrinkage as well as thermal strain and the convergence meant finish of cure reaction. It was also shown that this curing shrinkage should be evaluated with consideration on logarithmic change in stiffness of matrix resin. As for a RTM melding, both EFPI and FBC sensors were employed to measure strain. The results showed that FBG sensors hale also good potential for strain monitoring at cooling stage, while the non-uniform thermal residual strain of textile affected the FBG spectrum after molding. This study has proven that embedded fiber optic strain sensors hale practical ability of cure monitoring of FRP. However, development of automatic installation methods of sensors remains as a problem to be solved for applications to practical products.

• Journal of Sensor Science and Technology
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• v.8 no.6
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• pp.440-447
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• 1999

Tensile stress loaded on smart composite structures and thermal stress occurred during the during process of the smart composite materials with embedded optical fiber sensors affect directly the mechanical behavior of the embedded optical fiber sensors within the smart composite structures. Stress distribution within the optical fiber sensors varies with respect to the stacking sequence of the composite laminate and the coating conditions of the optical fibers. The cracks occurred within the composite laminate affect not only the fracture of the composite laminate but also the fracture of the optical fiber sensors embedded within the composite laminate. In this study, firstly, stress distribution of the optical fiber sensors embedded within the composite laminate which is subjected to the tensile and thermal stresses was analyzed using Finite Element Method. And, secondly, the effect of the stacking sequence of the composite laminate and the coating conditions of the optical fiber sensors on the stress distribution of the optical fiber sensors was investigated. Finally, the effect of the crack occurred within the smart composite laminate on the fracture behavior of the optical fiber sensors was also observed through the tensile test.

• Journal of Biosystems Engineering
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• v.41 no.4
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• pp.408-417
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• 2016

Purpose: Yield monitoring systems are an essential component of precision agriculture. They indicate the spatial variability of crop yield in fields, and have become an important factor in modern harvesters. The objective of this paper was to review research trends related to yield monitoring sensors for grain crops. Methods: The literature was reviewed for research on the major sensing components of grain yield monitoring systems. These major components included grain flow sensors, moisture content sensors, and cutting width sensors. Sensors were classified by sensing principle and type, and their performance was also reviewed. Results: The main targeted harvesting grain crops were rice, wheat, corn, barley, and grain sorghum. Grain flow sensors were classified into mass flow and volume flow methods. Mass flow sensors were mounted primarily at the clean grain elevator head or under the grain tank, and volume flow sensors were mounted at the head or in the middle of the elevator. Mass flow methods used weighing, force impact, and radiometric approaches, some of which resulted in measurement error levels lower than 5% ($R^2=0.99$). Volume flow methods included paddle wheel type and optical type, and in the best cases produced error levels lower than 3%. Grain moisture content sensing was in many cases achieved using capacitive modules. In some cases, errors were lower than 1%. Cutting width was measured by ultrasonic distance sensors mounted at both sides of the header dividers, and the errors were in some cases lower than 5%. Conclusions: The design and fabrication of an integrated yield monitoring system for a target crop would be affected by the selection of a sensing approach, as well as the layout and mounting of the sensors. For accurate estimation of yield, signal processing and correction measures should be also implemented.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.11
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• pp.3544-3564
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• 2022

With the development of the NB-IoT (Narrow band Internet of Things) and smart cities, coupled with the emergence of smart smoke sensors, new requirements and issues have been introduced to study on the deployment of sensors in large spaces. Previous research mainly focuses on the optimization of wireless sensors in some monitoring environments, including three-dimensional terrain or underwater space. There are relatively few studies on the optimization deployment problem of smart smoke sensors, and leaving large spaces with obstacles such as libraries out of consideration. This paper mainly studies the deployment issue of smart smoke sensors in large spaces by considering the fire probability of fire areas and the obstacles in a monitoring area. To cope with the problems of coverage blind areas and coverage redundancy when sensors are deployed randomly in large spaces, we proposed an optimized deployment strategy of smart smoke sensors based on the PSO (Particle Swarm Optimization) algorithm. The deployment problem is transformed into a multi-objective optimization problem with many constraints of fire probability and barriers, while minimizing the deployment cost and maximizing the coverage accuracy. In this regard, we describe the structure model in large space and a coverage model firstly, then a mathematical model containing two objective functions is established. Finally, a deployment strategy based on PSO algorithm is designed, and the performance of the deployment strategy is verified by a number of simulation experiments. The obtained experimental and numerical results demonstrates that our proposed strategy can obtain better performance than uniform deployment strategies in terms of all the objectives concerned, further demonstrates the effectiveness of our strategy. Additionally, the strategy we proposed also provides theoretical guidance and a practical basis for fire emergency management and other departments to better deploy smart smoke sensors in a large space.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.172-172
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• 2023

The use of low-cost IoT sensors for flow measurement in open channels has gained significant attention due to their potential to provide continuous and real-time data at a low cost. However, the accuracy and reliability of these sensors in real-world scenarios are not well understood. This study aims to compare the performance of low-cost IoT sensors in the laboratory and real-world conditions to evaluate their accuracy and reliability. Firstly, a low-cost IoT sensor was integrated with an IoT platform to acquire real-time flow rate data. The IoT sensors were calibrated in the laboratory environment to optimize their accuracy, including different types of low-cost IoT sensors (HC-SR04 ultrasonic sensor & YF-S201 sensor) using an open channel prototype. After calibration, the IoT sensors were then applied to a real-world case study in the Dorim-cheon stream, where they were compared to traditional flow measurement methods to evaluate their accuracy.The results showed that the low-cost IoT sensors provided accurate and reliable flow rate data under laboratory conditions, with an error range of less than 5%. However, when applied to the real-world case study, the accuracy of the IoT sensors decreased, which could be attributed to several factors such as the effects of water turbulence, sensor drift, and environmental factors. Overall, this study highlights the potential of low-cost IoT sensors for flow measurement in open channels and provides insights into their limitations and challenges in real-world scenarios.