• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.24 no.12
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• pp.977-984
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• 2014

Nanomaterial have grown from scientific interest to commercial products and the nanomaterial market has grown 19.1 % each year. As the nanomaterial market size increases, it is expected that nanomaterial production will increase and its contamination of outdoor environmental system will also increase in the form of industrial waste. Since most of nanomaterials are known as biologically non-degradable materials, nanomaterials will accumulate in the environment, and this will increase the potential threats to human health along the food chain. Recent studies have investigated the toxicity effect of nanomaterials due to their size, chemical composition and shape. For the development of nanomaterial while taking human health into consideration, a nanomaterial detecting sensor is required. In this paper, we have observed the trend of nanomaterial detecting sensor of mechanical, electrochemical, optical and kelvin probe force microscopy sensors and we believe that this trend will shed the light on the development of real-life nanomaterial detecting sensors.

• Journal of Communications and Networks
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• v.15 no.4
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• pp.422-429
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• 2013

Advances in wireless sensor network (WSN) technology have enabled small and low-cost sensors with the capability of sensing various types of physical and environmental conditions, data processing, and wireless communication. In the WSN, the sensor nodes have a limited transmission range and their processing and storage capabilities as well as their energy resources are limited. A triple umpiring system has already been proved for its better performance in WSNs. The clustering technique is effective in prolonging the lifetime of the WSN. In this study, we have modified the ad-hoc on demand distance vector routing by incorporating signal-to-noise ratio (SNR) based dynamic clustering. The proposed scheme, which is an efficient and secure routing protocol for wireless sensor networks through SNR-based dynamic clustering (ESRPSDC) mechanisms, can partition the nodes into clusters and select the cluster head (CH) among the nodes based on the energy, and non CH nodes join with a specific CH based on the SNR values. Error recovery has been implemented during the inter-cluster routing in order to avoid end-to-end error recovery. Security has been achieved by isolating the malicious nodes using sink-based routing pattern analysis. Extensive investigation studies using a global mobile simulator have shown that this hybrid ESRP significantly improves the energy efficiency and packet reception rate as compared with the SNR unaware routing algorithms such as the low energy aware adaptive clustering hierarchy and power efficient gathering in sensor information systems.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2013.10a
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• pp.799-804
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• 2013

The sensor/actuator active sensor diagnostics procedure, where the sensors/actuators are confirmed to be functioning properly during operation, is a critical component to successfully complete the structural health monitoring (SHM) process with large numbers of active sensors typically installed in a structure. The basis of this process is to track the changes in the capacitive value of piezoelectric materials, which shows up in measured admittance. Due to the temperature dependent nature of piezoelectric materials, we investigated the effects of temperature variations on sensor diagnostic process. The effect of temperature variations found to be remarkable, modifying the measured capacitive values significantly. In addition we analyzed the effect of bonding agents between a PZT patch and a host structure. This paper summarizes considerations needed to develop such sensor diagnostic processes, experimental procedures and results, and additional issues that can be used as guidelines for future investigations.

• Fashion & Textile Research Journal
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• v.16 no.1
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• pp.145-152
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• 2014

The usage of textile-based sensors has increased due to their many advantages (compared to IT sensors) when applied to body assessment and comfort. Textile-based sensors have different detecting factors such as pressure, voltage, current and capacitance to investigate the characteristics. In this study, textile-based sensor fabrics with sheath-core type conductive yarns were produced and the relationship between capacitance changes and applied load was investigated. The physical and electric properties of textile-based sensor fabrics were also investigated under various laminating conditions. A textile based pressure sensor that uses a sheath-core conductive yarn to ensure the stability of the pressure sensor in the textile-based sensor (the physical structure of the reaction characteristic of the capacitance) is important for the stability of the initial value of the initial capacitance value outside the characteristic of the textile structural environment. In addition, a textile based sensor is displaced relative to the initial value of the capacitance change according to pressure changes in the capacitance value of the sensor due to the fineness of the high risk of noise generation. Changing the physical structure of the fabric through the sensor characteristic of the pressure sensor via the noise generating element of laminating (temperature, humidity, and static electricity) to cut off the voltage output element to improve the data reliability could be secured.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.11a
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• pp.330-335
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• 2000

This paper deals with sensing ability of smart sensor that has a sensing ability of distinguish materials. We have developed new signal processing method that have distinguish different materials. We made the two type of smart sensors for experiment. The first type of smart sensor is H2 type. The second type of smart sensor is HH type. The smart sensor was developed for recognition of material. And then we developed estimation method of sensing ability of smart sensors. The first method(Sensing Ability Index) is developed for H2 smart sensor. The second method($R_{SAI}$ Index) is developed for HH smart sensor. We estimated sensing ability of smart sensor with new SAI and $R_{SAI}$ method. This paper describes our primary study for a new method of estimate sensing ability of smart sensor, which is need for precision work system. This is a study of dynamic characteristics of smart sensor according to frequency and displacement changing with new SAI and $R_{SAI}$ method. Experiment and analysis are executed for proper dynamic sensing condition. First, we developed advanced smart sensors. Second, we develop new SAI and $R_{SAI}$ methods that have a sensing ability of distinguish materials. Dynamic characteristics of smart sensor are evaluated through new SAI and $R_{SAI}$ method relatively. We can use the new SAI and $R_{SAI}$ method for finding materials. Applications of this method are finding abnormal condition of object(auto-manufacturing), feeling of object(medical product), robotics, safety diagnosis of structure, etc.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2009.10a
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• pp.314-315
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• 2009

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2009.04a
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• pp.466-467
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• 2009

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2012.04a
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• pp.210-211
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• 2012

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2010.05a
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• pp.126-127
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• 2010

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2010.05a
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• pp.248-249
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• 2010