• Fashion & Textile Research Journal
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• v.21 no.6
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• pp.697-707
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• 2019

This review paper deals with materials, classification, and a current article investigation on smart textile sensors for wearable vital signs monitoring (WVSM). Smart textile sensors can lose electrical conductivity during vital signs monitoring when applying them to clothing. Because they should have to endure severe conditions (bending, folding, and distortion) when wearing. Imparting electrical conductivity for application is a critical consideration when manufacturing smart textile sensors. Smart textile sensors fabricate by utilizing electro-conductive materials such as metals, allotrope of carbon, and intrinsically conductive polymers (ICPs). It classifies as performance level, fabric structure, intrinsic/extrinsic modification, and sensing mechanism. The classification of smart textile sensors by sensing mechanism includes pressure/force sensors, strain sensors, electrodes, optical sensors, biosensors, and temperature/humidity sensors. In the previous study, pressure/force sensors perform well despite the small capacitance changes of 1-2 pF. Strain sensors work reliably at 1 ㏀/cm or lower. Electrodes require an electrical resistance of less than 10 Ω/cm. Optical sensors using plastic optical fibers (POF) coupled with light sources need light in-coupling efficiency values that are over 40%. Biosensors can quantify by wicking rate and/or colorimetry as the reactivity between the bioreceptor and transducer. Temperature/humidity sensors require actuating triggers that show the flap opening of shape memory polymer or with a color-changing time of thermochromic pigment lower than 17 seconds.

• Journal of Multimedia Information System
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• v.9 no.3
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• pp.209-218
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• 2022

Industrial IoT applications, including smart factories, require two problem-solving to build data monitoring systems required by services from distributed IoT sensors (smart sensors). One is to overcome proprietary protocols, data formats, and hardware differences and to uniquely identify and connect IoT sensors, and the other is to overcome the problem of changing the server-side data storage structure and sensor data transmission format according to the addition or change of service or IoT sensors. The IEEE 1451.4 standard-based or IPMI specification-based smart sensor technology supports the development of plug-and-play sensors that solve the first problem. However, there is a lack of research that requires a second problem-solving, which requires support for the plug-in of IoT sensors into remote services. To propose a solution for the integration of these two problem-solving, we present a IoT sensor platform, a service system architecture, and a service plugin protocol for the MQTT-based IIoT application environment.

• Smart Structures and Systems
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• v.16 no.6
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• pp.1147-1167
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• 2015

Wireless smart sensors, which have become popular for monitoring applications, are an attractive option for implementing structural control systems, due to their onboard sensing, processing, and communication capabilities. However, wireless smart sensors pose inherent challenges for control, including delays from communication, acquisition hardware, and processing time. Previous research in wireless control, which focused on semi-active systems, has found that sampling rate along with time delays can significantly impact control performance. However, because semi-active systems are guaranteed stable, these issues are typically neglected in the control design. This work achieves active control with smart sensors in an experimental setting. Because active systems are not inherently stable, all the elements of the control loop must be addressed, including data acquisition hardware, processing performance, and control design at slow sampling rates. The sensing hardware is shown to have a significant impact on the control design and performance. Ultimately, the smart sensor active control system achieves comparable performance to the traditional tethered system.

• 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.

• Structural Monitoring and Maintenance
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• v.3 no.1
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• pp.91-114
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• 2016

In this paper, recent research trends and activities on structural health monitoring (SHM) of civil infrastructure in Korea are reviewed. Recently, there has been increasing need for adopting smart sensing technologies to SHM, so this review focuses on smart sensing, monitoring, and assessment for civil infrastructure. Firstly, the research activities on smart sensor technology is reviewed including optical fiber sensors, piezoelectric sensors, wireless smart sensors, and vision-based sensing system. Then, a brief overview is given to the recent advances in smart monitoring and assessment techniques such as vibration-based global monitoring techniques, local monitoring with piezoelectric materials, decentralized monitoring techniques for wireless sensors, wireless power supply and energy harvest. Finally, recent joint SHM activities on several test beds in Korea are discussed to share the up-to-date information and to promote the smart sensors and monitoring technologies for applications to civil infrastructure. It includes a Korea-US joint research on test bridges of the Korea Expressway Corporation (KEC), a Korea-US-Japan joint research on Jindo cable-stayed bridge, and a comparative study for cable tension measurement techniques on Hwamyung cable-stayed bridge, and a campaign test for displacement measurement techniques on Sorok suspension bridge.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.15 no.2
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• pp.99-106
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• 2006

In this paper, the new method that estimates a sensing ability of smart sensor will be proposed. A study is estimation method that evaluates sensing ability about smart sensor respectively. According to acceleration(g) and displacement changing, we estimated sensing ability of smart sensor using SAI(Sensing Ability Index) method respectively. Smart sensors was made fer experiment. The types of smart sensor are two types(hard and soft smart sensor). Smart sensors developed for recognition of material. Experiment and analysis are executed for estimate the SAI method. In develop a smart sensor, the SAI method will be useful for finding optical design condition of smart sensor that can sense a material. And then dynamic characteristics of smart sensors(frequency changing, acceleration changing, critical point, etc.) are evaluated respectively through new method(SAI) that use the power spectrum density. Dynamic characteristic of sensor is evaluated with SAI method relatively. We can use the SAI for finding critical point of smart sensor, too.

• Journal of Sensor Science and Technology
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• v.31 no.2
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• pp.79-84
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• 2022

Self-powered sensors play an important role in everyday life, and they cover a wide range of topics. These sensors are meant to measure the amount of relevant motion and transform the biomechanical activities into electrical signals using triboelectric nanogenerators (TENGs) since they are sensitive to external stimuli such as pressure, temperature, wetness, and motion. The present advancement of TENGs-based self-powered wearable, implantable, and patchable sensors for healthcare monitoring, human body motion, and medication delivery systems was carefully emphasized in this study. The use of TENG technology to generate electrical energy in real-time using self-powered sensors has been the topic of considerable research among various leading scholars. TENGs have been used in a variety of applications, including biomedical and healthcare physical sensors, wearable devices, biomedical, human-machine interface, chemical and environmental monitoring, smart traffic, smart cities, robotics, and fiber and fabric sensors, among others, as efficient mechanical-to-electric energy conversion technologies. In this evaluation, the progress accomplished by TENG in several areas is extensively reviewed. There will be a discussion on the future of self-powered sensors.

• Smart Structures and Systems
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• v.12 no.1
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• pp.23-39
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• 2013

In this article, a smart multifunctional optical system-on-a-chip (SOC) sensor platform is presented and its application for fiber Bragg grating (FBG) sensor interrogation in optical sensor networks is investigated. The smart SOC sensor platform consists of a superluminescent diode as a broadband source, a tunable microelectromechanical system (MEMS) based Fabry-P$\acute{e}$rot filter, photodetectors, and an integrated microcontroller for data acquisition, processing, and communication. Integrated with a wireless sensor network (WSN) module in a compact package, a smart optical sensor node is developed. The smart multifunctional sensor platform has the capability of interrogating different types of optical fiber sensors, including Fabry-P$\acute{e}$rot sensors and Bragg grating sensors. As a case study, the smart optical sensor platform is demonstrated to interrogate multiplexed FBG strain sensors. A time domain signal processing method is used to obtain the Bragg wavelength shift of two FBG strain sensors through sweeping the MEMS tunable Fabry-P$\acute{e}$rot filter. A tuning range of 46 nm and a tuning speed of 10 Hz are achieved. The smart optical sensor platform will open doors to many applications that require high performance optical WSNs.

• Journal of the Ergonomics Society of Korea
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• v.36 no.5
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• pp.395-409
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• 2017

Objective: The purpose of this study was to optimize the number and positions of foot pressure sensors using the reliability analysis of the center of pressure (COP) in smart shoes. Background: Foot pressure can be different according to foot region, and it is important which region of the foot pressure needs to be measured. Method: Thirty adults (age: $20.5{\pm}1.8years$, body weight: $71.4{\pm}6.5kg$, height: $1.76{\pm}0.04m$) participated in this study. The foot pressure data were collected using the insole of Pedar-X system (Novel GmbH, USA) with a sampling frequency of 100Hz during 1.3m/s speed walking on the treadmill (Instrumented treadmill, Bertec, USA). The intraclass correlation coefficients (ICC) were calculated between the COP positions using 4, 5, 6, 7, 8, and 99 sensors, while one-way repeated measure ANOVA was performed between the standard deviation (SD) of the COP positions. Results: The medio-lateral (M/L) COP position using 99 sensors was positively correlated with the M/L COP positions using 6, 7, and 8 sensors; however, it was not correlated with the M/L COP positions using 4 and 5 sensors during landing phase (1~4%) (p<.05). The antero-posterior (A/P) COP position using 99 sensors was positively correlated with the A/P COP positions using 4, 5, 6, 7, and 8 sensors (p<.05). The SD of the COP position using 99 sensors was smaller than the SD of the M/L COP positions using 4, 5, 6, 7, and 8 sensors (p<.05). Conclusion: Based on our findings, it is desirable to arrange at least 6 sensors in smart shoes. Application: The study of optimizing the number and positions of foot pressure sensors would contribute to developing more effective smart shoes using foot pressure technology.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.05a
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• pp.318-322
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• 2001

This paper deals with sensing ability of smart sensor that has a sensing ability to distinguish materials according to surface types of smart sensor. We have developed a new signal processing method that can distinguish among different materials. The smart sensor was developed for recognition of materials. We made two types of smart sensors in our experiment. Then, we estimated the ability to recognize objects according to smart sensor type. We estimated the sensing ability of smart sensor with the $R_{SAI}$ method. Experiments and analysis were executed to estimate the ability to recognize objects according to surface types of smart sensor. Sensing ability of smart sensors was evaluated relatively through a new $R_{SAI}$ method. Applications of smart sensors are for finding abnormal conditions of objects (auto-manufacturing), feeling of objects (medical product), robotics, safety diagnosis of structure, etc.etc.