• 한국방재학회:학술대회논문집
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• 2008.02a
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• pp.549-552
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• 2008

In this study, we deployed the cable tunnel inspection and monitoring system by wireless sensor network. It is shown that the wireless sensor network which is composed of sensor, wireless communication module, and gateway can be applied to cable tunnel monitoring system. Sensors considered herein are flame detection sensor, flood detection sensor, intruder detection sensor, and temperature sensor, etc. It is also found that the wireless sensor network can deliver sensing data reliably by wireless sensing technology. The gateway system that can transmit sensed data to server by CDMA is developed. Monitoring system is constructed by web service technology, and it is observed that this system can monitor the present state of tunnel without difficulties. The system provides an alternative to inspecting and monitoring the tunnel efficiently where the conventional wired system is infeasible.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.6
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• pp.1182-1189
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• 2007

As ubiquitous environment rapidly emerges due to the development of network and information communication technology, ubiquitous computing is being noticed as a technology that will take the lead in information technology industry of the future. To the end, the data recognized from each sensors, collected and processed need be transferred to applied service so that they may be used as data for providing sum to users. However, in reality as the definitions of metadata and platform for each sensor are not clear, approach from applied service for data use is difficult and there are limitations in transferring sensor measurement data of the applied service because of the difference of platform and protocol. In this paper, we designed sensor service technology language that expresses sensor and measurement data and describes the service though sensor data service. Also the researcher implemented a sensor data processing system that transfers sensor data to the applied service by using web service. The thesis will apply a reference model for standardization to guarantee mutual compatibility in exchanging sensor data through the system.

• Electronics and Telecommunications Trends
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• v.34 no.5
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• pp.14-25
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• 2019

As the Internet of Things, artificial intelligence and big data have received a lot of attention as key growth engines in the era of the fourth industrial revolution, data acquisition and utilization in mobile, automotive, robotics, manufacturing, agriculture, health care and national defense are becoming more important. Due to numerous data-based industrial changes, demand for sensor technologies is exploding, especially for intelligent sensor technologies that combine control, judgement, storage and communication functions with the sensors's own functions. Intelligent sensor technology can be defined as a convergence component technology that combines intelligent sensor units, intelligent algorithms, modules with signal processing circuits, and integrated plaform technologies. Intelligent sensor technology, which can be applied to variety of smart IT convergence services such as smart devices, smart homes, smart cars, smart factory, smart cities, and others, is evolving towards intelligent and convergence technologies that produce new high-value information through recognition, reasoning, and judgement based on artificial intelligence. As a result, development of intelligent sensor units is accelerating with strategies for miniaturization, low-power consumption and convergence, new form factor such as flexible and stretchable form, and integration of high-resolution sensor arrays. In the future, these intelligent sensor technologies will lead explosive sensor industries in the era of data-based artificial intelligence and will greatly contribute to enhancing nation's competitiveness in the global sensor market. In this report, we analyze and summarize the recent trends in intelligent sensor technologies, especially those for four core technologies.

• Structural Engineering and Mechanics
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• v.69 no.4
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• pp.407-416
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• 2019

This paper develops a wireless sensor for online fatigue crack detection and failure warning based on crack-induced nonlinear ultrasonic modulation. The wireless sensor consists of packaged piezoelectric (PZT) module, an excitation/sensing module, a data acquisition/processing module, a wireless communication module, and a power supply module. The packaged PZT and the excitation/sensing module generate ultrasonic waves on a structure and capture the response. Based on nonlinear ultrasonic modulation created by a crack, the data acquisition/processing module periodically performs fatigue crack diagnosis and provides failure warning if a component failure is imminent. The outcomes are transmitted to a base through the wireless communication module where two-levels duty cycling media access control (MAC) is implemented. The uniqueness of the paper lies in that 1) the proposed wireless sensor is developed specifically for online fatigue crack detection and failure warning, 2) failure warning as well as crack diagnosis are provided based on crack-induced nonlinear ultrasonic modulation, 3) event-driven operation of the sensor, considering rare extreme events such as earthquakes, is made possible with a power minimization strategy, and 4) the applicability of the wireless sensor to steel welded members is examined through field and laboratory tests. A fatigue crack on a steel welded specimen was successfully detected when the overall width of the crack was around $30{\mu}m$, and a failure warnings were provided when about 97.6% of the remaining useful fatigue lives were reached. Four wireless sensors were deployed on Yeongjong Grand Bridge in Souht Korea. The wireless sensor consumed 282.95 J for 3 weeks, and the processed results on the sensor were transmitted up to 20 m with over 90% success rate.

• Journal of Sensor Science and Technology
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• v.31 no.6
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• pp.423-427
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• 2022

A pressure sensor that mimics the sensing ability of human skin has emerged as high-profile technology because it shows remarkable applications in numerous fields such as robotics, human health monitoring, and artificial prosthetics. Whereas recent pressure sensors have achieved high sensitivity similar to that of human skin, they still show limited detection bandwidth. Moreover, once these e-skin are fabricated, their sensitivity and stiffness are fixed; therefore, they can be used for only limited applications. Our study proposes a new adaptive pressure sensor built with uniform gallium microdroplet-elastomer composite. Based on the phase transition of gallium microdroplets, the proposed sensor undergoes mode transformation, enabling it to have a higher sensitivity and wider detection bandwidth compared with those of human skin. In addition, we succeeded in extending a single adaptive pressure sensor to sensor arrays based on its high uniformity, reproducibility, and large-scale manufacturability. Finally, we designed an adaptive e-skin with the sensor array and demonstrated its applications on health monitoring tasks including blood pulse and body weight measurements.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.8
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• pp.3641-3655
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• 2016

Wireless sensor networks (WSNs) provide a promising approach to monitor the physical environments, to prolong the network lifetime by exploiting the mutual correlation among sensor readings has become a research focus. In this paper, we design a hierarchical network framework which guarantees layered-compression. Meanwhile, a data sorting-based adaptive spatial compression scheme (DS-ASCS) is proposed to explore the spatial correlation among signals. The proposed scheme reduces the amount of data transmissions and alleviates the network congestion. It also obtains high compression performance by sorting original sensor readings and selectively discarding the small coefficients in transformed matrix. Moreover, the compression ratio of this scheme varies according to the correlation among signals and the value of adaptive threshold, so the proposed scheme is adaptive to various deploying environments. Finally, the simulation results show that the energy of sorted data is more concentrated than the unsorted data, and the proposed scheme achieves higher reconstruction precision and compression ratio as compared with other spatial compression schemes.

• Current Optics and Photonics
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• v.6 no.2
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• pp.183-190
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• 2022

Based on the linear electro-optic (EO) effect of lithium niobite (LiNbO₃, LN) crystal, an intense two-dimensional (2D) electric field sensor was analyzed, fabricated and experimentally demonstrated. The linear polarized light beam transmits along the optical axis (z-axis) of the LN crystal, and the polarization direction of the polarized light is 45° to the y-axis. The sensor can detect the intensity of a 2D electric field that is perpendicular to the z-axis. Experimental results demonstrated that the minimum detectable electric field of the sensor is 10.5 kV/m. The maximum detected electric field of the sensor is larger than 178.9 kV/m. The sensitivity of the sensor is 0.444 mV/(kV·m^-1). The variation of the sensitivity is within ±0.16 dB when the sensor is rotated around a z-axis from 0° to 360°. The variation of the sensor output optical power is within ±1.4 dB during temperature change from 19 ℃ to 26 ℃ in a day (from 7:00 AM to 23:00 PM) and temperature change from 0 ℃ to 40 ℃ in a controllable temperature chamber. All theoretical and experimental results revealed that the fabricated sensor provides technology for the direct detection of intense 2D electric fields.

• Smart Structures and Systems
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• v.31 no.3
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• pp.247-257
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• 2023

In structural health monitoring of large-scale structures, optimal sensor placement plays an important role because of the high cost of sensors and their supporting instruments, as well as the burden of data transmission and storage. In this study, a vibration sensor placement algorithm based on deep reinforcement learning (DRL) is proposed, which can effectively solve non-convex, high-dimensional, and discrete combinatorial sensor placement optimization problems. An objective function is constructed to estimate the quality of a specific vibration sensor placement scheme according to the modal assurance criterion (MAC). Using this objective function, a DRL-based algorithm is presented to determine the optimal vibration sensor placement scheme. Subsequently, we transform the sensor optimal placement process into a Markov decision process and employ a DRL-based optimization algorithm to maximize the objective function for optimal sensor placement. To illustrate the applicability of the proposed method, two examples are presented: a 10-story braced frame and a sea-crossing bridge model. A comparison study is also performed with a genetic algorithm and particle swarm algorithm. The proposed DRL-based algorithm can effectively solve the discrete combinatorial optimization problem for vibration sensor placements and can produce superior performance compared with the other two existing methods.

• Smart Structures and Systems
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• v.5 no.3
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• pp.279-290
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• 2009

Two degrees of freedom resonant systems are employed to improve the resonant property of resonant sensor, as compared to a single degree of freedom resonant system. This paper presents design, development and testing of two degrees of freedom resonant sensor. To measure absolute mass, cantilever shaped two different masses (smaller/absorber mass and bigger/drive mass) with identical resonant frequency are mechanically linked to form 2 - Degree-of-Freedom (DOF) resonator which exhibits higher amplitude of displacement at the smaller mass. The same concept is extended for measuring differential quantity, by having two bigger mass and one smaller mass. The main features of this work are the 3 - DOF resonator for differential detection and the microcontroller based closed loop electronics for resonant sensor with piezoelectric sensing and excitation. The advantage of using microcontroller is that the method can be easily extended for any range of measurand.

• ETRI Journal
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• v.36 no.6
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• pp.1016-1022
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• 2014

This paper presents a hybrid model solution for user motion recognition. The use of a single classifier in motion recognition models does not guarantee a high recognition rate. To enhance the motion recognition rate, a hybrid model consisting of decision trees and artificial neural networks is proposed. We define six user motions commonly performed in an indoor environment. To demonstrate the performance of the proposed model, we conduct a real field test with ten subjects (five males and five females). Experimental results show that the proposed model provides a more accurate recognition rate compared to that of other single classifiers.