• Smart Structures and Systems
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• v.13 no.3
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• pp.389-406
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• 2014

Optimal sensor placement techniques play a significant role in enhancing the quality of modal data during the vibration based health monitoring of civil structures, where many degrees of freedom are available despite a limited number of sensors. The literature has shown a shift in the trends for solving such problems, from expansion or elimination approach to the employment of heuristic algorithms. Although these heuristic algorithms are capable of providing a global optimal solution, their greatest drawback is the requirement of high computational effort. Because a highly efficient optimisation method is crucial for better accuracy and wider use, this paper presents an improved simulated annealing (SA) algorithm to solve the sensor placement problem. The algorithm is developed based on the sensor locations' coordinate system to allow for the searching in additional dimensions and to increase SA's random search performance while minimising the computation efforts. The proposed method is tested on a numerical slab model that consists of two hundred sensor location candidates using three types of objective functions; the determinant of the Fisher information matrix (FIM), modal assurance criterion (MAC), and mean square error (MSE) of mode shapes. Detailed study on the effects of the sensor numbers and cooling factors on the performance of the algorithm are also investigated. The results indicate that the proposed method outperforms conventional SA and Genetic Algorithm (GA) in the search for optimal sensor placement.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.29 no.7
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• pp.1-7
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• 2015

In this paper, we demonstrated a polarimetric fiber pressure sensor using a polarization-diversity-loop-based Sagnac interferometer(PDLSI) composed of polarization-maintaining fiber(PMF) and a fiber Bragg grating(FBG). In order to compare the pressure sensitivity for various kinds of PMF, three kinds of bow-tie PMF were employed as sensor heads. The maximum pressure sensitivity was measured as approximately -15.07nm/MPa, and an R2 value to represent sensor linearity was measured as ~0.992 at the sensor system using corresponding PMF over a pressure range of 0-0.3MPa. An FBG was utilized and located adjacent to the PMF segment for compensating temperature-induced errors in the measurement of pressure. The pressure sensitivity of the proposed sensor was improved by approximately four times compared with the previously reported pressure sensor based on polarization-maintaining photonic crystal fiber.

• International journal of advanced smart convergence
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• v.9 no.3
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• pp.232-238
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• 2020

In this paper, we study on aerial objects detection and position estimation algorithm for the safety of UAV that flight in BVLOS. We use the vision sensor and LiDAR to detect objects. We use YOLOv2 architecture based on CNN to detect objects on a 2D image. Additionally we use a clustering method to detect objects on point cloud data acquired from LiDAR. When a single sensor used, detection rate can be degraded in a specific situation depending on the characteristics of sensor. If the result of the detection algorithm using a single sensor is absent or false, we need to complement the detection accuracy. In order to complement the accuracy of detection algorithm based on a single sensor, we use the Kalman filter. And we fused the results of a single sensor to improve detection accuracy. We estimate the 3D position of the object using the pixel position of the object and distance measured to LiDAR. We verified the performance of proposed fusion algorithm by performing the simulation using the Gazebo simulator.

• The Journal of the Korea institute of electronic communication sciences
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• v.5 no.6
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• pp.672-677
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• 2010

Recently, the efficient management system for large-scale sensor data has been required due to the increasing deployment of large-scale sensor networks. In this paper, we propose a cloud-based sensor data management system with low cast, high scalability, and efficiency. Sensor data in sensor networks are transmitted to the cloud through a cloud-gateway. At this point, outlier detection and event processing is performed. Transmitted sensor data are stored in the Hadoop HBase, distributed column-oriented database, and processed in parallel by query processing module designed as the MapReduce model. The proposed system can be work with the application of a variety of platforms, because processed results are provided through REST-based web service.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.3
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• pp.1014-1034
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• 2015

Location information of sensor nodes plays a critical role in many wireless sensor network (WSN) applications and protocols. Although many localization algorithms have been proposed in recent years, they usually target at dense networks and perform poorly in sparse networks. In this paper, we propose two component-based localization algorithms that can localize many more nodes in sparse networks than the state-of-the-art solution. We first develop the Basic Common nodes-based Localization Algorithm, namely BCLA, which uses both common nodes and measured distances between adjacent components to merge components. BCLA outperforms CALL, the state-of-the-art component-based localization algorithm that uses only distance measurements to merge components. In order to further improve the performance of BCLA, we further exploit the angular information among nodes to merge components, and propose the Component-based Localization with Angle and Distance information algorithm, namely CLAD. We prove the merging conditions for BCLA and CLAD, and evaluate their performance through extensive simulations. Simulations results show that, CLAD can locate more than 90 percent of nodes in a sparse network with average node degree 7.5, while CALL can locate only 78 percent of nodes in the same scenario.

• Journal of Sensor Science and Technology
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• v.25 no.3
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• pp.208-212
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• 2016

In this work, a loop sensor for Arc-Flash detections has been developed in order to trip a circuit breaker within 2.5 ms after an Arc-Flash event. For an efficient capturing of the flash light, plastic optical fibers, where light attenuations are larger than those in silica-based ones, with different diameters and surface conditions were utilized. The performance was comparatively analyzed with those of a point sensor and a commercialized product. The point sensor module was designed for hemisphere-like capturings of Arc-Flashes larger than 3 kA at 2 meters from the sensor. On the other hand, the loop sensor allowed 360-degree-detections around the fiber axis and the measurement range was dependent on the length of the fiber connected to the sensor module. The trip-level-dependent brightness measurement results showed that the fabricated point sensor and loop sensor satisfied a brightness condition, 10~40 klux, and the responses of the system to Arc-Flashes were completed within 2.5 ms.

• Smart Structures and Systems
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• v.6 no.5_6
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• pp.689-709
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• 2010

In this paper, a low cost, low power but multifunctional wireless sensor node is presented for the impedance-based SHM using piezoelectric sensors. Firstly, a miniaturized impedance measuring chip device is utilized for low cost and low power structural excitation/sensing. Then, structural damage detection/sensor self-diagnosis algorithms are embedded on the on-board microcontroller. This sensor node uses the power harvested from the solar energy to measure and analyze the impedance data. Simultaneously it monitors temperature on the structure near the piezoelectric sensor and battery power consumption. The wireless sensor node is based on the TinyOS platform for operation, and users can take MATLAB$^{(R)}$ interface for the control of the sensor node through serial communication. In order to validate the performance of this multifunctional wireless impedance sensor node, a series of experimental studies have been carried out for detecting loose bolts and crack damages on lab-scale steel structural members as well as on real steel bridge and building structures. It has been found that the proposed sensor nodes can be effectively used for local wireless health monitoring of structural components and for constructing a low-cost and multifunctional SHM system as "place and forget" wireless sensors.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.10
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• pp.3554-3570
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• 2021

With the rapid development of the Chinese water project, the safety monitoring of dams is urgently needed. Many drawbacks exist in dams, such as high monitoring costs, a limited equipment service life, long-term monitoring difficulties. MEMS sensors have the advantages of low cost, high precision, easy installation, and simplicity, so they have broad application prospects in engineering measurements. This paper designs intelligent monitoring based on the collaborative measurement of dual MEMS sensors. The system first determines the endpoint coordinates of the sensor array by the coordinate transformation relationship in the monitoring system and then obtains the dam settlement according to the endpoint coordinates. Next, this paper proposes a dual-MEMS sensor collaborative measurement algorithm that builds a mathematical model of the dual-sensor measurement. The monitoring system realizes mutual compensation between sensor measurement data by calculating the motion constraint matrix between the two sensors. Compared with the single-sensor measurement, the dual-sensor measurement algorithm is more accurate and can improve the reliability of long-term monitoring data. Finally, the experimental results show that the dam subsidence monitoring system proposed in this paper fully meets the engineering monitoring accuracy needs, and the dual-sensor collaborative measurement system is more stable than the single-sensor monitoring system.

• Journal of Applied Reliability
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• v.18 no.1
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• pp.20-32
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• 2018

Purpose: The purpose of this study is to set up an anomaly detection criteria for sensor data coming from a motorcycle. Methods: Five sensor values for accelerator pedal, engine rpm, transmission rpm, gear and speed are obtained every 0.02 second from a motorcycle. Exploratory data analysis is used to find any pattern in the data. Traditional process control methods such as X control chart and time series models are fitted to find any anomaly behavior in the data. Finally unsupervised learning algorithm such as k-means clustering is used to find any anomaly spot in the sensor data. Results: According to exploratory data analysis, the distribution of accelerator pedal sensor values is very much skewed to the left. The motorcycle seemed to have been driven in a city at speed less than 45 kilometers per hour. Traditional process control charts such as X control chart fail due to severe autocorrelation in each sensor data. However, ARIMA model found three abnormal points where they are beyond 2 sigma limits in the control chart. We applied a copula based Markov chain to perform statistical process control for correlated observations. Copula based Markov model found anomaly behavior in the similar places as ARIMA model. In an unsupervised learning algorithm, large sensor values get subdivided into two, three, and four disjoint regions. So extreme sensor values are the ones that need to be tracked down for any sign of anomaly behavior in the sensor values. Conclusion: Exploratory data analysis is useful to find any pattern in the sensor data. Process control chart using ARIMA and Joe's copula based Markov model also give warnings near similar places in the data. Unsupervised learning algorithm shows us that the extreme sensor values are the ones that need to be tracked down for any sign of anomaly behavior.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.9
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• pp.2442-2454
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• 2012

To ensure the security of wireless sensor networks, it is important to have a robust key management scheme. In this paper, we propose a Quorum-based key management scheme. A specific sensor, called as key distribution server (KDS), generates a key matrix and establishes a quorum system from the key matrix. The quorum system is a set system of subsets that the intersection of any two subsets is non-empty. In our scheme, each sensor is assigned a subset of the quorum system as its pre-distributed keys. Whenever any two sensors need a shared key, they exchange their IDs, and then each sensor by itself finds a common key from its assigned subset. A shared key is then generated by the two sensors individually based on the common key. By our scheme, no key is needed to be refreshed as a sensor leaves the network. Upon a sensor joining the network, the KDS broadcasts a message containing the joining sensor ID. After receiving the broadcast message, each sensor updates the key which is in common with the new joining one. Only XOR and hash operations are required to be executed during key update process, and each sensor needs to update one key only. Furthermore, if multiple sensors would like to have a secure group communication, the KDS broadcasts a message containing the partial information of a group key, and then each sensor in the group by itself is able to restore the group key by using the secret sharing technique without cooperating with other sensors in the group.