• Smart Structures and Systems
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• v.9 no.5
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• pp.461-472
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• 2012

This study proposes an optics-based active sensing system for continuous monitoring of underground pipelines in nuclear power plants (NPPs). The proposed system generates and measures guided waves using a single laser source and optical cables. First, a tunable laser is used as a common power source for guided wave generation and sensing. This source laser beam is transmitted through an optical fiber, and the fiber is split into two. One of them is used to actuate macro fiber composite (MFC) transducers for guided wave generation, and the other optical fiber is used with fiber Bragg grating (FBG) sensors to measure guided wave responses. The MFC transducers placed along a circumferential direction of a pipe at one end generate longitudinal and flexural modes, and the corresponding responses are measured using FBG sensors instrumented in the same configuration at the other end. The generated guided waves interact with a defect, and this interaction causes changes in response signals. Then, a damage-sensitive feature is extracted from the response signals using the axi-symmetry nature of the measured pitch-catch signals. The feasibility of the proposed system has been examined through a laboratory experiment.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.10B
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• pp.1216-1221
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• 2011

We propose a multiplexed fiber Bragg grating (FBG) sensor network for vessel monitoring to measure the variation of strain and temperature by environmental perturbation based on code division multiple access (CDMA). The center wavelength of FBG was linearly changed by environmental perturbation such as strain and temperature variation so that we could be monitoring the state of sensors. A RSOA was used as optical broadband source and which was modulated by using pseudo random binary sequence (PRBS) signal. The correlation peak of reflected signal from sensor networks was measured. In this paper, we used the sliding correlation techniques for high speed response and dynamic rage of sensors.

• Smart Structures and Systems
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• v.15 no.3
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• pp.523-541
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• 2015

Structural health monitoring of steel truss bridge based on changes in modal properties was investigated in this study. Vibration measurements with five sensors were conducted at an existing Warren truss bridge with partial fractures in diagonal members before and after an emergency repair work. Modal properties identified by the Eigensystem Realization Algorithm showed evidences of increases in modal damping due to the damage in diagonal member. In order to understand the dynamic behavior of the bridge and possible mechanism of those increases in modal damping, theoretical modal analysis was conducted with three dimensional frame models. It was found that vibrations of the main truss could be coupled internally with local vibrations of diagonal members and the degree of coupling could change with structural changes in diagonal members. Additional vibration measurements with fifteen sensors were then conducted so as to understand the consistency of those theoretical findings with the actual dynamic behavior. Modal properties experimentally identified showed that the damping change caused by the damage in diagonal member described above could have occurred in a diagonal-coupled mode. The results in this study imply that damages in diagonal members could be detected from changes in modal damping of diagonal-coupled modes.

• Smart Structures and Systems
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• v.6 no.7
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• pp.767-792
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• 2010

In this paper, the optimal design of vibration control system for smart structures has been investigated semi-analytically via the optimization of geometric parameters like the placements and sizes of piezoelectric sensors and actuators (S/As) bonded on the structures. The criterion based on the maximization of energy dissipation was adopted for the optimization of the control system. Based on the sensing and actuating equations, the total energy stored in the system which is used as the objective function was analytically derived with design variables explicitly presented. Two cases of single and combined vibration modes were addressed for a simply supported beam and a simply supported cylindrical shell. For single vibration mode, the optimal distributions of the piezoelectric S/As could be obtained analytically. However, the Sequential Quadratic Programming (SQP) method has to be employed to solve those which violated the prescribed constraints and to solve the case of combined vibration modes. The results of three examples, which include a simply supported beam, a simply supported cylindrical shell and a simply supported plate, showed good agreement with those obtained by the Genetic Algorithm (GA) method. Moreover, in comparison with the GA method, the proposed method is more effective in obtaining better optimization results and is much more efficient in terms of computation time.

• Structural Monitoring and Maintenance
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• v.2 no.2
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• pp.145-164
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• 2015

Bridge scour is the predominant cause of overwater bridge failures in North America and around the world. Several sensing systems have been developed over the years to detect the extent of scour so that preventative actions can be performed in a timely manner. These sensing systems have drawbacks, such as signal inaccuracy and discontinuity, installation difficulty, and high cost. Therefore, attempts to develop more efficient monitoring schemes continue. In this study, the viability of using optical dissolved oxygen (DO) probes for monitoring scour depths was explored. DO levels are very low in streambed sediments, as compared to the standard level of oxygen in flowing water. Therefore, scour depths can be determined by installing sensors to monitor DO levels at various depths along the buried length of a bridge pier or abutment. The measured DO is negligible when a sensor is buried but would increase significantly once scour occurs and exposes the sensor to flowing water. A set of experiments was conducted in which four dissolved oxygen probes were embedded at different soil depths in the vicinity of a mock bridge pier inside a laboratory flume simulating scour conditions. The results confirmed that DO levels jumped drastically when sensors became exposed during scour hole evolution, thereby providing discrete measurements of the maximum scour depth. Moreover, the DO probes could detect any subsequent refilling of the scour hole through the deposition of sediments. The effect of soil permeability on the sensing response time was also investigated.

• Smart Structures and Systems
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• v.18 no.3
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• pp.541-568
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• 2016

The goal of this study is to investigate the effect of non-synchronous sensing when using wireless sensors on structural identification and to attempt correcting such errors in order to obtain a better identification result. The sources causing non-synchronous sensing are discussed first and the magnitudes of such synchronization errors are estimated based on time stamps of data samples collected from Imote2 sensors; next the impact of synchronization errors on power spectral densities (PSDs) and correlation functions of output responses are derived analytically; finally a new method is proposed to correct such errors. In this correction method, the corrected PSDs of output responses are estimated using non-synchronous samples based on a modified FFT. The effect of synchronization errors in the measured output responses on structural identification and the application of this correction method are demonstrated using simulation examples. The simulation results show that even small synchronization errors in the output responses can distort the identified modal and stiffness parameters remarkably while the parameters identified using the proposed correction method can achieve high accuracy.

• Smart Structures and Systems
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• v.29 no.4
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• pp.561-576
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• 2022

Heritage timber structures represent the history and culture of a nation. These structures have been inherited from previous generations; however, they inevitably exhibit deterioration over time, potentially leading to structural deficiencies. Structural Health Monitoring (SHM) offers the potential to assess operational anomalies, deterioration, and damage through processing and analysis of data collected from transducers and sensors mounted on the structure. This paper reports on the design and implementation of a long-term SHM system on the Feiyun Wooden Pavilion in China, a three-story timber building built more than 500 years ago. The principles and features of the design and implementation of SHM systems for heritage timber buildings are systematically discussed. In total, 104 sensors of 6 different types are deployed on the structure to monitor the environmental effects and structural responses, including air temperature and humidity, wind speed and direction, structural temperatures, strain, inclination, and acceleration. In addition, integrated data acquisition and transmission subsystem using a newly developed software platform are implemented. Selected preliminary statistical and correlation analysis using one year of monitoring data are presented to demonstrate the condition assessment capability of the system based on the monitoring data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.2259-2277
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• 2023

We propose a probabilistic fish growth model for smart aquaculture systems equipped with IoT sensors that monitor the ecological environment. As IoT sensors permeate into smart aquaculture systems, environmental data such as oxygen level and temperature are collected frequently and automatically. However, there still exists data on fish weight, tank allocation, and other factors that are collected less frequently and manually by human workers due to technological limitations. Unlike sensor data, human-collected data are hard to obtain and are prone to poor quality due to missing data and reading errors. In a situation where different types of data are mixed, it becomes challenging to develop an effective fish growth model. This study explores the unique characteristics of such a combined environmental and weight dataset. To address these characteristics, we develop a preprocessing method and a probabilistic fish growth model using mixed data sampling (MIDAS) and overlapping mixtures of Gaussian processes (OMGP). We modify the OMGP to be applicable to prediction by setting a proper prior distribution that utilizes the characteristic that the ratio of fish groups does not significantly change as they grow. We conduct a numerical study using the eel dataset collected from a real smart aquaculture system, which reveals the promising performance of our model.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.385-392
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• 2022

As energy-saving techniques based on human behavior patterns have recently become an issue, the occupant-centered control system is adopted for estimating personal preference of indoor environment and optimizing environmental comfort and energy consumption. Accordingly, IoT devices have been used to collect indoor environmental quality (IEQ) data and personal data. However, the need to safely collect and manage data has been emerged due to cybersecurity issues. Therefore, this paper aims to present a framework that can safely transmit occupant-centered data collected from IoT to a private blockchain server using Hyperledger fabric. In the case study, the minimum value product of the mobile application and smartwatch application was developed to evaluate the usability of the proposed blockchain-based occupant-centered data collection framework. The results showed that the proposed framework could collect data safely and hassle-free in the daily life of occupants. In addition, the performance of the blockchain server was evaluated in terms of latency and throughput when ten people in a single office participated in the proposed data collection framework. Future works will further apply the proposed data collection framework to the building management system to automatically collect occupant data and be used in the HVAC system to reduce building energy consumption without security issues.

• Smart Structures and Systems
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• v.32 no.3
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• pp.179-193
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• 2023

This study explores an alternative to the existing centralized process for data anomaly detection in modern Internet of Things (IoT)-based structural health monitoring (SHM) systems. An edge intelligence framework is proposed for the early detection and classification of various data anomalies facilitating quality enhancement of acquired data before transmitting to a central system. State-of-the-art deep neural network pruning techniques are investigated and compared aiming to significantly reduce the network size so that it can run efficiently on resource-constrained edge devices such as wireless smart sensors. Further, depthwise separable convolution (DSC) is invoked, the integration of which with advanced structural pruning methods exhibited superior compression capability. Last but not least, quantization-aware training (QAT) is adopted for faster processing and lower memory and power consumption. The proposed edge intelligence framework will eventually lead to reduced network overload and latency. This will enable intelligent self-adaptation strategies to be employed to timely deal with a faulty sensor, minimizing the wasteful use of power, memory, and other resources in wireless smart sensors, increasing efficiency, and reducing maintenance costs for modern smart SHM systems. This study presents a theoretical foundation for the proposed framework, the validation of which through actual field trials is a scope for future work.