• 제어로봇시스템학회:학술대회논문집
• /
• 2001.10a
• /
• pp.127.4-127
• /
• 2001

Inspection and shape measurement of three-dimensional objects are widely needed in industries for quality monitoring and control. A number of visual or optical technologies have been successfully applied to measure three dimensional surfaces. Especially, the shape measurement using an interferometric principle becomes a successful methodology. However, those conventional interferometric methods to measure surface profile have an inherent shortcoming, namely 2∏ ambiguity problem. The problem inevitably happens when the object to be measured has discontinuous shape due to the repetition of interferometric signal with phase period of 2∏. Therefore, in this paper, we choose as a shape measuring method, ...

• Structural Monitoring and Maintenance
• /
• v.1 no.3
• /
• pp.309-322
• /
• 2014

Detection of fatigue cracks at an early stage of their development is important in structural health monitoring. The breathing of cracks in a structure generates higher harmonic components of the exciting frequency in the frequency spectrum. Previously, the residual operational deflection shape (R-ODS) method was successfully applied to beams with a single crack. The method is based on the ODSs at the exciting frequency and its higher harmonic components which consider both amplitude and phase information of responses to map the deflection pattern of structures. Although the R-ODS method shows the location of a single crack clearly, its identification for the location of multiple cracks in a structure is not always obvious. Therefore, an improvement to the R-ODS method is presented here to make the identification process distinct for the beams with multiple cracks. Numerical and experimental examples are utilised to investigate the effectiveness of the improved method.

• Journal of Welding and Joining
• /
• v.27 no.3
• /
• pp.85-91
• /
• 2009

A roll projection welding machine is introduced to fabricate metallic sandwich plate consisting of a structured inner sheet with projection-like shape and a pair of skin sheets. To fabricate the metallic sandwich plate of consistent and good quality, two process monitoring methods are introduced; dynamic resistance monitoring and skin sheet temperature monitoring. Dynamic resistance monitoring has no time delay but gives only averaged value over plate width. Skin sheet temperature monitoring has certain amount of time delay but is good for predicting weld quality of specified position. By the two complementary monitoring methods, the characteristics of the new welding process is successfully understood.

• Smart Structures and Systems
• /
• v.26 no.5
• /
• pp.591-603
• /
• 2020

Dynamic deflection monitoring is an essential and critical part of structural health monitoring for high-speed railway bridges. Two critical problems need to be addressed when using inclinometer sensors for such applications. These include constructing a general representation model of inclination-deflection and addressing the ill-posed inverse problem to obtain the accurate dynamic deflection. This paper provides a dynamic deflection monitoring method with the placement of optimal inclinometer sensors for high-speed railway bridges. The deflection shapes are reconstructed using the inclination-deflection transformation model based on the differential relationship between the inclination and displacement mode shape matrix. The proposed optimal sensor configuration can be used to select inclination-deflection transformation models that meet the required accuracy and stability from all possible sensor locations. In this study, the condition number and information entropy are employed to measure the ill-condition of the selected mode shape matrix and evaluate the prediction performance of different sensor configurations. The particle swarm optimization algorithm, genetic algorithm, and artificial fish swarm algorithm are used to optimize the sensor position placement. Numerical simulation and experimental validation results of a 5-span high-speed railway bridge show that the reconstructed deflection shapes agree well with those of the real bridge.

• International conference on construction engineering and project management
• /
• 2009.05a
• /
• pp.190-195
• /
• 2009

With the growth of information and communication technologies adoption in construction projects, it could be anticipated that more property owners and construction firms will attempt to use electronic gears and gadgets for site monitoring or surveillance purposes. As the construction workers may be the major group of project team members being monitored, from managerial perspectives and for ethical reasons, it is essential to investigate their degree of acceptance on site monitoring systems. Indeed studies on office workplace monitoring suggest that a monitoring system could shape or control the behaviors of employees. With adequate refinements, their research models could be applicable in the construction industry. This paper presents a model for analyzing the antecedences that affect workers' acceptance level on electronic monitoring, and investigating if there is any behavioral change.

• International Journal of Computer Science & Network Security
• /
• v.24 no.8
• /
• pp.191-204
• /
• 2024

IOT has carried out important function in converting the traditional fitness care corporation. With developing call for in population, traditional healthcare structures have reached their outmost functionality in presenting sufficient and as plenty as mark offerings. The worldwide is handling devastating developing antique population disaster and the right want for assisted-dwelling environments is turning into inevitable for senior citizens. There furthermore a determination by means of the use of way of countrywide healthcare organizations to increase crucial manual for individualized, right blanketed care to prevent and manipulate excessive coronial situations. Many tech orientated packages related to Health Monitoring have been delivered these days as taking advantage of net boom everywhere on globe, manner to improvements in cellular and in IOT generation. Such as optimized indoor networks insurance, community shape, and fairly-low device fee performances, advanced tool reliability, low device energy consumption, and hundreds higher unusual common usual performance in network safety and privacy. Studies have highlighted fantastic advantages of integrating IOT with health care location and as era is improving the rate also cannot be that terrific of a problem. However, many challenges in this new paradigm shift notwithstanding the fact that exist, that need to be addressed. So the out most purpose of this research paper is 3 essential departments: First, evaluation of key elements that drove the adoption and boom of the Internet of factors based totally domestic some distance off monitoring; Second, present fashionable improvement of IOT in home a long manner off monitoring shape and key building gadgets; Third, communicate future very last effects and distinct guidelines of such type a long way off monitoring packages going ahead. Such Research is a wonderful manner in advance now not outstanding in IOT Terminology but in standard fitness care location.

• Proceedings of the Korean Geotechical Society Conference
• /
• 2005.10a
• /
• pp.302-305
• /
• 2005

In this study, settlement characteristics of 38 CFRD was investigated from monitoring data and the method to estimate the dam settlements considering valley shape during constructions was proposed. The construction modulus of dam was found to be dependent on void ratios and valley shape factor. The construction modulus varied with valley shape and decreased with increasing void ratio. Also, the modulus was increased when the shape coefficient was less than 4. The settlement investigation results showed that the total settlement was proportional to the value of the settlement coefficient multiplied by the shape coefficient divided by void ratio.

• Smart Structures and Systems
• /
• v.30 no.2
• /
• pp.183-193
• /
• 2022

Structural health monitoring and structural vibration control are multidisciplinary and frontier research directions of civil engineering. As intelligent materials that integrate sensing and actuation capabilities, shape memory alloys (SMAs) exhibit multiple excellent characteristics, such as shape memory effect, superelasticity, corrosion resistance, fatigue resistance, and high energy density. Moreover, SMAs possess excellent resistance sensing properties and large deformation ability. Superfine NiTi SMA wires have potential applications in structural health monitoring and micro-drive system. In this study, the mechanical properties and electrical resistance sensing characteristics of superfine NiTi SMA wires were experimentally investigated. The mechanical parameters such as residual strain, hysteretic energy, secant stiffness, and equivalent damping ratio were analyzed at different training strain amplitudes and numbers of loading-unloading cycles. The results demonstrate that the detwinning process shortened with increasing training amplitude, while austenitic mechanical properties were not affected. In addition, superfine SMA wires showed good strain-resistance linear correlation, and the loading rate had little effect on their mechanical properties and electrical resistance sensing characteristics. This study aims to provide an experimental basis for the application of superfine SMA wires in engineering.

• Journal of Korean Society of Steel Construction
• /
• v.23 no.5
• /
• pp.629-636
• /
• 2011

This paper describes the smart structural system that uses smart materials for real-time monitoring and active control of bolted joints in steel structures. The impedance-based structural health monitoring (SHM) techniques, which utilize the electro-mechanical coupling property of piezoelectric materials, was used to detect loose bolts in bolted joints. By monitoring the measured electrical impedance and comparing it with the measured baseline, a bolt loosening damage was detected. The damage was evaluated quantitatively using the damage metrics in conductance signature with respect to the healthy states. When loosening damage was detected in the bolted joint, the external heater actuated the shape memory alloy (SMA) washer. Then the heated SMA washer expanded axially and adjusted the bolt tension to restore the lost torque. An experiment was conducted by integrating the piezoelectric-material-based SHM function and the SMA-based active control function on a bolted joint, after which the performance of thesmart self-healing joint system was investigated.

• Smart Structures and Systems
• /
• v.29 no.1
• /
• pp.93-103
• /
• 2022

With the wider availability of sensor technology through easily affordable sensor devices, several Structural Health Monitoring (SHM) systems are deployed to monitor vital civil infrastructure. The continuous monitoring provides valuable information about the health of the structure that can help provide a decision support system for retrofits and other structural modifications. However, when the sensors are exposed to harsh environmental conditions, the data measured by the SHM systems tend to be affected by multiple anomalies caused by faulty or broken sensors. Given a deluge of high-dimensional data collected continuously over time, research into using machine learning methods to detect anomalies are a topic of great interest to the SHM community. This paper contributes to this effort by proposing a relatively new time series representation named "Shapelet Transform" in combination with a Random Forest classifier to autonomously identify anomalies in SHM data. The shapelet transform is a unique time series representation based solely on the shape of the time series data. Considering the individual characteristics unique to every anomaly, the application of this transform yields a new shape-based feature representation that can be combined with any standard machine learning algorithm to detect anomalous data with no manual intervention. For the present study, the anomaly detection framework consists of three steps: identifying unique shapes from anomalous data, using these shapes to transform the SHM data into a local-shape space and training machine learning algorithms on this transformed data to identify anomalies. The efficacy of this method is demonstrated by the identification of anomalies in acceleration data from an SHM system installed on a long-span bridge in China. The results show that multiple data anomalies in SHM data can be automatically detected with high accuracy using the proposed method.