Computer Vision Based Structural Identification Framework for Bridge Health Mornitoring

The objective of this dissertation is to develop a comprehensive Structural Identification (St-Id) framework with damage for bridge type structures by using cameras and computer vision technologies. The traditional St-Id frameworks rely on using conventional sensors. In this study, the collected input and output data employed in the St-Id system are acquired by series of vision-based measurements. The following novelties are proposed, developed and demonstrated in this project: a) vehicle load (input) modeling using computer vision, b) bridge response (output) using full non-contact approach using video/image processing, c) image-based structural identification using input-output measurements and new damage indicators. The input (loading) data due vehicles such as vehicle weights and vehicle locations on the bridges, are estimated by employing computer vision algorithms (detection, classification, and localization of objects) based on the video images of vehicles. Meanwhile, the output data as structural displacements are also obtained by defining and tracking image key-points of measurement locations. Subsequently, the input and output data sets are analyzed to construct novel types of damage indicators, named Unit Influence Surface (UIS). Finally, the new damage detection and localization framework is introduced that does not require a network of sensors, but much less number of sensors. The main research significance is the first time development of algorithms that transform the measured video images into a form that is highly damage-sensitive/change-sensitive for bridge assessment within the context of Structural Identification with input and output characterization. The study exploits the unique attributes of computer vision systems, where the signal is continuous in space. This requires new adaptations and transformations that can handle computer vision data/signals for structural engineering applications. This research will significantly advance current sensor-based structural health monitoring with computer-vision techniques, leading to practical applications for damage detection of complex structures with a novel approach. By using computer vision algorithms and cameras as special sensors for structural health monitoring, this study proposes an advance approach in bridge monitoring through which certain type of data that could not be collected by conventional sensors such as vehicle loads and location, can be obtained practically and accurately

Tracking and Hands Motion Detection Approach for Monitoring Hand-Hygiene Compliance for Food Handling and Processing Industry

Hand-hygiene is a very critical issue for both food handling and processing industry and health care service providers. Poor hand-hygiene practice can easily lead to foodborne illness or large scale decease transmission. In this research, an automatic tracking and monitoring system was developed that used a 3D camera for hand washing and hands motion detection and a sensor-based monitoring system for hand-hygiene activities evaluation. An active Wi-Fi portable Radio Frequency Identification (RFID) tag was used for personal ID tracking. The effective hand washing time, soaping time were measured based on the hands motion detection and hand movement tracking. Water temperature, water flow, paper towel, soap and hand sanitizer usage were also measured for each hand washing event. All the data were forwarded to a system server for data recording, storage and management. Preliminary test data were collected to evaluate the system performance. The results showed that the system could effectively collect most of the hand-hygiene related factors including hand-hygiene product usage, hand washing time and soap lathering time for hand-hygiene evaluation.Biosystems & Agricultural Engineerin

Simulation verification techniques study. Subsystem simulation validation techniques

Techniques for validation of software modules which simulate spacecraft onboard systems are discussed. An overview of the simulation software hierarchy for a shuttle mission simulator is provided. A set of guidelines for the identification of subsystem/module performance parameters and critical performance parameters are presented. Various sources of reference data to serve as standards of performance for simulation validation are identified. Environment, crew station, vehicle configuration, and vehicle dynamics simulation software are briefly discussed from the point of view of their interfaces with subsystem simulation modules. A detailed presentation of results in the area of vehicle subsystems simulation modules is included. A list of references, conclusions and recommendations are also given

An Optofluidic Lens Biochip and an x-ray Readable Blood Pressure Microsensor: Versatile Tools for in vitro and in vivo Diagnostics.

Three different microfabricated devices were presented for use in vivo and in vitro diagnostic biomedical applications: an optofluidic-lens biochip, a hand held digital imaging system and an x-ray readable blood pressure sensor for monitoring restenosis. An optofluidic biochip–termed the ‘Microfluidic-based Oil-Immersion Lens’ (mOIL) biochip were designed, fabricated and test for high-resolution imaging of various biological samples. The biochip consists of an array of high refractive index (n = 1.77) sapphire ball lenses sitting on top of an oil-filled microfluidic network of microchambers. The combination of the high optical quality lenses with the immersion oil results in a numerical aperture (NA) of 1.2 which is comparable to the high NA of oil immersion microscope objectives. The biochip can be used as an add-on-module to a stereoscope to improve the resolution from 10 microns down to 0.7 microns. It also has a scalable field of view (FOV) as the total FOV increases linearly with the number of lenses in the biochip (each lens has ~200 microns FOV). By combining the mOIL biochip with a CMOS sensor, a LED light source in 3D printed housing, a compact (40 grams, 4cmx4cmx4cm) high resolution (~0.4 microns) hand held imaging system was developed. The applicability of this system was demonstrated by counting red and white blood cells and imaging fluorescently labelled cells. In blood smear samples, blood cells, sickle cells, and malaria-infected cells were easily identified. To monitor restenosis, an x-ray readable implantable blood pressure sensor was developed. The sensor is based on the use of an x-ray absorbing liquid contained in a microchamber. The microchamber has a flexible membrane that is exposed to blood pressure. When the membrane deflects, the liquid moves into the microfluidic-gauge. The length of the microfluidic-gauge can be measured and consequently the applied pressure exerted on the diaphragm can be calculated. The prototype sensor has dimensions of 1x0.6x10mm and adequate resolution (19mmHg) to detect restenosis in coronary artery stents from a standard chest x-ray. Further improvements of our prototype will open up the possibility of measuring pressure drop in a coronary artery stent in a non-invasively manner.PhDMacromolecular Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111384/1/toning_1.pd

A new method for residential side non-intrusive load monitoring

This thesis proposes a new non-intrusive method for residential load monitoring. The proposed method can detect appliance switching events, separate appliance electric features, and identify appliance types. Compared with other non-intrusive monitoring methods, the proposed method improves the monitoring accuracy and decreases the monitoring response time. Firstly, the monitoring hardware was designed and constructed to sample and acquire the aggregated electric data of one residential area. Secondly, the sampled data were processed and analysed with the proposed method, which achieves the monitoring of individual appliance running conditions and power consumption in this area in a non-intrusive way. The data analysis process includes three steps, 1) the appliance switching event is detected by the Heuristic detection method. 2) the working current of the switched appliance is separated according to the difference method, 3) the type of switched appliance is identified with the K-nearest neighbour method according to the appliance’s current harmonic components, and the identification result is modified and corrected according to appliance operation pattern with the aid of a Back Propagation Neural Network. Thirdly, the proposed NILM method was tested through offline and online applications. The offline application involves three days of pre-recorded data which were processed and analysed. The online application consists of two parts. The first part is a direct application for four domestic homes during one day (24 hours). As for the second part, the proposed monitoring method was applied to one domestic home for ninety days. All the online and offline tests, the running conditions and the power consumption of appliances were monitored and recorded. Due to the test results, the proposed method is reliable and offers a powerful monitoring method for demand side management.This thesis proposes a new non-intrusive method for residential load monitoring. The proposed method can detect appliance switching events, separate appliance electric features, and identify appliance types. Compared with other non-intrusive monitoring methods, the proposed method improves the monitoring accuracy and decreases the monitoring response time. Firstly, the monitoring hardware was designed and constructed to sample and acquire the aggregated electric data of one residential area. Secondly, the sampled data were processed and analysed with the proposed method, which achieves the monitoring of individual appliance running conditions and power consumption in this area in a non-intrusive way. The data analysis process includes three steps, 1) the appliance switching event is detected by the Heuristic detection method. 2) the working current of the switched appliance is separated according to the difference method, 3) the type of switched appliance is identified with the K-nearest neighbour method according to the appliance’s current harmonic components, and the identification result is modified and corrected according to appliance operation pattern with the aid of a Back Propagation Neural Network. Thirdly, the proposed NILM method was tested through offline and online applications. The offline application involves three days of pre-recorded data which were processed and analysed. The online application consists of two parts. The first part is a direct application for four domestic homes during one day (24 hours). As for the second part, the proposed monitoring method was applied to one domestic home for ninety days. All the online and offline tests, the running conditions and the power consumption of appliances were monitored and recorded. Due to the test results, the proposed method is reliable and offers a powerful monitoring method for demand side management

Selected Papers from 2020 IEEE International Conference on High Voltage Engineering (ICHVE 2020)

The 2020 IEEE International Conference on High Voltage Engineering (ICHVE 2020) was held on 6–10 September 2020 in Beijing, China. The conference was organized by the Tsinghua University, China, and endorsed by the IEEE Dielectrics and Electrical Insulation Society. This conference has attracted a great deal of attention from researchers around the world in the field of high voltage engineering. The forum offered the opportunity to present the latest developments and different emerging challenges in high voltage engineering, including the topics of ultra-high voltage, smart grids, and insulating materials

Physical model tests and numerical simulation for assessing the stability of tunnels

Nowadays, numerical modelling is increasingly used to assess the stability of tunnels and underground caverns. However, an analysis of the mechanical behaviour of existing brick-lined tunnels remains challenging due to the complex material components. One promising approach is to carry out a series of small-scale physical tunnel model tests representing the true behaviour of a prototype under extreme loading in order to validate and develop the corresponding numerical models. A physical model test is advisable before any field study, which might be dangerous and costly. During the tests, advanced monitoring techniques such as the laser scanning and photogrammetry would be used to register tunnel deformation and lining defects. This investigation will show how these may substitute or supplement the conventional manual procedures. Simultaneously, numerical models will be developed, primarily using FLAC and UDEC software, to simulate the physical models after comparing their results. In this way, numerical simulations of physical models would be achieved and verified. These numerical models could then be applied to the field study in the future research, enabling accurate prediction of the actual mechanical behaviour of a masonry tunnel, in combination with advanced monitoring techniques

Physical model tests and numerical simulation for assessing the stability of tunnels

Nowadays, numerical modelling is increasingly used to assess the stability of tunnels and underground caverns. However, an analysis of the mechanical behaviour of existing brick-lined tunnels remains challenging due to the complex material components. One promising approach is to carry out a series of small-scale physical tunnel model tests representing the true behaviour of a prototype under extreme loading in order to validate and develop the corresponding numerical models. A physical model test is advisable before any field study, which might be dangerous and costly. During the tests, advanced monitoring techniques such as the laser scanning and photogrammetry would be used to register tunnel deformation and lining defects. This investigation will show how these may substitute or supplement the conventional manual procedures. Simultaneously, numerical models will be developed, primarily using FLAC and UDEC software, to simulate the physical models after comparing their results. In this way, numerical simulations of physical models would be achieved and verified. These numerical models could then be applied to the field study in the future research, enabling accurate prediction of the actual mechanical behaviour of a masonry tunnel, in combination with advanced monitoring techniques

Physical model tests and numerical simulation for assessing the stability of tunnels

Nowadays, numerical modelling is increasingly used to assess the stability of tunnels and underground caverns. However, an analysis of the mechanical behaviour of existing brick-lined tunnels remains challenging due to the complex material components. One promising approach is to carry out a series of small-scale physical tunnel model tests representing the true behaviour of a prototype under extreme loading in order to validate and develop the corresponding numerical models. A physical model test is advisable before any field study, which might be dangerous and costly. During the tests, advanced monitoring techniques such as laser scanning and photogrammetry would be used to register tunnel deformation and lining defects. This investigation will show how these may substitute or supplement the conventional manual procedures. Simultaneously, numerical models will be developed, primarily using FLAC and UDEC software, to simulate the physical models after comparing their results. In this way, numerical simulations of physical models would be achieved and verified. These numerical models could then be applied to the field study in the future research, enabling accurate prediction of the actual mechanical behaviour of a masonry tunnel, in combination with advanced monitoring techniques

GPS Radio Occultation and the Role of Atmospheric Pressure on Spaceborne Gravity Estimation Over Antarctica

This report was prepared by Shengjie Ge, a graduate research associate in the Geodetic Science and surveying program of the Department of Geological Science at the Ohio State University, under the supervision of Professor C. K. Shum.This study was partially supported by grants from NASA Interdisciplinary Science Program NAG5-9518, and National Science National Space Weather Program ATM- 0418844.This report was also submitted to the Graduate School of the Ohio State University as a dissertation in partial fulfillment of the requirements for the Ph.D. degree.Dedicated satellite gravity missions are anticipated to significantly improve the current knowledge of the Earth’s mean gravity field and its time variable part–climate sensitive gravity signals. They could be measured by the Gravity Recovery and Climate Experiment (GRACE) twin-satellite with sub-centimeter accuracy in terms of column of water movement near the Earth’s surface with a spatial resolution of several hundred kilometers or larger, and a temporal resolution of one month or weeks. To properly recover the time variable gravity signals from space, the gravity measurements require the atmospheric pressure contribution to be accurately modeled and removed. The sparse coverage of measurements makes the weather products less accurate in the southern hemisphere, especially over the Southern Ocean and Antarctica. The asynoptic observation from GPS radio occultation could achieve dense spatial coverage even in remote regions. In this research, we investigate the potential use of GPS occultation to improve the pressure modeling over Antarctica. Atmospheric pressure profiles are retrieved and validated against ECMWF, NCEP and radiosonde observations. Our results show that occultation can provide compatible observations especially in the upper atmosphere. Large standard deviations and biases are found near the ground and in the Antarctic region. GPS occultation in the polar regions is less affected by multipath problem and can penetrate down near the surface. Through an experiment using a 1-D variational (1DVar) approach, we show that the high vertical accuracy of GPS occultation can be propagated down to reduce the uncertainty of surface pressure, indicating that GPS occultation can be expected to have positive impact on the pressure modeling over data-sparse areas after obtaining adequate number of observations (e.g., from Constellation Observing System for Meteorology, Ionosphere & Climate (COSMIC)). We also find that the retrieved profiles could be different due to various assumptions and retrieval algorithms. Pressure uncertainty degrades the GRACE recovered gravity change. We study the uncertainty of pressure modeling on various temporal scales. Global analysis models show large differences in the Antarctic region. The surface topography may introduce additional biases if it is not well treated. The atmospheric tides are non-negligible and need to be properly considered. The real magnitude of the mismodeled and un-modeled errors in the analysis is hard to evaluate, especially in Antarctica. We simulate the errors sensitive to GRACE using the differences between two global analysis models. Most of the very long wavelength errors are well ii captured by GRACE. Their changes in the form of short-period variation increase the errors of the middle to high degree spherical harmonic coefficients. After de-aliasing, middle to high degree coefficients are noticeably improved. The Inverted Barometer (IB) assumption decreases the amplitude of the aliasing error, and the pattern of the RMS difference is slightly changed over land by neglecting the large variations in the Southern Ocean. Our result using more recent ECMWF and NCEP operational analyses shows reduced aliasing effects, which indicates that two models are becoming increasingly close to each other. The model correlation and IB assumption may underestimate the true aliasing error. The analysis models are validated against the unevenly distributed Automatic Weather Station (AWS) surface pressure observations on the Antarctic continent. Spectral analysis shows that 6-hour analyzed model data can capture most of the power in pressure variations. ECMWF exhibits a much better agreement with AWS than NCEP reanalysis does. Large biases still exist due to the uncertainties of the station elevations. The comparison statistics show strong correlations with the topography with lower standard deviation values in the interior and higher standard deviation values around the coastal area. This result contradicts the distribution derived from the difference between two analysis models, which exhibits large difference in the interior of Antarctica. We also investigate the influences of different algorithms and assumptions of 2- D or 3-D atmospheric structures on the GRACE atmospheric de-aliasing product. Air density derived from the hydrostatic equation and the equation of state gives slightly different results, and the difference is above the expected GRACE sensitivity. We compare our results with the GRACE atmospheric de-aliasing product and find that the difference is almost below the GRACE sensitivity, although there are differences in the algorithms and we use a relatively low resolution model. We also find that the difference between 3-D hydrostatic formulation and 2-D algorithm is below the GRACE sensitivity. We discover that the atmospheric structure and latitudinal variations of gravity are largely compensated by removing their respective long-term means. Consequently, the 2-D method can greatly reduce the requirements for computational load and data storage. Removing the mean field does not help to reduce the discrepancies between ECMWF and NCEP. If the computational burden is not a concern, using our improved 3-D algorithm can bring a better result. After the full operation of the COSMIC satellites, some major improvement of the pressure modeling over Antarctica is anticipated. A reprocessing of the GRACE data using an improved pressure model could bring us better gravity solutions