This paper describes the work that is done by a group of I3 students at Philips CFT in Eindhoven, Netherlands. I3 is an initiative of Fontys University of Professional Education also located in Eindhoven. The work focuses on the use of computer vision in motion control. Experiments are done with several techniques for object recognition and tracking, and with the guidance of a robot movement by means of computer vision. These experiments involve detection of coloured objects, object detection based on specific features, template matching with automatically generated templates, and interaction of a robot with a physical object that is viewed by a camera mounted on the robot.
In sports, inertial measurement units are often used to measure the orientation of human body segments. A Madgwick (MW) filter can be used to obtain accurate inertial measurement unit (IMU) orientation estimates. This filter combines two different orientation estimates by applying a correction of the (1) gyroscope-based estimate in the direction of the (2) earth frame-based estimate. However, in sports situations that are characterized by relatively large linear accelerations and/or close magnetic sources, such as wheelchair sports, obtaining accurate IMU orientation estimates is challenging. In these situations, applying the MW filter in the regular way, i.e., with the same magnitude of correction at all time frames, may lead to estimation errors. Therefore, in this study, the MW filter was extended with machine learning to distinguish instances in which a small correction magnitude is beneficial from instances in which a large correction magnitude is beneficial, to eventually arrive at accurate body segment orientations in IMU-challenging sports situations. A machine learning algorithm was trained to make this distinction based on raw IMU data. Experiments on wheelchair sports were performed to assess the validity of the extended MW filter, and to compare the extended MW filter with the original MW filter based on comparisons with a motion capture-based reference system. Results indicate that the extended MW filter performs better than the original MW filter in assessing instantaneous trunk inclination (7.6 vs. 11.7◦ root-mean-squared error, RMSE), especially during the dynamic, IMU-challenging situations with moving athlete and wheelchair. Improvements of up to 45% RMSE were obtained for the extended MW filter compared with the original MW filter. To conclude, the machine learning-based extended MW filter has an acceptable accuracy and performs better than the original MW filter for the assessment of body segment orientation in IMU-challenging sports situations.
A damage estimation exercise has been carried out using the building stock inventory and population database of the Istanbul Metropolitan Municipality and selected European earthquake loss estimation packages: KOERILOSS, SELENA, ESCENARIS, SIGE, and DBELA. The input ground-motions, common to all models, correspond to a “credible worst case scenario” involving the rupture of the four segments of the Main Marmara Fault closest to Istanbul in a Mw 7.5 earthquake. The aim of the exercise is to assess the applicability of the selected software packages to earthquake loss estimation in the context of rapid post-earthquake response in European urban centers. The results in terms of predicted building damage and social losses are critically compared amongst each other, as well as with the results of previous scenario-based earthquake loss assessments carried out for the study area. The key methodological aspects and data needs for European rapid post-earthquake loss estimation are thus identified.
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The pipelines are buried structures. They move together with the soil during a seismic event. They are affected from ground motions. The project aims to find out the possible effects of Groningen earthquakes on pipelines of Loppersum and Slochteren.This project is devised for conducting an initial probe on the available data to see the possible actions that can be taken, initially on these two pilot villages, Loppersum and Slochteren, for detecting the potential relationship between the past damages and the seismic activity.Lifeline infrastructure, such as water mains and sewerage systems, covering our urbanised areas like a network, are most of the times, sensitive to seismic actions. This sensitivity can be in the form of extended damage during seismic events, or other collateral damages, such as what happened in Christchurch Earthquakes in 2011 in New Zealand when the sewerage system of the city was filled in with tonnes of sand due to liquefaction.Regular damage detection is one of key solutions for operational purposes. The earthquake mitigation, however, needs large scale risk studies with expected spatial distribution of damages for varying seismic hazard levels.
