Validation of a new method for ultrasonic structural health monitoring using advanced signal analysis. This paper presents the results of research on a new method for the monitoring of structural health using ultrasound. Conventional ultrasonic methods use the damping of the first arrival of the echo to determine imperfections, whereas this method uses the total complex echo, which has been subjected to multiple scattering and deflections within the tested material. It is experimentally demonstrated that the method works for health monitoring of a composite flat plate. A reference signal of an undamaged plate was recorded, which was correlated with recorded control signals of a damaged and a doubly damaged plate. To quantify this correlation the parameter fidelity was used. As the control signals are correlated with the reference signal the correlation is supposed to decrease as the plate is damaged and decrease further as the plate is doubly damaged.
Validation of a new method for ultrasonic structural health monitoring using advanced signal analysis. This paper presents the results of research on a new method for the monitoring of structural health using ultrasound. Conventional ultrasonic methods use the damping of the first arrival of the echo to determine imperfections, whereas this method uses the total complex echo, which has been subjected to multiple scattering and deflections within the tested material. It is experimentally demonstrated that the method works for health monitoring of a composite flat plate. A reference signal of an undamaged plate was recorded, which was correlated with recorded control signals of a damaged and a doubly damaged plate. To quantify this correlation the parameter fidelity was used. As the control signals are correlated with the reference signal the correlation is supposed to decrease as the plate is damaged and decrease further as the plate is doubly damaged.
This comprehensive document shares up-to-date knowledge on Early Warning Signals of business crisis, presents detection and intervention opportunities, and makes a clear case for their beneficial application to SME leadership and overall business resilience.
MULTIFILE
Carboxylated cellulose is an important product on the market, and one of the most well-known examples is carboxymethylcellulose (CMC). However, CMC is prepared by modification of cellulose with the extremely hazardous compound monochloracetic acid. In this project, we want to make a carboxylated cellulose that is a functional equivalent for CMC using a greener process with renewable raw materials derived from levulinic acid. Processes to achieve cellulose with a low and a high carboxylation degree will be designed.
Digital innovations in the field of immersive Augmented Reality (AR) can be a solution to offer adults who are mentally, physically or financially unable to attend sporting events such as premier league football a stadium and match experience. This allows them to continue to connect with their social networks. In the intended project, AR content will be further developed with the aim of evoking the stadium experience of home matches as much as possible. The extent to which AR enriches the experience is then tested in an experiment, in which the experience of a football match with and without AR enrichment is measured in a stadium setting and in a home setting. The experience is measured with physiological signals. In addition, a subjective experience measure is also being developed and benchmarked (the experience impact score). Societal issueInclusion and health: The joint experience of (top) sports competitions forms a platform for vulnerable adults, with a limited social capital, to build up and maintain the social networks that are so necessary for them. AR to fight against social isolation and loneliness.
The maximum capacity of the road infrastructure is being reached due to the number of vehicles that are being introduced on Dutch roads each day. One of the plausible solutions to tackle congestion could be efficient and effective use of road infrastructure using modern technologies such as cooperative mobility. Cooperative mobility relies majorly on big data that is generated potentially by millions of vehicles that are travelling on the road. But how can this data be generated? Modern vehicles already contain a host of sensors that are required for its operation. This data is typically circulated within an automobile via the CAN bus and can in-principle be shared with the outside world considering the privacy aspects of data sharing. The main problem is, however, the difficulty in interpreting this data. This is mainly because the configuration of this data varies between manufacturers and vehicle models and have not been standardized by the manufacturers. Signals from the CAN bus could be manually reverse engineered, but this process is extremely labour-intensive and time-consuming. In this project we investigate if an intelligent tool or specific test procedures could be developed to extract CAN messages and their composition efficiently irrespective of vehicle brand and type. This would lay the foundations that are required to generate big data-sets from in-vehicle data efficiently.
