Analytical and experimental modeling of a smart beam

Smart structures gain increasingly amount of attention in the past few years with the development of actuator and sensor technology, as well as real time and FPGA controllers. Piezoelectric strip transducers are very suitable for flexible beam structures due to their design and performances. Smart beam modeling is essential for analyses of the dynamic behavior of the beam, but simplified models are necessary for advanced model based control algorithms. In this paper few different approaches in analytical modeling of a smart beam are presented, as well as the methodology of experimental modeling. Data acquisitions guidelines and experimental models derived in LabVIEW are presented. All models are derived and verified on a laboratory experimental setup consisting of aluminum cantilever beam with a pair of collocated piezoelectric transducers

URBAN SOUND RECOGNITION USING DIFFERENT FEATURE EXTRACTION TECHNIQUES

The application of the advanced methods for noise analysis in the urban areas through the development of systems for classification of sound events significantly improves and simplifies the process of noise assessment. The main purpose of sound recognition and classification systems is to develop algorithms that can detect and classify sound events that occur in the chosen environment, giving an appropriate response to their users. In this research, a supervised system for recognition and classification of sound events has been established through the development of feature extraction techniques based on digital signal processing of the audio signals that are further used as an input parameter in the machine learning algorithms for classification of the sound events. Various audio parameters were extracted and processed in order to choose the best set of parameters that result in better recognition of the class to which the sounds belong. The created acoustic event detection and classification (AED/C) system could be further implemented in sound sensors for automatic control of environmental noise using the source classification that leads to reduced amount of required human validation of the sound level measurements since the target noise source is evidently defined

AI supported noise analyses for structure design requirements definition

The artificial intelligence (AI) field has encountered a turning point mainly due to advancements in machine learning, which allows systems to learn, improve, and perform a specific task through data without being explicitly programmed. Machine learning can be utilized with machining processes to improve product quality levels and productivity rates and to optimize design and process parameters. The systems for acoustic event detection and classification (AED/C) of noise events is a process consisted of feature extraction of the signals, meaning processing acoustic signals and converting them into symbolic descriptions that correspond to the various sound events present in the signals and their sources. The main objective of the AED/C systems is to develop algorithms able to recognize and classify sound events that occur in the chosen environment, giving an appropriate response to users. By utilizing the acoustic events detection and classification systems, a clear set of design requirements can be extracted based on the noise to be attenuated. A smart structure design for noise attenuation needs clear noise input for proper smart material choice, placement and active control. This paper shows a method for detection of noise events based on machinelearning algorithm that can be further used for definition of design requirements.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Transport Engineering and Logistic