194 research outputs found

    MECHANICAL ENERGY HARVESTER FOR POWERING RFID SYSTEMS COMPONENTS: MODELING, ANALYSIS, OPTIMIZATION AND DESIGN

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    Finding alternative power sources has been an important topic of study worldwide. It is vital to find substitutes for finite fossil fuels. Such substitutes may be termed renewable energy sources and infinite supplies. Such limitless sources are derived from ambient energy like wind energy, solar energy, sea waves energy; on the other hand, smart cities megaprojects have been receiving enormous amounts of funding to transition our lives into smart lives. Smart cities heavily rely on smart devices and electronics, which utilize small amounts of energy to run. Using batteries as the power source for such smart devices imposes environmental and labor cost issues. Moreover, in many cases, smart devices are in hard-to-access places, making accessibility for disposal and replacement difficult. Finally, battery waste harms the environment. To overcome these issues, vibration-based energy harvesters have been proposed and implemented. Vibration-based energy harvesters convert the dynamic or kinetic energy which is generated due to the motion of an object into electric energy. Energy transduction mechanisms can be delivered based on piezoelectric, electromagnetic, or electrostatic methods; the piezoelectric method is generally preferred to the other methods, particularly if the frequency fluctuations are considerable. In response, piezoelectric vibration-based energy harvesters (PVEHs), have been modeled and analyzed widely. However, there are two challenges with PVEH: the maximum amount of extractable voltage and the effective (operational) frequency bandwidth are often insufficient. In this dissertation, a new type of integrated multiple system comprised of a cantilever and spring-oscillator is proposed to improve and develop the performance of the energy harvester in terms of extractable voltage and effective frequency bandwidth. The new energy harvester model is proposed to supply sufficient energy to power low-power electronic devices like RFID components. Due to the temperature fluctuations, the thermal effect over the performance of the harvester is initially studied. To alter the resonance frequency of the harvester structure, a rotating element system is considered and analyzed. In the analytical-numerical analysis, Hamilton’s principle along with Galerkin’s decomposition approach are adopted to derive the governing equations of the harvester motion and corresponding electric circuit. It is observed that integration of the spring-oscillator subsystem alters the boundary condition of the cantilever and subsequently reforms the resulting characteristic equation into a more complicated nonlinear transcendental equation. To find the resonance frequencies, this equation is solved numerically in MATLAB. It is observed that the inertial effects of the oscillator rendered to the cantilever via the restoring force effects of the spring significantly alter vibrational features of the harvester. Finally, the voltage frequency response function is analytically and numerically derived in a closed-from expression. Variations in parameter values enable the designer to mutate resonance frequencies and mode shape functions as desired. This is particularly important, since the generated energy from a PVEH is significant only if the excitation frequency coming from an external source matches the resonance (natural) frequency of the harvester structure. In subsequent sections of this work, the oscillator mass and spring stiffness are considered as the design parameters to maximize the harvestable voltage and effective frequency bandwidth, respectively. For the optimization, a genetic algorithm is adopted to find the optimal values. Since the voltage frequency response function cannot be implemented in a computer algorithm script, a suitable function approximator (regressor) is designed using fuzzy logic and neural networks. The voltage function requires manual assistance to find the resonance frequency and cannot be done automatically using computer algorithms. Specifically, to apply the numerical root-solver, one needs to manually provide the solver with an initial guess. Such an estimation is accomplished using a plot of the characteristic equation along with human visual inference. Thus, the entire process cannot be automated. Moreover, the voltage function encompasses several coefficients making the process computationally expensive. Thus, training a supervised machine learning regressor is essential. The trained regressor using adaptive-neuro-fuzzy-inference-system (ANFIS) is utilized in the genetic optimization procedure. The optimization problem is implemented, first to find the maximum voltage and second to find the maximum widened effective frequency bandwidth, which yields the optimal oscillator mass value along with the optimal spring stiffness value. As there is often no control over the external excitation frequency, it is helpful to design an adaptive energy harvester. This means that, considering a specific given value of the excitation frequency, energy harvester system parameters (oscillator mass and spring stiffness) need to be adjusted so that the resulting natural (resonance) frequency of the system aligns with the given excitation frequency. To do so, the given excitation frequency value is considered as the input and the system parameters are assumed as outputs which are estimated via the neural network fuzzy logic regressor. Finally, an experimental setup is implemented for a simple pure cantilever energy harvester triggered by impact excitations. Unlike the theoretical section, the experimental excitation is considered to be an impact excitation, which is a random process. The rationale for this is that, in the real world, the external source is a random trigger. Harmonic base excitations used in the theoretical chapters are to assess the performance of the energy harvester per standard criteria. To evaluate the performance of a proposed energy harvester model, the input excitation type consists of harmonic base triggers. In summary, this dissertation discusses several case studies and addresses key issues in the design of optimized piezoelectric vibration-based energy harvesters (PVEHs). First, an advanced model of the integrated systems is presented with equation derivations. Second, the proposed model is decomposed and analyzed in terms of mechanical and electrical frequency response functions. To do so, analytic-numeric methods are adopted. Later, influential parameters of the integrated system are detected. Then the proposed model is optimized with respect to the two vital criteria of maximum amount of extractable voltage and widened effective (operational) frequency bandwidth. Corresponding design (influential) parameters are found using neural network fuzzy logic along with genetic optimization algorithms, i.e., a soft computing method. The accuracy of the trained integrated algorithms is verified using the analytical-numerical closed-form expression of the voltage function. Then, an adaptive piezoelectric vibration-based energy harvester (PVEH) is designed. This final design pertains to the cases where the excitation (driving) frequency is given and constant, so the desired goal is to match the natural frequency of the system with the given driving frequency. In this response, a regressor using neural network fuzzy logic is designed to find the proper design parameters. Finally, the experimental setup is implemented and tested to report the maximum voltage harvested in each test execution

    Parametric Excitation of Coupled Nonlinear Microelectromechanical Systems

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    The commencement of the semi-conductor industry in the second half of the last century gave a surprising new outlook for engineered dynamical mechanical systems. It enabled, thanks to the continuously evolving microfabrication methods, the implementation of Micro Electromechanical systems (MEMS) followed by their nano-counterpart or NEMS. Nowadays M/NEMS constitute a massive portion of the small-scaled sensors industry, in addition to electrical, optical and telecommunication components. Since these tiny dynamical electromechanical systems involve sometimes couplings between degrees of freedom as well as nonlinearities, the theory of stability in dynamical systems plays a significant role in their design and implementation. From a practical point of view, the approach to stability problems often takes two different perspectives. The first one, most commonly in linear systems, aims to avoid any instability which could cause destructive consequences for mechanical structures or for electrical and electronic components. On the contrary in nonlinear systems, the second perspective aims to drive the system into regions of instability for the trivial solution, while searching for stable nontrivial steady-state solutions of the underlying differential equations. With the advent of micro and nanosystems, the second perspective could acquire increased importance. This is attributed to their capability to exhibit typical nonlinear behavior and higher amplitudes at normal operation conditions, when compared to macroscale systems. Higher amplitudes, in this sense, allows for a better amplification of an input excitation, and thereby higher sensitivity for miniature sensors and measurement devices. In addition, if the system parameters were time-periodic, the trivial solution could turn to be unstable at the so called parametric resonances. Known as parametric pumping in micro and nanosystems, the system’s response is usually amplified at these resonance frequencies for higher sensitivity and accuracy. For these reasons, this work is mainly focused on parametrically excited nonlinear systems. Nevertheless, a systematic approach is followed in this thesis, where the origins of destabilization are surveyed in time-invariant systems before proceeding to carry out a theoretical study on time-periodic systems in general, and time-periodic nonlinear systems in particular. Through this theoretical study, a novel idea for the M/NEMS industry is presented, namely the broadband parametric amplification using a bimodal excitation method. This idea is then implemented in microsystems, by investigating a particular example, that is the microgyorscope. Given the low-cost of this device in comparison with other inertial sensors, it is being currently enhanced to reach a relatively higher sensitivity and accuracy. To this end, the theoretical findings, including the mentioned idea, are implemented in this device and prove to contribute effectively to its performance. Moreover, an experimental investigation is carried out on an analogous microsystem. Through the experimental study, an electronic system is introduced to apply the proposed bimodal parametric excitation method on the microsystem. By comparing the stability charts in theory and experiment, the theoretical model could be validated. In conclusion, a theoretical study is carried out through this work on parametrically excited nonlinear systems, then implemented on microgyroscopes, and finally experimentally validated. Thereby, this work puts a first milestone for the utilization of the proposed excitation method in the M/NEMS industry

    Using electrostatic nonlinearities to enhance the performance of ring-based Coriolis vibratory gyroscopes

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    This research investigates electrostatic nonlinearities in capacitively operated ring-based Coriolis vibrating gyroscopes (CVG’s). Large amplitude vibrations of the ring amplify the Coriolis force and are beneficial to achieving high-precision rate sensing. However, due to the miniature sizes of these devices and the narrow capacitive gaps, electrostatic nonlinearities manifest at relatively small ring displacements, thus resulting in the sensor output differing from what is expected of a standard linear device. As such, the current theory of operation commonly perceives electrostatic nonlinearities as an obstacle towards the development of high performance sensors. Electrostatic nonlinearities is the dominant source of nonlinearity in ring-based CVG’s. This work develops a mathematical model to analyse the influence of electrostatic nonlinearities on device performance. When the device operates using a basic electrostatic configuration incorporating only bias and drive voltages, it is found that the bias voltage induces single and mode-coupled cubic restoring forces, which are the main mechanisms through which electrostatic nonlinearities affect the ring dynamics and sensor output. These nonlinear restoring forces result in the amplitude-dependency of the drive and sense mode frequencies, and the presence of self-induced parametric excitation. These effects, in conjunction with the structural imperfections of the ring, degrade rate sensing performance by reducing the rate sensitivity and introducing bias rates and quadrature errors at larger drive amplitudes. A detailed theoretical analysis of the sense dynamics concludes that, depending on the interaction between the imperfections and the electrostatic nonlinearities, there are specific cases where the self-induced parametric excitation can enhance the rate sensitivity of the device. However, this enhancement cannot be achieved while retaining a trimmed sense response to keep the bias rate and quadrature error nullified. An analysis of the sense response and the modal forces shows that the imperfection-induced linear elastic coupling force and the nonlinear frequency imbalance are specifically responsible for the sensor output degradation. These nonlinear behaviours have also been validated against finite element results. The research also investigates the strategic use of electrostatic forces to counteract the effects of nonlinearity and enhance device performance. It is shown that through careful selection of the voltages applied to the electrodes, the form of the resulting electrostatic forces can be tailored to manipulate the sense mode dynamics for device performance enhancement. The presented work develops a general framework to achieve this direct electrostatic force manipulation by considering the variations of the capacitance, voltage and electrostatic potential energy from electrode to electrode, which then enables direct control of the form of the total electrostatic potential energy. Through the use of the framework, this research shows that the electrostatic nonlinearities can be manipulated to replicate the sensor outputs of a linear, trimmed device at larger drive amplitudes, or achieving parametric amplification of the sense response to enhance rate sensitivity without inducing bias rates and quadrature errors. The proposed general framework is used to determine the electrostatic configurations capable of negating self-induced parametric excitation by generating a separate parametric excitation in antiphase with the self-induced parametric excitation. The proposed implementation has potential to reduce sensor output nonlinearity and is most effective in devices where the drive amplitude dependencies of the drive and sense modes are equal, thus resulting in amplitude-insensitive frequency detuning in a manner similar to linear devices. This implementation can also be used in conjunction with a balancing voltage component to eliminate quadrature errors present in the sensor output caused by linear elastic coupling and nonlinear frequency imbalance. The combination of using parametric pumping and balancing voltage components trims the sensor output and have potential to suppress the sensor output nonlinearity further. The effectiveness of the chosen electrostatic configuration is validated against results from transient finite element studies. Rate measuring performance is enhanced further by parametrically exciting the sensor output to increase the quality factor of the device. To achieve enhanced performance the parametric excitation must be phase-tuneable and the proposed general framework is used to select electrostatic configurations capable of providing the required parametric excitation. Two approaches to develop the required parametric excitation are investigated. The first approach exploits linear electrostatic forces whilst the second approach uses quadratic electrostatic forces. Both approaches are shown to have potential to improve rate sensitivity through Q factor enhancing effects. However, the parametric excitation from the quadratic electrostatic forces is generally weaker unless compensated using larger parametric pumping voltages. On the other hand, it is found that the quadratic electrostatic forces promote nonlinear frequency balancing and so this approach is considered advantageous for achieving trimmed sensor output

    Hardware for Mobile Positioning : Considerations on Design and Development of Cost-Efficient Solutions

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    The estimation of a moving agent's position in an unknown environment is a problem formulated in its current form already in the 1980s. Emphasis on localization and mapping problems has grown rapidly in the last two decades driven by the increased computational capability of especially handheld systems and a large number of target applications in various fields, ranging from self-driving cars and geomatics to robotics and virtual/augmented reality. Besides the algorithms for positioning, hardware plays a major role as a backbone for enabling accurate, robust and flexible position estimation solutions. This thesis gives an overview of sensors utilized in mobile positioning with a focus on passive visual-inertial sensors as an alternative to more expensive active-ranging solutions. The main research interest of the thesis is the feasibility of developing and implementing a cost-efficient hardware solution for positioning. Visual, inertial and satellite positioning sensors' advantages, performance parameters, sources of error and physical requirements are considered. Sensor integration and both sensor and system-level calibration in a multisensor setup are discussed. Levels of developer involvement and options for hardware development approaches are presented, mainly ready-made modular solutions, building on top of intermediate products and development from scratch. Hardware development processes are demonstrated by implementing a synchronized visual-inertial positioning system including two pairs of stereo cameras, an inertial measurement unit and a Real-Time Kinematic capable satellite positioning solution. The system acts as a cost-efficient example for options and decisions required on the selection of sensors and computational subsystems supporting the sensor hardware, integration and continuous temporal synchronization of sensors as well as requirements and manufacturing options for system enclosures. Even though the direct costs of the solution seem inexpensive compared to competitive solutions, accounting for the development time and associated risk makes hardware development from scratch less attractive option compared to other approaches. For a proof-of-concept or a case in which a very limited number of end products are produced, implementation from the ground up is most likely time-consuming and thus ends up being an expensive endeavor compared to other approaches. Also, the benefits of control over the details of hardware and integration may not be fully utilized

    Manufacturing Metrology

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    Metrology is the science of measurement, which can be divided into three overlapping activities: (1) the definition of units of measurement, (2) the realization of units of measurement, and (3) the traceability of measurement units. Manufacturing metrology originally implicates the measurement of components and inputs for a manufacturing process to assure they are within specification requirements. It can also be extended to indicate the performance measurement of manufacturing equipment. This Special Issue covers papers revealing novel measurement methodologies and instrumentations for manufacturing metrology from the conventional industry to the frontier of the advanced hi-tech industry. Twenty-five papers are included in this Special Issue. These published papers can be categorized into four main groups, as follows: Length measurement: covering new designs, from micro/nanogap measurement with laser triangulation sensors and laser interferometers to very-long-distance, newly developed mode-locked femtosecond lasers. Surface profile and form measurements: covering technologies with new confocal sensors and imagine sensors: in situ and on-machine measurements. Angle measurements: these include a new 2D precision level design, a review of angle measurement with mode-locked femtosecond lasers, and multi-axis machine tool squareness measurement. Other laboratory systems: these include a water cooling temperature control system and a computer-aided inspection framework for CMM performance evaluation

    Wearable Smart Rings for Multi-Finger Gesture Recognition Using Supervised Learning

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    This thesis presents a wearable, smart ring with an integrated Bluetooth low-energy (BLE) module. The system uses an accelerometer and a gyroscope to collect fingers motion data. A prototype was manufactured, and its performance was tested. To detect complex finger movements, two rings are worn on the point and thumb fingers while performing the gestures. Nine pre-defined finger movements were introduced to verify the feasibility of the proposed method. Data pre-processing techniques, including normalization, statistical feature extraction, random forest recursive feature elimination (RF-RFE), and k-nearest neighbors sequential forward floating selection (KNN-SFFS), were applied to select well-distinguished feature vectors to enhance gesture recognition accuracy. Three supervised machine learning algorithms were used for gesture classification purposes, namely Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Naive Bayes (NB). We demonstrated that when utilizing the KNN-SFFS recommended features as the machine learning input, our proposed finger gesture recognition approach not only significantly decreases the dimension of the feature vector, results in faster response time and prevents overfitted model, but also provides approximately similar machine learning prediction accuracy compared to when all elements of feature vectors were used. By using the KNN as the primary classifier, the system can accurately recognize six one-finger and three two-finger gestures with 97.1% and 97.0% accuracy, respectively

    Proceedings of the 19th Sound and Music Computing Conference

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    Proceedings of the 19th Sound and Music Computing Conference - June 5-12, 2022 - Saint-Étienne (France). https://smc22.grame.f

    Technology and Management for Sustainable Buildings and Infrastructures

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    A total of 30 articles have been published in this special issue, and it consists of 27 research papers, 2 technical notes, and 1 review paper. A total of 104 authors from 9 countries including Korea, Spain, Taiwan, USA, Finland, China, Slovenia, the Netherlands, and Germany participated in writing and submitting very excellent papers that were finally published after the review process had been conducted according to very strict standards. Among the published papers, 13 papers directly addressed words such as sustainable, life cycle assessment (LCA) and CO2, and 17 papers indirectly dealt with energy and CO2 reduction effects. Among the published papers, there are 6 papers dealing with construction technology, but a majority, 24 papers deal with management techniques. The authors of the published papers used various analysis techniques to obtain the suggested solutions for each topic. Listed by key techniques, various techniques such as Analytic Hierarchy Process (AHP), the Taguchi method, machine learning including Artificial Neural Networks (ANNs), Life Cycle Assessment (LCA), regression analysis, Strength–Weakness–Opportunity–Threat (SWOT), system dynamics, simulation and modeling, Building Information Model (BIM) with schedule, and graph and data analysis after experiments and observations are identified
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