Analysis Of The Suspension Beam In Accelerometer For Stiffness Constant And Resonant Frequency By Using Analytical And Numerical Investigation

Mikro-meterpecut yang digunakan dalam pelbagai penerapan hanya akan tercapai dengan jayanya sekiranya keperluan frekuensi resonans dan kepekaan dapat dipenuhi dan konsisten. A successful and consistent performance of micro-accelerometer which has been applied in various applications can only be achieved when the resonant frequency and the sensitivity requirement are fulfilled

Analysis Of The Suspension Beam In Accelerometer For Stiffness Constant And Resonant Frequency By Using Analytical And Numerical Investigation [TL589.2.A3 W872 2007 f rb].

Mikro-meterpecut yang digunakan dalam pelbagai penerapan hanya akan tercapai dengan jayanya sekiranya keperluan frekuensi resonans dan kepekaan dapat dipenuhi dan konsisten. Berdasarkan syarat-syarat tersebut, analisis struktur pada pekali kekukuhan and frekuensi resonans bagi rasuk ampaian dalam meter pecut dan seterusnya pengoptimuman kepada kepekaan haruslah dilakukan. A successful and consistent performance of micro-accelerometer which has been applied in various applications can only be achieved when the resonant frequency and the sensitivity requirement are fulfilled. In view of this, structural analysis on stiffness constant and resonant frequency for the suspension beam in accelerometer, and subsequently optimization design of accelerometer with respect to sensitivity in term of displacement against acceleration must be performed

Design of Piezoresistive MEMS Accelerometer with Optimized Device Dimension

The advent of microfabrication has given a great impetus to MEMS inertial sensors particularly MEMS automobile sensors. In developing Microsystem technology, FEA has been acknowledged as the most cost and time effective alternative to building a prototype for simulation. Present work focuses on developing mathematical model in order to formulate a design procedure to determine the influence of geometric attributes of a four and an eight beam cross bridged accelerometer for automotive applications pertaining to lower inertial loads ( 2g). The configuration is so chosen to minimize cross-axis sensitivity and temperature variation. The proposed mathematical model takes both mechanical and electrical aspects into consideration. Both accelerometers are doped with p-type (boron diffused) silicon at two ends of its flexures. An optimization based on genetic algorithm has been carried out to determine the best possible geometric configuration while satisfying the specification of automotive inertia sensors. A solid model based on optimized dimensions has been simulated using ANSYS to determine stress, deformation, sensitivity for both configurations followed by validation with analytical results. The two configurations have been compared on the basis of output behaviour and performance parameters, and the obtained results are described in detail

Simulation for a Mems-Based CTRNN Ultra-Low Power Implementation of Human Activity Recognition

This paper presents an energy-efficient classification framework that performs human activity recognition (HAR). Typically, HAR classification tasks require a computational platform that includes a processor and memory along with sensors and their interfaces, all of which consume significant power. The presented framework employs microelectromechanical systems (MEMS) based Continuous Time Recurrent Neural Network (CTRNN) to performHAR tasks very efficiently. In a real physical implementation, we show that the MEMS-CTRNN nodes can perform computing while consuming power on a nano-watts scale compared to the micro-watts state-of-the-art hardware. We also confirm that this huge power reduction doesn’t come at the expense of reduced performance by evaluating its accuracy to classify the highly cited human activity recognition dataset (HAPT). Our simulation results show that the HAR framework that consists of a training module, and a network of MEMS-based CTRNN nodes, provides HAR classification accuracy for the HAPT that is comparable to traditional CTRNN and other Recurrent Neural Network (RNN) implantations. For example, we show that the MEMS-based CTRNN model average accuracy for the worst-case scenario of not using pre-processing techniques, such as quantization, to classify 5 different activities is 77.94% compared to 78.48% using the traditional CTRNN

Autonomous Navigation for Mars Exploration

The autonomous navigation technology uses the multiple sensors to percept and estimate the spatial locations of the aerospace prober or the Mars rover and to guide their motions in the orbit or the Mars surface. In this chapter, the autonomous navigation methods for the Mars exploration are reviewed. First, the current development status of the autonomous navigation technology is summarized. The popular autonomous navigation methods, such as the inertial navigation, the celestial navigation, the visual navigation, and the integrated navigation, are introduced. Second, the application of the autonomous navigation technology for the Mars exploration is presented. The corresponding issues in the Entry Descent and Landing (EDL) phase and the Mars surface roving phase are mainly discussed. Third, some challenges and development trends of the autonomous navigation technology are also addressed

Sijainnin estimointi inertiamittausyksikölla ilman paikannusjärjestelmää

This thesis aims to estimate the position of an inertial measurement unit (IMU) without any tracking device such as GPS. The work includes the calibration of the accelerometer with particle swarm optimization (PSO) to solve the equation, the gyrometer with the extended Kalman filter (EKF) and the magnetometer also with EKF. The calibration is realized with the data from the sensors and Matlab. When the calibration is done, the acceleration is obtained from the accelerometer and the gyrometer. The algorithm employs mostly rotation matrix theory. The performance of the algorithm depends on the success of the calibration. A small error in the estimation of the acceleration leads to a wrong result. This was, nevertheless, to be expected as a double integration with respect to time of a signal with remaining traces of bias is doomed to fail without any correction algorithms. Unfortunately, a working algorithm could not be achieved, pointing out that it may be difficult to realize one without external devices such as GPS.Tässä työssä estimoidaan inertiamittausyksikön (IMU) sijaintia käyttämättä GPS-laitetta. Työ sisältää kiihtyvyysanturin kalibroinnin hiukkasparvioptimointialgorithmilla (PSO), gyroskoopin laajennetulla Kalmanin suodattimella (EKF) ja kompassin EKF:lla. Kalibrointi on suoritettu vain anturien arvoilla ja Matlab-sovelluksella. Anturin kiihtyvyys saa kalibroiduilta kiihtyvyysanturilta ja kompassilta. Algorithmi käyttää rotaatiomatriisin teoria. Algorithmi tehokkuus riippuu kalibroinnista. Pienikin estimointivirhe aiheittaa väärän tuloksen.Työn tulokset voitiin ennustaa koska tuplaintegrointi pienellä virhellä johtaa helposti ja nopeasti tulokset väärään suuntaan. Työn algoritmi vaatii korjausalgoritmin joka pystyisi poistamaan integroinnin virheen. Valitettavasti toimivaa algoritmia ei löydettu, joka viittaa siihen, että sen toteutaminen saattaa olla vaikeaa ilman apulaitetta, kuten GPS-laitetta

GA-SVR and pseudo-position-aided GPS/INS integration during GPS outage

The performance of Global Positioning System and Inertial Navigation System (GPS/INS) integrated navigation is reduced when GPS is blocked. This paper proposes an algorithm to overcome the condition where GPS is unavailable. Together with a parameter-optimised Genetic Algorithm (GA), a Support Vector Regression (SVR) algorithm is used to construct the mapping function between the specific force, angular rate increments of INS measurements and the increments of the GPS position. During GPS outages, the real-time pseudo-GPS position is predicted with the mapping function, and the corresponding covariance matrix is estimated by an improved adaptive filtering algorithm. A GPS/INS integration scheme is demonstrated where the vehicle travels along a straight line and around a curve, with respect to both low-speed-stable and high-speed-unstable navigation platforms. The results show that the proposed algorithm provides a better performance when GPS is unavailable

Shape optimization of solid-air porous phononic crystal slabs with widest full 3D bandgap for in-plane acoustic waves

The use of Phononic Crystals (PnCs) as smart materials in structures and microstructures is growing due to their tunable dynamical properties and to the wide range of possible applications. PnCs are periodic structures that exhibit elastic wave scattering for a certain band of frequencies (called bandgap), depending on the geometric and material properties of the fundamental unit cell of the crystal. PnCs slabs can be represented by plane-extruded structures composed of a single material with periodic perforations. Such a configuration is very interesting, especially in Micro Electro-Mechanical Systems industry, due to the easy fabrication procedure. A lot of topologies can be found in the literature for PnCs with square-symmetric unit cell that exhibit complete 2D bandgaps; however, due to the application demand, it is desirable to find the best topologies in order to guarantee full bandgaps referred to in-plane wave propagation in the complete 3D structure. In this work, by means of a novel and fast implementation of the Bidirectional Evolutionary Structural Optimization technique, shape optimization is conducted on the hole shape obtaining several topologies, also with non-square-symmetric unit cell, endowed with complete 3D full bandgaps for in-plane waves. Model order reduction technique is adopted to reduce the computational time in the wave dispersion analysis. The 3D features of the PnC unit cell endowed with the widest full bandgap are then completely analyzed, paying attention to engineering design issues

Aplicação de meta-heurísticas para afinação de analisadores de espectro de vibração baseados em sistemas microeletromecânicos

Orientadores: Mateus Giesbrecht, Fabiano FruettDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: O espectro de vibração mecânica é uma característica do domínio da frequência utilizada para o monitoramento de sistemas diversos e é, tradicionalmente, calculado pela Transformada Rápida de Fourier (FFT) -- do termo em inglês Fast Fourier Transform -- de uma série temporal. Uma alternativa viável, com diversas vantagens operacionais, é o uso de microacelerômetros gêmeos para a obtenção do espectro diretamente no domínio da frequência. Essa estratégia possui sua maior limitação nas diferenças encontradas nos parâmetros físicos de acelerômetros -- devidas a seu processo de fabricação --, de tal forma que o nível de distorção do espectro pode ser consideravelmente superior àquele encontrado no espectro levantado pela FFT. Para contornar essas diferenças, neste trabalho a afinação do microdispositivo analisador de espectro é proposta através do ajuste das amplitudes das tensões de atuação dos acelerômetros. Para realizar a afinação, a Evolução Diferencial (DE, do termo em inglês Differential Evolution) é usada e o problema da afinação é abordado sob duas diferentes perspectivas de otimização: uma mono-objetivo e uma multi-objetivo. Para ambos os problemas de otimização, as funções objetivo e restrições são baseadas nas componentes da série de Fourier do ganho de malha fechada do sistema analisador de espectro -- composição essa que depende das tensões de excitação. Para a solução do problema de otimização multi-objetivo, o algoritmo DE é devidamente adaptado. As vantagens e desvantagens de ambas as estratégias de afinação são discutidas em detalhe, bem como os resultados obtidos para a aproximação do conjunto de Pareto. Esses resultados -- especialmente o compromisso distorção-sensibilidade -- são demonstrados e discutidos. A validade da estratégia de afinação proposta é evidenciada, uma vez que é capaz de determinar as amplitudes das tensões a serem aplicadas ao micro analisador de espectro para atender os requisitos de nível de distorção e sensibilidadeAbstract: The mechanical vibration spectrum is a frequency-domain characteristic used for monitoring various systems and is traditionally calculated by the Fast Fourier Transform (FFT) of a time series. Another possible alternative, with several operational advantages, is the use of twin-microaccelerometers to obtain the spectrum directly in the frequency domain. This strategy has its greatest limitation in the differences found in the accelerometers physical parameters -- due to their manufacturing process --, such that the spectrum distortion level may be considerably higher than that found in the spectrum raised by the FFT. To overcome these differences, in this work the tuning of the spectrum analyzer microdevice is proposed by adjusting the accelerometers actuation voltages amplitudes. To perform the tuning, the Differential Evolution (DE) is used and the tuning problem is approached in two different optimization perspectives: a mono-objective and a multi-objective. For both optimization problems, the objective functions and constraints are based on the Fourier series components of the spectrum analyzer system closed-loop gain -- a composition that depends on the excitation voltages. To solve the multi-objective optimization problem, the DE algorithm is properly adapted. The advantages and disadvantages of both tuning strategies are discussed in detail, as well as the results obtained for the Pareto-set approximation. The results -- specially the distortion-sensitivity compromise -- are demonstrated and discussed. The validity of the proposed tuning strategy is evidenced, since it is able to determine the voltages amplitudes to be applied to the micro spectrum analyzer to attend the distortion level and sensitivity requirementsMestradoAutomaçãoMestra em Engenharia Elétrica161153/2018-6CNP

Detection and Localization of Leaks in Water Networks

Today, 844 million humans around the world have no access to safe drinking water. Furthermore, every 90 seconds, one child dies from water-related illnesses. Major cities lose 15% - 50% of their water and, in some cases, losses may reach up to 70%, mostly due to leaks. Therefore, it is paramount to preserve water as an invaluable resource through water networks, particularly in large cities in which leak repair may cause disruption. Municipalities usually tackle leak problems using various detection systems and technologies, often long after leaks occur; however, such efforts are not enough to detect leaks at early stages. Therefore, the main objectives of the present research are to develop and validate a leak detection system and to optimize leak repair prioritization. The development of the leak detection models goes through several phases: (1) technology and device selection, (2) experimental work, (3) signal analysis, (4) selection of parameters, (5) machine learning model development and (6) validation of developed models. To detect leaks, vibration signals are collected through a variety of controlled experiments on PVC and ductile iron pipelines using wireless accelerometers, i.e., micro-electronic mechanical sensors (MEMS). The signals are analyzed to pinpoint leaks in water pipelines. Similarly, acoustic signals are collected from a pilot project in the city of Montreal, using noise loggers as another detection technology. The collected signals are also analyzed to detect and pinpoint the leaks. The leak detection system has presented promising results using both technologies. The developed MEMS model is capable of accurately pinpointing leaks within 12 centimeters from the exact location. Comparatively, for noise loggers, the developed model can detect the exact leak location within a 25-cm radius for an actual leak. The leak repair prioritization model uses two optimization techniques: (1) a well-known genetic algorithm and (2) a newly innovative Lazy Serpent Algorithm that is developed in the present research. The Lazy Serpent Algorithm has proved capable of surpassing the genetic algorithm in determining a more optimal schedule using much less computation time. The developed research proves that automated real-time leak detection is possible and can help governments save water resource and funds. The developed research proves the viability of accelerometers as a standalone leak detection technology and opens the door for further research and experimentations. The leak detection system model helps municipalities and water resource agencies rapidly detect leaks when they occur in real-time. The developed pinpointing models facilitate the leak repair process by precisely determine the leak location where the repair works should be conducted. The Lazy Serpent Algorithm helps municipalities better distribute their resources to maximize their desired benefits