In this paper, we analyze the dynamic response of a MEMS device made of a microplate supported by two microcantilever beams and electrically actuated via an underneath electrode. We employ the motion-induced current m...
In this paper, we analyze the dynamic response of a MEMS device made of a microplate supported by two microcantilever beams and electrically actuated via an underneath electrode. We employ the motion-induced current method to capture its dynamic response under varying pressure. We show the potential use of the MEMS device as a pressure sensor by leveraging squeeze-film damping and other nonlinear dynamic phenomena associated with its motion. The present study reveals the possible tuning of the MEMS device to operate in analog and binary modes. Analog-based detection is achieved within the low pressure range of 2.2-52 mbar. We also demonstrate the use of the MEMS device in binary mode, acting as a pressure switch, with a capability to detect low pressures down to sub-millibars.
Fast drumming presents a speed challenge to many robotic arms. To simultaneously meet the needs for speed, stroke, and force, we proposed and tested a new double-saddle dielectric elastomer actuator (DEA) as the artif...
详细信息
This paper presents a reduced and efficient alternate model to simulate the nonlinear dynamics of a thermally driven V-shaped MEMS actuator. The experimental observation of the dynamic voltage-displacement relationshi...
详细信息
ISBN:
(数字)9798331516963
ISBN:
(纸本)9798331516970
This paper presents a reduced and efficient alternate model to simulate the nonlinear dynamics of a thermally driven V-shaped MEMS actuator. The experimental observation of the dynamic voltage-displacement relationship shows an overdamped response with a variable rise-time and fall-time indicating the simultaneous presence of complex energy storage and energy dissipation mechanisms. To completely characterize these mechanisms and yet have a simple representation for control, we develop an alternate model consisting of a set of ordinary nonlinear differential equations representing the behavior of a nonlinear RC circuit with variable parameters that are a function of the applied voltage. The simulation results show good agreement with the measured data and confirm the accuracy of the proposed alternate model.
Machine learning algorithms are fundamentally driven by the data provided by humans;consequently, the decisions made by those algorithms are not free from human bias. This is particularly evident in the case of facial...
详细信息
ISBN:
(纸本)9789898704498
Machine learning algorithms are fundamentally driven by the data provided by humans;consequently, the decisions made by those algorithms are not free from human bias. This is particularly evident in the case of facial analysis systems that employ machine learning algorithms. Recent studies have shown that the decisions made by many of the commercially available facial analysis systems are prejudiced against certain groups of race, ethnicity, age, gender and culture. Further studies have identified that the underlying reason for such biased decisions is that the open source material available for facial image databases which are used in commerce and academia to train the algorithms has meager diversity in these categories. To compound this issue, facial analysis technology is promoted by influential companies and artificial intelligence service providers without affirming the fairness and accuracy of the decisions given by these systems. To minimize bias and ensure representation of the Middle Eastern population in the imminent growth of this technology, we propose the development of two Arab face databases along with an algorithmic audit involving seven commercially available facial analysis systems. Of the databases, the first, Arab-LEANA, will include 300 Arab subjects' face images with variation in lighting, expression, accessory, nationality and age (LEANA). The second, Arab Public Figures Faces (APFF), will contain images and videos of 300 Arab public figures captured "in the wild". Faces for APFF will be selected manually from the internet since manual selection of faces will result in a high degree of variability in scale, pose, expression, illumination, age, occlusion and make-up. These databases will provide the worldwide community of face recognition researchers with a large-scale, diverse collection of Arab face images for training and evaluating algorithms toward developing a more representative, and therefore more robust, capacity for facial analysis. T
The advancements in information and communication technologies have had a significant impact on the engineering educational system. Virtual laboratories are progressively being adopted to improve the way in which stud...
The advancements in information and communication technologies have had a significant impact on the engineering educational system. Virtual laboratories are progressively being adopted to improve the way in which students interact with simulations for control systems. The enhancement of visualization and interaction offered by modern computers presents an opportunity to teach the theoretical foundation with a more organic approach. In addition, there are optimization algorithms that can be employed to designing controllers in an optimal way without having extensive knowledge in the area of control theory. This paper delineates the utilization of CoppeliaSim software, the Moth Flame Optimization (MFO) algorithm, and the EVA mobile robot for teaching control theory with Single-Input, Single-Output systems (SISO), for mobile robot obstacle following/avoidance application. The approach employs an online multi-language (Spanish and Portuguese) methodology for students without knowledge of control theory.
This paper introduces an experimental method to detect the motion of an electrostatic Micro-Electro-Mechanical Systems (MEMS) resonator in aqueous media. The resonator comprises a micro cantilever beam subject to elec...
This paper introduces an experimental method to detect the motion of an electrostatic Micro-Electro-Mechanical Systems (MEMS) resonator in aqueous media. The resonator comprises a micro cantilever beam subject to electrostatic actuation through a side electrode. A Finite Element Method (FEM) model of the resonator is developed to determine the in-plane mode shapes and their natural frequencies in order to facilitate the experimental study. The motion of the resonator lead to variations in its capacitance and induce a current. The developed experiments demonstrate that motion-induced current can be measured and analyzed to detect the motion of the resonator's higher-order modes and can be used in chemical sensing.
In basketball game, players need to learn how to take a next appropriate action by judging various situations with surrounding players through team practice. Such decision-making capabilities in individual practice wi...
详细信息
Vehicle-to-Everything (V2X) networks require low-latency communications utilizing a broad spectrum while operating under jamming. In this sense, low complexity antenna array-based broadband jamming mitigation schemes ...
详细信息
ISBN:
(数字)9798350387414
ISBN:
(纸本)9798350387421
Vehicle-to-Everything (V2X) networks require low-latency communications utilizing a broad spectrum while operating under jamming. In this sense, low complexity antenna array-based broadband jamming mitigation schemes are crucial in order to allow low latency communication and low-cost hardware. In this paper we propose low-complexity algorithms for signal recovery in broadband processing scenario applied to V2X. Numerical simulation of a jamming scenario demonstrate the proposed algorithm achieving the same performance in terms of signal-to-interference plus noise ratio (SINR) as the state-of-the-art while taking signiflcantly less time to compute.
Robotic manipulators are multi-input multi-output (MIMO) systems with nonlinear points affected by numerous uncertainties and disturbances. PID controllers are widely used in industry for kinematic and dynamic control...
详细信息
ISBN:
(数字)9781665462808
ISBN:
(纸本)9781665462815
Robotic manipulators are multi-input multi-output (MIMO) systems with nonlinear points affected by numerous uncertainties and disturbances. PID controllers are widely used in industry for kinematic and dynamic control. However, when applied to MIMO systems, they are not easy to tune and require performance improvements. In this work, a PID controller is proposed with a fuzzy precompensator (FP-PID), both tuned by the bioinspired particle swarm optimization (PSO) algorithm to a two-degree of freedom (2-DOF) robotic manipulator representing a human leg. To validate the system, two real datasets of human gait were used: normal walking and stair climbing to estimate the error trajectory of the manipulator. The statistical analysis of the PSO algorithm with 16 experiments was satisfactory, and the addition of the fuzzy precompensator to the conventional PID resulted in a reduction of the mean square error of one of the manipulator links by up to 73 percent.
Oil spills represent a growing environmental challenge that poses a significant threat to living organisms. Moreover, the treatment of oil spills, especially in severe cases, has serious economic repercussions and req...
详细信息
ISBN:
(数字)9798331516963
ISBN:
(纸本)9798331516970
Oil spills represent a growing environmental challenge that poses a significant threat to living organisms. Moreover, the treatment of oil spills, especially in severe cases, has serious economic repercussions and requires substantial labor and time. Therefore, the effective detection of oil spills has become an important research problem. Traditional methods for detecting oil spills, such as manual patrolling and dynamic sensors, are often limited in accuracy and coverage. As a result, the automation of oil spills detection has emerged as a critical global imperative in scientific research. The aim of this paper is to employ deep learning technology to achieve effective detection of oil spills based on aerial images. Our approach is composed of two phases. In the first phase, a Deep Convolutional Neural Network (DCNN), namely ResNet50, is trained on a large dataset containing images showing oil spills at a seaport. The trained DCNN is used to classify the input image as "Oil Spill" or "No Oil Spill". In the second phase, the images classified as "Oil Spill" are analyzed using a deep learning detection model, namely You-Only-Look-Once (YOLOv4), to localize the oil spills. The results indicate the capability of the proposed method to achieve effective oil spill detection. In particular, the classification accuracy obtained by the ResNet50 model is equal to 98%. Moreover, the YOLOv4 model was able to obtain effective localization of the oil spills with mean-average precision of 62%.
暂无评论