Flight is an energetically expensive task. While aerial insects can effortlessly fly through natural environments, achieving power autonomous flights in insect-scale robots remains a major challenge. In prior works, w...
Flight is an energetically expensive task. While aerial insects can effortlessly fly through natural environments, achieving power autonomous flights in insect-scale robots remains a major challenge. In prior works, we developed soft-actuated insect-scale aerial robots that demonstrated unique capabilities such as in-flight collision recovery and somersaults. However, the soft dielectric elastomer actuators (DEAs) have low efficiency (< 20%) and require a high driving voltage (>600 V). These properties represent formidable obstacles for soft aerial robots to achieve power autonomous flights. In this work, we developed a 127 mg boost circuit that can convert a 7.7 V DC input into a 600 V and 400 Hz output for driving a 120 mg DEA. It has an equivalent capacitance and resistance of 20 nF and 5 $\mathbf{k}\Omega$ , respectively. The DEA is assembled into a 158 mg aerial robot, which can demonstrate liftoff while carrying the boost circuit as a payload. Although the robot remains tethered to an off-board power supply, this result represents a first step towards achieving power autonomy in soft aerial robots.
This paper considers a two-dimensional direction-of-arrival (DOA) estimation problem from a collaborative, distributed antenna array where each subarray is a distributed sensing node that is arbitrarily oriented. Whil...
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ISBN:
(数字)9798350329209
ISBN:
(纸本)9798350329216
This paper considers a two-dimensional direction-of-arrival (DOA) estimation problem from a collaborative, distributed antenna array where each subarray is a distributed sensing node that is arbitrarily oriented. While the relative locations of the subarrays are not precisely known, it is assumed that the configuration of each subarray is locally calibrated whereas the cross-covariance matrix between a pair of distributed nodes includes an unknown phase difference. Without explicitly estimating such unknown phase difference, subspace-based DOA estimation methods fail to coherently utilize the subarrays to locate the DOAs of the impinging signals. We propose a group sparsity-based approach to achieve accurate DOA estimation that is resilient to unknown phase disparities between subarrays. Simulation results clearly illustrate the effectiveness of the group sparsity-based approach using group LASSO, and the superiority over subspace-based methods, such as the MUSIC algorithm, is demonstrated.
Laser has been demonstrated to be a mature and versatile tool that presents great flexibility and applicability for the precision engineering of a wide range of materials over other established micromachining *** deca...
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Laser has been demonstrated to be a mature and versatile tool that presents great flexibility and applicability for the precision engineering of a wide range of materials over other established micromachining *** decades have witnessed its rapid development and extensive applications ranging from scientific researches to industrial *** hard materials remain several major technical challenges for conventional laser processing techniques due to their high hardness,great brittleness,and low optical absorption.A variety of hybrid laser processing technologies,such as laser-induced plasma-assisted ablation,laser-induced backside wet etching,and etching assisted laser micromachining,have been developed to overcome these barriers by introducing additional medium assistance or combining different process *** article reviews the basic principles and characteristics of these hybrid *** these technologies are used to precisely process transparent hard materials and their recent advancements are *** hybrid technologies show remarkable benefits in terms of efficiency,accuracy,and quality for the fabrication of microstructures and functional devices on the surface of or inside the transparent hard substrates,thus enabling widespread applications in the fields of microelectronics,bio-medicine,photonics,and microfluidics.A summary and outlook of the hybrid laser technologies are also highlighted.
Data-driven model discovery of complex dynamical systems is typically done using sparse optimization, but it has a fundamental limitation: sparsity in that the underlying governing equations of the system contain only...
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The fifth-generation (5G) cellular networks envisions achieving higher data rates, improved connectivity, reduced latency, and better quality of service (QoS) than the fourth-generation (4G) cellular networks. Such im...
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The fifth-generation (5G) cellular networks envisions achieving higher data rates, improved connectivity, reduced latency, and better quality of service (QoS) than the fourth-generation (4G) cellular networks. Such improved performance can be utilized to address the challenges in applications such as electricity generation in power systems. The traditional power grids responsible for electricity generation suffer from drawbacks such as life-threatening blackout crises, and energy storage proliferation as they are not robust to extreme climatic conditions. A recent study proposed the idea of extending the capabilities of advanced wireless technologies such as the current 5G to develop a robust, energy-efficient, and secure smart grids. However there are two main challenges associated with the integration of power systems and wireless technologies. First, it is imperative to understand the architecture and the enabling technologies of 5G to ensure that the performance requirements of the smart grids are met. Second, an end-to-end testbed is required to determine if the performance requirements are met by estimating the 5G characteristics such as latency, and throughput. Our proposed alleviates the aforementioned concerns in the following manner. To begin with, a systematic study of the 5G architecture including both the StandAlone (SA) and Non-Standalone (NSA) operations is presented. Furthermore, a detailed survey of the possible 5G enabling technologies is elicited. In addition to these, an end-to-end testbed that can estimate the 5G characteristics is explained in detail with appropriate preliminary results.
Predicting pedestrian behavior is a crucial task for intelligent driving systems. Accurate predictions require a deep understanding of various contextual elements that could impact the way pedestrians behave. To addre...
Predicting pedestrian behavior is a crucial task for intelligent driving systems. Accurate predictions require a deep understanding of various contextual elements that could impact the way pedestrians behave. To address this challenge, we propose a novel framework that relies on different data modalities to predict future trajectories and crossing actions of pedestrians from an egocentric perspective. Specifically, our model utilizes a cross-modal Transformer architecture to capture dependencies between different data types. The output of the Transformer is augmented with representations of interactions between pedestrians and other traffic agents conditioned on the pedestrian and ego-vehicle dynamics that are generated via a semantic attentive interaction module. Lastly, the context encodings are fed into a multi-stream decoder framework using a gated-shared network. We evaluate our algorithm on public pedestrian behavior benchmarks, PIE and JAAD, and show that our model improves state-of-the-art in trajectory and action prediction by up to 22% and 13% respectively on various metrics. The advantages of the proposed components are investigated via extensive ablation studies.
This paper proposes a time-varying inertia estimation approach for the virtual synchronous generator (VSG) control-based inverter. A Thevenin equivalent is first employed to formulate the relationship between the term...
This paper proposes a time-varying inertia estimation approach for the virtual synchronous generator (VSG) control-based inverter. A Thevenin equivalent is first employed to formulate the relationship between the terminal voltage phasor and the virtual frequency inside the VSG. This allows estimating virtual frequency directly, which is further integrated into the virtual swing equation and derived measurement function of the VSG. An improved adaptive Unscented Kalman Filter (IAUKF) is proposed to estimate the time-varying inertia. Numerical results show that, under various scenarios, the proposed inertia estimator is able to converge quickly, has high estimation accuracy, and filter out measurement noise.
A deep learning based non-linear predictive coding (NLPC) source compression and cognitive cooperative relay scheduling approach is developed for small-spacecraft swarms used in imaging based remote sensing missions. ...
A deep learning based non-linear predictive coding (NLPC) source compression and cognitive cooperative relay scheduling approach is developed for small-spacecraft swarms used in imaging based remote sensing missions. A LEO satellite swarm complemented by a number of earth stations and GEO satellite relays is specifically considered, although the developed approach is applicable for lunar and other planetary and space destinations as well. Each swarm node employs a recurrent convolutional neural network (RCNN) to predict its future observations based on immediate past observations and encodes the prediction error as its payload. A novel image reconstruction method that ensures maintaining an exact replica of the RCNN at the earth receiver without explicitly sharing the network weights is also proposed. Proposed cognitive cooperative relay scheduling algorithm also makes use of the same RCNN to predict future payload size based on which nodes are categorized as source and relay nodes. A multi-objective optimization algorithm is formulated that allows relay capacity allocation by taking into account various desired performance objectives and priority handling. Simulation results verify that the proposed NLPC and cooperative scheduling can significantly increase the amount of image data delivered to the earth while also drastically cutting down image delay and improving fairness among data delivered from each satellite node.
In this paper, a new pm-assisted model has been developed for the already introduced two-layer sub-harmonic synchronous machine. This work aims to increase the torque-producing capability of the brushless wound rotor ...
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In this paper, a new pm-assisted model has been developed for the already introduced two-layer sub-harmonic synchronous machine. This work aims to increase the torque-producing capability of the brushless wound rotor machines while keeping the use of rare earth magnets to a minimum. A 2D finite element analysis has been performed to validate the proposed model and compare the performance with the reference model. The results in the paper demonstrate that the proposed machine's average torque has increased.
This paper studies the synchronization problem of two-player multiagent systems through reinforcement learning methods. A Nash-minmax strategy is formulated, where the interactions of two players in the same agent are...
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ISBN:
(数字)9798350363012
ISBN:
(纸本)9798350363029
This paper studies the synchronization problem of two-player multiagent systems through reinforcement learning methods. A Nash-minmax strategy is formulated, where the interactions of two players in the same agent are non-zero-sum, while interactions of players between agents are zero-sum games. We propose an offline model-based reinforcement learning algorithm to identify Nash solutions for players within each agent, as well as the worst control solutions for players in neighboring antagonistic agents. On this basis, a data-driven off-policy algorithm is provided to alleviate the requirement for accurate system dynamics in the offline algorithm. Besides, the convergence of the proposed algorithms is analyzed. Finally, simulation results verify the effectiveness of the designed algorithms.
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