This paper considers the study scenario that the end-effector of a manipulator follows a desired trajectory and interacts with external environment. To maximize the interaction performance, admittance control is combi...
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ISBN:
(数字)9798350340266
ISBN:
(纸本)9798350340273
This paper considers the study scenario that the end-effector of a manipulator follows a desired trajectory and interacts with external environment. To maximize the interaction performance, admittance control is combined with adaptive dynamic programming (ADP). The optimal admittance parameters can be learned online without prior knowledge of the environment. A data-driven Hybrid Iteration is employed in the ADP, which can relax the initial stabilizing requirement and at the same time has a faster convergence rate compared with Value Iteration. In addition, a more accurate environment model is considered in the system control design, where a general iterative expression is proposed to describe the varying contour of the environment. At last, simulation and experimental studies are given to verify the effectiveness of the proposed method.
The technologies that are a part of the Internet of Things enable smart buildings to use energy more efficiently. The main focus of the research is on the use of internet-connected sensors and devices. Real-time monit...
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ISBN:
(数字)9798331543624
ISBN:
(纸本)9798331543631
The technologies that are a part of the Internet of Things enable smart buildings to use energy more efficiently. The main focus of the research is on the use of internet-connected sensors and devices. Real-time monitoring, analysis, and management of energy consumption are the goals of this. Data is gathered from various sources, such as appliances, lighting, HVAC (heating, ventilation, and air conditioning), and other systems, employing networked systems. This offers the chance for a more effective distribution of energy. The goal of the project is to foresee energy patterns and automatically adjust settings by focussing on the integration of cloud computing, big data analytics, and machine learning algorithms. Furthermore, it highlights how the Internet of Things can help cut down on energy waste and improve sustainability by giving consumers advice and information on how to use less energy. Providing consumers with insights and recommendations helps achieve this. By promoting the adoption of more ecologically friendly energy practices, the implementation of these technologies not only reduces operating costs but also decreases the environmental impact that buildings have. The study's conclusions indicate that the field of smart buildings has advanced significantly, which may help to promote sustainability and higher energy efficiency.
Effective data transmission plays a crucial role in federated learning (FL), which enables collaborative model training without centralizing data. This paper proposes a new coded transmission to enhance the communicat...
Effective data transmission plays a crucial role in federated learning (FL), which enables collaborative model training without centralizing data. This paper proposes a new coded transmission to enhance the communication quality for FL. The proposed coded transmission incorporates weight quantization, multilevel coding, set partitioning, and multi-stage decoding which are optimized to improve the FL performance. Furthermore, the unequal error protection (UEP) strategy is adopted in the proposed coded transmission, which allows the code rates to be optimized according to the significance of the quantized data. Simulation results demonstrate that the proposed UEP-based coded transmission outperforms conventional bit-interleaved coded modulation (BICM) scheme in terms of NMSE performance for FL, which, in return, improves the FL performance.
Photonics can be confined in real space with dispersion vanishing in the momentum space due to destructive interference. In this Letter, we report the experimental realization of flat bands with nontrivial topology in...
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Photonics can be confined in real space with dispersion vanishing in the momentum space due to destructive interference. In this Letter, we report the experimental realization of flat bands with nontrivial topology in a self-complementary plasmonic metasurface. The band diagram and compact localized states are measured. In these nontrivial band gaps, we observe the topological edge states by near-field measurements. Furthermore, we propose a digitalized metasurface by loading controllable diodes with C3 symmetry in every unit cell. By pumping a digital signal into the metasurface, we investigate the interaction between incident waves and the dynamic metasurface. Experimental results indicate that compact localized states in the nontrivial flat band could enhance the wave-matter interactions to convert more incident waves to time-modulated harmonic photonics. Although our experiments are conducted in the microwave regime, extending the related concepts into the optical plasmonic systems is feasible. Our findings pave an avenue toward planar integrated photonic devices with nontrivial flat bands and exotic transmission phenomena.
Detecting objects such as vehicles, buildings, pedestrians, and road signs is indispensable to advancing the concept of autonomous and self-driving cars. Furthermore, an autonomous vehicle (AV) must accurately detect ...
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ISBN:
(数字)9798331542726
ISBN:
(纸本)9798331542733
Detecting objects such as vehicles, buildings, pedestrians, and road signs is indispensable to advancing the concept of autonomous and self-driving cars. Furthermore, an autonomous vehicle (AV) must accurately detect its surrounding environment to operate reliably. Most object detection (OD) techniques perform adequately under typical weather conditions, including cloudy or sunny days. However, their efficiency decreases significantly when exposed to Adverse Weather Conditions (AWCs), including days with sandstorm, rain, fog or snow. Complex and computationally costly models are required to achieve high accuracy rates. In this study, we present an improved OD system in AWCs for autonomous vehicles (AVs) using the single-stage deep learning (DL) algorithm YOLO (You Only Look Once) version 10. To evaluate our system, Vehicle Detection in Adverse Weather Nature (DAWN) dataset is used. It comprises real-world images captured under various types of AWCs. The experimental findings confirm that the suggested method is effective and surpasses state-of-the-art OD approaches under AWCs.
There are various sources of ionizing radiation exposure, where medical exposure for radiation therapy or diagnosis is the most common human-made source. Understanding how gene expression is modulated after ionizing r...
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Unlike MANET (Mobil Ad Hoc Network), VANETs (Vehicular Ad Hoc Networks) are characterized by an unlimited number of vehicles can be equal to millions of vehicles and a high mobility. The objective of developing VANETs...
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We investigate multi-mode simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted multi-cell systems, consisting of multiple base stations (BSs), their own users (UEs), and on...
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The hybrid active power filter (HAPF) emerges as a cost-effective remedy for power quality challenges in medium voltage power systems. The success of HAPF, crucially, hinges on the efficacy of its current control mech...
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ISBN:
(数字)9798350330991
ISBN:
(纸本)9798350331004
The hybrid active power filter (HAPF) emerges as a cost-effective remedy for power quality challenges in medium voltage power systems. The success of HAPF, crucially, hinges on the efficacy of its current control mechanism. This paper introduces a novel approach by proposing a deterministic policy gradient based reinforcement learning (DPG-RL) as the current control strategy for HAPF. In stark contrast to conventional model-based control methods, the DPG-RL leverages artificial intelligence (AI) technology, rendering it model-free and capable of dynamically seeking the optimal control policy to enhance HAPF performance. A notable advantage lies in its significantly lower computational burden during each sampling period, distinguishing it from other contemporary AI-aided control methods. The paper outlines a systematic design process, encompassing feature selection and reward function design, to formulate the RL problem. The comprehensive design procedure of DPG-RL is then detailed. Simulation results are subsequently presented, validating the effectiveness and reliability of the proposed DPG-RL across diverse operational scenarios.
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