Programmable cell aggregation offers valuable insights into the natural development of synthetic multicellular systems and enables precise control over spatial organization and material structuring. Previous efforts h...
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This paper proposes an efficient, fully convolutional neural network to generate robotic grasps by using 300×300 depth images as input. Specifically, a residual squeeze-and-excitation network (RSEN) is introduced...
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This paper proposes an efficient, fully convolutional neural network to generate robotic grasps by using 300×300 depth images as input. Specifically, a residual squeeze-and-excitation network (RSEN) is introduced for deep feature extraction. Following the RSEN block, a multi-scale spatial pyramid module (MSSPM) is developed to obtain multi-scale contextual information. The outputs of each RSEN block and MSSPM are combined as inputs for hierarchical feature fusion. Then, the fused global features are upsampled to perform pixel-wise learning for grasping pose estimation. The experimental results on Cornell and Jacquard grasping datasets indicate that the proposed method has a fast inference speed of 5ms while achieving high grasp detection accuracy of 96.4% and 94.8% on Cornell and Jacquard, respectively, which strikes a balance between accuracy and running speed. Our method also gets a 90% physical grasp success rate with a UR5 robot arm.
Peak load management is very important for the electric power *** paper analyzes the impact of residential swimming pool pumps(RSPPs) on the peak *** this paper analyzes the challenges of non-intrusive energy consumpt...
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Peak load management is very important for the electric power *** paper analyzes the impact of residential swimming pool pumps(RSPPs) on the peak *** this paper analyzes the challenges of non-intrusive energy consumption estimation for ***,a novel reference based change-point(RCP) model is proposed for non-intrusive SPPs energy consumption *** advantages of the proposed RCP model are that it does not require high sampling rate data or prior information of the *** show that during pool season,under the assumption that the ratio of base loads(defined as the power consumption which is independent of the outdoor temperature) of houses with and with PPs remains the same during no pool season and pool season,6.3%of the total energy is consumed by PPs,while under the assumption that for houses with and without PPs,the ratio of base loads is equal to the ratio of the temperature-dependent power consumption during pool season,9.08% of the total energy is consumed by ***,we show that by shifting PPs activity period,under the first assumption,at least 1.27% of peak demand can be reduced,while under the second assumption,at least 4.53% of peak demand can be reduced.
Urban digital twin (UDT) technologies offer new opportunities for intelligent road inspection (IRI). This paper first reviews the state-of-the-art algorithms used in the two key components of UDT-based IRI systems: (1...
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ISBN:
(纸本)9781665480468
Urban digital twin (UDT) technologies offer new opportunities for intelligent road inspection (IRI). This paper first reviews the state-of-the-art algorithms used in the two key components of UDT-based IRI systems: (1) multi-temporal, multi-dimension, multi-score, and heterogeneous road data acquisition, and (2) road distress detection. This paper then summarizes the UDTIRI competition, organized in conjunction with IEEE Bigdata 2022. More details on our competition are available at ***/view/udtiri-workshop/bigdata-2022.
The health of people around the world and the global economy are under substantial threat from the outbreak of pandemics[1].controlling pandemics is extremely challenging,with preventing the spread of pathogens the mo...
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The health of people around the world and the global economy are under substantial threat from the outbreak of pandemics[1].controlling pandemics is extremely challenging,with preventing the spread of pathogens the most important and critical *** all preventative actions,body temperature screening is undoubtedly highly necessary and effective[2].
Temporal information plays a pivotal role in Bird’s-Eye-View (BEV) driving scene understanding, which can alleviate the visual information sparsity. However, the indiscriminate temporal fusion method will cause the b...
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In this paper, we propose a stiffness estimation and intention detection method for human-robot collaboration. The human arm endpoint stiffness can be obtained according to the muscle activation levels of the upper ar...
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ISBN:
(数字)9781728151694
ISBN:
(纸本)9781728151700
In this paper, we propose a stiffness estimation and intention detection method for human-robot collaboration. The human arm endpoint stiffness can be obtained according to the muscle activation levels of the upper arm and the human arm configurations. The estimated endpoint stiffness of human arm is matching to the robot arm joint stiffness through an appropriate mapping. The motion intention of human arm is detected based on the wrist configuration which is recognized by a Myo armband attached at the forearm of the operator. In order to reduce the time of feature engineering to ensure the performance of real-time collaboration, the wrist configuration recognition is realised based on the neural learning algorithm. The sEMG of the human forearm is directly fed into the neural network after processing by filters and sliding windows. The force sensor at the end of the robot arm is embedded in the feedback loop to make the robot arm better adapted to the operator's movement. The results of experiments performed on Baxter robot platform illustrate a good performance and verifies the proposed method.
For traditional parameter estimation schemes of uncertain robot, most of them were proposed to identify unknown parameter with desired precision, but few of them focused on the convergence time. Recently finite-time e...
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ISBN:
(数字)9781728151694
ISBN:
(纸本)9781728151700
For traditional parameter estimation schemes of uncertain robot, most of them were proposed to identify unknown parameter with desired precision, but few of them focused on the convergence time. Recently finite-time estimation techniques have been proposed by scholars to achieve estimation in finite time. In this paper, we proposed a novel estimation scheme for uncertain robot systems with fixed time instead of finite time. In order to avoid using acceleration signals during the estimation, a kind of auxiliary filtering technique was employed. Besides, a continuous and recursive update law was employed for the parameter estimation such that the computational burdens of real-time inversion of square matrices could be avoided. Finally the effectiveness of the identification algorithm is verified based on a 2-DOF uncertain robot model.
To address the performance degradation of wide-band spectrum sensing by sub-Nyquist sampling (SNS) in wireless fading channels, two compressive subspace learning (CSL) algorithms are proposed for signal subspace learn...
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To address the performance degradation of wide-band spectrum sensing by sub-Nyquist sampling (SNS) in wireless fading channels, two compressive subspace learning (CSL) algorithms are proposed for signal subspace learning based on antenna cross-correlations for further improving the sensing performance. Both algorithms are developed based on different organizations of SNS samples, and both exploit space diversity and noise uncorrelations between antennas. We further establish the expressions for statistical covariance matrices (SCMs) obtained by SNS samples in the multi-antenna SNS cognitive radio system. Based on the derived SCM expressions, conditions to ensure SCMs without noise contamination are given. Simulations validate the derived conditions and show the improvement on sensing performance over related works.
Vision sensors are widely applied in vehicles, robots, and roadside infrastructure. However, due to limitations in hardware cost and system size, camera Field-of-View (FoV) is often restricted and may not provide suff...
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