To mitigate the inspector’s workload and improve the quality of the product, computer vision-based anomaly detection (AD) techniques are gradually deployed in real-world industrial scenarios. Recent anomaly analysis ...
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In basketball videos, the ball is always so small in the camera that its appearance feature is hard to be extracted. In this paper, we introduce a deep-learning technology to detect the basketball. Specifically, we tr...
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The network connectivity maintenance problem in formation control of mobile agents with double-integrator dynamics is addressed in this paper. Distributed potential field-based controllers are proposed for double-inte...
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The network connectivity maintenance problem in formation control of mobile agents with double-integrator dynamics is addressed in this paper. Distributed potential field-based controllers are proposed for double-integrator agent group to maintain the connectivity of communication graph until the desired formation is achieved. We also investigate the connectivity preserving formation control with a virtual leader. Providing that at least one agent has information about the leader, all agents can asymptotically reach the desired formation and attain the same velocity as the virtual leader. The asymptotic stability analysis is presented with Lyapunov-like tools including LaSalle's invariance principle and Barbalat's lemma. Simulation results on a group of agents with double-integrator dynamics are presented to demonstrate the effectiveness of the proposed strategies.
The distribution network is developing towards the direction of Internet of Things in Electricity(IoTE). As an emerging technology of the Internet of Things(IoT), edge computing has great application potential in the ...
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ISBN:
(数字)9781728131375
ISBN:
(纸本)9781728131382
The distribution network is developing towards the direction of Internet of Things in Electricity(IoTE). As an emerging technology of the Internet of Things(IoT), edge computing has great application potential in the future IoTE. This paper presents an architecture of edge computing-driven IoTE, whose physical layer is an electrical network containing a high proportion of power electronic devices, and its information layer is a 5G-oriented power communication network containing a large amount of power terminal and mass communication data generated by the power terminal. For the physical layer, the virtual synchronous machine(VSM) algorithm are adopted to control the large number of power electronic components so as to guarantee the dynamic stability of physical power network. For the information layer, a new layered distributed information processing architecture, the Edge Computing Architecture, is introduced to address the needs of information monitoring and protection control in the smart distribution network. The architecture of the edge computing, and the applications that may be deployed in the edge computing gateways are introduced, and the challenges may be faced by the IoTE in the future development are further discussed.
Flexibility and robustness have become key points in the development of surgical robotcontroller for physical interactions. However, the conventional impedance control schemes unaware of the actual surgical scenario,...
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ISBN:
(数字)9781728158716
ISBN:
(纸本)9781728158723
Flexibility and robustness have become key points in the development of surgical robotcontroller for physical interactions. However, the conventional impedance control schemes unaware of the actual surgical scenario, including complex physical interaction on the robot arm, lead to the loss of accuracy. In this paper, a hierarchical task impedance control scheme is proposed for Minimally Invasive Surgery (MIS) based on an operational space formulation of a 7 DoFs redundant robot. Its redundancy is exploited to guarantee a remote center of motion (RCM) constraint and to provide a flexible workspace for the medical staff to assist physicians. In addition to the achievement of the classical whole-body impedance control, the issue of uncertain disturbances will be addressed by a decoupled adaptive approximation based on a radial basis function neural network (RBFNN) within the control framework. Task performances under the hierarchical task impedance controller were validated and compared with previous work in the literature. Experimental results showed its improved performance in terms of positional error and RCM constraint, regardless of the existing uncertain physical interaction.
Sparse reconstruction is an important method aiming at obtaining an approximation to an original signal from observed data. It can be deemed as a multiobjective optimization problem for the sparsity and the observatio...
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Assessment of myocardial viability is essential in diagnosis and treatment management of patients suffering from myocardial infarction, and classification of pathology on myocardium is the key to this assessment. This...
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Indirect methods for visual SLAM are gaining popularity due to their robustness to environmental variations. ORB-SLAM2 [1] is a benchmark method in this domain, however, it consumes significant time for computing desc...
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Existing scale estimation methods mainly use fixed-size tracking box for target tracking. However, when the moving direction of the target changes from near to far or from far to near, the size of the current tracking...
Existing scale estimation methods mainly use fixed-size tracking box for target tracking. However, when the moving direction of the target changes from near to far or from far to near, the size of the current tracking box cannot adapt to the scale change. In order to overcome the disadvantage of invariant size of tracking box in the scale change, this paper proposes a scale offset estimation method based on correlation filter by combining the state of previous frame and current frame of the target. Select the scale proposal box in the scale layer, and adjust the position and size of the actual tracking box in real time according to the size of the proposal box, target position and scale offset. In this paper, OTB-100 is used as the dataset. The obtained tracking results show that our algorithm Ours has better tracking performance than the existing others tracking algorithm in scale variation, and it has better tracking effect and stronger robustness in drift and occlusion events.
visual object tracking has been a concern topic these years, and many trackers have achieved good results in various fields. These researches and breakthroughs have made many improvements to solve problems such as dri...
visual object tracking has been a concern topic these years, and many trackers have achieved good results in various fields. These researches and breakthroughs have made many improvements to solve problems such as drift, lighting, deformation and occlusion. In this paper, we improve the structure of the AlexNet[1] network by designing the three important influencing factors of the receptive field size, total network step size, and feature filling of the twin network. Apart from this, we add a smoothing matrices and a background suppression matrices to effectively learn the features of the first few frames as much as possible. Fuse multilayer feature elements can learn online about target appearance changes and background suppression, and we train them by using continuous video sequences.
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