Gait planning of quadruped robots plays an important role in achieving less walking, including dynamic and static gait. In this article, a static and dynamic gait control method based on center of gravity stability ma...
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Detection of elliptical features is a challenging and important task in computer vision. In fact, ellipses can describe many objects in real images like manufactured objects, cells, ball, or traffic signs. Furthermore...
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The red color segmentation is widely used in image processing especially for traffic sign detection. The color segmentation under variety of light and weather conditions is a challenging task. These conditions make mo...
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ISBN:
(纸本)9781665411578
The red color segmentation is widely used in image processing especially for traffic sign detection. The color segmentation under variety of light and weather conditions is a challenging task. These conditions make modifications on traffic sign colors. In this paper, we aim to improve the precision of a state-of-the-art method in RGB and HSV spaces. Although the efficiency of this state-of-the-art method, the red segmentation in RGB space has some false positive detections especially at night, and the red segmentation in HSV space detects the black pixels with red pixels as a region of interest. In order to improve the precision of this method, we propose in this paper to combine the two segmentations. We also propose changing the threshold of the red segmentation in HSV to remove non-red pixels and to extract red pixels with low saturation. In addition, we make some modifications on the threshold of the red segmentation in RGB space to improve its performance. The results obtained in experiments show that our method detects more accurately red color of traffic signs under variety of light and weather conditions compared to the state-of-the-art method.
Data-free knowledge distillation aims to learn a compact student network from a pre-trained large teacher network without using the original training data of the teacher network. Existing collection-based and generati...
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Data-free knowledge distillation aims to learn a compact student network from a pre-trained large teacher network without using the original training data of the teacher network. Existing collection-based and generati...
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Gait planning of quadruped robots plays an important role in achieving less walking, including dynamic and static gait. In this article, a static and dynamic gait control method based on center of gravity stability ma...
Gait planning of quadruped robots plays an important role in achieving less walking, including dynamic and static gait. In this article, a static and dynamic gait control method based on center of gravity stability margin is proposed. Firstly, the robot model and kinematics modeling are introduced. Secondly, the robot’s foot static and dynamic gait were planned and the foot trajectory was designed. Finally, two types of gait of the robot were simulated using Vrep simulation software, and the differences in stability and speed between the coordinated gait with speed and stability in the static and dynamic gait of a 12 degree of freedom robot were analyzed, verifying the effectiveness of the gait control method proposed in this paper.
Out-of-distribution (OOD) detection aims to identify the test examples that do not belong to the distribution of training data. The distance-based methods, which identify OOD examples based on their distances from the...
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This paper proposes a principle of fully autonomous ground mobile landing recovery of Unmanned Aerial Vehicles (UAV) for the problems of relatively fixed landing point, passive recovery, poor flexibility, and environm...
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This paper proposes a principle of fully autonomous ground mobile landing recovery of Unmanned Aerial Vehicles (UAV) for the problems of relatively fixed landing point, passive recovery, poor flexibility, and environmental adaptability, which mainly includes localization, landing point tracking, and buffering landing for quadrotor UAV. Firstly, aiming at the problem that it is difficult to accurately obtain the position of a UAV in dynamic mobile landing recovery, a target location method based on Asynchronous Multisensor Information Fusion(AMIF) and servo turntable focus tracking is proposed. Secondly, to achieve fast and high-precision tracking of UAVs, a tracking control strategy of an independently driven landing recovery system and a Stewart six-degree of freedom platform is proposed. Then, to solve the problems of large impact force and center of gravity instability in the landing process of UAV, a stationarity control algorithm based on model prediction and a compliance control algorithm based on adaptive variable impedance are designed to achieve active compliance control while adjusting the position and attitude of the receiving surface in real-time. Finally, a quadrotor unmanned landing and recovery experimental platform is built to verify the feasibility of the ground mobile landing and recovery strategy proposed in this paper and the effectiveness of the control algorithm.
Modeling photovoltaic (PV) modules is fundamental for analyzing their efficiency and performance under different operating conditions. Generally, photovoltaic module modeling is based on a suitable equivalent circuit ...
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Modeling photovoltaic (PV) modules is fundamental for analyzing their efficiency and performance under different operating conditions. Generally, photovoltaic module modeling is based on a suitable equivalent circuit and a set of parameters representing the PV module's properties. Defining these parameters is not a trivial task, as they are usually not included in the technical documentation of the module. Therefore, the need to find suitable values for the model parameters becomes imperative. In this paper, we will test and compare two methods of extracting single-diode model (SDM) parameters: analytical and numerical, under standard test conditions (STC conditions). We examined the performance of six techniques: three analytical and three numerical. Except for Saleem technique, which tends to underestimate the I-V and P-V curves in the MPP zone, the results demonstrated that both approaches (analytical and iterative/numerical) are valid and accurate for predicting the I-V and P-V curves under STC conditions.
Estimating the Ratio of Edge-Users (REU) is an important issue in mobile networks, as it helps the subsequent adjustment of loads in different cells. However, existing approaches usually determine the REU manually, wh...
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Estimating the Ratio of Edge-Users (REU) is an important issue in mobile networks, as it helps the subsequent adjustment of loads in different cells. However, existing approaches usually determine the REU manually, which are experience-dependent and labor-intensive, and thus the estimated REU might be imprecise. Considering the inherited graph structure of mobile networks, in this paper, we utilize a graph-based deep learning method for automatic REU estimation, where the practical cells are deemed as nodes and the load switchings among them constitute edges. Concretely, Graph Attention Network (GAT) is employed as the backbone of our method due to its impressive generalizability in dealing with networked data. Nevertheless, conventional GAT cannot make full use of the information in mobile networks, since it only incorporates node features to infer the pairwise importance and conduct graph convolutions, while the edge features that are actually critical in our problem are disregarded. To accommodate this issue, we propose an Edge-Aware Graph Attention Network (EAGAT), which is able to fuse the node features and edge features for REU estimation. Extensive experimental results on two real-world mobile network datasets demonstrate the superiority of our EAGAT approach to several state-of-the-art methods.
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