The crystal structure shear and the electronic structure modulation through ion doping are both kinetically relevant to develop high-performance electrode materials for electrochemical energy storage, yet they remain ...
详细信息
In order to meet the operational needs in some narrow environments, the hyper-redundant snake-shaped manipulator has received extensive attention and has been widely studied due to its compact shape and multiple degre...
In order to meet the operational needs in some narrow environments, the hyper-redundant snake-shaped manipulator has received extensive attention and has been widely studied due to its compact shape and multiple degrees of freedom. At present, the research of hyper-redundant manipulator mainly focuses on the straight push platform. Considering the limitation of working space, this paper designed a hyper-redundant manipulator delivered by a curled turntable. According to the characteristics of turntable delivery, a tip-following algorithm based on curve fitting and a tip-following strategy based on terminal posture record are proposed. Finally, with the algorithm applied to the physical robot we made, we successfully realized the tip-following motion control of the hyper-redundant manipulator.
Recently, convolutional neural network has been pervasively adopted in visual object tracking for its potential in discriminating the target from the surrounding background. Most of the visual object trackers extract ...
详细信息
ISBN:
(纸本)9781665478977
Recently, convolutional neural network has been pervasively adopted in visual object tracking for its potential in discriminating the target from the surrounding background. Most of the visual object trackers extract deep features from a specific layer, generally from the last convolutional layer. However, these trackers are less effective, especially when the target undergoes drastic appearance variations caused by the presence of different challenging situations, such as occlusion, illumination change, background clutter and so on. In this research paper, a novel tracking algorithm is developed by introducing an elastic net constraint and a contextual information into the convolutional network to successfully track the desired target throughout a video sequence. Hierarchical features are extracted from the shallow and the deep convolutional layers to further improve the tracking accuracy and robustness. As the deep convolutional layers capture important semantic information, they are more robust to the target appearance variations. As for the shallow convolutional layers, they encode significant spatial details, which are more accurate to precisely localize the desired target. Moreover, Peak–Strength Context–Aware correlation filters are embedded to each convolutional layer output that produce multi–level convolutional response maps to collaboratively identify the estimated position of the target in a coarse–to–fine manner. Quantitative and qualitative experiments are performed on the widely used benchmark, the OTB–2015 dataset that shows impressive results compared to the state–of–the–art trackers.
As a rehabilitation aid,lower-limb rehabilitation robots help patients recover their walking *** paper presets a review of such a rehabilitation ***,we classify commercially available common types of lower-limb rehabi...
详细信息
As a rehabilitation aid,lower-limb rehabilitation robots help patients recover their walking *** paper presets a review of such a rehabilitation ***,we classify commercially available common types of lower-limb rehabilitation ***,we briefly describe the structure and characteristics of those robots,Then,we present the control methods used in those ***,we explain future research directions.
The current paper is concerned with the stability analysis of delayed neural networks. In the case that the delay derivative is restricted with an upper bound only, the augmented LKFs often contain high-degree terms o...
详细信息
The current paper is concerned with the stability analysis of delayed neural networks. In the case that the delay derivative is restricted with an upper bound only, the augmented LKFs often contain high-degree terms of the time-varying delay, resulting in the non-convex derivatives of LKFs, which can be solved by introducing extra delay-multiplied state variables to transform the non-convex delay-dependent terms into convex ones. To make fuller use of the delay-multiplied state variables and the delay-derivative-dependent information, these delay-multiplied state variables are introduced into an LKF and the integral inequality through the proper augmentation in this paper. Meanwhile, some free-matrix-based zero equations are introduced into this delay-dependent inequality to provide more freedom. By applying the augmented LKF and the novel integral inequality, a delay-dependent stability criterion of delayed neural networks with less conservatism is established, whose advantages are verified by three examples.
A new type of weak signal detection system that combines the memristor and Van der pol-Duffing chaotic system has been proposed in this paper, and the dynamic characteristics of the system are studied. It is observed ...
详细信息
With the increase of problem dimensions,most solutions of existing many-objective optimization algorithms are ***,the selection of individuals and the retention of elite individuals are *** algorithms cannot provide s...
详细信息
With the increase of problem dimensions,most solutions of existing many-objective optimization algorithms are ***,the selection of individuals and the retention of elite individuals are *** algorithms cannot provide sufficient solution precision and guarantee the diversity and convergence of solution sets when solving practical many-objective industrial ***,this work proposes an improved many-objective pigeon-inspired optimization(ImMAPIO)algorithm with multiple selection strategies to solve many-objective optimization *** selection strategies integrating hypervolume,knee point,and vector angles are utilized to increase selection pressure to the true Pareto ***,the accuracy,convergence,and diversity of solutions are *** is applied to the DTLZ and WFG test functions with four to fifteen objectives and compared against NSGA-III,GrEA,MOEA/D,RVEA,and many-objective Pigeon-inspired optimization *** results indicate the superiority of ImMAPIO on these test functions.
In evolutionary algorithms, how to effectively select interactive solutions for generating offspring is a challenging problem. Though many operators are proposed, most of them select interactive solutions (parents) ra...
详细信息
In evolutionary algorithms, how to effectively select interactive solutions for generating offspring is a challenging problem. Though many operators are proposed, most of them select interactive solutions (parents) randomly, having no specificity for the features of landscapes in various problems. To address this issue, this paper proposes a reinforcement-learning-based evolutionary algorithm to select solutions within the approximated basin of attraction. In the algorithm, the solution space is partitioned by the k-dimensional tree, and features of subspaces are approximated with respect to two aspects: objective values and uncertainties. Accordingly, two reinforcement learning (RL) systems are constructed to determine where to search: the objective-based RL exploits basins of attraction (clustered subspaces) and the uncertainty-based RL explores subspaces that have been searched comparatively less. Experiments are conducted on widely used benchmark functions, demonstrating that the algorithm outperforms three other popular multimodal optimization algorithms.
Modeling dynamic multi-objective optimization problems (DMOPs) has been one of the most challenging tasks in the field of dynamic evolutionary optimization. Based on the analysis of the existing DMOPs, several feature...
详细信息
Modeling dynamic multi-objective optimization problems (DMOPs) has been one of the most challenging tasks in the field of dynamic evolutionary optimization. Based on the analysis of the existing DMOPs, several features widely existed in real-world applications are not taken into account: different objectives may have different function models and variables to be optimized; and the number of conflicting variables should be independent from the number of objectives; the time-linkage property is not considered. In order to overcome the above issues, a novel framework for constructing DMOPs is proposed, where all objectives can be designed independently, and the number of the conflicting variables can be tuned by users. Moreover, it is easy to add new dynamic features to this framework. Several classical dynamic multi-objective optimization algorithms are tested on four scenarios, results show that these characteristics are challenging for the existing algorithms.
In this paper, we discuss the adaptive controller design for a class of fractional-order（FO） multi-agent systems（MAS）.All the agents are described by FO pure-feedback nonlinear systems. This is more general than t...
详细信息
In this paper, we discuss the adaptive controller design for a class of fractional-order（FO） multi-agent systems（MAS）.All the agents are described by FO pure-feedback nonlinear systems. This is more general than the existing works, which only considered strict-feedback or integer-order nonlinear systems. Meanwhile, asynchronous switching are considered in FO MAS,meaning the agents have heterogeneous switching dynamics. A new neural network based adaptive controller is proposed for the considered FO MAS, which can make the consensus tracking error converge to a small neighborhood of origin. Simulation verifies the results obtained.
暂无评论