In this paper, we focus on low-resolution human detection and propose a partial least squares-canonical correlation analysis （PLS-CCA） for outdoor video surveillance. The analysis relies on heterogeneous features fu...
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In this paper, we focus on low-resolution human detection and propose a partial least squares-canonical correlation analysis （PLS-CCA） for outdoor video surveillance. The analysis relies on heterogeneous features fusion-based human detection method. The proposed method can not only explore the relation between two individual heterogeneous features as much as possible, but also can robustly describe the visual appearance of humans with complementary information. Compared with some other methods, the experimental results show that the proposed method is effective and has a high accuracy, precision, recall rate and area under curve （AUC） value at the same time, and offers a discriminative and stable recognition performance.
Understanding and replicating the locomotion principles offish are fundamental in the development of artificial fishlike robotic systems,termed robotic *** paper has two objectives:(1) to review biological clues on bi...
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Understanding and replicating the locomotion principles offish are fundamental in the development of artificial fishlike robotic systems,termed robotic *** paper has two objectives:(1) to review biological clues on biomechanics and hydrodynamic flow control offish swimming and(2) to summarize design and control methods for efficient and stable swimming in robotic *** review of state-of-the-art research and future-oriented new directions indicates that fish-inspired biology and engineering interact in mutually beneficial *** strong interaction offers an important insight into the design and control of novel fish-inspired robots that addresses the challenge of environmental uncertainty and competing objectives;in addition,it also facilitates refinement of biological knowledge and robotic strategies for effective and efficient swimming.
作者：
Kiddee, PrasarnFang, ZaojunTan, MinInst Automat Chinese Acad Sci IACAS
State Key Lab Management & Control Complex Syst Control Sci & Engn Beijing 100190 Peoples R China IACAS
State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China IACAS
State Key Lab Management & Control Complex Syst Lab Complex Syst & Intelligence Sci Beijing 100190 Peoples R China
Feature detection is an essential and important part in weld seam tracking of automated welding robots. In thick plate seam tracking, a profilometer based on structured light is commonly employed. Features from light ...
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Feature detection is an essential and important part in weld seam tracking of automated welding robots. In thick plate seam tracking, a profilometer based on structured light is commonly employed. Features from light stripe of the structured light will be extracted and used as primary information in visual servoing control. The accuracy, robustness, and computational cost are the main aspects of the feature detection. They directly affect the quality of the tracking. In this paper, cross mark created by cross line structured light (CLSL) is taken into account, and handled as the feature. It can be used as a pinpoint for seam tracking. Firstly, the cross mark is hierarchically estimated by coarse-to-fine strategy. In coarse estimation step, the random sample consensus (RANSAC) algorithm is applied to compute the feature position. Subsequently, the mean shift algorithm is used to estimate the precise feature position in the fine estimation step. Finally, the robustness of the detection is improved by the modified Kalman filter algorithm. The experimental results verify that the feature position estimated by the proposed method is robust. Moreover, the coarse-to-fine strategy can reduce a huge computational cost in the detection. And therefore, the detection method is proper for being used in real-time thick plate seam tracking.
Anthropogenic land-use change is an important driver of global biodiversity loss and threatens public health through biological interactions. Understanding these landscape-ecological effects at local scales will help ...
Anthropogenic land-use change is an important driver of global biodiversity loss and threatens public health through biological interactions. Understanding these landscape-ecological effects at local scales will help achieve the United Nations Sustainable Development Goals by balancing urbanization, biodiversity and the spread of infectious diseases. Here, we address this knowledge gap by analysing a 43-year-long monthly dataset (1980-2022) of synanthropic rodents in Central China during intensive land-use change. We observed a notable increase in the mean patch size, coinciding with a substantial change in rodent community composition and a marked decline in rodent diversity;eight of the nine local rodent species experienced near-extirpation. Our analysis reveals that these irregular species replacements can be attributed to the effect of land consolidation on species competition among rodents, favouring striped field mice, a critical reservoir host of Hantaan virus (HTNV). Consequently, land consolidation has facilitated the proliferation of striped field mice and increased the prevalence of HTNV among them. This study highlights the importance of considering both direct and indirect effects of anthropogenic activities in the management of biodiversity and public health. A 43-year dataset of rodents in the Hu region of China reveals how urbanization-induced changes to land-use configuration affect rodent community composition, including benefitting striped field mice, the primary local hosts of the zoonotic pathogen Hantaan virus.
Optimal feedback design of dynamical systems is a significant topic in automatic control community and information *** for nonlinear systems,optimal control design always leads to coping with the nonlinear Hamilton-Ja...
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Optimal feedback design of dynamical systems is a significant topic in automatic control community and information *** for nonlinear systems,optimal control design always leads to coping with the nonlinear Hamilton-Jacobi-Bellman ***,it is intractable to acquire the analytic solution of the nonlinear Hamilton-JacobiBellman equation for general nonlinear systems.
A decentralized adaptive neural network sliding mode position/force control scheme is proposed for constrained reconfigurable manipulators. Different from the decentralized control strategy in multi-manipulator cooper...
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A decentralized adaptive neural network sliding mode position/force control scheme is proposed for constrained reconfigurable manipulators. Different from the decentralized control strategy in multi-manipulator cooperation, the proposed decentralized position/force control scheme can be applied to series constrained reconfigurable manipulators. By multiplying each row of Jacobian matrix in the dynamics by contact force vector, the converted joint torque is obtained. Furthermore, using desired information of other joints instead of their actual values, the dynamics can be represented as a set of interconnected subsystems by model decomposition technique. An adaptive neural network controller is introduced to approximate the unknown dynamics of subsystem. The interconnection and the whole error term are removed by employing an adaptive sliding mode term. And then, the Lyapunov stability theory guarantees the stability of the closed-loop system. Finally, two reconfigurable manipulators with different configurations are employed to show the effectiveness of the proposed decentralized position/force control scheme.
作者：
Yang, YanwuChinese Acad Sci
State Key Lab Management & Control Complex Syst Beijing 100864 Peoples R China
A spatial information search strategy based on the Bidirectional Neural Associative Memory model produces search results that are sensitive to user preferences.
A spatial information search strategy based on the Bidirectional Neural Associative Memory model produces search results that are sensitive to user preferences.
In this paper, an optimal tracking control scheme is proposed for a class of discrete-time chaotic systems using the approximation-error-based adaptive dynamic programming （ADP） algorithm. Via the system transformat...
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In this paper, an optimal tracking control scheme is proposed for a class of discrete-time chaotic systems using the approximation-error-based adaptive dynamic programming （ADP） algorithm. Via the system transformation, the optimal tracking problem is transformed into an optimal regulation problem, and then the novel optimal tracking control method is proposed. It is shown that for the iterative ADP algorithm with finite approximation error, the iterative performance index functions can converge to a finite neighborhood of the greatest lower bound of all performance index functions under some convergence conditions. Two examples are given to demonstrate the validity of the proposed optimal tracking control scheme for chaotic systems.
Broad learning system(BLS) has been proposed as an alternative method of deep learning. The architecture of BLS is that the input is randomly mapped into series of feature spaces which form the feature nodes, and the ...
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Broad learning system(BLS) has been proposed as an alternative method of deep learning. The architecture of BLS is that the input is randomly mapped into series of feature spaces which form the feature nodes, and the output of the feature nodes are expanded broadly to form the enhancement nodes, and then the output weights of the network can be determined analytically. The most advantage of BLS is that it can be learned incrementally without a retraining process when there comes new input data or neural nodes. It has been proven that BLS can overcome the inadequacies caused by training a large number of parameters in gradient-based deep learning algorithms. In this paper, a novel variant graph regularized broad learning system(GBLS) is proposed. Taking account of the locally invariant property of data, which means the similar images may share similar properties, the manifold learning is incorporated into the objective function of the standard BLS. In GBLS, the output weights are constrained to learn more discriminative information,and the classification ability can be further enhanced. Several experiments are carried out to verify that our proposed GBLS model can outperform the standard BLS. What is more, the GBLS also performs better compared with other state-of-the-art image recognition methods in several image databases.
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