The traditional Pavlov associative memory contains the process of learning and forgetting, which correspond to the reinforcement and extinction in classical conditional reflex respectively. In fact, besides reinforcem...
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The traditional Pavlov associative memory contains the process of learning and forgetting, which correspond to the reinforcement and extinction in classical conditional reflex respectively. In fact, besides reinforcement and extinction, classical conditional reflex also contains generalization and differentiation. In this paper, a memristor-based circuit is designed to implement generalization and differentiation based on Pavlov associative memory. This circuit can respond to one kind of conditional stimulus after the initial learning, then when another similar stimulus is applied to the circuit, the circuit responds similarily, this is the phenomenon of generalization. To make the circuit be sufficiently cognizant of these two similar stimuli and ultimately distinguish them, a special training method, which selectively reinforces one stimulus while inhibits another similar stimulus, is used to train the whole circuit. As a result, when these two similar stimuli are applied to the circuit separately again, it can respond to them differently. This is the phenomenon of differentiation. Finally, the correctness of implementing these functions described above are demonstrated by simulation results in PSPICE. (C) 2020 Elsevier B.V. All rights reserved.
DC microgrid as a microscale power system has drawn growing attention for its various applications, which calls for good power quality, proper power sharing, and fast response property. However, the existing researche...
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DC microgrid as a microscale power system has drawn growing attention for its various applications, which calls for good power quality, proper power sharing, and fast response property. However, the existing researches are not yet able to achieve the purpose in a predefined time. In this article, a new predefined-time secondary controller is proposed for DC microgrid to realize the objectives of both voltage regulation and current sharing among DGs within a predefined time. The controller is designed by employing a sign function and a K-1 function and by defining a new composite error, which does not need to sample the voltage of DC bus or the current of neighbors, but only the local current and voltage. The upper bound of the convergence time of both the voltage and current in the DC microgrid is just related to one parameter which can be predefined and is independent of initial error. The effectiveness of the proposed controller is verified by multiple simulations and experiments built on MATlab/Simulink and a hardware-in-the-loop experimental platform. The conducted simulations and experiments include several typical scenarios together with a comparison, which illustrate the advantages of the proposed controller such as fast convergence rate and small overshoot.
Tissue P systems are distributed parallel computing models inspired by the structure of tissue and the way of communicating substances between two cells or between a cell and the environment. In this work, we consider...
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Tissue P systems are distributed parallel computing models inspired by the structure of tissue and the way of communicating substances between two cells or between a cell and the environment. In this work, we consider a variant of tissue P systems, called tissue P systems with promoters, where the application of rules is regulated by promoters. The computational power of such P systems is investigated. Specifically, it is proved that such P systems using only antiport rules of length 2 or using only symport rules of length 1 are able to compute only finite sets of non-negative integers. However, such P systems with one cell and using antiport rules of length 2 and symport rules of length 1 or only using symport rules of length 2 are Turing universal. Moreover, a uniform solution to the SAT problem is provided by tissue P systems with promoters and cell division using only antiport rules of length 2. (C) 2016 Elsevier B.V. All rights reserved.
Since the manual inspection of analog instruments is inefficient, many computer vision-based automatic reading systems have been proposed recently. However, most of them use fixed cameras that are costly due to a larg...
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Since the manual inspection of analog instruments is inefficient, many computer vision-based automatic reading systems have been proposed recently. However, most of them use fixed cameras that are costly due to a large number of used cameras. Although some other systems adopting the pan-tilt-zoom camera and the movable inspection robot can avoid using many cameras, they have to overcome high computational cost in aligning the camera to the tested instrument. Meanwhile, most existing systems are instrument type-dependent and, hence, cannot handle multiple types of instruments simultaneously. In this article, first, based on an inspection robot, an automatic reading system equipped with a pan-tilt-zoom camera is designed for different types of round-shape analog instruments. Then, a fast camera alignment algorithm based on visual servo is proposed, in which YOLOv3 is applied and improved to locate the instrument and guide the camera to iteratively align with the instrument. Finally, a monocular-vision pointer reconstruction algorithm is proposed to accurately read the instrument. Experimental results demonstrated that our proposed system is fast and reliable in the camera-alignment process and is effective in reading different types of analog instruments during the robot-based inspection.
This study proposes an on-line predictor-corrector reentry guidance algorithm that satisfies path and no-fly zone constraints for hypersonic vehicles with a high lift-to-drag ratio. The proposed guidance algorithm can...
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This study proposes an on-line predictor-corrector reentry guidance algorithm that satisfies path and no-fly zone constraints for hypersonic vehicles with a high lift-to-drag ratio. The proposed guidance algorithm can generate a feasible trajectory at each guidance cycle during the entry flight. In the longitudinal profile, numerical predictor-corrector approaches are used to predict the flight capability from current flight states to expected terminal states and to generate an on-line reference drag acceleration profile. The path constraints on heat rate, aerodynamic load, and dynamic pressure are implemented as a part of the predictor-corrector algorithm. A tracking control law is then designed to track the reference drag acceleration profile. In the lateral profile, a novel guidance algorithm is presented. The velocity azimuth angle error threshold and artificial potential field method are used to reduce heading error and to avoid the no-fly zone. Simulated results for nominal and dispersed cases show that the proposed guidance algorithm not only can avoid the no-fly zone but can also steer a typical entry vehicle along a feasible 3D trajectory that satisfies both terminal and path constraints. (C) 2015 IAA. Published by Elsevier Ltd. All rights reserved.
How to control large complex networks is a great challenge. Recent studies have proved that the whole network can be sufficiently steered by injecting control signals into a minimum set of driver nodes, and the minimu...
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How to control large complex networks is a great challenge. Recent studies have proved that the whole network can be sufficiently steered by injecting control signals into a minimum set of driver nodes, and the minimum numbers of driver nodes for many real networks are high, indicating that it is difficult to control them. For some large natural and technological networks, it is impossible and not feasible to control the full network. For example, in biological networks like large-scale gene regulatory networks it is impossible to control all the genes. This prompts us to explore the question how to choose partial networks that are easy for controlling and important in networked systems. In this work, we propose a method to achieve this goal. By computing the minimum driver nodes densities of the partial networks of Erdos-Renyi (ER) networks, scale-free (SF) networks and 23 real networks, we find that our method performs better than random method that chooses nodes randomly. Moreover, we find that the nodes chosen by our method tend to be the essential elements of the whole systems, via studying the nodes chosen by our method of a real human signaling network and a human protein interaction network and discovering that the chosen nodes from these networks tend to be cancer-associated genes. The implementation of our method shows some interesting connections between the structure and the controllability of networks, improving our understanding of the control principles of complex systems. (C) 2016 Elsevier B.V. All rights reserved.
Person re-identification is a challenging problem that aims at matching persons across multiple non overlapping cameras. Previous works on person re-identification mainly focus on solving the problems caused by pose v...
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Person re-identification is a challenging problem that aims at matching persons across multiple non overlapping cameras. Previous works on person re-identification mainly focus on solving the problems caused by pose variations, backgrounds, and occlusion while ignoring the viewpoint variations. To address the viewpoint problem, we propose a Viewpoint Transform Matching (VTM) model, which reduces the inference of viewpoints by feature-level viewpoint transformation. Overall, we design a framework by using viewpoint specific branches to separately extract the representation of different viewpoints. Specifically, we first select the branch for an input image, according to its viewpoint information. In the branch, we transform the feature to other viewpoints by establishing a mutually transformable connection between the features of different viewpoints. To suppress the interference among viewpoints, we propose a viewpoint transform classifier module to independently train a classifier for each viewpoint. To improve the effectiveness of the transform, we propose a viewpoint transform loss to guarantee the consistency of the original features and the transformed features. Experiments conducted on Market-1501, DukeMTMC-reID and CUHK03 show the effectiveness of the proposed method. (c) 2021 Elsevier B.V. All rights reserved.
In this article, we propose a novel compressed latent distribution representation for 3D hand pose estimation from monocular RGB images to alleviate the channel correspondence problem. The channel correspondence probl...
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In this article, we propose a novel compressed latent distribution representation for 3D hand pose estimation from monocular RGB images to alleviate the channel correspondence problem. The channel correspondence problem occurs when the 2D and depth coordinates are estimated from independent feature maps, which means the 2D and depth channel sequences may not match during the cross-dataset inference. In contrast, we propose a compressed latent distribution representation that the 2D and depth feature maps for each joint are interconnected and inter-constrained more directly, effectively alleviating the channel correspondence problem and improving cross-dataset performance. Moreover, we design an efficient encoder-decoder network that can maintain the resolution of feature maps to enable better hand feature extraction from monocular RGB images. In this work, the overall pipeline contains two branches: one is the 2D hand pose estimation branch based on a latent heatmap representation (LHR);the other is the 3D hand pose estimation branch based on our proposed latent distribution representation (LDR). In this way, the 2D estimation branch serves as guidance for the 3D branch, which simplifies the optimization of the overall network and results in a more rapid convergence during training. The results on several benchmark datasets (including STB, RHD, and the most recently released InterHand2.6M) demonstrate that our proposed method achieves state-of-the-art (SOTA) performance.
Cell signaling governs the basic cellular activities and coordinates the actions in cell. Abnormal regulations in cell signaling processing are responsible for many human diseases, such as diabetes and cancers. With t...
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Cell signaling governs the basic cellular activities and coordinates the actions in cell. Abnormal regulations in cell signaling processing are responsible for many human diseases, such as diabetes and cancers. With the accumulation of massive data related to human cell signaling, it is feasible to obtain a human signaling network. Some studies have shown that interesting biological phenomenon and drug-targets could be discovered by applying structural controllability analysis to biological networks. In this work, we apply structural controllability to a human signaling network and detect driver nodes, providing a systematic analysis of the role of different proteins in controlling the human signaling network. We find that the proteins in the upstream of the signaling information flow and the low in-degree proteins play a crucial role in controlling the human signaling network. Interestingly, inputting different control signals on the regulators of the cancer-associated genes could cost less than controlling the cancer-associated genes directly in order to control the whole human signaling network in the sense that less drive nodes are needed. This research provides a fresh perspective for controlling the human cell signaling system.
Chaotic systems would degrade owing to finite computing precisions, and such degradation often seriously affects the performance of digital chaos-based applications. In this paper, a chaotification method is proposed ...
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Chaotic systems would degrade owing to finite computing precisions, and such degradation often seriously affects the performance of digital chaos-based applications. In this paper, a chaotification method is proposed to solve the dynamical degradation of digital chaotic systems based on a hybrid structure, where a continuous chaotic system is applied to control the digital chaotic system, and a unidirectional coupling controller that combines a linear external state control with a modular function is designed. Moreover, we proof rigorously that a class of digital chaotic systems can be driven to be chaotic in the sense that the system is sensitive to initial conditions. Different from the existing remedies, this method can recover the dynamical properties of system, and even make some properties better than those of the original chaotic system. Thus, this new approach can be applied to the fields of chaotic cryptography and secure communication. (C) 2013 Elsevier B.V. All rights reserved.
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