Natural videos provide rich visual contents for self-supervised learning. Yet most existing approaches for learning spatio-temporal representations rely on manually trimmed videos, leading to limited diversity in visu...
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In this paper, we address the consensus protocol design problem of the networked harmonic oscillators interacting through a directed graph with peer pressure from the perspective of the output information, in which th...
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In this paper, we address the consensus protocol design problem of the networked harmonic oscillators interacting through a directed graph with peer pressure from the perspective of the output information, in which the peer pressure is in the expression of the variant d-path Laplacian. For networked harmonic oscillators without time delay, sufficient conditions in terms of coupling strength are given, while for net-worked harmonic oscillators with time delay, sufficient conditions in the term of coupling strength and the upper bound on the time delay are proposed. Finally, simulation examples are provided to verify the theoretical results. (C) 2020 Elsevier B.V. All rights reserved.
The efficacy of urban rail transit in reducing the impact of road congestion is the subject of debate in policy and academic communities. Despite huge challenges arising from multifaceted factors such as data limitati...
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The efficacy of urban rail transit in reducing the impact of road congestion is the subject of debate in policy and academic communities. Despite huge challenges arising from multifaceted factors such as data limitation, the recently released annual average congestion data enables us to empirically analyze the effect of urban rail transits in terms of their contribution to the average accessibility under road congestion. This paper presents a framework to evaluate the accessibility improvement benefit of urban rail transit under road congestion from a mean-field perspective. A cross-regional analysis in 43 Chinese cities demonstrates that the rail transit networks enhance the potential accessibility and contour accessibility by up to 5.07% and 12.09%, respectively. Besides, the accessibility improvement benefit largely depends on the road congestion level, rail network size, and layout of rail network. Furthermore, simulations on randomly generated rail transit networks indicate that the population coverage and proximity to roads are two structural determinants on the performance of rail transit. The proposed framework, together with the empirical findings, provides insights for city authorities to plan urban rail networks.
In this paper, the input-to-state stability (ISS) problem of switched linear system with unstabilizable modes is investigated under denial-of-service (DoS) attacks and external disturbance. In contrast to earlier resu...
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In this paper, the input-to-state stability (ISS) problem of switched linear system with unstabilizable modes is investigated under denial-of-service (DoS) attacks and external disturbance. In contrast to earlier results which consider only stabilizable modes and usually impose certain constraints on the switching behavior and the DoS attacks, the switched system with unstabilizable modes is studied and a flexible description is proposed to specify the unstabilizable modes. Besides, a more general situation is considered that it allows switching during the active period of DoS attack and the attacks can take place arbitrarily independent of switching. Thus, it leads to a better description of switched system under DoS attacks and a more general result. Moreover, under the sampling scheme and DoS attacks, the asynchronous problem arises. Despite these added complications, a sufficient condition concerning DoS attacks frequency and duration is derived to ensure the ISS of the system under DoS attacks, and the result reveals the trade-off between the average time proportion of unstabilizable modes and the resilience of the system against DoS attacks. Finally, a numerical example and a comparative study are given to show the effectiveness of the proposed method. (c) 2022 Elsevier Ltd. All rights reserved.
The resilient consensus of multi-agent systems with trusted agents under switching topologies is addressed in this brief. A novel assumption on switching topologies is proposed, which is different from the existing wo...
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The resilient consensus of multi-agent systems with trusted agents under switching topologies is addressed in this brief. A novel assumption on switching topologies is proposed, which is different from the existing works dependent on the classical graph robustness condition. Correspondingly, the trusted-region-based sliding-window weighted (TSW) algorithm is proposed to filter the received sampled data. It is successfully shown that the resilient consensus can be guaranteed under the assumption on switching topologies and TSW algorithm. Finally, numerical examples are conducted to validate the results.
Electric vehicles are not widely adopted without proper charging infrastructure, despite their environmental benefits and growing popularity in transportation. This paper focuses on the location problem of charging in...
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Electric vehicles are not widely adopted without proper charging infrastructure, despite their environmental benefits and growing popularity in transportation. This paper focuses on the location problem of charging infrastructure to achieve a more optimized charging facility layout. The charging demands of electric vehicles can be divided into two categories. The first category is generated at network points such as shopping malls, office buildings, parking lots, and residential areas. The second category is generated along the flow of network paths, such as on the highway and on the way to and from work. The goal of this problem is to maximize both categories of charging demands using a nonlinear integer programming model. We introduce the spatial intersection model to obtain the data on path demand. The spatial intersection model is introduced to obtain data on path demand. In addition, future demand is taken into account in the optimization through data forecasting. Then, the greedy algorithm is designed to solve the optimization model. The effectiveness is proved by a lot of random experiments. Finally, the effects of parameters are analyzed by a case study. The location decision of charging stations for both demands is more reasonable than only one type of demand consideration. The proposed model ensures the coverage and appropriate extension of the charging network.
Chaos-based security applications are facing great challenges with various data analysis and prediction techniques. Focusing on the security risks in chaotic applications, this paper proposes a structure-varying delay...
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Chaos-based security applications are facing great challenges with various data analysis and prediction techniques. Focusing on the security risks in chaotic applications, this paper proposes a structure-varying delay -coupled chaotic model (SVDCCM), which has attack immunity and is proven to be chaotic mathematically. To choose suitable coupling structures for variation, the effects of different structures on chaotic performance are investigated in detail. We recommend loop structures for better chaotic behaviors and the structures in which the same coupling terms exist should be avoided to produce synchronous behaviors. Moreover, the general changing principles including random and fast varying are proposed to be the guidance on designing structure -varying systems, and a simple state-driven changing mechanism is given as an example. Experiment results show that SVDCCM has complex chaotic behaviors, good statistical properties, and high unpredictability. Its security when facing various attack challenges is also demonstrated, such as time-frequency attacks, phase space reconstruction attacks, change-point detection, etc. To illustrate its feasibility in the practical security application, a pseudo-random number generator (PRNG) based on SVDCCM is designed and implemented on the hardware platform. Performance tests indicate that the proposed PRNG can produce binary sequences with high reliability of randomness and unpredictability, which can be used in cryptosystems and other potential applications.
The application of unmanned vehicles in industrial cargo transportation is becoming increasingly widespread, particularly playing a significant role in transporting heavy-duty goods. However, when performing such task...
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ISBN:
(数字)9798331506056
ISBN:
(纸本)9798331506063
The application of unmanned vehicles in industrial cargo transportation is becoming increasingly widespread, particularly playing a significant role in transporting heavy-duty goods. However, when performing such tasks, whether in single-vehicle transportation or multi-vehicle collaborative transportation, the occurrence of wheel failures can potentially lead to serious safety accidents. Therefore, this paper proposes a fault-tolerant control scheme, establishing a kinematic model considering disturbances and failures, and adopts model predictive control for trajectory tracking. Additionally, an extended state observer is introduced to observe system states and counteract disturbances. To address potential failure issues, a controller reconstruction strategy is proposed. Finally, the effectiveness and superiority of the controller is validated through numerical simulation and comparative experiment.
Developing accurate algorithms for agricultural pattern recognition from aerial imagery has become increasingly important due to the prevalence of unmanned aerial vehicles (UAVs). This letter introduces a deep encoder...
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Developing accurate algorithms for agricultural pattern recognition from aerial imagery has become increasingly important due to the prevalence of unmanned aerial vehicles (UAVs). This letter introduces a deep encoderx2013;decoder network for semantic land cover segmentation, where the goal is to classify six anomaly categories from multispectral aerial imagery. Since aerial imagery exhibits specific characteristics and visual challenges in this imaging domain, existing semantic segmentation models are not plug-and-play. Starting from a state-of-the-art segmentation model, we present a step-by-step analysis of key challenges and also reveal our observations in addressing these challenges. In particular, we investigate on how to exploit data prior knowledge, how to deal with sample imbalance, and how to encode global semantic and contextual information to improve segmentation. Experiments on a recent large-scale aerial land cover data set demonstrate that our method achieves compelling performance against other state-of-the-art approaches. Our results and insights can provide references for practitioners working in this field when dealing with similar segmentation problems.
The development of renewable energy sources makes an important contribution to low-carbon sustainable ***, renewable energy such as wind and photovoltaic energy are highly random and intermittent, which brings great c...
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The development of renewable energy sources makes an important contribution to low-carbon sustainable ***, renewable energy such as wind and photovoltaic energy are highly random and intermittent, which brings great challenges to the operation and control of power networks. The traditional method is to build a large number of peak-shaving power stations and arrange a large number of energy storage devices to stabilize the fluctuations of renewable energy, and the cost may be prohibitive to be sustainable. In the future power network, advanced sensors, actuators, and communication equipment will be deployed on various systems such as generators, substations, transformers, distributed energy resources, air conditioners, and electric vehicles. A challengeable issue arises on how to manage these hundreds of millions of active endpoints. %power lines, Recently, distributed learning, optimization, and control methods for networked systems have received growing attention. Distributed learning, optimization, and control methods for managing hundreds of millions of active endpoints in the future power network are expected to enable a stable and economic operation of the power network with a high proportion of new energy, as illustrated in Fig. \ref{fig:illustration}.This issue contains nine research articles focusing on distributed control and optimization for power systems and smart power electronics, multi-agent reinforcement learning in power systems, advanced energy management and economic dispatch, *** paper by \href{https://***/articles/10.3389/fenrg.2022.1097319}{Ma et al.} proposes a multi-time-scale control method based on a deep Q network-deep deterministic policy gradient (DQN-DDPG) algorithm to maintain optimal voltage in distribution *** optimizes voltage regulation for longer times using the DQN algorithm and shorter times using the DDPG ***, based on the Markov decision process transformation, the design s
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