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The article investigates the issue of fixed-time control with adaptive output feedback for a twin-roll inclined casting system (TRICS) with disturbance. First, by using the mean value theorem, the nonaffine functions are decoupled to simplify the system. Second, radial basis function neural networks (RBFNNs) are introduced to approximate an unknown term, and a nonlinear neural state observer is created to handle the effects of unmeasured states. Then, the backstepping design framework is combined with prescribed performance and command filtering techniques to demonstrate that the scheme proposed in this article guarantees system performance within a fixed-time. The control design parameters determine the upper bound of settling time, regardless of the initial state of the system. Meanwhile, it ensures that all signals in the closed-loop system (CLS) remain bounded, and it can also maintain the tracking error within a predefined range within a fixed time. Finally, simulation results assert the effectiveness of the method.

The Multi-receiver Encryption (MRE) scheme can meet the secure data transmission requirements in multicast and broadcast scenarios. To meet compliance, critical information infrastructure in China should be protected with Chinese national commercial cryptographic algorithms. Designing an MRE scheme based on Elliptic Curve Cryptography (ECC) is one of the current design methods with better flexibility and performance. However, the research on MRE schemes based on SM2 elliptic curve public-key cryptography is still in a blank state. This paper proposes a Certificateless SM2-based Multi-receiver Encryption (CL-SM2-MRE) scheme. We prove the security of the CL-SM2-MRE scheme under the Random Oracle Model (ROM) and analyze the performance.

Generalized zero-shot learning (GZSL) aims to solve the category recognition tasks for unseen categories under the setting that training samples only contain seen classes while unseen classes are not available. This research is vital as there are always existing new categories and large amounts of unlabeled data in realistic scenarios. Previous work for GZSL usually maps the visual information of the visible classes and the semantic description of the invisible classes into the identical embedding space to bridge the gap between the disjointed visible and invisible classes, while ignoring the intrinsic features of visual images, which are sufficiently discriminative to classify themselves. To better use discriminative information from visual classes for GZSL, we propose the n-CADA-VAE. In our approach, we map the visual feature of seen classes to a high-dimensional distribution while mapping the semantic description of unseen classes to a low-dimensional distribution under the same latent embedding space, thus projecting information of different modalities to corresponding space positions more accurately. We conducted extensive experiments on four benchmark datasets (CUB, SUN, AWA1, and AWA2). The results show our model’s superior performance in generalized zero-shot as well as few-shot learning.

Abstract This paper considers the problem of adaptive fault estimation and fault‐tolerant tracking control for a class of uncertain nonlinear systems with model uncertainty and process fault. By utilising the approximation abilities of fuzzy logic systems, a novel fault estimation fuzzy observer is designed to estimate the unmeasured states, nonlinear functions and process fault. Based on backstepping technologies, a adaptive fuzzy fault‐tolerant controller with the adaptive updated laws is established, which can not only ensure the prescribed tracking performances, but also effectively compensate the process fault. It is proved that all the closed‐loop signals of systems are uniformly ultimately bounded and the tracking errors can constrain to the prescribed bounds. Finally, simulation results can verify the efficiency of the proposed approach.

Media reports and the number of rumors in the media have an important impact on the spread of rumors. Currently, studies that consider these two factors comprehensively on rumor propagation are uncommon. It is better to consider both effects comprehensively than to consider one of them alone. In this paper, we established a new propagation model, which regards the number of rumors in the media as a subclass that changes with time, and then comprehensively considered the effect of these two factors on the process of rumor propagation. We proved the existence and stability of equilibrium points of the model. Then, we selected two reasonable control variables: science popularization intensity and punishment intensity. We find that the control effects are optimal when these two control variables take the maximum value. Theoretical analysis and numerical simulation results showed that positive media publicity can reduce the spread of rumors, but cannot stop the spread of rumors. And under the parameters given in this paper, the optimal control parameter values satisfying the constraints can be calculated through quantitative analysis. This method provided a new idea for studying the optimal control of rumor propagation.

Data sharing through the cloud is flourishing with the development of cloud computing technology. The new wave of technology will also give rise to new security challenges, particularly the data confidentiality in cloud-based sharing applications. Searchable encryption is considered as one of the most promising solutions for balancing data confidentiality and usability. However, most existing searchable encryption schemes cannot simultaneously satisfy requirements for both high search efficiency and strong security due to lack of some must-have properties, such as parallel search and forward security. To address this problem, we propose a variant searchable encryption with parallelism and forward privacy, namely the parallel and forward private searchable public-key encryption (PFP-SPE). PFP-SPE scheme achieves both the parallelism and forward privacy at the expense of slightly higher storage costs. PFP-SPE has similar search efficiency with that of some searchable symmetric encryption schemes but no key distribution problem. The security analysis and the performance evaluation on a real-world dataset demonstrate that the proposed scheme is suitable for practical application.

Optimizing traffic signal timing can effectively alleviate urban traffic congestion. However, most of the existing signal timing methods struggle to deal with conflicting traffic flows in heavy traffic; therefore, more effective methods are urgently required. In this paper, we propose a flexible signal timing method that combines all-red control and adaptive timing (FMAA) to deal with conflicting traffic flows at an isolated intersection. First, we consider a Vehicle-to-Infrastructure (V2I) communication-based vehicular network environment, in which fine-grained traffic information can be collected by Road Side Units (RSUs) and uploaded to a cloud server for designing signal timing methods. Second, the congestion degree of Conflict Area (CA) is defined and utilized to trigger all-red control in congested cases. Third, the tolerance degree of the Waiting Area (WA) is defined and utilized to perform adaptive timing in other cases. Finally, simulations were conducted using SUMO, and the proposed FMAA method performed better than the comparative methods in terms of average speed, waiting time, and congested vehicles, thus improving traffic efficiency at an isolated intersection with conflicting traffic flows.

In this paper, we propose a novel ant colony optimization algorithm based on improved brainstorm optimization (IBSO-ACO) to solve the vehicle routing problem with soft time windows. Compared with the traditional ant colony algorithm, the proposed IBSO-ACO can better address the local optimum problem, since we have carefully designed an improved brainstorming optimization algorithm to update the solutions obtained by the ant colony algorithm, which enhance the solution diversity and the global search ability. Furthermore, we use the classification method to accelerate the convergence of the proposed algorithm. The extensive experimental results have confirmed that the proposed IBSO-ACO algorithm can achieve a lower routing cost at a high convergence rate than the traditional ant colony algorithm and the simulated annealing ant colony algorithm.

Recent years have witnessed that the new mobility Intelligent Transportation System is booming, especially the development of Vehicular Ad Hoc Networks (VANETs). It brings convenience and a good experience for drivers. Unfortunately, VANETs are suffering from potential security and privacy issues due to the inherent openness of VANETs. In the past few years, to address security and privacy-preserving problems, many identity-based privacy-preserving authentication schemes have been proposed by researchers. However, we found that these schemes fail to meet the requirements of user privacy protection and are vulnerable to attacks or have high computational complexity. Hence, we focus on enhancing privacy-preserving via authentication and achieving better performance. In this paper, first, we describe the vulnerabilities of the previous scheme. Furthermore, to enhance privacy protection and achieve better performance, we propose an efficient privacy-preserving mutual authentication protocol for secure V2V communication in VANETs. Through security analysis and comparison, we formally demonstrate that our scheme can accomplish security goals under dynamic topographical scenario compared with the previous scheme. Finally, the efficiency of the scheme is showed by performance evaluation. The results of our proposed scheme are computationally efficient compared with the previously proposed privacy-preserving authentication scheme.

The Internet of Things is one of the new emerging application domains that require delay tolerant network (DTN) support, where an end-to-end path between the source and the destination may not always exist. Due to the intermittent connectivity of DTN, the design of an efficient routing algorithm is the main challenge. In this paper, we first define a metric called message handling capacity to determine the ability of a node to forward messages. Then, we introduce a concept called connection strength to reflect the connection time between nodes and then integrate the concept into delivery predictability used by Prophet to determine the chance of a node completely delivering a message to the destination. Subsequently, we present a metric called quality of node (QoN), which is calculated by combining the relative weights of the message handling capacity and the improved delivery predictability. Finally, we present an adaptive spray and wait routing algorithm based on QoN (QoN-ASW). The QoN-ASW adaptively allocates the number of message copies between the encountered nodes according to the proportion of quality of node in the spray phase, which avoids the blindness of replica distribution. In addition, a forwarding scheme is implemented in the wait phase, which takes full advantage of encounter opportunities. In the simulation, we demonstrate the efficiency of integrating the connection strength into delivery predictability and compare the QoN-ASW with four existing DTN routing algorithms from four aspects. The simulation results show that the QoN-ASW can significantly improve the delivery rate and reduce the average delay while achieving a relatively low overhead.

The stable and fair data transmission of vehicular networks can improve transport efficiency and reduce traffic accident. It is challenging to ensure the stability and fairness of data transmission in dynamic vehicular networks. However, existing works based on opportunistic scheduling cannot support reliable transmission since stability and fairness are difficult to be guaranteed at the same time. In this paper, we propose a stable scheduling scheme for dynamic vehicular networks based on fair allocation of available channel resources. We formulate the problem of stability and fairness as network utility maximization, and propose an algorithm, called SF-NUM, to solve it. SF-NUM uses the stability scheduling method based on back pressure vector for the first time in vehicular networks. In order to achieve the fairness distribution of channel resources of the vehicular networks in the distributed computing way, multiple factors are considered in the SF-NUM algorithm. The experimental results show that the proposed algorithm outperforms other algorithms.

Wearable devices, e.g., smart glass and watch, could achieve fine-grained human-centric sensing nowadays, but still lack sufficient capacities to execute sophisticated data processing algorithms. This paper presents Outlet to explore the feasibility to leverage the available computing resources from user's ambience, e.g., from nearby smart phones, tablets, computers, Wi-Fi APs, and so on, to form a mobile computing edge. We can then outsource wearable computing tasks to this ambient edge to locally digest the sensory data for prompt user feedback and reaction, as it is perfectly located in user's vicinity. The Outlet design avoids the traditional offloading to the remote clouds, whose performance may highly depend on the high-speed Internet connectivity and availability. To enable the Outlet design, we identify three major challenges: context dependence, scattered resources, and resource dynamics, and propose a series of key techniques to address each of them. To examine Outlet's performance, we conduct extensive trace-driven evaluations and the results indicate promising performance achieved by Outlet, e.g., mostly within 97.6% to 99.5% closeness of the optimal performance and always outperforming the baseline approach under various system settings.

Due to the real-time requirement of message in vehicle ad hoc networks, it is a challenge to design an authentication for vehicle ad hoc networks to achieve security, efficiency, and conditional privacy-preserving. To address the challenge, many conditional privacy-preserving authentication schemes using bilinear pairing or ideal tamper-proof device have been proposed for vehicle ad hoc networks in recent years. However, the bilinear pairing operation is one of the most complex cryptographic operations and the assumption of tamper-proof device is very strong. In this article, an efficient location-based conditional privacy-preserving authentication scheme without the bilinear pairing and tamper-proof device is proposed. Compared with the most recently proposed authentication schemes, the proposed scheme markedly decreases the computation costs of message signing and message verification phase, while satisfies all security requirements of vehicle ad hoc networks and provides conditional privacy-preserving.