In this perspective article, we first recall the historic background of human-cyber-physical systems (HCPSs), and then introduce and clarify important concepts. We discuss the key challenges in establishing the scientific foundation from a system engineering point of view, including (1) complex heterogeneity, (2) lack of appropriate abstractions, (3) dynamic black-box integration of heterogeneous systems, (4) complex requirements for functionalities, performance, and quality of services, and (5) design, implementation, and maintenance of HCPS to meet requirements. Then we propose four research directions to tackle the challenges, including (1) abstractions and computational theory of HCPS, (2) theories and methods of HCPS architecture modelling, (3) specification and verification of model properties, and (4) software-defined HCPS. The article also serves as the editorial of this special section on cyber-physical systems and summarises the four articles included in this special section.
Cyber-physical systems (CPSs) are distributed assemblages of computing, communicating, and physical components that sense their environment, algorithmically assess the incoming information, and affect their physical environment. Thus, they share a common structure with other complex adaptive systems, and therefore share both the possible benefits and the probable harmful effects of emergent phenomena. Emergence is an often unexpected pattern that arises from the interactions among the individual system components and the environment. In this paper we focus on three major problems concerning emergence in the context of CPSs: how to successfully exploit emergence, how to avoid its detrimental effects in a single CPS, and how to avoid harmful emergence that arises due to unexpected interaction among several independently developed CPSs that are operating in the same environment. We review the state of the research with regard to these problems and outline several approaches that could be used to address them.
Cyber-physical systems (CPSs) have emerged as a potential enabling technology to handle the challenges in social and economic sustainable development. Since it was proposed in 2006, intensive research has been conducted, showing that the construction of a CPS is a hard and complex engineering process due to the nature of integrating a large number of heterogeneous subsystems. Among other approaches to dealing with the complex design issues, model-driven design of CPSs has shown its advantages. In this review paper, we present a survey of research on model-driven development of CPSs. We are concerned mainly with the widely used methods, techniques, and tools, and discuss how these are applied to CPSs. We also present comparative analyses on the surveyed techniques and tools from various perspectives, including their modeling languages, functionalities, and the challenges which they address in CPS design. With our understanding of the surveyed methods, we believe that model-driven approaches are an inevitable choice in building CPSs and further research effort is needed in the development of model-driven theories, techniques, and tools. We also argue that a unified modeling platform is needed. Such a platform would benefit research in the academic community and practical development in industry, and improve the collaboration between these two communities.
Robotic swarms are usually designed in a bottom-up way, which can make robotic swarms vulnerable to environmental impact. It is particularly true for the widely used control mode of robotic swarms, where it is often the case that neither the correctness of the swarming tasks at the macro level nor the safety of the interaction among agents at the micro level can be guaranteed. To ensure that the behaviors are safe at runtime, it is necessary to take into account the property guard approaches for robotic swarms in uncertain environments. Runtime enforcement is an approach which can guarantee the given properties in system execution and has no scalability issue. Although some runtime enforcement methods have been studied and applied in different domains, they cannot effectively solve the problem of property enforcement on robotic swarm tasks at present. In this paper, an enforcement method is proposed on swarms which should satisfy multi-level properties in uncertain environments. We introduce a macromicro property enforcing framework with the notion of agent shields and a discrete-time enforcing mechanism called D-time enforcing. To realize this method, a domain specification language and the corresponding enforcer synthesis algorithms are developed. We then apply the approach to enforce the properties of the simulated robotic swarm in the robotflocksim platform. We evaluate and show the effectiveness of the method with experiments on specific unmanned aerial vehicle swarm tasks.
Cyber-physical systems (CPSs) are becoming increasingly important in safety-critical systems. Particular risk analysis (PRA) is an essential step in the safety assessment process to guarantee the quality of a system in the early phase of system development. Human factors like the physical environment are the most important part of particular risk assessment. Therefore, it is necessary to analyze the safety of the system considering human factor and physical factor. In this paper, we propose a new particular risk model (PRM) to improve the modeling ability of the Architecture Analysis and Design Language (AADL). An architecture-based PRA method is presented to support safety assessment for the AADL model of a cyber-physical system. To simulate the PRM with the proposed PRA method, model transformation from PRM to a deterministic and stochastic Petri net model is implemented. Finally, a case study on the power grid system of CPS is modeled and analyzed using the proposed method.
Crowd counting has been applied to a variety of applications such as video surveillance, traffic monitoring, assembly control, and other public safety applications. Context information, such as perspective distortion and background interference, is a crucial factor in achieving high performance for crowd counting. While traditional methods focus merely on solving one specific factor, we aggregate sufficient context information into the crowd counting network to tackle these problems simultaneously in this study. We build a fully convolutional network with two tasks, i.e., main density map estimation and auxiliary semantic segmentation. The main task is to extract the multi-scale and spatial context information to learn the density map. The auxiliary semantic segmentation task gives a comprehensive view of the background and foreground information, and the extracted information is finally incorporated into the main task by late fusion. We demonstrate that our network has better accuracy of estimation and higher robustness on three challenging datasets compared with state-of-the-art methods.
Much recent progress in monaural speech separation (MSS) has been achieved through a series of deep learning architectures based on autoencoders, which use an encoder to condense the input signal into compressed features and then feed these features into a decoder to construct a specific audio source of interest. However, these approaches can neither learn generative factors of the original input for MSS nor construct each audio source in mixed speech. In this study, we propose a novel weighted-factor autoencoder (WFAE) model for MSS, which introduces a regularization loss in the objective function to isolate one source without containing other sources. By incorporating a latent attention mechanism and a supervised source constructor in the separation layer, WFAE can learn source-specific generative factors and a set of discriminative features for each source, leading to MSS performance improvement. Experiments on benchmark datasets show that our approach outperforms the existing methods. In terms of three important metrics, WFAE has great success on a relatively challenging MSS case, i.e., speaker-independent MSS.
In this paper, we investigate the secrecy outage performance in simultaneous wireless information and power transfer (SWIPT) systems taking artificial noise assistance into account. Multiple antennas in the source and a single antenna in both the legitimate receiver and the eavesdropper are assumed. Specifically, the transmitted signal at the source is composed of two parts, where the first part is the information symbols and the other is the noise for the eavesdropper. To avoid making noise in the legitimate receiver, these two parts in the transmitted signals are modulated into two orthogonal dimensions according to the instantaneous channel state between the source and the legitimate receiver. We derive an approximate closed-form expression for the secrecy outage probability (SOP) by adopting the Gauss-Laguerre quadrature (GLQ) method, where the gap between the exact SOP and our approximate SOP converges with increase of the summation terms in the GLQ. To obtain the secrecy diversity order and secrecy array gain for the considered SWIPT system, the asymptotic result of the SOP is also derived. This is tight in the high signal-to-noise ratio region. A novel and robust SOP approximation is also analyzed given a small variance of the signal-to-interference-plus-noise ratio at the eavesdropper. Some selected Monte-Carlo numerical results are presented to validate the correctness of the derived closed-form expressions.
An artificial intelligence enhanced star identification algorithm is proposed for star trackers in lost-inspace mode. A convolutional neural network model based on Vgg16 is used in the artificial intelligence algorithm to classify star images. The training dataset is constructed to achieve the networks’ optimal performance. Simulation results show that the proposed algorithm is highly robust to many kinds of noise, including position noise, magnitude noise, false stars, and the tracker’s angular velocity. With a deep convolutional neural network, the identification accuracy is maintained at 96% despite noise and interruptions, which is a significant improvement to traditional pyramid and grid algorithms.