Search

This article presents an automated, model-free, data-driven method for the safe tuning of PID cascade controller gains based on Bayesian optimization. The optimization objective is composed of data-driven performance metrics and modeled using Gaussian processes. The safety requirement is imposed via a barrier-like term in the objective, which is introduced to account for operational changes in the system. We further introduce a data-driven constraint that captures the stability requirements from system data. Numerical evaluation shows that the proposed approach outperforms relay feedback autotuning and quickly converges to the global optimum, thanks to a tailored stopping criterion. We demonstrate the performance of the method through simulations and experiments. For experimental implementation, in addition to the introduced safety constraint, we integrate a method for automatic detection of the critical gains and extend the optimization objective with a penalty depending on the proximity of the current candidate points to the critical gains. The resulting automated tuning method optimizes system performance while ensuring stability and standardization. [ABSTRACT FROM AUTHOR]

Extensive user check-in data incorporating user preferences for location is collected through Internet of Things (IoT) devices, including cell phones and other sensing devices in location-based social network. It can help traveling enterprises intelligently predict users' interests and preferences, provide them with scientific tourism paths, and increase the enterprises income. Thus, successive point-of-interest (POI) recommendation has become a hot research topic in augmented Intelligence of Things (AIoT). Presently, various methods have been applied to successive POI recommendations. Among them, the recurrent neural network-based approaches are committed to mining the sequence relationship between POIs, but ignore the high-order relationship between users and POIs. The graph neural network-based methods can capture the high-order connectivity, but it does not take the dynamic timeliness of POIs into account. Therefore, we propose an Interaction-enhanced and Time-aware Graph Convolution Network (ITGCN) for successive POI recommendation. Specifically, we design an improved graph convolution network for learning the dynamic representation of users and POIs. We also designed a self-attention aggregator to embed high-order connectivity into the node representation selectively. The enterprise management systems can predict the preferences of users, which is helpful for future planning and development. Finally, experimental results prove that ITGCN brings better results compared to the existing methods. [ABSTRACT FROM AUTHOR]

Currently, COVID-19 is circulating in crowded places as an infectious disease. COVID-19 can be prevented from spreading rapidly in crowded areas by implementing multiple strategies. The use of unmanned aerial vehicles (UAVs) as sensing devices can be useful in detecting overcrowding events. Accordingly, in this article, we introduce a real-time system for identifying overcrowding due to events such as congestion and abnormal behavior. For the first time, a monitoring approach is proposed to detect overcrowding through the UAV and social monitoring system (SMS). We have significantly improved identification by selecting the best features from the water cycle algorithm (WCA) and making decisions based on deep transfer learning. According to the analysis of the UAV videos, the average accuracy is estimated at 96.55%. Experimental results demonstrate that the proposed approach is capable of detecting overcrowding based on UAV videos' frames and SMS's communication even in challenging conditions. [ABSTRACT FROM AUTHOR]

Control inputs of fully constrained cable-driven parallel robots (CDPRs) are constrained by the positiveness of the cables tension, as cables merely apply tensile forces. The positive tension distribution (PTD) in CDPRs is usually guaranteed with iterative optimization techniques, utilizing the redundant actuation of the CDPR. The iterative nature of the conventional PTD limits their real-time application since the worst-case computation time of the iterative methods is not predictable. In addition, optimization methods are prone to model uncertainties. This article addresses the PTD problem in the fully constrained CDPRs with a control viewpoint. In the proposed approach, the PTD algorithm is an integral part of the controller, which explicitly generates positive values for the cables tension. To this aim, a saturation-type function is coupled with the controller, and its effect is compensated using a nonlinear disturbance observer. The stability of the proposed control scheme is also investigated in detail through Lyapunov’s second method, considering a nonsingular terminal sliding mode controller. Furthermore, the performance of the proposed methodology is compared with the conventional method for a six-degrees-of-freedom CDPR, in the presence of uncertainties. Finally, the effectiveness of the proposed control scheme is investigated through experiments. [ABSTRACT FROM AUTHOR]

The development of the Internet of vehicles (IoV) has spawned a series of driving assistance services (e.g., collision warning), which improves the safety and intelligence of transportation. In IoV, the driving assistance services need to be met in time due to the rapid speed of vehicles. By introducing edge computing into the IoV, the insufficiency of local computation resources in vehicles is improved, providing high quality services for users. Nevertheless, the resources provided by edge servers are often limited, which fail to meet all the needs of users in IoV simultaneously. Thereby, how to minimize the tasks processing latency of users in the case of limited edge server resources is still a challenge. To handle the above problem, a task offloading scheme fuzzy-task-offloading-and-resource-allocation (F-TORA) based on Takagi–Sugeno fuzzy neural network (T–S FNN) and game theory is designed. Primarily, the cloud server predicts the future traffic flow of each section through T–S FNN and transmits the prediction results to the roadside units (RSUs). Then, the RSU adjusts the current load based on the captured future traffic flow data. After the load balancing of each RSU, the optimal task offloading strategy is determined for the users by game theory. Following, the edge server acts as an agent to allocate computing resources for the offloaded tasks by $Q$ -learning algorithm. Finally, the robust performance of the proposed method is validated by comparative experiments. [ABSTRACT FROM AUTHOR]

Autonomous unmanned aerial vehicles (UAVs) are essential for detecting and tracking specific events, such as automatic navigation. The intelligent monitoring of people’s social distances in crowds is one of the most significant events caused by the coronavirus. The virus is spreading more quickly among the crowds, and the disease cycle continues in congested areas. Due to the error that occurs when humans monitor their activity, an automated model is required to alert to social distance violations in crowds. As a result, this article proposes a two-step framework based on autonomous UAV videos, including human tracking and deep learning-based recognition of the crowd’s social distance. The deep architecture is a modified-fast and lightweight ShuffleNet learning structure. First, the Kalman filter is used to determine the positions of individuals, and then the modified ShuffleNet is used to refine the bounding boxes obtained and determine the social distance. The social distance is calculated using the initial refinement of the bounding box obtained during the tracking step and the scale in frames of the human body. The observed average accuracy, average processing time (APT), and processed frame per second (FPS) for three congestion datasets were 97.5%, 84 milliseconds, and 11.5 FPS, respectively. Real-time decision-making was achieved by reducing the size and resolution of the frames. Additionally, the frames were re-labeled to reduce the computational complexity associated with detecting social distancing. The experimental results demonstrated that the proposed method could operate more quickly and accurately on various resolution frames of UAV videos with difficult conditions. [ABSTRACT FROM AUTHOR]

With the potential of implementing computing-intensive applications, edge computing is combined with digital twinning (DT)-empowered Internet of vehicles (IoV) to enhance intelligent transportation capabilities. By updating digital twins of vehicles and offloading services to edge computing devices (ECDs), the insufficiency in vehicles’ computational resources can be complemented. However, owing to the computational intensity of DT-empowered IoV, ECD would overload under excessive service requests, which deteriorates the quality of service (QoS). To address this problem, in this article, a multiuser offloading system is analyzed, where the QoS is reflected through the response time of services. Then, a service offloading (SOL) method with deep reinforcement learning, is proposed for DT-empowered IoV in edge computing. To obtain optimized offloading decisions, SOL leverages deep Q-network (DQN), which combines the value function approximation of deep learning and reinforcement learning. Eventually, experiments with comparative methods indicate that SOL is effective and adaptable in diverse environments. [ABSTRACT FROM AUTHOR]

In this article, we propose a performance-based autotuning method for cascade control systems, where the parameters of a linear axis drive motion controller from two control loops are tuned jointly. Using Bayesian optimization as all parameters are tuned simultaneously, the method is guaranteed to converge asymptotically to the global optimum of the cost. The data-efficiency and performance of the method are studied numerically for several training configurations and compared numerically to those achieved with classical tuning methods and to the exhaustive evaluation of the cost. On the real system, the tracking performance and robustness against disturbances are compared experimentally to nominal tuning. The numerical study and the experimental data both demonstrate that the proposed automated tuning method is efficient in terms of required tuning iterations, robust to disturbances, and results in improved tracking. [ABSTRACT FROM AUTHOR]

In the 5G-envisioned Internet of vehicles (IoV), a significant volume of data is exchanged through networks between intelligent transport systems (ITS) and clouds or fogs. With the introduction of Software-Defined Networking (SDN), the problems mentioned above are resolved by high-speed flow-based processing of data in network systems. To classify flows of packets in the SDN network, high throughput packet classification systems are needed. Although software packet classifiers are cheaper and more flexible than hardware classifiers, they could only deliver limited performance. A key idea to resolve this problem is parallelizing packet classification on graphical processing units (GPUs). In this paper, we study parallel forms of Tuple Space Search and Pruned Tuple Space Search algorithms for the flow classification suitable for GPUs using CUDA (Compute Unified Device Architecture). The key idea behind the offered methodology is to transfer the stream of packets from host memory to the global memory of the CUDA device, then assigning each of them to a classifier thread. To evaluate the proposed method, the GPU-based versions of the algorithms were implemented on two different CUDA devices, and two different CPU-based implementations of the algorithms were used as references. Experimental results showed that GPU computing enhances the performance of Pruned Tuple Space Search remarkably more than Tuple Space Search. Moreover, results evinced the computational efficiency of the proposed method for parallelizing packet classification algorithms. [ABSTRACT FROM AUTHOR]

Intelligent transportation systems (ITSs) have become popular in recent years as an essential requirement for safer and more efficient transportation systems. Internet of Electric vehicles (IoEV) as well as their hybrid forms provide an ideal means of supporting sustainability within an ITS. The control of charging/discharging of EV is still a challenge, despite the tremendous research progress to date in the field. In this paper, the use of charging station data and binary vectorization are proposed in order to provide timely insights on the dynamic behavior of charging processes. A Bag-of-Power-States model has been created for similarity measurement of charging stations within given time periods. The results of experimentations using synthetic data have shown that the proposed ${{Bag-of-Power-States}}$ model is computationally feasible and provides useful results for optimizing the scheduling of power supply to charging stations that may be located across a wide range of distances, over the same period of time. [ABSTRACT FROM AUTHOR]

As an important part of a smart city, the Intelligent Transportation System (ITS) is the key to the sustainable development of urban transportation considering the fuel consumption as well as traffic efficiency. Smart sensor can be integrated with the transportation infrastructure to achieve a sustainable ITS. Recent advancements in ITS suggest that the roads will gradually be filled with autonomous vehicles that are able to drive themselves while cooperating with each other to form the sustainable transportation systems. As a representative driving pattern of autonomous vehicles, the platooning technology has great potential to lower fuel consumption and increase traffic efficiency. In this paper, to reach above goals, we present a systematic framework for vehicular platooning forming. Firstly, the optimal speed of a platoon is determined by the optimal speed model, by which the total fuel consumption of this platoon could be saved to a great extend. After that, an optimal insert point decision is made for the vehicles to be grouped in the platoon, based on proposed Q-learning model. We also design a collision detection model to reduce the collision probability when a vehicle intents to join a platoon. Numerical results demonstrate that our proposed model could significantly improve the traffic efficiency, reduce the fuel consumption and decrease the platooning forming time, compared with other classic algorithms. [ABSTRACT FROM AUTHOR]