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Excessive spatial parallelization can introduce a performance bottleneck due to the communication overhead. While time-parallel method multigrid-reduction-in-time (MGRIT) provides an alternative to enhance concurrency, it generally requires large numbers of iterations to converge or even fails when applied to advection-dominated problems. To enhance the convergence of MGRIT, we propose the use of consecutive-step coarse-grid operators in MGRIT, rather than the standard rediscretized coarse-grid operators. The consecutive-step coarse-grid operator is defined as the multiplication of several fine-grid operators, which is able to track the advective characteristic more accurately than the standard rediscretized one. Numerical results show that multilevel MGRIT using the proposed operator is more efficient than the one using the standard rediscretized operator when applied to linear advection problems. Moreover, we perform time-parallel computing of the Euler equations and the Navier–Stokes equations by using the proposed method. Spatial coarsening is also considered. Compared with the MGRIT using the standard rediscretization approach, the developed method demonstrates enhanced robustness and efficiency in handling complex flow problems, including cases involving multidimensional shock waves and contact discontinuities. [ABSTRACT FROM AUTHOR]

Passive crowd counting using channel state information (CSI) is a promising technology for applications in fields such as smart cities and commerce. However, the most existing algorithms can only recognize the total number of people in the monitoring area and cannot simultaneously recognize the number and states of people and ignore the real-time performance of the algorithms. Therefore, they cannot be applied to the scenarios of multi-state crowd counting requiring high real-time performance. To address this issue, a lightweight passive multi-state crowd counting algorithm called TF-LPMCC is proposed. This algorithm constructs CSI amplitude data into amplitude and time–frequency images, extracts texture features using the gray-level co-occurrence matrix (GLCM) and gray-level difference statistic (GLDS) methods, and uses the linear discriminant analysis (LDA) algorithm to count the crowd in multi-states. Experiments show that the TF-LPMCC algorithm not only has low time complexity but also achieves an average recognition accuracy of 98.27% for crowd counting. [ABSTRACT FROM AUTHOR]

At present, it has become very convenient to collect channel state information (CSI) from ubiquitous commercial WiFi network cards, and the location or activity of a human who affects the CSI can be recognized by analyzing the change of the CSI. Therefore, wireless sensing technology based on the CSI has received widespread attention. However, the existing CSI-based gesture recognition methods still have some problems, which include that subcarrier selection is not optimized and motion interval extraction is not accurate enough, so the accuracy of gesture recognition methods still needs to be further improved. In response to the above problems, a gesture recognition method based on misalignment mean absolute deviation (MMAD) and KL divergence is proposed in the paper, which is called MMAD-KL-GR method. This method uses the proposed MMAD algorithm to extract the CSI amplitude intervals containing gesture information, then selects subcarriers by comparing the KL divergence of the CSI amplitude, and finally uses the subspace K-nearest neighbor (KNN) algorithm to recognize the gestures. Several experiments show that the MMAD-KL-GR method can effectively improve the accuracy of the gesture recognition. [ABSTRACT FROM AUTHOR]

Device-free localization (DFL) is a technology which helps locate the target without the need of equipping the target with a radio device. It uses the target-induced change in the received signal strength (RSS) of the wireless links to locate the target. It is useful for the applications in security monitoring and emergency rescue. However, it is difficult for the existing RSS-based DFL algorithms to acquire a high localization accuracy and a low computational complexity simultaneously. To address this issue, a novel hierarchic radio imaging (HRI)-based DFL algorithm is proposed in this article. The algorithm groups the wireless links according to the length of links and assigns a priority to each link group. Furthermore, a grid normalized model is proposed to calculate the grid values, and then a square human model is used to locate the target. In addition, in order to reduce the computational complexity, the location information of the target at the previous moment is used to limit the regional range of the target location. Extensive experiments are conducted with results showing that the HRI algorithm not only greatly reduces the computational complexity but also achieves high localization accuracy and high localization stability. Therefore, the HRI algorithm is very suitable for the real-time-based applications. [ABSTRACT FROM AUTHOR]

With the emergence of tools for extracting CSI data from commercial WiFi devices, CSI-based device-free activity recognition technology has developed rapidly and has been widely used in security monitoring, smart home, medical monitoring, and other fields. However, the existing CSI-based activity recognition algorithms need a large number of training samples to obtain the ideal recognition accuracy. To solve the problem, an attention-based bidirectional LSTM method using multidimensional features (called MF-ABLSTM method) is proposed. In this method, the signal preprocessing and continuous wavelet transform algorithms are used to construct time-frequency matrix, the sample entropy is used to characterize the statistical feature of CSI amplitudes, the energy difference at a fixed time interval is used to characterize the time-domain feature of activities, and the energy distribution of different frequency components is used to characterize the frequency-domain feature of activities. By expanding the training samples with the proposed tensor prediction algorithm, the accurate activity recognition can be realized with only a few samples. A large number of experiments verify the good performance of MF-ABLSTM method. [ABSTRACT FROM AUTHOR]

With the rapid development of modern industries, more and more risk factors exist in industrial operations, leading to a gradual increase in the frequency of industrial failures. The industrial enterprises have taken a variety of measures to analyze the risk factors in industrial operations, but establishing an effective monitoring and evaluation system remains a technical challenge. To overcome this challenge, a practical industrial risk monitoring system, based on the wireless sensor network (WSN), is established in this paper to improve resilience of the respective industrial operations. In the proposed system, the existing sensor nodes in the industrial environment are organized into WSN to transmit real-time data. A special inspection robot is used to monitor comprehensively the environmental and safety risk factors in the industrial environment. To obtain the location of the robot, the partial nodes of the WSN are organized dynamically as the anchor nodes of the localization system, and two grid cumulative probability localization (GCPL) algorithms are proposed based on the GCPL-circle intersection and GCPL-path loss methods, respectively. The GCPL algorithm determines the grid cumulative probability using prior location information of the target node and the received signal strength information between the anchor nodes and target node to locate the target node. Experimental results show that the two GCPL algorithms significantly improve the localization accuracy and stability, and hence can be implemented as a risk monitoring system for real-world applications. Note to Practitioners—In industrial operations, a lot of risk factors exist which result in many frequent operational disruptions and failures. It is hence critical to develop an effective risk monitoring and evaluation system. Motivated by the industrial needs, we develop in this paper a practical wireless sensor network (WSN)-based industrial risk monitoring system. In the system, the existing sensor nodes in the industrial environment are organized into WSN to transmit real-time data. A special inspection robot is used to monitor comprehensively the environmental and safety risk factors in the industrial environment. To obtain the location of the robot, the partial nodes of the WSN are organized dynamically. We further propose two grid cumulative probability localization algorithms to establish the system. We show via computational experiments that the proposed risk monitoring system performs well and can be implemented as a real application which helps to improve risk management of industrial operations. [ABSTRACT FROM AUTHOR]

Emergency incidents can trigger heated discussions on microblogging platforms, and a great number of tweets related to emergency incidents are retweeted by users. Consequently, social media big data related to the emergency incidents is generated from various social media platforms, which can be used to predict users' retweeting behavior. In this paper, the characteristics of individuals' retweeting behaviors in emergency incidents are analyzed, and then 11 important characteristics are extracted from recipient characteristics, retweeter characteristics, tweet content characteristics, and external media coverage. A back propagation neural network (BPNN) model called PRBBP is used to predict retweeting behavior in such emergency incidents. Based on PRBBP, an algorithm called PRABP is proposed to predict the number of retweets in emergency incidents. The experiments are performed on a large-scale dataset crawled from Sina weibo. The simulation results show that both the PRBBP model and the PRABP algorithm proposed by this paper have excellent predictive performance. [ABSTRACT FROM AUTHOR]

The era of big data has begun and an enormous amount of real-time data is used for the risk analysis of various industrial applications. However, a technical challenge exists in gathering real-time big data in a complex indoor industrial environment. Indoor wireless sensor networks (WSNs) technology can overcome this limitation by collecting the big data generated from source nodes and transmitting them to the data center in real time. In this study, typical residence, office, and manufacturing environments were chosen. The signal transmission characteristics of an indoor WSN were obtained by analyzing the test data. According to these characteristics, a real-time big data gathering (RTBDG) algorithm based on an indoor WSN is proposed for the risk analysis of industrial operations. In this algorithm, sensor nodes can screen the data collected from the environment and equipment according to the requirements of risk analysis. Clustering data transmission structure is then established on the basis of the received signal strength indicator (RSSI) and residual energy information. Experimental results show that RTBDG not only uses the limited energy of network nodes efficiently, but also balances the energy consumption of all nodes. In the near future, the algorithm will be widely applied to risk analysis in different industrial operations. [ABSTRACT FROM PUBLISHER]

This paper presents the design for an active trailer-steering system of multi-trailer articulated heavy vehicles using driver-software-in-the-loop real-time simulations. A linear yaw-plane multi-trailer articulated heavy-vehicle model is generated to derive an optimal active trailer-steering controller. Then, the controller is reconstructed in LabVIEW and integrated with a vehicle model for a tractor– double-trailer combination developed in TruckSim. The driver-software-in-the-loop real-time simulations are conducted on a vehicle simulator. The driver-software-in-the-loop real-time simulations indicate that the active trailer-steering controller can effectively improve the low-speed manoeuvrability and high-speed stability of the multi-trailer articulated heavy vehicle when testing using a low-speed 360° roundabout path-following manoeuvre and a high-speed single-lane-change manoeuvre respectively. The investigation based on the driver-software-in-the-loop real-time simulations paves the way to future development of electronic control units for the active trailer-steering system using driver-hardware-in-the-loop real-time simulations. [ABSTRACT FROM AUTHOR]

This paper presents a closed-loop dynamic simulation-based design method for articulated heavy vehicles (AHVs) with active trailer steering (ATS) systems. AHVs have poor manoeuvrability at low speeds and exhibit low lateral stability at high speeds. From the design point of view, there exists a trade-off relationship between AHVs’ manoeuvrability and stability. For example, fewer articulation points and longer wheelbases will improve high-speed lateral stability, but they will degrade low-speed manoeuvrability. To tackle this conflicting design problem, a systematic method is proposed for the design of AHVs with ATS systems. In order to evaluate vehicle performance measures under a well-defined testing manoeuvre, a driver model is introduced and it ‘drivers’ the vehicle model to follow a prescribed route at a given speed. Considering the interactions between the mechanical trailer and the ATS system, the proposed design method simultaneously optimises the active design variables of the controllers and passive design variables of the trailer in a single design loop (SDL). Through the design optimisation of an ATS system for an AHV with a truck and a drawbar trailer combination, this SDL method is compared against a published two design loop method. The benchmark investigation shows that the former can determine better trade-off design solutions than those derived by the latter. This SDL method provides an effective approach to automatically implement the design synthesis of AHVs with ATS systems. [ABSTRACT FROM PUBLISHER]