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In the rapidly evolving landscape of smart transportation, the passenger volume in urban rail transit consistently demonstrates an upward trajectory. In this context, precise and scientifically grounded short-term passenger flow prediction methods are essential for optimizing operational scheduling and ensuring safety in urban rail transit. Consequently, this paper introduces Temporal Graph Attention Long Short-Term Memory (TGALSTM), a spatiotemporal integrated prediction network model that incorporates the surrounding environment of the station. Initially, the paper enhanced the Temporal Convolutional Network (TCN) model to capture temporal features more accurately. Subsequently, the paper utilizes the Graph Attention Network (GAT) network module specifically to extract the topological structure and surrounding environmental features of the station. Lastly, the prediction task is accomplished by weighted fusion of various features, inputting them into the Attention Long Short-Term Memory (LSTM) network. Experiments were conducted on two authentic datasets, revealing that the TGALSTM model outperforms the baseline model in both single-step and double-step predictions, showcasing the model’s exceptional performance and robustness. This research offers a robust method and support to enhance the operational efficiency and passenger flow management of urban rail transit systems.

Online manipulation of multiple micro- and nanoscale agents is of major interest for various research applications. Among the biggest limitations of wireless external actuation are its global and coupled influences in the workspace, which limit the robust manipulation of multiple agents independently and simultaneously. In this paper, we propose novel motion planning algorithms, $\tt Bi$ - $\tt iSST$ and $\tt Ref$ - $\tt iSST$ , to quickly generate time-optimal trajectories for multiple agents sharing global external fields. Both algorithms are extended by the stable sparse rapidly-exploring random tree kinodynamic motion planning algorithm. The $\tt Bi$ - $\tt iSST$ uses a bidirectional approach to speed up the searching process. A novel connection process is proposed to connect the two trees efficiently by applying an optimization procedure. The $\tt Ref$ - $\tt iSST$ uses the workspace information to quickly generate global-routing trajectories as references, then guides the search process more effectively by getting more accurate heuristics according to the reference global-routing trajectories. A transition matrix similar to that in Markov Decision Processes is used to form the reference trajectory. Compared with the state-of-the-art $\tt iSST$ algorithm, the proposed algorithms quickly update feasible solutions and converge to a near-optimal, minimum-time solution to increase the efficiency of the simultaneous manipulation of multiple micro agents using global external fields. Extensive analysis and physical experiments are presented to confirm the effectiveness and the performance of the motion planning algorithms. Note to Practitioners—Autonomous manipulation of multiple micro- and nanoscale agents is of major interest for various research applications. Wireless actuation is a promising way to position those objects. The commonly used non-contact actuation techniques include magnetic actuation, electrical field actuation, optical tweezers, and actuated flows, etc. Among the biggest limitations of wireless external actuation are its global and coupled influences in the workspace, which limit the capability to robustly manipulate multiple agents independently and simultaneously. In this paper, we propose novel bidirectional informed sampling-based motion planning algorithms to quickly generate time-optimal trajectories for multiple agents sharing global external fields. Novel heuristics and informed reference trajectory are used to guide the search for manipulating multiple agents under global fields. Numerical simulations and physical experiments are presented to demonstrate the performance of the motion planning design. The proposed algorithm guarantees anytime performance and quickly converges to a near-optimal minimum time solution for multiple agents. Although we focus on the electric-field actuated multiple-micro-agent system, the proposed motion planning strategies are not limited to the actuation and can be generalized to other field-based applications, in which the actuation among a group of multiple agents are coupled or intertwined. [ABSTRACT FROM AUTHOR]

Adding haptic feedback has been reported to improve the outcome of minimally invasive robotic surgery. In this study, we seek to determine whether an algorithm based on simulating responses of a cutaneous afferent population can be implemented to improve the performance of presenting haptic feedback for robot-assisted surgery. We propose a bio-inspired controlling model to present vibration and force feedback to help surgeons localize underlying structures in phantom tissue. A single pair of actuators was controlled by outputs of a model of a population of cutaneous afferents based on the pressure signal from a single sensor embedded in surgical forceps. We recruited 25 subjects including 10 expert surgeons to evaluate the performance of the bio-inspired controlling model in an artificial palpation task using the da Vinci surgical robot. Among the control methods tested, the bio-inspired system was unique in allowing both novices and experts to easily identify the locations of all classes of tumors and did so with reduced contact force and tumor contact time. This work demonstrates the utility of our bio-inspired multi-modal feedback system, which resulted in superior performance for both novice and professional users, in comparison to a traditional linear and the existing piecewise discrete algorithms of haptic feedback. [ABSTRACT FROM AUTHOR]

Tactile information about an object can only be extracted from population responses of tactile receptors and their afferents. Thus, to best control tactile information in robots, neuroprostheses or haptic devices, inputs should represent responses from full populations of afferents. Here, we describe a simplified model that recreates afferent population responses of thousands of tactile afferents in a personal computer. The whole model includes a resistance network model to simplify the skin mechanics and an improved version of a single unit model that we have previously described. The whole model was implemented by short and efficient python code. The parameters of the model were fit based on a simple vibrating stimulus, but the simulated outputs generalize to match receptive field sizes, edge enhancement, and neurophysiological responses to dot textures, embossed letters and curved surfaces. We discuss how to use this work to model haptic perception and provide guidance in designing and controlling highly realistic tactile interfaces in robots, neural prostheses and haptic devices. [ABSTRACT FROM AUTHOR]

Automated, highly precise online manipulation of nanoscale and microscale objects is essential to achieve scalable nanomanufacturing. However, nanoparticles and microparticles exhibit as yet unpredictable and, therefore, uncontrolled variations in their structures or compositions that can limit their functions and properties. In this article, we present an electric field-based adaptive manipulation scheme to precisely control the motion of individual nanowires, online estimate their unknown mobilities, and rapidly plan the desired trajectories with the estimated mobilities. The input saturation is considered in the adaptive controller, and the closed-loop system is proved to be asymptotically stable. The proposed motion planners (MPs), ${{\tt Bi}}$ - ${{\tt iSST}}$ s, are built on and extended by the stable sparse rapidly exploring random tree (${{\tt SST}}$)-based kinodynamic motion-planning algorithms. Two sparse trees are maintained to increase the probability of finding a solution. The ${{\tt Bi}}$ - ${{\tt iSST}}$ algorithms use the workspace information, heuristics, and optimization to effectively guide the search process. When compared with the state-of-the-art algorithms, a ${{\tt Bi}}$ - ${{\tt iSST}}$ variant, the ${{\tt opt}}$ - ${{\tt Bi}}$ - ${{\tt iSST}}$ algorithm, quickly updates feasible solutions with the online estimated mobilities of multiple nanowires and converges to a near-optimal, minimum-time solution to increase the efficiency of simultaneous manipulation of the nanowires. Therefore, without complex characterization of each nanowire’s mobility, the nanowires can be steered simultaneously and efficiently to achieve precisely controlled positions without collisions. Simulation and experimental results confirm that the proposed integrated adaptive manipulation scheme precisely, independently, and simultaneously manipulates the motion of multiple nanowires. [ABSTRACT FROM AUTHOR]

We propose a novel seismic noise attenuation approach based on least-squares Gaussian beam transform (LSGBT). Gaussian beam transform uses time-domain Gaussian beam (TGB), which can be characterized by a particular location, arrival time, amplitude, slope, curvature, and width. We implement the local attributes such as beam center, spacing, and width to perform Gaussian beam decomposition. In this approach, we first introduce the plane-wave decomposition (PWD) theory to implement TGB decomposition of noisy seismic data and then apply data reconstruction. Unlike most state-of-the-art algorithms, random noise is attenuated in the process of Gaussian beam reconstruction. In the reconstruction records, the useful events are well preserved simultaneously removing random noise. Comparisons of experimental results on field data using traditional $f$ - $x$ deconvolution (FX Decon) and median filter (MF) methods are also provided, which suggest that our method achieves better denoising performance than FX Decon and MF methods. Taking into account that signal loss is sometimes unavoidable in almost all existing denoising methods. In addition to the signal-to-noise ratio (SNR) measurement, we also use local similarity as an efficient tool to evaluate denoising performance. A group of synthetic and field examples demonstrates the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

The dependence of the spectral broadening characteristics on the number of longitudinal modes is studied experimentally in a bi-directional pumping fiber amplifier. It is found that the linewidth of the amplifier decreases with reducing the longitudinal mode number of the seed source. In this letter, we present a 68 pm-linewidth kilowatt-level fiber laser using a simple master oscillator power amplifier structure. Meanwhile, we theoretically analyze the spectral broadening characteristics of the seeds with different longitudinal mode numbers. The conclusions are in agreement with the experimental results. This provides a simple and efficient method for obtaining the high power narrow linewidth fiber laser used in spectral beam combining. [ABSTRACT FROM AUTHOR]