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As the Internet of Things devices are deployed on a large scale, location-based services are being increasingly utilized. Among these services, kNN (k-nearest neighbor) queries based on road network constraints have gained importance. This study focuses on the CkNN (continuous k-nearest neighbor) queries for non-uniformly distributed moving objects with large-scale dynamic road network constraints, where CkNN objects are continuously and periodically queried based on their motion evolution. The present CkNN high-concurrency query under the constraints of a super-large road network faces problems, such as high computational cost and low query efficiency. The aim of this study is to ensure high concurrency nearest neighbor query requests while shortening the query response time and reducing global computation costs. To address this issue, we propose the DVTG-Index (Dynamic V-Tree Double-Layer Grid Index), which intelligently adjusts the index granularity by continuously merging and splitting subgraphs as the objects move, thereby filtering unnecessary vertices. Based on DVTG-Index, we further propose the DVTG-CkNN algorithm to calculate the initial kNN query and utilize the existing results to speed up the CkNN query. Finally, extensive experiments on real road networks confirm the superior performance of our proposed method, which has significant practical applications in large-scale dynamic road network constraints with non-uniformly distributed moving objects. [ABSTRACT FROM AUTHOR]

Physically implemented neural networks are subject to external perturbations and internal variations. Existing works focus on the adversarial attacks but seldom consider attack on the network structure and the corresponding recovery method. Inspired by the biological neural compensation mechanism and the neuromodulation technique in clinical practice, we propose a novel framework of reviving attacked reservoir computers, consisting of several strategies direct at different types of attacks on structure by adjusting only a minor fraction of edges in the reservoir. Numerical experiments demonstrate the efficacy and broad applicability of the framework and reveal inspiring insights into the mechanisms. This work provides a vehicle to improve the robustness of reservoir computers and can be generalized to broader types of neural networks. [ABSTRACT FROM AUTHOR]

The manufacturing accuracy of large-scale complex components determines the performance and quality of aircraft, ships, high-speed rail, and other equipment. High-precision 3D measurement plays a crucial role in ensuring manufacturing accuracy. At present, the existing measurement methods rely heavily on manual labor, which cannot satisfy the requirements of industry quality and efficiency. This paper introduces an integrated mobile robotic measurement system for the accurate and automatic 3D measurement of large-scale components with complex curved surfaces. The system consists of the mobile optical scanning measurement device, visual tracking device, and software platform, which can realize comprehensive and accurate data acquisition and stitching of large-scale complex components. The combination of visual tracking and 3D measurement based on the coordinated motion of the dual robot achieved the automatic data acquisition of large-scale complex components without sticking coded targets. Additionally, this paper also introduces a DeepMerge algorithm that combines local and global features of the point cloud, which effectively corrects the initial stitching error of the visual tracking system. The validity of the measurement system and method was shown by the measurement and stitching experiments on the surface of the vehicle nose, ensuring the accurate measurement of the robot's wide range of motion. [ABSTRACT FROM AUTHOR]

This paper presents a new manipulation theory for controlling compliant motions of a robotic manipulator. In previous closed loop control methods, both direct kinematics and inverse kinematics of a manipulator must be resolved to convert feedback force and position data from Cartesian space to joint space. However, in many cases, the solution of direct kinematics in a parallel manipulator or the solution of inverse kinematics in a serial manipulator is not easily available. In this study, the force and position data are packed into one set of 'motion feedback,' by replacing the force errors with virtual motion quantities, or one set of 'force feedback,' by replacing motion errors with virtual force quantities. The joint torques are adjusted based on this combined feedback package. Since only Jacobian of direct kinematics or Jacobian of inverse kinematics is used, the computational complexity is reduced significantly, and the control scheme is more stable at or near singular manipulator configurations. Furthermore, the complexities and oddities associated with hybrid control, such as nonuniformity of the space matrix and incompatibility of simultaneous position and force control in the same direction are circumvented. The applications of this theory are demonstrated in simulation experiments with both serial and parallel manipulators. [DOI: 10.1115/1.2406104] Keywords: target tracking, open loop, closed loop, manipulation, hybrid control

To provide stroke patients with good rehabilitation training, the rehabilitation robot should ensure that each joint of the limb of the patient does not exceed its joint range of motion. Based on the machine vision combined with an RGB-Depth (RGB-D) camera, a convenient and quick human-machine interaction method to measure the lower limb joint range of motion of the stroke patient is proposed. By analyzing the principle of the RGB-D camera, the transformation relationship between the camera coordinate system and the pixel coordinate system in the image is established. Through the markers on the human body and chair on the rehabilitation robot, an RGB-D camera is used to obtain their image data with relative position. The threshold segmentation method is used to process the image. Through the analysis of the image data with the least square method and the vector product method, the range of motion of the hip joint, knee joint in the sagittal plane, and hip joint in the coronal plane could be obtained. Finally, to verify the effectiveness of the proposed method for measuring the lower limb joint range of motion of human, the mechanical leg joint range of motion from a lower limb rehabilitation robot, which will be measured by the angular transducers and the RGB-D camera, was used as the control group and experiment group for comparison. The angle difference in the sagittal plane measured by the proposed detection method and angle sensor is relatively conservative, and the maximum measurement error is not more than 2.2 degrees. The angle difference in the coronal plane between the angle at the peak obtained by the designed detection system and the angle sensor is not more than 2.65 degrees. This paper provides an important and valuable reference for the future rehabilitation robot to set each joint range of motion limited in the safe workspace of the patient. [ABSTRACT FROM AUTHOR]

Diabetes mellitus, a chronic disease associated with elevated accumulation of glucose in the blood, is generally diagnosed through an invasive blood test such as oral glucose tolerance test (OGTT). An effective method is proposed to test type 2 diabetes using peripheral pulse waves, which can be measured fast, simply and inexpensively by a force sensor on the wrist over the radial artery. A self-designed pulse waves collection platform includes a wristband, force sensor, cuff, air tubes, and processing module. A dataset was acquired clinically for more than one year by practitioners. A group of 127 healthy candidates and 85 patients with type 2 diabetes, all between the ages of 45 and 70, underwent assessments in both OGTT and pulse data collection at wrist arteries. After preprocessing, pulse series were encoded as images using the Gramian angular field (GAF), Markov transition field (MTF), and recurrence plots (RPs). A four-layer multi-task fusion convolutional neural network (CNN) was developed for feature recognition, the network was well-trained within 30 minutes based on our server. Compared to single-task CNN, multi-task fusion CNN was proved better in classification accuracy for nine of twelve settings with empirically selected parameters. The results show that the best accuracy reached 90.6% using an RP with threshold $\epsilon$ of 6000, which is competitive to that using state-of-the-art algorithms in diabetes classification. [ABSTRACT FROM AUTHOR]

Reinforcement learning has been a promising approach in control and robotics since data-driven learning leads to non-necessity of engineering knowledge. However, it usually requires many interactions with environments to train a controller. This is a practical limitation in some real environments, for example, robots where interactions with environments are restricted and time inefficient. Thus, learning is generally conducted with a simulation environment, and after the learning, migration is performed to apply the learned policy to the real environment, but the differences between the simulation environment and the real environment, for example, friction coefficients at joints, changing loads, may cause undesired results on the migration. To solve this problem, most learning approaches concentrate on retraining, system or parameter identification, as well as adaptive policy training. In this article, we propose an approach where an adaptive policy is learned by extracting more information from the data. An environmental encoder, which indirectly reflects the parameters of an environment, is trained by explicitly incorporating model uncertainties into long-term planning and policy learning. This approach can identify the environment differences when migrating the learned policy to a real environment, thus increase the adaptability of the policy. Moreover, its applicability to autonomous learning in control tasks is also verified. [ABSTRACT FROM AUTHOR]