The modelling and application of cross-scale human behavior in realizing the shop-floor digital twin

The digital twin shop-floor has received much attention from the manufacturing industry as it is an important way to upgrade the shop-floor digitally and intelligently. As a key part of the shop-floor, humans' high autonomy and uncertainty leads to the difficulty in digital twin modeling of human behavior. Therefore, the modeling system for cross-scale human behavior in digital twin shop-floors was developed, powered by the data fusion of macro-behavior and micro-behavior virtual models. Shop-floor human macro-behavior mainly refers to the role of the human and their real-time position. Shop-floor micro-behavior mainly refers to real-time human limb posture and production behavior at their workstation. In this study, we reviewed and summarized a set of theoretical systems for cross-scale human behavior modeling in digital twin shop-floors. Based on this theoretical system, we then reviewed modeling theory and technology from macro-behavior and micro-behavior aspects to analyze the research status of shop-floor human behavior modeling. Lastly, we discuss and offer opinion on the application of cross-scale human behavior modeling in digital twin shop-floors. Cross-scale human behavior modeling is the key for realizing closed-loop interactive drive of human behavior in digital twin shop-floors.

Stability Analysis of Position Datum for Real-Time GPS/BDS/INS Positioning in a Platform System with Multiple Moving Devices

The rapid development of unmanned aerial vehicles (UAVs) in recent years has promoted their application in various fields, such as precise agriculture, formation flight, etc. In these applications, the accurate and reliable real-time position and attitude determination between each moving device in the same platform system are the key issue for safe and effective cooperative works. In traditional ways, static reference stations should be set up near the platform to keep the stable position datum of the platform system. In this paper, we abandoned the static stations and expected to achieve stable position datums with the platform system itself. To achieve this goal, we proposed an improved method based on both the Global Positioning System (GPS)/Beidou Navigation Satellite System (BDS) data and the inertial navigation system (INS) data to obtain precise positions of the moving devices. The time-differenced carrier phase (TDCP) was used to get the position variations and update the positions over time, and then, the INS data was integrated to further improve the accuracy and reliability of the updated positions; thus, this method is denoted as the TDCP/INS method. To evaluate the performance of this method and compare it with the traditional single-point positioning (SPP) method and the Kalman filtered SPP (KFSPP) method, a field vehicle experiment was conducted, and the position results achieved from these three methods were compared with those from the tightly combined real-time kinematic positioning (RTK)/INS method, where centimeter-level accuracy was obtained and regarded as the reference. The quantitative analysis where the position variations were evaluated and the qualitative analysis where the vehicle trajectories in three typical urban driving scenarios were discussed were both made for the three methods. The numerical results showed that the accuracy of the position variations from the SPP, KSPP, and TDCP methods was at the meter level, while that from the TDCP/INS method improved to the centimeter level, and the accuracies were 1.9 cm, 2.9 cm, and 3.1 cm in the east, north, and upward directions. The trajectory results also demonstrated a perfect consistency of the driving positions between the TDCP/INS method and the reference. As a contrast, the trajectories from the SPP and KFSPP methods had frequent jumps or sways when the vehicle drove along a large, curved road, turned at a crossroad, and passed under an urban viaduct.

How can we become more equal? Public policies and parents’ work–family preferences in Germany

This study examines how public policies affect parents’ preferences for a more egalitarian division of paid and unpaid work. Based on the assumption that individuals develop their preferences within a specific policy context, we examine how changes in three policies affect mothers’ and fathers’ work–family preferences: the availability of high-quality, affordable childcare; the right to return to a full-time job after having reduced hours to part-time and an increase in the number of ‘partner months’ in parental leave schemes. Analysing a unique probability sample of parents with young children in Germany from 2015 ( N = 1756), we find that fathers would want to work slightly fewer hours if they had the right to return to a full-time position after working part-time, and mothers would want to work slightly more hours if childcare opportunities were improved. Full-time working parents, moreover, are found to prefer fewer hours independent of the policy setting, while non-employed parents would like to work at least some hours. Last but not least, our analyses show that increasing the number of partner months in the parental leave scheme considerably increases fathers’ preferences for longer and mothers’ preferences for shorter leave. Increasing the number of partner months in parental schemes hence has the greatest potential to increase gender equality.

A robust bitmap-based real-time position tracking algorithm for rats in radial arm maze tests

This paper aims to develop a position tracking algorithm by which a rat in a radial arm maze can be accurately located in real time. An infrared (IR) night-vision camera was hung above the maze to capture IR images of the rat. The IR images were binarized and then duplicated for subsequent intersection and opening operations. Due to simple operations and a high robustness against the noise spots formed by the droppings of the rat, it took just minutes to process more than 9000 frames, and an accuracy above 99% was reached as well. The maze was intruded by an experimenter to further test the robustness, and the accuracy slightly fell to 98%. For comparison purposes, the same experiments were carried out using a pre-trained YOLO v2 model. The YOLO counterpart gave an accuracy beyond 97% in the absence and in the presence of the intruder. In other words, this work slightly outperformed the YOLO counterpart in terms of the accuracy in both cases, which indicates the robustness of this work. However, it took the YOLO counterpart an hour or so to locate a rat contained in the frames, which highlights the contribution of this work.

Estimation of the trajectory of magnetic nanoparticles in non-newtonian vascular fluid with cancer through neuronal networks

Treatments to combat cancer seek to reach specific regions to ensure maximum efficiency and reduce the possible adverse effects that occur in the treatment. One of these strategies include the treatment with magnetic nanoparticles (NPM), which has presented promising results, however, aspects involved in the trajectory of the nanoparticles are not yet known. The aim of this work is estimating the behavior of NPM through supervised neural networks, for this, artificial neural networks were implemented, such as multilayer perceptron, with optimization algorithms in which the Levenberg Marquardt algorithm stands out, different trajectories of NPM were simulated, including parameters such as time, position in X and Y, the speed that the nanoparticles can reach and physical factors that interact in the distribution were considered, such as the gravitational field, the magnetic field, the Stokes force, the force of pushing and dragging with different values of viscosity in the blood, generating a database with optimized reaction times that allows a more accurate prediction. The architecture obtained with the artificial neural the network that contains the optimization algorithm [5 4 3 2], presented the best performance with a training MSE of 1.763E-07, a validation uRMSE of 0.0049, and trend probabilities of X 0.62 % and 0.576 % in Y.

A Real-Time BLE/PDR Integrated System by Using an Improved Robust Filter for Indoor Position

Indoor position technologies have attracted the attention of many researchers. To provide a real-time indoor position system with high precision and stability is necessary under many circumstances. In a real-time position scenario, gross errors of the Bluetooth low energy (BLE) fingerprint method are more easily occurring and the heading angle of the pedestrian will drift without acceleration and magnetic field compensation. A real-time BLE/pedestrian dead-reckoning (PDR) integrated system by using an improved robust filter has been proposed. In the PDR method, the improved Mahony complementary filter based on the pedestrian motion states is adopted to estimate the heading angle reducing the drift error. Then, an improved robust filter is utilized to detect and restrain the gross error of the BLE fingerprint method. The robust filter detected the gross error at different granularity by constructing a robust vector changing the observation covariance matrix of the extended Kalman filter (EKF) adaptively when the application is running. Several experiments are conducted in the true position scenario. The mean position accuracy obtained by the proposed method in the experiment is 0.844 m and RMSE is 0.74 m. Compared with the classic EKF, these two values are increased by 38% and 18%, respectively. The results show that the improved filter can avoid the gross error in the BLE method and provide high precision and scalability in indoor position service.

An Indoor Navigation Methodology for Mobile Devices by Integrating Augmented Reality and Semantic Web

Indoor navigation systems incorporating augmented reality allow users to locate places within buildings and acquire more knowledge about their environment. However, although diverse works have been introduced with varied technologies, infrastructure, and functionalities, a standardization of the procedures for elaborating these systems has not been reached. Moreover, while systems usually handle contextual information of places in proprietary formats, a platform-independent model is desirable, which would encourage its access, updating, and management. This paper proposes a methodology for developing indoor navigation systems based on the integration of Augmented Reality and Semantic Web technologies to present navigation instructions and contextual information about the environment. It comprises four modules to define a spatial model, data management (supported by an ontology), positioning and navigation, and content visualization. A mobile application system was developed for testing the proposal in academic environments, modeling the structure, routes, and places of two buildings from independent institutions. The experiments cover distinct navigation tasks by participants in both scenarios, recording data such as navigation time, position tracking, system functionality, feedback (answering a survey), and a navigation comparison when the system is not used. The results demonstrate the system’s feasibility, where the participants show a positive interest in its functionalities.

Biomechanical device for quantification of parameters associated with the ‘sit-to-stand’ patients performance

Background In Daily Living Activities, the ‘Sit-to-Stand’ movement is essential to carry out several tasks, many of them starting from sitting and needing to get up. This movement stimulates a set of important muscles in the context of physical-motor activity and lower limb rehabilitation procedures. This work presents an interactive biomechanical device, based on two instrumented arms, to monitor the support force and arms angle, necessary to perform the ‘Sit-to-Stand’ movement. Methods The prototype incorporates aluminum support and two tubular arms instrumented with eight strain gauges, connected to a NI data board and a PC, allowing the quantification of the force applied during the sit-to-stand performance as well as the angle of the arms. A Labview user interface interacts with the user and the data can be visualized and recorded during the time. Results The device has been tested with a group of healthy volunteers, performing a sit-to-stand test protocol from a chair. In all the tests, the force applied, and the hand position have been quantified during the time necessary to perform the task. Conclusions The biomechanical device provides the quantification and identification of the harm force and position during the sit-to-stand movement. The system can be used as a force-time/position-time analysis, providing the analysis of recovery in patients who are undergoing physical-motor rehabilitation procedures of the lower limbs, such as post-stroke patients and institutionalized geriatric populations. The device can also allow biofeedback stimuli in rehabilitation activities through a graphical computer interface, such as a game.