A Real-time Nonlinear Model Predictive Controller for Yaw Motion Optimization of Distributed Drive Electric Vehicles

This paper proposes a real-time nonlinear model predictive control (NMPC) strategy for direct yaw moment control (DYC) of distributed drive electric vehicles (DDEVs). The NMPC strategy is based on a control-oriented model built by integrating a single track vehicle model with the Magic Formula (MF) tire model. To mitigate the NMPC computational cost, the continuation/generalized minimal residual (C/GMRES) algorithm is employed and modified for real-time optimization. Since the traditional C/GMRES algorithm cannot directly solve the inequality constraint problem, the external penalty method is introduced to transform inequality constraints into an equivalently unconstrained optimization problem. Based on the Pontryagin’s minimum principle (PMP), the existence and uniqueness for solution of the proposed C/GMRES algorithm are proven. Additionally, to achieve fast initialization in C/GMRES algorithm, the varying predictive duration is adopted so that the analytic expressions of optimally initial solutions in C/GMRES algorithm can be derived and gained. A Karush-Kuhn-Tucker (KKT) condition based control allocation method distributes the desired traction and yaw moment among four independent motors. Numerical simulations are carried out by combining CarSim and Matlab/Simulink to evaluate the effectiveness of the proposed strategy. Results demonstrate that the real-time NMPC strategy can achieve superior vehicle stability performance, guarantee the given safety constraints, and significantly reduce the computational efforts

A Computationally Efficient Path Following Control Strategy of Autonomous Electric Vehicles with Yaw Motion Stabilization

his paper proposes a computationally efficient path following control strategy of autonomous electric vehicles (AEVs) with yaw motion stabilization. First, the nonlinear control-oriented model including path following model, single track vehicle model, and Magic Formula tire model, are constructed. To handle the stability constraints with ease, the nonlinear model predictive control (NMPC) technique is applied for path following issue. Here NMPC control problem is reasonably established with the constraints of vehicle sideslip angle, yaw rate, steering angle, lateral position error, and Lyapunov stability. To mitigate the online calculation burden, the continuation/ generalized minimal residual (C/GMRES) algorithm is adopted. The deadzone penalty functions are employed for handling the inequality constraints and holding the smoothness of solution. Moreover, the varying predictive duration is utilized in this paper so as to fast gain the good initial solution by numerical algorithm. Finally, the simulation validations are carried out, which yields that the proposed strategy can achieve desirable path following and vehicle stability efficacy, while greatly reducing the computational burden compared with the NMPC controllers by active set algorithm or interior point algorithm

A robust estimator of mutual information for deep learning interpretability

We develop the use of mutual information (MI), a well-established metric in information theory, to interpret the inner workings of deep learning (DL) models. To accurately estimate MI from a finite number of samples, we present GMM-MI (pronounced ‘Jimmie’), an algorithm based on Gaussian mixture models that can be applied to both discrete and continuous settings. GMM-MI is computationally efficient, robust to the choice of hyperparameters and provides the uncertainty on the MI estimate due to the finite sample size. We extensively validate GMM-MI on toy data for which the ground truth MI is known, comparing its performance against established MI estimators. We then demonstrate the use of our MI estimator in the context of representation learning, working with synthetic data and physical datasets describing highly non-linear processes. We train DL models to encode high-dimensional data within a meaningful compressed (latent) representation, and use GMM-MI to quantify both the level of disentanglement between the latent variables, and their association with relevant physical quantities, thus unlocking the interpretability of the latent representation. We make GMM-MI publicly available in this GitHub repository

A robust estimator of mutual information for deep learning interpretability

We develop the use of mutual information (MI), a well-established metric in information theory, to interpret the inner workings of deep learning models. To accurately estimate MI from a finite number of samples, we present GMM-MI (pronounced $``$Jimmie$"$), an algorithm based on Gaussian mixture models that can be applied to both discrete and continuous settings. GMM-MI is computationally efficient, robust to the choice of hyperparameters and provides the uncertainty on the MI estimate due to the finite sample size. We extensively validate GMM-MI on toy data for which the ground truth MI is known, comparing its performance against established mutual information estimators. We then demonstrate the use of our MI estimator in the context of representation learning, working with synthetic data and physical datasets describing highly non-linear processes. We train deep learning models to encode high-dimensional data within a meaningful compressed (latent) representation, and use GMM-MI to quantify both the level of disentanglement between the latent variables, and their association with relevant physical quantities, thus unlocking the interpretability of the latent representation. We make GMM-MI publicly available.Comment: 30 pages, 8 figures. Minor changes to match version accepted for publication in Machine Learning: Science and Technology. GMM-MI available at https://github.com/dpiras/GMM-M

The Association of Problematic Smartphone Use with Family Well-Being Mediated by Family Communication in Chinese Adults: A Population-Based Study

Background and aims: Few studies have investigated the effects of problematic smartphone use (PSU) in the family context. We studied the association of PSU as a predictor with family well-being and the potential mediating role of family communication in Hong Kong Chinese adults. Methods: We analyzed data of 5,063 randomly selected adults [mean age (SD) = 48.1 (18.2) years; 45.0% men] from a dual landline and mobile telephone survey in 2017. PSU was assessed by the Smartphone Addiction Scale-Short Version with higher scores indicating higher levels. Family well-being was assessed by three questions on perceived family health, harmony, and happiness (3Hs) with higher scores indicating greater well-being. Perceived sufficiency and quality of family communication were rated. Multivariable regression analyses examined (a) associations of PSU with family 3Hs and well-being and (b) mediating role of family communication, adjusting for sociodemographic variables. Results: PSU was negatively associated with perceived family health (adjusted β = −0.008, 95% CI = −0.016, −0.0004), harmony (adjusted β =−0.009, 95% CI = −0.017, −0.002), happiness (adjusted β =−0.015, 95% CI = −0.022, −0.007), and well-being (adjusted β= −0.011, 95% CI = −0.018, −0.004). Perceived family communication sufficiency (adjusted β = −0.007, 95% CI =−0.010, −0.005) and quality (adjusted β = −0.009, 95% CI =−0.014, −0.005) mediated the association of PSU with family well-being, with 75% and 94% of total effects having mediated, respectively. Discussion and conclusions: PSU was negatively associated with family well-being, which was partially mediated by family communication. Such findings provide insights for health programs to prevent PSU and improve family well-being

Adverse childhood experiences on internet gaming disorder mediated through insomnia in Chinese young people

BackgroundAdverse childhood experiences (ACEs) have been associated with addictions such as substance use disorders. Few have examined ACEs on internet gaming disorder (IGD) as a newly established behavioral addiction, and the potential mediating role of insomnia remains unclear. We examined the associations between ACE number and types, IGD, and insomnia.MethodsParticipants included 1, 231 Chinese university students (54.5% male; 56.9% aged 18–20 years) who had played internet games at least once in the previous month. ACEs were measured using the 10-item ACE questionnaire (yes/no). Symptoms of insomnia and IGD were measured using the Insomnia Severity Index and the 9-item Internet Gaming Disorder Scale–Short-Form, respectively. Multivariable regressions examined the associations, adjusting for sex, age, maternal and paternal educational attainment, monthly household income, smoking, and alcohol drinking. The mediating role of insomnia symptoms was explored.ResultsThe prevalence of ACEs≥1 was 40.0%. Childhood verbal abuse was the most prevalent (17.4%), followed by exposure to domestic violence (17.1%) and childhood physical abuse (15.5%). More ACE numbers showed an association with IGD symptoms (adjusted OR = 1.11, 95% CI 1.04, 1.17). Specifically, IGD symptoms were observed for childhood physical neglect, emotional neglect, sexual abuse, parental divorce or separation, and household substance abuse. Insomnia symptoms mediated the associations of ACE number and types with IGD symptoms (proportion of total effect mediated range 0.23–0.89).ConclusionThe number and specific types of ACEs showed associations with IGD mediated through insomnia. Screening of ACEs is recommended in future studies on IGD. Longitudinal data are warranted to determine the causality of the observed associations

TencentPretrain: A Scalable and Flexible Toolkit for Pre-training Models of Different Modalities

Recently, the success of pre-training in text domain has been fully extended to vision, audio, and cross-modal scenarios. The proposed pre-training models of different modalities are showing a rising trend of homogeneity in their model structures, which brings the opportunity to implement different pre-training models within a uniform framework. In this paper, we present TencentPretrain, a toolkit supporting pre-training models of different modalities. The core feature of TencentPretrain is the modular design. The toolkit uniformly divides pre-training models into 5 components: embedding, encoder, target embedding, decoder, and target. As almost all of common modules are provided in each component, users can choose the desired modules from different components to build a complete pre-training model. The modular design enables users to efficiently reproduce existing pre-training models or build brand-new one. We test the toolkit on text, vision, and audio benchmarks and show that it can match the performance of the original implementations

A fast model predictive control allocation of distributed drive electric vehicles for tire slip energy saving with stability constraints

Abstract This paper proposes a fast model predictive control allocation (MPCA) approach to minimize the tire slip power loss on contact patches for distributed drive electric vehicles (DDEV). In this strategy, two assumptions are set up from a practical focus: (1) the vehicle acceleration and yaw rate are measurable by global position system (GPS)/ inertial navigation system (INS) and inertial measurement unit (IMU), respectively; (2) the longitudinal velocity, road adhesion factor, and vehicle yaw rate are arranged to be “already known” by advanced estimators. For the strategy design, a CarSim-embedded driver model and a linear quadratic regulator (LQR) based direct yaw moment controller, are respectively applied to calculate the desired longitudinal traction and yaw moment as a virtual input first. Then, a MPCA method is proposed to reasonably distribute the virtual input among four in-wheel motors in order to optimize the tire slip power loss and vehicle stability performance. To accurately characterize tire slip power loss in MPCA, a tire slip estimator is established for tire slip information acquirement. Moreover, addressing on the heavily computational challenge in MPCA, a modified continuation/generalized minimal residual (C/GMRES) algorithm is employed. Since the traditional C/GMRES algorithm cannot directly solve the inequality constraint problem, the barrier functions are applied for transforming the inequality constraints to equivalent cost. According to Pontryagin’s minimum principle (PMP) conditions, the existence and uniqueness for solution of the modified C/GMRES algorithm are strictly proved. Subsequently, a Karush–Kuhn–Tucker​ (KKT) condition based approach is developed to fast gain the optimally initial solution in C/GMRES algorithm for extending application. Finally, numerical simulation validations are implemented and demonstrate that the proposed MPCA can ensure the compatibility between the tire slip power loss reduction and vehicle stability in a computationally efficient way

Design of 3D-Printed Electronic Fiber Optic Sensor to Detect Rhodamine B Reagent: An Initiation to Potential Virus Detection

A fluorescence device based on ultraviolet light is proposed in this paper, which currently stands at the design stage with the eventual aim to potentially detect virus/antibody fluorescence reactions. The designed device is proposed to have the characteristics of high reflectivity, low power consumption, wide spectrum of light source, and proper silver coating. For fabrication and raising product quality, 3D printing technology and a sputtering test will be used. In this connection, this paper firstly introduces the design sources; then, the ideas of inventing fluorescence detection devices based on ultraviolet light, followed by the data analysis as well as discussing the results of computer simulations. The design process, materials, methods, and experiments are demonstrated following the reality work procedure. Instead of directly using a virus or antibodies for the experiment, at the current design stage, we focus on using this device to detect the rhodamine B reagent. Experiment shows that this reagent can be successfully detected. With this achievement, we logically believe that such type of an ultraviolet optical sensor, with further development and testing, may have the possible value to detect a single virus such as COVID-19, as well as other viruses or small molecules. Though there is long way to go to achieve such a goal, future works experimenting with the detection device on real virus or antibodies can take place more efficiently with a good foundation

Comparisons of Energy Management Methods for a Parallel Plug-In Hybrid Electric Vehicle between the Convex Optimization and Dynamic Programming

This paper proposes a comparison study of energy management methods for a parallel plug-in hybrid electric vehicle (PHEV). Based on detailed analysis of the vehicle driveline, quadratic convex functions are presented to describe the nonlinear relationship between engine fuel-rate and battery charging power at different vehicle speed and driveline power demand. The engine-on power threshold is estimated by the simulated annealing (SA) algorithm, and the battery power command is achieved by convex optimization with target of improving fuel economy, compared with the dynamic programming (DP) based method and the charging depleting–charging sustaining (CD/CS) method. In addition, the proposed control methods are discussed at different initial battery state of charge (SOC) values to extend the application. Simulation results validate that the proposed strategy based on convex optimization can save the fuel consumption and reduce the computation burden obviously