Human Driver Simulation Model

As part of the vehicle environmental certification process, the Environmental Protection Agency (EPA) requires automobile manufacturers to run a series of “drive cycle” tests to evaluate the efficiency of a vehicle (miles/gallon for internal combustion engine vehicles or Wh/mile for electric vehicles). For these tests, the dynamometer must be controlled by a human driver. The goal of this project is to create a simulation model of a human driver performing an automobile speed control task using MATLAB and Simulink. This model mimics human control tendencies and error as closely as possible by tuning parameter values to best-fit experimental data. Outputs from the model are brake pedal and accelerator pedal positions. Inputs to the model are the current desired vehicle speed, the current actual vehicle speed, and the desired vehicle speed a short time in the future. This preview of the desired speed is available to human drivers in the dynamometer test, and including preview as a control pathway in the simulation model was critical to producing reasonable results. Keywords – Simulation, Manual control, Parameter optimization, Grey-box mode

Verified Compositional Neuro-Symbolic Control for Stochastic Systems with Temporal Logic Tasks

Several methods have been proposed recently to learn neural network (NN) controllers for autonomous agents, with unknown and stochastic dynamics, tasked with complex missions captured by Linear Temporal Logic (LTL). Due to the sample-inefficiency of the majority of these works, compositional learning methods have been proposed decomposing the LTL specification into smaller sub-tasks. Then, separate controllers are learned and composed to satisfy the original task. A key challenge within these approaches is that they often lack safety guarantees or the provided guarantees are impractical. This paper aims to address this challenge. Particularly, we consider autonomous systems with unknown and stochastic dynamics and LTL-encoded tasks. We assume that the system is equipped with a finite set of base skills modeled by trained NN feedback controllers. Our goal is to check if there exists a temporal composition of the trained NN controllers - and if so, to compute it - that will yield a composite system behavior that satisfies the assigned LTL task with probability one. We propose a new approach that relies on a novel integration of automata theory and data-driven reachability analysis tools for NN-controlled stochastic systems. The resulting neuro-symbolic controller allows the agent to generate safe behaviors for unseen complex temporal logic tasks in a zero-shot fashion by leveraging its base skills. We show correctness of the proposed method and we provide conditions under which it is complete. To the best of our knowledge, this is the first work that designs verified temporal compositions of NN controllers for unknown and stochastic systems. Finally, we provide extensive numerical simulations and hardware experiments on robot navigation tasks to demonstrate the proposed method.Comment: arXiv admin note: substantial text overlap with arXiv:2209.0613

Text2Tree: Aligning Text Representation to the Label Tree Hierarchy for Imbalanced Medical Classification

Deep learning approaches exhibit promising performances on various text tasks. However, they are still struggling on medical text classification since samples are often extremely imbalanced and scarce. Different from existing mainstream approaches that focus on supplementary semantics with external medical information, this paper aims to rethink the data challenges in medical texts and present a novel framework-agnostic algorithm called Text2Tree that only utilizes internal label hierarchy in training deep learning models. We embed the ICD code tree structure of labels into cascade attention modules for learning hierarchy-aware label representations. Two new learning schemes, Similarity Surrogate Learning (SSL) and Dissimilarity Mixup Learning (DML), are devised to boost text classification by reusing and distinguishing samples of other labels following the label representation hierarchy, respectively. Experiments on authoritative public datasets and real-world medical records show that our approach stably achieves superior performances over classical and advanced imbalanced classification methods.Comment: EMNLP 2023 Findings. Code: https://github.com/jyansir/Text2Tre

Transfert de charge en fonction de la vitesse de course pour les coureurs du sexe masculin qui touchent le sol avec l’arrière-pied

The purpose of this study was to identify the influence of running speed on plantar pressure, and to use a load transfer algorithm to investigate the load transference in healthy recreational male runners who had a natural rear-foot strike pattern. Totally, 49 healthy males participated in this study, 39 of them (age 22.8 ± 1.8 years, weight 65.6 ± 7.9 kg, height 171.9 ± 4.0 cm) were identified as rear-foot strike runners. Data of pressure parameters, including maximum force, peak pressure, contact area and force-time integral (FTI) was recorded by Pedar-X insole plantar pressure measurement system at 8 different speeds (5, 6, 7, 8, 9, 10, 11, 12 km/h). The results indicated that with the increase of running speed, plantar pressure significantly increased under all foot regions except for the big toe. Faster running speeds resulted in significant lower FTI in all foot regions except for lateral midfoot and heel. Medial metatarsal, central metatarsal, and big toe were the main loading regions for rear-foot strike male runners during running. Load transferred from medial foot to lateral foot in transverse direction, and from toes to metatarsal, midfoot and heel in the longitudinal direction with increasing speeds. As a component of the spring mechanism, the arch played a key role in supporting and transferring loads. © 2020 by the author(s)

Mining Negative Temporal Contexts For False Positive Suppression In Real-Time Ultrasound Lesion Detection

During ultrasonic scanning processes, real-time lesion detection can assist radiologists in accurate cancer diagnosis. However, this essential task remains challenging and underexplored. General-purpose real-time object detection models can mistakenly report obvious false positives (FPs) when applied to ultrasound videos, potentially misleading junior radiologists. One key issue is their failure to utilize negative symptoms in previous frames, denoted as negative temporal contexts (NTC). To address this issue, we propose to extract contexts from previous frames, including NTC, with the guidance of inverse optical flow. By aggregating extracted contexts, we endow the model with the ability to suppress FPs by leveraging NTC. We call the resulting model UltraDet. The proposed UltraDet demonstrates significant improvement over previous state-of-the-arts and achieves real-time inference speed. To facilitate future research, we will release the code, checkpoints, and high-quality labels of the CVA-BUS dataset used in our experiments.Comment: 10 pages, 4 figures, MICCAI 2023 Early Accep

Research on low frequency ripple suppression technology of inverter based on model prediction

The low frequency ripple of the input side current of the single-phase inverter will reduce the efficiency of the power generation system and affect the overall performance of the system. Aiming at this problem, this paper proposes a two-modal modulation method and its MPC multi-loop composite control strategy on the circuit topology of a single-stage boost inverter with a buffer unit. The control strategy achieves the balance of active power on both sides of AC and DC by controlling the stable average value of the buffer capacitor voltage, and provides a current reference for inductance current of the DC input side. At the same time, the MPC controller uses the minimum inductor current error as the cost function to control inductor current to track its reference to achieve low frequency ripple suppression of the input current. In principle, it is expounded that the inverter using the proposed control strategy has better low frequency ripple suppression effect than the multi-loop PI control strategy, and the conclusion is proved by the simulation data. Finally, an experimental device of a single-stage boost inverter using MPC multi-loop composite control strategy is designed and fabricated, and the experimental results show that the proposed research scheme has good low frequency ripple suppression effect and strong adaptability to different types of loads

Knowledge, Attitudes, and Social Responsiveness Toward Corona Virus Disease 2019 (COVID-19) Among Chinese Medical Students—Thoughts on Medical Education

Purpose: To assess knowledge, attitudes, and social responsiveness toward COVID-19 among Chinese medical students.Methods: Self-administered questionnaires were used to collect data from 889 medical students in three well-known Chinese medical universities. The questionnaire was comprised of three domains which consisted of demographic characteristic collection, seven items for knowledge, and eight items for attitudes and social responsiveness toward COVID-19. Data from different universities were lumped together and were divided into different groups to compare the differences, including (1) students at the clinical learning stage (Group A) or those at the basic-medicine stage (Group B) and (2) students who have graduated and worked (Group C) or those newly enrolled (Group D).Results: Medical students at group B had a weaker knowledge toward COVID-19 than did students at group A, especially in the question of clinical manifestations (p < 0.001). The percentage of totally correct answers of COVID-19 knowledge in group C was higher than that in Group D (p < 0.001). There were significant differences between groups C and D in the attitudes and social responsiveness toward COVID-19. Surprisingly, we found that the idea of newly enrolled medical students could be easily affected by interventions.Conclusions: In light of this information, medical education should pay attention not only to the cultivation of professional knowledge and clinical skills but also to the positive interventions to better the comprehensive qualities including communicative abilities and empathy

MANSA: Learning Fast and Slow in Multi-Agent Systems

In multi-agent reinforcement learning (MARL), independent learning (IL) often shows remarkable performance and easily scales with the number of agents. Yet, using IL can be inefficient and runs the risk of failing to successfully train, particularly in scenarios that require agents to coordinate their actions. Using centralised learning (CL) enables MARL agents to quickly learn how to coordinate their behaviour but employing CL everywhere is often prohibitively expensive in real-world applications. Besides, using CL in value-based methods often needs strong representational constraints (e.g. individual-global-max condition) that can lead to poor performance if violated. In this paper, we introduce a novel plug & play IL framework named Multi-Agent Network Selection Algorithm (MANSA) which selectively employs CL only at states that require coordination. At its core, MANSA has an additional agent that uses switching controls to quickly learn the best states to activate CL during training, using CL only where necessary and vastly reducing the computational burden of CL. Our theory proves MANSA preserves cooperative MARL convergence properties, boosts IL performance and can optimally make use of a fixed budget on the number CL calls. We show empirically in Level-based Foraging (LBF) and StarCraft Multi-agent Challenge (SMAC) that MANSA achieves fast, superior and more reliable performance while making 40% fewer CL calls in SMAC and using CL at only 1% CL calls in LBF