Vidya Sumathy, Jakub Haluska, George Nikolakopoulos
In this work, an experimental characterization of the configuration space of a soft, pneumatically actuated morphing quadrotor is presented, with a focus on precise thrust characterization of its flexible arms, considering the effect of downwash. Unlike traditional quadrotors, the soft drone has pneumatically actuated arms, introducing complex, nonlinear interactions between motor thrust and arm deformation, which make precise control challenging. The silicone arms are actuated using differential pressure to achieve flexibility and thus have a variable workspace compared to their fixed counter-parts. The deflection of the soft arms during compression and expansion is controlled throughout the flight. However, in real time, the downwash from the motor attached at the tip of the soft arm generates a significant and random disturbance on the arm. This disturbance affects both the desired deflection of the arm and the overall stability of the system. To address this factor, an experimental characterization of the effect of downwash on the deflection angle of the arm is conducted.
Cats use the connection governing parallel transport in the space of shapes to land safely on their feet. Here we argue that this connection also explains the impressive performance of molecular motors by enabling molecules to evade conclusions of Feynman's ratchet-and-pawl analysis. We first demonstrate, using simple molecular models, how directed rotational motion can emerge from shape changes even without angular momentum. We then computationally design knotted polyalanine molecules and show how their shape space connection organizes individual atom thermal vibrations into collective rotational motion, independently of angular momentum. Our simulations show that rotational motion arises effortlessly even in ambient water, making the molecule an effective theory time crystal. Our findings have potential for practical molecular motor design and engineering and can be verified through high-precision nuclear magnetic resonance measurements.
Legged robots operating in real-world environments must possess the ability to rapidly adapt to unexpected conditions, such as changing terrains and varying payloads. This paper introduces the Synaptic Motor Adaptation (SMA) algorithm, a novel approach to achieving real-time online adaptation in quadruped robots through the utilization of neuroscience-derived rules of synaptic plasticity with three-factor learning. To facilitate rapid adaptation, we meta-optimize a three-factor learning rule via gradient descent to adapt to uncertainty by approximating an embedding produced by privileged information using only locally accessible onboard sensing data. Our algorithm performs similarly to state-of-the-art motor adaptation algorithms and presents a clear path toward achieving adaptive robotics with neuromorphic hardware.
Positioning accuracy is a critical requirement for vehicle-to-everything (V2X) use cases. Therefore, this paper derives the theoretical limits of estimation for the position and orientation of vehicles in a cooperative vehicle-to-vehicle (V2V) scenario, using a lens-based multiple-input multiple-output (lens-MIMO) system. Following this, we analyze the Cram$\acute{\text{e}}$r-Rao lower bounds (CRLBs) of the position and orientation estimation and explore a received signal model of a lens-MIMO for the particular angle of arrival (AoA) estimation with a V2V geometric model. Further, we propose a lower complexity AoA estimation technique exploiting the unique characteristics of the lens-MIMO for a single target vehicle; as a result, its estimation scheme is effectively extended by the successive interference cancellation (SIC) method for multiple target vehicles. Given these AoAs, we investigate the lens-MIMO estimation capability for the positions and orientations of vehicles. Subsequently, we prove that the lens-MIMO outperforms a conventional uniform linear array (ULA) in a certain configuration of a lens's structure. Finally, we confirm that the proposed localization algorithm is superior to ULA's CRLB as the resolution of the lens increases in spite of the lower complexity.
Abduallah Mohamed, Jundi Liu, Linda Ng Boyle
et al.
An ego vehicle following a virtual lead vehicle planned route is an essential component when autonomous and non-autonomous vehicles interact. Yet, there is a question about the driver's ability to follow the planned lead vehicle route. Thus, predicting the trajectory of the ego vehicle route given a lead vehicle route is of interest. We introduce a new dataset, the FollowMe dataset, which offers a motion and behavior prediction problem by answering the latter question of the driver's ability to follow a lead vehicle. We also introduce a deep spatio-temporal graph model FollowMe-STGCNN as a baseline for the dataset. In our experiments and analysis, we show the design benefits of FollowMe-STGCNN in capturing the interactions that lie within the dataset. We contrast the performance of FollowMe-STGCNN with prior motion prediction models showing the need to have a different design mechanism to address the lead vehicle following settings.
This article proposes an offline Energy Management System (EMS) for Parallel Hybrid Electric Vehicles (PHEVs). Dividing the torque between the Electric Motor (EM) and the Internal Combustion Engine (ICE) requires a suitable EMS. Batteries are vital to HEVs and significantly impact overall vehicle cost and performance. High temperature and high battery State of Charge (SOC) are the main factors that accelerate battery aging. SOC is the most critical state variable in EMS and was usually considered the only dynamic variable in previous studies. For simplicity, the battery temperature was often assumed to be constant, and the effect of EMS on temperature change was neglected. In this paper, we first apply Dynamic Programming (DP) to a PHEV without considering battery temperature variations. Then, the battery model is improved by modeling the cooling system to take into account temperature variations and show how neglecting the thermal dynamics of the battery in EMS is impractical. Finally, by integrating battery temperature as a state variable in the optimization problem, a new EMS is proposed to control battery temperature and SOC variation. Simulation results of the tested vehicle show that the proposed method controls battery charge and temperature. The proposed EMS method prevents uncontrolled fluctuations in battery temperature and reduces its deterioration rate.
Schizophrenia is a complicated mental illness characterized by a broad spectrum of symptoms affecting cognition, behavior, and emotion. The task of identifying reliable biomarkers to classify Schizophrenia accurately continues to be a challenge in the field of psychiatry. We investigate the temporal patterns within the motor activity data as a potential key to enhancing the categorization of individuals with Schizophrenia, using the dataset having motor activity recordings of 22 Schizophrenia patients and 32 control subjects. The dataset contains per-minute motor activity measurements collected for an average of 12.7 days in a row for each participant. We dissect each day into segments (Twelve, Eight, six, four, three, and two parts) and evaluate their impact on classification. We employ sixteen statistical features within these temporal segments and train them on Seven machine learning models to get deeper insights. LightGBM model outperforms the other six models. Our results indicate that the temporal segmentation significantly improves the classification, with AUC-ROC = 0.93, F1 score = 0.84( LightGBM- without any segmentation) and AUC-ROC = 0.98, F1 score = 0.93( LightGBM- with segmentation). Distinguishing between diurnal and nocturnal segments amplifies the differences between Schizophrenia patients and controls. However, further subdivisions into smaller time segments do not affect the AUC- ROC significantly. Morning, afternoon, evening, and night partitioning gives similar classification performance to day-night partitioning. These findings are valuable as they indicate that extensive temporal classification beyond distinguishing between day and night does not yield substantial results, offering an efficient approach for further classification, early diagnosis, and monitoring of Schizophrenia.
Vera Hagemann, Lara Watermann, Florian Klonek
et al.
Astronaut crews and ground control support teams are highly interdependent teams that need to communicate effectively to achieve a safe mission - despite being separated by large distances. Team communication quality with its facets clarity of objectives and information flow is a key coordination process to achieve high team performance and task satisfaction. Especially in interdependent teams working in extreme environments with time-delayed communications, the team's success depends on effective communication. We hypothesized that communication quality affects two key team outcomes, performance and task satisfaction, and that these effects can be explained by increased workload (effort and frustration). Hypotheses were tested during the AMADEE-20 analog Mars mission of the Austrian Space Forum. The analog astronauts (AA) were supported by an On-Site-Support (OSS) team and a remote Mission-Support-Centre (MSC) team. The MSC was the only contact for both AA and OSS, and the communication between them had a one-way delay of 10 minutes. Our study consisted of three runs in which members of each team had to exchange information to solve an interdependent task. We measured communication quality, effort and frustration, task satisfaction, and team performance. Results show that clarity of objectives and information flow positively impacted multiteam system performance. Furthermore, clarity of objectives reduced experienced effort which in turn enhanced team performance. High levels of information flow reduced experienced frustration, in turn enhancing task satisfaction. Our findings show that these facets of communication quality are essential for multiteam systems that work separated from each other. We stress that specific (team) communication training for astronauts and support personnel will be key to effective teamwork during future Mars missions, and thus to overall mission success.
Max van Meer, Rodrigo A. González, Gert Witvoet
et al.
Switched Reluctance Motors (SRMs) enable power-efficient actuation with mechanically simple designs. This paper aims to identify the nonlinear relationship between torque, rotor angle, and currents, to design commutation functions that minimize torque ripple in SRMs. This is achieved by conducting specific closed-loop experiments using purposely imperfect commutation functions and identifying the nonlinear dynamics via Bayesian estimation. A simulation example shows that the presented method is robust to position-dependent disturbances, and experiments suggest that the identification method enables the design of commutation functions that significantly increase performance. The developed approach enables accurate identification of the torque-current-angle relationship in SRMs, without the need for torque sensors, an accurate linear model, or an accurate model of position-dependent disturbances, making it easy to implement in production.
The quality of a laser deep penetration welding joint is closely related to porosity. However, the keyhole stability seriously affects the formation of porosity during the laser welding process. In this paper, a three-dimensional laser welding model with gas/liquid interface evolution characteristics is constructed based on the hydrodynamic interaction between the keyhole and molten pool during the laser welding process. The established model is used to simulate the flow and heat transfer process of molten. The Volume of Fluid (VOF) method is used to study the formation and collapse of the keyhole and the formation of bubbles. It is found that bubbles are easy to form when the keyhole depth abruptly changes. There are three main forms of bubbles formed by keyhole instability. The front wall of the keyhole collapses backward to form a bubble. The back wall of the keyhole inclines forward to form a bubble. The lower part of the keyhole produces a necking-down effect, and the lower part of the keyhole is isolated separately to form a bubble. In addition, when the keyhole does not penetrate the base metal, the stability of the keyhole is high and the percentage of porosity is low.
Serkan Kiranyaz, Ozer Can Devecioglu, Amir Alhams
et al.
Continuous long-term monitoring of motor health is crucial for the early detection of abnormalities such as bearing faults (up to 51% of motor failures are attributed to bearing faults). Despite numerous methodologies proposed for bearing fault detection, most of them require normal (healthy) and abnormal (faulty) data for training. Even with the recent deep learning (DL) methodologies trained on the labeled data from the same machine, the classification accuracy significantly deteriorates when one or few conditions are altered. Furthermore, their performance suffers significantly or may entirely fail when they are tested on another machine with entirely different healthy and faulty signal patterns. To address this need, in this pilot study, we propose a zero-shot bearing fault detection method that can detect any fault on a new (target) machine regardless of the working conditions, sensor parameters, or fault characteristics. To accomplish this objective, a 1D Operational Generative Adversarial Network (Op-GAN) first characterizes the transition between normal and fault vibration signals of (a) source machine(s) under various conditions, sensor parameters, and fault types. Then for a target machine, the potential faulty signals can be generated, and over its actual healthy and synthesized faulty signals, a compact, and lightweight 1D Self-ONN fault detector can then be trained to detect the real faulty condition in real time whenever it occurs. To validate the proposed approach, a new benchmark dataset is created using two different motors working under different conditions and sensor locations. Experimental results demonstrate that this novel approach can accurately detect any bearing fault achieving an average recall rate of around 89% and 95% on two target machines regardless of its type, severity, and location.
We introduce here the idea of Meta-Learning for training EEG BCI decoders. Meta-Learning is a way of training machine learning systems so they learn to learn. We apply here meta-learning to a simple Deep Learning BCI architecture and compare it to transfer learning on the same architecture. Our Meta-learning strategy operates by finding optimal parameters for the BCI decoder so that it can quickly generalise between different users and recording sessions -- thereby also generalising to new users or new sessions quickly. We tested our algorithm on the Physionet EEG motor imagery dataset. Our approach increased motor imagery classification accuracy between 60% to 80%, outperforming other algorithms under the little-data condition. We believe that establishing the meta-learning or learning-to-learn approach will help neural engineering and human interfacing with the challenges of quickly setting up decoders of neural signals to make them more suitable for daily-life.
M. Amin. Ghasemi, Sadjaad Ozgoli, Ali. M. NasrAbadi
Brain-computer interface (BCI) systems are usually designed specifically for each subject based on motor imagery. Therefore, the usability of these networks has become a significant challenge. The network has to be designed separately for each user, which is time-consuming for the user. Therefore, this study proposes a method by which the calibration time is significantly reduced while the classification accuracy is increased. In this method, we calibrated the features extracted from the motor imagery task by dividing the features extracted from the resting-state into both open-eye and closed-eye modes and the state in which the subject moves his eyes. The best classification accuracy was obtained using the SVM classifier using the resting-state signal in the open eye, which increased by 3.64% to 74.04%. In this paper, we also investigated the effect of recording time of the resting-state signal and the impact of eye state on the classification accuracy.
Sébastien B. Hausmann, Alessandro Marin Vargas, Alexander Mathis
et al.
The utility of machine learning in understanding the motor system is promising a revolution in how to collect, measure, and analyze data. The field of movement science already elegantly incorporates theory and engineering principles to guide experimental work, and in this review we discuss the growing use of machine learning: from pose estimation, kinematic analyses, dimensionality reduction, and closed-loop feedback, to its use in understanding neural correlates and untangling sensorimotor systems. We also give our perspective on new avenues where markerless motion capture combined with biomechanical modeling and neural networks could be a new platform for hypothesis-driven research.
As connectivity between and within vehicles increases, so does concern about safety and security. Various automotive serial protocols are used inside vehicles such as Controller Area Network (CAN), Local Interconnect Network (LIN) and FlexRay. CAN bus is the most used in-vehicle network protocol to support exchange of vehicle parameters between Electronic Control Units (ECUs). This protocol lacks security mechanisms by design and is therefore vulnerable to various attacks. Furthermore, connectivity of vehicles has made the CAN bus not only vulnerable from within the vehicle but also from outside. With the rise of connected cars, more entry points and interfaces have been introduced on board vehicles, thereby also leading to a wider potential attack surface. Existing security mechanisms focus on the use of encryption, authentication and vehicle Intrusion Detection Systems (IDS), which operate under various constrains such as low bandwidth, small frame size (e.g. in the CAN protocol), limited availability of computational resources and real-time sensitivity. We survey In-Vehicle Network (IVN) attacks which have been grouped under: direct interfaces-initiated attacks, telematics and infotainment-initiated attacks, and sensor-initiated attacks. We survey and classify current cryptographic and IDS approaches and compare these approaches based on criteria such as real time constrains, types of hardware used, changes in CAN bus behaviour, types of attack mitigation and software/ hardware used to validate these approaches. We conclude with potential mitigation strategies and research challenges for the future.
Francesco Concas, Jukka K. Nurminen, Tommi Mikkonen
et al.
As a part of the digital transformation, we interact with more and more intelligent gadgets. Today, these gadgets are often mobile devices, but in the advent of smart cities, more and more infrastructure---such as traffic and buildings---in our surroundings becomes intelligent. The intelligence, however, does not emerge by itself. Instead, we need both design techniques to create intelligent systems, as well as approaches to validate their correct behavior. An example of intelligent systems that could benefit smart cities are self-driving vehicles. Self-driving vehicles are continuously becoming both commercially available and common on roads. Accidents involving self-driving vehicles, however, have raised concerns about their reliability. Due to these concerns, the safety of self-driving vehicles should be thoroughly tested before they can be released into traffic. To ensure that self-driving vehicles encounter all possible scenarios, several millions of hours of testing must be carried out; therefore, testing self-driving vehicles in the real world is impractical. There is also the issue that testing self-driving vehicles directly in the traffic poses a potential safety hazard to human drivers. To tackle this challenge, validation frameworks for testing self-driving vehicles in simulated scenarios are being developed by academia and industry. In this chapter, we briefly introduce self-driving vehicles and give an overview of validation frameworks for testing them in a simulated environment. We conclude by discussing what an ideal validation framework at the state of the art should be and what could benefit validation frameworks for self-driving vehicles in the future.
The problem of step tracking control with a switching input and without any continuous-valued inputs is considered. The control objective is to reduce the number of switchings to a minimal value. This approach finds interesting applications when switching comprises costs and should be avoided. To solve the problem, a state dependent switching strategy should be designed and the resulting closed loop is indeed a hybrid system. Therefore, first we investigate the conditions on a hybrid system for being the desired solution. Then, we propose a method for designing the switching strategy such that the closed loop as a hybrid system solves the problem. The proposed method is applied to the induction motor control problem which results in relatively simple and efficient control algorithm. Comparison with the direct torque control for induction motors show that our method has a superior performance in reducing the number of mode switches.