pwc-archive/papers-with-abstracts huggingface dataset:
Duality-based Convex Optimization for Real-time Obstacle Avoidance between Polytopes with Control Barrier Functions
Authors: Akshay Thirugnanam, Jun Zeng, Koushil Sreenath
Date: 2021-07-18 00:00:00

Developing controllers for obstacle avoidance between polytopes is a challenging and necessary problem for navigation in tight spaces. Traditional approaches can only formulate the obstacle avoidance problem as an offline optimization problem. To address these challenges, we propose a duality-based safety-critical optimal control using nonsmooth control barrier functions for obstacle avoidance between polytopes, which can be solved in real-time with a QP-based optimization problem. A dual optimization problem is introduced to represent the minimum distance between polytopes and the Lagrangian function for the dual form is applied to construct a control barrier function. We validate the obstacle avoidance with the proposed dual formulation for L-shaped (sofa-shaped) controlled robot in a corridor environment. We demonstrate real-time tight obstacle avoidance with non-conservative maneuvers on a moving sofa (piano) problem with nonlinear dynamics.
Residual Attention Based Network for Automatic Classification of Phonation Modes
Authors: Xiaoheng Sun, Yiliang Jiang, Wei Li
Date: 2021-07-18 00:00:00
Tasks: Classification, Feature Engineering, Information Retrieval, Music Information Retrieval, Retrieval

Phonation mode is an essential characteristic of singing style as well as an important expression of performance. It can be classified into four categories, called neutral, breathy, pressed and flow. Previous studies used voice quality features and feature engineering for classification. While deep learning has achieved significant progress in other fields of music information retrieval (MIR), there are few attempts in the classification of phonation modes. In this study, a Residual Attention based network is proposed for automatic classification of phonation modes. The network consists of a convolutional network performing feature processing and a soft mask branch enabling the network focus on a specific area. In comparison experiments, the models with proposed network achieve better results in three of the four datasets than previous works, among which the highest classification accuracy is 94.58%, 2.29% higher than the baseline.
Solve the General Constrained Optimal Control Problem with Common Integration Method
Authors: Sheng Zhang, Jin-Mei Gao
Date: 2018-02-06 00:00:00
Tasks: Numerical Integration

Computation of general state- and/or control-constrained Optimal Control Problems (OCPs) is difficult for various constraints, especially the intractable path constraint. For such problems, the theoretical convergence of numerical algorithms is usually not guaranteed, and the right solution may not be successfully obtained. With the recently proposed Variation Evolving Method (VEM), the evolution equations, which guarantee the convergence towards the optimal solution in theory even for the general constrained OCPs, are derived. In particular, the costate-free optimality conditions are established. Besides the analytic expressions of the costates and the Lagrange multipliers adjoining the terminal constraint, the integral equation that determines the Karush-Kuhn-Tucker (KKT) multiplier variable is also derived. Upon the work in this paper, the general constrained OCPs may be transformed to the Initial-value Problems (IVPs) to be solved, with common Ordinary Differential Equation (ODE) numerical integration methods.
Delay-Compensated Distributed PDE Control of Traffic with Connected/Automated Vehicles
Authors: Jie Qi, Shurong Mo, Miroslav Krstic
Date: 2021-07-19 00:00:00

We develop an input delay-compensating design for stabilization of an Aw-Rascle-Zhang (ARZ) traffic model in congested regime which is governed by a $2\times 2$ first-order hyperbolic nonlinear PDE. The traffic flow consists of both adaptive cruise control-equipped (ACC-equipped) and manually-driven vehicles. The control input is the time gap of ACC-equipped and connected vehicles, which is subject to delays resulting from communication lag. For the linearized system, a novel three-branch bakcstepping transformation with explicit kernel functions is introduced to compensate the input delay. The transformation is proved {\ae}to be bounded, continuous and invertible, with explicit inverse transformation derived. Based on the transformation, we obtain the explicit predictor-feedback controller. We prove exponential stability of the closed-loop system with the delay compensator in $L_2$ norm. The performance improvement of the closed-loop system under the proposed controller is illustrated in simulation.
Design of robust controller applied for series elastic actuators in controlling humanoid's joint
Authors: Anh Khoa Lanh Luu, Van Tu Duong, Huy Hung Nguyen, Sang Bong Kim, Tan Tien Nguyen
Date: 2021-07-19 00:00:00

Although the application of Series elastic actuators (SEAs) in the biomechatronic field has proved its appropriation in many aspects so far, the problems of maintaining the stability for the SEAs still remains. This paper proposes a robust controller so that to overcome the drawbacks of the previous researches. Firstly, a mathematical model considering both the SEAs and the hip joint of humanoid UXA-90 is obtained. Secondly, a reference input of the proposed controller that is achieved from desired hip joint's angle in a walking cycle is utilized. Then, a backstepping based sliding mode force control approach is employed to ensure the precise movement of robot's link as well as meeting the requirement of robustness for the whole system, which is significant for the task of walking of a humanoid. Finally, some simulations are carried out to verify the quality and effectiveness of the proposed controller.
Employing Altruistic Vehicles at On-ramps to Improve the Social Traffic Conditions
Authors: Ruolin Li, Philip N. Brown, Roberto Horowitz
Date: 2021-07-18 00:00:00

Highway on-ramps are regarded as typical bottlenecks in transportation networks. In previous work, mainline vehicles' selfish lane choice behavior at on-ramps is studied and regarded as one cause leading to on-ramp inefficiency. When on-ramp vehicles plan to merge into the mainline of the highway, mainline vehicles choose to either stay steadfast on the current lane or bypass the merging area by switching to a neighboring lane farther from the on-ramp. Selfish vehicles make the decisions to minimize their own travel delay, which compromises the efficiency of the whole on-ramp. Results in previous work have shown that, if we can encourage a proper portion of mainline vehicles to bypass rather than to stay steadfast, the social traffic conditions can be improved. In this work, we consider employing a proportion of altruistic vehicles among the selfish mainline vehicles to improve the efficiency of the on-ramps. The altruistic vehicles are individual optimizers, making decisions whether to stay steadfast or bypass to minimize their own altruistic cost, which is a weighted sum of the travel delay and their negative impact on other vehicles. We first consider the ideal case that altruistic costs can be perfectly measured by altruistic vehicles. We give the conditions for the proportion of altruistic vehicles and the weight configuration of the altruistic costs, under which the social delay can be decreased or reach the optimal. Subsequently, we consider the impact of uncertainty in the measurement of altruistic costs and we give the optimal weight configuration for altruistic vehicles which minimizes the worst-case social delay under such uncertainty.
Byzantine-Resilient Distributed Hypothesis Testing With Time-Varying Network Topology
Authors: Bo Wu, Steven Carr, Suda Bharadwaj, Zhe Xu, Ufuk Topcu
Date: 2020-08-01 00:00:00

We study the problem of distributed hypothesis testing over a network of mobile agents with limited communication and sensing ranges to infer the true hypothesis collaboratively. In particular, we consider a scenario where there is an unknown subset of compromised agents that may deliberately share altered information to undermine the team objective. We propose two distributed algorithms where each agent maintains and updates two sets of beliefs (i.e., probability distributions over the hypotheses), namely local and actual beliefs (LB and AB respectively for brevity). In both algorithms, at every time step, each agent shares its AB with other agents within its communication range and makes a local observation to update its LB. Then both algorithms can use the shared information to update ABs under certain conditions. One requires receiving a certain number of shared ABs at each time instant; the other accumulates shared ABs over time and updates after the number of shared ABs exceeds a prescribed threshold. Otherwise, both algorithms rely on the agent's current LB and AB to update the new AB. We prove under mild assumptions that the AB for every non-compromised agent converges almost surely to the true hypothesis, without requiring connectivity in the underlying time-varying network topology. Using a simulation of a team of unmanned aerial vehicles aiming to classify adversarial agents among themselves, we illustrate and compare the proposed algorithms. Finally, we show experimentally that the second algorithm consistently outperforms the first algorithm in terms of the speed of convergence.
Dynamic Prioritization of Emergency Vehicles For Self-Organizing Traffic using VTL+EV *
Authors: Subash Humagain, Roopak Sinha
Date: 2021-07-17 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles

Cooperative vehicular technology in recent times has aided in realizing some state-of-art technologies like autonomous driving. Effective and efficient prioritization of emergency vehicles (EVs) using cooperative vehicular technology can undoubtedly aid in saving property and lives. Contemporary EV prioritization, called preemption, is highly dependent on existing traffic infrastructure. Accessing crucial decision parameters for preemption like speed, position and acceleration data in real-time is almost impossible in current systems. The connected vehicle can provide such data in real-time, which makes EV preemption more responsive and effective. Also, autonomous vehicles can help in optimizing the timing in traffic phases and minimize human-related loss like higher headway times and inconsistent inter-vehicle spacing when following each other. In this paper, we introduce self-coordinating a decentralized traffic control system termed as Virtual Traffic Light plus for Emergency Vehicle (VTL+EV) to prioritize EVs in an intersection. The proposed system can expedite EVs movement through intersections and impose minimal waiting time for ordinary vehicles. The VTL+EV algorithm also can improve overall throughput making an intersection more efficient.
Proactive Rolling-Horizon based Scheduling of Hydrogen Systems for Resilient Power Grids
Authors: Hamed Haggi, Wei Sun, James M. Fenton, Paul Brooker
Date: 2021-07-17 00:00:00
Tasks: Scheduling

Deploying distributed energy resources (DERs) and other smart grid technologies have increased the complexity of power grids and made them more vulnerable to natural disasters and cyber-physical-human (CPH) threats. To deal with these extreme events, proactive plans are required by utilities to minimize the damages caused by CPH threats. This paper proposes a proactive rolling-horizon-based scheme for the resilience-oriented operation of hydrogen (H2) systems in integrated distribution and transmission networks. The proposed framework is a bi-level model in which the upper-level is focused on distribution system operation in both normal and emergency operation modes, and the lower-level problem accounts for the transmission network operation. Two preeminent aspects of H2 systems are considered in this paper, 1) to show the flexibility of H2 systems, capacity-based demand response signals are considered for electrolyzers, stationary fuel cell (FC) units, and H2 storage tanks are considered in both normal and emergency operation modes; 2) unlike the batteries which can only charge and discharge energy based on maximum duration times and power ratings, H2 systems can be considered as the flexible long-term energy storage by storing H2 for days and supplying power to FC in the case of \textit{N-m} outages lasting for more than 10 hours. Moreover, H2 production cost based on water electrolysis and storage costs is calculated. Simulation results demonstrate that utilities can improve the system-level resilience using H2 systems as long-term backup power resources.
Routing Autonomous Emergency Vehicles in Smart Cities Using Real Time Systems Analogy: A Conceptual Model
Authors: Subash Humagain, Roopak Sinha
Date: 2021-07-17 00:00:00
Tasks: Autonomous Vehicles, Scheduling

Emergency service vehicles like ambulance, fire, police etc. should respond to emergencies on time. Existing barriers like increased congestion, multiple signalized intersections, queued vehicles, traffic phase timing etc. can prevent emergency vehicles (EVs) achieving desired response times. Existing solutions to route EVs have not been successful because they do not use dynamic traffic parameters. Real time information on increased congestion, halts on road, pedestrian flow, queued vehicles, real and adaptive speed, can be used to properly actuate pre-emption and minimise the impact that EV movement can have on other traffic. Smart cities provide the necessary infrastructure to enable two critical factors in EV routing: real-time traffic data and connectivity. In addition, using autonomous vehicles (AVs) in place of normal emergency service vehicles can have further advantages in terms of safety and adaptability in smart city environments. AVs feature several sensors and connectivity that can help them make real-time decisions. We propose a novel idea of using autonomous emergency vehicles (AEVs) that can meet the critical response time and drive through a complex road network in smart cities efficiently and safely. This is achieved by considering traffic network analogous to real-time systems (RTS) where we use mixed-criticality real-time system (MCRTS) task scheduling to schedule AEVs for meeting response time.
Effects of a pestilent species on the stability of cyclically dominant species
Authors: D. Bazeia, M. Bongestab, B. F. de Oliveira, A. Szolnoki
Date: 2021-07-19 00:00:00
Tasks: Diversity

Cyclic dominance is frequently believed to be a mechanism that maintains diversity of competing species. But this delicate balance could also be fragile if some of the members is weakened because an extinction of a species will involve the annihilation of its predator hence leaving only a single species alive. To check this expectation we here introduce a fourth species which chases exclusively a single member of the basic model composed by three cyclically dominant species. Interestingly, the coexistence is not necessarily broken and we have detected three consecutive phase transitions as we vary only the invasion strength of the fourth pestilent species. The resulting phases are analyzed by different techniques including the study of the Hamming distance density profiles. Some of our observations strengthen previous findings about cyclically dominant system, but they also offer new revelations and counter-intuitive phenomenon, like supporting pestilent species may result in its extinction, hence enriching our understanding about these very simple but still surprisingly complex systems.
Model-free prediction of emergence of extreme events in a parametrically driven nonlinear dynamical system by Deep Learning
Authors: J. Meiyazhagan, S. Sudharsan, M. Senthilvelan
Date: 2021-07-14 00:00:00
Tasks: Deep Learning, Time Series, Time Series Analysis

We predict the emergence of extreme events in a parametrically driven nonlinear dynamical system using three Deep Learning models, namely Multi-Layer Perceptron, Convolutional Neural Network and Long Short-Term Memory. The Deep Learning models are trained using the training set and are allowed to predict the test set data. After prediction, the time series of the actual and the predicted values are plotted one over the other in order to visualize the performance of the models. Upon evaluating the Root Mean Square Error value between predicted and the actual values of all three models, we find that the Long Short-Term Memory model can serve as the best model to forecast the chaotic time series and to predict the emergence of extreme events for the considered system.
Better force fields start with better data -- A data set of cation dipeptide interactions
Authors: Xiaojuan Hu, Maja-Olivia Lenz-Himmer, Carsten Baldauf
Date: 2021-07-19 00:00:00
Tasks: Benchmarking

We present a data set from a first-principles study of amino-methylated and acetylated (capped) dipeptides of the 20 proteinogenic amino acids - including alternative possible side chain protonation states and their interactions with selected divalent cations (Ca$^{2+}$, Mg$^{2+}$ and Ba$^{2+}$). The data covers 21,909 stationary points on the respective potential-energy surfaces in a wide relative energy range of up to 4 eV (390 kJ/mol). Relevant properties of interest, like partial charges, were derived for the conformers. The motivation was to provide a solid data basis for force field parameterization and further applications like machine learning or benchmarking. In particular the process of creating all this data on the same first-principles footing, i.e. density-functional theory calculations employing the generalized gradient approximation with a van der Waals correction, makes this data suitable for data-driven force field development. To make the data accessible across domain borders and to machines, we formalized the metadata in an ontology.
A Radiomics-Boosted Deep-Learning Model for COVID-19 and Non-COVID-19 Pneumonia Classification Using Chest X-ray Image
Authors: Zongsheng Hu, Zhenyu Yang, Kyle J. Lafata, Fang-Fang Yin, Chunhao Wang
Date: 2021-07-19 00:00:00
Tasks: Pneumonia Detection, Specificity

To develop a deep-learning model that integrates radiomics analysis for enhanced performance of COVID-19 and Non-COVID-19 pneumonia detection using chest X-ray image, two deep-learning models were trained based on a pre-trained VGG-16 architecture: in the 1st model, X-ray image was the sole input; in the 2nd model, X-ray image and 2 radiomic feature maps (RFM) selected by the saliency map analysis of the 1st model were stacked as the input. Both models were developed using 812 chest X-ray images with 262/288/262 COVID-19/Non-COVID-19 pneumonia/healthy cases, and 649/163 cases were assigned as training-validation/independent test sets. In 1st model using X-ray as the sole input, the 1) sensitivity, 2) specificity, 3) accuracy, and 4) ROC Area-Under-the-Curve of COVID-19 vs Non-COVID-19 pneumonia detection were 1) 0.90$\pm$0.07 vs 0.78$\pm$0.09, 2) 0.94$\pm$0.04 vs 0.94$\pm$0.04, 3) 0.93$\pm$0.03 vs 0.89$\pm$0.03, and 4) 0.96$\pm$0.02 vs 0.92$\pm$0.04. In the 2nd model, two RFMs, Entropy and Short-Run-Emphasize, were selected with their highest cross-correlations with the saliency maps of the 1st model. The corresponding results demonstrated significant improvements (p<0.05) of COVID-19 vs Non-COVID-19 pneumonia detection: 1) 0.95$\pm$0.04 vs 0.85$\pm$0.04, 2) 0.97$\pm$0.02 vs 0.96$\pm$0.02, 3) 0.97$\pm$0.02 vs 0.93$\pm$0.02, and 4) 0.99$\pm$0.01 vs 0.97$\pm$0.02. The reduced variations suggested a superior robustness of 2nd model design.
A Performance Investigation of Receive Beamforming Schemes in Specular Tissue Characterization
Authors: Gayathri Malamal, Mahesh Raveendranatha Panicker
Date: 2021-07-16 00:00:00

This work presents a comparison of the delay and sum (DAS), filtered delay multiply and sum (DMAS), minimum variance (MV) and specular receive beamforming schemes in the context of ultrasound imaging of specular reflectors. The main contributions of the study are, 1) a performance comparison of the four receive beamforming schemes through experimental studies for varying angulations of planar reflectors and for reflectors located at varying depths in the medium and, 2)an investigation on the influence of the sub-array length in MV beamforming on the imaging of specular structures. The qualitative conclusions are quantitatively validated in terms of contrast and generalized contrast-to-noise ratios. The study examines the benefits and drawbacks of each receive beamforming technique and highlights the significance of application-tailored beamforming schemes for imaging specular structures.
Testing and Isolation Efficacy: Insights from a Simple Epidemic Model
Authors: Ali Gharouni, F. M. Abdelmalek, David J. D. Earn, Jonathan Dushoff, Benjamin M. Bolker
Date: 2021-07-17 00:00:00

Testing individuals for pathogens can affect the spread of epidemics. Understanding how individual-level processes of sampling and reporting test results can affect community- or population-level spread is a dynamical modeling question. The effect of testing processes on epidemic dynamics depends on factors underlying implementation, particularly testing intensity and on whom testing is focused. Here, we use a simple model to explore how the individual-level effects of testing might directly impact population-level spread. Our model development was motivated by the COVID-19 epidemic, but has generic epidemiological and testing structures. To the classic SIR framework we have added a per capita testing intensity, and compartment-specific testing weights, which can be adjusted to reflect different testing emphases -- surveillance, diagnosis, or control. We derive an analytic expression for the relative reduction in the basic reproductive number due to testing, test-reporting and related isolation behaviours. Intensive testing and fast test reporting are expected to be beneficial at the community level because they can provide a rapid assessment of the situation, identify hot spots, and may enable rapid contact-tracing. Direct effects of fast testing at the individual level are less clear, and may depend on how individuals' behaviour is affected by testing information. Our simple model shows that under some circumstances both increased testing intensity and faster test reporting can reduce the effectiveness of control, and allows us to explore the conditions under which this occurs. Conversely, we find that focusing testing on infected individuals always acts to increase effectiveness of control.
Hamiltonian Monte Carlo for Regression with High-Dimensional Categorical Data
Authors: Szymon Sacher, Laura Battaglia, Stephen Hansen
Date: 2021-07-16 00:00:00
Tasks: regression, valid, Vocal Bursts Intensity Prediction

Latent variable models are increasingly used in economics for high-dimensional categorical data like text and surveys. We demonstrate the effectiveness of Hamiltonian Monte Carlo (HMC) with parallelized automatic differentiation for analyzing such data in a computationally efficient and methodologically sound manner. Our new model, Supervised Topic Model with Covariates, shows that carefully modeling this type of data can have significant implications on conclusions compared to a simpler, frequently used, yet methodologically problematic, two-step approach. A simulation study and revisiting Bandiera et al. (2020)'s study of executive time use demonstrate these results. The approach accommodates thousands of parameters and doesn't require custom algorithms specific to each model, making it accessible for applied researchers