pwc-archive/papers-with-abstracts huggingface dataset:
Measurement of Trustworthiness of the Online Reviews
Authors: Dipankar Das
Date: 2022-10-03 00:00:00
Tasks: Decision Making

In electronic commerce (e-commerce)markets, a decision-maker faces a sequential choice problem. Third-party intervention plays an important role in making purchase decisions in this choice process. For instance, while purchasing products/services online, a buyer's choice or behavior is often affected by the overall reviewers' ratings, feedback, etc. Moreover, the reviewer is also a decision-maker. After purchase, the decision-maker would put forth their reviews for the product, online. Such reviews would affect the purchase decision of another potential buyer, who would read the reviews before conforming to his/her final purchase. The question that arises is \textit{how trustworthy are these review reports and ratings?} The trustworthiness of these review reports and ratings is based on whether the reviewer is a rational or an irrational person. Indexing the reviewer's rationality could be a way to quantify a reviewer's rationality but it does not communicate the history of his/her behavior. In this article, the researcher aims at formally deriving a rationality pattern function and thereby, the degree of rationality of the decision-maker or the reviewer in the sequential choice problem in the e-commerce markets. Applying such a rationality pattern function could make it easier to quantify the rational behavior of an agent who participates in the digital markets. This, in turn, is expected to minimize the information asymmetry within the decision-making process and identify the paid reviewers or manipulative reviews.
Incentive Mechanism and Path Planning for UAV Hitching over Traffic Networks
Authors: Ziyi Lu, Na Yu, Xuehe Wang
Date: 2022-10-02 00:00:00

Package delivery via the UAVs is a promising transport mode to provide efficient and green logistic services, especially in urban areas or complicated topography. However, the energy storage limit of the UAV makes it difficult to perform long-distance delivery tasks. In this paper, we propose a novel multimodal logistics framework, in which the UAVs can call on ground vehicles to provide hitch services to save their own energy and extend their delivery distance. This multimodal logistics framework is formulated as a two-stage model to jointly consider the incentive mechanism design for ground vehicles and path planning for UAVs. In Stage I, to deal with the motivations for ground vehicles to assist UAV delivery, a dynamic pricing scheme is proposed to best balance the vehicle response time and payments to ground vehicles. It shows that a higher price should be decided if the vehicle response time is long to encourage more vehicles to offer a ride. In Stage II, the task allocation and path planning of the UAVs over traffic network is studied based on the vehicle response time obtained in Stage I. To address pathfinding with restrictions and the performance degradation of the pathfinding algorithm due to the rising number of conflicts in multi-agent pathfinding, we propose the suboptimal conflict avoidance-based path search (CABPS) algorithm, which has polynomial time complexity. Finally, we validate our results via simulations. It is shown that our approach is able to increase the success rate of UAV package delivery. Moreover, we estimate the delivery time of the UAV in a pessimistic case, it is still twice as fast as the delivery time of the ground vehicle only.
Optimized Decoders for Mixed-Order Ambisonics
Authors: Aaron Heller, Eric Benjamin, Fernando Lopez-Lezcano
Date: 2022-10-01 00:00:00
Tasks: Decoder

In this paper we discuss the motivation, design, and analysis of ambisonic decoders for systems where the vertical order is less than the horizontal order, known as mixed-order Ambisonic systems. This can be due to the use of microphone arrays that emphasize horizontal spatial resolution or speaker arrays that provide sparser coverage vertically. First, we review Ambisonic reproduction criteria, as defined by Gerzon, and summarize recent results on the relative perceptual importance of the various criteria. Then we show that using full-order decoders with mixed-order program material results in poorer performance than with a properly designed mixed-order decoder. We then introduce a new implementation of a decoder optimizer that draws upon techniques from machine learning for quick and robust convergence, discuss the construction of the objective function, and apply it to the problem of designing two-band decoders for mixed-order signal sets and non-uniform loudspeaker layouts. Results of informal listening tests are summarized and future directions discussed.
NTFields: Neural Time Fields for Physics-Informed Robot Motion Planning
Authors: Ruiqi Ni, Ahmed H. Qureshi
Date: 2022-09-30 00:00:00
Tasks: Motion Planning, Robot Navigation

Neural Motion Planners (NMPs) have emerged as a promising tool for solving robot navigation tasks in complex environments. However, these methods often require expert data for learning, which limits their application to scenarios where data generation is time-consuming. Recent developments have also led to physics-informed deep neural models capable of representing complex dynamical Partial Differential Equations (PDEs). Inspired by these developments, we propose Neural Time Fields (NTFields) for robot motion planning in cluttered scenarios. Our framework represents a wave propagation model generating continuous arrival time to find path solutions informed by a nonlinear first-order PDE called Eikonal Equation. We evaluate our method in various cluttered 3D environments, including the Gibson dataset, and demonstrate its ability to solve motion planning problems for 4-DOF and 6-DOF robot manipulators where the traditional grid-based Eikonal planners often face the curse of dimensionality. Furthermore, the results show that our method exhibits high success rates and significantly lower computational times than the state-of-the-art methods, including NMPs that require training data from classical planners.
Pre-trained Speech Representations as Feature Extractors for Speech Quality Assessment in Online Conferencing Applications
Authors: Bastiaan Tamm, Helena Balabin, Rik Vandenberghe, Hugo Van hamme
Date: 2022-10-01 00:00:00

Speech quality in online conferencing applications is typically assessed through human judgements in the form of the mean opinion score (MOS) metric. Since such a labor-intensive approach is not feasible for large-scale speech quality assessments in most settings, the focus has shifted towards automated MOS prediction through end-to-end training of deep neural networks (DNN). Instead of training a network from scratch, we propose to leverage the speech representations from the pre-trained wav2vec-based XLS-R model. However, the number of parameters of such a model exceeds task-specific DNNs by several orders of magnitude, which poses a challenge for resulting fine-tuning procedures on smaller datasets. Therefore, we opt to use pre-trained speech representations from XLS-R in a feature extraction rather than a fine-tuning setting, thereby significantly reducing the number of trainable model parameters. We compare our proposed XLS-R-based feature extractor to a Mel-frequency cepstral coefficient (MFCC)-based one, and experiment with various combinations of bidirectional long short term memory (Bi-LSTM) and attention pooling feedforward (AttPoolFF) networks trained on the output of the feature extractors. We demonstrate the increased performance of pre-trained XLS-R embeddings in terms a reduced root mean squared error (RMSE) on the ConferencingSpeech 2022 MOS prediction task.
Robust Set Stability of Logic Dynamical Systems with respect to Uncertain Switching
Authors: Yuqian Guo, Zhitao Li
Date: 2022-10-03 00:00:00

This paper proposes several definitions of robust stability for logic dynamical systems (LDSs) with uncertain switching, including robust/uniform robust set stability and asymptotical (or infinitely convergent)/finite-time set stability with ratio one. It is proved herein that an LDS is robustly set stable if and only if the destination set contains all loops (i.e., the paths from each state to itself); an LDS is uniformly robustly set stable, or finite-time set stable with ratio one, if and only if all states outside the destination set are unreachable from any self-reachable state; and an LDS is asymptotically set stable with ratio one if and only if the largest robustly invariant subset (LRIS) in the destination set is reachable from any state. In addition, it is proved that uniform robust set stability implies robust set stability, and robust set stability implies asymptotical set stability with ratio one. However, the inverse claims are not generally true. The relations between robust stability and stability under random switching are revealed, that is, the asymptotical/finite-time set stability with ratio one under uncertain switching is equivalent to asymptotical/finite-time set stability of the LDS under random switching. Furthermore, it is proved that, for uniform set stability and asymptotical/finite-time set stability with ratio one, the set stability is equivalent to the stability with respect to the LRIS in the destination set. However, robust set stability does not imply robust stability with respect to the LRIS in the destination set. This finding corrects a result in a previous study.
Dissipative Imitation Learning for Robust Dynamic Output Feedback
Authors: Amy K. Strong, Ethan J. LoCicero, Leila Bridgeman
Date: 2022-10-03 00:00:00
Tasks: Imitation Learning

Robust imitation learning seeks to mimic expert controller behavior while ensuring stability, but current methods require accurate plant models. Here, robust imitation learning is addressed for stabilizing poorly modeled plants with linear dynamic output feedback. Open-loop input-output properties are used to characterize an uncertain plant, and the feedback matrix of the dynamic controller is learned while enforcing stability through the controller's open-loop QSR-dissipativity properties. The imitation learning method is applied to two systems with parametric uncertainty.
Design of the PID temperature controller for an alkaline electrolysis system with time delays
Authors: Ruomei Qi, Jiarong Li, Jin Lin, Yonghua Song, Jiepeng Wang, Qiangqiang Cui, Yiwei Qiu, Ming Tang, Jian Wang
Date: 2022-10-03 00:00:00

Electrolysis systems use proportional-integral-derivative (PID) temperature controllers to maintain stack temperatures around set points. However, heat transfer delays in electrolysis systems cause manual tuning of PID temperature controllers to be time-consuming, and temperature oscillations often occur. This paper focuses on the design of the PID temperature controller for an alkaline electrolysis system to achieve fast and stable temperature control. A thermal dynamic model of an electrolysis system is established in the frequency-domain for controller designs. Based on this model, the temperature stability is analysed by the root distribution, and the PID parameters are optimized considering both the temperature overshoot and the settling time. The performance of the optimal PID controllers is verified through experiments. Furthermore, the simulation results show that the before-stack temperature should be used as the feedback variable for small lab-scale systems to suppress stack temperature fluctuations, and the after-stack temperature should be used for larger systems to improve the economy. This study is helpful in ensuring the temperature stability and control of electrolysis systems.
Data-Driven Convex Approach to Off-road Navigation via Linear Transfer Operators
Authors: Joseph Moyalan, Yongxin Chen, Umesh Vaidya
Date: 2022-10-03 00:00:00

We consider the problem of optimal navigation control design for navigation on off-road terrain. We use traversability measure to characterize the degree of difficulty of navigation on the off-road terrain. The traversability measure captures the property of terrain essential for navigation, such as elevation map, terrain roughness, slope, and terrain texture. The terrain with the presence or absence of obstacles becomes a particular case of the proposed traversability measure. We provide a convex formulation to the off-road navigation problem by lifting the problem to the density space using the linear Perron-Frobenius (P-F) operator. The convex formulation leads to an infinite-dimensional optimal navigation problem for control synthesis. The finite-dimensional approximation of the infinite-dimensional convex problem is constructed using data. We use a computational framework involving the Koopman operator and the duality between the Koopman and P-F operator for the data-driven approximation. This makes our proposed approach data-driven and can be applied in cases where an explicit system model is unavailable. Finally, we demonstrate the application of the developed framework for the navigation of vehicle dynamics with Dubin's car model.
Evolution of flexible industrial assembly
Authors: Ali Ahmad Malik
Date: 2022-10-03 00:00:00
Tasks: Scheduling

Assembly is a key industrial process to achieve finished goods. Driven by market demographics and technological advancements, industrial assembly has evolved through several phases i.e. craftmanship, bench assembly, assembly lines and flexible assembly cells. Due to the complexity and variety of assembly tasks, besides significant advancement of automation technologies in other manufacturing activities, humans are still considered vital for assembly operations. The rationalization of manufacturing automation has considerably remained away from assembly systems. The advancement in assembly has only been in terms of better scheduling of work tasks and avoiding of wastes. With smart manufacturing technologies such as collaborative robots, additive manufacturing, and digital twins, the opportunities have arisen for the next reshaping of assembly systems. The new paradigm promises a higher degree of automation yet remaining flexible. This may result into a new manufacturing paradigm driven by the advancement of new technologies, new customer expectations and by establishing new kinds of manufacturing systems. This study explores the future collaborative assembly cells, presents a generic framework to develop them and the basic building blocks.
Optimal Weight Adaptation of Model Predictive Control for Connected and Automated Vehicles in Mixed Traffic with Bayesian Optimization
Authors: Viet-Anh Le, Andreas A. Malikopoulos
Date: 2022-10-03 00:00:00
Tasks: Bayesian Optimization, Model Predictive Control

In this paper, we develop an optimal weight adaptation strategy of model predictive control (MPC) for connected and automated vehicles (CAVs) in mixed traffic. We model the interaction between a CAV and a human-driven vehicle (HDV) as a simultaneous game and formulate a game-theoretic MPC problem to find a Nash equilibrium of the game. In the MPC problem, the weights in the HDV's objective function can be learned online using moving horizon inverse reinforcement learning. Using Bayesian optimization, we propose a strategy to optimally adapt the weights in the CAV's objective function so that the expected true cost when using MPC in simulations can be minimized. We validate the effectiveness of the optimal strategy by numerical simulations of a vehicle crossing example at an unsignalized intersection.
Self-Tuning Tube-based Model Predictive Control
Authors: Damianos Tranos, Alessio Russo, Alexandre Proutiere
Date: 2022-10-02 00:00:00
Tasks: model, Model Predictive Control

We present Self-Tuning Tube-based Model Predictive Control (STT-MPC), an adaptive robust control algorithm for uncertain linear systems with additive disturbances based on the least-squares estimator and polytopic tubes. Our algorithm leverages concentration results to bound the system uncertainty set with prescribed confidence, and guarantees robust constraint satisfaction for this set, along with recursive feasibility and input-to-state stability. Persistence of excitation is ensured without compromising the algorithm's asymptotic performance or increasing its computational complexity. We demonstrate the performance of our algorithm using numerical experiments.
Observer-based Event-triggered Boundary Control of the One-phase Stefan Problem
Authors: Bhathiya Rathnayake, Mamadou Diagne
Date: 2022-10-02 00:00:00

This paper provides an observer-based event-triggered boundary control strategy for the one-phase Stefan problem using the position and velocity measurements of the moving interface. The infinite-dimensional backstepping approach is used to design the underlying observer and controller. For the event-triggered implementation of the continuous-time observer-based controller, a dynamic event triggering condition is proposed. The triggering condition determines the times at which the control input needs to be updated. In between events, the control input is applied in a \textit{Zero-Order-Hold} fashion. It is shown that the dwell-time between two triggering instances is uniformly bounded below excluding \textit{Zeno behavior}. Under the proposed event-triggered boundary control approach, the well-posedness of the closed-loop system along with certain model validity conditions is provided. Further, using Lyapunov approach, the global exponential convergence of the closed-loop system to the setpoint is proved. A simulation example is provided to illustrate the theoretical results.
Re-Routing Strategy of Connected and Automated Vehicles Considering Coordination at Intersections
Authors: Heeseung Bang, Andreas A. Malikopoulos
Date: 2022-10-02 00:00:00
Tasks: All

In this paper, we propose a re-routing strategy for connected and automated vehicles (CAVs), considering coordination and control of all the CAVs in the network. The objective for each CAV is to find the route that minimizes the total travel time of all CAVs. We coordinate CAVs at signal-free intersections to accurately predict the travel time for the routing problem. While it is possible to find a system-optimal solution by comparing all the possible combinations of the routes, this may impose a computational burden. Thus, we instead find a person-by-person optimal solution to reduce computational time while still deriving a better solution than selfish routing. We validate our framework through simulations in a grid network.
Differentially Private Timeseries Forecasts for Networked Control
Authors: Po-han Li, Sandeep P. Chinchali, Ufuk Topcu
Date: 2022-10-01 00:00:00

We analyze a cost-minimization problem in which the controller relies on an imperfect timeseries forecast. Forecasting models generate imperfect forecasts because they use anonymization noise to protect input data privacy. However, this noise increases the control cost. We consider a scenario where the controller pays forecasting models incentives to reduce the noise and combines the forecasts into one. The controller then uses the forecast to make control decisions. Thus, forecasting models face a trade-off between accepting incentives and protecting privacy. We propose an approach to allocate economic incentives and minimize costs. We solve a biconvex optimization problem on linear quadratic regulators and compare our approach to a uniform incentive allocation scheme. The resulting solution reduces control costs by 2.5 and 2.7 times for the synthetic timeseries and the Uber demand forecast, respectively.
RIS Design for CRB Optimization in Source Localization with Electromagnetic Interference
Authors: Yuhua Jiang, Yuanwan Mai, Feifei Gao
Date: 2022-10-01 00:00:00

Reconfigurable Intelligent Surface (RIS) plays an important role in enhancing source localization accuracy. Based on the information inequality of Fisher information analyses, the Cram\'{e}r-Rao Bound (CRB) of the localization error can be used to evaluate the localization accuracy for a given set of RIS coefficients. In this paper, we adopt the manifold optimization method to derive the optimal RIS coefficients that minimize the CRB of the localization error with the presence of electromagnetic interference (EMI), where the RIS coefficients are restricted to lie on the complex circle manifold. Simulation results are provided to validate the proposed studies under various circumstances.
Learning-Based Adaptive Optimal Control of Linear Time-Delay Systems: A Policy Iteration Approach
Authors: Leilei Cui, Bo Pang, Zhong-Ping Jiang
Date: 2022-10-01 00:00:00
Tasks: Autonomous Driving, Reinforcement Learning (RL)

This paper studies the adaptive optimal control problem for a class of linear time-delay systems described by delay differential equations (DDEs). A crucial strategy is to take advantage of recent developments in reinforcement learning and adaptive dynamic programming and develop novel methods to learn adaptive optimal controllers from finite samples of input and state data. In this paper, the data-driven policy iteration (PI) is proposed to solve the infinite-dimensional algebraic Riccati equation (ARE) iteratively in the absence of exact model knowledge. Interestingly, the proposed recursive PI algorithm is new in the present context of continuous-time time-delay systems, even when the model knowledge is assumed known. The efficacy of the proposed learning-based control methods is validated by means of practical applications arising from metal cutting and autonomous driving.
A high performance globally exponentially stable sensorless observer for the IPMSM: Theoretical and experimental results
Authors: Bowen Yi, Romeo Ortega, Jongwon Choi, Kwanghee Nam
Date: 2022-10-01 00:00:00
Tasks: regression

In a recent paper [18] the authors proposed the first solution to the problem of designing a {\em globally exponentially stable} (GES) flux observer for the interior permanent magnet synchronous motor. However, the establishment of the stability proof relies on the assumption that the adaptation gain is sufficiently {\em small} -- a condition that may degrade the quality of the transient behavior. In this paper we propose a new GES flux observer that overcomes this limitation ensuring a high performance behavior. The design relies on the use of a novel theoretical tool -- the generation of a {\em ``virtual" invariant manifold} -- that allows the use of the more advanced Kreisselmeier's regression extension estimator, instead of a simple gradient descent one. We illustrate its superior transient behavior via extensive simulations and {\em experiments}.
A Novel Power-Band based Data Segmentation Method for Enhancing Meter Phase and Transformer-Meter Pairing Identification
Authors: Han Pyo Lee, PJ Rehm, Matthew Makdad, Edmond Miller, Ning Lu
Date: 2022-10-01 00:00:00

This paper presents a novel power-band-based data segmentation (PBDS) method to enhance the identification of meter phase and meter-transformer pairing. Meters that share the same transformer or are on the same phase typically exhibit strongly correlated voltage profiles. However, under high power consumption, there can be significant voltage drops along the line connecting a customer to the distribution transformer. These voltage drops significantly decrease the correlations among meters on the same phase or supplied by the same transformer, resulting in high misidentification rates. To address this issue, we propose using power bands to select highly correlated voltage segments for computing correlations, rather than relying solely on correlations computed from the entire voltage waveforms. The algorithm's performance is assessed by conducting tests using data gathered from 13 utility feeders. To ensure the credibility of the identification results, utility engineers conduct field verification for all 13 feeders. The verification results unequivocally demonstrate that the proposed algorithm surpasses existing methods in both accuracy and robustness.