pwc-archive/papers-with-abstracts huggingface dataset:
Robust Joint Active-Passive Beamforming Design for IRS-Assisted ISAC Systems
Authors: Mahmoud AlaaEldin, Emad Alsusa, Karim G. Seddik, Christos Masouros, Iman Valiulahi
Date: 2023-09-02 00:00:00
Tasks: Integrated sensing and communication, ISAC

The idea of Integrated Sensing and Communication (ISAC) offers a promising solution to the problem of spectrum congestion in future wireless networks. This paper studies the integration of intelligent reflective surfaces (IRS) with ISAC systems to improve the performance of radar and communication services. Specifically, an IRS-assisted ISAC system is investigated where a multi-antenna base station (BS) performs multi-target detection and multi-user communication. A low complexity and efficient joint optimization of transmit beamforming at the BS and reflective beamforming at the IRS is proposed. This is done by jointly optimizing the BS beamformers and IRS reflection coefficients to minimize the Frobenius distance between the covariance matrices of the transmitted signal and the desired radar beam pattern. This optimization aims to satisfy the signal-to-interference-and-noise ratio (SINR) constraints of the communication users, the total transmit power limit at the BS, and the unit modulus constraints of the IRS reflection coefficients. To address the resulting complex non-convex optimization problem, an efficient alternating optimization (AO) algorithm combining fractional programming (FP), semi-definite programming (SDP), and second order cone programming (SOCP) methods is proposed. Furthermore, we propose robust beamforming optimization for IRS-ISAC systems by adapting the proposed optimization algorithm to the IRS channel uncertainties that may exist in practical systems. Using advanced tools from convex optimization theory, the constraints containing uncertainty are transformed to their equivalent linear matrix inequalities (LMIs) to account for the channels' uncertainty radius. The results presented quantify the benefits of IRS-ISAC systems under various conditions and demonstrate the effectiveness of the proposed algorithm.
Network Topology Inference with Sparsity and Laplacian Constraints
Authors: Jiaxi Ying, Xi Han, Rui Zhou, Xiwen Wang, Hing Cheung So
Date: 2023-09-02 00:00:00
Tasks: Time Series

We tackle the network topology inference problem by utilizing Laplacian constrained Gaussian graphical models, which recast the task as estimating a precision matrix in the form of a graph Laplacian. Recent research \cite{ying2020nonconvex} has uncovered the limitations of the widely used $\ell_1$-norm in learning sparse graphs under this model: empirically, the number of nonzero entries in the solution grows with the regularization parameter of the $\ell_1$-norm; theoretically, a large regularization parameter leads to a fully connected (densest) graph. To overcome these challenges, we propose a graph Laplacian estimation method incorporating the $\ell_0$-norm constraint. An efficient gradient projection algorithm is developed to solve the resulting optimization problem, characterized by sparsity and Laplacian constraints. Through numerical experiments with synthetic and financial time-series datasets, we demonstrate the effectiveness of the proposed method in network topology inference.
A Multi-Head Ensemble Multi-Task Learning Approach for Dynamical Computation Offloading
Authors: Ruihuai Liang, Bo Yang, Zhiwen Yu, Xuelin Cao, Derrick Wing Kwan Ng, Chau Yuen
Date: 2023-09-02 00:00:00
Tasks: Edge-computing, Multi-Task Learning

Computation offloading has become a popular solution to support computationally intensive and latency-sensitive applications by transferring computing tasks to mobile edge servers (MESs) for execution, which is known as mobile/multi-access edge computing (MEC). To improve the MEC performance, it is required to design an optimal offloading strategy that includes offloading decision (i.e., whether offloading or not) and computational resource allocation of MEC. The design can be formulated as a mixed-integer nonlinear programming (MINLP) problem, which is generally NP-hard and its effective solution can be obtained by performing online inference through a well-trained deep neural network (DNN) model. However, when the system environments change dynamically, the DNN model may lose efficacy due to the drift of input parameters, thereby decreasing the generalization ability of the DNN model. To address this unique challenge, in this paper, we propose a multi-head ensemble multi-task learning (MEMTL) approach with a shared backbone and multiple prediction heads (PHs). Specifically, the shared backbone will be invariant during the PHs training and the inferred results will be ensembled, thereby significantly reducing the required training overhead and improving the inference performance. As a result, the joint optimization problem for offloading decision and resource allocation can be efficiently solved even in a time-varying wireless environment. Experimental results show that the proposed MEMTL outperforms benchmark methods in both the inference accuracy and mean square error without requiring additional training data.
Finite Element Analysis and Machine Learning Guided Design of Carbon Fiber Organosheet-based Battery Enclosures for Crashworthiness
Authors: Shadab Anwar Shaikh, M. F. N. Taufique, Kranthi, Balusu, Shank S. Kulkarni, Forrest Hale, Jonathan Oleson, Ram Devanathan, Ayoub Soulami
Date: 2023-08-22 00:00:00

Carbon fiber composite can be a potential candidate for replacing metal-based battery enclosures of current electric vehicles (E.V.s) owing to its better strength-to-weight ratio and corrosion resistance. However, the strength of carbon fiber-based structures depends on several parameters that should be carefully chosen. In this work, we implemented high throughput finite element analysis (FEA) based thermoforming simulation to virtually manufacture the battery enclosure using different design and processing parameters. Subsequently, we performed virtual crash simulations to mimic a side pole crash to evaluate the crashworthiness of the battery enclosures. This high throughput crash simulation dataset was utilized to build predictive models to understand the crashworthiness of an unknown set. Our machine learning (ML) models showed excellent performance (R2 > 0.97) in predicting the crashworthiness metrics, i.e., crush load efficiency, absorbed energy, intrusion, and maximum deceleration during a crash. We believe that this FEA-ML work framework will be helpful in down select process parameters for carbon fiber-based component design and can be transferrable to other manufacturing technologies.
LEAP Hand: Low-Cost, Efficient, and Anthropomorphic Hand for Robot Learning
Authors: Kenneth Shaw, Ananye Agarwal, Deepak Pathak
Date: 2023-09-12 00:00:00

Dexterous manipulation has been a long-standing challenge in robotics. While machine learning techniques have shown some promise, results have largely been currently limited to simulation. This can be mostly attributed to the lack of suitable hardware. In this paper, we present LEAP Hand, a low-cost dexterous and anthropomorphic hand for machine learning research. In contrast to previous hands, LEAP Hand has a novel kinematic structure that allows maximal dexterity regardless of finger pose. LEAP Hand is low-cost and can be assembled in 4 hours at a cost of 2000 USD from readily available parts. It is capable of consistently exerting large torques over long durations of time. We show that LEAP Hand can be used to perform several manipulation tasks in the real world -- from visual teleoperation to learning from passive video data and sim2real. LEAP Hand significantly outperforms its closest competitor Allegro Hand in all our experiments while being 1/8th of the cost. We release detailed assembly instructions, the Sim2Real pipeline and a development platform with useful APIs on our website at https://leap-hand.github.io/
Generalized Regret Analysis of Thompson Sampling using Fractional Posteriors
Authors: Prateek Jaiswal, Debdeep Pati, Anirban Bhattacharya, Bani K. Mallick
Date: 2023-09-12 00:00:00
Tasks: Thompson Sampling

Thompson sampling (TS) is one of the most popular and earliest algorithms to solve stochastic multi-armed bandit problems. We consider a variant of TS, named $\alpha$-TS, where we use a fractional or $\alpha$-posterior ($\alpha\in(0,1)$) instead of the standard posterior distribution. To compute an $\alpha$-posterior, the likelihood in the definition of the standard posterior is tempered with a factor $\alpha$. For $\alpha$-TS we obtain both instance-dependent $\mathcal{O}\left(\sum_{k \neq i^*} \Delta_k\left(\frac{\log(T)}{C(\alpha)\Delta_k^2} + \frac{1}{2} \right)\right)$ and instance-independent $\mathcal{O}(\sqrt{KT\log K})$ frequentist regret bounds under very mild conditions on the prior and reward distributions, where $\Delta_k$ is the gap between the true mean rewards of the $k^{th}$ and the best arms, and $C(\alpha)$ is a known constant. Both the sub-Gaussian and exponential family models satisfy our general conditions on the reward distribution. Our conditions on the prior distribution just require its density to be positive, continuous, and bounded. We also establish another instance-dependent regret upper bound that matches (up to constants) to that of improved UCB [Auer and Ortner, 2010]. Our regret analysis carefully combines recent theoretical developments in the non-asymptotic concentration analysis and Bernstein-von Mises type results for the $\alpha$-posterior distribution. Moreover, our analysis does not require additional structural properties such as closed-form posteriors or conjugate priors.
Using conservative voltage reduction and dynamic thermal rating for congestion management of power network
Authors: Ramin Nourollahi, Rasoul Esmaeilzadeh
Date: 2023-09-12 00:00:00
Tasks: Management

Increasing the amount of electric power that is used on the demand side has brought more attention to the peak-load management of the distribution network (DN). The creation of infrastructures for smart grids, the efficient utilization of the distributed network's components, and the appropriate administration of the distributed network would result in a valuable solution for the operators of the distributed network. As a result, a framework for peak-load management is given in this research. Within this framework, the real-time rating of the components and the voltage-dependent characteristics of the electric loads work together to assist the DN operator in effectively navigating peak periods. The combination of the conservation voltage reduction (CVR) and the dynamic thermal rating (DTR) of the components that make up the DN produces outcomes that are more helpful than any of these factors alone could provide. This is true even though each of these factors contributes to the efficient functioning of the DN. According to the findings, as compared to the individual implementation of CVR, the simultaneous utilization of DTR and CVR results in a cost-savings rise at peak events which is 58.75 percentage points more than the individual implementation. In addition, a discussion is offered concerning the current difficulties that are being experienced by the feeders that are providing the voltage-dependent constant-power loads during the utilization of the CVR, which are handled by the dynamic rating of the components that make up the DN.
Risk-Aware Reinforcement Learning through Optimal Transport Theory
Authors: Ali Baheri
Date: 2023-09-12 00:00:00
Tasks: Decision Making, Management, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In the dynamic and uncertain environments where reinforcement learning (RL) operates, risk management becomes a crucial factor in ensuring reliable decision-making. Traditional RL approaches, while effective in reward optimization, often overlook the landscape of potential risks. In response, this paper pioneers the integration of Optimal Transport (OT) theory with RL to create a risk-aware framework. Our approach modifies the objective function, ensuring that the resulting policy not only maximizes expected rewards but also respects risk constraints dictated by OT distances between state visitation distributions and the desired risk profiles. By leveraging the mathematical precision of OT, we offer a formulation that elevates risk considerations alongside conventional RL objectives. Our contributions are substantiated with a series of theorems, mapping the relationships between risk distributions, optimal value functions, and policy behaviors. Through the lens of OT, this work illuminates a promising direction for RL, ensuring a balanced fusion of reward pursuit and risk awareness.
Adopting Dynamic VAR Compensators to Mitigate PV Impacts on Unbalanced Distribution Systems
Authors: Han Pyo Lee, Keith DSouza, Ke Chen, Ning Lu, Mesut Baran
Date: 2023-09-12 00:00:00

The growing integration of distributed energy resources into distribution systems poses challenges for voltage regulation. Dynamic VAR Compensators (DVCs) are a new generation of power electronics-based Volt/VAR compensation devices designed to address voltage issues in distribution systems with a high penetration of renewable generation resources. Currently, the IEEE Std. 1547-based Volt/VAR Curve (VV-C) is widely used as the local control scheme for controlling a DVC. However, the effectiveness of this scheme is not well documented, and there is limited literature on alternative control and placement schemes that can maximize the effective use of a DVC. In this paper, we propose an optimal dispatch and control mechanism to enhance the conventional VV-C based localized DVC control. First, we establish a multi-objective optimization framework to identify the optimal dispatch strategy and suitable placement for the DVC. Next, we introduce two supervisory control strategies to determine the appropriate instances for adjusting the VV-C when the operating condition changes. The outlined scheme comprises two primary stages: time segmentation and VV-C fitting. Within this framework, each time segment aims to produce optimized Q-V trajectories. The proposed method is tested on a modified IEEE 123-bus test system using OpenDSS for a wide range of operating scenarios, including sunny and cloudy days. Simulation results demonstrate that the proposed scheme effectively reduces voltage variations compared to the standard VV-C specified in IEEE Std. 1547.
A General Framework for Verification and Control of Dynamical Models via Certificate Synthesis
Authors: Alec Edwards, Andrea Peruffo, Alessandro Abate
Date: 2023-09-12 00:00:00

An emerging branch of control theory specialises in certificate learning, concerning the specification of a desired (possibly complex) system behaviour for an autonomous or control model, which is then analytically verified by means of a function-based proof. However, the synthesis of controllers abiding by these complex requirements is in general a non-trivial task and may elude the most expert control engineers. This results in a need for automatic techniques that are able to design controllers and to analyse a wide range of elaborate specifications. In this paper, we provide a general framework to encode system specifications and define corresponding certificates, and we present an automated approach to formally synthesise controllers and certificates. Our approach contributes to the broad field of safe learning for control, exploiting the flexibility of neural networks to provide candidate control and certificate functions, whilst using SMT-solvers to offer a formal guarantee of correctness. We test our framework by developing a prototype software tool, and assess its efficacy at verification via control and certificate synthesis over a large and varied suite of benchmarks.
Computation of safe disturbance sets using implicit RPI sets
Authors: Sampath Kumar Mulagaleti, Alberto Bemporad, Mario Zanon
Date: 2023-09-12 00:00:00
Tasks: Computational Efficiency

Given a stable linear time-invariant (LTI) system subject to output constraints, we present a method to compute a set of disturbances such that the reachable set of outputs matches as closely as possible the output constraint set, while being included in it. This problem finds application in several control design problems, such as the development of hierarchical control loops, decentralized control, supervisory control, robustness-verification, etc. We first characterize the set of disturbance sets satisfying the output constraint inclusion using corresponding minimal robust positive invariant (mRPI) sets, following which we formulate an optimization problem that minimizes the distance between the reachable output set and the output constraint set. We tackle the optimization problem using an implicit RPI set approach that provides a priori approximation error guarantees, and adopt a novel disturbance set parameterization that permits the encoding of the set of feasible disturbance sets as a polyhedron. Through extensive numerical examples, we demonstrate that the proposed approach computes disturbance sets with reduced conservativeness improved computational efficiency than state-of-the-art methods.
A Note on the Extended Kalman Filter on a Manifold
Authors: Yixiao Ge, Pieter van Goor, Robert Mahony
Date: 2023-09-12 00:00:00
Tasks: State Estimation

The kinematics of many control systems, especially in the robotics field, naturally live on smooth manifolds. Most classical state-estimation algorithms, including the extended Kalman filter, are posed on Euclidean space. Although any filter algorithm can be adapted to a manifold setting by implementing it in local coordinates and ignoring the geometric structure, it has always been clear that there would be advantages in taking the geometric structure into consideration in developing the algorithm. In this paper, we argue that the minimum geometric structure required to adapt the extended Kalman filter to a manifold is that of an affine connection. With this structure, we show that a naive coordinate implementation of the EKF fails to account for geometry of the manifold in the update step and in the reset step. We provide geometric modifications to the classical EKF based on parallel transport of the measurement covariance (for the update) and a-posteriori state covariance (for the reset) that address these limitations. Preliminary results for attitude estimation with two directional measurements demonstrate that the proposed modifications significantly improve the transient behavior of the filter.
A Computationally Efficient Bi-level Coordination Framework for CAVs at Unsignalized Intersections
Authors: Jiping Luo, Tingting Zhang, Qinyu Zhang
Date: 2023-09-12 00:00:00
Tasks: Scheduling, Traffic Signal Control, Trajectory Planning

In this paper, we investigate cooperative vehicle coordination for connected and automated vehicles (CAVs) at unsignalized intersections. To support high traffic throughput while reducing computational complexity, we present a novel collision region model and decompose the optimal coordination problem into two sub-problems: \textit{centralized} priority scheduling and \textit{distributed} trajectory planning. Then, we propose a bi-level coordination framework which includes: (i) a Monte Carlo Tree Search (MCTS)-based high-level priority scheduler aims to find high-quality passing orders to maximize traffic throughput, and (ii) a priority-based low-level trajectory planner that generates optimal collision-free control inputs. Simulation results demonstrate that our bi-level strategy achieves near-optimal coordination performance, comparable to state-of-the-art centralized strategies, and significantly outperform the traffic signal control systems in terms of traffic throughput. Moreover, our approach exhibits good scalability, with computational complexity scaling linearly with the number of vehicles. Video demonstrations can be found online at \url{https://youtu.be/WYAKFMNnQfs}.
Reset Controller Synthesis by Reach-avoid Analysis for Delay Hybrid Systems
Authors: Han Su, Jiyu Zhu, Shenghua Feng, Yunjun Bai, Bin Gu, Jiang Liu, Mengfei Yang, Naijun Zhan
Date: 2023-09-12 00:00:00

A reset controller plays a crucial role in designing hybrid systems. It restricts the initial set and redefines the reset map associated with discrete transitions, in order to guarantee the system to achieve its objective. Reset controller synthesis, together with feedback controller synthesis and switching logic controller synthesis, provides a correct-by-construction approach to designing hybrid systems. However, time-delay is an inevitable factor in hybrid systems, which can degrade control performance and render verification certificates obtained by abstracting away time-delay invalid in practice. In this paper, we investigate this issue in a practical manner by taking time-delay into account. We propose an approach that reduces the synthesis of reset controllers to the generation of reach-avoid sets for the hybrid system under consideration, which can be efficiently solved using off-the-shell convex optimization solvers.
Correct-by-Construction for Hybrid Systems by Synthesizing Reset Controller
Authors: Jiang Liu, Han Su, Yunjun Bai, Bin Gu, Bai Xue, Mengfei Yang, Naijun Zhan
Date: 2023-09-12 00:00:00

Controller synthesis, including reset controller, feedback controller, and switching logic controller, provides an essential mechanism to guarantee the correctness and reliability of hybrid systems in a correct-by-construction manner. Unfortunately, reset controller synthesis is still in an infant stage in the literature, although it makes theoretical and practical significance. In this paper, we propose a convex programming based method to synthesize reset controllers for polynomial hybrid systems subject to safety, possibly together with liveness. Such a problem essentially corresponds to computing an initial set of continuous states in each mode and a reset map associated with each discrete jump such that any trajectory starting from any computed initial state keeps safe if only safety constraints are given or reaches the target set eventually and keeps safe before that if both safety and liveness are given, through the computed reset maps. Both cases can be reduced to reach-avoid and/or differential invariant generation problems, further encoded as convex optimization problems. Finally, several examples are provided to demonstrate the efficiency and effectiveness of our method.
Geometry Enhanced Optimal Control Technique for Acrobatic Flip Motion of Quadcopter
Authors: Jie Yao
Date: 2023-09-12 00:00:00
Tasks: Computational Efficiency

A nonlinear optimal control strategy, named the geometry enhanced finite time $\boldsymbol{\theta-}$D technique, is proposed to manipulate the acrobatic flip flight of variable pitch (VP) quadcopter unmanned aerial vehicles (abbreviated as VP copter). A unique superiority of the VP copter, which can provide the thrust in both positive and negative vertical directions by varying the pitch angles of blades, facilitates the acrobatic flip motion. The finite time $\boldsymbol{\theta-}$D technique can offer a closed-form near-optimal state feedback control law with online computational efficiency as compared with the finite time state-dependent Riccati equation (SDRE) technique. Meanwhile, by virtue of the geometric technique, the singularity issue of the rotation matrix in the acrobatic flip maneuver can be avoided. The simulation experiments verify the proposed control strategy is effective and efficient.