pwc-archive/papers-with-abstracts huggingface dataset:
CNN-DST: ensemble deep learning based on Dempster-Shafer theory for vibration-based fault recognition
Authors: Vahid Yaghoubi, Liangliang Cheng, Wim Van Paepegem, Mathias Kersemans
Date: 2021-10-14 00:00:00
Tasks: Deep Learning, Fault Detection

Nowadays, using vibration data in conjunction with pattern recognition methods is one of the most common fault detection strategies for structures. However, their performances depend on the features extracted from vibration data, the features selected to train the classifier, and the classifier used for pattern recognition. Deep learning facilitates the fault detection procedure by automating the feature extraction and selection, and classification procedure. Though, deep learning approaches have challenges in designing its structure and tuning its hyperparameters, which may result in a low generalization capability. Therefore, this study proposes an ensemble deep learning framework based on a convolutional neural network (CNN) and Dempster-Shafer theory (DST), called CNN-DST. In this framework, several CNNs with the proposed structure are first trained, and then, the outputs of the CNNs selected by the proposed technique are combined by using an improved DST-based method. To validate the proposed CNN-DST framework, it is applied to an experimental dataset created by the broadband vibrational responses of polycrystalline Nickel alloy first-stage turbine blades with different types and severities of damage. Through statistical analysis, it is shown that the proposed CNN-DST framework classifies the turbine blades with an average prediction accuracy of 97.19%. The proposed CNN-DST framework is benchmarked with other state-of-the-art classification methods, demonstrating its high performance. The robustness of the proposed CNN-DST framework with respect to measurement noise is investigated, showing its high noise-resistance. Further, bandwidth analysis reveals that most of the required information for detecting faulty samples is available in a small frequency range.
Scheduler-Pointed False Data Injection Attack for Event-Based Remote State Estimation
Authors: Qiulin Xu, Junlin Xiong
Date: 2021-10-14 00:00:00
Tasks: State Estimation

In this paper, an attack problem is investigated for event-based remote state estimation in cyber-physical systems. Our objective is to degrade the effect of the event-based scheduler while bypassing a $\chi^2$ false data detector. A two-channel scheduler-pointed false data injection attack strategy is proposed by modifying the numerical characteristics of innovation signals. The attack strategy is proved to be always existent, and an algorithm is provided to find it. Under the proposed attack strategy, the scheduler becomes almost invalid and the performance of the remote estimator is degraded. Numerical simulations are used to illustrate our theoretical results.
Relation-aware Heterogeneous Graph for User Profiling
Authors: Qilong Yan, Yufeng Zhang, Qiang Liu, Shu Wu, Liang Wang
Date: 2021-10-14 00:00:00
Tasks: Node Classification, Relation

User profiling has long been an important problem that investigates user interests in many real applications. Some recent works regard users and their interacted objects as entities of a graph and turn the problem into a node classification task. However, they neglect the difference of distinct interaction types, e.g. user clicks an item v.s.user purchases an item, and thus cannot incorporate such information well. To solve these issues, we propose to leverage the relation-aware heterogeneous graph method for user profiling, which also allows capturing significant meta relations. We adopt the query, key, and value mechanism in a transformer fashion for heterogeneous message passing so that entities can effectively interact with each other. Via such interactions on different relation types, our model can generate representations with rich information for the user profile prediction. We conduct experiments on two real-world e-commerce datasets and observe a significant performance boost of our approach.
A Framework for Risk Assessment and Optimal Line Upgrade Selection to Mitigate Wildfire Risk
Authors: Sofia Taylor, Line A. Roald
Date: 2021-10-14 00:00:00

As wildfires in the United States are becoming more frequent and severe, mitigating wildfire ignition risk from power line faults is an increasingly crucial effort. Long-term ignition prevention strategies, especially converting overhead lines to underground cables, are expensive. Thus, it is important to prioritize upgrades on lines that will reduce wildfire ignition risk the most. However, since so many factors contribute to ignition risk, it is difficult to quantify the wildfire risk associated with power lines. This paper examines how various risk definitions based on historical wildfire risk maps can be used to inform transmission upgrade planning. These risk metrics are evaluated using an optimization model that determines which overhead lines should be undergrounded such that the total wildfire risk in the network is minimized. The risk assignment and upgrade selection are tested on both a synthetic network and the actual transmission lines in California.
Optimization-based modelling and game-theoretic framework for techno-economic analysis of demand-side flexibility: a real case study
Authors: Timur Sayfutdinov, Charalampos Patsios, David Greenwood, Meltem Peker, Ilias Sarantakos
Date: 2021-10-14 00:00:00

This paper proposes a two-step framework for techno-economic analysis of a demand-side flexibility service in distribution networks. Step one applies optimization-based modelling to propose a generic problem formulation which determines the offer curve, in terms of available flexible capacity and its marginal cost, for flexible distribution-connected assets. These offer curves form an input to the second step, which uses a multi-agent iterative game framework to determine the benefits of demand-side flexibility for the Distribution System Operator (DSO) and the service providers. The combined two-step framework simultaneously accounts for the objectives of each flexibility provider, technical constraints of flexible assets, customer preferences, market clearing mechanisms, and strategic bidding by service providers, omission of any of which can lead to erroneous results. The proposed two-step framework has been applied to a real case study in the North East of England to examine four market mechanisms and three bidding strategies. The results showed that among all considered market mechanisms, flexibility markets that operate under discriminatory pricing, such as pay-as-bid and Dutch reverse auctions, are prone to manipulations, especially in the lack of competition. In contrast, uniform pricing pay-as-cleared auction provides limited opportunities for manipulation even when competition is low.
Synchronization and Balancing around Simple Closed Polar Curves with Bounded Trajectories and Control Saturation
Authors: Aditya Hegde, Anoop Jain
Date: 2021-10-14 00:00:00

The problem of synchronization and balancing around simple closed polar curves is addressed for unicycle-type multi-agent systems. Leveraging the concept of barrier Lyapunov function in conjunction with bounded Lyapunov-like curve-phase potential functions, we propose distributed feedback control laws and show that the agents asymptotically stabilize to the desired closed curve, their trajectories remain bounded within a compact set, and their turn-rates adhere to the saturation limits. We also characterize the explicit nature of the boundary of this trajectory-constraining set based on the magnitude of the safe distance of the exterior boundary from the desired curve. We further establish a connection between the perimeters and areas of the trajectory-constraining set with that of the desired curve. We obtain bounds on different quantities of interest in the post-design analysis and provide simulation results to illustrate the theoretical findings.
Ego4D: Around the World in 3,000 Hours of Egocentric Video
Authors: Kristen Grauman, Andrew Westbury, Eugene Byrne, Zachary Chavis, Antonino Furnari, Rohit Girdhar, Jackson Hamburger, Hao Jiang, Miao Liu, Xingyu Liu, Miguel Martin, Tushar Nagarajan, Ilija Radosavovic, Santhosh Kumar Ramakrishnan, Fiona Ryan, Jayant Sharma, Michael Wray, Mengmeng Xu, Eric Zhongcong Xu, Chen Zhao, Siddhant Bansal, Dhruv Batra, Vincent Cartillier, Sean Crane, Tien Do, Morrie Doulaty, Akshay Erapalli, Christoph Feichtenhofer, Adriano Fragomeni, Qichen Fu, Abrham Gebreselasie, Cristina Gonzalez, James Hillis, Xuhua Huang, Yifei HUANG, Wenqi Jia, Weslie Khoo, Jachym Kolar, Satwik Kottur, Anurag Kumar, Federico Landini, Chao Li, Yanghao Li, Zhenqiang Li, Karttikeya Mangalam, Raghava Modhugu, Jonathan Munro, Tullie Murrell, Takumi Nishiyasu, Will Price, Paola Ruiz Puentes, Merey Ramazanova, Leda Sari, Kiran Somasundaram, Audrey Southerland, Yusuke Sugano, Ruijie Tao, Minh Vo, Yuchen Wang, Xindi Wu, Takuma Yagi, Ziwei Zhao, Yunyi Zhu, Pablo Arbelaez, David Crandall, Dima Damen, Giovanni Maria Farinella, Christian Fuegen, Bernard Ghanem, Vamsi Krishna Ithapu, C. V. Jawahar, Hanbyul Joo, Kris Kitani, Haizhou Li, Richard Newcombe, Aude Oliva, Hyun Soo Park, James M. Rehg, Yoichi Sato, Jianbo Shi, Mike Zheng Shou, Antonio Torralba, Lorenzo Torresani, Mingfei Yan, Jitendra Malik
Date: 2021-10-13 00:00:00
Proceeding: CVPR 2022 1
Tasks: De-identification, Ethics

We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite. It offers 3,670 hours of daily-life activity video spanning hundreds of scenarios (household, outdoor, workplace, leisure, etc.) captured by 931 unique camera wearers from 74 worldwide locations and 9 different countries. The approach to collection is designed to uphold rigorous privacy and ethics standards with consenting participants and robust de-identification procedures where relevant. Ego4D dramatically expands the volume of diverse egocentric video footage publicly available to the research community. Portions of the video are accompanied by audio, 3D meshes of the environment, eye gaze, stereo, and/or synchronized videos from multiple egocentric cameras at the same event. Furthermore, we present a host of new benchmark challenges centered around understanding the first-person visual experience in the past (querying an episodic memory), present (analyzing hand-object manipulation, audio-visual conversation, and social interactions), and future (forecasting activities). By publicly sharing this massive annotated dataset and benchmark suite, we aim to push the frontier of first-person perception. Project page: https://ego4d-data.org/
Singular Zone in Quadrotor Yaw-Position Feedback Linearization
Authors: Zhe Shen, Takeshi Tsuchiya
Date: 2021-10-14 00:00:00
Tasks: Position

It is well known that the conventional quadrotor is an under-actuated MIMO system. The number of the inputs is less than the degree of freedom. One approach in controlling this non-holonomic system is feedback linearization. In the frequently cited (305 times) application, the yaw and the position are selected as the controlled variables. It is reported that no singularity is found in their delta matrix, making it possible to apply the inversion within a wide range. However, we find the ignored singular zone within the range of interest. The unreported singular area can cause the failure in the controller design. This paper visualizes this uninvertible area and details the deduction process.
On the Sample Complexity of Decentralized Linear Quadratic Regulator with Partially Nested Information Structure
Authors: Lintao Ye, Hao Zhu, Vijay Gupta
Date: 2021-10-14 00:00:00

We study the problem of control policy design for decentralized state-feedback linear quadratic control with a partially nested information structure, when the system model is unknown. We propose a model-based learning solution, which consists of two steps. First, we estimate the unknown system model from a single system trajectory of finite length, using least squares estimation. Next, based on the estimated system model, we design a control policy that satisfies the desired information structure. We show that the suboptimality gap between our control policy and the optimal decentralized control policy (designed using accurate knowledge of the system model) scales linearly with the estimation error of the system model. Using this result, we provide an end-to-end sample complexity result for learning decentralized controllers for a linear quadratic control problem with a partially nested information structure.
Cloud-Assisted Collaborative Road Information Discovery with Gaussian Process: Application to Road Profile Estimation
Authors: Mohammad R. Hajidavalloo, Zhaojian Li, Xin Xia, Ali Louati, Minghui Zheng, Weichao Zhuang
Date: 2021-10-13 00:00:00

There is an increasing popularity in exploiting modern vehicles as mobile sensors to obtain important road information such as potholes, black ice and road profile. Availability of such information has been identified as a key enabler for next-generation vehicles with enhanced safety, efficiency, and comfort. However, existing road information discovery approaches have been predominately performed in a single-vehicle setting, which is inevitably susceptible to vehicle model uncertainty and measurement errors. To overcome these limitations, this paper presents a novel cloud-assisted collaborative estimation framework that can utilize multiple heterogeneous vehicles to iteratively enhance estimation performance. Specifically, each vehicle combines its onboard measurements with a cloud-based Gaussian process (GP), crowdsourced from prior participating vehicles as "pseudo-measurements", into a local estimator to refine the estimation. The resultant local onboard estimation is then sent back to the cloud to update the GP, where we utilize a noisy input GP (NIGP) method to explicitly handle uncertain GPS measurements. We employ the proposed framework to the application of collaborative road profile estimation. Promising results on extensive simulations and hardware-in-the-loop experiments show that the proposed collaborative estimation can significantly enhance estimation and iteratively improve the performance from vehicle to vehicle, despite vehicle heterogeneity, model uncertainty, and measurement noises.
Dominant Mixed Feedback Design for Stable Oscillations
Authors: Weiming Che, Fulvio Forni
Date: 2021-10-13 00:00:00

We present a design framework to induce stable oscillations through mixed feedback control. We provide conditions on the feedback gain and on the balance between positive and negative feedback contributions to guarantee robust oscillations. Using linear matrix inequalities, we later derive a systematic design for robustness to bounded dynamic uncertainties and for passive interconnections. The results of the paper provide a system-theoretic justification to several observations from system biology and neuroscience pointing at the mixed feedback as a fundamental enabler for robust oscillations. Our results are illustrated through a distributed electrical circuit mimicking (simplified) neural dynamics.
On Networks Over Finite Rings
Authors: Daizhan Cheng
Date: 2021-10-13 00:00:00

A (control) network over a finite ring is proposed. Using semi-tensor product (STP) of matrices, a set of algebraic equations are provided to verify whether a finite set with two binary operators is a ring. It is then shown that the STP-based technique developed for logical (control) networks are applicable to (control) networks over finite rings. The sub-(control) network over an ideal of the bearing ring is revealed. Then the product ring is proposed and the (control) network over product ring is investigated. As a key result, the decomposition theorem, called the Decomposition Principle (DP), is proposed, which shows a (control) network over a product ring is decomposable into sub-(control)networks over each factor rings, which makes the (control) properties of a network can be revealed by its factor sub-networks over factor rings. Using DP, the control problems of a control network are investigated via its sub control networks. Particularly, using DP, the control of linear networks over product rings is discussed in detail. Finally, the representation theorem is presented, which shows that each (control) network over a finite set can be expressed as a (control) network over a product ring.
Enhanced Sequential Covariance Intersection Fusion
Authors: Zhongyao Hu, Bo Chen, Wen-An Zhang, Li Yu
Date: 2021-10-13 00:00:00

This paper is concerned with the sequential covariance intersection (CI) fusion problem that the fusion result is independent of fusion structure including the fusion order and the number of estimates fused in each sequential fusion. An enhanced sequential CI fusion is first developed to better meet the practical requirements as compared with the existing batch and sequential CI fusion. Meanwhile, it is proved that the enhanced sequential CI fusion ensures the fusion estimate and covariance are unbiased and consistent. Notice that the fusion structure of the enhanced sequential CI fusion is unpredictable in practice, which may have negative impacts on the fusion performance. To this end, a weighting fusion criterion with analytical form is further proposed, and can be depicted by different formulas when choosing different performance indexes. For this criterion, it is proved that the fusion results are not affected by the fusion structure, and thus the fusion performance can be guaranteed. Finally, simulation examples are utilized to demonstrate the effectiveness and advantages of the proposed methods.
Control Co-Design for Buoyancy-Controlled MHK Turbine: A Nested Optimization of Geometry and Spatial-Temporal Path Planning
Authors: Arezoo Hasankhani, Yufei Tang, Austin Snyder, James VanZwieten, Wei Qiao
Date: 2021-10-13 00:00:00

Recent research progress has confirmed that using advanced controls can result in massive increases in energy capture for marine hydrokinetic (MHK) energy systems, including ocean current turbines (OCTs) and wave energy converters (WECs); however, to realize maximum benefits, the controls, power-take-off system, and basic structure of the device must all be co-designed from early stages. This paper presents an OCT turbine control co-design framework, accounting for the plant geometry and spatial-temporal path planning to optimize the performance. Developing a control co-design framework means that it is now possible to evaluate the effects of changing plant geometry on a level playing field when accounting for the OCT plant power optimization. The investigated framework evaluates the key design parameters, including the sizes of the generator, rotor, and variable buoyancy tank in the OCT system, and formulates these parameters' effect on the OCT model and harnessed power through defining a power-to-weight ratio, subject to the design and operational constraints. The control co-design is formulated as a nested optimization problem, where the outer loop optimizes the plant geometry and the inner loop accounts for the spatial-temporal path planning to optimize the harnessed power with respect to the linear model of the OCT system and ocean current uncertainties. Compared with a baseline design, results verify the efficacy of the proposed framework in co-designing an optimal OCT system to gain the maximum power-to-weight ratio.
Incentive-aware Electric Vehicle Routing Problem: a Bi-level Model and a Joint Solution Algorithm
Authors: Canqi Yao, Shibo Chen, Mauro Salazar, Zaiyue Yang
Date: 2021-10-13 00:00:00

Fixed pickup and delivery times can strongly limit the performance of freight transportation. Against this backdrop, fleet operators can use compensation mechanisms such as monetary incentives to buy delay time from their customers, in order to improve the fleet efficiency and ultimately minimize the costs of operation. To make the most of such an operational model, the fleet activities and the incentives should be jointly optimized accounting for the customers' reactions. Against this backdrop, this paper presents an incentive-aware electric vehicle routing scheme in which the fleet operator actively provides incentives to the customers in exchange of pickup or delivery time flexibility. Specifically, we first devise a bi-level model whereby the fleet operator optimizes the routes and charging schedules of the fleet jointly with an incentive rate to reimburse the delivery delays experienced by the customers. At the same time, the customers choose the admissible delays by minimizing a monetarily-weighted combination of the delays minus the reimbursement offered by the operator. Second, we tackle the complexity resulting from the bi-level and nonlinear problem structure with an equivalent transformation method, reformulating the problem as a single-level optimization problem that can be solved with standard mixed-integer linear programming algorithms. We demonstrate the effectiveness of our framework via extensive numerical experiments using VRP-REP data from Belgium. Our results show that by jointly optimizing routes and incentives subject to the customers' preferences, the operational costs can be reduced by up to 5%, whilst customers can save more than 30% in total delivery fees.
On the Behaviour of Under-tuned Super-Twisting Sliding Mode Control Loops
Authors: Dimitrios Papageorgiou, Christopher Edwards
Date: 2021-10-13 00:00:00

This paper investigates the stability properties and performance of super-twisting sliding-mode control loops subject to periodic perturbations. Although there exist conditions on the control gains that guarantee finite-time stability of the closed-loop system, such conditions are often too restrictive from a practical standpoint, especially in relation to actuator limitations and induced chatter. Using regularisation and averaging theory, it is proven that under milder conditions for the control gains, the trajectories of the periodically perturbed closed-loop system converge to a stable limit cycle of the same period containing the origin. Additionally, guidelines for selecting the controller gains are provided based on bounds of the closed-loop system states. Finally, the theoretical findings are validated through simulations.
Distributed Kalman Filters for Relative Formation Control of Mobile Agents
Authors: Martijn van der Marel, Raj Thilak Rajan
Date: 2021-10-12 00:00:00
Tasks: Position

Formation control (FC) of multi-agent plays a critical role in a wide variety of fields. In the absence of absolute positioning, agents in FC systems rely on relative position measurements with respect to their neighbors. In distributed filter design literature, relative observation models are comparatively unexplored, and in FC literature, uncertainty models are rarely considered. In this article, we aim to bridge the gap between these domains, by exploring distributed filters tailored for relative FC of swarms. We propose statistically robust data models for tracking relative positions of agents in a FC network, and subsequently propose optimal Kalman filters for both centralized and distributed scenarios. Our simulations highlight the benefits of these estimators, and we identify future research directions based on our proposed framework.
A Reference Governor for linear systems with polynomial constraints
Authors: Laurent Burlion, Rick Schieni, Ilya Kolmanovsky
Date: 2021-10-12 00:00:00

The paper considers the application of reference governors to linear discrete-time systems with constraints given by polynomial inequalities. We propose a novel algorithm to compute the maximal output admissible invariant set in the case of polynomial constraints. The reference governor solves a constrained nonlinear minimization problem at initialization and then uses a bisection algorithm at the subsequent time steps. The effectiveness of the method is demonstrated by two numerical examples.