pwc-archive/papers-with-abstracts huggingface dataset:
The tourism forecasting competition
Authors: George Athanasopoulos, Rob J. Hyndman, Haiyan Song, Doris C.Wu
Date: 2010-09-02 00:00:00
Proceeding: International Journal of Forecasting 2010 9
Tasks: Time Series, Time Series Analysis

We evaluate the performances of various methods for forecasting tourism data. The data used include 366 monthly series, 427 quarterly series and 518 annual series, all supplied to us by either tourism bodies or academics who had used them in previous tourism forecasting studies. The forecasting methods implemented in the competition are univariate and multivariate time series approaches, and econometric models. This forecasting competition differs from previous competitions in several ways: (i) we concentrate on tourism data only; (ii) we include approaches with explanatory variables; (iii) we evaluate the forecast interval coverage as well as the point forecast accuracy; (iv) we observe the effect of temporal aggregation on the forecasting accuracy; and (v) we consider the mean absolute scaled error as an alternative forecasting accuracy measure. We find that pure time series approaches provide more accurate forecasts for tourism data than models with explanatory variables. For seasonal data we implement three fully automated pure time series algorithms that generate accurate point forecasts, and two of these also produce forecast coverage probabilities which are satisfactorily close to the nominal rates. For annual data we find that Naïve forecasts are hard to beat.
Human-Machine Collaboration for Smart Decision Making: Current Trends and Future Opportunities
Authors: Baocheng Geng, Pramod K. Varshney
Date: 2023-01-18 00:00:00
Tasks: Autonomous Driving, Decision Making

Recently, modeling of decision making and control systems that include heterogeneous smart sensing devices (machines) as well as human agents as participants is becoming an important research area due to the wide variety of applications including autonomous driving, smart manufacturing, internet of things, national security, and healthcare. To accomplish complex missions under uncertainty, it is imperative that we build novel human machine collaboration structures to integrate the cognitive strengths of humans with computational capabilities of machines in an intelligent manner. In this paper, we present an overview of the existing works on human decision making and human machine collaboration within the scope of signal processing and information fusion. We review several application areas and research domains relevant to human machine collaborative decision making. We also discuss current challenges and future directions in this problem domain.
Cooperative Artificial Neural Networks for Rate-Maximization in Optical Wireless Networks
Authors: Ahmad Adnan Qidan, Taisir El-Gorashi, Jaafar M. H. Elmirghani
Date: 2023-01-19 00:00:00
Tasks: valid

Recently, Optical wireless communication (OWC) have been considered as a key element in the next generation of wireless communications due to its potential in supporting unprecedented communication speeds. In this paper, infrared lasers referred to as vertical-cavity surface-emitting lasers (VCSELs) are used as transmitters sending information to multiple users. In OWC, rate-maximization optimization problems are usually complex due to the high number of optical access points (APs) needed to ensure coverage. Therefore, practical solutions with low computational time are essential to cope with frequent updates in user-requirements that might occur. In this context, we formulate an optimization problem to determine the optimal user association and resource allocation in the network, while the serving time is partitioned into a series of time periods. Therefore, cooperative ANN models are designed to estimate and predict the association and resource allocation variables for each user such that sub-optimal solutions can be obtained within a certain period of time prior to its actual starting, which makes the solutions valid and in accordance with the demands of the users at a given time. The results show the effectiveness of the proposed model in maximizing the sum rate of the network compared with counterpart models. Moreover, ANN-based solutions are close to the optimal ones with low computational time.
Cooperative Distributed MPC via Decentralized Real-Time Optimization: Implementation Results for Robot Formations
Authors: Gösta Stomberg, Henrik Ebel, Timm Faulwasser, Peter Eberhard
Date: 2023-01-19 00:00:00
Tasks: Model Predictive Control

Distributed model predictive control (DMPC) is a flexible and scalable feedback control method applicable to a wide range of systems. While the stability analysis of DMPC is quite well understood, there exist only limited implementation results for realistic applications involving distributed computation and networked communication. This article approaches formation control of mobile robots via a cooperative DMPC scheme. We discuss the implementation via decentralized optimization algorithms. To this end, we combine the alternating direction method of multipliers with decentralized sequential quadratic programming to solve the underlying optimal control problem in a decentralized fashion with nominal convergence guarantees. Our approach only requires coupled subsystems to communicate and does not rely on a central coordinator. Our experimental results showcase the efficacy of DMPC for formation control and they demonstrate the real-time feasibility of the considered algorithms.
Magnetoreological spring as element of vibration control system of dynamicly active equipment for biofuel production
Authors: K. A. Bashmur, V. A. Kachaeva, V. V. Bukhtoyarov, M. V. Saramud
Date: 2023-01-19 00:00:00

The development of vibration protection systems that ensure efficiency and safety in the operation of process equipment and pipelines is one of the main tasks of controlling the dynamic state of machines. One of the effective methods of vibration isolation of the equipment of these installations is the use of vibration mounts. Today, both passive and active methods of extinguishing are actively used. The combination of two methods of damping will ensure the stability and adaptability of vibration protection systems to the operating conditions of process equipment. The paper presents and investigates the device of a hybrid vibration support, including a magnetorheological spring, as an element of vibration damping. A distinctive feature of the vibration mount is an extended range of performance and reduced wear rate of the components. An analysis of the damping characteristics of a hybrid vibration support in passive and active modes of operation is presented. A simulation model of the vibration support under consideration has been developed in the MATLAB Simulink simulation environment using the PIB controller. The simulation results indicate the effectiveness of the use of vibration damping systems with a magnetorheological spring in conjunction with various technological equipment.
Architecting Safer Autonomous Aviation Systems
Authors: Jane Fenn, Mark Nicholson, Ganesh Pai, Michael Wilkinson
Date: 2023-01-09 00:00:00

The aviation literature gives relatively little guidance to practitioners about the specifics of architecting systems for safety, particularly the impact of architecture on allocating safety requirements, or the relative ease of system assurance resulting from system or subsystem level architectural choices. As an exemplar, this paper considers common architectural patterns used within traditional aviation systems and explores their safety and safety assurance implications when applied in the context of integrating artificial intelligence (AI) and machine learning (ML) based functionality. Considering safety as an architectural property, we discuss both the allocation of safety requirements and the architectural trade-offs involved early in the design lifecycle. This approach could be extended to other assured properties, similar to safety, such as security. We conclude with a discussion of the safety considerations that emerge in the context of candidate architectural patterns that have been proposed in the recent literature for enabling autonomy capabilities by integrating AI and ML. A recommendation is made for the generation of a property-driven architectural pattern catalogue.
A Benchmark Generator for Combinatorial Testing
Authors: Carlos Ansotegui, Eduard Torres
Date: 2022-12-29 00:00:00
Tasks: Autonomous Driving

Combinatorial Testing (CT) tools are essential to test properly a wide range of systems (train systems, Graphical User Interfaces (GUIs), autonomous driving systems, etc). While there is an active research community working on developing CT tools, paradoxically little attention has been paid to making available enough resources to test the CT tools themselves. In particular, the set of available benchmarks to asses their correctness, effectiveness and efficiency is rather limited. In this paper, we introduce a new generator of CT benchmarks that essentially borrows the structure contained in the plethora of available Combinatorial Problems from other research communities in order to create meaningful benchmarks. We additionally perform an extensive evaluation of CT tools with these new benchmarks. Thanks to this study we provide some insights on under which circumstances a particular CT tool should be used.
Channel Reuse for Backhaul in UAV Mobile Networks with User QoS Guarantee
Authors: Mohammadsaleh Nikooroo, Zdenek Becvar, Omid Esrafilian, David Gesbert
Date: 2023-01-19 00:00:00

In mobile networks, unmanned aerial vehicles (UAVs) acting as flying base stations (FlyBSs) can effectively improve performance. Nevertheless, such potential improvement requires an efficient positioning of the FlyBS. In this paper, we study the problem of sum downlink capacity maximization in FlyBS-assisted networks with mobile users and with a consideration of wireless backhaul with channel reuse while a minimum required capacity to every user is guaranteed. The problem is formulated under constraints on the FlyBS's flying speed, propulsion power consumption, and transmission power for both of flying and ground base stations. None of the existing solutions maximizing the sum capacity can be applied due to the combination of these practical constraints. This paper pioneers in an inclusion of all these constraints together with backhaul to derive the optimal 3D positions of the FlyBS and to optimize the transmission power allocation for the channels at both backhaul and access links as the users move over time. The proposed solution is geometrical based, and it shows via simulations a significant increase in the sum capacity (up by 19%-47%) compared with baseline schemes where one or more of the aspects of backhaul communication, transmission power allocation, and FlyBS's positioning are not taken into account.
Learning stability of partially observed switched linear systems
Authors: Zheming Wang, Raphaël M. Jungers, Mihály Petreczky, Bo Chen, Li Yu
Date: 2023-01-19 00:00:00
Tasks: Learning Theory

This paper deals with learning stability of partially observed switched linear systems under arbitrary switching. Such systems are widely used to describe cyber-physical systems which arise by combining physical systems with digital components. In many real-world applications, the internal states cannot be observed directly. It is thus more realistic to conduct system analysis using the outputs of the system. Stability is one of the most frequent requirement for safety and robustness of cyber-physical systems. Existing methods for analyzing stability of switched linear systems often require the knowledge of the parameters and/or all the states of the underlying system. In this paper, we propose an algorithm for deciding stability of switched linear systems under arbitrary switching based purely on observed output data. The proposed algorithm essentially relies on an output-based Lyapunov stability framework and returns an estimate of the joint spectral radius (JSR). We also prove a probably approximately correct error bound on the quality of the estimate of the JSR from the perspective of statistical learning theory.
From prosumer to flexumer: Case study on the value of flexibility in decarbonizing the multi-energy system of a manufacturing company
Authors: Markus Fleschutz, Markus Bohlayer, Marco Braun, Michael D. Murphy
Date: 2023-01-19 00:00:00

Digitalization and sector coupling enable companies to turn into flexumers. By using the flexibility of their multi-energy system (MES), they reduce costs and carbon emissions while stabilizing the electricity system. However, to identify the necessary investments in energy conversion and storage technologies to leverage demand response (DR) potentials, companies need to assess the value of flexibility. Therefore, this study quantifies the flexibility value of a production company's MES by optimizing the synthesis, design, and operation of a decarbonizing MES considering self-consumption optimization, peak shaving, and integrated DR based on hourly prices and carbon emission factors (CEFs). The detailed case study of a beverage company in northern Germany considers vehicle-to-X of powered industrial trucks, power-to-heat on multiple temperatures, wind turbines, photovoltaic systems, and energy storage systems (thermal energy, electricity, and hydrogen). We propose and apply novel data-driven metrics to evaluate the intensity of price-based and CEF-based DR. The results reveal that flexibility usage reduces decarbonization costs (by 19-80% depending on electricity and carbon removal prices), total annual costs, operating carbon emissions, energy-weighted average prices and CEFs, and fossil energy dependency. The results also suggest that a net-zero operational carbon emission MES requires flexibility, which, in an economic case, is provided by a combination of different flexible technologies and storage systems that complement each other. While the value of flexibility depends on various market and consumer-specific factors such as electricity or carbon removal prices, this study highlights the importance of demand flexibility for the decarbonization of MESs.
Sequential learning and control: Targeted exploration for robust performance
Authors: Janani Venkatasubramanian, Johannes Köhler, Julian Berberich, Frank Allgöwer
Date: 2023-01-19 00:00:00
Tasks: Scheduling

We present a novel dual control strategy for uncertain linear systems based on targeted harmonic exploration and gain-scheduling with performance and excitation guarantees. In the proposed sequential approach, robust control is implemented after exploration with the main feature that the exploration is optimized with respect to the robust control performance. Specifically, we leverage recent results on finite excitation using spectral lines to determine a high probability lower bound on the resultant finite excitation of the exploration data. This provides an a priori upper bound on the remaining model uncertainty after exploration, which can further be leveraged in a gain-scheduling controller design that guarantees robust performance. This leads to a semidefinite program-based design which computes an exploration strategy with finite excitation bounds and minimal energy, and a gain-scheduled controller with probabilistic performance bounds that can be implemented after exploration. The effectiveness of our approach and its benefits over common random exploration strategies are demonstrated with an example of a system which is 'hard to learn'.
Mitigating Motion Sickness with Optimization-based Motion Planning
Authors: Yanggu Zheng, Barys Shyrokau, Tamas Keviczky
Date: 2023-01-19 00:00:00
Tasks: Motion Planning

The acceptance of automated driving is under the potential threat of motion sickness. It hinders the passengers' willingness to perform secondary activities. In order to mitigate motion sickness in automated vehicles, we propose an optimization-based motion planning algorithm that minimizes the distribution of acceleration energy within the frequency range that is found to be the most nauseogenic. The algorithm is formulated into integral and receding-horizon variants and compared with a commonly used alternative approach aiming to minimize accelerations in general. The proposed approach can reduce frequency-weighted acceleration by up to 11.3% compared with not considering the frequency sensitivity for the price of reduced overall acceleration comfort. Our simulation studies also reveal a loss of performance by the receding-horizon approach over the integral approach when varying the preview time and nominal sampling time. The computation time of the receding-horizon planner is around or below the real-time threshold when using a longer sampling time but without causing significant performance loss. We also present the results of experiments conducted to measure the performance of human drivers on a public road section that the simulated scenario is actually based on. The proposed method can achieve a 19\% improvement in general acceleration comfort or a 32% reduction in squared motion sickness dose value over the best-performing participant. The results demonstrate considerable potential for improving motion comfort and mitigating motion sickness using our approach in automated vehicles.
Analysis of the Reliability of a Biofuel Production Plant from Waste Cooking Oil
Authors: Ivan Nekrasov, Aleksandr Zagulyaev, Vladimir Bukhtoyarov, Svetlana Eremeeva, Elena Filyushina, Aleksey Gorodov, Natalia Shepeta
Date: 2023-01-19 00:00:00

The article considers the issue of increasing the structural reliability of a biofuel production plant. A review of the existing basic technological schemes of the biofuel production plant has been carried out. The main structural elements are determined and a functional diagram is constructed. Processed cooking oil was chosen as the input raw material. A structural analysis of the reliability of each element and the entire system as a whole was carried out. The least reliable elements are determined, options for improving the overall reliability of the installation are proposed.
A Light-Weight Communication-Efficient Data Sharing Approach in 5G NR V2X
Authors: Ran Wei, Lyutianyang Zhang
Date: 2023-01-19 00:00:00
Tasks: Autonomous Driving, Scheduling

Timeliness of information is critical for Basic Safety Messages (BSMs) in Vehicle-to-Everything (V2X) communication to enable highly reliable autonomous driving. However, the current semi-persistent scheduling (SPS) algorithms in the 5th generation New Radio (5G NR) standard can still generate collisions probability, close to 20% with 100 vehicles per kilometer, such that they cannot meet this requirement for BSMs. This paper proposes a Ledger concept, which can communicate collision information to every vehicle in the network within one Resource Reservation Interval (RRI) through the broadcasting of Ledger information. The colliding vehicle is aware that it has collided with other vehicles and will reselect for the next SPS period. Besides that, other protocols follow the SPS. Although it sacrifices 14.29% of resources by including Ledger, it can eventually reduce the collision probability. In this paper, a Monte Carlo simulator is used to verify and analyze the performance of the Ledger system. Numerical results show that abide by the SPS protocol, the Ledger system can make the collision probability converge to zero after amount of RRIs.
Distributed Reproduction Numbers of Networked Epidemics
Authors: Baike She, Philip E. Paré, Matthew Hale
Date: 2023-01-19 00:00:00

Reproduction numbers are widely used for the estimation and prediction of epidemic spreading processes over networks. However, reproduction numbers do not enable estimation and prediction in individual communities within networks, and they can be difficult to compute due to the aggregation of infection data that is required to do so. Therefore, in this work we propose a novel concept of distributed reproduction numbers to capture the spreading behaviors of each entity in the network, and we show how to compute them using certain parameters in networked SIS and SIR epidemic models. We use distributed reproduction numbers to derive new conditions under which an outbreak can occur. These conditions are then used to derive new conditions for the existence, uniqueness, and stability of equilibrium states. Finally, in simulation we use synthetic infection data to illustrate how distributed reproduction numbers provide more fine-grained analyses of networked spreading processes than ordinary reproduction numbers.
Resilient Containment Control of Heterogeneous Multi-Agent Systems Against Unbounded Sensor and Actuator Attacks
Authors: Shan Zuo, Yi Zhang, Yichao Wang
Date: 2023-01-18 00:00:00

Accurate local state measurement is important to ensure the reliable operation of distributed multi-agent systems (MAS). Existing fault-tolerant control strategies generally assume the sensor faults to be bounded and uncorrelated. In this paper, we study the ramifications of allowing the sensor attack injections to be unbounded and correlated. These malicious sensor attacks may bypass the conventional attack-detection methods and compromise the cooperative performance and even stability of the distributed networked MAS. Moreover, the attackers may gain access to the actuation computing channels and manipulate the control input commands. To this end, we consider the resilient containment control problem of general linear heterogeneous MAS in the face of correlated and unbounded sensor attacks, as well as general unbounded actuator attacks. We propose an attack-resilient control framework to guarantee the uniform ultimate boundedness of the closed-loop dynamical systems and preserve the bounded containment performance. Compared with existing literature addressing bounded faults and/or disturbances that are unintentionally caused in the sensor and actuator channels, the proposed control protocols are resilient against unknown unbounded attack signals simultaneously injected into sensor and actuator channels, and hence are more practical in the real-world security applications. A numerical example illustrates the efficacy of the proposed result, by highlighting the resilience improvement over the conventional cooperative control method.
GrAVITree: Graph-based Approximate Value Function In a Tree
Authors: Patrick H. Washington, David Fridovich-Keil, Mac Schwager
Date: 2023-01-18 00:00:00

In this paper, we introduce GrAVITree, a tree- and sampling-based algorithm to compute a near-optimal value function and corresponding feedback policy for indefinite time-horizon, terminal state-constrained nonlinear optimal control problems. Our algorithm is suitable for arbitrary nonlinear control systems with both state and input constraints. The algorithm works by sampling feasible control inputs and branching backwards in time from the terminal state to build the tree, thereby associating each vertex in the tree with a feasible control sequence to reach the terminal state. Additionally, we embed this stochastic tree within a larger graph structure, rewiring of which enables rapid adaptation to changes in problem structure due to, e.g., newly detected obstacles. Because our method reasons about global problem structure without relying on (potentially imprecise) derivative information, it is particularly well suited to controlling a system based on an imperfect deep neural network model of its dynamics. We demonstrate this capability in the context of an inverted pendulum, where we use a learned model of the pendulum with actuator limits and achieve robust stabilization in settings where competing graph-based and derivative-based techniques fail.