pwc-archive/papers-with-abstracts huggingface dataset:
LEI2JSON: Schema-based Validation and Conversion of Livestock Event Information
Authors: Mahir Habib, Muhammad Ashad Kabir, Lihong Zheng
Date: 2023-10-26 00:00:00

Livestock producers often need help in standardising (i.e., converting and validating) their livestock event data. This article introduces a novel solution, LEI2JSON (Livestock Event Information To JSON). The tool is an add-on for Google Sheets, adhering to the livestock event information (LEI) schema. The core objective of LEI2JSON is to provide livestock producers with an efficient mechanism to standardise their data, leading to substantial savings in time and resources. This is achieved by building the spreadsheet template with the appropriate column headers, notes, and validation rules, converting the spreadsheet data into JSON format, and validating the output against the schema. LEI2JSON facilitates the seamless storage of livestock event information locally or on Google Drive in JSON. Additionally, we have conducted an extensive experimental evaluation to assess the effectiveness of the tool.
Estimating True Beliefs in Opinion Dynamics with Social Pressure
Authors: Jennifer Tang, Aviv Adler, Amir Ajorlou, Ali Jadbabaie
Date: 2023-10-26 00:00:00
Tasks: parameter estimation

Social networks often exert social pressure, causing individuals to adapt their expressed opinions to conform to their peers. An agent in such systems can be modeled as having a (true and unchanging) inherent belief while broadcasting a declared opinion at each time step based on her inherent belief and the past declared opinions of her neighbors. An important question in this setting is parameter estimation: how to disentangle the effects of social pressure to estimate inherent beliefs from declared opinions. This is useful for forecasting when agents' declared opinions are influenced by social pressure while real-world behavior only depends on their inherent beliefs. To address this, Jadbabaie et al. formulated the Interacting P\'olya Urn model of opinion dynamics under social pressure and studied it on complete-graph social networks using an aggregate estimator, and found that their estimator converges to the inherent beliefs unless majority pressure pushes the network to consensus. In this work, we studythis model on arbitrary networks, providing an estimator which converges to the inherent beliefs even in consensus situations. Finally, we bound the convergence rate of our estimator in both consensus and non-consensus scenarios; to get the bound for consensus scenarios (which converge slower than non-consensus) we additionally found how quickly the system converges to consensus.
Adaptive Resource Management for Edge Network Slicing using Incremental Multi-Agent Deep Reinforcement Learning
Authors: Haiyuan Li, Yuelin Liu, Xueqing Zhou, Xenofon Vasilakos, Reza Nejabati, Shuangyi Yan, Dimitra Simeonidou
Date: 2023-10-26 00:00:00
Tasks: Deep Reinforcement Learning, Edge-computing, Incremental Learning, Management, Scheduling

Multi-access edge computing provides local resources in mobile networks as the essential means for meeting the demands of emerging ultra-reliable low-latency communications. At the edge, dynamic computing requests require advanced resource management for adaptive network slicing, including resource allocations, function scaling and load balancing to utilize only the necessary resources in resource-constraint networks. Recent solutions are designed for a static number of slices. Therefore, the painful process of optimization is required again with any update on the number of slices. In addition, these solutions intend to maximize instant rewards, neglecting long-term resource scheduling. Unlike these efforts, we propose an algorithmic approach based on multi-agent deep deterministic policy gradient (MADDPG) for optimizing resource management for edge network slicing. Our objective is two-fold: (i) maximizing long-term network slicing benefits in terms of delay and energy consumption, and (ii) adapting to slice number changes. Through simulations, we demonstrate that MADDPG outperforms benchmark solutions including a static slicing-based one from the literature, achieving stable and high long-term performance. Additionally, we leverage incremental learning to facilitate a dynamic number of edge slices, with enhanced performance compared to pre-trained base models. Remarkably, this approach yields superior reward performance while saving approximately 90% of training time costs.
Proposal on Model Based Current Overshoot Suppression of Receiver Side Coil in Drone Wireless Power Transfer System
Authors: Kota Fujimoto, Takumi Hamada, Hiroshi Fujimoto
Date: 2023-10-26 00:00:00

This paper proposes a model-based control method in the wireless power transfer (WPT) system by operating a semi-bridgeless active rectifier (SBAR) to suppress the secondary coil current overshoot. By damping the current overshoot, it is possible to reduce the rectifier's rated current and decrease the rectifier's size, which is beneficial for the lightweight-oriented system such as drones. In the control method, an inverse of the plant model is used to calculate the reference input to the system. The current overshoot is reduced by operating the SBAR under the duty ratio calculated from the model. To confirm the performance of the proposed method, the simulation and the experiment using the WPT prototype are conducted. The experimental results show that the proposed method can suppress the secondary coil current overshoot. The results suggest it is possible to realize the lighter secondary system by applying the proposed method.
Free Space Optical Communication for Inter-Satellite Link: Architecture, Potentials and Trends
Authors: Guanhua Wang, Fang Yang, Jian Song, Zhu Han
Date: 2023-10-26 00:00:00
Tasks: Integrated sensing and communication, Scheduling

The sixth-generation (6G) network is expected to achieve global coverage based on the space-air-ground integrated network, and the latest satellite network will play an important role in it. The introduction of inter-satellite links (ISLs) can significantly improve the throughput of the satellite network, and recently gets lots of attention from both academia and industry. In this paper, we illustrate the advantages of using the laser for ISLs due to its longer communication distance, higher data speed, and stronger security. Specifically, space-borne laser terminals with the acquisition, pointing and tracking mechanism which realize long-distance communication are illustrated, advanced modulation and multiplexing modes that make high communication rates possible are introduced, and the security of ISLs ensured by the characteristics of both laser and the optical channel is also analyzed. Moreover, some open issues such as advanced optical beam steering, routing and scheduling algorithm, and integrated sensing and communication are discussed to direct future research.
Efficient safe learning for controller tuning with experimental validation
Authors: Marta Zagorowska, Christopher König, Hanlin Yu, Efe C. Balta, Alisa Rupenyan, John Lygeros
Date: 2023-10-26 00:00:00
Tasks: Computational Efficiency

Optimization-based controller tuning is challenging because it requires formulating optimization problems explicitly as functions of controller parameters. Safe learning algorithms overcome the challenge by creating surrogate models from measured data. To ensure safety, such data-driven algorithms often rely on exhaustive grid search, which is computationally inefficient. In this paper, we propose a novel approach to safe learning by formulating a series of optimization problems instead of a grid search. We also develop a method for initializing the optimization problems to guarantee feasibility while using numerical solvers. The performance of the new method is first validated in a simulated precision motion system, demonstrating improved computational efficiency, and illustrating the role of exploiting numerical solvers to reach the desired precision. Experimental validation on an industrial-grade precision motion system confirms that the proposed algorithm achieves 30% better tracking at sub-micrometer precision as a state-of-the-art safe learning algorithm, improves the default auto-tuning solution, and reduces the computational cost seven times compared to learning algorithms based on exhaustive search.
The Newtonian Mechanics of Demand
Authors: Max Mendel
Date: 2023-10-26 00:00:00
Tasks: Electrical Engineering, Friction

Economic engineering is a new field wherein economic systems are modelled in the same manner as traditional mechanical and electrical engineering systems. In this paper, we use Newton's theory of motion as the basis for the theory of demand; thereby establishing a theoretical foundation for economic engineering. We follow Newton's original development, as set forth in the Principia, to determine economic analogs to his three laws of motion. The pivotal result is an operational definition for an economic force, i.e. a want or a desire, in terms of a price adjustment. With this, we model the price effects of scarcity and trade friction in analogy with the models for the spring and damping force. In turn, we define economic benefits and surplus as analogous to the definitions of mechanical work and energy. These are then used to interpret the various types of economic equilibrium considered by economists from a mechanical perspective. The effectiveness of the analogy is illustrated by applying it to modelling the price and inventory dynamics of various economic agents -- including consumers, dealers, holders, spot and futures traders -- using linear-time invariant systems theory.
Proactive Emergency Collision Avoidance for Automated Driving in Highway Scenarios
Authors: Leila Gharavi, Azita Dabiri, Jelske Verkuijlen, Bart De Schutter, Simone Baldi
Date: 2023-10-26 00:00:00
Tasks: Collision Avoidance, Computational Efficiency, Model Predictive Control

Uncertainty in the behavior of other traffic participants is a crucial factor in collision avoidance for automated driving; here, stochastic metrics could avoid overly conservative decisions. This paper introduces a Stochastic Model Predictive Control (SMPC) planner for emergency collision avoidance in highway scenarios to proactively minimize collision risk while ensuring safety through chance constraints. To guarantee that the emergency trajectory can be attained, we incorporate nonlinear tire dynamics in the prediction model of the ego vehicle. Further, we exploit Max-Min-Plus-Scaling (MMPS) approximations of the nonlinearities to avoid conservatism, enforce proactive collision avoidance, and improve computational efficiency in terms of performance and speed. Consequently, our contributions include integrating a dynamic ego vehicle model into the SMPC planner, introducing the MMPS approximation for real-time implementation in emergency scenarios, and integrating SMPC with hybridized chance constraints and risk minimization. We evaluate our SMPC formulation in terms of proactivity and efficiency in various hazardous scenarios. Moreover, we demonstrate the effectiveness of our proposed approach by comparing it with a state-of-the-art SMPC planner and we validate that the generated trajectories can be attained using a high-fidelity vehicle model in IPG CarMaker.
Distributed Adaptive Control for Uncertain Networks
Authors: Venkatraman Renganathan, Anders Rantzer, Olle Kjellqvist
Date: 2023-10-26 00:00:00

Control of network systems with uncertain local dynamics has remained an open problem for a long time. In this paper, a distributed minimax adaptive control algorithm is proposed for such networks whose local dynamics has an uncertain parameter possibly taking finite number of values. To hedge against this uncertainty, each node in the network collects the historical data of its neighboring nodes to decide its control action along its edges by finding the parameter that best describes the observed disturbance trajectory. Our proposed distributed adaptive controller is scalable and we give both lower and upper bounds for its $\ell_{2}$ gain. Numerical simulations demonstrate that once each node has sufficiently estimated its local uncertainty, the distributed minimax adaptive controller behaves like the optimal distributed $\mathcal{H}_{\infty}$ controller in hindsight.
Controllability of networked multiagent systems based on linearized Turing's model
Authors: TianHao Li, Ruichang Zhang, Zhixin Liu, Zhuo Zou, Xiaoming Hu
Date: 2023-10-26 00:00:00

Turing's model has been widely used to explain how simple, uniform structures can give rise to complex, patterned structures during the development of organisms. However, it is very hard to establish rigorous theoretical results for the dynamic evolution behavior of Turing's model since it is described by nonlinear partial differential equations. We focus on controllability of Turing's model by linearization and spatial discretization. This linearized model is a networked system whose agents are second order linear systems and these agents interact with each other by Laplacian dynamics on a graph. A control signal can be added to agents of choice. Under mild conditions on the parameters of the linearized Turing's model, we prove the equivalence between controllability of the linearized Turing's model and controllability of a Laplace dynamic system with agents of first order dynamics. When the graph is a grid graph or a cylinder grid graph, we then give precisely the minimal number of control nodes and a corresponding control node set such that the Laplace dynamic systems on these graphs with agents of first order dynamics are controllable.
Controlling Automated Vehicles on Large Lane-free Roundabouts (Extended Version)
Authors: Mehdi Naderi, Markos Papageorgiou, Dimitrios Troullinos, Iasson Karafyllis, Ioannis Papamichail
Date: 2023-10-26 00:00:00
Tasks: Navigate

Controlling automated vehicles on large lane-free roundabouts is challenging because of the geometrical complexity and frequent conflicts among entering, rotating, and exiting vehicles. This paper proposes a comprehensive methodology to control the vehicles within the roundabout and the connected road branches. The developed real-time vehicle movement strategy relies on offline-computed wide overlapping movement corridors, one for each Origin-Destination (OD) movement, which delineate the admissible movement zones of corresponding OD vehicles. Also, space-dependent desired orientations are determined by destination, so as to mitigate potential vehicle conflicts and reduce trip distance. A distributed (per vehicle) movement control strategy, using two nonlinear feedback controllers (NLFC), for circular and straight movements, respectively, is employed to navigate each vehicle within the respective OD corridor toward its destination, accounting for the desired orientation and avoiding collisions with other vehicles; while boundary controllers guarantee that the corridor boundaries will not be violated, and the exit will not be missed. As an overly complicated case study, we consider the famous roundabout of Place Charles de Gaulle in Paris, featuring a width of 38 m and comprising a dozen of bidirectional radial streets, hence a total of 144 ODs. The pertinence and effectiveness of the presented method is verified via microscopic simulation and evaluation of macroscopic data.
Mode Selection for Component Mode Synthesis with Guaranteed Assembly Accuracy
Authors: Lars A. L. Janssen, Rob H. B. Fey, Bart Besselink, Nathan van de Wouw
Date: 2023-10-26 00:00:00

In this work, a modular approach is introduced to select the most important eigenmodes for each component of a composed structural dynamics system to obtain the required accuracy of the reduced-order assembly model. To enable the use of models of complex (structural) dynamical systems in engineering practice, e.g., in a design, optimization and/or control context, the complexity of the models needs to be reduced. When the model consist of an assembly of multiple interconnected structural components, component mode synthesis is often the preferred model reduction method. The standard approach to component mode synthesis for such system is to select the eigenmodes of a component that are most important to accurately model the dynamic behavior of this component in a certain frequency range of interest. However, often, a more relevant goal is to obtain, in this frequency range, an accurate model of the assembly. In the proposed approach, accuracy requirements on the level of the assembly are translated to accuracy requirements on component level, by employing techniques from the field of systems and control. With these component-level requirements, the eigenmodes that are most important to accurately model the dynamic behavior of the assembly can be selected in a modular fashion. We demonstrate with two structural dynamics benchmark systems that this method based on assembly accuracy allows for a computationally efficient selection of eigenmodes that 1) guarantees satisfaction of the assembly accuracy requirements and 2) results in most cases in reduced-order models of significantly lower order with respect to the industrial standard approach in which component eigenmodes are selected using a frequency criterion.
A Forward Reachability Perspective on Robust Control Invariance and Discount Factors in Reachability Analysis
Authors: Jason J. Choi, Donggun Lee, Boyang Li, Jonathan P. How, Koushil Sreenath, Sylvia L. Herbert, Claire J. Tomlin
Date: 2023-10-26 00:00:00
Tasks: valid

Control invariant sets are crucial for various methods that aim to design safe control policies for systems whose state constraints must be satisfied over an indefinite time horizon. In this article, we explore the connections among reachability, control invariance, and Control Barrier Functions (CBFs). Unlike prior formulations based on backward reachability concepts, by examining a forward reachability problem, we are able to establish a strong link between these three concepts. First, our findings show that the inevitable Forward Reachable Tube (FRT), which is the set of states such that every trajectory reaching the FRT must have passed through a given initial set of states, is precisely this initial set of states itself if it is a robust control invariant set with a differentiable boundary. We highlight that this statement may not hold if the boundary is not differentiable. Next, we formulate a differential game between the control and disturbance, where the inevitable FRT is characterized by the zero-superlevel set of the value function. By incorporating a discount factor in the cost function of the game, the barrier constraint of the CBF naturally arises as the constraint that is imposed on the optimal control policy. Combining these results, the value function of our FRT formulation serves as a CBF-like function, and conversely, any valid CBF is also a forward reachability value function inside the control invariant set, thereby revealing the inverse optimality of the CBF. This strong link we establish between the reachability problem and the barrier constraint, while guaranteeing the continuity of the value function, is not achievable by previous backward reachability-based formulations. As such, our work fills a crucial gap in the existing literature that is vital for constructing valid CBFs to ensure safety.
Linking intra- and extra-cellular metabolic domains via neural-network surrogates for dynamic metabolic control
Authors: Sebastián Espinel-Ríos, José L. Avalos
Date: 2023-10-26 00:00:00

We outline a modeling and optimization strategy for investigating dynamic metabolic engineering interventions. Our framework is particularly useful at the early stages of research and development, often constrained by limited knowledge and experimental data. Elucidating a priori optimal trajectories of manipulatable intracellular fluxes can guide the design of suitable control schemes, e.g., cyber(ge)netic or in-cell approaches, and the selection of appropriate actuators, e.g., at the transcriptional or post-translational levels. Model-based dynamic optimization is proposed to predict optimal trajectories of target manipulatable intracellular fluxes. A challenge emerges as existing models are often oversimplified, lacking insights into metabolism, or excessively complex, making them difficult to build and implement. Here, we use surrogates derived from steady-state solutions of constraint-based metabolic models to link manipulatable intracellular fluxes to the process exchange rates of structurally simple hybrid dynamic models. The latter can be conveniently used in optimal control problems of metabolism. As a proof of concept, we apply our method to a reduced metabolic network of $\textit{Escherichia coli}$ considering two different scenarios of dynamic metabolic engineering.
Electric Vehicle Aggregation Review: Benefits and Vulnerabilities of Managing a Growing EV Fleet
Authors: Kelsey Nelson, Javad Mohammadi, Yu Chen, Erik Blasch, Alex Aved, David Ferris, Erika Ardiles Cruz, Philip Morrone
Date: 2023-10-26 00:00:00

Electric vehicles (EVs) are becoming more popular within the United States, making up an increasingly large portion of the US's electricity consumption. Hence, there is much attention has been directed on how to manage EVs within the power sector. A well-investigated strategy for managing the increase in electricity demand from EV charging is aggregation, which allows for an intermediary to manage electricity flow between EV owners and their utilities. When implemented effectively, EV aggregation provides key benefits to power grids by relieving electrical loads.. These benefits are aggregation's ability to shift EV loads to peak shave, which often leads to lower emissions, electricity generation prices, and consumer costs depending on the penetration levels of non-dispatchable electricity sources. This review seeks to appropriately highlight the broad vulnerabilities of EV aggregation alongside its benefits, namely those regarding battery degradation, rebound peaks, and cybersecurity. The holistic overview of EV aggregation provides comparisons that balance expectations with realistic performance.
Single-pixel imaging based on deep learning
Authors: Kai Song, Yaoxing Bian, Ku Wu, Hongrui Liu, Shuangping Han, Jiaming Li, Jiazhao Tian, Chengbin Qin, Jianyong Hu, Liantuan Xiao
Date: 2023-10-25 00:00:00
Tasks: Deep Learning, Super-Resolution

Single-pixel imaging can collect images at the wavelengths outside the reach of conventional focal plane array detectors. However, the limited image quality and lengthy computational times for iterative reconstruction still impede the practical application of single-pixel imaging. Recently, deep learning has been introduced into single-pixel imaging, which has attracted a lot of attention due to its exceptional reconstruction quality, fast reconstruction speed, and the potential to complete advanced sensing tasks without reconstructing images. Here, this advance is discussed and some opinions are offered. Firstly, based on the fundamental principles of single-pixel imaging and deep learning, the principles and algorithms of single-pixel imaging based on deep learning are described and analyzed. Subsequently, the implementation technologies of single-pixel imaging based on deep learning are reviewed. They are divided into super-resolution single-pixel imaging, single-pixel imaging through scattering media, photon-level single-pixel imaging, optical encryption based on single-pixel imaging, color single-pixel imaging, and image-free sensing according to diverse application fields. Finally, major challenges and corresponding feasible approaches are discussed, as well as more possible applications in the future.