pwc-archive/papers-with-abstracts huggingface dataset:
Continuous Time-Delay Estimation From Sampled Measurements
Authors: Mohamed Abdalmoaty, Alexander Medvedev
Date: 2022-11-20 00:00:00

An algorithm for continuous time-delay estimation from sampled output data and known input of finite energy is presented. The continuous time-delay modeling allows for the estimation of subsample delays. The proposed estimation algorithm consists of two steps. First, the continuous Laguerre spectrum of the output signal is estimated from discrete-time (sampled) noisy measurements. Second, an estimate of the delay value is obtained in Laguerre domain given a continuous-time description of the input. The second step of the algorithm is shown to be intrinsically biased, the bias sources are established, and the bias itself is modeled. The proposed delay estimation approach is compared in a Monte-Carlo simulation with state-of-the-art methods implemented in time, frequency, and Laguerre domain demonstrating comparable or higher accuracy for the considered case.
Bode Integral Limitation For Irrational Systems
Authors: William Chang, Fariba Ariaei, Edmond Jonckheere
Date: 2022-11-20 00:00:00
Tasks: Sensitivity

Bode integrals of sensitivity and sensitivity-like functions along with complementary sensitivity and complementary sensitivity-like functions are conventionally used for describing performance limitations of a feedback control system. In this paper, we investigate the Bode integral and evaluate what happens when a fractional order Proportional-Integral-Derivative (PID) controller is used in a feedback control system. We extend our analysis to when fractal PID controllers are applied to irrational systems. We split this into two cases: when the sequence of infinitely many right half plane open-loop poles doesn't have any limit points and when it does have a limit point. In both cases, we prove that the structure of the Bode Integral is similar to the classical version under certain conditions of convergence. We also provide a sufficient condition for the controller to lower the Bode sensitivity integral.
Distributed Optimization with Quantized Gradient Descent
Authors: Apostolos I. Rikos, Wei Jiang, Themistoklis Charalambous, Karl H. Johansson
Date: 2022-11-20 00:00:00
Tasks: Distributed Optimization

In this paper, we consider the unconstrained distributed optimization problem, in which the exchange of information in the network is captured by a directed graph topology, thus, nodes can only communicate with their neighbors. Additionally, in our problem, the communication channels among the nodes have limited bandwidth. In order to alleviate this limitation, quantized messages should be exchanged among the nodes. For solving this distributed optimization problem, we combine a gradient descent method with a distributed quantized consensus algorithm (which requires the nodes to exchange quantized messages and converges in a finite number of steps). Specifically, at every optimization step, each node (i) performs a gradient descent step (i.e., subtracts the scaled gradient from its current estimate), and (ii) performs a finite-time calculation of the quantized average of every node's estimate in the network. As a consequence, this algorithm approximately mimics the centralized gradient descent algorithm. We show that our algorithm asymptotically converges to a neighborhood of the optimal solution with linear convergence rate. The performance of the proposed algorithm is demonstrated via simple illustrative examples.
A two dimensional fluid model for TCP/AQM analysis
Authors: Sadek Belamfedel Alaoui, Alejandro J. Rojas, Abdelaziz Hmamed, El Houssaine Tissir
Date: 2022-11-20 00:00:00
Tasks: Vocal Bursts Valence Prediction

This work proposes a new mathematical model for the TCP/AQM system that aims to improve the accuracy of existing fluid models, especially with respect to the sequential events that occur in the network. The analysis is based on the consideration of two time bases, one at the queue's router level and the other at the congestion window level, which leads to the derivation of a new nonlinear two-dimensional fluid model for Internet congestion control. To avoid the difficult task of assessing stability of a 2D nonlinear dynamic model, we perform a local stability analysis of a 2D linear TCP AQM model. By constructing a new two dimensional second order Bessel Legendre Lyapunov functional, new matrix inequalities are derived to evaluate the stability of the 0-input system and to synthesize a feedback controller. Finally, two Internet traffic scenarios, with state space matrices for replicability, are presented, demonstrating the validity of the theoretical results.
Identifiability of dynamic networks: the essential rôle of dources and dinks
Authors: Eduardo Mapurunga, Michel Gevers, Alexandre S. Bazanella
Date: 2022-11-20 00:00:00
Tasks: All, valid

The paper [1] presented the first results on generic identifiability of dynamic networks with partial excitation and partial measurements, i.e. networks where not all nodes are excited or not all nodes are measured. One key contribution of that paper was to establish a set of necessary conditions on the excitation and measurement pattern (EMP) that guarantee generic identifiability. In a nutshell, these conditions established that all sources must be excited and all sinks measured, and that all other nodes must be either excited or measured. In the present paper, we show that two other types of nodes, which are defined by the local topology of the network, play an essential r\^ole in the search for a valid EMP, i.e. one that guarantees generic identifiability. We have called these nodes dources and dinks. We show that a network is generically identifiable only if, in addition to the above mentioned conditions, all dources are excited and all dinks are measured. We also show that sources and dources are the only nodes in a network that always need to be excited, and that sinks and dinks are the only nodes that need to be measured for an EMP to be valid.
Passivity and Immersion based-modified gradient estimator: A control perspective in parameter estimation
Authors: Syed Shadab Nayyer, G. Revati, S. R. Wagh, N. M. Singh
Date: 2022-11-19 00:00:00
Tasks: parameter estimation

In this paper, a constructive and systematic strategy with more apparent degrees of freedom to achieve the accurate estimation of unknown parameters via a control perspective is proposed. By adding a virtual control in the final equation of the gradient dynamics, the Gradient Estimator (GE) and Memory Regressor and Extension (MRE) approaches are extended. The solution of the virtual control law is identified by the P&I approach. The P&I approach is based on the choice of an appropriate implicit manifold and the generation of a suitable passive output and a related storage function. This facilitates the virtual control law being obtained in a way that the parametric error converges asymptotically to zero. Because the above ideas connect with the P&I approach and GE, the developed methodology is labeled the passivity and immersion-based modified gradient estimator (MGE). The proposed P&I-based modified gradient estimator is extended via the MRE approach. This modification provides improved transient response and fast convergence. Based on certain PE and non-PE examples, a comparative analysis is carried out to show the efficacy of the proposed approaches.
Optical IRS Aided B5G V2V Solution for Road Safety Applications
Authors: Tathagat Pal, Gurinder Singh, Vivek Ashok Bohara, Anand Srivastava
Date: 2022-11-19 00:00:00

In this work, we showcase the potential benefit of employing optical intelligent reflecting surfaces (O-IRS) for improving safety message dissemination for vehicular visible light communication (V-VLC) systems particularly at the road intersections. Buildings, roadside structures, signboards, and other impediments commonly hinder line-of-sight (LoS) communication between vehicles at urban crossroads scenarios. We propose using O-IRS at road intersection to improve the communication link's reliability. We compare the performance of proposed scheme with baseline scenarios such as optical relay and non line-of-sight (NLOS) road reflection (NRR) aided vehicle-to-vehicle (V2V) communication. From obtained results, it has been shown that O-IRS offers considerable performance enhancement as compared to the baseline scenarios. In particular, O-IRS can achieve longer communication range as compared to the optical relay aided V-VLC systems while ensuring desired quality-of-service (QoS).
An Information-State Based Approach to Linear Time Varying System Identification and Control
Authors: Mohamed Naveed Gul Mohamed, Raman Goyal, Suman Chakravorty, Ran Wang
Date: 2022-11-19 00:00:00

This paper considers the problem of system identification for linear time varying systems. We propose a new system realization approach that uses an "information-state" as the state vector, where the "information-state" is composed of a finite number of past inputs and outputs. The system identification algorithm uses input-output data to fit an autoregressive moving average model (ARMA) to represent the current output in terms of finite past inputs and outputs. This information-state-based approach allows us to directly realize a state-space model using the estimated time varying ARMA paramters linear time varying (LTV) systems. The paper develops the theoretical foundation for using ARMA parameters-based system representation using only the concept of linear observability, details the reasoning for exact output modeling using only the finite history, and shows that there is no need to separate the free and the forced response for identification. The paper also discusses the implications of using the information-state system for optimal output feedback control and shows that the solution obtained using a suitably posed information state problem is optimal for the original problem. The proposed approach is tested on various different systems, and the performance is compared with state-of-the-art LTV system identification techniques.
A 2030 United States Macro Grid Unlocking Geographical Diversity to Accomplish Clean Energy Goals
Authors: Yixing Xu, Daniel Olsen, Bainan Xia, Dan Livengood, Victoria Hunt, YiFan Li, Lane Smith
Date: 2022-11-19 00:00:00
Tasks: Diversity

Some U.S. states have set clean energy goals and targets in an effort to decarbonize their electricity sectors. There are many reasons for such goals and targets, including the increasingly apparent effects of climate change. A handful of states (Washington, California, New York, and Virginia) are aiming for deep decarbonization by 2050 or earlier, a mere 30 years or less from today. The urgency of substantial carbon emissions reduction (50% or more by 2030) needed to avoid catastrophic climate impacts requires even more ambitious efforts than some of the original targets (e.g., a 30% renewable portfolio standard) set for between now and 2030. With the cost of solar and wind energy falling faster than expected in recent years, economics are also driving rapid expansion of clean energy investments. With this in mind, this report examines combinations of interregional AC and High-Voltage DC (HVDC) transmission upgrades and additions to evaluate the benefits of large-scale transmission expansion.
Analysis of Reinforcement Learning Schemes for Trajectory Optimization of an Aerial Radio Unit
Authors: Hossein Mohammadi, Vuk Marojevic, Bodong Shang
Date: 2022-11-18 00:00:00
Tasks: Q-Learning, reinforcement-learning, Reinforcement Learning (RL)

This paper introduces the deployment of unmanned aerial vehicles (UAVs) as lightweight wireless access points that leverage the fixed infrastructure in the context of the emerging open radio access network (O-RAN). More precisely, we propose an aerial radio unit that dynamically serves an under served area and connects to the distributed unit via a wireless fronthaul between the UAV and the closest tower. In this paper we analyze the UAV trajectory in terms of artificial intelligence (AI) when it serves both UEs and central units (CUs) at the same time in multi input multi output (MIMO) fading channel. We first demonstrate the nonconvexity of the problem of maximizing the overall network throughput based on UAV location, and then we use two different machine learning approaches to solve it. We first assume that the environment is a gridworld and then let the UAV explore the environment by flying from point A to point B, using both the offline Q-learning and the online SARSA algorithm and the achieved path-loss as the reward. With the intention of maximizing the average payoff, the trajectory in the second scenario is described as a Markov decision process (MDP). According to simulations, MDP produces better results in a smaller setting and in less time. In contrast, SARSA performs better in larger environments at the expense of a longer flight duration.
System architecture of a four-wheel drive Formula Student vehicle
Authors: Adriano Schommer, Gordana Collier, Robert Norris, Denise Morrey, Ludmila Nesi Maria, Chris Johnston
Date: 2022-11-18 00:00:00

Formula Student vehicles are becoming increasingly complex, especially with the current shift from internal combustion engines toward electric powertrains. The interaction between software and hardware is complex and imposes additional challenges for systems integration. This paper provides a structured introduction to the OBR22 Oxford Brookes Racing Formula Student electric vehicle. From a system architecture perspective, the four-wheel drive in-hub motors topology is described. Diagrams of the hardware components, the architecture of the high voltage and communication systems are presented. This paper also demonstrates the model-based development process, including an overview of the model-in-the-loop (MiL) and hardware-in-the-loop (HiL) control design phases.
Iterative Learning Control -- Deep Dive
Authors: Shane Rupert Koscielniak
Date: 2022-10-21 00:00:00

The stability and convergence of an Iterative Learning Controller (ILC) may be assessed either by directly iterating the equations for a variety of inputs, or by finding the eigenvalues of the iterated system, or by forming the Z-transform and applying pole-zero or equivalent root locus. Two often-used criteria are (i) Asymptotic Convergence (AC) of the difference vectors, and (ii) mono-tonic convergence (MC) of the vector norm. The latter (MC) has a Z- domain counterpart. In this paper we apply all three methods and both convergence tests to a simple plant with an ILC wrapper. One, two and three-term learning functions are used. We can then ask the questions: do all the tests work, and do they agree on the stability?
Dynamic Interactional And Cooperative Network For Shield Machine
Authors: Dazhi Gao, Rongyang Li, Hongbo Wang, Lingfeng Mao, Huansheng Ning
Date: 2022-11-17 00:00:00
Tasks: Anomaly Detection

The shield machine (SM) is a complex mechanical device used for tunneling. However, the monitoring and deciding were mainly done by artificial experience during traditional construction, which brought some limitations, such as hidden mechanical failures, human operator error, and sensor anomalies. To deal with these challenges, many scholars have studied SM intelligent methods. Most of these methods only take SM into account but do not consider the SM operating environment. So, this paper discussed the relationship among SM, geological information, and control terminals. Then, according to the relationship, models were established for the control terminal, including SM rate prediction and SM anomaly detection. The experimental results show that compared with baseline models, the proposed models in this paper perform better. In the proposed model, the R2 and MSE of rate prediction can reach 92.2\%, and 0.0064 respectively. The abnormal detection rate of anomaly detection is up to 98.2\%.
Thermodynamic bounds on ultrasensitivity in covalent switching
Authors: Jeremy A. Owen, Pranay Talla, John W. Biddle, Jeremy Gunawardena
Date: 2022-11-18 00:00:00

Switch-like motifs are among the basic building blocks of biochemical networks. A common motif that can serve as an ultrasensitive switch consists of two enzymes acting antagonistically on a substrate, one making and the other removing a covalent modification. To work as a switch, such covalent modification cycles must be held out of thermodynamic equilibrium by continuous expenditure of energy. Here, we exploit the linear framework for timescale separation to establish tight bounds on the performance of any covalent-modification switch, in terms of the chemical potential difference driving the cycle. The bounds apply to arbitrary enzyme mechanisms, not just Michaelis-Menten, with arbitrary rate constants, and thereby reflect fundamental physical constraints on covalent switching.
Theory of the asynchronous state of structured rotator networks and its application to recurrent networks of excitatory and inhibitory units
Authors: Jonas Ranft, Benjamin Lindner
Date: 2022-11-19 00:00:00

Recurrently coupled oscillators that are sufficiently heterogeneous and/or randomly coupled can show an asynchronous activity in which there are no significant correlations among the units of the network. The asynchronous state can nevertheless exhibit a rich temporal correlation statistics, that is generally difficult to capture theoretically. For randomly coupled rotator networks, it is possible to derive differential equations that determine the autocorrelation functions of the network noise and of the single elements in the network. So far, the theory has been restricted to statistically homogeneous networks, making it difficult to apply this framework to real-world networks, which are structured with respect to the properties of the single units and their connectivity. A particularly striking case are neural networks for which one has to distinguish between excitatory and inhibitory neurons, which drive their target neurons towards or away from firing threshold. To take into account network structures like that, here we extend the theory for rotator networks to the case of multiple populations. Specifically, we derive a system of differential equations that govern the self-consistent autocorrelation functions of the network fluctuations in the respective populations. We then apply this general theory to the special but important case of recurrent networks of excitatory and inhibitory units in the balanced case and compare our theory to numerical simulations. We inspect the effect of the network structure on the noise statistics by comparing our results to the case of an equivalent homogeneous network devoid of internal structure. Our results show that structured connectivity and heterogeneity of the oscillator type can both enhance or reduce the overall strength of the generated network noise and shape its temporal correlations.
Influence of Economic Decoupling in assessing carbon budget quotas for the European Union
Authors: Ilaria Perissi, Aled Jones
Date: 2022-11-21 00:00:00

In the present study, for the first time, an effort sharing approach based on Inertia and Capability principles is proposed to assess European Union (EU27) carbon budget distribution among the Member States. This is done within the context of achieving the Green Deal objective and EU27 carbon neutrality by 2050. An in-depth analysis is carried out about the role of Economic Decoupling embedded in the Capability principle to evaluate the correlation between the expected increase of economic production and the level of carbon intensity in the Member States. As decarbonization is a dynamic process, the study proposes a simple mathematical model as a policy tool to assess and redistribute Member States carbon budgets as frequently as necessary to encourage progress or overcome the difficulties each Member State may face during the decarbonization pathways.
Borrowing Constraints in Emerging Markets
Authors: Santiago Camara, Maximo Sangiacomo
Date: 2022-11-20 00:00:00

Borrowing constraints are a key component of modern international macroeconomic models. The analysis of Emerging Markets (EM) economies generally assumes collateral borrowing constraints, i.e., firms access to debt is constrained by the value of their collateralized assets. Using credit registry data from Argentina for the period 1998-2020 we show that less than 15% of firms debt is based on the value of collateralized assets, with the remaining 85% based on firms cash flows. Exploiting central bank regulations over banks capital requirements and credit policies we argue that the most prevalent borrowing constraints is defined in terms of the ratio of their interest payments to a measure of their present and past cash flows, akin to the interest coverage borrowing constraint studied by the corporate finance literature. Lastly, we argue that EMs exhibit a greater share of interest sensitive borrowing constraints than the US and other Advanced Economies. From a structural point of view, we show that in an otherwise standard small open economy DSGE model, an interest coverage borrowing constraints leads to significantly stronger amplification of foreign interest rate shocks compared to the standard collateral constraint. This greater amplification provides a solution to the Spillover Puzzle of US monetary policy rates by which EMs experience greater negative effects than Advanced Economies after a US interest rate hike. In terms of policy implications, this greater amplification leads to managed exchange rate policy being more costly in the presence of an interest coverage constraint, given their greater interest rate sensitivity, compared to the standard collateral borrowing constraint.
Radon-based Image Reconstruction for MPI using a continuously rotating FFL
Authors: Stephanie Blanke, Christina Brandt
Date: 2022-11-21 00:00:00
Tasks: Image Reconstruction

Magnetic particle imaging is a relatively new tracer-based medical imaging technique exploiting the non-linear magnetization response of magnetic nanoparticles to changing magnetic fields. If the data are generated by using a field-free line, the sampling geometry resembles the one in computerized tomography. Indeed, for an ideal field-free line rotating only in between measurements it was shown that the signal equation can be written as a convolution with the Radon transform of the particle concentration. In this work, we regard a continuously rotating field-free line and extend the forward operator accordingly. We obtain a similar result for the relation to the Radon data but with two additive terms resulting from the additional time-dependencies in the forward model. We jointly reconstruct particle concentration and corresponding Radon data by means of total variation regularization yielding promising results for synthetic data.
Temporal error concealment for fisheye video sequences based on equisolid re-projection
Authors: Andrea Eichenseer, Jürgen Seiler, Michel Bätz, André Kaup
Date: 2022-11-21 00:00:00
Tasks: Decoder

Wide-angle video sequences obtained by fisheye cameras exhibit characteristics that may not very well comply with standard image and video processing techniques such as error concealment. This paper introduces a temporal error concealment technique designed for the inherent characteristics of equisolid fisheye video sequences by applying a re-projection into the equisolid domain after conducting part of the error concealment in the perspective domain. Combining this technique with conventional decoder motion vector estimation achieves average gains of 0.71 dB compared against pure decoder motion vector estimation for the test sequences used. Maximum gains amount to up to 2.04 dB for selected frames.