pwc-archive/papers-with-abstracts huggingface dataset:
Translating Assembly Accuracy Requirements to Cut-Off Frequencies for Component Mode Synthesis
Authors: Lars A. L. Janssen, Bart Besselink, Rob H. B. Fey, Nathan van de Wouw
Date: 2023-04-11 00:00:00

One of the most popular methods for reducing the complexity of assemblies of finite element models in the field of structural dynamics is component mode synthesis. A main challenge of component mode synthesis is balancing model complexity and model accuracy, because it is difficult to predict how component reduction influences assembly model accuracy. This work introduces an approach that allows for the translation of assembly model accuracy requirements in the frequency domain to the automatic selection of the cut-off frequencies for the model-order reduction (MOR) of components. The approach is based on a mathematical approach for MOR for coupled linear systems in the field of systems and control. We show how this approach is also applicable to structural dynamics models. We demonstrate the use of this approach in the scope of component mode synthesis (CMS) methods with the aim to reduce the complexity of component models while guaranteeing accuracy requirements of the assembly model. The proposed approach is illustrated on a mechanical, three-component structural dynamics system for which reduced-order models are computed that are reduced further compared to reduction using standard methods. This results in lower simulation cost, while maintaining the required accuracy.
Reinforcement Learning Based Minimum State-flipped Control for the Reachability of Boolean Control Networks
Authors: Jingjie Ni, Yang Tang, Fangfei Li
Date: 2023-04-11 00:00:00
Tasks: Q-Learning, Transfer Learning

This paper proposes model-free reinforcement learning methods for minimum-cost state-flipped control in Boolean control networks (BCNs). We tackle two questions: 1) finding the flipping kernel, namely the flip set with the smallest cardinality ensuring reachability, and 2) deriving optimal policies to minimize the number of flipping actions for reachability based on the obtained flipping kernel. For question 1), Q-learning's capability in determining reachability is demonstrated. To expedite convergence, we incorporate two improvements: i) demonstrating that previously reachable states remain reachable after adding elements to the flip set, followed by employing transfer learning, and ii) initiating each episode with special initial states whose reachability to the target state set are currently unknown. Question 2) requires optimal control with terminal constraints, while Q-learning only handles unconstrained problems. To bridge the gap, we propose a BCN-characteristics-based reward scheme and prove its optimality. Questions 1) and 2) with large-scale BCNs are addressed by employing small memory Q-learning, which reduces memory usage by only recording visited action-values. An upper bound on memory usage is provided to assess the algorithm's feasibility. To expedite convergence for question 2) in large-scale BCNs, we introduce adaptive variable rewards based on the known maximum steps needed to reach the target state set without cycles. Finally, the effectiveness of the proposed methods is validated on both small- and large-scale BCNs.
Universal dual-port grid-forming control: bridging the gap between grid-forming and grid-following control
Authors: Irina Subotić, and Dominic Groß
Date: 2023-04-11 00:00:00

We analyze a dual-port grid-forming (GFM) control for power systems containing ac and dc transmission, converter-interfaced generation and energy storage, and legacy generation. To operate such a system and provide standard services, state-of-the-art control architectures i) require assigning grid-following (GFL) and GFM controls to different converters, and ii) result in highly complex system dynamics. In contrast, dual-port GFM control (i) subsumes common functions of GFM and GFL controls in a simple controller, ii) can be applied to a wide range of emerging technologies independently of the network configuration, and iii) significantly reduces system complexity. In this work, we provide i) an end-to-end modeling framework that allows to model complex topologies through composition of reduced-order device models, ii) an in-depth discussion of universal dual-port GFM control for emerging power systems, and iii) end-to-end stability conditions that cover a wide range of network topologies, emerging technologies, and legacy technologies. Finally, we validate our findings in detailed case studies.
Data-Driven Fast Frequency Control using Inverter-Based Resources
Authors: Etinosa Ekomwenrenren, John W. Simpson-Porco, Evangelos Farantatos, Mahendra Patel, Aboutaleb Haddadi, Lin Zhu
Date: 2023-04-11 00:00:00

To address the control challenges associated with the increasing share of inverter-connected renewable energy resources, this paper proposes a direct data-driven approach for fast frequency control in the bulk power system. The proposed control scheme partitions the power system into control areas, and leverages local dispatchable inverter-based resources to rapidly mitigate local power imbalances upon events. The controller design is based directly on historical measurement sequences, and does not require identification of a parametric power system model. Theoretical results are provided to support the approach. Simulation studies on a nonlinear three-area test system demonstrate that the controller provides fast and localized frequency control under several types of contingencies.
Exact Set-valued Estimation using Constrained Convex Generators for uncertain Linear Systems
Authors: Daniel Silvestre
Date: 2023-04-10 00:00:00
Tasks: Autonomous Vehicles, State Estimation, Unity

Set-valued state estimation when in the presence of uncertainties in the model have been addressed in the literature essentially following three main approaches: i) interval arithmetic of the uncertain dynamics with the estimates; ii) factorizing the uncertainty into matrices with unity rank; and, iii) performing the convex hull for the vertices of the uncertainty space. Approach i) and ii) introduce a lot of conservatism because both disregard the relationship of the parameters with the entries of the dynamics matrix. On the other hand, approach iii) has a large growth on the number of variables required to represent the set or is approximated losing its main advantage in comparison with i) and ii). In this paper, with the application of autonomous vehicles in GPS-denied areas that resort to beacon signals for localization, we develop an exact (meaning no added conservatism) and optimal (smallest growth in the number of variables) closed-form definition for the convex hull of Convex Constrained Generators (CCGs). This results in a more efficient method to represent the minimum volume convex set corresponding to the state estimation. Given that reductions methods are still lacking in the literature for CCGs, we employ an approximation using ray-shooting that is comparable in terms of accuracy with methods for Constrained Zonotopes as the ones implemented in CORA. Simulations illustrate the greater accuracy of CCGs with the proposed convex hull operation in comparison to Constrained Zonotopes.
Non-Linear Estimation using the Weighted Average Consensus-Based Unscented Filtering for Various Vehicles Dynamics towards Autonomous Sensorless Design
Authors: Bambang L. Widjiantoro, Moh Kamalul Wafi, Katherin Indriawati
Date: 2023-04-09 00:00:00
Tasks: Autonomous Vehicles

The concerns to autonomous vehicles have been becoming more intriguing in coping with the more environmentally dynamics non-linear systems under some constraints and disturbances. These vehicles connect not only to the self-instruments yet to the neighborhoods components, making the diverse interconnected communications which should be handled locally to ease the computation and to fasten the decision. To deal with those interconnected networks, the distributed estimation to reach the untouched states, pursuing sensorless design, is approached, initiated by the construction of the modified pseudo measurement which, due to approximation, led to the weighted average consensus calculation within unscented filtering along with the bounded estimation errors. Moreover, the tested vehicles are also associated to certain robust control scenarios subject to noise and disturbance with some stability analysis to ensure the usage of the proposed estimation algorithm. The numerical instances are presented along with the performances of the control and estimation method. The results affirms the effectiveness of the method with limited error deviation compared to the other centralized and distributed filtering. Beyond these, the further research would be the directed sensorless design and fault-tolerant learning control subject to faults to negate the failures.
Fault-Tolerant Control Design in Scrubber Plant with Fault on Sensor Sensitivity
Authors: Moh Kamalul Wafi, Katherin Indriawati
Date: 2023-04-09 00:00:00
Tasks: Sensitivity

The concept of fault-tolerant control has extensively been explored with various mapping of development. It starts from the system characteristic, the robustness of the controller, estimation methods and optimization, to the combination of the faults such that it can touch the true observed system. The mathematical concepts of the scrubber plant taking into account the pressure parameter along with sensing element and actuator are proposed. The data to construct the designs derive from the true values in one of Indonesian company. The performances coming from the simulations depict that the open- and closed-loop system could be the same as those of the real results. Furthermore, the observer is proposed to give the estimates of the states of $(\hat{x})$ and $(\hat{f}_s)$ showing the positive trace on the set-point of the residual fault followed by designing the fault-tolerant control with sensor fault on sensitivity. The scenarios are to give the lack of reading in sensor with $70\%$ and $85\%$ sensitivity and those are contrasted to the system without FTC (only PI controller). The yields portray that the system with FTC could deal with those sensor fault scenarios while its counterpart cannot drawing the faulty performance instead of tracking the set-point. The next project associated with this paper is also mentioned in the last section.
Distributed Estimation with Decentralized Control for Quadruple-Tank Process
Authors: Moh Kamalul Wafi, Bambang L. Widjiantoro
Date: 2023-04-09 00:00:00

This paper proposes the design of quadruple-tank process due to the unique multivariable MIMO system under minimum and non-minimum scenario with respect to the valve ratio. This model is then implemented the distributed estimation algorithm with decentralized control. The inputs are set in divergent gains of pumps while the four-tank process is interconnected so that the stability properties are different, making the usage of decentralized control is reasonable. The number of outputs is designed the same as those of inputs which are also that of distributed Luenberger observer with the continuous linearized dynamical system. This distributed comprises local estimates only in certain output, meaning that it would lead to insufficiency so that the neighbouring links under some network topologies are required in the dynamical system. This concept fortunately works in two different characteristic stability of the tank process regarding estimating the states. This success leads to the further research of the more large-scale complex system.
Entropic contribution to phenotype fitness
Authors: Pablo Catalán, Juan Antonio García-Martín, Jacobo Aguirre, José A. Cuesta, Susanna Manrubia
Date: 2023-04-11 00:00:00

All possible phenotypes are not equally accessible to evolving populations. In fact, only phenotypes of large size, i.e. those resulting from many different genotypes, are found in populations of sequences, presumably because they are easier to discover and maintain. Genotypes that map to these phenotypes usually form mostly connected genotype networks that percolate the space of sequences, thus guaranteeing access to a large set of alternative phenotypes. Within a given environment, where specific phenotypic traits become relevant for adaptation, the replicative ability of a phenotype and its overall fitness (in competition experiments with alternative phenotypes) can be estimated. Two primary questions arise: how do phenotype size, reproductive capability and topology of the genotype network affect the fitness of a phenotype? And, assuming that evolution is only able to access large phenotypes, what is the range of unattainable fitness values? In order to address these questions, we quantify the adaptive advantage of phenotypes of varying size and spectral radius in a two-peak landscape. We derive analytical relationships between the three variables (size, topology, and replicative ability) which are then tested through analysis of genotype-phenotype maps and simulations of population dynamics on such maps. Finally, we analytically show that the fraction of attainable phenotypes decreases with the length of the genotype, though its absolute number increases. The fact that most phenotypes are not visible to evolution very likely forbids the attainment of the highest peak in the landscape. Nevertheless, our results indicate that the relative fitness loss due to this limited accessibility is largely inconsequential for adaptation.
Mapping job complexity and skills into wages
Authors: Sabrina Aufiero, Giordano De Marzo, Angelica Sbardella, Andrea Zaccaria
Date: 2023-04-11 00:00:00

We use algorithmic and network-based tools to build and analyze the bipartite network connecting jobs with the skills they require. We quantify and represent the relatedness between jobs and skills by using statistically validated networks. Using the fitness and complexity algorithm, we compute a skill-based complexity of jobs. This quantity is positively correlated with the average salary, abstraction, and non-routinarity level of jobs. Furthermore, coherent jobs - defined as the ones requiring closely related skills - have, on average, lower wages. We find that salaries may not always reflect the intrinsic value of a job, but rather other wage-setting dynamics that may not be directly related to its skill composition. Our results provide valuable information for policymakers, employers, and individuals to better understand the dynamics of the labor market and make informed decisions about their careers.
Five guidelines to improve context-aware process selection: an Australian banking perspective
Authors: Nigel Adams, Adriano Augusto, Michael Davern, Marcello La Rosa
Date: 2023-04-11 00:00:00
Tasks: Decision Making, Management

As the first phase in the Business Process Management (BPM) lifecycle, process identification addresses the problem of identifying which processes to prioritize for improvement. Process selection plays a critical role in this phase, but it is a step with known pitfalls. Decision makers rely frequently on subjective criteria, and their knowledge of the alternative processes put forward for selection is often inconsistent. This leads to poor quality decision-making and wastes resources. In recent years, a rejection of a one-size-fits-all approach to BPM in favor of a more context-aware approach has gained significant academic attention. In this study, the role of context in the process selection step is considered. The context is qualitative, subjective, sensitive to decision-making bias and politically charged. We applied a design-science approach and engaged industry decision makers through a combination of research methods to assess how different configurations of process inputs influence and ultimately improve the quality of the process selection step. The study highlights the impact of framing effects on context and provides five guidelines to improve effectiveness.
Iterated Revelation: How to Incentive Experts to Complete Incomplete Contracts
Authors: Evan Piermont
Date: 2023-04-11 00:00:00

This paper examines how a decision maker might incentivize an expert to reveal novel aspects of the decision problem, expanding the set of contracts from which the decision maker can choose. The chosen contract will determine, along with the resolution of uncertainty, the payoffs to both players. I show that the set of achievable outcomes under any (incentive compatible) mechanism is characterized by a small and tractable class of iterated revelation mechanisms (IRMs). An IRM is a dynamic interaction wherein each round the expert chooses to reveal some novel contingencies and the decision maker proposes a contract that the expert can accept or reject; the IRM ends after rejection or when nothing novel is revealed. I then consider the set of robust IRMs -- those that maximize the worst case outcome across all types of expert -- and show these are characterized by a principle of myopic optimality: at each round, the decision maker maximizes his payoff as if the expert had nothing further to reveal. The set of robust IRMs also delineate the payoffs achievable by any efficient mechanism.
Real time enhancement of operator's ergonomics in physical human - robot collaboration scenarios using a multi-stereo camera system
Authors: Gerasimos Arvanitis, Nikos Piperigkos, Christos Anagnostopoulos, Aris S. Lalos, Konstantinos Moustakas
Date: 2023-04-11 00:00:00

In collaborative tasks where humans work alongside machines, the robot's movements and behaviour can have a significant impact on the operator's safety, health, and comfort. To address this issue, we present a multi-stereo camera system that continuously monitors the operator's posture while they work with the robot. This system uses a novel distributed fusion approach to assess the operator's posture in real-time and to help avoid uncomfortable or unsafe positions. The system adjusts the robot's movements and informs the operator of any incorrect or potentially harmful postures, reducing the risk of accidents, strain, and musculoskeletal disorders. The analysis is personalized, taking into account the unique anthropometric characteristics of each operator, to ensure optimal ergonomics. The results of our experiments show that the proposed approach leads to improved human body postures and offers a promising solution for enhancing the ergonomics of operators in collaborative tasks.
Toward Whole-Brain Minimally-Invasive Vascular Imaging
Authors: Anatole Jimenez, Bruno Osmanski, Denis Vivien, Mickael Tanter, Thomas Gaberel, Thomas Deffieux
Date: 2023-04-11 00:00:00

Imaging the brain vasculature can be critical for cerebral perfusion monitoring in the context of neurocritical care. Although ultrasensitive Doppler (UD) can provide good sensitivity to cerebral blood volume (CBV) in a large field of view, it remains difficult to perform through the skull. In this work, we investigate how a minimally invasive burr hole, performed for intracranial pressure (ICP) monitoring, could be used to map the entire brain vascular tree. We explored the use of a small motorized phased array probe with a non-implantable preclinical prototype in pigs. The scan duration (18 min) and coverage (62 $\pm$ 12 % of the brain) obtained allowed global CBV variations detection (relative in brain Dopplerdecrease =-3[-4-+16]% \& Dopplerincrease. = +1[-3-+15]%, n = 6 \& 5) and stroke detection (relative in core Dopplerstroke. =-25%, n = 1). This technology could one day be miniaturized to be implanted for brain perfusion monitoring in neurocritical care.
SFT-KD-Recon: Learning a Student-friendly Teacher for Knowledge Distillation in Magnetic Resonance Image Reconstruction
Authors: Matcha Naga Gayathri, Sriprabha Ramanarayanan, Mohammad Al Fahim, Rahul G S, Keerthi Ram, Mohanasankar Sivaprakasam
Date: 2023-04-11 00:00:00
Tasks: Image Reconstruction, Knowledge Distillation

Deep cascaded architectures for magnetic resonance imaging (MRI) acceleration have shown remarkable success in providing high-quality reconstruction. However, as the number of cascades increases, the improvements in reconstruction tend to become marginal, indicating possible excess model capacity. Knowledge distillation (KD) is an emerging technique to compress these models, in which a trained deep teacher network is used to distill knowledge to a smaller student network such that the student learns to mimic the behavior of the teacher. Most KD methods focus on effectively training the student with a pre-trained teacher unaware of the student model. We propose SFT-KD-Recon, a student-friendly teacher training approach along with the student as a prior step to KD to make the teacher aware of the structure and capacity of the student and enable aligning the representations of the teacher with the student. In SFT, the teacher is jointly trained with the unfolded branch configurations of the student blocks using three loss terms - teacher-reconstruction loss, student-reconstruction loss, and teacher-student imitation loss, followed by KD of the student. We perform extensive experiments for MRI acceleration in 4x and 5x under-sampling on the brain and cardiac datasets on five KD methods using the proposed approach as a prior step. We consider the DC-CNN architecture and setup teacher as D5C5 (141765 parameters), and student as D3C5 (49285 parameters), denoting a compression of 2.87:1. Results show that (i) our approach consistently improves the KD methods with improved reconstruction performance and image quality, and (ii) the student distilled using our approach is competitive with the teacher, with the performance gap reduced from 0.53 dB to 0.03 dB.
Timing Recovery for Point-to-Multi-Point Coherent Passive Optical Networks
Authors: Ji Zhou, Jinyang Yang, Haide Wang, Jianrui Zeng, Changyuan Yu
Date: 2023-04-11 00:00:00

We propose a timing recovery for point-to-multi-point coherent passive optical networks. The results show that the proposed algorithm has low complexity and better robustness against the residual chromatic dispersion.
On heavy-tailed risks under Gaussian copula: the effects of marginal transformation
Authors: Bikramjit Das, Vicky Fasen-Hartmann
Date: 2023-04-11 00:00:00

In this paper, we compute multivariate tail risk probabilities where the marginal risks are heavy-tailed and the dependence structure is a Gaussian copula. The marginal heavy-tailed risks are modeled using regular variation which leads to a few interesting consequences. First, as the threshold increases, we note that the rate of decay of probabilities of tail sets vary depending on the type of tail sets considered and the Gaussian correlation matrix. Second, we discover that although any multivariate model with a Gaussian copula admits the so called asymptotic tail independence property, the joint tail behavior under heavier tailed marginal variables is structurally distinct from that under Gaussian marginal variables. The results obtained are illustrated using examples and simulations.