pwc-archive/papers-with-abstracts huggingface dataset:
Multi-Metric Optimization using Generative Adversarial Networks for Near-End Speech Intelligibility Enhancement
Authors: Haoyu Li, Junichi Yamagishi
Date: 2021-04-17 00:00:00

The intelligibility of speech severely degrades in the presence of environmental noise and reverberation. In this paper, we propose a novel deep learning based system for modifying the speech signal to increase its intelligibility under the equal-power constraint, i.e., signal power before and after modification must be the same. To achieve this, we use generative adversarial networks (GANs) to obtain time-frequency dependent amplification factors, which are then applied to the input raw speech to reallocate the speech energy. Instead of optimizing only a single, simple metric, we train a deep neural network (DNN) model to simultaneously optimize multiple advanced speech metrics, including both intelligibility- and quality-related ones, which results in notable improvements in performance and robustness. Our system can not only work in non-realtime mode for offline audio playback but also support practical real-time speech applications. Experimental results using both objective measurements and subjective listening tests indicate that the proposed system significantly outperforms state-ofthe-art baseline systems under various noisy and reverberant listening conditions.
Network modeling methods for precision medicine
Authors: Elio Nushi, Victor-Bogdan Popescu, Jose Angel Sanchez Martin, Sergiu Ivanov, Eugen Czeizler, Ion Petre
Date: 2021-04-19 00:00:00

We discuss in this survey several network modeling methods and their applicability to precision medicine. We review several network centrality methods (degree centrality, closeness centrality, eccentricity centrality, betweenness centrality, and eigenvector-based prestige) and two systems controllability methods (minimum dominating sets and network structural controllability). We demonstrate their applicability to precision medicine on three multiple myeloma patient disease networks. Each network consists of protein-protein interactions built around a specific patient's mutated genes, around the targets of the drugs used in the standard of care in multiple myeloma, and around multiple myeloma-specific essential genes. For each network we demonstrate how the network methods we discuss can be used to identify personalized, targeted drug combinations uniquely suited to that patient.
How Does the Vulnerability of an Evolving Power Grid Change?
Authors: Balint Hartmann
Date: 2021-04-19 00:00:00

In this paper the seven-decade historical dataset of the Hungarian power system is used to perform vulnerability assessment applying a complex network approach.
Oracle-based economic predictive control
Authors: Jose Maria Manzano, David Muñoz de la Peña, Daniel Limon
Date: 2021-04-19 00:00:00

This paper presents an economic model predictive controller, under the assumption that the only measurable signal of the plant is the economic cost to be minimized. In order to forecast the evolution of this economic cost for a given input trajectory, a prediction model with a NARX structure, the so-called oracle, is proposed. Sufficient conditions to ensure the existence of such oracle are studied, proving that it can be derived for a general nonlinear system if the economic cost function is a Morse function. Based on this oracle, economic model predictive controllers are proposed, and their stability is demonstrated in nominal conditions under a standard dissipativity assumption. The viability of these controllers in practical settings (where the oracle may provide imperfect predictions for generic inputs) is proven by means of input-to-state stability. These properties have been illustrated in a case study based on a continuously stirred tank reactor.
Data-Driven Controller Design via Finite-Horizon Dissipativity
Authors: Nils Wieler, Julian Berberich, Anne Koch, Frank Allgöwer
Date: 2021-01-15 00:00:00

Given one open-loop measured trajectory of a single-input single-output discrete-time linear time-invariant system, we present a framework for data-driven controller design for closed-loop finite-horizon dissipativity. First, we parametrize all closed-loop trajectories using the given data of the plant and a model of the controller. We then provide an approach to validate the controller by verifying closed-loop dissipativity in the standard feedback loop based on this parametrization. We use these conditions to design controllers leading to closed-loop dissipativity based on a quadratic matrix inequality feasibility problem. Finally, the results are illustrated with a simulation example.
Voltage Collapse Stabilization in Star DC Networks
Authors: Charalampos Avraam, Enrique Mallada
Date: 2021-04-19 00:00:00

Voltage collapse is a type of blackout-inducing dynamic instability that occurs when power demand exceeds the maximum power that can be transferred through a network. The traditional (preventive) approach to avoid voltage collapse is based on ensuring that the network never reaches its maximum capacity. However, such an approach leads to inefficient use of network resources and does not account for unforeseen events. To overcome this limitation, this paper seeks to initiate the study of voltage collapse stabilization, i.e., the design of load controllers aimed at stabilizing the point of voltage collapse. We formulate the problem of voltage stability for a star direct current network as a dynamic problem where each load seeks to achieve a constant power consumption by updating its conductance as the voltage changes. We introduce a voltage collapse stabilization controller and show that the high-voltage equilibrium is stabilized. More importantly, we are able to achieve proportional load shedding under extreme loading conditions. We further highlight the key features of our controller using numerical illustrations.
Cyber-Resilient Self-Triggered Distributed Control of Networked Microgrids Against Multi-Layer DoS Attacks
Authors: Pudong Ge, Boli Chen, Fei Teng
Date: 2021-04-19 00:00:00

Networked microgrids with high penetration of distributed generators have ubiquitous remote information exchange, which may be exposed to various cyber security threats. This paper, for the first time, addresses a consensus problem in terms of frequency synchronisation in networked microgrids subject to multi-layer denial of service (DoS) attacks, which could simultaneously affect communication, measurement and control actuation channels. A unified notion of Persistency-of-Data-Flow (PoDF) is proposed to characterise the data unavailability in different information network links, and further quantifies the multi-layer DoS effects on the hierarchical system. With PoDF, we provide a sufficient condition of the DoS attacks under which the consensus can be preserved with the proposed edge-based self-triggered distributed control framework. In addition, to mitigate the conservativeness of offline design against the worst-case attack across all agents, an online self-adaptive scheme of the control parameters is developed to fully utilise the latest available information of all data transmission channels. Finally, the effectiveness of the proposed cyber-resilient self-triggered distributed control is verified by representative case studies.
Additive Networks of Chen-Fliess Series: Local Convergence and Relative Degree
Authors: W. Steven Gray, Luis A. Duffaut Espinosa, Kurusch Ebrahimi-Fard
Date: 2021-04-18 00:00:00

Given an additive network of input-output systems where each node of the network is modeled by a locally convergent Chen-Fliess series, two basic properties of the network are established. First, it is shown that every input-output map between a given pair of nodes has a locally convergent Chen-Fliess series representation. Second, sufficient conditions are given under which the input-output map between a pair of nodes has a well defined relative degree as defined by its generating series. This analysis leads to the conclusion that this relative degree property is generic in a certain sense.
Invariant Subspace Approach to Boolean (Control) Networks
Authors: Daizhan Cheng, Lijun Zhang, Dongyao Bi
Date: 2021-04-18 00:00:00

A logical function can be used to characterizing a property of a state of Boolean network (BN), which is considered as an aggregation of states. To illustrate the dynamics of a set of logical functions, which characterize our concerned properties of a BN, the invariant subspace containing the set of logical functions is proposed, and its properties are investigated. Then the invariant subspace of Boolean control network (BCN) is also proposed. The dynamics of invariant subspace of BCN is also invariant. Finally, using outputs as the set of logical functions, the minimum realization of BCN is proposed, which provides a possible solution to overcome the computational complexity of large scale BNs/BCNs.
Ecological Adaptive Cruise Control for City Buses based on Hybrid Model Predictive Control using PnG and Traffic Light Information
Authors: Sai Krishna Chada, Jitin Mathew Thomas, Daniel Görges, Achim Ebert, Roman Teutsch
Date: 2021-04-18 00:00:00
Tasks: Model Predictive Control

This paper proposes an ecological adaptive cruise control (EACC) concept with the primary goal to minimize the fuel consumption in a city bus with an internal combustion engine (ICE). A hybrid model predictive control (HMPC) is implemented in this work to control both continuous and discrete-time variables. Moreover, a multi-objective optimization problem for EACC is formulated in time-domain as a mixed-integer quadratically constrained quadratic programming (MIQCQP) problem. The proposed HMPC-EACC performs robust vehicle-following while tracking a leading vehicle and plans fuel-efficient acceleration and deceleration maneuvers for the host vehicle. Additionally, it uses the signal phase and timing (SPaT) information to compute a green wave reference speed for the host vehicle to cross the signalized intersections at a green phase. Moreover, the proposed controller performs pulse and glide (PnG) to optimally control the engine ON and OFF states and save additional fuel. Furthermore, the performance of the proposed strategy is evaluated on a real-world driving profile and compared against a baseline controller from the literature. Finally, the influence of different prediction horizons on the fuel savings and computation times are studied. The results reveal significant reduction in fuel consumption with HMPC-EACC and demonstrate that the proposed controller is real-time capable.
Optimizing Furnace efficiency for Factory of Future using Cooperative Games
Authors: Sreenath Shaju, Mohak Sukhwani, Ankit Kala
Date: 2021-04-17 00:00:00

Approximately 75% of energy used in petrochemical and refining industries is consumed by furnaces. Operating furnaces at optimal conditions results in huge amounts of savings. In this paper, we model the furnace efficiency optimization as a multi-objective problem involving multiple interactions among the controlled variables and propose a cooperative game based formulation for the factory of future. The controlled variables are Absorbed Duty and Coil Outlet Temperature. We propose a comprehensive solution to select the best combination of manipulated variables (fired duty, throughput and coil inlet temperature) satisfying multiple criteria using a cooperative game theory approach. We compare this approach with the standard multi-objective optimization using NSGA-II and RNSGA-II algorithms.
Exploring Cybersecurity Issues in 5G Enabled Electric Vehicle Charging Station with Deep Learning
Authors: Manoj Basnet, M. Hasan Ali
Date: 2021-04-17 00:00:00
Tasks: Intrusion Detection

The surging usage of electric vehicles (EVs) demand the robust deployment of trustworthy electric vehicle charging station (EVCS) with millisecond range latency and massive machine to machine communications where 5G could act. However, 5G suffers from inherent protocols, hardware, and software vulnerabilities that seriously threaten the communicating entities' cyber-physical security. To overcome these limitations in the EVCS system, this paper analyses the impact of False Data Injection (FDI) and Distributed Denial of Services (DDoS) attacks on the operation of EVCS. This work is an extension of our previously published conference paper about the EVCS. As new features, this paper simulates the FDI attack and the syn flood DDoS attacks on 5G enabled remote Supervisory Control and Data Acquisition (SCADA) system that controls the solar photovoltaics (PV) controller, Battery Energy Storage (BES) controller, and EV controller of the EVCS. The attacks make the EVCS system oscillate or shift the DC operating point. The frequency of oscillation, its damping, and the system's resiliency are found to be related to the attacks' intensity and target controller. Finally, we propose the novel stacked Long Short-Term Memory (LSTM) based intrusion detection systems (IDS) solely based on the electrical fingerprint. This model can detect the stealthy cyberattacks that bypass the cyber layer and go unnoticed in the monitoring system with nearly 100% detection accuracy.
Coordination of Heterogeneous Deferrable Loads using the F-MBC Mechanism
Authors: Subhitcha Ramkumar, Hazem A. Abdelghany, Simon H. Tindemans
Date: 2020-11-30 00:00:00
Tasks: Scheduling

Increasing participation of prosumers in the electricity grid calls for efficient operational strategies for utilizing the flexibility offered by Distributed Energy Resources (DER) to match supply and demand. This paper investigates the coordination performance of a recently proposed coordination scheme for deferrable loads: Forecast Mediated Market Based Control (F-MBC). Enhancements are made to the simulation setup to enable an analysis of performance in realistic scenarios, with heterogeneous loads and an open-ended simulation horizon. Operational scenarios were formulated to showcase the ability of F-MBC to schedule heterogeneous populations of deferrable loads with dynamic load profiles, supported by a mix of renewable and flexible generation. Availability patterns of devices were generated to take into account varying user preferences. Simulation results indicate that F-MBC was able to achieve good distributed scheduling performance for devices with a high initial power consumption. However, performance for devices with low initial power consumption has been found to be less satisfactory. Several directions for further improvement of the F-MBC scheme and its applications are identified.
Efficient Assessment of Electricity Distribution Network Adequacy with the Cross-Entropy Method
Authors: Julian N. Betge, Barbera Droste, Jacco Heres, Simon H. Tindemans
Date: 2020-11-30 00:00:00
Tasks: Computational Efficiency, Demand Forecasting

Identifying future congestion points in electricity distribution networks is an important challenge distribution system operators face. A proven approach for addressing this challenge is to assess distribution grid adequacy using probabilistic models of future demand. However, computational cost can become a severe challenge when evaluating large probabilistic electricity demand forecasting models with long forecasting horizons. In this paper, Monte Carlo methods are developed to increase the computational efficiency of obtaining asset overload probabilities from a bottom-up stochastic demand model. Cross-entropy optimised importance sampling is contrasted with conventional Monte Carlo sampling. Benchmark results of the proposed methods suggest that the importance sampling-based methods introduced in this work are suitable for estimating rare overload probabilities for assets with a small number of customers.
Models and Predictive Control for Nonplanar Vehicle Navigation
Authors: Thomas Fork, H. Eric Tseng, Francesco Borrelli
Date: 2021-04-17 00:00:00
Tasks: Model Predictive Control

We present a simplified model of a vehicle driving on a nonplanar road. A parametric surface is used to describe the nonplanar road which can describe any combination of curvature, bank and slope. We show that the proposed modeling approach generalizes planar vehicle models that reference a centerline, such as the Frenet model. We use the proposed approach for vehicle path planning and following using model predictive control. We also model and control vehicle contact with the road surface. We demonstrate that the proposed controller improves speed and lane following on complex roads compared to planar vehicle controllers, and mitigates loss of control on complex road surfaces including off-camber turns.
H_\infty Almost Output and Regulated Output Synchronization of Heterogeneous Multi-agent Systems: A Scale-free Protocol Design
Authors: Donya Nojavanzadeh, Zhenwei Liu, Ali Saberi, Anton A. Stoorvogel
Date: 2021-04-16 00:00:00

This paper studies scale-free protocol design for H_\infty almost output and regulated output synchronization of heterogeneous multi-agent systems with linear, right-invertible, and introspective agents in presence of external disturbances. The collaborative linear protocol designs are based on localized information exchange over the same communication network, which do not require any knowledge of the directed network topology and spectrum of the associated Laplacian matrix. Moreover, the proposed scale-free protocols achieve H_\infty almost synchronization with a given arbitrary degree of accuracy for any size of the network.
Steric interactions and out-of-equilibrium processes control the internal organization of bacteria
Authors: Ander Movilla Miangolarra, Sophia Hsin-Jung Li, Jean-François Joanny, Ned S. Wingreen, Michele Castellana
Date: 2021-04-19 00:00:00
Tasks: Translation

Despite the absence of a membrane-enclosed nucleus, the bacterial DNA is typically condensed into a compact body - the nucleoid. This compaction influences the localization and dynamics of many cellular processes including transcription, translation, and cell division. Here, we develop a model that takes into account steric interactions among the components of the Escherichia coli transcriptional-translational machinery (TTM) and out-of-equilibrium effects of mRNA transcription, translation, and degradation, in order to explain many observed features of the nucleoid. We show that steric effects, due to the different molecular shapes of the TTM components, are sufficient to drive equilibrium phase separation of the DNA, explaining the formation and size of the nucleoid. In addition, we show that the observed positioning of the nucleoid at midcell is due to the out-of-equilibrium process of messenger RNA (mRNA) synthesis and degradation: mRNAs apply a pressure on both sides of the nucleoid, localizing it to midcell. We demonstrate that, as the cell grows, the production of these mRNAs is responsible for the nucleoid splitting into two lobes, and for their well-known positioning to 1/4 and 3/4 positions on the long cell axis. Finally, our model quantitatively accounts for the observed expansion of the nucleoid when the pool of cytoplasmic mRNAs is depleted. Overall, our study suggests that steric interactions and out-of-equilibrium effects of the TTM are key drivers of the internal spatial organization of bacterial cells.
A devil's advocate view on 'self-organized' brain criticality
Authors: Claudius Gros
Date: 2021-04-19 00:00:00

Stationarity of the constituents of the body and of its functionalities is a basic requirement for life, being equivalent to survival in first place. Assuming that the resting state activity of the brain serves essential functionalities, stationarity entails that the dynamics of the brain needs to be regulated on a time-averaged basis. The combination of recurrent and driving external inputs must therefore lead to a non-trivial stationary neural activity, a condition which is fulfilled for afferent signals of varying strengths only close to criticality. In this view, the benefits of working vicinity of a second-order phase transition, such as signal enhancements, are not the underlying evolutionary drivers, but side effects of the requirement to keep the brain functional in first place. It is hence more appropriate to use the term 'self-regulated' in this context, instead of 'self-organized'.
Paris Agreement requires substantial, broad, and sustained engagements beyond COVID-19 public stimulus packages
Authors: Katsumasa Tanaka, Christian Azar, Olivier Boucher, Philippe Ciais, Yann Gaucher, Daniel J. A. Johansson
Date: 2021-04-16 00:00:00

It has been claimed that COVID-19 public stimulus packages could be sufficient to meet the short-term energy investment needs to leverage a shift toward a pathway consistent with the 1.5 degrees C target of the Paris Agreement. Here we provide complementary perspectives to reiterate that substantial, broad, and sustained engagements beyond stimulus packages will be needed for achieving the Paris Agreement long-term targets. Low-carbon investments will need to scale up and persist over the next several decades following short-term stimulus packages. The required total energy investments in the real world can be larger than the currently available estimates from Integrated Assessment Models (IAMs). Existing databases from IAMs are not sufficient for analyzing the effect of public spending on emission reduction. To inform what role COVID-19 stimulus packages and public investments may play for reaching the Paris Agreement targets, explicit modelling of such policies is required.
Unsupervised Hyperspectral Stimulated Raman Microscopy Image Enhancement: Denoising and Segmentation via One-Shot Deep Learning
Authors: Pedram Abdolghader, Andrew Ridsdale, Tassos Grammatikopoulos, Gavin Resch, Francois Legare, Albert Stolow, Adrian F. Pegoraro, Isaac Tamblyn
Date: 2021-04-14 00:00:00
Tasks: Clustering, Denoising, Image Enhancement, Image Segmentation, One-Shot Learning, Semantic Segmentation, Unsupervised Image Segmentation

Hyperspectral stimulated Raman scattering (SRS) microscopy is a label-free technique for biomedical and mineralogical imaging which can suffer from low signal to noise ratios. Here we demonstrate the use of an unsupervised deep learning neural network for rapid and automatic denoising of SRS images: UHRED (Unsupervised Hyperspectral Resolution Enhancement and Denoising). UHRED is capable of one-shot learning; only one hyperspectral image is needed, with no requirements for training on previously labelled datasets or images. Furthermore, by applying a k-means clustering algorithm to the processed data, we demonstrate automatic, unsupervised image segmentation, yielding, without prior knowledge of the sample, intuitive chemical species maps, as shown here for a lithium ore sample.
Machine-Learning Classification of Closed and Open Radiating Wires from Near Magnetic or Electric Field Scan Images
Authors: Amir Geranmayeh
Date: 2021-03-16 00:00:00

Sets of intelligent classifiers are applied to the near-field scan-data in order to automatically classify the shape of radiating wirings. The support vector machine, k-nearest neighbors algorithm, and Gaussian process classifications are trained using the near-field radiation pattern of diverse radiating wire configurations. Leave-one-out cross-validation is used for estimating the performance of the predictive models. The output of this research is a software package well-suited to be retrained based on any measured near-field databank to automate the identification of magnetic-type or electric-type of the radiating coupling sources.