pwc-archive/papers-with-abstracts huggingface dataset:
Turbulence-based load alleviation control for wind turbine in extreme turbulence situation
Authors: Liang Dong, Wai Hou Lio
Date: 2021-04-16 00:00:00

The extreme loads experienced by the wind turbine in the extreme wind events are critical for the evaluation of structural reliability. Hence, the load alleviation control methods need to be designed and deployed to reduce the adverse effects of extreme wind events. This work demonstrates that the extreme loads are highly correlated to wind conditions such as turbulence-induced wind shears. Based on this insight, this work proposes a turbulence-based load alleviation control strategy for adapting the controller to changes in wind condition. The estimation of the rotor averaged wind shear based on the rotor loads is illustrated, and is herein used to statistically characterize the extreme wind events for control purpose. To demonstrates the benefits, simulations are carried out using high-fidelity aero-elastic tool and the DTU 10 MW reference turbine in normal and extreme turbulence wind conditions. The results indicate that the proposed method can effectively decrease the exceedance probability of the extreme loads. Meanwhile, the method can minimize the loss of annual energy production in normal operating condition.
Negative Imaginary State Feedback Equivalence for Systems of Relative Degree One and Relative Degree Two
Authors: Kanghong Shi, Ian R. Petersen, Igor G. Vladimirov
Date: 2021-03-09 00:00:00

This paper presents necessary and sufficient conditions under which a linear system of relative degree either one or two is state feedback equivalent to a negative imaginary (NI) system. More precisely, we show for a class of linear time-invariant strictly proper systems, that such a system can be rendered minimal and NI using full state feedback if and only if it is controllable and weakly minimum phase. A strongly strict negative imaginary state feedback equivalence result is also provided. The NI state feedback equivalence result is then applied in a robust stabilization problem for an uncertain system with a strictly negative imaginary uncertainty.
Significance of Low-level Controller for String Stability under Adaptive Cruise Control
Authors: Hao Zhou, Anye Zhou, Tienan Li, Danjue Chen, Srinivas Peeta, Jorge Laval
Date: 2021-04-15 00:00:00

Current commercial adaptive cruise control (ACC) systems consist of an upper-level planner controller that decides the optimal trajectory that should be followed, and a low-level controller in charge of sending the gas/brake signals to the mechanical system to actually move the vehicle. We find that the low-level controller has a significant impact on the string stability (SS) even if the planner is string stable: (i) a slow controller deteriorates the SS, (ii) slow controllers are common as they arise from insufficient control gains, from a "weak" gas/brake system or both, and (iii) the integral term in a slow controller causes undesired overshooting which affects the SS. Accordingly, we suggest tuning up the proportional/feedforward gain and ensuring the gas/brake is not "weak". The study results are validated both numerically and empirically with data from commercial cars.
The Immersion and Invariance Wind Speed Estimator Revisited and New Results
Authors: Yichao Liu, Atindriyo Kusumo Pamososuryo, Riccardo M. G. Ferrari, Jan-Willem van Wingerden
Date: 2021-04-15 00:00:00

The Immersion and Invariance (I&I) wind speed estimator is a powerful and widely-used technique to estimate the rotor effective wind speed on horizontal axis wind turbines. Anyway, its global convergence proof is rather cumbersome, which hinders the extension of the method and proof to time-delayed and/or uncertain systems. In this letter, we illustrate that the circle criterion can be used as an alternative method to prove the global convergence of the I\&I estimator. This also opens up the inclusion of time-delays and uncertainties. First, we demonstrate that the I&I wind speed estimator is equivalent to a torque balance estimator with a proportional correction term. As the nonlinearity in the estimator is sector bounded, the well-known circle criterion is applied to the estimator to guarantee its global convergence for time-delayed systems. By looking at the theoretical framework from this new perspective, this letter further proposes the addition of an integrator to the correction term to improve the estimator performance. Case studies show that the proposed estimator with an additional integral correction term is effective at wind speed estimation. Furthermore, its global convergence can be guaranteed by the circle criterion for time-delayed systems.
Homomorphic Encryption-Enabled Distance-Based Distributed Formation Control with Distance Mismatch Estimators
Authors: Mariano Perez Chaher, Bayu Jayawardhana, Junsoo Kim
Date: 2021-04-15 00:00:00
Tasks: Quantization

This paper considers the use of fully homomorphic encryption for the realisation of distributed formation control of multi-agent systems via edge computer. In our proposed framework, the distributed control computation in the edge computer uses only the encrypted data without the need for a reset mechanism that is commonly required to avoid error accumulation. Simulation results show that, despite the use of encrypted data on the controller and errors introduced by the quantization process prior to the encryption, the formation is able to converge to the desired shape. The proposed architecture offers insight on the mechanism for realising distributed control computation in an edge/cloud computer while preserving the privacy of local information coming from each agent.
Controlling extended criticality via modular connectivity
Authors: Nikita Gutjahr, Philipp Hövel, Aline Viol
Date: 2021-04-16 00:00:00
Tasks: Open-Ended Question Answering

Criticality has been conjectured as an integral part of neuronal network dynamics. Operating at a critical threshold requires precise parameter tuning and a corresponding mechanism remains an open question. Recent studies have suggested that topological features observed in brain networks give rise to a Griffiths phase, leading to power-laws in brain activity dynamics and the operational benefits of criticality in an extended parameter region. Motivated by growing evidence of neural correlates of different states of consciousness, we investigate how topological changes affect the expression of a Griffiths phase. We analyze the activity decay in modular networks using a Susceptible-Infected-Susceptible propagation model and find that we can control the extension of the Griffiths phase by altering intra- and intermodular connectivity. We find that by adjusting system parameters, we can counteract changes in critical behavior and maintain a stable critical region despite changes in network topology. Our results give insight into how structural network properties affect the emergence of a Griffiths phase and how its features are linked to established topological network metrics. We discuss how those findings can contribute to understand the observed changes in functional brain networks. Finally, we indicate how our results could be useful in the study of disease spreading.
Predicting synchronized gene coexpression patterns from fibration symmetries in gene regulatory networks in bacteria
Authors: Ian Leifer, Mishael Sánchez-Pérez, Cecilia Ishida, Hernán A. Makse
Date: 2021-04-16 00:00:00

Background: Gene regulatory networks coordinate the expression of genes across physiological states and ensure a synchronized expression of genes in cellular subsystems, critical for the coherent functioning of cells. Here we address the questions whether it is possible to predict gene synchronization from network structure alone. We have recently shown that synchronized gene expression may be predicted from symmetries in the gene regulatory networks (GRN) and described by the concept of symmetry fibrations. We showed that symmetry fibrations partition the genes into groups called fibers based on the symmetries of their 'input trees', the set of paths in the network through which signals can reach a gene. In idealized dynamic gene expression models, all genes in a fiber are perfectly synchronized, while less idealized models - with gene input functions differencing between genes - predict symmetry breaking and desynchronization. Results: To study the functional role of gene fibers and to test whether some of the fiber-induced coexpression remains in reality, we analyze gene fibrations for the gene regulatory networks of E. coli and B. subtilis and confront them with expression data. We find approximate gene coexpression patterns consistent with symmetry fibrations with idealized gene expression dynamics. This shows that network structure alone provides useful information about gene synchronization, and suggest that gene input functions within fibers may be further streamlined by evolutionary pressures to realize a coexpression of genes. Conclusions: Thus, gene fibrations provides a sound conceptual tool to describe tunable coexpression induced by network topology and shaped by mechanistic details of gene expression.
Improving Transparency in IDIQ Contracts: A Comparison of Common Procurement Issues Affecting Economies Across the Atlantic and Suggested Solutions
Authors: Sareesh Rawat
Date: 2021-04-16 00:00:00

Expansions in the size and scope of public procurement across the Atlantic have increased calls for accountability of democratic governments. Indefinite Delivery, Indefinite Quantity contracts by their very nature are less transparent but serve as major tools of public procurement in both the European and American economies. This paper utilizes a cross-Atlantic perspective to discuss common challenges faced by governments and contracting entities while highlighting the need for balancing transparency with efficiency to avoid negative economic outcomes. It concludes by discussing and providing potential solutions to certain common challenges.
Exploratory Data Analysis of Electric Tricycle as Sustainable Public Transport Mode in General Santos City Using Logistic Regression
Authors: Geoffrey L. Cueto, Francis Aldrine A. Uy, Keith Anshilo Diaz
Date: 2021-04-16 00:00:00
Tasks: Management

General Santos City, as the tuna capital of the Philippines, relies with the presence of tricycles in moving people and goods. Considered as a highly-urbanized city, General Santos City serves as vital link of the entire SOCKSARGEN region's economic activities. With the current thrust of the city in providing a sustainable transport service, several options were identified to adopt in the entire city, that includes cleaner and better transport mode. Electric tricycle is an after sought alternative that offers better choice in terms of identified factors of sustainable transport: reliability, safety, comfort, environment, affordability, and facility. A literature review was conducted to provide a comparison of cost and emission between a motorized tricycle and an e-tricycle. The study identified the existing tricycle industry of the city and reviewed the modal share with the city's travel pattern. The survey revealed a number of hazards were with the current motorized tricycle that needs to address for the welfare of the passengers and drivers. The study favors the shift to adopting E-tricycle. The model derived from binary logistics regression provided a 72.72% model accuracy. Based from the results and findings, electric tricycle can be an alternative mode of public transport in the city that highly support sustainable option that provides local populace to improve their quality of life through mobility and economic activity. Further recommendation to local policy makers in the transport sector of the city include the clustering of barangays for better traffic management and franchise regulation, the inclusion of transport-related infrastructure related to tricycle service with their investment planning and programming, the roll out and implementation of tricycle code of the city, and the piloting activity of introducing e-tricycle in the city.
Contribuição do ecoturismo para o uso sustentável dos recursos hídricos do município de Rondonópolis-MT
Authors: Manoel Benedito Nirdo da Silva Campos
Date: 2021-04-16 00:00:00
Tasks: Diversity

The Municipality of Rondon\'opolis possesses several touristic attractions such as a great diversity of waterfalls and little beaches located in the surroundings of the urban area, which attract tourists from various locations. Aiming to understand how ecotourism can contribute to the conservation of water resources in the leisure areas, as well as their potential development of touristic activities in those places. The procedures included the use of various techniques subsidized in remote sensing and geoprocessing tools that allowed the analysis and spatial distribution of tourism activities of the main leisure areas. The spatial distribution of the waterfalls and its surroundings, we observe the biophysical characters such as: the endemic vegetation, the cachoeiras, the waterfalls, the rocky outcrops, rivers, little beaches and espraiados. The results showed a correct perception of respondents on existing inter-relationships between ecotourism practices and the sustainable use of water resources. In conclusion though, a long way must be performed in order to prevent the economic benefits of ecotourism generate an inappropriate exploitation of natural resources, causing environmental problems, particularly to water resources in the surroundings.
Optimal Algorithmic Monetary Policy
Authors: Luyao Zhang, Yulin Liu
Date: 2021-04-16 00:00:00

Centralized monetary policy, leading to persistent inflation, is often inconsistent, untrustworthy, and unpredictable. Algorithmic stablecoins enabled by blockchain technology are promising in solving this problem. Algorithmic stablecoins utilize a monetary policy that is entirely rule-based. However, there is little understanding of how to optimize the rule. We propose a model that trade-off the price for supply stability. We further study the comparative statics by varying several design features. Finally, we discuss the empirical implications for designing stablecoins by the private sector and Central Bank Digital Currency (CBDC) by the public sector.
Efficient Keyword Spotting by capturing long-range interactions with Temporal Lambda Networks
Authors: Biel Tura, Santiago Escuder, Ferran Diego, Carlos Segura, Jordi Luque
Date: 2021-04-16 00:00:00
Tasks: Keyword Spotting, speech-recognition, Speech Recognition

Models based on attention mechanisms have shown unprecedented speech recognition performance. However, they are computationally expensive and unnecessarily complex for keyword spotting, a task targeted to small-footprint devices. This work explores the application of Lambda networks, an alternative framework for capturing long-range interactions without attention, for the keyword spotting task. We propose a novel \textit{ResNet}-based model by swapping the residual blocks by temporal Lambda layers. Furthermore, the proposed architecture is built upon uni-dimensional temporal convolutions that further reduce its complexity. The presented model does not only reach state-of-the-art accuracies on the Google Speech Commands dataset, but it is 85% and 65% lighter than its Transformer-based (KWT) and convolutional (Res15) counterparts while being up to 100 times faster. To the best of our knowledge, this is the first attempt to explore the Lambda framework within the speech domain and therefore, we unravel further research of new interfaces based on this architecture.
Algorithm-driven Advances for Scientific CT Instruments: From Model-based to Deep Learning-based Approaches
Authors: S. V. Venkatakrishnan, K. Aditya Mohan, Amir Koushyar Ziabari, Charles A. Bouman
Date: 2021-04-16 00:00:00
Tasks: Computed Tomography (CT), Image Reconstruction

Multi-scale 3D characterization is widely used by materials scientists to further their understanding of the relationships between microscopic structure and macroscopic function. Scientific computed tomography (CT) instruments are one of the most popular choices for 3D non-destructive characterization of materials at length scales ranging from the angstrom-scale to the micron-scale. These instruments typically have a source of radiation that interacts with the sample to be studied and a detector assembly to capture the result of this interaction. A collection of such high-resolution measurements are made by re-orienting the sample which is mounted on a specially designed stage/holder after which reconstruction algorithms are used to produce the final 3D volume of interest. The end goal of scientific CT scans include determining the morphology,chemical composition or dynamic behavior of materials when subjected to external stimuli. In this article, we will present an overview of recent advances in reconstruction algorithms that have enabled significant improvements in the performance of scientific CT instruments - enabling faster, more accurate and novel imaging capabilities. In the first part, we will focus on model-based image reconstruction algorithms that formulate the inversion as solving a high-dimensional optimization problem involving a data-fidelity term and a regularization term. In the last part of the article, we will present an overview of recent approaches using deep-learning based algorithms for improving scientific CT instruments.
Conditional Coding for Flexible Learned Video Compression
Authors: Théo Ladune, Pierrick Philippe, Wassim Hamidouche, Lu Zhang, Olivier Déforges
Date: 2021-04-16 00:00:00
Proceeding: ICLR Workshop Neural_Compression 2021 5
Tasks: Image Compression, Video Compression

This paper introduces a novel framework for end-to-end learned video coding. Image compression is generalized through conditional coding to exploit information from reference frames, allowing to process intra and inter frames with the same coder. The system is trained through the minimization of a rate-distortion cost, with no pre-training or proxy loss. Its flexibility is assessed under three coding configurations (All Intra, Low-delay P and Random Access), where it is shown to achieve performance competitive with the state-of-the-art video codec HEVC.
Rate-Splitting Multiple Access for Joint Radar-Communications with Low-Resolution DACs
Authors: Onur Dizdar, Aryan Kaushik, Bruno Clerckx, Christos Masouros
Date: 2021-04-16 00:00:00
Tasks: Joint Radar-Communication

In this paper, we introduce the design of a multi-antenna Joint Radar-Communication (JRC) system with Rate Splitting Multiple Access (RSMA) and low resolution Digital-to-Analog Converter (DAC) units. Using RSMA, the communication messages are split into private and common parts, then precoded and quantized before transmission. We use a problem formulation to design the JRC system with RSMA and low resolution DACs by maximizing communication sum-rate and the proximity of the resulting JRC waveform to an optimal radar beampattern under an average transmit power constraint. We solve the joint sum-rate maximization and beampattern error minimization problem using Alternating Direction Method of Multipliers (ADMM) method. The numerical results show that RSMA achieves a significantly higher sum-rate compared to Space Division Multiple Access (SDMA) while providing the same Normalized Mean Square Error (NMSE) for the designed radar beampattern.
Hardware Efficient Joint Radar-Communications with Hybrid Precoding and RF Chain Optimization
Authors: Aryan Kaushik, Christos Masouros, Fan Liu
Date: 2021-04-16 00:00:00
Tasks: Joint Radar-Communication

In this paper, we aim to achieve energy efficient design with minimum hardware requirement for hybrid precoding, which enables a large number of antennas with minimal number of RF chains, and sub-arrayed multiple-input multiple-output (MIMO) radar based joint radar-communication (JRC) systems. A dynamic active RF chain selection mechanism is implemented in the baseband processing and the energy efficiency (EE) maximization problem is solved using fractional programming to obtain the optimal number of RF chains at the current channel state. Subsequently hybrid precoders are computed employing a sub-arrayed MIMO structure for EE maximization with weighted formulation of the communication and radar metrics, and the solution is based on alternating minimization. The simulation results show that the proposed method with minimum hardware achieves the best EE while maintaining the rate performance, and an efficient trade-off between sensing and communication.
Efficient QAM Signal Detector for Massive MIMO Systems via PS-ADMM Approach
Authors: Quan Zhang, Jiangtao Wang, Yongchao Wang
Date: 2021-04-16 00:00:00

In this paper, we design an efficient quadrature amplitude modulation (QAM) signal detector for massive multiple-input multiple-output (MIMO) communication systems via the penalty-sharing alternating direction method of multipliers (PS-ADMM). Its main content is as follows: first, we formulate QAM-MIMO detection as a maximum-likelihood optimization problem with bound relaxation constraints. Decomposing QAM signals into a sum of multiple binary variables and exploiting introduced binary variables as penalty functions, we transform the detection optimization model to a non-convex sharing problem; second, a customized ADMM algorithm is presented to solve the formulated non-convex optimization problem. In the implementation, all variables can be solved analytically and in parallel; third, it is proved that the proposed PS-ADMM algorithm converges under mild conditions. Simulation results demonstrate the effectiveness of the proposed approach.
Federated Learning for Internet of Things: A Comprehensive Survey
Authors: Dinh C. Nguyen, Ming Ding, Pubudu N. Pathirana, Aruna Seneviratne, Jun Li, H. Vincent Poor
Date: 2021-04-16 00:00:00
Tasks: Federated Learning, Survey

The Internet of Things (IoT) is penetrating many facets of our daily life with the proliferation of intelligent services and applications empowered by artificial intelligence (AI). Traditionally, AI techniques require centralized data collection and processing that may not be feasible in realistic application scenarios due to the high scalability of modern IoT networks and growing data privacy concerns. Federated Learning (FL) has emerged as a distributed collaborative AI approach that can enable many intelligent IoT applications, by allowing for AI training at distributed IoT devices without the need for data sharing. In this article, we provide a comprehensive survey of the emerging applications of FL in IoT networks, beginning from an introduction to the recent advances in FL and IoT to a discussion of their integration. Particularly, we explore and analyze the potential of FL for enabling a wide range of IoT services, including IoT data sharing, data offloading and caching, attack detection, localization, mobile crowdsensing, and IoT privacy and security. We then provide an extensive survey of the use of FL in various key IoT applications such as smart healthcare, smart transportation, Unmanned Aerial Vehicles (UAVs), smart cities, and smart industry. The important lessons learned from this review of the FL-IoT services and applications are also highlighted. We complete this survey by highlighting the current challenges and possible directions for future research in this booming area.