pwc-archive/papers-with-abstracts huggingface dataset:
Structural Health Monitoring using deep learning with optimal finite element model generated data
Date: 2020-12-01 00:00:00
Tasks: Structural Health Monitoring

Identifying damage through Structural Health Monitoring (SHM) methods is increasingly attracting attention due to multiple maintenance and failure prevention applications. In order to create reliable SHM systems for structural damage identification (DI) tasks, access to large amounts of data containing measured structural responses is usually necessary. The data acquisition is mostly based on direct experimental responses up to now and requires time consuming measurements in various working and ambient conditions of the structure. In the present work, a novel SHM method is tested where all data is solely derived from FE calculated responses, after an initial experimental cost for FE model updating on the healthy structure state. The proposed method can be especially applied in cases where specific damage types are expected or anomalies are adequately defined so they can be effectively simulated by FE models. Origin of such models may then be the healthy experimental status. To test the proposed SHM system, the optimal FE model of an experimental benchmark linear beam structure is constructed, simulating an undamaged condition. In order to check the robustness of the proposed method the damage magnitudes imposed on the benchmark are kept small and combined with random excitations. Next, the optimal FE model is used for generating labeled SHM vibration data through a repetitive load case scheme which also includes uncertainties simulation. The data derived from the optimal FE model is finally used to train a Deep Learning (DL) Convolutional Neural Network (CNN) classifier which is after experimentally validated on the benchmark structure. The optimal FE generated data proves to be able to train an accurate CNN that can predict adequately the experimental benchmark states. A comparison is also given with a CNN trained by the corresponding nominal FE model data which is found not reliable on the experimental validations. The presented combination of optimal FE and DL is a potential solution for future SHM tools and further investigation is encouraged.
Self Supervision for Attention Networks
Authors: Badri N Patro, Kasturi GS, Ansh Jain, Vinay P Namboodiri
Date: 2021-01-06 00:00:00
Tasks: image-classification, Image Classification, Language Modelling, Question Answering, text-classification, Text Classification, Visual Question Answering, Visual Question Answering (VQA)

In recent years, the attention mechanism has become a fairly popular concept and has proven to be successful in many machine learning applications. However, deep learning models do not employ supervision for these attention mechanisms which can improve the model's performance significantly. Therefore, in this paper, we tackle this limitation and propose a novel method to improve the attention mechanism by inducing" self-supervision". We devise a technique to generate desirable attention maps for any model that utilizes an attention module. This is achieved by examining the model's output for different regions sampled from the input and obtaining the attention probability distributions that enhance the proficiency of the model. The attention distributions thus obtained are used for supervision. We rely on the fact, that attenuation of the unimportant parts, allows a model to attend to more salient regions, thus strengthening the prediction accuracy. The quantitative and qualitative results published in this paper show that this method successfully improves the attention mechanism as well as the model's accuracy. In addition to the task of Visual Question Answering (VQA), we also show results on the task of Image classification and Text classification to prove that our method can be generalized to any vision and language model that uses an attention module
Designing metabolic division of labor in microbial communities
Authors: Meghan Thommes, Taiyao Wang, Qi Zhao, Ioannis C. Paschalidis, Daniel Segrè
Date: 2019-04-30 00:00:00

Microbes face a trade-off between being metabolically independent and relying on neighboring organisms for the supply of some essential metabolites. This balance of conflicting strategies affects microbial community structure and dynamics, with important implications for microbiome research and synthetic ecology. A “gedanken” (thought) experiment to investigate this trade-off would involve monitoring the rise of mutual dependence as the number of metabolic reactions allowed in an organism is increasingly constrained. The expectation is that below a certain number of reactions, no individual organism would be able to grow in isolation and cross-feeding partnerships and division of labor would emerge. We implemented this idealized experiment using in silico genome-scale models. In particular, we used mixed-integer linear programming to identify trade-off solutions in communities of Escherichia coli strains. The strategies that we found revealed a large space of opportunities in nuanced and nonintuitive metabolic division of labor, including, for example, splitting the tricarboxylic acid (TCA) cycle into two separate halves. The systematic computation of possible solutions in division of labor for 1-, 2-, and 3-strain consortia resulted in a rich and complex landscape. This landscape displayed a nonlinear boundary, indicating that the loss of an intracellular reaction was not necessarily compensated for by a single imported metabolite. Different regions in this landscape were associated with specific solutions and patterns of exchanged metabolites. Our approach also predicts the existence of regions in this landscape where independent bacteria are viable but are outcompeted by cross-feeding pairs, providing a possible incentive for the rise of division of labor.

IMPORTANCE Understanding how microbes assemble into communities is a fundamental open issue in biology, relevant to human health, metabolic engineering, and environmental sustainability. A possible mechanism for interactions of microbes is through cross-feeding, i.e., the exchange of small molecules. These metabolic exchanges may allow different microbes to specialize in distinct tasks and evolve division of labor. To systematically explore the space of possible strategies for division of labor, we applied advanced optimization algorithms to computational models of cellular metabolism. Specifically, we searched for communities able to survive under constraints (such as a limited number of reactions) that would not be sustainable by individual species. We found that predicted consortia partition metabolic pathways in ways that would be difficult to identify manually, possibly providing a competitive advantage over individual organisms. In addition to helping understand diversity in natural microbial communities, our approach could assist in the design of synthetic consortia.
DeepCGH: 3D computer-generated holography using deep learning
Authors: M. Hossein Eybposh, NICHOLAS W. CAIRA, MATHEW ATISA, PRANEETH CHAKRAVARTHULA, NICOLAS C. PÉGARD
Date: 2020-08-31 00:00:00
Tasks: Deep Learning

The goal of computer-generated holography (CGH) is to synthesize custom illu-
mination patterns by modulating a coherent light beam. CGH algorithms typically rely on
iterative optimization with a built-in trade-off between computation speed and hologram accuracy
that limits performance in advanced applications such as optogenetic photostimulation. We
introduce a non-iterative algorithm, DeepCGH, that relies on a convolutional neural network
with unsupervised learning to compute accurate holograms with fixed computational complexity.
Simulations show that our method generates holograms orders of magnitude faster and with up to
41% greater accuracy than alternate CGH techniques. Experiments in a holographic multiphoton
microscope show that DeepCGH substantially enhances two-photon absorption and improves
performance in photostimulation tasks without requiring additional laser power.
Transmit Beamspace DDMA Based Automotive MIMO Radar
Authors: Feng Xu, Sergiy A. Vorobyov, Fawei Yang
Date: 2021-03-25 00:00:00
Tasks: parameter estimation

The time division multiple access (TDMA) technique has been applied in automotive multiple-input multiple-output (MIMO) radar. However, it suffers from the transmit energy loss, and as a result the parameter estimation performance degradation when the number of transmit elements increases. To tackle these problem, a transmit beamspace (TB) Doppler division multiple access (DDMA) approach is proposed. First, a phase modulation matrix with empty Doppler spectrum is introduced. By exploiting the empty Doppler spectrum, a test function based on sequential detection is developed to mitigate the Doppler ambiguity in DDMA waveform. Then, a discrete Fourier transform (DFT)-based TB in slow-time is formed.The proposed method can achieve waveform diversity in Doppler domain and generate a TB in slow-time that concentrates the transmitted power in a fixed spatial region to improve the transmit energy distribution for automotive MIMO radar, which is favored by medium/long range radar (MRR/LRR) applications. As compared to the conventional TDMA technique, the proposed TB DDMA approach can fully exploit the transmission capabilities of all transmit elements to ensure that the emitted power is efficiently used and inherits easy implementation. Moreover, the proposed TB DDMA method avoids the trade-off between the active time for each transmit antenna and the frame time. Simulation results verify the effectiveness of the proposed TB DDMA approach for automotive MIMO radar.
Plasma Spray Process Parameters Configuration using Sample-efficient Batch Bayesian Optimization
Authors: Xavier Guidetti, Alisa Rupenyan, Lutz Fassl, Majid Nabavi, John Lygeros
Date: 2021-03-25 00:00:00
Tasks: Bayesian Optimization

Recent work has shown constrained Bayesian optimization to be a powerful technique for the optimization of industrial processes. In complex manufacturing processes, the possibility to run extensive sequences of experiments with the goal of finding good process parameters is severely limited by the time required for quality evaluation of the produced parts. To accelerate the process parameter optimization, we introduce a parallel acquisition procedure tailored on the process characteristics. We further propose an algorithm that adapts to equipment status to improve run-to-run reproducibility. We validate our optimization method numerically and experimentally, and demonstrate that it can efficiently find input parameters that produce the desired outcome and minimize the process cost.
Source Aware Deep Learning Framework for Hand Kinematic Reconstruction using EEG Signal
Authors: Sidharth Pancholi, Amita Giri, Anant Jain, Lalan Kumar, Sitikantha Roy
Date: 2021-03-25 00:00:00
Tasks: Brain Computer Interface, channel selection, EEG, Electroencephalogram (EEG), Trajectory Prediction

The ability to reconstruct the kinematic parameters of hand movement using non-invasive electroencephalography (EEG) is essential for strength and endurance augmentation using exosuit/exoskeleton. For system development, the conventional classification based brain computer interface (BCI) controls external devices by providing discrete control signals to the actuator. A continuous kinematic reconstruction from EEG signal is better suited for practical BCI applications. The state-of-the-art multi-variable linear regression (mLR) method provides a continuous estimate of hand kinematics, achieving maximum correlation of upto 0.67 between the measured and the estimated hand trajectory. In this work, three novel source aware deep learning models are proposed for motion trajectory prediction (MTP). In particular, multi layer perceptron (MLP), convolutional neural network - long short term memory (CNN-LSTM), and wavelet packet decomposition (WPD) CNN-LSTM are presented. Additional novelty of the work includes utilization of brain source localization (using sLORETA) for the reliable decoding of motor intention mapping (channel selection) and accurate EEG time segment selection. Performance of the proposed models are compared with the traditionally utilised mLR technique on the real grasp and lift (GAL) dataset. Effectiveness of the proposed framework is established using the Pearson correlation coefficient and trajectory analysis. A significant improvement in the correlation coefficient is observed when compared with state-of-the-art mLR model. Our work bridges the gap between the control and the actuator block, enabling real time BCI implementation.
Real-Time Vehicular Wireless System-Level Simulation
Authors: Anja Dakić, Markus Hofer, Benjamin Rainer, Stefan Zelenbaba, Laura Bernadó, Thomas Zemen
Date: 2020-12-22 00:00:00

Future automation and control units for advanced driver assistance systems (ADAS) will exchange sensor and kinematic data with nearby vehicles using wireless communication links to improve traffic safety. In this paper we present an accurate real-time system-level simulation for multi-vehicle communication scenarios to support the development and test of connected ADAS systems. The physical and data-link layer are abstracted and provide the frame error rate (FER) to a network simulator. The FER is strongly affected by the non-stationary doubly dispersive fading process of the vehicular radio communication channel. We use a geometry-based stochastic channel model (GSCM) to enable a simplified but still accurate representation of the non-stationary vehicular fading process. The propagation path parameters of the GSCM are used to efficiently compute the time-variant condensed radio channel parameters per stationarity region of each communication link during run-time. Five condensed radio channel parameters mainly determine the FER forming a parameter vector: path loss, root mean square delay spread, Doppler bandwidth, $K$-factor, and line-of-sight Doppler shift. We measure the FER for a pre-defined set of discrete grid points of the parameter vector using a channel emulator and a given transmitter-receiver modem pair. The FER data is stored in a table and looked up during run-time of the real-time system-level simulation. We validate our methodology using empirical measurement data from a street crossing scenarios demonstrating a close match in terms of FER between simulation and measurement.
Abstracting the Sampling Behaviour of Stochastic Linear Periodic Event-Triggered Control Systems
Authors: Giannis Delimpaltadakis, Luca Laurenti, Manuel Mazo Jr
Date: 2021-03-25 00:00:00

Recently, there have been efforts towards understanding the sampling behaviour of event-triggered control (ETC), for obtaining metrics on its sampling performance and predicting its sampling patterns. Finite-state abstractions, capturing the sampling behaviour of ETC systems, have proven promising in this respect. So far, such abstractions have been constructed for non-stochastic systems. Here, inspired by this framework, we abstract the sampling behaviour of stochastic narrow-sense linear periodic ETC (PETC) systems via Interval Markov Chains (IMCs). Particularly, we define functions over sequences of state-measurements and interevent times that can be expressed as discounted cumulative sums of rewards, and compute bounds on their expected values by constructing appropriate IMCs and equipping them with suitable rewards. Finally, we argue that our results are extendable to more general forms of functions, thus providing a generic framework to define and study various ETC sampling indicators.
Multi-Objective $H_{\infty}$ Control for String Stability of Cooperative Adaptive Cruise Control Systems
Authors: Erkan Kayacan
Date: 2021-03-25 00:00:00

Autonomous vehicle following systems are playing a decisive role to increase vehicle density on roads by shortening inter-vehicle time gaps. However, disturbance attenuation along a platoon of vehicles, i.e., string stability, is being a challenging task while time gap is getting shorter. In order to guarantee the string stability of a vehicle platoon, a multi-objective $H_{\infty}$ control formulation for adaptive cruise control and cooperative adaptive cruise control structures has been investigated in this paper. The proposed control method solves an optimization problem and achieves a controller that is able to provide not only the system stability, but also the string stability as distinct from the traditional $H_{\infty}$ control.
Regularized Covariance Estimation for Polarization Radar Detection in Compound Gaussian Sea Clutter
Authors: Lei Xie, Zishu He, Jun Tong, Tianle Liu, Jun Li, Jiangtao Xi
Date: 2021-03-17 00:00:00

This paper investigates regularized estimation of Kronecker-structured covariance matrices (CM) for polarization radar in sea clutter scenarios where the data are assumed to follow the complex, elliptically symmetric (CES) distributions with a Kronecker-structured CM. To obtain a well-conditioned estimate of the CM, we add penalty terms of Kullback-Leibler divergence to the negative log-likelihood function of the associated complex angular Gaussian (CAG) distribution. This is shown to be equivalent to regularizing Tyler's fixed-point equations by shrinkage. A sufficient condition that the solution exists is discussed. An iterative algorithm is applied to solve the resulting fixed-point iterations and its convergence is proved. In order to solve the critical problem of tuning the shrinkage factors, we then introduce two methods by exploiting oracle approximating shrinkage (OAS) and cross-validation (CV). The proposed estimator, referred to as the robust shrinkage Kronecker estimator (RSKE), is shown to achieve better performance compared with several existing methods when the training samples are limited. Simulations are conducted for validating the RSKE and demonstrating its high performance by using the IPIX 1998 real sea data.
Modelling the emergence of phenotypic heterogeneity in vascularised tumours
Authors: Chiara Villa, Mark A. J. Chaplain, Tommaso Lorenzi
Date: 2019-10-18 00:00:00

We present a mathematical study of the emergence of phenotypic heterogeneity in vascularised tumours. Our study is based on formal asymptotic analysis and numerical simulations of a system of non-local parabolic equations that describes the phenotypic evolution of tumour cells and their nonlinear dynamic interactions with the oxygen, which is released from the intratumoural vascular network. Numerical simulations are carried out both in the case of arbitrary distributions of intratumour blood vessels and in the case where the intratumoural vascular network is reconstructed from clinical images obtained using dynamic optical coherence tomography. The results obtained support a more in-depth theoretical understanding of the eco-evolutionary process which underpins the emergence of phenotypic heterogeneity in vascularised tumours. In particular, our results offer a theoretical basis for empirical evidence indicating that the phenotypic properties of cancer cells in vascularised tumours vary with the distance from the blood vessels, and establish a relation between the degree of tumour tissue vascularisation and the level of intratumour phenotypic heterogeneity.
Low emission zones: Effects on alternative-fuel vehicle uptake and fleet CO2 emissions
Authors: Jens F. Peters, Mercedes Burguillo, Jose M. Arranz
Date: 2021-03-25 00:00:00

This study analyses the actual effect of a representative low-emission zone (LEZ) in terms of shifting vehicle registrations towards alternative fuel technologies and its effectiveness for reducing vehicle fleet CO2 emissions. Vehicle registration data is combined with real life fuel consumption values on individual vehicle model level, and the impact of the LEZ is then determined via an econometric approach. The increase in alternative fuel vehicles (AFV) registration shares due to the LEZ is found to be significant but fosters rather fossil fuel powered AFV and plug-in hybrid electric vehicles than zero emission vehicles. This is reflected in the average CO2 emissions of newly registered vehicles, which do not decrease significantly. In consequence, while the LEZ is an effective measure for stimulating the shift towards low emission vehicles, the support of non-electric AFV as low emission vehicles jeopardizes its effectiveness for decarbonizing the vehicle fleet.
Experimental Validation of Linear and Nonlinear MPC on an Articulated Unmanned Ground Vehicle
Authors: Erkan Kayacan, Wouter Saeys, Herman Ramon, Calin Belta, Joshua M. Peschel
Date: 2021-03-25 00:00:00
Tasks: Model Predictive Control

This paper focuses on the trajectory tracking control problem for an articulated unmanned ground vehicle. We propose and compare two approaches in terms of performance and computational complexity. The first uses a nonlinear mathematical model derived from first principles and combines a nonlinear model predictive controller (NMPC) with a nonlinear moving horizon estimator (NMHE) to produce a control strategy. The second is based on an input-state linearization (ISL) of the original model followed by linear model predictive control (LMPC). A fast real-time iteration scheme is proposed, implemented for the NMHE-NMPC framework and benchmarked against the ISL-LMPC framework, which is a traditional and cheap method. The experimental results for a time-based trajectory show that the NMHE-NMPC framework with the proposed real-time iteration scheme gives better trajectory tracking performance than the ISL-LMPC framework and the required computation time is feasible for real-time applications. Moreover, the ISL-LMPC produces results of a quality comparable to the NMHE-NMPC framework at a significantly reduced computational cost.
An Intelligent Bed Sensor System for Non-Contact Respiratory Rate Monitoring
Authors: Qingju Liu, Mark Kenny, Ramin Nilforooshan, Payam Barnaghi
Date: 2021-03-25 00:00:00

We present an IoT-based intelligent bed sensor system that collects and analyses respiration-associated signals for unobtrusive monitoring in the home, hospitals and care units. A contactless device is used, which contains four load sensors mounted under the bed and one data processing unit (data logger). Various machine learning methods are applied to the data streamed from the data logger to detect the Respiratory Rate (RR). We have implemented Support Vector Machine (SVM) and also Neural Network (NN)-based pattern recognition methods, which are combined with either peak detection or Hilbert transform for robust RR calculation. Experimental results show that our methods could effectively extract RR using the data collected by contactless bed sensors. The proposed methods are robust to outliers and noise, which are caused by body movements. The monitoring system provides a flexible and scalable way for continuous and remote monitoring of sleep, movement and weight using the embedded sensors.
AoA-Based Pilot Assignment in Massive MIMO Systems Using Deep Reinforcement Learning
Authors: Yasaman Omid, Seyed MohammadReza Hosseini, Seyyed MohammadMahdi Shahabi, Mohammad Shikh-Bahaei, Arumugam Nallanathan
Date: 2021-03-25 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning (RL)

In this paper, the problem of pilot contamination in a multi-cell massive multiple input multiple output (M-MIMO) system is addressed using deep reinforcement learning (DRL). To this end, a pilot assignment strategy is designed that adapts to the channel variations while maintaining a tolerable pilot contamination effect. Using the angle of arrival (AoA) information of the users, a cost function, portraying the reward, is presented, defining the pilot contamination effects in the system. Numerical results illustrate that the DRL-based scheme is able to track the changes in the environment, learn the near-optimal pilot assignment, and achieve a close performance to that of the optimum pilot assignment performed by exhaustive search, while maintaining a low computational complexity.