pwc-archive/papers-with-abstracts huggingface dataset:
Kalman-based interacting multiple-model wind speed estimator for wind turbines
Authors: Wai Hou Lio, Fanzhong Meng
Date: 2021-04-20 00:00:00
Tasks: State Estimation

The use of state estimation technique offers a means of inferring the rotor-effective wind speed based upon solely standard measurements of the turbine. For the ease of design and computational concerns, such estimators are typically built based upon simplified turbine models that characterise the turbine with rigid blades. Large model mismatch, particularly in the power coefficient, could lead to degradation in estimation performance. Therefore, in order to effectively reduce the adverse impact of parameter uncertainties in the estimator model, this paper develops a wind sped estimator based on the concept of interacting multiple-model adaptive estimation. The proposed estimator is composed of a bank of extended Kalman filters and each filter model is developed based on different power coefficient mapping to match the operating turbine parameter. Subsequently, the algorithm combines the wind speed estimates provided by each filter based on their statistical properties. In addition, the proposed estimator not only can infer the rotor-effective wind speed, but also the uncertain system parameters, namely, the power coefficient. Simulation results demonstrate the proposed estimator achieved better improvement in estimating the rotor-effective wind speed and power coefficient compared to the standard Kalman filter approach.
Nonlinear Tracking and Rejection using Linear Parameter-Varying Control
Authors: Patrick J. W. Koelewijn, Roland Tóth, Henk Nijmeijer, Siep Weiland
Date: 2021-04-20 00:00:00

The Linear Parameter-Varying (LPV) framework has been introduced with the intention to provide stability and performance guarantees for analysis and controller synthesis for Nonlinear (NL) systems via convex methods. By extending results of the Linear Time-Invariant framework, mainly based on quadratic stability and performance using dissipativity theory, it has been assumed that they generalize tracking and disturbance rejection guarantees for NL systems. However, as has been shown in literature, stability and performance through standard dissipativity is not sufficient in order to satisfy the desired guarantees in case of reference tracking and disturbance rejection for nonlinear systems. We propose to solve this problem by the application of incremental dissipativity, which does ensure these specifications. A novel approach is proposed to synthesize and realize an NL controller which is able to guarantee incremental stability and performance for NL systems via convex optimization using methods from the LPV framework. Through simulations and experiments, the presented method is compared to standard LPV controller designs, showing significant performance improvements.
GPS-denied Navigation: Attitude, Position, Linear Velocity, and Gravity Estimation with Nonlinear Stochastic Observer
Authors: Hashim A Hashim
Date: 2021-04-20 00:00:00
Tasks: Position

Successful navigation of a rigid-body traveling with six degrees of freedom (6 DoF) requires accurate estimation of attitude , position, and linear velocity. The true navigation dynamics are highly nonlinear and are modeled on the matrix Lie group of SE2(3). This paper presents novel geometric nonlinear continuous stochastic navigation observers on SE2(3) capturing the true nonlinearity of the problem. The proposed observers combines IMU and landmark measurements. It efficiently handles the IMU measurement noise. The proposed observers are guaranteed to be almost semi-globally uniformly ultimately bounded in the mean square. Quaternion representation is provided. A real-world quadrotor measurement dataset is used to validate the effectiveness of the proposed observers in its discrete form. Keywords: Inertial navigation, stochastic system, Brownian motion process, stochastic filter algorithm, stochastic differential equation, Lie group, SE(3), SO(3), pose estimator, position, attitude, feature measurement, inertial measurement unit, IMU.
Singular Perturbation-based Reinforcement Learning of Two-Point Boundary Optimal Control Systems
Authors: Vasanth Reddy, Hoda Eldardiry, Almuatazbellah Boker
Date: 2021-04-19 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning (RL)

This work presents a technique for learning systems, where the learning process is guided by knowledge of the physics of the system. In particular, we solve the problem of the two-point boundary optimal control problem of linear time-varying systems with unknown model dynamics using reinforcement learning. Borrowing techniques from singular perturbation theory, we transform the time-varying optimal control problem into a couple of time-invariant subproblems. This allows the utilization of an off-policy iteration method to learn the controller gains. We show that the performance of the learning-based controller approximates that of the model-based optimal controller and the accuracy of the approximation improves as the time horizon of the control problem increases. Finally, we provide a simulation example to verify the results of the paper.
Selective Harmonic Elimination in a Cascaded H-Bridge Multilevel Inverter fed by High-Frequency Isolated DC-DC Converter
Authors: Amirhossein Pourdadashnia, Milad Sadoughi, Mohammad Farhadi-Kangarlu, Behrouz Tousi
Date: 2021-04-19 00:00:00

In recent years, the use of multilevel inverter has become popular due to its many advantages. Due to the popularity of multilevel inverters in industries and in applications that require a wide range of voltages, there have been many challenges to achieve high-quality voltage. Many pieces of research have been done to solve the problem of annoying harmonics in multilevel inverters. In this study, pulse width modulation (PWM) has been proposed as a switching method. Using the Selective Harmonic Elimination (SHE) technique, the inverter switching can be carried out in low frequency, and also, this method can reduce the annoying harmonics significantly. However, in the lower modulation index, eliminating such harmonics is challenging, resulting in a considerable increase in output voltage distortion. This study suggests a way to solve the problem. In this research, eliminating selected harmonics in a multilevel inverter with variable DC links is proposed. The DC-link variable method in this study is to use a high-frequency isolated DC-DC converter. The proposed method is verified on a 5-level Cascaded H-Bridge (CHB) inverter using the SHE-PWM method solved by particle swarm optimization (PSO) algorithm.
Reducing Harmonic Distortion in a 5-Level Cascaded H-bridge Inverter Fed by a 12-Pulse Thyristor Rectifier
Authors: Milad Sadoughi, Amirhossein Pourdadashnia, Mohammad Farhadi-Kangarlu, Sadjad Galvani
Date: 2021-04-19 00:00:00

This paper investigates a multilevel inverter with a capability that produces a wide voltage range with high quality. The selective harmonic elimination (SHE) method is considered for a single-phase 5-level cascaded H-bridge (CHB) inverter, in which the particle swarm optimization (PSO) algorithm solves the nonlinear equations. However, eliminating the low-order harmonics has been challenging when a low range of output voltage is required. To surmount such challenges and access to a wide range of output voltage, an adjustable dc-link is introduced that allows the inverter to increase the modulation index, resulting in a significant decrease in total harmonic distortion (THD). In this paper, to regulate the dc-link voltage amount, a 12-pulse rectifier is employed to allow the inverter to produce the output voltage requirements with less distortion. To prove such claims, the PSO algorithm is modified to calculate the optimal angles, as a result, the switching angles are applied in SIMULINK MATLAB to generate a 5-level output voltage.
Dissensus Algorithms for Opinion Dynamics on the Sphere
Authors: Ziqiao Zhang, Said Al-Abri, Fumin Zhang
Date: 2021-04-19 00:00:00

In this paper, novel dissensus algorithms based on the Oja principal component analysis (PCA) flow are proposed to model opinion dynamics on the unit sphere. The information of the covariance formed by the opinion state of each agent is used to achieve a dissensus equilibrium on unsigned graphs. This differs from most of the existing work where antagonistic interactions represented by negative weights in signed graphs are used to achieve a dissensus equilibrium. The nonlinear algorithm is analyzed under both constant covariance and time-varying covariance leading to different behaviors. Stability analysis for the unstable consensus and stable dissensus equilibria is provided under various conditions. The performance of the algorithm is illustrated through a simulation experiment of a multi-agent system.
Assume/Guarantee Contracts for Dynamical Systems: Theory and Computational Tools
Authors: Miel Sharf, Bart Besselink, Adam Molin, Qiming Zhao, Karl Henrik Johansson
Date: 2020-12-23 00:00:00
Tasks: Autonomous Driving

Modern engineering systems include many components of different types and functions. Verifying that these systems satisfy given specifications can be an arduous task, as most formal verification methods are limited to systems of moderate size. Recently, contract theory has been proposed as a modular framework for defining specifications. In this paper, we present a contract theory for discrete-time dynamical control systems relying on assume/guarantee contracts, which prescribe assumptions on the input of the system and guarantees on the output. We then focus on contracts defined by linear constraints, and develop efficient computational tools for verification of satisfaction and refinement based on linear programming. We exemplify these tools in a simulation example, proving a certain safety specification for a two-vehicle autonomous driving setting.
Modeling biological networks: from single gene systems to large microbial communities
Authors: Lana Descheemaeker
Date: 2021-04-20 00:00:00
Tasks: Time Series, Time Series Analysis

In this research, we study biological networks at different scales: a gene autoregulatory network at the single-cell level and the gut microbiota at the population level. Proteins are the main actors in cells, they are the building blocks, act as enzymes and antibodies. The production of proteins is mediated by transcription factors. In some cases, a protein acts as its own transcription factor, this is called autoregulation. It is known that autorepression speeds up the response and that autoactivation can lead to multiple stable equilibria. In this thesis, we study the effects of the combination of activation and repression in autoregulation, as a case study we investigate the possible dynamics of the leucine responsive protein B of the archaeon Sulfolobus solfataricus (Ss-LrpB), a protein that regulates itself in a unique and non-monotonic way via three binding boxes. We examine for which conditions this type of network leads to oscillations or bistability. In the second part, much larger biological systems are considered. Ecological systems, among which the human gut microbiome, are characterized by heavy-tailed abundance profiles. We study how these distributions can arise from population-based models by adding saturation effects and linear noise. Moreover, we examine different characteristics of experimental time series of microbial communities, such as the noise color and neutrality of the biodiversity, and look at the influence of the parameters on these characteristics. With the first research topic we want to lay a foundation for the understanding of non-monotonic gene regulation and take the first steps toward synthetic biology in archaea. In the second part of the thesis, we investigate experimental time series from complex ecosystems and seek theoretical models reproducing all observed characteristics in view of building predictive models.
Ghost Handwritten Digit Recognition based on Deep Learning
Authors: Xing He, Shengmei Zhao, Le Wang
Date: 2020-04-05 00:00:00
Tasks: Deep Learning, Handwritten Digit Recognition

We present a ghost handwritten digit recognition method for the unknown handwritten digits based on ghost imaging (GI) with deep neural network, where a few detection signals from the bucket detector, generated by the Cosine Transform speckle, are used as the characteristic information and the input of the designed deep neural network (DNN), and the classification is designed as the output of the DNN. The results show that the proposed scheme has a higher recognition accuracy (as high as 98.14% for the simulations, and 92.9% for the experiments ) with a smaller sampling ratio (say 12.76%). With the increase of the sampling ratio, the recognition accuracy is enhanced greatly. Compared with the traditional recognition scheme using the same DNN structure, the proposed scheme has a little better performance with a lower complexity and non-locality property. The proposed scheme provides a promising way for remote sensing.
Inferring Multi-Period Optimal Portfolios via Detrending Moving Average Cluster Entropy
Authors: P. Murialdo, L. Ponta, A. Carbone
Date: 2021-04-20 00:00:00
Tasks: Diversity

Despite half a century of research, there is still no general agreement about the optimal approach to build a robust multi-period portfolio. We address this question by proposing the detrended cluster entropy approach to estimate the portfolio weights of high-frequency market indices. The information measure produces reliable estimates of the portfolio weights gathered from the real-world market data at varying temporal horizons. The portfolio exhibits a high level of diversity, robustness and stability as it is not affected by the drawbacks of traditional mean-variance approaches.
Dynamics of COVID-19 mitigation inefficiency in Brazil
Authors: Borko D. Stosic
Date: 2021-04-20 00:00:00
Tasks: Decision Making

In this work Data Envelopment Analysis (DEA) is employed in thirty-day windows to quantify temporal evolution of relative pandemic mitigation inefficiency of Brazilian municipalities. For each thirty-day window the results of inefficiency scores of over five thousand Brazilian municipalities are displayed on maps, to address the spatial distribution of the corresponding values. This phenomenological spatiotemporal approach reveals location of the hotspots, in terms of relative pandemic inefficiency of the municipalities, at different points in time along the pandemic. It is expected that the current approach may subsidize decision making through comparative analysis of previous practice, and thus contribute to the future pandemic mitigation efforts.
CATE meets ML -- The Conditional Average Treatment Effect and Machine Learning
Authors: Daniel Jacob
Date: 2021-04-20 00:00:00
Tasks: BIG-bench Machine Learning

For treatment effects - one of the core issues in modern econometric analysis - prediction and estimation are two sides of the same coin. As it turns out, machine learning methods are the tool for generalized prediction models. Combined with econometric theory, they allow us to estimate not only the average but a personalized treatment effect - the conditional average treatment effect (CATE). In this tutorial, we give an overview of novel methods, explain them in detail, and apply them via Quantlets in real data applications. We study the effect that microcredit availability has on the amount of money borrowed and if 401(k) pension plan eligibility has an impact on net financial assets, as two empirical examples. The presented toolbox of methods contains meta-learners, like the Doubly-Robust, R-, T- and X-learner, and methods that are specially designed to estimate the CATE like the causal BART and the generalized random forest. In both, the microcredit and 401(k) example, we find a positive treatment effect for all observations but conflicting evidence of treatment effect heterogeneity. An additional simulation study, where the true treatment effect is known, allows us to compare the different methods and to observe patterns and similarities.
Coverage Characterization of STAR-RIS Networks: NOMA and OMA
Authors: Chenyu Wu, Yuanwei Liu, Xidong Mu, Xuemai Gu, Octavia A. Dobre
Date: 2021-04-20 00:00:00

The novel concept of simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) is investigated, where incident signals can be transmitted and reflected to users located at different sides of the surface. In particular, the fundamental coverage range of STAR-RIS aided two-user communication networks is studied. A sum coverage range maximization problem is formulated for both non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA), where the resource allocation at the access point and the transmission and reflection coefficients at the STAR-RIS are jointly optimized to satisfy the communication requirements of users. For NOMA, we transform the non-convex decoding order constraint into a linear constraint and the resulting problem is convex, which can be optimally solved. For OMA, we first show that the optimization problem for given time/frequency resource allocation is convex. Then, we employ the one dimensional search-based algorithm to obtain the optimal solution. Numerical results reveal that the coverage can be significantly extended by the STAR-RIS compared with conventional RISs.
A General 3D Space-Time-Frequency Non-Stationary THz Channel Model for 6G Ultra-Massive MIMO Wireless Communication Systems
Authors: Jun Wang, Cheng-Xiang Wang, Jie Huang, Haiming Wang, Xiqi Gao
Date: 2021-04-20 00:00:00

In this paper, a novel three-dimensional (3D) space-time-frequency (STF) non-stationary geometry-based stochastic model (GBSM) is proposed for the sixth generation (6G) terahertz (THz) wireless communication systems. The proposed THz channel model is very general having the capability to capture different channel characteristics in multiple THz application scenarios such as indoor scenarios, device-to-device (D2D) communications, ultra-massive multiple-input multiple-output (MIMO) communications, and long traveling paths of users. Also, the generality of the proposed channel model is demonstrated by the fact that it can easily be reduced to different simplified channel models to fit specific scenarios by properly adjusting model parameters. The proposed general channel model takes into consideration the non-stationarities in space, time, and frequency domains caused by ultra-massive MIMO, long traveling paths, and large bandwidths of THz communications, respectively. Statistical properties of the proposed general THz channel model are investigated. The accuracy and generality of the proposed channel model are verified by comparing the simulation results of the relative angle spread and root mean square (RMS) delay spread with corresponding channel measurements.
Coverage Analysis for 3D Terahertz Communication Systems
Authors: Akram Shafie, Nan Yang, Salman Durrani, Xiangyun Zhou, Chong Han, Markku Juntti
Date: 2021-04-20 00:00:00

We conduct novel coverage probability analysis of downlink transmission in a three-dimensional (3D) terahertz (THz) communication (THzCom) system. In this system, we address the unique propagation properties in THz band, e.g., absorption loss, super-narrow directional beams, and high vulnerability towards blockage, which are fundamentally different from those at lower frequencies. Different from existing studies, we characterize the performance while considering the effect of 3D directional antennas at both access points (APs) and user equipments (UEs), and the joint impact of the blockage caused by the user itself, moving humans, and wall blockers in a 3D environment. Under such consideration, we develop a tractable analytical framework to derive a new expression for the coverage probability by examining the regions where dominant interferers (i.e., those can cause outage by themselves) can exist, and the average number of interferers existing in these regions. Aided by numerical results, we validate our analysis and reveal that ignoring the impact of the vertical heights of THz devices in the analysis leads to a substantial underestimation of the coverage probability. We also show that it is more worthwhile to increase the antenna directivity at the APs than at the UEs, to produce a more reliable THzCom system.
Neural tuning and representational geometry
Authors: Nikolaus Kriegeskorte, Xue-Xin Wei
Date: 2021-04-20 00:00:00

A central goal of neuroscience is to understand the representations formed by brain activity patterns and their connection to behavior. The classical approach is to investigate how individual neurons encode the stimuli and how their tuning determines the fidelity of the neural representation. Tuning analyses often use the Fisher information to characterize the sensitivity of neural responses to small changes of the stimulus. In recent decades, measurements of large populations of neurons have motivated a complementary approach, which focuses on the information available to linear decoders. The decodable information is captured by the geometry of the representational patterns in the multivariate response space. Here we review neural tuning and representational geometry with the goal of clarifying the relationship between them. The tuning induces the geometry, but different sets of tuned neurons can induce the same geometry. The geometry determines the Fisher information, the mutual information, and the behavioral performance of an ideal observer in a range of psychophysical tasks. We argue that future studies can benefit from considering both tuning and geometry to understand neural codes and reveal the connections between stimulus, brain activity, and behavior.
A model of COVID-19 pandemic evolution in African countries
Authors: Kossi Amouzouvi, Kétévi A. Assamagan, Somiéalo Azote, Simon H. Connell, Jean Baptiste Fankam Fankam, Fenosoa Fanomezana, Aluwani Guga, Cyrille E. Haliya, Toivo S. Mabote, Francisco Fenias Macucule, Dephney Mathebula, Azwinndini Muronga, Kondwani C. C. Mwale, Ann Njeri, Ebode F. Onyie, Laza Rakotondravohitra, George Zimba
Date: 2021-04-19 00:00:00

We studied the COVID-19 pandemic evolution in selected African countries. For each country considered, we modeled simultaneously the data of the active, recovered and death cases. In this study, we used a year of data since the first cases were reported. We estimated the time-dependent basic reproduction numbers, $R_0$, and the fractions of infected but unaffected populations, to offer insights into containment and vaccine strategies in African countries. We found that $R_0\leq 4$ at the start of the pandemic but has since fallen to $R_0 \sim 1$. The unaffected fractions of the populations studied vary between $1-10$\% of the recovered cases.