pwc-archive/papers-with-abstracts huggingface dataset:
Analyzing Distribution Transformer Degradation with Increased Power Electronic Loads
Authors: Bhaskar Mitra, Ankit Singhal, Soumya Kundu, James P. Ogle
Date: 2022-10-26 00:00:00

The influx of non-linear power electronic loads into the distribution network has the potential to disrupt the existing distribution transformer operations. They were not designed to mediate the excessive heating losses generated from the harmonics. To have a good understanding of current standing challenges, a knowledge of the generation and load mix as well as the current harmonic estimations are essential for designing transformers and evaluating their performance. In this paper, we investigate a mixture of essential power electronic loads for a household designed in PSCAD/EMTdc and their potential impacts on transformer eddy current losses and derating using harmonic analysis. The various scenarios have been studied with increasing PV penetrations. The peak load conditions are chosen for each scenario to perform a transformer derating analysis. Our findings reveal that in the presence of high power electronic loads (especially third harmonics), along with increasing PV generation may worsen transformer degradation. However, with a low amount of power electronic loads, additional PV generation helps to reduce the harmonic content in the current and improve transformer performance.
Optimization based coordination of autonomous vehicles in confined areas
Authors: Stefan Kojchev, Robert Hult, Jonas Fredriksson
Date: 2022-10-26 00:00:00
Tasks: Autonomous Vehicles, Collision Avoidance

Confined areas present an opportunity for early deployment of autonomous vehicles (AV) due to the absence of non-controlled traffic participants. In this paper, we present an approach for coordination of multiple AVs in confined sites. The method computes speed-profiles for the AVs such that collisions are avoided in cross-intersection and merge crossings. Specifically, this is done through the solution of an optimal control problem where the motion of all vehicles is optimized jointly. The order in which the vehicles pass the crossings is determined through the solution of a Mixed Integer Quadratic Program (MIQP). Through simulation results, we demonstrate the capability of the algorithm in terms of performance and satisfaction of collision avoidance constraints.
Singularity-free Formation Path Following of Underactuated AUVs: Extended Version
Authors: Josef Matouš, Kristin Y. Pettersen, Damiano Varagnolo, Claudio Paliotta
Date: 2022-10-26 00:00:00
Tasks: Collision Avoidance

This paper proposes a method for formation path following control of a fleet of underactuated autonomous underwater vehicles. The proposed method combines several hierarchic tasks in a null space-based behavioral algorithm to safely guide the vehicles. Compared to the existing literature, the algorithm includes both inter-vehicle and obstacle collision avoidance, and employs a scheme that keeps the vehicles within given operation limits. The algorithm is applied to a six degree-of-freedom model, using rotation matrices to describe the attitude to avoid singularities. Using the results of cascaded systems theory, we prove that the closed-loop system is uniformly semiglobally exponentially stable. We use numerical simulations to validate the results.
MIMA -- Multifunctional IoT Integrated Menstrual Aid
Authors: Jyothish Kumar J, Subhankar Mishra, Amish Bibhu, Shreya Shivangi, Sulagna Saha
Date: 2022-10-26 00:00:00

Menstruation is the monthly shedding of the endometrium lining of a woman's uterus. The average age when girls start menstruating is around the age of 12 years (menarche), and the cycle continues until they attain menopause (about the age of 51). Medical research and analysis in this field reveal that most women have to go through a painful cycle of abdominal cramps along with sanitary pad rashes, while painkillers or endurance ability are their go-to solution. Heat pads or hot water bags also help in pain reduction. Currently, the concept of period pants revolves around pad-free and hassle-free periods for women, whereas most women still prefer sanitary pads during their period cycle. MIMA aims at the development of IoT integrated smart, functional intimate wear for women that would help women comfort during menstruation by catering to issues of menstrual cramps, rashes, leakage and stains, malodor, etc. The proposed methodology has been implemented by referring to the online survey conducted from Indian women (17-58 years old). MIMA can provide comfort during the menstruation cycle with IoT integrated Heat-Pad and functional alterations in the garment for a rash-free, anti-odor, and leak-proof period.
Safe and Efficient Switching Mechanism Design for Uncertified Linear Controller
Authors: Yiwen Lu, Yilin Mo
Date: 2022-10-26 00:00:00

Sustained research efforts have been devoted to learning optimal controllers for linear stochastic dynamical systems with unknown parameters, but due to the corruption of noise, learned controllers are usually uncertified in the sense that they may destabilize the system. To address this potential instability, we propose a "plug-and-play" modification to the uncertified controller which falls back to a known stabilizing controller when the norm of the difference between the uncertified and the fall-back control input exceeds a certain threshold. We show that the switching strategy is both safe and efficient, in the sense that: 1) the linear-quadratic cost of the system is always bounded even if original uncertified controller is destabilizing; 2) in case the uncertified controller is stabilizing, the performance loss caused by switching converges super-exponentially to $0$ for Gaussian noise, while the converging polynomially for general heavy-tailed noise. Finally, we demonstrate the effectiveness of the proposed switching strategy via numerical simulation on the Tennessee Eastman Process.
Differentially Private Stochastic Convex Optimization for Network Routing Applications
Authors: Matthew Tsao, Karthik Gopalakrishnan, Kaidi Yang, Marco Pavone
Date: 2022-10-26 00:00:00

Network routing problems are common across many engineering applications. Computing optimal routing policies requires knowledge about network demand, i.e., the origin and destination (OD) of all requests in the network. However, privacy considerations make it challenging to share individual OD data that would be required for computing optimal policies. Privacy can be particularly challenging in standard network routing problems because sources and sinks can be easily identified from flow conservation constraints, making feasibility and privacy mutually exclusive. In this paper, we present a differentially private algorithm for network routing problems. The main ingredient is a reformulation of network routing which moves all user data-dependent parameters out of the constraint set and into the objective function. We then present an algorithm for solving this formulation based on a differentially private variant of stochastic gradient descent. In this algorithm, differential privacy is achieved by injecting noise, and one may wonder if this noise injection compromises solution quality. We prove that our algorithm is both differentially private and asymptotically optimal as the size of the training set goes to infinity. We corroborate the theoretical results with numerical experiments on a road traffic network which show that our algorithm provides differentially private and near-optimal solutions in practice.
Scale-free linear protocol design for global regulated state synchronization of discrete-time double-integrator multi-agent systems subject to actuator saturation
Authors: Zhenwei Liu, Ali Saberi, Anton A. Stoorvogel
Date: 2022-10-26 00:00:00

This paper studies global regulated state synchronization of discrete-time double-integrator multi-agent systems subject to actuator saturation by utilizing localized information exchange. We propose a scale-free linear protocol that achieves global regulated state synchronization for any network with arbitrary number of agents and arbitrarily directed communication graph that has a path between each agent and exosystem which generates the reference trajectory.
A formal process of hierarchical functional requirements development for Set-Based Design
Authors: Minghui Sun, Zhaoyang Chen, Georgios Bakirtzis, Hassan Jafarzadeh, Cody Fleming
Date: 2022-10-26 00:00:00

The design of complex systems is typically uncertain and ambiguous at early stages. Set-Based Design is a promising approach to complex systems design as it supports alternative exploration and gradual uncertainty reduction. When designing a complex system, functional requirements decomposition is a common and effective approach to progress the design incrementally. However, the current literature on Set-Based Design lacks formal guidance in functional requirements decomposition. To bridge the gap, we propose a formal process to hierarchically decompose the functional requirements for Set-Based Design. A four-step formal process is proposed to systematically define, reason, and narrow the sets, and eventually decompose the functional requirement into the sub-requirements. Such a process can be used by the individual suppliers working in parallel at multiple levels of abstraction and guarantee that the resulting system will eventually satisfy the top-level functional requirements. An example of designing a cruise control system is applied to demonstrate the feasibility of the proposed process.
Stealthy Measurement-Aided Pole-Dynamics Attacks with Nominal Models
Authors: Dajun Du, Changda Zhang, Chen Peng, Minrui Fei, Huiyu Zhou
Date: 2022-10-26 00:00:00

When traditional pole-dynamics attacks (TPDAs) are implemented with nominal models, model mismatch between exact and nominal models often affects their stealthiness, or even makes the stealthiness lost. To solve this problem, our current paper presents a novel stealthy measurement-aided pole-dynamics attacks (MAPDAs) method with model mismatch. Firstly, the limitations of TPDAs using exact models are revealed, where exact models help ensure the stealthiness of TPDAs but model mismatch severely influences its stealthiness. Secondly, to handle model mismatch, the proposed MAPDAs method is designed by using a model reference adaptive control strategy, which can keep the stealthiness. Moreover, it is easier to implement as only the measurements are needed in comparison with the existing methods requiring both the measurements and control inputs. Thirdly, the performance of the proposed MAPDAs method is explored using convergence of multivariate measurements, and MAPDAs with model mismatch have the same stealthiness and similar destructiveness as TPDAs. Specifically, MAPDAs with adaptive gains will remain stealthy at an acceptable detection threshold till destructiveness occurs. Finally, experimental results from a networked inverted pendulum system confirm the feasibility and effectiveness of the proposed method.
Optimizing the Homogeneity and Efficiency of an SOEC Based on Multiphysics Simulation and Data-driven Surrogate Model
Authors: Yingtian Chi, Kentaro Yokoo, Hironori Nakajima, Kohei Ito, Jin Lin, Yonghua Song
Date: 2022-10-25 00:00:00

Inhomogeneous current and temperature distributions are harmful to the durability of the solid oxide electrolysis cell (SOEC). Segmented SOEC experiments reveal that a high steam utilization, which is favorable for system efficiency, leads to local steam starvation and enhanced the inhomogeneity. It is necessary to consider inhomogeneity and efficiency jointly in optimization studies. Three-dimensional (3D) multiphysics models validated with experiments can simulate the inhomogeneity in a reliable manner, but they are unsuitable for optimization due to the high computational cost. This study proposes a method that combines segmented SOEC experiments, multiphysics simulation, and artificial intelligence to optimize the inhomogeneity and efficiency of SOEC jointly. A 3D cell model is first built and verified by segmented SOEC experiments. Then, fast neural network surrogate models are built from the simulation data and integrated into a multi-objective optimization problem. Its solutions form a Pareto front reflecting the conflicting relationships among different objectives. It is found that the down-stream current is 60%-65% of the up-stream current when the steam utilization is 0.7. To increase the steam utilization to 0.8, the down-stream current will further drop to 50%-60% of the up-stream current. The Pareto fronts enable system operators to achieve a balance between efficiency and inhomogeneity.
Equivariant Filters are Equivariant
Authors: Hiya Gada, Pieter van Goor, Ravi Banavar, Robert Mahony
Date: 2022-10-25 00:00:00

Observers for systems with Lie group symmetries are an active area of research that is seeing significant impact in a number of practical domains, including aerospace, robotics, and mechatronics. This paper builds on the theory of the recently proposed Equivariant Filter (EqF), which is a general observer design for systems on homogeneous spaces that takes advantage of symmetries to yield significant performance advantages. It is shown that the EqF error dynamics are invariant to transformation of the input signal and equivariant as a parametrised vector field. The main theorem shows that two EqF's with different choices of local coordinates and origins and with equivalent noise modelling yield identical performance. In other words, the EqF is intrinsic to the system equations and symmetry. This is verified in a simulation of a 2D robot localisation problem, which also shows how the ability to choose an origin for the EqF can yield practical performance advantages by mitigating floating point precision errors.
Flowers of immortality
Authors: Thomas Fink, Yang-Hui He
Date: 2022-10-24 00:00:00

There has been a recent surge of interest in what causes aging. This has been matched by unprecedented research investment in the field from tech companies. But, despite considerable effort from a broad range of researchers, we do not have a rigorous mathematical theory of programmed aging. To address this, we recently derived a mortality equation that governs the transition matrix of an evolving population with a given maximum age. Here, we characterize the spectrum of eigenvalues of the solution to this equation. The eigenvalues fall into two classes. The complex and negative real eigenvalues, which we call the flower, are always contained in the unit circle in the complex plane. They play a negligible role in controlling the dynamics of an aging population. The positive real eigenvalues, which we call the stem, are the only eigenvalues which can exceed the unit circle. They control the most important properties of the dynamics. In particular, the spectral radius increases with the maximum allowed age. This suggests that programmed aging confers no advantage in a constant environment. However, the spectral gap, which governs the rate of convergence to equilibrium, decreases with the maximum allowed age. This opens the door to an evolutionary advantage in a changing environment.
Two 3-Branes in Randall-Sundrum Setup and Current Acceleration of the Universe
Authors: Anzhong Wang, Rong-Gen Cai, N. O. Santos
Date: 2006-07-17 00:00:00
Tasks: valid

Five-dimensional spacetimes of two orbifold 3-branes are studied, by assuming that {\em the two 3-branes are spatially homogeneous, isotropic, and independent of time}, following the so-called "bulk-based" approach. The most general form of the metric is obtained, and the corresponding field equations are divided into three groups, one is valid on each of the two 3-branes, and the third is valid in the bulk. The Einstein tensor on the 3-branes is expressed in terms of the discontinuities of the first-order derivatives of the metric coefficients. Thus, once the metric is known in the bulk, the distribution of the Einstein tensor on the two 3-branes is uniquely determined. As applications, we consider two different cases, one is in which the bulk is locally $AdS_{5}$, and the other is where it is vacuum. In some cases, it is shown that the universe is first decelerating and then accelerating. The global structure of the bulk as well as the 3-branes is also studied, and found that in some cases the solutions may represent the collision of two orbifold 3-branes. The applications of the formulas to the studies of the cyclic universe and the cosmological constant problem are also pointed out.
Robust real-time imaging through flexible multimode fibers
Authors: Abdullah Abdulaziz, Simon Peter Mekhail, Yoann Altmann, Miles J. Padgett, Stephen McLaughlin
Date: 2022-10-25 00:00:00

Conventional endoscopes comprise a bundle of optical fibers, associating one fiber for each pixel in the image. In principle, this can be reduced to a single multimode optical fiber (MMF), the width of a human hair, with one fiber spatial-mode per image pixel. However, images transmitted through a MMF emerge as unrecognizable speckle patterns due to dispersion and coupling between the spatial modes of the fiber. Furthermore, speckle patterns change as the fiber undergoes bending, making the use of MMFs in flexible imaging applications even more complicated. In this paper, we propose a real-time imaging system using flexible MMFs, but which is robust to bending. Our approach does not require access or feedback signal from the distal end of the fiber during imaging. We leverage a variational autoencoder (VAE) to reconstruct and classify images from the speckles and show that these images can still be recovered when the bend configuration of the fiber is changed to one that was not part of the training set. We utilize a MMF 300 mm long with a 62.5 um core for imaging 10 x 10 cm objects placed approximately at 20 cm from the fiber and the system can deal with a change in fiber bend of 50 degrees and range of movement of 8 cm.
Quantitative elemental imaging in eukaryotic algae
Authors: Stefan Schmollinger, Si Chen, Sabeeha S. Merchant
Date: 2022-10-26 00:00:00

All organisms, fundamentally, are made from the same raw material, namely the elements of the periodic table. Biochemical diversity is achieved with how these elements are utilized, for what purpose and in which physical location. Determining elemental distributions, especially those of trace elements that facilitate metabolism as cofactors in the active centers of essential enzymes, can determine the state of metabolism, the nutritional status or the developmental stage of an organism. Photosynthetic eukaryotes, especially algae, are excellent subjects for quantitative analysis of elemental distribution. These microbes utilize unique metabolic pathways that require various trace nutrients at their core to enable its operation. Photosynthetic microbes also have important environmental roles as primary producers in habitats with limited nutrient supply or toxin contaminations. Accordingly, photosynthetic eukaryotes are of great interest for biotechnological exploitation, carbon sequestration and bioremediation, with many of the applications involving various trace elements and consequently affecting their quota and intracellular distribution. A number of diverse applications were developed for elemental imaging allowing subcellular resolution, with X-ray fluorescence microscopy (XFM) being at the forefront, enabling quantitative descriptions of intact cells in a non-destructive method. This Tutorial Review summarizes the workflow of a quantitative, single-cell elemental distribution analysis of a eukaryotic alga using XFM.
Linear Response Theory of Evolved Metabolic Systems
Authors: Jumpei F. Yamagishi, Tetsuhiro S. Hatakeyama
Date: 2022-10-26 00:00:00

Predicting cellular metabolic states is a central problem in biophysics. Conventional approaches, however, sensitively depend on the microscopic details of individual metabolic systems. In this Letter, we derived a universal linear relationship between the metabolic responses against nutrient conditions and metabolic inhibition, with the aid of a microeconomic theory. The relationship holds in arbitrary metabolic systems as long as the law of mass conservation stands, as supported by extensive numerical calculations. It offers quantitative predictions without prior knowledge of systems.
Grounding Answers for Visual Questions Asked by Visually Impaired People
Authors: Chongyan Chen; Samreen Anjum; Danna Gurari
Date: 2022-06-20 00:00:00
Proceeding: CVPR 2022 6
Tasks: Question Answering, Visual Question Answering, Visual Question Answering (VQA)

Visual question answering is the task of answering questions about images. We introduce the VizWiz-VQAGrounding dataset, the first dataset that visually grounds answers to visual questions asked by people with visual impairments. We analyze our dataset and compare it with five VQA-Grounding datasets to demonstrate what makes it similar and different. We then evaluate the SOTA VQA and VQA-Grounding models and demonstrate that current SOTA algorithms often fail to identify the correct visual evidence where the answer is located. These models regularly struggle when the visual evidence occupies a small fraction of the image, for images that are higher quality, as well as for visual questions that require skills in text recognition. The dataset, evaluation server, and leaderboard all can be found at the following link: https: //vizwiz.org/tasks-and-datasets/answergrounding-for-vqa/.
NSCGCN: A novel deep GCN model to diagnosis COVID-19
Authors: Chaosheng Tang, Chaochao Hu, Junding Sun, Shui-Hua Wang, Yu-Dong Zhang
Date: 2022-10-13 00:00:00
Proceeding: Computers in Biology and Medicine 2022 10
Tasks: Computed Tomography (CT), COVID-19 Diagnosis, Transfer Learning

Aim
Corona Virus Disease 2019 (COVID-19) was a lung disease with high mortality and was highly contagious. Early diagnosis of COVID-19 and distinguishing it from pneumonia was beneficial for subsequent treatment.

Objectives
Recently, Graph Convolutional Network (GCN) has driven a significant contribution to disease diagnosis. However, limited by the nature of the graph convolution algorithm, deep GCN has an over-smoothing problem. Most of the current GCN models are shallow neural networks, which do not exceed five layers. Furthermore, the objective of this study is to develop a novel deep GCN model based on the DenseGCN and the pre-trained model of deep Convolutional Neural Network (CNN) to complete the diagnosis of chest X-ray (CXR) images.

Methods
We apply the pre-trained model of deep CNN to perform feature extraction on the data to complete the extraction of pixel-level features in the image. And then, to extract the potential relationship between the obtained features, we propose Neighbourhood Feature Reconstruction Algorithm to reconstruct them into graph-structured data. Finally, we design a deep GCN model that exploits the graph-structured data to diagnose COVID-19 effectively. In the deep GCN model, we propose a Node-Self Convolution Algorithm (NSC) based on feature fusion to construct a deep GCN model called NSCGCN (Node-Self Convolution Graph Convolutional Network).

Results
Experiments were carried out on the Computed Tomography (CT) and CXR datasets. The results on the CT dataset confirmed that: compared with the six state-of-the-art (SOTA) shallow GCN models, the accuracy and sensitivity of the proposed NSCGCN had improve 8% as sensitivity (Sen.) = 87.50%, F1 score = 97.37%, precision (Pre.) = 89.10%, accuracy (Acc.) = 97.50%, area under the ROC curve (AUC) = 97.09%. Moreover, the results on the CXR dataset confirmed that: compared with the fourteen SOTA GCN models, sixteen SOTA CNN transfer learning models and eight SOTA COVID-19 diagnosis methods on the COVID-19 dataset. Our proposed method had best performances as Sen. = 96.45%, F1 score = 96.45%, Pre. = 96.61%, Acc. = 96.45%, AUC = 99.22%.

Conclusion
Our proposed NSCGCN model is effective and performed better than the thirty-eight SOTA methods. Thus, the proposed NSC could help build deep GCN models. Our proposed COVID-19 diagnosis method based on the NSCGCN model could help radiologists detect pneumonia from CXR images and distinguish COVID-19 from Ordinary Pneumonia (OPN).