arXiv subjects

Yi Guo

Publications and source records attributed to Yi Guo.

At least 127 records · Page 7Linked to original sources

arXiv2022

Multiple Ancillary Services Provision by Distributed Energy Resources in Active Distribution Networks

The electric power system is currently experiencing radical changes stemming from the increasing share of renewable energy resources and the consequent decommissioning of conventional power plants based on synchronous generators. Since the principal providers of ancillary services are being phased out, new flexibility and reserve providers are needed. The proliferation of Distributed Energy Resources (DERs) in modern distribution networks has opened new possibilities for distribution system operators, enabling them to fill the market gap by harnessing the DER flexibility. This paper introduces a novel centralized MPC-based controller that enables the concurrent provision of voltage support, primary and secondary frequency control by adjusting the setpoints of a heterogeneous group of DERs in active distribution grids. The input-multirate control framework is used to accommodate the distinct timescales and provision requirements of each ancillary service and to ensure that the available resources are properly allocated. Furthermore, an efficient way for incorporating network constraints in the formulation is proposed, where network decomposition is applied to a linear power flow formulation together with network reduction. In addition, different timescale dynamics of the employed DERs and their capability curves are included. The performance of the proposed controller is evaluated on several case studies via dynamic simulations of the IEEE 33-bus system.

eess.SY↗

arXiv2022

Tractable Data Enriched Distributionally Robust Chance-Constrained CVR

This paper proposes a tractable distributionally robust chance-constrained conservation voltage reduction (DRCC-CVR) method with enriched data-based ambiguity set in unbalanced three-phase distribution systems. The increasing penetration of distributed renewable energy not only brings clean power but also challenges the voltage regulation and energy-saving performance of CVR by introducing high uncertainties to distribution systems. In most cases, the conventional robust optimization methods for CVR only provide conservative solutions. To better consider the impacts of load and PV generation uncertainties on CVR implementation in distribution systems and provide less conservative solutions, this paper develops a data-based DRCC-CVR model with tractable reformulation and data enrichment method. Even though the uncertainties of load and photovoltaic (PV) can be captured by data, the availability of smart meters (SMs) and micro-phasor measurement units (PMUs) is restricted by cost budget. The limited data access may hinder the performance of the proposed DRCC-CVR. Thus, we further present a data enrichment method to statistically recover the high-resolution load and PV generation data from low-resolution data with Gaussian Process Regression (GPR) and Markov Chain (MC) models, which can be used to construct a data-based moment ambiguity set of uncertainty distributions for the proposed DRCC-CVR. Finally, the nonlinear power flow and voltage dependant load models and DRCC with moment-based ambiguity set are reformulated to be computationally tractable and tested on a real distribution feeder in Midwest U. S. to validate the effectiveness and robustness of the proposed method.

eess.SY↗

arXiv2022

Optimal Power Flow with State Estimation In the Loop for Distribution Networks

We propose a framework for integrating optimal power flow (OPF) with state estimation (SE) in the loop for distribution networks. Our approach combines a primal-dual gradient-based OPF solver with a SE feedback loop based on a limited set of sensors for system monitoring, instead of assuming exact knowledge of all states. The estimation algorithm reduces uncertainty on unmeasured grid states based on a few appropriate online state measurements and noisy "pseudo-measurements". We analyze the convergence of the proposed algorithm and quantify the statistical estimation errors based on a weighted least squares (WLS) estimator. The numerical results on a 4521-node network demonstrate that this approach can scale to extremely large networks and provide robustness to both large pseudo measurement variability and inherent sensor measurement noise.

math.OC↗

arXiv2022

Optimal Pump Control for Water Distribution Networks via Data-based Distributional Robustness

In this paper, we propose a data-based methodology to solve a multi-period stochastic optimal water flow (OWF) problem for water distribution networks (WDNs). The framework explicitly considers the pump schedule and water network head level with limited information of demand forecast errors for an extended period simulation. The objective is to determine the optimal feedback decisions of network-connected components, such as nominal pump schedules and tank head levels and reserve policies, which specify device reactions to forecast errors for accommodation of fluctuating water demand. Instead of assuming the uncertainties across the water network are generated by a prescribed certain distribution, we consider ambiguity sets of distributions centered at an empirical distribution, which is based directly on a finite training data set. We use a distance-based ambiguity set with the Wasserstein metric to quantify the distance between the real unknown data-generating distribution and the empirical distribution. This allows our multi-period OWF framework to trade off system performance and inherent sampling errors in the training dataset. Case studies on a three-tank water distribution network systematically illustrate the tradeoff between pump operational cost, risks of constraint violation, and out-of-sample performance.

math.OC↗

arXiv2022

Cosmological Implications of Axion-Matter Couplings

Axions and other light particles appear ubiquitously in physics beyond the Standard Model, with a variety of possible couplings to ordinary matter. Cosmology offers a unique probe of these particles as they can thermalize in the hot environment of the early universe for any such coupling. For sub-MeV particles, their entropy must leave a measurable cosmological signal, usually via the effective number of relativistic particles, $N_\mathrm{eff}$. In this paper, we will revisit the cosmological constraints on the couplings of axions and other pseudo-Nambu-Goldstone bosons to Standard Model fermions from thermalization below the electroweak scale, where these couplings are marginal and give contributions to the radiation density of $ΔN_\mathrm{eff} > 0.027$. We update the calculation of the production rates to eliminate unnecessary approximations and find that the cosmological bounds on these interactions are complementary to astrophysical constraints, e.g. from supernova SN 1987A. We additionally provide quantitative explanations for these bounds and their relationship.

astro-ph.CO↗

arXiv2021

Energy-Efficient Design for a NOMA assisted STAR-RIS Network with Deep Reinforcement Learning

Simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) has been considered as a promising auxiliary device to enhance the performance of the wireless network, where users located at the different sides of the surfaces can be simultaneously served by the transmitting and reflecting signals. In this paper, the energy efficiency (EE) maximization problem for a non-orthogonal multiple access (NOMA) assisted STAR-RIS downlink network is investigated. Due to the fractional form of the EE, it is challenging to solve the EE maximization problem by the traditional convex optimization solutions. In this work, a deep deterministic policy gradient (DDPG)-based algorithm is proposed to maximize the EE by jointly optimizing the transmission beamforming vectors at the base station and the coefficients matrices at the STAR-RIS. Simulation results demonstrate that the proposed algorithm can effectively maximize the system EE considering the time-varying channels.

cs.IT↗

arXiv2021

GDP: Stabilized Neural Network Pruning via Gates with Differentiable Polarization

Model compression techniques are recently gaining explosive attention for obtaining efficient AI models for various real-time applications. Channel pruning is one important compression strategy and is widely used in slimming various DNNs. Previous gate-based or importance-based pruning methods aim to remove channels whose importance is smallest. However, it remains unclear what criteria the channel importance should be measured on, leading to various channel selection heuristics. Some other sampling-based pruning methods deploy sampling strategies to train sub-nets, which often causes the training instability and the compressed model's degraded performance. In view of the research gaps, we present a new module named Gates with Differentiable Polarization (GDP), inspired by principled optimization ideas. GDP can be plugged before convolutional layers without bells and whistles, to control the on-and-off of each channel or whole layer block. During the training process, the polarization effect will drive a subset of gates to smoothly decrease to exact zero, while other gates gradually stay away from zero by a large margin. When training terminates, those zero-gated channels can be painlessly removed, while other non-zero gates can be absorbed into the succeeding convolution kernel, causing completely no interruption to training nor damage to the trained model. Experiments conducted over CIFAR-10 and ImageNet datasets show that the proposed GDP algorithm achieves the state-of-the-art performance on various benchmark DNNs at a broad range of pruning ratios. We also apply GDP to DeepLabV3Plus-ResNet50 on the challenging Pascal VOC segmentation task, whose test performance sees no drop (even slightly improved) with over 60% FLOPs saving.

cs.CV↗

arXiv2021

Clinical Relation Extraction Using Transformer-based Models

The newly emerged transformer technology has a tremendous impact on NLP research. In the general English domain, transformer-based models have achieved state-of-the-art performances on various NLP benchmarks. In the clinical domain, researchers also have investigated transformer models for clinical applications. The goal of this study is to systematically explore three widely used transformer-based models (i.e., BERT, RoBERTa, and XLNet) for clinical relation extraction and develop an open-source package with clinical pre-trained transformer-based models to facilitate information extraction in the clinical domain. We developed a series of clinical RE models based on three transformer architectures, namely BERT, RoBERTa, and XLNet. We evaluated these models using 2 publicly available datasets from 2018 MADE1.0 and 2018 n2c2 challenges. We compared two classification strategies (binary vs. multi-class classification) and investigated two approaches to generate candidate relations in different experimental settings. In this study, we compared three transformer-based (BERT, RoBERTa, and XLNet) models for relation extraction. We demonstrated that the RoBERTa-clinical RE model achieved the best performance on the 2018 MADE1.0 dataset with an F1-score of 0.8958. On the 2018 n2c2 dataset, the XLNet-clinical model achieved the best F1-score of 0.9610. Our results indicated that the binary classification strategy consistently outperformed the multi-class classification strategy for clinical relation extraction. Our methods and models are publicly available at https://github.com/uf-hobi-informatics-lab/ClinicalTransformerRelationExtraction. We believe this work will improve current practice on clinical relation extraction and other related NLP tasks in the biomedical domain.

cs.CL↗

arXiv2021

OpenCDA:An Open Cooperative Driving Automation Framework Integrated with Co-Simulation

Although Cooperative Driving Automation (CDA) has attracted considerable attention in recent years, there remain numerous open challenges in this field. The gap between existing simulation platforms that mainly concentrate on single-vehicle intelligence and CDA development is one of the critical barriers, as it inhibits researchers from validating and comparing different CDA algorithms conveniently. To this end, we propose OpenCDA, a generalized framework and tool for developing and testing CDA systems. Specifically, OpenCDA is composed of three major components: a co-simulation platform with simulators of different purposes and resolutions, a full-stack cooperative driving system, and a scenario manager. Through the interactions of these three components, our framework offers a straightforward way for researchers to test different CDA algorithms at both levels of traffic and individual autonomy. More importantly, OpenCDA is highly modularized and installed with benchmark algorithms and test cases. Users can conveniently replace any default module with customized algorithms and use other default modules of the CDA platform to perform evaluations of the effectiveness of new functionalities in enhancing the overall CDA performance. An example of platooning implementation is used to illustrate the framework's capability for CDA research. The codes of OpenCDA are available in the https://github.com/ucla-mobility/OpenCDA.

cs.RO↗

arXiv2021

A Study of Social and Behavioral Determinants of Health in Lung Cancer Patients Using Transformers-based Natural Language Processing Models

Social and behavioral determinants of health (SBDoH) have important roles in shaping people's health. In clinical research studies, especially comparative effectiveness studies, failure to adjust for SBDoH factors will potentially cause confounding issues and misclassification errors in either statistical analyses and machine learning-based models. However, there are limited studies to examine SBDoH factors in clinical outcomes due to the lack of structured SBDoH information in current electronic health record (EHR) systems, while much of the SBDoH information is documented in clinical narratives. Natural language processing (NLP) is thus the key technology to extract such information from unstructured clinical text. However, there is not a mature clinical NLP system focusing on SBDoH. In this study, we examined two state-of-the-art transformer-based NLP models, including BERT and RoBERTa, to extract SBDoH concepts from clinical narratives, applied the best performing model to extract SBDoH concepts on a lung cancer screening patient cohort, and examined the difference of SBDoH information between NLP extracted results and structured EHRs (SBDoH information captured in standard vocabularies such as the International Classification of Diseases codes). The experimental results show that the BERT-based NLP model achieved the best strict/lenient F1-score of 0.8791 and 0.8999, respectively. The comparison between NLP extracted SBDoH information and structured EHRs in the lung cancer patient cohort of 864 patients with 161,933 various types of clinical notes showed that much more detailed information about smoking, education, and employment were only captured in clinical narratives and that it is necessary to use both clinical narratives and structured EHRs to construct a more complete picture of patients' SBDoH factors.

cs.CL↗

arXiv2021

A Self-Supervised Gait Encoding Approach with Locality-Awareness for 3D Skeleton Based Person Re-Identification

Person re-identification (Re-ID) via gait features within 3D skeleton sequences is a newly-emerging topic with several advantages. Existing solutions either rely on hand-crafted descriptors or supervised gait representation learning. This paper proposes a self-supervised gait encoding approach that can leverage unlabeled skeleton data to learn gait representations for person Re-ID. Specifically, we first create self-supervision by learning to reconstruct unlabeled skeleton sequences reversely, which involves richer high-level semantics to obtain better gait representations. Other pretext tasks are also explored to further improve self-supervised learning. Second, inspired by the fact that motion's continuity endows adjacent skeletons in one skeleton sequence and temporally consecutive skeleton sequences with higher correlations (referred as locality in 3D skeleton data), we propose a locality-aware attention mechanism and a locality-aware contrastive learning scheme, which aim to preserve locality-awareness on intra-sequence level and inter-sequence level respectively during self-supervised learning. Last, with context vectors learned by our locality-aware attention mechanism and contrastive learning scheme, a novel feature named Constrastive Attention-based Gait Encodings (CAGEs) is designed to represent gait effectively. Empirical evaluations show that our approach significantly outperforms skeleton-based counterparts by 15-40% Rank-1 accuracy, and it even achieves superior performance to numerous multi-modal methods with extra RGB or depth information. Our codes are available at https://github.com/Kali-Hac/Locality-Awareness-SGE.

cs.CV↗

arXiv2021

XCloud-pFISTA: A Medical Intelligence Cloud for Accelerated MRI

Machine learning and artificial intelligence have shown remarkable performance in accelerated magnetic resonance imaging (MRI). Cloud computing technologies have great advantages in building an easily accessible platform to deploy advanced algorithms. In this work, we develop an open-access, easy-to-use and high-performance medical intelligence cloud computing platform (XCloud-pFISTA) to reconstruct MRI images from undersampled k-space data. Two state-of-the-art approaches of the Projected Fast Iterative Soft-Thresholding Algorithm (pFISTA) family have been successfully implemented on the cloud. This work can be considered as a good example of cloud-based medical image reconstruction and may benefit the future development of integrated reconstruction and online diagnosis system.

eess.IV↗

arXiv2021

A Discussion On the Validity of Manifold Learning

Dimensionality reduction (DR) and manifold learning (ManL) have been applied extensively in many machine learning tasks, including signal processing, speech recognition, and neuroinformatics. However, the understanding of whether DR and ManL models can generate valid learning results remains unclear. In this work, we investigate the validity of learning results of some widely used DR and ManL methods through the chart mapping function of a manifold. We identify a fundamental problem of these methods: the mapping functions induced by these methods violate the basic settings of manifolds, and hence they are not learning manifold in the mathematical sense. To address this problem, we provide a provably correct algorithm called fixed points Laplacian mapping (FPLM), that has the geometric guarantee to find a valid manifold representation (up to a homeomorphism). Combining one additional condition(orientation preserving), we discuss a sufficient condition for an algorithm to be bijective for any d-simplex decomposition result on a d-manifold. However, constructing such a mapping function and its computational method satisfying these conditions is still an open problem in mathematics.

cs.LG↗

arXiv2021

Development of local plasticity around voids during tensile deformation

Voids can limit the life of engineering components. This motivates us to understand local plasticity around voids in a nickel base superalloy combining experiments and simulations. Single crystal samples were deformed in tension with in-situ high angular resolution electron back scatter diffraction to probe the heterogeneous local stress field under load; the reference stress is informed by crystal plasticity finite element simulations. This information is used to understand the activation of plastic deformation around the void. Our investigation indicates that while the resolved shear stress would indicate slip activity on multiple slip systems, slip is reduced to specific systems due to image forces and forest hardening. This study rationalizes the observed development of plastic deformation around the void, aiding in our understanding of component failure and engineering design.

cond-mat.mtrl-sci↗

arXiv2021

When Text Simplification Is Not Enough: Could a Graph-Based Visualization Facilitate Consumers' Comprehension of Dietary Supplement Information?

Dietary supplements are widely used but not always safe. With the rapid development of the Internet, consumers usually seek health information including dietary supplement information online. To help consumers access quality online dietary supplement information, we have identified trustworthy dietary supplement information sources and built an evidence-based knowledge base of dietary supplement information-the integrated DIetary Supplement Knowledge base (iDISK) that integrates and standardizes dietary supplement related information across these different sources. However, as information in iDISK was collected from scientific sources, the complex medical jargon is a barrier for consumers' comprehension. To assess how different approaches to simplify and represent dietary supplement information from iDISK will affect lay consumers' comprehension, using a crowdsourcing platform, we recruited participants to read dietary supplement information in four different representations from iDISK: original text, syntactic and lexical text simplification, manual text simplification, and a graph-based visualization. We then assessed how the different simplification and representation strategies affected consumers' comprehension of dietary supplement information in terms of accuracy and response time to a set of comprehension questions. With responses from 690 qualified participants, our experiments confirmed that the manual approach had the best performance for both accuracy and response time to the comprehension questions, while the graph-based approach ranked the second outperforming other representations. In some cases, the graph-based representation outperformed the manual approach in terms of response time. A hybrid approach that combines text and graph-based representations might be needed to accommodate consumers' different information needs and information seeking behavior.

cs.CY↗

arXiv2021

Casting voids in nickel superalloy and the mechanical behaviour under room temperature tensile deformation

The microstructure of a second-generation nickel base superalloy is studied using X-ray computed tomography (XCT) and scanning electron microscopy (SEM). The as-cast material contains 0.15 (+-0.001) vol% voids and these are distributed in the inter-dendritic region. The volume fraction of the voids increases to 0.21 (+-0.001) vol% after tensile deformation. Surface observations show evidence of dislocation emissions from the void surface, a mechanism possibly facilitates the expansion of the voids and contributes to the increased void volume fraction. Phenomenological parameters such as stress triaxiality, often believed to control void growth, are investigated through crystal plasticity simulation and compared with literature reported data. The results indicate weak correlation between stress triaxiality and void growth, but this may be possibly due to the lack of data at higher level of plastic deformation, which is limited by the ductility of the material. The distribution of the stress triaxiality field within the sample is heterogeneous and the peak of the triaxiality field is a function of the ratio between notch diameter and sample width. A smaller notch diameter to sample width ratio tend to distribute the triaxiality peaks towards the centre of the sample but also lead to higher strain localisation, an effect that results in early sample failure.

cond-mat.mtrl-sci↗

arXiv2020

Revisiting the Water Quality Sensor Placement Problem: Optimizing Network Observability and State Estimation Metrics

Real-time water quality (WQ) sensors in water distribution networks (WDN) have the potential to enable network-wide observability of water quality indicators, contamination event detection, and closed-loop feedback control of WQ dynamics. To that end, prior research has investigated a wide range of methods that guide the geographic placement of WQ sensors. These methods assign a metric for fixed sensor placement (SP) followed by \textit{metric-optimization} to obtain optimal SP. These metrics include minimizing intrusion detection time, minimizing the expected population and amount of contaminated water affected by an intrusion event. In contrast to the literature, the objective of this paper is to provide a computational method that considers the overlooked metric of state estimation and network-wide observability of the WQ dynamics. This metric finds the optimal WQ sensor placement that minimizes the state estimation error via the Kalman filter for noisy WQ dynamics -- a metric that quantifies WDN observability. To that end, the state-space dynamics of WQ states for an entire WDN are given and the observability-driven sensor placement algorithm is presented. The algorithm takes into account the time-varying nature of WQ dynamics due to changes in the hydraulic profile -- a collection of hydraulic states including heads (pressures) at nodes and flow rates in links which are caused by a demand profile over a certain period of time. Thorough case studies are given, highlighting key findings, observations, and recommendations for WDN operators. Github codes are included for reproducibility.

eess.SY↗

arXiv2020

Algebraic 3D Graphic Statics: reciprocal constructions

The recently developed 3D graphic statics (3DGS) lacks a rigorous mathematical definition relating the geometrical and topological properties of the reciprocal polyhedral diagrams as well as a precise method for the geometric construction of these diagrams. This paper provides a fundamental algebraic formulation for 3DGS by developing equilibrium equations around the edges of the primal diagram and satisfying the equations by the closeness of the polygons constructed by the edges of the corresponding faces in the dual/reciprocal diagram. The research provides multiple numerical methods for solving the equilibrium equations and explains the advantage of using each technique. The approach of this paper can be used for compression-and-tension combined form-finding and analysis as it allows constructing both the form and force diagram based on the interpretation of the input diagram. Besides, the paper expands on the geometric/static degrees of (in)determinacies of the diagrams using the algebraic formulation and shows how these properties can be used for the constrained manipulation of the polyhedrons in an interactive environment without breaking the reciprocity between the two.

cs.CG↗