In the last decade, researchers have been investigating the severity of insulation breakdown caused by partial discharge (PD) in overhead transmission lines with covered conductors or electrical equipment such as generators and motors used in various industries. Developing an effective partial discharge detection system can lead to significant savings on maintenance and prevent power disruptions. Traditional methods rely on hand-crafted features and domain expertise to identify partial discharge patterns in the electrical current. Many data-driven deep learning-based methods have been proposed in recent years to remove these ad hoc feature extraction. However, most of these methods either operate in the time-domain or frequency-domain. Many research approaches have been developed to generate phase-resolved partial discharge (PRPD) patterns from raw PD sensor data. These PRPD diagrams suggest a correlation between partial discharge activities occurring in an alternating electrical waveform's positive and negative half-cycles. However, this correlation criterion between half-cycles has been remained unexplored in deep learning-based methods. This work proposes a novel feature-fusion-based Dual-CyCon Net that can utilize all time, frequency, and phase domain features for joint learning in one cohesive framework. Our proposed cycle-consistency loss exploits any relation between an alternating electrical signal's positive and negative half-cycles to calibrate the model's sensitivity. This loss explores cycle-invariant PD-specific features, enabling the model to learn more robust, noise-invariant features for PD detection. A case study of our proposed framework on a public real-world noisy measurement from high-frequency voltage sensors to detect damaged power lines has achieved a state-of-the-art MCC score of 0.8455.
Partial discharge (PD) is a common indication of faults in power systems, such as generators, and cables. These PD can eventually result in costly repairs and substantial power outages. PD detection traditionally relies on hand-crafted features and domain expertise to identify very specific pulses in the electrical current, and the performance declines in the presence of noise or of superposed pulses. In this paper, we propose a novel end-to-end framework based on convolutional neural networks. The framework has two contributions. First, it does not require any feature extraction and enables r…
IBM Research（IBM研究院）；IBM Watson Health；Vanderbilt University（范德堡大学）；Vanderbilt University Medical Center（范德堡大学医学中心）
Deep learning architectures have an extremely high-capacity for modeling complex data in a wide variety of domains. However, these architectures have been limited in their ability to support complex prediction problems using insurance claims data, such as readmission at 30 days, mainly due to data sparsity issue. Consequently, classical machine learning methods, especially those that embed domain knowledge in handcrafted features, are often on par with, and sometimes outperform, deep learning approaches. In this paper, we illustrate how the potential of deep learning can be achieved by blendin…
Physics-separating artificial neural networks for predicting sputtering and thin film deposition of AlN in Ar/N$_2$ discharges on experimental timescales
Ruhr University Bochum（波鸿鲁尔大学）；Kiel University（基尔大学）
Mederrata Research and Sound Prediction（Mederrata研究与声音预测公司）；NIH NIDDK（美国国立卫生研究院国家糖尿病、消化与肾脏疾病研究所）；Mederrata Research（Mederrata研究公司）；Blue Health Intelligence（蓝色健康智能公司）；Walter Reed Army Institute of Research（沃尔特·里德陆军研究所）；Sound Prediction and Mederrata Research（声音预测与Mederrata研究公司）
We developed an inherently interpretable multilevel Bayesian framework for representing variation in regression coefficients that mimics the piecewise linearity of ReLU-activated deep neural networks. We used the framework to formulate a survival model for using medical claims to predict hospital readmission and death that focuses on discharge placement, adjusting for confounding in estimating causal local average treatment effects. We trained the model on a 5% sample of Medicare beneficiaries from 2008 and 2011, based on their 2009--2011 inpatient episodes, and then tested the model on 2012 e…
Pennsylvania State University（宾夕法尼亚州立大学）；Stanford University（斯坦福大学）
Recent observations with varied schedules and types (moving average, snapshot, or regularly spaced) can help to improve streamflow forecasts, but it is challenging to integrate them effectively. Based on a long short-term memory (LSTM) streamflow model, we tested multiple versions of a flexible procedure we call data integration (DI) to leverage recent discharge measurements to improve forecasts. DI accepts lagged inputs either directly or through a convolutional neural network (CNN) unit. DI ubiquitously elevated streamflow forecast performance to unseen levels, reaching a record continental-…
Wenzhou University（温州大学）；Wenzhou Polytechnic（温州职业技术学院）；National Yunlin University of Science and Technology（云林科技大学）；Hong Kong Polytechnic University（香港理工大学）；Obuda University（奥布达大学）；Slovak University of Technology in Bratislava（斯洛伐克布拉迪斯拉发工业大学）
Streamlined weirs which are a nature-inspired type of weir have gained tremendous attention among hydraulic engineers, mainly owing to their established performance with high discharge coefficients. Computational fluid dynamics (CFD) is considered as a robust tool to predict the discharge coefficient. To bypass the computational cost of CFD-based assessment, the present study proposes data-driven modeling techniques, as an alternative to CFD simulation, to predict the discharge coefficient based on an experimental dataset. To this end, after splitting the dataset using a k fold cross validatio…
Oden Institute（奥登研究所）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）
Accurate and efficient models for rainfall runoff (RR) simulations are crucial for flood risk management. Most rainfall models in use today are process-driven; i.e. they solve either simplified empirical formulas or some variation of the St. Venant (shallow water) equations. With the development of machine-learning techniques, we may now be able to emulate rainfall models using, for example, neural networks. In this study, a data-driven RR model using a sequence-to-sequence Long-short-Term-Memory (LSTM) network was constructed. The model was tested for a watershed in Houston, TX, known for sev…
Huazhong University of Science and Technology（华中科技大学）；Institute of Plasma Physics, HFIPS, Chinese Academy of Sciences（中国科学院合肥物质科学研究院等离子体物理研究所）；Shenzhen University（深圳大学）
Predicting disruptions across different tokamaks is a great obstacle to overcome. Future tokamaks can hardly tolerate disruptions at high performance discharge. Few disruption discharges at high performance can hardly compose an abundant training set, which makes it difficult for current data-driven methods to obtain an acceptable result. A machine learning method capable of transferring a disruption prediction model trained on one tokamak to another is required to solve the problem. The key is a disruption prediction model containing a feature extractor that is able to extract common disrupti…
University of California, Davis（加州大学戴维斯分校）；University of Exeter（埃克塞特大学）
A Health Service Area (HSA) is a group of geographic regions served by similar health care facilities. The delineation of HSAs plays a pivotal role in the characterization of health care services available in an area, enabling a better planning and regulation of health care services. Though Dartmouth HSAs have been the standard delineation for decades, previous work has recently shown an improved HSA delineation using a network-based approach, in which HSAs are the communities extracted by the Louvain algorithm in hospital-patient discharge networks. Given the existent heterogeneity of communi…
SeqBattNet: A Discrete-State Physics-Informed Neural Network with Aging Adaptation for Battery Modeling
AIWARE Limited Company（AIWARE有限公司）；Faculty of Information Technology, Ton Duc Thang University（Ton Duc Thang大学信息科技学院）；Software Engineering Department, FPT University（FPT大学软件工程系）；Laboratory for Applied and Industrial Mathematics, Institute for Computational Science and Artificial Intelligence, Van Lang University（Van Lang大学应用与工业数学实验室）；Faculty of Mechanical-Electrical and Computer Engineering, School of Technology, Van Lang University（Van Lang大学机械-电子与计算机工程学院）；Laboratory for Computational Mechanics, Institute for Computational Science and Artificial Intelligence, Van Lang University（Van Lang大学计算力学实验室）；Faculty of Civil Engineering, School of Technology, Van Lang University（Van Lang大学土木工程学院）
Pacific Northwest National Laboratory（太平洋西北国家实验室）；University of Minnesota（明尼苏达大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Numerical modeling and simulation have become indispensable tools for advancing a comprehensive understanding of the underlying mechanisms and cost-effective process optimization and control of flow batteries. In this study, we propose an enhanced version of the physics-constrained deep neural network (PCDNN) approach [1] to provide high-accuracy voltage predictions in the vanadium redox flow batteries (VRFBs). The purpose of the PCDNN approach is to enforce the physics-based zero-dimensional (0D) VRFB model in a neural network to assure model generalization for various battery operation condi…
CERFACS（欧洲科学计算研究与高级培训中心）；ISAE-SUPAERO（法国国立高等航空航天学院）；Université de Toulouse（图卢兹大学）；SAFRAN AIRCRAFT ENGINES（赛峰飞机发动机公司）
The Poisson equation is critical to get a self-consistent solution in plasma fluid simulations used for Hall effect thrusters and streamer discharges, since the Poisson solution appears as a source term of the unsteady nonlinear flow equations. As a first step, solving the 2D Poisson equation with zero Dirichlet boundary conditions using a deep neural network is investigated using multiple-scale architectures, defined in terms of number of branches, depth and receptive field. One key objective is to better understand how neural networks learn the Poisson solutions and provide guidelines to ach…
Centers for Disease Control and Prevention（美国疾病控制与预防中心）；Sandia National Laboratories（桑迪亚国家实验室）
Syndromic surveillance detects and monitors individual and population health indicators through sources such as emergency department records. Automated classification of these records can improve outbreak detection speed and diagnosis accuracy. Current syndromic systems rely on hand-coded keyword-based methods to parse written fields and may benefit from the use of modern supervised-learning classifier models. In this paper we implement two recurrent neural network models based on long short-term memory (LSTM) and gated recurrent unit (GRU) cells and compare them to two traditional bag-of-word…
Tokyo University of Agriculture and Technology（东京农工大学）；Juntendo University Nerima Hospital（顺天堂大学练马医院）
Electroencephalography (EEG) is essential for the diagnosis of epilepsy, but it requires expertise and experience to identify abnormalities. It is thus crucial to develop automated models for the detection of abnormalities in EEGs related to epilepsy. This paper describes the development of a novel class of compact convolutional neural networks (CNNs) for detecting abnormal patterns and electrodes in EEGs for epilepsy. The designed model is inspired by a CNN developed for brain-computer interfacing called multichannel EEGNet (mEEGNet). Unlike the EEGNet, the proposed model, mEEGNet, has the sa…
CIMS, New York University（纽约大学库朗数学科学研究所）；CSAIL & IMES, Massachusetts Institute of Technology（麻省理工学院计算机科学与人工智能实验室及医学工程与科学研究所）
In this work, we present the Grounded Recurrent Neural Network (GRNN), a recurrent neural network architecture for multi-label prediction which explicitly ties labels to specific dimensions of the recurrent hidden state (we call this process "grounding"). The approach is particularly well-suited for extracting large numbers of concepts from text. We apply the new model to address an important problem in healthcare of understanding what medical concepts are discussed in clinical text. Using a publicly available dataset derived from Intensive Care Units, we learn to label a patient's diagnoses a…
Feature engineering remains a major bottleneck when creating predictive systems from electronic medical records. At present, an important missing element is detecting predictive regular clinical motifs from irregular episodic records. We present Deepr (short for Deep record), a new end-to-end deep learning system that learns to extract features from medical records and predicts future risk automatically. Deepr transforms a record into a sequence of discrete elements separated by coded time gaps and hospital transfers. On top of the sequence is a convolutional neural net that detects and combin…
Hall effect thrusters are one of the most versatile and popular electric propulsion systems for space use. Industry trends towards interplanetary missions arise advances in design development of such propulsion systems. It is understood that correct sizing of discharge channel in Hall effect thruster impact performance greatly. Since the complete physics model of such propulsion system is not yet optimized for fast computations and design iterations, most thrusters are being designed using so-called scaling laws. But this work focuses on rather novel approach, which is outlined less frequently…
KAIST（韩国科学技术院）；National Fusion Research Institute（国家核聚变研究所）；Mobiis Co., Ltd.（Mobiis有限公司）
A neural network solving Grad-Shafranov equation constrained with measured magnetic signals to reconstruct magnetic equilibria in real time is developed. Database created to optimize the neural network's free parameters contain off-line EFIT results as the output of the network from $1,118$ KSTAR experimental discharges of two different campaigns. Input data to the network constitute magnetic signals measured by a Rogowski coil (plasma current), magnetic pick-up coils (normal and tangential components of magnetic fields) and flux loops (poloidal magnetic fluxes). The developed neural networks …
According to the United Nations World Water Assessment Programme, every day, 2 million tons of sewage and industrial and agricultural waste are discharged into the worlds water. In order to address this pervasive issue of increasing water pollution, while ensuring that the global population has an efficient, accurate, and low cost method to assess whether the water they drink is contaminated, we propose AquaSight, a novel mobile application that utilizes deep learning methods, specifically Convolutional Neural Networks, for automated water impurity detection. After comprehensive training with …
This study proposes two straightforward yet effective approaches to reduce the required computational time of the training process for time-series modeling through a recurrent neural network (RNN) using multi-time-scale time-series data as input. One approach provides coarse and fine temporal resolutions of the input time-series to RNN in parallel. The other concatenates the coarse and fine temporal resolutions of the input time-series data over time before considering them as the input to RNN. In both approaches, first, finer temporal resolution data are utilized to learn the fine temporal sc…
North Carolina A&T State University（北卡罗来纳农工州立大学）；Arizona State University（亚利桑那州立大学）
Given the high power density low discharge rate and decreasing cost rechargeable lithium-ion batteries LiBs have found a wide range of applications such as power grid level storage systems electric vehicles and mobile devices. Developing a framework to accurately model the nonlinear degradation process of LiBs which is indeed a supervised learning problem becomes an important research topic. This paper presents a classical-quantum hybrid machine learning approach to capture the LiB degradation model that assesses battery cell life loss from operating profiles. Our work is motivated by recent a…
