Study develops ML emulators for generator models from terminal bus data.
problem Reconstruct generator models from terminal bus measurements.
method Used machine learning techniques, including VAR and LSTM models.
result Established trade-offs between linear AR and powerful LSTM models.
This paper evaluates various bus arrival time prediction models.
problem Improving prediction accuracy of bus arrival times.
method General evaluation framework for various models, including raw data pre-processing.
result Preliminary results show strengths and weaknesses of common models.
BusTr predicts bus travel times from real-time traffic forecasts.
problem Improving accuracy of bus travel time predictions.
method Neural sequence model trained on real-time traffic forecasts.
result BusTr outperforms DeepTTE by 30% in Mean Absolute Percentage Error (MAPE).
Two neural network models analyze bus system efficiency and demand.
problem Identify service gaps and quantify demand in public transportation.
method Two neural network models considering demographic data and metrics.
result Models can generalize to other cities' bus systems.
Research presents a dataset and algorithm for optimizing bus timetables in New Delhi.
problem Improving efficiency of public transport in New Delhi.
method Real-time GPS data, constrained clustering algorithm, statistical analysis.
result Algorithm reduces waiting time and provides an efficient timetable.
The increasing penetration of distributed energy resources poses numerous reliability issues to the urban distribution grid. The topology estimation is a critical step to ensure the robustness of distribution grid operation. However, the bus connectivity and grid topology estimation are usually hard in distribution gri…
There is an increasing need for monitoring and controlling uncertainties brought by distributed energy resources in distribution grids. For such goal, accurate multi-phase topology is the basis for correlating measurements in unbalanced distribution networks. Unfortunately, such topology knowledge is often unavailable …
Generative model predicts ETA for bus routes using local data.
problem Accurate ETA prediction for public transit, especially buses, in cities with limited data.
method Generative deep learning model trained on local bus route data.
result Model updates ETA in real-time based on current trip information.
Paper tackles adversarial attacks on ANN state estimation in smart grids.
problem Adversarial attacks degrade ANN state estimation accuracy without detection.
method Proposes population-based and gradient-based algorithms to generate attack vectors.
result DE algorithm is more effective than SLSQP in generating attack vectors.
DDSTN improves breast cancer diagnosis by leveraging imbalanced ultrasound modalities.
problem Imbalanced ultrasound modalities in diagnosing breast cancer.
method Integrates LUPI and MMD into a deep transfer learning framework.
result Outperforms state-of-the-art algorithms in BUS-based CAD.
The wave equation utt=c2uxx is generally regarded as a linear approximation to the equation describing the amplitude of a transversely vibrating elastic string in the plane. But, as is shown in \cite{BC96}, the assumption of transverse vibration in fact implies that the wave equation describes the vibration…
The increasing complexity of the power grid, due to higher penetration of distributed resources and the growing availability of interconnected, distributed metering devices re- quires novel tools for providing a unified and consistent view of the system. A computational framework for power systems data fusion, based on…
Two novel models predict bus travel times with uncertainty, improving connection assurance.
problem Improving bus connection assurance by handling travel time uncertainty.
method Two novel approaches: Deep Quantile Regression (DQR) and Bayesian Recurrent Neural Networks (BRNN).
result DQR model performs best for 80%, 90%, and 95% prediction intervals, with small underestimation.
Thompson Sampling with bilateral uncertainty improves performance in Bayesian Optimization.
problem Twin difficulties of modeling and searching complex functions in high dimensions.
method Exploiting conditional independence, Thompson Sampling respecting bilateral uncertainty (BU).
result Thompson Sampling with BU is more effective than the additive approximation in small budgets.
This paper presents a bus travel time prediction system using deep neural networks.
problem Accurate travel time predictions for urban buses to compete with other modes of transport.
method Multi-output, multi-time-step deep neural network combining convolutional and LSTM layers.
result The proposed model significantly outperforms other methods and detects small irregular peaks quickly.
Diverse fault types, fast re-closures, and complicated transient states after a fault event make real-time fault location in power grids challenging. Existing localization techniques in this area rely on simplistic assumptions, such as static loads, or require much higher sampling rates or total measurement availabilit…
Optimizes bus schedules to improve on-time performance.
problem Improving on-time performance of public transit systems.
method Formulated as a single-objective optimization task, solved using greedy algorithm, GA, and PSO.
result Enhanced bus timetables leading to better on-time performance.
Given a complete, smooth metric measure space (M,g,e−fdv) with the Bakry-Émery Ricci curvature bounded from below, various gradient estimates for solutions of the following general f-heat equations ut=Δfu+aulogu+bu+Aup+Bu−q and \[ u_t=Δ_f u+Ae^{pu}+Be^{-pu}+D \] are studied. As by-product, we obt…
A robust model handles up to 25% of outliers in time-series data for power flow calculations.
problem Handling outliers in time-series data for accurate power flow calculations.
method Robust data-driven process model with Schweppe-type generalized maximum likelihood estimator and projection statistics for outlier weighting.
result The model can handle up to 25% of outliers in the training data set.
Proposes BU-SPO method to improve text classification robustness.
problem Vulnerability of deep models in text classification.
method Bigram and unigram based adaptive Semantic Preservation Optimization (BU-SPO) method.
result Achieves highest attack success rates and semantic similarity by changing the smallest number of words.
New test for SGD in binary classification reduces computation time.
problem Determining optimal stopping for SGD in binary classification.
method Proposes a new, simple, computationally inexpensive termination criterion for SGD.
result Termination criterion reduces expected misclassification probability.
Enhanced Gaussian process regression for multi-fidelity data fusion.
problem Combining data of varying fidelity levels for accurate predictions.
method Gradient-enhanced Cokriging method (GE-Cokriging) for QoI and its gradients.
result GE-Cokriging outperforms conventional multi-fidelity Cokriging in predicting QoI and gradients.
New method preserves distances in time series data.
problem Preserving distances in time series data under interpolation.
method Developed lines-preserving terminal embeddings.
result First dimension-free coresets for Fréchet distance clustering.
We compute the rings H∗(N;F2) for N a closed Sol3-manifold and then determine the Borsuk-Ulam indices BU(N,φ) with φ=0 in H1(N;F2).
New method identifies drivers from car logs without reverse-engineering CAN protocol.
problem Identifying drivers from in-vehicle network logs without access to exact signal semantics.
method Machine learning techniques applied to off-the-shelf data.
result Driver re-identification accuracy of 75-85% on a dataset of 33 drivers.
New operations defined on moduli spaces for bundles with orientations.
problem Pushforward operations for principal bundles with orientations.
method Developed a general theory of pushforward operations for principal G-bundles, constructing specific operations for G=BU(1). result Classified all stable pushforward operations and showed they are generated by the projective Euler and rank operations.
The paper provides gradient estimates for a specific equation on Riemannian manifolds.
problem Gradient estimates for positive solutions to a specific equation on Riemannian manifolds.
method Obtained gradient bounds for positive solutions without depending on the solution's bounds or the Laplacian of the distance function.
result Gradient bound of a positive solution does not depend on the solution's bounds or the Laplacian of the distance function.
A new method uses Gaussian Processes to solve power flow problems with uncertain renewable and load inputs.
problem Solving power flow problems with uncertain renewable and load inputs.
method Non-parametric Bayesian inference-based uncertainty propagation using Gaussian Processes.
result The method provides reasonably accurate solutions with fewer samples and time compared to Monte-Carlo simulations.
New method for computing terminal embeddings in sublinear time.
problem Efficiently computing terminal embeddings with sublinear time complexity.
method Developed a data structure to compute terminal embeddings in sublinear time.
result Achieved sublinear time computation of terminal embeddings.
Optimal asset allocation strategy outperforms stochastic benchmark.
problem Achieving higher terminal wealth than a stochastic benchmark.
method Data-driven Neural Network optimization framework for dynamic asset allocation.
result Optimal adaptive strategy outperforms benchmark with higher median and right-skewed terminal wealth.
The article develops a model for predicting aircraft motion in terminal airspace.
problem Predicting aircraft motion for collision avoidance and safety analysis.
method Fits a probabilistic generative model to historical position data of aircraft landings and takeoffs.
result The model generates realistic trajectories, provides accurate predictions, and captures statistical properties of aircraft trajectories.
We propose a decentralized Maximum Likelihood solution for estimating the stochastic renewable power generation and demand in single bus Direct Current (DC) MicroGrids (MGs), with high penetration of droop controlled power electronic converters. The solution relies on the fact that the primary control parameters are se…
Proposes a new model to optimize investment plans with varying terminal times.
problem Improving the classical mean-variance model for continuous time investments.
method Uses stochastic optimal control and varying terminal time to determine optimal strategies.
result Optimal strategies and terminal times can be determined to minimize portfolio variance.
Proposes a new bound on generalization error using conditional mutual information.
problem Improving the generalization error bound in machine learning.
method Combines error decomposition and conditional mutual information techniques.
result New bound is order-wise better than previous ones in a simple Gaussian setting.
Deep active learning improves solvability prediction in power systems.
problem Inconclusive conservatism in traditional solvability region analysis methods.
method Proposes a deep active learning framework to reduce labeled dataset size.
result Significantly reduces the size of labeled dataset for training.
This study predicts parking availability using multi-source data and a self-supervised learning enhanced transformer.
problem Accurate parking availability prediction to support urban planning and management.
method Proposes SST-iTransformer, a self-supervised learning enhanced spatio-temporal inverted transformer, integrating multi-source data.
result SST-iTransformer achieves state-of-the-art performance in parking availability prediction.
For unbounded operators A,B and C in general, with C closure of [A,B] does not lead to the uncertainty relation ||Au|| ||Bu|| >= |<C u,u> |/2. If A,B and C are part of the generators of a unitary representation of a Lie group then the uncertainty principle above holds.
TVM improves generative modeling by matching terminal velocities.
problem Creating high-fidelity one- and few-step generative models.
method TVM generalizes flow matching, modeling transitions between diffusion timesteps and regularizing terminal behavior.
result TVM achieves state-of-the-art FID scores with minimal architectural changes and fused attention kernel.
A new method identifies EMTE causes using CNN and unsupervised feature learning.
problem Identifying causes of EMTEs in power grids.
method Spatiotemporal unsupervised feature learning with CNN and softmax.
result Validation through EMTP and RTDS simulations of various events.
A new BO termination criterion for HPO reduces optimization time without sacrificing test performance.
problem Determining an optimal budget for hyperparameter optimization.
method A new termination criterion based on the discrepancy between predictive and computable target performance.
result The proposed termination criterion achieves a better trade-off between test performance and optimization time.
Ridesourcing platforms like Uber and Didi are getting more and more popular around the world. However, unauthorized ridesourcing activities taking advantages of the sharing economy can greatly impair the healthy development of this emerging industry. As the first step to regulate on-demand ride services and eliminate b…
Termination proof for Cartan's method in constant type problems.
problem Proving termination of Cartan's equivalence method for constant type problems.
method Groupoid approach to Lie pseudo-groups and Cartan-Kuranishi theorem.
result Cartan's method terminates at involution or complete reduction for constant type problems.
We study the existence of a minimal supersolution for backward stochastic differential equations when the terminal data can take the value +∞ with positive probability. We deal with equations on a general filtered probability space and with generators satisfying a general monotonicity assumption. With this minim…
We use Bott periodicity to relate previously defined quantum classes to certain "exotic Chern classes" on BU. This provides an interesting computational and theoretical framework for some Gromov-Witten invariants connected with cohomological field theories. This framework has applications to study of higher dimension…
Researchers find Kähler-Einstein metrics near isolated log terminal singularities.
problem Existence of Kähler-Einstein metrics with positive curvature near isolated log terminal singularities.
method Solving complex Monge-Ampère equations to analyze the existence of metrics.
result Existence of smooth solutions in subcritical regimes, with critical exponent expressed in terms of normalized volume.
The paper analyzes and proposes a new stopping criterion for recursive Bayesian classification.
problem Limitations of conventional stopping criteria in recursive Bayesian classification.
method Geometric interpretation of state posterior progression and analysis of conventional criteria.
result Proposes a new stopping criterion to overcome limitations of conventional methods.
A new algorithm discovers useful reinforcement learning options by focusing on termination compressibility.
problem Discovering useful reinforcement learning options.
method Proposes an algorithm that focuses on the termination condition, using a critic to learn the option transition model and optimize compressibility.
result The resulting options are non-trivial, meaningful, and useful for learning and planning.
Deregulation of energy markets, penetration of renewables, advanced metering capabilities, and the urge for situational awareness, all call for system-wide power system state estimation (PSSE). Implementing a centralized estimator though is practically infeasible due to the complexity scale of an interconnection, the c…