Predict and explain storage failures using RNNs with event extraction.
problem Predict and explain failures in storage environments from time series data.
method Extract anomalous spikes as events, then build an RNN classifier with attention mechanisms.
result Comparable accuracy to traditional RNNs with improved explainability.
Paper proposes a method to predict disk failures using multi-layer domain adaptive learning.
problem Traditional machine learning models struggle to predict disk failures due to limited data.
method Multi-layer domain adaptive learning with source and target domains.
result The proposed method improves failure prediction accuracy on disk data with few failure samples.
Proposes a deep neural network for early disk drive failure prediction.
problem Early prediction of disk drive failure using multivariate time series sensor data.
method Enriched features derived from sensor data through transformations, combined with ensemble learning and deep neural network architecture.
result Significantly improved classification accuracy in predicting disk drive failure.
Proposes a federated learning approach for RUL prediction from nonparametric degradation and failure signals.
problem Cost-effective RUL prediction from limited, non-shared CM signals with unknown parametric forms.
method Joint modeling of nonlinear degradation signals and failure events using federated learning.
result Superior RUL prediction compared to alternatives, validated through simulations and real data.
Novel asynchronous SGD method resists Byzantine attacks without server storage.
problem Asynchronous distributed learning with Byzantine attacks and failures.
method Buffered Asynchronous SGD (BASGD) and its momentum variant (BASGDm).
result BASGD and BASGDm resist non-omniscient and omniscient attacks without server storage.
Deep learning detects corrosion in nuclear fuel canisters.
problem Ensuring safety and integrity of used nuclear fuel dry storage canisters.
method Residual neural networks (ResNets) for real-time corrosion detection of canister images.
result Deep learning approach accurately detects corrosion and classifies canisters as corroded or intact.
DFMR improves robustness of learning finite mixture models in distributed settings.
problem Learning finite mixture models in distributed settings with Byzantine failures.
method DFMR leverages pairwise L2 distances to filter and retain local estimates, ensuring robust aggregation.
result DFMR achieves optimal convergence rate and asymptotic equivalence to global maximum likelihood estimate.
Game theory models storage investment to balance market competition and profits.
problem Strategic storage investment impacts electricity market prices and revenues.
method Formulated a non-cooperative game between investors to model strategic storage decisions.
result Increasing storage capacity reduces individual profits but increases total investment.
New method assesses energy storage value beyond cost reduction.
problem Improving energy storage value beyond cost reduction.
method Market potential method to evaluate and compare energy storage technologies.
result High-cost hydrogen storage can be more valuable than low-cost hydrogen storage.
Study on energy storage's impact on electricity prices and profitability.
problem Analyzing the profitability of energy storage in electricity markets.
method Characterized optimal operating strategy for storage systems, determined equilibrium price in a market with storage, renewables, and conventional producers, and characterized price process using stochastic differential equations.
result Increased average revenues and interquantile ranges for storage assets in energy transition scenarios.
Paper provides a method to price electricity storage contracts using COS technique.
problem Valuation of electricity storage contracts considering physical and operational constraints.
method Uses Fourier-based COS method to price contracts based on stochastic polynomial process.
result The COS method accurately and efficiently prices electricity storage contracts.
Generative models improve carbon storage site prediction using Bayesian inversion.
problem Predicting suitable geologic sites for long-term carbon dioxide storage.
method Generative adversarial networks and Bayesian inversion to condition models on physical measurements and historic data.
result Improved resolution of carbon dioxide storage capacity forecasts.
Paper proposes a risk-averse approach to energy storage price arbitrage using conformal uncertainty quantification.
problem Inherent volatility and uncertainty of real-time electricity prices create financial risks for storage arbitrage.
method Two-layer prediction model with conformal uncertainty quantification for high coverage of real-time price uncertainty.
result The framework achieves good profit margins with minimal losses, demonstrating effectiveness in real-time market.
It has been known for a long time that the classical spherical perceptrons can be used as storage memories. Seminal work of Gardner, \cite{Gar88}, started an analytical study of perceptrons storage abilities. Many of the Gardner's predictions obtained through statistical mechanics tools have been rigorously justified. …
Framework uses RL and simulation for optimal microgrid energy storage planning.
problem Optimal investment in diverse energy storage technologies for microgrids.
method Combines reinforcement learning with simulation-based optimization for long-term planning.
result Derives better engineering solutions for future microgrid applications.
Paper proposes a new model to assess risks in energy storage systems considering both exogenous and endogenous uncertainties.
problem Current risk assessment ignores the stochastic nature of energy storage availability.
method Data-driven unified model with exogenous and endogenous uncertainty description for four types of generic energy storage.
result Comparative results show more severe risks for endogenous uncertainty, suggesting new strategies for system operators.
Study estimates risks of nuclear waste storage projects.
problem Cost and schedule risks in nuclear waste storage projects.
method Reference class of 216 past projects for cost risk, 200 for schedule risk.
result Cost and schedule risks are substantial for nuclear waste storage projects.
AppStreamer reduces mobile game storage by predicting needed files.
problem Expanding storage needs of mobile games and apps.
method Predictive streaming of app files from cloud or edge servers.
result Reduces storage by 87% for Dead Effect 2 and 86% for Fire Emblem Heroes.
Deep learning optimizes gas storage operations.
problem Optimizing underground natural gas storage operations.
method Reinforcement learning techniques applied to high-dimensional forward markets with constraints.
result Performance of deep learning method superior to least-squares Monte-Carlo approach.
New framework compresses and recovers scientific data efficiently.
problem Efficiently managing and recovering from large scientific datasets.
method Grounded in learning exponential families, preserves uncertainty and supports trade-offs.
result Preserves physical features and quantities of interest in compressed representations.
RL agent learns to save costs by managing household energy storage.
problem Maximizing cost savings in smart grids with household energy storage.
method Data-driven RL agent learns from tariff structures and storage capacity.
result RL agent explains its learning process and strategies.
SER tool predicts storage capacity risk with probabilistic accuracy.
problem Managing data storage growth to avoid financial losses.
method Stochastic Estimated Risk (SER) using Brownian motion with drift model.
result Probabilistic approach is more accurate for non-linear storage patterns.
With the development of cloud computing and big data, the reliability of data storage systems becomes increasingly important. Previous researchers have shown that machine learning algorithms based on SMART attributes are effective methods to predict hard drive failures. In this paper, we use SMART attributes to predict…
Study on neural networks' storage capacity and solution space structure.
problem Understanding the storage capacity and solution space structure of neural networks.
method Replica method from statistical physics.
result Storage capacity per parameter remains finite even with infinite width and weights exhibit negative correlations.
New approach for feature evolution in streaming data with limited storage.
problem Rarely-provided labels in feature evolving streams.
method Incorporates manifold regularization and a buffer to adapt to different storage budgets.
result Preserves the performance of feature evolving learning across different storage budgets.
This study optimizes energy storage scheduling under price uncertainty, balancing risk and reward.
problem Optimizing energy storage operation under price uncertainty and risk.
method Two-stage stochastic risk-constrained approach using conditional value-at-risk.
result Increasing risk aversion leads to substantial benefits in terms of risk reduction and expected reward.
Introduces an unobservable intrinsic electricity price to link storage theory with risk premium.
problem Connecting storage theory with risk premium in electricity markets.
method Introduces an unobservable intrinsic electricity price and derives prices for various contracts.
result Finds an overall negative risk premium in empirical analysis.
New framework predicts future shipments with less error and saves labor.
problem Optimizing storage assignment in warehouses under uncertainty.
method Introduces a new framework that combines neural networks to predict future shipments and integrates it into a storage assignment system.
result Achieves up to 29% decrease in MAPE compared to CNN-LSTM on unseen future shipments.
Communication networks have evolved from specialized, research and tactical transmission systems to large-scale and highly complex interconnections of intelligent devices, increasingly becoming more commercial, consumer-oriented, and heterogeneous. Propelled by emergent social networking services and high-definition st…
Deep learning reconstructs pressure fields and classifies leakage rates in CCS storage sites.
problem Monitoring CO2 leakage in CCS storage sites.
method Variational auto-encoder tailored for pressure field reconstruction and leakage rate classification.
result Uncertainty estimates of predictions illustrated on synthetic data.
Paper introduces a new volatility model for natural gas markets and discusses swing option pricing.
problem Modeling price and storage dynamics in natural gas markets with path-dependent volatility.
method Developed a novel stochastic path-dependent volatility model and used deep learning for swing option pricing.
result Proposed a deep learning method for numerical approximations of swing option pricing.
We consider solution of stochastic storage problems through regression Monte Carlo (RMC) methods. Taking a statistical learning perspective, we develop the dynamic emulation algorithm (DEA) that unifies the different existing approaches in a single modular template. We then investigate the two central aspects of regres…
The paper optimizes daily storage trading of electricity using dynamic spread densities.
problem Optimizing daily storage trading of electricity based on price spreads.
method Formulated dynamic density functions based on skewed-t representations to model hourly electricity price spreads. Selected the best specification for each spread using the Pinball Loss function and calculated risk associated with spread arbitrages.
result Optimal daily operation of a battery storage facility determined from spread densities.
New insights into binary perceptron reveal phase transitions and algorithmic thresholds.
problem Understanding the statistical-computational gap in binary perceptron models.
method Application of fully lifted random duality theory (fl RDT) to uncover structural changes.
result Numerical estimates of constraint density thresholds align with theoretical predictions.
Economics tool predicts failure times in reliability systems.
problem Predicting optimal failure times in weighted k-out-of-n reliability systems with heterogeneous component failure.
method Using rational expectations to analyze and predict failure times in reliability systems with heterogeneous component failure.
result Different measures are optimal for predicting system failure depending on component failure distributions.
Distributed, controllable energy storage devices offer several benefits to electric power system operation. Three such benefits include reducing peak load, providing standby power, and enhancing power quality. These benefits, however, are only realized during peak load or during an outage, events that are infrequent. T…
Unified model predicts multi-mode failure with multi-sensor data.
problem Independent failure mode and RUL prediction ignores inherent relationship.
method Hierarchical Bayesian framework with Cox model, Gaussian process, and multinomial distributions.
result Robust uncertainty quantification and accurate prediction of multi-mode failure.
Extends DAMs to Gaussian distributions for efficient pattern storage and retrieval.
problem Limited storage capacity and retrieval methods for non-vector pattern representations.
method Introduces a log-sum-exp energy function over Gaussian distributions, using optimal transport maps for retrieval dynamics.
result Proves exponential storage capacity and provides quantitative retrieval guarantees.
Framework classifies machine learning failures into intentional and unintentional.
problem Understanding and preventing failures in machine learning systems.
method Developed a taxonomy of machine learning failure modes.
result Stakeholders found the framework useful for discussing machine learning failures.
Develops a deep RL algorithm for ESS control in electricity markets.
problem Controlling ESSs for arbitrage in real-time electricity markets under price uncertainty.
method Formulated as a Markov decision process, developed a deep RL algorithm using a recurrent neural network.
result Effectiveness of the algorithm verified using real-time PJM electricity prices.
In this paper we consider an energy storage optimization problem in finite time in a model with partial information that allows for a changing economic environment. The state process consists of the storage level controlled by the storage manager and the energy price process, which is a diffusion process the drift of w…
GAN-FP uses GANs to predict equipment failures from imbalanced data.
problem Accurately predicting equipment failures with limited data and high imbalance.
method GAN-FP employs two GAN networks to generate and classify imbalanced data, optimizing a weighted loss objective and a consistency GAN.
result GAN-FP outperforms traditional methods in imbalanced failure prediction.
We describe a project, called the "Discretization in Geometry and Dynamics Gallery", or DGD Gallery for short, whose goal is to store geometric data and to make it publicly available. The DGD Gallery offers an online web service for the storage, sharing, and publication of digital research data.
Holographic Invariant Storage uses vector architectures to ensure LLM safety at design time.
problem Mitigating context drift in large language models (LLMs) during deployment.
method Introduces Holographic Invariant Storage (HIS) protocol that combines known properties of bipolar Vector Symbolic Architectures into a design-time safety contract.
result Closed-form guarantees for single-signal recovery fidelity, continuous-noise robustness, and multi-signal capacity degradation are provided and validated.
PAGER detects failures in deep regression models using a new framework.
problem Detecting failures in deep regression models.
method PAGER uses a combination of epistemic uncertainty and manifold non-conformity scores.
result PAGER accurately characterizes and detects failures in deep regressors.
Study shows how activation functions impact the storage capacity of treelike neural networks.
problem Understanding the role of activation functions in neural network expressive power.
method Analysis of treelike two-layer networks with various activation functions in the infinite-width limit.
result Activation functions affect storage capacity and robustness, with nonlinearity increasing capacity and decreasing robustness.
New method for decentralized learning reduces data and computation needs.
problem High complexity and data/compute requirements for neural network training.
method Asynchronous updates over unreliable network using Distributed Averaging Consensus.
result Models can be learned on highly biased datasets with intermittent communication.
Predict and explain service failures in supply-chain networks using data models.
problem Predict and explain service failures in supply-chain networks, particularly last-mile pickup and delivery.
method Used supervised classification with Random Forests and Association Rules on a dataset of 500,000 services.
result Classifier reaches an average sensitivity of 0.7 and specificity of 0.7 for 5 types of failure.