A question answering (QA) system is a research direction in the field of artificial intelligence and natural language processing (NLP) that has attracted much attention and has broad development prospects. As one of the main components in the QA syst...
To address the insufficient accuracy of short-term load forecasting caused by the strong randomness of distributed energy output, variable electricity consumption patterns, and complex meteorological factors, this study proposes a load forecasting me...
Human activity recognition (HAR) based on wearable sensors is a key technology in the fields of smart sensing and health monitoring. With the rapid development of deep learning, its powerful feature extraction capabilities have significantly enhanced...
Background: Modern aquaculture is increasingly adopting industrialized recirculating aquaculture systems, in which the stable operation of its circulating water pump is essential. Yet, given the complex working conditions, this pump is prone to malfu...
Accurate deformation prediction is crucial for ensuring the safety and longevity of bridges. However, the complex fluctuations of deformation pose a challenge to achieving this goal. To improve the prediction accuracy, a bridge deformation prediction...
In aerospace manufacturing, the reliability of machining equipment, particularly spindle bearings, is critical to maintaining productivity, as bearing health significantly constrains operational efficiency. Accurate prediction of the remaining useful...
In high-concurrency distributed systems, log data often exhibits sequence uncertainty and redundancy, which pose significant challenges to the accuracy and efficiency of anomaly detection. To address these issues, we propose BiGRMT, a hybrid architec...
This paper addresses emotion recognition in low-resource language settings for healthcare and human-computer interaction (HCI). Most existing multimodal systems rely on resource-intensive transformers or high-resource languages, limiting their applic...
This study compares deep learning models for point-level anomaly detection in multichannel satellite telemetry data. Raw event-based telemetry was converted into segment-based multivariate time series without windowing or feature extraction, allowing...
This study addresses a lexical ambiguity issue in Sesotho sa Leboa that arises from terms with various meanings, often known as homonyms or polysemous words. When compared to, for instance, European languages, this lexical ambiguity in Sesotho sa Leb...
Non-Intrusive Load Monitoring (NILM) aims to disaggregate a household’s total aggregated power consumption into appliance-level usage, enabling intelligent energy management without the need for intrusive metering. While deep learning has impro...
With the deepening of the Industrial Revolution and the rapid development of the chemical industry, the large-scale emissions of corrosive dust and gases from numerous factories have become a significant source of air pollution. Mercury in the atmosp...
The integration of distributed generation (DG) changes the fault current path, magnitude, direction, and transient characteristics of distribution networks, which increases the difficulty of fault type identification. In particular, weak fault featur...
Reliable bearing fault diagnosis is essential for the steady running of mechanical systems. However, existing diagnostic models still face significant limitations in feature extraction, primarily due to the non-stationary and nonlinear characteristic...
Aiming at improving the prediction accuracy of the gas dissolved in transformer oil which occurs with strong nonlinearity, this paper presents a method named CEEMDAN-PWOA-VMD-BIGRU for gas content prediction. First, Complete Ensemble Empirical Mode D...
Grammatical error correction (GEC) for Chinese learner English is still dominated by sentence-level modeling, which limits discourse-level consistency and weakens adaptation to learner-specific error profiles. From an instructional perspective, these...
(1) Background: Ultra-short-term photovoltaic (PV) power prediction is crucial for optimizing grid scheduling and enhancing energy utilization efficiency. Existing prediction methods face challenges of missing data, noise interference, and insufficie...
Due to the limitations of seismic exploration instruments and the impact of the high frequencies absorption by the earth layers during subsurface propagation of seismic waves, recorded seismic data usually lack high and low frequency information that...
In recent years, deep learning (DL) methods, such as recurrent neural networks (RNN). have been used for streamflow prediction. In this study, the monthly inflow into the Dez Dam reservoir from 1955 to 2018 in southwestern Iran was simulated using va...
Lithium-ion batteries are widely used in various energy sectors, and accurately predicting their early remaining useful life (RUL) is crucial for shortening battery evaluation time and accelerating battery commercialization. However, information on d...
The growth of greenhouse gas emissions, driven by the use of internal combustion engines (ICE), highlights the urgent need for sustainable solutions, particularly in the shipping sector. Non-invasive predictive maintenance using acoustic signal analy...
Electric vehicles (EVs) contribute to reducing fossil fuel dependence and environmental pollution problems. However, due to complex charging behaviors and the high demand for charging, EVs have imposed significant burdens on power systems. By providi...
This article aims to assess the effectiveness of state-of-the-art artificial neural network (ANN) models in time series analysis, specifically focusing on their application in prediction tasks of critical infrastructures (CIs). To accomplish this, sh...
To address wind power fluctuations causing curtailment and high costs, this study proposes an integrated method combining wind power forecasting with substation optimization. An enhanced Bidirectional Gated Recurrent Unit (BiGRU) model is developed b...
The current mainstream methods for online detection of transformers all have shortcomings such as low sensitivity and susceptibility to interference from the testing environment. Aiming at the shortcomings of the existing online detection methods for...
Remaining Useful Life (RUL) prediction is a crucial task in predictive maintenance. Currently, gated recurrent networks, hybrid models, and attention-enhanced models used for predictive maintenance face the challenge of balancing prediction accuracy...
Emotion recognition plays an increasingly important role in today’s society and has a high social value. However, current emotion recognition technology faces the problems of insufficient feature extraction and imbalanced samples when processin...
Advanced Metering Infrastructure (AMI), as a critical data collection and communication hub within the smart grid architecture, is highly vulnerable to network intrusions due to its open bidirectional communication network. A significant challenge in...
Emotion detection using electroencephalogram (EEG) signals is a rapidly evolving field with significant applications in mental health diagnostics, affective computing, and human–computer interaction. However, existing approaches often face chal...
To address the challenges of high labeling costs and insufficient cross-condition generalization in training cutting tool wear state prediction models, this paper proposes a model optimization method based on knowledge distillation. By constructing a...
Forecasting electronic current transformer (ECT) metering errors is challenging because long-term operation produces nonstationary and multiscale variations under changing load and environmental conditions. This study proposes an improved variational...
A great deal of operational information exists in the form of text. Therefore, extracting operational information from unstructured military text is of great significance for assisting command decision making and operations. Military relation extract...
In various domains, including everyday activities, agricultural practices, and medical treatments, the escalating challenge of antibiotic resistance poses a significant concern. Traditional approaches to studying antibiotic resistance genes (ARGs) of...
Solar insecticidal lamps are important physical control devices for green pest management, but faults in their photovoltaic power supply units can reduce trapping efficiency and shorten service life. To improve fault identification under complex agri...
DNS over HTTPS (DoH) protects name-resolution traffic but can also conceal command-and-control and data-exfiltration channels. Detection is challenging because benign and malicious flows share similar statistics, packet relationships span long sequen...
The bidirectional gated recurrent unit (BiGRU) method based on dissolved gas analysis (DGA) has been studied in the field of power transformer fault diagnosis. However, there are still some shortcomings such as the fuzzy boundaries of DGA data, and t...
Forecasting USD/CNY exchange rate volatility is of substantial practical significance for the management of cross-border capital flows and the formulation of monetary policies. In recent years, multiple external shocks arising from China–US geo...
Lithium-ion batteries are an indispensable component of numerous contemporary applications, such as electric vehicles and renewable energy systems. However, accurately predicting their remaining service life is a significant challenge due to the comp...
Constructing a knowledge graph of geological hazards literature can facilitate the reuse of geological hazards literature and provide a reference for geological hazard governance. Named entity recognition (NER), as a core technology for constructing...
Accurately predicting the long-term trajectory of a surface drifting buoy (SDB) is challenging. This paper proposes a promising solution to the SDB trajectory prediction based on artificial intelligence (AI) technologies. Initially, a scalable mathem...
In response to the insufficient adaptability of power transformer remaining useful life (RUL) prediction under complex working conditions and the difficulty of multi-scale feature fusion, this study proposes an industrial time series prediction model...
Accurate soil salinization monitoring in arid oases is crucial for agricultural sustainability and ecological security. However, existing deep learning-based approaches often suffer from insufficient use of multi-scale information and inadequate mode...
Shipping Enterprise Credit Named Entity Recognition (NER) aims to recognize shipping enterprise credit entities from unstructured shipping enterprise credit texts. Aiming at the problem of low entity recognition rate caused by complex and diverse ent...
Nonlinear devices and channel interference can significantly impact the received signal in visible light communication (VLC). While recent research has explored receiver recovery using deep learning, existing approaches often involve replacing tradit...
Accurate prediction of failure in industrial machinery and engines is critical for minimizing unexpected downtimes and enabling cost-effective maintenance. Existing predictive models often struggle to generalize across diverse datasets and require ex...
With the rapid development of social network platforms, Sina Weibo has become the main carrier for modern netizens to express public views and emotions. How to obtain the tendency of public opinion and analyze the text’s emotion more accurately...
Wave energy is a promising source of sustainable clean energy, yet its inherent intermittency and irregularity pose challenges for stable grid integration. Accurate forecasting of wave energy power is crucial for reliable grid management. This paper...
Accurate reactive power optimization in wind farms (WFs) is critical for optimizing operations and ensuring grid stability, yet it faces challenges from noisy, nonlinear, and dynamic Supervisory Control and Data Acquisition (SCADA) data. This study p...
In rolling bearing fault diagnosis, weak features are often masked by complex environmental conditions, blurring the original fault signals and reducing diagnostic accuracy. To address this issue, we propose the VMD/FFT-Quadratic-BiGRU diagnostic mod...
In this article, we introduce a novel deep learning hybrid model that integrates attention Transformer and gated recurrent unit (GRU) architectures to improve the accuracy of cryptocurrency price predictions. By combining the Transformer’s stre...
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