The progressive proliferation of sensor deployment in distribution networks (DNs), propelled by the dual drivers of power automation and ubiquitous IoT infrastructure development, has precipitated exponential growth in real-time data generated by mul...
To address the limitations of traditional single-point detection methods in monitoring the sulfur content of ship fuel (FSC), which are inadequate in meeting the regulatory demands of high-traffic ports, this study proposes an integrated analytical a...
As monitoring technologies and data collection methodologies advance, landslide disaster data reflects attributes such as diverse sources, heterogeneity, substantial volumes, and stringent real-time requirements. To bolster the data support capabilit...
Accurate model aggregation is pivotal for the efficient dispatch and control of massive distributed photovoltaic (PV) and energy storage (ES) resources. However, the lack of unified standards across equipment manufacturers results in inconsistent dat...
Rock grade is a key indicator guiding tunnel construction. In order to ensure the efficiency and safety of construction, it is necessary to accurately predict the rock grade of the unexcavated part of a tunnel. Currently, geological sketches and geop...
Artificial intelligence (AI), particularly through advanced large language model (LLM) technologies, is reshaping coal mine safety assessment methods with its powerful cognitive capabilities. Given the dynamic, multi-source, and heterogeneous charact...
Compared to traditional measurement devices, the micro-synchrophasor measurement unit (D-PMU or μPMU) in the distribution power system has great differences in data acquisition frequency, data format, data dimension, time-stamped information, etc....
The development and utilization of urban underground space represents a crucial strategy for achieving sustainable urban development. Three-dimensional (3D) geological models provide a data foundation and technical support for research in urban plann...
To address the fragmentation and weak correlation of knowledge in the design method domain, this paper proposes a framework for constructing a knowledge graph driven by multi-source heterogeneous data. The process involves collecting multi-source het...
With the globalization of trade, maritime transport is playing an increasingly strategic role in sustaining international commerce. As a result, research into the tracking and fusion of multi-source vessel data in canal environments has become critic...
With the application of new-generation information technology in the full life cycle process of a complex product, it is showing the characteristics of multi-source, real-time, heterogeneous, cross-domain transmission. Large data volume and low value...
As geological exploration technology advances, geoscience relies on digitization and intelligence to address challenges such as data fragmentation, multi-source heterogeneity, and visual analysis. This study develops a big data-driven 3D visual analy...
Multi-source heterogeneous data has been widely adopted in developing artificial intelligence systems in recent years. In real-world scenarios, raw multi-source data are generally unlabeled and inherently contain multi-view noise and feature redundan...
This review comprehensively summarizes the recent research progress of multi-source sensing technologies and intelligent modeling methods in vegetation monitoring. It elucidates the physical mechanisms and complementary natures of core active and pas...
Air temperature (Ta) is essential for studying surface processes and human activities, particularly agricultural cultivation, which is strongly influenced by temperature. Remote sensing techniques that integrate multi-source data can estimate Ta with...
With the rapid development of underground space, the issue of safety in construction processes is becoming more and more significant. The purpose of this paper is to solve the problem of the existing underground space monitoring technology not being...
Aimed at the characteristics of multi-source heterogeneity and the rapid generation of data in digital twin workshops, as well as the current situation where communication protocols between equipment within the workshop vary greatly and are difficult...
Item description information plays a crucial role in helping users understand the basic situation of an item and is also vital auxiliary information in recommendation systems. Traditional methods obtain this data through platform backend data or web...
To address the challenges of insufficient transformer winding fault samples and the effective fusion of heterogeneous multi-source data, this study proposes an intelligent fault diagnosis method based on a time–frequency diffusion model and Con...
The determination of the land geoid and the marine geoid involves different data sets and calculation strategies. It is a hot issue at present to construct the unified land–ocean quasi-geoid by fusing multi-source data in coastal areas, which i...
This paper is concerned with the airborne-laser-data-based sag estimation for wide-area transmission lines. A systematic data processing framework is established for multi-source data collected from power lines, which is applicable to various operati...
Land cover data are important basic data for earth system science and other fields. Multi-source remote sensing images have become the main data source for land cover classification. There are still many uncertainties in the scale effect of image spa...
As complex agricultural machinery, traditional combine harvesters face numerous challenges during operation due to their reliance on manual observation. To meet the demands of modern agriculture, intelligent combine harvesters have emerged. Intellige...
Accurate acquisition of low-observable targets with a minimal radar cross-section (RCS) poses a significant challenge for multi-source remote sensing systems, such as integrated radar–electro-optical (REO) platforms, particularly in complex ele...
The rapid advancement of autonomous agricultural systems (AASs) is transforming modern agriculture, where labor shortages, sustainability imperatives, and demands for precision farming are driving the adoption of intelligent agricultural platforms. A...
Flood hazards caused by intense short-term precipitation have led to significant social and economic losses and pose serious threats to human life and property. Accurate disaster risk assessment plays a critical role in verifying disaster statistics...
With the rapid growth of new energy vehicles (NEVs), the number of NEV-related traffic accidents has risen sharply. To address the challenge of low accuracy in real-time risk assessment caused by the coupling of multi-source heterogeneous data, this...
Among the most prevalent natural hazards, flooding has been threatening human lives and properties. Robust flood simulation is required for effective response and prevention. Machine learning is widely used in flood modeling due to its high performan...
Cyanobacterial blooms occur frequently in lakes worldwide, disrupting aquatic ecosystem balance and directly threatening drinking water safety and fisheries production. Establishing a reliable bloom early-warning system has therefore become an urgent...
Soybean yield prediction is one of the most critical activities for increasing agricultural productivity and ensuring food security. Traditional models often underestimate yields because of limitations associated with single data sources and simplist...
Intelligent mining technology, as the core driving force for the digital transformation of the mining industry, integrates cyber-physical systems, artificial intelligence, and industrial internet technologies to establish a “cloud–edge&nd...
Due to the diversity and complexity of distribution networks, the classical modular software development method may face the difficulty in modular division, data sharing and collaboration of different specialties. Inspired by the idea of separating t...
Accurate air quality forecasting requires the effective integration of heterogeneous data sources that vary in spatial coverage, temporal resolution, and sensing reliability. This paper presents a unified spatio-temporal data processing framework des...
With the development of urbanization, building maintenance units (BMUs) have been widely used in super high-rise buildings. As aerial work machinery, condition monitoring plays a vital role in the safety maintenance and management of BMUs. However, B...
The rapid development of the Industrial Internet of Things (IIoT) generates massive heterogeneous sensor data, complicating data cleaning and normalization. Existing algorithmcentric methods often treat quality issues in isolation and lack unified go...
Time-series Interferometric Synthetic Aperture Radar (InSAR) techniques encounter substantial reliability challenges, primarily due to the presence of gross errors arising from phase unwrapping failures. These errors propagate through the processing...
Complex underwater environments induce difficult-to-quantify ranging errors, constraining the localization accuracy and robustness of heterogeneous networks. To address this, a node localization method based on a Geometric Topology Perception-Enhance...
The traditional way of studying fluorinated materials by adjusting parameters throughout multiple trials can no longer meet the needs of the processing and analysis of multi-source, heterogeneous, and numerous complex data. Due to the high confidenti...
With the increasing demand for accurate and reliable hydrological information, geo-spatial analysis plays a more and more important role in hydrological studies. The development of the geo-spatial technique advances our understanding of the complex a...
As an urban infrastructure project, subway engineering exhibits the characteristics of large scale, trans-regional presence, and complex environmental conditions. The three-dimensional (3D) real scene of subway engineering aims to visually present th...
Multi-source cross-domain hyperspectral image (HSI) classification is challenged by heterogeneous sensor configurations, scene-dependent distribution shifts, and limited labeled target data, which hinder effective knowledge transfer across multiple s...
It is extremely important to extract valuable information and achieve efficient integration of remote sensing data. The multi-source and heterogeneous nature of remote sensing data leads to the increasing complexity of these relationships, and means...
Natural disasters occur continuously across the globe, posing severe threats to human life and property. Remote sensing technology has provided powerful technical means for large-scale and rapid disaster monitoring. However, the deep integration of r...
The cross section is the basic data for building 3D geological models. It is inefficient to draw a large number of cross sections to build an accurate model. This paper reports the use of multi-source and heterogeneous geological data, such as geolog...
Multi-source heterogeneous data in substation secondary systems are typically characterized by high entropy and disorder, which pose significant challenges for cross-modal information integration and efficient retrieval. Therefore, a method for multi...
To address the problems of the lack of an online data simulation test environment, the poor openness of data collection, and the low degree of data visualization in the online control process of port cranes, an operation state monitoring system frame...
In the landslide chain from pre-disaster conditions to landslide mitigation and recovery, time is an important factor in understanding the geological hazards process and managing landsides. Static knowledge graphs are unable to capture the temporal d...
Current traditional tea processing production lines suffer from issues such as fragmented data and low levels of intelligence. This paper proposes a three-dimensional visualization system for tea processing production lines based on digital twins. Fi...
Internet of Things (IoT) devices equipped with electromagnetic sensors, such as millimeter-wave radars, generate massive amounts of computation-intensive and latency-sensitive sensing data. However, their limited computational capability and energy s...
Water conservancy safety management faces several challenges. These include the integration of multi-source heterogeneous data and inefficient knowledge utilization. To address these issues, this study proposes a knowledge graph (KG) construction met...
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