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Earlier sources: Fusion
This energy thread follows fusion through original research, public projects, and source reports. Read the evidence below to see which developments are represented in SYMTRI’s archive.
SYMTRI organizes observations from public sources into a living map. Connections reflect shared subject matter; they do not establish causation or endorse a source.
Open in the map ↗128 classified signals in the fourteen-day window. These counts include signals awaiting readable evidence.
Earlier sources
Up to ten sources with readable evidence, ordered by their feed timestamp. These archive sources can be older than the live fourteen-day window. This list is a sample, not a measure of total activity. Dates distinguish paper publication, repository creation, model/dataset activity, Hacker News posts, and KEV catalog additions. “First archived” records when SYMTRI first stored the signal.
- OpenAlex · Published · First archived
Adjusting modal contributions via differential-driven SoftMax fusion for RGB-IR object detection ↗
Visible light (RGB) and infrared (IR) fusion enhances object detection in complex environments by leveraging complementary modalities. However, adaptively balancing modal contributions under extreme distribution imbalance remains challenging, while transformer-based methods incur heavy computational costs. To address this, we propose Diff-Fusion, which uses semantically prepared inter-modal differences to generate spatially varying complementary weights for modal contributions in efficient RGB-IR detection. To improve modality-specific feature encoding, we apply lightweight RGB and IR detail e…
- OpenAlex · Published · First archived
Unsupervised Cross-Condition Fault Diagnosis Using Dual-Branch Time–Frequency Adaptive Fusion and MMD Alignment ↗
Cross-condition bearing diagnosis is challenged by simultaneous changes in transient impacts, spectral-energy patterns, and source–target feature distributions as speed and load vary. Handcrafted indicators and single-representation networks often capture only part of this coupled variation, whereas global alignment alone may reduce marginal discrepancy without preserving diagnostic structure. This study proposes DBAF-Net, which couples a raw vibration sequence with a five-channel, multi-resolution, source-domain-calibrated Kaiser short-time Fourier transform tensor. The time-domain branch mod…
- OpenAlex · Published · First archived
Sensor-Rich Robotic Grasping in Unstructured Scenes: A Review of Vision, Multimodal Fusion, Learning Architectures, and Closed-Loop Manipulation ↗
Vision-based robotic grasping is a core capability for autonomous manipulation, yet reliable operation in unstructured scenes remains difficult because sensing, representation, contact mechanics, control, and hardware are tightly coupled. This structured qualitative review examines a 117-publication corpus spanning grasp detection and pose estimation, multimodal and tactile sensing, learning architectures, closed-loop correction, active interaction, and deployment-oriented evaluation. The update began with 91 candidate records; DOI and normalized-title deduplication produced 83 unique records,…
- OpenAlex · Published · First archived
Visual–frequency bimodal fusion networks for oscillation detection and quantification in industrial control loops ↗
Oscillations arising from performance degradation in industrial control loops threaten operational safety and efficiency. Existing automated detection methods often rely on fixed assumptions and single-modality signals, limiting their robustness and generalization. In this work, we propose a visual–frequency bimodal fusion network (VFBFN) for oscillation detection and quantification. VFBFN employs a dual-branch encoder consisting of a two-dimensional (2D) convolutional neural network (CNN) for image-based signal embeddings and a one-dimensional (1D) CNN for frequency spectra to extract complem…
- OpenAlex · Published · First archived
Unified Embedding and Composable Fusion for Arbitrary Multimodal Person Re-Identification ↗
Driven by the advancement of intelligent security, person re-identification has evolved from single-modality imagery to complex multimodal tasks involving infrared, text, sketches, and paintings. However, most existing methods are restricted to fixed modality pairs and struggle to accommodate the flexible fusion requirements of variable inputs, making the integration of arbitrary modality combinations a critical challenge. To address this, we propose the Unified Embedding Representation Fusion Network (UERF-Net), a framework based on unified embedding representation fusion. This method first e…
- OpenAlex · Published · First archived
Physically Constrained Temporal Fusion Transformer with LLM-Assisted Hyperparameter Optimization for Multi-Scenario Energy Time Series Forecasting ↗
Accurate forecasting of battery state of health (SOH), wind power, and photovoltaic (PV) power is challenged by non-stationarity, multiscale dependencies, and physically implausible outputs. This study proposes a physically constrained Temporal Fusion Transformer (PC-TFT) that coordinates enhanced temporal representation, physical regularization, and LLM-assisted Bayesian optimization. Mogrifier-LSTM and a feature-enhanced attention module capture transient fluctuations and long-range dependencies, while spectral normalization stabilizes training under physical constraints. Scenario-specific d…
- OpenAlex · Published · First archived
Automated safety interpretation of crack-aperture for concrete dams using mutation-aware visual-sensor fusion and knowledge-augmented reasoning ↗
Automated safety interpretation of concrete-dam cracks remains challenging because crack-gauge records may be incomplete, abnormal changes are difficult to distinguish from normal evolution, and monitoring outputs are not readily translated into traceable decisions. This paper proposes a mutation-aware visual–sensor fusion and knowledge-augmented framework. A deep learning model, multiscale dynamic spatial YOLOv11n (MDS-YOLOv11n), extracts crack widths from images and aligns them with crack-gauge measurements to supplement missing observations. Autoformer models the fused sequence; a dynamic t…
- OpenAlex · Published · First archived
DSFormer: An Enhanced Dual-Selective Fusion Transformer Framework for Hyperspectral Image Classification++ ↗
Hyperspectral image classification requires joint modeling of spectral signatures and spatial structure. In patch-based classification, each input patch is assigned the label of its center pixel, although neighboring pixels may belong to different land-cover classes. This setting motivates coordinating shallow spatial features, contextual refinement within the patch, and supervision of the resulting representation. We propose DSFormer++, an enhanced framework based on the original Dual Selective Fusion Transformer Network (DSFormer). Shallow Feature Compensation (SFC) combines shallow and patc…
- OpenAlex · Published · First archived
Physics-guided data fusion for settlement prediction of high-fill railway embankments ↗
Settlement prediction for high-fill railway embankments is challenged by monitoring noise and stage-dependent deformation across construction and operation. This paper presents a physics-guided framework integrating multivariate construction information with settlement monitoring data. Monitoring sequences are denoised by a hybrid autoencoder with a signal-to-noise-ratio-based cleaning mechanism. A temporal graph convolutional neural network embeds a modified layer-wise summation formulation to guide variable interactions and settlement prediction. On the analysed dataset, the model achieved a…
- arXiv · Published · First archived
A New Degree of Freedom in Stellarator Coil Design ↗
Reversed toroidal field (RTF) coils introduce a new degree of freedom in stellarator coil design. While the total plasma-linked current is fixed by Ampere's law, compensating RTF currents allow the modular-coil current to exceed this value without altering the net linkage. This redistribution decouples key coil constraints and modifies the external coil geometry without significantly compromising the confining magnetic field. As a result, the achievable coil-plasma separation can be increased in the configurations studied, potentially enabling more compact stellarator designs.
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How to assess evidence about Fusion
Check each original source for its methods, release status, limitations, and supporting data. A repository announcement, a preprint, and a published study provide different kinds of evidence. SYMTRI’s subject classification helps discovery; it does not independently validate the source’s claims.
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