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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 ↗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
MambaFusion: Registration-assisted lumbar CT-MRI image fusion via Mamba and dynamic feature modeling ↗
Medical images play a pivotal role in clinical diagnosis, yet a single imaging modality often fails to provide complete anatomical and pathological information. Multimodal image fusion addresses this limitation by integrating complementary information from different modalities. In this study, a CT-MRI registration and preprocessing procedure was first introduced to reduce spatial mismatch between lumbar CT and MRI images. The MRI volume was registered and resampled into the CT coordinate space, ensuring spatial consistency before image fusion. Landmark-based registration evaluation across 30 p…
- OpenAlex · Published · First archived
Hierarchical Detection and D-S Evidence Fusion-Based Insulation Diagnosis of Oil-Immersed Current Transformers ↗
Partial discharge monitoring of oil-immersed current transformers (CTs) is susceptible to electromagnetic interference, and single-parameter detection is biased due to incomplete information. To further improve the safety of the oil-immersed CT, an insulation diagnosis method combining hierarchical detection with Dempster–Shafer evidence (D-S) theory is proposed in this paper. A three-step diagnostic framework is constructed. At the first step, the liquid level is monitored since it changes with the gas generated by partial discharge. If an abnormality is identified in the first-step screening…
- OpenAlex · Published · First archived
A Hybrid Deep Learning Approach for Early Detection of Wildfires Using Transformer Models and Feature Fusion Techniques ↗
Natural disasters, particularly wildfires, pose a serious threat to ecosystems, human lives, and infrastructure, making rapid and accurate fire detection essential for effective disaster management. Despite recent advances in deep learning-based wildfire detection, conventional CNN-based approaches are inherently limited in modeling long-range spatial dependencies, while relying on a single deep architecture may provide an incomplete representation of complex and multi-scale fire patterns. Furthermore, high-dimensional deep features may contain redundant information, increasing computational c…
- OpenAlex · Published · First archived
Segmentation-free hybrid CNN–transformer CAD framework with handcrafted feature fusion for explainable Leukaemia diagnosis ↗
Diagnosing leukaemia from microscopic blood smear images is nevertheless challenging for traditional Computer-Aided Diagnosis (CAD) systems. Many current methods depend on segmentation and basic feature representations. This makes it hard to tell apart morphologically similar subtypes. This work presents an extended hybrid CAD framework to address these limitations by fusing handcrafted texture descriptors, deep CNN representations, Transformer-based contextual modelling and explainable AI. Multi-resolution handcrafted features using the Discrete Wavelet Transform (DWT), Grey-Level Co-occurren…
- OpenAlex · Published · First archived
Elevated substrate preheating tailors microstructural evolution and enhances fatigue resistance in laser powder bed fusion H13 tool steel ↗
Fatigue performance remains a key limitation for laser powder bed fusion (LPBF) tool steels due to the strong coupling between process-induced defects and microstructural heterogeneity. Elevated substrate preheating has been proposed as an effective strategy to improve the processability of hard-to-print alloys; however, its combined influence on defect characteristics, microstructure, and fatigue cracking behavior remains insufficiently understood. In this study, the fatigue behavior of LPBF-fabricated H13 tool steel was investigated by increasing the substrate preheating temperature from 200…
- arXiv · Published · First archived
Reliability-Gated Fusion of Consumer Head and Foot IMUs for Lower-Body 3D Pose ↗
Sparse inertial pose estimation promises camera-free motion capture from consumer devices, but consumer sensors are unreliable: firmware-fused orientations are biased, mounting varies between sessions, and streams drift or drop out. On a new 35-take single-subject benchmark pairing an earbud head inertial measurement unit (IMU) with two smart-insole foot IMUs (SAM-3D-Body pseudo-ground-truth labels), we show the reliability problem is channel-level: a channel ablation isolates foot acceleration as the most informative input (66.6 mm vs. 79.0 mm head-only) and the firmware-fused foot orientatio…
- arXiv · Published · First archived
LQR-ArUco Fusion: Robust Hierarchical Control for Navigation and Asymmetric Manipulation in Two-Wheeled Robots ↗
We propose a hierarchical control framework to address severe dynamic instabilities and navigational drift that arise when a two-wheeled inverted pendulum (TWIP) robot attempts asymmetric object manipulation. While two-wheeled platforms are highly manoeuvrable, their constant balancing adjustments make onboard odometry highly unreliable for precise navigation. Furthermore, the addition of a side-mounted robotic arm introduces unactuated lateral roll moments when a payload is lifted, a challenge heavily compounded on uneven terrain. To solve these coupled problems, our architecture divides the…
- arXiv · Published · First archived
CacheRepair: Learning to Repair Cross-Chunk Context in RAG for KV Cache Fusion ↗
Multi-document retrieval-augmented generation (RAG) requires a language model to process multiple retrieved text chunks before answering a question. Precomputing each chunk's KV cache independently and concatenating the caches when the chunks are retrieved can accelerate this step. However, the assembled cache lacks cross-chunk attention information, reducing answer quality. Selective recomputation methods recover the missing cross-chunk context by rerunning the target LLM on selected tokens, incurring substantial online computation. We introduce CacheRepair, a lightweight network that learns…
- GitHub · Repository created · First archived
Alpha-Park/genpark-cosine-bm25-reciprocal-rank-fusion-skill ↗
Zero-dependency hybrid retrieval engine fusing sparse lexical BM25 Okapi and dense Cosine vector similarities via Reciprocal Rank Fusion.
- GitHub · Repository created · First archived
alphaparkinc/genpark-cosine-bm25-reciprocal-rank-fusion-skill ↗
Zero-dependency hybrid retrieval engine fusing sparse lexical BM25 Okapi and dense Cosine vector similarities via Reciprocal Rank Fusion.
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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