Vertical-Driven Architectures
Collection
Physical AI system designs by CodeNinja, reasoned on Praxis, made living with Hyper Ontology. • 49 items • Updated
Datasets:
Modalities:
Text
Formats:
csv
Languages:
English
Size:
< 1K
Tags:
physical-ai
sovereign-ai
united-states
rail-and-transportation
predictive-maintenance
wayside-detectors
License:
choice stringlengths 6 36 | picked stringlengths 40 148 | why stringlengths 78 146 |
|---|---|---|
Work surface model | gpt-oss-120b, 120B MoE, MXFP4, Apache-2.0, central tier | Largest permissive US-origin open weights; GLM 5.3 set aside on the Entity List; one 141 GB card in the railroad's own data center |
Portal detector | D-FINE, Apache-2.0, COCO checkpoints, wayside NPU box | No field-of-use restriction; Objects365 terms not cleared; runs at yard speed on the edge |
Portal OCR | PP-OCRv6 small, 7.7M parameters, CTC head pinned | Car number becomes a join key at the edge; runs on CPU well inside 1 GB of RAM |
Trend forecasting | Chronos-2, Apache-2.0, zero-shot, central tier | Multivariate forecasts with covariates; residual thresholding; nothing fine tuned under the system of context |
Central hardware class | 141 GB HBM GPU class, one card, one node | About 65.2 GB of MXFP4 and 16 bit weights plus a 13 GB planning reserve fits with about 63 GB spare |
Edge compute sizing | Octa-core NPU edge box sized from its streams | D-FINE and PP-OCRv6 occupy a fraction of the box; buffer and video pipeline take the rest |
Wayside sensing | 29 detectors: 22 hot box and hot wheel, 4 wheel impact load, 2 acoustic bearing, 1 wheel profile; pilot locomotive vibration and temperature sensing | The existing network is the trend corpus; new onboard sensing is pilot only, rolled out through scheduled shop visits |
Portal sensing | Four cameras at yard entry: left side, right side, wheel line, yard throat overview | Bought by class against pixel-density and blur-budget arithmetic; detections span car, door, wheel, brake shoe, journal box and dragging equipment |
Pattern: trend, do not build | Cross-detector trending over the existing 29 detectors, keyed to serialized bearings and axles | Shortens the observed gap without new detector sites, which are the railroad's capital decision |
Pattern: findings cross, pixels stay | Edge inference at the portal with store-and-forward buffers | A cellular link that drops for hours cannot carry video; detections, OCR strings and evidence frames cross |
Pattern: advise, never act | No train control, signal or PTC interface | The single-reading stop alarm to the dispatcher stays exactly as it is; people decide every set out |
Ground | The railroad's own data center and wayside boxes, Harbor air-gapped registry | Open weights let the operator hold its weights, its data and its decision record with no permission to renew |
Wayside Trend Watch ontology and model register for the United States
The object model and the model and equipment register from Wayside Trend Watch: Predictive Maintenance From Wayside Detectors for a Freight Railroad in the United States, an open reference architecture by CodeNinja Atoms, in rail and transportation. Part of the Vertical-Driven Architectures series; every design in the series is also a row in the cumulative dataset https://huggingface.co/datasets/CodeNinjatools/vertical-driven-architectures.
- Read the paper: https://codeatoms.ai/wayside-detector-trending-rail-us/
- DOI: https://doi.org/10.5281/zenodo.23556421
- Source files and PDF: https://github.com/muhammadumar89/codeninja-research
- Live view: https://huggingface.co/spaces/CodeNinjatools/wayside-detector-trending-rail-us
Files
| File | What it holds |
|---|---|
objects.json |
14 typed objects (Freight Car, Locomotive, Axle Bearing (Roller Bearing), Wheelset, Traction Motor, Wayside Detector, Track Segment, Detector Reading, Trend Flag, Work Order, Track Geometry Exception, Rail Flaw Indication, AAR Interchange Record, Train), each with its properties and 13 typed links written from chapter 5. Format hyper-ontology/1: designed with CodeNinja Praxis, implemented with Hyper Ontology. |
models.csv |
The model and equipment register: every model, tool and hardware class with its licence and the reason for each choice. |
How to use it
import json
from huggingface_hub import hf_hub_download
p = hf_hub_download("CodeNinjatools/wayside-detector-trending-rail-us-ontology", "objects.json", repo_type="dataset")
objects = json.load(open(p))["objects"]
print([o["label"] for o in objects])
Citation
CodeNinja Engineering Team and Muhammad Umar. 2026. Wayside Trend Watch: Predictive Maintenance From Wayside Detectors for a Freight Railroad in the United States. CodeNinja Atoms. https://doi.org/10.5281/zenodo.23556421. CC BY 4.0.
One of the CodeNinja Atoms sovereign AI reference architectures: every published design with its sector, country, compute and three-year cost. Its object model sits with the other industrial ontology examples.
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