TALON-project
@talon_project
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Joined November 2022
š® Milestone: All pilots are running. Final KPI metrics in D5.4 (month 36). Weāll deliver: ⢠Zero-touch AI orchestration ⢠Blockchain-traced AI/ML artefacts ⢠Federated learning for distributed edge models
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š· TALONās HRC pilot marries drones and AI to flag PPE violations in under 4 seconds. Current accuracy: 90%, inspection rate up 4x. š Next target: 70% fewer safety incidents on site.
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šÆ TALON I5.0 field trials reveal: every 1% quality gain equates to ā¬10K saved. AI-driven models target scrap reduction and dynamic machine uptime. š Latest quality ratio: 98.54% (goal: 99.0%).
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ā” TALONās untethered drone validation trial lowers average power draw to 20 W (down from 30 W+), achieving 94.7% accuracy in object detection. šæReduce transfer size to 4 bytes (down from 16B) without loss.
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š TALON delivers 100% preservation of personally identifiable information. Our facial and metadata anonymization techniques secure worker privacy in hazardous environments, while maintaining high detection precision. ā
Oversight audit reported zero re-identification instances.
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š¤ TALONās accelerates safety inspection cycles by 75%. Units navigate industrial sites in 4 to 6 minutes (versus 20 to 25 minutes for manual teams) and enforce 100% PPE compliance through anonymized computer vision. š”ļø Quicker notifications and more secure workplaces.
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š TALON decreases AR-to-node latency by 90% (from 2 s to 200 ms). Our AR/VR pilot delivers expedited maintenance guidance and training with a 90.03% accuracy in environmental recognition. š§Personal protective equipment detection reached 96.4% mean average precision.
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š TALONās Industry 5.0 Automation and Planning work package delivers unprecedented OEE gains. Target metrics include: ⢠Quality yield raised to 99% (prior baseline 98%) ⢠79% machinery availability, translating to a monthly uptime increase of 7.2 hours
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š TALONās unified autonomous UAV and autonomous vehicle trial achieves sub-10ms inter-drone coordination latency. AI-optimized swarm management now reduces energy draw by 30% while maintaining precision. š„ Data payload size downshifted from kilobytes to 16 bytes per message.
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š Through a seamless edge-cloud architecture, TALON maximizes resource use, elevates performance, and reduces energy consumption across manufacturing ecosystems. ā¢Ā 90% reduction in latency between drone and edge nodes ā¢Ā 20% decrease in energy per autonomous flight
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⨠Ready to Orchestrate the Unmanned Factory? TALONās architecture is open, scalable, and battle-tested. Explore the deliverables. Pilot with us. Letās build the self-driving industrial futureātogether.
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šĀ TALONās Recipe: 1 Part AI, 2 PartsĀ Sustainability. Federated learning + energy-aware benchmarking + smart pricing = 20% less COā.
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š® TALONās Next Act? AI Forecasting. Weāre evolving from reactive to predictive: Kafka queues + online learning + pre-emptive scaling. The future is autonomous.
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šĀ TALONās Pilots: Where Theory MetĀ Factory Floors. In Pilot 2, we: ⢠Cut energy 20% ⢠Automated 100% of healing ⢠Hit 70% non-commercial device use Real industry. Real results.
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ā»ļø Wasted Device Power? TALON Turns It Into Cash. Our SPS incentivizes sharing idle phones/laptops for edge computing. Earn discounts + boost participation.
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šØ Why 70% CPU Usage Triggers an Intervention. TALONās Grafana rules (like excess_CPU_any_node) are tripwires for autonomous healing. No tickets. No delays. Just silent fixes.
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š„Ā TALONās Self-Healing Duo: Scale vs. Reschedule RULE 1: Scale replicas during CPU overload. RULE 2: Taint nodes + pod resets. Which won in Pilot 2?Ā Spoiler: Both did.Ā See the Node-RED flows in action!
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šĀ TALON's Dashboard Just Got Upgraded. TALONās Grafana panels donāt justĀ showĀ cluster healthātheyĀ predictĀ fires. CPU/Memory/Disk trends + auto-healing alerts = ops teams breathing easier.
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š āMove Aside, Overloaded Node!āāTALONās Task Offloader Why waste resources? Our orchestrator live-migrates tasks to idle nodes, cutting hotspots by 35%. Efficiency isnāt magicāitās math. š§®
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šøĀ TALONās SPS API Fights Back Against Cloud Costs. Dynamic pricing + demand forecasting = 25% savings. Choose bundles (Basic to Ultimate) or pay-per-useāall optimized via Stackelberg game theory.
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