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Selected work

Work that made it out of the notebook.

15 projects delivered by the Saerosense founding team for companies, hospitals and research programs — each with its workflow, the techniques behind it, and the results it produced.

WiFi log intelligence with RAG — project visualAgents & RAGRAG + expert memory

Telecom · home networking

WiFi log intelligence with RAG

Turns raw router logs into plain-language root causes — “Why is my internet slow?” answered with evidence and a next action.

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Agents & RAG
한국어RAG chatbot · voice channel

Insurance · South Korea

Korean insurance chatbot & voice assistant

A Korean-language RAG chatbot grounded in an insurer’s own documents, benchmarked against ChatGPT, with a phone channel built on speech-to-text and text-to-speech.

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Multi-agent equity research — project visualAgents & RAG8 agents · local LLM

Finance · investment research

Multi-agent equity research

Eight specialist agents turn 50+ market metrics into a scored BUY/HOLD/SELL research report — on a local LLM, so data stays in-house.

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Concrete defect segmentation — project visualComputer vision5 damage classes

Infrastructure inspection · South Korea

Concrete defect segmentation

Pixel-level detection of cracks, spalling, rebar exposure and efflorescence for a GPR & vision scanner platform.

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Blurry license plate recognition — project visualComputer vision>90% accuracy

Smart city · traffic & parking

Blurry license plate recognition

A five-stage pipeline that reads low-resolution, motion-blurred CCTV plates with above 90% accuracy.

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PPE compliance detection — project visualComputer vision7 PPE classes

Industrial safety

PPE compliance detection

A YOLO-NAS detector that checks seven types of protective equipment on workers at construction, manufacturing and mining sites.

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Computer vision
94.8%AUC-ROC · genuine vs counterfeit

Retail · brand protection

Luxury logo authentication

A ConvNeXt-Base pipeline that separates genuine from counterfeit luxury-brand logos — up to 94.8% AUC-ROC.

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Environment perception for self-driving — project visualComputer visionDrivable space + lanes

Autonomous driving

Environment perception for self-driving

Drivable-space and lane estimation from semantic segmentation, with RANSAC ground-plane fitting.

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Stereo visual-inertial odometry (MSCKF) — project visualComputer vision<0.2 m error

Robotics · drones · AR/VR

Stereo visual-inertial odometry (MSCKF)

Drift-reduced motion estimation fusing stereo cameras and an IMU — position error under 0.2 m across the evaluated trajectory.

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Brain tumor segmentation in MRI — project visualComputer vision3D U-Net

Medical imaging

Brain tumor segmentation in MRI

A 3D U-Net that segments brain tumors from multi-modal MRI volumes, trained with a multi-class soft Dice loss.

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Generative AI for knee X-rays — project visualComputer vision3 generative families

Medical imaging · radiology

Generative AI for knee X-rays

Knee radiograph re-generation with StyleGAN3, Stable Diffusion and Pix2Pix GAN, in partnership with a university hospital in Seoul.

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Network & telemetry
93.5%Test accuracy · 230 KB on-device model

Smart home · telecom

On-device WiFi sensing

CNN + BiLSTM + attention recognizes human activity from WiFi signals — 93.5% accuracy in a 230 KB model on a 1 GB ARM device.

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Auto-reclosing with particle filter & CNN — project visualNetwork & telemetryIEEE TPWRD

Power systems · transmission

Auto-reclosing with particle filter & CNN

Detects secondary-arc extinction and permanent faults on transmission lines so breakers reclose only when it is safe. Published in IEEE Transactions on Power Delivery.

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Network & telemetry
90.5%Permanent-fault accuracy on unseen lines

Power systems · transmission

Pattern-recognition auto-reclosing with Bi-LSTM

A Bi-LSTM predicts the secondary-arc extinction interval and fault type — 90.5% permanent-fault accuracy on unseen line models. Published in IEEE Access.

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Network & telemetry
0.0018RMSE · CatBoost + PCA

Urban infrastructure · climate risk

Flood depth prediction

CatBoost and PCA over multi-resolution simulation rasters predict flood depth — RMSE 0.0018, MAE 0.0002.

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