Projects & Applied Research

0→1 Digital Platform Build & Launch

Independent Build | 2026

End-to-end owner: architecture, development, testing, production launch

Took a multi-role service platform from architecture design and full-stack development through automated testing to production deployment on cloud infrastructure — completed end to end in personal time (described generically; no project-specific details).

Source:

AI Process & Engineering Parameter Optimization

Workplace Project Research | Changshu Jingyuan

Participated in engineering pull-force prediction and supported manufacturing AI applications through plant management.

Translated manufacturing and design problems into measurable parameters, prediction targets and validation workflows. Studied key pressing, paint fixtures, pogo-pin force, keycap pull force and laser-engraving parameter optimization.

Research Question & Method

The keycap pull-force project addressed hook-opening dimensions that relied on experience and tooling trials. It used material and historical force data to explore dimension prediction and parameter recommendations. The presentation records 20 modeling samples and 3 additional test samples: preliminary validation requiring more data across specifications and operating conditions.

Manufacturing Validation

The key-pressing case combined cylinder pressure, ejection time, pressing time and rubber hardness. Following model recommendations and offline testing, the team reported a metric increase from 200 to 1,113 pcs per operation. The original unit and limited scope are retained.

Benefits & Research Limitations

The pull-force project estimated RMB 104,800 per year across tooling modification, experimental labor and new-hire training. This is a scenario estimate, not realized savings, and is not added to the RMB 2M+ annual result in the resume. Further work includes tolerance recommendations, larger samples and continued offline validation.

Source: AI Project Optimization Summary, slides 20, 22, 62, 66–67, 71, 74 and 79–80. Named participation applies to the pull-force project; other cases summarize team reports.

Jingtek E-bike App & IoT Service System

Product Planning & Entrepreneurship Research | E-bike / IoT

Draws on product development and App / administration architecture planning during the co-founder / executive vice president role.

Explored integration of e-bike hardware communications, mobile applications and administration systems, extending product capabilities into online transactions, after-sales services and user communities.

App & Administration Planning

The presentation covers vehicle anomaly detection, nearby repair locations, shopping, wish lists and points redemption. Administration includes member, product and maintenance management, plus customer-behavior and sales analysis, considering product development and after-sales workflows together.

IoT & Business Model Research

Explored community features, test rides and maintenance services to support engagement, alongside online transactions and experience sharing. Rental concepts proposed GPS, anti-theft and geofencing requirements; hardware planning included communication compatibility and an OTA update library.

Scope of Experience

This case demonstrates system planning, hardware–software integration and business model research. The presentation provides no revenue, user-count or full-launch validation, so proposed features are not presented as realized commercial results. Its requirement to support over 60% of communication protocols is a different metric from the resume’s over 65% hardware compatibility result.

Source: Jingtek E_bike app system, slides 3 and 5–16. Personal responsibilities are contextualized by the entrepreneurship and App architecture experience in the September 15 resume.

Agentic Workflows & Manufacturing Governance

Continuing Study | Agentic Workflow

Studied Morris Fan’s lecture materials to explore workflow integration and governance for private enterprise AI agents.

Used The Practice and Challenges of Agentic Workflows as a study reference, focusing on manufacturing problem definition, data and tool integration, human review and traceable decision processes.

Architecture & Workflow Study

Reviewed the lecture’s data and tool layers, agent core, workflow orchestration and governance architecture. Explored turning operational needs into executable, observable tasks with clear boundaries for queries, tool actions and result verification.

Manufacturing Governance

Focused on human-in-the-loop review, tool permissions, evidence logs, exception handling and stop mechanisms. Considered irreversibility, production risk and data sensitivity, alongside task success rate, cost, latency and failure recovery.

Applications & Attribution

Equipment maintenance, quality knowledge, production scheduling, demand forecasting and supply-chain early warning are directions for further study. This is a review of lecture materials authored by Morris Fan; the lecture’s demonstration systems, code and results are not claimed as personal development or deployment outcomes.

Source: Morris Fan, The Practice and Challenges of Agentic Workflows, pages 4–13, 27–32, 73–82 and 84–91. Third-party study reference, not a personal publication.

E-Vehicle Controller Embedded Development

Ongoing Development | Embedded Systems

Personal Technical Research & Development (ongoing)

Building on 10+ years of embedded development experience, ongoing firmware and tooling development for an e-bike controller (three-phase BLDC motor control): motor control algorithms, migration of production-proven protection logic, communication protocol design, Windows tuning tools and AI-assisted diagnostics over logged operating data.

Motor Control & Firmware

Re-implemented production-proven control logic on a modern dual-core MCU with integrated CAN/BLE: three-phase BLDC commutation and SVPWM sinusoidal control, overcurrent / undervoltage / stall protection, and firmware-level electromagnetic braking (E-ABS) with regenerative energy recovery and safety management.

Protocols & Toolchain

Designed serial communication protocols and debug frames (controller–display–BMS) and built a Windows tuning tool covering parameter read/write, Hall calibration, live charts and diagnostic export, supporting motor matching and production calibration.

AI Applications

Exported operating data (current, speed, voltage, load state) as structured diagnostic packages; explored LLM-assisted fault diagnosis and motor-parameter self-learning as a bridge to IoT services and after-sales applications.

Source: Personal development records (GitHub)

E-Vehicle Battery Management System (BMS) Development

Ongoing Development | Battery Management

Personal Technical Research & Development (ongoing)

End-to-end design of a 36V (10S) lithium battery management system: analog front-end (AFE) selection, host MCU and CAN architecture, safety-gated regenerative braking with advance negotiation, and online terminal contact-resistance monitoring — with full schematic, pin-level wiring table and CAN protocol draft delivered.

Architecture & Hardware

BQ76930 AFE + STM32F103 (built-in bxCAN) + CAN transceiver: 10S cell sampling and balancing, charge/discharge MOS power stage, 42V-to-5V buck with integrated Bluetooth speaker; full schematic and pin-level connection table delivered.

Protocol & Regen Safety

Mapped the legacy RS232 battery protocol onto J1939-style CAN frames for controller integration; regenerative braking gated by a two-layer mechanism (safety admission + advance no-regen negotiation) with multi-level SOC/temperature/current thresholds, protecting both battery and controller lifespan across a two-phase rollout plan.

Reliability & Differentiating Features

Self-developed B+/P+ dual-point online contact-resistance monitoring that estimates power-loop contact resistance in real time with tiered alerts, catching outdoor plug corrosion and fretting degradation early; plus an anthropomorphic voice-prompt feature (battery / health / connection status, language and voice switchable via app).

Source: Personal development records (GitHub: ebikeBMS)