Yoshio Takaeda
Data Engineer / Researcher / Lecturer
Working with manufacturing data to develop predictive maintenance and stability control systems.
Let AI organize the chaos; humans courageously add dimensions.
Overview
With over 30 years of experience in manufacturing process design, data engineering, and analytics infrastructure development. From chemical plant design to genome analysis engines, polymer material databases, and current high-dimensional time series analysis—consistently pursuing data-driven problem solving.
Currently serving as a lecturer at Sophia University Graduate School, teaching “Practical High-Dimensional Data Analysis” and bridging theory with implementation.
Experience
Education
Kyoto University Graduate School of Engineering / Ph.D. (Engineering)
Polymer Chemistry, Polymer Molecular Theory Laboratory (1995)
Professional Experience
Sophia University Graduate School, Applied Data Science Program / Lecturer
September 2023 - Present
- Teaching “Practical High-Dimensional Data Analysis”
- Integrated education from theory to implementation with practical case studies
toor Inc. / Founder & Chief Research Officer
January 2012 - Present
- Research and development of high-dimensional time series analysis technology (toorPIA)
- Proof-of-concept studies for manufacturing data infrastructure and predictive maintenance systems
- Open source project development
Public Tech Company / Chief Technology Officer
August 2006 - January 2012
- Technical development of intellectual property management systems and data infrastructure design
- Led technology strategy and operational optimization as CTO/COO
Mitsubishi Research Institute / Senior Research Scientist
July 1998 - July 2006
- International Human Genome Project: Development of genome homology search engine
- Polymer Materials Database: Design, development, and implementation
- Industrial data analysis and simulation technology R&D
TonenGeneral Sekiyu Chemical / Research Engineer
April 1995 - June 1998
- Design and implementation of PP/PE polymer manufacturing plants
- Process data engineering and analysis
- Chemical process optimization and quality control
Technical Expertise
Core Skills
30+ Years of Data Engineering in Manufacturing
From chemical plant design to genome analysis, polymer material databases, and manufacturing equipment predictive maintenance—consistently pursuing data-driven solutions
High-Dimensional Time Series Analysis
Handling hundreds to thousands of manufacturing process parameters as multidimensional vectors, enabling seamless (continuous) visualization of complex system state transitions across optimal time scales for dynamic characterization and predictive maintenance
Acoustic Signal-Based Predictive Maintenance
High-quality audio data acquisition using DSP processing with Raspberry Pi + ADC + PreAmp + piezoelectric sensors to detect anomalies in rotating machinery and other manufacturing equipment
Data Infrastructure Design
SQLite3 for rapid POC validation, PostgreSQL/TimescaleDB for production-scale time series data processing. Emphasis on appropriate technology selection
Algorithm Development
Homology search (genomics), material property prediction (polymers), anomaly detection (manufacturing processes and acoustic signals)
Technology Stack
- Analysis & AI: Python (NumPy/Pandas/scikit-learn/librosa), machine learning model implementation
- Signal Processing: DSP, FFT, wavelet transforms, acoustic feature extraction
- Backend: Node.js (API/real-time processing), Ruby on Rails (web applications), Go, C/C++ (high-performance processing, numerical computation, control systems)
- Frontend: JavaScript/TypeScript, React/Next.js
- Data Infrastructure: SQLite3 (POC/edge), PostgreSQL/TimescaleDB (production), Docker/Kubernetes
- Edge Computing: Raspberry Pi, high-precision ADC & PreAmp selection, piezoelectric sensor selection
- Parallel Processing: OpenMP, Go concurrency
Research Projects
🧭 toorPIA (High-Dimensional Time Series Analysis Engine)
Research project for real-time analysis of manufacturing equipment parameters, automatic anomaly detection, and root cause identification.
Research Focus
- Predictive Maintenance: Early detection of anomalies using statistical methods
- Root Cause Analysis: Automatic identification of causal sensors and parameters
- Adjustment Recommendations: Generation of control parameter optimization proposals
- Implementation Validation: Proof-of-concept studies with CSV integration, GUI, and lightweight scripts
Technology Stack
Python API (signal processing & analysis), BFF (Node.js + React/Next.js), PostgreSQL + TimescaleDB, Docker/Kubernetes
🔊 Acoustic-Based Predictive Maintenance System
Research project for acquiring audio data from rotating machinery and other manufacturing equipment to detect anomalies.
Technical Architecture
- Hardware: Raspberry Pi + high-precision ADC + PreAmp + piezoelectric sensors
- Signal Processing: DSP, FFT, STFT, Wavelet, spectrogram analysis
- Data Management: SQLite3 (edge) → PostgreSQL (aggregation)
- Analysis: Detection of deviations from normal sound patterns, tracking time-series changes in frequency features
Proof-of-Concept Results
- Early detection of bearing degradation
- Classification of motor abnormal sounds
- Visualization and anomaly detection of vibration patterns
🪶 Local LLM Analysis
Experimental research project analyzing internal representations of local large language models to visualize proposition distances and bias structures.
→ GitHub: toorpia-labs/local-llm-analysis
Research Chronology
2012-Present: Manufacturing Predictive Maintenance (toor)
Consolidating 30 years of experience to develop technology that keeps manufacturing sites running. Dual approach using both process parameters and acoustic signals.
2006-2012: IP Management Systems (Venture Company)
Solving the complexity of intellectual property management through data infrastructure. Integration of technology and business.
1998-2006: Genomics & Materials DB (Mitsubishi Research Institute)
Large-scale data processing in the International Human Genome Project, structured data design for polymer materials database.
1995-1998: Chemical Plants (TonenGeneral)
Process design and data analysis for PP/PE polymer manufacturing plants. The foundation of manufacturing data engineering.
Patents & Publications
Patents (United States)
Multidimensional Correlated Data Extracting Device and Method
US Patent 10,353,892 (2019)
Method for extracting correlated subsets and identifying featured elements in multidimensional space
State Determining Device and Method
US Patent 10,621,028 (2020)
Time-series combination of device data and similarity-based state analysis
Data Analysis Apparatus and Method
US Patent Application 20160109355
Segment analysis using similarity indices based on frequency spectrum
Document Retrieving Apparatus and Method
US Patent 8,818,979 (2014)
Interactive document retrieval and query vector combination on 2D maps
Analyzer, Analysis System, and Analysis Method
US Patent Application 20210232567
Method for plotting new data on reference maps
Information Display Method and Device
US Patent Application 20170213249
Advertisement display clarifying relevance in search services
Academic Publications
Genome Analysis
- DIGIT: a novel gene finding program by combining gene-finders. T. Yada, T. Takagi, Y. Totoki, Y. Sakaki, Y. Takaeda. Pacific Symposium on Biocomputing, 2003
Polymer Solution Light Scattering & Nuclear Magnetic Relaxation Studies (1993-1997)
- Dynamic depolarized light scattering and nuclear magnetic relaxation studies of isotactic oligo- and poly(methyl methacrylate)s in dilute solution. Macromolecules, 1997, 30(9), 2751-2758
- Dynamic depolarized light scattering and nuclear magnetic relaxation studies of oligo- and poly(methyl methacrylate)s in dilute solution. Macromolecules, 1995, 28(3), 682-693
- Mean-square optical anisotropy of isotactic oligo- and poly(methyl methacrylate)s in dilute solution. Macromolecules, 1995, 28(12), 4167-4172
- Dynamic depolarized light scattering and nuclear magnetic relaxation studies of oligo- and polystyrenes in dilute solutions. Macromolecules, 1994, 27(15), 4248-4258
- On the correlation between the negative intrinsic viscosity and the rotatory relaxation time of solvent molecules in dilute polymer solutions. Macromolecules, 1993, 26(25), 6891-6896
- Mean-square optical anisotropy of oligo- and poly(methyl methacrylate)s in dilute solutions. Macromolecules, 1993, 26(15), 3742-3749
Research Philosophy
Field-Oriented Approach
Pursuing structures that “continue to operate” rather than just theory. Always staying grounded in the field—from chemical plants to genome analysis to manufacturing equipment.
Appropriate Technology Selection
Avoiding over-engineering. Start small and iterate quickly.
Critical Thinking
Incorporating opposing viewpoints to surface risks early. An attitude learned from 30 years of failures and successes.
Learning Cycle
Taking technology back to the field and learning from the field again. Mutually reinforcing education, practice, and research—emphasizing the trinity of these three elements.
Public Individual Statement I believe that individual engineers should not be consumed by corporate structures, but instead deliver their value directly to the fields that truly need them.
Contact
GitHub: github.com/takaeda
Email: takaeda@gmail.com
Location: Japan
This site showcases personal research and educational activities and is non-commercial.
© 2025 Yoshio Takaeda | Personal Research Portfolio