Why NUBISON AX
Technology Innovation
Oracle AD
A principle-based industrial AI technology setting new standards for complex multivariate time-series anomaly detection in industrial environments
Key Features
- Anomaly detection based on multivariate time-series data
- Precise identification of anomaly occurrence time and intervals
- Root cause tracing at variable (sensor) level
- Predictive maintenance and downtime minimization
- Detection of complex long-term anomaly patterns
Key Applications
- Semiconductor and display ultra-precision manufacturing
- Robotic automation and smart factory equipment
- Large-scale sensing-based industrial facilities
- Energy, environmental, and plant operations
Technical Differentiators & Advantages
Innovative First-Principle Based Approach
World-Class Performance Verification
Proven technical excellence on major benchmarks including PSM, SMD, and SWaT, simultaneously improving detection performance and anomaly localization accuracy
Principle-Based Anomaly Detection
Breaking away from model complexity competition, achieving high reliability and stability through design based on fundamental principles of anomaly signal generation
Stability and Reproducibility
Structurally defining inter-variable relationships in latent space under normal conditions, enabling precise detection of long-term and cumulative anomalies
Dual Scoring Mechanism
Combining prediction error and structural deviation to minimize false positives while simultaneously enhancing detection sensitivity and stability
High Interpretability
Clearly identifying root cause variables from latent structures when anomalies occur, supporting engineers' immediate root cause analysis and predictive maintenance
Industrial Field Optimization
Optimized for complex sensor data in high-precision manufacturing environments such as semiconductors, displays, and robotic automation, ready for immediate deployment in real operational environments
Neuro-symbolic AI
An AI paradigm that integrates deep learning-based learning capabilities with rule, logic, and knowledge graph-based reasoning into a unified system for simultaneous prediction, explanation, and verification
Key Features
- Neural network-based perception and pattern recognition (images, video, time-series, text)
- Ontology, knowledge graph, and logic rule-based reasoning and consistency verification
- Constraint checking on neural model outputs with automatic rule violation detection and correction
- Background knowledge-based reasoning and generalization in low-data environments
- LLM-integrated natural language interface for Q&A and verification pipelines
Key Applications
- Smart factory and manufacturing process analysis (equipment, sensors, process history)
- Quality inspection and vision inspection automation (video analysis + quality specification rule checking)
- Process and equipment compliance and regulatory automatic inspection
- Knowledge graph-based manufacturing analysis and root cause tracing
Technical Differentiators & Advantages
Structural Integration of Neural Learning and Symbolic Reasoning
Neural Learning + Symbolic Reasoning Integration
Combining logic, rules, and knowledge graphs on top of deep learning's high-performance prediction capabilities to implement logically verifiable decision-making beyond simple probability outputs
Explainability and Transparency
Explicitly traceable reasoning paths based on rules, ontologies, and logical expressions, mitigating the black-box problem and suitable for industrial and regulatory environments
Embedded Domain Rules and Constraints
Reflecting medical guidelines, manufacturing specifications, and safety/compliance rules in the symbolic layer, enabling automatic verification, blocking, and correction when AI outputs violate rules
Advanced Reasoning with Limited Data
Performing reasoning, analogy, and abstraction using domain knowledge and logic even in environments lacking large-scale data
LLM Hallucination Mitigation
Achieving enterprise-level reliability through pipelines that verify and provide feedback on LLM-generated results using symbolic reasoners
Industry and Manufacturing Scalability
A general-purpose AI architecture immediately applicable to industrial sites with clear domain structures such as processes, equipment, quality, safety, and knowledge graphs
INSPIRE
A core technology that fundamentally resolves modality imbalance in asynchronous multimodal environments and overcomes the limitations of real industrial data integration
Key Features
- Fully asynchronous multimodal data integration
- Mitigation of information imbalance between modalities
- Enhanced expressiveness through indirect synthesis
- Stable performance maintenance in noisy and missing data environments
- Advanced multimodal classification and prediction performance
Key Applications
- Industrial equipment and process monitoring (sensors + unstructured data)
- Manufacturing and robotics-based multi-sensor systems
- Sports analysis (images + trajectory and time-series data)
- Healthcare multimodal diagnostic data
- Complex AI analysis systems in asynchronous collection environments
Technical Differentiators & Advantages
Innovative Multimodal Integration via Indirect Synthesis
Pre-emptive Modality Imbalance Resolution
Unlike conventional direct fusion approaches, each modality absorbs meaningful information from other modalities before fusion, fundamentally solving the problem of weaker modalities being overshadowed
Cycle Consistency-Based Domain Preservation
Enabling information exchange while maintaining original domain characteristics through bidirectional transformation between modalities, achieving stable representation learning even in asynchronous and heterogeneous environments
Dual Alignment Mechanism
Simultaneously applying sample-level proximity alignment and distribution-level statistical alignment to achieve both local and global representation consistency
Optimized for Asynchronous Environments
Not requiring temporal alignment or common timestamps, enabling immediate application to irregular and asynchronous data collection environments in real industrial sites
Strong Performance and Robustness
Achieving state-of-the-art accuracy, F1, and AUC performance on the RallyPose benchmark compared to existing single-modal, ensemble, direct fusion, and cross-attention approaches
Scalability and Versatility
A general-purpose framework extensible to various modality combinations beyond image-time-series, applicable across complex data environments including industrial, healthcare, and sports
Data Blind Spot Removal
A data integration technology that eliminates data blind spots in industrial sites through a collection system encompassing both standard and non-standard equipment
Key Features
- Unified collection of standard and non-standard equipment and system data
- Support for multiple communication methods including TCP/IP, PLC, Bluetooth, MQTT/S, REST API
- Edge Gateway-based data collection and on-site integration
- Collection system configuration based on Link Server / Driver Server
- Transformation of non-standardized data into usable formats
Key Applications
- Manufacturing sites with mixed standard communication interfaces
- Environments operating both legacy and new equipment
- Non-computerized equipment environments with manual input or display-based systems
- Facilities establishing equipment data-based analysis and management systems
Technical Differentiators & Advantages
Innovation in Standard and Non-Standard Data Integration
Unified Standard/Non-Standard Data Collection
Enabling integration of industrial standards (Modbus, OPC-UA, etc.) and non-standard equipment data into a single collection system
Diverse Link-Based Data Integration
Providing flexible integration structures including Gateway, Mobile, Direct, LPWA, System, and Digital Convert Links according to field environment requirements
Data Blind Spot Minimization
Expanding data collection scope to include equipment areas where data collection was previously difficult
RPA and OCR-Based Non-Computerized Equipment Data Collection
For single-output port or display-based equipment: RPA-based automation, predefined region-of-interest data extraction, and OCR recognition for standard text and non-standard display values
Centralized Data Collection Architecture
Enabling consistent data management by transmitting extracted data to central data collection servers
Scalable Collection Architecture
An extensible architecture that flexibly accommodates new equipment additions and communication protocol changes
Legacy Data Integration
A data integration technology that restructures distributed legacy data on a time basis to enhance process data usability and AI applicability
Key Features
- Integration of legacy system data from MES, PLM, and other systems
- Mapping data without time information to time-axis references
- Transformation of individual variables into time-series data format Xn(t)
- Thing Driver code-based data transformation and time mapping
- Web-based editing tools for immediate code modification and application
- Real-time integration through dynamic Thing Driver application
Key Applications
- Manufacturing sites operating MES-based production management systems
- Industrial environments with legacy system-centered process management
- Sites with AI application constraints due to insufficient time information
- Industrial data lake framework implementation environments
Technical Differentiators & Advantages
Thing Driver-Based Time Information Reconstruction
Time-Centric Data Reconstruction
Reorganizing existing production data without time information along time-axis references to enable process-level analysis
Thing Driver-Based Integration Architecture
Applying Thing Driver codes to individual data for time information mapping and unified management through a single Driver
Flexible Code Management and Extensibility
Mapped codes can be immediately modified and applied through web-based editing tools, ensuring field responsiveness
Large-Scale Information Retrieval Support
Enabling retrieval of thousands of diverse information types using encapsulated code collections
Real-Time Integration Design
Completing data integration in real-time by dynamically applying generated Thing Drivers to the system
Maximized AI Applicability
Expanding the scope of machine learning and AI analysis model applications through data transformed into time-series format