Mathematical Models TRIZ Cases

A guidance term aligns synthetic and real feature spans during training, improving object classification on real-world data.

Probability-grid task allocation and DDQN control help multiple USVs search underwater targets more accurately with less manual intervention.

Out-of-distribution detection and expert-likelihood checks help machine learning models avoid unreliable classifications and outputs.

Machine-learning alignment of heterogeneous vehicle sensor data updates HD maps with missing objects while improving map accuracy and consistency.

Routes out-of-distribution inputs away from standard ML classification using likelihood checks and expert fallback to reduce misclassification risk.

Uses tempo-invariant beat-based audio graphs and AI controls to generate personalized music in real time across changing user contexts.

Variability across multiple Q-functions lets an autonomous vehicle block uncertain RL actions and execute only confident decisions.

Outlier, relationship, and property weights rank likely causes when warehouse abnormalities occur independently rather than in repeated patterns.

Probability-based promotion timing shifts video service notifications to later content when early engagement is unlikely, reducing user dissatisfaction.

Outlier, relationship, and property weights help identify causes of objective-variable changes even when warehouse abnormalities occur independently.

Expert-corrected vibration diagnoses retrain Bayesian models to cut false results and reduce reliance on specialized technicians.

Manufacturer-specific ML models infer device types from port and traffic-volume patterns, improving profiling accuracy without manual rules.

Tracks changing relationships between multiple sensors with precision matrices to detect abnormal device behavior with clearer explainability.

A hierarchical probabilistic model predicts top tool configurations for cylindrical grinding, avoiding slow trials and rigid rule-based selection.

Voting logic validates pilot command data, filters faulty sensors, and adjusts propulsor thrust to keep electric aircraft stable.

Real-time illuminance sensing lets an autonomous work machine adjust direction, speed, and work mode without complex plant-growth analysis.

Sensors and neural control adjust tint, slat angle, and shade coverage to maintain brightness and visibility with less manual intervention.

Cloud AI and biometric feedback recommend dialysis settings at home, reducing caregiver burden while improving treatment accuracy.

Temporal classification and RNN planning shift heavy computation offline, enabling robust vehicle trajectory generation under full constraints.

Models human preference distributions and interference risk to plan socially appropriate robot paths with real-time multi-agent equilibrium inference.

Negative rewards for intervention behaviors help a robot learn dynamic navigation with fewer interactions, better transport efficiency, and collision avoidance.

Flight time-series embeddings learn engine aging across maintenance intervals, improving condition assessment without expert models.

Rule-based scheme selection combined with DDPG parameter tuning improves unmanned system controllability while keeping operations safe.

Failure-time prediction and dynamic task timing cut maintenance cost, avoid premature replacement, and reduce equipment downtime.

Maps polynomial-curve uncertainty to a point and uses statistical distance to improve noisy proximity measurement in autonomous systems.

Flight-data learning replaces static aircraft models to predict next-state behavior more accurately and support fuel-efficient operations.

Monitored prediction gaps trigger automatic ML model retraining and replacement in industrial control code to keep closed-loop processes optimized.

kNN and logistic regression analyze time-window transitions to catch short signal perturbations, cut false positives, and improve fault classification.

Bayesian polytopic uncertainty modeling improves sample-efficient feedback control while preserving stability under partial dynamics knowledge.

Preliminary learning from PID or manual control cuts reinforcement learning time and improves convergence in delayed process control.

Pre-validating device responses lets blockchain control transactions confirm only executable IoT commands, reducing wasted validation and storage.

Multiple path perception inputs are cross-checked in real time to improve lane mapping reliability on curved roads and complex intersections.

Hyperparameter search compares correctly and incorrectly labeled training to curb memorization and improve ML control system generalization.

Centralized MADDPG training with empowerment terms helps autonomous entities learn coordinated actions without losing decentralized operation.

Pressure waveform analysis with AI detects short-volume aspiration and gel pick-up within 100 msec, helping avoid bad results and downtime.

HD map object attributes populate radar occupancy grids to build reference maps before driving, improving vehicle localization reliability.

AI-driven analysis of signal and external data reveals user-device spatial-temporal patterns to improve network planning and resource allocation.

Uses surrogate models and optimized test inputs to separate similar fault responses and identify the true fault mode faster.

Probability graphs aligned with binary signal timelines make abnormality and outlier detection easier without obscuring average signal behavior.

Hidden Markov route prediction and sensor-data bootstrapping improve aircraft maintenance scheduling by capturing non-linearity and seasonality.

By combining route transition probabilities with bootstrapped sensor history, this case improves aircraft maintenance timing under non-linear, seasonal use.

By fusing shunt resistor and Hall sensor readings as probability distributions, this case improves current measurement under temperature noise and EMI.

Batch updates from robot navigation logs keep static maps accurate over time without offline remapping or SLAM drift.

Filters multiple out-of-phase seasonality modes from asset sensor signals using spectral phase analysis to improve early anomaly detection.

A corrected failure-probability threshold validates supervised machine learning for safety-critical technical processes with stronger safety evidence.

Corrected failure probability thresholds and split validation data improve supervised ML reliability for safety-relevant technical processes.

A trained safety model predicts resource depletion across shared agents and adjusts behaviors to keep task execution above safe thresholds.

A probabilistic filter updates robot state and motion uncertainty in real time while enforcing structural constraints for stable control.

Dynamic assessment of production steps and quality checks finds a Pareto-optimal balance of quality, time, and cost in flexible manufacturing.

Dual linear programming converts CMDP occupation-measure policies into logical rules, making constrained control actions easier to understand and modify.