Condition
Instrument corrections, baselines and quality checks turn the raw response into a stable analytical input. Invalid or incomplete measurements are identified before inference.
Classification and diagnostics
AI
Artificial intelligence helps HT-Nova interpret high-dimensional sensor data that is difficult to separate with fixed thresholds alone. Models can support spectral identification, fine-grained pattern recognition and instrument diagnostics at the edge or within a connected platform.
Introduction
HT-Nova utilizes artificial intelligence algorithms to process high-dimensional response data from sensors, achieving data characteristic differentiation that traditional analysis methods cannot accomplish. This approach is applied in fields such as Raman spectroscopy and gas monitoring sensor fine-grained characteristic differentiation.
Our proprietary technology, HT-MARS® artificial intelligence identification algorithm is applied in handheld Raman spectrometers. By using a large amount of spectral data to construct deep neural network models, this algorithm effectively enhances the recall rate, accuracy, and mixture identification accuracy of substance identification. Moreover, it can perform artificial intelligence identification analysis without the need for internet connection. Compared with fixed-rule matching, HT-MARS® is designed to improve robustness to defined spectral variation. Performance claims should remain tied to documented test sets, comparators, operating conditions and false-positive and false-negative results.
In a networked environment, connecting to the HT-Vision system platform enables integration with higher-level cloud-based multimodal fusion deep recognition algorithms. Even as the substance types in the cloud database continue to expand, the recognition speed of the deep neural network can still reach millisecond-level performance.
Technical principleTalk to a technology expert
Learn the structure inside complex sensor data.
A trained model receives a controlled representation of the sensor signal - such as a corrected Raman spectrum, a multi-sensor response pattern or a time-series feature set. The model maps that representation to a class, score, estimate or diagnostic state based on patterns learned from labelled examples.
HT-Nova’s HT-MARS® approach applies this principle to handheld Raman identification, using spectral data to improve differentiation of similar materials and mixtures. Edge inference can operate without a continuous network connection, while connected systems can combine device results with broader data and updated models where the deployment permits it.
The model is only one part of the measurement chain. Training-set coverage, reference labels, preprocessing, uncertainty thresholds, version control and performance monitoring determine whether an AI output remains valid when instruments, users and environments change.
Signal path
From raw response to a controlled inference.
Reliable sensor intelligence preserves the signal’s scientific meaning before, during and after model inference.
Represent
Relevant spectral, spatial or temporal structure is encoded for the model. The representation should retain discriminating evidence without embedding avoidable artefacts.
Infer
The validated model produces a class, score or estimate with defined thresholds. Edge deployment can keep response fast and available when connectivity is limited.
Verify
Confidence, quality rules and reference evidence are presented with the result. Feedback and performance monitoring reveal when the model or data domain needs review.
Engineering considerations
Model performance must survive the real world.
A strong laboratory score is not enough; the model, instrument and operating procedure must remain controlled as one analytical system.
Representative data
Training and validation data should cover relevant instruments, substances, mixtures, users and environments. Poor labels or hidden sampling bias can create confident but incorrect outputs.
Robustness and drift
Signal distributions change with ageing, maintenance, environmental conditions and new sample types. Monitoring and controlled updates are needed to detect performance drift.
Traceability and oversight
Model version, input quality and decision rationale should be recoverable for review. Human confirmation and alternative methods remain important when consequences or uncertainty are high.
Key information
Technology Features
Data Processing and Analysis Capability
Sensor data is typically vast and complex, requiring effective processing and analysis to extract useful information. Artificial intelligence (AI) techniques can be applied to the processing and analysis of sensor data, utilizing machine learning, pattern recognition, and other algorithms to intelligently process the data, thus rapidly and accurately extracting the desired information
Real-time Decision-making and Feedback
AI-based sensor systems can perform real-time analysis and processing of data, making corresponding decisions and providing feedback based on the analysis results. This enables sensor systems to possess a higher level of intelligence during real-time monitoring and control processes, allowing them to quickly respond to various changes and events
Adaptability and Optimization
Validated AI models can support adaptation or optimisation within predefined limits when suitable inputs, controls and fallback behaviour are engineered into the system. This adaptability and optimization capability enables sensor systems to adapt to different work scenarios and application requirements, enhancing system flexibility and adaptability
Fault Detection and Prediction
With the help of AI technology, sensor systems can monitor and analyze their own states, detecting sensor faults or abnormal conditions in a timely manner and providing predictions and warnings. This helps to take timely measures to repair faults, ensuring the stable operation and reliability of the system
Intelligent Optimization and Energy Conservation
Through the application of AI technology, sensor systems can achieve intelligent optimization control, effectively adjusting and optimizing the system's operating status and energy consumption to achieve energy conservation and emission reduction, and efficient resource utilization. This helps to reduce operating costs, improve energy utilization efficiency, and reduce environmental impact
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Discuss AI with our technical team.
Tell us about your environment, target substances and operational requirements. We’ll help map the right path.
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