Drift Compensation Research Articles

Phase Change Memory Drift Compensation in Spiking Neural Networks Using a Non-Linear Current Scaling Strategy

The non-ideality aspects of phase change memory (PCM) such as drift and resistance variability can pose significant obstacles in neuromorphic hardware implementations. A unique drift and variability compensation strategy is demonstrated and implemented in an FD-SOI SNN hardware unit composed of embedded phase change memories (ePCMs), current attenuators, and spiking neurons. The effect of drift and variability compensation on inference accuracy is tested on the MNIST dataset to show that our drift and variability mitigation strategy is effective in sustaining its accuracy over time. The variability is reduced by up to 5% while the drift coefficient is reduced by up to 57.8%. The drift is compensated and the SNN classification accuracy is sustained for up to 2 years with intrinsic control-free hardware that tracks the ePCM current over time and consumes less than 30 µW. The results are based on ePCM chip experimental data and pos-layout simulation of a test chip comprising the proposed circuit solution.

A Drift Compensation System of Autocollimator Based on Reciprocal Roll Angles and Feedback Combination Target

A Drift Compensation System of Autocollimator Based on Reciprocal Roll Angles and Feedback Combination Target

Semi-supervised comparative learning compensation method for chemical gas sensor drift.

The gradual and unpredictable variation in chemo-sensory signal responses when exposed to the same analyte under identical conditions, commonly referred to as sensor drift, has long been recognized as one of the most serious challenges faced by chemical sensors. The traditional drift compensation method is both labor-intensive and expensive, as it requires frequent collection and labeling of gas samples for recalibration. Introducing a small number of meaningful drift calibration samples can be an attractive strategy to reduce the computational load and improve the performance of the updated classifier. However, under the influence of drift, new challenges arise due to the difference in the distribution of source and target domain data. This paper proposes a novel algorithm framework called semi-supervised contrastive learning drift compensation (SSCLDC). The framework automatically extracts high-level abstract features based on a multilayer perceptron to better represent the structure of the source data. In addition, to address the issue of data distribution differences caused by drift between the source and target domains. We add a small number of reference sample pairs into the training for semi-supervised learning. Combining a contrastive loss function that can represent the matching degree of paired samples effectively overcomes the problem of sensor drift. The Kennard-Stone sequential algorithm is used to select the representative reference sample from the set of candidate reference samples. Experiments conducted on a widely used long-term chemical gas sensor drift dataset demonstrate that the proposed method outperforms several classic drift compensation techniques, highlighting its effectiveness and practical applicability.

Prototype-Optimized unsupervised domain adaptation via dynamic Transformer encoder for sensor drift compensation in electronic nose systems

In the field of electronic nose systems, sensor drift poses a significant challenge, affecting the reliability and accuracy of gas detection. Current solutions often require labeled data and fail to generalize well across different domains. This paper presents a novel, unsupervised domain adaptation framework for sensor drift compensation, leveraging a dynamic Transformer-based encoder and prototype learning. Our approach extracts semantic representations from source domain data and aligns instances to prototypes for knowledge transfer across domains. A dynamic prototype-guided classification model is deployed for drift compensation. The key contributions of this work include the introduction of prototype-optimized unsupervised learning and the development of an end-to-end drift compensation model. Experimental results on two gas sensor datasets demonstrate superior performance over existing unsupervised and semi-supervised methods, validating the effectiveness of our approach.

An electronic nose drift compensation algorithm based on semi-supervised adversarial domain adaptive convolutional neural network

Sensor drift is a significant challenge leading to performance degradation in electronic nose(E-nose) systems. Effectively addressing sensor drift represents the most daunting problem in E-nose technology. This work proposes a domain adaptive approach called Semi-Supervised Adversarial Domain Adaptive Convolutional Neural Network (SAD-CNN) to tackle the long-term drift in E-nose and device displacement. SAD-CNN leverages adversarial learning to minimize distribution disparities between the source and target domains. Unlike traditional methods employing projection matrices, SAD-CNN utilizes one-dimensional convolutional neural networks as feature extractors, circumventing the complexities associated with parameter adjustments and matrix calculations in the projection process. During the training process, utilizing pseudo-labels generated by the semi-supervised self-training method to train the model, thereby reducing the labeling costs. Additionally, confidence threshold screening is introduced during the self-training phase to minimize erroneous pseudo-labels. Furthermore, the regenerative Hilbert space’s Maximum Mean Difference combined with Minimum Variance is introduced as a domain constraint function to mitigate distribution discrepancies between domains and enhance feature discriminability across domains. The experimental results demonstrate that the SAD-CNN method outperforms others. Within the long-term drift dataset, the classification accuracies for different scenarios are 78.01 % and 82.53 %, respectively. Meanwhile, the instrument change dataset yields a classification accuracy of 96.45 %.

An enhanced time synchronization method for a network based on Kalman filtering

This paper introduces an enhanced time synchronization method different from IEEE 1588 (PTP). The proposed algorithm employs a unique synchronous message packet structure with a fixed length of 10 bytes and the highest system priority. This design enables preemptive transmission, effectively reducing transmission and queuing delays. Additionally, it applies a Kalman filtering model to mitigate noise interference, including clock drift, network delay jitter, and network asymmetry. The algorithm also features a clock drift compensation mechanism to continuously adjust the clock, ensuring high precision and stability in time synchronization. The algorithm proposed in this paper has the characteristics of being easy to implement, requiring minimal hardware resources, and being applicable to a variety of networks. Simulation results show that, with an 80 MHz crystal oscillator, the time offset between master and slave clocks does not exceed ± 1 clock cycle in symmetric communication links. In asymmetric links, the maximum time offset is within ± 3 clock cycles. Compared to the original PTP algorithm and the Kalman filter-based time synchronization algorithm, this method reduces the time offset from several microseconds to less than 40 ns.

Joint Distributed Manifold Preserving Domain Adaptation for Drift Compensation in E-Nose

Joint Distributed Manifold Preserving Domain Adaptation for Drift Compensation in E-Nose

Advances in drift compensation algorithms for electronic nose technology

Purpose This paper aims to explore recent advances in drift compensation algorithms for Electronic Nose (E-nose) technology and addresses sensor drift challenges through offline, online and neural network-based strategies. It offers a comprehensive review and covers causes of drift, compensation methods and future directions. This synthesis provides insights for enhancing the reliability and effectiveness of E-nose systems in drift issues. Design/methodology/approach The article adopts a comprehensive approach and systematically explores the causes of sensor drift in E-nose systems and proposes various compensation strategies. It covers both offline and online compensation methods, as well as neural network-based approaches, and provides a holistic view of the available techniques. Findings The article provides a comprehensive overview of drift compensation algorithms for E-nose technology and consolidates recent research insights. It addresses challenges like sensor calibration and algorithm complexity, while discussing future directions. Readers gain an understanding of the current state-of-the-art and emerging trends in electronic olfaction. Originality/value This article presents a comprehensive review of the latest advancements in drift compensation algorithms for electronic nose technology and covers the causes of drift, offline drift compensation algorithms, online drift compensation algorithms and neural network drift compensation algorithms. The article also summarizes and discusses the current challenges and future directions of drift compensation algorithms in electronic nose systems.

Development of Highly Sensitive and Thermostable Microelectromechanical System Pressure Sensor Based on Array-Type Aluminum-Silicon Hybrid Structures.

In order to meet the better performance requirements of pressure detection, a microelectromechanical system (MEMS) piezoresistive pressure sensor utilizing an array-type aluminum-silicon hybrid structure with high sensitivity and low temperature drift is designed, fabricated, and characterized. Each element of the 3 × 3 sensor array has one stress-sensitive aluminum-silicon hybrid structure on the strain membrane for measuring pressure and another temperature-dependent structure outside the strain membrane for measuring temperature and temperature drift compensation. Finite-element numerical simulation has been adopted to verify that the array-type pressure sensor has an enhanced piezoresistive effect and high sensitivity, and then this sensor is fabricated based on the standard MEMS process. In order to further reduce the temperature drift, a thermodynamic control system whose heating feedback temperature is measured by the temperature-dependent structure is adopted to keep the working temperature of the sensor constant by using the PID algorithm. The experiment test results show that the average sensitivity of the proposed sensor after temperature compensation reaches 0.25 mV/ (V kPa) in the range of 0-370 kPa, the average nonlinear error is about 1.7%, and the thermal sensitivity drift coefficient (TCS) is reduced to 0.0152%FS/°C when the ambient temperature ranges from -20 °C to 50 °C. The research results may provide a useful reference for the development of a high-performance MEMS array-type pressure sensor.

A novel temperature drift compensation method based on LSTM for NMR sensor

Nuclear Magnetic Resonance (NMR) sensors have become one of the best choices for angular velocity sensors in future Inertial Navigation Systems (INS) due to their small size and high precision. The performance of NMR sensors can be affected by temperature changes, resulting in temperature drift. Therefore, temperature compensation is essential. Accurate temperature compensation is challenging due to the lack of direct temperature observations in the core sensitive area (cell) and the complex temperature drift model. Considering the time correlation of temperatures inside and outside the cell, a new temperature drift compensation method based on the Long-Short Term Memory (LSTM) network is proposed. The memory characteristics of the LSTM enable it to fully utilize current and previous data to learn complex models. The main contributions of this study are as follows: (1) the mechanism of temperature drift in NMR sensors was analyzed; (2) a multi-time scale input–output model for temperature changes, temperature change rates, and ambient temperature versus sensor output bias was established; (3) a temperature drift model identification method based on LSTM was designed. Experimental results show that, compared to traditional polynomial fitting and memory-free methods, the proposed method improves temperature compensation accuracy by 26.0% to 47.0%. This method effectively reduces the adverse impact of temperature changes on the NMR sensor.

A Multibranch LSTM-Attention Ensemble Classification Network for Sensor Drift Compensation

A Multibranch LSTM-Attention Ensemble Classification Network for Sensor Drift Compensation

Safety assessment of asenapine in the FAERS database: real adverse event analysis and discussion on neurological and psychiatric side effects

PurposeThis study aims to comprehensively assess the safety of Asenapine by conducting an comprehensive statistical analysis of adverse event reports in the FAERS database, with a particular focus on potential adverse reactions related to its use in the treatment of psychiatric disorders.MethodsEvent reports from the first quarter of 2009 to the third quarter of 2023 were collected and analyzed. Detailed examinations of gender, age, reporter identity, and other aspects were conducted to reveal the fundamental characteristics of Asenapine-related adverse events. Signal mining techniques were employed to systematically evaluate various adverse reactions associated with Asenapine.ResultsThe study found that adverse event reports involving Asenapine were more common among female patients, with the age group mainly distributed between 18 and 45 years. Physicians were the primary reporters of adverse events, and psychiatric disorders, neurological disorders, and gastrointestinal disorders were the most common areas affected by adverse reactions. In addition to known adverse reactions, potential risks not mentioned in the drug label were identified, such as anosognosia, attentional drift, and psychogenic compensation disorder.ConclusionAsenapine carries the risk of various adverse reactions alongside its therapeutic effects. In clinical practice, physicians should closely monitor the occurrence of neurological disorders, psychiatric disorders, and gastrointestinal system disorders.

Optimization and realignment of OAM mode excitation in ring-core optical fibers using machine learning.

Light beams carrying orbital angular momentum (OAM) in free space or within optical fibers have a wide range of applications in optics; however, exciting these modes with both high purity and low loss generally requires demanding optimization of excitation conditions in a high dimensional space. Furthermore, mechanical drift can significantly degrade the mode purity over time, which may limit practical deployment of OAM modes in concrete applications. Here, combining an iterative wavefront matching approach and a genetic algorithm, we demonstrate rapid and automated excitation of OAM modes with optimized purity and reduced loss. Our approach allows for systematic computational realignment of the system enabling drift compensation over extended durations. Our experimental results indicate that OAM purity can be optimized and maintained over periods exceeding 24 h, paving the way for the applications of stable OAM beams in optics.

Startup drift compensation of RLG based on monotone constrained RBF neural network

Serious startup drift of the Ring Laser Gyroscope (RLG) is observed during cold startup process, which will dramatically degrade the performances of the corresponding Inertial Navigation System (INS). In this paper, correlation analysis method, which analyzes the relationship between the startup drift of the RLG and the temperature change, is used to determine the significant temperature-related terms during gyroscope startup. Based on the significant temperature-related terms and the startup time length, a startup drift compensation model for RLG based on monotonicity-constrained Radial Basis Function (RBF) neural network is proposed and validated. Compared with the raw RLG data without compensation, the standard deviation of the RLG output with the proposed constrained RBF network model is decreased by more than 46%, and the peak-to-peak value is decreased by more than 35%. Compared with the traditional multiple regression model, the standard deviation and peak-to-peak value of the RLG output are decreased by more than 10% and 6%, respectively. Compared with the common RBF network model, the standard deviation and peak-to-peak value of the RLG output are decreased by more than 8% and 3%, respectively. Navigation experiments also validate the effectiveness of the compensation model.

Mobile Phone Based Indoor Mapping

Abstract. We presented a mobile phone scanning solution that offers a workflow for scanning not only small spaces, where drift can be neglected, but also larger spaces where it becomes a major accuracy issue. The LiDAR and image data is combined to build 3D representations of indoor spaces. The paper does focus on the drift compensation for larger scans on the mobile phone by using AutoTags detections. We show that those can also be used to combine scans from multiple independent scans.

A gas sensor based on free-standing SWCNT film for selective recognition of toxic and flammable gases under thermal cycling protocols

The widespread adoption of e-nose devices based on chemiresistive materials has been hindered by issues related to sensor device complexity and reliability, specifically sensor drift, necessitating frequent recalibration and retraining of pattern recognition models. This study introduces a method for thermocycling a single sensor based on a free-standing network of single-walled carbon nanotubes (SWCNTs) to acquire signal patterns for selective analyte detection. Additionally, it employs a data filtering technique to compensate for the sensor drift. A free-standing SWCNT film, only a few nanometers thick, is thermally cycled via Joule heating between room temperature and 120 °C. Under these conditions, the sensitivity was tested towards NO2, H2S, and acetone vapors (10–25 ppm) in the mixture with dry air. Signal patterns produced through thermocycling were processed using CatBoost and LSTM algorithms. The accuracy of detection reached 90 % in the classification task, and the average root mean squared error of analyte concentration detection in the multioutput regression task was below 4 ppm. By combining original sensor design, thermocycling, signal filtering for drift compensation, and advanced pattern recognition models, this work contributes to overcoming the challenges in multivariate sensing systems, paving the way for practical applications of the more reliable chemiresistive sensors.

A Novel Detection Framework via Drift Compensation for Inter-Board Differences

A Novel Detection Framework via Drift Compensation for Inter-Board Differences

Characterization of Magnetoresistive Shunts and Its Sensitivity Temperature Compensation.

The main purpose of the paper is to show how a magnetoresistive (MR) element can work as a current sensor instead of using a Wheatstone bridge composed by four MR elements, defining the concept of a magnetoresistive shunt (MR-shunt). This concept is reached by considering that once the MR element is biased at a constant current, the voltage drop between its terminals offers information, by the MR effect, of the current to be measured, as happens in a conventional shunt resistor. However, an MR-shunt has the advantage of being a non-dissipative shunt since the current of interest does not circulate through the material, preventing its self-heating. Moreover, it provides galvanic isolation. First, we propose an electronic circuitry enabling the utilization of the available MR sensors integrated into a Wheatstone bridge as sensing elements (MR-shunt). This circuitry allows independent characterization of each of the four elements of the bridge. An independently implemented MR element is also analyzed. Secondly, we propose an electronic conditioning circuit for the MR-shunt, which allows both the bridge-integrated element and the single element to function as current sensors in a similar way to the sensing bridge. Third, the thermal variation in the sensitivity of the MR-shunt, and its temperature coefficient, are obtained. An electronic interface is proposed and analyzed for thermal drift compensation of the MR-shunt current sensitivity. With this hardware compensation, temperature coefficients are experimentally reduced from 0.348%/°C without compensation to -0.008%/°C with compensation for an element integrated in a sensor bridge and from 0.474%/°C to -0.0007%/°C for the single element.

Fourier transform-based post-processing drift compensation and calibration method for scanning probe microscopy

Scanning probe microscopy (SPM) is ubiquitous in nanoscale science allowing the observation of features in real space down to the angstrom resolution. The scanning nature of SPM, wherein a sharp tip rasters the surface during which a physical setpoint is maintained via a control feedback loop, often implies that the image is subject to drift effects, leading to distortion of the resulting image. While there are in-operando methods to compensate for the drift, correcting the residual linear drift in obtained images is often neglected. In this paper, we present a reciprocal space-based technique to compensate the linear drift in atomically-resolved scanning probe microscopy images without distinction of the fast and slow scanning directions; furthermore this method does not require the set of SPM images obtained for the different scanning directions. Instead, the compensation is made possible by the a priori knowledge of the lattice parameters. The method can also be used to characterize and calibrate the SPM instrument.

Development of fixed-point two-degree-of-freedom angular error measurement system with precision improvement function.

In the field of precision industries, measuring micro-angular errors plays a crucial role in improving the accuracy of equipment. Therefore, this study aims to develop a two-degree-of-freedom (two-DOF) angular error measurement system for simultaneously measuring the assembly angle errors of the bond head in a die bonding machine. This system is based on the laser collimation method and constructed by using components such as a fiber laser and position-sensitive detectors. It utilizes the angular drift compensation of the laser beam and averaging function to improve the measurement accuracy. In addition, the mathematical model of the proposed system was established by using skew-ray tracing. Through experiments, the measuring accuracy of ±0.25 arcsec and measuring repeatability of 0.3 arcsec could be achieved.