Calibration Data Research Articles

EMG-based Multi-User Hand Gesture Classification via Unsupervised Transfer Learning Using Unknown Calibration Gestures.

The poor generalization performance and heavy training burden of the gesture classification model contribute as two main barriers that hinder the commercialization of sEMG-based human-machine interaction (HMI) systems. To overcome these challenges, eight unsupervised transfer learning (TL) algorithms developed on the basis of convolutional neural networks (CNNs) were explored and compared on a dataset consisting of 10 gestures from 35 subjects. The highest classification accuracy obtained by CORrelation Alignment (CORAL) reaches more than 90%, which is 10% higher than the methods without using TL. In addition, the proposed model outperforms 4 common traditional classifiers (KNN, LDA, SVM, and Random Forest) using the minimal calibration data (two repeated trials for each gesture). The results also demonstrate the model has a great transfer robustness/flexibility for cross-gesture and cross-day scenarios, with an accuracy of 87.94% achieved using calibration gestures that are different with model training, and an accuracy of 84.26% achieved using calibration data collected on a different day, respectively. As the outcomes confirm, the proposed CNN TL method provides a practical solution for freeing new users from the complicated acquisition paradigm in the calibration process before using sEMG-based HMI systems.

Flow Rate and Volume Estimates from Variable Frequency Drive Operated Drainage Sump Pumps

Highlights This article provides an overview of operations and benefits of using a variable frequency drive to operate agricultural subsurface drainage system sump pumps. A methodology is proposed to monitor flow rates leaving agricultural subsurface drainage pump stations via a MATLAB algorithm. The results of this research were mixed because the algorithm estimates did not always match with the corresponding flowmeter measurements. Abstract. Subsurface drainage plays a crucial role in excess water removal through perforated pipes buried in the soil. The Red River Valley of eastern North Dakota and west central Minnesota often requires pumped drainage outlets due to its flat topography, with many modern systems incorporating variable frequency drives (VFD) for improved efficiency. VFDs adjust pump speeds to match drainage needs, but this dynamic operation complicates accurate flow rate estimation for edge-of-field monitoring. This project aimed to devise an automated method for calculating flow rate estimates from VFD pumping systems involving tank geometry, operational water levels, and pump duty cycling. Linear regressions with calibration data from a transit-time ultrasonic flowmeter produced mixed outcomes. Some linear regressions revealed errors in calculated versus measured flow rate of =14%, while other regressions showed errors nearing 40%. To address this, future efforts should explore methods incorporating the pump’s electrical characteristics. This approach would provide a more accurate representation of the unstable and intricate pumping operations during transient conditions. Keywords: Edge-of-field monitoring, Flow rate, Pump, Red River Basin, Subsurface drainage, Variable frequency drive

AC susceptibility measurement of magnetic nanoparticles using an optically pumped magnetometer and a flux transformer

Magnetic particle imaging (MPI) is used to detect small magnetic fields in magnetic nanoparticles (MNPs). As a first step in MPI systems, we proposed an AC susceptibility measurement system with an optically pumped magnetometer (OPM) based on a flux transformer. First, the magnetic frequency response of the OPM was obtained for calibration. Second, the AC susceptibility of the Resovist MNPs in liquid and solid phases was obtained using the calibration data. These results are consistent with those of previous studies. Therefore, the proposed method is useful for detecting weak MNP magnetic signals in relatively strong magnetic fields and is expected to be applicable to MPI.

Multi-Stimulus Least-Squares Transformation With Online Adaptation Scheme to Reduce Calibration Effort for SSVEP-Based BCIs.

The proposed ms-LST-OA consists of two parts. Firstly, to improve the precision of the transformation matrices, we propose the multi-stimulus LST (ms-LST) using cross-stimulus learning scheme as the cross-subject data transformation method. The ms-LST uses the data from neighboring stimuli to construct a higher precision transformation matrix for each stimulus to reduce the differences between transformed data and real data. Secondly, to further optimize the constructed spatial filters and reference templates, we use an online adaptation scheme to learn more features of the EEG signals of the target subject through an iterative process trial-by-trial. ms-LST-OA performance was measured for three datasets (Benchmark Dataset, BETA Dataset, and UCSD Dataset). Using few calibration data, the ITR of ms-LST-OA achieved 210.01±10.10 bits/min, 172.31±7.26 bits/min, and 139.04±14.90 bits/min for all three datasets, respectively. Using ms-LST-OA can reduce calibration effort for SSVEP-based BCIs.

Autonomous Calibration of Blood Pressure Dependent Data Using Second-Order Blood Pressure Variation for a Future Mobile Diagnostic: Requirements for a Calibration

Autonomous Calibration of Blood Pressure Dependent Data Using Second-Order Blood Pressure Variation for a Future Mobile Diagnostic: Requirements for a Calibration

METHODS OF MACHINE LEARNING IN MODERN METROLOGY

In the modern world of scientific and technological progress, the requirements for the accuracy and reliability of measurements are becoming increasingly stringent. The rapid development of machine learning (ML) methods opens up perspectives for improving metrological processes and enhancing the quality of measurements. This article explores the potential application of ML methods in metrology, outlining the main types of ML models in automatic instrument calibration, analysis, and prediction of data. Attention is paid to the development of hybrid approaches that combine ML methods with traditional metrological methods for the optimal solution of complex measurement tasks.

Subgraph Propagation and Contrastive Calibration for Incomplete Multiview Data Clustering.

The success of multiview raw data mining relies on the integrity of attributes. However, each view faces various noises and collection failures, which leads to a condition that attributes are only partially available. To make matters worse, the attributes in multiview raw data are composed of multiple forms, which makes it more difficult to explore the structure of the data especially in multiview clustering task. Due to the missing data in some views, the clustering task on incomplete multiview data confronts the following challenges, namely: 1) mining the topology of missing data in multiview is an urgent problem to be solved; 2) most approaches do not calibrate the complemented representations with common information of multiple views; and 3) we discover that the cluster distributions obtained from incomplete views have a cluster distribution unaligned problem (CDUP) in the latent space. To solve the above issues, we propose a deep clustering framework based on subgraph propagation and contrastive calibration (SPCC) for incomplete multiview raw data. First, the global structural graph is reconstructed by propagating the subgraphs generated by the complete data of each view. Then, the missing views are completed and calibrated under the guidance of the global structural graph and contrast learning between views. In the latent space, we assume that different views have a common cluster representation in the same dimension. However, in the unsupervised condition, the fact that the cluster distributions of different views do not correspond affects the information completion process to use information from other views. Finally, the complemented cluster distributions for different views are aligned by contrastive learning (CL), thus solving the CDUP in the latent space. Our method achieves advanced performance on six benchmarks, which validates the effectiveness and superiority of our SPCC.

Performance Evaluation of IoT Sensors in Urban Air Quality Monitoring: Insights from the IoT Sensor Performance Test

In this paper, we report on extensive experiments conducted to evaluate Internet of Things (IoT) sensor performance in monitoring urban air quality. As certified sensors showed a considerably reduced air quality measurement error of 4.3% compared to uncalibrated sensors at 8.5%, our results highlight the crucial function of sensor calibration. The performance of sensors was impacted by environmental factors; higher temperatures produced better accuracy (3.6%), while high humidity levels caused sensors to react more quickly (2.3 seconds). The average air quality index (AQI) recorded by inside sensors was 45, but outside sensors reported an AQI of 60. This indicates that the positioning of the sensors had a substantial influence on the air quality data. Additionally, the methods of data transmission were examined, and it was found that Wi-Fi-transmitting sensors had lower latency (0.6 seconds) and data loss (1.8%) than cellular-transmitting sensors. These results emphasize the significance of environmental factors, sensor placement strategy, sensor calibration, and suitable data transmission techniques in maximizing IoT sensor performance for urban air quality monitoring, ultimately leading to more accurate and dependable air quality assessment.

Landslide assessment through integrated geoelectrical and seismic methods: A case study in Thungsong site, southern Thailand

Many landslides can cause significant damage to infrastructure, property, and human life. To study landslide structure and processes, geophysical techniques are most productive when employed in combination with other survey and monitoring tools, such as intrusive sampling. Here, the integration of electrical resistivity tomography (ERT) and seismic refraction tomography (SRT) methods is used to assess landslides in Thungsong district, Nakhon Si Thammarat, the south of Thailand, where is a hilly and seasons of prolonged rainfall region. The 2D cross-plot analysis of P-wave velocity and resistivity values obtained by these two methods is introduced to identify potential landslide-prone zones in this region. The results of the 2D cross-plot model reveal detailed image of the subsurface conditions, highlighting areas of low P-wave velocity (lower than 600 m/s) and low resistivity (lower than 600 Ωm). These areas are indicative of weak zone and are potential to be sliding materials. Moreover, an intrusive sampling data from boreholes is also used for the calibration and validation geophysical data with geological data. This can improve the accuracy of landslide assessment and develop effective mitigation strategies to reduce the risk of landslides in this area. In addition of the 2D cross-plot, the volume of sliding material is also determined from the difference of the surface and slipping plane elevations. The volume calculation of sliding material is roughly 33447.76 m3. This approach provides a preliminary tool for landslide studies and monitoring landslides in this region, thus enabling an improved understanding of slope failure processes in this context, and the basis of a landslide mitigation strategy in the future.

Real-time target point identification and automated log grasping by a forwarder, using a single stereo camera for both object detection and boom-tip control

The forest industry is constantly striving to increase productivity and cut costs, and many research and innovation projects are currently focusing on semi-automated or autonomous systems. A key element, with several possible solutions, is automated log grasping, where researchers and manufacturers are looking for efficient and sturdy ways to solve the task in real-time forwarding operations. This study presents a simple method for automated log grasping using only a single stereo camera for object detection (log and grapple) and a simple controller moving the boom, with feedback from the camera as boom-tip control. The accuracy, precision, and repeatability of the method was tested on a full-scale forwarder. Boom movements were examined from two different start positions in relation to the target position, with the log placed at three different angles. The overall log-grasping success was also evaluated. The tests were performed in a full-scale, real-time operation, without hand-eye calibration or other sensor data from the machine. The method was precise, with high repeatability, but the grasping point showed a minor systematic offset, depending on log angle. However, the deviation in accuracy was too small to affect the success rate. In practice, the most difficult log angles can be avoided by moving the machine slightly. The log grasping method may become part of an autonomous forwarding system or could provide operator support in semi-automated systems.

The O2 software framework and GPU usage in ALICE online and offline reconstruction in Run 3

The O² software framework and GPU usage in ALICE online and offline reconstruction in Run 3

Biopsychosocial factors in oral and systemic diseases: a scoping review.

The association between chronic oral diseases and other major systemic health conditions, commonly referred to as the oral-systemic health connection, has been previously studied with several underlying common risk factors and pathways linking both groups of diseases. Psychosocial factors contribute to an increased susceptibility to chronic oral and non-oral diseases. The aim of this review is to summarize the current state of knowledge on the role of psychosocial stress in chronic oral and systemic diseases. A search strategy was built and a literature search was conducted using four databases (CINAHL, Embase, Medline, PsycINFO). A combination of search terms related to psychosocial stress, systemic disease, and oral conditions were used. Studies were eligible for inclusion if they included human adults (aged 18 years and older), included psychosocial factors as an exposure measure, and outcome measures of both an oral and systemic condition. Only English-language articles were considered. Pilot testing of the data extraction form and calibration were conducted and data were extracted independently by one researcher. A total of fifteen articles out of eighty full-text articles screened were determined to be eligible for inclusion in this review. Periodontal disease was the most commonly studied oral disease, measured in 53% of included articles, with the most commonly studied systemic diseases being of mental health conditions (40%) and diabetes (47%). Psychosocial stress was measured using a range of psychometric indicators and/or biomarkers, including perceived stress, individual behaviours, childhood adversity, and cortisol. In total, fourteen studies found a positive association between measures of psychosocial stress and oral-systemic health. Psychosocial stress may be a common contributor to both chronic oral and non-oral diseases.

Modelling of a multi-seam hard coal deposit using IT tools

With the rising demand for mineral resources, efficient deposit management has become a critical challenge in modern mining. This article underscores the significance of geological modeling in coal deposit management, emphasizing the essential role that digital deposit models play across various mine departments. It outlines the methodology for creating geological models, detailing steps such as data digitization, validation, model construction, calibration, and continuous updates. The article examines three specific deposit modelling software programs (Minex, MineScape, and Carlson) highlighting their unique features and suitability for multi-seam coal deposits. The findings include the development of contour maps, structural and qualitative models, and conducting economic analyses. Additionally, the article discusses the importance of modelling tectonic disturbances and creating qualitative maps for effective deposit exploitation planning. It concludes by emphasizing that the integration of information technology with geological expertise is crucial for precise planning and optimization of mining operations, leading to more efficient and sustainable resource management.

Crowdsourced Meteorological Data to Supplement Limited Official Sources: A Survey and Case Study of Precipitation Monitoring in Guangzhou, China

Abstract Crowdsourced meteorological data may provide a useful supplement to operational observations. However, the willingness of various parties to share their data remains unclear. Here, a survey on data applications was carried out to investigate the willingness to participate in crowdsourcing observations. Of the 21 responses, 71% expressed difficulty in meeting the requirement of data services using only their own observations and revealed that they would be willing to exchange data with other parties under some framework; moreover, 90% expressed a willingness to participate in crowdsourcing observations. The findings suggest that in a way the social foundation of crowdsourcing has been established in China. Additionally, a case study on precipitation monitoring was performed in Guangzhou, the capital city of Guangdong Province, South China. Three sources of hourly measurements were combined after data quality control and calibration and interpolated over Guangzhou (gridded precipitation was based on combined data, and it is referred to as the COM grid). Subsequently, the COM grid was compared with the grid data based only on observations from the China Meteorological Administration using three indices, namely, cumulative precipitation, precipitation intensity, and heavy rain hours. The results indicate that requirement for more observations could benefit from crowdsourced data, especially on uneven terrain and in regions covered by sparse surface stations.

The irtQ R package: a user-friendly tool for item response theory-based test data analysis and calibration.

Computerized adaptive testing (CAT) has become a widely adopted test design for high-stakes licensing and certification exams, particularly in the health professions in the United States, due to its ability to tailor test difficulty in real time, reducing testing time while providing precise ability estimates. A key component of CAT is item response theory (IRT), which facilitates the dynamic selection of items based on examinees' ability levels during a test. Accurate estimation of item and ability parameters is essential for successful CAT implementation, necessitating convenient and reliable software to ensure precise parameter estimation. This paper introduces the irtQ R package, which simplifies IRT-based analysis and item calibration under unidimensional IRT models. While it does not directly simulate CAT, it provides essential tools to support CAT development, including parameter estimation using marginal maximum likelihood estimation via the expectation-maximization algorithm, pretest item calibration through fixed item parameter calibration and fixed ability parameter calibration methods, and examinee ability estimation. The package also enables users to compute item and test characteristic curves and information functions necessary for evaluating the psychometric properties of a test. This paper illustrates the key features of the irtQ package through examples using simulated datasets, demonstrating its utility in IRT applications such as test data analysis and ability scoring. By providing a user-friendly environment for IRT analysis, irtQ significantly enhances the capacity for efficient adaptive testing research and operations. Finally, the paper highlights additional core functionalities of irtQ, emphasizing its broader applicability to the development and operation of IRT-based assessments.

Optimization of lane based signalized intersections through vissim at outer ring road Bengaluru

Signalized intersections are indispensable parts of urban traffic networks. In developing countries, there are many types of vehicles, which causes heterogenous traffic conditions. Lack of lane discipline, varying composition, and a variety of vehicle situations are all possible issues that get worse with time. For such a dynamic traffic situation, microsimulation modeling is appropriate. The traffic flow characteristics of a particular road are frequently used in traffic simulation research on signalized intersections. The methodology outlines a systematic process for optimizing traffic signals using VISSIM software, including data collection, calibration, and signal timing adjustments at the Iblur and Silkboard Junction, Bengaluru, Karnataka, India. Results show significant improvements in key performance metrics after optimizing signal timings, leading to reduced congestion and smoother traffic flow. The study underscores the significance of accurate data collection for developing and validating simulation models. The successful development and validation of a simulation model in VISSIM, along with efforts to enhance intersection performance through signal timing adjustments, have been achieved. Future research directions include expanding calibration efforts, incorporating real-time field data, and exploring adaptive signal control systems.

IN-FLIGHT AND ON-ORBIT CALIBRATION OF LUNAR IMAGING SPECTROMETERS – A REVIEW

Lunar imaging spectrometers play a leading role in the study of the Moon’s mineral composition. The accuracy and reliability of the acquired data depend on the calibration process. Key stages of it include laboratory calibration, in-flight validation, on-orbit calibration, and cross-calibration. During these stages, a variety of techniques and methods are used for calibration with the aim of achieving higher radiometric accuracy when recording the spectral reflective characteristics of materials in scenes from the lunar surface. These methods include capturing well-known calibration astronomical targets and calibration sites, comparing data from previous lunar surface studies obtained from orbital devices or ground-based telescopes. On-board sources for calibration, such as lamps with a standardized emission spectrum, are also used. Capturing the Earth’s atmosphere for validation and calibration of data recorded by optical spectrometers is another method. The review presents an overview of techniques and methods used to calibrate optical spectrometers in flight to the Moon and in lunar orbit. This paper provides a review of the techniques and methods utilized for in-flight and on-orbit calibration of lunar imaging spectrometers, drawing from an extensive overview of referenced science papers.

Leveraging Transfer Superposition Theory for StableState Visual Evoked Potential Cross-Subject Frequency Recognition.

In steady-state visual evoked potential (SSVEP)based brain-computer interfaces (BCIs), various spatial filtering methods based on individual calibration data have been proposed to alleviate the interference of spontaneous activities in SSVEP signals for enhancing the SSVEP detection performance. However, the necessary calibration procedures take time, cause visual fatigue and reduce usability. For the calibration-free scenario, we propose a cross-subject frequency identification method based on transfer superimposed theory for SSVEP frequency decoding. First, a multi-channel signal decomposition model was constructed. Next, we used the cross least squares iterative method to create individual specific transfer spatial filters as well as source subject transfer superposition templates in the source subject. Then, we identified common knowledge among source subjects using a prototype spatial filter to make common transfer spatial filters and common impulse responses. Following, we reconstructed a global transfer superimposition template with SSVEP frequency characteristics. Finally, an ensemble cross-subject transfer learning method was proposed for SSVEP frequency recognition by combining the sourcesubject transfer mode, the global transfer mode, and the sinecosine reference template. Offline tests on two public datasets show that the proposed method significantly outperforms the FBCCA, TTCCA, and CSSFT methods. More importantly, the proposed method can be directly used in online SSVEP recognition without calibration. The proposed algorithm was robust, which is important for a practical BCI.

Analysis of the effect of motion on highly accelerated 3D FatNavs in 3D brain images acquired at 3T.

3D FatNavs are rapid acquisitions of MRI fat-volumes within the head that can be used for retrospective motion correction for brain MRI. 3D FatNavs typically use very high acceleration factors and are reconstructed with the GRAPPA parallel imaging technique. However, the GRAPPA reconstruction is not expected to perform well on 3D FatNavs volumes in the presence of strong motion due to the mismatched calibration data acquired once at the start of the scan, leading to motion-parameter misestimation. This study aims to assess the accuracy and precision of 3D FatNav-derived motion-estimates in the presence of large changes in head position. Rigid motion parameters were simulated and applied retrospectively to the 3D FatNav volumes from MPRAGE datasets acquired at 3T. The transformed images were then re-reconstructed using GRAPPA to simulate real motion deterioration of the fat-navigator, and used to estimate the motion applied and evaluate the tracking inaccuracy. This information was then used to estimate the residual motion after 3D FatNav-based motion correction and applied to the original MPRAGE volumes. The effect of the misestimation was assessed using an image quality metric and the evaluation scores from two observers. Quality boundaries were then estimated to assess the motion tolerance when 3D FatNavs are used. The GRAPPA reconstruction was shown to deteriorate for large changes in the head position, affecting the quality of 3D FatNav volumes and consequently degrading the accuracy of the motion-estimates. Based on our simulations, the estimated threshold of motion that led to a noticeable degradation in the MPRAGE image quality was up to RMS values of 3.7° and 3 mm for rotations and translations respectively. 3D FatNavs were shown to be able to correct for a wide range of motion levels and types. Boundaries of acceptable motion magnitudes for different levels of acceptable loss of image quality were determined.

Calibration and limitations of a fixed-partly fixed resonant column apparatus

The small-strain shear modulus of soils and rocks is an important input parameter in geotechnical analyses. Its estimation using the resonant column apparatus relies on accurate knowledge of the rotational inertia of the apparatus’s active end. Due to the complex geometry of the active end, its inertia is typically found from a calibration exercise. In this study a range of aluminium calibration bars and steel masses were used to compare two different calibration exercises presented in the literature. The resulting values of active end inertia are shown to be highly method-dependent. The method of compensating for the drive connection stiffness by finding the apparatus resonant frequency is found to introduce errors at low stiffness. The combined use of calibration bars and masses reduces scatter but yields poor results for stiff bars. An alternative approach to mitigate both of these problems is suggested. Active end inertia is found to be constant using this alternative approach. While effects of base inertia and reaction mass inertia are found to be small for this apparatus, compliance of the drive system is shown to significantly reduce resonant frequencies at high stiffnesses which increases the apparent active end inertia. This reduction in resonant frequency is modelled and used to derive frequency correction factors, which reduce errors in shear modulus to within acceptable limits when using the constant value of active end inertia. This demonstrates the potential for increasing the limit of sample stiffness where sufficient calibration data are available.