The journal Measurement Science Review has been published online since 2001. PUBLISHER: De Gruyter Brill, Paradigm Publishing Services https://reference-global.com/journal/MSR ARCHIVE from 2001 to 2024 is available at https://www.measurement.sk/ If you want to submit your article to Measurement Science Review you can use this site. Measurement Science Review is a scientific peer-reviewed journal of the Institute of Measurement Science, Slovak Academy of Sciences. The aim of the journal Measurement Science Review is to publish papers from scientific disciplines covering Measurement Science with an orientation toward the theory of measurement, measurement of physical quantities and measurement in biomedicine. The journal Measurement Science Review is published since 2001. Since 07 Mar 2008 the Journal has been published by: De Gruyter Poland Sp. z o.o., Sciendo; As of 01 Oct 2025, the Sciendo publishing platform operates under a new name — Paradigm — available at https://reference-global.com/journal/MSR Since Volume 8 (2008), each published paper is provided with a DOI (Digital Object Identifier). Please note that this web page related to Open Journal System is in use from January 2023, i.e. starting from Volume 23. Measurement Science Review is a fully open access scientific journal licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0) eISSN: 1335-8871 Open volume since January 2025 Impact Factor: 0.8 Five Year Impact Factor: 0.9 Cite Score: 1.9 SJR: 0.240 SNIP: 0.538 JCI: 0.22 MNiSW: 70 Google Scholar h5-index: 13 Google Scholar h5-median: 21 Journal is indexed in 40 databases Languages: English Journal Subjects: Engineering, Electrical Engineering, Control Engineering, Metrology and Testing Scope of the Journal: Theoretical Problems of Measurement Measurement of Physical Quantities Measurement in Biomedicine
Slovak Academy of Sciences In partnership with: Paradigm Publishing Services
A Compact Design of a Two-stage Piecewise Linear ADC Used for Sensor Linearity Improvement
This paper presents a compact two-stage piecewise linear analog-to-digital converter (ADC) that enhances sensor linearity while featuring a reduced design complexity. Unlike a traditional two-stage piecewise linear ADC, the proposed architecture achieves the same resolution with fewer components by using a single flash ADC for both conversion stages and by adding a comparator at the start of each conversion stage. The flash ADC has a resolution of (n/2−1) bits, where n is the total resolution of the two-stage piecewise linear ADC. This compact architecture enables efficient and simultaneous linearization and digitization of sensor output while significantly reducing energy consumption and implementation costs. Numerical results confirm the compactness and cost-effectiveness of the proposed design, showing up to a 75 % reduction in comparator count and up to a 25 % reduction in resistor count compared to the traditional design of the two-stage piecewise linear ADC with the same total resolution. These significant savings make the proposed two-stage piecewise linear ADC design well-suited for applications with strict energy and space constraints.
Copyright (c) 2025 Slovak Academy of Sciences - Institute of Measurement Science https://creativecommons.org/licenses/by-nc-nd/4.0/
Robust Pose Estimation by Fusing Partial Color and Depth Imagery
Pose estimation algorithms are an extensively studied research topic in the field of computer vision and machine learning. Even though many algorithms attempt to solve the problem, most algorithms are still not accurate enough to recover poses in real-world applications. Therefore, we have developed a new approach that utilizes depth cues and optical flow measurements that presents improved pose recovery in real-world pose estimation applications. We also present a camera calibration method that creates projection matrices for pose estimation from cameras, which enables angular comparison for relative pose estimates from two sensor systems positioned at different locations. We applied and tested the proposed algorithm in the laboratory settings and compared our findings with a commercial and a gold standard pose estimation system. Angular pose errors were reported.
The Three Most Common Needs for Training on Measurement Uncertainty
Measurement uncertainty is key to assessing, stating and improving the reliability of measurements. An understanding of measurement uncertainty is the basis for confidence in measurements and is required by many communities; among others in national metrology institutes, accreditation bodies, calibration and testing laboratories, as well as in legal metrology, at universities and in different metrology fields. An important cornerstone to convey an understanding of measurement uncertainty is to provide training. This article identifies the status and the needs for training on measurement uncertainty in each of the above communities as well as among those teaching uncertainty. It is the first study to do so across many different disciplines, and it merges many different sources of information with a focus on Europe. As a result, awareness on the training needs of different communities is raised and teachers of uncertainty are supported in addressing their audiences’ needs as well as in improving their uncertainty-specific pedagogical and technology-related knowledge. The three needs that are most commonly encountered in the communities requiring an understanding of measurement uncertainty, are 1) to address a general lack of training on measurement uncertainty, 2) to gain a better overview of existing training on measurement uncertainty in several communities, and 3) to deliver more training on specific technical topics including use of a Monte Carlo method for propagating probability distributions and treating multivariate measurands and measurement models. These needs will serve to guide future developments in uncertainty training and will, ultimately, contribute to increasing the understanding of uncertainty.
A Nested DUAL-axis Accelerometer with EnhancedTemperature Robustness for Testing Across the EntireOperational Temperature Range
This paper investigates a nested biaxial micro-electro-mechanical systems (MEMS) accelerometer with low coupling error, derives the kinetic model equations of the accelerometer, and performs finite element simulations of the operating modes and mechanical coupling of the structure. The accelerometer uses an inner and outer nested structure with non-rigid links to reduce cross-coupling between orthogonal sensitive axes. Closed-loop control is employed to further improve the device stability and reduce the coupling coefficient. The preparation and testing of the structure was finally completed. Without compensation, tests show that the accelerometer works in closed-loop mode with low cross-coupling, and the inter-axis cross-coupling coefficients are less than 1.31 % and 2.4 %, respectively. The accelerometer exhibits an X-axis output drift of 1.26 mg/°C and a Y-axis output drift of 0.91 mg/°C in its natural state over the full temperature range of −40 °C to 60 °C. After compensation by the temperature compensation system, the X-axis output bias is optimized to 0.02008 mg/°C and the Y-axis output bias to 0.02041 mg/°C.
micro-electro-mechanical systems accelerometer
TRI-BCC: Tri-Level Breast Cancer Classification via Transfer Learning Networks with Histopathogical Images
Breast cancer (BC) is the second most common hereditary cancer in women and is most likely to affect women. The high cost of treating BC has made its prevention an important health issue. However, early detection of BC is a time-consuming and difficult task when using mammogram images. This work introduces a novel TRI-BCC model for BC detection using transfer learning-based tri-level classification with histopathological images. Initially, input images undergo a denoising phase using the Adaptive Brightness Contrast Dynamic Histogram Equalization (ABCDE) technique, which removes noisy artifacts to enhance overall image quality. Following this pre-processing step, a data augmentation phase is employed to synthetically expand the training dataset, thereby improving model generalization and robustness against overfitting. The augmented and pre-processed images are subsequently provided as input to the hybrid Golden Eagle-Whale Optimization (GWO) algorithm, for the segmentation of lesions. In the tri-level classification, transfer learning (TL) models: CapsuleNet, EfficientNet, ShuffleNet, GoogleNet, MobileNet and ResNet are employed to categorize images as benign or malignant (Level-I). Benign cases (Level-II) include Adenosis (AS), Phyllodes tumor (PT), Tubular adenoma (TA) and Fibroadenoma (FA), while malignant cases (Level-II) include Papillary (PC), Mucinous (MC), Lobular (LC), and Ductal (DC) carcinoma. Malignant images are processed using a Randomized Decision Tree (RDT) to classify them into different cancer stages (Level-III) for precise diagnosis. Based on experimental analysis, the TRI-BCC model achieves an overall accuracy of 99.06 % for the categorization of BC. The proposed TRI-BCC model improves overall accuracy by 6.11 %, 0.78 %, 2.66 %, and 13.18 % compared to UNet+YOLO, fine-tuned networks, hybrid deep neural network, and Pa-DBN-BC, respectively.
Analysis and Graphical Evaluation of Pressure Changes in Pneumatic Circuits for Industrial Applications
Measuring and evaluating the condition of pneumatic systems (PS) is an important identification tool for many industrial applications. Therefore, this paper focuses on the detection of changes (faults) in these circuits by analysing system pressures. The proposed approach allows dynamic monitoring of the pressure profile when the run-in processes have an identical sequence (i.e. deviations and changes become steady at a specific frequency). The analysis performed and the subsequent graphical representation provide valuable insights into the running process for management. Proof-of-concept experiments are carried out with a sensor (AVENTICS) on a simple pneumatic circuit with constant motion, which serves as a sample example. Their results show the usefulness of this method, and the collected data will serve as a suitable basis for implementation in machine learning models.
pneumatic systems, pressure changes (faults), filtering of unwanted states, failure detection
Filter and Sampling Rate Optimization for PPG-Based Detection of Autonomic Dysfunction: An ECG-guided Approach
Photoplethysmography (PPG) is well suited for wearable health applications, but has a lower frequency spectrum than electrocardiography (ECG) and is more affected by motion artifacts. In this study, ten signal filters from three categories were investigated in combination with different sampling rates to evaluate their effects on PPG signal quality. A correlation and accuracy analysis was performed comparing the interbeat intervals detected in PPG and ECG using Pearson correlation and absolute error. The results showed that specific filters with sampling rates as low as 40 Hz perform well in detecting autonomic neuropathy. The results highlight the potential of PPG with optimized filters and sampling rates for clinical screening of the autonomic nervous system (ANS) in wearable health monitoring.
Detection of Laryngeal Pathologies from Voice using EMD-based Mel-Spectrograms and Scalograms with AlexNet
In this paper, a novel method for detecting of laryngeal pathologies using deep neural networks and time–frequency signal processing techniques is presented. The proposed approach combines empirical mode decomposition (EMD) and wavelet analysis to extract discriminative features from healthy and pathological voice recordings obtained from the Saarbrücken Voice Database (SVD). Each voice signal is pre-processed and decomposed into intrinsic mode functions (IMFs), from which the most relevant IMF is selected based on a temporal energy criterion. Two sets of features are derived from the selected IMF: Mel-frequency cepstral coefficients (MFCCs) and continuous wavelet transform (CWT) coefficients. These features are converted into Mel-spectrogram and scalogram images, respectively, which serve as inputs to the AlexNet convolutional neural network (AlexNet-CNN) for automatic binary classification. To the best of our knowledge, this is the first study to incorporate scalogram representations with AlexNet-CNN in the context of pathological voice detection. The results show that the proposed method achieves a classification accuracy of 85.66 % when using Mel-spectrograms and 86.4 % when using scalograms, demonstrating its potential for effective and interpretable voice pathology screening.
Adaptive Measurement Selection for Scalable Distributed Graph Optimization in Multi-UAV Relative Positioning
Autonomous relative positioning is a critical challenge for unmanned aerial vehicle (UAV) swarms. In this study, we address the optimization of measurement constraint selection in the distributed relative positioning process of large-scale UAV swarms. We introduce a distributed graph optimization (DGO) scheme for swarm relative positioning, which enables global consistent relative position estimation through limited inter-UAV information sharing. To prevent the calculation time from escalating with swarm size, this method allows users to specify the number of relative measurement constraints used in the calculation. Building on the Cramér-Rao Lower Bound (CRLB) and Fisher Information Matrix (FIM) theory, we further propose a theoretically optimal method for selecting relative measurement constraints. Different from traditional distributed optimization methods that use fixed constraints, the proposed method can adaptively select the most theoretically advantageous constraints to improve accuracy, resulting in higher precision and improved adaptability to dynamic environments. To validate the effectiveness of the proposed method, we conducted numerical experiments with different swarm sizes and sensing error conditions. The results show that the proposed method has higher accuracy and stability compared to self-pose estimation methods and other measurement selection approaches, while having a low computational load. This work represents the first attempt to incorporate the FIM into constraint selection for distributed localization of UAV swarms.
Exploration of Infrared Thermography as an Alternate Tool for the Detection of Gastric Diseases
The electrical impulses, known as electrogastrograms (EGGs), originate from the muscles of the stomach and are due to the physiological activity of the digestive system. The characteristics of these signals can be used to diagnose various human gastrointestinal disorders. Although the recording of EGG signals is a well-recognized diagnostic procedure, acquiring these signals from human subjects is difficult due to movement artifacts, and difficulties in electrode placement, and is also a time-consuming technique. Infrared thermography (IRT) is a non-contact and non-invasive technique used to detect various pathologies in humans. In this work, an attempt was made to establish the correlation between the information content (IC) of electrogastrograms and the information content of abdominal thermal images, in both normal and bradygastric subjects using Rényi entropy. The results show a strong correlation between the entropy of EGG signals and abdominal IRT images, in both normal and bradygastric subjects, with a correlation coefficient of 0.95 and 0.92, respectively. Since the information in the EGG signals and IRT images are strongly correlated, IRT can therefore be proposed as a diagnostic technique to replace EGG in the assessment of digestive diseases.
Copyright (c) 2025 Measurement Science Review https://creativecommons.org/licenses/by-nc-nd/4.0
KazStandard Primary Dew/Frost Point Generator
This article presents studies on the dew point/frost temperature generator using the single-pressure (1-P) and single-temperature (1-T) method first developed by KazStandard to improve the calibration accuracy of dew point hygrometers. The method of applying the dew point temperature scale is based on the supply of humid air with high instability through a thermostatic saturator, which enables increased measurement accuracy. The characteristics include a study of the saturator's efficiency, the stability of the dew point temperature reproduction and a comparison with a calibrated dew point hygrometer. The study was conducted in the dew point temperature range from −50°C to 20°C, and the sources of measurement uncertainty were analyzed.
GGLCM: A Real-Time Early Anomaly Detection Method for Mechanical Vibration Data with Missing Labels
Early anomaly detection plays a central role in the scientific maintenance of mechanical equipment. Although the application is limited by weak monitoring, it encounters the problem of missing labels. To overcome this challenge, the Gramian gray level co-occurrence matrix (GGLCM) analysis method is proposed, which includes three phases: first, the time-series are input into the Gramian angular field (GAF) in real time for signal dimension reconstruction. Second, the gray level co-occurrence matrix (GLCM) is applied to the reconstructed signal. Since the GAF preserves the dependencies in the time-series, the limitation of missing labels is significantly weakened. Third, a continuous alarm mechanism is developed for reliable detection. Finally, the GGLCM is verified by actual vibration datasets of overloaded bearings.
Feed Rate-Adaptive Predictive PH Interpolation for Real-Time NURBS Toolpath Execution in High-Speed CNC Machining
This study presents a novel feed rate-adaptive predictive interpolation algorithm that combines Pythagorean-hodograph (PH) curves with non-uniform rational b-splines (NURBS) to improve real-time toolpath generation in high-speed CNC machining. Unlike traditional NURBS interpolators, which are prone to feed rate fluctuations and poor curvature handling, the proposed method incorporates a curvature-aware predictive module that dynamically adjusts the feed rate based on local geometry and system dynamics. Key innovations include sharp corner detection, PH-based smoothing, and jerk-limited feed rate profiling. Simulation and experimental results show that the proposed algorithm significantly reduces path-following errors (on average by more than 60 % RMS error reduction) while ensuring smoother, bounded-acceleration trajectories. The algorithm consistently maintains geometric accuracy across different toolpath orientations, making it a robust solution for precision manufacturing tasks that require both real-time responsiveness and high accuracy.
non-uniform rational b-splines machining
Performance Estimation of Low Power and Area-Efficient Parallel Pipelined FFT
We present a novel parallel and pipelined fast Fourier transform (FFT) architecture for high-speed and low-power applications, a critical component in wireless communications and digital signal processors. The new FFT model implements a data-inverted Vedic multiplier in the FFT architecture, which reduces data switching activity in the input patterns to minimize dynamic power consumption and computational delay. The proposed architecture incorporates a low-power bit inversion (BI) multiplier scheme for a minimum number of complex multiplications with a high-speed partial product generation technique in FFT computation. This research focuses on the investigation and implementation of a modified butterfly unit as the best choice compared to other low-power and high-speed multipliers, such as Booth and Wallace multipliers for FFT processors. The BI multiplier design was synthesized in a field programmable gate array (FPGA), and the results show that the area efficiency could be improved by about 30 % and the power consumption and delay could be reduced by 56 %. The proposed FFT processor utilizes only 8 % of the available look-up tables (LUTs) with a 1:3 ratio in resource utilization and a 56 % reduction in delays compared to previous research. This makes this architecture best suited for high-speed wireless communications and 5G applications. This BI-Vedic multiplier is used in convolutions, FFT, and digital signal processing (DSP) filters where fast multiplication is critical. Throughput in applications with real-time signals is improved. It is also used in image and video processing and is critical for algorithms that manipulate pixels, scale, and compress data when many multiplications need to be performed quickly. IoT and embedded systems are beneficial for low-power systems as BI reduces power consumption and switching activity.
Adulteration Control of Lavender Essential Oil by using Its Electrical Properties in the Low-Frequency Range
With the development of technology, analysis methods have also increased and, as with many products, the properties of essential oils can be studied using methods based on electrical properties. In this study, the electrical properties of lavender essential oil mixed with various fixed oils were to be investigated to determine the adulteration rate more quickly and easily. Corn oil, olive oil, and sunflower oil were selected as fixed oils to obtain the oil samples with different adulteration rates. In this context, the real and imaginary parts of the permittivity, the loss tangents, the electrical conductivities, the amplitudes, and the phases of the impedances of the oil samples were measured. The measurements were also used to create an equivalent Debye circuit model for the lavender essential oil in order to analyze its electrical properties. The results showed that the lavender essential oil and the oil samples mixed at different rates showed different results in terms of electrical properties at different frequencies. According to the measurements, electrical conductivity was a critical parameter for a mixture of pure lavender essential oil and corn oil. The loss tangent was investigated for the mixture of pure lavender essential oil and olive oil, and the mixture of pure lavender essential oil and sunflower oil. It was observed that the electrical conductivity in the studied frequency band increased when the adulteration rate in the mixed corn oil samples increased. In addition, the loss tangent of the olive oil mixture and the sunflower oil mixture increased as the adulteration rate of said oil samples increased. By measuring the electrical properties, it may be possible to determine whether there is an adulteration and dilution process for particularly valuable essential oils, and if so, in what proportion.
System Maintainability Estimation with Multi-Peak Time Distribution based on the Bayesian Melding Method
In some test phases of equipment, the small sample size of test data and the absence of some maintenance operations may lead to a multi-peak phenomenon in data distribution, which is a challenge for Bayesian information fusion based on maintainability assessment. In this paper, prior information at two levels, the system level and the maintenance operation level, is integrated with the field test data via the Bayesian melding method (BMM). Mixture priors are used to avoid prior-data conflicts in the Bayesian framework, and a Bayesian posterior distribution is used to estimate system maintainability. Adaptive sampling importance resampling (ASIR) is used to overcome computational difficulties in simulation algorithms. Compared to the other methods, the proposed method provides more information sources for maintainability estimation, whose estimation effect is shown to be satisfactory based on two validation cases.
Efficiency Assessment of a High-Speed Tracked Vehicle Hybrid Powertrain
The paper analyzes the difference in power balance and efficiency between a modernized, hybrid high-speed tracked vehicle powertrain model and a mechanical powertrain model corresponding to the real vehicle developed and verified in previous research. This is to prove the argument of the efficiency benefits of electrifying the vehicle turning mechanism and eliminating the friction elements slip. The simulation models of both powertrains presented in this paper are subjected to the same simulation conditions, with the powertrain design solutions being the only variables. The results presented show that the vehicle with a hybrid powertrain achieves the required turning radius about four seconds earlier, with about 50 % less internal combustion engine (ICE) power required for the analyzed working regime. The hybrid powertrain offers an infinite number of calculated turning radii within the range of electric motor rpm, instead of one calculated turning radius in an existing powertrain. This results in a reduction in the total power required for the turning process as there are no losses due to friction element slip.
Precision Measurement and Feature Selection in Medical Diagnostics using Hybrid Genetic Algorithm and Support Vector Machine
The rapid development of intelligent systems has had a significant impact on healthcare, forensics, and medicine, offering innovative solutions to critical problems. Breast cancer, which affects a large number of women each year, requires effective methods for early detection and accurate diagnosis to improve patient outcomes. This study introduces a hybrid feature selection method based on genetic algorithm (GA) and Bucket of Models (BoM) approach to improve breast cancer detection and classification. In the proposed method, GA is used to identify the most relevant features from the breast cancer diagnosis data, to improve the efficiency of the classification process. BoM is then used to select the optimal classification model from a set of candidates, to further improve the accuracy of diagnosis. The support vector machine (SVM) is used as the primary classifier due to its robustness in classifying medical data. The GA feature selection process includes encoding chromosomes, initializing the population, evaluating fitness, and iterating through reproduction steps, that systematically evaluate and select the most informative features for breast cancer diagnosis. In this study, a breast cancer detection accuracy of 97.16 % was achieved, which is a superior performance compared to existing state-of-the-art methods. This study contributes to the development of more accurate and efficient breast cancer screening tools to help healthcare providers make informed diagnostic decisions.
A Bearing Fault Diagnosis Model based on Minimum Average Composite Entropy and Parallel Attention Mechanism Convolutional Neural Network
This work addresses the issues of low diagnostic accuracy and weak generalization in rotating machinery bearing fault diagnosis, especially under complex noise conditions. In this paper, a novel bearing fault diagnosis method is proposed. This method, known as MACE + PFACNN, combines the minimum average composite entropy (MACE) with a parallel fusion attention convolutional neural network (PFACNN). In MACE, the minimum average composite entropy, which is composed of the Renyi entropy and the sample entropy, is used as a fitness function to guide the dung beetle optimization algorithm for fault feature extraction. Then, the extracted signal features are converted into angle and field and angular difference fields by Gramian angle field transformation. Finally, a PFACNN is used for fault diagnosis. Experimental data and bench tests show that the proposed model achieves a classification accuracy of 99.93 %. Compared with the baseline model, the noise resistance under complex noise conditions has improved by more than 15 %, and the generalization ability has increased by 3.68 %.
The Joint Calibration Method of Multi-line Laser and Tracking System based on Conjugate Gradient Iteration
The multi-line laser 3D reconstruction system mainly relies on marking points to acquire 3D data. To simplify the acquisition of 3D data for objects, we use a binocular tracking method to achieve unmarked point stitching of the multi-line laser reconstructed 3D data. The key challenge with this system is the joint calibration between the multi-line laser system and the tracking ball cage. Traditionally, planar calibration plates are used for calibration. However, due to the extensive calibration field, the production of large calibration plates incurs high costs and compromises machining accuracy. As a result, significant joint calibration errors occur between the tracking ball and the multi-line laser system, making high-precision calibration impossible. To solve these problems, an iterative method based on multi-position attitude and conjugate gradient is proposed to achieve high-precision joint calibration. A simple and convenient cross pole with multiple coding points is used as a calibrator. The 3D data of these coding points are determined beforehand using a coordinate measuring machine (CMM). First, the internal and external parameters of the binocular tracking system are calibrated using this cross pole. During the joint calibration process, in which both the multi-line laser system and the tracking ball cage are involved, the cross pole is imaged at different positions simultaneously with the binocular tracking system and the multi-line laser system. This allows us to determine the positions and orientations of both systems relative to each other and relative to the cross pole. The transformation relationship between the multi-line laser system and the tracking ball cage is calibrated using an iterative conjugate gradient optimization algorithm based on these positions and orientations, which completes the entire system calibration and eventually achieves three-dimensional reconstruction of the unmarked points. Compared to conventional planar calibration plate-based methods, our proposed approach requires only one cross pole to perform two crucial calibration steps, improving the joint calibration accuracy. While the final reconstruction accuracy of conventional methods is about 0.1 mm, our proposed method can achieve an accuracy of about 0.02 mm.
Hybrid Regression Models for Predicting Hydration: A Case Study in Pediatric Hemodialysis
With the rapid development of artificial intelligence (AI) and machine learning (ML), the medical field is experiencing significant advances that make it easier for doctors to monitor and personalize the hemodialysis process in pediatric patients. This case study focuses on the prediction of hydration in pediatric hemodialysis using hybrid ML models with hyperparameters for prediction accuracy. Data were collected from pediatric hemodialysis at the University Children's Hospital in Tiršova, and parameters such as weight, blood pressure, lean tissue index (LTI), fat tissue index (FTI), body mass index (BMI), total body water, extracellular water (ECW), adipose tissue mass, body cell mass, and bioimpedance were adjusted for training. The model was configured for each pediatric patient and retrained after each treatment to make individualized predictions with the highest accuracy. The proposed model uses measurable parameters to estimate hydration and provide better recommendations to the physician, leading to better results than commonly used state-of-the-art competing methods. The hybrid ML models with hyperparameters represent a novel, simplified, safe, and efficient method for predicting hydration in children, making it easier for doctors to monitor the hemodialysis process for children.
Grain Truck Compartment Localization Method based on Point Cloud Projection
Quality control is an essential step before grain storage. It requires the localization of grain truck compartments and guiding robotic arms to automatically sample grains. However, the diverse types of grain trucks and the variability in parking lead to difficulties in compartment localization and inaccurate measurements. To solve this problem, a rotating 3D laser scanner is proposed to scan grain trucks. After ground calibration, the XOY plane of the rotating scanned point cloud is aligned parallel to the ground. To avoid complex point cloud segmentation, grain truck point clouds are clipped using pre-defined regions of interest (ROI). Since only 2D corner points are required, this paper presents a projection-based point cloud processing method. Here, the points of the grain truck are projected onto the XOY plane and then the points of the rear and side panels of the projected compartment are extracted for line fitting. To robustly extract compartment corners, a region growing method based on density variations is proposed. Along the fitted line, the 2D corners of the rear and side panels are extracted to obtain the length and width dimensions of the compartment. Extensive tests have shown that the proposed method can accommodate various grain truck models with a corner extraction accuracy of less than 9.8 cm, making it suitable for the automated grain truck localization and measurement tasks.
A common problem since the early development of MRI systems, which persists to this day, is the induction of eddy currents (EC) in the conductive parts of the MR scanner during the rise and decay of gradient pulses. The magnitude of the eddy currents, among other factors, is proportional to the magnitude of the gradient pulses and the magnitude of change in the gradient field over time. These eddy currents cause several problems, ranging from heating of the cryostat to undesirable time-varying gradient fields that produce various artefacts in the acquired images or undesirable effects on tissue structures in patients. There are several ways to suppress the eddy current effect. In this article, we focus on eddy current compensation by modulating gradient pre-emphasis.
Recent Advances on Vibration Sensors and Calibration Methods for the Operation and Maintenance of Mechanical Equipment
Vibration is a common phenomenon in construction, military, manufacturing and other fields. Dangerous vibrations can directly affect the service life and efficiency of equipment. Vibration analysis is the most important method to prevent the occurrence of these equipment malfunctions. Vibration measurements are indispensable for vibration analysis. Currently, there is a great demand for vibration measurement and detection in industrial systems, and the requirements for the accuracy of measurement results are increasing. Therefore, we provide a brief literature review on the accurate measurement of the vibration, including the sensors, vibration sensor calibration equipment and procedures.
An Enhanced Measurement of Epicardial Fat Segmentation and Severity Classification using Modified U-Net and FOA-guided XGBoost
The amount of epicardial fat around the heart has a significant impact on cardiovascular function and requires precise measurement for timely treatment. In this work, an improved U-Net architecture is proposed for accurate segmentation of epicardial fat in computer tomography (CT) images. The proposed method integrates a modified squeeze-and-excitation (MSE) block and a multi-scale dense (MS-D) convolutional neural network (CNN) to improve feature extraction. In addition, a metaheuristic optimization algorithm from falcon optimization algorithm (FOA) is used for efficient feature selection. The selected features are then classified using the XGBoost algorithm to determine the fat severity. Experimental evaluations on a CT image dataset show the superior segmentation performance of the proposed U-Net compared to existing architectures. It achieves a mean intersection over union (Mean IOU) of 89.5 %, a mean Dice score (MDS) of 94.3 %, and a Pearson correlation coefficient (PCC) of 0.973. FOA-guided feature selection further increases the accuracy of severity classification. The overall classification accuracy of the model is 98 %. These results highlight the technological advancements and measurement accuracy of the proposed U-Net architecture. They also demonstrate the suitability of the model to improve cardiovascular risk assessment and management.
Contribution of using Vibration Diagnostic Tools in Assessing the Condition of Gear Elements in Off-Road Vehicle Gearboxes
The aim of this paper is to contribute to the possibility of using vibration diagnostics as a modern, non-destructive tool for assessing the condition of gears in the gearbox of off-road vehicles when the vehicles are used in real operating conditions, despite the non-stationary change in load during work. The method of vibration analysis in a vehicle gearbox under real operating conditions was determined and the adaptability of vibration analysis tools in determining the conditions of gears in off-road vehicles was considered. The data collected in the research shows that the vibration diagnostic method as a non-destructive tool for determining the condition of gears in off-road vehicle gearboxes under real operating conditions provides data on the condition of gear elements at different stages of vehicle operation. In this way, research time is shortened, there is no need to develop special test-tables for gearbox tests, the research method is non-destructive and it can identify processes that are characteristic of both early and later operating stages of the vehicle. Finally, the decision as to whether certain forms of gearbox maintenance are required can be made based on the assessed condition of the gears in the gearbox.
Research on the Error Estimation Method for Electric Energy Meters of Electric Vehicle Charging Piles based on Deep Learning
In the context of the increasing spread of electric vehicle (EV) charging stations, the accuracy and reliability of electric energy measurement is becoming increasingly important for consumers. Degradation in the performance of smart meters at these stations is often due to factors such as aging and malfunctions. Traditional approaches to solving this problem usually involve manual on-site inspections, which require significant investment in manpower and materials. To overcome this challenge, this study proposes an error estimation method that integrates highway convolutional neural networks with bidirectional long short-term memory (LSTM) networks, which enables real-time prediction of measurement performance at charging piles. First, the convolutional module is combined with the highway network to extract spatial features from smart meter data for charging facilities while retaining some original information to improve model prediction performance. The features are then fed into a bidirectional LSTM network to obtain temporal characteristics, which improves the accuracy of relative error predictions. Empirical validation of this method at a charging station in the region has shown that it has higher efficiency compared to existing advanced models.
bidirectional long short-term memory network
Experimental Platform for Investigation of Low-Frequency Magnetic Field Effects on Cells
This study presents a novel experimental platform designed to systematically investigate the effects of low-frequency (LF) magnetic fields (MFs) on biological systems. By overcoming key methodological challenges, including variability in environmental conditions and poor reproducibility, this platform sets a new standard for experimental reliability. It integrates precise temperature regulation, high MF homogeneity, and a modular structure, that ensures adaptability to diverse experimental setups. The platform was validated using Saccharomyces cerevisiae CCY 21-4-99 as a model organism, cultivated under controlled conditions with and without MF exposure. The minimal growth variations observed between chambers confirm the ability of the platform to maintain reproducible conditions and support statistically robust experimental designs. The platform can be applied to diverse biological systems and can be technically adapted to different experimental requirements, including different electromagnetic field sources. It provides a highly controlled environment for investigating the cellular and subcellular effects of LF MF, creating a solid foundation for future research into its biological mechanisms and potential applications.
Implementation and Evaluation of Machine Learning Algorithms in Ball Bearing Fault Detection
The subject of this research is the development of a classifier based on machine learning (ML) that is able to recognize defective and healthy ball bearings. For this purpose, vibration measurements were performed on the bearings, on a total of 196 samples. For each recorded vibration signal, a feature extraction was performed by digital processing in the time domain. The following ML algorithms were used to develop the classifier: K-nearest neighbor (KNN) and support vector machine (SVM) as well as improved versions of the aforementioned algorithms. Improved versions of the mentioned algorithms were obtained by optimizing their hyperparameters. The corresponding models of the KNN and SVM algorithms showed a high percentage of success in classification, 98.5 % and 99.5 %, respectively. By optimizing the hyperparameters, models with a maximum classification success of 100 % were achieved.
Parametric Faults Detection in Analog Circuits using Variable Ranking-based Feature Selection Method and Optimized SVM Model
This work proposes an optimized support vector model and a variable ranking-based test node selection approach for identifying parametric faults in analog circuits using a fault dictionary. Test node selection is essential for fault dictionary-based fault detection to reduce the dimensionality and test process complexity. To determine an appropriate set of test nodes, a feature selection technique based on variable ranking is used, as it is computationally efficient and involves sorting and score estimation. In the proposed method, test nodes are ranked using a score function based on data variability, where the nodes with the highest data variability are assigned the highest rank. This ranking ensures that the most informative test nodes are prioritized for fault detection. An optimized support vector model is used for fault diagnosis to improve classification accuracy. The results show the effectiveness of this approach. The performance of the proposed method is validated by measuring the fault detection accuracy on benchmark circuits.