This website is ready to use since January 2023, The 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; Supported by Slovak National Committee IMEKO. The aim of the journal Measurement Science Review is to publish papers from scientific disciplines covering the 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 by 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
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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.
Copyright (c) 2025 Slovak Academy of Sciences - Institute of Measurement Science https://creativecommons.org/licenses/by-nc-nd/4.0/
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.
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.
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.
Elevator Operation Health Diagnosis using Vibration Region Segmentation Algorithm via Internet
The safety of elevator operation is an indispensable issue in the elevator industry. The most important factor affecting the elevator performance is car vibration. For this reason, vibration analysis is considered an important topic for elevator maintenance as it can be used to detect potential problems before breakdown. Currently, vibration measurement is typically performed using vibration analyzers operated by personnel, resulting in a time-consuming process and experience-dependent interpretation. While there are some machine learning algorithms that are used to diagnose elevator condition, their computational complexity still makes it difficult to translate into a real-world application. Therefore, in this study, the elevator condition diagnosis model is developed using a simple vibration region segmentation method. Based on the elevator operation characteristics, the cut-off point is determined by the abrupt acceleration variation condition to define the acceleration segment region. With the binarization process, the digital array is used at each time of acceleration variation to evaluate the state of elevator operation. Normally, the elevator operation can be divided into five segments, e.g., start up, acceleration, steady state, deceleration, and stop. The rule for determining the critical point for segmentation is thus formulated based on an abrupt acceleration change. If the number of segmented areas exceeds five, it can be considered as an abnormal case. To develop the system, the elevator vibration data is collected by a 3D accelerometer and then processed in the PC using the proposed algorithm. The results are then transferred to the cloud for online monitoring. The experimental results show that the proposed model is quite simple but effective for elevator diagnosis and maintenance.
elevator, diagnosis, vibration, segmentation, accelerometer
Dynamic Trust-based Access Control with Hybrid Encryption for Secure IoT Applications
The rapid growth of internet of things (IoT) applications, especially in wireless sensor networks (WSNs), has led to the generation of large amounts of real-time data from interconnected devices. This growth leads to challenges in securing data access and managing resources efficiently. To address these challenges, we propose a dynamic trust-based access control (DTAC) model for IoT and WSN applications. The DTAC model integrates behavioral trust evaluation and context-aware decision making to dynamically adapt access permissions to network conditions in real-time. the trust scores are calculated using fuzzy logic and machine learning techniques, which enable adaptive decision-making. To increase security, the model uses a hybrid encryption scheme that combines elliptic curve cryptography (ECC) with lightweight symmetric encryption, ensuring data confidentiality with minimal computational overhead. In addition, access control decisions are refined by contextual factors such as user roles, device locations, and data sensitivity. The model includes a collaborative re-evaluation mechanism that periodically updates trust scores and isolates malicious nodes without compromising network stability. The DTAC model is evaluated on key metrics such as security resilience, energy efficiency, and latency and demonstrates better performance than existing solutions. This model provides a scalable, energy-efficient, and secure framework for IoT and WSN applications that ensures reliable data access and privacy in diverse environments.
A Model Reference Adaptive Controller based Flamingo Search Algorithm for Liquid Level Control in Non-Linear Conical Tank System
Conical tanks (CTs) play a vital role in the process industry as they can prevent residues in the tank. On the other hand, liquid level controlling in a CT is a complicated process due to the non-linear cross-sectional area. The complexity in CTs is also increased by the limitation to manipulated variables, interactions in measurements, and frequent disturbances. The control of liquid levels in CTs is of great importance in the process industry. In this paper, the model reference adaptive control (MRAC) with the modified proportional integral derivative (MPID) controller is proposed to maintain the liquid level at the set points. Two proportional integral derivative (PID) controller blocks are used in the proposed MRAC. The controller parameters are tuned using flamingo search optimization algorithm (FSA) based on minimum mean square error (MSE). The proposed method is implemented in MATLAB/Simulink platform and the results are considered with respect to different set points and disturbance conditions. Also, the results are compared with the existing controllers such as proportional integral (PI), PID, fractional-order proportional integral (FOPI) and MRAC-PID. The results show that the proposed MRAC provides lower integral square error (ISE) and integral absolute error (IAE) values of 38.25 and 2167, respectively.
Energy Efficient Multimedia Transmission in Wireless Sensor Networks using Enhanced Adaptive Transmission Control Algorithm and Measurement Techniques
Wireless multimedia sensor networks (WMSNs) face challenges such as high energy consumption, latency, and ensuring secure data transmission, especially for real-time multimedia applications. To address these issues, this study proposes the enhanced adaptive transmission control algorithm (EATCA), which integrates a bidirectional long short-term memory (LSTM) model and chaos-based lightweight cryptography. EATCA optimizes energy consumption, reduces latency, and improves data security through the use of adaptive clustering and predictive transmission techniques. The algorithm uses meta-heuristic optimization for cluster head selection and fuzzy logic for route prioritization to ensure efficient data transmission. A chaos-based encryption scheme ensures data confidentiality with minimal computational overhead. The simulation results show that EATCA achieves 43.53 % lower energy consumption compared to traditional low energy adaptive cluster hierarchy (LEACH) protocols, a 96 % packet delivery ratio (PDR), and a 42 ms latency, making it suitable for time-sensitive multimedia applications. The system also achieves an 85 % compression ratio while maintaining security overhead as low as 7 %. These results make EATCA a reliable and energy-efficient solution for optimizing WMSNs and pave the way for scalable and secure network implementations.
measurement techniques for network optimization
Copyright (c) 2024 Slovak Academy of Sciences - Institute of Measurement Science https://creativecommons.org/licenses/by-nc-nd/4.0/
Prediction of the Natural Gas Compressibility Factor by using MLP and RBF Artificial Neural Networks
The compressibility factor indicates the deviation of the real natural gas from the ideal behavior. It is one of the most important parameters in the natural gas industry. In the present study, two different types of neural networks – multi-layer perceptron (MLP) and radial basis functions (RBF) – were used to predict the compressibility factor Z of natural gas. The pressure, temperature, and speed of sound (SoS) were chosen as input parameters for the artificial neural network (ANN) models. The training and testing of the MLP-ANN and RBF-ANN were carried out on the basis of 151 days of continuous measurements. Different variants of both types of neural networks were implemented and a comparative analysis of their modeling capabilities was performed. The models developed show a very high prediction accuracy, with the results obtained showing a certain advantage of the RBF-ANN. The comparative analysis was performed on the basis of standard performance indicators such as R2, root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE). The present study shows an intelligent method implemented in two different variants to determine the compressibility factor of natural gas without the need to use the equation of state.
Verification of Coordinate Measuring Machine using a Gauge Block
Two orthogonal least squares methods of the points approximation by a set of parallel planes are presented. Such an approximation can be used to study the measurement details of using a coordinate measuring machine (CMM). A calibrated gauge block and a CMM with a touch measuring probe were used in the experimental verification. A comparison of the different CMM strategies is provided.