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Hydrophobic properties belong to the most important factors which determine functional properties of paper products. A number of methods related to the measurement of the interaction between water and paper are already used but most of them give results after specific period of time whereas many unit operations in modern industrial processes (e.g. printing) last usually less than one second. As a result, most of the existing methods are not corresponding to today’s requirements. The aim of the presented research was to develop the method based on image analysis for measuring the dynamics of penetration and permeation of liquid through paper. Special device named eXtended Liquid Penetration Analyser (XLPA) was built for this purpose. Based on the obtained results, the method developed allows to observe and analyse the dynamics of liquid permeation through paper. The proposed method fully replaces the method implemented in the PLG Sizing Tester, which estimated the time of a 50% decrease in brightness of a tested paper sample permeated by ink. Presented method offers the possibility of quantitative measurement of changes in liquid absorption with time resolution of 1/60 s. At the same time, thanks to the use of a camera and image analysis, it is possible to detect local areas of non-homogeneity in paper structure that differ with regards to their capability to absorb liquid, as well as to determine quantitative measures that characterize the non-homogeneity of the permeation process with reference to paper surface. [ABSTRACT FROM AUTHOR]

During the folding process substrates are exposed to high-localized stresses, which in the case of coated papers and boards, can lead to decreased aesthetic features or complete loss of functionality. Production efficiency of the folding process could be improved by an automated, computer vision-based inspection system. For such a task, different existing computer-aided fold-crack evaluation approaches were analyzed. A detailed research was conducted to propose an image processing based fold cracking assessment via finding optimal sample preparation and digitization techniques and developing an algorithm for the digital image analysis and feature extraction. The analysis of the applicability of different sample preparation and digitization parameters, as well as the proposed digital image feature, was done by correlation evaluation, one-way analysis of variance (ANOVA) and corresponding post hoc tests. The results indicated that the developed algorithm fulfils the set requirements and the proposed feature of digitized samples faithfully describes the analyzed fold-cracks. [ABSTRACT FROM AUTHOR]

• Novel rapid moisture test for oil-paper insulation. • Microstrip ring resonator analyzes dielectrics properties. • Precise moisture level distinction found. • New model assesses the moisture content accurately. Moisture ingress significantly accelerates the aging of oil-paper insulated equipment, making accurate assessment of moisture content critical for the operation and maintenance of oil-immersed insulated power equipment. In this paper, a novel method for rapid moisture content evaluation is proposed based on a microstrip ring resonator (MRR) designed with a fundamental frequency of 1.16 GHz. A calculation model for the dielectric response analysis using MRR is established to characterize dielectric properties of the material layered on the substrate. ANSYS HFSS electromagnetic simulation software was used to study the impact of dielectric material thickness and the substrate on the MRR test results. Experimental measurement demonstrated that the maximum deviation in resonance frequency was merely 0.542 %. The dielectric properties of uniformly and non-uniformly damp insulating pressboard were analyzed across the frequency band from 1 to 6 GHz. The MRR test results were compared with those of the standard resonant cavity method and frequency domain dielectric spectroscopy (FDS) test. Using the regression algorithm and curve analysis, moisture assessment models for both uniformly and non-uniformly damp oil-paper insulation were established. The experimental results show that the MRR test can clearly distinguish between the pressboard samples with varying moisture levels under uniform conditions, with a maximum moisture evaluation error of 8.19 %. The results demonstrate that the MRR test method, requiring only 56 s, can quickly and effectively evaluate the moisture content of insulating pressboard. [ABSTRACT FROM AUTHOR]

• A QD-based colorimetric sensing system with high selectivity was developed. • A ternary colorimetric probe combining aptamer recognition with a QD-based photocatalytic reaction was created. • Automatic data readout through smartphones was achieved. • The system is convenient, universal, and multi-target sensing available. Quantum dots colorimetric sensing systems (QDs-CSSs) are promising tools for on-site monitoring or point-of-care testing, owing to their high sensitivity, low cost, and direct visual readout. Despite these advantages, a notable challenge hindering their practical applications lies in the limited selectivity. To date, scant attention has been devoted to addressing this issue. In this study, we designed a ternary colorimetric probe integrating aptamer recognition with a QDs-based photocatalytic reaction. The target can specifically modulate the photocatalytic oxidation of o -phenylenediamine through its specific aptamer, thereby triggering a color change within the probe. We fabricated a multi-layer paper chip to load the probe and perform multi-target detection. We manufactured a multi-functional smartphone-based device to support the chip and provide automatic readout of the colorimetric sensing results. By putting these elements together, we obtained a QDs-CSS with high selectivity, good reproducibility, and low consumption. To prove its practical performance, this system was used for the simultaneous sensing of Pb(II), As(III), and Cd(II). A nM level detection limit was achieved and the testing results in actual samples agreed with those from the standard method. The proposed system opens up a new avenue to develop QDs-CSSs with high selectivity. Moreover, it shows promising prospects to be a general sensing system for various targets. [ABSTRACT FROM AUTHOR]

Measurement related to different machine vision functions is the base for developing of cyber-physical systems able to see and make decisions. These kinds of systems are emerging in all areas of our daily lives. They can be found in the medical area, in industry, in the agriculture, in all those interconnected cloud computing-based systems related to flying/terrestrial robotics, navigation, automated surgery, smart cities, smart health monitoring, etc. All of them are extremely dependent on the same: adequate coordinates measurement, properly selected data processing and data fusion algorithms, evaluation procedures for performance analysis of measurement within Machine Vision systems, processes and algorithms (both traditional and artificial intelligence), mathematical models for 3D-measurement purposes (measurement of displacements, surface profiles, deformations, data augmentation/interpolation, etc.), and distributed visual measurement systems, as well as distributed memory and sensory part. Cyber-physical systems can be implemented on almost any application, especially on those dotted by robots and automated guided devices (from aerospace applications to domestic cleaners). The success of the measurement process depends on the kind of sensors and their optoelectronics characteristics and intrinsic parameters, as well as their respective operating and processing. The correct approach selection for the application, the data acquisition and collection efficiency, the data processing algorithms, the hardware processors response time, and the intelligent auto adaptability to changing environments or conditions. Recently, the emergence of artificial intelligence algorithms and the internet of things have powerful development of such systems, highlighting the importance and the impact of the measurement accuracy related to machine vision performance. [ABSTRACT FROM AUTHOR]

• Density of a paper has a decisive impact on its air permeability. • Exposure of both historical and common papers to A. niger decreases permeability. • Nano-wollastonite is recommended to improve biological resistance of paper. The effect of nano-wollastonite and Aspergillus niger fungus on permeability of historical and common paper was studied here. Specimens were immersed in aqueous wollastonite nano-suspension (NW) at 10, 20, 30, and 40% concentration, and their permeability was finally compared with that obtained from control specimens. Permeability of each specimen was measured before and after the immersion in NW. To fix solid particles on NW-treated specimens, polyvinyl acetate (PVA) was mixed in all treatments. Results showed that wollastonite significantly limited the growth of A. niger. Permeability of historical specimens was significantly lower than that of the common paper, 29 in comparison to 190 cm3.s−1, respectively. This was due to the higher density in the historical paper, providing lower empty voids and spaces for fluid to pass through. Fungi culture resulted in significant decrease in both paper types. In fact, fungal hyphae penetrated into the specimens, eventually blocking the paths through which fluid could transfer. High significant R-square values were found between permeability values before and after the exposure to A. niger. It was concluded that permeability is closely correlated to the density of paper, and it has a significant effect on the potentiality of fungi hyphae to be penetrated into the texture of papers, affecting the weight loss values. Moreover, NW-suspension can be used to decrease permeability as well as susceptibility to fungal attack. [ABSTRACT FROM AUTHOR]

• Numerical model for solution of heat diffusion equation. • Thermographic investigation in optically semi-transparent materials. • Non-destructive detection of graphic features buried in cultural heritage artefacts. • Selective configurations for optical and thermal properties investigation in paper. In this paper, infrared thermography is applied to the quantitative characterization of optically semi-transparent media by using a newly introduced mathematical model. The method is specifically aimed to the detection and evaluations in graphic features buried beneath the surface of some particular kind of cultural heritage artefacts, such as books and documents. A numerical approach based on the finite element method, is proposed for solving the model equations. The computed thermographic signal depends on different inspected material physical properties, which have been selectively evaluated by comparing the theoretical results with the experimental ones in specifically designed test samples. Thereafter, the model is applied to the analysis of the results obtained in the case of graphic elements buried within samples constituted by the same previously characterized materials. [ABSTRACT FROM AUTHOR]

• At steady-state excitation, the transient electric field distribution in typical multi layer oil-paper insulation at different aging stages was obtained. • The equivalent model of polarization conductivity was proposed, which characterized the nonlinear phenomena at different polarization voltages. • Based on the charge escape trap model, the mechanism of the nonlinear phenomenon of response current in the process of dielectric polarization was revealed. The oil-immersed transformer plays a pivotal role in the power network. Its insulation stability is essential. At present, the application of time-domain dielectric response technology in insulation detection of oil-immersed equipment is relatively mature. However, there is a contradiction between increasing the test excitation amplitude to improve the test accuracy and the nonlinear response characteristics of the test dielectric at high-voltage excitation. The assessment theories all depend on the laboratory test results, and inaccurate test results will affect the assessment results. Therefore, this paper prepared several groups of insulation-scaled aging models in the laboratory and studied the effects of temperature and test excitation amplitude on the polarization and depolarization characteristics of the models. During the polarization process, the electric intensity variation was calculated. Meanwhile, at the time-varying electric field, the influence mechanism of electric intensity on the response current caused by the charge escaping from the trap during the polarization process was obtained. This paper quantified the relationship between the aging state and the height of the interfacial polarization barrier. The study of nonlinear dielectric response in the time domain will help to improve the accuracy of test results when diagnosing the insulation performance of oil-immersed transformers. [ABSTRACT FROM AUTHOR]

In parallel with the development of nanoparticle coatings for protection of paper substrates, detailed descriptions of the surface topography with micro- to nanoscale roughness features are needed. In this work, papers have been coated with poly(styrene-co-maleic anhydride) nanoparticles including different types of vegetable oils and the surface roughness was evaluated at 2000 × 2000 μm 2 to 0.2 × 0.2 μm 2 length scales by combining non-contact optical profilometry (NCP) and atomic force microscopy (AFM). The relationships between roughness data were studied for statistical roughness parameters, spatial roughness parameters and in the frequency domain. In order to compare AFM roughness more accurately, the original images were flattened to remove effects of the underlaying fibrous substrate and highlight features of the nanoparticle coating. More detailed information on the coating topography could be obtained by considering bearing ratio curves and histograms, where it was concluded that the oil-filled coatings form a rather thin and continuous coating that closely follows the shape of the cellulose fibers. The relation between statistical roughness parameters from NCP and AFM follows an exponential trend with relatively low coefficient of determination. The increase in surface roughness with length scale showed a transition point attributed to short- and long-range surface features. Therefore, the correlation length was used as a spatial roughness parameter that provides a successful extrapolation of the average roughness over different length scales in a double logarithmic diagram with very high coefficient of determination. Based on the power spectral density, it was difficult to exactly distinguish between the different types of SMI/oil coatings, as they include similar nanoscale features. The frequency roughness parameters were better suited for extrapolation than statistical roughness parameters but little less efficient than the spatial roughness parameters. [ABSTRACT FROM AUTHOR]

Abstract: Aiming at the problem that paper smoothness cannot be measured on line, a new method for soft measurement of paper smoothness is put forward on the basis of studying paper quantization distribution. A soft measurement system is established, by which, paper quantization noise is processed by time–frequency analysis and non-linear transformation, so that online paper smoothness can be obtained through a Radial Basis Function Neural Networks (RBFNNs). The results show that this soft computing method is a feasible way with high measuring accuracy. [Copyright &y& Elsevier]

Abstract: The automatic control of product moisture content in the paper industry is based upon online moisture measurement. Furthermore, the automatic control of other quality variables such as coat weight may also rely on the moisture measurement. The integrity of infrared radiation (IR) based online moisture measurement systems was studied by performing basic calibrations at the mills with references sealed between two glass plates. The calibrations were performed according to the EN ISO/IEC 17025 standard which is a requirement for calibration laboratories. The observed measurement errors were 2.9% moisture in the first time calibrations at the 95% confidence level. It is demonstrated that these errors can be reduced to under 0.5% moisture by applying traceable methods in the basic calibration, thus improving the integrity of online moisture measurements. Also a standardised algorithm, based on the experience from the performed calibrations, is proposed to ease the adjustment of different moisture measurement systems. [Copyright &y& Elsevier]

Abstract: Paper raw materials contain impurities, such as waxes, coating binders, hot melts and pressure-sensitive adhesives, that have a tendency to attach to the surfaces of machinery during paper production. Drying cylinders located at the beginning of the drying section are in an especially challenging position because sticky materials can easily transfer from the paper web to the hot cylinder surfaces at this point. Usually, one to four drying cylinders are coated with a nonstick coating material in order to decrease contamination. The nonstick properties of coating materials are at their best when the coatings are new, but their performance usually decreases as the materials age. To date, there is no practical method available for the on-site evaluation of the nonstick properties of coating materials or a method to monitor the condition of the coating during its life cycle. A new practical method for the on-site evaluation of nonstick cylinder coating materials has been developed. This method also provides the opportunity to compare the performance of different drying cylinder coating materials. The analysis is performed using an adhesive tape attached to a clean cylinder surface. The tape is immediately peeled off of the surface at a constant speed and peel angle. The temperature compensation is used to facilitate the comparison of surfaces at different temperatures. The force required to peel off the adhesive tape is monitored with a force meter. Our results show a linear relationship between the release force and the nonstick qualities of the coating. In this paper, a method for testing drying cylinder coatings and the results of mill tests are presented. [Copyright &y& Elsevier]

[Display omitted] • Through test and simulation analysis, this paper obtained the variation of electric field distribution in the oil gap in the aging oil-paper insulation model at different test temperatures. • The critical field strength/frequency calculation method of nonlinear loss factor in the pores of oil-paper insulation was proposed. The influence of insulation aging test temperature and field strength on the change of pore limiting proportion coefficient was obtained. • This paper clarified the reasons for the positive and negative nonlinear changes in the test curve of the loss factor at different excitation amplitudes. Meanwhile, considering the ion recombination process, the quantitative calculation method for the nonlinear loss factor of the aging oil-paper insulation model was proposed, and the effectiveness of the method was verified by experiments. Frequency domain dielectric response technology is widely used to detect the major insulation status of transformers. The influence of the test excitation amplitude on the nonlinear change of the dielectric characteristics of oil-paper insulation will interfere with the accuracy of the assessment results. In this paper, the electric field intensity distribution law of the oil gap in the oil-paper insulation model is obtained through test and simulation analysis. The influence of insulation aging, test temperature, and excitation frequency on the electric field distribution in the oil gap is clarified, it provides a reference for field strength for nonlinear loss calculation of oil-paper insulation. Meanwhile, based on the Garton's ion motion nonlinear response model, considering the influence of the external electric field on the ion recombination in the oil gap, the nonlinear loss calculation model of transformer oil-paper insulation is corrected. Furthermore, the mechanism of positive and negative nonlinear loss variation for the oil-paper insulation model in a wide frequency range is revealed, and the calculation method of critical frequency and critical field strength of nonlinear loss variation in the oil-paper insulation model is proposed. Finally, according to the test results of the frequency domain dielectric response of oil-paper insulation with different aging and the apparent ion mobility in transformer oil, combined with the corrected calculation method proposed in this paper, the influence of the test excitation amplitude on the nonlinear change of the loss factor of oil-paper insulation is eliminated at different temperatures. The mean square error (MSE) of the corrected loss factor is reduced by 88.03% on average compared with that before. [ABSTRACT FROM AUTHOR]

A pinhole detection system for glass wool paper has been developed based on electric discharge phenomenon. The main part of the system consists of two electrodes, i.e. anode and cathode, through which the glass wool paper passes. The line cathode consists of many needles with an equidistant separation of 6 mm. Three sets of line cathode, with needles at intervals of 6 mm, were shifted 2 mm from each other. A high resistance of 100 MΩ was used in the electric circuit to control the discharge current, i.e. damage to the paper. The detectable pinhole size using this system was between 0.1 and 1 mm. The detection rate was above 70%. The production rate of satisfactory goods was increased to approximately 33% as demonstrated in a manufacturing plant. [Copyright &y& Elsevier]

• Integrated single-step procedure of blood-plasma creatinine estimation using CREA-SENSE app from one drop of whole blood. • Paper-based microfluidic analysis with simple design and easy fabrication steps. • Requires tiny volume of blood sample (10 microleter) and very short testing time (within 2 min) • Includes spontaneous plasma separation stage facilitated by mere salt functionalization of the substrate. • Favorable agreement with results from the gold-standard automated biochemical analyzer. • Usable for accurate point-of-care diagnostics in resource-limited settings. Measuring creatinine levels in blood plasma is one of the most commonly used means of routine monitoring of renal function. Point-of-care devices for creatinine level estimation potentially allow rapid measurement, facilitating continuous monitoring of vulnerable patients. However, a majority of such devices currently available is constrained by several factors such as lack of simplicity in fabrication and operation (including plasma separation), a limited regime of efficacy, compromised reagent stability, and elevated costs. Here, we report the invention of a simple-to-fabricate and easy-to-use paper strip for rapid evaluation of plasma creatinine levels (ranging from 0.1 to 15 mg dL-1) in an integrated single-step process. For the testing, one drop of whole blood is first dripped onto the calcium salt-functionalized sample pad of the strip. Subsequently, blood plasma separation occurs due to spontaneous aggregation of red blood cells (RBCs) in the presence of calcium ions. The plasma spontaneously traverses into the reaction zone of the test-strip via capillary action where it reacts with previously dispensed alkaline picrate reagent, resulting in a colorimetric signal. Smartphone-interfaced imaging and image analytics of the reaction spot renders the dissemination of the test result in a turn-around time of about 2–3 min, without necessitating additional device interfacing. High efficacy of performance is established by validating with the results obtained from laboratory-benchmarked biochemical auto-analyzer, exhibiting an accuracy of 94%, and the coefficient of variation ranging from 0.64 to 6.4%. Suitability of use in extreme resource-limited settings is established by favorable stability of the reagents outside controlled laboratory ambience and engagement of unskilled personnel to execute the testing procedure with uncompromised accuracy. [ABSTRACT FROM AUTHOR]

The objective of this study was to characterize the properties of pulp and paper manufactured from two agricultural residues namely, wheat straw ( Triticum aestivum L. em Thell.) and date palm midribs ( Phoenix dactylifera L.) widely available in Saudi Arabia by employing soda-anthraquinone (AQ) pulping process. Specimens from cedar wood ( Juniperus procera Hochst. ex Endl.) wood was also used as control samples. Chemical and the morphological characteristics of all three types of raw materials in addition to basic properties of hand sheets made from such resources were evaluated. The results indicated that wheat straw had the highest hemicellulose and ash values of 35% and 7% and the lowest lignin content value of 18%, respectively. The fibre length of the both palm fronds and wheat straw was lower than that of 2.2 mm which was found for J . procera . Overall strength properties of the handsheet made from two agricultural raw materials were significantly lower than those made from J . procera pulp. Wheat straw handsheets also had higher strength properties than those made from date palm midribs with an exception of their tear index. Based on the results of this study it appears that both types of raw materials, date palm midribs and wheat straw could have a potential to manufacture pulp and paper with accepted properties so that such underutilized species can be converted into value-added products. [ABSTRACT FROM AUTHOR]

The paper entitled “Experimental investigation of machinability characteristics and multiresponse optimization of end milling in aluminium composites using response surface methodology (RSM) based grey relational analysis (GRA)”. The Authors adopted RSM based grey relational method to achieve high productivity in terms of maximum material removal rate MRR and least surface roughness, tool wear and cutting forces. Experimental design was made using RSM (central composite design) and responses were measured according to standard methods. In this study, authors had proposed the optimized Grey relational grade (GRG) ranking for machinability characteristics. However, there are some errors in their analysis, which are likely to affect the significance of their proposed ranking. So, this comment highlights the miscalculations and gives the corrected results. [ABSTRACT FROM AUTHOR]

• Versatile and low-cost 3D printer method for building point-of-use device was proposed. • A disposable µPAD was used for determination of pH, water hardness and total phenols. • Smartphones were used for the quantification of analytical interest species. • Simple and fast colorimetric method was applied for monitoring the quality index in water samples. The measurements by lab-on-a-chip devices are very interesting regarding the reduction of reagents, time and cost. In this paper, we have described the construction and application of a new device based on colorimetric measurements using a smartphone camera and a microfluidic paper-based device (μPAD), both fixed in supports obtained through 3D printing, employing a polylactic acid filament. The μPAD was evaluated as a water quality index device by colorimetric monitoring of water hardness (Ca2+ and Mg2+), total phenols (catechol), and pH. Under optimized parameters, the proposed method presented a linear range between 20 and 560 mg L−1 for Ca2+ and Mg2+(hardness), 0.20 to 16 mg L−1 of catechol and pH from 4.7 to 12, and limits of detection of 0.083 mg L−1, 0.124 mg L−1 and 0.262, for water hardness, catechol, and pH, respectively. The proposed device was successfully applied for the quality monitoring of the distribution of water samples. [ABSTRACT FROM AUTHOR]

Abstract: A method is described for the simultaneous calibration of online grammage and moisture measurement by applying machine reel sampling and traceable sample area measurement. The area measurement is based on a flatbed scanner and image analysis. It is demonstrated that a relative uncertainty of 0.5% can be achieved in dry grammage calibration at the 95% confidence level. The determination of grammage and moisture were performed by applying the EN ISO 536 and the EN ISO 287 standards, respectively. The described technique can also be applied with the calibrations performed for the paper industry. [Copyright &y& Elsevier]

This paper proposes an original method to determine the oil mist impingement over an insert based on different machining configurations (rotation velocities) and different inner canalization designs, without material removal, on milling tool. The oil mist spray is determined using different holders as an insert. Glass plate and blotting paper are used and evaluated in this work to get as much information as possible, based on micro–macro scale observations. The imprint size of the oil mist spray distribution, the particle sizes and the fluid film formation have been studied to determine the effects of the different configurations on the oil mist impingement behavior. The droplet impingements and distributions on the glass plate are analyzed under a 3-D profilometer (microscopic scale). On the blotting paper, the distributions are analyzed with pictures of the oil marks (macroscopic scale). This original method gives quite fast particle distributions that can be used in industry. The different experimental results give large information about the oil mist behavior sprayed on the glass plate. Increasing rotation velocities increases the oil mist amount in the cutting area. Increasing the canalization orientation design gives a better focused spray. The rake angle has strictly no influence on the oil mist spray distribution. But sputtering effects have been highlighted and should be avoided to keep MQL efficiency. [ABSTRACT FROM AUTHOR]

[Display omitted] • A novel colorimetric sensing approach based on nanozymes (MoS 2 -SrTiO 3) was constructed by using a cheap platform i.e. filter paper for H 2 O 2 detection. • A lower detection limit (LOD) of 0.08 µM was achieved and the excellent response could be seen even from the naked eye. • The proposed method is quite appropriate for the detection of H 2 O 2 for onsite monitoring in biological and environmental samples. In the present study a quick sensing assay was introduced based on novel biomimetic oxidase like behavior of Molybdenum Sulfide and Strontium Titanate (MoS 2 -SrTiO 3) nanocomposite for colorimetric detection of H 2 O 2. In order to improve the adhesion nanocomposite was encapsulated into the gel and loaded on the surface of filter paper. The synergistic effect of MoS 2 and SrTiO 3 on sensing exhibited the intrinsic peroxidase-like activity by catalyzing the oxidation of 3,3′,5,5′-tetramethylbenzidine (TMB) as substrate and as a result an improved naked eye response was observed. This enhanced response was well supported by characterizations techniques like FT-IR, SEM, XRD and Raman spectroscopy. For better sensitivity and selectivity, the following conditions like gel concentration (0.3 g/6mL) at room temperature, incubation time (30 min) and concentration of nanocomposite (2 mg/mL) were optimized. A better sensitivity was obtained with a lower limit of detection (LOD) of 0.08 µM in a relatively wide range of 0.1 µM to 5 µM and high correlation coefficient (R 2) of 0.978. The ability of the sensor in real samples like milk and tap water was assessed with good recovery values ranging from 91 − 98 % in the presence of interfering compounds. This colorimetric detection using filter paper modified with enzyme mimetic nanocomposites is a step forward towards the point of care devices for onsite detection and monitoring of the dieases which was otherwise quite difficult and expensive. [ABSTRACT FROM AUTHOR]

In order to ensure quality maintenance of rotary machinery, proper interpretation of the malfunction causes is required. The behaviour of the mechanical components of the machinery is often reflected in vibrations responding to the action of internal as well as external forces. The paper focuses on the issues of the application of the tools of technical diagnostics in verification of the technical condition of two-stage bevel-spur gearbox operating in difficult conditions of paper industry. Pinion bearing dismantled from malfunctioned gearbox was analysed. After repair, measurements were performed in order to determine the dynamic properties of the bearings. The values measured on the machine after repair were compared in two operating modes - depending on motor rotation speed. The acquired values of dynamic parameters and temperature (after repair) were in correlation to the condition found on the malfunctioned bearing (after dismantling form the malfunctioned gearbox). [ABSTRACT FROM AUTHOR]

• First survey of relevant research activities on Digital Calibration Certificate-DCC. • A thorough analysis of both the calibration process and the calibration certificate. • Modeling the calibration process, the calibration certificate life-cycle. • Identifying Calibration use-cases and stakeholders. • Advantages and disadvantages of various potential DCC formats. Calibration Certificates (CC) are essential for maintaining the accuracy of measurement instruments, which in turn are necessary to maintain the quality of products and services. Such certificates are mainly used in legal metrology for quality audits, accreditation and examination processes; and also in the operational metrology to adjust measurement results according to device uncertainty. Paper-based CC (PCC) or a digital copy of the paper format has been successfully used for this purpose for many decades. However, the PCC starts to fail short with the emergence of new technologies and applications that require automated creation, processing and updates of the calibration certificates. The last years have witnessed an intense effort on digital transformation in metrology and especially developing Digital Calibration Certificates (DCC). Until recently, we noticed the lack of agreement on the nature and the usage of such important new metrology artifacts as well as some repetition in the work by different researchers. To help reaching a minimum consensus on the subject, we present in this paper, a thorough analysis of the calibration certificate and the calibration process. Besides that, we aim by this work to provide an overview of the carried research on that point to help peer researchers to view the big picture and to avoid wasting their efforts in repeating same work on same ideas. Consequently, they can recognize the peer researchers and organizations working on the same topics, find available resources, identify research gaps and identify the best potential directions of their efforts as well. Finally, our paper aims also to establish a bridge between the metrological concepts and information technology concepts such that more computer science researchers can be involved in solving the problem. [ABSTRACT FROM AUTHOR]

• A kinematic analysis method of FCM based on CGA and equipment pose fusion is proposed. • The monitoring system in longwall mining is improved based on infrared range sensor. • The DEF/DPS-VRF is proposed to improve the comprehensiveness of straightness monitoring. Full-pose monitoring of the Floating Connection Mechanism (FCM) still exists blind zone, unreliable sensing data, etc., this paper proposes a digital twin bi-directional deduction method for the pose of starting/end point of the FCM. Firstly, considering the pose of the connection equipment, an optimization of the motion law of the FCM based on Conformal Geometry Algebra (CGA) is realized; secondly, the virtual application of optimization motion laws of the FCM is achieved by marking key points and extracting key information; then, referring to the information enhancement scheme obtained by adding digital twin infrared range sensors and a bi-directional deduction method is proposed for the full-pose of the FCM; finally, the experimental validation shows that the motion law of the FCM proposed in this paper is in line with the actual motion and generates better analytical results, the pose deduction accuracy is over 87%, providing new ideas for equipment pose monitoring. [ABSTRACT FROM AUTHOR]

• Characterization of ground-penetrating radar images of pipeline leakage. • Leakage diagnostic method for water supply pipeline; • Image correlation analysis; • Uncertainty analysis; • Water supply pipe leakage location diagnosis. In this paper, a new leakage diagnostic method for water supply pipeline based on ground penetrating radar(GPR), image correlation algorithm and uncertainty analysis method is proposed. The presented leakage diagnosis based on GPR can identify the leakage by using the change of hyperbolic echo in the radar echo map before and after leakage. However, the determination of leakage regions needs to be done manually and the accuracy of leakage diagnostic results are affected by subjectivity and experience of technicians. Additionally, the adverse effects of testing errors and calculation errors on identification results are not being considered in the presented leakage diagnostic method. In this paper, the change of hyperbolic echo in radar echo map caused by leakage is explained firstly based on the experimental results. And then, the image correlation algorithm is introduced to calculate the correlation coefficients of radar echo maps before and after leakage, which can be used as the leakage diagnostic indicator. Finally, the uncertainty analysis method is used to consider the influence of testing errors and calculation errors on the leakage diagnostic indicator and the new leakage diagnostic method for water supply pipeline is proposed. The proposed leakage diagnostic method is the novelty of this paper and its workability is verified in the laboratory and the field tests. The results show that the proposed method can effectively identify the leakage region in water supply pipeline. [ABSTRACT FROM AUTHOR]

• A novel square electromagnetic sensor for weak excitation conditions is constructed. • The influence of changes in the structural and operating parameters of the sensor on its detection performance is analyzed. • Several commonly used open architecture sensors were constructed to compare their heating performance with the sensor proposed in this paper. • The detection performance of the square sensor was experimentally verified. Nondestructive testing of internal corrosion defects is a key task to evaluate whether the material is repaired and replaced. In this paper, a novel square ferrite magnetic open sensing structure is designed for detecting volumetric corrosion defects on the inner surface of oil and gas storage tanks. A theoretical model of the structure is developed and the characteristics of the guided distribution of the electromagnetic field are resolved. The proposed model provides a region of interest with a uniform magnetic field. This greatly improves the detectability and thermal comparison of corrosion defects on the inner surface. In addition, the inspection is performed under weak excitation conditions and in the full open field of view of the infrared camera. This configuration greatly improves the portability and efficiency of workpiece inspection. Several open architecture sensors are evaluated and compared regarding temperature rise uniformity, model passability, and lightweight level. Meanwhile, the geometrical parameters of the square sensor, the operating parameters, and the material properties of the detected samples are investigated, including the coil height, the cross-arm length, the lifting distance, the operating frequency, the relative magnetic permeability of the sample, and the electrical conductivity of the sample. Finally, the detection capability of the sensors was verified through experiments with different steel plate thicknesses and defect morphologies. [ABSTRACT FROM AUTHOR]

• An improved K-means clustering method of workpiece point-cloud is proposed. • A data-driven parameter identification method of intersection model is proposed. • A model-driven path planning method of laser scanning and robot welding is proposed. To improve the accuracy, an approach to robot welding path planning of the intersection weldment based on 3D visual perception is proposed. It adopts the two-stage location scheme of "global 3D reconstruction + local scanning correction". First, this paper proposes an improved K-means clustering method based on the geometric features of workpiece point-cloud, to achieve accurate and fast point-cloud segmentation. Second, the intersection parameters of workpiece can be quickly calculated by the proposed parameter identification method. Third, this paper plans the scanning path of laser scanner according to the identified parameters, and puts forward an algorithm of weld path points recognition combined with the actual weldments state. Then, the pose of robot welding torch is obtained by using spline fitting algorithm and welding torch attitude estimation algorithm. Finally, the experiments show the path planning error is about 0.3 mm, which has increased a lot than the published methods. [ABSTRACT FROM AUTHOR]

• When utilizing the acoustic droplet ejection (ADE) system, it is crucial to ensure that droplets fly upward at a specific velocity and efficiently coalesce on the target substrate. This relies on precise adjustment of the acoustic input power. • In this paper, we first utilize the oscillating drop technique to achieve non-contact measurements of surface tension and viscosity. The measurement results of surface tension and viscosity showed a good agreement with that obtained by the standard instruments. • In addition, an adaptive power regulation method was proposed based on real-time detection of the liquid properties. By using the power regulation method, the relative errors between the obtained initial velocity and the given velocity are mostly less than 6.7%. This work not only achieved non-contact and rapid measurement of liquid properties, but also significantly improved the operational efficiency of the ADE system for high-throughput liquid transfer. The constantly evolving pace of life science research has necessitated fundamental changes in liquid handling systems. With its high-throughput liquid handling capability without physical contact, the acoustic droplet ejection (ADE) technology has been proven to be highly beneficial in simplifying experimental operations and enhancing reagent compatibility. And it is becoming one of the important tools in life science research. When utilizing the ADE system, it is crucial to ensure that droplets fly upward at a specific velocity and efficiently coalesce on the target substrate. This relies on precise adjustment of the acoustic input power. However, the existing power regulation methods are either cumbersome to operate or rely on additional devices to measure the liquid properties in advance, making it difficult to meet the demand for rapid and accurate assessment of input power. The determination of viscosity and surface tension is the prerequisite for accurate liquid transfer during the ADE. In this paper, we first utilize the oscillating drop technique to achieve non-contact and synchronous measurements of surface tension and viscosity. The measurement results showed a good agreement with that obtained by the standard instruments. Based on the real-time detection of the liquid properties, an adaptive power regulation method was proposed to precisely control droplet initial velocity. Using the power regulation method, the relative errors between the obtained initial velocity and the given velocity are mostly less than 6.7%. This work not only achieved the measurement of liquid properties, but also significantly improved the operational efficiency of the ADE system for high-throughput liquid transfer. [ABSTRACT FROM AUTHOR]

• A CFRP subsurface defects collection and detection platform was designed. • A CFRP subsurface defects dataset was created specifically for practical production purposes. • A novel network was designed to effectively address the challenges associated with intrinsic similarly and edge disruption in CFRP infrared images. • Our model demonstrates state-of-the-art performance on the CFRP dataset and is suitable for COD detection tasks. Infrared thermography, due to its fast detection speed, low cost, and intuitive results, is widely used in carbon fiber reinforced plastics (CFRP) materials detection. However, CFRP infrared image noise and lower resolution lead to intrinsic similarly and edge disruption, making it impossible to obtain satisfactory results. Additionally, the current methods require a lot of preprocessing and post-processing operations. Since camouflaged object detection has achieved good results in solving intrinsic similarly and edge disruption. Therefore, this paper proposes an edge and high-level semantic guided network (EHSGNet) based on camouflaged object detection for infrared thermography CFRP subsurface defect detection, achieving end-to-end detection of CFRP. This paper also constructed a dataset containing CFRP samples with both natural defects and artificially simulated internal defects to validate our method. Since the temperature change is achieved in the temperature-controlled chamber by means of hot air heating, it can heat the sample uniformly and improve the quality of CFRP infrared images, so our experiments will be carried out in the temperature-controlled chamber. The experiments were conducted on CFRP specimens with different sizes, shapes and depths of artificial defects. The experimental results demonstrate that the proposed EHSGNet outperforms current approaches significantly on our dataset, with precision (PRC) and recall (RCL) reaching 94.3% and 93.1%, respectively. [ABSTRACT FROM AUTHOR]

Bridges have become an essential part of our transport networks. Therefore, the issue of cable damage detection in bridge health monitoring, specifically through acoustic emission (AE) technology, is of paramount importance for the nation and its citizens. This paper extensively investigates and compares various bridge cable damage identification techniques applied to AE. It can be categorized into two major classes: conventional parameter identification and machine learning identification. For the former, the signal's own feature parameter identification technique and the signal's mathematical index identification method are reviewed, summarizing their pros and cons. For the latter, both unsupervised and supervised learning are extensively surveyed. This paper introduces insights from AE monitoring in other six research fields, such as rail crack monitoring, and presents a basic process. Finally, the directions and recommendations for future research are proposed, providing useful suggestions for research in bridge cable damage identification and related domains. [ABSTRACT FROM AUTHOR]

• A hybrid broadband and narrowband active sound profiling algorithm is proposed for active hybrid noise control. • Simulations of stationary and non-stationary hybrid noise sources prove the stability and robustness of the algorithm. • The proposed algorithm can selectively control the desired narrowband noise components with specific magnitudes in hybrid noise. • Real vehicle experiments, conducted using headrests, validate the theoretical analysis. The vehicle interior noise consists of both broadband road noise and narrowband engine order noise. Currently, the road noise control (RNC) system and engine noise control (EOC) system operate independently in engineering applications. This paper introduces a novel multi-channel hybrid broadband and narrowband active noise control (HBNANC) algorithm, designed to simultaneously attenuate road noise and engine order noise inside vehicles. On this basis, by integrating an error signal design (ESD) subsystem and a sinusoidal error separation (SES) subsystem, a multi-channel hybrid broadband and narrowband active sound profiling (HBNASP) algorithm is further proposed, enabling specific attenuation or amplification of individual narrowband components to meet subjective acoustic comfort requirements. The paper validates the algorithms' effectiveness and robustness through simulations, and real vehicle experimental measurements under various conditions. The research offers a comprehensive approach to enhancing the auditory experience inside vehicles, contributing to the advancement of active noise control technology. [ABSTRACT FROM AUTHOR]

• This paper proposes a high-accuracy single-frame 3D reconstruction method with color speckle projection for pantograph sliders. • There is currently a scarcity of research papers on three-dimensional reconstruction of pantograph carbon sliders. The proposed method not only enables the acquisition of a comprehensive and dense point cloud of the worn surface through a single frame but also achieves an average error limitation within one pixel. • We have successfully tackled the persistent challenges of poor imaging accuracy and limited generalization in carbon slider imaging by combining traditional structured light, active imaging with speckle, and deep learning-based stereo matching methods. • The proposed method holds significant implications for the health monitoring of train pantographs. The carbon slider of the pantograph is a crucial component in the high-speed rail power system, and regular maintenance is of great significance. 2D-based approach is susceptible to inaccuracies caused by defects, rainwater, stains, and other factors. Due to the weak texture and uneven reflectance of the slider, it is difficult to obtain accurate 3D information through a single-frame approach. To address this issue, this paper proposes a high-accuracy 3D imaging method for sliders, successfully resolving the long-standing problem of low reconstruction accuracy and limited generalization in pantograph reconstruction. Firstly, high-precision disparity maps of industrial component are generated using the binocular Gray Code phase-shifting method as the Ground Truth, and the color speckle pattern is subsequently utilized as the input for the model. Secondly, we propose a split-cost volume approach that achieves cost aggregation using only 2D decoding-encoding operations. Furthermore, disparity regression is utilized to provide the initial disparity for the GRU(Gated Recurrent Unit), thereby accelerating the convergence speed of the model. Quantitative experiments of optical standard sphere validated that the proposed method could achieve 3D reconstruction with a radius error of no more than 0.07 mm. The pantograph reconstruction results showed that the average absolute error of the carbon slider did not exceed 0.21 mm under reasonable exposure time and the working distance at which focus has been achieved, which meets the requirements of industrial inspection. [ABSTRACT FROM AUTHOR]

• Denoising and harmonic interference removal from the measurement signal is crucial for determining the actual parameters of the power generating unit model and requires various transient signals data recorded during a well-defined testing procedure. However, the recorded signals usually contain noise and harmonic interferences which are difficult to remove due to a presence of different kinds of discontinuities. • The paper presents a proposal of optimal denoising and harmonic interference removal method from transient measurement signals of 200 MW power generating unit. The method combines notch filters for harmonic rejection followed by SASS (Sparsity Assisted Signal Smoothing) algorithm for wideband noise rejection. Parameters of the notch filters as well as the SASS are determined using popular optimization algorithms (simulated annealing, Nelder-Mead simplex and a simple gradient algorithm). • Performance of the proposed method is compared to its direct Linear Time-Invariant (LTI) competitors such as low-pass and notch filters and to the optimal SASS method alone. The performance evaluation is based on a set of specially generated test signals which are using the developed mathematical model of the signals (to set the level of noise and interferences) as well as the mathematical model of the power generating unit (to provide the reference clean signals). The obtained results confirms that the optimal notch-SASS method leads to superior results than the LTI filters or the SASS algorithm alone. • The authors believe that the methodology presented in the paper and obtained results may encourage other scientists to try the SASS or the proposed optimal notch-SASS method. Especially that according to the authors knowledge, this is the first paper where the SASS method was used for this kind of signals, which makes the SASS method relatively unknown in this field. Denoising and harmonic interference removal from the measurement signal is crucial for accurate parameters calculation of a generating unit model. Determining the actual parameters values requires various unit transient signals data recorded during a well-defined testing procedure. However, the recorded signals usually contain noise and harmonic interferences which are difficult to remove due to a presence of different kinds of discontinuities. The paper presents a proposal of optimal denoising and harmonic interference removal method from transient measurement signals of 200 MW power generating unit. The method combines notch filters for harmonic rejection followed by SASS (Sparsity Assisted Signal Smoothing) algorithm for wideband noise rejection. Parameters of the notch filters as well as the SASS are determined using popular optimization algorithms (simulated annealing, Nelder-Mead simplex and gradient algorithm). Performance of the proposed method is compared to its direct Linear Time-Invariant (LTI) competitors such as low-pass and notch filters alone, notch and low-pass filters combined and to the optimal SASS method alone. The performance evaluation is based on a set of specially generated test signals which are using the developed mathematical model of the signals (to set the level of noise and interferences) as well as the mathematical model of the power generating unit (to provide the reference clean signals). The obtained results confirm that the optimal notch-SASS method might be a valuable noise removal tool for considered class of signals and leads to superior results than the LTI filters or the SASS method alone. [ABSTRACT FROM AUTHOR]

[Display omitted] • Analytical error variance propagation. • Analysis of white noise, bias instability, rate random walk, and correlated noise. • Comparison of the relative importance of each error factor. • Using a more suitable model for bias instability. Understanding and mitigating the various stochastic errors in gyroscope measurements is crucial for accurate attitude determination. White noise and bias instability can introduce drift errors, reducing the system's accuracy. Efficient sensor selection and algorithm design necessitate a thorough understanding of these influences and how they propagate throughout the system. This paper addresses the propagation of significant stochastic errors in the attitude determination system, including white noise (angle random walk), bias instability, rate random walk, and correlated noise. The paper discusses the modeling strategy and statistical characteristics of these error contributors. It then evaluates the variance propagation of these errors within the output of a single-axis attitude determination system. The presented analysis calculates and compares the impact of each source of error on the system's precision over time. Experiments with the MPU6050 and L3G4200D sensors are conducted to validate the analytical results. [ABSTRACT FROM AUTHOR]

• The impact of capacitive nonlinearity on the angular measurement is studied. • A practical method for identifying assembly and nonlinear errors is presented. • The joint compensation of error parameters is achieved by signal processing. • The fourth harmonic component in the gyro output is greatly suppressed. • The findings of this study are suitable for various axisymmetric vibrating gyros. This paper presents a novel nonlinear error identification and compensation method for the Hemispherical Resonator Gyro (HRG) from the signal processing perspective. Firstly, the paper establishes an HRG nonlinear error model that reveals the quantitative correlation between angle measurement error and the amplitude-gap ratio. Subsequently, a dynamic analysis model for gyro drift is developed using nonlinear motion equations. Based on this, a scalar factor nonlinear identification model encompassing assembly and nonlinear errors is proposed. Nonlinear optimization is then used to identify error parameters accurately. Finally, a joint compensation method is devised to mitigate assembly and nonlinear error issues. Empirical findings provide concrete validation for the precision of the identification and compensation method. The experimental results demonstrate that reductions of 97%, 89%, and 40% after the joint compensation are achieved in the fourth harmonic component, scale factor nonlinearity, and bias instability of the gyro, respectively. [ABSTRACT FROM AUTHOR]

• The computational spectrometers' calibration requires more special measurements. • A calibration and measurement system is developed for specialized objects. • An efficient data processing method based on envelope-centroid is proposed. • Feasibility of computational imaging spectrometer is validated. To enhance the performance of the optical payload, a novel computational imaging spectrometer using a board-bandpass film-filter line array (IS-BFLA) was developed. Since there is an obvious difference between the IS-BFLA and the one utilizing dispersion, conventional calibration and evaluation methods are not suitable. In this paper, we present a calibration method for the IS-BFLA. The calibration objects are determined by the mathematical model. We develop an automated calibration system and design a set of data processing procedures. Two evaluation methods: self-evaluation of multi-measurement based on Euclidean distance and multi-pixel inter-evaluation based on spectral angle, are developed to evaluate the precision and accuracy of the calibration. Furthermore, comparing the calibration value with the design value and spectral reconstruction are also utilized. The accuracy of spectral reconstruction: R2 is greater than 0.99, which determines the feasibility of the calibrated data. We hope that the work in this paper will assist in subsequent related studies. [ABSTRACT FROM AUTHOR]

• Divide fire area by equipment function. • Calculate the number of fire detectors required to utilize the floor area and the height of the room. • The substation fire alarm system is established based on the building automation system. • Wavelet transform is used to eliminate the signal noise of fire detector equipment. • The fuzzy characteristic analysis model of fire detector operating state is established. Aiming at the problem that it is difficult to obtain the life cycle law and detection coverage of fire detectors and the low reliability of fire alarm process in urban integrated substation, an optimal layout scheme and operation condition monitoring technology of fire detectors based on local outlier factor and neural network are proposed. Divide fire zones by equipment functions, calculate the number of fire detectors required by using ground area and indoor height, and integrate building automation system to create substation fire alarm system; The signal noise of fire detector equipment is eliminated by wavelet transform, the fuzzy characteristic analysis model of fire detector operation state is established, the local outlier factor is simulated as abnormal data, the past operation data is trained by BP(Back propagation) neural network, the difference between the current state and the normal operation state is obtained, and the detector operation state monitoring is completed. The operation reliability of fire detector is analyzed from the aspects of temperature and smoke perception. The simulation results show that the proposed technology can successfully obtain the life cycle law and detection coverage of fire detector, and ensure the safe use of urban integrated substation. In addition, through a series of experiments, the feasibility and effectiveness of this technology in practical applications are verified.The maximum time consumption of detector working condition monitoring in this method is only 3.3 s. The fitting degree of the method in this paper is 5.7 ml/m3, and the fitting degree of the actual smoke curve is 5.7 ml/m3. The temperature of the method in this paper is lower than 33 °C. In the future development, the introduction of more advanced self-diagnosis technology can be real-time monitoring and analysis of the status of the detector itself, timely detection of faults or anomalies, and automatic alarm or notification of maintenance personnel for repair and maintenance. [ABSTRACT FROM AUTHOR]

• The influence of meteorological factors on the icing of transmission lines was analyzed in experimental studies. • The proposed Gray Correlation Pearson Combined Analysis (GPCA) combines two types of traditional methods for more effective data analysis. • The GPCA-SVM model established by Gaussian kernel is more effective than the ordinary model under the consideration of data balance. • In this paper, the objective law between multi-dimensional meteorological factors and icing degree is explored, which lays the foundation for the intelligent monitoring system of power grid and provides guarantee for the operation and maintenance of power system. In recent years, ice and snow disasters have occurred frequently, and transmission line icing can pose a great threat to the operation of the power system. In order to solve this problem, the power industry should introduce an intelligent monitoring system to recognize the risk of ice-covered disasters in advance and take timely countermeasures to reduce the impact of extreme weather on the safe operation of the power system. In this paper, we first use Python to sort out and summarize multidimensional meteorological data from multiple locations in the Northeast China, and propose a method called Grey Relational Pearson Combination Analysis (GPCA). Through this method, we determine the correlation between multidimensional meteorological data and icing degree, and select the optimal combination of icing factors as the feature vector for a support vector machine. In addition, we also compare the differences in classification effects of four types of kernel functions, such as linear kernel, polynomial kernel and Gaussian kernel. Finally, considering data balance, we use Gaussian kernel to establish GPCA-SVM model and conduct experimental analysis. There was a 1.04% increase in accuracy, a 1.36% increase in recall, and 0.31% increase in F1 value when comparing gray correlation accuracy and a 21.66% increase in precision when comparing Pearson accuracy at light icing. Comparison of gray correlation accuracy at heavy icing improved by 1.04%, precision by 17.44%, and F1 value by 10.29%; comparison of Pearson accuracy improved by 0.31%, precision by 9.93%, and F1 value by 1.67%. These research results lay the foundation for the intelligent monitoring system of power grid, and provide guarantee for the operation and maintenance of power system. [ABSTRACT FROM AUTHOR]

Surface defects in steel manufacturing can compromise product quality and safety. Detecting these diverse and complex defects under industrial conditions is challenging. This paper proposes WSS-YOLO, a YOLOv8-based model, for accurate defect detection on industrial steel. Firstly, the dynamic non-monotonic focusing mechanism based on WIoU loss was employed to focus on anchor boxes with ordinary quality, thereby improving the overall performance of the detector. Secondly, the C2f-DSC module based on dynamic snake convolution is designed to enable the model to adaptively adjust the receptive field. Finally, GSConv and VOV-GSCSP modules are introduced into the neck network to reduce the computational complexity and parameter quantity while ensuring the accuracy of the model. This paper conducts extensive experiments on the public datasets NEU-DET and GC10-DET, achieving mAP of 82.3% and 72.0%, respectively, outperforming other excellent models. Moreover, the effectiveness of the proposed method in industrial defect detection is also validated. • Utilizing the WIoU loss to mitigate the harm of low-quality samples. • Designing the C2f-DSC module, employing dynamic snake convolution (DSC) to enhance the model's feature extraction capability. • Implementing a slim-neck paradigm design to achieve lightweight design while maintaining detection accuracy. • Validating the effectiveness of the proposed methods through extensive experimental results. [ABSTRACT FROM AUTHOR]

• An original multielectrode embedded capacitive sensor is developed. • Multielectrode embedded capacitive sensor allows to assess permittivity in concrete. • Optimum dimensions chosen for capacitive electrodes are rectangular 30 × 10 mm2. • Measurements in liquids and concrete show a very repeatable and reproducible system. • Permittivities obtained with the developed embedded sensor are validated. This paper discusses the development of a capacitive sensor embedded within concrete blocks in order to continuously monitor water content, which is a crucial parameter in the assessment of concrete degradation. The embedded capacitive sensor undergoes continuous interrogation by an automatic monitoring system, in order to track permittivity changes over time through a frequency measurement system. The methodology involves a two-step calibration process: first, obtaining permittivity from frequency, then converting permittivity profiles into water content profiles. This paper focuses on the sensor design and first calibration in the aim of assessing permittivity profiles. Numerical methods enable optimizing both electrode geometry and design of the embedded capacitive sensor. Experimental validation of the sensor in liquid and concrete environments, along with a metrological validation, reveals the sensor's ability to monitor permittivity profiles as well as its uncertainty at 8 different depths inside the concrete structure. Permittivity measurements were also conducted using reference methods. The results demonstrate very good agreement with less than 10% difference between the embedded sensor and the reference results. This step provides insights into its applicability and potential perspectives for future research. [ABSTRACT FROM AUTHOR]

Deep learning is widely used in Bearing Fault Diagnosis (BFD). Nonetheless, practical industrial production often generates a large amount of industrial noise. These noises exhibit randomness and complexity, which puts forward higher requirements for diagnosis algorithms. Certain studies have tackled the issue of anti-interference in high-noise environments (S N R ≤ 0 dB) by increasing the complexity of the model. However, due to the excessive number of parameters and computation, such models cannot be deployed on low-end edge devices. Balancing resource consumption and accuracy has become a major challenge in BFD modeling research. To solve the above problems, this paper proposes a new Transformer architecture model called LTFAFormer. The LTFAFormer is capable of achieving high-precision diagnostics on low-end edge devices and shows greater noise resistance. In terms of processing sequence information, Transformer has proven to be superior to other solutions. However, when dealing with longer sensor signal data containing complex noise, the traditional self-attention mechanism not only cannot effectively extract fault features, but also generates more computational complexity than CNN. To address this issue, we propose a novel time–frequency dual-channel parallel attention mechanism. Our approach enhances the feature extraction capability of the model by expanding the attention computation scale and reduces the computational resource consumption of the model by optimizing the model structure. To validate the effectiveness of LTFAFormer, we present two cases to demonstrate that LTFAFormer has higher prediction accuracy while satisfying lightweight. Especially in high-noise environments, LTFAFormer has stronger robustness. In this paper provides a new set of feasible strategies for the practical deployment of BFD models in practical industrial environments. • The LTFAFormer, a lightweight and noise-robust model, is ideal for low-end devices. • Added a Dual-Channel Attention Module to expand traditional self-attention scale. • Developed a Cross-fusion mechanism for attention to enhance feature fusion. • Optimized the model's embedding module based on sensor signal traits. • Our model outperforms others in bearing fault diagnosis and is robust in noise. [Display omitted] [ABSTRACT FROM AUTHOR]

A novel position estimator is proposed to address the issue of position detection for micromanipulator in surgical robots. Firstly, the single joint micromanipulator experimental prototype proposed in the paper is built based on the real micromanipulator. To identify the parameters of the position estimator dynamics model, we enhance the Honey Badger Algorithm (HBA) and utilize Improved Honey Badger Algorithm (IHBA). With global parameters, the maximum absolute estimation error of the position estimator is only 1.10 deg. To further enhance position estimation accuracy, this paper introduces an online updating parameters model and integrates it with IHBA to achieve online updating parameters. The high-precision estimation of position estimator proposed in this paper is obtained. The experiments demonstrate the effective of the position estimator with maximum absolute estimation error is only 0.28 deg and the excellent robustness. • By incorporating the Lévy flight strategy and chaotic tent map, an improve HBA algorithm named IHBA is proposed. • An online updating parameters model is proposed and integrated with IHBA, significantly enhances the estimation accuracy of the position estimator. • A high-precision position estimator that accurately represents the performance of real micromanipulator is proposed. [ABSTRACT FROM AUTHOR]

• A method for constructing grayscale images of vibration signals has been proposed. • An unconventional convolutional kernel design method has been proposed. • The validity of the fault diagnosis method in this paper were verified. Rolling bearings are widely used in various industries, including rail transit, aerospace, and wind power generation, playing a critical role. However, bearing failures can lead to serious consequences, impacting equipment operation and even causing safety accidents. Hence, the diagnosis of bearing faults is of utmost importance. However, the variable speed conditions experienced during bearing operation pose significant challenges to fault diagnosis. To overcome the limitations of traditional methods in diagnosing bearing faults under variable speed conditions, this paper proposes a fault diagnosis method based on the gray-level co-occurrence matrix (GLCM) and Dual Channel Convolutional Neural Network (DCCNN). The method introduces a two-dimensional grayscale matrix construction (2D-GMC) technique to extract grayscale texture features for fault diagnosis. Additionally, an unconventional kernel design method, based on grayscale image contrast, is proposed to reduce the complexity associated with traditional square kernels. A new DCCNN architecture is developed accordingly. Furthermore, the transfer learning concept is utilized to train the proposed DCCNN model using fault signals at specific rotational speeds. The method intercepts the variable speed into multi-speed short-time series, then constructs gray image under different speed to realize the rapid fault diagnosis of bearings under variable speed conditions. [ABSTRACT FROM AUTHOR]

• In this paper, a mechanical property analysis method based on multistage decomposition − equivalence − modeling − reconstruction of structures is presented. • The transfer mechanism of force identified the weak stiffness link and the main flexible function area of the component under different stress directions. • Based on the basic theory of elastic mechanics and material mechanics, the deformation function model under the action of forces in different directions is established. • Subsequently, the mechanical model of the component is reconstructed, leading to the derivation of FMC global deformation function. • Finally, the accuracy of the deformation function is verified through a deformation measurement experiment conducted on the FMC. The force measuring component (FMC) is the key structure of the thrust vector measuring device. The precise analysis of FMCs' performance is crucial in elucidating the mechanism of vector force measurement and decoupling output data. The current testing technology solely utilizes FMCs, with no investigation into their mechanical properties. This paper presents an innovative and comprehensive mechanical analysis and modeling of FMC performance. The force transfer mechanism within the FMC is examined in each direction, and the structure is decomposed into its functional components, which are then treated as thin plate and beam structures. By applying principles of elastic mechanics, the local structure's deformation function is analyzed. The complete deformation function for the FMC is established based on incorporating local and global deformation regulations. Finally, force-loading experiments were conducted, and deformation measurements were used to validate the FMC's deformation function. The experimental results demonstrate a strong agreement between the theoretical calculations and experimental results. The maximum errors of axial and lateral deformation function is less than values of 4.77 % and 5.19 %, respectively, confirming that the theoretical function accurately characterizes the mechanical properties of the FMC. [ABSTRACT FROM AUTHOR]

• In this paper, we design and fabricate a twist sensor MZI with an MSM fiber structure, and combines it with a fiber collimator to achieve control of the number of cladding modes in SMF. • After cascading with an FPI, twist sensing enhancement based on optical vernier effect has been achieved. • The experimental results indicate that the sensing MZI has a torsional sensitivity of −0.011 nm/(rad/m) within a large detection range of 0–84 rad/m. • After amplification by optical vernier effect, it reaches −0.218 nm/(rad/m) with an amplification factor of 19.8 times. • The sensor has a relatively small response to other physical quantities, with a strain sensitivity of −1.1 pm/με, a temperature sensitivity of 60.4 pm/℃, and a refractive index sensitivity of 1.1543 nm/RIU, exhibiting excellent anti crosstalk performance. • In addition, this article discusses a phase demodulation scheme for interferometric sensors based on optical vernier effect sensitization for the first time, which can filter out noise caused by dispersion or loss, eliminate the influence of initial phase of the sensing system, and point out that improving the basic sensitivity of the sensing interferometer is a further direction for sensitization. • This article uses a graded index fiber collimator to successfully modulate the optical mode transmitted in an interferometric sensor. • The design concept of integrating functional optical devices into interferometric sensing systems will be the future development direction in the field. • The new torsion sensor has a simple processing method, is easy to promote, and has practicality, which can play an important role in engineering detection, human activity recognition, and other fields. Fiber interferometric sensor can achieve sensing enhancement of various physical quantities based on the optical vernier effect, but there are currently no proposed torsion detection enhancement schemes. In this paper, we control the number of cladding modes in the sensing single-mode fiber by graded index fiber collimator and form a dual beam interference with the core mode, resulting in a transmission spectrum with regular waveform. The experimental results indicate that the sensing Mach-Zehnder interferometer (MZI) has a torsional sensitivity of −0.014 nm/(rad/m) within a large detection range of 0–60 rad/m. After amplification by optical cursor effect, it reaches −0.361 nm/(rad/m) with an amplification factor of 25.8 times, the detection limit for envelope valley is 0.345 rad/m. The sensor has a relatively small response to other physical quantities, with a strain sensitivity of 20.4 pm/με, a temperature sensitivity of 1.792 nm/℃, and a refractive index sensitivity of 77.885 nm/RIU, exhibiting excellent anti refractive index crosstalk performance. In addition, we discuss the phase demodulation scheme for interferometric sensor based on optical vernier effect sensitivity enhancement for the first time, which can filter out noise caused by dispersion or loss, eliminate the influence of the initial phase of the sensing system, and evaluate the direction of improving the detection performance of the phase demodulation system. [ABSTRACT FROM AUTHOR]

• Uncertainties in measurement. • LVDT sensor. • Upper bound of dynamic error. • Absolute error. The upper bound of dynamic error (UBDE) and the associated uncertainties determined for the Linear Variable Differential Transformer (LVDT) sensor with the inter-winding capacitance are analysed in this paper. This analysis considers the absolute error (AE) by assuming the numerical excitation signals with the magnitude (one constraint) and the magnitude and rate of change (two constraints). The values of the AE, the associated uncertainties, and the corresponding signals with constraints are determined. The developed procedure for determining the UBDE is also validated. The solutions presented in this paper include the numerical calculation methods based on the results of the parametric identification (via practical experiments) for the LVDT sensor. These results are obtained through the measurement of the corresponding frequency responses. The practical experiment was carried out for the Miran LVDT sensor, while the numerical calculations were performed using MathCad 15 software. The solutions presented in this paper can be used to compare the different types of LVDTs in terms of their the UBDE values and the associated uncertainties. [ABSTRACT FROM AUTHOR]

• The novel concept of the nondestructive thickness measurement of the plate-like structures subjected to corrosion degradation has been presented. • The study explores the influence of actuator-sensor distance on corrosion monitoring, emphasizing the crucial role of the number of sensors over their spatial separation. • The Bilateral and Rotational Central Symmetry Degrees are introduced as reliable indicators, demonstrating a consistent decrease with higher corrosion levels. • The experimental campaign was supported with numerical simulations conducted for plates with irregular thickness defined using random fields. Metallic structures often face degradation, and corrosion ranks among the most prevalent forms of deterioration. Accurate quantification of corrosion is crucial, especially for structures exposed to harsh environmental conditions, such as marine vessels and offshore installations. Because the traditional measurement methods based on scanning by ultrasonic gauge are time-consuming and provide only rough information on the thickness variability, there is a need to develop new robust and accurate diagnostics methods. This paper aims to investigate the corrosion monitoring of metal plates using guided wave propagation. Guided waves propagate within the entire volume of the specimen. If it is undamaged and isotropic, the velocity in all directions is the same, and the spreading wavefront takes a circular shape. The primary assumption presented in this paper is that the potential damage caused by corrosion leads to disturbance of this symmetry. The study shows Bilateral and Rotational Corrosion Symmetry Degree (BCSD and RCSD) functions, demonstrating a consistent decrease in symmetry values with increasing degree of degradation (DoD) caused by the corrosion process. The main aim of the study was to test the possibility of corrosion monitoring by using variable number of sensors comprising the transducer network. Also, the influence of the distance between the sensors affecting the size of the monitored area on the corrosion monitoring procedure was investigated. The paper contains the results of numerical and experimental campaigns conducted for corroded plates characterized by variable DoD and monitored using nine different transducers configurations. It is the first step in developing a novel measurement procedure specially designed for the ship and offshore industry. Therefore, because of the initial stage of the study, the last part of the paper discusses the limitations and drawbacks of the presented wave symmetry-based approach. [ABSTRACT FROM AUTHOR]

• An innovative calibration method of coal gangue size based on perspective correction and parameter calibration is proposed. • An innovative coal gangue image acquisition and processing system that can adaptively adjust the acquisition conditions is established. • Through experimental comparison, the optimal conditions for the calibration of coal gangue size are determined. The size calibration of coal gangue can help to determine the size range of coal gangue, which has a great influence on the grabbing strategy of coal gangue in the process of coal gangue sorting. However, at present, the size calibration of coal gangue has not been effectively studied. Based on this, this paper innovatively proposes a gangue size calibration method based on perspective correction-parameter calibration. Firstly, a coal gangue image acquisition and processing system was built to collect coal gangue images and preprocess the images. Secondly, the size calibration system model is established, including mathematical model and operation model. Finally, the influence of different heights and tilt angles on the accuracy and stability of the size calibration results were compared. The results show that when the camera height and the coal gangue height are 600 mm and the camera is perpendicular to the ground, the calibration accuracy and stability are the best, and the accuracy rate is more than 97 %. The research content of this paper provides theoretical support for the accurate positioning and precise sorting of coal gangue in the process of belt transportation. [ABSTRACT FROM AUTHOR]

• Single input/channnel signal phase measurement method. • Enhanced phase Measurement System (PMS) prototype. • Best prototype uncertainty of 0.004 phase. • Protopype frequency range of 0.1 Hz ti 100 MHz. In this paper, a new prototype of a Phase Measurement System (PMS) is presented. The PMS uses a Sample & Hold (S&H) circuit-based architecture, and it is capable of performing phase measurements with very low uncertainty over a wide bandwidth. The prototype covers a frequency range from 100 Hz to 100 MHz, and it consists of three main subsystems: (i) a bank of four S&H circuits, (ii) a pulse train signal generator with coarse and fine delay control, and (iii) a data acquisition circuit followed by digital signal processing. The paper describes the method and system-level architecture of the PMS, as well as its capabilities. Experimental results show that the PMS can provide high-accuracy time measurements from high-accuracy amplitude measurements. For a sinewave frequency of 10 MHz, the maximum standard deviation obtained was 0.019°, while for a frequency of 100 MHz, the maximum standard deviation was 0.73°. These results represent a significant improvement compared to the previous PMS prototype and reference measurement instrumentation. [ABSTRACT FROM AUTHOR]

• Enhanced security risks are emerging from handling of numerous UAVs (drones), especially in particularly crowded areas or areas of strategic importance for national security. • Implementing drone detection systems can involve various sensor types, including imaging, radar, acoustic, and radio frequency (RF) sensors. Localizing the target drone through direction finding (DF) methods is a crucial aspect of these systems. • An RF-based direction-finding testbed (employing a DJI Mavic Air UAV as target, software defined radio (SDR) platforms from the USRP family as hardware and MUSIC as signal processing algorithm) is designed and experimentally assessed. • The measurement scenarios include a static one (46 selected points) and a dynamic one (38 selected points) measured in approx. 15 mins (total flight time), determining Latitude, Longitude, Altitude (relative to sea level), and Height (above ground). • The testbed's performance is assessed by comparing the direction derived from the drone's flight record with that estimated using the proposed testbed. This comparison yields an average estimation error of 1.15 degrees from the drone's actual position for the static scenario and of 1.86 degrees for the dynamic one, highlighting the successful implementation of this approach. Nowadays, drones, also known as unmanned aerial vehicles (UAVs), are used for a wide range of purposes in the civil, industrial, and military domains. The greater the number of UAVs, the more serious the potential security risks correlated to them. To address these risks, it became necessary to develop systems that can detect, locate, and possibly neutralize such an object. The detection can be performed using different sensors such as imaging, radar, acoustic, and radio frequency (RF) sensors. The localization of the target drone, which can be done using direction finding (DF) methods, is an essential part of such a system. Thus, an RF-based DF testbed is designed and experimentally assessed in this paper. The testbed is implemented using software defined radio (SDR) platforms from the Universal Software Radio Peripheral (USRP) family as hardware equipment, and the MUSIC algorithm for processing the signals received on four different phase-coherent RF channels. A DJI Mavic Air UAV is used as target. For assessing the DF estimation performance, the direction obtained from the drone flight record is compared with the one estimated using the proposed DF testbed. Two different scenarios, a static one and a dynamic one, are considered to highlight the effectiveness of the proposed DF testbed across various conditions. The obtained results indicate an average estimation error of 1.15 degrees from the drone's actual position, in the case of the static scenario, and of 1.86 degrees in the case of the dynamic scenario, results which are on parity with or surpassed those of other drone DF systems described in the literature. The paper also outlines the benefits and challenges of the current approach. [ABSTRACT FROM AUTHOR]