Search Results (36)

Gradient damage processes in cementitious materials are generally produced by chemical and/or physical processes that travel from outside to inside. Depending on the type of damage, it can cause different effects such as decreased porosity, cracking, or steel corrosion in the case of carbonation, or increased porosity, micro-cracks, expansion, and spalling (also present in thermal damage) in the case of external attack by sulphates or acid attack. Therefore, estimating the boundaries of this damage is an essential task for concrete quality assessment. The first objective of this work was to use neural networks (NNs) for ultrasound tomographic reconstruction of concrete samples in order to estimate the advance front in gradient damage. Unlike the usual X-ray tomography, ultrasound tomography is affected by diffraction, among other factors. NNs can learn to compensate for these effects; however, they require a large amount of training data to achieve accurate results. In the case of cement-based materials, obtaining and measuring a real training database could be complicated, expensive, and time-consuming. For this purpose, a training process using simulated measurements was carried out. The second objective of this work was to demonstrate the feasibility of training neural networks through simulations, which reduces costs. Finally, the trained neural network for tomographic reconstruction was evaluated using real cylindrical concrete specimens. Each specimen consisted of an outer cylinder, representing externally exposed cement, and an inner cylinder, simulating the unaffected core. The Structural Similarity Index (SSIM) was used as a metric to assess the reconstruction accuracy, achieving values of 0.95 for simulated signals and up to 0.82 for real signals. Full article

The performance of steam traps plays an important role in the normal operation of steam systems. It also contributes to the improvement of thermal efficiency of steam-using equipment and the rational use of energy. As an important component of the steam system, it is crucial to monitor the state of the steam trap and establish a correlation between the acoustic emission signal and the internal leakage state. However, in actual test environments, the acoustic emission sensor often collects various background noises alongside the valve internal leakage acoustic emission signal. Therefore, to minimize the impact of environmental noise on valve internal leakage identification, it is necessary to preprocess the original acoustic emission signals through noise reduction before identification. To address the above problems, a denoising method based on a sparrow search algorithm, variational modal decomposition, and improved wavelet thresholding is proposed. The sparrow search algorithm, using minimum envelope entropy as the fitness function, optimizes the decomposition level K and the penalty factor α of the variational modal decomposition algorithm. This removes modes with higher entropy in the modal envelopes. Subsequently, wavelet threshold denoising is applied to the remaining modes, and the denoised signal is reconstructed. Validation analysis demonstrates that the combination of SSA-VMD and the improved wavelet threshold function effectively filters out noise from the signal. Compared to traditional thresholding methods, this approach increases the signal-to-noise ratio and reduces the root-mean-square error, significantly enhancing the noise reduction effect on the steam trap’s background noise signal. Full article

Accurately characterizing the internal porosity rate of thermal barrier coatings (TBCs) was essential for prolonging their service life. This work concentrated on atmospheric plasma spray (APS)-prepared TBCs and proposed the utilization of terahertz non-destructive detection technology to evaluate their internal porosity rate. The internal porosity rates were ascertained through a metallographic analysis and scanning electron microscopy (SEM), followed by the reconstruction of the TBC model using a four-parameter method. Terahertz time-domain simulation data corresponding to various porosity rates were generated employing the time-domain finite difference method. In simulating actual test signals, white noise with a signal-to-noise ratio of 10 dB was introduced, and various wavelet transforms were utilized for denoising purposes. The effectiveness of different signal processing techniques in mitigating noise was compared to extract key features associated with porosity. To address dimensionality challenges and further enhance model performance, kernel principal component analysis (kPCA) was employed for data processing. To tackle issues related to limited sample sizes, this work proposed to use the Siamese neural network (SNN) and generative adversarial network (GAN) algorithms to solve this challenge in order to improve the generalization ability and detection accuracy of the model. The efficacy of the constructed model was assessed using multiple evaluation metrics; the results indicate that the novel hybrid WT-kPCA-GAN model achieves a prediction accuracy exceeding 0.9 while demonstrating lower error rates and superior predictive performance overall. Ultimately, this work presented an innovative, convenient, non-destructive online approach that was safe and highly precise for measuring the porosity rate of TBCs, particularly in scenarios involving small sample sizes facilitating assessments regarding their service life. Full article

Railways play a pivotal role in national economic development, freight transportation, national defense, and regional connectivity. The detection of rolling contact fatigue (RCF) defects in rail tracks is essential for railway safety and maintenance. Due to its efficiency and non-contact capability in detecting surface and near-surface defects, Eddy Current Pulsed Thermography (ECPT) has garnered significant attention from researchers. However, detecting multi-layer RCF defects remains a challenge. This paper introduces a sweeping Eddy Current Pulsed Thermal-Tomography system (ECPTT) to detect multi-layer RCF defects effectively. This system utilizes varying excitation frequencies to heat defects, altering skin depth and facilitating feature extraction to distinguish multi-layer RCF defects. Skewness and thermographic signal reconstruction (TSR) values are employed as features in the experiments. These features are qualitatively analyzed to differentiate the layers and depths of multi-layer RCF defects. Additionally, five different coils were compared and analyzed quantitatively. The results indicate that the ECPTT system can effectively detect and distinguish multi-layer RCF defects, thereby providing more detailed defect information and enhancing railway safety and maintenance efficiency. Full article

Photoacoustic imaging (PAI) is a rapidly developing emerging non-invasive biomedical imaging technique that combines the strong contrast from optical absorption imaging and the high resolution from acoustic imaging. Abnormal biological tissues (such as tumors and inflammation) generate different levels of thermal expansion after absorbing optical energy, producing distinct acoustic signals from normal tissues. This technique can detect small tissue lesions in biological tissues and has demonstrated significant potential for applications in tumor research, melanoma detection, and cardiovascular disease diagnosis. During the process of collecting photoacoustic signals in a PAI system, various factors can influence the signals, such as absorption, scattering, and attenuation in biological tissues. A single ultrasound transducer cannot provide sufficient information to reconstruct high-precision photoacoustic images. To obtain more accurate and clear image reconstruction results, PAI systems typically use a large number of ultrasound transducers to collect multi-channel signals from different angles and positions, thereby acquiring more information about the photoacoustic signals. Therefore, to reconstruct high-quality photoacoustic images, PAI systems require a significant number of measurement signals, which can result in substantial hardware and time costs. Compressed sensing is an algorithm that breaks through the Nyquist sampling theorem and can reconstruct the original signal with a small number of measurement signals. PAI based on compressed sensing has made breakthroughs over the past decade, enabling the reconstruction of low artifacts and high-quality images with a small number of photoacoustic measurement signals, improving time efficiency, and reducing hardware costs. This article provides a detailed introduction to PAI based on compressed sensing, such as the physical transmission model-based compressed sensing method, two-stage reconstruction-based compressed sensing method, and single-pixel camera-based compressed sensing method. Challenges and future perspectives of compressed sensing-based PAI are also discussed. Full article

To enhance the accuracy of micro-Newton thrust measurements via a torsion pendulum, addressing microgravity coupling effects caused by platform tilt and pendulum mass eccentricity is crucial. This study focuses on analyzing and minimizing these effects by alleviating reference surface tilt and calibrating the center of mass during thrust measurements. The study introduced analysis techniques and compensation measures. It first examined the impact of reference tilt and center of mass eccentricity on the stiffness and compliance of the torsion pendulum by reconstructing its dynamic model. Simscape Multibody was initially employed for numerical analysis to assess the dynamic coupling effects of the tilted pendulum. The results showed the influence of reference tilt on the stiffness and compliance of the torsion pendulum through simulation. An inverted pendulum was developed to amplify the platform’s tilt angle for microgravity drag-free control. Center of mass calibration can identify the gravity coupling caused by the center of mass position. Based on the displacement signal from the capacitive sensor located at the end of the inverted pendulum, which represents the platform’s tilt angle, the pendulum’s vibration at 0.1 mHz was reduced from 5.7 $\mathsf{\mu }$m/Hz^1/2 to 0.28 $\mathsf{\mu }$m/Hz^1/2 by adjusting the voltage of piezoelectric actuator. Finally, a new two-stage torsion pendulum structure was proposed to decouple the tilt coupling buried in both pitch and roll angle. The study utilized theoretical models, numerical analysis, and experimental testing to validate the analysis methods and compensation measures for microgravity coupling effects in torsion pendulums. This led to a reduction in low-frequency noise caused by ground vibrations and thermal strains, ultimately improving the micro-Newton thrust measurement accuracy of the torsion pendulum through the platform’s drag-free control. Full article

Plasmonic nanostructures with a high density of confined areas with high local electromagnetic fields (hot spots) are sine qua nonto increase the efficiency of surface-enhanced Raman spectroscopy (SERS). These nanostructures can be used both to identify biological molecules and to monitor photochemical reactions occurring on the metal surface. In this work, using the method of pulsed laser deposition, three-dimensional (3D) porous wedge-shaped arrays of gold nanoparticles (Au NPs) were obtained with structural parameters varying along the substrate, such as film thickness, porosity, nanoparticles size, and the distance between them. The resulting arrays were structures with a regularly changing density of hot spots along the substrate, in which the enhancement of the electromagnetic field strength is due to the geometric parameters of the nanostructure.By analyzing the evolution of fluorescence and Raman scattering of fluorescein molecules adsorbed on the surface of porous gold under illumination at 532 nm, the processes in the region of extreme values of the electromagnetic field of surface nanostructures was studied. A correlation has been established between the amplification of optical signals and the structural features of the surface. A correlation between SERS and fluorescence signals indicates the predominant contribution of hot spots to the electromagnetic amplification of optical signals. The observed time evolution of the fluorescence and SERS intensity of fluorescein can be explained by the combination of molecular photodegradation, the reconstruction of the hot spot architecture due to local heating, and potent relocation of analyte molecules outside the area of measurement owing to the effects of thermal gradients. Full article

The present work, unlike others, does not try to reduce the noise in hyperspectral images to increase the semantic segmentation performance metrics; rather, we present a classification framework for noisy Hyperspectral Images (HSI), studying the classification performance metrics for different SNR levels and where the inputs are compressed. This framework consists of a 3D Convolutional Neural Network (3DCNN) that uses as input data a spectrally compressed version of the HSI, obtained from the Tucker Decomposition (TKD). The advantage of this classifier is the ability to handle spatial and spectral features from the core tensor, exploiting the spatial correlation of remotely sensed images of the earth surface. To test the performance of this framework, signal-independent thermal noise and signal-dependent photonic noise generators are implemented to simulate an extensive collection of tests, from 60 dB to −20 dB of Signal-to-Noise Ratio (SNR) over three datasets: Indian Pines (IP), University of Pavia (UP), and Salinas (SAL). For comparison purposes, we have included tests with Support Vector Machine (SVM), Random Forest (RF), 1DCNN, and 2DCNN. For the test cases, the datasets were compressed to only 40 tensor bands for a relative reconstruction error less than 1%. This framework allows us to classify the noisy data with better accuracy and significantly reduces the computational complexity of the Deep Learning (DL) model. The framework exhibits an excellent performance from 60 dB to 0 dB of SNR for 2DCNN and 3DCNN, achieving a Kappa coefficient from 0.90 to 1.0 in all the noisy data scenarios for a representative set of labeled samples of each class for training, from 5% to 10% for the datasets used in this work. The source code and log files of the experiments used for this paper are publicly available for research purposes. Full article

Stratigraphy is a fundamental classification tool for archaeology on which modern excavation techniques are based, and essentially consists of a sedimentological, pedological and archaeological interpretation of the multiple cultural layers found while digging; this concept can be adopted when studying monumental buildings and, in particular, their hidden parts or elements. The precious and delicate surfaces of monuments need non-invasive techniques such as geophysical methods and in the present article, the use of GPR technique has been exploited through a dataset collected over the nave of the church of San Leonardo de Siete Fuentes in Sardinia. First, the georadar results have been jointly analyzed by means of the B- and C-scans, in which some most significant patterns were detected and analyzed by looking at their signal features over the investigated volume. Following the analysis, elements from the signal attribute analysis and horizon detection and visualization, with a 3D approach, were used. To strengthen the reliability of the GPR results, a thermal infrared survey was simultaneously carried out. Thanks to the integrated geophysical and historical analysis of the monument, the ancient layout of the church has been reconstructed and other targets of potential archaeological interest identified Full article

Emissivity variations are one of the most critical challenges in thermography technologies; this is due to the temperature calculation strongly depending on emissivity settings for infrared signal extraction and evaluation. This paper describes an emissivity correction and thermal pattern reconstruction technique based on physical process modelling and thermal feature extraction, for eddy current pulsed thermography. An emissivity correction algorithm is proposed to address the pattern observation issues of thermography in both spatial and time domains. The main novelty of this method is that the thermal pattern can be corrected based on the averaged normalization of thermal features. In practice, the proposed method brings benefits in enhancing the detectability of the faults and characterization of the materials without the interference of the emissivity variation problem at the object’s surfaces. The proposed technique is verified in several experimental studies, such as the case-depth evaluation of heat-treatment steels, failures, and fatigues of gears made of the heat-treated steels that are used for rolling stock applications. The proposed technique can improve the detectability of the thermography-based inspection methods and would improve the inspection efficiency for high-speed NDT&E applications, such as rolling stock applications. Full article

This paper discusses optimal design of the series proportional–integral–derivative–accelerative (PIDA) controller for integral-plus-dead-time (IPDT) plants. The article starts with the design of disturbance reconstruction and compensation based on proportional-derivative-accelerative (PDA) stabilizing controllers. It shows that by introducing positive feedback by a low-pass filter from the (limited) output of the stabilizing PDA controller, one gets disturbance observer (DOB) for the reconstruction and compensation of input disturbances. Thereby, the DOB functionality is based on evaluating steady-state controller output. This DOB interpretation is in full agreement with the results of the analysis of the optimal setting of the stabilizing PDA controller and of its expanded PIDA version with positive feedback from the controller output. By using the multiple real dominant pole (MRDP) method, it confirms that the low-pass filter time constant in positive feedback must be much longer than the dominant time constant of the stabilized loop. This paper also shows that the constrained PIDA controller with the MRDP setting leads to transient responses with input and output overshoots. Experimentally, such a constrained series PIDA controller can be shown as equivalent to a constrained MRDP tuned parallel PIDA controller in anti-windup connection using conditional integration. Next, the article explores the possibility of removing overshoots of the output and input of the process achieved for MRDP tuning by interchanging the parameters of the controller transfer function, which was proven as very effective in the case of the series PID controller. It shows that such a modification of the controller can only be implemented approximately, when the factorization of the controller numerator, which gives complex conjugate zeros, will be replaced by a double real zero. Neglecting the imaginary part and specifying the feedback time constant with a smaller approximative time constant results in the removal of overshoots, but the resulting dynamics will not be faster than for the previously mentioned solutions. A significant improvement in the closed-loop performance can finally be achieved by the optimal setting of the constrained series PIDA controller calculated using the performance portrait method. This article also points out the terminologically incorrect designation of the proposed structure as series PIDA controller, because it does not contain any explicit integral action. Instead, it proposes a more thorough revision of the interpretation of controllers based on automatic reset from the controller output, which do not contain any integrator, but at the same time represent the core of the most used industrial automation. In the end, constrained structures using automatic reset of the stabilizing controller output can ensure a higher performance of transient responses than the usually preferred solutions based on parallel controllers with integral action that, in order to respect the control signal limitation, must be supplemented with anti-windup circuitry. The excellent properties of the constrained series PIDA controller are demonstrated by an example of controlling a thermal process and proven by the circle criterion of absolute stability. Full article

Intelligent drilling technologies, such as downhole signal and power transmission, can be used to measure key downhole data and obtain thermal insulation cores. This technology is of great significance for the accurate assessment of deep oil and gas resources, the reconstruction of oil and gas resource extraction systems, and the realization of efficient, intelligent and safe resource extraction. In order to meet the needs of underground communication and power supply for active thermal insulation coring, a new type of conductive center pole was innovatively designed. Using the theory of innovation problem solving (TRIZ) and axiomatic design (AD) to analyze the functional requirements of the conductive central pole, establish and solve the original design matrix. Based on the axiomatic design theory, the non-coupling matrix is decoupled by using the TRIZ solving tool, and the key indicators of the design scheme that meet the independent axiom are evaluated. In view of the contradictions and conflicts, the TRIZ solution tool was continually used to solve, optimize and obtain a design scheme with a higher comprehensive evaluation. Thus, the self-adaptive non-winding connection and power conduction of the conductive center pole was realized. Finally, the strength of the newly designed center pole was checked, and a physical prototype was made. Pre-research experiments on its conductivity and electrothermal conversion efficiency were carried out under different simulation environments to verify its conductivity. It provides innovative solutions to related problems in the field of deep insulation coring and intelligent drilling and provides effective technical means for related needs. Full article

When evaluating the energy efficiency of buildings and implementing the necessary measures to increase energy efficiency levels, thermal technical characteristics are determined. For this purpose, in situ measurements of the thermal resistance of external enclosing structures were carried out. One of the methods most often used by researchers is the non-destructive method—the heat flow meter (HFM) method regulated by ISO 9869. In the case of surveying a building with a high level of thermal resistance, researchers are faced with low-density heat flux measurements, which is always a difficult task due to significant fluctuations and the influence of external factors on the measurement results. This is due to the fact that it is difficult to determine what is a useful signal and what is a consequence of the effects of non-stationarity and heat transfer conditions. The article provides an example of low-density heat flux measurements when determining the thermal resistance of a building and proposes a data pre-processing procedure that allows for the reduction of heat flux fluctuations, which has a significant impact on the final result at low density. The proposed use of wavelet analysis in the pre-processing of low-density heat flux measurement data makes it possible to reconstruct them or reduce disturbances that occur during research. A comparison of the obtained results with the results of the calculation according to ISO 9869-1 showed a decrease in the standard deviation of the measurements from 5.74 to 2.81%. The results of this study can be used to reduce the noise of low-density heat flux and, as a result, reduce the standard deviation of the measurement when applying the HFM method of determining the thermal resistance of external enclosing structures. Full article

Low-intensity pulsed ultrasound (LIPUS) serves as a non-invasive treatment tool that reaches the lesion site in the form of ultrasound. Due to its low toxicity, low thermal effect, and low immunogenicity, LIPUS has attracted wide interest in disease treatment. It has been demonstrated that LIPUS can activate multiple signal pathways in the shape of sound wave and one of the most acknowledged downstream response components is integrin/focal adhesion kinase (FAK) complex. In recent years, the functions of LIPUS in bone regeneration, bone healing, bone mass maintenance, and cellular metabolism were found. Various oral diseases and their treatments mainly involve hard/soft tissue regeneration and reconstruction, including periodontitis, orthodontic tooth movement (OTM), dental implant, mandibular deficiency, and dentin-pulp complex injury. Thus, more and more researchers pay close attention to the application prospects of LIPUS in stomatology. We searched these articles in PubMed with keywords LIPUS, temporomandibular joint (TMJ), periodontitis, orthodontics, and pulp, then classified the retrieved literature in the past five years by disease type. In this review, the function effects and possible mechanisms of LIPUS in periodontal tissue regeneration, orthodontic treatment, implant osseointegration, TMJ bone formation/cartilage protection, and dentin-pulp complex repair after injury will be summarized. The challenges LIPUS faced and the research limitations of LIPUS will also be elucidated. Therefore, this paper intends to provide new insights into oral disease treatments, explore the optimal application specification of LIPUS, and probe the future research orientation and the prospect of LIPUS in the dental field. Full article

Heat shock transcription factors (Hsfs) are key regulators in plant heat stress response, and therefore, they play vital roles in signal transduction pathways in response to environmental stresses, as well as in plant growth and development. Canavalia rosea (Sw.) DC. is an extremophile halophyte with good adaptability to high temperature and salt-drought tolerance, and it can be used as a pioneer species for ecological reconstruction on tropical coral islands. To date, very little is known regarding the functions of Hsfs in the adaptation mechanisms of plant species with specialized habitats, especially in tropical leguminous halophytes. In this study, a genome-wide analysis was performed to identify all the Hsfs in C. rosea based on whole-genome sequencing information. The chromosomal location, protein domain or motif organization, and phylogenetic relationships of 28 CrHsfs were analyzed. Promoter analyses indicated that the expression levels of different CrHsfs were precisely regulated. The expression patterns also revealed clear transcriptional changes among different C. rosea tissues, indicating that the regulation of CrHsf expression varied among organs in a developmental or tissue-specific manner. Furthermore, the expression levels of most CrHsfs in response to environmental conditions or abiotic stresses also implied a possible positive regulatory role of this gene family under abiotic stresses, and suggested roles in adaptation to specialized habitats such as tropical coral islands. In addition, some CrHsfAs were cloned and their possible roles in abiotic stress tolerance were functionally characterized using a yeast expression system. The CrHsfAs significantly enhanced yeast survival under thermal and oxidative stress challenges. Our results contribute to a better understanding of the plant Hsf gene family and provide a basis for further study of CrHsf functions in environmental thermotolerance. Our results also provide valuable information on the evolutionary relationships among CrHsf genes and the functional characteristics of the gene family. These findings are beneficial for further research on the natural ecological adaptability of C. rosea to tropical environments. Full article