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Appl. Sci., Volume 12, Issue 21 (November-1 2022) – 616 articles

Cover Story (view full-size image): To understand, study, and optimize optical imaging systems from the information–theoretic viewpoint has been an important research subfield. However, the “direct point-to-point” image information acquisition mode of traditional optical imaging limits the development of further imaging capabilities. In this review, the connection between ghost imaging (GI) and information optical imaging is firstly illustrated by combining viewpoints of both optical coherence theory and information theory, and several specific GI systems and studies with various extended imaging capabilities are reviewed, followed by a detailed prospect for future studies. This review emphasizes the potential of GI for conducting information optical imaging studies. View this paper
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Article
Study of the Evolution of the Performance Ratio of Photovoltaic Plants Operating in a Utility-Scale Installation Located at a Subtropical Climate Zone Using Mixed-Effects Linear Modeling
Appl. Sci. 2022, 12(21), 11306; https://doi.org/10.3390/app122111306 - 07 Nov 2022
Viewed by 736
Abstract
This paper assessed the evolution of the performance ratio (PR) of a utility-scale photovoltaic (PV) installation that operates at subtropical climate conditions. The period of study encompassed 8 years, and the PR was calculated according to the ICE 61724 standard with a monthly [...] Read more.
This paper assessed the evolution of the performance ratio (PR) of a utility-scale photovoltaic (PV) installation that operates at subtropical climate conditions. The period of study encompassed 8 years, and the PR was calculated according to the ICE 61724 standard with a monthly resolution. A linear mixed effects model (LME) is a suitable tool for analyzing longitudinal data. Three LME models were assessed to provide the degradation rate. The “null model” evaluates the general relationship between PR and time with a monthly declination rate (ΔPR%) of 0.0391%/month. The “typology model” considered the relationship between PR and, as covariates, time, Manufacturer, Technology, and NominalP. Only the ΔPR% related to NominalP was found to be significant, so that, when the nominal power of a type of PV module used for a PV production unit is increased by one unit, the ΔPR% of the corresponding unit increases by 0.000897%/month. Finally, the “location model” took into account the relationship between PR and, as covariates, time, Edge, and LengthSt. These last two factors were significant, resulting in an increase of 0.0132%/month for a PV unit located at the edge of the facility and 0.00117%/month and per PV production unit when considering the length of a street, respectively. Full article
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Article
Distinction of Scrambled Linear Block Codes Based on Extraction of Correlation Features
Appl. Sci. 2022, 12(21), 11305; https://doi.org/10.3390/app122111305 - 07 Nov 2022
Viewed by 566
Abstract
Aiming to solve the problem of the distinction of scrambled linear block codes, a method for identifying the scrambling types of linear block codes by combining correlation features and convolution long short-term memory neural networks is proposed in this paper. First, the cross-correlation [...] Read more.
Aiming to solve the problem of the distinction of scrambled linear block codes, a method for identifying the scrambling types of linear block codes by combining correlation features and convolution long short-term memory neural networks is proposed in this paper. First, the cross-correlation characteristics of the scrambling sequence symbols are deduced, the partial autocorrelation function is constructed, the superiority of the partial autocorrelation function is determined by derivation, and the two are combined as the input correlation characteristics. A shallow network combining a convolutional neural network and LSTM is constructed; finally, the linear block code scrambled dataset is input into the network model, and the training and recognition test of the network is completed. The simulation results show that, compared with the traditional algorithm based on a multi-fractal spectrum, the proposed method can identify a synchronous scrambler, and the recognition accuracy is higher under a high bit error rate. Moreover, the method is suitable for classification under noise. The proposed method lays a foundation for future improvements in scrambler parameter identification. Full article
(This article belongs to the Special Issue Advance in Digital Signal, Image and Video Processing)
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Article
Low-Cost Assessment Method for Existing Adjacent Beam Bridges
Appl. Sci. 2022, 12(21), 11304; https://doi.org/10.3390/app122111304 - 07 Nov 2022
Viewed by 504
Abstract
Damage in grouted joints is an unavoidable early disease in adjacent box beam bridges and hollow-core slab bridges. Joint damage will lead to degradation of the transverse load transmission capacity of the bridge, causing beams of the bridge superstructure to bear loads higher [...] Read more.
Damage in grouted joints is an unavoidable early disease in adjacent box beam bridges and hollow-core slab bridges. Joint damage will lead to degradation of the transverse load transmission capacity of the bridge, causing beams of the bridge superstructure to bear loads higher than the designed value, and eventually fail prematurely. Precise assessment of bearing--capacity degradation degree of adjacent box beam bridges and hollow-core slab bridges that are of great number is the keypoint to maintaining the serviceability of traffic network. The current specifications regard grouted joints as individual components and cannot correctly assess the degradation degree of bearing capacity caused by joint damage. In this paper, the traditional hinge connected beam method is improved by modifying deformation compatibility conditions at grouted joints. By using a modified hinge connected beam method, the relationship of joints at different locations with the lateral load distribution factor (LLDF) is analyzed. Based on analysis results, this paper proposes a new low-cost assessment method and a new assessment index that can utilize visual inspection results. Based on the concept of standard deviation, the proposed method assesses the degradation degree of the lateral load transmission performance of bridge superstructures by calculating the variation in LLDFs of beams, which is expressed by the lateral load distribution performance rating number LDN. The proposed method is applied to three real bridges. The accuracy of the calculation results is verified by comparing the ranking of LDNs of three bridges with the ranking of the variation degrees of lateral deflection influence lines of three bridges obtained from static-load test results. Full article
(This article belongs to the Section Civil Engineering)
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Article
Graph-Based Multi-Label Classification for WiFi Network Traffic Analysis
Appl. Sci. 2022, 12(21), 11303; https://doi.org/10.3390/app122111303 - 07 Nov 2022
Viewed by 669
Abstract
Network traffic analysis, and specifically anomaly and attack detection, call for sophisticated tools relying on a large number of features. Mathematical modeling is extremely difficult, given the ample variety of traffic patterns and the subtle and varied ways that malicious activity can be [...] Read more.
Network traffic analysis, and specifically anomaly and attack detection, call for sophisticated tools relying on a large number of features. Mathematical modeling is extremely difficult, given the ample variety of traffic patterns and the subtle and varied ways that malicious activity can be carried out in a network. We address this problem by exploiting data-driven modeling and computational intelligence techniques. Sequences of packets captured on the communication medium are considered, along with multi-label metadata. Graph-based modeling of the data are introduced, thus resorting to the powerful GRALG approach based on feature information granulation, identification of a representative alphabet, embedding and genetic optimization. The obtained classifier is evaluated both under accuracy and complexity for two different supervised problems and compared with state-of-the-art algorithms. We show that the proposed preprocessing strategy is able to describe higher level relations between data instances in the input domain, thus allowing the algorithms to suitably reconstruct the structure of the input domain itself. Furthermore, the considered Granular Computing approach is able to extract knowledge on multiple semantic levels, thus effectively describing anomalies as subgraphs-based symbols of the whole network graph, in a specific time interval. Interesting performances can thus be achieved in identifying network traffic patterns, in spite of the complexity of the considered traffic classes. Full article
(This article belongs to the Special Issue Applied and Innovative Computational Intelligence Systems)
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Article
Laser Spot Centering Algorithm of Double-Area Shrinking Iteration Based on Baseline Method
Appl. Sci. 2022, 12(21), 11302; https://doi.org/10.3390/app122111302 - 07 Nov 2022
Viewed by 489
Abstract
High-precision laser spot center detection occupies an important position in optical measurement technology. In this paper, we propose a laser spot centering method to improve positioning accuracy. This method is an iterative double-area shrinkage approach based on the baseline method. The background noise [...] Read more.
High-precision laser spot center detection occupies an important position in optical measurement technology. In this paper, we propose a laser spot centering method to improve positioning accuracy. This method is an iterative double-area shrinkage approach based on the baseline method. The background noise baseline is calculated from the noise statistics of multiple background image frames acquired, and then the background noise is subtracted during the calculation while retaining the effective information of the spot region. The real spot area is located in the end by double-area shrinkage iteration to calculate the position of the spot center. Simulation and experimental results showed that our proposed method has strong anti-background noise interference ability, as well as higher positioning accuracy in locating the spot center than commonly used approaches; the maximum localization accuracy could reach 0.05 pixels, meeting the real-time requirements of the algorithm. The fluctuation range of measurement results was small when continuously detecting the center of the same laser spot, which could reach 0.04 and 0.03 pixels in the x- and y-directions, respectively. The result indicates that the method can meet the requirements of laser high-precision positioning. Full article
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Article
Research on Generalized Hybrid Probability Convolutional Neural Network
Appl. Sci. 2022, 12(21), 11301; https://doi.org/10.3390/app122111301 - 07 Nov 2022
Viewed by 504
Abstract
This paper first studies the generalization ability of the convolutional layer as a feature mapper (CFM) for extracting image features and the classification ability of the multilayer perception (MLP) in a CNN. Then, a novel generalized hybrid probability convolutional neural network (GHP-CNN) is [...] Read more.
This paper first studies the generalization ability of the convolutional layer as a feature mapper (CFM) for extracting image features and the classification ability of the multilayer perception (MLP) in a CNN. Then, a novel generalized hybrid probability convolutional neural network (GHP-CNN) is proposed to solve abstract feature classification with an unknown distribution form. To measure the generalization ability of the CFM, a new index is defined and the positive correlation between it and the CFM is researched. Generally, a fully trained CFM can extract features that are beneficial to classification, regardless of whether the data participate in training the CFM. In the CNN, the fully connected layer in the MLP is not always optimal, and the extracted abstract feature has an unknown distribution. Thus, an improved classifier called the structure-optimized probabilistic neural network (SOPNN) is used for abstract feature classification in the GHP-CNN. In the SOPNN, the separability information is not lost in the normalization process, and the final classification surface is close to the optimal classification surface under the Bayesian criterion. The proposed GHP-CNN utilizes the generalization ability of the CFM and the classification ability of the SOPNN. Experiments show that the proposed network has better classification ability than the existing hybrid neural networks. Full article
(This article belongs to the Special Issue Deep Convolutional Neural Networks)
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Article
Impact of the Power-Dependent Beam Diameter during Electron Beam Additive Manufacturing: A Case Study with γ-TiAl
Appl. Sci. 2022, 12(21), 11300; https://doi.org/10.3390/app122111300 - 07 Nov 2022
Viewed by 750
Abstract
The development of process parameters for electron beam powder bed fusion (PBF-EB) is usually made with simple geometries and uniform scan lengths. The transfer to complex parts with various scan lengths can be achieved by adapting beam parameters such as beam power and [...] Read more.
The development of process parameters for electron beam powder bed fusion (PBF-EB) is usually made with simple geometries and uniform scan lengths. The transfer to complex parts with various scan lengths can be achieved by adapting beam parameters such as beam power and scan speed. Under ideal conditions, this adaption results in a constant energy input into the powder bed despite of the local scan length. However, numerous PBF-EB machines show deviations from the ideal situation because the beam diameter is subject to significant changes if the beam power is changed. This study aims to demonstrate typical scaling issues when applying process parameters to scan lengths up to 45 mm using a fourth generation γ-TiAl alloy. Line energy, area energy, return time, and lateral velocity are kept constant during the additive manufacturing process by adjusting beam power and beam velocity to various scan lengths. Samples produced in this way are examined by light microscopy regarding lateral melt pool extension, melt pool depth, porosity, and microstructure. The process-induced aluminum evaporation is measured by electron probe microanalysis. The experiments reveal undesired changes in melt pool geometry, gas porosity, and aluminum evaporation by increasing the beam power. In detail, beam widening is identified as the reason for the change in melt pool dimensions and microstructure. This finding is supported by numerical calculations from a semi-analytic heat conduction model. This study demonstrates that in-depth knowledge of the electron beam diameter is required to thoroughly control the PBF-EB process, especially when scaling process parameters from simply shaped geometries to complex parts with various scan lengths. Full article
(This article belongs to the Special Issue The Physics of Joining and Additive Manufacturing)
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Article
Hyperspectral Image Classification Using 3D Capsule-Net Based Architecture
Appl. Sci. 2022, 12(21), 11299; https://doi.org/10.3390/app122111299 - 07 Nov 2022
Viewed by 611
Abstract
Convolution neural networks have received much interest recently in the categorization of hyperspectral images (HSI). Deep learning requires a large number of labeled samples in order to optimize numerous parameters due to the expansion of architecture depth and feature aggregation. Unfortunately, only few [...] Read more.
Convolution neural networks have received much interest recently in the categorization of hyperspectral images (HSI). Deep learning requires a large number of labeled samples in order to optimize numerous parameters due to the expansion of architecture depth and feature aggregation. Unfortunately, only few examples with labels are accessible, and the majority of spectral images are not labeled. For HSI categorization, the difficulty is how to acquire richer features with constrained training data. In order to properly utilize HSI features at various scales, a 3D Capsule-Net based supervised architecture is presented in this paper for HSI classification. First, the input data undergo incremental principal component analysis (IPCA) for dimensionality reduction. The reduced data are then divided into windows and given to a 3D convolution layer to get the shallow features. These shallow features are then used by 3D Capsule-Net to compute high-level features for classification of HSI. Experimental investigation on three common datasets demonstrates that the categorization performance by Capsule-Net based architecture exceeds a number of other state-of-the-art approaches. Full article
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Article
CBA-CLSVE: A Class-Level Soft-Voting Ensemble Based on the Chaos Bat Algorithm for Intrusion Detection
Appl. Sci. 2022, 12(21), 11298; https://doi.org/10.3390/app122111298 - 07 Nov 2022
Cited by 1 | Viewed by 464
Abstract
Various machine-learning methods have been applied to anomaly intrusion detection. However, the Intrusion Detection System still faces challenges in improving Detection Rate and reducing False Positive Rate. In this paper, a Class-Level Soft-Voting Ensemble (CLSVE) scheme based on the Chaos Bat Algorithm (CBA), [...] Read more.
Various machine-learning methods have been applied to anomaly intrusion detection. However, the Intrusion Detection System still faces challenges in improving Detection Rate and reducing False Positive Rate. In this paper, a Class-Level Soft-Voting Ensemble (CLSVE) scheme based on the Chaos Bat Algorithm (CBA), called CBA-CLSVE, is proposed for intrusion detection. The Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Decision Tree (DT) are selected as the base learners of the ensemble. The Chaos Bat Algorithm is used to generate class-level weights to create the weighted voting ensemble. A weighted fitness function considering the tradeoff between maximizing Detection Rate and minimizing False Positive Rate is proposed. In the experiments, the NSL-KDD, UNSW-NB15 and CICIDS2017 datasets are used to verify the scheme. The experimental results show that the class-level weights generated by CBA can be used to improve the combinative performance. They also show that the same ensemble performance can be achieved using about half the total number of features or fewer. Full article
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Article
Functionality Analysis of Derailment Containment Provisions through Full-Scale Testing—I: Collision Load and Change in the Center of Gravity
Appl. Sci. 2022, 12(21), 11297; https://doi.org/10.3390/app122111297 - 07 Nov 2022
Viewed by 523
Abstract
In order to reduce the large damage caused by train derailment, protective facilities of various shapes and conditions can be installed on railroad tracks. These protective facilities are referred to as derailment containment provisions (DCPs) and three different types are used worldwide. However, [...] Read more.
In order to reduce the large damage caused by train derailment, protective facilities of various shapes and conditions can be installed on railroad tracks. These protective facilities are referred to as derailment containment provisions (DCPs) and three different types are used worldwide. However, there are no clear standards for DCP design such as installation location, size, and design load, and the performance verification of DCPs installed in the actual railway field is not sufficiently performed. In this paper, the functionality of DCP type I was analyzed experimentally. A method for estimating the collision (impact) load acting on the DCP was proposed. In addition, the containment effect of DCP type I according to the change in the vehicle’s center of gravity was identified through a comparative analysis of the dynamic motion such as roll, pitch, and yaw. Full article
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Article
Co-Operative Binary Bat Optimizer with Rough Set Reducts for Text Feature Selection
Appl. Sci. 2022, 12(21), 11296; https://doi.org/10.3390/app122111296 - 07 Nov 2022
Viewed by 535
Abstract
The process of eliminating irrelevant, redundant and noisy features while trying to maintain less information loss is known as a feature selection problem. Given the vast amount of the textual data generated and shared on the internet such as news reports, articles, tweets [...] Read more.
The process of eliminating irrelevant, redundant and noisy features while trying to maintain less information loss is known as a feature selection problem. Given the vast amount of the textual data generated and shared on the internet such as news reports, articles, tweets and product reviews, the need for an effective text-feature selection method becomes increasingly important. Recently, stochastic optimization algorithms have been adopted to tackle this problem. However, the efficiency of these methods is decreased when tackling high-dimensional problems. This decrease could be attributed to premature convergence where the population diversity is not well maintained. As an innovative attempt, a cooperative Binary Bat Algorithm (BBACO) is proposed in this work to select the optimal text feature subset for classification purposes. The proposed BBACO uses a new mechanism to control the population’s diversity during the optimization process and to improve the performance of BBA-based text-feature selection method. This is achieved by dividing the dimension of the problem into several parts and optimizing each of them in a separate sub-population. To evaluate the generality and capability of the proposed method, three classifiers and two standard benchmark datasets in English, two in Malay and one in Arabic were used. The results show that the proposed method steadily improves the classification performance in comparison with other well-known feature selection methods. The improvement is obtained for all of the English, Malay and Arabic datasets which indicates the generality of the proposed method in terms of the dataset language. Full article
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Article
Development of a Finite Element Model of the Cervical Spine and Validation of a Functional Spinal Unit
Appl. Sci. 2022, 12(21), 11295; https://doi.org/10.3390/app122111295 - 07 Nov 2022
Viewed by 588
Abstract
The cervical spine is a common site of injury in the vertebral column, with severe injuries often associated with damage to the spinal cord. Several studies have been performed to better understand the mechanisms of such situations and develop ways to treat or [...] Read more.
The cervical spine is a common site of injury in the vertebral column, with severe injuries often associated with damage to the spinal cord. Several studies have been performed to better understand the mechanisms of such situations and develop ways to treat or even prevent them. Among the most advantageous and most widely used methods are computational models, as they offer unique features such as providing information on strains and stresses that would otherwise be difficult to obtain. Therefore, the main objective of this work is to help better understand the mechanics of the neck by creating a new finite element model of the human cervical spine that accurately represents most of its components. The initial geometry of the cervical spine was obtained using the computer tomography scans of a 46-year-old female. The complete model was then sectioned, and a functional spinal unit consisting of the C6–C7 segment was simulated to initiate the validation process. The reduced model was validated against experimental data obtained from in vitro tests that evaluated the range of motion of various cervical segments in terms of flexion–extension, axial rotation, and lateral bending. Full article
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Article
Experimental Study on Improvement Mechanism of Electric Heating-Assisted Cyclic Steam Stimulation of Horizontal Well
Appl. Sci. 2022, 12(21), 11294; https://doi.org/10.3390/app122111294 - 07 Nov 2022
Viewed by 489
Abstract
To resolve the issues of the high porous medium flow resistance, low oil production rate, high oil decline rate, and low oil recovery factor for the cyclic steam stimulation (CSS) of horizontal wells in heavy oil reservoirs, the CSS method assisted by the [...] Read more.
To resolve the issues of the high porous medium flow resistance, low oil production rate, high oil decline rate, and low oil recovery factor for the cyclic steam stimulation (CSS) of horizontal wells in heavy oil reservoirs, the CSS method assisted by the electric heating (E-CSS) of horizontal wells was proposed in this study. Combining the heat from electric heating and steam during E-CSS, the analytical model of formation temperature rise was established for the three phases of electric-assisted CSS (i.e., injection, soaking, production), and physical experiments were carried out to compare the performance of conventional CSS and E-CSS. The experimental results were used to validate the analytical model and reveal the impact of the key electric heating mechanism on the horizontal CSS performance. Meanwhile, the typical well model was used to forecast the E-CSS potential. The results indicate that electric heating can achieve uniform heating in the steam injection phase, maintain heating around the wellbore in the soak phase, and reduce flow resistance and enhance oil output in the production phase. Forecasts of the typical well model indicate that electric heating can enhance the oil recovery factor by 9.4% and the oil-steam ratio from 0.14 to 0.23, implying a significant application potential in heavy oil reservoirs developed by horizontal CSS. Full article
(This article belongs to the Special Issue Advances in Enhanced Heavy Oil Recovery Technologies)
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Article
Influence of Process Parameters and Reducing Agent on the Size of MoS2 Nanoparticles Obtained in Impinging Jet Reactor
Appl. Sci. 2022, 12(21), 11293; https://doi.org/10.3390/app122111293 - 07 Nov 2022
Viewed by 502
Abstract
Molybdenum disulfide (MoS2) is an emerging material with exciting properties. Many consider it an excellent catalyst, particularly for hydrogen evolution reaction. Currently, it is used on a larger scale as a lubricant. The size of MoS2 is a crucial parameter [...] Read more.
Molybdenum disulfide (MoS2) is an emerging material with exciting properties. Many consider it an excellent catalyst, particularly for hydrogen evolution reaction. Currently, it is used on a larger scale as a lubricant. The size of MoS2 is a crucial parameter defining its properties. A preparation method that is easily scalable and cheap is currently being sought. A solution might be a wet chemical synthesis method carried out in an impinging jet reactor. The simple design of the reactor and the possibility of continuous operation make this method unique. In this study, the influence of the reactor was investigated using numerical simulations. The S-type reactor showed better mixing and more control over the working conditions than T-type. Therefore, the S-type reactor was chosen as better for nanoparticle synthesis. We also investigated the influence of the process conditions on the size of the precipitated MoS2 particles. The best operating conditions (i.e., Mo concentration of 0.2 mol/m3, reagent flow velocity of 20 mL/min, reaction temperature of 20 °C) were chosen to obtain the smallest particles (~200 µm). Additionally, two different reducing agents were also tested. The use of formic acid allowed obtaining smaller particle sizes but these were less stable than in the case of citric acid. Full article
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Article
Microbial Growth Dynamics in Minced Meat Enriched with Plant Powders
Appl. Sci. 2022, 12(21), 11292; https://doi.org/10.3390/app122111292 - 07 Nov 2022
Viewed by 982
Abstract
Plant powders with antimicrobial properties can be used in food manufacturing and must comply with the demands of consumers regarding microbiological safety, nutritional value, and sensory properties of foods. The present study aimed to assess the microbial growth inhibitory ability of different plant [...] Read more.
Plant powders with antimicrobial properties can be used in food manufacturing and must comply with the demands of consumers regarding microbiological safety, nutritional value, and sensory properties of foods. The present study aimed to assess the microbial growth inhibitory ability of different plant powders, including by-products of horticultural primary processing (e.g., pomace) in raw and cooked minced pork. The total counts of aerobic mesophilic bacteria, pseudomonads, yeasts, and moulds were studied to assess the microbial growth dynamics in meat samples. Additionally, for the plant powders, which were able to suppress the microbial growth in a total counts dynamics study, the growth potential of Listeria monocytogenes in ready-to-eat (RTE) minced meat samples was estimated by challenge testing. The results showed that the most effective combinations of plant powders in raw minced pork, in relation to the total counts of microorganisms, were 3% apple+1% onion+2% blackcurrant berries (Apple+On+BCber); 3% apple+1% garlic+2% tomato (Apple+Ga+Tom); and 3% apple+2% tomato+1% rhubarb petioles (Apple+Tom+Rhub). However, challenge tests revealed that some plant powders were unable to inhibit the growth of L. monocytogenes. The lowest L. monocytogenes growth potential (δ = 2.74 log cfu/g) was determined for cooked minced pork samples enriched with 2% rhubarb petioles, followed by Apple+On+BCber (δ = 3.63 log cfu/g) and Apple+Tom+Rhub (δ = 3.74 log cfu/g). In minced pork samples without plant additives, the L. monocytogenes growth potential was 7.30 log cfu/g. In conclusion, blends of plant powders may have good potential for developing meat products with acceptable microbiological quality. Full article
(This article belongs to the Special Issue Antibacterial Activity of Plant Extracts)
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Communication
A Wideband Folded Dipole Antenna with an Improved Cross-Polarization Level for Millimeter-Wave Applications
Appl. Sci. 2022, 12(21), 11291; https://doi.org/10.3390/app122111291 - 07 Nov 2022
Viewed by 636
Abstract
A low-profile planar millimeter-wave (MMW) folded dipole antenna fed by substrate integrated waveguide (SIW) is proposed in this letter. By etching the gaps at the proper position of 1.5λ dipole, an additional resonant mode is generated. Accordingly, the working bandwidth is greatly broadened. [...] Read more.
A low-profile planar millimeter-wave (MMW) folded dipole antenna fed by substrate integrated waveguide (SIW) is proposed in this letter. By etching the gaps at the proper position of 1.5λ dipole, an additional resonant mode is generated. Accordingly, the working bandwidth is greatly broadened. In addition, by appropriately adjusting the length of the dual-side parallel strip line (DSPSL), the radiated electric fields generated by the aperture of the feeding SIW and the connecting metallic vias of the folded dipole are designed with an out-of-phase potential. Hence, the cross-polarization of the presented folded dipole antenna is improved as well. As a demonstration, a prototype is fabricated and measured. The experimental results exhibit that the proposed folded dipole has a −10 dB impedance bandwidth of 58.5% (from 30.3 GHz to 53.7 GHz), a gain of around 5 dBi with more than 120 degrees beamwidth in H-plane, and a cross-polarization levels below −15 dB, covering the working frequency band. Compared with the up-to-date planar dipole antenna, the proposed folded dipole achieves the widest working bandwidth and low cross-polarization level. The proposed antenna can be used as the terminal antenna of the MMW communication system. Full article
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Article
Section Margin Allocation Method for Renewable Energy Power Generation Clusters Considering the Randomness of Source and Load Power
Appl. Sci. 2022, 12(21), 11290; https://doi.org/10.3390/app122111290 - 07 Nov 2022
Viewed by 417
Abstract
It is difficult to adapt the traditional section margin distribution method to the power system with a high proportion of renewable energy generation (REG). This paper proposes a section margin allocation method of REG clusters considering the randomness of source and load power. [...] Read more.
It is difficult to adapt the traditional section margin distribution method to the power system with a high proportion of renewable energy generation (REG). This paper proposes a section margin allocation method of REG clusters considering the randomness of source and load power. Firstly, the probability density function of the predicted REG power error is estimated, the probability that REG stations meet the output command after the section margin distribution is calculated, and the output realization probability of each REG station is discussed. The typical operation mode set of the REG cluster is then obtained by clustering the REG stations according to the operation history, and the load rise space of the REG cluster under each typical operation mode is calculated. Considering the randomness of REG power and its load power, the section margin is allocated to each REG station in the REG cluster so as to ensure that each REG station reaches the highest output probability. On the premise of ensuring the safe and stable operation of the grid, this method facilitates the management of REG clusters and the accommodation capacity of the power system for REG. Simulations of the power system of IEEE 39 nodes verify the rationality and validity of the section margin allocation method. Full article
(This article belongs to the Section Electrical, Electronics and Communications Engineering)
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Article
Effectiveness of LRB in Curved Bridge Isolation: A Numerical Study
Appl. Sci. 2022, 12(21), 11289; https://doi.org/10.3390/app122111289 - 07 Nov 2022
Cited by 1 | Viewed by 594
Abstract
Lead Rubber Bearings (LRBs) represent one of the most widely employed devices for the seismic protection of structures. However, the effectiveness of the same in the case of curved bridges has not been judged well because of the complexity involved in curved bridges, [...] Read more.
Lead Rubber Bearings (LRBs) represent one of the most widely employed devices for the seismic protection of structures. However, the effectiveness of the same in the case of curved bridges has not been judged well because of the complexity involved in curved bridges, especially in controlling torsional moments. This study investigates the performance of an LRB-isolated horizontally curved continuous bridge under various seismic loadings. The effectiveness of LRBs on the bridge response control was determined by considering various aspects, such as the changes in ground motion characteristics, multidirectional effects, the degree of seismic motion, and the variation of incident angles. Three recorded ground motions were considered in this study, representing historical earthquakes with near-field, far-field, and forward directivity effects. The effectiveness of the bi-directional behavior considering the interaction effect of the bearing and pier was also studied. The finite element method was adopted. A sensitivity study of the bridge response related to the bearing design parameters was carried out for the considered ground motions. The importance of non-linearity and critical design parameters of LRBs were assessed. It was found that LRBs resulted in a significant increase in deck displacement for Turkey ground motion, which might be due to the forward directivity effect. The bi-directional effect is crucial for the curved bridge as it enhances the displacement significantly compared to uni-directional motion. Full article
(This article belongs to the Special Issue Bridge Dynamics: Volume III)
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Article
Mechanical Mechanism and Dynamic Characteristics of Barge–Whole Bridge Collision Behaviours
Appl. Sci. 2022, 12(21), 11288; https://doi.org/10.3390/app122111288 - 07 Nov 2022
Viewed by 489
Abstract
Collision between a moving ship and a bridge in inner rivers is a frequent occurrence that seriously endanger the safety of the bridge. Existing studies mostly address the action of a ship colliding with a bridge pier that is used as a substitution [...] Read more.
Collision between a moving ship and a bridge in inner rivers is a frequent occurrence that seriously endanger the safety of the bridge. Existing studies mostly address the action of a ship colliding with a bridge pier that is used as a substitution of the associated whole bridge. Such a simplification necessarily induces errors in reflecting the mechanical mechanism and dynamic characteristics of ship–whole bridge collisions. To circumvent this problem, the mechanical behavior of collision between a barge and a whole bridge was studied via elaborating a delicate barge–whole bridge collision simulation underpinned by impact mechanics and materials theories. The main contributions of this study are fourfold: (i) the entire process of the collision between the barge and a whole bridge was fully inspected; (ii) the progressive evolution of collision-induced damage in the bridge pier as well as in the barge was investigated; (iii) the effect of impact velocity, impact angle and barge mass on the collision behavior were elucidated, and (iv) the influences of the superstructure of the whole bridge on the peak value and temporal feature of the impact force, evolution of damage, and top displacement of the bridge pier were clarified. This study yielded more accurate, comprehensive, and reliable results on dynamics and damage evolution of collision in comparison with the outcome of a barge colliding a pier. These findings collectively reveal the mechanical mechanism and dynamic characteristics of collision, providing a scientific basis for developing post-collision damage assessment methods and anti-collision facilities. Full article
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Article
Geometric–Statistical Model for Middle-Ear Anatomy and Ventilation
Appl. Sci. 2022, 12(21), 11287; https://doi.org/10.3390/app122111287 - 07 Nov 2022
Viewed by 410
Abstract
The ventilation of the middle-ear (ME) is achieved by the mucosa covering the bony cavities of this segment, which we have previously defined as consisting of two distinct epithelial areas, each representing an independent organ with characteristic function: the D-Organ and the F-Organ. [...] Read more.
The ventilation of the middle-ear (ME) is achieved by the mucosa covering the bony cavities of this segment, which we have previously defined as consisting of two distinct epithelial areas, each representing an independent organ with characteristic function: the D-Organ and the F-Organ. The D-Organ corresponds to the epithelium covering the Antrum walls (belonging to the central cavities of the middle-ear) and the walls of mastoid and petrous cavities (peripheral cavities of the ME); it ensures the D-Function, the biophysical process comparable to that of energy-consuming ionic membrane pumps, works against electrical trans-membrane gradients to transfer gas molecules against trans-membrane and trans-cellular pressure gradients. The F-Organ corresponds to the epithelium covering the Protympanum, Tympanic Cavity and Aditus ad Antrum (central cavities of ME). The F-Function is represented by the permeability of cell membranes for respiratory gases. This is a general function of all cells and the size of the cellular membrane surface (luminal and basal) and the height of the cell (distance between the two membranes) determines the diffusion flow for each molecular type of gas. The present work aims to give an original point of view on middle-ear geometry and precedence over ME mucosa affliction or structural-anatomic type of the mastoid (pneumatic, pneumato-diploic, diploic, sclerotic). This type of approach to the problem has never been attempted since the two organs have never been previously defined. We aim to establish a clear topographic structure for these two organs within the reference system represented by the anatomy of ME cavities and to establish the reasons why the mastoid and petrous cavitary system grow or stop growing at a certain point in the life of an individual. Full article
(This article belongs to the Section Biomedical Engineering)
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Review
A Review on Carbon Quantum Dots Modified g-C3N4-Based Photocatalysts and Potential Application in Wastewater Treatment
Appl. Sci. 2022, 12(21), 11286; https://doi.org/10.3390/app122111286 - 07 Nov 2022
Cited by 1 | Viewed by 594
Abstract
Carbon quantum dots (CDs) are a fascinating class of carbon nanomaterials (less than 10 nm in size) with unique optical, electrical, and physicochemical properties. In addition to these properties, CQDs exhibit the desired advantages of aqueous stability, low toxicity, high surface area, economic [...] Read more.
Carbon quantum dots (CDs) are a fascinating class of carbon nanomaterials (less than 10 nm in size) with unique optical, electrical, and physicochemical properties. In addition to these properties, CQDs exhibit the desired advantages of aqueous stability, low toxicity, high surface area, economic feasibility, chemical inertness, and highly tunable photoluminescence behaviour. Recently, graphitic carbon nitride (g-C3N4) has appeared as one of the required stable carbon-based polymers due to its varied applications in several fields. In this regard, modification strategies have been made in the g-C3N4 semiconductor using CQDs to enhance the adsorptive and photocatalytic activity. In comparison to other semiconductor quantum dots, g-C3N4 shows strong fluorescent properties, such as wide excitation spectra, photostability, and tunable photo-luminescent emission spectra. The interaction inside this multicomponent photocatalyst further promotes the photocatalytic activity by improving charge transference, which plays a vital role in electrochemistry. Therefore, CQDs are auspicious nanomaterials in the field of photocatalysis, wastewater treatment and water adsorption treatment. This particular article featured the recent progression in the field of CDs/g-C3N4-based photocatalysts focusing on their luminescent mechanism and potential applications in wastewater treatment. Full article
(This article belongs to the Special Issue Advances in the Removal of Pollutants in Wastewater)
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Article
Degreasing Efficiency of Electroplating Pretreatment Process Using Secondary Alcohol Ethoxylate as Nonionic Surfactant
Appl. Sci. 2022, 12(21), 11285; https://doi.org/10.3390/app122111285 - 07 Nov 2022
Viewed by 499
Abstract
In this study, the effect of the hydrophilic–lipophilic balance (HLB) number and cloud point (CP) of a secondary-alcohol ethoxylated nonionic surfactant on degreasing efficiency was investigated. A degreasing process was conducted for steel samples with different surfactants in a degreasing solution. The HLB [...] Read more.
In this study, the effect of the hydrophilic–lipophilic balance (HLB) number and cloud point (CP) of a secondary-alcohol ethoxylated nonionic surfactant on degreasing efficiency was investigated. A degreasing process was conducted for steel samples with different surfactants in a degreasing solution. The HLB number and CP increased with the increasing n of the hydrophilic ethylene oxide (OCH2CH2)n group. For a constant temperature of the degreasing solution (30–80 °C), the degreasing efficiency was investigated as a function of degreasing time. The highest degreasing efficiency was observed near the cloud point of the surfactant, and the degreasing efficiency decreased significantly at temperatures lower and greater than the cloud point. A Hogaboom test was carried out to observe oil stains on the surface of samples. Additionally, the contact angle of the surface with water droplets was measured after degreasing with various surfactants. Full article
(This article belongs to the Section Surface Sciences and Technology)
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Article
Simulation and Implementation of a Mobile Robot Trajectory Planning Solution by Using a Genetic Micro-Algorithm
Appl. Sci. 2022, 12(21), 11284; https://doi.org/10.3390/app122111284 - 07 Nov 2022
Cited by 1 | Viewed by 621
Abstract
Robots able to roll and jump are used to solve complex trajectories. These robots have a low level of autonomy, and currently, only teleoperation is available. When researching the literature about these robots, limitations were found, such as a high risk of damage [...] Read more.
Robots able to roll and jump are used to solve complex trajectories. These robots have a low level of autonomy, and currently, only teleoperation is available. When researching the literature about these robots, limitations were found, such as a high risk of damage by testing, lack of information, and nonexistent tools. Therefore, the present research is conducted to minimize the dangers in actual tests, increase the documentation through a platform repository, and solve the autonomous trajectory of a maze with obstacles. The methodology consisted of: replicating a scenario with the parrot robot in the gazebo simulator; then the computational resources, the mechanism, and the available commands of the robot were studied; subsequently, it was determined that the genetic micro-algorithm met the minimum requirements of the robot; in the last part, it was programmed in simulation and the solution was validated in the natural environment. The results were satisfactory and it was possible to create a parrot robot in a simulation environment analogous to the typical specifications. The genetic micro-algorithm required only 100 generations to converge; therefore, the demand for computational resources did not affect the execution of the essential tasks of the robot. Finally, the maze problem could be solved autonomously in a real environment from the simulations with an error of less than 10% and without damaging the robot. Full article
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Article
Analysis of Jet Structure and Physical Properties in the Coalfields of Northern China
Appl. Sci. 2022, 12(21), 11283; https://doi.org/10.3390/app122111283 - 07 Nov 2022
Viewed by 565
Abstract
Archeological discoveries have identified China as one of the first countries in the world to use jet. However, many differences are evident between the jet currently found in existing mines and the archaeological discoveries of cultural relics in terms of texture and quality [...] Read more.
Archeological discoveries have identified China as one of the first countries in the world to use jet. However, many differences are evident between the jet currently found in existing mines and the archaeological discoveries of cultural relics in terms of texture and quality according to the definition of organic gem jet in gemology. This paper reports the results of microscopic analysis and coal quality analysis of the coal and jet samples from coal seams in Fushun Open-pit Mine and Datong Coalfield. The findings reveal that the physical and chemical composition of coal in different mining areas differs markedly. However, the differences between jet and coal in both mining areas are similar; that is, jet has lighter density and greater hardness (2–4) compared to coal, as well as elasticity (engravability), and both jet and coal occur in the (rock slurry) hydrothermal environment. Lastly, the analysis shows that the formation of jet depends on rubber-like hydrocarbon coal with a high degree of corruption in a sedimentary environment under the vulcanization of a hydrothermal, high-sulfur environment. Full article
(This article belongs to the Special Issue Mineralogy of Critical Elements Deposits)
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Article
Skin Byproducts of Reinhardtius hippoglossoides (Greenland Halibut) as Ecosustainable Source of Marine Collagen
Appl. Sci. 2022, 12(21), 11282; https://doi.org/10.3390/app122111282 - 07 Nov 2022
Cited by 2 | Viewed by 630
Abstract
Collagen is a ubiquitous protein present in the extracellular matrix of all major metazoan animals, with approximately 28 different human collagen types described in the literature, each with unique physicochemical properties. Collagens found broad application in the cosmeceutical, pharmaceutical, and biomedical fields and [...] Read more.
Collagen is a ubiquitous protein present in the extracellular matrix of all major metazoan animals, with approximately 28 different human collagen types described in the literature, each with unique physicochemical properties. Collagens found broad application in the cosmeceutical, pharmaceutical, and biomedical fields and can be isolated from environmentally sustainable sources such as marine byproducts, which are abundant in the fish processing industry and are highly appealing low-cost sources. In this study, marine collagen was isolated from the skins of Greenland halibut (Reinhardtius hippoglossoides), an unexplored byproduct from fish processing plants, using three different collagen extraction methods, due to the use of distinct salting-out methods using a solution of 2.6 M NaCl + 0.05 M Tris-HCl pH = 7.5, (method I); a combination of 0.7 M NaCl followed by a solution of 2.3 M NaCl + 0.05 M Tris-HCl pH = 7.5 (method II); and one method using only 0.9 M NaCl (method III), yielding COLRp_I, COLRp_II, and COLRp_III collagens. These extracted type I collagens were produced with a yield of around 2 and 4% and characterized regarding the physicochemical properties, considering possible biotechnological applications. This work evidenced that the typical triple helix structure conformation was preserved in all extraction methods, but influenced the thermal behavior, intrinsic morphology, and moisture capacity of the collagens, with interest for biotechnological application, as the incorporation as an ingredient in cosmetic formulation. Furthermore, the use of collagen isolated from skin byproducts represents a high economic value with decreasing collagen cost for industrial purposes and is also an environmentally sustainable source for industrial uses. Full article
(This article belongs to the Special Issue New Trends on Marine Biomaterials)
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Communication
Development of a Holographic Waveguide with Thermal Compensation for Augmented Reality Devices
Appl. Sci. 2022, 12(21), 11281; https://doi.org/10.3390/app122111281 - 07 Nov 2022
Viewed by 513
Abstract
In this research, studies were conducted on the possibility of providing thermal compensation of the information display device circuit based on a holographic waveguide when the wavelength of the radiation source ch as a result of changes in ambient temperature. A variant of [...] Read more.
In this research, studies were conducted on the possibility of providing thermal compensation of the information display device circuit based on a holographic waveguide when the wavelength of the radiation source ch as a result of changes in ambient temperature. A variant of implementing the waveguide structure in terms of the geometry of the diffraction gratings arrangement is proposed, its main parameters (grating period, thickness, refractive index) and the dependencies between them affecting the quality of the reproduced image are determined. Full article
(This article belongs to the Special Issue Holographic Technologies: Theory and Practice)
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Article
Three-Phase Fault Arc Phase Selection Based on Global Attention Temporal Convolutional Neural Network
Appl. Sci. 2022, 12(21), 11280; https://doi.org/10.3390/app122111280 - 07 Nov 2022
Viewed by 445
Abstract
For low-voltage three-phase systems, the deep fault arc features are difficult to extract, and the phase information has strong timing. This phenomenon leads to the problem of low accuracy of fault phase selection. This paper proposes a three-phase fault arc phase selection method [...] Read more.
For low-voltage three-phase systems, the deep fault arc features are difficult to extract, and the phase information has strong timing. This phenomenon leads to the problem of low accuracy of fault phase selection. This paper proposes a three-phase fault arc phase selection method based on a global temporal convolutional network. First, this method builds a low-voltage three-phase arc fault data acquisition platform and establishes a dataset. Second, the experimental data were decomposed by variational mode decomposition and analyzed in the time-frequency domain. The decomposed data are reconstructed and used as input to the model. Finally, in order to reduce the fault features lost during the causal convolution operation, the global attention mechanism is used to extract deep fault characterization to identify faults and their differences. The experimental results show that the accuracy of the three-phase arc fault arc phase selection of the model can reach 98.62%, and the accuracy of single-phase fault detection can reach 99.39%. This model can effectively extract three-phase arc fault and phase characteristics. This paper provides a new idea for series fault arc detection and three-phase fault arc phase selection research. Full article
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Article
Characteristics of a Novel FinFET with Multi-Enhanced Operation Gates (MEOG FinFET)
Appl. Sci. 2022, 12(21), 11279; https://doi.org/10.3390/app122111279 - 07 Nov 2022
Viewed by 727
Abstract
This study illustrates a type of novel device. Integrating fin field-effect transistors (FinFETs) with current silicon-on-insulator (SOI) wafers provides an excellent platform to fabricate advanced specific devices. An SOI FinFET device consists of three independent gates. By connecting the various gates, multiple working [...] Read more.
This study illustrates a type of novel device. Integrating fin field-effect transistors (FinFETs) with current silicon-on-insulator (SOI) wafers provides an excellent platform to fabricate advanced specific devices. An SOI FinFET device consists of three independent gates. By connecting the various gates, multiple working modes are obtained. Compared with traditional FinFETs, the multi-enhanced operation gate fin field-effect transistor in this study combines independent gates by connecting the selection modes; thus, a possible operation can be performed to attain a FinFET with five equivalent working states in only one device. This novel function can enable the device to work with multiple specific voltages and currents by connecting the corresponding gate combinations, augmenting the integrated degrees and shifting the working modes, thereby meeting the different needs of high-speed, low-power, and other potential applications. Further, the potential applications are highlighted. Full article
(This article belongs to the Section Electrical, Electronics and Communications Engineering)
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Article
RSVN: A RoBERTa Sentence Vector Normalization Scheme for Short Texts to Extract Semantic Information
Appl. Sci. 2022, 12(21), 11278; https://doi.org/10.3390/app122111278 - 07 Nov 2022
Viewed by 506
Abstract
With the explosive growth in short texts on the Web and an increasing number of Web corpora consisting of short texts, short texts are playing an important role in various Web applications. Entity linking is a crucial task in knowledge graphs and a [...] Read more.
With the explosive growth in short texts on the Web and an increasing number of Web corpora consisting of short texts, short texts are playing an important role in various Web applications. Entity linking is a crucial task in knowledge graphs and a key technology in the field of short texts that affects the accuracy of many downstream tasks in natural language processing. However, compared to long texts, the entity-linking task of Chinese short text is a challenging problem due to the serious colloquialism and insufficient contexts. Moreover, existing methods for entity linking in Chinese short text underutilize semantic information and ignore the interaction between label information and the original short text. In this paper, we propose a RoBERTa sentence vector normalization scheme for short texts to fully extract the semantic information. Firstly, the proposed model utilizes RoBERTa to fully capture contextual semantic information. Secondly, the anisotropy of RoBERTa’s output sentence vectors is revised by utilizing the standard Gaussian of flow model, which enables the sentence vectors to more precisely characterize the semantics. In addition, the interaction between label embedding and text embedding is employed to improve the NIL entity classification. Experimental results demonstrate that the proposed model outperforms existing research results and mainstream deep learning methods for entity linking in two Chinese short text datasets. Full article
(This article belongs to the Special Issue Intelligent Control Using Machine Learning)
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Article
A Fast Wavelet-Based Bridge Condition Assessment Approach Using Only Moving Vehicle Measurements
Appl. Sci. 2022, 12(21), 11277; https://doi.org/10.3390/app122111277 - 07 Nov 2022
Viewed by 461
Abstract
Recently, the ‘drive-by’ or vehicle scanning technique has attracted increasing attention over the last decade for the purpose of bridge health monitoring. The feasibility of this technique has been demonstrated by many field tests. In comparison to conventional bridge SHM, the concept of [...] Read more.
Recently, the ‘drive-by’ or vehicle scanning technique has attracted increasing attention over the last decade for the purpose of bridge health monitoring. The feasibility of this technique has been demonstrated by many field tests. In comparison to conventional bridge SHM, the concept of the drive-by bridge technique shows many advantages in terms of efficiency, economy, convenience, and mobility. It has been verified that wavelet transforms can successfully identify bridge damage and its location using the responses of a moving vehicle. However, the validity of this method is challenged by road roughness. This paper proposes a wavelet-based approach to detect bridge defects using wavelet energy. In addition, a damage index based on component wavelet energy is developed to localize the damage. A numerical simulation is modeled to verify the feasibility of the proposed approach, and the result shows that the proposed approach performs well even when considering road roughness in the vehicle and bridge interaction. Moreover, the effects of road surface profile, vehicle velocity, vehicle mass, noise signal, and different damage severity on the proposed approach are investigated. The proposed approach shows a great potential application in bridge health monitoring using indirect measurements from a moving vehicle. Full article
(This article belongs to the Special Issue State-of-the-Art Structural Health Monitoring in Civil Engineering)
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