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Appl. Sci., Volume 9, Issue 13 (July-1 2019)

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Cover Story (view full-size image) Using semi-crystalline polymers as feedstock for fused filament fabrication still poses some major [...] Read more.
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Open AccessArticle
Classifying Degraded Three-Dimensionally Printed Polylactic Acid Specimens Using Artificial Neural Networks based on Fourier Transform Infrared Spectroscopy
Appl. Sci. 2019, 9(13), 2772; https://doi.org/10.3390/app9132772
Received: 10 June 2019 / Revised: 2 July 2019 / Accepted: 6 July 2019 / Published: 9 July 2019
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Abstract
Fused filament fabrication (FFF) is commonly employed in multiple domains to realize inexpensive and flexible material extrusion systems with thermoplastic materials. Among the several types of thermoplastic materials, polylactic acid (PLA), an environment-friendly bio-plastic, is commonly used for FFF for the sake of [...] Read more.
Fused filament fabrication (FFF) is commonly employed in multiple domains to realize inexpensive and flexible material extrusion systems with thermoplastic materials. Among the several types of thermoplastic materials, polylactic acid (PLA), an environment-friendly bio-plastic, is commonly used for FFF for the sake of the safety of the manufacturing process. However, thermal degradation of three-dimensionally (3D)-printed PLA products is inevitable, and it is one of the failure mechanisms of thermoplastic products. The present study focuses on the thermal degradation of 3D-printed PLA specimens. A classification methodology using artificial neural networks (ANNs) based on Fourier transform infrared (FTIR) and was developed. Under the given experimental conditions, the ANN model could classify four levels of thermal degradation. Among the FTIR spectra recorded from 650 cm−1 to 4000 cm−1, the ANN model could suggest the best wavenumber ranges for classification. Full article
(This article belongs to the Section Mechanical Engineering)
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Open AccessReview
A Review of Non-Destructive Damage Detection Methods for Steel Wire Ropes
Appl. Sci. 2019, 9(13), 2771; https://doi.org/10.3390/app9132771
Received: 22 May 2019 / Revised: 24 June 2019 / Accepted: 5 July 2019 / Published: 9 July 2019
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Abstract
As an important load-bearing component, steel wire ropes (WRs) are widely used in complex systems such as mine hoists, cranes, ropeways, elevators, oil rigs, and cable-stayed bridges. Non-destructive damage detection for WRs is an important way to assess damage states to guarantee WR’s [...] Read more.
As an important load-bearing component, steel wire ropes (WRs) are widely used in complex systems such as mine hoists, cranes, ropeways, elevators, oil rigs, and cable-stayed bridges. Non-destructive damage detection for WRs is an important way to assess damage states to guarantee WR’s reliability and safety. With intelligent sensors, signal processing, and pattern recognition technology developing rapidly, this field has made great progress. However, there is a lack of a systematic review on technologies or methods introduced and employed, as well as research summaries and prospects in recent years. In order to bridge this gap, and to promote the development of non-destructive detection technology for WRs, we present an overview of non-destructive damage detection research of WRs and discuss the core issues on this topic in this paper. First, the WRs’ damage type is introduced, and its causes are explained. Then, we summarize several main non-destructive detection methods for WRs, including electromagnetic detection method, optical detection method, ultrasonic guided wave detection method, and acoustic emission detection method. Finally, a prospect is put forward. Based on the review of papers, we provide insight about the future of the non-destructive damage detection methods for steel WRs to a certain extent. Full article
(This article belongs to the Special Issue Nondestructive Testing in Composite Materials)
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Open AccessArticle
Trajectory Planning and Optimization for a Par4 Parallel Robot Based on Energy Consumption
Appl. Sci. 2019, 9(13), 2770; https://doi.org/10.3390/app9132770
Received: 7 May 2019 / Revised: 13 June 2019 / Accepted: 4 July 2019 / Published: 9 July 2019
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Abstract
A study on trajectory planning and optimization for a Par4 parallel robot was carried out, based on energy consumption in high-speed picking and placing. In the end-effector operating space of the Par4 parallel robot, the rectangular transition of the pick-and-place trajectory was rounded [...] Read more.
A study on trajectory planning and optimization for a Par4 parallel robot was carried out, based on energy consumption in high-speed picking and placing. In the end-effector operating space of the Par4 parallel robot, the rectangular transition of the pick-and-place trajectory was rounded by a Lamé curve. A piecewise design method was adopted to accomplish trajectory shape planning for displacement, velocity and acceleration. To make the Par4 robot’s end run more smoothly and to reduce residual vibration, asymmetric fifth-order and sixth-order polynomial motion laws were employed. With the aim of reaching the minimum mechanical energy consumption for the Par4 parallel robot, the recently proposed Grey Wolf Optimizer (GWO) algorithm was adopted to optimize the planning trajectory. The validity of the design method was verified by experiments, and it was found that the minimum mechanical energy consumption of the optimal trajectory planned under the law of fifth-order polynomial motion is lower than that of sixth-order polynomial motion. In addition, the experiments also revealed the optimal values of Parameters e and f, which were the parameters of the Lamé curve function. Parameter e can be calculated as half the pick-up span for the minimum mechanical energy consumption, unlike parameter f, whose optimal value depends on specific circumstances such as the pick-and-place coordinates and the pick-up height. Full article
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Open AccessArticle
Global Maximum Power Point Tracking of PV Systems under Partial Shading Condition: A Transfer Reinforcement Learning Approach
Appl. Sci. 2019, 9(13), 2769; https://doi.org/10.3390/app9132769
Received: 11 June 2019 / Accepted: 2 July 2019 / Published: 9 July 2019
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Abstract
This paper aims to introduce a novel maximum power point tracking (MPPT) strategy called transfer reinforcement learning (TRL), associated with space decomposition for Photovoltaic (PV) systems under partial shading conditions (PSC). The space decomposition is used for constructing a hierarchical searching space of [...] Read more.
This paper aims to introduce a novel maximum power point tracking (MPPT) strategy called transfer reinforcement learning (TRL), associated with space decomposition for Photovoltaic (PV) systems under partial shading conditions (PSC). The space decomposition is used for constructing a hierarchical searching space of the control variable, thus the ability of the global search of TRL can be effectively increased. In order to satisfy a real-time MPPT with an ultra-short control cycle, the knowledge transfer is introduced to dramatically accelerate the searching speed of TRL through transferring the optimal knowledge matrices of the previous optimization tasks to a new optimization task. Four case studies are conducted to investigate the advantages of TRL compared with those of traditional incremental conductance (INC) and five other conventional meta-heuristic algorithms. The case studies include a start-up test, step change in solar irradiation with constant temperature, stepwise change in both temperature and solar irradiation, and a daily site profile of temperature and solar irradiation in Hong Kong. Full article
(This article belongs to the Special Issue Standalone Renewable Energy Systems—Modeling and Controlling)
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Open AccessArticle
A Damage Model for Concrete under Fatigue Loading
Appl. Sci. 2019, 9(13), 2768; https://doi.org/10.3390/app9132768
Received: 3 June 2019 / Revised: 25 June 2019 / Accepted: 25 June 2019 / Published: 9 July 2019
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Abstract
For concrete, fatigue is an essential mechanical behavior. Concrete structures subjected to fatigue loads usually experience a progressive degradation/damage process and even an abrupt failure. However, in the literature, certain essential damage behaviors are not well considered in the study of the mechanism [...] Read more.
For concrete, fatigue is an essential mechanical behavior. Concrete structures subjected to fatigue loads usually experience a progressive degradation/damage process and even an abrupt failure. However, in the literature, certain essential damage behaviors are not well considered in the study of the mechanism for fatigue behaviors such as the development of irreversible/residual strains. In this work, a damage model with the concept of mode-II microcracks on the crack face and nearby areas contributing to the development of irreversible strains was proposed. By using the micromechanics method, a micro-cell-based damage model under multi-axial loading was introduced to understand the damage behaviors for concrete. By a thermodynamic interpretation of the damage behaviors, a novel fatigue damage variable (irreversible deformation fatigue damage variable) was defined. This variable is able to describe irreversible strains generated by both mode-II microcracks and irreversible frictional sliding. The proposed model considered both elastic and irreversible deformation fatigue damages. It is found that the prediction by the proposed model of cyclic creep, stiffness degradation and post-fatigue stress-strain relationship of concrete agrees well with experimental results. Full article
(This article belongs to the Special Issue Fatigue and Fracture of Non-metallic Materials and Structures)
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Open AccessFeature PaperArticle
The Development of Tyrosyl-DNA Phosphodiesterase 1 Inhibitors. Combination of Monoterpene and Adamantine Moieties via Amide or Thioamide Bridges
Appl. Sci. 2019, 9(13), 2767; https://doi.org/10.3390/app9132767
Received: 12 June 2019 / Revised: 2 July 2019 / Accepted: 3 July 2019 / Published: 9 July 2019
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Abstract
Eleven amide and thioamide derivatives with monoterpene and adamantine substituents were synthesised. They were tested for their activity against the tyrosyl-DNA phosphodiesterase 1 DNA (Tdp1) repair enzyme with the most potent compound 47a, having an IC50 value of 0.64 µM. When [...] Read more.
Eleven amide and thioamide derivatives with monoterpene and adamantine substituents were synthesised. They were tested for their activity against the tyrosyl-DNA phosphodiesterase 1 DNA (Tdp1) repair enzyme with the most potent compound 47a, having an IC50 value of 0.64 µM. When tested in the A-549 lung adenocarcinoma cell line, no or very limited cytotoxic effect was observed for the ligands. However, in conjunction with topotecan, a well-established Topoisomerase 1 (Top1) poison in clinical use against cancer, derivative 46a was very cytotoxic at 5 µM concentration, displaying strong synergism. This effect was only seen for 46a (IC50—3.3 µM) albeit some other ligands had better IC50 values. Molecular modelling into the catalytic site of Tdp1 predicted plausible binding mode of 46a, effectively blocking access to the catalytic site. Full article
(This article belongs to the Section Chemistry)
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Open AccessFeature PaperTechnical Note
Performance Test of MicroAeth® AE51 at Concentrations Lower than 2 μg/m3 in Indoor Laboratory
Appl. Sci. 2019, 9(13), 2766; https://doi.org/10.3390/app9132766
Received: 6 June 2019 / Revised: 27 June 2019 / Accepted: 5 July 2019 / Published: 9 July 2019
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Abstract
It is difficult to find information about how the microAeth® AE51 performs, in spite of its versatility for about a decade in various research fields such as black carbon measurements and personal exposure studies. Stimulated by this, we conducted real-time tests for [...] Read more.
It is difficult to find information about how the microAeth® AE51 performs, in spite of its versatility for about a decade in various research fields such as black carbon measurements and personal exposure studies. Stimulated by this, we conducted real-time tests for indoor aerosol in order to provide performance characteristics toward proper usage. We calculated the attenuation (ATN) using the reference signal together with the sensing signal to compare it with the ATN recorded in a microAeth® AE51. Performance was evaluated under extremely low concentration through the zero test, using filtered clean air. Ten-day-long continuous measurements were done for both indoor aerosol and filtered particle free air to examine the feasibility of microAeth® AE51 in an indoor use. Generally, MicroAeth® AE51 exhibited excellent performance, though it showed relatively low performance under some conditions. Noise was intensified while it was directly connected to a power adaptor. Another issue includes the occurrence of negative concentrations for extremely clean air. The noise amplification turned out to be related to a power source independent on the internal battery, and it was able to be removed by post-processing. Uncertainty analysis was carried out to better understand the origin of unwanted noise. Technical perspective of a proper usage will be addressed with regard to what will play a role for a long-term monitoring. Full article
(This article belongs to the Special Issue Air Pollution)
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Open AccessArticle
State of Charge Estimation of a Lithium Ion Battery Based on Adaptive Kalman Filter Method for an Equivalent Circuit Model
Appl. Sci. 2019, 9(13), 2765; https://doi.org/10.3390/app9132765
Received: 30 April 2019 / Revised: 1 July 2019 / Accepted: 4 July 2019 / Published: 9 July 2019
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Abstract
Due to its accuracy, simplicity, and other advantages, the Kalman filter method is one of the common algorithms to estimate the state-of-charge (SOC) of batteries. However, this method still has its shortcomings. The Kalman filter method is an algorithm designed for linear systems [...] Read more.
Due to its accuracy, simplicity, and other advantages, the Kalman filter method is one of the common algorithms to estimate the state-of-charge (SOC) of batteries. However, this method still has its shortcomings. The Kalman filter method is an algorithm designed for linear systems and requires precise mathematical models. Lithium-ion batteries are not linear systems, so the establishment of the battery equivalent circuit model (ECM) is necessary for SOC estimation. In this paper, an adaptive Kalman filter method and the battery Thevenin equivalent circuit are combined to estimate the SOC of an electric vehicle power battery dynamically. Firstly, the equivalent circuit model is studied, and the battery model suitable for SOC estimation is established. Then, the parameters of the corresponding battery charge and the discharge experimental detection model are designed. Finally, the adaptive Kalman filter method is applied to the model in the unknown interference noise environment and is also adopted to estimate the SOC of the battery online. The simulation results show that the proposed method can correct the SOC estimation error caused by the model error in real time. The estimation accuracy of the proposed method is higher than that of the Kalman filter method. The adaptive Kalman filter method also has a correction effect on the initial value error, which is suitable for online SOC estimation of power batteries. The experiment under the BBDST (Beijing Bus Dynamic Stress Test) working condition fully proves that the proposed SOC estimation algorithm can hold the satisfactory accuracy even in complex situations. Full article
(This article belongs to the Section Electrical, Electronics and Communications Engineering)
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Open AccessArticle
Performance Analysis of Feature Selection Methods in Software Defect Prediction: A Search Method Approach
Appl. Sci. 2019, 9(13), 2764; https://doi.org/10.3390/app9132764
Received: 26 April 2019 / Revised: 10 May 2019 / Accepted: 14 May 2019 / Published: 9 July 2019
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Abstract
Software Defect Prediction (SDP) models are built using software metrics derived from software systems. The quality of SDP models depends largely on the quality of software metrics (dataset) used to build the SDP models. High dimensionality is one of the data quality problems [...] Read more.
Software Defect Prediction (SDP) models are built using software metrics derived from software systems. The quality of SDP models depends largely on the quality of software metrics (dataset) used to build the SDP models. High dimensionality is one of the data quality problems that affect the performance of SDP models. Feature selection (FS) is a proven method for addressing the dimensionality problem. However, the choice of FS method for SDP is still a problem, as most of the empirical studies on FS methods for SDP produce contradictory and inconsistent quality outcomes. Those FS methods behave differently due to different underlining computational characteristics. This could be due to the choices of search methods used in FS because the impact of FS depends on the choice of search method. It is hence imperative to comparatively analyze the FS methods performance based on different search methods in SDP. In this paper, four filter feature ranking (FFR) and fourteen filter feature subset selection (FSS) methods were evaluated using four different classifiers over five software defect datasets obtained from the National Aeronautics and Space Administration (NASA) repository. The experimental analysis showed that the application of FS improves the predictive performance of classifiers and the performance of FS methods can vary across datasets and classifiers. In the FFR methods, Information Gain demonstrated the greatest improvements in the performance of the prediction models. In FSS methods, Consistency Feature Subset Selection based on Best First Search had the best influence on the prediction models. However, prediction models based on FFR proved to be more stable than those based on FSS methods. Hence, we conclude that FS methods improve the performance of SDP models, and that there is no single best FS method, as their performance varied according to datasets and the choice of the prediction model. However, we recommend the use of FFR methods as the prediction models based on FFR are more stable in terms of predictive performance. Full article
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Open AccessReview
VPNFilter Malware Analysis on Cyber Threat in Smart Home Network
Appl. Sci. 2019, 9(13), 2763; https://doi.org/10.3390/app9132763
Received: 8 June 2019 / Revised: 4 July 2019 / Accepted: 4 July 2019 / Published: 9 July 2019
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Abstract
Recently, the development of smart home technologies has played a crucial role in enhancing several real-life smart applications. They help improve the quality of life through systems designed to enhance convenience, comfort, entertainment, health of the householders, and security. Note, however, that malware [...] Read more.
Recently, the development of smart home technologies has played a crucial role in enhancing several real-life smart applications. They help improve the quality of life through systems designed to enhance convenience, comfort, entertainment, health of the householders, and security. Note, however, that malware attacks on smart home devices are increasing in frequency and volume. As people seek to improve and optimize comfort in their home and minimize their daily home responsibilities at the same time, this makes them attractive targets for a malware attack. Thus, attacks on smart home-based devices have emerged. The goals of this paper are to analyze the different aspects of cyber-physical threats on the smart home from a security perspective, discuss the types of attacks including advanced cyber-attacks and cyber-physical system attacks, and evaluate the impact on a smart home system in daily life. We have come up with a taxonomy focusing on cyber threat attacks that can also have potential impact on a smart home system and identify some key issues about VPNFilter malware that constitutes large-scale Internet of Things (IoT)-based botnet malware infection. We also discuss the defense mechanism against this threat and mention the most infected routers. The specific objective of this paper is to provide efficient task management and knowledge related to VPNFilter malware attack. Full article
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Open AccessArticle
Experimental Investigation on Chemical Grouting in a Permeated Fracture Replica with Different Roughness
Appl. Sci. 2019, 9(13), 2762; https://doi.org/10.3390/app9132762
Received: 9 May 2019 / Revised: 26 June 2019 / Accepted: 4 July 2019 / Published: 9 July 2019
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Abstract
This paper presents an experimental investigation on chemical grouting in a permeated fracture replica considering its roughness. Tests of grouting with flowing water in the fracture replica were carried out under different Bardon’s standard roughness profiles. The interactions between influential factors were considered [...] Read more.
This paper presents an experimental investigation on chemical grouting in a permeated fracture replica considering its roughness. Tests of grouting with flowing water in the fracture replica were carried out under different Bardon’s standard roughness profiles. The interactions between influential factors were considered and an experimental platform for grouting in rough fractures with flowing water was established. The effect of chemical grouting in fractures with flowing water was investigated using orthogonal experiment. The joint roughness coefficient (JRC), the initial water flow rate, the gel time, and the fracture opening were selected as factors in the orthogonal experiment. The results show that there is a positive correlation between the water plugging rate and JRC, and negative correlations between the water plugging rate and the initial water flow rate, gel time, and fracture opening. The change curve of the water flow rate is divided into three categories: Single platform decreasing type, double platform decreasing type, and multi-peak fluctuating type. The curve of seepage pressure contains three categories: Single peak type, multi-peak type and platform type. The results provide a reference for grouting in rock fractures. Full article
(This article belongs to the Special Issue Fatigue and Fracture of Non-metallic Materials and Structures)
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Open AccessArticle
Restricted Boltzmann Machine Vectors for Speaker Clustering and Tracking Tasks in TV Broadcast Shows
Appl. Sci. 2019, 9(13), 2761; https://doi.org/10.3390/app9132761
Received: 21 May 2019 / Revised: 23 June 2019 / Accepted: 2 July 2019 / Published: 9 July 2019
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Abstract
Restricted Boltzmann Machines (RBMs) have shown success in both the front-end and backend of speaker verification systems. In this paper, we propose applying RBMs to the front-end for the tasks of speaker clustering and speaker tracking in TV broadcast shows. RBMs are trained [...] Read more.
Restricted Boltzmann Machines (RBMs) have shown success in both the front-end and backend of speaker verification systems. In this paper, we propose applying RBMs to the front-end for the tasks of speaker clustering and speaker tracking in TV broadcast shows. RBMs are trained to transform utterances into a vector based representation. Because of the lack of data for a test speaker, we propose RBM adaptation to a global model. First, the global model—which is referred to as universal RBM—is trained with all the available background data. Then an adapted RBM model is trained with the data of each test speaker. The visible to hidden weight matrices of the adapted models are concatenated along with the bias vectors and are whitened to generate the vector representation of speakers. These vectors, referred to as RBM vectors, were shown to preserve speaker-specific information and are used in the tasks of speaker clustering and speaker tracking. The evaluation was performed on the audio recordings of Catalan TV Broadcast shows. The experimental results show that our proposed speaker clustering system gained up to 12% relative improvement, in terms of Equal Impurity (EI), over the baseline system. On the other hand, in the task of speaker tracking, our system has a relative improvement of 11% and 7% compared to the baseline system using cosine and Probabilistic Linear Discriminant Analysis (PLDA) scoring, respectively. Full article
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Open AccessArticle
Deep Learning Application to Ensemble Learning—The Simple, but Effective, Approach to Sentiment Classifying
Appl. Sci. 2019, 9(13), 2760; https://doi.org/10.3390/app9132760
Received: 5 June 2019 / Revised: 21 June 2019 / Accepted: 22 June 2019 / Published: 8 July 2019
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Abstract
Sentiment analysis is an active research area in natural language processing. The task aims at identifying, extracting, and classifying sentiments from user texts in post blogs, product reviews, or social networks. In this paper, the ensemble learning model of sentiment classification is presented, [...] Read more.
Sentiment analysis is an active research area in natural language processing. The task aims at identifying, extracting, and classifying sentiments from user texts in post blogs, product reviews, or social networks. In this paper, the ensemble learning model of sentiment classification is presented, also known as CEM (classifier ensemble model). The model contains various data feature types, including language features, sentiment shifting, and statistical techniques. A deep learning model is adopted with word embedding representation to address explicit, implicit, and abstract sentiment factors in textual data. The experiments conducted based on different real datasets found that our sentiment classification system is better than traditional machine learning techniques, such as Support Vector Machines and other ensemble learning systems, as well as the deep learning model, Long Short-Term Memory network, which has shown state-of-the-art results for sentiment analysis in almost corpuses. Our model’s distinguishing point consists in its effective application to different languages and different domains. Full article
(This article belongs to the Section Computing and Artificial Intelligence)
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Open AccessCase Report
Detection and Monitoring of Tunneling-Induced Riverbed Deformation Using GPS and BeiDou: A Case Study
Appl. Sci. 2019, 9(13), 2759; https://doi.org/10.3390/app9132759
Received: 7 June 2019 / Revised: 2 July 2019 / Accepted: 4 July 2019 / Published: 8 July 2019
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Abstract
Shield tunneling under rivers often requires monitoring riverbed deformations in near real-time. However, it is challenging to measure riverbed deformation with conventional survey techniques. This study introduces a comprehensive method that uses the Global Positioning System (GPS) of the USA and the BeiDou [...] Read more.
Shield tunneling under rivers often requires monitoring riverbed deformations in near real-time. However, it is challenging to measure riverbed deformation with conventional survey techniques. This study introduces a comprehensive method that uses the Global Positioning System (GPS) of the USA and the BeiDou navigation satellite system (BeiDou) of China to monitor riverbed deformation during the construction of twin tunnels beneath the Hutuo River in Shijiazhuang, China. A semi-permanent GPS network with one base station outside the river and six rover stations within the river was established for conducting near real-time and long-term monitoring. The distances between the base and the rover antennas are within two kilometers. The network was continuously operating for eight months from April to December 2018. The method is comprised of three components: (1) Monitoring the stability of the base station using precise point positioning (PPP) method, a stable regional reference frame, and a seasonal ground deformation model; (2) monitoring the relative positions of rover stations using the carrier-phase double-difference (DD) positioning method in near real-time; and (3) detecting abrupt and gradual displacements at both base and rover stations using an automated change point detection algorithm. The method is able to detect abrupt positional-changes as minor as five millimeters in near real-time and gradual positional-changes at a couple of millimeters per day within a week. The method has the flexibility of concurrent processing different GPS and BeiDou data sessions (e.g., every 15 minutes, 30 minutes, one hour, one day) for diffident monitoring purposes. This study indicates that BeiDou observations can also achieve few-millimeter-accuracy for measuring displacements. Parallel processing GPS and BeiDou observations can improve the reliability of near real-time structural deformation monitoring and minimize false alerts. The method introduced in this article can be applied to other urban areas for near real-time and long-term structural health monitoring. Full article
(This article belongs to the Special Issue Structural Damage Detection and Health Monitoring)
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Open AccessArticle
Recognition of Urdu Handwritten Characters Using Convolutional Neural Network
Appl. Sci. 2019, 9(13), 2758; https://doi.org/10.3390/app9132758
Received: 2 June 2019 / Revised: 26 June 2019 / Accepted: 1 July 2019 / Published: 8 July 2019
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Abstract
In the area of pattern recognition and pattern matching, the methods based on deep learning models have recently attracted several researchers by achieving magnificent performance. In this paper, we propose the use of the convolutional neural network to recognize the multifont offline Urdu [...] Read more.
In the area of pattern recognition and pattern matching, the methods based on deep learning models have recently attracted several researchers by achieving magnificent performance. In this paper, we propose the use of the convolutional neural network to recognize the multifont offline Urdu handwritten characters in an unconstrained environment. We also propose a novel dataset of Urdu handwritten characters since there is no publicly-available dataset of this kind. A series of experiments are performed on our proposed dataset. The accuracy achieved for character recognition is among the best while comparing with the ones reported in the literature for the same task. Full article
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Open AccessArticle
Algorithmically Optimized Hemispherical Dome as a Secondary Optical Element for the Fresnel Lens Solar Concentrator
Appl. Sci. 2019, 9(13), 2757; https://doi.org/10.3390/app9132757
Received: 16 May 2019 / Revised: 29 June 2019 / Accepted: 3 July 2019 / Published: 8 July 2019
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Abstract
The significance of this work lies in the development of a novel code-based, detailed, and deterministic geometrical approach that couples the optimization of the Fresnel lens primary optical element (POE) and the dome-shaped secondary optical element (SOE). The objective was to maximize the [...] Read more.
The significance of this work lies in the development of a novel code-based, detailed, and deterministic geometrical approach that couples the optimization of the Fresnel lens primary optical element (POE) and the dome-shaped secondary optical element (SOE). The objective was to maximize the concentration acceptance product (CAP), while using the minimum SOE and receiver geometry at a given f-number and incidence angle (also referred to as the tracking error angle). The laws of polychromatic light refraction along with trigonometry and spherical geometry were utilized to optimize the POE grooves, SOE radius, receiver size, and SOE–receiver spacing. Two literature case studies were analyzed to verify this work’s optimization, both with a spot Fresnel lens POE and a spherical dome SOE. Case 1 had a 625 cm2 POE at an f-number of 1.5, and Case 2 had a 314.2 cm2 POE at an f-number of 1.34. The equivalent POE designed by this work, with optimized SOE radiuses of 13.6 and 11.4 mm, respectively, enhanced the CAP value of Case 1 by 52% to 0.426 and that of Case 2 by 32.4% to 0.45. The SOE’s analytical optimization of Case 1 was checked by a simulated comparative analysis to ensure the validity of the results. Fine-tuning this design for thermal applications and concentrated photovoltaics is also discussed in this paper. The algorithm can be further improved for more optimization parameters and other SOE shapes. Full article
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Open AccessArticle
Augmented Reality in Heritage Apps:Current Trends in Europe
Appl. Sci. 2019, 9(13), 2756; https://doi.org/10.3390/app9132756
Received: 8 May 2019 / Revised: 3 June 2019 / Accepted: 5 July 2019 / Published: 8 July 2019
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Abstract
Although augmented reality (AR) has come to play an increasingly important role in a wide range of areas, its use remains rather limited in the realm of heritage education. This paper sets out to analyze which heritage-related apps can be found in Europe [...] Read more.
Although augmented reality (AR) has come to play an increasingly important role in a wide range of areas, its use remains rather limited in the realm of heritage education. This paper sets out to analyze which heritage-related apps can be found in Europe that partly or wholly use AR as a tool to help users learn about different types of heritage. Our study only identified a limited number of such apps and we used this sample both to paint a portrait of the current state of the question and also to highlight certain observable trends. The results showed that most such apps used AR to reconstruct spaces and buildings, and to a lesser extent, objects. Many of these apps used an academic mode of communication to provide a temporal perspective of monumental and (mainly) historical heritage. The paper also outlines future lines of research dedicated to finding more apps that could be used to increase the current sample size. This would allow for a more comprehensive assessment of such apps from an educational point of view. Several case studies are proffered in order to highlight the keys to successful use of AR in heritage apps. Full article
(This article belongs to the Special Issue Augmented Reality: Current Trends, Challenges and Prospects)
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Open AccessArticle
Geometry of the Vocal Tract and Properties of Phonation near Threshold: Calculations and Measurements
Appl. Sci. 2019, 9(13), 2755; https://doi.org/10.3390/app9132755
Received: 24 May 2019 / Revised: 28 June 2019 / Accepted: 2 July 2019 / Published: 8 July 2019
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Abstract
In voice research, analytically-based models are efficient tools to investigate the basic physical mechanisms of phonation. Calculations based on lumped element models describe the effects of the air in the vocal tract upon threshold pressure (Pth) by its inertance. The [...] Read more.
In voice research, analytically-based models are efficient tools to investigate the basic physical mechanisms of phonation. Calculations based on lumped element models describe the effects of the air in the vocal tract upon threshold pressure (Pth) by its inertance. The latter depends on the geometrical boundary conditions prescribed by the vocal tract length (directly) and its cross-sectional area (inversely). Using Titze’s surface wave model (SWM) to account for the properties of the vocal folds, the influence of the vocal tract inertia is examined by two sets of calculations in combination with experiments that apply silicone-based vocal folds. In the first set, a vocal tract is constructed whose cross-sectional area is adjustable from 2.7 cm2 to 11.7 cm2. In the second set, the length of the vocal tract is varied from 4.0 cm to 59.0 cm. For both sets, the pressure and frequency data are collected and compared with calculations based on the SWM. In most cases, the measurements support the calculations; hence, the model is suited to describe and predict basic mechanisms of phonation and the inertial effects caused by a vocal tract. Full article
(This article belongs to the Special Issue Computational Methods and Engineering Solutions to Voice)
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Open AccessArticle
Maximization of Eigenfrequency Gaps in a Composite Cylindrical Shell Using Genetic Algorithms and Neural Networks
Appl. Sci. 2019, 9(13), 2754; https://doi.org/10.3390/app9132754
Received: 10 June 2019 / Revised: 1 July 2019 / Accepted: 5 July 2019 / Published: 8 July 2019
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Abstract
This paper presents a novel method for the maximization of eigenfrequency gaps around external excitation frequencies by stacking sequence optimization in laminated structures. The proposed procedure enables the creation of an array of suggested lamination angles to avoid resonance for each excitation frequency [...] Read more.
This paper presents a novel method for the maximization of eigenfrequency gaps around external excitation frequencies by stacking sequence optimization in laminated structures. The proposed procedure enables the creation of an array of suggested lamination angles to avoid resonance for each excitation frequency within the considered range. The proposed optimization algorithm, which involves genetic algorithms, artificial neural networks, and iterative retraining of the networks using data obtained from tentative optimization loops, is accurate, robust, and significantly faster than typical genetic algorithm optimization in which the objective function values are calculated using the finite element method. The combined genetic algorithm–neural network procedure was successfully applied to problems related to the avoidance of vibration resonance, which is a major concern for every structure subjected to periodic external excitations. The presented examples illustrate a combined approach to avoiding resonance through the maximization of a frequency gap around external excitation frequencies complemented by the maximization of the fundamental natural frequency. The necessary changes in natural frequencies are caused only by appropriate changes in the lamination angles. The investigated structures are thin-walled, laminated one- or three-segment shells with different boundary conditions. Full article
(This article belongs to the Section Acoustics and Vibrations)
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Open AccessArticle
On Sharing Spatial Data with Uncertainty Integration Amongst Multiple Robots Having Different Maps
Appl. Sci. 2019, 9(13), 2753; https://doi.org/10.3390/app9132753
Received: 30 May 2019 / Revised: 3 July 2019 / Accepted: 4 July 2019 / Published: 8 July 2019
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Abstract
Information sharing is a powerful feature of multi-robot systems. Sharing information precisely and accurately is important and has many benefits. Particularly, smart information sharing can improve robot path planning. If a robot finds a new obstacle or blocked path, it can share this [...] Read more.
Information sharing is a powerful feature of multi-robot systems. Sharing information precisely and accurately is important and has many benefits. Particularly, smart information sharing can improve robot path planning. If a robot finds a new obstacle or blocked path, it can share this information with other remote robots allowing them to plan better paths. However, there are two problems with such information sharing. First, the maps of the robots may be different in nature (e.g., 2D grid-map, 3D semantic map, feature map etc.) as the sensors used by the robots for mapping and localization may be different. Even the maps generated using the same sensor (e.g., Lidar) can vary in scale or rotation and the sensors used might have different specifications like resolution or range. In such scenarios, the ‘correspondence problem’ in different maps is a critical bottleneck in information sharing. Second, the transience of the obstacles has to be considered while also considering the positional uncertainty of the new obstacles while sharing information. In our previous work, we proposed a ‘node-map’ with a confidence decay mechanism to solve this problem. However, the previous work had many limitations due to the decoupling of new obstacle’s positional uncertainty and confidence decay. Moreover, the previous work applied only to homogeneous maps. In addition, the previous model worked only with static obstacles in the environment. The current work extends our previous work in three main ways: (1) we extend the previous work by integrating positional uncertainty in the confidence decay mechanism and mathematically model the transience of newly added or removed obstacles and discuss its merits; (2) we extend the previous work by considering information sharing in heterogeneous maps build using different sensors; and (3) we consider dynamic obstacles like moving people in the environment and test the proposed method in complex scenarios. All the experiments are performed in real environments and with actual robots and results are discussed. Full article
(This article belongs to the Special Issue Multi-Robot Systems: Challenges, Trends and Applications)
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Open AccessArticle
Efficient Transmit Delay Calculation in Ultrasound Coherent Plane-Wave Compound Imaging for Curved Array Transducers
Appl. Sci. 2019, 9(13), 2752; https://doi.org/10.3390/app9132752
Received: 11 March 2019 / Revised: 21 June 2019 / Accepted: 4 July 2019 / Published: 8 July 2019
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Abstract
The recently introduced plane-wave compounding method based on multiple plane-wave excitation has enabled several new applications due to its high frame rate (>1000 Hz). In this paper, a new efficient transmit delay calculation method in plane-wave compound imaging for a curved array transducer [...] Read more.
The recently introduced plane-wave compounding method based on multiple plane-wave excitation has enabled several new applications due to its high frame rate (>1000 Hz). In this paper, a new efficient transmit delay calculation method in plane-wave compound imaging for a curved array transducer is presented. In the proposed method, the transmit delay is only calculated for a steering angle of 0° and is shifted along the element of the transducer to obtain other transmit delays for different steering angles. To evaluate the performance of the proposed method, the computational complexity was measured for various transmission conditions. For the number of elements and plane-wave excitations of 128 and 65, respectively, the number of operations was substantially decreased in the proposed method compared with the conventional method (256 vs. 8320). The benefits of the proposed method were demonstrated with phantom and in vivo experiments, where coherent plane-wave compounding with 65 excitations provided larger CR and CNR values compared to nine excitations (−22.5 dB and 2.7 vs. −11.3 dB and 1.9, respectively). These results indicate the proposed method can effectively reduce the computational complexity for plane-wave compound imaging in curved array transducers. Full article
(This article belongs to the Special Issue Advanced Ultrasound Technology for Medical Application)
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Open AccessArticle
NO Removal by Plasma-Enhanced NH3-SCR Using Methane as an Assistant Reduction Agent at Low Temperature
Appl. Sci. 2019, 9(13), 2751; https://doi.org/10.3390/app9132751
Received: 18 June 2019 / Revised: 1 July 2019 / Accepted: 2 July 2019 / Published: 8 July 2019
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Abstract
The effects of using CH4 as an assistant reduction agent in plasma-assisted NH3–SCR were investigated. The new hybrid reaction system performed better than DBD–NH3–SCR when the O2 concentration varied from 2% to 12%. Compared with DBD–NH3 [...] Read more.
The effects of using CH4 as an assistant reduction agent in plasma-assisted NH3–SCR were investigated. The new hybrid reaction system performed better than DBD–NH3–SCR when the O2 concentration varied from 2% to 12%. Compared with DBD–NH3–SCR, DBD–NH3–CH4–SCR (NH3:CH4 = 1:1) showed a more significant promotion effect on the performance and N2 selectivity for NOX abatement. When the O2 concentration was 6% and the SIE was 512 J/L, the NO removal efficiency of the new hybrid system reached 84.5%. The outlet gas components were observed via FTIR to reveal the decomposition process and its mechanism. This work indicated that CH4, as an assistant agent, enhances DBD–NH3–SCR in excess oxygen to achieve a new process with significantly higher activity at a low temperature (≤348 K) for NOX removal. Full article
(This article belongs to the Special Issue Air Pollution)
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Open AccessArticle
Supplementation of Non-Dairy Creamer-Enriched High-Fat Diet with D-Allulose Ameliorated Blood Glucose and Body Fat Accumulation in C57BL/6J Mice
Appl. Sci. 2019, 9(13), 2750; https://doi.org/10.3390/app9132750
Received: 26 April 2019 / Revised: 19 June 2019 / Accepted: 5 July 2019 / Published: 8 July 2019
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Abstract
D-allulose, which has 70% of the sweet taste of sucrose but nearly no calories, has been reported to inhibit the absorption of lipids and suppress body weight gain in obese mice. Fats in non-dairy creamer consist of highly saturated fatty acids, which can [...] Read more.
D-allulose, which has 70% of the sweet taste of sucrose but nearly no calories, has been reported to inhibit the absorption of lipids and suppress body weight gain in obese mice. Fats in non-dairy creamer consist of highly saturated fatty acids, which can cause various lipid disorders when consumed over a long period. We investigated whether D-allulose supplementation alleviates the effects of a non-dairy creamer-enriched high-fat diet on lipid metabolism. High-fat diets enriched with non-dairy creamer were administered to C57BL/6J mice with or without D-allulose supplementation for eight weeks by the pair-feeding design. Lipid metabolic markers were compared between the non-dairy creamer control group (NDC) and non-dairy creamer allulose group (NDCA). Body, adipose tissue, and liver weights, and fasting blood glucose levels, were significantly lower in the NDCA group than in the NDC group. Fecal fatty acid and triglyceride levels were significantly higher in the NDCA group than in the NDC group. Supplementing a non-dairy creamer-enriched high-fat diet with D-allulose improved overall lipid metabolism, including the plasma and hepatic lipid profiles, hepatic and adipose tissue morphology, and plasma inflammatory adipokine levels in mice. These results suggest that D-allulose can be used as a functional food component for preventing body fat accumulation from a high-fat diet that includes hydrogenated plant fats. Full article
(This article belongs to the Section Applied Biosciences and Bioengineering)
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Open AccessArticle
Post-FEC Performance of Pilot-Aided Carrier Phase Estimation over Cycle Slip
Appl. Sci. 2019, 9(13), 2749; https://doi.org/10.3390/app9132749
Received: 27 June 2019 / Revised: 2 July 2019 / Accepted: 3 July 2019 / Published: 8 July 2019
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Abstract
The POST-forward error correction (FEC) bit error rate (BER) performance and the cycle-slip (CS) probability of the carrier phase estimation (CPE) scheme based on Viterbi–Viterbi phase estimation (VVPE) algorithm and the VV cascaded by pilot-aided-phase-unwrap (PAPU) algorithm have been experimentally investigated in a [...] Read more.
The POST-forward error correction (FEC) bit error rate (BER) performance and the cycle-slip (CS) probability of the carrier phase estimation (CPE) scheme based on Viterbi–Viterbi phase estimation (VVPE) algorithm and the VV cascaded by pilot-aided-phase-unwrap (PAPU) algorithm have been experimentally investigated in a 56 Gbit/s quadrature phase-shift keying (QPSK) coherent communication system. Experimental results show that, with 0.78% pilot overhead, the VVPE + PAPU scheme greatly improves the POST-FEC performance degraded by continuous CS, maintaining a low CS probability with less influence of filter length. Comparing with the VVPE scheme, the VVPE + PAPU scheme can respectively obtain about 3.1 dB, 1.3 dB, 0.6 dB PRE-FEC optical signal noise ratio (OSNR) gains at PRE-BER of 1.8 × 10−2. Meanwhile, the VVPE + PAPU scheme respectively achieves about 3 dB, 1 dB, and 0.5 dB POST-FEC OSNR gain and improves the FEC limit from 2.5 × 10−3 to 1.4 × 10−2, from 8.9 × 10−3 to 1.8 × 10−2, and from 1.6 × 10−2 to 1.9 × 10−2 under the CPE filter length of 8, 16, and 20. Full article
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Open AccessArticle
The Effects of Differences in Individual Characteristics and Regional Living Environments on the Motivation to Immigrate to Hometowns: A Decision Tree Analysis
Appl. Sci. 2019, 9(13), 2748; https://doi.org/10.3390/app9132748
Received: 23 May 2019 / Revised: 20 June 2019 / Accepted: 2 July 2019 / Published: 7 July 2019
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Abstract
Population decline and rural–urban population disparities are serious problems in Japan. This study aims to investigate the relationship between people’s motivations to migrate to their hometowns (“U-turn migration”) and their evaluations of the living environments of both their hometowns and current places of [...] Read more.
Population decline and rural–urban population disparities are serious problems in Japan. This study aims to investigate the relationship between people’s motivations to migrate to their hometowns (“U-turn migration”) and their evaluations of the living environments of both their hometowns and current places of residence. An online questionnaire survey was conducted for people living in multiple places in Japan. By using the data of respondents’ U-turn motivations and their evaluations of living environments, we conducted a decision tree analysis to quantitatively describe the multilayered relationship between the environments and people’s motivations, and we focused on patterns that can ameliorate the population disparities. These are the major findings: first, living environments in both the hometown and at the current place of residence affected the U-turn motivations. Second, respondents were divided into several groups based on similar U-turn motivation structures, and with different demographic characters among the groups. Additionally, the evaluations of some living environments tend to depend on the city size or geographic locations. Although some groups’ U-turn migrations may improve population disparities, the improvement and maintenance of living environments are necessary. The results can help local governments in identifying the living environments that need development, and in estimating the feasibility of U-turn migrations. Full article
(This article belongs to the Section Civil Engineering)
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Open AccessArticle
The Effects of Asphalt Migration on the Dynamic Modulus of Asphalt Mixture
Appl. Sci. 2019, 9(13), 2747; https://doi.org/10.3390/app9132747
Received: 31 May 2019 / Revised: 28 June 2019 / Accepted: 3 July 2019 / Published: 7 July 2019
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Abstract
Asphalt migration is one of the significant detrimental effects on asphalt pavement performance. In order to simulate the state after the occurrence of asphalt migration amid asphalt pavement layers and further investigate the effects of asphalt migration on the dynamic modulus of asphalt [...] Read more.
Asphalt migration is one of the significant detrimental effects on asphalt pavement performance. In order to simulate the state after the occurrence of asphalt migration amid asphalt pavement layers and further investigate the effects of asphalt migration on the dynamic modulus of asphalt mixture, samples with different asphalt contents layers were firstly separated into the upper and lower half portions and then compacted together. By conducting the dynamic modulus test with the Superpave Simple Performance Tester (SPT), the variation laws of the dynamic modulus (|E*|) and the phase angle (δ) at different testing temperatures and loading frequencies were analyzed in this paper. Further, the dynamic modulus and the stiffness parameter (|E*|/sinδ) at the loading frequency of 10 Hz and testing temperature of 50 °C were illustrated. Simultaneously, the master curves of the dynamic modulus and phase angle of asphalt mixtures under different testing conditions were constructed to better investigate the effects of asphalt migration on the dynamic modulus by means of Williams–Landel–Ferry (WLF) equation and Sigmoidal function. Results show that, after the asphalt migration, the dynamic modulus of asphalt mixtures increase with the increasing loading frequency while they decrease with the increasing testing temperature; the dynamic modulus and the stiffness parameter are the highest when asphalt mixtures have the optimum asphalt content layers, and then decrease with the incremental difference of asphalt content in the upper and lower half portions. Besides this, different from the master curves of dynamic modulus, the master curves of phase angle firstly increase with the increase of loading frequency to the highest point and then decrease with the further increase of loading frequency and are not as smooth as that of dynamic modulus. It can be concluded that the asphalt migration has compromised the mixture’s mechanical structure, and the more asphalt migrates, the weaker the mechanical properties of asphalt mixture will be. Additionally, based on the shift factors and master curves in the time–temperature superposition principle (TTSP), the effects of asphalt migration on the dynamic modulus and the variation laws of the dynamic modulus of asphalt mixture after the occurrence of asphalt migration can be better construed at the quantitative level. Full article
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Open AccessArticle
The Integration of Azure Sphere and Azure Cloud Services for Internet of Things
Appl. Sci. 2019, 9(13), 2746; https://doi.org/10.3390/app9132746
Received: 5 May 2019 / Revised: 29 June 2019 / Accepted: 2 July 2019 / Published: 7 July 2019
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Abstract
Internet of Things (IoT) has become one of the key factors that enables, drives and accelerates the digital transformation all over the world. The vision of the IoT is not only the immediate access to the data but also the ability to turning [...] Read more.
Internet of Things (IoT) has become one of the key factors that enables, drives and accelerates the digital transformation all over the world. The vision of the IoT is not only the immediate access to the data but also the ability to turning data into intelligence. As such, there is a growing number of public cloud computing providers offering IoT related services, including data processing, data analyzing and data visualization. However, with tens of billions of microcontroller-powered devices getting involved in the era of IoT, the concerns for overall security, privacy and cost are rising constantly and exponentially. Furthermore, these issues cannot be solved by public cloud computing providers since they mainly focus on the software and services rather than on the end devices. In this article, an integrated solution including Azure Sphere devices and Azure cloud services is proposed to provide a comprehensive and efficient way to ensure security that starts in the device and extends to the cloud with limited budgets. Moreover, the implementation details including hardware components, software design and Azure cloud integration are presented to demonstrate the feasibility and efficiency of the proposed solution. Full article
(This article belongs to the Section Computing and Artificial Intelligence)
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Open AccessArticle
A PEC Thrice Subtraction Method for Obtaining Permeability Invariance Feature in Conductivity Measurement of Ferromagnetic Samples
Appl. Sci. 2019, 9(13), 2745; https://doi.org/10.3390/app9132745
Received: 17 May 2019 / Revised: 4 July 2019 / Accepted: 5 July 2019 / Published: 7 July 2019
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Abstract
Conductivity, as an important index of structural health monitoring, can be used to evaluate heat treatment condition, and sort different materials or measure the stress of mechanical parts. However, the permeability of a measured sample has significant impact on the detected signal in [...] Read more.
Conductivity, as an important index of structural health monitoring, can be used to evaluate heat treatment condition, and sort different materials or measure the stress of mechanical parts. However, the permeability of a measured sample has significant impact on the detected signal in pulsed eddy current (PEC) testing, which is prone to measurement errors due to the effect of permeability change. In this paper, a thrice subtraction method is investigated and utilized to obtain a permeability invariance (PI) feature for reducing permeability effect in conductivity measurement of ferromagnetic samples. The thrice subtraction method is based on the PEC signals of sample and air, the difference signal between the difference PEC signal and its normalization signal, and the difference signal between the difference normalization signal and its standard deviation. In the thrice subtraction signals, the behavior of the obtained PI feature is analyzed by experiments and simulations. The results demonstrate that the thrice subtraction method is a practicable program and the PI feature is potential to measure the conductivity of ferromagnetic samples. The work reported in this paper provides an effective approach to obtain a PI feature for estimating the conductivity of ferromagnetic samples without a permeability effect. Full article
(This article belongs to the Special Issue Nondestructive Testing (NDT))
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Open AccessArticle
Power Control and Link Selection for Wireless Relay Networks with Hybrid Energy Sources
Appl. Sci. 2019, 9(13), 2744; https://doi.org/10.3390/app9132744
Received: 9 June 2019 / Revised: 1 July 2019 / Accepted: 3 July 2019 / Published: 6 July 2019
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Abstract
The Hybrid energy supply (HES) wireless relay system is a new green network technology, where the source node is powered by the grid and relay is powered by harvested renewable energy. However, the network’s performance may degrade due to the intermittent nature of [...] Read more.
The Hybrid energy supply (HES) wireless relay system is a new green network technology, where the source node is powered by the grid and relay is powered by harvested renewable energy. However, the network’s performance may degrade due to the intermittent nature of renewable energy. In this paper, our purpose is to minimize grid energy consumption and maximize throughput. However, improving the throughput requires increasing the transmission power of the source node, which will lead to a higher grid energy consumption. Linear weighted summation method is used to turn the two conflicting objectives into a single objective. Link assignment and a power control strategy are adopted to maximize the total reward of the network. The problem is formulated as a discrete Markov decision model. In addition, a backwards induction method based on state deletion is proposed to reduce the computational complexity. Simulation results show that the proposed algorithm can effectively alleviate performance degradation caused by the lack of renewable energy, and present the trade-off between energy consumption and throughput. Full article
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Open AccessArticle
Fault Diagnosis of Rolling Bearing Based on Multiscale Intrinsic Mode Function Permutation Entropy and a Stacked Sparse Denoising Autoencoder
Appl. Sci. 2019, 9(13), 2743; https://doi.org/10.3390/app9132743
Received: 28 April 2019 / Revised: 14 June 2019 / Accepted: 2 July 2019 / Published: 6 July 2019
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Abstract
Effective intelligent fault diagnosis of bearings is important for improving safety and reliability of machine. Benefiting from the training advantages, deep learning method can automatically and adaptively learn more abstract and high-level features without much priori knowledge. To realize representative features mining and [...] Read more.
Effective intelligent fault diagnosis of bearings is important for improving safety and reliability of machine. Benefiting from the training advantages, deep learning method can automatically and adaptively learn more abstract and high-level features without much priori knowledge. To realize representative features mining and automatic recognition of bearing health condition, a diagnostic model of stacked sparse denoising autoencoder (SSDAE) which combines sparse autoencoder (SAE) and denoising autoencoder (DAE) is proposed in this paper. The sparse criterion in SAE, corrupting operation in DAE and reasonable designing of the stack order of autoencoders help to mine essential information of the input and improve fault pattern classification robustness. In order to provide better input features for the constructed network, the raw non-stationary and nonlinear vibration signals are processed with ensemble empirical mode decomposition (EEMD) and multiscale permutation entropy (MPE). MPE features which are extracted based on both the selected characteristic frequency-related intrinsic mode function components (IMFs) and the raw signal, are used as low-level feature for the input of the proposed diagnostic model for health condition recognition and classification. Two experiments based on the Case Western Reserve University (CWRU) dataset and the measurement dataset from laboratory were conducted, and results demonstrate the effectiveness of the proposed method and highlight its excellent performance relative to existing methods. Full article
(This article belongs to the Special Issue Fault Diagnosis of Rotating Machine)
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