Applied Sciences

20 pages, 1734 KiB

Open AccessEditor’s ChoiceReview

Biochar as a Multifunctional Component of the Environment—A Review

by Bogdan Saletnik, Grzegorz Zaguła, Marcin Bajcar, Maria Tarapatskyy, Gabriel Bobula and Czesław Puchalski

Appl. Sci. 2019, 9(6), 1139; https://doi.org/10.3390/app9061139 - 18 Mar 2019

Cited by 82 | Viewed by 8378

Abstract

The growing demand for electricity, caused by dynamic economic growth, leads to a decrease in the available non-renewable energy resources constituting the foundation of global power generation. A search for alternative sources of energy that can support conventional energy technologies utilizing fossil fuels [...] Read more.

The growing demand for electricity, caused by dynamic economic growth, leads to a decrease in the available non-renewable energy resources constituting the foundation of global power generation. A search for alternative sources of energy that can support conventional energy technologies utilizing fossil fuels is not only of key significance for the power industry but is also important from the point of view of environmental conservation and sustainable development. Plant biomass, with its specific chemical structure and high calorific value, is a promising renewable source of energy which can be utilized in numerous conversion processes, enabling the production of solid, liquid, and gaseous fuels. Methods of thermal biomass conversion include pyrolysis, i.e., a process allowing one to obtain a multifunctional product known as biochar. The article presents a review of information related to the broad uses of carbonization products. It also discusses the legal aspects and quality standards applicable to these materials. The paper draws attention to the lack of uniform legal and quality conditions, which would allow for a much better use of biochar. The review also aims to highlight the high potential for a use of biochar in different environments. The presented text attempts to emphasize the importance of biochar as an alternative to classic products used for energy, environmental and agricultural purposes. Full article

(This article belongs to the Special Issue New Carbon Materials from Biomass and Their Applications)

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14 pages, 3312 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Assessing Dynamic Conditions of the Retaining Wall: Developing Two Hybrid Intelligent Models

by Hui Chen, Panagiotis G. Asteris, Danial Jahed Armaghani, Behrouz Gordan and Binh Thai Pham

Appl. Sci. 2019, 9(6), 1042; https://doi.org/10.3390/app9061042 - 13 Mar 2019

Cited by 128 | Viewed by 6546

Abstract

The precise estimation and forecast of the safety factor (SF) in civil engineering applications is considered as an important issue to reduce engineering risk. The present research investigates new artificial intelligence (AI) techniques for the prediction of SF values of retaining walls, as [...] Read more.

The precise estimation and forecast of the safety factor (SF) in civil engineering applications is considered as an important issue to reduce engineering risk. The present research investigates new artificial intelligence (AI) techniques for the prediction of SF values of retaining walls, as important and resistant structures for ground forces. These structures have complicated performances in dynamic conditions. Consequently, more than 8000 designs of these structures were dynamically evaluated. Two AI models, namely the imperialist competitive algorithm (ICA)-artificial neural network (ANN), and the genetic algorithm (GA)-ANN were used for the forecasting of SF values. In order to design intelligent models, parameters i.e., the wall thickness, stone density, wall height, soil density, and internal soil friction angle were examined under different dynamic conditions and assigned as inputs to predict SF of retaining walls. Various models of these systems were constructed and compared with each other to obtain the best one. Results of models indicated that although both hybrid models are able to predict SF values with a high accuracy and they can be introduced as new models in the field, the retaining wall performance could be properly predicted in dynamic conditions using the ICA-ANN model. Under these conditions, a combination of engineering design and artificial intelligence techniques can be used to control and secure retaining walls in dynamic conditions. Full article

(This article belongs to the Special Issue Soft Computing Techniques in Structural Engineering and Materials)

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22 pages, 4394 KiB

Open AccessEditor’s ChoiceReview

Recent Advances in Plasmonic Sensor-Based Fiber Optic Probes for Biological Applications

by M. S. Aruna Gandhi, Suoda Chu, K. Senthilnathan, P. Ramesh Babu, K. Nakkeeran and Qian Li

Appl. Sci. 2019, 9(5), 949; https://doi.org/10.3390/app9050949 - 6 Mar 2019

Cited by 119 | Viewed by 13769

Abstract

The survey focuses on the most significant contributions in the field of fiber optic plasmonic sensors (FOPS) in recent years. FOPSs are plasmonic sensor-based fiber optic probes that use an optical field to measure the biological agents. Owing to their high sensitivity, high [...] Read more.

The survey focuses on the most significant contributions in the field of fiber optic plasmonic sensors (FOPS) in recent years. FOPSs are plasmonic sensor-based fiber optic probes that use an optical field to measure the biological agents. Owing to their high sensitivity, high resolution, and low cost, FOPS turn out to be potential alternatives to conventional biological fiber optic sensors. FOPS use optical transduction mechanisms to enhance sensitivity and resolution. The optical transduction mechanisms of FOPS with different geometrical structures and the photonic properties of the geometries are discussed in detail. The studies of optical properties with a combination of suitable materials for testing the biosamples allow for diagnosing diseases in the medical field. Full article

(This article belongs to the Special Issue Recent Progress in Fiber Optic Sensors: Bringing Light to Measurement)

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29 pages, 10285 KiB

Open AccessEditor’s ChoiceReview

Review of Artificial Intelligence Adversarial Attack and Defense Technologies

by Shilin Qiu, Qihe Liu, Shijie Zhou and Chunjiang Wu

Appl. Sci. 2019, 9(5), 909; https://doi.org/10.3390/app9050909 - 4 Mar 2019

Cited by 234 | Viewed by 27792

Abstract

In recent years, artificial intelligence technologies have been widely used in computer vision, natural language processing, automatic driving, and other fields. However, artificial intelligence systems are vulnerable to adversarial attacks, which limit the applications of artificial intelligence (AI) technologies in key security fields. [...] Read more.

In recent years, artificial intelligence technologies have been widely used in computer vision, natural language processing, automatic driving, and other fields. However, artificial intelligence systems are vulnerable to adversarial attacks, which limit the applications of artificial intelligence (AI) technologies in key security fields. Therefore, improving the robustness of AI systems against adversarial attacks has played an increasingly important role in the further development of AI. This paper aims to comprehensively summarize the latest research progress on adversarial attack and defense technologies in deep learning. According to the target model’s different stages where the adversarial attack occurred, this paper expounds the adversarial attack methods in the training stage and testing stage respectively. Then, we sort out the applications of adversarial attack technologies in computer vision, natural language processing, cyberspace security, and the physical world. Finally, we describe the existing adversarial defense methods respectively in three main categories, i.e., modifying data, modifying models and using auxiliary tools. Full article

(This article belongs to the Special Issue Advances in Deep Learning)

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15 pages, 11368 KiB

Open AccessEditor’s ChoiceArticle

Behavior of Fiber-Reinforced and Lime-Stabilized Clayey Soil in Triaxial Tests

by Yixian Wang, Panpan Guo, Xian Li, Hang Lin, Yan Liu and Haiping Yuan

Appl. Sci. 2019, 9(5), 900; https://doi.org/10.3390/app9050900 - 3 Mar 2019

Cited by 89 | Viewed by 7060

Abstract

The beneficial role of combining fiber reinforcement with lime stabilization in altering soil behavior has been established in the literature. However, the coupling effect of their combination still remains unclear in terms of its magnitude and microscopic mechanism, especially for natural fibers with [...] Read more.

The beneficial role of combining fiber reinforcement with lime stabilization in altering soil behavior has been established in the literature. However, the coupling effect of their combination still remains unclear in terms of its magnitude and microscopic mechanism, especially for natural fibers with special microstructures. The objective of this study was to investigate the coupling effect of wheat straw fiber reinforcement and lime stabilization on the mechanical behavior of Hefei clayey soil. To achieve this, an experimental program including unconsolidated–undrained (UU) triaxial tests and SEM analysis was implemented. Static compaction test samples were prepared on untreated soil, fiber-reinforced soil, lime-stabilized soil, and lime-stabilized/fiber-reinforced soil at optimum moisture content with determining of the maximum dry density of the untreated soil. The lime was added in three different contents of 2%, 4%, and 6%, and 13 mm long wheat straw fiber slices with a cross section one-quarter that of the intact ones were mixed in at 0.2%, 0.4%, and 0.6% by dry weight of soil. Analysis of the derived results indicated that the addition of a small amount of wheat straw fibers into lime-stabilized soil improved the intensity of the strain-softening behavior associated with mere lime stabilization. The observed evidence that the shear strength increase brought by a combination of 0.4% fiber reinforcement and 4% lime stabilization was smaller than the summation of the shear strength increases brought by their presence alone in a sample demonstrated a coupling effect between fiber reinforcement and lime stabilization. This coupling effect was also detected in the comparisons of the secant modulus and failure pattern between the combined treatment and the individual treatments. These manifestations of the coupling effect were explained by a microscopic mechanism wherein the fiber reinforcing effect was made more effective by the ways in which lime chemically stabilized the soil and lime stabilization development was quickened by the water channels passing through the surfaces and honeycomb pores of the wheat straw fibers. Full article

(This article belongs to the Section Civil Engineering)

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44 pages, 6655 KiB

Open AccessFeature PaperEditor’s ChoiceReview

Organic Crystals for THz Photonics

by Mojca Jazbinsek, Uros Puc, Andreja Abina and Aleksander Zidansek

Appl. Sci. 2019, 9(5), 882; https://doi.org/10.3390/app9050882 - 1 Mar 2019

Cited by 140 | Viewed by 9982

Abstract

Organic crystals with second-order optical nonlinearity feature very high and ultra-fast optical nonlinearities and are therefore attractive for various photonics applications. During the last decade, they have been found particularly attractive for terahertz (THz) photonics. This is mainly due to the very intense [...] Read more.

Organic crystals with second-order optical nonlinearity feature very high and ultra-fast optical nonlinearities and are therefore attractive for various photonics applications. During the last decade, they have been found particularly attractive for terahertz (THz) photonics. This is mainly due to the very intense and ultra-broadband THz-wave generation possible with these crystals. We review recent progress and challenges in the development of organic crystalline materials for THz-wave generation and detection applications. We discuss their structure, intrinsic properties, and advantages compared to inorganic alternatives. The characteristic properties of the most widely employed organic crystals at present, such as DAST, DSTMS, OH1, HMQ-TMS, and BNA are analyzed and compared. We summarize the most important principles for THz-wave generation and detection, as well as organic THz-system configurations based on either difference-frequency generation or optical rectification. In addition, we give state-of-the-art examples of very intense and ultra-broadband THz systems that rely on organic crystals. Finally, we present some recent breakthrough demonstrations in nonlinear THz photonics enabled by very intense organic crystalline THz sources, as well as examples of THz spectroscopy and THz imaging using organic crystals as THz sources for various scientific and technological applications. Full article

(This article belongs to the Special Issue Nonlinear Optical Materials and Phenomena)

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17 pages, 2852 KiB

Open AccessEditor’s ChoiceArticle

Optimization of EPB Shield Performance with Adaptive Neuro-Fuzzy Inference System and Genetic Algorithm

by Khalid Elbaz, Shui-Long Shen, Annan Zhou, Da-Jun Yuan and Ye-Shuang Xu

Appl. Sci. 2019, 9(4), 780; https://doi.org/10.3390/app9040780 - 22 Feb 2019

Cited by 88 | Viewed by 5630

Abstract

The prediction of earth pressure balance (EPB) shield performance is an essential part of project scheduling and cost estimation of tunneling projects. This paper establishes an efficient multi-objective optimization model to predict the shield performance during the tunneling process. This model integrates the [...] Read more.

The prediction of earth pressure balance (EPB) shield performance is an essential part of project scheduling and cost estimation of tunneling projects. This paper establishes an efficient multi-objective optimization model to predict the shield performance during the tunneling process. This model integrates the adaptive neuro-fuzzy inference system (ANFIS) with the genetic algorithm (GA). The hybrid model uses shield operational parameters as inputs and computes the advance rate as output. GA enhances the accuracy of ANFIS for runtime parameters tuning by multi-objective fitness function. Prior to modeling, datasets were established, and critical operating parameters were identified through principal component analysis. Then, the tunneling case for Guangzhou metro line number 9 was adopted to verify the applicability of the proposed model. Results were then compared with those of the ANFIS model. The comparison showed that the multi-objective ANFIS-GA model is more successful than the ANFIS model in predicting the advance rate with a high accuracy, which can be used to guide the tunnel performance in the field. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications to Smart City and Smart Enterprise)

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17 pages, 1996 KiB

Open AccessEditor’s ChoiceArticle

S-Box Based Image Encryption Application Using a Chaotic System without Equilibrium

by Xiong Wang, Ünal Çavuşoğlu, Sezgin Kacar, Akif Akgul, Viet-Thanh Pham, Sajad Jafari, Fawaz E. Alsaadi and Xuan Quynh Nguyen

Appl. Sci. 2019, 9(4), 781; https://doi.org/10.3390/app9040781 - 22 Feb 2019

Cited by 107 | Viewed by 5801

Abstract

Chaotic systems without equilibrium are of interest because they are the systems with hidden attractors. A nonequilibrium system with chaos is introduced in this work. Chaotic behavior of the system is verified by phase portraits, Lyapunov exponents, and entropy. We have implemented a [...] Read more.

Chaotic systems without equilibrium are of interest because they are the systems with hidden attractors. A nonequilibrium system with chaos is introduced in this work. Chaotic behavior of the system is verified by phase portraits, Lyapunov exponents, and entropy. We have implemented a real electronic circuit of the system and reported experimental results. By using this new chaotic system, we have constructed S-boxes which are applied to propose a novel image encryption algorithm. In the designed encryption algorithm, three S-boxes with strong cryptographic properties are used for the sub-byte operation. Particularly, the S-box for the sub-byte process is selected randomly. In addition, performance analyses of S-boxes and security analyses of the encryption processes have been presented. Full article

(This article belongs to the Special Issue Applied Sciences Based on and Related to Computer and Control)

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17 pages, 1393 KiB

Open AccessEditor’s ChoiceReview

Role of Wetland Plants and Use of Ornamental Flowering Plants in Constructed Wetlands for Wastewater Treatment: A Review

by Luis Sandoval, Sergio Aurelio Zamora-Castro, Monserrat Vidal-Álvarez and José Luis Marín-Muñiz

Appl. Sci. 2019, 9(4), 685; https://doi.org/10.3390/app9040685 - 17 Feb 2019

Cited by 123 | Viewed by 11397

Abstract

The vegetation in constructed wetlands (CWs) plays an important role in wastewater treatment. Popularly, the common emergent plants in CWs have been vegetation of natural wetlands. However, there are ornamental flowering plants that have some physiological characteristics similar to the plants of natural [...] Read more.

The vegetation in constructed wetlands (CWs) plays an important role in wastewater treatment. Popularly, the common emergent plants in CWs have been vegetation of natural wetlands. However, there are ornamental flowering plants that have some physiological characteristics similar to the plants of natural wetlands that can stimulate the removal of pollutants in wastewater treatments; such importance in CWs is described here. A literature survey of 87 CWs from 21 countries showed that the four most commonly used flowering ornamental vegetation genera were Canna, Iris, Heliconia and Zantedeschia. In terms of geographical location, Canna spp. is commonly found in Asia, Zantedeschia spp. is frequent in Mexico (a country in North America), Iris is most commonly used in Asia, Europe and North America, and species of the Heliconia genus are commonly used in Asia and parts of the Americas (Mexico, Central and South America). This review also compares the use of ornamental plants versus natural wetland plants and systems without plants for removing pollutants (organic matter, nitrogen, nitrogen and phosphorous compounds). The removal efficiency was similar between flowering ornamental and natural wetland plants. However, pollutant removal was better when using ornamental plants than in unplanted CWs. The use of ornamental flowering plants in CWs is an excellent option, and efforts should be made to increase the adoption of these system types and use them in domiciliary, rural and urban areas. Full article

(This article belongs to the Special Issue Advanced Roles of Constructed Wetlands: Environmental Mitigation, Ecoservices and Resource Recovery)

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13 pages, 1138 KiB

Open AccessEditor’s ChoiceReview

Dielectric-Based Rear Surface Passivation Approaches for Cu(In,Ga)Se₂ Solar Cells—A Review

by Gizem Birant, Jessica de Wild, Marc Meuris, Jef Poortmans and Bart Vermang

Appl. Sci. 2019, 9(4), 677; https://doi.org/10.3390/app9040677 - 16 Feb 2019

Cited by 47 | Viewed by 4836

Abstract

This review summarizes all studies which used dielectric-based materials as a passivation layer at the rear surface of copper indium gallium (di)selenide, Cu(In,Ga)Se₂, (CIGS)-based thin film solar cells, up to 2019. The results regarding the kind of dielectric materials, the deposition [...] Read more.

This review summarizes all studies which used dielectric-based materials as a passivation layer at the rear surface of copper indium gallium (di)selenide, Cu(In,Ga)Se₂, (CIGS)-based thin film solar cells, up to 2019. The results regarding the kind of dielectric materials, the deposition techniques, contacting approaches, the existence of additional treatments, and current–voltage characteristics (J–V) of passivated devices are emphasized by a detailed table. The techniques used to implement the passivation layer, the contacting approach for the realization of the current flow between rear contact and absorber layer, additional light management techniques if applicable, the solar simulator results, and further characterization techniques, i.e., external quantum efficiency (EQE) and photoluminescence (PL), are shared and discussed. Three graphs show the difference between the reference and passivated devices in terms of open-circuit voltage (V_oc), short-circuit current (J_sc), and efficiency (η), with respect to the thicknesses of the absorber layer. The effects of the passivation layer at the rear surface are discussed based on these three graphs. Furthermore, an additional section is dedicated to the theoretical aspects of the passivation mechanism. Full article

(This article belongs to the Special Issue CIGS Thin Films and Solar Cells)

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15 pages, 4609 KiB

Open AccessEditor’s ChoiceArticle

Effects of Graphene Nanoplatelets and Multiwall Carbon Nanotubes on the Structure and Mechanical Properties of Poly(lactic acid) Composites: A Comparative Study

by Todor Batakliev, Ivanka Petrova-Doycheva, Verislav Angelov, Vladimir Georgiev, Evgeni Ivanov, Rumiana Kotsilkova, Marcello Casa, Claudia Cirillo, Renata Adami, Maria Sarno and Paolo Ciambelli

Appl. Sci. 2019, 9(3), 469; https://doi.org/10.3390/app9030469 - 30 Jan 2019

Cited by 109 | Viewed by 6941

Abstract

Poly(lactic acid)/graphene and poly(lactic acid)/carbon nanotube nanocomposites were prepared by an easy and low-cost method of melt blending of preliminary grinded poly(lactic acid) (PLA) with nanosized carbon fillers used as powder. Morphological, structural and mechanical properties were investigated to reveal the influence of [...] Read more.

Poly(lactic acid)/graphene and poly(lactic acid)/carbon nanotube nanocomposites were prepared by an easy and low-cost method of melt blending of preliminary grinded poly(lactic acid) (PLA) with nanosized carbon fillers used as powder. Morphological, structural and mechanical properties were investigated to reveal the influence of carbon nanofiller on the PLA–based composite. The dependence of tensile strength on nanocomposite loading was defined by a series of experiments over extruded filaments using a universal mechanical testing instrument. The applying the XRD technique disclosed that compounds crystallinity significantly changed upon addition of multi walled carbon nanotubes. We demonstrated that Raman spectroscopy can be used as a quick and unambiguous method to determine the homogeneity of the nanocomposites in terms of carbon filler dispersion in a polymer matrix. Full article

(This article belongs to the Special Issue Polymer Nanocomposite for 3D Printing and Applications)

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25 pages, 7254 KiB

Open AccessFeature PaperEditor’s ChoiceReview

A Review: Thermal Stability of Methylammonium Lead Halide Based Perovskite Solar Cells

by Tanzila Tasnim Ava, Abdullah Al Mamun, Sylvain Marsillac and Gon Namkoong

Appl. Sci. 2019, 9(1), 188; https://doi.org/10.3390/app9010188 - 7 Jan 2019

Cited by 178 | Viewed by 18723

Abstract

Perovskite solar cells have achieved photo-conversion efficiencies greater than 20%, making them a promising candidate as an emerging solar cell technology. While perovskite solar cells are expected to eventually compete with existing silicon-based solar cells on the market, their long-term stability has become [...] Read more.

Perovskite solar cells have achieved photo-conversion efficiencies greater than 20%, making them a promising candidate as an emerging solar cell technology. While perovskite solar cells are expected to eventually compete with existing silicon-based solar cells on the market, their long-term stability has become a major bottleneck. In particular, perovskite films are found to be very sensitive to external factors such as air, UV light, light soaking, thermal stress and others. Among these stressors, light, oxygen and moisture-induced degradation can be slowed by integrating barrier or interface layers within the device architecture. However, the most representative perovskite absorber material, CH₃NH₃PbI₃ (MAPbI₃), appears to be thermally unstable even in an inert environment. This poses a substantial challenge for solar cell applications because device temperatures can be over 45 °C higher than ambient temperatures when operating under direct sunlight. Herein, recent advances in resolving thermal stability problems are highlighted through literature review. Moreover, the most recent and promising strategies for overcoming thermal degradation are also summarized. Full article

(This article belongs to the Special Issue Next Generation Photovoltaic Solar Cells)

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26 pages, 7494 KiB

Open AccessEditor’s ChoiceArticle

Landslide Susceptibility Modeling Using Integrated Ensemble Weights of Evidence with Logistic Regression and Random Forest Models

by Wei Chen, Zenghui Sun and Jichang Han

Appl. Sci. 2019, 9(1), 171; https://doi.org/10.3390/app9010171 - 4 Jan 2019

Cited by 135 | Viewed by 7029

Abstract

The main aim of this study was to compare the performances of the hybrid approaches of traditional bivariate weights of evidence (WoE) with multivariate logistic regression (WoE-LR) and machine learning-based random forest (WoE-RF) for landslide susceptibility mapping. The performance of the three landslide [...] Read more.

The main aim of this study was to compare the performances of the hybrid approaches of traditional bivariate weights of evidence (WoE) with multivariate logistic regression (WoE-LR) and machine learning-based random forest (WoE-RF) for landslide susceptibility mapping. The performance of the three landslide models was validated with receiver operating characteristic (ROC) curves and area under the curve (AUC). The results showed that the areas under the curve obtained using the WoE, WoE-LR, and WoE-RF methods were 0.720, 0.773, and 0.802 for the training dataset, and were 0.695, 0.763, and 0.782 for the validation dataset, respectively. The results demonstrate the superiority of hybrid models and that the resultant maps would be useful for land use planning in landslide-prone areas. Full article

(This article belongs to the Special Issue Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing)

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21 pages, 8610 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Recognition of Acoustic Signals of Commutator Motors

by Adam Glowacz

Appl. Sci. 2018, 8(12), 2630; https://doi.org/10.3390/app8122630 - 15 Dec 2018

Cited by 52 | Viewed by 4412

Abstract

Most faults can stop a motor, and time is lost in fixing the damaged motor. This is a reason why it is essential to develop fault-detection methods. This paper describes the acoustic-based fault detection of two commutator motors: the commutator motor of an [...] Read more.

Most faults can stop a motor, and time is lost in fixing the damaged motor. This is a reason why it is essential to develop fault-detection methods. This paper describes the acoustic-based fault detection of two commutator motors: the commutator motor of an electric impact drill and the commutator motor of a blender. Acoustic signals were recorded by a smartphone. Five states of the electric impact drill and three states of the blender were analysed: for the electric impact drill, these states were healthy, damaged gear train, faulty fan with five broken rotor blades, faulty fan with 10 broken rotor blades, and shifted brush (motor off); for the blender, these states were healthy, faulty fan with two broken rotor blades, and faulty fan with five broken rotor blades. A feature extraction method, MSAF-RATIO-27-MULTIEXPANDED-4-GROUPS (Method of Selection of Amplitudes of Frequency Ratio of 27% Multiexpanded 4 Groups), was developed and used for the computation of feature vectors. The nearest mean (NM) and support vector machine (SVM) classifiers were used for data classification. Analysis of the recognition of acoustic signals was carried out. The analysed value of TE_EID (the total efficiency of recognition of the electric impact drill) was equal to 96% for the NM classifier and 88.8% for SVM. The analysed value of TE_B (the total efficiency of recognition of the blender) was equal to 100% for the NM classifier and 94.11% for SVM. Full article

(This article belongs to the Special Issue Fault Diagnosis of Rotating Machine)

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14 pages, 2918 KiB

Open AccessEditor’s ChoiceArticle

Classification of Heart Sound Signal Using Multiple Features

by Yaseen, Gui-Young Son and Soonil Kwon

Appl. Sci. 2018, 8(12), 2344; https://doi.org/10.3390/app8122344 - 22 Nov 2018

Cited by 225 | Viewed by 17013

Abstract

Cardiac disorders are critical and must be diagnosed in the early stage using routine auscultation examination with high precision. Cardiac auscultation is a technique to analyze and listen to heart sound using electronic stethoscope, an electronic stethoscope is a device which provides the [...] Read more.

Cardiac disorders are critical and must be diagnosed in the early stage using routine auscultation examination with high precision. Cardiac auscultation is a technique to analyze and listen to heart sound using electronic stethoscope, an electronic stethoscope is a device which provides the digital recording of the heart sound called phonocardiogram (PCG). This PCG signal carries useful information about the functionality and status of the heart and hence several signal processing and machine learning technique can be applied to study and diagnose heart disorders. Based on PCG signal, the heart sound signal can be classified to two main categories i.e., normal and abnormal categories. We have created database of 5 categories of heart sound signal (PCG signals) from various sources which contains one normal and 4 are abnormal categories. This study proposes an improved, automatic classification algorithm for cardiac disorder by heart sound signal. We extract features from phonocardiogram signal and then process those features using machine learning techniques for classification. In features extraction, we have used Mel Frequency Cepstral Coefficient (MFCCs) and Discrete Wavelets Transform (DWT) features from the heart sound signal, and for learning and classification we have used support vector machine (SVM), deep neural network (DNN) and centroid displacement based k nearest neighbor. To improve the results and classification accuracy, we have combined MFCCs and DWT features for training and classification using SVM and DWT. From our experiments it has been clear that results can be greatly improved when Mel Frequency Cepstral Coefficient and Discrete Wavelets Transform features are fused together and used for classification via support vector machine, deep neural network and k-neareast neighbor(KNN). The methodology discussed in this paper can be used to diagnose heart disorders in patients up to 97% accuracy. The code and dataset can be accessed at “https://github.com/yaseen21khan/Classification-of-Heart-Sound-Signal-Using-Multiple-Features-/blob/master/README.md”. Full article

(This article belongs to the Special Issue Deep Learning and Big Data in Healthcare)

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14 pages, 7505 KiB

Open AccessEditor’s ChoiceArticle

Feasibility Study of Steel Bar Corrosion Monitoring Using a Piezoceramic Transducer Enabled Time Reversal Method

by Linsheng Huo, Chuanbo Li, Tianyong Jiang and Hong-Nan Li

Appl. Sci. 2018, 8(11), 2304; https://doi.org/10.3390/app8112304 - 19 Nov 2018

Cited by 68 | Viewed by 4325

Abstract

Steel bars, which are commonly used as reinforcements in concrete structures, are slender rods and are good conduits for stress wave propagation. In this paper, a lead zirconate titanate (PZT)-based steel bar corrosion monitoring approach was proposed. Two PZT transducers are surface-bonded on [...] Read more.

Steel bars, which are commonly used as reinforcements in concrete structures, are slender rods and are good conduits for stress wave propagation. In this paper, a lead zirconate titanate (PZT)-based steel bar corrosion monitoring approach was proposed. Two PZT transducers are surface-bonded on the two ends of a steel rod, respectively. One works as actuator to generate stress waves, and the other functions as a sensor to detect the propagated stress waves. Time reverse technology was applied in this research to monitor the corrosion of the steel bars with a high signal to noise ratio (SNR). Accelerated corrosion experiments of steel bars were conducted. The anti-corrosion performance of the protected piezoceramic transducers was tested first, and then they were used to monitor the corrosion of the steel bar using the time reversal method. The degree of corrosion in the steel bar was determined by the ratio of mass loss during the experiment. The experimental results show that the peak values of the signal that were obtained by time reversal operation are linearly related to the degree of corrosion of the steel bar, which demonstrates the feasibility of the proposed approach for monitoring the corrosion of steel bars using the time reversal method enabled by piezoceramic transducers. Full article

(This article belongs to the Special Issue Structural Damage Detection and Health Monitoring)

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20 pages, 5345 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Cu-Doped TiO₂: Visible Light Assisted Photocatalytic Antimicrobial Activity

by Snehamol Mathew, Priyanka Ganguly, Stephen Rhatigan, Vignesh Kumaravel, Ciara Byrne, Steven J. Hinder, John Bartlett, Michael Nolan and Suresh C. Pillai

Appl. Sci. 2018, 8(11), 2067; https://doi.org/10.3390/app8112067 - 26 Oct 2018

Cited by 189 | Viewed by 14668

Abstract

Surface contamination by microbes is a major public health concern. A damp environment is one of potential sources for microbe proliferation. Smart photocatalytic coatings on building surfaces using semiconductors like titania (TiO₂) can effectively curb this growing threat. Metal-doped titania in [...] Read more.

Surface contamination by microbes is a major public health concern. A damp environment is one of potential sources for microbe proliferation. Smart photocatalytic coatings on building surfaces using semiconductors like titania (TiO₂) can effectively curb this growing threat. Metal-doped titania in anatase phase has been proven as a promising candidate for energy and environmental applications. In this present work, the antimicrobial efficacy of copper (Cu)-doped TiO₂ (Cu-TiO₂) was evaluated against Escherichia coli (Gram-negative) and Staphylococcus aureus (Gram-positive) under visible light irradiation. Doping of a minute fraction of Cu (0.5 mol %) in TiO₂ was carried out via sol-gel technique. Cu-TiO₂ further calcined at various temperatures (in the range of 500–700 °C) to evaluate the thermal stability of TiO₂ anatase phase. The physico-chemical properties of the samples were characterized through X-ray diffraction (XRD), Raman spectroscopy, X-ray photo-electron spectroscopy (XPS) and UV–visible spectroscopy techniques. XRD results revealed that the anatase phase of TiO₂ was maintained well, up to 650 °C, by the Cu dopant. UV–vis results suggested that the visible light absorption property of Cu-TiO₂ was enhanced and the band gap is reduced to 2.8 eV. Density functional theory (DFT) studies emphasize the introduction of Cu⁺ and Cu²⁺ ions by replacing Ti⁴⁺ ions in the TiO₂ lattice, creating oxygen vacancies. These further promoted the photocatalytic efficiency. A significantly high bacterial inactivation (99.9999%) was attained in 30 min of visible light irradiation by Cu-TiO₂. Full article

(This article belongs to the Special Issue Cu and Cu-Based Nanoparticles: Applications in Catalysis)

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17 pages, 8699 KiB

Open AccessEditor’s ChoiceArticle

Visualization and Interpretation of Convolutional Neural Network Predictions in Detecting Pneumonia in Pediatric Chest Radiographs

by Sivaramakrishnan Rajaraman, Sema Candemir, Incheol Kim, George Thoma and Sameer Antani

Appl. Sci. 2018, 8(10), 1715; https://doi.org/10.3390/app8101715 - 20 Sep 2018

Cited by 217 | Viewed by 18029

Abstract

Pneumonia affects 7% of the global population, resulting in 2 million pediatric deaths every year. Chest X-ray (CXR) analysis is routinely performed to diagnose the disease. Computer-aided diagnostic (CADx) tools aim to supplement decision-making. These tools process the handcrafted and/or convolutional neural network [...] Read more.

Pneumonia affects 7% of the global population, resulting in 2 million pediatric deaths every year. Chest X-ray (CXR) analysis is routinely performed to diagnose the disease. Computer-aided diagnostic (CADx) tools aim to supplement decision-making. These tools process the handcrafted and/or convolutional neural network (CNN) extracted image features for visual recognition. However, CNNs are perceived as black boxes since their performance lack explanations. This is a serious bottleneck in applications involving medical screening/diagnosis since poorly interpreted model behavior could adversely affect the clinical decision. In this study, we evaluate, visualize, and explain the performance of customized CNNs to detect pneumonia and further differentiate between bacterial and viral types in pediatric CXRs. We present a novel visualization strategy to localize the region of interest (ROI) that is considered relevant for model predictions across all the inputs that belong to an expected class. We statistically validate the models’ performance toward the underlying tasks. We observe that the customized VGG16 model achieves 96.2% and 93.6% accuracy in detecting the disease and distinguishing between bacterial and viral pneumonia respectively. The model outperforms the state-of-the-art in all performance metrics and demonstrates reduced bias and improved generalization. Full article

(This article belongs to the Special Issue Advanced Intelligent Imaging Technology)

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29 pages, 1513 KiB

Open AccessFeature PaperEditor’s ChoiceReview

A Review of the Synthesis and Applications of Polymer–Nanoclay Composites

by Feng Guo, Saman Aryana, Yinghui Han and Yunpeng Jiao

Appl. Sci. 2018, 8(9), 1696; https://doi.org/10.3390/app8091696 - 19 Sep 2018

Cited by 270 | Viewed by 18480

Abstract

Recent advancements in material technologies have promoted the development of various preparation strategies and applications of novel polymer–nanoclay composites. Innovative synthesis pathways have resulted in novel polymer–nanoclay composites with improved properties, which have been successfully incorporated in diverse fields such as aerospace, automobile, [...] Read more.

Recent advancements in material technologies have promoted the development of various preparation strategies and applications of novel polymer–nanoclay composites. Innovative synthesis pathways have resulted in novel polymer–nanoclay composites with improved properties, which have been successfully incorporated in diverse fields such as aerospace, automobile, construction, petroleum, biomedical and wastewater treatment. These composites are recognized as promising advanced materials due to their superior properties, such as enhanced density, strength, relatively large surface areas, high elastic modulus, flame retardancy, and thermomechanical/optoelectronic/magnetic properties. The primary focus of this review is to deliver an up-to-date overview of polymer–nanoclay composites along with their synthesis routes and applications. The discussion highlights potential future directions for this emerging field of research. Full article

(This article belongs to the Special Issue Nanoclays for Technological Applications)

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19 pages, 743 KiB

Open AccessEditor’s ChoiceArticle

Enabling Technologies for Operator 4.0: A Survey

by Tamás Ruppert, Szilárd Jaskó, Tibor Holczinger and János Abonyi

Appl. Sci. 2018, 8(9), 1650; https://doi.org/10.3390/app8091650 - 13 Sep 2018

Cited by 167 | Viewed by 12383

Abstract

The fast development of smart sensors and wearable devices has provided the opportunity to develop intelligent operator workspaces. The resultant Human-Cyber-Physical Systems (H-CPS) integrate the operators into flexible and multi-purpose manufacturing processes. The primary enabling factor of the resultant Operator 4.0 paradigm is [...] Read more.

The fast development of smart sensors and wearable devices has provided the opportunity to develop intelligent operator workspaces. The resultant Human-Cyber-Physical Systems (H-CPS) integrate the operators into flexible and multi-purpose manufacturing processes. The primary enabling factor of the resultant Operator 4.0 paradigm is the integration of advanced sensor and actuator technologies and communications solutions. This work provides an extensive overview of these technologies and highlights that the design of future workplaces should be based on the concept of intelligent space. Full article

(This article belongs to the Special Issue Advanced Internet of Things for Smart Infrastructure System)

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17 pages, 13919 KiB

Open AccessEditor’s ChoiceReview

Mini-LED and Micro-LED: Promising Candidates for the Next Generation Display Technology

by Tingzhu Wu, Chin-Wei Sher, Yue Lin, Chun-Fu Lee, Shijie Liang, Yijun Lu, Sung-Wen Huang Chen, Weijie Guo, Hao-Chung Kuo and Zhong Chen

Appl. Sci. 2018, 8(9), 1557; https://doi.org/10.3390/app8091557 - 5 Sep 2018

Cited by 565 | Viewed by 46167

Abstract

Displays based on inorganic light-emitting diodes (LED) are considered as the most promising one among the display technologies for the next-generation. The chip for LED display bears similar features to those currently in use for general lighting, but it size is shrunk to [...] Read more.

Displays based on inorganic light-emitting diodes (LED) are considered as the most promising one among the display technologies for the next-generation. The chip for LED display bears similar features to those currently in use for general lighting, but it size is shrunk to below 200 microns. Thus, the advantages of high efficiency and long life span of conventional LED chips are inherited by miniaturized ones. As the size gets smaller, the resolution enhances, but at the expense of elevating the complexity of fabrication. In this review, we introduce two sorts of inorganic LED displays, namely relatively large and small varieties. The mini-LEDs with chip sizes ranging from 100 to 200 μm have already been commercialized for backlight sources in consumer electronics applications. The realized local diming can greatly improve the contrast ratio at relatively low energy consumptions. The micro-LEDs with chip size less than 100 μm, still remain in the laboratory. The full-color solution, one of the key technologies along with its three main components, red, green, and blue chips, as well color conversion, and optical lens synthesis, are introduced in detail. Moreover, this review provides an account for contemporary technologies as well as a clear view of inorganic and miniaturized LED displays for the display community. Full article

(This article belongs to the Special Issue Group III-V Nitride Semiconductor Microcavities and Microemitters)

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31 pages, 970 KiB

Open AccessEditor’s ChoiceReview

Swarm Intelligence Algorithms for Feature Selection: A Review

by Lucija Brezočnik, Iztok Fister and Vili Podgorelec

Appl. Sci. 2018, 8(9), 1521; https://doi.org/10.3390/app8091521 - 1 Sep 2018

Cited by 254 | Viewed by 17562

Abstract

The increasingly rapid creation, sharing and exchange of information nowadays put researchers and data scientists ahead of a challenging task of data analysis and extracting relevant information out of data. To be able to learn from data, the dimensionality of the data should [...] Read more.

The increasingly rapid creation, sharing and exchange of information nowadays put researchers and data scientists ahead of a challenging task of data analysis and extracting relevant information out of data. To be able to learn from data, the dimensionality of the data should be reduced first. Feature selection (FS) can help to reduce the amount of data, but it is a very complex and computationally demanding task, especially in the case of high-dimensional datasets. Swarm intelligence (SI) has been proved as a technique which can solve NP-hard (Non-deterministic Polynomial time) computational problems. It is gaining popularity in solving different optimization problems and has been used successfully for FS in some applications. With the lack of comprehensive surveys in this field, it was our objective to fill the gap in coverage of SI algorithms for FS. We performed a comprehensive literature review of SI algorithms and provide a detailed overview of 64 different SI algorithms for FS, organized into eight major taxonomic categories. We propose a unified SI framework and use it to explain different approaches to FS. Different methods, techniques, and their settings are explained, which have been used for various FS aspects. The datasets used most frequently for the evaluation of SI algorithms for FS are presented, as well as the most common application areas. The guidelines on how to develop SI approaches for FS are provided to support researchers and analysts in their data mining tasks and endeavors while existing issues and open questions are being discussed. In this manner, using the proposed framework and the provided explanations, one should be able to design an SI approach to be used for a specific FS problem. Full article

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35 pages, 7341 KiB

Open AccessEditor’s ChoiceReview

Graphene Nanoplatelets-Based Advanced Materials and Recent Progress in Sustainable Applications

by Pietro Cataldi, Athanassia Athanassiou and Ilker S. Bayer

Appl. Sci. 2018, 8(9), 1438; https://doi.org/10.3390/app8091438 - 23 Aug 2018

Cited by 219 | Viewed by 19040

Abstract

Graphene is the first 2D crystal ever isolated by mankind. It consists of a single graphite layer, and its exceptional properties are revolutionizing material science. However, there is still a lack of convenient mass-production methods to obtain defect-free monolayer graphene. In contrast, graphene [...] Read more.

Graphene is the first 2D crystal ever isolated by mankind. It consists of a single graphite layer, and its exceptional properties are revolutionizing material science. However, there is still a lack of convenient mass-production methods to obtain defect-free monolayer graphene. In contrast, graphene nanoplatelets, hybrids between graphene and graphite, are already industrially available. Such nanomaterials are attractive, considering their planar structure, light weight, high aspect ratio, electrical conductivity, low cost, and mechanical toughness. These diverse features enable applications ranging from energy harvesting and electronic skin to reinforced plastic materials. This review presents progress in composite materials with graphene nanoplatelets applied, among others, in the field of flexible electronics and motion and structural sensing. Particular emphasis is given to applications such as antennas, flexible electrodes for energy devices, and strain sensors. A separate discussion is included on advanced biodegradable materials reinforced with graphene nanoplatelets. A discussion of the necessary steps for the further spread of graphene nanoplatelets is provided for each revised field. Full article

(This article belongs to the Special Issue Graphene Nanoplatelets)

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29 pages, 6619 KiB

Open AccessEditor’s ChoiceArticle

Wavelet Decomposition and Convolutional LSTM Networks Based Improved Deep Learning Model for Solar Irradiance Forecasting

by Fei Wang, Yili Yu, Zhanyao Zhang, Jie Li, Zhao Zhen and Kangping Li

Appl. Sci. 2018, 8(8), 1286; https://doi.org/10.3390/app8081286 - 1 Aug 2018

Cited by 143 | Viewed by 10480

Abstract

Solar photovoltaic (PV) power forecasting has become an important issue with regard to the power grid in terms of the effective integration of large-scale PV plants. As the main influence factor of PV power generation, solar irradiance and its accurate forecasting are the [...] Read more.

Solar photovoltaic (PV) power forecasting has become an important issue with regard to the power grid in terms of the effective integration of large-scale PV plants. As the main influence factor of PV power generation, solar irradiance and its accurate forecasting are the prerequisite for solar PV power forecasting. However, previous forecasting approaches using manual feature extraction (MFE), traditional modeling and single deep learning (DL) models could not satisfy the performance requirements in partial scenarios with complex fluctuations. Therefore, an improved DL model based on wavelet decomposition (WD), the Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) is proposed for day-ahead solar irradiance forecasting. Given the high dependency of solar irradiance on weather status, the proposed model is individually established under four general weather type (i.e., sunny, cloudy, rainy and heavy rainy). For certain weather types, the raw solar irradiance sequence is decomposed into several subsequences via discrete wavelet transformation. Then each subsequence is fed into the CNN based local feature extractor to automatically learn the abstract feature representation from the raw subsequence data. Since the extracted features of each subsequence are also time series data, they are individually transported to LSTM to construct the subsequence forecasting model. In the end, the final solar irradiance forecasting results under certain weather types are obtained via the wavelet reconstruction of these forecasted subsequences. This case study further verifies the enhanced forecasting accuracy of our proposed method via a comparison with traditional and single DL models. Full article

(This article belongs to the Special Issue Solar Power System Planning & Design: Resource Assessment, Site Evaluation, System Design, Production Forecasting and Feasibility Studies)

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36 pages, 9540 KiB

Open AccessEditor’s ChoiceReview

A Review of AlGaN-Based Deep-Ultraviolet Light-Emitting Diodes on Sapphire

by Yosuke Nagasawa and Akira Hirano

Appl. Sci. 2018, 8(8), 1264; https://doi.org/10.3390/app8081264 - 31 Jul 2018

Cited by 171 | Viewed by 13037

Abstract

This paper reviews the progress of AlGaN-based deep-ultraviolet (DUV) light emitting diodes (LEDs), mainly focusing in the work of the authors’ group. The background to the development of the current device structure on sapphire is described and the reason for using a (0001) [...] Read more.

This paper reviews the progress of AlGaN-based deep-ultraviolet (DUV) light emitting diodes (LEDs), mainly focusing in the work of the authors’ group. The background to the development of the current device structure on sapphire is described and the reason for using a (0001) sapphire with a miscut angle of 1.0° relative to the m-axis is clarified. Our LEDs incorporate uneven quantum wells (QWs) grown on an AlN template with dense macrosteps. Due to the low threading dislocation density of AlGaN and AlN templates of about 5 × 10⁸/cm², the number of nonradiative recombination centers is decreased. In addition, the uneven QW show high external quantum efficiency (EQE) and wall-plug efficiency, which are considered to be boosted by the increased internal quantum efficiency (IQE) by enhancing carrier localization adjacent to macrosteps. The achieved LED performance is considered to be sufficient for practical applications. The advantage of the uneven QW is discussed in terms of the EQE and IQE. A DUV-LED die with an output of over 100 mW at 280–300 nm is considered feasible by applying techniques including the encapsulation. In addition, the fundamental achievements of various groups are reviewed for the future improvements of AlGaN-based DUV-LEDs. Finally, the applications of DUV-LEDs are described from an industrial viewpoint. The demonstrations of W/cm²-class irradiation modules are shown for UV curing. Full article

(This article belongs to the Special Issue Highly Efficient UV and Visible Light Sources)

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21 pages, 3290 KiB

Open AccessEditor’s ChoiceReview

Research Progress of Gas Sensor Based on Graphene and Its Derivatives: A Review

by Wenchao Tian, Xiaohan Liu and Wenbo Yu

Appl. Sci. 2018, 8(7), 1118; https://doi.org/10.3390/app8071118 - 11 Jul 2018

Cited by 176 | Viewed by 10894

Abstract

Gas sensors are devices that convert a gas volume fraction into electrical signals, and they are widely used in many fields such as environmental monitoring. Graphene is a new type of two-dimensional crystal material that has many excellent properties including large specific surface [...] Read more.

Gas sensors are devices that convert a gas volume fraction into electrical signals, and they are widely used in many fields such as environmental monitoring. Graphene is a new type of two-dimensional crystal material that has many excellent properties including large specific surface area, high conductivity, and high Young’s modulus. These features make it ideally suitable for application for gas sensors. In this paper, the main characteristics of gas sensor are firstly introduced, followed by the preparation methods and properties of graphene. In addition, the development process and the state of graphene gas sensors are introduced emphatically in terms of structure and performance of the sensor. The emergence of new candidates including graphene, polymer and metal/metal oxide composite enhances the performance of gas detection significantly. Finally, the clear direction of graphene gas sensors for the future is provided according to the latest research results and trends. It provides direction and ideas for future research. Full article

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32 pages, 3614 KiB

Open AccessFeature PaperEditor’s ChoiceReview

Bimetallic Nanoparticles: Enhanced Magnetic and Optical Properties for Emerging Biological Applications

by Pannaree Srinoi, Yi-Ting Chen, Varadee Vittur, Maria D. Marquez and T. Randall Lee

Appl. Sci. 2018, 8(7), 1106; https://doi.org/10.3390/app8071106 - 9 Jul 2018

Cited by 206 | Viewed by 13861

Abstract

Metal nanoparticles are extensively studied due to their unique chemical and physical properties, which differ from the properties of their respective bulk materials. Likewise, the properties of heterogeneous bimetallic structures are far more attractive than those of single-component nanoparticles. For example, the incorporation [...] Read more.

Metal nanoparticles are extensively studied due to their unique chemical and physical properties, which differ from the properties of their respective bulk materials. Likewise, the properties of heterogeneous bimetallic structures are far more attractive than those of single-component nanoparticles. For example, the incorporation of a second metal into a nanoparticle structure influences and can potentially enhance the optical/plasmonic and magnetic properties of the material. This review focuses on the enhanced optical/plasmonic and magnetic properties offered by bimetallic nanoparticles and their corresponding impact on biological applications. In this review, we summarize the predominant structures of bimetallic nanoparticles, outline their synthesis methods, and highlight their use in biological applications, both diagnostic and therapeutic, which are dictated by their various optical/plasmonic and magnetic properties. Full article

(This article belongs to the Special Issue Biological Applications of Magnetic Nanoparticles)

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17 pages, 7005 KiB

Open AccessEditor’s ChoiceArticle

A Novel Image Feature for the Remaining Useful Lifetime Prediction of Bearings Based on Continuous Wavelet Transform and Convolutional Neural Network

by Youngji Yoo and Jun-Geol Baek

Appl. Sci. 2018, 8(7), 1102; https://doi.org/10.3390/app8071102 - 8 Jul 2018

Cited by 141 | Viewed by 10640

Abstract

In data-driven methods for prognostics, the remaining useful lifetime (RUL) is predicted based on the health indicator (HI). The HI detects the condition of equipment or components by monitoring sensor data such as vibration signals. To construct the HI, multiple features are extracted [...] Read more.

In data-driven methods for prognostics, the remaining useful lifetime (RUL) is predicted based on the health indicator (HI). The HI detects the condition of equipment or components by monitoring sensor data such as vibration signals. To construct the HI, multiple features are extracted from signals using time domain, frequency domain, and time–frequency domain analyses, and which are then fused. However, the process of selecting and fusing features for the HI is very complex and labor-intensive. We propose a novel time–frequency image feature to construct HI and predict the RUL. To convert the one-dimensional vibration signals to a two-dimensional (2-D) image, the continuous wavelet transform (CWT) extracts the time–frequency image features, i.e., the wavelet power spectrum. Then, the obtained image features are fed into a 2-D convolutional neural network (CNN) to construct the HI. The estimated HI from the proposed model is used for the RUL prediction. The accuracy of the RUL prediction is improved by using the image features. The proposed method compresses the complex process including feature extraction, selection, and fusion into a single algorithm by adopting a deep learning approach. The proposed method is validated using a bearing dataset provided by PRONOSTIA. The results demonstrate that the proposed method is superior to related studies using the same dataset. Full article

(This article belongs to the Special Issue Fault Detection and Diagnosis in Mechatronics Systems)

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33 pages, 3386 KiB

Open AccessEditor’s ChoiceReview

Review of Auxetic Materials for Sports Applications: Expanding Options in Comfort and Protection

by Olly Duncan, Todd Shepherd, Charlotte Moroney, Leon Foster, Praburaj D. Venkatraman, Keith Winwood, Tom Allen and Andrew Alderson

Appl. Sci. 2018, 8(6), 941; https://doi.org/10.3390/app8060941 - 6 Jun 2018

Cited by 215 | Viewed by 22238

Abstract

Following high profile, life changing long term mental illnesses and fatalities in sports such as skiing, cricket and American football—sports injuries feature regularly in national and international news. A mismatch between equipment certification tests, user expectations and infield falls and collisions is thought [...] Read more.

Following high profile, life changing long term mental illnesses and fatalities in sports such as skiing, cricket and American football—sports injuries feature regularly in national and international news. A mismatch between equipment certification tests, user expectations and infield falls and collisions is thought to affect risk perception, increasing the prevalence and severity of injuries. Auxetic foams, structures and textiles have been suggested for application to sporting goods, particularly protective equipment, due to their unique form-fitting deformation and curvature, high energy absorption and high indentation resistance. The purpose of this critical review is to communicate how auxetics could be useful to sports equipment (with a focus on injury prevention), and clearly lay out the steps required to realise their expected benefits. Initial overviews of auxetic materials and sporting protective equipment are followed by a description of common auxetic materials and structures, and how to produce them in foams, textiles and Additively Manufactured structures. Beneficial characteristics, limitations and commercial prospects are discussed, leading to a consideration of possible further work required to realise potential uses (such as in personal protective equipment and highly conformable garments). Full article

(This article belongs to the Special Issue Sports Materials)

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29 pages, 2531 KiB

Open AccessEditor’s ChoiceReview

A State-of-the-Art Review of Nanoparticles Application in Petroleum with a Focus on Enhanced Oil Recovery

by Madhan Nur Agista, Kun Guo and Zhixin Yu

Appl. Sci. 2018, 8(6), 871; https://doi.org/10.3390/app8060871 - 25 May 2018

Cited by 212 | Viewed by 15262

Abstract

Research on nanotechnology application in the oil and gas industry has been growing rapidly in the past decade, as evidenced by the number of scientific articles published in the field. With oil and gas reserves harder to find, access, and produce, the pursuit [...] Read more.

Research on nanotechnology application in the oil and gas industry has been growing rapidly in the past decade, as evidenced by the number of scientific articles published in the field. With oil and gas reserves harder to find, access, and produce, the pursuit of more game-changing technologies that can address the challenges of the industry has stimulated this growth. Nanotechnology has the potential to revolutionize the petroleum industry both upstream and downstream, including exploration, drilling, production, and enhanced oil recovery (EOR), as well as refinery processes. It provides a wide range of alternatives for technologies and materials to be utilized in the petroleum industry. Nanoscale materials in various forms such as solid composites, complex fluids, and functional nanoparticle-fluid combinations are key to the new technological advances. This paper aims to provide a state-of-the-art review on the application of nanoparticles and technology in the petroleum industry, and focuses on enhanced oil recovery. We briefly summarize nanotechnology application in exploration and reservoir characterization, drilling and completion, production and stimulation, and refinery. Thereafter, this paper focuses on the application of nanoparticles in EOR. The different types of nanomaterials, e.g., silica, aluminum oxides, iron oxide, nickel oxide, titanium oxide, zinc oxide, zirconium oxide, polymers, and carbon nanotubes that have been studied in EOR are discussed with respect to their properties, their performance, advantages, and disadvantages. We then elaborate upon the parameters that will affect the performance of nanoparticles in EOR, and guidelines for promising recovery factors are emphasized. The mechanisms of the nanoparticles in the EOR processes are then underlined, such as wettability alteration, interfacial tension reduction, disjoining pressure, and viscosity control. The objective of this review is to present a wide range of knowledge and expertise related to the nanotechnology application in the petroleum industry in general, and the EOR process in particular. The challenges and future research directions for nano-EOR are pinpointed. Full article

(This article belongs to the Special Issue Nanotech for Oil and Gas)

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11 pages, 1346 KiB

Open AccessEditor’s ChoiceLetter

Small Object Detection in Optical Remote Sensing Images via Modified Faster R-CNN

by Yun Ren, Changren Zhu and Shunping Xiao

Appl. Sci. 2018, 8(5), 813; https://doi.org/10.3390/app8050813 - 18 May 2018

Cited by 220 | Viewed by 13753

Abstract

The PASCAL VOC Challenge performance has been significantly boosted by the prevalently CNN-based pipelines like Faster R-CNN. However, directly applying the Faster R-CNN to the small remote sensing objects usually renders poor performance. To address this issue, this paper investigates on how to [...] Read more.

The PASCAL VOC Challenge performance has been significantly boosted by the prevalently CNN-based pipelines like Faster R-CNN. However, directly applying the Faster R-CNN to the small remote sensing objects usually renders poor performance. To address this issue, this paper investigates on how to modify Faster R-CNN for the task of small object detection in optical remote sensing images. First of all, we not only modify the RPN stage of Faster R-CNN by setting appropriate anchors but also leverage a single high-level feature map of a fine resolution by designing a similar architecture adopting top-down and skip connections. In addition, we incorporate context information to further boost small remote sensing object detection performance while we apply a simple sampling strategy to solve the issue about the imbalanced numbers of images between different classes. At last, we introduce a simple yet effective data augmentation method named ‘random rotation’ during training. Experimental results show that our modified Faster R-CNN algorithm improves the mean average precision by a large margin on detecting small remote sensing objects. Full article

(This article belongs to the Special Issue Complex Networks and Machine Learning: From Molecular to Social Sciences)

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17 pages, 6453 KiB

Open AccessFeature PaperEditor’s ChoiceReview

Overview of Lithium-Ion Battery Modeling Methods for State-of-Charge Estimation in Electrical Vehicles

by Jinhao Meng, Guangzhao Luo, Mattia Ricco, Maciej Swierczynski, Daniel-Ioan Stroe and Remus Teodorescu

Appl. Sci. 2018, 8(5), 659; https://doi.org/10.3390/app8050659 - 25 Apr 2018

Cited by 236 | Viewed by 18835

Abstract

As a critical indictor in the Battery Management System (BMS), State of Charge (SOC) is closely related to the reliable and safe operation of lithium-ion (Li-ion) batteries. Model-based methods are an effective solution for accurate and robust SOC estimation, the performance of which [...] Read more.

As a critical indictor in the Battery Management System (BMS), State of Charge (SOC) is closely related to the reliable and safe operation of lithium-ion (Li-ion) batteries. Model-based methods are an effective solution for accurate and robust SOC estimation, the performance of which heavily relies on the battery model. This paper mainly focuses on battery modeling methods, which have the potential to be used in a model-based SOC estimation structure. Battery modeling methods are classified into four categories on the basis of their theoretical foundations, and their expressions and features are detailed. Furthermore, the four battery modeling methods are compared in terms of their pros and cons. Future research directions are also presented. In addition, after optimizing the parameters of the battery models by a Genetic Algorithm (GA), four typical battery models including a combined model, two RC Equivalent Circuit Model (ECM), a Single Particle Model (SPM), and a Support Vector Machine (SVM) battery model are compared in terms of their accuracy and execution time. Full article

(This article belongs to the Special Issue Battery Management and State Estimation)

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15 pages, 2546 KiB

Open AccessEditor’s ChoiceReview

Polydimethylsiloxane (PDMS)-Based Flexible Resistive Strain Sensors for Wearable Applications

by Jing Chen, Jiahong Zheng, Qinwu Gao, Jinjie Zhang, Jinyong Zhang, Olatunji Mumini Omisore, Lei Wang and Hui Li

Appl. Sci. 2018, 8(3), 345; https://doi.org/10.3390/app8030345 - 28 Feb 2018

Cited by 203 | Viewed by 20117

Abstract

There is growing attention and rapid development on flexible electronic devices with electronic materials and sensing technology innovations. In particular, strain sensors with high elasticity and stretchability are needed for several potential applications including human entertainment technology, human–machine interface, personal healthcare, and sports [...] Read more.

There is growing attention and rapid development on flexible electronic devices with electronic materials and sensing technology innovations. In particular, strain sensors with high elasticity and stretchability are needed for several potential applications including human entertainment technology, human–machine interface, personal healthcare, and sports performance monitoring, etc. This article presents recent advancements in the development of polydimethylsiloxane (PDMS)-based flexible resistive strain sensors for wearable applications. First of all, the article shows that PDMS-based stretchable resistive strain sensors are successfully fabricated by different methods, such as the filtration method, printing technology, micromolding method, coating techniques, and liquid phase mixing. Next, strain sensing performances including stretchability, gauge factor, linearity, and durability are comprehensively demonstrated and compared. Finally, potential applications of PDMS-based flexible resistive strain sensors are also discussed. This review indicates that the era of wearable intelligent electronic systems has arrived. Full article

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16 pages, 2249 KiB

Open AccessFeature PaperEditor’s ChoiceReview

Inkjet-Printed and Paper-Based Electrochemical Sensors

by Ryan P. Tortorich, Hamed Shamkhalichenar and Jin-Woo Choi

Appl. Sci. 2018, 8(2), 288; https://doi.org/10.3390/app8020288 - 14 Feb 2018

Cited by 102 | Viewed by 12889

Abstract

It is becoming increasingly more important to provide a low-cost point-of-care diagnostic device with the ability to detect and monitor various biological and chemical compounds. Traditional laboratories can be time-consuming and very costly. Through the combination of well-established materials and fabrication methods, it [...] Read more.

It is becoming increasingly more important to provide a low-cost point-of-care diagnostic device with the ability to detect and monitor various biological and chemical compounds. Traditional laboratories can be time-consuming and very costly. Through the combination of well-established materials and fabrication methods, it is possible to produce devices that meet the needs of many patients, healthcare and medical professionals, and environmental specialists. Existing research has demonstrated that inkjet-printed and paper-based electrochemical sensors are suitable for this application due to advantages provided by the carefully selected materials and fabrication method. Inkjet printing provides a low cost fabrication method with incredible control over the material deposition process, while paper-based substrates enable pump-free microfluidic devices due to their natural wicking ability. Furthermore, electrochemical sensing is incredibly selective and provides accurate and repeatable quantitative results without expensive measurement equipment. By merging each of these favorable techniques and materials and continuing to innovate, the production of low-cost point-of-care sensors is certainly within reach. Full article

(This article belongs to the Special Issue Printed Electronics 2017)

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12 pages, 1727 KiB

Open AccessEditor’s ChoiceArticle

Variety Identification of Single Rice Seed Using Hyperspectral Imaging Combined with Convolutional Neural Network

by Zhengjun Qiu, Jian Chen, Yiying Zhao, Susu Zhu, Yong He and Chu Zhang

Appl. Sci. 2018, 8(2), 212; https://doi.org/10.3390/app8020212 - 31 Jan 2018

Cited by 213 | Viewed by 9759

Abstract

The feasibility of using hyperspectral imaging with convolutional neural network (CNN) to identify rice seed varieties was studied. Hyperspectral images of 4 rice seed varieties at two different spectral ranges (380–1030 nm and 874–1734 nm) were acquired. The spectral data at the ranges [...] Read more.

The feasibility of using hyperspectral imaging with convolutional neural network (CNN) to identify rice seed varieties was studied. Hyperspectral images of 4 rice seed varieties at two different spectral ranges (380–1030 nm and 874–1734 nm) were acquired. The spectral data at the ranges of 441–948 nm (Spectral range 1) and 975–1646 nm (Spectral range 2) were extracted. K nearest neighbors (KNN), support vector machine (SVM) and CNN models were built using different number of training samples (100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1500, 2000, 2500 and 3000). KNN, SVM and CNN models in the Spectral range 2 performed slightly better than those in the Spectral range 1. The model performances improved with the increase in the number of training samples. The improvements were not significant when the number of training samples was large. CNN model performed better than the corresponding KNN and SVM models in most cases, which indicated the effectiveness of using CNN to analyze spectral data. The results of this study showed that CNN could be adopted in spectral data analysis with promising results. More varieties of rice need to be studied in future research to extend the use of CNNs in spectral data analysis. Full article

(This article belongs to the Special Issue Hyperspectral Chemical Imaging for Food Authentication)

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15 pages, 3070 KiB

Open AccessEditor’s ChoiceArticle

Energy Management Strategy for a Hybrid Electric Vehicle Based on Deep Reinforcement Learning

by Yue Hu, Weimin Li, Kun Xu, Taimoor Zahid, Feiyan Qin and Chenming Li

Appl. Sci. 2018, 8(2), 187; https://doi.org/10.3390/app8020187 - 26 Jan 2018

Cited by 193 | Viewed by 11264

Abstract

An energy management strategy (EMS) is important for hybrid electric vehicles (HEVs) since it plays a decisive role on the performance of the vehicle. However, the variation of future driving conditions deeply influences the effectiveness of the EMS. Most existing EMS methods simply [...] Read more.

An energy management strategy (EMS) is important for hybrid electric vehicles (HEVs) since it plays a decisive role on the performance of the vehicle. However, the variation of future driving conditions deeply influences the effectiveness of the EMS. Most existing EMS methods simply follow predefined rules that are not adaptive to different driving conditions online. Therefore, it is useful that the EMS can learn from the environment or driving cycle. In this paper, a deep reinforcement learning (DRL)-based EMS is designed such that it can learn to select actions directly from the states without any prediction or predefined rules. Furthermore, a DRL-based online learning architecture is presented. It is significant for applying the DRL algorithm in HEV energy management under different driving conditions. Simulation experiments have been conducted using MATLAB and Advanced Vehicle Simulator (ADVISOR) co-simulation. Experimental results validate the effectiveness of the DRL-based EMS compared with the rule-based EMS in terms of fuel economy. The online learning architecture is also proved to be effective. The proposed method ensures the optimality, as well as real-time applicability, in HEVs. Full article

(This article belongs to the Section Energy Science and Technology)

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