bluuebunny/crossref_metadata_2025 huggingface dataset:
Behavior of Polymer Materials Exposed to Aging in the Swimming Pool: Focus on Properties That Assure Comfort and Durability
Authors: Salopek Čubrić Ivana , Čubrić Goran , Potočić Matković Vesna Marija
Published: July 2021

The degradation of polyamide (PA) and polyester (PES) polymers is under intense study due to growing concerns about the accumulation of plastics in soils and oceans. Previous studies confirm that ageing degrades PA and PES at the molecular level. However, researchers have not addressed the development of protocols for aging textile materials in swimming pools, and few data are available on the effects of aging on comfort and durability. This research addresses the development of the aging protocol for PA and PES swimwear materials, its implementation, and the evaluation of properties that assure comfort and durability after specific periods of exposure. The tests include microscopic analysis, tensile tests, determination of fluid transport phases and drying period. The results revealed changes in the surface of the material in terms of fibrillation (more pronounced after outdoor aging). There is a positive correlation between the exposure duration and the breaking force (R2 ranges from 0.85 to 0.98), with a stronger correlation for the PA materials. The decrease in breaking force due to aging is up to 40%, indicating significantly reduced durability. The change in breaking force follows the changes in mass (R2 = 0.867). In terms of comfort assurance, outdoor aging of materials should have a greater negative impact than indoor ageing.
The Influence of Urban Sprawl on Air Pollution and the Mediating Effect of Vehicle Ownership
Authors: Tao Aiping , Liang Qun , Kuai Peng , Ding Tao
Published: July 2021

Based on the panel data of 224 prefecture-level and above cities in China from 2003 to 2016, this paper empirically studies the impact of urban sprawl on air pollution and introduces a mediating effect model to test the mediating role of vehicle ownership concerning the impact of urban sprawl on air pollution. The research in this paper arrives at three conclusions. First, urban sprawl has a significant positive effect on air pollution, and this conclusion is still valid after solving the endogeneity problem and conducting a robustness test. Second, the results of mediating effect test show that urban sprawl indirectly affects air pollution through the partial mediating effect of vehicle ownership. By removing the mediating effect, urban sprawl has a significant negative impact on air pollution, indicating that the mediating effect of vehicle ownership is higher concerning the impact of urban sprawl on air pollution. Third, further panel quantile regression results show that the higher the level of air pollution, the weaker the mediating effect of vehicle ownership and the stronger the direct effect of urban sprawl on air pollution. These conclusions can provide some empirical support for solving the air pollution problems caused by urban sprawl in China.
Influence of Process Design on the Preparation of Solid Lipid Nanoparticles by an Ultrasonic-Nanoemulsification Method
Authors: Pucek-Kaczmarek Agata
Published: July 2021

In recent years, lipid-based nanosystems have emerged as a promising class of nanocarriers for encapsulating many active agents. Solid lipid nanoparticles (SLNs) provide good stability (colloidal as well as physical) and high biocompatibility. Appropriate design of the carrier structure through a selection of components and preparation methods allows us to obtain formulations with desired physicochemical parameters and biological properties. The present contribution has been carried out to investigate SLNs containing biocompatible phosphatidylcholine mixed with non-ionic surfactant Tween 60 as stabilizing agents. The internal lipid phase consisted of glyceryl monostearate was confirmed as safe for drug delivery by the Food and Drug Administration. The SLNs were fabricated by ultrasonic-nanoemulsification method. The preparation process was optimized in regard to variable parameters such as ultrasonication time and used amplitude and number of cycles. The sizes of the studied nanoparticles along with the size distribution were determined by dynamic light scattering (DLS), while shape and morphology were determined by atomic force microscopy (AFM) and transmission electron microscopy (TEM). The colloidal stability was measured by a turbidimetric method. The physical state of SLNs was characterized using differential scanning calorimetry (DSC). The obtained results indicate that the proposed SLNs may provide great potential for design and preparation of novel delivery nanosystems with a variety of possible applications.
Ecofriendly Simple UV Spectrophotometric and Chemometric Methods for Simultaneous Estimation of Paracetamol Aceclofenac and Eperisone Hydrochloride in Pharmaceutical Formulation: Assessment of Greenness Profile
Authors: Rathinam Seetharaman , Karunanidhi Santhana Lakshmi
Published: July 2021

This work introduces three eco-friendly UV spectrophotometric methods for the simultaneous estimation of Paracetamol, Aceclofenac and Eperisone Hydrochloride in pharmaceutical tablet formulation. The procedures employed were simultaneous equation method and multivariate chemometric methods with phosphate buffer pH 7.80 as diluent. The simultaneous equation method encompasses absorbance measurement at three different wavelengths (λmax of the drugs). It exhibits linearity between 12–18 µg mL−1 for paracetamol, 3.69–5.53 µg mL−1 for Aceclofenac, and 2.76–4.15 µg mL−1 Eperisone hydrochloride. The results obtained for accuracy and precision by the simultaneous equation method were within the permissible limits. Principal component regression and partial least squares were the tools used for chemometric methods. The calibration set and prediction set were constructed, and the UV spectra were recorded in zero order mode, further subjected to chemometric analysis. The % recoveries obtained for Paracetamol, Aceclofenac, and Eperisone Hydrochloride by chemometric techniques showed good accuracy, and the results obtained for analytical figures of merit were acceptable. Statistical comparison of the assay results obtained for the proposed methods showed no significant difference found among the methods using one way analysis of variance. Greenness evaluation tools revealed the greenness profile of the proposed methods and found them to be ecofriendly. The described methods were appropriate for routine quality control laboratories, facilitating eco-friendly, fast, and cost effective determination of Paracetamol, Aceclofenac, and Eperisone Hydrochloride in Acemyoset P tablets.
Thy Kingdom Come in BAM as It Is in Heaven: Implications on Defining the Kingdom of God in BAM Businesses
Authors: Ward Ben
Published: July 2021

Business as Mission (BAM) is a subcategory of Social Entrepreneurship as it seeks cultural innovation from a Christian perspective focusing specifically on economic uplift and religious direction. Most BAM authors describe the kingdom of God as the reign of God. In a theological review, I will show that defining kingdom simply as God’s rule is not a complete view of the kingdom. Rather, a more robust definition of the kingdom is preferred in biblical and theological studies that focuses on God’s people in God’s place under God’s rule. Therefore, the BAM community can adopt a refined definition that helps them move forward in the core vision of holistic ministry. This research provides a biblical and theological understanding for business practitioners to pursue a spiritual bottom line alongside local churches.
Working in Time: From Barbarism to Repetition
Authors: Bowen Amber
Published: July 2021

The task of ethicists, philosophers, and theologians to restore the dignity of human labor and vocation in a (post)industrial, techno-driven society is motivated by an often unacknowledged concern to restore the underlying spirituality of the human experience of work. Due to its ability to interrogate the range of givenness in human experience, phenomenology is a method particularly suited to explore this spiritual dimension. In this essay, I offer a phenomenological analysis that attends to the way our experience of time either suppresses or discloses the underlying spirituality of work. (Post)industrial societies reduce time to “clock time”, or an objective unit of measurement of production. Since increased production per unit of time is necessary for profit, we live and work in a society that is continually racing against the clock, and we find ourselves existentially pitted against it. I diagnose this reductionistic perspective of time, and its ensuing consequences, as a form of what Michel Henry calls “barbarism”. Setting aside the assumption of time as exclusively “clock time”, I then attend phenomenologically to other ways in which time gives itself to consciousness, namely, in cuisine, music, and craftsmanship. Finally, while Henry is helpful in analyzing the spiritual destitution of such an approach to time (and, consequently, to work), ultimately I turn to Kierkegaard’s account of temporality, specifically as articulated in the philosophical category of repetition, to disclose time as constitutive of our work and thus to demonstrate the spiritual significance of human vocation.
True-Color Reconstruction Based on Hyperspectral LiDAR Echo Energy
Authors: Wang Tengfeng , Wan Xiaoxia , Chen Bowen , Shi Shuo
Published: July 2021

With the development of remote sensing technology, the simultaneous acquisition of 3D point cloud and color information has become the constant goal for scientific research and commercial applications in this field. However, since radar echo data in practice refer to the value of the spectral channel and its corresponding energy, it is still impossible to obtain accurate tristimulus values of the point through color integral calculation after traditional normalization and multispectral correction. Furthermore, the reflectance of the target, the laser transmission power and other factors lead to the problems of no echo energy or weak echo energy in some bands of the visible spectrum, which further leads to large chromatic difference compared to the color calculated from the spectral reflectance of standard color card. In response to these problems, the hyperbolic tangent spectrum correction model with parameters is proposed for the spectrum correction of the acquired hyperspectral LiDAR in the 470–700 nm band. In addition, the improved gradient boosting decision tree sequence prediction algorithm is proposed for the reconstruction of missing spectrum in the 400–470 nm band where the echo energy is weak and missing. Experimental results show that there is relatively small chromatic difference between the obtained spectral information after correction and reconstruction and the spectrum of standard color card, achieving the purpose of true color reconstruction.
Dynamic Cosine Method for Normalizing Incidence Angle Effect on C-band Radar Backscattering Coefficient for Maize Canopies Based on NDVI
Authors: Feng Zhuangzhuang , Zheng Xingming , Li Lei , Li Bingze , Chen Si , Guo Tianhao , Wang Xigang , Jiang Tao , Li Xiaojie , Li Xiaofeng
Published: July 2021

Wide mode SAR images have an apparent incidence angle effect. The existing incident angle normalization methods assume that the relationship between the incident angle (θ) and the backscattering coefficient (σPQ) does not change with the growth stage of crops, which is in conflict with the real-life situation. Therefore, the normalization results of σPQ based on these existing methods will affect the accuracy of object classification, target recognition, and land surface parameter inversion. Here, the change in θ-σPQ relationship was investigated based on time-series (April to October) σPQ of maize canopies in northeast China, and a dynamic method based on normalized difference vegetation index (NDVI) was developed to normalize the effect of θ on σPQ. Through the accuracy evaluation, the following conclusions are obtained: (1) the dependence (referring to N) of Sentinel 1 C-band σPQ on θ varies with maize NDVI. In addition, the value of N changed from 9.35 to 0.66 at VV polarization from bare soil to biomass peak, and from 6.26 to 0.99 at VH polarization; (2) a dynamic method was proposed to quantify the change of N based on its strong correlation with NDVI, indicated by R2 of 0.82 and 0.80 for VV and VH polarization, respectively; and (3) the overall root mean square error of normalized σPQ based on the newly-developed dynamic method is 0.51 dB, and this accuracy outperforms the original first-order cosine method (1.37 dB) and cosine square law method (1.08 dB) by about 63% and 53% on the whole. This study provides a dynamic framework for normalizing radar backscatter coefficient, improving the retrieval accuracy of land surface parameters from radar remote sensing.
Driving Factors of Land Surface Temperature in Urban Agglomerations: A Case Study in the Pearl River Delta, China
Authors: Liu Wenxiu , Meng Qingyan , Allam Mona , Zhang Linlin , Hu Die , Menenti Massimo
Published: July 2021

Land surface temperature (LST) in urban agglomerations plays an important role for policymakers in urban planning. The Pearl River Delta (PRD) is one of the regions with the highest urban densities in the world. This study aims to explore the spatial patterns and the dominant drivers of LST in the PRD. MODIS LST (MYD11A2) data from 2005 and 2015 were used in this study. First, spatial analysis methods were applied in order to determine the spatial patterns of LST and to identity the hotspot areas (HSAs). Second, the hotspot ratio index (HRI), as a metric of thermal heterogeneity, was developed in order to identify the features of thermal environment across the nine cities in the PRD. Finally, the geo-detector (GD) metric was employed to explore the dominant drivers of LST, which included elevation, land use/land cover (LUCC), the normalized difference vegetation index (NDVI), impervious surface distribution density (ISDD), gross domestic product (GDP), population density (POP), and nighttime light index (NLI). The GD metric has the advantages of detecting the dominant drivers without assuming linear relationships and measuring the combined effects of the drivers. The results of Moran’s Index showed that the daytime and nighttime LST were close to the cluster pattern. Therefore, this process led to the identification of HSAs. The HSAs were concentrated in the central PRD and were distributed around the Pearl River estuary. The results of the HRI indicated that the spatial distribution of the HSAs was highly heterogeneous among the cities for both daytime and nighttime. The highest HRI values were recorded in the cities of Dongguan and Shenzhen during the daytime. The HRI values in the cities of Zhaoqing, Jiangmen, and Huizhou were relatively lower in both daytime and nighttime. The dominant drivers of LST varied from city to city. The influence of land cover and socio-economic factors on daytime LST was higher in the highly urbanized cities than in the cities with low urbanization rates. For the cities of Zhaoqing, Huizhou, and Jiangmen, elevation was the dominant driver of daytime LST during the study period, and for the other cities in the PRD, the main driver changed from land cover in 2005 to NLI in 2015. This study is expected to provide useful guidance for planning of the thermal environment in urban agglomerations.
Statistical Characteristics of Raindrop Size Distribution in Monsoon Season over South China Sea
Authors: Huang Chaoying , Chen Sheng , Zhang Asi , Pang Ying
Published: July 2021

The South China Sea (SCS) is the largest and southernmost sea in China. Water vapor from the SCS is the primary source of precipitation over coastal areas during the summer monsoon season and may cause the uneven distribution of rainfall in southern China. Deep insight into the spatial variability of raindrop size distribution (DSD) is essential for understanding precipitation microphysics, since DSD contains abundant information about rainfall microphysics processes. However, compared to the studies of DSDs over mainland China, very little is known about DSDs over Chinese ocean areas, especially over the South China Sea (SCS). This study investigated the statistical characteristics of the DSD in summer monsoon seasons using the second-generation Particle Size and Velocity (Parsivel2) installed on the scientific research vessel that measured the size and velocity of raindrops over the SCS. In this study, the characteristics of precipitation over the SCS for daytime and nighttime rains were analyzed for different precipitation systems and upon different rain rates. It was found that: (1) rain events were more frequent during the late evening to early morning; (2) more than 78.2% of the raindrops’ diameters were less than 2 mm, and the average value of mass-weighted mean diameter Dm (1.46 mm) of the SCS is similar to that over land in the southern China; (3) the stratiform precipitation features a relatively high concentration of medium to large-sized rain drops compared to other regions; (4) the DSD in the SCS agreed with a three-parameter gamma distribution for the small raindrop diameter. Furthermore, a possible factor for significant DSD variability in the ocean compared with the coast and large islands is also discussed.
Comparison of Coniferous Plantation Heights Using Unmanned Aerial Vehicle (UAV) Laser Scanning and Stereo Photogrammetry
Authors: Li Mei , Li Zengyuan , Liu Qingwang , Chen Erxue
Published: July 2021

Plantation forests play a critical role in forest products and ecosystems. Unmanned aerial vehicle (UAV) remote sensing has become a promising technology in forest related applications. The stand heights will reflect the growth and competition of individual trees in plantation. UAV laser scanning (ULS) and UAV stereo photogrammetry (USP) can both be used to estimate stand heights using different algorithms. Thus, this study aimed to deeply explore the variations of four kinds of stand heights including mean height, Lorey’s height, dominated height, and median height of coniferous plantations using different models based on ULS and USP data. In addition, the impacts of thinned point density of 30 pts to 10 pts, 5 pts, 1 pts, and 0.8 pts/m2 were also analyzed. Forest stand heights were estimated from ULS and USP data metrics by linear regression and the prediction accuracy was assessed by 10-fold cross validation. The results showed that the prediction accuracy of the stand heights using metrics from USP was basically as good as that of ULS. Lorey’s height had the highest prediction accuracy, followed by dominated height, mean height, and median height. The correlation between height percentiles metrics from ULS and USP increased with the increased height. Different stand heights had their corresponding best height percentiles as variables based on stand height characteristics. Furthermore, canopy height model (CHM)-based metrics performed slightly better than normalized point cloud (NPC)-based metrics. The USP was not able to extract exact terrain information in a continuous coniferous plantation for forest canopy cover (CC) over 0.49. The combination of USP and terrain from ULS can be used to estimate forest stand heights with high accuracy. In addition, the estimation accuracy of each forest stand height was slightly affected by point density, which can also be ignored.
Development and Assessment of High-Resolution Radar-Based Precipitation Intensity-Duration-Curve (IDF) Curves for the State of Texas
Authors: Ghebreyesus Dawit T. , Sharif Hatim O.
Published: July 2021

Conventionally, in situ rainfall data are used to develop Intensity Duration Frequency (IDF) curves, which are one of the most effective tools for modeling the probability of the occurrence of extreme storm events at different timescales. The rapid recent technological advancements in precipitation sensing, and the finer spatio-temporal resolution of data have made the application of remotely sensed precipitation products more dominant in the field of hydrology. Some recent studies have discussed the potential of remote sensing products for developing IDF curves. This study employs a 19-year NEXRAD Stage-IV high-resolution radar data (2002–2020) to develop IDF curves over the entire state of Texas at a fine spatial resolution. The Annual Maximum Series (AMS) were fitted to four widely used theoretical Extreme Value statistical distributions. Gumble distribution, a unique scenario of the Generalized Extreme Values (GEV) family, was found to be the best model for more than 70% of the state’s area for all storm durations. Validation of the developed IDFs against the operational Atlas 14 IDF values shows a ±27% difference in over 95% of the state for all storm durations. The median of the difference stays between −10% and +10% for all storm durations and for all return periods in the range of (2–100) years. The mean difference ranges from −5% for the 100-year return period to 8% for the 10-year return period for the 24-h storm. Generally, the western and northern regions of the state show an overestimation, while the southern and southcentral regions show an underestimation of the published values.
Fiber Optic Sensors Embedded in Textile-Reinforced Concrete for Smart Structural Health Monitoring: A Review
Authors: Alwis Lourdes , Bremer Kort , Roth Bernhard
Published: July 2021

The last decade has seen rapid developments in the areas of carbon fiber technology, additive manufacturing technology, sensor engineering, i.e., wearables, and new structural reinforcement techniques. These developments, although from different areas, have collectively paved way for concrete structures with non-corrosive reinforcement and in-built sensors. Therefore, the purpose of this effort is to bridge the gap between civil engineering and sensor engineering communities through an overview on the up-to-date technological advances in both sectors, with a special focus on textile reinforced concrete embedded with fiber optic sensors. The introduction section highlights the importance of reducing the carbon footprint resulting from the building industry and how this could be effectively achieved by the use of state-of-the-art reinforcement techniques. Added to these benefits would be the implementations on infrastructure monitoring for the safe operation of structures through their entire lifespan by utilizing sensors, specifically, fiber optic sensors. The paper presents an extensive description on fiber optic sensor engineering that enables the incorporation of sensors into the reinforcement mechanism of a structure at its manufacturing stage, enabling effective monitoring and a wider range of capabilities when compared to conventional means of structural health monitoring. In future, these developments, when combined with artificial intelligence concepts, will lead to distributed sensor networks for smart monitoring applications, particularly enabling such distributed networks to be implemented/embedded at their manufacturing stage.
A Survey on the Application of WirelessHART for Industrial Process Monitoring and Control
Authors: Devan P. Arun Mozhi , Hussin Fawnizu Azmadi , Ibrahim Rosdiazli , Bingi Kishore , Khanday Farooq Ahmad
Published: July 2021

Industrialization has led to a huge demand for a network control system to monitor and control multi-loop processes with high effectiveness. Due to these advancements, new industrial wireless sensor network (IWSN) standards such as ZigBee, WirelessHART, ISA 100.11a wireless, and Wireless network for Industrial Automation-Process Automation (WIA-PA) have begun to emerge based on their wired conventional structure with additional developments. This advancement improved flexibility, scalability, needed fewer cables, reduced the network installation and commissioning time, increased productivity, and reduced maintenance costs compared to wired networks. On the other hand, using IWSNs for process control comes with the critical challenge of handling stochastic network delays, packet drop, and external noises which are capable of degrading the controller performance. Thus, this paper presents a detailed study focusing only on the adoption of WirelessHART in simulations and real-time applications for industrial process monitoring and control with its crucial challenges and design requirements.
Audio-Based Drone Detection and Identification Using Deep Learning Techniques with Dataset Enhancement through Generative Adversarial Networks
Authors: Al-Emadi Sara , Al-Ali Abdulla , Al-Ali Abdulaziz
Published: July 2021

Drones are becoming increasingly popular not only for recreational purposes but in day-to-day applications in engineering, medicine, logistics, security and others. In addition to their useful applications, an alarming concern in regard to the physical infrastructure security, safety and privacy has arisen due to the potential of their use in malicious activities. To address this problem, we propose a novel solution that automates the drone detection and identification processes using a drone’s acoustic features with different deep learning algorithms. However, the lack of acoustic drone datasets hinders the ability to implement an effective solution. In this paper, we aim to fill this gap by introducing a hybrid drone acoustic dataset composed of recorded drone audio clips and artificially generated drone audio samples using a state-of-the-art deep learning technique known as the Generative Adversarial Network. Furthermore, we examine the effectiveness of using drone audio with different deep learning algorithms, namely, the Convolutional Neural Network, the Recurrent Neural Network and the Convolutional Recurrent Neural Network in drone detection and identification. Moreover, we investigate the impact of our proposed hybrid dataset in drone detection. Our findings prove the advantage of using deep learning techniques for drone detection and identification while confirming our hypothesis on the benefits of using the Generative Adversarial Networks to generate real-like drone audio clips with an aim of enhancing the detection of new and unfamiliar drones.
Failure Detection Methods for Pipeline Networks: From Acoustic Sensing to Cyber-Physical Systems
Authors: Wong Boon , McCann Julie A.
Published: July 2021

Pipeline networks have been widely utilised in the transportation of water, natural gases, oil and waste materials efficiently and safely over varying distances with minimal human intervention. In order to optimise the spatial use of the pipeline infrastructure, pipelines are either buried underground, or located in submarine environments. Due to the continuous expansion of pipeline networks in locations that are inaccessible to maintenance personnel, research efforts have been ongoing to introduce and develop reliable detection methods for pipeline failures, such as blockages, leakages, cracks, corrosion and weld defects. In this paper, a taxonomy of existing pipeline failure detection techniques and technologies was created to comparatively analyse their respective advantages, drawbacks and limitations. This effort has effectively illuminated various unaddressed research challenges that are still present among a wide array of the state-of-the-art detection methods that have been employed in various pipeline domains. These challenges include the extension of the lifetime of a pipeline network for the reduction of maintenance costs, and the prevention of disruptive pipeline failures for the minimisation of downtime. Our taxonomy of various pipeline failure detection methods is also presented in the form of a look-up table to illustrate the suitability, key aspects and data or signal processing techniques of each individual method. We have also quantitatively evaluated the industrial relevance and practicality of each of the methods in the taxonomy in terms of their respective deployability, generality and computational cost. The outcome of the evaluation made in the taxonomy will contribute to our future works involving the utilisation of sensor fusion and data-centric frameworks to develop efficient, accurate and reliable failure detection solutions.
Potential of Point-of-Care and At-Home Assessment of Immune Status via Rapid Cytokine Detection and Questionnaire-Based Anamnesis
Authors: Jamaludeen Noor , Beyer Christian , Billing Ulrike , Vogel Katrin , Brunner-Weinzierl Monika , Spiliopoulou Myra
Published: July 2021

Monitoring the immune system’s status has emerged as an urgent demand in critical health conditions. The circulating cytokine levels in the blood reflect a thorough insight into the immune system status. Indeed, measuring one cytokine may deliver more information equivalent to detecting multiple diseases at a time. However, if the reported cytokine levels are interpreted with considering lifestyle and any comorbid health conditions for the individual, this will promote a more precise assessment of the immune status. Therefore, this study addresses the most recent advanced assays that deliver rapid, accurate measuring of the cytokine levels in human blood, focusing on add-on potentials for point-of-care (PoC) or personal at-home usage, and investigates existing health questionnaires as supportive assessment tools that collect all necessary information for the concrete analysis of the measured cytokine levels. We introduced a ten-dimensional featuring of cytokine measurement assays. We found 15 rapid cytokine assays with assay time less than 1 h; some could operate on unprocessed blood samples, while others are mature commercial products available in the market. In addition, we retrieved several health questionnaires that addressed various health conditions such as chronic diseases and psychological issues. Then, we present a machine learning-based solution to determine what makes the immune system fit. To this end, we discuss how to employ topic modeling for deriving the definition of immune fitness automatically from literature. Finally, we propose a prototype model to assess the fitness of the immune system through leveraging the derived definition of the immune fitness, the cytokine measurements delivered by a rapid PoC immunoassay, and the complementary information collected by the health questionnaire about other health factors. In conclusion, we discovered various advanced rapid cytokine detection technologies that are promising candidates for point-of-care or at-home usage; if paired with a health status questionnaire, the assessment of the immune system status becomes solid and we demonstrated potentials for promoting the assessment tool with data mining techniques.
Railway Overhead Contact System Point Cloud Classification
Authors: Chen Xiao , Chen Zhuang , Liu Guoxiang , Chen Kun , Wang Lu , Xiang Wei , Zhang Rui
Published: July 2021

As the railway overhead contact system (OCS) is the key component along the high-speed railway, it is crucial to detect the quality of the OCS. Compared with conventional manual OCS detection, the vehicle-mounted Light Detection and Ranging (LiDAR) technology has advantages such as high efficiency and precision, which can solve the problems of OCS detection difficulty, low efficiency, and high risk. Aiming at the contact cables, return current cables, and catenary cables in the railway vehicle-mounted LiDAR OCS point cloud, this paper used a scale adaptive feature classification algorithm and the DBSCAN (density-based spatial clustering of applications with noise) algorithm considering OCS characteristics to classify the OCS point cloud. Finally, the return current cables, catenary cables, and contact cables in the OCS were accurately classified and extracted. To verify the accuracy of the method presented in this paper, we compared the experimental results of this article with the classification results of TerraSolid, and the classification results were evaluated in terms of four accuracy indicators. According to statistics, the average accuracy of using this method to extract two sets of OCS point clouds is 99.83% and 99.89%, respectively; the average precision is 100% and 99.97%, respectively; the average recall is 99.16% and 99.42%, respectively; and the average overall accuracy is 99.58% and 99.69% respectively, which is overall better than TerraSolid. The experimental results showed that this approach could accurately and quickly extract the complete OCS from the point cloud. It provides a new method for processing railway OCS point clouds and has high engineering application value in railway component detection.
Pupil Size Prediction Techniques Based on Convolution Neural Network
Authors: Whang Allen Jong-Woei , Chen Yi-Yung , Tseng Wei-Chieh , Tsai Chih-Hsien , Chao Yi-Ping , Yen Chieh-Hung , Liu Chun-Hsiu , Zhang Xin
Published: July 2021

The size of one’s pupil can indicate one’s physical condition and mental state. When we search related papers about AI and the pupil, most studies focused on eye-tracking. This paper proposes an algorithm that can calculate pupil size based on a convolution neural network (CNN). Usually, the shape of the pupil is not round, and 50% of pupils can be calculated using ellipses as the best fitting shapes. This paper uses the major and minor axes of an ellipse to represent the size of pupils and uses the two parameters as the output of the network. Regarding the input of the network, the dataset is in video format (continuous frames). Taking each frame from the videos and using these to train the CNN model may cause overfitting since the images are too similar. This study used data augmentation and calculated the structural similarity to ensure that the images had a certain degree of difference to avoid this problem. For optimizing the network structure, this study compared the mean error with changes in the depth of the network and the field of view (FOV) of the convolution filter. The result shows that both deepening the network and widening the FOV of the convolution filter can reduce the mean error. According to the results, the mean error of the pupil length is 5.437% and the pupil area is 10.57%. It can operate in low-cost mobile embedded systems at 35 frames per second, demonstrating that low-cost designs can be used for pupil size prediction.
Pre-Processing Method to Improve Cross-Domain Fault Diagnosis for Bearing
Authors: Kim Taeyun , Chai Jangbom
Published: July 2021

Models trained with one system fail to identify other systems accurately because of domain shifts. To perform domain adaptation, numerous studies have been conducted in many fields and have successfully aligned different domains into one domain. The domain shift problem is caused by the difference of distributions between two domains, which is solved by reducing this difference. Source domain data are labeled and used for training the models to extract the features while the target domain data are unlabeled or partially labeled and only used for aligning. Bearings play important roles in rotating machines, so many artificial intelligent models have been developed to diagnose bearings. Bearing diagnosis has also faced a domain shift problem due to various operating conditions such as experimental environment, number of balls, degree of defects, and rotational speed. Cross-domain fault diagnosis has been successfully performed when the systems are the same but operating conditions are different. However, the results are poor when diagnosing different bearing systems because the characteristics of the signals such as specific frequencies depend on the specifications. In this paper, the pre-processing method was used for improving the diagnosis without prior knowledge such as fault frequencies. The signals were first transformed to a common pattern space before entering the models. To develop and to validate the proposed method for different domains, vibration signals measured from two ball-bearing systems (Case Western Reserve University datasets and Paderborn University datasets) were used. One dimensional CNN models were utilized for verification of the proposed method and the results of the models using raw datasets and pre-processed datasets were compared. Even though each of the ball-bearing systems have their own specifications, using the proposed method was very helpful for domain adaptation, and cross-domain fault diagnosis was performed with high accuracy.
Target Classification in Synthetic Aperture Radar Images Using Quantized Wavelet Scattering Networks
Authors: Raj Raghu G. , Fox Maxine R. , Narayanan Ram M.
Published: July 2021

The need to classify targets and features in high-resolution imagery is of interest in applications such as detection of landmines in ground penetrating radar and tumors in medical ultrasound images. Convolutional neural networks (CNNs) trained using extensive datasets are being investigated recently. However, large CNNs and wavelet scattering networks (WSNs), which share similar properties, have extensive memory requirements and are not readily extendable to other datasets and architectures—and especially in the context of adaptive and online learning. In this paper, we quantitatively study several quantization schemes on WSNs designed for target classification using X-band synthetic aperture radar (SAR) data and investigate their robustness to low signal-to-noise ratio (SNR) levels. A detailed study was conducted on the tradeoffs involved between the various quantization schemes and the means of maximizing classification performance for each case. Thus, the WSN-based quantization studies performed in this investigation provide a good benchmark and important guidance for the design of quantized neural networks architectures for target classification.
Recent Advances in Generation and Detection of Orbital Angular Momentum Optical Beams—A Review
Authors: Fatkhiev Denis M. , Butt Muhammad A. , Grakhova Elizaveta P. , Kutluyarov Ruslan V. , Stepanov Ivan V. , Kazanskiy Nikolay L. , Khonina Svetlana N. , Lyubopytov Vladimir S. , Sultanov Albert K.
Published: July 2021

Herein, we have discussed three major methods which have been generally employed for the generation of optical beams with orbital angular momentum (OAM). These methods include the practice of diffractive optics elements (DOEs), metasurfaces (MSs), and photonic integrated circuits (PICs) for the production of in-plane and out-of-plane OAM. This topic has been significantly evolved as a result; these three methods have been further implemented efficiently by different novel approaches which are discussed as well. Furthermore, development in the OAM detection techniques has also been presented. We have tried our best to bring novel and up-to-date information to the readers on this interesting and widely investigated topic.
A Composite Fabry-Perot Interferometric Sensor with the Dual-Cavity Structure for Simultaneous Measurement of High Temperature and Strain
Authors: Xia Ping , Tan Yuegang , Yang Caixia , Zhou Zude , Yun Kang
Published: July 2021

In this paper, an optical fiber composite Fabry-Perot interferometric (CFPI) sensor capable of simultaneous measurement of high temperature and strain is presented. The CFPI sensor consists of a silica-cavity intrinsic Fabry-Perot interferometer (IFPI) cascading an air-cavity extrinsic Fabry-Perot interferometer (EFPI). The IFPI is constructed at the end of the transmission single-mode fiber (SMF) by splicing a short piece of photonic crystal fiber (PCF) to SMF and then the IFPI is inserted into a quartz capillary with a reflective surface to form a single-ended sliding EFPI. In such a configuration, the IFPI is only sensitive to temperature and the EFPI is sensitive to strain, which allows the achieving of temperature-compensated strain measurement. The experimental results show that the proposed sensor has good high-temperature resistance up to 1000 °C. Strain measurement under high temperatures is demonstrated for high-temperature suitability and stable strain response. Featuring intrinsic safety, compact structure and small size, the proposed CFPI sensor may find important applications in the high-temperature harsh environment.
Classification Learning of Latent Bruise Damage to Apples Using Shortwave Infrared Hyperspectral Imaging
Authors: Nturambirwe Jean Frederic Isingizwe , Perold Willem Jacobus , Opara Umezuruike Linus
Published: July 2021

Bruise damage is a very commonly occurring defect in apple fruit which facilitates disease occurrence and spread, leads to fruit deterioration and can greatly contribute to postharvest loss. The detection of bruises at their earliest stage of development can be advantageous for screening purposes. An experiment to induce soft bruises in Golden Delicious apples was conducted by applying impact energy at different levels, which allowed to investigate the detectability of bruises at their latent stage. The existence of bruises that were rather invisible to the naked eye and to a digital camera was proven by reconstruction of hyperspectral images of bruised apples, based on effective wavelengths and data dimensionality reduced hyperspectrograms. Machine learning classifiers, namely ensemble subspace discriminant (ESD), k-nearest neighbors (KNN), support vector machine (SVM) and linear discriminant analysis (LDA) were used to build models for detecting bruises at their latent stage, to study the influence of time after bruise occurrence on detection performance and to model quantitative aspects of bruises (severity), spanning from latent to visible bruises. Over all classifiers, detection models had a higher performance than quantitative ones. Given its highest speed in prediction and high classification performance, SVM was rated most recommendable for detection tasks. However, ESD models had the highest classification accuracy in quantitative (>85%) models and were found to be relatively better suited for such a multiple category classification problem than the rest.
ADS-B Crowd-Sensor Network and Two-Step Kalman Filter for GNSS and ADS-B Cyber-Attack Detection
Authors: Leonardi Mauro , Sirbu Gheorghe
Published: July 2021

Automatic Dependent Surveillance-Broadcast is an Air Traffic Control system in which aircraft transmit their own information (identity, position, velocity, etc.) to ground sensors for surveillance purposes. This system has many advantages compared to the classical surveillance radars: easy and low-cost implementation, high accuracy of data, and low renewal time, but also limitations: dependency on the Global Navigation Satellite System, a simple unencrypted and unauthenticated protocol. For these reasons, the system is exposed to attacks like jamming/spoofing of the on-board GNSS receiver or false ADS-B messages’ injection. After a mathematical model derivation of different types of attacks, we propose the use of a crowd sensor network capable of estimating the Time Difference Of Arrival of the ADS-B messages together with a two-step Kalman filter to detect these attacks (on-board GNSS/ADS-B tampering, false ADS-B message injection, GNSS Spoofing/Jamming). Tests with real data and simulations showed that the algorithm can detect all these attacks with a very high probability of detection and low probability of false alarm.
Qualitative Recognition of Primary Taste Sensation Based on Surface Electromyography
Authors: Wang You , Wang Hengyang , Li Huiyan , Ullah Asif , Zhang Ming , Gao Han , Hu Ruifen , Li Guang
Published: July 2021

Based on surface electromyography (sEMG), a novel recognition method to distinguish six types of human primary taste sensations was developed, and the recognition accuracy was 74.46%. The sEMG signals were acquired under the stimuli of no taste substance, distilled vinegar, white granulated sugar, instant coffee powder, refined salt, and Ajinomoto. Then, signals were preprocessed with the following steps: sample augments, removal of trend items, high-pass filter, and adaptive power frequency notch. Signals were classified with random forest and the classifier gave a five-fold cross-validation accuracy of 74.46%, which manifested the feasibility of the recognition task. To further improve the model performance, we explored the impact of feature dimension, electrode distribution, and subject diversity. Accordingly, we provided an optimized feature combination that reduced the number of feature types from 21 to 4, a preferable selection of electrode positions that reduced the number of channels from 6 to 4, and an analysis of the relation between subject diversity and model performance. This study provides guidance for further research on taste sensation recognition with sEMG.
Increasing the Reliability of Simulation Tests in Navigation and Maneuvering Simulators Using the k-Epsilon Model Based on the RANS Method
Authors: Czaplewski Krzysztof , Swierczynski Slawomir , Zwolan Piotr
Published: July 2021

The influence of wind on the maneuverability of sea-going vessels is a known factor limiting their maneuverability, especially in the case of very large vessels. Adverse weather conditions often limit the maneuverability of vessels or even make it impossible to enter the port. This results in longer delivery times for transported goods as well as measurable material losses for both carriers and their owners. This situation is often caused by a lack of information on differences in the prevailing weather conditions at the entrance to the port and at the seaport itself. There are simulation tools, such as the methods of computational fluid dynamics (CFD), which, after their appropriate adaptation and use in a virtual environment, have become important decision-making tools supporting the port administration when deciding about the movement of vessels. In this article, the authors present the results of research aimed at adapting one of the CFD methods for the needs of maritime navigation. The effects of the work were verified in a virtual environment and were successfully implemented in the port waters of Gdansk, Poland.
Fabrication and Characterization of Iridium Oxide pH Microelectrodes Based on Sputter Deposition Method
Authors: Xi Ye , Guo Zhejun , Wang Longchun , Xu Qingda , Ruan Tao , Liu Jingquan
Published: July 2021

pH value plays an important role in many fields such as chemistry and biology; therefore, rapid and accurate pH measurement is very important. Because of its advantages in preparation, wide test range, rapid response, and good biocompatibility, iridium oxide material has received more and more attention. In this paper, we present a method for preparing iridium oxide pH microelectrodes based on the sputter deposition method. The sputtering parameters of iridium oxide are also studied and optimized. Open-circuit potential tests show that microelectrodes exhibit near-Nernstian pH response with good linearity (about 60 mV/pH), fast response, high stability (a slight periodic fluctuation of potential change <2.5 mV in 24 h), and good reversibility in the pH range of 1.00–13.00.
Rebirth of Distributed AI—A Review of eHealth Research
Authors: Khan Manzoor Ahmed , Alkaabi Najla
Published: July 2021

The envisioned smart city domains are expected to rely heavily on artificial intelligence and machine learning (ML) approaches for their operations, where the basic ingredient is data. Privacy of the data and training time have been major roadblocks to achieving the specific goals of each application domain. Policy makers, the research community, and the industrial sector have been putting their efforts into addressing these issues. Federated learning, with its distributed and local training approach, stands out as a potential solution to these challenges. In this article, we discuss the potential interplay of different technologies and AI for achieving the required features of future smart city services. Having discussed a few use-cases for future eHealth, we list design goals and technical requirements of the enabling technologies. The paper confines its focus on federated learning. After providing the tutorial on federated learning, we analyze the Federated Learning research literature. We also highlight the challenges. A solution sketch and high-level research directions may be instrumental in addressing the challenges.