bluuebunny/crossref_metadata_2025 huggingface dataset:
Improvement of Phenolic Compounds, Essential Oil Content and Antioxidant Properties of Sweet Basil (Ocimum basilicum L.) Depending on Type and Concentration of Selenium Application
Authors: Skrypnik Liubov , Novikova Anastasia , Tokupova Elina
Published: October 2019

The effect of selenium biofortification on phytomass yield, selenium, essential oil and phenolic compounds content as well as antioxidant properties of basil leaves was investigated. Selenium in form of sodium selenate was applied either in nutrient solution or by foliar spraying at three levels (2.0, 5.0 and 10.0 μM). Selenium treatment significantly increased Se concentration in leaves up to 20.23 μg g−1 (addition in nutrient solution) and 10.74 μg g−1 (foliar application). Neither a toxic nor a beneficial effect of Se addition on the plants was observed. Se application of 2 µM Se in nutrient solution and of 5 µM Se by foliar spraying successfully enhanced production of essential oils, hydroxycinnamic acids, total phenolics and antioxidant activity of basil leaves. The anthocyanin content was positively affected only by application of Se in nutrient solution. Considering both an increase in the Se concentration in basil leaves and an increase in the production of phytonutrients, the optimal doses of selenium can be considered to be 5 and 10 μM for Se addition in nutrient solution and by foliar treatment, respectively. The results confirm the possibility of the enrichment of basil plants with selenium and thereby improving the nutritional qualities of the human diet.
Effect of Oxygen Plasma on Sprout and Root Growth, Surface Morphology and Yield of Garlic
Authors: Holc Matej , Primc Gregor , Iskra Jernej , Titan Primož , Kovač Janez , Mozetič Miran , Junkar Ita
Published: October 2019

Depending on the climate, garlic can be planted either in the fall or spring for a harvest in the summer, but spring planting might require the strengthening of the plant by external techniques. We have used low pressure, inductively coupled, radio frequency oxygen plasma for the treatment of peeled garlic cloves of a spring-planted Slovenian autochthonous cultivar. The aim of the study was to assess the effects of plasma treatment on garlic clove shoot and root growth and, ultimately, the yield. The roles of surface chemistry, surface morphology, and water uptake in these effects were also evaluated. The plasma treatment of cloves induced increases in water uptake. The increases were explained by changes in surface morphology that were determined by using scanning electron microscopy (SEM) and atomic force microscopy (AFM). Nanostructured epicuticular wax structures appeared at the cuticle surface. The optimal treatment parameters accelerated root growth, but not shoot growth, in a laboratory setting. After growth in the field, the trends indicated that plant height and dried bulb mass increase, but the improvements were not statistically significant.
Biological Control of Alternaria Leaf Spot Caused by Alternaria spp. in Jerusalem Artichoke (Helianthus tuberosus L.) under Two Fertilization Regimes
Authors: Viriyasuthee Wanalai , Jogloy Sanun , Saksirirat Weerasak , Saepaisan Suwita , Gleason Mark L. , Chen Ruey Shyang
Published: October 2019

The objectives of this study were to evaluate the efficacy of integrating resistant genotypes of Jerusalem artichoke with Trichoderma harzianum isolate T9 to control Alternaria leaf spot caused by Alternaria spp. under two fertilization regimes and to determine whether T9 application induced chitinase and β-1,3-glucanase activity in Jerusalem artichoke leaves. Six Jerusalem artichoke varieties (resistant varieties JA15, JA86, and JA116 and susceptible varieties HEL246, HEL293, and JA109) and three disease control methods (a non-inoculated control, application of T. harzianum T9, and fungicide sprays (propiconazole at a rate of 30 mL/20 L of water, 375 ppm)) was conducted in two separate trials (different fertilization regimes) at the experimental farm of the Faculty of Agriculture, Khon Kaen University, Khon Kaen, Thailand. Resistant genotypes controlled Alternaria leaf spot effectively. Application of Trichoderma showed low efficacy to control Alternaria leaf spot, but in specific susceptible genotypes—HEL246 and HEL293—the application of Trichoderma could reduce disease severity up to 10%. The application of Trichoderma was associated with a rise in production of chitinase and β-1,3-glucanase in HEL246 seedlings. The number of Trichoderma propagules in soil, as well as the extent of colonization of roots and leaves, were monitored. The results indicated that application of Trichoderma had higher propagules than non-inoculated control. Neither varietal resistance nor the disease control methods used in this study impacted the yield or yield components of Jerusalem artichoke.
Polymer Inclusion Membranes (PIMs) Doped with Alkylimidazole and their Application in the Separation of Non-Ferrous Metal Ions
Authors: Radzyminska-Lenarcik Elzbieta , Ulewicz Malgorzata
Published: October 2019

The study involved the transport of zinc(II), cadmium(II), and nickel(II) ions from acidic aqueous solutions using polymer inclusion membranes (PIMs). PIMs consisted of cellulose triacetate (CTA) as a support; o-nitrophenyl pentyl ether (o-NPPE) as a plasticizer; and 1-octylimidazole (1), 1-octyl-2-methylimidazole (2), 1-octyl-4-methylimidazole (3), or 1-octyl-2,4-dimethylimidazole (4) as ion carriers. The membranes were characterized by means of atomic force microscopy (AFM) and scanning electron microscopy (SEM). The results show that Zn(II) and Cd(II) are effectively transported across PIMs, while Ni(II) transport is not effective. The rate of transport of metal ions across PIMs is determined by the diffusion rate of the M(II)–carrier complex across the membrane. The best result achieved for Zn(II) removal after 24 h was 95.5% for the ternary Zn(II)–Cd(II)–Ni(II) solution for PIM doped (4). For this membrane, the separation coefficients for Zn(II)/Cd(II), Zn(II)/Ni(II), and Cd(II)/Ni(II) were 2.8, 104.5, and 23.5, respectively. Additionally, the influence of basicity and structure of carrier molecules on transport kinetics was discussed.
Multi-Objective Predictive Control Optimization with Varying Term Objectives: A Wind Farm Case Study
Authors: Ionescu Clara M. , Caruntu Constantin F. , Cajo Ricardo , Ghita Mihaela , Crevecoeur Guillaume , Copot Cosmin
Published: October 2019

This paper introduces the incentive of an optimization strategy taking into account short-term and long-term cost objectives. The rationale underlying the methodology presented in this work is that the choice of the cost objectives and their time based interval affect the overall efficiency/cost balance of wide area control systems in general. The problem of cost effective optimization of system output is taken into account in a multi-objective predictive control formulation and applied on a windmill park case study. A strategy is proposed to enable selection of optimality criteria as a function of context conditions of system operating conditions. Long-term economic objectives are included and realistic simulations of a windmill park are performed. The results indicate the global optimal criterium is no longer feasible when long-term economic objectives are introduced. Instead, local sub-optimal solutions are likely to enable long-term energy efficiency in terms of balanced production of energy and costs for distribution and maintenance of a windmill park.
Taste Masking of Nizatidine Using Ion-Exchange Resins
Authors: Panraksa Pattaraporn , Boonsermsukcharoen Kasidech , Hwang Kyu-Mok , Park Eun-Seok , Jantrawut Pensak
Published: October 2019

The purpose of this study was to mask the bitter taste of nizatidine (NZD) using cation-exchange resins. Amberlite IRP-69 and Dowex-50 containing cross-linked polystyrene backbones were used. The drug resin complexes were prepared by batch process using drug: resin ratios of 1:1, 1:3, and 1:5. The optimum drug: resin ratio and the time required for maximum percentage drug loading into the complexes were determined. The selected drug-resin complexes were evaluated for morphology, drug release, and taste. The NZD-Dowex complex was obtained at a drug: resin ratio of 1:5 using a stirring time of 1 h in order to get 100% loading of NZD. The NZD-Dowex complex had a spherical shape and smooth texture similar to Dowex resin. The NZD-Dowex complex with a ratio of 1:5 showed that in vitro drug release of 4.27% at 5 min in simulated salivary fluid of pH 6.8 and 99.67% at 1 h in simulated gastric fluid of pH 1.2. NZD’s bitter taste was effectively masked when it formed a complex with Dowex at a ratio of 1:5. This was proved by an electronic tongue and human test panel.
Innovating the Concept and Practice of Two-Dimensional Gel Electrophoresis in the Analysis of Proteomes at the Proteoform Level
Authors: Zhan Xianquan , Li Biao , Zhan Xiaohan , Schlüter Hartmut , Jungblut Peter R. , Coorssen Jens R.
Published: October 2019

Two-dimensional gel electrophoresis (2DE) is an important and well-established technical platform enabling extensive top-down proteomic analysis. However, the long-held but now largely outdated conventional concepts of 2DE have clearly impacted its application to in-depth investigations of proteomes at the level of protein species/proteoforms. It is time to popularize a new concept of 2DE for proteomics. With the development and enrichment of the proteome concept, any given “protein” is now recognized to consist of a series of proteoforms. Thus, it is the proteoform, rather than the canonical protein, that is the basic unit of a proteome, and each proteoform has a specific isoelectric point (pI) and relative mass (Mr). Accordingly, using 2DE, each proteoform can routinely be resolved and arrayed according to its different pI and Mr. Each detectable spot contains multiple proteoforms derived from the same gene, as well as from different genes. Proteoforms derived from the same gene are distributed into different spots in a 2DE pattern. High-resolution 2DE is thus actually an initial level of separation to address proteome complexity and is effectively a pre-fractionation method prior to analysis using mass spectrometry (MS). Furthermore, stable isotope-labeled 2DE coupled with high-sensitivity liquid chromatography-tandem MS (LC-MS/MS) has tremendous potential for the large-scale detection, identification, and quantification of the proteoforms that constitute proteomes.
Life Cycle Assessment of Oyster Farming in the Po Delta, Northern Italy
Authors: Tamburini Elena , Fano Elisa Anna , Castaldelli Giuseppe , Turolla Edoardo
Published: October 2019

Oysters represent an important portion of the world’s total aquaculture production. In recent years, in Italy, oyster farming has progressively increased its role in the economic growth of the aquaculture sector and still has great potential for growth. As in any other production, oyster farming generates environmental impacts over an oyster’s life cycle, due to material, energy, fuel, and water use. The aim of this work was to carry out a cradle-to-gate life cycle assessment (LCA) of 1 kg of fresh oysters of commercial size produced in the Po delta area, northern Italy. Two scenarios were considered. The current scenario provides for oyster seed purchasing from France and transport to Italy, whereas the alternative scenario includes in situ seed production in order to realize a complete local and traceable supply chain. Eco-indicator® 99-H and ReCiPe® midpoint (H) v.1.12 were used to perform the impact assessments. The overall impacts of the two scenarios were very similar and indicated that the main hotspots were the fattening and prefattening phases of farming, which were common in both scenarios. Focusing the analysis on the first stages, transport from France had a greater impact than did local seed production, emphasizing the importance of a short supply chain in aquaculture production.
Multi-Scale Geospatial Object Detection Based on Shallow-Deep Feature Extraction
Authors: AL-Alimi Dalal , Shao Yuxiang , Feng Ruyi , Al-qaness Mohammed A. A. , Elaziz Mohamed Abd , Kim Sunghwan
Published: October 2019

Multi-class detection in remote sensing images (RSIs) has garnered wide attention and introduced several service applications in many fields, including civil and military fields. However, several reasons make detection from aerial images very challenging and more difficult than nature scene images: Objects do not have a fixed size, often appear at very various scales and sometimes appear in dense groups, like vehicles and storage tanks, and have different surroundings or background areas. Furthermore, all of this makes the manual annotation of objects very complex and costly. The powerful effect of the feature extraction methods on object detection and the successes of deep convolutional neural networks (CNN) extract deep features more than traditional methods. This study introduced a novel network structure and designed a unique feature extraction which employs squeeze and excitation network (SENet) and residual network (ResNet) to obtain feature maps, named a shallow-deep feature extraction (SDFE), that improves the resolution and the localization at the same time. Furthermore, this novel model reduces the loss of dense groups and small objects, and provides higher and more stable detection accuracy which is not significantly affected by changing the value of the threshold of the intersection over union (IoU) and overcomes the difficulties of RSIs. Moreover, this study introduced strong evidence about the factors that affect the detection of RSIs. The proposed shallow-deep and multi-scale (SD-MS) method outperforms other approaches for the given ten classes of the NWPU VHR-10 dataset.
Monitoring Soil Surface Mineralogy at Different Moisture Conditions Using Visible Near-Infrared Spectroscopy Data
Authors: Ymeti Irena , Pikha Shrestha Dhruba , van der Meer Freek
Published: October 2019

The soil minerals determine essential soil properties such as the cation exchange capacity, texture, structure, and their capacity to form bonds with organic matter. Any alteration of these organo-mineral interactions due to the soil moisture variations needs attention. Visible near-infrared imaging spectroscopy is capable of assessing spectral soil constituents that are responsible for the organo-mineral interactions. In this study, we hypothesized that the alterations of the surface soil mineralogy occur due to the moisture variations. For eight weeks, under laboratory conditions, imaging spectroscopy data were collected on a 72 h basis for three Silty Loam soils varying in the organic matter (no, low and high) placed at the drying-field capacity, field capacity and waterlogging-field capacity treatments. Using the Spectral Information Divergence image classifier, the image area occupied by the Mg-clinochlore, goethite, quartz coated 50% by goethite, hematite dimorphous with maghemite was detected and quantified (percentage). Our results showed these minerals behaved differently, depending on the soil type and soil treatment. While for the soils with organic matter, the mineralogical alterations were evident at the field capacity state, for the one with no organic matter, these changes were insignificant. Using imaging spectroscopy data on the Silty Loam soil, we showed that the surface mineralogy changes over time due to the moisture conditions.
Improving the Accuracy of Landslide Detection in “Off-site” Area by Machine Learning Model Portability Comparison: A Case Study of Jiuzhaigou Earthquake, China
Authors: Hu Qiao , Zhou Yi , Wang Shixing , Wang Futao , Wang Hongjie
Published: October 2019

The rising machine learning (ML) models have become the preferred way for landslide detection based on remote sensing images, but the performance of these models in a sample-free area are rarely concerned in many studies. In this study, we used a cross-validation method (training model in one area and validation in another) to compare the model portability of trained ML models applied in an “off-site” area, as a consideration of the landslide detection ability of these models in sample-free areas. We integrate nighttime light imagery, multi-seasonal optical Landsat time-series and digital elevation data, and we employed support vector machines (SVM), artificial neural networks (ANN) and random forest (RF) models to classify the satellite imagery and identify landslides. Samples of two scenarios generated from two subareas of the Jiuzhaigou disaster-stricken region are used for the cross-application and accuracy evaluation of three ML models. The results revealed that when the trained models are applied in areas outside those in which they were developed, the landslide identification accuracy of these three models has declined. Especially for the SVM and ANN models, the accuracy is greatly reduced and there appears a seriously imbalanced user’s and producer’s accuracy. However, although the performance of the RF model is lower than that of SVM and ANN models in their local area, the RF model exhibits stable portability, and retains the original performance and achieves a satisfactory balance between overestimation and underestimation in “off-site” areas. An additional validation from a new area proved that the landslide detection performance of the RF model with stable portability is higher than that of the SVM and ANN models in “off-site” areas. The results suggest that evaluating the model portability through cross-application can be a useful way to determine the most suitable model for landslide detection in “off-site” areas with a similar geographic environment to model development areas, so as to maximize the accuracy of landslide detection based on limited samples.
Context-Aware Human Activity and Smartphone Position-Mining with Motion Sensors
Authors: Gao Zhiqiang , Liu Dawei , Huang Kaizhu , Huang Yi
Published: October 2019

Today’s smartphones are equipped with embedded sensors, such as accelerometers and gyroscopes, which have enabled a variety of measurements and recognition tasks. In this paper, we jointly investigate two types of recognition problems in a joint manner, e.g., human activity recognition and smartphone on-body position recognition, in order to enable more robust context-aware applications. So far, these two problems have been studied separately without considering the interactions between each other. In this study, by first applying a novel data preprocessing technique, we propose a joint recognition framework based on the multi-task learning strategy, which can reduce computational demand, better exploit complementary information between the two recognition tasks, and lead to higher recognition performance. We also extend the joint recognition framework so that additional information, such as user identification with biometric motion analysis, can be offered. We evaluate our work systematically and comprehensively on two datasets with real-world settings. Our joint recognition model achieves the promising performance of 0.9174 in terms of F 1 -score for user identification on the benchmark RealWorld Human Activity Recognition (HAR) dataset. On the other hand, in comparison with the conventional approach, the proposed joint model is shown to be able to improve human activity recognition and position recognition by 5.1 % and 9.6 % respectively.
Satellite Retrieval of Downwelling Shortwave Surface Flux and Diffuse Fraction under All Sky Conditions in the Framework of the LSA SAF Program (Part 1: Methodology)
Authors: Carrer Dominique , Ceamanos Xavier , Moparthy Suman , Vincent Chloé , C. Freitas Sandra , Trigo Isabel F.
Published: October 2019

Several studies have shown that changes in incoming solar radiation and variations of the diffuse fraction can significantly modify the vegetation carbon uptake. Hence, monitoring the incoming solar radiation at large scale and with high temporal frequency is crucial for this reason along with many others. The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility for Land Surface Analysis (LSA SAF) has operationally disseminated in near real time estimates of the downwelling shortwave radiation at the surface since 2005. This product is derived from observations provided by the SEVIRI instrument onboard the Meteosat Second Generation series of geostationary satellites, which covers Europe, Africa, the Middle East, and part of South America. However, near real time generation of the diffuse fraction at the surface level has only recently been initiated. The main difficulty towards achieving this goal was the general lack of accurate information on the aerosol particles in the atmosphere. This limitation is less important nowadays thanks to the improvements in atmospheric numerical models. This study presents an upgrade of the LSA SAF operational retrieval method, which provides the simultaneous estimation of the incoming solar radiation and its diffuse fraction from the satellite every 15 min. The upgrade includes a comprehensive representation of the influence of aerosols based on physical approximations of the radiative transfer within an atmosphere-surface associated medium. This article explains the retrieval method, discusses its limitations and differences with the previous method, and details the characteristics of the output products. A companion article will focus on the evaluation of the products against independent measurements of solar radiation. Finally, the access to the source code is provided through an open access platform in order to share the expertise on the satellite retrieval of this variable with the community.
A Strategy to Mitigate the Ionospheric Scintillation Effects on BDS Precise Point Positioning: Cycle-Slip Threshold Model
Authors: Luo Xiaomin , Lou Yidong , Gu Shengfeng , Song Weiwei
Published: October 2019

Because of the special design of BeiDou navigation satellite system (BDS) constellation, the effects of ionospheric scintillation on operational BDS generally are more serious than on the global positioning system (GPS). As BDS is currently providing global services, it is increasingly important to seek strategies to mitigate the scintillation effects on BDS navigation and positioning services. In this study, an improved cycle-slip threshold model is proposed to decrease the high false-alarm rate of cycle-slips under scintillation conditions, thus avoiding the frequent unnecessary ambiguity resets in BDS precise point positioning (PPP) solution. We use one-year (from 23 March 2015 to 23 March 2016) BDS dataset from Hong Kong Sha Tin (HKST) station (22.4°N, 114.2°E; geomagnetic latitude: 15.4°N) to model the cycle-slip threshold and try to make it suitable for three types of BDS satellites and multiple scintillation levels. The availability of our mitigation strategy is validated by using three months (from 1 September 2015 to 30 November 2015) BDS dataset collected at 10 global navigation satellite system (GNSS) stations in Hong Kong. Positioning results demonstrate that our mitigated BDS PPP can prevent the sudden fluctuations of positioning errors induced by the ionospheric scintillation. Statistical results of BDS PPP experiments show that the mitigated solution can maintain an accuracy of about 0.08 m and 0.10 m in the horizontal and vertical components, respectively. Compared with standard BDS PPP, the accuracy of mitigated PPP can be improved by approximately 24.1%, 38.2%, and 47.9% in the east, north, and up directions, respectively. Our study demonstrates that considering different scintillation levels to establish appropriate cycle-slip threshold model in PPP processing can efficiently mitigate the ionospheric scintillation effects on BDS PPP.
waveformlidar: An R Package for Waveform LiDAR Processing and Analysis
Authors: Zhou Tan , Popescu Sorin
Published: October 2019

A wealth of Full Waveform (FW) LiDAR (Light Detection and Ranging) data are available to the public from different sources, which is poised to boost extensive applications of FW LiDAR data. However, we lack a handy and open source tool that can be used by potential users for processing and analyzing FW LiDAR data. To this end, we introduce waveformlidar, an R package dedicated to FW LiDAR processing, analysis and visualization as a solution to the constraint. Specifically, this package provides several commonly used waveform processing methods such as Gaussian, Adaptive Gaussian and Weibull decompositions and deconvolution approaches (Gold and Richard-Lucy (RL)) with users’ customized settings. In addition, we also developed functions to derive commonly used waveform metrics for characterizing vegetation structure. Moreover, a new way to directly visualize FW LiDAR data is developed by converting waveforms into points to form the Hyper Point Cloud (HPC), which can be easily adopted and subsequently analyzed with existing discrete-return LiDAR processing tools such as LAStools and FUSION. Basic explorations of the HPC such as 3D voxelization of the HPC and conversion from original waveforms to composite waveforms are also available in this package. All of these functions are developed based on small-footprint FW LiDAR data but they can be easily transplanted to the large footprint FW LiDAR data such as Geoscience Laser Altimeter System (GLAS) and Global Ecosystem Dynamics Investigation (GEDI) data analysis. It is anticipated that these functions will facilitate the widespread use of FW LiDAR and be beneficial for better estimating biomass and characterizing vegetation structure at various scales.
Improving Clock Prediction Algorithm for BDS-2/3 Satellites Based on LS-SVM Method
Authors: He Lina , Zhou Hairui , Liu Zhiqiang , Wen Yuanlan , He Xiufeng
Published: October 2019

The satellite clock prediction is crucial to support real-time global satellite precise positioning services. Currently, the clock prediction for the Chinese BeiDou navigation satellite system (BDS) is still challenging to satisfy the precise positioning applications. Based on the exploration of existing prediction models, an improved model combing the spectrum analysis model (SAM) and the least-squares support-vector machine (LS-SVM) is proposed especially for BDS-2/3 satellites. Considering satellite-specific characteristics, the parameters of the LS-SVM method are optimized satellite by satellite, including input length, regularization and kernel parameters. The improved model is evaluated by comparing the predicted clocks of existing methods and the improved model. The bias of the predicted clock offsets are within ±1.0 ns for most medium Earth orbits (MEOs) over three hours employing the improved model, which is better than that of the existing methods and can be applied for several real-time precise positioning applications. The predicted clock offsets are further evaluated by applying clock corrections to precise point positioning (PPP) in both static and kinematic modes for 10 international GNSS service (IGS) Multi-GNSS Experiment (MGEX) stations, including five stations in the Asia-Pacific region. According to the practical engineering experience, 2 dm and 5 dm are defined for static and kinematic PPP, respectively, as a convergence threshold. Then, in the static PPP, the improved model is demonstrated to be effective, and positioning accuracies of some stations obtain more than 15% improvements on average for each direction, which enables them to get sub-decimeter positioning, especially in the Asia-Pacific region. In the kinematic PPP, the improved model performs much better than the others in terms of both the convergence time and the positioning accuracy. The convergence time can be shortened from 1.0 h to below 0.5 h, while the positioning accuracies are enhanced by 16.3%, 10.8%, and 18.9% on average in east, north, and up direction, respectively.
Daytime Rainy Cloud Detection and Convective Precipitation Delineation Based on a Deep Neural Network Method Using GOES-16 ABI Images
Authors: Liu Qian , Li Yun , Yu Manzhu , Chiu Long S. , Hao Xianjun , Duffy Daniel Q. , Yang Chaowei
Published: October 2019

Precipitation, especially convective precipitation, is highly associated with hydrological disasters (e.g., floods and drought) that have negative impacts on agricultural productivity, society, and the environment. To mitigate these negative impacts, it is crucial to monitor the precipitation status in real time. The new Advanced Baseline Imager (ABI) onboard the GOES-16 satellite provides such a precipitation product in higher spatiotemporal and spectral resolutions, especially during the daytime. This research proposes a deep neural network (DNN) method to classify rainy and non-rainy clouds based on the brightness temperature differences (BTDs) and reflectances (Ref) derived from ABI. Convective and stratiform rain clouds are also separated using similar spectral parameters expressing the characteristics of cloud properties. The precipitation events used for training and validation are obtained from the IMERG V05B data, covering the southeastern coast of the U.S. during the 2018 rainy season. The performance of the proposed method is compared with traditional machine learning methods, including support vector machines (SVMs) and random forest (RF). For rainy area detection, the DNN method outperformed the other methods, with a critical success index (CSI) of 0.71 and a probability of detection (POD) of 0.86. For convective precipitation delineation, the DNN models also show a better performance, with a CSI of 0.58 and POD of 0.72. This automatic cloud classification system could be deployed for extreme rainfall event detection, real-time forecasting, and decision-making support in rainfall-related disasters.
Dual-NMS: A Method for Autonomously Removing False Detection Boxes from Aerial Image Object Detection Results
Authors: Lin Zhiyuan , Wu Qingxiao , Fu Shuangfei , Wang Sikui , Zhang Zhongyu , Kong Yanzi
Published: October 2019

In the field of aerial image object detection based on deep learning, it’s difficult to extract features because the images are obtained from a top-down perspective. Therefore, there are numerous false detection boxes. The existing post-processing methods mainly remove overlapped detection boxes, but it’s hard to eliminate false detection boxes. The proposed dual non-maximum suppression (dual-NMS) combines the density of detection boxes that are generated for each detected object with the corresponding classification confidence to autonomously remove the false detection boxes. With the dual-NMS as a post-processing method, the precision is greatly improved under the premise of keeping recall unchanged. In vehicle detection in aerial imagery (VEDAI) and dataset for object detection in aerial images (DOTA) datasets, the removal rate of false detection boxes is over 50%. Additionally, according to the characteristics of aerial images, the correlation calculation layer for feature channel separation and the dilated convolution guidance structure are proposed to enhance the feature extraction ability of the network, and these structures constitute the correlation network (CorrNet). Compared with you only look once (YOLOv3), the mean average precision (mAP) of the CorrNet for DOTA increased by 9.78%. Commingled with dual-NMS, the detection effect in aerial images is significantly improved.
Heat Source Forecast of Ball Screw Drive System Under Actual Working Conditions Based on On-Line Measurement of Temperature Sensors
Authors: Li Zhenjun , Lu Zechen , Zhao Chunyu , Liu Fangchen , Chen Ye
Published: October 2019

In view of the time-varying complexity of the heat source for the ball screw feed system, this paper proposes an adaptive inverse problem-solving method to estimate the time-varying heat source and temperature field of the feed system under working conditions. The feed system includes multiple heat sources, and the rapid change of the moving heat source increases the difficulty of its identification. This paper attempts to develop a numerical calculation method for identifying the heat source by combining the experiment with the optimization algorithm. Firstly, based on the theory of heat transfer, a new dynamic thermal network model was proposed. The temperature data signal and the position signal of the moving nut captured by the sensors are used as input to optimize the solution of the time-varying heat source. Then, based on the data obtained from the experiment, finite element software parametric programming was used to optimize the estimate of the heat source, and the results of the two heat source prediction methods are compared and verified. The other measured temperature points obtained by the experiment were used to compare and verify the inverse method of this numerical calculation, which illustrates the reliability and advantages of the dynamic thermal network combined with the genetic algorithm for the inverse method. The method based on the on-line monitoring of temperature sensors proposed in this paper has a strong application value for heat source and temperature field estimation of complex mechanical structures.
Spatial Landslide Susceptibility Assessment Based on Novel Neural-Metaheuristic Geographic Information System Based Ensembles
Authors: Moayedi Hossein , Osouli Abdolreza , Tien Bui Dieu , Foong Loke Kok
Published: October 2019

Regular optimization techniques have been widely used in landslide-related problems. This paper outlines two novel optimizations of artificial neural network (ANN) using grey wolf optimization (GWO) and biogeography-based optimization (BBO) metaheuristic algorithms in the Ardabil province, Iran. To this end, these algorithms are synthesized with a multi-layer perceptron (MLP) neural network for optimizing its computational parameters. The used spatial database consists of fourteen landslide conditioning factors, namely elevation, slope aspect, land use, plan curvature, profile curvature, soil type, distance to river, distance to road, distance to fault, rainfall, slope degree, stream power index (SPI), topographic wetness index (TWI) and lithology. 70% of the identified landslides are randomly selected to train the proposed models and the remaining 30% is used to evaluate the accuracy of them. Also, the frequency ratio theory is used to analyze the spatial interaction between the landslide and conditioning factors. Obtained values of area under the receiver operating characteristic curve, as well as mean square error and mean absolute error showed that both GWO and BBO hybrid algorithms could efficiently improve the learning capability of the MLP. Besides, the BBO-based ensemble surpasses other implemented models.
LEO-Augmented GNSS Based on Communication Navigation Integrated Signal
Authors: Wang Lei , Lü Zhicheng , Tang Xiaomei , Zhang Ke , Wang Feixue
Published: October 2019

Low Earth Orbit (LEO) is of great benefit for the positioning performance of Global Navigation Satellite System (GNSS). To realize the system of LEO-augmented GNSS, three methods to integrate communication and navigation signal for LEO communication system with the least influence on the communication performance are analyzed. The analysis adopts the parameters of IRIDIUM signal as restrictions. This paper gives quantitative comparison of these methods considering CN0(carrier noise power spectral density rate) margin, pseudorange accuracy, Doppler accuracy, and communication loss. For method 1, a low-power navigation signal is added to the communication signal. For method 2, the navigation signal is launched in one or more frames. For method 3, the navigation signal is launched in the frequency band separated to the communication signal. The result shows that the pseudorange accuracy of method 2 is far below method 1 and method 3. However, the difference of Doppler accuracy among the three methods can be emitted. Detailed analysis shows that method 1 is practicable when the communication and navigation signal power rate is 15 dB. It achieves the balance of pseudorange accuracy and bit error rate (BER) performance under this condition. Comprehensive comparison of these methods is given in the last. The result shows that the CN0 margin of the navigation signal for method 3 can be 13.04 dB higher than method 1, based on the accuracy threshold considered in this paper. Methods 1 and 3 have the advantage of high accuracy and high CN0 margin respectively. However, method 3 causes high communication capacity loss. Considering that the main disadvantage of GNSS signals is low CN0, method 3 is a good choice for the LEO-augmented GNSS system. Methods 1 and 3 can be combined to realize both high accuracy and high CN0 margin if possible.
Suitability of a Non-Dispersive Infrared Methane Sensor Package for Flux Quantification Using an Unmanned Aerial Vehicle
Authors: Shah Adil , Pitt Joseph , Kabbabe Khristopher , Allen Grant
Published: October 2019

Point-source methane emission flux quantification is required to help constrain the global methane budget. Facility-scale fluxes can be derived using in situ methane mole fraction sampling, near-to-source, which may be acquired from an unmanned aerial vehicle (UAV) platform. We test a new non-dispersive infrared methane sensor by mounting it onto a small UAV, which flew downwind of a controlled methane release. Nine UAV flight surveys were conducted on a downwind vertical sampling plane, perpendicular to mean wind direction. The sensor was first packaged in an enclosure prior to sampling which contained a pump and a recording computer, with a total mass of 1.0 kg. The packaged sensor was then characterised to derive a gain factor of 0.92 ± 0.07, independent of water mole fraction, and an Allan deviation precision (at 1 Hz) of ±1.16 ppm. This poor instrumental precision and possible short-term drifts made it non-trivial to define a background mole fraction during UAV surveys, which may be important where any measured signal is small compared to sources of instrumental uncertainty and drift. This rendered the sensor incapable of deriving a meaningful flux from UAV sampling for emissions of the order of 1 g s−1. Nevertheless, the sensor may indeed be useful when sampling mole fraction enhancements of the order of at least 10 ppm (an order of magnitude above the 1 Hz Allan deviation), either from stationary ground-based sampling (in baseline studies) or from mobile sampling downwind of sources with greater source flux than those observed in this study. While many methods utilising low-cost sensors to determine methane flux are being developed, this study highlights the importance of adequately characterising and testing all new sensors before they are used in scientific research.
Adversarially Learned Total Variability Embedding for Speaker Recognition with Random Digit Strings
Authors: Kang Woo Hyun , Kim Nam Soo
Published: October 2019

Over the recent years, various research has been conducted to investigate methods for verifying users with a short randomized pass-phrase due to the increasing demand for voice-based authentication systems. In this paper, we propose a novel technique for extracting an i-vector-like feature based on an adversarially learned inference (ALI) model which summarizes the variability within the Gaussian mixture model (GMM) distribution through a nonlinear process. Analogous to the previously proposed variational autoencoder (VAE)-based feature extractor, the proposed ALI-based model is trained to generate the GMM supervector according to the maximum likelihood criterion given the Baum–Welch statistics of the input utterance. However, to prevent the potential loss of information caused by the Kullback–Leibler divergence (KL divergence) regularization adopted in the VAE-based model training, the newly proposed ALI-based feature extractor exploits a joint discriminator to ensure that the generated latent variable and the GMM supervector are more realistic. The proposed framework is compared with the conventional i-vector and VAE-based methods using the TIDIGITS dataset. Experimental results show that the proposed method can represent the uncertainty caused by the short duration better than the VAE-based method. Furthermore, the proposed approach has shown great performance when applied in association with the standard i-vector framework.
The Influence of Air Pressure on the Dynamics of Flexural Ultrasonic Transducers
Authors: Feeney Andrew , Kang Lei , Somerset William E. , Dixon Steve
Published: October 2019

The flexural ultrasonic transducer comprises a piezoelectric ceramic disc bonded to a membrane. The vibrations of the piezoelectric ceramic disc induce flexural modes in the membrane, producing ultrasound waves. The transducer is principally utilized for proximity or flow measurement, designed for operation at atmospheric pressure conditions. However, there is rapidly growing industrial demand for the flexural ultrasonic transducer in applications including water metering or in petrochemical plants where the pressure levels of the gas or liquid environment can approach 100 bar. In this study, characterization methods including electrical impedance analysis and pitch-catch ultrasound measurement are employed to demonstrate the dynamic performance of flexural ultrasonic transducers in air at elevated pressures approaching 100 bar. Measurement principles are discussed, in addition to modifications to the transducer design for ensuring resilience at increasing air pressure levels. The results highlight the importance of controlling the parameters of the measurement environment and show that although the conventional design of flexural ultrasonic transducer can exhibit functionality towards 100 bar, its dynamic performance is unsuitable for accurate ultrasound measurement. It is anticipated that this research will initiate new developments in ultrasound measurement systems for fluid environments at elevated pressures.
Outlier Detection Using Improved Support Vector Data Description in Wireless Sensor Networks
Authors: Shi Pei , Li Guanghui , Yuan Yongming , Kuang Liang
Published: October 2019

Wireless sensor networks (WSNs) are susceptible to faults in sensor data. Outlier detection is crucial for ensuring the quality of data analysis in WSNs. This paper proposes a novel improved support vector data description method (ID-SVDD) to effectively detect outliers of sensor data. ID-SVDD utilizes the density distribution of data to compensate SVDD. The Parzen-window algorithm is applied to calculate the relative density for each data point in a data set. Meanwhile, we use Mahalanobis distance (MD) to improve the Gaussian function in Parzen-window density estimation. Through combining new relative density weight with SVDD, this approach can efficiently map the data points from sparse space to high-density space. In order to assess the outlier detection performance, the ID-SVDD algorithm was implemented on several datasets. The experimental results demonstrated that ID-SVDD achieved high performance, and could be applied in real water quality monitoring.
A Low-Cost Strain Gauge Displacement Sensor Fabricated via Shadow Mask Printing
Authors: Yi Ying , Wang Bo , Bermak Amine
Published: October 2019

This work presents a cost-effective shadow mask printing approach to fabricate flexible sensors. The liquid-state sensing material can be directly brushed on a flexible substrate through a shadow mask. The ink leakage issue which often occurs in printed electronics is addressed with a custom taping scheme. A simple thermal compression bonding approach is also proposed to package the functional area of the sensor. To verify the feasibility and robustness of the proposed fabrication approach, a prototyped strain gauge displacement sensor is fabricated using carbon ink as the sensing material and a flexible polyimide (PI) film as the substrate. Once the substrate is deformed, cracks in the solidified ink layer can cause an increased resistance in the conductive path, thus achieving function of stable displacement/strain sensing. As a demonstration for displacement sensing application, this sensor is evaluated by studying its real-time resistance response under both static and dynamic mechanical loading. The fabricated sensor shows a comparable performance (with a gauge factor of ~17.6) to those fabricated using costly lithography or inkjet printing schemes, while with a significantly lower production cost.
A Bootstrapped Comparator-Switched Active Rectifying Circuit for Wirelessly Powered Integrated Miniaturized Energy Sensing Systems
Authors: Gong Cihun-Siyong Alex , Li Shiang-Wei , Shiue Muh-Tian
Published: October 2019

Human life expectancy has gradually increased in part through rapid advances in technology, including the development and use of wearable and implantable biomedical electronic devices and sensing monitors. A new architecture is proposed in this paper to replace the traditional diode circuit implementation in wireless power supply systems applied to the above-mentioned devices and monitors. By achieving near-ideal power transistor switching and leveraging the characteristics of conventional diodes to prevent reverse current, the proposed approach greatly improves the performance of the energy harvester in power conversion. The MOS harvester used in the uninterrupted permanent wireless near-field power supply described here for use in biomedical systems was designed and verified using the Taiwan Semiconductor Manufacturing Company (TSMC) standard 180-nm process, achieving performance results of Voltage conversion efficiency (VCE) = 73.55–95.12% and Power conversion efficiency (PCE) = 80.36–90.08% with the output load (0.1–1 kΩ) under 3.3 V ac input with an overall area of 1.189 mm2. These results are expected to create an important technical niche for new “green-energy” miniaturized energy sensing systems including cutting edge wirelessly powered biomedical electronics applications.
An Intra-Oral Optical Sensor for the Real-Time Identification and Assessment of Wine Intake
Authors: Faragó Paul , Gălătuș Ramona , Hintea Sorin , Boșca Adina Bianca , Feurdean Claudia Nicoleta , Ilea Aranka
Published: October 2019

Saliva has gained considerable attention as a diagnostics alternative to blood analyses. A wide spectrum of salivary compounds is correlated to blood concentrations of biomarkers, providing informative and discriminative data regarding the state of health. Intra-oral detection and assessment of food and beverage intake can be correlated and provides valuable information to forecast the formation and modification of salivary biomarkers. In this context, the present work proposes a novel intra-oral optical fiber sensor, developed around an optical coupler topology, and exemplified on the detection and assessment of wine intake, which is accounted for example for the formation of Nε-carboxymethyllysine Advanced Glycation End-products. A laboratory proof of concept validates the proposed solution on four white and four red wine samples. The novel optical sensor geometry shows good spectral properties, accounting for selectivity with respect to grape-based soft drinks. This enables intra-oral detection and objective quality assessment of wine. Moreover, its implementation exploits the advantages of fiber-optics sensing and facilitates integration into a mouthguard, holding considerable potential for real-time biomedical applications to investigate Advanced Glycation End-products in the saliva and their connection with consumption of wine, for the evaluation of risk factors in diet-related diseases.