bluuebunny/crossref_metadata_2025 huggingface dataset:
Study Rheological Behavior of Polymer Solution in Different-Medium-Injection-Tools
Authors: Huang Bin , Li Xiaohui , Fu Cheng , Wang Ying , Cheng Haoran
Published: February 2019

Previous studies showed the difficulty during polymer flooding and the low producing degree for the low permeability layer. To solve the problem, Daqing, the first oil company, puts forward the polymer-separate-layer-injection-technology which separates mass and pressure in a single pipe. This technology mainly increases the control range of injection pressure of fluid by using the annular de-pressure tool, and reasonably distributes the molecular weight of the polymer injected into the thin and poor layers through the shearing of the different-medium-injection-tools. This occurs, in order to take advantage of the shearing thinning property of polymer solution and avoid the energy loss caused by the turbulent flow of polymer solution due to excessive injection rate in different injection tools. Combining rheological property of polymer and local perturbation theory, a rheological model of polymer solution in different-medium-injection-tools is derived and the maximum injection velocity is determined. The ranges of polymer viscosity in different injection tools are mainly determined by the structures of the different injection tools. However, the value of polymer viscosity is mainly determined by the concentration of polymer solution. So, the relation between the molecular weight of polymer and the permeability of layers should be firstly determined, and then the structural parameter combination of the different-medium-injection-tool should be optimized. The results of the study are important for regulating polymer injection parameters in the oilfield which enhances the oil recovery with reduced the cost.
Valorization of Alkaline Peroxide Mechanical Pulp by Metal Chloride-Assisted Hydrotropic Pretreatment for Enzymatic Saccharification and Cellulose Nanofibrillation
Authors: Bian Huiyang , Wu Xinxing , Luo Jing , Qiao Yongzhen , Fang Guigan , Dai Hongqi
Published: February 2019

Developing economical and sustainable fractionation technology of lignocellulose cell walls is the key to reaping the full benefits of lignocellulosic biomass. This study evaluated the potential of metal chloride-assisted p-toluenesulfonic acid (p-TsOH) hydrolysis at low temperatures and under acid concentration for the co-production of sugars and lignocellulosic nanofibrils (LCNF). The results indicated that three metal chlorides obviously facilitated lignin solubilization, thereby enhancing the enzymatic hydrolysis efficiency and subsequent cellulose nanofibrillation. The CuCl2-assisted hydrotropic pretreatment was most suitable for delignification, resulting in a relatively higher enzymatic hydrolysis efficiency of 53.2%. It was observed that the higher residual lignin absorbed on the fiber surface, which exerted inhibitory effects on the enzymatic hydrolysis, while the lower lignin content substrates resulted in less entangled LCNF with thinner diameters. The metal chloride-assisted rapid and low-temperature fractionation process has a significant potential in achieving the energy-efficient and cost-effective valorization of lignocellulosic biomass.
Extended Regression Models for Predicting the Pumping Capability and Viscous Dissipation of Two-Dimensional Flows in Single-Screw Extrusion
Authors: Roland Wolfgang , Kommenda Michael , Marschik Christian , Miethlinger Jürgen
Published: February 2019

Generally, numerical methods are required to model the non-Newtonian flow of polymer melts in single-screw extruders. Existing approximation equations for modeling the throughput–pressure relationship and viscous dissipation are limited in their scope of application, particularly when it comes to special screw designs. Maximum dimensionless throughputs of Π V < 2.0 , implying minimum dimensionless pressure gradients Π p , z ≥ − 0.5 for low power-law exponents are captured. We present analytical approximation models for predicting the pumping capability and viscous dissipation of metering channels for an extended range of influencing parameters ( Π p , z ≥ − 1.0 , and t / D b ≤ 2.4 ) required to model wave- and energy-transfer screws. We first rewrote the governing equations in dimensionless form, identifying three independent influencing parameters: (i) the dimensionless down-channel pressure gradient Π p , z , (ii) the power-law exponent n , and (iii) the screw-pitch ratio t / D b . We then carried out a parametric design study covering an extended range of the dimensionless influencing parameters. Based on this data set, we developed regression models for predicting the dimensionless throughput-pressure relationship and the viscous dissipation. Finally, the accuracy of all three models was proven using an independent data set for evaluation. We demonstrate that our approach provides excellent approximation. Our models allow fast, stable, and accurate prediction of both throughput-pressure behavior and viscous dissipation.
Composites of Rigid Polyurethane Foams Reinforced with POSS
Authors: Członka Sylwia , Strąkowska Anna , Strzelec Krzysztof , Adamus-Włodarczyk Agnieszka , Kairytė Agnė , Vaitkus Saulius
Published: February 2019

Rigid polyurethane foams (RPUFs) were successfully modified with different weight ratios (0.5 wt%, 1.5 wt% and 5 wt%) of APIB-POSS and AEAPIB-POSS. The resulting foams were evaluated by their processing parameters, morphology (Scanning Electron Microscopy analysis, SEM), mechanical properties (compressive test, three-point bending test and impact strength), viscoelastic behavior (Dynamic Mechanical Analysis, DMA), thermal properties (Thermogravimetric Analysis, TGA, and thermal conductivity) and application properties (contact angle, water absorption and dimensional analysis). The results showed that the morphology of modified foams is significantly affected by the type of the filler and filler content, which resulted in inhomogeneous, irregular, large cell shapes and further affected the physical and mechanical properties of resulting materials. RPUFs modified with APIB-POSS represent better mechanical and thermal properties compared to the RPUFs modified with AEAPIB-POSS. The results showed that the best results were obtained for RPUFs modified with 0.5 wt% of APIB-POSS. For example, in comparison with unfilled foam, compositions modified with 0.5 wt% of APIB-POSS provide greater compression strength, better flexural strength and lower water absorption.
Aquinas and Scotus on the Metaphysical Foundations of Morality
Authors: Clanton J. , Martin Kraig
Published: February 2019

This paper retraces some of the contrast between Aquinas and Scotus with respect to the metaphysical foundations of morality in order to highlight how subtle differences pertaining to the relationship between the divine will and the divine intellect can tip a thinker toward either an unalloyed natural law theory (NLT) or something that at least starts to move in the direction of divine command theory (DCT). The paper opens with a brief consideration of three distinct elements in Aquinas’s work that might tempt one to view him in a DCT light, namely: his discussion of the divine law in addition to the natural law; his position on the so-called immoralities of the patriarchs; and some of his assertions about the divine will in relation to justice. We then respond to each of those considerations. In the second and third of these cases, following Craig Boyd, we illustrate how Aquinas’s conviction that the divine will follows the ordering of the divine intellect can help inform the interpretive disputes in question. We then turn our attention to Scotus’s concern about the freedom of the divine will, before turning to his discussion of the natural law in relation to the Decalogue as a way of stressing how his two-source theory of the metaphysical foundations of morality represents a clear departure from Aquinas in the direction of DCT.
Assessing Bioremediation of Soils Polluted with Fuel Oil 6 by Means of Diffuse Reflectance Spectroscopy
Authors: García Víctor J. , Márquez Carmen O. , Cedeño Andrés R. , Montesdeoca Kleber G.
Published: February 2019

This study aimed to assess the bioremediation of soils polluted with fuel oil 6 (FO6) using diffuse reflectance (DR) spectroscopy in the visible and near infrared (Vis-NIR) electromagnetic spectrum. To achieve our goal, we determined the spectral signature of fuel oil 6 (FO6), developed a calibration model to quantify the total petroleum hydrocarbons (TPH), and assessed the bioremediation in soils contaminated with FO6 and inoculated with Pseudomonas aeruginosa. Surface soil samples (SS) (0–30 cm depth) from uncontaminated Entisol soil from Termoesmeraldas Thermal Power Plant, Ecuador and quart sand (QS) samples were spiked with FO6 at a known contamination of 0.5, 1, 3, 6, 9, 12 wt.% on a gravimetric basis. A sample of contaminated Entisol soil was taken to isolate P. aeruginosa from a spill site located in Termoesmeraldas. P. aeruginosa was successfully augmented in a molasses medium. The results suggested that the C–H stretch combination overtone band around 2300 nm is the one that makes the significant contribution to the FO6 spectral signature and for the analysis of FO6 contaminated Entisols soil. The calibration model for QS samples and SS showed an excellent agreement with experimental data R2 = 0.9989 and R2 = 0.9968, respectively. The TPH at 0, 7, 14, 21, and 23 days after inoculation were found using a calibration model developed and the Unach hydrocarbon index (UHI). While the QS samples showed the lower recovery rate (13.6%), the Entisols SS showed the higher recovery rate (45.8%) in 23 days. The use of DR spectroscopy and determination of the FO6 spectral signature allowed the assessment of the bioremediation process of QS and Entisols SS samples. The results showed that DR decreased with increasing the FO6 concentration and soil properties affected the degree of biodegradation.
High-Coverage Satellite-Based Coastal Bathymetry through a Fusion of Physical and Learning Methods
Authors: Danilo Céline , Melgani Farid
Published: February 2019

An up-to-date knowledge of water depth is essential for a wide range of coastal activities, such as navigation, fishing, study of coastal erosion, or the observation of the rise of water levels due to climate change. This paper presents a coastal bathymetry estimation method that takes a single satellite acquisition as input, aimed at scenarios where in situ data are not available or would be too costly to obtain. The method uses free multispectral images that are easy to obtain for any region of the globe from sources such as the Sentinel-2 or Landsat-8 satellites. In order to address the shortcomings of existing image-only approaches (low resolution, scarce spatial coverage especially in the shallow water zones, dependence on specific physical conditions) we derive a new bathymetry estimation approach that combines a physical wave model with a statistical method based on Gaussian Process Regression learned in an unsupervised way. The resulting system is able to provide a nearly complete coverage of the 2–12-m-depth zone at a resolution of 80 m. Evaluated on three sites around the Hawaiian Islands, our method obtained estimates with a correlation coefficient in the range of 0.7–0.9. Furthermore, the trained models provide equally good results in nearby zones that lack exploitable waves, extending the scope of applicability of the method.
Infrared Small Target Detection Based on Partial Sum of the Tensor Nuclear Norm
Authors: Zhang Landan , Peng Zhenming
Published: February 2019

Excellent performance, real time and strong robustness are three vital requirements for infrared small target detection. Unfortunately, many current state-of-the-art methods merely achieve one of the expectations when coping with highly complex scenes. In fact, a common problem is that real-time processing and great detection ability are difficult to coordinate. Therefore, to address this issue, a robust infrared patch-tensor model for detecting an infrared small target is proposed in this paper. On the basis of infrared patch-tensor (IPT) model, a novel nonconvex low-rank constraint named partial sum of tensor nuclear norm (PSTNN) joint weighted l1 norm was employed to efficiently suppress the background and preserve the target. Due to the deficiency of RIPT which would over-shrink the target with the possibility of disappearing, an improved local prior map simultaneously encoded with target-related and background-related information was introduced into the model. With the help of a reweighted scheme for enhancing the sparsity and high-efficiency version of tensor singular value decomposition (t-SVD), the total algorithm complexity and computation time can be reduced dramatically. Then, the decomposition of the target and background is transformed into a tensor robust principle component analysis problem (TRPCA), which can be efficiently solved by alternating direction method of multipliers (ADMM). A series of experiments substantiate the superiority of the proposed method beyond state-of-the-art baselines.
Boreal Forest Snow Damage Mapping Using Multi-Temporal Sentinel-1 Data
Authors: Tomppo Erkki , Antropov Oleg , Praks Jaan
Published: February 2019

Natural disturbances significantly influence forest ecosystem services and biodiversity. Accurate delineation and early detection of areas affected by disturbances are critical for estimating extent of damage, assessing economical influence and guiding forest management activities. In this study we focus on snow load damage detection from C-Band SAR images. Snow damage is one of the least studied forest damages, which is getting more common due to current climate trends. The study site was located in the southern part of Northern Finland and the SAR data were represented by the time series of C-band SAR scenes acquired by the Sentinel-1 sensor. Methods used in the study included improved k nearest neighbour method, logistic regression analysis and support vector machine classification. Snow damage recordings from a large snow damage event that took place in Finland during late 2018 were used as reference data. Our results showed an overall detection accuracy of 90%, indicating potential of C-band SAR for operational use in snow damage mapping. Additionally, potential of multitemporal Sentinel-1 data in estimating growing stock volume in damaged forest areas were carried out, with obtained results indicating strong potential for estimating the overall volume of timber within the affected areas. The results and research questions for further studies are discussed.
Statistical Properties of an Unassisted Image Quality Index for SAR Imagery
Authors: Gomez Luis , Ospina Raydonal , Frery Alejandro C.
Published: February 2019

The M estimator is a recently proposed image-quality index used to evaluate the despeckling operation in SAR (Synthetic Aperture Radar) data. It is used also to rank despeckling filters and to improve their design. As a difference with traditional image-quality estimators, it operates not on the filtered result but on a derived one, i.e., the ratio image. However, a deep statistical analysis of its properties remains open and, with it, the ability to use it as a test statistic. In this work, we focus on obtaining insights into its distribution as well as on exploring other remarkable statistical properties of this unassisted estimator. This study is performed through EDA (Exploratory Data Analysis) and the well-known ANOVA (ANalysis Of VAriance). We test our results on a set of simulated SAR data and provide guides to enrich the M estimator to extend its capabilities.
Spatiotemporal Filtering and Noise Analysis for Regional GNSS Network in Antarctica Using Independent Component Analysis
Authors: Li Wenhao , Li Fei , Zhang Shengkai , Lei Jintao , Zhang Qingchuan , Yuan Lexian
Published: February 2019

The common mode error (CME) and optimal noise model are the two most important factors affecting the accuracy of time series in regional Global Navigation Satellite System (GNSS) networks. Removing the CME and selecting the optimal noise model can effectively improve the accuracy of GNSS coordinate time series. The CME, a major source of error, is related to the spatiotemporal distribution; hence, its detrimental effects on time series can be effectively reduced through spatial filtering. Independent component analysis (ICA) is used to filter the time series recorded by 79 GPS stations in Antarctica from 2010 to 2018. After removing stations exhibiting strong local effects using their spatial responses, the filtering results of residual time series derived from principal component analysis (PCA) and ICA are compared and analyzed. The Akaike information criterion (AIC) is then used to determine the optimal noise model of the GPS time series before and after ICA/PCA filtering. The results show that ICA is superior to PCA regarding both the filter results and the consistency of the optimal noise model. In terms of the filtering results, ICA can extract multisource error signals. After ICA filtering, the root mean square (RMS) values of the residual time series are reduced by 14.45%, 8.97%, and 13.27% in the east (E), north (N), and vertical (U) components, respectively, and the associated speed uncertainties are reduced by 13.50%, 8.06% and 11.82%, respectively. Furthermore, different GNSS time series in Antarctica have different optimal noise models with different noise characteristics in different components. The main noise models are the white noise plus flicker noise (WN+FN) and white noise plus power law noise (WN+PN) models. Additionally, the spectrum index of most PN is close to that of FN. Finally, there are more stations with consistent optimal noise models after ICA filtering than there are after PCA filtering.
Predicting Rice Grain Yield Based on Dynamic Changes in Vegetation Indexes during Early to Mid-Growth Stages
Authors: Zhang Ke , Ge Xiaokang , Shen Pengcheng , Li Wanyu , Liu Xiaojun , Cao Qiang , Zhu Yan , Cao Weixing , Tian Yongchao
Published: February 2019

Predicting the grain yield during early to mid-growth stages is important for initial diagnosis of rice and quantitative regulation of topdressing. In this study, we conducted four experiments using different nitrogen (N) application rates (0–400 kg N∙ha−1) in three Japonica rice cultivars (Wuyunjing24, Ningjing4, and Lianjing7) grown in Jiangsu province, Eastern China, from 2015–2016. Spectral reflectance data were collected multiple times during early to mid-growth stages using an active mounted sensor (RapidScan CS-45, Holland Scientific Inc., Lincoln, NE, USA). Data were then used to calculate optimal vegetation indexes (normalized difference red edge, NDRE; normalized difference vegetation index, NDVI; ratio vegetation index, RVI; red-edge ratio vegetation index, RERVI), which were used to develop a dynamic change model and in-season grain yield prediction model. The NDRE index was more stable than other indexes (NDVI, RVI, RERVI), showing less standard deviation at the same N fertilizer rate. The R2 of the relationships between leaf area index (LAI), plant nitrogen accumulation (PNA), and NDRE also increased compared to other indexes. These findings suggest that NDRE is suitable for analysis of paddy rice N nutrition. According to real-time series changes in NDRE, the resulting dynamic model followed a sigmoid curve, with a coefficient of determination (R2) >0.9 and relative root-mean-square error <5%. Moreover, the feature platform value (saturation value, SV) of the NDRE-based model accurately predicted the differences between treatments and the final grain yield levels. R2 values of the relationship between SV and yield were >0.7. For every 0.1 increase in SV, grain yield increased by 3608.1 kg·ha−1. Overall, our new dynamic model effectively predicted grain yield at stem elongation and booting stages, providing real-time crop N nutrition data for management of N fertilizer topdressing in rice production.
Correlation between Spectral Characteristics and Physicochemical Parameters of Soda-Saline Soils in Different States
Authors: Li Xiaojie , Ren Jianhua , Zhao Kai , Liang Zhengwei
Published: February 2019

The spectral features of soils are a comprehensive representation of their physicochemical parameters, surface states, and internal structures. To date, spectral measurements have been mostly performed for powdered soils and smooth aggregate soils, but rarely for cracked soils; a common state of soda-saline soils. In this study, we measured the spectral features of 57 saline soil samples in powdered, aggregate, and cracked states for comparison. We then explored in depth the factors governing soil spectral features to build up simple and multiple linear regression models between the spectral features and physicochemical parameters (salt content, Na+, pH, and electronic conductivity (EC)) of saline soils in different states. We randomly selected 40 samples to construct the models, and used the remaining 17 samples for validation. Our results indicated that the regression models worked more effectively in predicting physicochemical parameters for cracked soils than for other soils. Subsequently, the crack ratio (CR) was introduced into the regression models to modify the spectra of soils in powdered and aggregate states. The accuracy of prediction was improved, evidenced by a 2–11% decrease in the parameters mean absolute error (MAE).
Editorial for Special Issue “Remote Sensing of Precipitation”
Authors: Michaelides Silas
Published: February 2019

This Special Issue hosts papers on all aspects of remote sensing of precipitation, including applications that embrace the use of remote-sensing techniques of precipitation in tackling issues, such as precipitation estimations and retrievals, along with their methodologies and corresponding error assessment; precipitation modelling including validation, instrument comparison, and calibration; understanding of cloud and precipitation microphysical properties; precipitation downscaling; precipitation droplet size distribution; assimilation of remotely sensed precipitation into numerical weather prediction models; and measurement of precipitable water vapor.
High Three-Dimensional Detection Accuracy in Piezoelectric-Based Touch Panel in Interactive Displays by Optimized Artificial Neural Networks
Authors: Gao Shuo , Dai Yanning , Kitsos Vasileios , Wan Bo , Qu Xiaolei
Published: February 2019

High detection accuracy in piezoelectric-based force sensing in interactive displays has gained global attention. To achieve this, artificial neural networks (ANN)—successful and widely used machine learning algorithms—have been demonstrated to be potentially powerful tools, providing acceptable location detection accuracy of 95.2% and force level recognition of 93.3% in a previous study. While these values might be acceptable for conventional operations, e.g., opening a folder, they must be boosted for applications where intensive operations are performed. Furthermore, the relatively high computational cost reported prevents the popularity of ANN-based techniques in conventional artificial intelligence (AI) chip-free end-terminals. In this article, an ANN is designed and optimized for piezoelectric-based touch panels in interactive displays for the first time. The presented technique experimentally allows a conventional smart device to work smoothly with a high detection accuracy of above 97% for both location and force level detection with a low computational cost, thereby advancing the user experience, and serviced by piezoelectric-based touch interfaces in displays.
A Novel Method for Early Gear Pitting Fault Diagnosis Using Stacked SAE and GBRBM
Authors: Li Jialin , Li Xueyi , He David , Qu Yongzhi
Published: February 2019

Research on data-driven fault diagnosis methods has received much attention in recent years. The deep belief network (DBN) is a commonly used deep learning method for fault diagnosis. In the past, when people used DBN to diagnose gear pitting faults, it was found that the diagnosis result was not good with continuous time domain vibration signals as direct inputs into DBN. Therefore, most researchers extracted features from time domain vibration signals as inputs into DBN. However, it is desirable to use raw vibration signals as direct inputs to achieve good fault diagnosis results. Therefore, this paper proposes a novel method by stacking spare autoencoder (SAE) and Gauss-Binary restricted Boltzmann machine (GBRBM) for early gear pitting faults diagnosis with raw vibration signals as direct inputs. The SAE layer is used to compress the raw vibration data and the GBRBM layer is used to effectively process continuous time domain vibration signals. Vibration signals of seven early gear pitting faults collected from a gear test rig are used to validate the proposed method. The validation results show that the proposed method maintains a good diagnosis performance under different working conditions and gives higher diagnosis accuracy compared to other traditional methods.
Histogram-Based CRC for 3D-Aided Pose-Invariant Face Recognition
Authors: Shi Liang , Song Xiaoning , Zhang Tao , Zhu Yuquan
Published: February 2019

Traditional Collaborative Representation-based Classification algorithms for face recognition (CRC) usually suffer from data uncertainty, especially if it includes various poses and illuminations. To address this issue, in this paper, we design a new CRC method using histogram statistical measurement (H-CRC) combined with a 3D morphable model (3DMM) for pose-invariant face classification. First, we fit a 3DMM to raw images in the dictionary to reconstruct the 3D shapes and textures. The fitting results are used to render numerous virtual samples of 2D images that are frontalized from arbitrary poses. In contrast to other distance-based evaluation algorithms for collaborative (or sparse) representation-based methods, the histogram information of all the generated 2D face images is subsequently exploited. Second, we use a histogram-based metric learning to evaluate the most similar neighbours of the test sample, which aims to obtain ideal result for pose-invariant face recognition using the designed histogram-based 3DMM model and online pruning strategy, forming a unified 3D-aided CRC framework. The proposed method achieves desirable classification results that are conducted on a set of well-known face databases, including ORL, Georgia Tech, FERET, FRGC, PIE and LFW.
Heavy Metal Soil Contamination Detection Using Combined Geochemistry and Field Spectroradiometry in the United Kingdom
Authors: Lamine Salim , Petropoulos George P. , Brewer Paul A. , Bachari Nour-El-Islam , Srivastava Prashant K. , Manevski Kiril , Kalaitzidis Chariton , Macklin Mark G.
Published: February 2019

Technological advances in hyperspectral remote sensing have been widely applied in heavy metal soil contamination studies, as they are able to provide assessments in a rapid and cost-effective way. The present work investigates the potential role of combining field and laboratory spectroradiometry with geochemical data of lead (Pb), zinc (Zn), copper (Cu) and cadmium (Cd) in quantifying and modelling heavy metal soil contamination (HMSC) for a floodplain site located in Wales, United Kingdom. The study objectives were to: (i) collect field- and lab-based spectra from contaminated soils by using ASD FieldSpec® 3, where the spectrum varies between 350 and 2500 nm; (ii) build field- and lab-based spectral libraries; (iii) conduct geochemical analyses of Pb, Zn, Cu and Cd using atomic absorption spectrometer; (iv) identify the specific spectral regions associated to the modelling of HMSC; and (v) develop and validate heavy metal prediction models (HMPM) for the aforementioned contaminants, by considering their spectral features and concentrations in the soil. Herein, the field- and lab-based spectral features derived from 85 soil samples were used successfully to develop two spectral libraries, which along with the concentrations of Pb, Zn, Cu and Cd were combined to build eight HMPMs using stepwise multiple linear regression. The results showed, for the first time, the feasibility to predict HMSC in a highly contaminated floodplain site by combining soil geochemistry analyses and field spectroradiometry. The generated models help for mapping heavy metal concentrations over a huge area by using space-borne hyperspectral sensors. The results further demonstrated the feasibility of combining geochemistry analyses with filed spectroradiometric data to generate models that can predict heavy metal concentrations.
Off-Site Indoor Localization Competitions Based on Measured Data in a Warehouse
Authors: Ichikari Ryosuke , Kaji Katsuhiko , Shimomura Ryo , Kourogi Masakatsu , Okuma Takashi , Kurata Takeshi
Published: February 2019

The performance of indoor localization methods is highly dependent on the situations in which they are used. Various competitions on indoor localization have been held for fairly comparing the existing indoor localization methods in shared and controlled testing environments. However, it is difficult to evaluate the practical performance in industrial scenarios through the existing competitions. This paper introduces two indoor localization competitions, which are named the “PDR Challenge in Warehouse Picking 2017” and “xDR Challenge for Warehouse Operations 2018” for tracking workers and vehicles in a warehouse scenario. For the PDR Challenge in Warehouse Picking 2017, we conducted a unique competition based on the data measured during the actual picking operation in an actual warehouse. We term the dead-reckoning of a vehicle as vehicle dead-reckoning (VDR), and the term “xDR” is derived from pedestrian dead-reckoning (PDR) plus VDR. As a sequel competition of the PDR Challenge in Warehouse Picking 2017, the xDR Challenge for Warehouse Operations 2018 was conducted as the world’s first competition that deals with tracking forklifts by VDR with smartphones. In the paper, first, we briefly summarize the existing competitions, and clarify the characteristics of our competitions by comparing them with other competitions. Our competitions have the unique capability of evaluating the practical performance in a warehouse by using the actual measured data as the test data and applying multi-faceted evaluation metrics. As a result, we successfully organize the competitions due to the many participants from many countries. As a conclusion of the paper, we summarize the findings of the competitions.
A Novel Surface Descriptor for Automated 3-D Object Recognition and Localization
Authors: Chen Liang-Chia , Nguyen Thanh-Hung
Published: February 2019

This paper presents a novel approach to the automated recognition and localization of 3-D objects. The proposed approach uses 3-D object segmentation to segment randomly stacked objects in an unstructured point cloud. Each segmented object is then represented by a regional area-based descriptor, which measures the distribution of surface area in the oriented bounding box (OBB) of the segmented object. By comparing the estimated descriptor with the template descriptors stored in the database, the object can be recognized. With this approach, the detected object can be matched with the model using the iterative closest point (ICP) algorithm to detect its 3-D location and orientation. Experiments were performed to verify the feasibility and effectiveness of the approach. With the measured point clouds having a spatial resolution of 1.05 mm, the proposed method can achieve both a mean deviation and standard deviation below half of the spatial resolution.
Optimization of Reconfigurable Satellite Constellations Using Simulated Annealing and Genetic Algorithm
Authors: Paek Sung Wook , Kim Sangtae , de Weck Olivier
Published: February 2019

Agile Earth observation can be achieved with responsiveness in satellite launches, sensor pointing, or orbit reconfiguration. This study presents a framework for designing reconfigurable satellite constellations capable of both regular Earth observation and disaster monitoring. These observation modes are termed global observation mode and regional observation mode, constituting a reconfigurable satellite constellation (ReCon). Systems engineering approaches are employed to formulate this multidisciplinary problem of co-optimizing satellite design and orbits. Two heuristic methods, simulated annealing (SA) and genetic algorithm (GA), are widely used for discrete combinatorial problems and therefore used in this study to benchmark against a gradient-based method. Point-based SA performed similar or slightly better than the gradient-based method, whereas population-based GA outperformed the other two. The resultant ReCon satellite design is physically feasible and offers performance-to-cost(mass) superior to static constellations. Ongoing research on observation scheduling and constellation management will extend the ReCon applications to radar imaging and radio occultation beyond visible wavelengths and nearby spectrums.
Robust Inertial Measurement Unit-Based Attitude Determination Kalman Filter for Kinematically Constrained Links
Authors: Lee Jung Keun , Choi Mi Jin
Published: February 2019

The external acceleration of a fast-moving body induces uncertainty in attitude determination based on inertial measurement unit (IMU) signals and thus, frequently degrades the determination accuracy. Although previous works adopt acceleration-compensating mechanisms to deal with this problem, they cannot completely eliminate the uncertainty as they are, inherently, approaches to an underdetermined problem. This paper presents a novel constraint-augmented Kalman filter (KF) that eliminates the acceleration-induced uncertainty for a robust IMU-based attitude determination when IMU is attached to a constrained link. Particularly, this research deals with an acceleration-level kinematic constraint derived on the basis of a ball joint. Experimental results demonstrate the superiority of the proposed constrained KF over the conventional unconstrained KF: The average accuracy improved by 1.88° with a maximum of 4.18°. More importantly, whereas the accuracy of conventional KF is dependent to some extent on test acceleration conditions, that of the proposed KF is independent of these conditions. Due to the robustness of the proposed KF, it may be applied when accurate attitude estimation is needed regardless of dynamic conditions.
Study on Health Assessment Method of a Braking System of a Mine Hoist
Authors: Li Juanjuan , Meng Guoying , Xie Guangming , Wang Aiming , Ding Jun , Zhang Wei , Wan Xingwei
Published: February 2019

This paper presents a method for calculating the health degree (HD) of a braking system of a mine hoist combined with three-level fuzzy comprehensive assessment (TLFCA) and a back-propagation neural network (BPNN). Firstly, the monitored values of a sensor are fused by multi-time fusion and the fuzzy comprehensive assessment values (FCAVs) of the health condition (HC) of the sensor are obtained. Secondly, the FCAVs of all sensors in a subsystem are fused by multi-sensor fusion, and FCAVs of the subsystem are obtained. Then the FCAVs of all subsystems are fused by multi-subsystem fusion and FCAVs of the system are obtained. All the FCAVs are fed into a pre-trained neural network, and the corresponding HD of the sensor, subsystem and system is obtained. Finally, the practicability, reliability and sensitivity of the proposed method are verified by the monitored values of the test rig. This paper presents a method to provide technical support for intelligent maintenance, and also provides necessary data for further prognostics health management (PHM) of the braking system. The method presented in this paper can also be used as a reference for the HD calculation of the whole hoist and other complicated equipment.
Fully Open-Access Passive Dry Electrodes BIOADC: Open-Electroencephalography (EEG) Re-Invented
Authors: Gargiulo Gaetano D. , Bifulco Paolo , Cesarelli Mario , McEwan Alistair , Nikpour Armin , Jin Craig , Gunawardana Upul , Sreenivasan Neethu , Wabnitz Andrew , Hamilton Tara J.
Published: February 2019

The Open-electroencephalography (EEG) framework is a popular platform to enable EEG measurements and general purposes Brain Computer Interface experimentations. However, the current platform is limited by the number of available channels and electrode compatibility. In this paper we present a fully configurable platform with up to 32 EEG channels and compatibility with virtually any kind of passive electrodes including textile, rubber and contactless electrodes. Together with the full hardware details, results and performance on a single volunteer participant (limited to alpha wave elicitation), we present the brain computer interface (BCI)2000 EEG source driver together with source code as well as the compiled (.exe). In addition, all the necessary device firmware, gerbers and bill of materials for the full reproducibility of the presented hardware is included. Furthermore, the end user can vary the dry-electrode reference circuitry, circuit bandwidth as well as sample rate to adapt the device to other generalized biopotential measurements. Although, not implemented in the tested prototype, the Biomedical Analogue to Digital Converter BIOADC naturally supports SPI communication for an additional 32 channels including the gain controller. In the appendix we describe the necessary modification to the presented hardware to enable this function.
Segmentation of the ECG Signal by Means of a Linear Regression Algorithm
Authors: Aspuru Javier , Ochoa-Brust Alberto , Félix Ramón A. , Mata-López Walter , Mena Luis J. , Ostos Rodolfo , Martínez-Peláez Rafael
Published: February 2019

The monitoring and processing of electrocardiogram (ECG) beats have been actively studied in recent years: new lines of research have even been developed to analyze ECG signals using mobile devices. Considering these trends, we proposed a simple and low computing cost algorithm to process and analyze an ECG signal. Our approach is based on the use of linear regression to segment the signal, with the goal of detecting the R point of the ECG wave and later, to separate the signal in periods for detecting P, Q, S, and T peaks. After pre-processing of ECG signal to reduce the noise, the algorithm was able to efficiently detect fiducial points, information that is transcendental for diagnosis of heart conditions using machine learning classifiers. When tested on 260 ECG records, the detection approach performed with a Sensitivity of 97.5% for Q-point and 100% for the rest of ECG peaks. Finally, we validated the robustness of our algorithm by developing an ECG sensor to register and transmit the acquired signals to a mobile device in real time.
Review on Wearables to Monitor Foot Temperature in Diabetic Patients
Authors: Martín-Vaquero Jesús , Hernández Encinas Ascensión , Queiruga-Dios Araceli , José Bullón Juan , Martínez-Nova Alfonso , Torreblanca González Jose , Bullón-Carbajo Cristina
Published: February 2019

One of the diseases that could affect diabetic patients is the diabetic foot problem. Unnoticed minor injuries and subsequent infection can lead to ischemic ulceration, and may end in a foot amputation. Preliminary studies have shown that there is a positive relationship between increased skin temperature and the pre–ulceration phase. Hence, we have carried out a review on wearables, medical devices, and sensors used specifically for collecting vital data. In particular, we are interested in the measure of the foot–temperature. Since there is a large amount of this type of medical wearables, we will focus on those used to measure temperature and developed in Spain.
Fluid Pressure Monitoring-Based Strategy for Delicate Grasping of Fragile Objects by A Robotic Hand with Fluid Fingertips
Authors: Nishimura Toshihiro , Suzuki Yosuke , Tsuji Tokuo , Watanabe Tetsuyou
Published: February 2019

In this study, we propose a strategy for delicately grasping fragile objects using a robotic gripper with highly deformable fluid fingertips. In an earlier study, we developed a soft fingertip, referred to as a fluid fingertip, which was fabricated from a deformable rubber bag filled with incompressible fluid. The proposed strategy detects the preferable grasping point where fracturing of the target object is avoided while the applied force or pressure from the fluid fingertip is substantially transmitted to the target. In this grasping point, the behavior of the fluid pressure changes with respect to the pushing distance when pushing at a constant speed. The other features of the strategy determining the grasping point are as follows: (1) the threshold for the detection of the grasping point is fixed or constant with respect to the type of target object; (2) information regarding the deformation and stiffness of the fingertips and target object is not required. The detection of the grasping point through behavioral changes in the pressure is performed by comparing the fitting accuracies of fitting models utilizing information of the fluid pressure and pushing distance. The validity of the proposed approach is verified through several experiments.
Positioning, Navigation, and Book Accessing/Returning in an Autonomous Library Robot using Integrated Binocular Vision and QR Code Identification Systems
Authors: Yu Xiaojun , Fan Zeming , Wan Hao , He Yuye , Du Junye , Li Nan , Yuan Zhaohui , Xiao Gaoxi
Published: February 2019

With rapid advancements in artificial intelligence and mobile robots, some of the tedious yet simple jobs in modern libraries, like book accessing and returning (BAR) operations that had been fulfilled manually before, could be undertaken by robots. Due to the limited accuracies of the existing positioning and navigation (P&N) technologies and the operational errors accumulated within the robot P&N process, however, most of the current robots are not able to fulfill such high-precision operations. To address these practical issues, we propose, for the first time (to the best of our knowledge), to combine the binocular vision and Quick Response (QR) code identification techniques together to improve the robot P&N accuracies, and then construct an autonomous library robot for high-precision BAR operations. Specifically, the binocular vision system is used for dynamic digital map construction and autonomous P&N, as well as obstacle identification and avoiding functions, while the QR code identification technique is responsible for both robot operational error elimination and robotic arm BAR operation determination. Both simulations and experiments are conducted to verify the effectiveness of the proposed technique combination, as well as the constructed robot. Results show that such a technique combination is effective and robust, and could help to significantly improve the P&N and BAR operation accuracies, while reducing the BAR operation time. The implemented autonomous robot is fully-autonomous and cost-effective, and may find applications far beyond libraries with only sophisticated technologies employed.
Origin of the 16S Ribosomal Molecule from Ancestor tRNAs
Authors: de Farias Savio Torres , Rêgo Thais Gaudêncio , José Marco V.
Published: February 2019

We tested the hypothesis that concatemers of ancestral tRNAs gave rise to the 16S ribosomal RNA. We built an ancestral sequence of proto-tRNAs that showed a significant identity of 51.69% and a percentage of structural identity of 0.941 with the 16S ribosomal molecule. We also propose a hypothesis for the emergence of translation.
Online Store Locator: An Essential Resource for Retailers in the 21st Century
Authors: Wandosell Gonzalo , Parra-Meroño María Concepción , Baños Raul
Published: February 2019

Most retailers use their websites and social media to increase their visibility, while potential customers get information about these retailers using the Internet on electronic devices. Many papers have previously studied online marketing strategies used by retailers, but little attention has been paid to determine how these companies provide information through the Internet about the location and characteristics of their stores. This paper aims to obtain evidence about the inclusion of interactive web maps on retailers’ websites to provide information about the location of their stores. With this purpose, the store locator interactive tools of specialty retailers’ websites included in the report “Global Powers of Retailing 2015” are studied in detail using different procedures, such as frequency analysis and word clouds. From the results obtained, it was concluded that most of these firms use interactive maps to provide information about their offline stores, but today some of them still use non-interactive (static) maps or text format to present this information. Moreover, some differences were observed among the search filters used in the store locator services, according to the retailer’s specialty. These results provided insight into the important role of online store locator tools on retailers’ websites.
Keto-Adaptation and Endurance Exercise Capacity, Fatigue Recovery, and Exercise-Induced Muscle and Organ Damage Prevention: A Narrative Review
Authors: Ma Sihui , Suzuki Katsuhiko
Published: February 2019

A ketogenic diet (KD) could induce nutritional ketosis. Over time, the body will acclimate to use ketone bodies as a primary fuel to achieve keto-adaptation. Keto-adaptation may provide a consistent and fast energy supply, thus improving exercise performance and capacity. With its anti-inflammatory and anti-oxidative properties, a KD may contribute to muscle health, thus preventing exercise-induced fatigue and damage. Given the solid basis of its potential to improve exercise capacity, numerous investigations into KD and exercise have been carried out in recent years. This narrative review aims to summarize recent research about the potential of a KD as a nutritional approach during endurance exercise, focusing on endurance capacity, recovery from fatigue, and the prevention of exhaustive exercise-induced muscle and organ damage.
Rate of Force Development and Muscle Architecture after Fast and Slow Velocity Eccentric Training
Authors: Stasinaki Angeliki-Nikoletta , Zaras Nikolaos , Methenitis Spyridon , Bogdanis Gregory , Terzis Gerasimos
Published: February 2019

The aim of the study was to investigate the rate of force development (RFD) and muscle architecture early adaptations in response to training with fast- or slow-velocity eccentric squats. Eighteen young novice participants followed six weeks (two sessions/week) of either fast-velocity (Fast) or slow-velocity (Slow) squat eccentric-only training. Fast eccentric training consisted of nine sets of nine eccentric-only repetitions at 70% of 1-RM with <1 s duration for each repetition. Slow eccentric training consisted of five sets of six eccentric-only repetitions at 90% of 1-RM with ~4 sec duration for each repetition. Before and after training, squat 1-RM, countermovement jump (CMJ), isometric leg press RFD, and vastus lateralis muscle architecture were evaluated. Squat 1-RM increased by 14.5 ± 7.0% (Fast, p < 0.01) and by 5.4 ± 5.1% (Slow, p < 0.05). RFD and fascicle length increased significantly in the Fast group by 10–19% and 10.0 ± 6.2%, p < 0.01, respectively. Muscle thickness increased only in the Slow group (6.0 ± 6.8%, p < 0.05). Significant correlations were found between the training induced changes in fascicle length and RFD. These results suggest that fast eccentric resistance training may be more appropriate for increases in rapid force production compared to slow eccentric resistance training, and this may be partly due to increases in muscle fascicle length induced by fast eccentric training.