bluuebunny/crossref_metadata_2025 huggingface dataset:
Metabolic Changes Induced by Silver Ions in Carlina acaulis
Authors: Dresler Sławomir , Hawrylak-Nowak Barbara , Strzemski Maciej , Wójciak-Kosior Magdalena , Sowa Ireneusz , Hanaka Agnieszka , Gołoś Iwona , Skalska-Kamińska Agnieszka , Cieślak Małgorzata , Kováčik Jozef
Published: November 2019

Silver is one of the most toxic heavy metals for plants, inducing various toxic symptoms and metabolic changes. Here, the impact of Ag(I) on Carlina acaulis physiology and selected metabolites was studied using two Ag concentrations (1 or 10 µM) after 14 days of exposure. The higher concentration of Ag(I) evoked reduction of growth, while 1 µM Ag had a growth-promoting effect on root biomass. The translocation factor (<0.04) showed that Ag was mainly retained in the roots. The 1 µM Ag concentration increased the level of low-molecular-weight organic acids (LMWOAs), while 10 µM Ag depleted these compounds in the roots. The increased concentration of Ag(I) elevated the accumulation of phytochelatins (PCs) in the roots and reduced glutathione (GSH) in the shoots (but not in the roots). At 1 µM, Ag(I) elevated the level of phenolic and triterpene acids, while the 10 µM Ag treatment increased the carlina oxide content in the roots. The obtained results indicate an alteration of metabolic pathways of C. acaulis to cope with different levels of Ag(I) stress. Our data imply that the intracellular binding of Ag(I) and nonenzymatic antioxidants contribute to the protection against low concentrations of Ag ions.
Continuous, Strong, Porous Silk Firoin-Based Aerogel Fibers toward Textile Thermal Insulation
Authors: Yang Haiwei , Wang Zongqian , Liu Zhi , Cheng Huan , Li Changlong
Published: November 2019

Aerogel fiber, with the characteristics of ultra-low density, ultra-high porosity, and high specific surface area, is the most potential candidate for manufacturing wearable thermal insulation material. However, aerogel fibers generally show weak mechanical properties and complex preparation processes. Herein, through firstly preparing a cellulose acetate/polyacrylic acid (CA/PAA) hollow fiber using coaxial wet-spinning followed by injecting the silk fibroin (SF) solution into the hollow fiber, the CA/PAA-wrapped SF aerogel fibers toward textile thermal insulation were successfully constructed after freeze-drying. The sheath (CA/PAA hollow fiber) possesses a multiscale porous structure, including micropores (11.37 ± 4.01 μm), sub-micron pores (217.47 ± 46.16 nm), as well as nanopores on the inner (44.00 ± 21.65 nm) and outer (36.43 ± 17.55 nm) surfaces, which is crucial to the formation of a SF aerogel core. Furthermore, the porous CA/PAA-wrapped SF aerogel fibers have many advantages, such as low density (0.21 g/cm3), high porosity (86%), high strength at break (2.6 ± 0.4 MPa), as well as potential continuous and large-scale production. The delicate structure of multiscale porous sheath and ultra-low-density SF aerogel core synergistically inhibit air circulation and limit convective heat transfer. Meanwhile, the high porosity of aerogel fibers weakens heat transfer and the SF aerogel cellular walls prevent infrared radiation. The results show that the mat composed of these aerogel fibers exhibits excellent thermal insulating properties with a wide working temperature from −20 to 100 °C. Therefore, this SF-based aerogel fiber can be considered as a practical option for high performance thermal insulation.
High Efficiency Gas Permeability Membranes from Ethyl Cellulose Grafted with Ionic Liquids
Authors: Xu Jingyu , Jia Hongge , Yang Nan , Wang Qingji , Yang Guoxing , Zhang Mingyu , Xu Shuangping , Zang Yu , Ma Liqun , Jiang Pengfei , Zhou Hailiang , Wang Honghan
Published: November 2019

Ethyl cellulose was grafted with ionic liquids in optimal yields (62.5–64.1%) and grafting degrees (5.93–7.90%) by the esterification of the hydroxyl groups in ethyl cellulose with the carboxyl groups in ionic liquids. In IR spectra of the ethyl cellulose derivatives exhibited C=O bond stretching vibration peaks at 1760 or 1740 cm−1, confirming the formation of the ester groups and furnishing the evidence of the successful grafting of ethyl cellulose with ionic liquids. The ethyl cellulose grafted with ionic liquids could be formed into membranes by using the casting solution method. The resulting membranes exhibited good membrane forming ability and mechanical properties. The EC grafted with ionic liquids-based membranes demonstrated PCO2/PCH4 separation factors of up to 18.8, whereas the PCO2/PCH4 separation factor of 9.0 was obtained for pure EC membrane (both for CO2/CH4 mixture gas). The membranes also demonstrated an excellent gas permeability coefficient PCO2, up to 199 Barrer, which was higher than pure EC (PCO2 = 46.8 Barrer). Therefore, it can be concluded that the ionic liquids with imidazole groups are immensely useful for improving the gas separation performances of EC membranes.
Interaction of Wu’s Slip Features in Bioconvection of Eyring Powell Nanoparticles with Activation Energy
Authors: Alwatban Anas M. , Khan Sami Ullah , Waqas Hassan , Tlili Iskander
Published: November 2019

The current continuation aim is to explore the rheological consequences of Eyring Powell nanofluid over a moving surface in the presence of activation energy and thermal radiation. The bioconvection of magnetized nanoparticles is executed with the evaluation of motile microorganism. The most interesting Wu’s slip effects are also assumed near the surface. The evaluation of nanoparticles for current flow problems has been examined by using Buongiorno’s model. The governing equations for the assumed flow problem are constituted under the boundary layer assumptions. After converting these equations in dimensionless form, the famous shooting technique is executed. A detailed physical significance is searched out in the presence of slip features. The variation of physical quantities, namely velocity, nanoparticles temperature, nano particles concentration, motile microorganism density, skin friction coefficient, local Nusselt number and motile organism density number are observed with detailed physical aspects for various flow controlling parameters.
A Pedestrian Avoidance Method Considering Personal Space for a Guide Robot
Authors: Hiroi Yutaka , Ito Akinori
Published: November 2019

Many methods have been proposed for avoiding obstacles in robotic systems. However, a robotic system that moves without colliding with obstacles and people, while still being mentally safe to the persons nearby, has not yet been realized. In this paper, we describe the development of a method for a mobile robot to avoid a pedestrian approaching from the front and to pass him/her by while preserving the “public distance” of personal space. We assume a robot that moves along a prerecorded path. When the robot detects a pedestrian using a laser range finder (LRF), it calculates the trajectory to avoid the pedestrian considering their personal space, passes by the pedestrian, and returns to the original trajectory. We introduce a virtual target to control the robot moving along the path, such that it can use the same control strategy as for human-following behavior. We carry out experiments to evaluate the method along three routes, in which the robot functioned without problems. The distance between the robot and the pedestrian was 9.3 m, on average, when the robot started to use avoiding behavior, which is large enough to keep a public distance from a pedestrian. When the robot passed by the pedestrian, the minimum distance between them was 1.19 m, which was large enough for passing safely.
Assessment of China’s Offshore Wind Resources Based on the Integration of Multiple Satellite Data and Meteorological Data
Authors: Guo Qiaoying , Huang Ran , Zhuang Liwei , Zhang Kangyu , Huang Jingfeng
Published: November 2019

Wind resources assessment plays a significant role in site selection for the construction of offshore wind farms. Mean wind speeds (MWS), wind power densities (WPD), and Weibull parameters are the most important variables for wind resources assessment. These variables were estimated with the synergetic use of multiple satellite data (QuikSCAT + WindSAT + ASCAT) and meteorological data from coastal stations using spatial interpolation methods, including inverse distance weighting (IDW), ordinary kriging (OK), and ordinary co-kriging (OCK). The spatial variability of offshore wind energy resources over the China Sea is assessed at heights of 10 m and 100 m (hub height of wind turbine). Then, 8 buoy measurements were used to evaluate the accuracy of the offshore wind resources assessment. Our results show that combining multiple satellite data and coastal meteorological data improves the accuracy of wind resources assessment in the offshore areas and the OCK method show the best performance for accuracy in most cases. The statistical results comparing buoy-derived MWS and interpolated MWS show a root mean square error (RMSE) of 0.17 m/s and correlation coefficient (Corr.) of 0.987 at a height of 10 m. Statistics of the comparison between buoy-derived WPD and interpolated WPD by OCK show a RMSE of 23.38 W/m2 at a height of 10 m. The results show that the highest wind resources are mainly found in the Taiwan Strait and offshore regions in Fujian province.
Tracking Reforestation in the Loess Plateau, China after the “Grain for Green” Project through Integrating PALSAR and Landsat Imagery
Authors: Zhou Hui , Xu Fu , Dong Jinwei , Yang Zhiqi , Zhao Guosong , Zhai Jun , Qin Yuanwei , Xiao Xiangming
Published: November 2019

An unprecedented reforestation process happened in the Loess Plateau, China due to the ecological restoration project ‘Grain for Green Project’, which has affected regional carbon and water cycles as well as brought climate feedbacks. Accurately mapping the area and spatial distribution of emerged forests in the Loess Plateau over time is essential for forest management but a very challenging task. Here we investigated the changes of forests in the Loess Plateau after the forest reconstruction project. First, we used a pixel and rule-based algorithm to identify and map the annual forests from 2007 to 2017 in the Loess Plateau by integrating 30 m Landsat data and 25 m resolution PALSAR data in this study. Then, we carried out the accuracy assessment and comparison with several existing forest products. The overall accuracy (OA) and Kappa coefficient of the resultant map, were about 91% and 0.77 in 2010, higher than those of the other forest products (FROM-GLC, GlobeLand30, GLCF-VCF, JAXA, and OU-FDL) with OA ranging from 83.57% to 87.96% and Kappa coefficients from 0.52 to 0.68. Based on the annual forest maps, we found forest area in the Loess Plateau has increased by around 15,000 km2 from 2007 to 2017. This study clearly demonstrates the advantages of data fusion between PALSAR and Landsat images for monitoring forest cover dynamics in the Loess Plateau, and the resultant forest maps with lower uncertainty would contribute to the regional forest management.
Hyperspectral Pansharpening Based on Spectral Constrained Adversarial Autoencoder
Authors: He Gang , Zhong Jiaping , Lei Jie , Li Yunsong , Xie Weiying
Published: November 2019

Hyperspectral (HS) imaging is conducive to better describing and understanding the subtle differences in spectral characteristics of different materials due to sufficient spectral information compared with traditional imaging systems. However, it is still challenging to obtain high resolution (HR) HS images in both the spectral and spatial domains. Different from previous methods, we first propose spectral constrained adversarial autoencoder (SCAAE) to extract deep features of HS images and combine with the panchromatic (PAN) image to competently represent the spatial information of HR HS images, which is more comprehensive and representative. In particular, based on the adversarial autoencoder (AAE) network, the SCAAE network is built with the added spectral constraint in the loss function so that spectral consistency and a higher quality of spatial information enhancement can be ensured. Then, an adaptive fusion approach with a simple feature selection rule is induced to make full use of the spatial information contained in both the HS image and PAN image. Specifically, the spatial information from two different sensors is introduced into a convex optimization equation to obtain the fusion proportion of the two parts and estimate the generated HR HS image. By analyzing the results from the experiments executed on the tested data sets through different methods, it can be found that, in CC, SAM, and RMSE, the performance of the proposed algorithm is improved by about 1.42%, 13.12%, and 29.26% respectively on average which is preferable to the well-performed method HySure. Compared to the MRA-based method, the improvement of the proposed method in in the above three indexes is 17.63%, 0.83%, and 11.02%, respectively. Moreover, the results are 0.87%, 22.11%, and 20.66%, respectively, better than the PCA-based method, which fully illustrated the superiority of the proposed method in spatial information preservation. All the experimental results demonstrate that the proposed method is superior to the state-of-the-art fusion methods in terms of subjective and objective evaluations.
Spatiotemporal Fusion of Satellite Images via Very Deep Convolutional Networks
Authors: Zheng Yuhui , Song Huihui , Sun Le , Wu Zebin , Jeon Byeungwoo
Published: November 2019

Spatiotemporal fusion provides an effective way to fuse two types of remote sensing data featured by complementary spatial and temporal properties (typical representatives are Landsat and MODIS images) to generate fused data with both high spatial and temporal resolutions. This paper presents a very deep convolutional neural network (VDCN) based spatiotemporal fusion approach to effectively handle massive remote sensing data in practical applications. Compared with existing shallow learning methods, especially for the sparse representation based ones, the proposed VDCN-based model has the following merits: (1) explicitly correlating the MODIS and Landsat images by learning a non-linear mapping relationship; (2) automatically extracting effective image features; and (3) unifying the feature extraction, non-linear mapping, and image reconstruction into one optimization framework. In the training stage, we train a non-linear mapping between downsampled Landsat and MODIS data using VDCN, and then we train a multi-scale super-resolution (MSSR) VDCN between the original Landsat and downsampled Landsat data. The prediction procedure contains three layers, where each layer consists of a VDCN-based prediction and a fusion model. These layers achieve non-linear mapping from MODIS to downsampled Landsat data, the two-times SR of downsampled Landsat data, and the five-times SR of downsampled Landsat data, successively. Extensive evaluations are executed on two groups of commonly used Landsat–MODIS benchmark datasets. For the fusion results, the quantitative evaluations on all prediction dates and the visual effect on one key date demonstrate that the proposed approach achieves more accurate fusion results than sparse representation based methods.
Multi-Scale Association between Vegetation Growth and Climate in India: A Wavelet Analysis Approach
Authors: Sebastian Dawn Emil , Ganguly Sangram , Krishnaswamy Jagdish , Duffy Kate , Nemani Ramakrishna , Ghosh Subimal
Published: November 2019

Monsoon climate over India has high degree of spatio-temporal heterogeneity characterized by the existence of multi-climatic zones along with strong intra-seasonal, seasonal, and inter-annual variability. Vegetation growth of Indian forests relates to this climate variability, though the dependence structure over space and time is yet to be explored. Here, we present a comprehensive analysis of this association with quality-controlled satellite-based remote sensing dataset of vegetation greenness and radiation along with station based gridded precipitation datasets. A spatio-temporal time-frequency analysis using wavelets is performed to understand the relative association of vegetation growth with precipitation and radiation at different time scales. The inter-annual variation of forest greenness over the Tropical India are observed to be correlated with the seasonal monsoon precipitation. However, at inter and intra-seasonal scales, vegetation has a strong association with radiation in regions of high precipitation like the Western Ghats, Eastern Himalayas, and Northeast hills. Forests in Western Himalayas were found to be correlated more on the winter precipitation from western disturbances than the south west monsoon precipitation. Our results provide new and useful region-specific information for dynamic vegetation modelling in the Indian monsoon region that may further be used in understanding global vegetation-land-atmosphere interactions.
A Quantum Dot-Based FLIM Glucose Nanosensor
Authors: Ripoll Consuelo , Orte Angel , Paniza Lorena , Ruedas-Rama Maria Jose
Published: November 2019

In the last few years, quantum dot (QD) nanoparticles have been employed for bioimaging and sensing due to their excellent optical features. Most studies have used photoluminescence (PL) intensity-based techniques, which have some drawbacks, especially when working with nanoparticles in intracellular media, such as fluctuations in the excitation power, fluorophore concentration dependence, or interference from cell autofluorescence. Some of those limitations can be overcome with the use of time-resolved spectroscopy and fluorescence lifetime imaging microscopy (FLIM) techniques. In this work, CdSe/ZnS QDs with long decay times were modified with aminophenylboronic acid (APBA) to achieve QD-APBA conjugates, which can act as glucose nanosensors. The attachment of the boronic acid moiety on the surface of the nanoparticle quenched the PL average lifetime of the QDs. When glucose bonded to the boronic acid, the PL was recovered and its lifetime was enhanced. The nanosensors were satisfactorily applied to the detection of glucose into MDA-MB-231 cells with FLIM. The long PL lifetimes of the QD nanoparticles made them easily discernible from cell autofluorescence, thereby improving selectivity in their sensing applications. Since the intracellular levels of glucose are related to the metabolic status of cancer cells, the proposed nanosensors could potentially be used in cancer diagnosis.
Rotating Stall Induced Non-Synchronous Blade Vibration Analysis for an Unshrouded Industrial Centrifugal Compressor
Authors: Zhao Xinwei , Zhou Qiang , Yang Shuhua , Li Hongkun
Published: November 2019

Rotating stall limits the operating range and stability of the centrifugal compressor and has a significant impact on the lifetime of the impeller blade. This paper investigates the relationship between stall pressure wave and its induced non-synchronous blade vibration, which will be meaningful for stall resonance avoidance at the early design phase. A rotating disc under a time-space varying load condition is first modeled to understand the physics behind stall-induced vibration. Then, experimental work is conducted to verify the model and reveal the mechanism of stall cells evolution process within flow passage and how blade vibrates when suffering such aerodynamic load. The casing mounted pressure sensors are used to capture the low-frequency pressure wave. Strain gauges and tip timing sensors are utilized to monitor the blade vibration. Based on circumferentially distributed pressure sensors and stall parameters identification method, a five stall cells mode is found in this compressor test rig and successfully correlates with the blade non-synchronous vibration. Furthermore, with the help of tip timing measurement, all blades vibration is also evaluated under different operating mass flow rate. Analysis results verify that the proposed model can show the blade forced vibration under stall flow condition. The overall approach presented in this paper is also important for stall vibration and resonance free design with effective experimental verification.
Energy Modeling of Neighbor Discovery in Bluetooth Low Energy Networks
Authors: Luo Bingqing , Gao Jincheng , Sun Zhixin
Published: November 2019

Given that current Internet of Things (IoT) applications employ many different sensors to provide information, a large number of the Bluetooth low energy (BLE) devices will be developed for IoT systems. Developing low-power and low-cost BLE advertisers is one of most challenging tasks for supporting the neighbor discovery process (NDP) of such a large number of BLE devices. Since the parameter setting is essential to achieve the required performance for the NDP, an energy model of neighbor discovery in BLE networks can provide beneficial guidance when determining some significant parameter metrics, such as the advertising interval, scan interval, and scan window. In this paper, we propose a new analytical model to characterize the energy consumption using all possible parameter settings during the NDP in BLE networks. In this model, the energy consumption is derived based on the Chinese remainder theorem (CRT) for an advertising event and a scanning event during the BLE NDP. In addition, a real testbed is set up to measure the energy consumption. The measurement and experimental results reveal the relationship between the average energy consumption and the key parameters. On the basis of this model, beneficial guidelines for BLE network configuration are presented to help choose the proper parameters to optimize the power consumption for a given IoT application.
Modeling and Analysis of Spatial Inter-Symbol Interference for RGB Image Sensors Based on Visible Light Communication
Authors: Liu Peng , Zheng Peijun , Yang Shiwei , Chen Ziyu
Published: November 2019

In this paper, RGB image sensors based on visible light communication system are designed and researched. In our study, the spatial inter-symbol interference is considered and the formation mechanism of the stray light, the influence of the spatial inter-symbol interference, and the influence of RGB image sensors are analyzed. The mathematics expression of the system signal-to-noise ratio (SNR) and bit error ratio (BER) is given. The simulation result indicates that there is a critical communication distance in the system. Once the communication distance exceeds the critical value, the system BER performance increases sharply. In addition, an adaptive threshold detection method is introduced and the performance is simulated. By means of estimating the spatial inter-symbol interference noise power, the optimal detection threshold can be obtained and the system BER performance increases significantly.
Detection of Micro-Defects on Irregular Reflective Surfaces Based on Improved Faster R-CNN
Authors: Zhou Zhuangzhuang , Lu Qinghua , Wang Zhifeng , Huang Haojie
Published: November 2019

The detection of defects on irregular surfaces with specular reflection characteristics is an important part of the production process of sanitary equipment. Currently, defect detection algorithms for most irregular surfaces rely on the handcrafted extraction of shallow features, and the ability to recognize these defects is limited. To improve the detection accuracy of micro-defects on irregular surfaces in an industrial environment, we propose an improved Faster R-CNN model. Considering the variety of defect shapes and sizes, we selected the K-Means algorithm to generate the aspect ratio of the anchor box according to the size of the ground truth, and the feature matrices are fused with different receptive fields to improve the detection performance of the model. The experimental results show that the recognition accuracy of the improved model is 94.6% on a collected ceramic dataset. Compared with SVM (Support Vector Machine) and other deep learning-based models, the proposed model has better detection performance and robustness to illumination, which proves the practicability and effectiveness of the proposed method.
Sport-Related Human Activity Detection and Recognition Using a Smartwatch
Authors: Zhuang Zhendong , Xue Yang
Published: November 2019

As an active research field, sport-related activity monitoring plays an important role in people’s lives and health. This is often viewed as a human activity recognition task in which a fixed-length sliding window is used to segment long-term activity signals. However, activities with complex motion states and non-periodicity can be better monitored if the monitoring algorithm is able to accurately detect the duration of meaningful motion states. However, this ability is lacking in the sliding window approach. In this study, we focused on two types of activities for sport-related activity monitoring, which we regard as a human activity detection and recognition task. For non-periodic activities, we propose an interval-based detection and recognition method. The proposed approach can accurately determine the duration of each target motion state by generating candidate intervals. For weak periodic activities, we propose a classification-based periodic matching method that uses periodic matching to segment the motion sate. Experimental results show that the proposed methods performed better than the sliding window method.
Voltammetric Electronic Tongue for the Simultaneous Determination of Three Benzodiazepines
Authors: Herrera-Chacón Anna , Torabi Farzad , Faridbod Farnoush , Ghasemi Jahan B. , González-Calabuig Andreu , del Valle Manel
Published: November 2019

The presented manuscript reports the simultaneous detection of a ternary mixture of the benzodiazepines diazepam, lorazepam, and flunitrazepam using an array of voltammetric sensors and the electronic tongue principle. The electrodes used in the array were selected from a set of differently modified graphite epoxy composite electrodes; specifically, six electrodes were used incorporating metallic nanoparticles of Cu and Pt, oxide nanoparticles of CuO and WO3, plus pristine electrodes of epoxy-graphite and metallic Pt disk. Cyclic voltammetry was the technique used to obtain the voltammetric responses. Multivariate examination using Principal Component Analysis (PCA) justified the choice of sensors in order to get the proper discrimination of the benzodiazepines. Next, a quantitative model to predict the concentrations of mixtures of the three benzodiazepines was built employing the set of voltammograms, and was first processed with the Discrete Wavelet Transform, which fed an artificial neural network response model. The developed model successfully predicted the concentration of the three compounds with a normalized root mean square error (NRMSE) of 0.034 and 0.106 for the training and test subsets, respectively, and coefficient of correlation R ≥ 0.938 in the predicted vs. expected concentrations comparison graph.
Detection of Tennis Activities with Wearable Sensors
Authors: Benages Pardo Luis , Buldain Perez David , Orrite Uruñuela Carlos
Published: November 2019

This paper aims to design and implement a system capable of distinguishing between different activities carried out during a tennis match. The goal is to achieve the correct classification of a set of tennis strokes. The system must exhibit robustness to the variability of the height, age or sex of any subject that performs the actions. A new database is developed to meet this objective. The system is based on two sensor nodes using Bluetooth Low Energy (BLE) wireless technology to communicate with a PC that acts as a central device to collect the information received by the sensors. The data provided by these sensors are processed to calculate their spectrograms. Through the application of innovative deep learning techniques with semi-supervised training, it is possible to carry out the extraction of characteristics and the classification of activities. Preliminary results obtained with a data set of eight players, four women and four men have shown that our approach is able to address the problem of the diversity of human constitutions, weight and sex of different players, providing accuracy greater than 96.5% to recognize the tennis strokes of a new player never seen before by the system.
Computer Vision and IoT-Based Sensors in Flood Monitoring and Mapping: A Systematic Review
Authors: Arshad Bilal , Ogie Robert , Barthelemy Johan , Pradhan Biswajeet , Verstaevel Nicolas , Perez Pascal
Published: November 2019

Floods are amongst the most common and devastating of all natural hazards. The alarming number of flood-related deaths and financial losses suffered annually across the world call for improved response to flood risks. Interestingly, the last decade has presented great opportunities with a series of scholarly activities exploring how camera images and wireless sensor data from Internet-of-Things (IoT) networks can improve flood management. This paper presents a systematic review of the literature regarding IoT-based sensors and computer vision applications in flood monitoring and mapping. The paper contributes by highlighting the main computer vision techniques and IoT sensor approaches utilised in the literature for real-time flood monitoring, flood modelling, mapping and early warning systems including the estimation of water level. The paper further contributes by providing recommendations for future research. In particular, the study recommends ways in which computer vision and IoT sensor techniques can be harnessed to better monitor and manage coastal lagoons—an aspect that is under-explored in the literature.
New Algorithms for Autonomous Inertial Navigation Systems Correction with Precession Angle Sensors in Aircrafts
Authors: Chen Danhe , Neusypin Konstantin , Selezneva Maria , Mu Zhongcheng
Published: November 2019

This paper presents new algorithmic methods for accuracy improvement of autonomous inertial navigation systems of aircrafts. Firstly, an inertial navigation system platform and its nonlinear error model are considered, and the correction schemes are presented for autonomous inertial navigation systems using internal information. Next, a correction algorithm is proposed based on signals from precession angle sensors. A vector of reduced measurements for the estimation algorithm is formulated using the information about the angles of precession. Finally, the accuracy of the developed correction algorithms for autonomous inertial navigation systems of aircrafts is studied. Numerical solutions for the correction algorithm are presented by the adaptive Kalman filter for the measurement data from the sensors. Real data of navigation system Ts-060K are obtained in laboratory experiments, which validates the proposed algorithms.
Determining a Threshold to Delimit the Amazonian Forests from the Tree Canopy Cover 2000 GFC Data
Authors: Gasparini Kaio Allan Cruz , Silva Junior Celso Henrique Leite , Shimabukuro Yosio Edemir , Arai Egidio , Aragão Luiz Eduardo Oliveira Cruz e , Silva Carlos Alberto , Marshall Peter L.
Published: November 2019

Open global forest cover data can be a critical component for Reducing Emissions from Deforestation and Forest Degradation (REDD+) policies. In this work, we determine the best threshold, compatible with the official Brazilian dataset, for establishing a forest mask cover within the Amazon basin for the year 2000 using the Tree Canopy Cover 2000 GFC product. We compared forest cover maps produced using several thresholds (10%, 30%, 50%, 80%, 85%, 90%, and 95%) with a forest cover map for the same year from the Brazilian Amazon Deforestation Monitoring Project (PRODES) data, produced by the National Institute for Space Research (INPE). We also compared the forest cover classifications indicated by each of these maps to 2550 independently assessed Landsat pixels for the year 2000, providing an accuracy assessment for each of these map products. We found that thresholds of 80% and 85% best matched with the PRODES data. Consequently, we recommend using an 80% threshold for the Tree Canopy Cover 2000 data for assessing forest cover in the Amazon basin.
Sensing Properties of Fused Silica Single-Mode Optical Fibers Based on PPP-BOTDA in High-Temperature Fields
Authors: Shen Jiahui , Li Ting , Zhu Hong , Yang Caiqian , Zhang Kai
Published: November 2019

The strain of fiber-reinforced polymer (FRP) bars at high temperatures is currently difficult to measure. To overcome this difficulty, a method of smart FRP bars embedded with optical fibers was proposed and studied, in which an ordinary single-mode optical fiber was applied as a distributed sensor. In this paper, both the distributed temperature and strain-sensing characteristics of optical fiber were studied based on pulse pre-pump Brillouin optical time-domain analysis (PPP-BOTDA) under high temperature. The temperature and strain coefficients were investigated under a thermomechanical coupling environment with consideration of large strain levels. The experimental results show that the temperature and strain coefficients decreased as the temperature increased, because the properties of silica and coating materials changed with temperature. Then, the formulas for determining the temperature and strain coefficients at high temperatures were introduced and discussed. The excellent sensing performance of the optical fiber indicated that smart FRP bars have the potential for use at high temperatures.
Identification of Gait Motion Patterns Using Wearable Inertial Sensor Network
Authors: Moon Kee S. , Lee Sung Q , Ozturk Yusuf , Gaidhani Apoorva , Cox Jeremiah A.
Published: November 2019

Gait signifies the walking pattern of an individual. It may be normal or abnormal, depending on the health condition of the individual. This paper considers the development of a gait sensor network system that uses a pair of wireless inertial measurement unit (IMU) sensors to monitor the gait cycle of a user. The sensor information is used for determining the normality of movement of the leg. The sensor system places the IMU sensors on one of the legs to extract the three-dimensional angular motions of the hip and knee joints while walking. The wearable sensor is custom-made at San Diego State University with wireless data transmission capability. The system enables the user to collect gait data at any site, including in a non-laboratory environment. The paper also presents the mathematical calculations to decompose movements experienced by a pair of IMUs into individual and relative three directional hip and knee joint motions. Further, a new approach of gait pattern classification based on the phase difference angles between hip and knee joints is presented. The experimental results show a potential application of the classification method in the areas of smart detection of abnormal gait patterns.
A Quantitative Comparison of Overlapping and Non-Overlapping Sliding Windows for Human Activity Recognition Using Inertial Sensors
Authors: Dehghani Akbar , Sarbishei Omid , Glatard Tristan , Shihab Emad
Published: November 2019

The sliding window technique is widely used to segment inertial sensor signals, i.e., accelerometers and gyroscopes, for activity recognition. In this technique, the sensor signals are partitioned into fix sized time windows which can be of two types: (1) non-overlapping windows, in which time windows do not intersect, and (2) overlapping windows, in which they do. There is a generalized idea about the positive impact of using overlapping sliding windows on the performance of recognition systems in Human Activity Recognition. In this paper, we analyze the impact of overlapping sliding windows on the performance of Human Activity Recognition systems with different evaluation techniques, namely, subject-dependent cross validation and subject-independent cross validation. Our results show that the performance improvements regarding overlapping windowing reported in the literature seem to be associated with the underlying limitations of subject-dependent cross validation. Furthermore, we do not observe any performance gain from the use of such technique in conjunction with subject-independent cross validation. We conclude that when using subject-independent cross validation, non-overlapping sliding windows reach the same performance as sliding windows. This result has significant implications on the resource usage for training the human activity recognition systems.
Proposal of the Tactile Glove Device
Authors: Junior José C. V. S. , Torquato Matheus F. , Noronha Daniel H. , Silva Sérgio N. , Fernandes Marcelo A. C.
Published: November 2019

This project aims to develop a tactile glove device and a virtual environment inserted in the context of tactile internet. The tactile glove allows a human operator to interact remotely with objects from a 3D environment through tactile feedback or tactile sensation. In other words, the human operator is able to feel the contour and texture from virtual objects. Applications such as remote diagnostics, games, remote analysis of materials, and others in which objects could be virtualized can be significantly improved using this kind of device. These gloves have been an essential device in all research on the internet next generation called “Tactile Internet”, in which this project is inserted. Unlike the works presented in the literature, the novelty of this work is related to architecture, and tactile devices developed. They are within the 10 ms round trip latency limits required in a tactile internet environment. Details of hardware and software designs of a tactile glove, as well as the virtual environment, are described. Results and comparative analysis about round trip latency time in the tactile internet environment is developed.
Automatic Extraction of Structural and Non-Structural Road Edges from Mobile Laser Scanning Data
Authors: Yang Mengmeng , Liu Xianlin , Jiang Kun , Xu Jingzhong , Sheng Peng , Yang Diange
Published: November 2019

Accurate road information is important for applications involving road maintenance, intelligent transportation, and road network updates. Mobile laser scanning (MLS) can effectively extract road information. However, accurately extracting road edges based on large-scale data for complex road conditions, including both structural and non-structural road types, remains difficult. In this study, a robust method to automatically extract structural and non-structural road edges based on a topological network of laser points between adjacent scan lines and auxiliary surfaces is proposed. The extraction of road and curb points was achieved mainly from the roughness of the extracted surface, without considering traditional thresholds (e.g., height jump, slope, and density). Five large-scale road datasets, containing different types of road curbs and complex road scenes, were used to evaluate the practicality, stability, and validity of the proposed method via qualitative and quantitative analyses. Measured values of the correctness, completeness, and quality of extracted road edges were over 95.5%, 91.7%, and 90.9%, respectively. These results confirm that the proposed method can extract road edges from large-scale MLS datasets without the need for auxiliary information on intensity, image, or geographic data. The proposed method is effective regardless of whether the road width is fixed, the road is regular, and the existence of pedestrians and vehicles. Most importantly, the proposed method provides a valuable solution for road edge extraction that is useful for road authorities when developing intelligent transportation systems, such as those required by self-driving vehicles.
Classification and Identification of Industrial Gases Based on Electronic Nose Technology
Authors: Li Hui , Luo Dehan , Sun Yunlong , GholamHosseini Hamid
Published: November 2019

Rapid detection and identification of industrial gases is a challenging problem. They have a complex composition and different specifications. This paper presents a method based on the kernel discriminant analysis (KDA) algorithm to identify industrial gases. The smell prints of four typical industrial gases were collected by an electronic nose. The extracted features of the collected gases were employed for gas identification using different classification algorithms, including principal component analysis (PCA), linear discriminant analysis (LDA), PCA + LDA, and KDA. In order to obtain better classification results, we reduced the dimensions of the original high-dimensional data, and chose a good classifier. The KDA algorithm provided a high classification accuracy of 100% by selecting the offset of the kernel function c = 10 and the degree of freedom d = 5. It was found that this accuracy was 4.17% higher than the one obtained using PCA. In the case of standard deviation, the KDA algorithm has the highest recognition rate and the least time consumption.
Most Plant Nutrient Elements Are Retained by Biochar in Soil
Authors: Limwikran Tanawan , Kheoruenromne Irb , Suddhiprakarn Anchalee , Prakongkep Nattaporn , Gilkes Robert J.
Published: November 2019

Biochar may contain substantial amounts of plant nutrient elements, and at typical rates of application, may supply luxury levels of K, Ca, P, and other plant nutrients. However, little is known of the agronomic effectiveness of these nutrients because they exist in diverse compounds and are located in the microporous matrix of biochar particles. We have identified the compounds and location of nutrient elements in three biochars and observed their release from biochar particles in soil. Much K was quickly released from biochar but little or no Ca, Mg, S, and P were released over eight months, which represents a very different behavior from chemical fertilizers that are mostly water soluble. There is clearly a need to determine the availability to plant nutrients in biochar. Appropriate laboratory methods should be developed for measuring the availability of plant nutrients as standard methods of fertilizer analysis are ineffective.
Fuzzy Linear Programming Models for a Green Logistics Center Location and Allocation Problem under Mixed Uncertainties Based on Different Carbon Dioxide Emission Reduction Methods
Authors: Sun Yan , Lu Yue , Zhang Cevin
Published: November 2019

This study explores a foundational logistics center location and allocation problem in a three-stage logistics network that consists of suppliers, logistics centers, and customers. In this study, the environmental sustainability of the logistics network is improved by optimizing the carbon dioxide emissions of the logistics network based on multi-objective optimization and carbon tax regulation. Mixed uncertainties in the planning stage, including the supply capacities of suppliers, operation capacities of logistics centers, and demands of customers, are modeled using triangular fuzzy numbers based on the fuzzy set theory to order to enhance the reliability of the logistics center location and allocation planning. To solve the green logistics center location and allocation problem under mixed uncertainties, we establish two fuzzy mixed integer linear programming models. The fuzzy credibilistic chance-constrained programming is then adopted to obtain the crisp and linear reformulations of the fuzzy programming models. A numerical case is given to verify the feasibility of the proposed methods, in which the performance of carbon tax regulation in reducing carbon dioxide emissions is then tested based on the benchmark provided by the multi-objective optimization. Lastly, sensitivity analysis and fuzzy simulation are utilized to reveal the effect of the mixed uncertainties on the logistics location and allocation planning and further determine the best confidence level in the fuzzy chance constraints to provide decision makers with a crisp plan.
Feruloylated Arabinoxylans from Maize Distiller’s Dried Grains with Solubles: Effect of Feruloyl Esterase on their Macromolecular Characteristics, Gelling, and Antioxidant Properties
Authors: Marquez-Escalante Jorge A. , Carvajal-Millan Elizabeth
Published: November 2019

Distiller’s dried grains with solubles (DDGS) are co-products of the maize ethanol industry. DDGS contains feruloylated arabinoxylans (AXs), which can present gelling, antioxidant, and health-promoting effects. However, AXs presenting high ferulic acid (FA) content can exhibit delayed fermentation by the colonic microbiota. Therefore, partial deferuloylation of AXs from DDGS while preserving the polysaccharide gelling and antioxidant properties could add value and favor the sustainable development of bioethanol plants. The aim of this work was to partially deferuloylated AXs from DDGS using feruloyl esterase and to evaluate the polysaccharide macromolecular characteristics, gelling, and antioxidant properties. The AXs presented FA and FA dimer contents of 3.27 and 0.30 µg/mg polysaccharide, respectively, which decreased to 1.26 and 0.20 µg/mg polysaccharide, respectively, in feruloyl esterase-treated AXs (FAXs). The molecular weight and intrinsic viscosity of FAXs were slightly less than those of AXs. The Fourier transform infrared spectroscopy data of AXs and FAXs were similar, confirming that the enzyme did not modify the polysaccharide molecular identity. FAX gels (2% w/v) exhibited a decrease in elasticity by 43% in relation to that of AXs gels. The antioxidant capacity of FAXs was reduced by 32% and 43% (DPPH and ABTS method, respectively), compared with that of AXs. The FAX gelling and antioxidant properties were -comparable to those reported for other AXs in the literature. Feruloyl esterase may offer an interesting approach for the design of functional FAXs as value-added products recovered from DDGS.
A Novel Head Mounted Display Based Methodology for Balance Evaluation and Rehabilitation
Authors: Lee Eun-Young , Tran Van Thanh , Kim Dongho
Published: November 2019

In this paper, we present a new augmented reality (AR) head mounted display (HMD)-based balance rehabilitation method. This method assesses the individual’s postural stability quantitatively by measuring head movement via the inertial measurement unit sensor integrated in the AR HMD. In addition, it provides visual feedback to train through holographic objects, which interacts with the head position in real-time. We implemented applications for Microsoft HoloLens and conducted experiments with eight participants to verify the method we proposed. Participants performed each of three postural tasks three times depending on the presence or absence of augmented reality, the center of pressure (COP) displacement was measured through the Wii Balance Board, and the head displacement was measured through the HoloLens. There are significant correlations (p < 0.05) between COP and head displacement and significant differences (p < 0.05) between with/without AR feedback, although most of them were not statistically significant likely due to the small sample. Despite the results, we confirmed the applicability and potential of the AR HMD-based balance rehabilitation method we proposed. We expect the proposed method could be used as a convenient and effective rehabilitation tool for both patients and therapists in the future.
Towards a More Transparent Disclosure for Corporate Sustainability: Focusing on the Regulation of Unfaithful Disclosure Designation
Authors: Lee Jaehong , Cho Eunjoo , Park Jong Sung
Published: November 2019

This study explored the effect of corporate governance on disclosure transparency, coming from the changes of the largest shareholder and the designation as an unfaithful disclosure firm. In addition, the study verified whether this designation on a firm increases voluntary disclosure level the following year. According to results of analyzing nonfinancial firms listed in the Korea stock market from 2009 to 2017, the more frequently the largest shareholder changed in the recent three years, the significantly higher the possibility of the firm in being designated as an unfaithful disclosure firm. For firms that were designated as unfaithful disclosure firms, voluntary disclosure significantly increased the following year. These results were strengthened by the addition of several more analyses including quality of management forecasts, consideration of fixed-effects regression and the mediation modeling. These results imply the necessity for the supervision of authorities to take caution, while it shows the effectiveness of the unfaithful disclosure designation regulation in improving disclosure transparency.