bluuebunny/crossref_metadata_2025 huggingface dataset:
Properties of Poplar Fiber/PLA Composites: Comparison on the Effect of Maleic Anhydride and KH550 Modification of Poplar Fiber
Authors: Yang Zhaozhe , Feng Xinhao , Xu Min , Rodrigue Denis
Published: March 2020

To improve the interfacial adhesion and dispersion of a poplar fiber in a polylactic acid (PLA) matrix, maleic anhydride (MA) and a silane coupling agent (KH550) were used to modify the poplar fiber. The poplar fiber/PLA composites were produced with different modifier contents. The mechanical, thermal, rheological, and physical properties of composites were investigated. A comparison of different natural fiber modifications on the properties of composites was also analyzed. The results showed that both MA and KH550 could improve the interfacial adhesion between the poplar fiber and PLA, resulting in the enhanced mechanical properties of the composite, with 17% and 23% increases of tensile strength for 0.5% MA and 2% KH550, respectively. The thermal properties of the composites were improved at 6% KH550 (a 9% enhancement of T90%) and decreased at 0.5% MA (a 6% decrement of T90%). The wettability of the composites obtained a 11.3% improvement at 4% KH550 and a 5% reduction at 4% MA. Therefore, factors such as mechanical properties, economic efficiency, and durability should be carefully considered when choosing the modifier to improve the property of the composite.
Intelligent Colored Token Petri Nets for Modeling, Control, and Validation of Dynamic Changes in Reconfigurable Manufacturing Systems
Authors: Kaid Husam , Al-Ahmari Abdulrahman , Li Zhiwu , Davidrajuh Reggie
Published: March 2020

The invention of reconfigurable manufacturing systems (RMSs) has created a challenging problem: how to quickly and effectively modify an RMS to address dynamic changes in a manufacturing system, such as processing failures and rework, machine breakdowns, addition of new machines, addition of new products, removal of old machines, and changes in processing routes induced by the competitive global market. This paper proposes a new model, the intelligent colored token Petri net (ICTPN), to simulate dynamic changes or reconfigurations of a system. The main idea is that intelligent colored tokens denote part types that represent real-time knowledge about changes and status of a system. Thus, dynamic configurations of a system can be effectively modeled. The developed ICTPN can model dynamic changes of a system in a modular manner, resulting in the development of a very compact model. In addition, when configurations appear, only the changed colored token of the part type from the current model has to be modified. Based on the resultant ICTPN model, deadlock-free, conservative, and reversible behavioral properties, among others, are guaranteed. The developed ICTPN model was tested and validated using the GPenSIM tool and compared with existing methods from the literature.
Ketone Solvent to Reduce the Minimum Miscibility Pressure for CO2 Flooding at the South Sumatra Basin, Indonesia
Authors: Novriansyah Adi , Bae Wisup , Park Changhyup , Permadi Asep K. , Sri Riswati Shabrina
Published: March 2020

This paper experimentally analyzes the chemical additives, i.e., methanol and ethanol, as alcohol solvents, and acetone as a ketone solvent, and the temperature influencing the minimum miscibility pressure (MMP) that is essential to design miscible CO2 flooding at an oil field, the South Sumatra basin, Indonesia. The experiments were designed to measure CO2-oil interfacial tension with the vanishing interfacial tension (VIT) method in the ranges up to 3000 psi (208.6 bar) and 300 degrees Celsius. The experiment results show that lower temperatures, larger solvent volumes, and the acetone were effective in reducing MMP. The acetone, an aprotic ketone solvent, reduced MMP more than the methanol and the ethanol in the CO2-oil system. The high temperature was negative to obtain the high CO2 solubility into the oil as well as the lower MMP. The experimental results confirm that the aprotic ketone solvent could be effective in decreasing the MMP for the design of miscible CO2 flooding at the shallow mature oilfields with a low reservoir temperature.
Journey Making: Applying PSE Principles to Complex Curriculum Designs
Authors: Cameron Ian , Birkett Greg
Published: March 2020

Since the 1950s, Process Systems Engineering (PSE) concepts have traditionally been applied to the process industries, with great effect and with significant benefit. However, the same general approaches and principles in designing complex process designs can be applied to the design of higher education (HE) curricula. Curricula represent intended learning journeys, these being similar to the design of process flowsheets. In this paper, we set out the formal framework and concepts that underlie the challenges in design of curricula. The approaches use generic and fundamental concepts that can be applied by any discipline to curriculum design. We show how integration of discipline-specific concepts, across time and space, can be combined through design choices, to create learning journeys for students. These concepts are captured within a web-based design tool that permits wide choices for designers to build innovative curricula. The importance of visualization of curricula is discussed and illustrated, using a range of tools that permit insight into the nature of the designs. The framework and tool presented in this paper have been widely used across many disciplines, such as science, engineering, nursing, philosophy and pharmacy. As a special issue in memory of Professor Roger W.H. Sargent; we show these new developments in curriculum design are similar to the development of process flowsheets. Professor Sargent was not only an eminent research leader and pioneer, but an influential educator who gave rise to a new area in Chemical Engineering, influencing its many directions for more than 50 years.
Effects of Low-Temperature Drying with Intermittent Gaseous Chlorine Dioxide Treatment on Texture and Shelf-Life of Rice Cakes
Authors: Oyinloye Timilehin Martins , Yoon Won Byong
Published: March 2020

We investigated the effect of chlorine dioxide (ClO2) under low temperature drying to suppress rice cake stickiness during the cutting process by initiating the onset of retrogradation until the stickiness is minimized for shelf-life extension. The intermittent ClO2 application at low-temperature drying was conducted at 10 °C for different drying periods (0, 6, 12, 18, and 24 h). Texture analysis showed significant differences with increasing values of hardness (901.39 ± 53.87 to 12,653 ± 1689.35 g) and reduced values of modified adhesiveness (3614.37 ±578.23 to 534.81 ± 89.37 g). The evaluation of rice cake stickiness during the cutting process revealed an optimum drying period of 18 h with no significant difference (p ≤ 0.05) compared to the 24 h drying process. Microbial contamination during the drying process increased, with microbial load from 6.39 ± 0.37 to 7.94 ± 0.29 CFU/g. Intermittent ClO2 application at 22 ppm successfully reduced the microbial load by 63% during drying process. The inhibitory property of ClO2 was further analyzed on a sample with high initial microbial load (3.01 ± 0.14 CFU/g) using primary and modified secondary growth models fitted to all experimental storage temperatures (5–25 °C) with R2 values > 0.99. The model demonstrated a strong inhibition by ClO2 with microbial growth not exceeding the accepted population threshold (106 CFU/g) for toxin production. The shelf-life of rice cake was increased by 86 h and 432 h at room temperature (25 °C) and 5 °C respectively. Microbial inactivation via ClO2 treatment is a novel method for improved food storage without additional thermal sterilization or the use of an additional processing unit.
The Influence of Freezing and Thawing on the Yield and Energy Consumption of the Celeriac Juice Pressing Process
Authors: Nadulski Rafał , Kobus Zbigniew , Guz Tomasz
Published: March 2020

The aim of this study is to indicate the influence of pretreatment, consisting of grinding vegetables and then freezing and thawing the raw material before pressing on the process efficiency and quality of obtained juice. The subject of the research was celeriac root (Apium graveolens L) of the Edward variety. Juice pressing was carried out in a laboratory basket press. The pressing yield and specific energy were values characterizing the pressing process, while pH and the extracted content in the juice were used to assess the juice quality. The experiment was performed according to three procedures. In each of them, the ground celeriac root (pulp or chips) was initially pretreated through freezing and thawing and then pressed twice. Among the examined methods of obtaining juice, the most beneficial method was pressing juice from the pulp, then freezing and thawing the pomace obtained in the first cycle, and finally, pressing the pomace. It is an energy-optimal method and guarantees a high pressing yield as well as obtaining juice with a higher soluble solid content than during the process of pressing chips.
The Polarimetric Sensitivity of SMAP-Reflectometry Signals to Crop Growth in the U.S. Corn Belt
Authors: Rodriguez-Alvarez Nereida , Misra Sidharth , Morris Mary
Published: March 2020

Crop growth is an important parameter to monitor in order to obtain accurate remotely sensed estimates of soil moisture, as well as assessments of crop health, productivity, and quality commonly used in the agricultural industry. The Soil Moisture Active Passive (SMAP) mission has been collecting Global Positioning System (GPS) signals as they reflect off the Earth’s surface since August 2015. The L-band dual-polarization reflection measurements enable studies of the evolution of geophysical parameters during seasonal transitions. In this paper, we examine the sensitivity of SMAP-reflectometry signals to agricultural crop growth related characteristics: crop type, vegetation water content (VWC), crop height, and vegetation opacity (VOP). The study presented here focuses on the United States “Corn Belt,” where an extensive area is planted every year with mostly corn, soybean, and wheat. We explore the potential to generate regularly an alternate source of crop growth information independent of the data currently used in the soil moisture (SM) products developed with the SMAP mission. Our analysis explores the variability of the polarimetric ratio (PR), computed from the peak signals at V- and H-polarization, during the United States Corn Belt crop growing season in 2017. The approach facilitates the understanding of the evolution of the observed surfaces from bare soil to peak growth and the maturation of the crops until harvesting. We investigate the impact of SM on PR for low roughness scenes with low variability and considering each crop type independently. We analyze the sensitivity of PR to the selected crop height, VWC, VOP, and Normalized Differential Vegetation Index (NDVI) reference datasets. Finally, we discuss a possible path towards a retrieval algorithm based on Global Navigation Satellite System-Reflectometry (GNSS-R) measurements that could be used in combination with passive SMAP soil moisture algorithms to correct simultaneously for the VWC and SM effects on the electromagnetic signals.
Hyperspectral and Multispectral Remote Sensing Image Fusion Based on Endmember Spatial Information
Authors: Feng Xiaoxiao , He Luxiao , Cheng Qimin , Long Xiaoyi , Yuan Yuxin
Published: March 2020

Hyperspectral (HS) images usually have high spectral resolution and low spatial resolution (LSR). However, multispectral (MS) images have high spatial resolution (HSR) and low spectral resolution. HS–MS image fusion technology can combine both advantages, which is beneficial for accurate feature classification. Nevertheless, heterogeneous sensors always have temporal differences between LSR-HS and HSR-MS images in the real cases, which means that the classical fusion methods cannot get effective results. For this problem, we present a fusion method via spectral unmixing and image mask. Considering the difference between the two images, we firstly extracted the endmembers and their corresponding positions from the invariant regions of LSR-HS images. Then we can get the endmembers of HSR-MS images based on the theory that HSR-MS images and LSR-HS images are the spectral and spatial degradation from HSR-HS images, respectively. The fusion image is obtained by two result matrices. Series experimental results on simulated and real datasets substantiated the effectiveness of our method both quantitatively and visually.
Associations between Benthic Cover and Habitat Complexity Metrics Obtained from 3D Reconstruction of Coral Reefs at Different Resolutions
Authors: Fukunaga Atsuko , Burns John H. R. , Pascoe Kailey H. , Kosaki Randall K.
Published: March 2020

Quantifying the three-dimensional (3D) habitat structure of coral reefs is an important aspect of coral reef monitoring, as habitat architecture affects the abundance and diversity of reef organisms. Here, we used photogrammetric techniques to generate 3D reconstructions of coral reefs and examined relationships between benthic cover and various habitat metrics obtained at six different resolutions of raster cells, ranging from 1 to 32 cm. For metrics of 3D structural complexity, fractal dimension, which utilizes information on 3D surface areas obtained at different resolutions, and vector ruggedness measure (VRM) obtained at 1-, 2- or 4-cm resolution correlated well with benthic cover, with a relatively large amount of variability in these metrics being explained by the proportions of corals and crustose coralline algae. Curvature measures were, on the other hand, correlated with branching and mounding coral cover when obtained at 1-cm resolution, but the amount of variability explained by benthic cover was generally very low when obtained at all other resolutions. These results show that either fractal dimension or VRM obtained at 1-, 2- or 4-cm resolution, along with curvature obtained at 1-cm resolution, can effectively capture the 3D habitat structure provided by specific benthic organisms.
Hierarchical Multi-View Semi-Supervised Learning for Very High-Resolution Remote Sensing Image Classification
Authors: Shi Cheng , Lv Zhiyong , Yang Xiuhong , Xu Pengfei , Bibi Irfana
Published: March 2020

Traditional classification methods used for very high-resolution (VHR) remote sensing images require a large number of labeled samples to obtain higher classification accuracy. Labeled samples are difficult to obtain and costly. Therefore, semi-supervised learning becomes an effective paradigm that combines the labeled and unlabeled samples for classification. In semi-supervised learning, the key issue is to enlarge the training set by selecting highly-reliable unlabeled samples. Observing the samples from multiple views is helpful to improving the accuracy of label prediction for unlabeled samples. Hence, the reasonable view partition is very important for improving the classification performance. In this paper, a hierarchical multi-view semi-supervised learning framework with CNNs (HMVSSL) is proposed for VHR remote sensing image classification. Firstly, a superpixel-based sample enlargement method is proposed to increase the number of training samples in each view. Secondly, a view partition method is designed to partition the training set into two independent views, and the partitioned subsets are characterized by being inter-distinctive and intra-compact. Finally, a collaborative classification strategy is proposed for the final classification. Experiments are conducted on three VHR remote sensing images, and the results show that the proposed method performs better than several state-of-the-art methods.
A Deep Convolutional Neural Network for Oil Spill Detection from Spaceborne SAR Images
Authors: Zeng Kan , Wang Yixiao
Published: March 2020

Classification algorithms for automatically detecting sea surface oil spills from spaceborne Synthetic Aperture Radars (SARs) can usually be regarded as part of a three-step processing framework, which briefly includes image segmentation, feature extraction, and target classification. A Deep Convolutional Neural Network (DCNN), named the Oil Spill Convolutional Network (OSCNet), is proposed in this paper for SAR oil spill detection, which can do the latter two steps of the three-step processing framework. Based on VGG-16, the OSCNet is obtained by designing the architecture and adjusting hyperparameters with the data set of SAR dark patches. With the help of the big data set containing more than 20,000 SAR dark patches and data augmentation, the OSCNet can have as many as 12 weight layers. It is a relatively deep Deep Learning (DL) network for SAR oil spill detection. It is shown by the experiments based on the same data set that the classification performance of OSCNet has been significantly improved compared to that of traditional machine learning (ML). The accuracy, recall, and precision are improved from 92.50%, 81.40%, and 80.95% to 94.01%, 83.51%, and 85.70%, respectively. An important reason for this improvement is that the distinguishability of the features learned by OSCNet itself from the data set is significantly higher than that of the hand-crafted features needed by traditional ML algorithms. In addition, experiments show that data augmentation plays an important role in avoiding over-fitting and hence improves the classification performance. OSCNet has also been compared with other DL classifiers for SAR oil spill detection. Due to the huge differences in the data sets, only their similarities and differences are discussed at the principle level.
Multistage Dynamic Optimization with Different Forms of Neural-State Constraints to Avoid Many Object Collisions Based on Radar Remote Sensing
Authors: Lisowski Józef
Published: March 2020

This article presents the possibility of helping navigators direct the movement of an object, while safely passing through other objects, using an artificial neural network and optimization methods. It has been shown that the best trajectory of an object in terms of optimality and security, from among many possible options, can be determined by the method of dynamic programming with the simultaneous use of an artificial neural network, by depicting the encountered objects as moving in forbidden domains. Analytical considerations are illustrated with examples of simulation studies of the developed calculation program on real navigational situations at sea. This research took into account both the number of objects encountered and the different shapes of domains assigned to the objects encountered. Finally, the optimal value of the safe object trajectory time was compared on the setpoint value of the safe passing distance of objects in given visibility conditions at sea, and the degree of discretization of calculations was determined by the density of the location of nodes along the route of objects.
Phenology-Based Rice Paddy Mapping Using Multi-Source Satellite Imagery and a Fusion Algorithm Applied to the Poyang Lake Plain, Southern China
Authors: Ding Mingjun , Guan Qihui , Li Lanhui , Zhang Huamin , Liu Chong , Zhang Le
Published: March 2020

Accurate information about the spatiotemporal patterns of rice paddies is essential for the assessment of food security, management of agricultural resources, and sustainability of ecosystems. However, accurate spatial datasets of rice paddy fields and multi-cropping at fine resolution are still lacking. Landsat observation is the primary source of remote sensing data that has continuously mapped regional rice paddy fields at a 30-m spatial resolution since the 1980s. However, Landsat data used for rice paddy studies reveals some challenges, especially data quality issues (e.g., cloud cover). Here, we present an algorithm that integrates time-series Landsat and MODIS (Moderate-resolution Imaging Spectroradiometer) images with a phenology-based approach (ILMP) to map rice paddy planting fields and multi-cropping patterns. First, a fusion of MODIS and Landsat data was used to reduce the cloud contamination, which added more information to the Landsat time series data. Second, the unique biophysical features of rice paddies during the flooding and open-canopy periods (which can be captured by the dynamics of the vegetation indices) were used to identify rice paddy regions as well as those of multi-cropping. This algorithm was tested for 2015 in Nanchang County, which is located on the Poyang Lake plain in southern China. We evaluated the resultant map of the rice paddy and multi-cropping systems using ground-truth data and Google Earth images. The overall accuracy and kappa coefficient of the rice paddy planting areas were 93.66% and 0.85, respectively. The overall accuracy and kappa coefficient of the multi-cropping regions were 92.95% and 0.89, respectively. In addition, our algorithm was more capable of capturing detailed information about areas with fragmented cropland than that of the National Land Cover Dataset (NLCD) from 2015. These results demonstrated the great potential of our algorithm for mapping rice paddy fields and using the multi-cropping index in complex landscapes in southern China.
An Approach of Identifying and Extracting Urban Commercial Areas Using the Nighttime Lights Satellite Imagery
Authors: Duan Xuzhe , Hu Qingwu , Zhao Pengcheng , Wang Shaohua , Ai Mingyao
Published: March 2020

Urban commercial areas can reflect the spatial distribution of business activities. However, the scope of urban commercial areas cannot be easily detected by traditional methods because of difficulties in data collection. Considering the positive correlation between business scale and nighttime lighting, this paper proposes a method of urban commercial areas detection based on nighttime lights satellite imagery. First, an imagery preprocess model is proposed to correct imageries and improve efficiency of cluster analysis. Then, an exploratory spatial data analysis and hotspots clustering method is employed to detect commercial areas by geographic distribution metric with urban commercial hotspots. Furthermore, four imageries of Wuhan City and Shenyang City are selected as an example for urban commercial areas detection experiments. Finally, a comparison is made to find out the time and space factors that affect the detection results of the commercial areas. By comparing the results with the existing map data, we are convinced that the nighttime lights satellite imagery can effectively detect the urban commercial areas. The time of image acquisition and the vegetation coverage in the area are two important factors affecting the detection effect. Harsh weather conditions and high vegetation coverage are conducive to the effective implementation of this method. This approach can be integrated with traditional methods to form a fast commercial areas detection model, which can then play a role in large-scale socio-economic surveys and dynamic detection of commercial areas evolution. Hence, a conclusion can be reached that this study provides a new method for the perception of urban socio-economic activities.
PreciPatch: A Dictionary-based Precipitation Downscaling Method
Authors: Xu Mengchao , Liu Qian , Sha Dexuan , Yu Manzhu , Duffy Daniel , Putman William , Carroll Mark , Lee Tsengdar , Yang Chaowei
Published: March 2020

Climate and weather data such as precipitation derived from Global Climate Models (GCMs) and satellite observations are essential for the global and local hydrological assessment. However, most climatic popular precipitation products (with spatial resolutions coarser than 10km) are too coarse for local impact studies and require “downscaling” to obtain higher resolutions. Traditional precipitation downscaling methods such as statistical and dynamic downscaling require an input of additional meteorological variables, and very few are applicable for downscaling hourly precipitation for higher spatial resolution. Based on dynamic dictionary learning, we propose a new downscaling method, PreciPatch, to address this challenge by producing spatially distributed higher resolution precipitation fields with only precipitation input from GCMs at hourly temporal resolution and a large geographical extent. Using aggregated Integrated Multi-satellitE Retrievals for GPM (IMERG) data, an experiment was conducted to evaluate the performance of PreciPatch, in comparison with bicubic interpolation using RainFARM—a stochastic downscaling method, and DeepSD—a Super-Resolution Convolutional Neural Network (SRCNN) based downscaling method. PreciPatch demonstrates better performance than other methods for downscaling short-duration precipitation events (used historical data from 2014 to 2017 as the training set to estimate high-resolution hourly events in 2018).
Estimating Meltwater Drainage Onset Timing and Duration of Landfast Ice in the Canadian Arctic Archipelago Using AMSR-E Passive Microwave Data
Authors: Tanaka Yasuhiro
Published: March 2020

Meltwater drainage onset (DO) timing and drainage duration (DD) related to snowmelt-water redistribution are both important for understanding not only the Arctic energy and heat budgets but also the salt/heat balance of the mixed layer in the ocean and sea-ice ecosystem. We present DO and DD as determined from the time series of Advanced Microwave Scanning Radiometer-Earth observing system (AMSR-E) melt pond fraction (MPF) estimates in an area with Canadian landfast ice. To address the lack of evaluation on a day-by-day basis for the AMSR-E MPF estimate, we first compared AMSR-E MPF with the daily Medium Resolution Imaging Spectrometer (MERIS) MPF. The AMSR-E MPF estimate correlates significantly with the MERIS MPF (r = 0.73–0.83). The estimate has a product quality similar to the MERIS MPF only when the albedo is around 0.5–0.7 and a positive bias of up to 10% in areas with an albedo of 0.7–0.9, including melting snow. The DO/DD estimates are determined by using a polynomial regression curve fitted on the time series of the AMSR-E MPF. The DOs/DDs from time series of the AMSR-E and MERIS MPFs are compared, revealing consistency in both DD and DO. The DO timing from 2006 to 2011 is correlated with melt onset timing. To the best of our knowledge, our study provides the first large-scale information on both DO timing and DD.
Development of a 3D Real-Time Atmospheric Monitoring System (3DREAMS) Using Doppler LiDARs and Applications for Long-Term Analysis and Hot-and-Polluted Episodes
Authors: YIM Steve Hung Lam
Published: March 2020

Heatwaves and air pollution are serious environmental problems that adversely affect human health. While related studies have typically employed ground-level data, the long-term and episodic characteristics of meteorology and air quality at higher altitudes have yet to be fully understood. This study developed a 3-Dimensional Real-timE Atmospheric Monitoring System (3DREAMS) to measure and analyze the vertical profiles of horizontal wind speed and direction, vertical wind velocity as well as aerosol backscatter. The system was applied to Hong Kong, a highly dense city with complex topography, during each season and including hot-and-polluted episodes (HPEs) in 2019. The results reveal that the high spatial wind variability and wind characteristics in the lower atmosphere in Hong Kong can extend upwards by up to 0.66 km, thus highlighting the importance of mountains for the wind environment in the city. Both upslope and downslope winds were observed at one site, whereas downward air motions predominated at another site. The high temperature and high concentration of fine particulate matter during HPEs were caused by a significant reduction in both horizontal and vertical wind speeds that established conditions favorable for heat and air pollutant accumulation, and by the prevailing westerly wind promoting transboundary air pollution. The findings of this study are anticipated to provide valuable insight for weather forecasting and air quality studies. The 3DREAMS will be further developed to monitor upper atmosphere wind and air quality over the Greater Bay Area of China.
Long-Term (2005–2017) View of Atmospheric Pollutants in Central China Using Multiple Satellite Observations
Authors: Li Rong , Mei Xin , Chen Liangfu , Wang Lili , Wang Zifeng , Jing Yingying
Published: March 2020

The air quality in China has experienced dramatic changes during the last few decades. To improve understanding of distribution, variations, and main influence factors of air pollution in central China, long-term multiple satellite observations from moderate resolution imaging spectroradiometer (MODIS) and ozone monitoring instrument (OMI) are used to characterize particle pollution and their primary gaseous precursors, sulfur dioxide (SO2), and nitrogen dioxide (NO2) in Hubei province during 2005–2017. Unlike other regions in eastern China, particle and gaseous pollutants exhibit distinct spatial and temporal patterns in central China due to differences in emission sources and control measures. OMI SO2 of the whole Hubei region reached the highest value of ~0.2 Dobson unit (DU) in 2007 and then declined by more than 90% to near background levels. By contrast, OMI NO2 grew from ~3.2 to 5.9 × 1015 molecules cm−2 during 2005–2011 and deceased to ~3.9 × 1015 molecules cm−2 in 2017. Unlike the steadily declining SO2, variations of OMI NO2 flattened out in 2016 and increased ~0.5 × 1015 molecules cm−2 during 2017. As result, MODIS AOD at 550 nm increased from 0.55 to the peak value of 0.7 during 2005–2011 and then decreased continuously to 0.38 by 2017. MODIS AOD and OMI SO2 has a high correlation (R > 0.8), indicating that annual variations of SO2 can explain most changes of AOD. The air pollution in central China has notable seasonal variations, which is heaviest in winter and light in summer. While air quality in eastern Hubei is dominated by gaseous pollution such as O3 and NOx, particle pollutants are mainly concentrated in central Hubei. The high consistency with ground measurements demonstrates that satellite observation can well capture variations of air pollution in regional scales. The increasing ozone (O3) and NO2 since 2016 suggests that more control measures should be made to reduce O3-related emissions. To improve the air quality in regional scale, it is necessary to monitor the dynamic emission sources with satellite observations at a finer resolution.
Forestry Remote Sensing from Unmanned Aerial Vehicles: A Review Focusing on the Data, Processing and Potentialities
Authors: Guimarães Nathalie , Pádua Luís , Marques Pedro , Silva Nuno , Peres Emanuel , Sousa Joaquim J.
Published: March 2020

Currently, climate change poses a global threat, which may compromise the sustainability of agriculture, forestry and other land surface systems. In a changing world scenario, the economic importance of Remote Sensing (RS) to monitor forests and agricultural resources is imperative to the development of agroforestry systems. Traditional RS technologies encompass satellite and manned aircraft platforms. These platforms are continuously improving in terms of spatial, spectral, and temporal resolutions. The high spatial and temporal resolutions, flexibility and lower operational costs make Unmanned Aerial Vehicles (UAVs) a good alternative to traditional RS platforms. In the management process of forests resources, UAVs are one of the most suitable options to consider, mainly due to: (1) low operational costs and high-intensity data collection; (2) its capacity to host a wide range of sensors that could be adapted to be task-oriented; (3) its ability to plan data acquisition campaigns, avoiding inadequate weather conditions and providing data availability on-demand; and (4) the possibility to be used in real-time operations. This review aims to present the most significant UAV applications in forestry, identifying the appropriate sensors to be used in each situation as well as the data processing techniques commonly implemented.
A High-Resolution Open Source Platform for Building Envelope Thermal Performance Assessment Using a Wireless Sensor Network
Authors: Lihakanga Romwald , Ding Yuan , Medero Gabriela M. , Chapman Samuel , Goussetis George
Published: March 2020

This paper presents an in-situ wireless sensor network (WSN) for building envelope thermal transmission analysis. The WSN is able to track heat flows in various weather conditions in real-time. The developed system focuses on long-term in-situ building material variation analysis, which cannot be readily achieved using current approaches, especially when the number of measurement hotspots is large. This paper describes the implementation of the proposed system using the heat flow method enabled through an adaptable and low-cost wireless network, validated via a laboratory experiment.
A Novel Approach for Mixed Manual/Connected Automated Freeway Traffic Management
Authors: Li Duo , Wagner Peter
Published: March 2020

Freeway traffic management and control often rely on input from fixed-point sensors. A sufficiently high sensor density is required to ensure data reliability and accuracy, which results in high installation and maintenance costs. Moreover, fixed-point sensors encounter difficulties to provide spatiotemporally and wide-ranging information due to the limited observable area. This research exploits the utilization of connected automated vehicles (CAVs) as an alternative data source for freeway traffic management. To handle inherent uncertainty associated with CAV data, we develop an interval type 2 fuzzy logic-based variable speed limit (VSL) system for mixed traffic. The simulation results demonstrate that when more 10% CAVs are deployed, the performance of the proposed CAV-based system can approach that of the detector-based system. It is demonstrated in addition that the introduction of CAVs may make VSL obsolete at very high CAV-equipment rates.
Sensing Performance Investigations on Two-Photon Fluorescent Probes for Detecting β-Amyloid in Alzheimer’s Disease
Authors: Zhang Yujin , Luan Ni , Li Kan , Leng Jiancai , Hu Wei
Published: March 2020

Alzheimer’s disease (AD) is one of the most common forms of senile disease. In recent years, the incidence of AD has been increasing significantly with the acceleration of the aging process of the global population. However, current clinical drugs can only alleviate the symptoms of AD patients without healing the disease fundamentally. Therefore, it is of great significance to develop an effective small molecule diagnostic reagent for the early diagnosis of AD. In this paper, we employ an integrated approach, including molecular docking simulation and quantum mechanics/molecular mechanics calculation, to investigate the sensing performance of a series of donor–acceptor structural probes for the marker protein of AD (β-amyloid). Results show that the probes display evident fluorescence enhancement when bound to the β-amyloid, suggesting the effect of the environment on the molecular properties. Especially, the two-photon absorption cross-section of the probes increase drastically in the β-amyloid compared to that in vacuum, which results from the larger electron delocalization and dipole moment in the fibrillary-like environment. Thus, one can propose that the studied probes are capable of application in two-photon fluorescent imaging, particularly those containing naphthalene rings as the donor or with a longer spacer group. Our calculations elucidate the experimental measurements reasonably, and further establish possible structure–property relationships that can be used to design novel biocompatible two-photon fluorescent probes for the diagnosis of Alzheimer’s.
Using Deep Learning to Forecast Maritime Vessel Flows
Authors: Zhou Xiangyu , Liu Zhengjiang , Wang Fengwu , Xie Yajuan , Zhang Xuexi
Published: March 2020

Forecasting vessel flows is important to the development of intelligent transportation systems in the maritime field, as real-time and accurate traffic information has favorable potential in helping a maritime authority to alleviate congestion, mitigate emission of GHG (greenhouse gases) and enhance public safety, as well as assisting individual vessel users to plan better routes and reduce additional costs due to delays. In this paper, we propose three deep learning-based solutions to forecast the inflow and outflow of vessels within a given region, including a convolutional neural network (CNN), a long short-term memory (LSTM) network, and the integration of a bidirectional LSTM network with a CNN (BDLSTM-CNN). To apply those solutions, we first divide the given maritime region into

M
×
N

grids, then we forecast the inflow and outflow for all the grids. Experimental results based on the real AIS (Automatic Identification System) data of marine vessels in Singapore demonstrate that the three deep learning-based solutions significantly outperform the conventional method in terms of mean absolute error and root mean square error, with the performance of the BDLSTM-CNN-based hybrid solution being the best.
Bagging Ensemble of Multilayer Perceptrons for Missing Electricity Consumption Data Imputation
Authors: Jung Seungwon , Moon Jihoon , Park Sungwoo , Rho Seungmin , Baik Sung Wook , Hwang Eenjun
Published: March 2020

For efficient and effective energy management, accurate energy consumption forecasting is required in energy management systems (EMSs). Recently, several artificial intelligence-based techniques have been proposed for accurate electric load forecasting; moreover, perfect energy consumption data are critical for the prediction. However, owing to diverse reasons, such as device malfunctions and signal transmission errors, missing data are frequently observed in the actual data. Previously, many imputation methods have been proposed to compensate for missing values; however, these methods have achieved limited success in imputing electric energy consumption data because the period of data missing is long and the dependency on historical data is high. In this study, we propose a novel missing-value imputation scheme for electricity consumption data. The proposed scheme uses a bagging ensemble of multilayer perceptrons (MLPs), called softmax ensemble network, wherein the ensemble weight of each MLP is determined by a softmax function. This ensemble network learns electric energy consumption data with explanatory variables and imputes missing values in this data. To evaluate the performance of our scheme, we performed diverse experiments on real electric energy consumption data and confirmed that the proposed scheme can deliver superior performance compared to other imputation methods.
Object Identification for Task-Oriented Communication with Industrial Robots
Authors: Rogowski Adam , Skrobek Piotr
Published: March 2020

To make the human-robot collaboration effective, it may be necessary to provide robots with “senses” like vision and hearing. Task-oriented man-machine speech communication often relies on the use of abstract terms to describe objects. Therefore it is necessary to correctly map those terms into images of proper objects in a camera’s field of view. This paper presents the results of our research in this field. A novel method for contour identification, based on flexible editable contour templates (FECT), has been developed. We demonstrate that existing methods are not appropriate for this purpose because it is difficult to formulate general rules that humans employ to rank shapes into proper classes. Therefore, the rules for shape classification should be individually formulated by the users for each application. Our aim was to create appropriate tool facilitating formulation of those rules as it could potentially be a very labor-intensive task. The core of our solution is FCD (flexible contour description) format for description of flexible templates. Users will be able to create and edit flexible contour templates, and thus, adjust image recognition systems to their needs, in order to provide task-oriented communication between humans and robots.
A Method of Interstory Drift Monitoring Using a Smartphone and a Laser Device
Authors: Li Jinke , Xie Botao , Zhao Xuefeng
Published: March 2020

Interstory drift is an important engineering parameter in building design and building structural health monitoring. However, many problems exist in current interstory drift monitoring methods. The traditional method is imprecise—double numerical integration of acceleration data—and other direct monitoring methods need professional equipment. This paper proposes a method to solve these problems by monitoring the interstory drift with a smartphone and a laser device. In this method, a laser device is installed on the ceiling while a smartphone is fixed on a steel projection plate on the floor. Compared with a reference sensor, the method designed in this study shows that a smartphone is competent in monitoring the interstory drift. This method utilizes a smartphone application (APP) named D-Viewer to implement monitoring and data storage just in one place, which is also inexpensive. The results showed that this method has an average percent error of 3.37%, with a standard deviation of 2.67%. With the popularization of the smartphone, this method is promising in acquiring large amounts of data, which will be significant for building assessment after an earthquake.
Photoplethysmographic Time-Domain Heart Rate Measurement Algorithm for Resource-Constrained Wearable Devices and its Implementation
Authors: Wójcikowski Marek , Pankiewicz Bogdan
Published: March 2020

This paper presents an algorithm for the measurement of the human heart rate, using photoplethysmography (PPG), i.e., the detection of the light at the skin surface. The signal from the PPG sensor is processed in time-domain; the peaks in the preprocessed and conditioned PPG waveform are detected by using a peak detection algorithm to find the heart rate in real time. Apart from the PPG sensor, the accelerometer is also used to detect body movement and to indicate the moments in time, for which the PPG waveform can be unreliable. This paper describes in detail the signal conditioning path and the modified algorithm, and it also gives an example of implementation in a resource-constrained wrist-wearable device. The algorithm was evaluated by using the publicly available PPG-DaLia dataset containing samples collected during real-life activities with a PPG sensor and accelerometer and with an ECG signal as ground truth. The quality of the results is comparable to the other algorithms from the literature, while the required hardware resources are lower, which can be significant for wearable applications.
Heterogeneous Iris One-to-One Certification with Universal Sensors Based On Quality Fuzzy Inference and Multi-Feature Fusion Lightweight Neural Network
Authors: Shuai Liu , Yuanning Liu , Xiaodong Zhu , Guang Huo , Zukang Wu , Xinlong Li , Chaoqun Wang , Jingwei Cui
Published: March 2020

Due to the unsteady morphology of heterogeneous irises generated by a variety of different devices and environments, the traditional processing methods of statistical learning or cognitive learning for a single iris source are not effective. Traditional iris recognition divides the whole process into several statistically guided steps, which cannot solve the problem of correlation between various links. The existing iris data set size and situational classification constraints make it difficult to meet the requirements of learning methods under a single deep learning framework. Therefore, aiming at a one-to-one iris certification scenario, this paper proposes a heterogeneous iris one-to-one certification method with universal sensors based on quality fuzzy inference and a multi-feature entropy fusion lightweight neural network. The method is divided into an evaluation module and a certification module. The evaluation module can be used by different devices to design a quality fuzzy concept inference system and an iris quality knowledge concept construction mechanism, transform human logical cognition concepts into digital concepts, and select appropriate concepts to determine iris quality according to different iris quality requirements and get a recognizable iris. The certification module is a lightweight neural network based on statistical learning ideas and a multi-source feature fusion mechanism. The information entropy of the iris feature label was used to set the iris entropy feature category label and design certification module functions according to the category label to obtain the certification module result. As the requirements for the number and quality of irises changes, the category labels in the certification module function were dynamically adjusted using a feedback learning mechanism. This paper uses iris data collected from three different sensors in the JLU (Jilin University) iris library. The experimental results prove that for the lightweight multi-state irises, the abovementioned problems are ameliorated to a certain extent by this method.
Cognitive Radio-Assisted NOMA Broadcasting for 5G Cellular V2X Communications: Model of Roadside Unit Selection and SWIPT
Authors: Do Dinh-Thuan , Le Anh-Tu , Hoang Thi-Anh , Lee Byung Moo
Published: March 2020

The outage performance is a significant problem to implement the Cognitive Radio (CR) paradigm in the Vehicle to Everything (V2X) networks. Recently, more interest has focused on Non-Orthogonal Multiple Access (NOMA) in wireless-powered communication. In the conventional CR-enabled V2X-NOMA network, spectrum sensing and limited battery capacity at the Roadside Unit (RSU) may cause serious outage performance. In this study, RSU selection scheme is adopted. This paper presents an interesting model of a system with Simultaneous Wireless Information and Power Transfer (SWIPT) and a CR-enabled V2X-NOMA network. In the downlink, the RSU harvests wireless energy from Radio Frequency (RF) signals and senses the spectrum state at the same time. A CR-enabled V2X-NOMA system performance is presented by deriving exact expressions of outage probability of distant vehicles. In the overlay CR-enabled V2X-NOMA network, the constraints are transmit power and the number of designed RSU that make significant impacts on system performance. Simulation results show that the CR-enabled V2X-NOMA get benefits from energy harvesting and RSU selection scheme.
A Learning-Enhanced Two-Pair Spatiotemporal Reflectance Fusion Model for GF-2 and GF-1 WFV Satellite Data
Authors: Ge Yanqin , Li Yanrong , Chen Jinyong , Sun Kang , Li Dacheng , Han Qijin
Published: March 2020

Since requirements of related applications for time series remotely-sensed images with high spatial resolution have been hard to be satisfied under current observation conditions of satellite sensors, it is key to reconstruct high-resolution images at specified dates. As an effective data reconstruction technique, spatiotemporal fusion can be used to generate time series land surface parameters with a clear geophysical significance. In this study, an improved fusion model based on the Sparse Representation-Based Spatiotemporal Reflectance Fusion Model (SPSTFM) is developed and assessed with reflectance data from Gaofen-2 Multi-Spectral (GF-2 MS) and Gaofen-1 Wide-Field-View (GF-1 WFV). By introducing a spatially enhanced training method to dictionary training and sparse coding processes, the developed fusion framework is expected to promote the description of high-resolution and low-resolution overcomplete dictionaries. Assessment indices including Average Absolute Deviation (AAD), Root-Mean-Square Error (RMSE), Peak Signal to Noise Ratio (PSNR), Correlation Coefficient (CC), spectral angle mapper (SAM), structure similarity (SSIM) and Erreur Relative Global Adimensionnelle de Synthèse (ERGAS) are then used to test employed fusion methods for a parallel comparison. The experimental results show that more accurate prediction of GF-2 MS reflectance than that from the SPSTFM can be obtained and furthermore comparable with popular two-pair based reflectance fusion models like the Spatial and Temporal Adaptive Fusion Model (STARFM) and the Enhanced-STARFM (ESTARFM).