bluuebunny/crossref_metadata_2025 huggingface dataset:
Spatial Assessment of Solar Radiation by Machine Learning and Deep Neural Network Models Using Data Provided by the COMS MI Geostationary Satellite: A Case Study in South Korea
Authors: Yeom Jong-Min , Park Seonyoung , Chae Taebyeong , Kim Jin-Young , Lee Chang Suk
Published: May 2019

Although data-driven methods including deep neural network (DNN) were introduced, there was not enough assessment about spatial characteristics when using limited ground observation as reference. This work aimed to interpret the feasibility of several machine learning approaches to assess the spatial distribution of solar radiation on Earth based on the Communication, Ocean, and Meteorological Satellite (COMS) Meteorological Imager (MI) geostationary satellite. Four data-driven models were selected (artificial neural network (ANN), random forest (RF), support vector regression (SVR), and DNN), to compare their accuracy and spatial estimating performance. Moreover, we used a physical model to probe the ability of data-driven methods, implementing hold-out and k-fold cross-validation approaches based on pyranometers located in South Korea. The results of analysis showed the RF had the highest accuracy in predicting performance, although the difference between RF and the second-best technique (DNN) was insignificant. Temporal variations in root mean square error (RMSE) were dependent on the number of data samples, while the physical model showed relatively less sensitivity. Nevertheless, DNN and RF showed less variability in RMSE than the others. To examine spatial estimation performance, we mapped solar radiation over South Korea for each model. The data-driven models accurately simulated the observed cloud pattern spatially, whereas the physical model failed to do because of cloud mask errors. These exhibited different spatial retrieval performances according to their own training approaches. Overall analysis showed that deeper layers of networks approaches (RF and DNN), could best simulate the challenging spatial pattern of thin clouds when using satellite multispectral data.
A Novel Traveling-Wave-Based Method Improved by Unsupervised Learning for Fault Location of Power Cables via Sheath Current Monitoring
Authors: Li Mingzhen , Liu Jianming , Zhu Tao , Zhou Wenjun , Zhou Chengke
Published: May 2019

In order to improve the practice in maintenance of power cables, this paper proposes a novel traveling-wave-based fault location method improved by unsupervised learning. The improvement mainly lies in the identification of the arrival time of the traveling wave. The proposed approach consists of four steps: (1) The traveling wave associated with the sheath currents of the cables are grouped in a matrix; (2) the use of dimensionality reduction by t-SNE (t-distributed Stochastic Neighbor Embedding) to reconstruct the matrix features in a low dimension; (3) application of the DBSCAN (density-based spatial clustering of applications with noise) clustering to cluster the sample points by the closeness of the sample distribution; (4) the arrival time of the traveling wave can be identified by searching for the maximum slope point of the non-noise cluster with the fewest samples. Simulations and calculations have been carried out for both HV (high voltage) and MV (medium voltage) cables. Results indicate that the arrival time of the traveling wave can be identified for both HV cables and MV cables with/without noise, and the method is suitable with few random time errors of the recorded data. A lab-based experiment was carried out to validate the proposed method and helped to prove the effectiveness of the clustering and the fault location.
Electrochemical Sensor Based on Poly(Azure B)-DNA Composite for Doxorubicin Determination
Authors: Porfireva Anna , Vorobev Vyatseslav , Babkina Sofya , Evtugyn Gennady
Published: May 2019

A new voltammetric DNA sensor has been developed for doxorubicin determination on the platform of a glassy carbon electrode (GCE) covered with electropolymerized Azure B film and physically adsorbed native DNA. The redox properties of polymeric Azure B were monitored at various pH and scan rates. DNA application decreased the peak currents related to polymeric and monomeric forms of the dye, whereas incubation in doxorubicin solution partially restored the peaks in accordance with the drug and DNA concentration. The relative shift of the cathodic peak current caused by doxorubicin depended on the nominal DNA concentration and its application mode. In optimal conditions, the DNA sensor makes it possible to determine between 0.1 μM to 0.1 nM doxorubicin (limit of detection 7 × 10−11 M). The DNA sensor was tested on commercial doxorubicin formulations and on artificial samples the mimicked electrolyte content of human serum.
An Improved Multi-temporal and Multi-feature Tea Plantation Identification Method Using Sentinel-2 Imagery
Authors: Zhu Jun , Pan Ziwu , Wang Hang , Huang Peijie , Sun Jiulin , Qin Fen , Liu Zhenzhen
Published: May 2019

As tea is an important economic crop in many regions, efficient and accurate methods for remotely identifying tea plantations are essential for the implementation of sustainable tea practices and for periodic monitoring. In this study, we developed and tested a method for tea plantation identification based on multi-temporal Sentinel-2 images and a multi-feature Random Forest (RF) algorithm. We used phenological patterns of tea cultivation in China’s Shihe District (such as the multiple annual growing, harvest, and pruning stages) to extracted multi-temporal Sentinel-2 MSI bands, their derived first spectral derivative, NDVI and textures, and topographic features. We then assessed feature importance using RF analysis; the optimal combination of features was used as the input variable for RF classification to extract tea plantations in the study area. A comparison of our results with those achieved using the Support Vector Machine method and statistical data from local government departments showed that our method had a higher producer’s accuracy (96.57%) and user’s accuracy (96.02%). These results demonstrate that: (1) multi-temporal and multi-feature classification can improve the accuracy of tea plantation recognition, (2) RF classification feature importance analysis can effectively reduce feature dimensions and improve classification efficiency, and (3) the combination of multi-temporal Sentinel-2 images and the RF algorithm improves our ability to identify and monitor tea plantations.
On Placement, Location and Orientation of Wrist-Worn Tri-Axial Accelerometers during Free-Living Measurements
Authors: Straczkiewicz Marcin , Glynn Nancy W. , Harezlak Jaroslaw
Published: May 2019

Wearable accelerometers have recently become a standalone tool for the objective assessment of physical activity (PA). In free-living studies, accelerometers are placed by protocol on a pre-defined body location (e.g., non-dominant wrist). However, the protocol is not always followed, e.g., the sensor can be moved between wrists or reattached in a different orientation. Such protocol violations often result in PA miscalculation. We propose an approach, PLOE (“Placement, Location and Orientation Evaluation method”), to determine the sensor position using statistical features from the raw accelerometer measurements. We compare the estimated position with the study protocol and identify discrepancies. We apply PLOE to the measurements collected from 45 older adults who wore ActiGraph GT3X+ accelerometers on the left and right wrist for seven days. We found that 15.6% of participants who wore accelerometers violated the protocol for one or more days. The sensors were worn on the wrong hand during 6.9% of the days of simultaneous wearing of devices. During the periods of discrepancies, the daily PA was miscalculated by more than 20%. Our findings show that correct placement of the device has a significant effect on the PA estimates. These results demonstrate a need for the evaluation of sensor position.
Bearing Fault Diagnosis Based on a Hybrid Classifier Ensemble Approach and the Improved Dempster-Shafer Theory
Authors: Wang Yanxue , Liu Fang , Zhu Aihua
Published: May 2019

Bearing fault diagnosis of a rotating machine plays an important role in reliable operation. A novel intelligent fault diagnosis method for roller bearings has been developed based on a proposed hybrid classifier ensemble approach and the improved Dempster-Shafer theory. The improved Dempster-Shafer theory well considered the combination of unreliable evidence sources, the uncertainty information of basic probability assignment, and the relative credibility of the evidence on the weights in the process of decision making under the framework of fuzzy preference relations, which can effectively deal with conflicts of the evidences and then well improve the diagnostic accuracy for the hybrid classifier ensemble. The effectiveness of the improved Dempster-Shafer theory has been verified via a numerical example. In addition, deep neural networks, a support vector machine, and extreme learning machine techniques have been utilized in the single-stage classification based on singular spectrum entropy, power spectrum entropy, time-frequency entropy, and wavelet packet energy spectrum entropy in this work. Performances of the proposed hybrid ensemble classifier has been demonstrated on a bearing test-rig, compared with the original Dempster-Shafer theory. It can be found that the overall error rate can be greatly reduced with the hybrid ensemble classifier and the improved Dempster-Shafer theory.
An Enhanced Lightweight IoT-based Authentication Scheme in Cloud Computing Circumstances
Authors: Martínez-Peláez Rafael , Toral-Cruz Homero , Parra-Michel Jorge R. , García Vicente , Mena Luis J. , Félix Vanessa G. , Ochoa-Brust Alberto
Published: May 2019

With the rapid deployment of the Internet of Things and cloud computing, it is necessary to enhance authentication protocols to reduce attacks and security vulnerabilities which affect the correct performance of applications. In 2019 a new lightweight IoT-based authentication scheme in cloud computing circumstances was proposed. According to the authors, their protocol is secure and resists very well-known attacks. However, when we evaluated the protocol we found some security vulnerabilities and drawbacks, making the scheme insecure. Therefore, we propose a new version considering login, mutual authentication and key agreement phases to enhance the security. Moreover, we include a sub-phase called evidence of connection attempt which provides proof about the participation of the user and the server. The new scheme achieves the security requirements and resists very well-known attacks, improving previous works. In addition, the performance evaluation demonstrates that the new scheme requires less communication-cost than previous authentication protocols during the registration and login phases.
Focal Vibration Training (Equistasi®) to Improve Posture Stability. A Retrospective Study in Parkinson’s Disease
Authors: Serio Francesco , Minosa Cosimo , De Luca Matteo , Conte Pierguido , Albani Giovanni , Peppe Antonella
Published: May 2019

Background: For people with Parkinson’s disease (PD), falls are a critical point. Focal vibration training (FVT) may represent a valid tool to improve postural performances and reduce the risk of falls. The aim of this study was to evaluate the efficacy of FVT to improve the postural stability in PD patients. Methods: Since October 2015, 55 consecutive PD patients have been selected (T0) for an approach including FVT associated with a rehabilitative protocol (RP); after eight weeks (T1), those patients showing a relevant improvement in the clinical rating scales ((Timed Up and Go (TUG), Tinetti, Unified Parkinson’s disease rating scale (UPDRS) Part III, Berg Balance scale (BBS) and falls rate scale), continued with the FVT protocol (FVTRP group). The remaining patients continued with only the RP (RP group). In July 2018, we have extrapolated the data of the last clinical visit (T2) to observe any differences in the rate of falls. Results: The FVTRP group shows a decrement in the rate of falls from 2.1 to 1.25 (p 0.036) and a stability of the levodopa equivalent daily dosage (LEDD). The RP group shows an increment of LEDD and stability in falls. Conclusions: FVT has been confirmed as a valid tool to enhance the effect of the rehabilitation protocol aimed at postural training.
User Identification across Asynchronous Mobility Trajectories
Authors: Qi Mengjun , Wang Zhongyuan , He Zheng , Shao Zhenfeng
Published: May 2019

With the popularity of location-based services and applications, a large amount of mobility data has been generated. Identification through mobile trajectory information, especially asynchronous trajectory data has raised great concerns in social security prevention and control. This paper advocates an identification resolution method based on the most frequently distributed TOP-N (the most frequently distributed N regions regarding user trajectories) regions regarding user trajectories. This method first finds TOP-N regions whose trajectory points are most frequently distributed to reduce the computational complexity. Based on this, we discuss three methods of trajectory similarity metrics for matching tracks belonging to the same user in two datasets. We conducted extensive experiments on two real GPS trajectory datasets GeoLife and Cabspotting and comprehensively discussed the experimental results. Experimentally, our method is substantially effective and efficiency for user identification.
Spatial Baseline Optimization for Spaceborne Multistatic SAR Tomography Systems
Authors: Zhao Jiuchao , Yu Anxi , Zhang Yongsheng , Zhu Xiaoxiang , Dong Zhen
Published: May 2019

Spaceborne multistatic synthetic aperture radar (SAR) tomography (SMS-TomoSAR) systems take full advantage of the flexible configuration of multistatic SAR in the space, time, phase, and frequency dimensions, and simultaneously achieve high-precision height resolution and low-deformation measurement of three-dimensional ground scenes. SMS-TomoSAR currently poses a series of key issues to solve, such as baseline optimization, spatial transmission error estimation and compensation, and the choice of imaging algorithm, which directly affects the performance of height-dimensional imaging and surface deformation measurement. This paper explores the impact of baseline distribution on height-dimensional imaging performance for the baseline optimization issue, and proposes a feasible baseline optimization method. Firstly, the multi-base multi-pass baselines of an SMS-TomoSAR system are considered equivalent to a group of multi-pass baselines from monostatic SAR. Secondly, we establish the equivalent baselines as a symmetric-geometric model to characterize the non-uniform characteristic of baseline distribution. Through experimental simulation and model analysis, an approximately uniform baseline distribution is shown to have better SMS-TomoSAR imaging performance in the height direction. Further, a baseline design method under uniform-perturbation sampling with Gaussian distribution error is proposed. Finally, the imaging performance of different levels of perturbation is compared, and the maximum baseline perturbation allowed by the system is given.
Lateral Motion Prediction of On-Road Preceding Vehicles: A Data-Driven Approach
Authors: Wang Chen , Delport Jacques , Wang Yan
Published: May 2019

Drivers’ behaviors and decision making on the road directly affect the safety of themselves, other drivers, and pedestrians. However, as distinct entities, people cannot predict the motions of surrounding vehicles and they have difficulty in performing safe reactionary driving maneuvers in a short time period. To overcome the limitations of making an immediate prediction, in this work, we propose a two-stage data-driven approach: classifying driving patterns of on-road surrounding vehicles using the Gaussian mixture models (GMM); and predicting vehicles’ short-term lateral motions (i.e., left/right turn and left/right lane change) based on real-world vehicle mobility data, provided by the U.S. Department of Transportation, with different ensemble decision trees. We considered several important kinetic features and higher order kinematic variables. The research results of our proposed approach demonstrate the effectiveness of pattern classification and on-road lateral motion prediction. This methodology framework has the potential to be incorporated into current data-driven collision warning systems, to enable more practical on-road preprocessing in intelligent vehicles, and to be applied in autopilot-driving scenarios.
Estimation of Heart Rate Recovery after Stair Climbing Using a Wrist-Worn Device
Authors: Sokas Daivaras , Petrėnas Andrius , Daukantas Saulius , Rapalis Andrius , Paliakaitė Birutė , Marozas Vaidotas
Published: May 2019

Heart rate recovery (HRR) after physical exercise is a convenient method to assess cardiovascular autonomic function. Since stair climbing is a common daily activity, usually followed by a slow walking or rest, this type of activity can be considered as an alternative HRR test. The present study explores the feasibility to estimate HRR parameters after stair climbing using a wrist-worn device with embedded photoplethysmography and barometric pressure sensors. A custom-made wrist-worn device, capable of acquiring heart rate and altitude, was used to estimate the time-constant of exponential decay τ , the short-term time constant S , and the decay of heart rate in 1 min D . Fifty-four healthy volunteers were instructed to climb the stairs at three different climbing rates. When compared to the reference electrocardiogram, the absolute and percentage errors were found to be ≤ 21.0 s (≤ 52.7%) for τ , ≤ 0.14 (≤ 19.2%) for S , and ≤ 7.16 bpm (≤ 20.7%) for D in 75% of recovery phases available for analysis. The proposed approach to monitoring HRR parameters in an unobtrusive way may complement information provided by personal health monitoring devices (e.g., weight loss, physical activity), as well as have clinical relevance when evaluating the efficiency of cardiac rehabilitation program outside the clinical setting.
Land Use Change and Climate Variation in the Three Gorges Reservoir Catchment from 2000 to 2015 Based on the Google Earth Engine
Authors: Hao Binfei , Ma Mingguo , Li Shiwei , Li Qiuping , Hao Dalei , Huang Jing , Ge Zhongxi , Yang Hong , Han Xujun
Published: May 2019

Possible environmental change and ecosystem degradation have received increasing attention since the construction of Three Gorges Reservoir Catchment (TGRC) in China. The advanced Google Earth Engine (GEE) cloud-based platform and the large number of Geosciences and Remote Sensing datasets archived in GEE were used to analyze the land use and land cover change (LULCC) and climate variation in TGRC. GlobeLand30 data were used to evaluate the spatial land dynamics from 2000 to 2010 and Landsat 8 Operational Land Imager (OLI) images were applied for land use in 2015. The interannual variations in the Land Surface Temperature (LST) and seasonally integrated normalized difference vegetation index (SINDVI) were estimated using Moderate Resolution Imaging Spectroradiometer (MODIS) products. The climate factors including air temperature, precipitation and evapotranspiration were investigated based on the data from the Global Land Data Assimilation System (GLDAS). The results indicated that from 2000 to 2015, the cultivated land and grassland decreased by 2.05% and 6.02%, while the forest, wetland, artificial surface, shrub land and waterbody increased by 3.64%, 0.94%, 0.87%, 1.17% and 1.45%, respectively. The SINDVI increased by 3.209 in the period of 2000-2015, while the LST decreased by 0.253 °C from 2001 to 2015. The LST showed an increasing trend primarily in urbanized area, with a decreasing trend mainly in forest area. In particular, Chongqing City had the highest LST during the research period. A marked decrease in SINDVI occurred primarily in urbanized areas. Good vegetation areas were primarily located in the eastern part of the TGRC, such as Wuxi County, Wushan County, and Xingshan County. During the 2000–2015 period, the air temperature, precipitation and evapotranspiration rose by 0.0678 °C/a, 1.0844 mm/a, and 0.4105 mm/a, respectively. The climate change in the TGRC was influenced by LULCC, but the effect was limited. What is more, the climate change was affected by regional climate change in Southwest China. Marked changes in land use have occurred in the TGRC, and they have resulted in changes in the LST and SINDVI. There was a significantly negative relationship between LST and SINDVI in most parts of the TGRC, especially in expanding urban areas and growing forest areas. Our study highlighted the importance of environmental protection, particularly proper management of land use, for sustainable development in the catchment.
A Bi-Directional Carrier Sense Collision Avoidance Neighbor Discovery Algorithm in Directional Wireless Ad Hoc Sensor Networks
Authors: Yang Annan , Li Bo , Yan Zhongjiang , Yang Mao
Published: May 2019

From the perspective of media protocol control and routing of directional wireless ad hoc sensors networks, neighbor discovery protocol is an important problem to be solved first. In the past period of time, some methods have been studied on neighbor discovery protocol, but they have a common defect of link collision. The collision is caused by mutual interference of multiple transmitting nodes which are in one reception beam of the receiving node. To solve this problem, we propose a neighbor discovery algorithm using a bi-directional carrier sense collision avoidance and multi subchannels based on a scan-based algorithm (BD-SBA). Based on a scan-based algorithm (SBA), bi-directional carrier sense of the BD-SBA algorithm is performed in the first broadcast step which can reduce the collision of broadcasting the scanning request (SREQ) frames. In the second step (the reply step), the mechanism of multiple subchannels and multiple slots is applied to reduce the collision of the scanning response (SRES) frames. From the analysis and simulation, we can see that nodes using proposed algorithm can discover their neighbor nodes in fewer time. Moreover, the proposed algorithm has better performance for different beamwidths and densely distributed scenes. So it has great significance in engineering application.
Gas Sensors Based on Mechanically Exfoliated MoS2 Nanosheets for Room-Temperature NO2 Detection
Authors: Li Wenli , Zhang Yong , Long Xia , Cao Juexian , Xin Xin , Guan Xiaoxiao , Peng Jinfeng , Zheng Xuejun
Published: May 2019

The unique properties of MoS2 nanosheets make them a promising candidate for high-performance room temperature gas detection. Herein, few-layer MoS2 nanosheets (FLMN) prepared via mechanical exfoliation are coated on a substrate with interdigital electrodes for room-temperature NO2 detection. Interestingly, compared with other NO2 gas sensors based on MoS2, FLMN gas sensors exhibit high responsivity for room-temperature NO2 detection, and NO2 is easily desorbed from the sensor surface with an ultrafast recovery behavior, with recovery times around 2 s. The high responsivity is related to the fact that the adsorbed NO2 can affect the electron states within the entire material, which is attributed to the very small thickness of the MoS2 nanosheets. First-principles calculations were carried out based on the density functional theory (DFT) to verify that the ultrafast recovery behavior arises from the weak van der Waals binding between NO2 and the MoS2 surface. Our work suggests that FLMN prepared via mechanical exfoliation have a great potential for fabricating high-performance NO2 gas sensors.
Intensity-Assisted ICP for Fast Registration of 2D-LIDAR
Authors: Tian Yingzhong , Liu Xining , Li Long , Wang Wenbin
Published: May 2019

Iterative closest point (ICP) is a method commonly used to perform scan-matching and registration. To be a simple and robust algorithm, it is still computationally expensive, and it has been regarded as having a crucial challenge especially in a real-time application as used for the simultaneous localization and mapping (SLAM) problem. For these reasons, this paper presents a new method for the acceleration of ICP with an assisted intensity. Unlike the conventional ICP, this method is proposed to reduce the computational cost and avoid divergences. An initial transformation guess is computed with an assisted intensity for their relative rigid-body transformation. Moreover, a target function is proposed to determine the best initial transformation guess based on the statistic of their spatial distances and intensity residuals. Additionally, this method is also proposed to reduce the iteration number. The Anderson acceleration is utilized for increasing the iteration speed which has better ability than the Picard iteration procedure. The proposed algorithm is operated in real time with a single core central processing unit (CPU) thread. Hence, it is suitable for the robot which has limited computation resources. To validate the novelty, this proposed method is evaluated on the SEMANTIC3D.NET benchmark dataset. According to comparative results, the proposed method is declared as having better accuracy and robustness than the conventional ICP methods.
Multi-Sensor Data Fusion Algorithm Based on Trust Degree and Improved Genetics
Authors: Sun Guiling , Zhang Ziyang , Zheng Bowen , Li Yangyang
Published: May 2019

Aiming at the problems of low data fusion precision and poor stability in greenhouse wireless sensor networks (WSNs), a multi-sensor data fusion algorithm based on trust degree and improved genetics is proposed. The original data collected by the sensor nodes are sent to the gateway through the sink node, and data preprocessing based on cubic exponential smoothing is performed at the gateway to eliminate abnormal data and noise data. In fuzzy theory, the range of membership functions is determined, according to this feature, the data fusion algorithm based on exponential trust degree is used to fuse the smooth data to avoid the absolute degree of mutual trust between data. In this paper, we have improved the crossover and mutation operations in the standard genetic algorithm, the variation is separated from the intersection, the chaotic sequence is used to determine the intersection, and the weakest single-point intersection is implemented to improve the convergence accuracy of the algorithm, weaken and avoid jitter problems during optimization. The chaotic sequence is used to mutate multiple genes in the chromosome to avoid premature algorithm maturity. Finally, the improved genetic algorithm is used to optimize the fusion estimation value. The experimental results show that the cubic exponential smoothing can significantly reduce the data fluctuation and improve the stability of the system. Compared with the commonly used data fusion algorithms such as arithmetic average method and adaptive weighting method, the data fusion algorithm based on trust degree and improved genetics has higher fusion precision. At the same time, the execution time of the algorithm is greatly reduced.
A Turn-On Fluorescence-Based Fibre Optic Sensor for the Detection of Mercury
Authors: Nguyen T. Hien , Sun Tong , Grattan Kenneth T. V.
Published: May 2019

The design, development, and evaluation of an optical fibre sensor for the detection of Hg2+ in aqueous media are discussed in detail in this paper. A novel fluorescent polymeric material for Hg2+ detection, based on a coumarin derivative (acting as the fluorophore) and an azathia crown ether moiety (acting as the mercury ion receptor), has been synthesized. The fluorophore was covalently immobilized onto the fibre surface by polymerisation using the ion imprinting technique and exhibited a significant increase in fluorescence intensity in response to Hg2+ via a photoinduced electron transfer (PET) mechanism. The sensor provided a response over a concentration range of 0–28 µM with an acceptable response rate of around 11 min and a recovery rate of around 30 min in a Tris-EDTA buffer solution. A detection limit of 0.15 µM was obtained with a possibility of improvement by changing the thickness of the polymer layer and using a more sensitive detector. High-quality performance is seen through a high selectivity for Hg2+ over other metal ions, excellent photo-stability and reversibility which was also demonstrated, making this type of sensor potentially well suited for in-situ monitoring of mercury in the environment.
Joint Optimization of Interference Coordination Parameters and Base-Station Density for Energy-Efficient Heterogeneous Networks
Authors: Sun Yanzan , Xu Han , Zhang Shunqing , Wu Yating , Wang Tao , Fang Yong , Xu Shugong
Published: May 2019

Heterogeneous networks (HetNets), consisting of macro-cells and overlaying pico-cells, have been recognized as a promising paradigm to support the exponential growth of data traffic demands and high network energy efficiency (EE). However, for two-tier heterogeneous architecture deployment of HetNets, the inter-tier interference will be challenging. Time domain further-enhanced inter-cell interference coordination (FeICIC) proposed in 3GPP Release-11 becomes necessary to mitigate the inter-tier interference by applying low power almost blank subframe (ABS) scheme. Therefore, for HetNets deployment in reality, the pico-cell range expansion (CRE) bias, the power of ABS and the density of pico base stations (PBSs) are three important factors for the network EE improvement. Aiming to improve the network EE, the above three factors are jointly considered in this paper. In particular, we first derive the closed-form expression of the network EE as a function of pico CRE bias, power reduction factor of low power ABS and PBS density based on stochastic geometry model. Then, the approximate relationship between pico CRE bias and power reduction factor is deduced, followed by a linear search algorithm to get the near-optimal pico CRE bias and power reduction factor together at a given PBS density. Next, a linear search algorithm is further proposed to optimize PBS density based on fixed pico CRE bias and power reduction factor. Due to the fact that the above pico CRE bias and power reduction factor optimization and PBS density optimization are optimized separately, a heuristic algorithm is further proposed to optimize pico CRE bias, power reduction factor and PBS density jointly to achieve global network EE maximization. Numerical simulation results show that our proposed heuristic algorithm can significantly enhance the network EE while incurring low computational complexity.
Identification of Ground Intrusion in Underground Structures Based on Distributed Structural Vibration Detected by Ultra-Weak FBG Sensing Technology
Authors: Gan Weibing , Li Sheng , Li Zhengying , Sun Lizhi
Published: May 2019

It is challenging for engineers to timely identify illegal ground intrusions in underground systems such as subways. In order to prevent the catastrophic collapse of subway tunnels from intrusion events, this paper investigated the capability of detecting the ground intrusion of underground structures based on dynamic measurement of distributed fiber optic sensing. For an actual subway tunnel monitored by the ultra-weak fiber optic Bragg grating (FBG) sensing fiber with a spatial resolution of five meters, a simulated experiment of the ground intrusion along the selected path was designed and implemented, in which a hydraulic excavator was chosen to exert intrusion perturbations with different strengths and modes at five selected intrusion sites. For each intrusion place, the distributed vibration responses of sensing fibers mounted on the tunnel wall and the track bed were detected to identify the occurrence and characteristics of the intrusion event simulated by the discrete and continuous pulses of the excavator under two loading postures. By checking the on-site records of critical moments in the intrusion process, the proposed detection approach based on distributed structural vibration responses for the ground intrusion can detect the occurrence of intrusion events, locate the intrusion ground area, and distinguish intrusion strength and typical perturbation modes.
Aircraft Pose Estimation Based on Geometry Structure Features and Line Correspondences
Authors: Teng Xichao , Yu Qifeng , Luo Jing , Wang Gang , Zhang Xiaohu
Published: May 2019

A robust and accurate aircraft pose estimation method is proposed in this paper. The aircraft pose reflects the flight status of the aircraft and accurate pose measurement is of great importance in many aerospace applications. This work aims to establish a universal framework to estimate the aircraft pose based on generic geometry structure features. In our method, line features are extracted to describe the structure of an aircraft in single images and the generic geometry features are exploited to form line groups for aircraft structure recognition. Parallel line clustering is utilized to detect the fuselage reference line and bilateral symmetry property of aircraft provides an important constraint for the extraction of wing edge lines under weak perspective projection. After identifying the main structure of the aircraft, a planes intersection method is used to obtain the 3D pose parameters based on the established line correspondences. Our proposed method can increase the measuring range of binocular vision sensors and has the advantage of not relying on 3D models, cooperative marks or other feature datasets. Experimental results show that our method can obtain reliable and accurate pose information of different types of aircraft.
Smart Tourism as a Pillar for Sustainable Urban Development: An Alternate Smart City Strategy from Mauritius
Authors: Dabeedooal Yeshna Jessie , Dindoyal Vickramsingh , Allam Zaheer , Jones David S.
Published: May 2019

Mauritius, a small island developing state (SIDS), has an approved government-issued smart city framework geared at facilitating the development of smart cities through an application of Internet of things. In an attempt to move away from privately-operated urban developments in the form of newly built smart cities, an alternate framework has been proposed by Allam and Newman to redefine this timely concept for existing cities with the main dimensions being metabolism, culture, and governance. This new framework focuses on liveability and sustainability that builds on the cultural and historical dimensions of existing cities. This study adds to the redefined smart city paradigm by proposing a new pillar in the form of smart tourism for the city of Port Louis, Mauritius. This paper reviews models of smart tourism and explores how this can be grafted to the Allam and Newman’s smart city model. The findings of this study seek to inform policy makers on alternate and the more relevant economic potential of smart tourism for the city of Port Louis.
Sustainability of Impacts of Poplar Growth on Soil Organic Matter in Eutric Cambisols
Authors: Baum Christel , Barth Martin , Henkel Kathrin , Siebers Meike , Eckhardt Kai-Uwe , Hilke Ines , Gleixner Gerd , Leinweber Peter
Published: May 2019

Short rotation coppices (SRC) with poplar on arable soils constitute no-till management in combination with a changed litter quality compared to annual crops. Both tillage and litter quality impact soil organic matter (SOM) composition, but little is known on the sustainability of this impact at the molecular level. We compared the microbial colonization and SOM quantity and quality of a young (4 years), old (17 years) and a former SRC with hybrid poplar (Populus maximoviczii × Populus nigra cv. Max) to adjacent arable sites with annual crops or grass. Total fungal and arbsucular mycorrhizal fungal phospholipid fatty acid (PLFA) markers were increased under no-till treatments with permanent crops (SRC and grass) compared to tilled cereals. Enrichments in fungal biomass coincided with C accumulation close to the soil surface (0–5 cm) but was abolished under former SRC after return to annual tillage. This management change altered the spatial distribution but not the accumulation of SOM within the topsoil (0–30 cm). However, lasting qualitative changes in SOM with increased proportions of lignin, lipids and sterols were found under current and former SRC. Increased colonization by arbuscular mycorrhizal fungi was correlated with increased invertase activity (R = 0.64; p < 0.05), carbohydrate consumption and a corresponding accumulation of lignins and lipids in the SOM. This link indicates a regulatory impact of mycorrhizal fungi on soil C dynamics by changing the quality of SOM. Increased stability of SOM to microbial degradation by higher portions of lipids and sterols in the SOM were assumed to be a sustainable effect of poplar growth at Eutric Cambisols.
Soil Degradation Mapping in Drylands Using Unmanned Aerial Vehicle (UAV) Data
Authors: Krenz Juliane , Greenwood Philip , Kuhn Nikolaus J.
Published: May 2019

Arid and semi-arid landscapes often show a patchwork of bare and vegetated spaces. Their heterogeneous patterns can be of natural origin, but may also indicate soil degradation. This study investigates the use of unmanned aerial vehicle (UAV) imagery to identify the degradation status of soils, based on the hypothesis that vegetation cover can be used as a proxy for estimating the soils’ health status. To assess the quality of the UAV-derived products, we compare a conventional field-derived map (FM) with two modelled maps based on (i) vegetation cover (RGB map), and (ii) vegetation cover, topographic information, and a flow accumulation analysis (RGB+DEM map). All methods were able to identify areas of soil degradation but differed in the extent of classified soil degradation, with the RGB map classifying the least amount as degraded. The RGB+DEM map classified 12% more as degraded than the FM, due to the wider perspective of the UAV compared to conventional field mapping. Overall, conventional UAVs provide a valuable tool for soil mapping in heterogeneous landscapes where manual field sampling is very time consuming. Additionally, the UAVs’ planform view from a bird’s-eye perspective can overcome the limited view from the surveyors’ (ground-based) vantage point.
The Risk Reduction and Sustainable Development of Shared Transportation: The Chinese Online Car-hailing Policy Evaluation in the Digitalization Era
Authors: Gao Yuchen , Chen Jingrui
Published: May 2019

Online car-hailing services (such as DiDi and Uber) are one of the typical sharing economy forms of transportation service in the digital era. Related public policies are expected to reduce the risk of online car-hailing and ensure the social sustainable development. However, the empirical evidence regarding the effect of relevant policy implementations is still scarce. This study takes an online car-hailing service in China as a research object to understand and evaluate the effect of implementing related policies. The risk related to an online car-hailing service is classified into three dimensions in this study, namely, institutional, economic and safety perspectives. The empirical results indicate that public policies have significant impacts on reducing conflicts and risk of shared transportation in China, especially the institutional risk. Furthermore, the effects of different policy factors differ for different risk types. Several insights of developing policies and regulations related to sustainable shared transportation in the digitalization era are also provided. The interaction between government authorities, private firms and citizens should be attached to great importance in policy design, which will consequently enhance the sustainable development in the transportation sector under sharing economy.
Direct Power Control of Matrix Converter-Fed DFIG with Fixed Switching Frequency
Authors: Yousefi-Talouki Arzhang , Zalzar Shaghayegh , Pouresmaeil Edris
Published: May 2019

In this paper, a direct power control (DPC) technique is proposed for matrix converter-fed grid-connected doubly fed induction generators (DFIGs). In contrast to what has been investigated in the past for direct torque control (DTC) or DPC of matrix converter-fed DFIGs, the active and reactive powers are regulated in a fixed switching frequency using indirect space vector modulation (ISVM) technique. Hence, designing input filters for matrix converters (MCs) becomes convenient. In addition, the reactive component of input side of MC is controlled which leads to reduction of distortion in grid current waveform. Also, an extensive discussion is addressed for nonlinear voltage errors of MC that may cause inaccurate power control. Simulation results done in MATLAB/Simulink show the effectiveness of the proposed method.
Reforming Health, Safety, and Environmental Regulation for Offshore Operations in China: Risk and Resilience Approaches?
Authors: Yang Yuan
Published: May 2019

Offshore drilling accidents have triggered regulatory reforms in China. The reforms aim to explore proper regulatory approaches to supervise offshore operations and improve their health, safety and environmental (HSE) performance. This study offers a review on the roles of risk and resilience in managing offshore operations and a well-defined analysis on their integrations with Chinese laws and regulations. The study finds risk and resilience approaches can promote the effectiveness of HSE regulation for offshore operations, while both are difficult to be transposed into legally binding rules in China. To fully develop and implement risk regulation for offshore operations, the study suggests to decentralize China’s command-and-control regulatory regime and encourage self-regulation in offshore petroleum companies. Transposing resilience thinking into legal practice is also highlighted so that various regulatory powers can keep proactive and flexible to any possible changes and uncertainties.
Does Climate Change or Human Activity Lead to the Degradation in the Grassland Ecosystem in a Mountain-Basin System in an Arid Region of China?
Authors: Yan Junjie , Zhang Guangpeng , Deng Xiaoya , Ling Hongbo , Xu Hailiang , Guo Bin
Published: May 2019

In mountain-basin systems in the arid region, grasslands are sensitive to the impacts of climate change and human activities. In this study, we aimed to resolve two key scientific issues: (1) distinguish and explain the laws of grassland ecosystem deterioration in a mountain-basin system and identify the key factors related; and (2) evaluate whether damaged grasslands ecosystem have the potential for natural revegetation. Hence, by combining spatial analysis with statistical methods, we studied the trends of the deterioration of the grassland ecosystem and its spatial characteristics in Kulusitai, a mountain-basin system in the arid region of Northwest China. According to our results, vegetation coverage and productivity exhibited significant decreasing trends, while the temperature vegetation drought index (TVDI) exhibited a significant increasing trend. Drainage of groundwater, because of increase in irrigation for the expanded irrigated area around Kulusitai, and climate warming were the critical triggers that leaded to the soil drought. Soil drought and overgrazing, resulting from the impact of human activities, were the main factors responsible for the deterioration of the grassland ecosystems. However, limiting the number of livestock to a reasonable scale and reducing the irrigated area may help to increase the soil moisture, thus promoting the germination of soil seed banks and facilitating the normal growth of grassland vegetation. Furthermore, based on analysis of the phenology of the grassland vegetation, the reasonable period for harvesting and storage is from July 29 to August 5. The results of this study provide a scientific basis and practical guide for restoring mountain-basin grassland systems in arid regions.
Examination on the Influence Area of Transit-Oriented Development: Considering Multimodal Accessibility in New Delhi, India
Authors: Ann Sangeetha , Jiang Meilan , Mothafer Ghasak Ibrahim , Yamamoto Toshiyuki
Published: May 2019

As Indian cities adopt the concept of transit-oriented development (TOD), concerns have arisen regarding the applicability of TOD standards formulated in developed countries in the Indian context. This study aims to estimate the TOD influence areas in New Delhi by examining the last mile connectivity patterns of passengers on the Delhi Metro Railway (DMR). Questionnaire surveys conducted on the last mile connectivity reveals use of various access modes for metro stations in India, although current research only considers walking and cycling to be universal forms of access. Therefore, this study focuses on the DMR’s multimodal accessibility to investigate the last mile distance of each mode. In order to offset the rounding errors of reported distance, a heaping model and multiple imputation (MI) were employed to improve the accuracy of the reported distance. Afterward, distance decay analysis and receiver operating characteristic (ROC) curves were used to determine the thresholds of last mile distances. The findings show that the influence area differs across travel modes and increases in the order of walking, informal transit, buses, and private transport, respectively.
Combined Impact of Socioeconomic Forces and Policy Implications: Spatial-Temporal Dynamics of the Ecosystem Services Value in Yangtze River Delta, China
Authors: Chen Sha , Li Guan , Xu Zhongguo , Zhuo Yuefei , Wu Cifang , Ye Yanmei
Published: May 2019

Water can carry or overturn a boat. Natural resources form the foundation of human survival and development. However, land use change caused by human urban civilization has damaged the natural environment and in turn threatened the continuation of human civilization. Accordingly, it is crucial to analyze the impacts of human activities on land use change and consequent dynamics of ecosystem service value (ESV). For the sustainable development of human beings, an investigation should be conducted to explore what type of land use behavior will be considerably beneficial to improve our relationship with the natural environment. This study analyzes the spatial–temporal dynamics of ESV of 148 counties in the Yangtze River Delta in China over three five-year periods (2000–2015) and examines the influence of socioeconomic forces and policy implications. Exploratory spatial data analysis and spatial regression were applied to facilitate the analysis. Results show that the averages of the ESV change ratios of the 148 counties in each of the aforementioned periods are −0.667%, −2.690%, and −4.668%, respectively. The number of counties that showed an ESV loss trend in the three periods are 72 (48.6%), 125 (84.4%), and 139 (93.9%), respectively. In terms of spatial pattern, ESV change demonstrates the spatial distribution characteristic of “high loss spreading from the northeast to the middle and west” with a tendency to strengthen spatial agglomeration. Results of the spatial regression analysis determine the overwhelming importance of population growth and economic advancement. The results also indicate that the development mode characterized by industrial structure, capital input, and technology upgrades can exert considerable influence on socioeconomic development, thereby affecting the change of ESV. Moreover, the constraints of policy substantially affect the changes of ESV from 2010 to 2015. Policy makers should consider the relationship between land use patterns and the ESV variation in different development stages to formulate appropriate measures, thereby reducing or preventing the loss of ecological service value and promoting sustainable development.