bluuebunny/crossref_metadata_2025 huggingface dataset:
Super-Resolution-Based Snake Model—An Unsupervised Method for Large-Scale Building Extraction Using Airborne LiDAR Data and Optical Image
Authors: Nguyen Thanh Huy , Daniel Sylvie , Guériot Didier , Sintès Christophe , Le Caillec Jean-Marc
Published: May 2020

Automatic extraction of buildings in urban and residential scenes has become a subject of growing interest in the domain of photogrammetry and remote sensing, particularly since the mid-1990s. Active contour model, colloquially known as snake model, has been studied to extract buildings from aerial and satellite imagery. However, this task is still very challenging due to the complexity of building size, shape, and its surrounding environment. This complexity leads to a major obstacle for carrying out a reliable large-scale building extraction, since the involved prior information and assumptions on building such as shape, size, and color cannot be generalized over large areas. This paper presents an efficient snake model to overcome such a challenge, called Super-Resolution-based Snake Model (SRSM). The SRSM operates on high-resolution Light Detection and Ranging (LiDAR)-based elevation images—called z-images—generated by a super-resolution process applied to LiDAR data. The involved balloon force model is also improved to shrink or inflate adaptively, instead of inflating continuously. This method is applicable for a large scale such as city scale and even larger, while having a high level of automation and not requiring any prior knowledge nor training data from the urban scenes (hence unsupervised). It achieves high overall accuracy when tested on various datasets. For instance, the proposed SRSM yields an average area-based Quality of 86.57% and object-based Quality of 81.60% on the ISPRS Vaihingen benchmark datasets. Compared to other methods using this benchmark dataset, this level of accuracy is highly desirable even for a supervised method. Similarly desirable outcomes are obtained when carrying out the proposed SRSM on the whole City of Quebec (total area of 656 km2), yielding an area-based Quality of 62.37% and an object-based Quality of 63.21%.
Estimating Yields of Household Fields in Rural Subsistence Farming Systems to Study Food Security in Burkina Faso
Authors: Karst Isabel G. , Mank Isabel , Traoré Issouf , Sorgho Raissa , Stückemann Kim-Jana , Simboro Séraphin , Sié Ali , Franke Jonas , Sauerborn Rainer
Published: May 2020

Climate change has an increasing impact on food security and child nutrition, particularly among rural smallholder farmers in sub-Saharan Africa. Their limited resources and rainfall dependent farming practices make them sensitive to climate change-related effects. Data and research linking yield, human health, and nutrition are scarce but can provide a basis for adaptation and risk management strategies. In support of studies on child undernutrition in Burkina Faso, this study analyzed the potential of remote sensing-based yield estimates at household level. Multi-temporal Sentinel-2 data from the growing season 2018 were used to model yield of household fields (median 1.4 hectares (ha), min 0.01 ha, max 12.6 ha) for the five most prominent crops in the Nouna Health and Demographic Surveillance (HDSS) area in Burkina Faso. Based on monthly metrics of vegetation indices (VIs) and in-situ harvest measurements from an extensive field survey, yield prediction models for different crops of high dietary importance (millet, sorghum, maize, and beans) were successfully generated producing R² between 0.4 and 0.54 (adj. R² between 0.32 and 0.5). The models were spatially applied and resulted in a yield estimation map at household level, enabling predictions of up to 2 months prior to harvest. The map links yield on a 10-m spatial resolution to households and consequently can display potential food insecurity. The results highlight the potential for satellite imagery to provide yield predictions of smallholder fields and are discussed in the context of health-related studies such as child undernutrition and food security in rural Africa under climate change.
UAV Applications for Determination of Land Deformations Caused by Underground Mining
Authors: Ćwiąkała Paweł , Gruszczyński Wojciech , Stoch Tomasz , Puniach Edyta , Mrocheń Dawid , Matwij Wojciech , Matwij Karolina , Nędzka Michał , Sopata Paweł , Wójcik Artur
Published: May 2020

This article presents a case study that demonstrates the applicability of unmanned aerial vehicle (UAV) photogrammetric data to land surface deformation monitoring in areas affected by underground mining. The results presented include data from two objects located in the Upper Silesian Coal Basin in Poland. The limits of coordinate and displacement accuracy are determined by comparing UAV-derived photogrammetric products to reference data. Vertical displacements are determined based on differences between digital surface models created using UAV imagery from several measurement series. Interpretation problems related to vegetation growth on the terrain surface that significantly affect vertical displacement error are pointed out. Horizontal displacements are determined based on points of observation lines established in the field for monitoring purposes, as well as based on scattered situational details. The use of this type of processing is limited by the need for unambiguous situational details with clear contours. Such details are easy to find in urbanized areas but difficult to find in fields and meadows. In addition, various types of discontinuous deformations are detected and their development over time is presented. The results are compared to forecasted land deformations. As a result of the data processing, it has been estimated that the accuracy of the determination of XY coordinates and the horizontal displacements (RMS) in best case scenario is on the level of 1.5–2 GSD, and about 2–3 GSD for heights and subsidence.
Landslide Detection with Himawari-8 Geostationary Satellite Data: A Case Study of a Torrential Rain Event in Kyushu, Japan
Authors: Miura Tomoaki , Nagai Shin
Published: May 2020

In this study, we investigated the utility of Himawari-8 Advanced Himawari Imager (AHI), one of third-generation geostationary satellite sensors, for mapping landslides caused by torrential rain that hit the northern Kyushu area in Japan in the summer of 2017. AHI normalized difference vegetation index (NDVI) time series data had distinctive temporal signatures over landslide areas where the NDVI abruptly decreased after the rain event. The observed changes in the NDVI were linearly correlated with the percent landslide area, the percentage of landslide areas within the AHI pixel footprint, obtained with aerial survey (r = 0.78). AHI 10 min resolution data obtained near cloud-free coverage of the landslide region by the 8th day after the disaster event. This was comparable to the amount of time it took to obtain near cloud-free image coverage with aerial survey, and better than those with the polar-orbiting satellite sensors of Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite, Landsat-8 Operational Land Imager, and Sentinel-2A/B MultiSpectral Instrument. These results suggest that third-generation geostationary satellite data can serve as another useful resource for post-event, region-wide initial assessment of landslide areas after a heavy rain event.
Inline Inspection with an Industrial Robot (IIIR) for Mass-Customization Production Line
Authors: Wu Zai-Gen , Lin Chao-Yi , Chang Hao-Wei , Lin Po Ting
Published: May 2020

Robots are essential for the rapid development of Industry 4.0. In order to truly achieve autonomous robot control in customizable production lines, robots need to be accurate enough and capable of recognizing the geometry and orientation of an arbitrarily shaped object. This paper presents a method of inline inspection with an industrial robot (IIIR) for mass-customization production lines. A 3D scanner was used to capture the geometry and orientation of the object to be inspected. As the object entered the working range of the robot, the end effector moved along with the object and the camera installed at the end effector performed the requested optical inspections. The detailed information about the developed methodology was introduced in this paper. The experiments showed there was a relative movement between the moving object and the following camera and the speed was around 0.34 mm per second (worst case was around 0.94 mm per second). For a camera of 60 frames per second, the relative moving speed between the object and the camera was around 6 micron (around 16 micron for the worst case), which was stable enough for most industrial production inspections.
A New Approach to Explore the Surface Profile of Clay Soil Using White Light Interferometry
Authors: Yang Suchun , Liu Junwei , Xu Longfei , Zhang Mingyi , Jeng Dong-Sheng
Published: May 2020

In order to have a better understanding of the real contact area of granular materials, the white light interference method is applied to explore the real surface morphology of clay soils under high stress. Analysis of the surface profile indicates that there exists a support point height z0 with the highest distribution frequency. A concept of a real contact region (from z0 to z0 + d90; d90 represents the particle size corresponding to 90% of the volume fraction) is proposed by combining a surface profile with the particle size distribution of clay soil. It was found that under the compressive stress of 106 MPa–529 MPa, the actual contact area ratio of clay soil varies between 0.375 and 0.431. This demonstrates an increasing trend with the rise of stress. On the contrary, the apparent porosity decreases with an increasing stress, varying between 0.554 and 0.525. In addition, as the compressive stress increases, the cumulative frequency of apparent profile height (from z0 − d90 to z0 + d90) has a concentrated tendency with a limited value of 0.9.
Behavioral Acoustic Emanations: Attack and Verification of PIN Entry Using Keypress Sounds
Authors: Panda Sourav , Liu Yuanzhen , Hancke Gerhard Petrus , Qureshi Umair Mujtaba
Published: May 2020

This paper explores the security vulnerability of Personal Identification Number (PIN) or numeric passwords. Entry Device (PEDs) that use small strings of data (PINs, keys or passwords) as means of verifying the legitimacy of a user. Today, PEDs are commonly used by personnel in different industrial and consumer electronic applications, such as entry at security checkpoints, ATMs and customer kiosks, etc. In this paper, we propose a side-channel attack on a 4–6 digit random PIN key, and a PIN key user verification method. The intervals between two keystrokes are extracted from the acoustic emanation and used as features to train machine-learning models. The attack model has a 60% chance to recover the PIN key. The verification model has an 88% accuracy on identifying the user. Our attack methods can perform key recovery by using the acoustic side-channel at low cost. As a countermeasure, our verification method can improve the security of PIN entry devices.
Wireless Module for Nondestructive Testing/Structural Health Monitoring Applications Based on Solitary Waves
Authors: Misra Ritesh , Jalali Hoda , Dickerson Samuel J. , Rizzo Piervincenzo
Published: May 2020

In recent years, there has been an increasing interest in the use of highly nonlinear solitary waves (HNSWs) for nondestructive evaluation and structural health monitoring applications. HNSWs are mechanical waves that can form and travel in highly nonlinear systems, such as granular particles in Hertzian contact. The easiest setup consists of a built-in transducer in drypoint contact with the structure or material to be inspected/monitored. The transducer is made of a monoperiodic array of spherical particles that enables the excitation and detection of the solitary waves. The transducer is wired to a data acquisition system that controls the functionality of the transducer and stores the time series for post-processing. In this paper, the design and testing of a wireless unit that enables the remote control of a transducer without the need to connect it to sophisticated test equipment are presented. Comparative tests and analyses between the measurements obtained with the newly designed wireless unit and the conventional wired configuration are provided. The results are corroborated by an analytical model that predicts the dynamic interaction between solitary waves and materials with different modulus. The advantages and limitations of the proposed wireless platform are given along with some suggestions for future developments.
The Power Gain Difference Method Analysis
Authors: Veselý Jiří , Hubáček Petr , Olivová Jana
Published: May 2020

In this paper, we propose a new approach to passively locate the 3D position of a signal source. This novel technique, called the power gain difference (PGD), is based only on measuring the received signal strength (RSS) with multiple sensors deployed in the area of interest, while the target transmit power or the equivalent isotropic radiated power (EIRP) is assumed to be unknown. Next, the signal source position is estimated using the knowledge of the ratios of RSS measured on different sensors. First, this article presents the geometric representation and the analytical solution of the model of the PGD technique. Second, the PGD dilution of precision was analyzed in order to gauge the accuracy of measuring the RSS. Finally, a numerical simulation of the performance of the proposed method was carried out and the results are discussed. It seems that the PGD technique has the potential to be a simple and effective solution of the 3D localization problem.
Flight and Interaction Control of an Innovative Ducted Fan Aerial Manipulator
Authors: Zhang Yibo , Xu Bin , Xiang Changle , Fan Wei , Ai Tianfu
Published: May 2020

An innovative aerial manipulator with ducted fans is proposed to achieve side-on aerial manipulation tasks in a confined environment, such as canopy sampling in dense forests. The dynamic model of the novel design is studied, and on this basis a composite controller is proposed to address the challenges of arm extension and physical interaction during the manipulation process. An adaptive controller is proposed for the aerial platform to achieve good stability and tracking performance under the manipulator motion, and an impedance controller is designed for the manipulator to ensure compliance and stability during physical contact. The experimental tests validate the effectiveness of the proposed prototype structure and controller design.
A Comparative Study of Vaginal Labor and Caesarean Section Postpartum Uterine Myoelectrical Activity
Authors: Diaz-Martinez Alba , Mas-Cabo Javier , Prats-Boluda Gema , Garcia-Casado Javier , Cardona-Urrego Karen , Monfort-Ortiz Rogelio , Lopez-Corral Angel , De Arriba-Garcia Maria , Perales Alfredo , Ye-Lin Yiyao
Published: May 2020

Postpartum hemorrhage (PPH) is one of the major causes of maternal mortality and morbidity worldwide, with uterine atony being the most common origin. Currently there are no obstetrical techniques available for monitoring postpartum uterine dynamics, as tocodynamometry is not able to detect weak uterine contractions. In this study, we explored the feasibility of monitoring postpartum uterine activity by non-invasive electrohysterography (EHG), which has been proven to outperform tocodynamometry in detecting uterine contractions during pregnancy. A comparison was made of the temporal, spectral, and non-linear parameters of postpartum EHG characteristics of vaginal deliveries and elective cesareans. In the vaginal delivery group, EHG obtained a significantly higher amplitude and lower kurtosis of the Hilbert envelope, and spectral content was shifted toward higher frequencies than in the cesarean group. In the non-linear parameters, higher values were found for the fractal dimension and lower values for Lempel-Ziv, sample entropy and spectral entropy in vaginal deliveries suggesting that the postpartum EHG signal is extremely non-linear but more regular and predictable than in a cesarean. The results obtained indicate that postpartum EHG recording could be a helpful tool for earlier detection of uterine atony and contribute to better management of prophylactic uterotonic treatment for PPH prevention.
Enhancement of Withstand Voltage in Silicon Strain Gauges Using a Thin Alkali-Free Glass
Authors: Kim Joon Hyub , Han Ji-Hoon , Park Chan Won , Min Nam Ki
Published: May 2020

We present a cost-effective approach to produce silicon strain gauges that can withstand very high voltage without using any complex package design and without sacrificing any sensor performance. This is achieved by a special silicon strain gauge structure created on an alkali-free glass substrate that has a high breakdown voltage. A half-bridge silicon strain gauge is designed, fabricated, and then tested to measure its output characteristics. The device has a glass layer that is only 25–55 µm thick; it shows it is able to withstand a voltage of over 2000 V while maintaining a high degree of linearity with correlation coefficients higher than 0.9990 and an average sensitivity of 104.13. Due to their unique electrical properties, silicon strain gauges-on-glass chips hold much promise for use in advanced force and pressure sensors.
Development and Characterization of UV-Resin Coated Fiber Bragg Gratings
Authors: Leal-Junior Arnaldo , Frizera Anselmo , Marques Carlos
Published: May 2020

We report the development and characterizations of a fiber Bragg grating (FBG) sensor coated with different ultraviolet (UV) curable resins. The UV-curable resins were applied on the fiber after the FBG inscription and cured with an UV lamp. One set of samples used the NOA 68 resin and the other used NOA 88. The samples were characterized with respect to the temperature, moisture absorption and strain response. Furthermore, in order to understand the influence of the resin coating on the optical fiber’s mechanical properties, tensile tests were performed with the samples. Results show that all samples presented negligible sensitivity to moisture absorption in the 50-min long tests with the fibers immersed in a container filled with distillated water. Regarding the temperature responses, the coated FBGs presented higher sensitivity (13.84 pm/°C for NOA 88 and 12.76 pm/°C for NOA 68) than the uncoated FBGs due to the thermal expansion of the coatings. In the strain tests, all coated and uncoated samples presented similar sensitivities, but with a larger strain range applied for the coated samples (strains higher than 5500 µε) when compared with the uncoated samples (3500 µε). Moreover, the stress-strain curves of the coated samples indicated a Young’s modulus one order with magnitude lower than the one of the uncoated silica fiber, where the lowest Young’s modulus is 3.84 GPa and was obtained with the NOA 68 coating, which indicates the possibility of obtaining highly sensitive pressure and force sensors.
MVDT-SI: A Multi-View Double-Triangle Algorithm for Star Identification
Authors: Sun Lijian , Zhou Yun
Published: May 2020

Recently, the triangle algorithm has become the most widely used star identification algorithm because of its simplicity and convenience, where the magnitude information plays a key role in the construction of star map features. However, in practice, the magnitude information of the observed star map is often difficult to use, because they might contain errors or be lost in some worst cases. To solve this problem, we proposed a multi-view double-triangle algorithm for star identification in this paper. This algorithm constructs double-triangle features of stars with the angle and distance information of star points. Moreover, to reduce the influence of noise interference on the identification accuracy of the model, we built multi-view double-triangle features for the observed star map to improve the robustness of the algorithm. Synthetic and real experiments show that our algorithm has a high identification accuracy of more than 98.4% in face of “false star” noises and “missing star” noises, and our algorithm is not affected by the focal length and the shooting angle of the star sensor. Moreover, the results also show that our algorithm has good robustness, short identification time and reduced storage costs, which could be beneficial in practice.
Enhancing BCI-Based Emotion Recognition Using an Improved Particle Swarm Optimization for Feature Selection
Authors: Li Zina , Qiu Lina , Li Ruixin , He Zhipeng , Xiao Jun , Liang Yan , Wang Fei , Pan Jiahui
Published: May 2020

Electroencephalogram (EEG) signals have been widely used in emotion recognition. However, the current EEG-based emotion recognition has low accuracy of emotion classification, and its real-time application is limited. In order to address these issues, in this paper, we proposed an improved feature selection algorithm to recognize subjects’ emotion states based on EEG signal, and combined this feature selection method to design an online emotion recognition brain-computer interface (BCI) system. Specifically, first, different dimensional features from the time-domain, frequency domain, and time-frequency domain were extracted. Then, a modified particle swarm optimization (PSO) method with multi-stage linearly-decreasing inertia weight (MLDW) was purposed for feature selection. The MLDW algorithm can be used to easily refine the process of decreasing the inertia weight. Finally, the emotion types were classified by the support vector machine classifier. We extracted different features from the EEG data in the DEAP data set collected by 32 subjects to perform two offline experiments. Our results showed that the average accuracy of four-class emotion recognition reached 76.67%. Compared with the latest benchmark, our proposed MLDW-PSO feature selection improves the accuracy of EEG-based emotion recognition. To further validate the efficiency of the MLDW-PSO feature selection method, we developed an online two-class emotion recognition system evoked by Chinese videos, which achieved good performance for 10 healthy subjects with an average accuracy of 89.5%. The effectiveness of our method was thus demonstrated.
Motion Sensors-Based Machine Learning Approach for the Identification of Anterior Cruciate Ligament Gait Patterns in On-the-Field Activities in Rugby Players
Authors: Tedesco Salvatore , Crowe Colum , Ryan Andrew , Sica Marco , Scheurer Sebastian , Clifford Amanda M. , Brown Kenneth N. , O’Flynn Brendan
Published: May 2020

Anterior cruciate ligament (ACL) injuries are common among athletes. Despite a successful return to sport (RTS) for most of the injured athletes, a significant proportion do not return to competitive levels, and thus RTS post ACL reconstruction still represents a challenge for clinicians. Wearable sensors, owing to their small size and low cost, can represent an opportunity for the management of athletes on-the-field after RTS by providing guidance to associated clinicians. In particular, this study aims to investigate the ability of a set of inertial sensors worn on the lower-limbs by rugby players involved in a change-of-direction (COD) activity to differentiate between healthy and post-ACL groups via the use of machine learning. Twelve male participants (six healthy and six post-ACL athletes who were deemed to have successfully returned to competitive rugby and tested in the 5–10 year period following the injury) were recruited for the study. Time- and frequency-domain features were extracted from the raw inertial data collected. Several machine learning models were tested, such as k-nearest neighbors, naïve Bayes, support vector machine, gradient boosting tree, multi-layer perceptron, and stacking. Feature selection was implemented in the learning model, and leave-one-subject-out cross-validation (LOSO-CV) was adopted to estimate training and test errors. Results obtained show that it is possible to correctly discriminate between healthy and post-ACL injury subjects with an accuracy of 73.07% (multi-layer perceptron) and sensitivity of 81.8% (gradient boosting). The results of this study demonstrate the feasibility of using body-worn motion sensors and machine learning approaches for the identification of post-ACL gait patterns in athletes performing sport tasks on-the-field even a number of years after the injury occurred.
Priority-Based Data Collection for UAV-Aided Mobile Sensor Network
Authors: Ma Xiaoyan , Liu Tianyi , Liu Song , Kacimi Rahim , Dhaou Riadh
Published: May 2020

In this work, we study data collection in multiple unmanned aerial vehicle (UAV)-aided mobile wireless sensor networks (WSNs). The network topology is changing due to the mobility of the UAVs and the sensor nodes, so the design of efficient data collection protocols is a major concern. We address such high dynamic network and propose two mechanisms: prioritized-based contact-duration frame selection mechanism (PCdFS), and prioritized-based multiple contact-duration frame selection mechanisms (PMCdFS) to build collision-free scheduling and balance the nodes between the multi-UAV respectively. Based on the two mechanisms, we proposed a Balance algorithm to conduct the collision-free communication between the mobile nodes and the multi-UAVs. Two key design ideas for a Balance algorithm are: (a) no need of higher priority for those nodes that have lower transmission rate between them and the UAV and (b) improve the communication opportunity for those nodes that have shorter contact duration with the UAVs. We demonstrate the performance of proposed algorithms through extensive simulations, and real experiments. These experiments using 15 mobile nodes at a path with 10 intersections and 1 island, present that network fairness is efficiently enhanced. We also confirm the applicability of proposed algorithms in a challenging and realistic scenario through numerous experiments on a path at Tongji campus in Shanghai, China.
A Deep Reinforcement Learning-Based MPPT Control for PV Systems under Partial Shading Condition
Authors: Phan Bao Chau , Lai Ying-Chih , Lin Chin E.
Published: May 2020

On the issues of global environment protection, the renewable energy systems have been widely considered. The photovoltaic (PV) system converts solar power into electricity and significantly reduces the consumption of fossil fuels from environment pollution. Besides introducing new materials for the solar cells to improve the energy conversion efficiency, the maximum power point tracking (MPPT) algorithms have been developed to ensure the efficient operation of PV systems at the maximum power point (MPP) under various weather conditions. The integration of reinforcement learning and deep learning, named deep reinforcement learning (DRL), is proposed in this paper as a future tool to deal with the optimization control problems. Following the success of deep reinforcement learning (DRL) in several fields, the deep Q network (DQN) and deep deterministic policy gradient (DDPG) are proposed to harvest the MPP in PV systems, especially under a partial shading condition (PSC). Different from the reinforcement learning (RL)-based method, which is only operated with discrete state and action spaces, the methods adopted in this paper are used to deal with continuous state spaces. In this study, DQN solves the problem with discrete action spaces, while DDPG handles the continuous action spaces. The proposed methods are simulated in MATLAB/Simulink for feasibility analysis. Further tests under various input conditions with comparisons to the classical Perturb and observe (P&O) MPPT method are carried out for validation. Based on the simulation results in this study, the performance of the proposed methods is outstanding and efficient, showing its potential for further applications.
Autonomous Toy Drone via Coresets for Pose Estimation
Authors: Nasser Soliman , Jubran Ibrahim , Feldman Dan
Published: May 2020

A coreset of a dataset is a small weighted set, such that querying the coreset provably yields a ( 1 + ε )-factor approximation to the original (full) dataset, for a given family of queries. This paper suggests accurate coresets ( ε = 0 ) that are subsets of the input for fundamental optimization problems. These coresets enabled us to implement a “Guardian Angel” system that computes pose-estimation in a rate > 20 frames per second. It tracks a toy quadcopter which guides guests in a supermarket, hospital, mall, airport, and so on. We prove that any set of n matrices in R d × d whose sum is a matrix S of rank r, has a coreset whose sum has the same left and right singular vectors as S, and consists of O ( d r ) = O ( d 2 ) matrices, independent of n. This implies the first (exact, weighted subset) coreset of O ( d 2 ) points to problems such as linear regression, PCA/SVD, and Wahba’s problem, with corresponding streaming, dynamic, and distributed versions. Our main tool is a novel usage of the Caratheodory Theorem for coresets, an algorithm that computes its set in time that is linear in its cardinality. Extensive experimental results on both synthetic and real data, companion video of our system, and open code are provided.
Double Role of Diphenylpyridine Derivatives as Fluorescent Sensors for Monitoring Photopolymerization and the Determination of the Efficiencies of the Generation of Superacids by Cationic Photoinitiators
Authors: Topa Monika , Petko Filip , Galek Mariusz , Ortyl Joanna
Published: May 2020

Novel fluorescent sensors with electron-donating or electron-withdrawing substituents incorporated into a chromophore group based on 2,6-diphenylpyridine were designed and synthesised. The spectroscopic properties of these compounds were studied. Moreover, the positive solvatochromism of 2,6-bis-(4-methylsulphanylphenyl)pyridine (PT-SCH3) in selected solvents was studied by measurement of the absorption and emission spectra and analysed using the Dimroth–Reichardt solvent parameter set. After that, the performance of a series of 2,6-diphenylpyridine derivatives as fluorescent molecular sensors for monitoring free-radical and cationic photopolymerization processes by the Fluorescence Probe Technique (FPT) was studied. As a consequence of this stage of research, the effect of substituents on the sensitivity of the 2,6-diphenylpyridine derivatives as sensors during photopolymerization has been evaluated and discussed. It has been found that compounds containing strong electron-donating substituent (PT-SCH3) slightly shift their fluorescence spectrum during the free-radical polymerization of monomer, which enables the monitoring of the polymerization progress using the fluorescence intensity ratio measured at two different wavelengths as the progress indicator. The position of the fluorescence spectrum of 2,6-diphenylpyridine derivatives with electron-withdrawing substituents is practically insensitive to changes occurring in their environment. Hence, it is recommended to use these compounds with different indicators of the progress of the photopolymerization process based on normalised intensity of fluorescence (Imax/I0). Among the compounds studied, 2,6-bis(4-methylsulphanylphenyl)pyridine (PT-SCH3) turned out to be the best fluorescent sensor for the purpose of monitoring free-radical polymerization by FPT. Consequently, the dual application of the selected 2,6-diphenylpyridine derivatives is proposed: (a) as fluorescent sensors for monitoring the free-radical photopolymerization progress, and (b) as spectroscopic sensors for the determination of efficiencies of the generation of superacids by cationic photoinitiators during the cationic photopolymerization process. Finally, a new method for determining the relative efficiency of the photogeneration of superacids during the photo cleavage of onium salt has been devised and applied for the evaluation of the performance of 2,6-diphenylpyridine derivatives.
WatchPose: A View-Aware Approach for Camera Pose Data Collection in Industrial Environments
Authors: Yang Cong , Simon Gilles , See John , Berger Marie-Odile , Wang Wenyong
Published: May 2020

Collecting correlated scene images and camera poses is an essential step towards learning absolute camera pose regression models. While the acquisition of such data in living environments is relatively easy by following regular roads and paths, it is still a challenging task in constricted industrial environments. This is because industrial objects have varied sizes and inspections are usually carried out with non-constant motions. As a result, regression models are more sensitive to scene images with respect to viewpoints and distances. Motivated by this, we present a simple but efficient camera pose data collection method, WatchPose, to improve the generalization and robustness of camera pose regression models. Specifically, WatchPose tracks nested markers and visualizes viewpoints in an Augmented Reality- (AR) based manner to properly guide users to collect training data from broader camera-object distances and more diverse views around the objects. Experiments show that WatchPose can effectively improve the accuracy of existing camera pose regression models compared to the traditional data acquisition method. We also introduce a new dataset, Industrial10, to encourage the community to adapt camera pose regression methods for more complex environments.
Data Handling in Industry 4.0: Interoperability Based on Distributed Ledger Technology
Authors: Sun Shengjing , Zheng Xiaochen , Villalba-Díez Javier , Ordieres-Meré Joaquín
Published: May 2020

Information-intensive transformation is vital to realize the Industry 4.0 paradigm, where processes, systems, and people are in a connected environment. Current factories must combine different sources of knowledge with different technological layers. Taking into account data interconnection and information transparency, it is necessary to enhance the existing frameworks. This paper proposes an extension to an existing framework, which enables access to knowledge about the different data sources available, including data from operators. To develop the interoperability principle, a specific proposal to provide a (public and encrypted) data management solution to ensure information transparency is presented, which enables semantic data treatment and provides an appropriate context to allow data fusion. This proposal is designed also considering the Privacy by Design option. As a proof of application case, an implementation was carried out regarding the logistics of the delivery of industrial components in the construction sector, where different stakeholders may benefit from shared knowledge under the proposed architecture.
An Online SBAS Service to Improve Drone Navigation Performance in High-Elevation Masked Areas
Authors: Yoon Hyojung , Seok Hyojeong , Lim Cheolsoon , Park Byungwoon
Published: May 2020

Owing to the high demand for drone operation in high-elevation masked areas, it is necessary to develop a more effective method of transmitting and applying Satellite-Based Augmentation System (SBAS) messages for drones. This study proposes an onboard module including correction conversion, integrity information calculation, and fast initialization requests, which can enable the application of an online SBAS to drone operation. The proposed system not only improves the position accuracy with timely and proper protection levels in an open sky, but also reduces the initialization time from 70–100 s to 1 s, enabling a drone of short endurance to perform its mission successfully. In SBAS signal-denied cases, the position accuracy was improved by 40% and the uncorrected 13.4 m vertical error was reduced to 5.6 m by applying an SBAS message delivered online. The protection levels calculated with the accurate position regardless of the current location could denote the thrust level and availability of the navigation solution. The proposed system can practically solve the drawbacks of the current SBAS, considering the characteristics of the low-cost receivers on the market. Our proposed system is expected to be a useful and practical solution to integrate drones into the airspace in the near future.
Optimised Autocalibration Algorithm of Weigh-In-Motion Systems for Direct Mass Enforcement
Authors: Burnos Piotr , Gajda Janusz
Published: May 2020

Dynamic vehicle weighing systems, also known as Weigh-In-Motion (WIM), are sensitive to factors which interfere with the measurement, including weather and climate conditions. This is a result of the sensitivity of the axle load sensors used in the systems. As a result, a significant change in the precision of weighing can be observed over short periods of time (even less than 1 h). This fact is a deterrent to the use of such systems for direct mass enforcement. In this article, we present a solution for this problem using an optimised autocalibration algorithm. We show the results of simulation studies which we conducted on the proposed algorithm. These were then verified experimentally at an in-road site. We demonstrated that autocalibration of the WIM system allows for effective limitation of the sensitivity of weighing results to interfering factors. This is, however, conditioned on a sufficiently high frequency of reference vehicles crossing the WIM site. The required frequency depends on the speed of change in the concentration of influencing factors.
Big Data Driven Detection of Trees in Suburban Scenes Using Visual Spectrum Eye Level Photography
Authors: Thirlwell Andrew , Arandjelović Ognjen
Published: May 2020

The aim of the work described in this paper is to detect trees in eye level view images. Unlike previous work that universally considers highly constrained environments, such as natural parks and wooded areas, or simple scenes with little clutter and clear tree separation, our focus is on much more challenging suburban scenes, which are rich in clutter and highly variable in type and appearance (houses, falls, shrubs, cars, bicycles, pedestrians, hydrants, lamp posts, etc.). Thus, we motivate and introduce three different approaches: (i) a conventional computer vision based approach, employing manually engineered steps and making use of explicit human knowledge of the application domain, (ii) a more machine learning oriented approach, which learns from densely extracted local features in the form of scale invariant features (SIFT), and (iii) a machine learning based approach, which employs both colour and appearance models as a means of making the most of available discriminative information. We also make a significant contribution in regards to the collection of training and evaluation data. In contrast to the existing work, which relies on manual data collection (thus risking unintended bias) or corpora constrained in variability and limited in size (thus not allowing for reliable generalisation inferences to be made), we show how large amounts of representative data can be collected automatically using freely available tools, such as Google’s Street View, and equally automatically processed to produce a large corpus of minimally biased imagery. Using a large data set collected in the manner and comprising tens of thousands of images, we confirm our theoretical arguments that motivated our machine learning based and colour-aware histograms of oriented gradients based method, which achieved a recall of 95% and precision of 97%.
Frontal EEG Changes with the Recovery of Carotid Blood Flow in a Cardiac Arrest Swine Model
Authors: Kim Heejin , Kim Ki Hong , Hong Ki Jeong , Ku Yunseo , Shin Sang Do , Kim Hee Chan
Published: May 2020

Monitoring cerebral circulation during cardiopulmonary resuscitation (CPR) is essential to improve patients’ prognosis and quality of life. We assessed the feasibility of non-invasive electroencephalography (EEG) parameters as predictive factors of cerebral resuscitation in a ventricular fibrillation (VF) swine model. After 1 min untreated VF, four cycles of basic life support were performed and the first defibrillation was administered. Sustained return of spontaneous circulation (ROSC) was confirmed if a palpable pulse persisted for 20 min. Otherwise, one cycle of advanced cardiovascular life support (ACLS) and defibrillation were administered immediately. Successfully defibrillated animals were continuously monitored. If sustained ROSC was not achieved, another cycle of ACLS was administered. Non-ROSC was confirmed when sustained ROSC did not occur after 10 ACLS cycles. EEG and hemodynamic parameters were measured during experiments. Data measured for approximately 3 s right before the defibrillation attempts were analyzed to investigate the relationship between the recovery of carotid blood flow (CBF) and non-invasive EEG parameters, including time- and frequency-domain parameters and entropy indices. We found that time-domain magnitude and entropy measures of EEG correlated with the change of CBF. Further studies are warranted to evaluate these EEG parameters as potential markers of cerebral circulation during CPR.
Embedded Corrosion Sensing with ZnO-PVDF Sensor Textiles
Authors: Chowdhury Tonoy , D’Souza Nandika , Ho Yee Hsien , Dahotre Narendra , Mahbub Ifana
Published: May 2020

Corrosion in underground and submerged steel pipes is a global problem. Coatings serve as an impermeable barrier or a sacrificial element to the transport of corrosive fluids. When this barrier fails, corrosion in the metal initiates. There is a critical need for sensors at the metal/coating interface as an early alert system. Current options utilize metal sensors, leading to accelerating corrosion. In this paper, a non-conductive sensor textile as a viable solution was investigated. For this purpose, non-woven Zinc (II) Oxide-Polyvinylidene Fluoride (ZnO-PVDF) nanocomposite fiber textiles were prepared in a range of weight fractions (1%, 3%, and 5% ZnO) and placed at the coating/steel interface. The properties of ZnO-PVDF nanocomposite meshes were characterized using scanning electron microscopy (SEM), X-ray diffraction (XRD), Fourier transform infrared (FTIR) and d33 meter. Electrochemical impedance spectroscopy (EIS) testing was performed during the immersion of the coated samples to validate the effectiveness of the sensor textile. The results offer a new option for sub-surface corrosion sensing using low cost, easily fabricated sensor textiles.
MorphoCluster: Efficient Annotation of Plankton Images by Clustering
Authors: Schröder Simon-Martin , Kiko Rainer , Koch Reinhard
Published: May 2020

In this work, we present MorphoCluster, a software tool for data-driven, fast, and accurate annotation of large image data sets. While already having surpassed the annotation rate of human experts, volume and complexity of marine data will continue to increase in the coming years. Still, this data requires interpretation. MorphoCluster augments the human ability to discover patterns and perform object classification in large amounts of data by embedding unsupervised clustering in an interactive process. By aggregating similar images into clusters, our novel approach to image annotation increases consistency, multiplies the throughput of an annotator, and allows experts to adapt the granularity of their sorting scheme to the structure in the data. By sorting a set of 1.2 M objects into 280 data-driven classes in 71 h (16 k objects per hour), with 90% of these classes having a precision of 0.889 or higher. This shows that MorphoCluster is at the same time fast, accurate, and consistent; provides a fine-grained and data-driven classification; and enables novelty detection.
Energy-Optimal Latency-Constrained Application Offloading in Mobile-Edge Computing
Authors: Gu Xiaohui , Ji Chen , Zhang Guoan
Published: May 2020

Mobile-edge computation offloading (MECO) is a promising emerging technology for battery savings in mobile devices (MD) and/or in latency reduction in the execution of applications by (either total or partial) offloading highly demanding applications from MDs to nearby servers such as base stations. In this paper, we provide an offloading strategy for the joint optimization of the communication and computational resources by considering the blue trade-off between energy consumption and latency. The strategy is formulated as the solution to an optimization problem that minimizes the total energy consumption while satisfying the execution delay limit (or deadline). In the solution, the optimal transmission power and rate and the optimal fraction of the task to be offloaded are analytically derived to meet the optimization objective. We further establish the conditions under which the binary decisions (full-offloading and no offloading) are optimal. We also explore how such system parameters as the latency constraint, task complexity, and local computing power affect the offloading strategy. Finally, the simulation results demonstrate the behavior of the proposed strategy and verify its energy efficiency.
On Singular Perturbation of Neutron Point Kinetics in the Dynamic Model of a PWR Nuclear Power Plant
Authors: Chen Xiangyi , Ray Asok
Published: May 2020

This short communication makes use of the principle of singular perturbation to approximate the ordinary differential equation (ODE) of prompt neutron (in the point kinetics model) as an algebraic equation. This approximation is shown to yield a large gain in computational efficiency without compromising any significant accuracy in the numerical simulation of primary coolant system dynamics in a PWR nuclear power plant. The approximate (i.e., singularly perturbed) model has been validated with a numerical solution of the original set of neutron point-kinetic and thermal–hydraulic equations. Both models use variable-step Runge–Kutta numerical integration.
Can Countermovement Jump Neuromuscular Performance Qualities Differentiate Maximal Horizontal Deceleration Ability in Team Sport Athletes?
Authors: Harper Damian J. , Cohen Daniel D. , Carling Christopher , Kiely John
Published: May 2020

This investigation aimed to determine the countermovement jump (CMJ) neuromuscular performance (NMP) qualities that differentiate between athletes with high or low horizontal deceleration ability. Twenty-seven male university team sport athletes performed a CMJ on vertical axis force plates and a maximal horizontal deceleration following a 20 m maximal horizontal sprint acceleration. The instantaneous velocity throughout the maximal horizontal deceleration test was measured using a radar device. The deceleration ability was evaluated using the average deceleration (HDEC, m·s−2) and change in momentum—referred to as the horizontal braking impulse (HBI, N·s·kg−1). Participants were dichotomised into high and low HDEC and HBI according to a median-split analysis, and CMJ variables calculated for the overall eccentric, eccentric-deceleration and concentric phases. When horizontal deceleration ability was defined by HDEC, the CMJ concentric (effect size (ES) = 0.95) and eccentric (ES = 0.72) peak forces were the variables with the largest difference between groups. However, when defined using HBI, the largest difference was the concentric (ES = 1.15) and eccentric (ES = −1.00) peak velocities. Only the concentric mean power was significantly different between the high and low groups for both HDEC (ES = 0.85) and HBI (ES = 0.96). These findings show that specific eccentric and concentric NMP qualities may underpin the horizontal deceleration abilities characterised by HDEC and HBI. Specific NMP training interventions may be beneficial to target improvements in either of these measures of horizontal deceleration abilities.