bluuebunny/crossref_metadata_2025 huggingface dataset:
Text Mining of Hazard and Operability Analysis Reports Based on Active Learning
Authors: Wang Zhenhua , Zhang Beike , Gao Dong
Published: July 2021

In the field of chemical safety, a named entity recognition (NER) model based on deep learning can mine valuable information from hazard and operability analysis (HAZOP) text, which can guide experts to carry out a new round of HAZOP analysis, help practitioners optimize the hidden dangers in the system, and be of great significance to improve the safety of the whole chemical system. However, due to the standardization and professionalism of chemical safety analysis text, it is difficult to improve the performance of traditional models. To solve this problem, in this study, an improved method based on active learning is proposed, and three novel sampling algorithms are designed, Variation of Token Entropy (VTE), HAZOP Confusion Entropy (HCE) and Amplification of Least Confidence (ALC), which improve the ability of the model to understand HAZOP text. In this method, a part of data is used to establish the initial model. The sampling algorithm is then used to select high-quality samples from the data set. Finally, these high-quality samples are used to retrain the whole model to obtain the final model. The experimental results show that the performance of the VTE, HCE, and ALC algorithms are better than that of random sampling algorithms. In addition, compared with other methods, the performance of the traditional model is improved effectively by the method proposed in this paper, which proves that the method is reliable and advanced.
Effect of Air Injection on the Internal Flow Characteristics in the Draft Tube of a Francis Turbine Model
Authors: Kim Seung-Jun , Cho Yong , Kim Jin-Hyuk
Published: July 2021

Under low flow-rate conditions, a Francis turbine exhibits precession of a vortex rope with pressure fluctuations in the draft tube. These undesirable flow phenomena can lead to deterioration of the turbine performance as manifested by torque and power output fluctuations. In order to suppress the rope with precession and a swirl component in the tube, the use of anti-swirl fins was investigated in a previous study. However, vortex rope generation still occurred near the cone of the tube. In this study, unsteady-state Reynolds-averaged Navier–Stokes analyses were conducted with a scale-adaptive simulation shear stress transport turbulence model. This model was used to observe the effects of the injection in the draft tube on the unsteady internal flow and pressure phenomena considering both active and passive suppression methods. The air injection affected the generation and suppression of the vortex rope and swirl component depending on the flow rate of the air. In addition, an injection level of 0.5%Q led to a reduction in the maximum unsteady pressure characteristics.
Chemical Composition and Low-Temperature Fluidity Properties of Jet Fuels
Authors: Benavides Alirio , Benjumea Pedro , Cortés Farid B. , Ruiz Marco A.
Published: July 2021

The physicochemical properties of petroleum-derived jet fuels mainly depend on their chemical composition, which can vary from sample to sample as a result of the diversity of the crude diet processed by the refinery. Jet fuels are exposed to very low temperatures both at altitude and on the ground in places subject to extreme climates and must be able to maintain their fluidity at these low temperatures otherwise the flow of fuel to turbine engines will be reduced or even stopped. In this work, an experimental evaluation of the effect of chemical composition on low-temperature fluidity properties of jet fuels (freezing point, crystallization onset temperature and viscosity at −20 °C) was carried out. Initially, a methodology based on gas chromatography coupled to mass spectrometry (GC–MS) was adapted to determine the composition of 70 samples of Jet A1 and Jet A fuels. This methodology allowed quantifying the content, in weight percentage, of five main families of hydrocarbons: paraffinic, naphthenic, aromatic, naphthalene derivatives, and tetralin- and indane-derived compounds. Fuel components were also grouped into 11 classes depending on structural characteristics and the number of carbon atoms in the compound. The latter compositional approach allowed obtaining more precise model regressions for predicting the composition–property dependence and identifying individual components or hydrocarbon classes contributing to increased or decreased property values.
The Diminished Public, and Black Christian Promotion of American Civic Ideals
Authors: Smith R. Drew
Published: July 2021

Black public activism has been guided largely by black affinities toward the U.S. Constitution, including its core democratic liberalist premises. This range of constitutionally defined political possibilities has both animated (and confined) a sense of public imagination and agency for many black Christians. Divergences and convergences between black religion-based public confidence and dissent are examined here, with reference to three paradigmatic approaches: (1) civil religious patriotism; (2) religious counter-publics; and (3) socio-religious liminality and semi-publics. Contrasts and continuities between these approaches are examined with attention to the impact of these approaches on a beleaguered and diminished American public realm and their relative affirmations or negations of broad understandings and undertakings of public purposes.
To Be or Not to Be God—The Issue of Authorial Power in Dostoevsky
Authors: Zholkovsky Alexander
Published: July 2021

This paper problematizes the now widely accepted concept of Dostoevsky’s dialogism, which alleges the ‘Author’s’ equal empowerment of all his characters. Using examples from Crime and Punishment and The Brothers Karamazov, Zholkovsky focuses on instances of ‘scene-staging’ based on the ‘scripts’ devised and enacted by some characters, that are ‘read,’ with varying success, by their targets. He documents the resulting ‘discursive combat’ among the characters, with special attention paid to those ‘playing god’ and thus, the more ‘authorial’ among them. In several cases, the would-be ‘divine’ manipulation is shown to be consistently subverted by the Dostoevskian narrative. However, in one instance, where Aliosha Karamazov charitably scripts Captain Snegirev’s behavior, the ensuing discussion of this episode, in Aliosha’s conversations with Lise Khokhlakova, upholds Aliosha’s right to play god with the Other—“for the Other’s own good”, of course (not unlike the Grand Inquisitor).
Risk Factors Affecting Bancassurance Development in Poland
Authors: Śliwiński Adam , Dropia Joanna , Duczkowski Norbert
Published: July 2021

The aim of the article is to identify the risk factors affecting bancassurance development in Poland. The development is understood here as a change of gross written premiums obtained through banks in Poland. The group of risk factors selected in a survey conducted among financial sector employees was subject to statistical verification. The analysis used both variables directly related to the insurance product (e.g., a regulatory restriction of insurance acquisition costs) as well as those resulting from the specificity of the bancassurance channel, such as the sales of banking products, i.e., cash loans, housing loans and the value of funds placed by customers on deposits. The study was conducted on the basis of data on the gross premiums written in Poland in the years 2004–2019. The result of the applied model confirms the assumptions and the importance of insurance distribution in banks. Significant risk factors (statistically significant) which determine gross premiums written in the bancassurance channel are: the size of policyholder’s family (number of children, dependants) represented by the average number of people in a household in Poland, demand on mortgage loans represents by bank housing loans for households and agent’s commission, represented by the ratio of acquisition costs to gross written premium. The results of the econometric model obtained are consistent with expectations arising from the principles and practice of cooperation between banks and insurers as well as the specificity of insurance products distribution (also local) in the bancassurance channel.
Sparse Label Assignment for Oriented Object Detection in Aerial Images
Authors: Ming Qi , Miao Lingjuan , Zhou Zhiqiang , Song Junjie , Yang Xue
Published: July 2021

Object detection in aerial images has received extensive attention in recent years. The current mainstream anchor-based methods directly divide the training samples into positives and negatives according to the intersection-over-unit (IoU) of the preset anchors. This label assignment strategy assigns densely arranged samples for training, which leads to a suboptimal learning process and cause the model to suffer serious duplicate detections and missed detections. In this paper, we propose a sparse label assignment strategy (SLA) to select high-quality sparse anchors based on the posterior IoU of detections. In this way, the inconsistency between classification and regression is alleviated, and better performance can be achieved through balanced training. Next, to accurately detect small and densely arranged objects, we use a position-sensitive feature pyramid network (PS-FPN) with a coordinate attention module to extract position-sensitive features for accurate localization. Finally, the distance rotated IoU loss is proposed to eliminate the inconsistency between the training loss and the evaluation metric for better bounding box regression. Extensive experiments on the DOTA, HRSC2016, and UCAS-AOD datasets demonstrate the superiority of the proposed approach.
Workflow for Off-Site Bridge Inspection Using Automatic Damage Detection-Case Study of the Pahtajokk Bridge
Authors: Mirzazade Ali , Popescu Cosmin , Blanksvärd Thomas , Täljsten Björn
Published: July 2021

For the inspection of structures, particularly bridges, it is becoming common to replace humans with autonomous systems that use unmanned aerial vehicles (UAV). In this paper, a framework for autonomous bridge inspection using a UAV is proposed with a four-step workflow: (a) data acquisition with an efficient UAV flight path, (b) computer vision comprising training, testing and validation of convolutional neural networks (ConvNets), (c) point cloud generation using intelligent hierarchical dense structure from motion (DSfM), and (d) damage quantification. This workflow starts with planning the most efficient flight path that allows for capturing of the minimum number of images required to achieve the maximum accuracy for the desired defect size, then followed by bridge and damage recognition. Three types of autonomous detection are used: masking the background of the images, detecting areas of potential damage, and pixel-wise damage segmentation. Detection of bridge components by masking extraneous parts of the image, such as vegetation, sky, roads or rivers, can improve the 3D reconstruction in the feature detection and matching stages. In addition, detecting damaged areas involves the UAV capturing close-range images of these critical regions, and damage segmentation facilitates damage quantification using 2D images. By application of DSfM, a denser and more accurate point cloud can be generated for these detected areas, and aligned to the overall point cloud to create a digital model of the bridge. Then, this generated point cloud is evaluated in terms of outlier noise, and surface deviation. Finally, damage that has been detected is quantified and verified, based on the point cloud generated using the Terrestrial Laser Scanning (TLS) method. The results indicate this workflow for autonomous bridge inspection has potential.
Habitat Suitability Modeling for the Feeding Ground of Immature Albacore in the Southern Indian Ocean Using Satellite-Derived Sea Surface Temperature and Chlorophyll Data
Authors: Mondal Sandipan , Vayghan Ali Haghi , Lee Ming-An , Wang Yi-Chen , Semedi Bambang
Published: July 2021

In the current study, remotely sensed sea surface ocean temperature (SST) and sea surface chlorophyll (SSC), an indicator of tuna abundance, were used to determine the optimal feeding habitat zone of the southern Indian Ocean (SIO) albacore using a habitat suitability model applied to the 2000–2016 Taiwanese longline fishery data. The analysis showed a stronger correlation between the 2-month lag SSC and standardized catch per unit effort (CPUE) than 0-, 1-, 3-, and 4-month lag SSC. SST also exhibited a stronger correlation with standardized CPUE. Therefore, SST and SSC_2 were selected as final variables for model construction. An arithmetic mean model with SST and SSC_2 was deemed suitable to predict the albacore feeding habitat zone in the SIO. The preferred ranges of SSC_2 and SST for the feeding habitat of immature albacore were 0.07–0.09 mg m−3 and 16.5–18.5 °C, respectively, and mainly centralized at 17.5 °C SST and 0.08 mg m−3 SSC_2. The selected habitat suitability index model displayed a high correlation (R2 = 0.8276) with standardized CPUE. Overall, temperature and ocean chlorophyll were found to be essential for albacore habitat formation in the SIO, consistent with previous studies. The results of this study can contribute to ecosystem-based fisheries management in the SIO by providing insights into the habitat preference of immature albacore tuna in the SIO.
Improved Understanding of Groundwater Storage Changes under the Influence of River Basin Governance in Northwestern China Using GRACE Data
Authors: Liu Xin , Hu Litang , Sun Kangning , Yang Zhengqiu , Sun Jianchong , Yin Wenjie
Published: July 2021

Groundwater is crucial for economic development in arid and semiarid areas. The Shiyang River Basin (SRB) has the most prominent water use issues in northwestern China, and overexploited groundwater resources have led to continuous groundwater-level decline. The key governance planning project of the SRB was issued in 2007. This paper synthetically combines remote-sensing data from Gravity Recovery and Climate Experiment (GRACE) data and precipitation, actual evapotranspiration, land use, and in situ groundwater-level data to evaluate groundwater storage variations on a regional scale. Terrestrial water storage anomalies (TWSA) and groundwater storage anomalies (GWSA), in addition to their influencing factors in the SRB since the implementation of the key governance project, are analyzed in order to evaluate the effect of governance. The results show that GRACE-derived GWS variations are consistent with in situ observation data in the basin, with a correlation coefficient of 0.68. The GWS in the SRB had a slow downward trend from 2003 to 2016, and this increased by 0.38 billion m³/year after 2018. As the meteorological data did not change significantly, the changes in water storage are mainly caused by human activities, which are estimated by using the principle of water balance. The decline in GWS in the middle and lower reaches of the SRB has been curbed since 2009 and has gradually rebounded since 2014. GWS decreased by 2.2 mm EWH (equivalent water height) from 2011 to 2016, which was 91% lower than that from 2007 to 2010. The cropland area in the middle and lower reaches of the SRB also stopped increasing after 2011 and gradually decreased after 2014, while the area of natural vegetation gradually increased, indicating that the groundwater level and associated ecology significantly recovered after the implementation of the project.
Remote Sensing-Guided Spatial Sampling Strategy over Heterogeneous Surface Ground for Validation of Vegetation Indices Products with Medium and High Spatial Resolution
Authors: Lv Tingting , Zhou Xiang , Tao Zui , Sun Xiaoyu , Wang Jin , Li Ruoxi , Xie Futai
Published: July 2021

Remote sensing (RS)-derived vegetation indices (VIs) with medium and high spatial resolution have emerged as a promising dataset for fine-scale ecosystem modeling and agricultural monitoring at local or global scales. Before they can be used as reliable inputs for other research, conducting in situ measurements for validation is very critical. However, the spatial heterogeneity due to the diversity of land cover and its spatial organization in the landscape increases the uncertainty of validation, so design of optimal sampling is an important basis for the reliability of the validation. In this paper, we propose an integrative stratified sampling strategy (INTEG-STRAT) based on normalized difference vegetation index (NDVI) data as prior knowledge. The basic idea is to realize a sampling optimization by determining the optimal combination of the spatial sampling method (e.g., simple random sampling (SRS), spatial system sampling (SYS), stratified sampling, generalized random tessellation stratified (GRTS), balanced acceptance sampling (BAS)) and spatial stratification scheme with an objective rule. The objective rule in this paper is to minimize the root mean square error (RMSE) of 10-fold cross validation between estimated values (sample are not included) and the corresponding values on prior knowledge. Relative precision, correlation coefficient, and RMSE are used to compare the effectiveness of the proposed sampling strategy with each sampling method without considering sampling optimization. After comparing, we find that the INTEG-STRAT requires fewer samples to become stable and has higher accuracy. At site 1, when the correlation coefficient between NDVI image and the simulated NDVI surface reached 80%, INTEG-STRAT needed only 70 sampling points while other methods require more sampling points. At the same time, INTEG-STRAT strategy has a smaller RMSE between the estimated values and the corresponding values on prior knowledge image. In general, INTEG-STRAT is an effective method in the selection of representative samples to support the validation of vegetation indices products with medium and high spatial resolution.
Systematic Water Fraction Estimation for a Global and Daily Surface Water Time-Series
Authors: Mayr Stefan , Klein Igor , Rutzinger Martin , Kuenzer Claudia
Published: July 2021

Fresh water is a vital natural resource. Earth observation time-series are well suited to monitor corresponding surface dynamics. The DLR-DFD Global WaterPack (GWP) provides daily information on globally distributed inland surface water based on MODIS (Moderate Resolution Imaging Spectroradiometer) images at 250 m spatial resolution. Operating on this spatiotemporal level comes with the drawback of moderate spatial resolution; only coarse pixel-based surface water quantification is possible. To enhance the quantitative capabilities of this dataset, we systematically access subpixel information on fractional water coverage. For this, a linear mixture model is employed, using classification probability and pure pixel reference information. Classification probability is derived from relative datapoint (pixel) locations in feature space. Pure water and non-water reference pixels are located by combining spatial and temporal information inherent to the time-series. Subsequently, the model is evaluated for different input sets to determine the optimal configuration for global processing and pixel coverage types. The performance of resulting water fraction estimates is evaluated on the pixel level in 32 regions of interest across the globe, by comparison to higher resolution reference data (Sentinel-2, Landsat 8). Results show that water fraction information is able to improve the product’s performance regarding mixed water/non-water pixels by an average of 11.6% (RMSE). With a Nash-Sutcliffe efficiency of 0.61, the model shows good overall performance. The approach enables the systematic provision of water fraction estimates on a global and daily scale, using only the reflectance and temporal information contained in the input time-series.
Fine-Tuning Heat Stress Algorithms to Optimise Global Predictions of Mass Coral Bleaching
Authors: Lachs Liam , Bythell John C , East Holly K , Edwards Alasdair J , Mumby Peter J , Skirving William J , Spady Blake L , Guest James R.
Published: July 2021

Increasingly intense marine heatwaves threaten the persistence of many marine ecosystems. Heat stress-mediated episodes of mass coral bleaching have led to catastrophic coral mortality globally. Remotely monitoring and forecasting such biotic responses to heat stress is key for effective marine ecosystem management. The Degree Heating Week (DHW) metric, designed to monitor coral bleaching risk, reflects the duration and intensity of heat stress events and is computed by accumulating SST anomalies (HotSpot) relative to a stress threshold over a 12-week moving window. Despite significant improvements in the underlying SST datasets, corresponding revisions of the HotSpot threshold and accumulation window are still lacking. Here, we fine-tune the operational DHW algorithm to optimise coral bleaching predictions using the 5 km satellite-based SSTs (CoralTemp v3.1) and a global coral bleaching dataset (37,871 observations, National Oceanic and Atmospheric Administration). After developing 234 test DHW algorithms with different combinations of the HotSpot threshold and accumulation window, we compared their bleaching prediction ability using spatiotemporal Bayesian hierarchical models and sensitivity–specificity analyses. Peak DHW performance was reached using HotSpot thresholds less than or equal to the maximum of monthly means SST climatology (MMM) and accumulation windows of 4–8 weeks. This new configuration correctly predicted up to an additional 310 bleaching observations globally compared to the operational DHW algorithm, an improved hit rate of 7.9%. Given the detrimental impacts of marine heatwaves across ecosystems, heat stress algorithms could also be fine-tuned for other biological systems, improving scientific accuracy, and enabling ecosystem governance.
Improved Accuracy of Phenological Detection in Rice Breeding by Using Ensemble Models of Machine Learning Based on UAV-RGB Imagery
Authors: Ge Haixiao , Ma Fei , Li Zhenwang , Tan Zhengzheng , Du Changwen
Published: July 2021

Accurate and timely detection of phenology at plot scale in rice breeding trails is crucial for understanding the heterogeneity of varieties and guiding field management. Traditionally, remote sensing studies of phenology detection have heavily relied on the time-series vegetation index (VI) data. However, the methodology based on time-series VI data was often limited by the temporal resolution. In this study, three types of ensemble models including hard voting (majority voting), soft voting (weighted majority voting) and model stacking, were proposed to identify the principal phenological stages of rice based on unmanned aerial vehicle (UAV) RGB imagery. These ensemble models combined RGB-VIs, color space (e.g., RGB and HSV) and textures derived from UAV-RGB imagery, and five machine learning algorithms (random forest; k-nearest neighbors; Gaussian naïve Bayes; support vector machine and logistic regression) as base models to estimate phenological stages in rice breeding. The phenological estimation models were trained on the dataset of late-maturity cultivars and tested independently on the dataset of early-medium-maturity cultivars. The results indicated that all ensemble models outperform individual machine learning models in all datasets. The soft voting strategy provided the best performance for identifying phenology with the overall accuracy of 90% and 93%, and the mean F1-scores of 0.79 and 0.81, respectively, in calibration and validation datasets, which meant that the overall accuracy and mean F1-scores improved by 5% and 7%, respectively, in comparison with those of the best individual model (GNB), tested in this study. Therefore, the ensemble models demonstrated great potential in improving the accuracy of phenology detection in rice breeding.
Development and Validation of an End-to-End Simulator and Gas Concentration Retrieval Processor Applied to the MERLIN Lidar Mission
Authors: Cassé Vincent , Armante Raymond , Bousquet Philippe , Chomette Olivier , Crevoisier Cyril , Delahaye Thibault , Edouart Dimitri , Gibert Fabien , Millet Bruno , Nahan Frédéric , Pierangelo Clémence
Published: July 2021

In the context of MERLIN (MEthane Remote LIdar missioN), a French–German spatial lidar mission dedicated to monitoring the atmospheric methane content, two software programs have been developed: LIDSIM (LIDar SIMulator) and PROLID (PROcessor LIDar). The objectives are to assess whether the instrument design meets the performance requirements and to study the sensitivity of this performance to geophysical parameters. LIDSIM is an end-to-end mission simulator and PROLID is a retrieval processor that provides mole fractions of methane in dry air, averaged over an atmospheric column. These two tools are described in this paper. Results of the validation tests and the first full orbit simulations are reported. Merlin target performance does not seem to be reachable but breakthrough performance is reached.
Characterization of Carrier Phase-Based Positioning in Real-World Jamming Conditions
Authors: Larsen Søren Skaarup , Jensen Anna B. O. , Olesen Daniel H.
Published: July 2021

GNSS signals arriving at receivers at the surface of the Earth are weak and easily susceptible to interference and jamming. In this paper, the impact of jamming on the reference station in carrier phase-based relative baseline solutions is examined. Several scenarios are investigated in order to assess the robustness of carrier phase-based positioning towards jamming. Among others, these scenarios include a varying baseline length, the use of single- versus dual-frequency observations, and the inclusion of the Galileo and GLONASS constellations to a GPS only solution. The investigations are based on observations recorded at physical reference stations in the Danish TAPAS network during actual jamming incidents, in order to realistically evaluate the impact of real-world jamming on carrier phase-based positioning accuracy. The analyses performed show that, while there are benefits of using observations from several frequencies and constellations in positioning solutions, special care must be taken in solution processing. The selection of which GNSS constellations and observations to include, as well as when they are included, is essential, as blindly adding more jamming-affected observations may lead to worse positioning accuracy.
Classification of Motor Imagery Electroencephalography Signals Based on Image Processing Method
Authors: Chen Zhongye , Wang Yijun , Song Zhongyan
Published: July 2021

In recent years, more and more frameworks have been applied to brain-computer interface technology, and electroencephalogram-based motor imagery (MI-EEG) is developing rapidly. However, it is still a challenge to improve the accuracy of MI-EEG classification. A deep learning framework termed IS-CBAM-convolutional neural network (CNN) is proposed to address the non-stationary nature, the temporal localization of excitation occurrence, and the frequency band distribution characteristics of the MI-EEG signal in this paper. First, according to the logically symmetrical relationship between the C3 and C4 channels, the result of the time-frequency image subtraction (IS) for the MI-EEG signal is used as the input of the classifier. It both reduces the redundancy and increases the feature differences of the input data. Second, the attention module is added to the classifier. A convolutional neural network is built as the base classifier, and information on the temporal location and frequency distribution of MI-EEG signal occurrences are adaptively extracted by introducing the Convolutional Block Attention Module (CBAM). This approach reduces irrelevant noise interference while increasing the robustness of the pattern. The performance of the framework was evaluated on BCI competition IV dataset 2b, where the mean accuracy reached 79.6%, and the average kappa value reached 0.592. The experimental results validate the feasibility of the framework and show the performance improvement of MI-EEG signal classification.
Human-Machine Shared Driving Control for Semi-Autonomous Vehicles Using Level of Cooperativeness
Authors: Nguyen Anh-Tu , Rath Jagat Jyoti , Lv Chen , Guerra Thierry-Marie , Lauber Jimmy
Published: July 2021

This paper proposes a new haptic shared control concept between the human driver and the automation for lane keeping in semi-autonomous vehicles. Based on the principle of human-machine interaction during lane keeping, the level of cooperativeness for completion of driving task is introduced. Using the proposed human-machine cooperative status along with the driver workload, the required level of haptic authority is determined according to the driver’s performance characteristics. Then, a time-varying assistance factor is developed to modulate the assistance torque, which is designed from an integrated driver-in-the-loop vehicle model taking into account the yaw-slip dynamics, the steering dynamics, and the human driver dynamics. To deal with the time-varying nature of both the assistance factor and the vehicle speed involved in the driver-in-the-loop vehicle model, a new ℓ∞ linear parameter varying control technique is proposed. The predefined specifications of the driver-vehicle system are guaranteed using Lyapunov stability theory. The proposed haptic shared control method is validated under various driving tests conducted with high-fidelity simulations. Extensive performance evaluations are performed to highlight the effectiveness of the new method in terms of driver-automation conflict management.
Hough Transform-Based Angular Features for Learning-Free Handwritten Keyword Spotting
Authors: Kundu Subhranil , Malakar Samir , Geem Zong Woo , Moon Yoon Young , Singh Pawan Kumar , Sarkar Ram
Published: July 2021

Handwritten keyword spotting (KWS) is of great interest to the document image research community. In this work, we propose a learning-free keyword spotting method following query by example (QBE) setting for handwritten documents. It consists of four key processes: pre-processing, vertical zone division, feature extraction, and feature matching. The pre-processing step deals with the noise found in the word images, and the skewness of the handwritings caused by the varied writing styles of the individuals. Next, the vertical zone division splits the word image into several zones. The number of vertical zones is guided by the number of letters in the query word image. To obtain this information (i.e., number of letters in a query word image) during experimentation, we use the text encoding of the query word image. The user provides the information to the system. The feature extraction process involves the use of the Hough transform. The last step is feature matching, which first compares the features extracted from the word images and then generates a similarity score. The performance of this algorithm has been tested on three publicly available datasets: IAM, QUWI, and ICDAR KWS 2015. It is noticed that the proposed method outperforms state-of-the-art learning-free KWS methods considered here for comparison while evaluated on the present datasets. We also evaluate the performance of the present KWS model using state-of-the-art deep features and it is found that the features used in the present work perform better than the deep features extracted using InceptionV3, VGG19, and DenseNet121 models.
Histogram Adjustment of Images for Improving Photogrammetric Reconstruction
Authors: Łabędź Piotr , Skabek Krzysztof , Ozimek Paweł , Nytko Mateusz
Published: July 2021

The accuracy of photogrammetric reconstruction depends largely on the acquisition conditions and on the quality of input photographs. This paper proposes methods of improving raster images that increase photogrammetric reconstruction accuracy. These methods are based on modifying color image histograms. Special emphasis was placed on the selection of channels of the RGB and CIE L*a*b* color models for further improvement of the reconstruction process. A methodology was proposed for assessing the quality of reconstruction based on premade reference models using positional statistics. The analysis of the influence of image enhancement on reconstruction was carried out for various types of objects. The proposed methods can significantly improve the quality of reconstruction. The superiority of methods based on the luminance channel of the L*a*b* model was demonstrated. Our studies indicated high efficiency of the histogram equalization method (HE), although these results were not highly distinctive for all performed tests.
Automation Pyramid as Constructor for a Complete Digital Twin, Case Study: A Didactic Manufacturing System
Authors: Martinez Edwin Mauricio , Ponce Pedro , Macias Israel , Molina Arturo
Published: July 2021

Nowadays, the concept of Industry 4.0 aims to improve factories’ competitiveness. Usually, manufacturing production is guided by standards to segment and distribute its processes and implementations. However, industry 4.0 requires innovative proposals for disruptive technologies that engage the entire production process in factories, not just a partial improvement. One of these disruptive technologies is the Digital Twin (DT). This advanced virtual model runs in real-time and can predict, detect, and classify normal and abnormal operating conditions in factory processes. The Automation Pyramid (AP) is a conceptual element that enables the efficient distribution and connection of different actuators in enterprises, from the shop floor to the decision-making levels. When a DT is deployed into a manufacturing system, generally, the DT focuses on the low-level that is named field level, which includes the physical devices such as controllers, sensors, and so on. Thus, the partial automation based on the DT is accomplished, and the information between all manufacturing stages could be decremented. Hence, to achieve a complete improvement of the manufacturing system, all the automation pyramid levels must be included in the DT concept. An artificial intelligent management system could create an interconnection between them that can manage the information. As a result, this paper proposed a complete DT structure covering all automation pyramid stages using Artificial Intelligence (AI) to model each stage of the AP based on the Digital Twin concept. This work proposes a virtual model for each level of the traditional AP and the interactions among them to flow and control information efficiently. Therefore, the proposed model is a valuable tool in improving all levels of an industrial process. In addition, It is presented a case study where the DT concept for modular workstations underpins the development of technologies within the framework of the Automation Pyramid model is implemented into a didactic manufacturing system.
A Long-Lasting Textile-Based Anatomically Realistic Head Phantom for Validation of EEG Electrodes
Authors: Tseghai Granch Berhe , Malengier Benny , Fante Kinde Anlay , Van Langenhove Lieva
Published: July 2021

During the development of new electroencephalography electrodes, it is important to surpass the validation process. However, maintaining the human mind in a constant state is impossible which in turn makes the validation process very difficult. Besides, it is also extremely difficult to identify noise and signals as the input signals are not known. For that reason, many researchers have developed head phantoms predominantly from ballistic gelatin. Gelatin-based material can be used in phantom applications, but unfortunately, this type of phantom has a short lifespan and is relatively heavyweight. Therefore, this article explores a long-lasting and lightweight (−91.17%) textile-based anatomically realistic head phantom that provides comparable functional performance to a gelatin-based head phantom. The result proved that the textile-based head phantom can accurately mimic body-electrode frequency responses which make it suitable for the controlled validation of new electrodes. The signal-to-noise ratio (SNR) of the textile-based head phantom was found to be significantly better than the ballistic gelatin-based head providing a 15.95 dB ± 1.666 (±10.45%) SNR at a 95% confidence interval.
Multi-Camera Vessel-Speed Enforcement by Enhancing Detection and Re-Identification Techniques
Authors: Zwemer Matthijs H. , Groot Herman G. J. , Wijnhoven Rob , Bondarev Egor , de With Peter H. N.
Published: July 2021

This paper presents a camera-based vessel-speed enforcement system based on two cameras. The proposed system detects and tracks vessels per camera view and employs a re-identification (re-ID) function for linking vessels between the two cameras based on multiple bounding-box images per vessel. Newly detected vessels in one camera (query) are compared to the gallery set of all vessels detected by the other camera. To train and evaluate the proposed detection and re-ID system, a new Vessel-reID dataset is introduced. This extensive dataset has captured a total of 2474 different vessels covered in multiple images, resulting in a total of 136,888 vessel bounding-box images. Multiple CNN detector architectures are evaluated in-depth. The SSD512 detector performs best with respect to its speed (85.0% Recall@95Precision at 20.1 frames per second). For the re-ID of vessels, a large portion of the total trajectory can be covered by the successful detections of the SSD model. The re-ID experiments start with a baseline single-image evaluation obtaining a score of 55.9% Rank-1 (49.7% mAP) for the existing TriNet network, while the available MGN model obtains 68.9% Rank-1 (62.6% mAP). The performance significantly increases with 5.6% Rank-1 (5.7% mAP) for MGN by applying matching with multiple images from a single vessel. When emphasizing more fine details by selecting only the largest bounding-box images, another 2.0% Rank-1 (1.4% mAP) is added. Application-specific optimizations such as travel-time selection and applying a cross-camera matching constraint further enhance the results, leading to a final 88.9% Rank-1 and 83.5% mAP performance.
Fully Learnable Model for Task-Driven Image Compressed Sensing
Authors: Zheng Bowen , Zhang Jianping , Sun Guiling , Ren Xiangnan
Published: July 2021

This study primarily investigates image sensing at low sampling rates with convolutional neural networks (CNN) for specific applications. To improve the image acquisition efficiency in energy-limited systems, this study, inspired by compressed sensing, proposes a fully learnable model for task-driven image-compressed sensing (FLCS). The FLCS, based on Deep Convolution Generative Adversarial Networks (DCGAN) and Variational Auto-encoder (VAE), divides the image-compressed sensing model into three learnable parts, i.e., the Sampler, the Solver and the Rebuilder. To be specific, a measurement matrix suitable for a type of image is obtained by training the Sampler. The Solver calculates the image’s low-dimensional representation with the measurements. The Rebuilder learns a mapping from the low-dimensional latent space to the image space. All the mentioned could be trained jointly or individually for a range of application scenarios. The pre-trained FLCS reconstructs images with few iterations for task-driven compressed sensing. As indicated from the experimental results, compared with existing approaches, the proposed method could significantly improve the reconstructed images’ quality while decreasing the running time. This study is of great significance for the application of image-compressed sensing at low sampling rates.
Effect of BoNT/A in the Surface Electromyographic Characteristics of the Pelvic Floor Muscles for the Treatment of Chronic Pelvic Pain
Authors: Albaladejo-Belmonte Monica , Nohales-Alfonso Francisco J. , Tarazona-Motes Marta , De-Arriba Maria , Alberola-Rubio Jose , Garcia-Casado Javier
Published: July 2021

Chronic pelvic pain (CPP) is a complex condition with a high economic and social burden. Although it is usually treated with botulinum neurotoxin type A (BoNT/A) injected into the pelvic floor muscles (PFM), its effect on their electrophysiological condition is unknown. In this study, 24 CPP patients were treated with BoNT/A. Surface electromyographic signals (sEMG) were recorded at Weeks 0 (infiltration), 8, 12 and 24 from the infiltrated, non-infiltrated, upper and lower PFM. The sEMG of 24 healthy women was also recorded for comparison. Four parameters were computed: root mean square (RMS), median frequency (MDF), Dimitrov’s index (DI) and sample entropy (SampEn). An index of pelvic electrophysiological impairment (IPEI) was also defined with respect to the healthy condition. Before treatment, the CPP and healthy parameters of almost all PFM sides were significantly different. Post-treatment, there was a significant reduction in power (MDF), lower fatigue index (SampEn) in all sites in patients, mainly during PFM contractions, which brought their electrophysiological condition closer to that of healthy women (<IPEI). sEMG can be used to assess the PFM electrophysiological condition of CPP patients and the effects of therapies such as BoNT/A infiltration.
Optimal Scheduling of Dynamic Pricing Based V2G and G2V Operation in Microgrid Using Improved Elephant Herding Optimization
Authors: Jadoun Vinay Kumar , Sharma Nipun , Jha Piyush , S. Jayalakshmi N. , Malik Hasmat , Garcia Márquez Fausto Pedro
Published: July 2021

The unpredictable nature of the loads and non-linearity of the components of microgrid systems make optimal scheduling more complex. In this paper, a deterministic optimal load-scheduling problem is developed for microgrids operating in both islanding and grid-connected mode under different energy scenarios. Various cases are considered in this research, based on the interaction and dynamic behavior of the microgrid, considering electric vehicles (EVs) in the scenario. The aim of this research is to minimize the overall cost of microgrid operations. The concept of dynamic pricing has also been introduced in order to optimize the energy cost for the consumers. For ensuring the stability of the microgrids, a load variance index has been considered, and the fuzzy-based approach has been used for cost and load variance minimization to reduce the operation cost without compromising the stability of the microgrid. The grid-to-vehicle (G2V) and vehicle-to-grid (V2G) operations of EVs are integrated into the microgrid, which would help in valley filling and peak shaving of the loads during the off-peak and peak hours, respectively. In order to solve the proposed complex combinatorial optimization problem, elephant herding optimization (EHO) is modified and implemented. The performance of the proposed improved EHO (IEHO) is first tested on the latest CEC test functions. The results obtained by IEHO after 100 different trials are compared with the latest published methods and are found to be better based on the average value and the standard deviation for different CEC test functions. In addition, the simulation results obtained by particle swarm optimization (PSO), EHO, and proposed IEHO on a microgrid test system for different scenarios with all cases reveal that the proposed model with a mix of energy resources in the dynamic load dispatch environment bring the maximum benefits of microgrid systems. Furthermore, the results obtained from the simulation verifies that if free trade of power is allowed between the microgrids and the main grid, the process of power generation can be more economical, and further introduction of dynamic pricing into the scenario proves to be even cheaper. The implementation of the G2V and V2G operations of EVs operations in the proposed scenario not only helped in cost minimization but also helped in stabilizing the grid.
Sustainability-Oriented Multi-Criteria Analysis of Different Continuous Flight Auger Piles
Authors: Josa Irene , Tošić Nikola , Marinković Snežana , de la Fuente Albert , Aguado Antonio
Published: July 2021

With increasing construction activity and concrete consumption globally, the economic, environmental, and social impacts of human activities continue to increase rapidly. Therefore, it is imperative to assess the choice and construction of each structure and structural component from a sustainability-based perspective. In this study, such a multi-criteria decision-making approach using the MIVES method is applied to the choice of grouped continuous flight auger (CFA) piles. Different alternatives of CFA piles are studied: length (10 and 20 m), reinforcement (steel cage reinforcement and structural fibers), and aggregates (natural crushed aggregates and recycled aggregate concrete sourced from stationary and mobile recycling plants), based on experimentally verified mix designs. All alternatives were analyzed considering economic, environmental, and social requirements, using a decision-making tree with eight criteria and eleven indicators, with weights assigned by an expert panel. The results of the analysis showed a clear advantage in terms of all three sustainability requirements for CFA piles with steel fibers and recycled aggregate concrete, with all solutions with steel cage reinforcement having significantly lower values of the sustainability index. Such results demonstrate the need for implementing innovative solutions even in structural members such as CFA piles that are often considered in insufficient detail.
The Consumption of Alcoholic Beverages Can Be Reduced by Fiscal Means? Study on the Case of Romania
Authors: Bostan Ionel , Rusu Valentina Diana
Published: July 2021

World Health Organization gives great importance to excessive alcohol consumption among the population and its negative effects. It proposes a set of measures to reduce alcohol consumption and its harmful effects. The objective of our study was to estimate the effects of excise tax on alcohol in Romania, from two perspectives. On the one hand, we analyze the effects of alcohol excise on the consumption of alcoholic beverages among the Romanian population. We also consider the relationship between alcohol consumption and the incidence of diseases and deaths caused by it. On the other hand, we analyze the effects of the excise tax on alcohol on the state budget, through the revenues from the sales of alcohol. For achieving the main purpose of the paper, we analyze secondary data using both graphical and statistical methods. The statistical methods imply testing correlation between variables and also a regression model. The obtained results highlight the fact that the excise taxes on alcohol have not a significant effect on alcohol consumption among the population. At the same time, we observed that the revenues from excise taxes in alcohol at the state budget had an increasing trend. These results highlight the fact that the increase of excise duties for alcoholic beverages, in Romania, does not reduce their consumption. So, in order to limit the effects of this harmful habit of a certain part of the Romanian population, the decision-makers must take additional measures, the fiscal ones being relatively inefficient.