bluuebunny/crossref_metadata_2025 huggingface dataset:
Transferable Deep Learning from Time Series of Landsat Data for National Land-Cover Mapping with Noisy Labels: A Case Study of China
Authors: Zhao Xuemei , Hong Danfeng , Gao Lianru , Zhang Bing , Chanussot Jocelyn
Published: October 2021

Large-scale land-cover classification using a supervised algorithm is a challenging task. Enormous efforts have been made to manually process and check the production of national land-cover maps. This has led to complex pre- and post-processing and even the production of inaccurate mapping products from large-scale remote sensing images. Inspired by the recent success of deep learning techniques, in this study we provided a feasible automatic solution for improving the quality of national land-cover maps. However, the application of deep learning to national land-cover mapping remains limited because only small-scale noisy labels are available. To this end, a mutual transfer network MTNet was developed. MTNet is capable of learning better feature representations by mutually transferring pre-trained models from time-series of data and fine-tuning current data. An interactive training strategy such as this can effectively alleviate the effects of inaccurate or noisy labels and unbalanced sample distributions, thus yielding a relatively stable classification system. Extensive experiments were conducted by focusing on several representative regions to evaluate the classification results of our proposed method. Quantitative results showed that the proposed MTNet outperformed its baseline model about 1%, and the accuracy can be improved up to 6.45% compared with the model trained by the training set of another year. We also visualized the national classification maps generated by MTNet for two different time periods to quantitatively analyze the performance gain. It was concluded that the proposed MTNet provides an efficient method for large-scale land cover mapping.
YOLO-RTUAV: Towards Real-Time Vehicle Detection through Aerial Images with Low-Cost Edge Devices
Authors: Koay Hong Vin , Chuah Joon Huang , Chow Chee-Onn , Chang Yang-Lang , Yong Keh Kok
Published: October 2021

Object detection in aerial images has been an active research area thanks to the vast availability of unmanned aerial vehicles (UAVs). Along with the increase of computational power, deep learning algorithms are commonly used for object detection tasks. However, aerial images have large variations, and the object sizes are usually small, rendering lower detection accuracy. Besides, real-time inferencing on low-cost edge devices remains an open-ended question. In this work, we explored the usage of state-of-the-art deep learning object detection on low-cost edge hardware. We propose YOLO-RTUAV, an improved version of YOLOv4-Tiny, as the solution. We benchmarked our proposed models with various state-of-the-art models on the VAID and COWC datasets. Our proposed model can achieve higher mean average precision (mAP) and frames per second (FPS) than other state-of-the-art tiny YOLO models, especially on a low-cost edge device such as the Jetson Nano 2 GB. It was observed that the Jetson Nano 2 GB can achieve up to 12.8 FPS with a model size of only 5.5 MB.
Toward a More Representative Monitoring of Land-Use and Land-Cover Dynamics: The Use of a Sample-Based Assessment through Augmented Visual Interpretation Using Open Foris Collect Earth
Authors: Maniatis Danae , Dionisio Daniel , Guarnieri Laura , Marchi Giulio , Mollicone Danilo , Morales Carmen , Sanchez-Paus Díaz Alfonso
Published: October 2021

High-quality data for REDD+ monitoring, measurement, and reporting are critical for the continued success of REDD+ implementation and Results-Based Payments. Collect Earth is a free, user-friendly, and open-source software for land monitoring developed by the Food and Agriculture Organization of the United Nations (FAO). The tool allows countries to undertake land monitoring easily and rapidly through a sample-based approach and generate Activity Data (data on the magnitude of human activity resulting in emissions or removals during a given period of time) through augmented visual interpretation with low costs. Under the Paris Agreement, countries will have to update the greenhouse gas inventories that they report to the United Nations Framework Convention on Climate Change every two years through the Biennial Update Reports. One of the important benefits of using sample-based approaches such as the one proposed by Collect Earth is the possibility to achieve a detailed classification of the land-use sub-categories with high accuracy of the estimates for land-use changes occurring since 2000. However, most guidance documents developed for capacity building in developing countries for REDD+ reporting only advocate developing land-cover and land-cover change maps using remote sensing. As several countries already use Collect Earth and the sample-based methodology to report on REDD+, this commentary advocates for a more representative approach and a methodological debate on the potential of sample-based approaches using remote sensing, and when possible combined with ground truthing, to estimate Activity Data for REDD+ and countries’ greenhouse gas inventories for the Agriculture, Forestry and Other Land Use sector in general.
Estimation of Maize Photosynthesis Traits Using Hyperspectral Lidar Backscattered Intensity
Authors: Bi Kaiyi , Niu Zheng , Xiao Shunfu , Bai Jie , Sun Gang , Wang Ji , Han Zeying , Gao Shuai
Published: October 2021

High-throughput measurement of plant photosynthesis ability presents a challenge for the breeding process aimed to improve crop yield. As a novel technique, hyperspectral lidar (HSL) has the potential to characterize the spatial distribution of plant photosynthesis traits under less confounding factors. In this paper, HSL reflectance spectra of maize leaves were utilized for estimating the maximal velocity of Rubisco carboxylation (Vcmax) and maximum rate of electron transport at a specific light intensity (J) based on both reflectance-based and trait-based methods, and the results were compared with the commercial Analytical Spectral Devices (ASD) system. A linear combination of the Lambertian model and the Beckmann law was conducted to eliminate the angle effect of the maize point cloud. The results showed that the reflectance-based method (R2 ≥ 0.42, RMSE ≤ 28.1 for J and ≤4.32 for Vcmax) performed better than the trait-based method (R2 ≥ 0.31, RMSE ≤ 33.7 for J and ≤5.17 for Vcmax), where the estimating accuracy of ASD was higher than that of HSL. The Lambertian–Beckmann model performed well (R2 ranging from 0.74 to 0.92) for correcting the incident angle at different wavelength bands, so the spatial distribution of photosynthesis traits of two maize plants was visually displayed. This study provides the basis for the further application of HSL in high-throughput measurements of plant photosynthesis.
Impact of Winter Snowfall on Vegetation Greenness in Central Asia
Authors: Huang Farong , Feng Ting , Guo Zengkun , Li Lanhai
Published: October 2021

Snowfall is an important climatic variable with remarkable impacts on vegetation greenness in the alpine and extra-tropical regions. Central Asia (CA) is located in the middle latitude with high mountains, and the ecosystem is vulnerable to climate change in CA. In this region, the vegetation response to winter snowfall and its interactions with growing season climatic factors on vegetation greenness is still unclear. Thus, this study attempted to examine the impact of winter snowfall on vegetation greenness with remote-sensing vegetation index and reanalysis climatic data, and to investigate the interactions between winter snowfall and growing season climatic factors and their influence on vegetation growth via path analysis. The results showed that there is a generally positive correlation between winter snowfall and vegetation greenness from May to September and during the whole growing season (April to September). This positive correlation was significant in 8% of the study area for the whole growing season. However, the increase in winter snowfall was not beneficial to the regional vegetation growth in April. The vegetation response to winter snowfall also relates to land-cover type and elevation. The vegetation greenness in grassland was depicted to be more sensitive to winter snowfall than that in forestland. The response turned from positive to negative when the elevation increased from below 3000 m to above 3000 m. Moreover, the impact of winter snowfall on vegetation greenness was not regulated by temperature and precipitation in the growing season in more than 70% of CA. The impact relates to the interaction with April temperature in Central Kazakhstan, and is regulated by growing season precipitation in North Kazakhstan where annual precipitation mainly occurs in the growing season. The impact of winter snowfall on vegetation greenness is more important than growing season precipitation and temperature in some areas, since annual precipitation does not concentrate in the growing season or the relative increase of winter snowfall is great in these places. The results of the present study improve the understanding of vegetation response to climate change, and provide a scientific reference for environmental protection in CA and similar regions.
A High-Resolution, Random Forest Approach to Mapping Depth-to-Bedrock across Shallow Overburden and Post-Glacial Terrain
Authors: Furze Shane , O’Sullivan Antóin M. , Allard Serge , Pronk Toon , Curry R. Allen
Published: October 2021

Regolith, or unconsolidated materials overlying bedrock, exists as an active zone for many geological, geomorphological, hydrological and ecological processes. This zone and its processes are foundational to wide-ranging human needs and activities such as water supply, mineral exploration, forest harvesting, agriculture, and engineered structures. Regolith thickness, or depth-to-bedrock (DTB), is typically unavailable or restricted to finer scale assessments because of the technical and cost limitations of traditional drilling, seismic, and ground-penetrating radar surveys. The objective of this study was to derive a high-resolution (10 m2) DTB model for the province of New Brunswick, Canada as a case study. This was accomplished by developing a DTB database from publicly available soil profiles, boreholes, drill holes, well logs, and outcrop transects (n = 203,238). A Random Forest model was produced by modeling the relationships between DTB measurements in the database to gridded datasets derived from both a LiDAR-derived digital elevation model and photo-interpreted surficial geology delineations. In developing the Random Forest model, DTB measurements were split 70:30 for model development and validation, respectively. The DTB model produced an R2 = 92.8%, MAE = 0.18 m, and RMSE = 0.61 m for the training, and an R2 = 80.3%, MAE = 0.18 m, and RMSE = 0.66 m for the validation data. This model provides an unprecedented resolution of DTB variance at a landscape scale. Additionally, the presented framework provides a fundamental understanding of regolith thickness across a post-glacial terrain, with potential application at the global scale.
The Detection of Motor Bearing Fault with Maximal Overlap Discrete Wavelet Packet Transform and Teager Energy Adaptive Spectral Kurtosis
Authors: Yang D.-M.
Published: October 2021

Motor bearings are one of the most critical components in rotating machinery. Envelope demodulation analysis has been widely used to demodulate bearing vibration signals to extract bearing defect frequency components but one of the main challenges is to accurately locate the major fault-induced frequency band with a high signal-to-noise ratio (SNR) for demodulation. Hence, an enhanced fault detection method combining the maximal overlap discrete wavelet packet transform (MODWPT) and the Teager energy adaptive spectral kurtosis (TEASK) denoising algorithms is proposed for identifying the weak periodic impulses. The Teager energy power spectrum (TEPS) defines the sparse representation of the filtered signals of the MODWPT in the frequency domain via the Teager energy operator (TEO); the TEASK helps determine the most informative frequency band for demodulation. The methodology is compared in terms of performance with the fast Kurtogram and the Autogram methods. The simulation and practical application examples have shown that the proposed MODWPT-TEASK method outperforms the above two methods in diagnosing defects of motor bearings.
Drought Vulnerability Assessment Using Geospatial Techniques in Southern Queensland, Australia
Authors: Hoque Muhammad , Pradhan Biswajeet , Ahmed Naser , Alamri Abdullah
Published: October 2021

In Australia, droughts are recurring events that tremendously affect environmental, agricultural and socio-economic activities. Southern Queensland is one of the most drought-prone regions in Australia. Consequently, a comprehensive drought vulnerability mapping is essential to generate a drought vulnerability map that can help develop and implement drought mitigation strategies. The study aimed to prepare a comprehensive drought vulnerability map that combines drought categories using geospatial techniques and to assess the spatial extent of the vulnerability of droughts in southern Queensland. A total of 14 drought-influencing criteria were selected for three drought categories, specifically, meteorological, hydrological and agricultural. The specific criteria spatial layers were prepared and weighted using the fuzzy analytical hierarchy process. Individual categories of drought vulnerability maps were prepared from their specific indices. Finally, the overall drought vulnerability map was generated by combining the indices using spatial analysis. Results revealed that approximately 79.60% of the southern Queensland region is moderately to extremely vulnerable to drought. The findings of this study were validated successfully through the receiver operating characteristics curve (ROC) and the area under the curve (AUC) approach using previous historical drought records. Results can be helpful for decision makers to develop and apply proactive drought mitigation strategies.
Simulation of the Urban Space Thermal Environment Based on Computational Fluid Dynamics: A Comprehensive Review
Authors: Huo Hongyuan , Chen Fei , Geng Xiaowei , Tao Jing , Liu Zhansheng , Zhang Wenzhi , Leng Pei
Published: October 2021

Rapid urbanization has made urban space thermal environment (USTE) problems increasingly prominent. USTE research is important for improving urban ecological environment and building energy consumption. Most studies on USTE research progress have focused on meteorological observations and remote sensing methods, and few studies on USTE are based on computational fluid dynamics (CFD). During the past two decades, with the increasing applications of CFD in USTE research, comprehensively summarizing the phased results have become necessary tasks. This paper analyzes the current research status of CFD-based USTE simulation from six perspectives. First, we summarize the current research status of USTE simulation with CFD models that integrate ground observations and remote sensing technology. Second, we define and classify the spatial scope of CFD-based USTE simulations at different scales. Third, we systematically analyze the quantitative relationships among urban land type, the underlying surface structure, water bodies, green space and the corresponding changes in CFD-based USTE simulations. Fourth, we quantitatively analyze the impact of anthropogenic heat in CFD-based USTE simulations. Fifth, we summarize the corresponding USTE mitigation measures and methods based on the CFD simulation results. Finally, the outlooks and the existing problems in current research on CFD simulations of the USTE are analyzed.
Ground Moving Target Tracking Filter Considering Terrain and Kinematics
Authors: Kim Do-Un , Lee Woo-Cheol , Choi Han-Lim , Park Joontae , An Jihoon , Lee Wonjun
Published: October 2021

This paper addresses ground target tracking (GTT) for airborne radar. Digital terrain elevation data (DTED) are widely used for GTT as prior information under the premise that ground targets are constrained on terrain. Existing works fuse DTED to a tracking filter in a way that adopts only the assumption that the position of the target is constrained on the terrain. However, by kinematics, it is natural that the velocity of the moving ground target is constrained as well. Furthermore, DTED provides neither continuous nor accurate measurement of terrain elevation. To overcome such limitations, we propose a novel soft terrain constraint and a constraint-aided particle filter. To resolve the difficulties in applying the DTED to the GTT, first, we reconstruct the ground-truth terrain elevation using a Gaussian process and treat DTED as a noisy observation of it. Then, terrain constraint is formulated as joint soft constraints of position and velocity. Finally, we derive a Soft Terrain Constrained Particle Filter (STC-PF) that propagates particles while approximately satisfying the terrain constraint in the prediction step. In the numerical simulations, STC-PF outperforms the Smoothly Constrained Kalman Filter (SCKF) in terms of tracking performance because SCKF can only incorporate hard constraints.
A Taxonomy of Food Supply Chain Problems from a Computational Intelligence Perspective
Authors: Angarita-Zapata Juan S. , Alonso-Vicario Ainhoa , Masegosa Antonio D. , Legarda Jon
Published: October 2021

In the last few years, the Internet of Things, and other enabling technologies, have been progressively used for digitizing Food Supply Chains (FSC). These and other digitalization-enabling technologies are generating a massive amount of data with enormous potential to manage supply chains more efficiently and sustainably. Nevertheless, the intricate patterns and complexity embedded in large volumes of data present a challenge for systematic human expert analysis. In such a data-driven context, Computational Intelligence (CI) has achieved significant momentum to analyze, mine, and extract the underlying data information, or solve complex optimization problems, striking a balance between productive efficiency and sustainability of food supply systems. Although some recent studies have sorted the CI literature in this field, they are mainly oriented towards a single family of CI methods (a group of methods that share common characteristics) and review their application in specific FSC stages. As such, there is a gap in identifying and classifying FSC problems from a broader perspective, encompassing the various families of CI methods that can be applied in different stages (from production to retailing) and identifying the problems that arise in these stages from a CI perspective. This paper presents a new and comprehensive taxonomy of FSC problems (associated with agriculture, fish farming, and livestock) from a CI approach; that is, it defines FSC problems (from production to retail) and categorizes them based on how they can be modeled from a CI point of view. Furthermore, we review the CI approaches that are more commonly used in each stage of the FSC and in their corresponding categories of problems. We also introduce a set of guidelines to help FSC researchers and practitioners to decide on suitable families of methods when addressing any particular problems they might encounter. Finally, based on the proposed taxonomy, we identify and discuss challenges and research opportunities that the community should explore to enhance the contributions that CI can bring to the digitization of the FSC.
Simple Modification of a Commercial Laser Triangulation Sensor for Distance Measurement of Slot and Bore Side Surfaces
Authors: Hošek Jan , Linduška Petr
Published: October 2021

The aim of the research is to analyze the possibility of the development and realization of a common laser triangulation sensor arrangement-based probe for the measurement of slots and bore sides with the help of a mirror attachment. The analysis shows the feasibility and limits of the solution with respect to the maximum measurement depth and surface distance measurement working range. We propose two possible solutions: one for maximizing the ratio of the measurement depth to the measured bore size and the second for maximizing the total depth, intended for the measurement of slots and large bore sizes. We analyzed measurement error sources. We found that the errors related to the reflection mirror misalignment can be fully compensated. We proved the validity of the proposed solution with the realization of a commercial laser triangulation sensor-based probe and demonstrated a slot side and a bore side surface distance scanning measurement. The probe working range was assessed with regard to the obscuration effect of optical beams.
Piezoelectric Sensor for the Monitoring of Arterial Pulse Wave: Detection of Arrhythmia Occurring in PAC/PVC Patients
Authors: Guo Cheng-Yan , Wang Kuan-Jen , Hsieh Tung-Li
Published: October 2021

Previous studies have found that the non-invasive blood pressure measurement method based on the oscillometric method is inaccurate when an arrhythmia occurs. Therefore, we propose a high-sensitivity pulse sensor that can measure the hemodynamic characteristics of the pulse wave and then estimate the blood pressure. When an arrhythmia occurs, the hemodynamics of the pulse wave are abnormal and change the morphology of the pulse wave. Our proposed sensor can measure the occurrence of ectopic beats from the radial artery, and the detection algorithm can reduce the error of blood pressure estimation caused by the distortion of ectopic beats that occurs when the pulse wave is measured. In this study, we tested patients with premature atrial contraction (PAC) or premature ventricular contraction (PVC) and analyzed the morphology of the pulse waves when the sensor detected the ectopic beats. We discuss the advantages of using the Moens–Korteweg equation to estimate the blood pressure of patients with arrhythmia, which is different from the oscillometric method. Our research provides a possible arrhythmia detection method for wearable devices and can accurately estimate blood pressure in a non-invasive way during an arrhythmia.
Noninvasive Methods for Fault Detection and Isolation in Internal Combustion Engines Based on Chaos Analysis
Authors: Lima Thyago L. , Filho Abel C. L. , Belo Francisco A. , Souto Filipe V. , Silva Thaís C. B. , Mishina Koje V. , Rodrigues Marcelo C.
Published: October 2021

The classic monitoring methods for detecting faults in automotive vehicles based on on-board diagnostics (OBD) are insufficient when diagnosing several mechanical failures. Other sensing techniques present drawbacks such as high invasiveness and limited physical range. The present work presents a fully noninvasive system for fault detection and isolation in internal combustion engines through sound signals processing. An acquisition system was developed, whose data are transmitted to a smartphone in which the signal is processed, and the user has access to the information. A study of the chaotic behavior of the vehicle was carried out, and the feasibility of using fractal dimensions as a tool to diagnose engine misfire and problems in the alternator belt was verified. An artificial neural network was used for fault classification using the fractal dimension data extracted from the sound of the engine. For comparison purposes, a strategy based on wavelet multiresolution analysis was also implemented. The proposed solution allows a diagnosis without having any contact with the vehicle, with low computational cost, without the need for installing sensors, and in real time. The system and method were validated through experimental tests, with a success rate of 99% for the faults under consideration.
HARNAS: Human Activity Recognition Based on Automatic Neural Architecture Search Using Evolutionary Algorithms
Authors: Wang Xiaojuan , Wang Xinlei , Lv Tianqi , Jin Lei , He Mingshu
Published: October 2021

Human activity recognition (HAR) based on wearable sensors is a promising research direction. The resources of handheld terminals and wearable devices limit the performance of recognition and require lightweight architectures. With the development of deep learning, the neural architecture search (NAS) has emerged in an attempt to minimize human intervention. We propose an approach for using NAS to search for models suitable for HAR tasks, namely, HARNAS. The multi-objective search algorithm NSGA-II is used as the search strategy of HARNAS. To make a trade-off between the performance and computation speed of a model, the F1 score and the number of floating-point operations (FLOPs) are selected, resulting in a bi-objective problem. However, the computation speed of a model not only depends on the complexity, but is also related to the memory access cost (MAC). Therefore, we expand the bi-objective search to a tri-objective strategy. We use the Opportunity dataset as the basis for most experiments and also evaluate the portability of the model on the UniMiB-SHAR dataset. The experimental results show that HARNAS designed without manual adjustments can achieve better performance than the best model tweaked by humans. HARNAS obtained an F1 score of 92.16% and parameters of 0.32 MB on the Opportunity dataset.
Microwave Hydration Monitoring: System Assessment Using Fasting Volunteers
Authors: Besler Brendon C. , Fear Elise C.
Published: October 2021

Hydration is an important aspect of human health, as water is a critical nutrient used in many physiological processes. However, there is currently no clinical gold standard for non-invasively assessing hydration status. Recent work has suggested that permittivity in the microwave frequency range provides a physiologically meaningful metric for hydration monitoring. Using a simple time of flight technique for estimating permittivity, this study investigates microwave-based hydration assessment using a population of volunteers fasting during Ramadan. Volunteers are measured throughout the day while fasting during Ramadan and while not fasting after Ramadan. Comparing the estimated changes in permittivity to changes in weight and the time s fails to establish a clear relationship between permittivity and hydration. Assessing the subtle changes in hydration found in a population of sedentary, healthy adults proves difficult and more work is required to determine approaches suitable for tracking subtle changes in hydration over time with microwave-based hydration assessment techniques.
A Robust and Fast Method for Sidescan Sonar Image Segmentation Based on Region Growing
Authors: Wang Xuyang , Wang Luyu , Li Guolin , Xie Xiang
Published: October 2021

For high-resolution side scan sonar images, accurate and fast segmentation of sonar images is crucial for underwater target detection and recognition. However, due to the characteristics of low signal-to-noise ratio (SNR) and complex environmental noise of sonar, the existing methods with high accuracy and good robustness are mostly iterative methods with high complexity and poor real-time performance. For this purpose, a region growing based segmentation using the likelihood ratio testing method (RGLT) is proposed. This method obtains the seed points in the highlight and the shadow regions by likelihood ratio testing based on the statistical probability distribution and then grows them according to the similarity criterion. The growth avoids the processing of the seabed reverberation regions, which account for the largest proportion of sonar images, thus greatly reducing segmentation time and improving segmentation accuracy. In addition, a pre-processing filtering method called standard deviation filtering (STDF) is proposed to improve the SNR and remove the speckle noise. Experiments were conducted on three sonar databases, which showed that RGLT has significantly improved quantitative metrics such as accuracy, speed, and segmentation visual effects. The average accuracy and running times of the proposed segmentation method for 100 × 400 images are separately 95.90% and 0.44 s.
Interactive Fluid Coupling Effects of Non-Neighbouring Members
Authors: Manickavasagam Arun Kumar , Gutschmidt Stefanie , Sellier Mathieu
Published: October 2021

Broadband, multi-functional and parallel-processing devices are often built on coupled oscillators or arrays of resonators. Different length scales and applications determine the dominating coupling mechanism of the device. In this paper we investigate the effects of interactive fluid coupling between members of a one-dimensional array wherein only one member is actuated. We are specifically interested in studying the influence of non-neighbouring members in small-size arrays comprising of three and five members for different Reynolds numbers and gap widths between members. Our model and analysis is based on the Navier–Stokes equation for incompressible flow which is solved using a boundary integral technique resulting in the hydrodynamic coupling matrix through which added mass and damping effects are inferred. Results clearly suggest that non-neighbouring members play a significant role for most typical array configurations and therefore cannot be ignored. In particular, arrays with more than three members must account for the behaviour of such a device with all member interactions. Thus, predicting the performance of most new and emerging technologies such as sensors and biomedical devices is determined by array effects rather than local, nearest neighbour influences.
Low-Computational-Cost Hybrid FEM-Analytical Induction Machine Model for the Diagnosis of Rotor Eccentricity, Based on Sparse Identification Techniques and Trigonometric Interpolation
Authors: Terron-Santiago Carla , Martinez-Roman Javier , Puche-Panadero Ruben , Sapena-Bano Angel
Published: October 2021

Since it is not efficient to physically study many machine failures, models of faulty induction machines (IMs) have attracted a rising interest. These models must be accurate enough to include fault effects and must be computed with relatively low resources to reproduce different fault scenarios. Moreover, they should run in real time to develop online condition-monitoring (CM) systems. Hybrid finite element method (FEM)-analytical models have been recently proposed for fault diagnosis purposes since they keep good accuracy, which is widely accepted, and they can run in real-time simulators. However, these models still require the full simulation of the FEM model to compute the parameters of the analytical model for each faulty scenario with its corresponding computing needs. To address these drawbacks (large computing power and memory resources requirements) this paper proposes sparse identification techniques in combination with the trigonometric interpolation polynomial for the computation of IM model parameters. The proposed model keeps accuracy similar to a FEM model at a much lower computational effort, which could contribute to the development and to the testing of condition-monitoring systems. This approach has been applied to develop an IM model under static eccentricity conditions, but this may extend to other fault types.
ECG Authentication Based on Non-Linear Normalization under Various Physiological Conditions
Authors: Hwang Ho Bin , Kwon Hyeokchan , Chung Byungho , Lee Jongshill , Kim In Young
Published: October 2021

The development and use of wearable devices require high levels of security and have sparked interest in biometric authentication research. Among the available approaches, electrocardiogram (ECG) technology is attracting attention because of its strengths in spoofing. However, morphological changes of ECG, which are affected by physical and psychological factors, can make authentication difficult. In this paper, we propose authentication using non-linear normalization of ECG beats that is robust to changes in ECG waveforms according to heart rate fluctuations in various daily activities. We performed a non-linear normalization method through the analysis of ECG alongside heart rate, evaluating similarities and authenticating the performance of our new method compared to existing methods. Compared with beats before normalization, the average similarity of the proposed method increased 23.7% in the resting state and 43% in the non-resting state. After learning in the resting state, authentication performance reached 99.05% accuracy for the resting state and 88.14% for the non-resting state. The proposed method can be applicable to an ECG-based authentication system under various physiological conditions.
Machine Learning Based Identification of Microseismic Signals Using Characteristic Parameters
Authors: Peng Kang , Tang Zheng , Dong Longjun , Sun Daoyuan
Published: October 2021

Microseismic monitoring system is one of the effective means to monitor ground stress in deep mines. The accuracy and speed of microseismic signal identification directly affect the stability analysis in rock engineering. At present, manual identification, which heavily relies on manual experience, is widely used to classify microseismic events and blasts in the mines. To realize intelligent and accurate identification of microseismic events and blasts, a microseismic signal identification system based on machine learning was established in this work. The discrimination of microseismic events and blasts was established based on the machine learning framework. The microseismic monitoring data was used to optimize the parameters and validate the classification methods. Subsequently, ten machine learning algorithms were used as the preliminary algorithms of the learning layer, including the Decision Tree, Random Forest, Logistic Regression, SVM, KNN, GBDT, Naive Bayes, Bagging, AdaBoost, and MLP. Then, training set and test set, accounting for 50% of each data set, were prospectively examined, and the ACC, PPV, SEN, NPV, SPE, FAR and ROC curves were used as evaluation indexes. Finally, the performances of these machine learning algorithms in microseismic signal identification were evaluated with cross-validation methods. The results showed that the Logistic Regression classifier had the best performance in parameter identification, and the accuracy of cross-validation can reach more than 0.95. Random Forest, Decision Tree, and Naive Bayes also performed well in this data set. There were some differences in the accuracy of different classifiers in the training set, test set, and all data sets. To improve the accuracy of signal identification, the database of microseismic events and blasts should be expanded, to avoid the inaccurate data distribution caused by the small training set. Artificial intelligence identification methods, including Random Forest, Logistic Regression, Decision Tree, Naive Bayes, and AdaBoost algorithms, were applied to signal identification of the microseismic monitoring system in mines, and the identification results were consistent with the actual situation. In this way, the confusion caused by manual classification between microseismic events and blasts based on the characteristics of waveform signals is solved, and the required source parameters are easily obtained, which can ensure the accuracy and timeliness of microseismic events and blasts identification.
Toward the Integration of Cyber and Physical Security Monitoring Systems for Critical Infrastructures
Authors: Fausto Alessandro , Gaggero Giovanni Battista , Patrone Fabio , Girdinio Paola , Marchese Mario
Published: October 2021

Critical Infrastructures (CIs) are sensible targets. They could be physically damaged by natural or human actions, causing service disruptions, economic losses, and, in some extreme cases, harm to people. They, therefore, need a high level of protection against possible unintentional and intentional events. In this paper, we show a logical architecture that exploits information from both physical and cybersecurity systems to improve the overall security in a power plant scenario. We propose a Machine Learning (ML)-based anomaly detection approach to detect possible anomaly events by jointly correlating data related to both the physical and cyber domains. The performance evaluation showed encouraging results—obtained by different ML algorithms—which highlights how our proposed approach is able to detect possible abnormal situations that could not have been detected by using only information from either the physical or cyber domain.
The State of the Practice in Validation of Model-Based Safety Analysis in Socio-Technical Systems: An Empirical Study
Authors: Sadeghi Reyhaneh , Goerlandt Floris
Published: October 2021

Even though validation is an important concept in safety research, there is comparatively little empirical research on validating specific safety assessment, assurance, and ensurance activities. Focusing on model-based safety analysis, scant work exists to define approaches to assess a model’s adequacy for its intended use. Rooted in a wider concern for evidence-based safety practices, this paper intends to provide an understanding of the extent of this problem of lack of validation to establish a baseline for future developments. The state of the practice in validation of model-based safety analysis in socio-technical systems is analyzed through an empirical study of relevant published articles in the Safety Science journal spanning a decade (2010–2019). A representative sample is first selected using the PRISMA protocol. Subsequently, various questions concerning validation are answered to gain empirical insights into the extent, trends, and patterns of validation in this literature on model-based safety analysis. The results indicate that no temporal trends are detected in the ratio of articles in which models are validated compared to the total number of papers published. Furthermore, validation has no clear correlation with the specific model type, safety-related concept, different system life cycle stages, industries, or with the countries from which articles originate. Furthermore, a wide variety of terminology for validation is observed in the studied articles. The results suggest that the safety science field concerned with developing and applying models in safety analyses would benefit from an increased focus on validation. Several directions for future work are discussed.
Vortex-Assisted Dispersive Molecularly Imprinted Polymer-Based Solid Phase Extraction of Acetaminophen from Water Samples Prior to HPLC-DAD Determination
Authors: Mpayipheli Neliswa , Mpupa Anele , Nomngongo Philiswa Nosizo
Published: October 2021

In the present study, acetaminophen (ACT) molecularly imprinted polymer (ACT-MIP) were successfully synthesized via surface imprinting polymerization. The structural and morphological properties of ACT-MIP were characterized using various analytical techniques. ACT-MIP were used as an adsorbent in a vortex-assisted dispersive molecularly imprinted solid-phase micro-extraction (VA-d-μ-MISPE), coupled with a high-performance liquid chromatography–diode array detector (HPLC-DAD) method for the determination of ACT in water samples. Influential parameters such as the mass of adsorbent, vortex speed, extraction time, desorption volume, and desorption time were optimized using a multivariate approach. Under optimum conditions, the maximum binding capacities of ACT-MIP and NIP (non-imprinted polymers) were 191 mg/g and 71.5 mg/g, respectively. The linearity was attained across concentrations ranging from 0.630 to 500 µg/L, with a coefficient of determination of 0.9959. For ACT-MIP, the limit of detection (LOD) and limit of quantification (LOQ), enhancement factor, and precision of the method were 0.19 ng/L, 0.63 ng/L, 79, and <5%, respectively. The method was applied in the analysis of spiked water samples, and satisfactory percentage recoveries in the range of 95.3–99.8% were obtained.
On Process Intensification through Membrane Storage Reactors
Authors: Lowd John , Tsotsis Theodore , Manousiouthakis Vasilios I.
Published: October 2021

In this work, a dynamic, one-dimensional, first principle-based model of a novel membrane storage reactor (MSR) process is developed and simulated. The resulting governing equations are rendered dimensionless and are shown to feature two dimensionless groups that can be used to affect process performance. The novel process is shown to intensify production of a desired species through the creation of two physically distinct domains separated by a semipermeable boundary, and dynamic operation. A number of metrics are then introduced and applied to a case study on Steam Methane Reforming, for which a parametric study is carried out which establishes the superior performance of the MSR when compared to a reactor operating at steady state (SSR).
The Impossibility of Home: Displacement and Border Practices in Times of Crisis
Authors: Fiske Lucy , Briskman Linda
Published: October 2021

We launched the call for papers for this issue in March 2020, as the coronavirus pandemic was spreading rapidly around the globe, disrupting lives and stalling movement as country after country went into lockdown, and death tolls starkly revealed racial and economic inequalities within and between nations [...]
Gender Equality and E-Scooters: Mind the Gap! A Statistical Analysis of the Sicily Region, Italy
Authors: Campisi Tiziana , Skoufas Anastasios , Kaltsidis Alexandros , Basbas Socrates
Published: October 2021

Mobility since 2000 has undergone enormous changes due to new modes of transport and related technologies as well as catastrophic and pandemic events. Several strategies have been implemented by European states to mitigate impacts and assess possible risks in a preventive way. Today, mobility pursues the objectives of sustainability and resilience through a series of short-, medium- and long-term strategies that encourage the collaboration of the population to the choices of urban planning and design. Among the different modes of transport that have had a rise in recent years are scooters. Such modes are well suited to connecting spaces within the first and last mile. Similar to other modes of transportation, scooters are also characterized to date by reduced gender equity. The present work investigates through the administration of an online survey the participants’ perceptions concerning the factors that most affect this gender balance considering the metropolitan areas of Catania and Palermo in Sicily. The development of an ordinal regression model revealed the most influential factors of the gender equality variable. Specifically, age, job occupation and perceived safety level of micromobility modes play the most important role. The present findings can be effectively utilized in the planning stage of e-scooter services towards the bridging of the gender gap.
Implementation of the E-Learning Model for Sustainability of Driver Rehabilitation Program
Authors: Jovanov Nemanja , Vranješ Đorđe , Jovanov Goran , Otić Goran , Vasiljević Jovica , Petrić Željko , Aleksić Stojan
Published: October 2021

In this work, we show the experience of the driver rehabilitation process in the Republic of Serbia, with the analysis of the rehabilitation process and the changing of the drivers’ attitudes. Before performing the analysis, we define the basic hypothesis (implementation of the sustainability model of E learning for the driver rehabilitation process will impact the driver attitude and minimize the mistakes). In the analysis, we take the most recognized mistakes in the process of education. Implementation of this model is exclusive for a group of candidates who finished the driver rehabilitation process. Using the database of drivers who made mistakes, we made the electronic database (video material with comments) and subsequently implemented the driver education model to assess who made mistakes in the rehabilitation process. In the test process, we used the two groups of candidates (age 18–30 and 31–40), before and after the implementation of this e-learning model. In order to process the data, taking into account the small number of observations further in the research, correlation analysis was used, which determines whether there is a correlation between the amount of alcohol in blood (expressed in g/kg) and the reaction time (in seconds) of a driver. In the second part of the work, correlation analysis was performed with the aim to examine which type of relation exists between the candidates who participated in the driver rehabilitation program and candidates that participated in the program of the proposed e-learning model. The results of this study offer a direction in which learning takes place by itself and affects the effective environment of e-learning, multimedia teaching, virtual practical content, and learning under the instruction of the lecturer in this target group of participants in the rehabilitation program. How many times have we believed that we have an ability and overestimated our-selves in that? You can be convinced in traffic only if you have to prove that ability. The results of the application of this model make it possible to prove that ability and change the opinion about the stated mistakes. The key goal of this work is to make the sustainability e-learning model based on mistakes, which will eliminate the same mistakes made by drivers. The result would be top training and high-quality performance in traffic.
The Human Capital of the Freelancers and Their Satisfaction with the Quality of Life
Authors: Huđek Ivona , Tominc Polona , Širec Karin
Published: October 2021

Digital technologies are underpinning a wide range of products, services, processes, and business model innovations that are significantly transforming industries, organizations, and society. They are increasingly permeating every aspect of our daily lives. In this paper, we looked at the challenges of digitalization, leading to a new way of working in the market and new business models, such as the gig economy. Individuals are becoming more mobile and self-directed in their careers, as opposed to traditional careers. We investigated the demographic characteristics of the new, growing group of self-employed, so-called freelancers, at the national level in Slovenia and investigated the relationships between human capital and their subjective (job and career satisfaction, life satisfaction) and objective success (income satisfaction). The survey was conducted among 200 freelancers (respondents). The empirical results show that most Slovenian freelancers working from home work in professional, scientific, and technical occupations; are between 35 and 44 years old; have completed the second level of tertiary education; work more than 40 hours per week; and have 16 years or more of work experience. To explore the quality of life of freelancers, empirical analysis showed that human capital factors are important for the success of a new career as a freelancer. The results show that freelancers’ human capital factors (education and experience, skills, and training) positively influence subjective and objective success. Using structural equation modeling, we also confirmed that human capital has a greater impact on freelancers’ subjective success (life satisfaction, career satisfaction) than on objective success (income satisfaction), suggesting that job and life satisfaction should be considered an important resource that freelancers rely on to determine their quality of life.