bluuebunny/crossref_metadata_2025 huggingface dataset:
Investigation of Parameter Sensitivity and the Physical Mechanism for the Formation of a Core-Skin-Core (CSC) Structure in Two-Stage Co-Injection Molding
Authors: Huang Chao-Tsai , Rao You-Ti , Ko Kuan-Yu , Hsu Chih-Chung , Zhou You-Sheng , Hsu Chia-Hsiang , Chang Rong-Yue , Tseng Shi-Chang , Chen Likey
Published: November 2022

One of the main challenges in co-injection molding is how to predict the skin to core morphology accurately and then manage it properly, especially after skin material has been broken through. In this study, the formation of the Core-Skin-Core (CSC) structure and its physical mechanism in a two-stage co-injection molding has been studied based on the ASTM D638 TYPE V system by using both numerical simulation and experimental observation. Results showed that when the skin to core ratio is selected properly (say 30/70), the CSC structure can be observed clearly at central location for 30SFPP/30SFPP system. When the skin to core ratio and operation conditions are fixed, regardless of material arrangement (including 30SFPP/30SFPP; PP/PP; 30SFPP/PP; and PP/30SFPP systems), the morphologies of the CSC structures are very close for all systems. This CSC structure can be further validated by using μ-CT scan and image analysis technologies perfectly. Furthermore, the influences of various operation parameters on the CSC structure variation have been investigated. Results exhibited that the CSC structure does not change significantly irrespective of the flow rate changing, melt temperature varying, or even mold temperature being modified. Moreover, the mechanism to generate the CSC structure can be derived using the melt front movement of the numerical simulation. It is worth noting that after the skin material was broken through, the core material travelled ahead with fountain flow to occupy the flow front. In the same period, the proper amount of skin material with certain inertia of enough kinetic energy will keep going to penetrate the new coming core material to travel until the end of filling. It ends up with this special CSC structure.
Collagen Obtained from Leather Production Waste Provides Suitable Gels for Biomedical Applications
Authors: Maistrenko Lesia , Iungin Olga , Pikus Polina , Pokholenko Ianina , Gorbatiuk Oksana , Moshynets Olena , Okhmat Olena , Kolesnyk Tetiana , Potters Geert , Mokrousova Olena
Published: November 2022

Collagen and its derivates are typically obtained by extracting them from fresh animal tissues. Lately, however, there has been an increased interest in obtaining collagen from other sources, such as waste material, because of the growing trend to replace synthetic materials with sustainable, natural counterparts in various industries, as well as to ensure a rational waste revalorization. In this paper, collagen was obtained from non-tanned waste of leather production, taken at different stages of the production process: limed pelt, delimed pelt, and fleshings. A stepwise extraction through acid hydrolysis in 0.5 M acetic acid and subsequent precipitation with NaCl lead to collagen-containing protein extracts. The highest collagen yield was achieved in extracts based on delimed pelt (2.3% m/m after a first extraction round, and an additional 1.4% m/m after the second round). Hyp/Hyl molar ratios of 10.91 in these extracts suggest the presence of type I collagen. Moreover, gels based on these collagen extracts promote adhesion and spreading of HEK293 cells, with cells grown on collagen from delimed pelt showing a larger nuclear and cell expansion than cells grown on traditional bovine tendon atelocollagen. This suggests that these collagen gels are promising natural biomedical carriers and could be used in a wide range of medical and cosmetic applications.
Wear Behaviour of Multilayer Al-PVD-Coated Polymer Gears
Authors: Bončina Tonica , Polanec Brigita , Zupanič Franc , Glodež Srečko
Published: November 2022

A comprehensive experimental investigation of the wear behaviour of coated spur polymer gears made of POM is performed in this study. Three different thicknesses of aluminium (Al) coatings were investigated and deposited by the Physical Vapour Deposition (PVD) process. The Al coating was deposited in three steps: By plasma activation, metallisation of the aluminium by the magnetron sputtering process, and plasma polymerisation. The wear of the gears was tested on an in-house developed testing rig for different torques (16, 20, and 24 Nm) and a rotational speed of 1000 rpm. The duration of the experiments was set to 13 h, when the tooth thickness and, consequently, the wear of the tooth flank were recorded. The experimental results showed that the influence of metallisation with aluminium surface coatings on the wear behaviour of the analysed polymer gear is not significantly important. The results also showed that the gears with a thicker aluminium coating showed greater wear than gears with a thinner coating or even without a coating. This is probably due to the fact that the aluminium particles that started to deviate during gear operation represented the abrasive material, which led to the faster wear of the contacting surfaces of the meshing gear flanks.
Alginate Particles for Encapsulation of Phenolic Extract from Spirulina sp. LEB-18: Physicochemical Characterization and Assessment of In Vitro Gastrointestinal Behavior
Authors: Machado Adriana R. , Silva Pedro M. P. , Vicente António A. , Souza-Soares Leonor A. , Pinheiro Ana C. , Cerqueira Miguel A.
Published: November 2022

Encapsulation can be used as a strategy to protect and control the release of bioactive extracts. In this work, an extract from Spirulina sp. LEB-18, rich in phenolic compounds, was encapsulated in biopolymeric particles (i.e., composed of alginate) and characterized concerning their thermal behavior using differential scanning calorimetry (DSC), size, morphology, swelling index (S), and encapsulation efficiency (EE%); the release profile of the phenolic compounds at different pHs and the particle behavior under in vitro gastrointestinal digestion were also evaluated. It was shown that it is possible to encapsulate the phenolic extract from Spirulina sp. LEB-18 in alginate particles with high encapsulation efficiency (88.97%). It was also observed that the particles are amorphous and that the encapsulated phenolic compounds were released at a pH 7.2 but not at pH 1.5, which means that the alginate particles are able to protect the phenolic compounds from the harsh stomach conditions but lose their integrity under intestinal pH conditions. Regarding bioaccessibility, it was observed that the encapsulated phenolic compounds showed higher bioaccessibility compared to phenolic compounds in free form. This work increases the knowledge about the behavior of alginate particles encapsulating phenolic compounds during in vitro gastrointestinal digestion. It also provides essential information for designing biopolymeric particle formulations encapsulating phenolic compounds for application in pharmaceutical and food products.
Coarse-Grained Simulations of Release of Drugs Housed in Flexible Nanogels: New Insights into Kinetic Parameters
Authors: Quesada-Pérez Manuel , Pérez-Mas Luis , Carrizo-Tejero David , Maroto-Centeno José-Alberto , Ramos-Tejada María del Mar , Martín-Molina Alberto
Published: November 2022

The diffusion-controlled release of drugs housed in flexible nanogels has been simulated with the help of a coarse-grained model that explicitly considers polymer chains. In these in silico experiments, the effect of its flexibility is assessed by comparing it with data obtained for a rigid nanogel with the same volume fraction and topology. Our results show that the initial distribution of the drug can exert a great influence on the release kinetics. This work also reveals that certain surface phenomena driven by steric interactions can lead to apparently counterintuitive behaviors. Such phenomena are not usually included in many theoretical treatments used for the analysis of experimental release kinetics. Therefore, one should be very careful in drawing conclusions from these formalisms. In fact, our results suggest that the interpretation of drug release curves in terms of kinetic exponents (obtained from the Ritger–Peppas Equation) is a tricky question. However, such curves can provide a first estimate of the drug diffusion coefficient.
Effect of the Current on the Fire Characteristics of Overloaded Polyvinyl Chloride Copper Wires
Authors: Li Zhe , Lin Qingwen , Li Yang , Lyu Huifei , Wang Huaibin , Sun Junli
Published: November 2022

In this study, the fire behavior variation of unenergized polyvinyl chloride (PVC) copper wires subjected to overload with different currents was investigated by a cone calorimeter. Overload currents were selected from 1 times safe-rated current (Ie) to 3.5 times Ie to obtain tested sample wires. The mass fraction, time to ignition (TTI), heat release rate (HRR), gas emission, and residue were measured. If the current flowing through the wire increased up to 3.5 times Ie, the TTI of this unenergized wire increased drastically and the peak HRR (pHRR) decreased notably so that the flame growing index (FGI) reduced considerably. When the wire carried less than three times Ie, the FGI remained stable. For all overloaded PVC copper wires, the increase in the heat flux resulted in a higher pHRR and a lower burning duration. However, regardless of the external heat flux exposure, the FGI of copper wires overloaded at 3.5 times Ie was lower than that of copper wires carrying less than other times Ie. Moreover, the consumption of O2 and generation of CO2 as the heat flux varied were consistent with that of the HRR. Opposed to expectation, the flame propagation of unenergized PVC copper wires would decline in a fire, if the wire has been damaged by overload with some currents.
Deep Learning-Based Human Body Posture Recognition and Tracking for Unmanned Aerial Vehicles
Authors: Lee Min-Fan Ricky , Chen Yen-Chun , Tsai Cheng-Yo
Published: November 2022

For many applications (e.g., surveillance and disaster response), situational awareness is essential. In these applications, human body posture recognition in real time plays a crucial role for corresponding response. Traditional posture recognition suffers from accuracy, due to the low robustness against uncertainty. Those uncertainties include variation from the environment (e.g., viewpoint, illumination and occlusion) and the postures (e.g., ambiguous posture and the overlap of multiple people). This paper proposed a drone surveillance system to distinguish human behaviors among violent, normal and help needed based on deep learning approach under the influence of those uncertainties. First, the real-time pose estimation is performed by the OpenPose network, and then the DeepSort algorithm is applied for tracking multi-person. The deep neural network model (YOLO) is trained to recognize each person’s postures based on a single frame of joints obtained from OpenPose. Finally, the fuzzy logic is applied to interpret those postures. The trained deep learning model is evaluated via the metrics (accuracy, precision, recall, P-R curve and F1 score). The empirical results show the proposed drone surveillance system can effectively recognize the targeted human behaviors with strong robustness in the presence of uncertainty and operated efficiently with high real-time performance.
Removal of Hydrophobic Organic Pollutants and Copper by Alginate-Based and Polycaprolactone Materials
Authors: Marques-da-Silva Dorinda , Lopes João M. , Correia Iris , Silva João S. , Lagoa Ricardo
Published: November 2022

Organic pollutants (OPs) and heavy metals are environmental toxicants associated with great concerns. Decontamination processes are urgent for both, and the possibility to achieve their simultaneous removal from polluted waters is highly interesting. Additionally, in many cases, the effect of organic matter in the removal process is overlooked and must be considered. This work aimed to study the potential of alginate-based and polycaprolactone (PCL) materials to remove OPs and copper ions in the absence and presence of organic matter. The OPs investigated were the polycyclic aromatic hydrocarbons anthracene and benzo[a]pyrene, and the pesticide chlorpyrifos, both hydrophobic compounds. Copper (II) ions were used as a model of heavy metals. Alginate-based spheres were prepared by gelation, and PCL microparticles were obtained by oil/water emulsion solvent evaporation. The materials with the highest efficiencies for OP removal from aqueous solutions were those with activated carbon and PCL. Furthermore, the spheres with activated carbon could remove anthracene and copper simultaneously, even in the presence of humic acid. This work points to activated carbon–alginate spheres as a multifunctional adsorbent able to remove different pollutants and to PCL for potential applications in OP decontamination processes.
Dual Input Fuzzy Logic Controllers for Closed Loop Hemorrhagic Shock Resuscitation
Authors: Berard David , Vega Saul J. , Avital Guy , Snider Eric J.
Published: November 2022

Hemorrhage remains a leading cause of preventable death in emergency situations, including combat casualty care. This is partially due to the high cognitive burden that constantly adjusting fluid resuscitation rates can require, especially in austere or mass casualty situations. Closed-loop control systems have the potential to simplify hemorrhagic shock resuscitation if properly tuned for the application. We have previously compared 4 different controller types using a hardware-in-loop test platform that simulates hemorrhagic shock conditions, and we found that a dual input—(1) error from target and (2) rate of error change—fuzzy logic (DFL) controller performed best. Here, we highlight a range of DFL designs to showcase the tunability the controller can have for different hemorrhage scenarios. Five different controller setups were configured with different membership function logic to create more and less aggressive controller designs. Overall, the results for the different controller designs ranged from reaching the setup rapidly but often overshooting the target to more conservatively approaching the target, resulting in not reaching the target during high active hemorrhage rates. In conclusion, DFL controllers are well-suited for hemorrhagic shock resuscitation and can be tuned to meet the response rates set by clinical practice guidelines for this application.
The Effect of Medical Device Regulations on Deploying a Lean Six Sigma Project
Authors: McGrane Vincent , McDermott Olivia , Trubetskaya Anna , Rosa Angelo , Sony Michael
Published: November 2022

This paper investigates the differences in timelines involved in Lean Six Sigma (LSS) project deployment in a regulated industry versus in an unregulated one. Two case studies utilising Lean Six Sigma methods—in order to compare the transfer of manual manufacturing lines within a medical device and electronics manufacturing site—are discussed and utilised. This research aims to show the effects of regulatory procedures on LSS project implementation and timelines. This study particularly highlights how a regulatory environment can be a barrier, or bottleneck, to project management, continuous improvement, and engineering changes in the MedTech or medical device manufacturing industry. The results of this study represent an important first step towards a full understanding of the influence of regulations on operations in medical devices and, by extension, on pharmaceutical manufacturing industries on a global scale. The research limitations are that the data collected were from two specific case study comparisons alone.
Tertiary Wastewater Treatment Technologies: A Review of Technical, Economic, and Life Cycle Aspects
Authors: Zagklis Dimitris P. , Bampos Georgios
Published: November 2022

The activated sludge process is the most widespread sewage treatment method. It typically consists of a pretreatment step, followed by a primary settling tank, an aerobic degradation process, and, finally, a secondary settling tank. The secondary effluent is then usually chlorinated and discharged to a water body. Tertiary treatment aims at improving the characteristics of the secondary effluent to facilitate its reuse. In this work, through a literature review of the most prominent tertiary treatment methods, a benchmarking of their technical efficiency, economic feasibility, and environmental impact was carried out. The photo-Fenton method proved to be the most technically efficient process, significantly reducing the microbial load and pharmaceutical content (by 4.9 log and 84%, respectively) of the secondary effluent. Chlorination and UV irradiation exhibited the lowest treatment costs (0.004 EUR/m−3) and the lowest global warming potential (0.04 and 0.09 kg CO2eq. m−3, respectively). After all the data were aggregated, a decision-making tool was constructed in the form of a ternary diagram, which indicates the most appropriate tertiary treatment method according to the weight-per-process aspect (technical, economic, and environmental) selected by the user, with chlorination, UV irradiation, ozonation, microalgae cultivation, and constructed wetlands prevailing in the final results.
A Review on Parallel Development of Flux Design and Thermodynamics Subject to Submerged Arc Welding
Authors: Zhang Jin , Shao Guoyou , Fan Jun , Wang Li , Zhang Dan
Published: November 2022

Submerged arc welding is a complex metallurgical process with a temperature of nearly 2000 ∘C (a temperature much higher than that in traditional steelmaking) and different phases, including flux (slag), metal, and plasma. Flux serves vital functions in order to produce the weld metal with desired qualities. It is well known that understandings of the thermodynamic properties regarding flux and slag are essential to aid in flux design and optimization. Actually, the developments of flux design and thermodynamics have been promoting each other. Within this review, the flux design stages have been documented and reviewed in detail from the perspective of thermodynamics. The thermodynamic design principles for fluxes have been evaluated systematically, the limitations of each flux have been elucidated, and the thermodynamic significance of the designed fluxes upon the development of welding thermodynamics has been analyzed. Based on the hypothesis that thermodynamic equilibrium is attained locally considering that the high temperatures and surface-to-volume ratio counteract the short time available for chemical reactions to be completed, both slag–metal and gas–slag–metal equilibrium models have been evaluated, which may provide technical assistance for flux design and matching. Then, recent applications of Calphad (Computer Coupling of Phase Diagrams and Thermochemistry) technology in the fields of flux design and matching have been introduced. The incumbent review demonstrates that thermodynamic consideration is essential to develop new fluxes or upgrade existing ones to meet the growing demands concerning submerged arc welding quality. Furthermore, it is revealed that the thermodynamic approach is capable of facilitating the flux design process geared toward submerged arc welding. Finally, further investigation into welding thermodynamics is proposed to better aid in flux design and matching.
Utilization of Carbon Dioxide via Catalytic Hydrogenation Processes during Steam-Based Enhanced Oil Recovery
Authors: Aliev Firdavs , Mirzaev Oybek , Kholmurodov Temurali , Slavkina Olga , Vakhin Alexey
Published: November 2022

The concentration of carbon dioxide in the atmosphere has been increasing since immediately after the boom of industrialization. Novel technologies are required for carbon dioxide (CO2) capture, storage, and its chemical conversion into value-added products. In this study, we present a novel in situ CO2 utilization method via a hydrogenation process in the presence of nickel tallates during steam-based enhanced oil recovery. The light n-alkanes are the preferred products of in situ catalytic hydrogenation of CO2 due to their effective solubility, viscosity-reducing capacity, and hydrogen-donating capacity. A nickel tallate was evaluated for its carbon dioxide hydrogenation and oil-upgrading performance at 300 °C. The results showed that the content of saturated and aromatic fractions increased, while the content of heavier fragments decreased. Moreover, the relative content of normal C10–C20 alkanes doubled after the catalytic hydrogenation of CO2. Despite the noncatalytic hydrogenation of CO2, the viscosity was altered from 3309 mPa.s to 1775 mPa.s at a shear rate of 0.66 s−1. The addition of the catalyst further contributed to the reduction of the viscosity, down to 1167 mPa.s at the same shear rate. Thus, in situ catalytic hydrogenation of CO2 not only significantly reduces the concentration of anthropogenic carbon dioxide gas in the atmosphere, but it also enhances the oil-recovery factor by improving the quality of the upgraded crude oil and its mobility.
The Influence of Reservoir Clay Composition on Heavy Oil In Situ Combustion
Authors: Minkhanov Ilgiz F. , Bolotov Alexander V. , Tazeev Aidar R. , Chalin Vladislav V. , Kacou Anini Franck D. , Galeev Ranel I. , Sagirov Rustam N. , Al-Muntaser Ameen A. , Emelianov Dmitrii A. , Khelkhal Mohammed Amine , Varfolomeev Mikhail A.
Published: November 2022

Thermally enhanced oil recovery methods, such as in situ combustion and steam injection, are generating considerable interest in terms of improving oil reserve exploitation and satisfying oil demand and economic growth. However, the early breakthrough of the in situ combustion front and the significant amount of heat loss associated with steam injection for deeper reservoir applications are the main challenges that require urgent solutions. Further data collection related to the effects of a reservoir’s physical and chemical properties, temperature, and pressure on in situ combustion front propagation and steam injection heat transfer inefficiency would be needed to achieve better reservoir oil recovery. Most studies have focused on the application of catalytic systems and the investigation of minerals’ effects on combustion front stabilization; however, the effect of clay interlayers’ minerals on the performance of in situ combustion is still poorly understood. This paper takes a new look at the role played by the interlayers’ minerals in stabilizing the combustion front using X-ray diffraction (XRD), thermogravimetry (TG), differential scanning calorimetry (DSC) combined with nuclear magnetic resonance (NMR), and combustion tube experiments. The studied samples’ compositions were analyzed by XRD, TG/DSC, and NMR techniques. Meanwhile, the effects of interlayers’ minerals on oil production were screened by combustion tube experiments. The data obtained from this study suggest that clay dispersion within a reservoir would improve oil recovery via in situ combustion, and our study led us to obtain an 80.5% recovery factor. However, the experiments of models with clay interlayers showed less recovery factors, and the model with interlayers led to a 0% recovery factor in the presence of air injection. Meanwhile, the same model in hydrothermal and air injection conditions led to a 13.9% recovery factor. This was due to the hydrothermal effect improving permeability and pore enlargement, which allowed the transfer of heat and matter. Moreover, our study found that clay minerals exhibit excellent catalytic effects on the formation of fuel deposition and the coke oxidation process. This effect was reflected in the significant role played by clay minerals in decreasing the number of heteroatoms by breaking down the C-S, C-N, and C-O bonds and by stimulating the processes of hydrocarbon polymerization during the in situ combustion. Our results add to a growing body of literature related to in situ combustion challenges and underline the importance of a reservoir’s physical parameters in the successful application of in situ combustion.
Where Reinforcement Learning Meets Process Control: Review and Guidelines
Authors: Faria Ruan de Rezende , Capron Bruno Didier Olivier , Secchi Argimiro Resende , de Souza Maurício B.
Published: November 2022

This paper presents a literature review of reinforcement learning (RL) and its applications to process control and optimization. These applications were evaluated from a new perspective on simulation-based offline training and process demonstrations, policy deployment with transfer learning (TL) and the challenges of integrating it by proposing a feasible approach to online process control. The study elucidates how learning from demonstrations can be accomplished through imitation learning (IL) and reinforcement learning, and presents a hyperparameter-optimization framework to obtain a feasible algorithm and deep neural network (DNN). The study details a batch process control experiment using the deep-deterministic-policy-gradient (DDPG) algorithm modified with adversarial imitation learning.
Salivary Biomarkers in Oral Squamous Cell Carcinoma: A Proteomic Overview
Authors: Riccardi Gabriele , Bellizzi Mario Giuseppe , Fatuzzo Irene , Zoccali Federica , Cavalcanti Luca , Greco Antonio , Vincentiis Marco de , Ralli Massimo , Fiore Marco , Petrella Carla , Minni Antonio , Barbato Christian
Published: November 2022

Background: Oral squamous cell carcinoma (OSCC) is one of the most frequent cancers worldwide. Endoscopic methods may be useful in the evaluation of oral injuries even though the diagnostic gold standard is a biopsy. Targeted screenings could be considered the best way to prevent the occurrence of oral cancer. Aimed to elucidate the potential identification of specific biomarkers of OSCC, the use of saliva is convenient and noninvasive. Many studies reported more than a hundred putative saliva biomarkers for OSCC, and proteogenomic approaches were fundamental to disclosing this issue. Methods: Relevant literature published in the last few years was systematically searched on PubMed and we focused on articles about the use and study of salivary biomarkers in the diagnostics of head and neck cancer (n = 110). Thereafter, we performed a selection focusing on diagnosis with salivary proteomics in OSCC (n = 8). Results: Saliva proteomics can be a source of biomarkers for OSCC. We reviewed literature of biomarker proteins in saliva that could also be evaluated as probable targets for non-invasive screening of oral neoplasm such as cytokines, matrix metalloproteinases, and acute-phase response proteins. Conclusions: The measurement of salivary biomarkers is a highly hopeful technique for the diagnosis of OSCC. Proteogenomic approaches could permit an accurate and early diagnosis of OSCC. This review seeks to generate an up-to-date view on translational OSCC issues by raising awareness of researchers, physicians, and surgeons. Renewed clinical studies, which will validate the sensitivity and specificity of salivary biomarkers, are necessary to translate these results into possible strategies for early diagnosis of OSCC, thus improving patient outcomes.
Challenges and Response of Korean Religions to the COVID-19 Pandemic in Korea
Authors: Lim Jeonok , Kim Jae-ik , Nam Mi-hee , Jun Chulhoo , Park Kwangsoo
Published: November 2022

The ongoing COVID-19 pandemic has triggered many lifestyle changes and caused serious social issues such as economic disruption, restriction of social and religious activities, and an increase in anxiety and conflict. This study first elaborates the pre-pandemic status of Korean religions and the Korean government’s response to the COVID-19 outbreak. Second, the study examines the debates regarding the role of religion at the initial stage of the COVID-19 outbreak, as some members of religious denominations such as Shinchonji Church of Jesus and some Protestant churches led by Pastor Chŏn’s Love First Church (K. Sarangjeil) significantly contributed to its spread through their religious services and gatherings. In this regard, this study examines how the media portrayed religion in this period. When the Korean government implemented strong distance and prohibition policies for social and religious gatherings, the issue of religious freedom became a serious social issue, driving the public to think about the social role of religions when it comes to public health. Without a doubt, the COVID-19 pandemic impacted Korean society and religions, producing some negative symptoms such as the corona-blue syndrome. In this context, this study examines the role of religion in public health. The role of religion in Korea may need to change. Given that most religions in Korea such as Buddhism, Catholicism, and Won-Buddhism have actively cooperated with the government policy to overcome COVID-19, these religions can help heal the post-pandemic traumas and suffering from the ensuing natural or social disasters.
EDTRS: A Superpixel Generation Method for SAR Images Segmentation Based on Edge Detection and Texture Region Selection
Authors: Yu Hang , Jiang Haoran , Liu Zhiheng , Zhou Suiping , Yin Xiangjie
Published: November 2022

The generation of superpixels is becoming a critical step in SAR image segmentation. However, most studies on superpixels only focused on clustering methods without considering multi-feature in SAR images. Generating superpixels for complex scenes is a challenging task. It is also time consuming and inconvenient to manually adjust the parameters to regularize the shapes of superpixels. To address these issues, we propose a new superpixel generation method for SAR images based on edge detection and texture region selection (EDTRS), which takes into account the different features of SAR images. Firstly, a Gaussian function is applied in the neighborhood of each pixel in eight directions, and a Sobel operator is used to determine the redefined region. Then, 2D entropy is introduced to adjust the edge map. Secondly, local outlier factor (LOF) detection is used to eliminate speckle-noise interference in SAR images. We judge whether the texture has periodicity and introduce an edge map to select the appropriate region and extract texture features for the target pixel. A gray-level co-occurrence matrix (GLCM) and principal component analysis (PCA) are combined to extract texture features. Finally, we use a novel approach to combine the features extracted, and the pixels are clustered by the K-means method. Experimental results with different SAR images show that the proposed method outperforms existing superpixel generation methods with an increase of 5–10% in accuracy and produces more regular shapes.
Automatic Identification of Liquefaction Induced by 2021 Maduo Mw7.3 Earthquake Based on Machine Learning Methods
Authors: Liang Peng , Xu Yueren , Li Wenqiao , Zhang Yanbo , Tian Qinjian
Published: November 2022

Rapid extraction of liquefaction induced by strong earthquakes is helpful for earthquake intensity assessment and earthquake emergency response. Supervised classification methods are potentially more accurate and do not need pre-earthquake images. However, the current supervised classification methods depend on the precisely delineated polygons of liquefaction by manual and landcover maps. To overcome these shortcomings, this study proposed two binary classification methods (i.e., random forest and gradient boosting decision tree) based on typical samples. The proposed methods trained the two machine learning methods with different numbers of typical samples, then used the trained binary classification methods to extract the spatial distribution of liquefaction. Finally, a morphological transformation method was used for the postprocessing of the extracted liquefaction. The recognition accuracies of liquefaction were estimated by four evaluation indices, which all showed a score of about 90%. The spatial distribution of liquefaction pits is also consistent with the formation principle of liquefaction. This study demonstrates that the proposed binary classification methods based on machine learning could efficiently and quickly provide the spatial distribution of liquefaction based on post-earthquake emergency satellite images.
UltraHi-PrNet: An Ultra-High Precision Deep Learning Network for Dense Multi-Scale Target Detection in SAR Images
Authors: Zhou Zheng , Cui Zongyong , Zang Zhipeng , Meng Xiangjie , Cao Zongjie , Yang Jianyu
Published: November 2022

Multi-scale target detection in synthetic aperture radar (SAR) images is one of the key techniques of SAR image interpretation, which is widely used in national defense and security. However, multi-scale targets include several types. For example, targets with similar-scale, large-scale, and ultra-large-scale differences coexist in SAR images. In particular, it is difficult for existing target detection methods to detect both ultra-large-scale targets and ultra-small-scale targets in SAR images, resulting in poor detection results for these two types of targets. To solve these problems, this paper proposes an ultra-high precision deep learning network (UltraHi-PrNet) to detect dense multi-scale targets. Firstly, a novel scale transfer layer is constructed to transfer the features of targets of different scales from bottom networks to top networks, ensuring that the features of ultra-small-scale, small-scale, and medium-scale targets in SAR images can be extracted more easily. Then, a novel scale expansion layer is constructed to increase the range of the receptive field of feature extraction without increasing the feature resolution, ensuring that the features of large-scale and ultra-large-scale targets in SAR images can be extracted more easily. Next, the scale expansion layers with different expansion rates are densely connected to different stages of the backbone network, and the features of the target with ultra-large-scale differences are extracted. Finally, the classification and regression of targets were achieved based on Faster R-CNN. Based on the SAR ship detection dataset (SSDD), AIR-SARShip-1.0, high-resolution SAR ship detection dataset-2.0 (high-resolution SSDD-2.0), the SAR-ship-dataset, and the Gaofen-3 airport dataset, the experimental results showed that this method can detect similar-scale, large-scale, and ultra-large-scale targets more easily. At the same time, compared with other advanced SAR target detection methods, the proposed method can achieve higher accuracy.
Contribution of Sentinel-3A Radar Altimetry Data to the Study of the Water Level Variations in Lake Buyo (West of Côte d’Ivoire)
Authors: Oularé Sékouba , Sokeng Valère-Carin Jofack , Kouamé Koffi Fernand , Komenan Christian Armel Kouassi , Danumah Jean Homian , Mertens Benoit , Akpa You Lucette , Catry Thibault , Pillot Benjamin
Published: November 2022

The artificial Lake Buyo is an important water reservoir that ensures the availability of water for multiple purposes: drinking water supply, fishing, and energy. In the last five years, this lake has experienced extreme variations in its surface area and water levels, including very significant declines, which has impacted the supply of electricity. This study aimed to assess temporal variations in the water levels of Lake Buyo using radar altimetry. Altimetric data from the Sentinel-3A satellite on Lake Buyo (tracks 16 (orbit 8) and 743 (orbit 372)) were selected over the period from 31 May 2016 to 12 June 2021 and compared to the in situ measurements provided by the Direction de la Production de l’Electricité de Côte d’Ivoire (DPE-CI). The extraction of the time series of the Sentinel-3A altimetric water levels and their corrections (geophysical and environmental corrections) were carried out with the ALTiS software. The results showed an overall agreement between the altimetric water levels and the in situ measurements, with a correlation coefficient (R2) ranging from 0.98 to 0.99 obtained, as well as a Nash–Sutcliffe Efficiency (NSE) coefficient also between 0.98 and 0.99. Further, the bias (0.12 m and 0.13 m) and root mean square error (RMSE) (0.38 and 0.67 m) values showed that the results were acceptable. The analysis of the water levels time series allowed for the identification of two main periods: March to October and November to February. The first period corresponded to a high level period, recording a maximum level of 200.06 m. The second period, from November to March, was characterized by a drop in the water level, recording a minimum level of 187.42 m. The water levels time series provided by Sentinel-3 allowed us to appreciate the respective influences of seasonal and interannual variations on rainfall and the contributions of the Sassandra River tributaries to the water levels of Lake Buyo.
Dependence of Skin-Electrode Contact Impedance on Material and Skin Hydration
Authors: Goyal Krittika , Borkholder David A. , Day Steven W.
Published: November 2022

Dry electrodes offer an accessible continuous acquisition of biopotential signals as part of current in-home monitoring systems but often face challenges of high-contact impedance that results in poor signal quality. The performance of dry electrodes could be affected by electrode material and skin hydration. Herein, we investigate these dependencies using a circuit skin-electrode interface model, varying material and hydration in controlled benchtop experiments on a biomimetic skin phantom simulating dry and hydrated skin. Results of the model demonstrate the contribution of the individual components in the circuit to total impedance and assist in understanding the role of electrode material in the mechanistic principle of dry electrodes. Validation was performed by conducting in vivo skin-electrode contact impedance measurements across ten normative human subjects. Further, the impact of the electrode on biopotential signal quality was evaluated by demonstrating an ability to capture clinically relevant electrocardiogram signals by using dry electrodes integrated into a toilet seat cardiovascular monitoring system. Titanium electrodes resulted in better signal quality than stainless steel electrodes. Results suggest that relative permittivity of native oxide of electrode material come into contact with the skin contributes to the interface impedance, and can lead to enhancement in the capacitive coupling of biopotential signals, especially in dry skin individuals.
Passive Sonar Target Identification Using Multiple-Measurement Sparse Bayesian Learning
Authors: Shin Myoungin , Hong Wooyoung , Lee Keunhwa , Choo Youngmin
Published: November 2022

Accurate estimation of the frequency component is an important issue to identify and track marine objects (e.g., surface ship, submarine, etc.). In general, a passive sonar system consists of a sensor array, and each sensor receives data that have common information of the target signal. In this paper, we consider multiple-measurement sparse Bayesian learning (MM-SBL), which reconstructs sparse solutions in a linear system using Bayesian frameworks, to detect the common frequency components received by each sensor. In addition, the direction of arrival estimation was performed on each detected common frequency component using the MM-SBL based on beamforming. The azimuth for each common frequency component was confirmed in the frequency-azimuth plot, through which we identified the target. In addition, we perform target tracking using the target detection results along time, which are derived from the sum of the signal spectrum at the azimuth angle. The performance of the MM-SBL and the conventional target detection method based on energy detection were compared using in-situ data measured near the Korean peninsula, where MM-SBL displays superior detection performance and high-resolution results.
A Machine-Learning-Based Robust Classification Method for PV Panel Faults
Authors: Memon Sufyan Ali , Javed Qaiser , Kim Wan-Gu , Mahmood Zahid , Khan Uzair , Shahzad Mohsin
Published: November 2022

Renewable energy resources have gained considerable attention in recent years due to their efficiency and economic benefits. Their proportion of total energy use continues to grow over time. Photovoltaic (PV) cell and wind energy generation are the least-expensive new energy sources in most countries. Renewable energy technologies significantly contribute to climate mitigation and provide economic benefits. Apart from these advantages, renewable energy sources, particularly solar energy, have drawbacks, for instance restricted energy supply, reliance on weather conditions, and being affected by several kinds of faults, which cause a high power loss. Usually, the local PV plants are small in size, and it is easy to trace any fault and defect; however, there are many PV cells in the grid-connected PV system where it is difficult to find a fault. Keeping in view the aforedescribed facts, this paper presents an intelligent model to detect faults in the PV panels. The proposed model utilizes the Convolutional Neural Network (CNN), which is trained on historic data. The dataset was preprocessed before being fed to the CNN. The dataset contained different parameters, such as current, voltage, temperature, and irradiance, for five different classes. The simulation results showed that the proposed CNN model achieved a training accuracy of 97.64% and a testing accuracy of 95.20%, which are much better than the previous research performed on this dataset.
Research on Underwater Complex Scene SLAM Algorithm Based on Image Enhancement
Authors: Wu Renhan , Gao Yuzhuo
Published: November 2022

Underwater images typically suffer from less explicit feature point information and more redundant information due to wild conditions. To solve these degradation problems, we propose the VINS-MONO algorithm to enhance the quality of the underwater image. Specifically, we first used the FAST feature point extraction algorithm to improve the extraction speed. Then, the inverse optical flow method was used to improve the accuracy of feature extraction. At the same time, several kinds of residual information were extracted and marginalized, separately, in the marginalization part of the back-end, in order to improve the marginalization speed. Extensive experiments on underwater dataset HAUD-Dataset and public dataset EuRoC show that our approach is superior to the original VINS-MONO algorithm. In addition, the original algorithm optimizes the situation in which the feature point information is not obvious, and the redundant information is more complex in the underwater environment, which effectively improves the visual quality of the underwater image.
Wafer Center Alignment System of Transfer Robot Based on Reduced Number of Sensors
Authors: Kim Hyungjong
Published: November 2022

This brief presents a wafer alignment algorithm with reduced sensor number that obtains the relative distance of the wafer center and the robot hand. By ‘reduced number’, in spite of smaller number of sensors than the conventional method, we mean an improved method which achieves the similar results to pre-existing algorithms. Indeed, it can be designed with only three sensor data, less than four sensors of the conventional algorithm. Thus, some advantages of the proposed alignment algorithm include that it can be designed with low cost and less computing power. The proposed alignment algorithm is applied to a transfer robot for coater/developer system in semiconductor processing for verifying the performance of the method. The performance of the proposed method is validated by both simulation and experimental results.
Salinity and Temperature Dual-Parameter Sensor Based on Fiber Ring Laser with Tapered Side-Hole Fiber Embedded in Sagnac Interferometer
Authors: Zhao Fang , Lin Weihao , Hu Jie , Liu Shuaiqi , Yu Feihong , Chen Xingwei , Wang Guoqing , Shum Perry Ping , Shao Liyang
Published: November 2022

This paper presented a new kind of salinity and temperature dual-parameter sensor based on a fiber ring laser (FRL) with tapered side-hole fiber (SHF) embedded in a Sagnac interferometer. The sensing structure is majorly composed of tapered SHF located in the middle of SHF inside the Sagnac interferometer loop structure. The influences of the SHF’s diameters of different tapered in the Sagnac interferometer loop on the FRL sensing system are studied. The presence of air holes in the SHF makes the cladding mode easier to excite, and the interaction between the cladding mode with its surroundings is enhanced, thus having higher salinity sensitivity. Besides, the unique advantages of high resolution, narrower linewidth, and high signal-to-noise ratio (SNR) of fiber laser make the measurement results more accurate. In this experiment, the SHF with different taper diameters was made, and it was found that reducing the diameter of the taper waist diameter could further improve the salinity sensitivity. When the waist diameter was 9.70 μm, the maximum salinity sensitivity of 0.2867 nm/‰ was achieved. Temperature sensing experiments were also carried out. The maximum temperature sensitivity of the FRL sensing system was −0.3041 nm/°C at the temperature range from 20 to 30 °C. The sensor has the characteristics of easy manufacture, good selectivity, and high sensitivity, proving the feasibility of simultaneous measurement of seawater salinity and temperature.
An Improved Split-Ring Resonator-Based Sensor for Microfluidic Applications
Authors: Ye Wei , Wang Da-Wei , Wang Jing , Wang Gaofeng , Zhao Wen-Sheng
Published: November 2022

This study proposes an ultrahigh-sensitivity split-ring resonator-based microwave sensor for retrieving the complex permittivity of liquid samples. An interdigital capacitor structure was used to expand the sensing area and the sensitivity. A defected ground structure and A parallel dual split-ring resonator were introduced to improve the quality factor. A polydimethylsiloxane microfluidic channel substrate was placed above the interdigital capacitor structure. The channel route coincided with the interdigital gap to fully utilize the strong electric field. Ethanol–water solutions with varying ethanol fractions were injected into the channel as the testing liquid. It was demonstrated that the variation in resonant frequency can be used to retrieve the dielectric properties of liquid samples. The proposed sensor used a small liquid volume of ~0.68 μL and provided values in good agreement with the reference data.
Identification and Compensation for D-Dot Measurement System in Transient Electromagnetic Pulse Measurement
Authors: Jin Mengzhe , Li Hao , Liu Shanghe
Published: November 2022

The measurement of the transient pulsed electromagnetic (EM) field is essential for analyzing electromagnetic compatibility. Due to their good performance, D-dot sensors, combined with numerical integration computation for signal recovery, are commonly used to measure electromagnetic pulses (EMPs). However, the integration approach is occasionally flawed due to a non-ideal frequency response or noise, causing distortions in the reconstructed signal. In order to better understand the dynamic performance of the sensor, a nonlinear Hammerstein model is employed in the system identification for the sensor with the calibration data collected in the laboratory environment. When identifying the linear component based on the ultra-wideband characteristics of the measured transient pulse, a two-step identification approach with two different pulse excitation modes, low frequency and high frequency, is utilized to conduct the modeling across the entire frequency range. Based on the reliable identification and modeling of the D-dot sensor, a compensation system that corresponds to the nonlinear Hammerstein model has been developed for the practical signal recovery of the incident E-field. After compensation, the dynamic characteristics of the sensor are significantly improved, and the system compensation approach outperforms the integration method in signal recovery for the incident E-field.