bluuebunny/crossref_metadata_2025 huggingface dataset:
Statistical Treatment of Clinical Investigations in Pediatric Cardiology
Authors: Rao P. Syamasundar
Published: April 2021

This paper describes various statistical methods used by the author during multiple studies conducted by the author. Initially, the data were scrutinized to ensure normal distribution, and expressed as mean ± standard deviation (SD) or standard error of mean (SEM) for normally distributed variables. Medians and ranges were given for the data with skewed distribution. Two tailed, paired t tests or independent sample t tests (analysis of variance) were used for normally distributed data, while non-parametric chi-square or similar other tests were utilized for data with skewed distribution. Statistical significance was set at a p value of < 0.05. Bonferroni correction was applied when the study involves multiple comparisons. A number of other statistical methods used during these studies were also discussed. Finally, special methods used in evaluating aortic remodeling subsequent to balloon angioplasty of native aortic coarctation were reviewed.
Internal Factors Affecting the Surface Rumpling of a β-NiAlHf Coating
Authors: Liu Liang , He Jian , Dong Yaoge , Guo Hongbo
Published: April 2021

β-NiAl coatings on a superalloy substrate will inevitably result in severe rumpling at elevated temperatures; however, the associated rumpling mechanisms are not completely understood. The scale rumpling behavior of a β-NiAlHf coating deposited by electron beam physical vapor deposition (EB-PVD) on single crystal superalloy IC21 was investigated in this work. Some internal factors, including the mismatch in the coefficient of thermal expansion and the stress induced by the growth of oxide scale and the phase transformation, were taken into consideration. The thermal mismatch stress between the coating and substrate was the main internal factor responsible for rumpling behavior during thermal cyclic loads, while the phase degradation from β-NiAl to γ’-Ni3Al in the coating played a dominant role during static thermal loads.
Tuning the Optical Properties of WO3 Films Exhibiting a Zigzag Columnar Microstructure
Authors: Sakkas Charalampos , Rauch Jean-Yves , Cote Jean-Marc , Tissot Vincent , Gavoille Joseph , Martin Nicolas
Published: April 2021

Tungsten oxide WO3 thin films are deposited by DC reactive magnetron sputtering. The Reactive Gas Pulsing Process (RGPP) associated with the GLancing Angle Deposition method (GLAD) are implemented to produce zigzag columnar structures. The oxygen injection time (tON time) and the pulsing period are kept constant. Three tilt angles α are used: 75, 80, and 85° and the number of zigzags N is progressively changed from N = 0.5, 1, 2, 4, 8 to 16. For each film, refractive index, extinction coefficient, and absorption coefficient are calculated from optical transmission spectra of the films measured in the visible region from wavelength values only. Absorption and extinction coefficients monotonously drop as the number of zigzags increases. Refractive indices are the lowest for the most grazing tilt angle α = 85°. The highest refractive index is nevertheless obtained for a number of zigzags close to four. This optimized optical property is directly correlated to changes of the microstructure, especially a porous architecture, which is favored for high tilt angles, and tunable as a function of the number of zigzags.
Entropy Generation Incorporating γ-Nanofluids under the Influence of Nonlinear Radiation with Mixed Convection
Authors: Khan Umair , Zaib Aurang , Khan Ilyas , Nisar Kottakkaran Sooppy
Published: April 2021

Nanofluids offer the potential to improve heat transport performance. In light of this, the current exploration gives a numerical simulation of mixed convection flow (MCF) using an effective Prandtl model and comprising water- and ethylene-based γγ−Al2O3 particles over a stretched vertical sheet. The impacts of entropy along with non-linear radiation and viscous dissipation are analyzed. Experimentally based expressions of thermal conductivity as well as viscosity are utilized for γγ−Al2O3 nanoparticles. The governing boundary-layer equations are stimulated numerically utilizing bvp4c (boundary-value problem of fourth order). The outcomes involving flow parameter found for the temperature, velocity, heat transfer and drag force are conferred via graphs. It is determined from the obtained results that the temperature and velocity increase the function of the nanoparticle volume fraction for H2O\C2H6O2 based γγ−Al2O3 nanofluids. In addition, it is noted that the larger unsteady parameter results in a significant advancement in the heat transport and friction factor. Heat transfer performance in the fluid flow is also augmented with an upsurge in radiation.
Effects of Polarity Inversion Layer on Performances of Lateral-Field-Excitation Piezoelectric Sensors Based on Lithium Niobate Single Crystal
Authors: Zheng Yuanzhen , Chen Dudu , Sun Fei , Xu Kuanxiang , Ma Tingfeng , Yuan Lili , Wu Rongxing
Published: April 2021

In this work, lateral-field-excitation (LFE) piezoelectric sensors based on polarity inversion layer are designed and fabricated, then frequency stabilities and sensitivities on electric property changes of liquids are tested. Because the polarity inversion layer can suppress the spurious modes, the stabilities of the LFE devices with a polarity inversion layer are obviously better than that of LFE devices with no polarity inversion layer. On the changes of liquid conductivity and permittivity, the sensitivities of the LFE devices with a polarity inversion layer are 2.4 times and 2.1 times higher than that of LFE devices with no polarity inversion layer, respectively. The polarity inversion layer of the lithium niobate crystal plate can be realized conveniently by heat treatment, therefore, the technology of the polarity inversion layer can play an important role in improving the sensing performances of LFE sensors.
Terahertz Emission and Ultrafast Carrier Dynamics of Ar-Ion Implanted Cu(In,Ga)Se2 Thin Films
Authors: Kang Chul , Lee Gyuseok , Lee Woo-Jung , Cho Dae-Hyung , Maeng Inhee , Chung Yong-Duck , Kee Chul-Sik
Published: April 2021

We investigated THz emission from Ar-ion-implanted Cu(In,Ga)Se2 (CIGS) films. THz radiation from the CIGS films increases as the density of implanted Ar ions increases. This is because Ar ions contribute to an increase in the surface surge current density. The effect of Ar-ion implantation on the carrier dynamics of CIGS films was also investigated using optical pump THz probe spectroscopy. The fitted results imply that implanted Ar ions increase the charge transition of intra-and carrier–carrier scattering lifetimes and decrease the bandgap transition lifetime.
Engineering the Uniform Lying Helical Structure in Chiral Nematic Liquid Crystals: From Morphology Transition to Dimension Control
Authors: Jia Zhixuan , Pawale Tejal , Guerrero-García Guillermo , Hashemi Sid , Martínez-González José , Li Xiao
Published: April 2021

Chiral nematic liquid crystals (CLCs), with a unique helix structure, have attracted immense recognition over the last few decades owing to the abundant presence in natural phenomena and their diverse applications. However, the optical properties of CLC are usually hindered by the abundance of the so-called fingerprint domains. Up to now, studies have worked on controlling the in-plane orientation of the lying helix through surface rubbing and external stimuli. It remains challenging to achieve a steady and uniform lying helical structure. Here, by varying the surface anchoring strength, a uniform lying helical structure with long-range order is achieved as thermodynamically stable state without any external support. Poly (6-(4-methoxy-azobenzene-4’-oxy) hexyl methacrylate) (PMMAZO)—a liquid crystalline polymer—is deposited onto the silicon substrate to fine-tune the surface anchoring. By changing the grafting density of PMMAZO, both pitch size and morphology of the lying helical structure can be controlled. As the grafting density increases, the enhanced titled deformation of helical structure suppresses the pitch size of CLC at the same cell thickness; as the cell thickness increases, the morphology transition from long-range order stripes to small fingerprint domains is facilitated.
Exploring Inner-City Residents’ and Foreigners’ Commitment to Improving Air Pollution: Evidence from a Field Survey in Hanoi, Vietnam
Authors: Vuong Quan-Hoang , Phu Tri Vu , Le Tuyet-Anh T. , Van Khuc Quy
Published: April 2021

Solutions for mitigating and reducing environmental pollution are important priorities for many developed and developing countries. This study was conducted to better understand the degree to which inner-city citizens and foreigners perceive air pollution and respond to it, particularly how much they willingly contribute to improving air quality in Vietnam, a lower-middle-income nation in Southeast Asia. During mid-December 2019, a stratified random sampling technique and a contingent valuation method (CVM) were employed to survey 199 inhabitants and 75 foreigners who reside and travel within the inner-city of Hanoi. The data comprises four major groups of information on: (1) perception of air pollution and its impacts, (2) preventive measures used to mitigate polluted air, (3) commitments on willingness-to-pay (WTP) for reducing air pollution alongside reasons for the yes-or-no-WTP decision, and (4) demographic information of interviewees. The findings and data of this study could offer many policy implications for better environmental management in the study area and beyond.
Isolation of Microsatellite Markers from De Novo Whole Genome Sequences of Coptotermes gestroi (Wasmann) (Blattodea: Rhinotermitidae)
Authors: Lim Li Yang , Cheng Shawn , Ab Majid Abdul Hafiz
Published: April 2021

Coptotermes gestroi (Wasmann) (Blattodea: Rhinotermitidae) is a subterranean termite species from Southeast Asia which has been unintentionally introduced to many parts of the world through commerce and modern transportation. Known for causing extensive damage to timber used in the built environment, the termite also has a habit of nesting in carton nests in wood and wooden structures in buildings. As so little is known of its breeding system, colony, and genetic structure, we initiated work to sequence its genome with an Illumina HiSeq™ 2000 sequencer. In this publication, we announce our paired-end sequencing data and report the isolation of 119,190 microsatellite markers from our DNA assembly. The microsatellite marker reported in this publication can be used to elucidate the mating system and genetic structure of this highly invasive termite species. Additionally, in this announcement the study authors make the Bio Project sequence accession number SRR13105492 accessible from the Sequence Read Archive database.
Bayesian and Frequentist Inferences on a Type I Half-Logistic Odd Weibull Generator with Applications in Engineering
Authors: EL-Morshedy Mahmoud , Alshammari Fahad Sameer , Tyagi Abhishek , Elbatal Iberahim , Hamed Yasser S. , Eliwa Mohamed S.
Published: April 2021

In this article, we have proposed a new generalization of the odd Weibull-G family by consolidating two notable families of distributions. We have derived various mathematical properties of the proposed family, including quantile function, skewness, kurtosis, moments, incomplete moments, mean deviation, Bonferroni and Lorenz curves, probability weighted moments, moments of (reversed) residual lifetime, entropy and order statistics. After producing the general class, two of the corresponding parametric statistical models are outlined. The hazard rate function of the sub-models can take a variety of shapes such as increasing, decreasing, unimodal, and Bathtub shaped, for different values of the parameters. Furthermore, the sub-models of the introduced family are also capable of modelling symmetric and skewed data. The parameter estimation of the special models are discussed by numerous methods, namely, the maximum likelihood, simple least squares, weighted least squares, Cramér-von Mises, and Bayesian estimation. Under the Bayesian framework, we have used informative and non-informative priors to obtain Bayes estimates of unknown parameters with the squared error and generalized entropy loss functions. An extensive Monte Carlo simulation is conducted to assess the effectiveness of these estimation techniques. The applicability of two sub-models of the proposed family is illustrated by means of two real data sets.
A Novel Blind Signal Detector Based on the Entropy of the Power Spectrum Subband Energy Ratio
Authors: Li Han , Hu Yanzhu , Wang Song
Published: April 2021

In this paper, we present a novel blind signal detector based on the entropy of the power spectrum subband energy ratio (PSER), the detection performance of which is significantly better than that of the classical energy detector. This detector is a full power spectrum detection method, and does not require the noise variance or prior information about the signal to be detected. According to the analysis of the statistical characteristics of the power spectrum subband energy ratio, this paper proposes concepts such as interval probability, interval entropy, sample entropy, joint interval entropy, PSER entropy, and sample entropy variance. Based on the multinomial distribution, in this paper the formulas for calculating the PSER entropy and the variance of sample entropy in the case of pure noise are derived. Based on the mixture multinomial distribution, the formulas for calculating the PSER entropy and the variance of sample entropy in the case of the signals mixed with noise are also derived. Under the constant false alarm strategy, the detector based on the entropy of the power spectrum subband energy ratio is derived. The experimental results for the primary signal detection are consistent with the theoretical calculation results, which proves that the detection method is correct.
Neural Dynamics under Active Inference: Plausibility and Efficiency of Information Processing
Authors: Da Costa Lancelot , Parr Thomas , Sengupta Biswa , Friston Karl
Published: April 2021

Active inference is a normative framework for explaining behaviour under the free energy principle—a theory of self-organisation originating in neuroscience. It specifies neuronal dynamics for state-estimation in terms of a descent on (variational) free energy—a measure of the fit between an internal (generative) model and sensory observations. The free energy gradient is a prediction error—plausibly encoded in the average membrane potentials of neuronal populations. Conversely, the expected probability of a state can be expressed in terms of neuronal firing rates. We show that this is consistent with current models of neuronal dynamics and establish face validity by synthesising plausible electrophysiological responses. We then show that these neuronal dynamics approximate natural gradient descent, a well-known optimisation algorithm from information geometry that follows the steepest descent of the objective in information space. We compare the information length of belief updating in both schemes, a measure of the distance travelled in information space that has a direct interpretation in terms of metabolic cost. We show that neural dynamics under active inference are metabolically efficient and suggest that neural representations in biological agents may evolve by approximating steepest descent in information space towards the point of optimal inference.
Key Roles of Crypto-Exchanges in Generating Arbitrage Opportunities
Authors: Kabašinskas Audrius , Šutienė Kristina
Published: April 2021

The evolving crypto-currency market is seen as dynamic, segmented, and inefficient, coupled with a lack of regulatory oversight, which together becomes conducive to observing the arbitrage. In this context, a crypto-network is designed using bid/ask data among 20 crypto-exchanges over a 2-year period. The graph theory technique is employed to describe the network and, more importantly, to determine the key roles of crypto-exchanges in generating arbitrage opportunities by estimating relevant network centrality measures. Based on the proposed arbitrage ratio, Gatecoin, Coinfloor, and Bitsane are estimated as the best exchanges to initiate arbitrage, while EXMO and DSX are the best places to close it. Furthermore, by means of canonical correlation analysis, we revealed that higher volatility and the decreasing price of dominating crypto-currencies and CRIX index signal bring about a more likely arbitrage appearance in the market. The findings of research include pre-tax and after-tax arbitrage opportunities.
Preschool Children’s Reasoning about Sound from an Inferential-Representational Approach
Authors: Gallegos-Cázares Leticia , Flores-Camacho Fernando , Calderón-Canales Elena
Published: April 2021

The purpose of this article is to present an analysis to identify the reasoning processes and representations that preschool students develop about sound based on the inferential-representational approach. Participants were 18 preschool students between the ages of four and five attending three rural schools located in the Sierra Norte of Puebla, Mexico. Data were obtained through a 14 question semi-structured interview. From children’s answers to the formulated questions, an inferential analysis method was applied to identify intentionality, representation elements, sign-material expressions, representations, inferences, and coordination rules in students’ constructions. The results show that children build a basic set of epistemic tools to give meaning to their interpretations and can use them as surrogate reasoning to make inferences. This research constitutes the first approximation toward the understanding of preschool children’s reasoning forms with an inferential-representational approach and constitutes a new approach that puts forward new referents to analyze students of different ages. We consider that the described results and analysis have implications on science education at this educational level.
Improving Characteristics of LUT-Based Mealy FSMs with Twofold State Assignment
Authors: Barkalov Alexander , Titarenko Larysa , Krzywicki Kazimierz , Saburova Svetlana
Published: April 2021

Practically, any digital system includes sequential blocks. This article is devoted to a case when sequential blocks are represented by models of Mealy finite state machines (FSMs). The performance (maximum operating frequency) is one of the most important characteristics of an FSM circuit. In this article, a method is proposed which aims at increasing the operating frequency of LUT-based Mealy FSMs with twofold state assignment. This is done using only extended state codes. Such an approach allows excluding a block of transformation of binary state codes into extended state codes. The proposed approach leads to LUT-based Mealy FSM circuits having two levels of logic blocks. Each function for any logic level is represented by a circuit including a single LUT. The proposed method is illustrated by an example of synthesis. The results of experiments conducted with standard benchmarks show that the proposed approach produces LUT-based circuits with significantly higher operating frequency than it is for circuits produced by other investigated methods (Auto and One-hot of Vivado, JEDI, twofold state assignment). The performance is increased by an average of 15.9 to 25.49 percent. These improvements are accompanied by a small growth of the numbers of LUTs compared with circuits based on twofold state assignment. Our approach provides the best area-time products compared with other investigated methods. The advantages of the proposed approach increase as the number of FSM inputs and states increases.
Conformal Array Geometry for Hemispherical Coverage
Authors: Munno Fortuna
Published: April 2021

Conformal arrays may be a viable solution in many antenna applications requiring a wide angular coverage with sufficiently high directivity values, so it is worth comparing different 2D conformal array geometries to satisfy these requirements. To this end, first, the singular value decomposition (SVD) of the radiation operator is exploited to determine the maximum directivity values an array can reach in the whole observation domain. A numerical study based on the maximum directivity and, hence, on the SVD is then proposed to select the array geometry complying with some given requirements. Therefore, the performances achievable by some array geometries (a semi-circumference, a trapezoidal, and an angle array) are analyzed, and the one assuring a better hemispherical coverage is suggested. Furthermore, such an SVD-based study is usefully exploited to determine which panels of a multi-faceted array must be fed to reach some assigned specifications.
Knowledge-Based System for Crop Pests and Diseases Recognition
Authors: Rodríguez-García Miguel Ángel , García-Sánchez Francisco , Valencia-García Rafael
Published: April 2021

With the rapid increase in the world’s population, there is an ever-growing need for a sustainable food supply. Agriculture is one of the pillars for worldwide food provisioning, with fruits and vegetables being essential for a healthy diet. However, in the last few years the worldwide dispersion of virulent plant pests and diseases has caused significant decreases in the yield and quality of crops, in particular fruit, cereal and vegetables. Climate change and the intensification of global trade flows further accentuate the issue. Integrated Pest Management (IPM) is an approach to pest control that aims at maintaining pest insects at tolerable levels, keeping pest populations below an economic injury level. Under these circumstances, the early identification of pests and diseases becomes crucial. In this work, we present the first step towards a fully fledged, semantically enhanced decision support system for IPM. The ultimate goal is to build a complete agricultural knowledge base by gathering data from multiple, heterogeneous sources and to develop a system to assist farmers in decision making concerning the control of pests and diseases. The pest classifier framework has been evaluated in a simulated environment, obtaining an aggregated accuracy of 98.8%.
Power Transfer Analysis of an Asymmetric Wireless Transmission System Using the Scattering Parameters
Authors: Despotović Živadin , Reljić Dejan , Vasić Veran , Oros Djura
Published: April 2021

The most widely adopted category of the mid-range wireless power transmission (WPT) systems is based on the magnetic resonance coupling (MRC), which is appropriate for a very wide range of applications. The primary concerns of the WPT/MRC system design are the power transfer capabilities. Using the scattering parameters based on power waves, the power transfer of an asymmetric WPT/MRC system with the series-series compensation structure is studied in this paper. This approach is very convenient since the scattering parameters can provide all the relevant characteristics of the WPT/MRC system related to power propagation. To maintain the power transfer capability of the WPT/MRC system at a high level, the scattering parameter S21 is used to determine the operating frequency of the power source. Nevertheless, this condition does not coincide with the maximum possible power transfer efficiency of the system. In this regard, the WPT/MRC system is thereafter configured with a matching circuit, whereas the scattering parameter S21′ S21’is used to calculate and then adjust the matching frequency of the system. This results in the maximum available power transfer efficiency and thereby increases the overall performance of the system. Theoretical investigations are followed by numerical simulation and experimental validation.
Improving the Active Disturbance Rejection Controller Tracking Quality by the Input-Gain Underestimation for a Second-Order Plant
Authors: Patelski Radosław , Pazderski Dariusz
Published: April 2021

In this paper, a problem of influence of an input-gain uncertainty on the tracking performance of a control structure designed according to the Active Disturbance Rejection Control (ADRC) paradigm is investigated. This problem is exemplified using the second-order plant. It is presented that conscious choice of the input-gain parameter different to that of the real plant may lead to a significant improvement of the control precision if the controller is designed in an error domain. Obtained results indicate that the closed-loop system remains stable, and the tracking errors decrease if the value of this parameter follows from multiplying the plant gain input by a positive factor smaller than a certain threshold value. The upper limit for this factor is investigated in the paper and outcomes are presented for various implementation variants. Results of numerical computations, simulations and experiments are presented to consider this bounding value in individual cases.
Feature Extraction for StarCraft II League Prediction
Authors: Lee Chan Min , Ahn Chang Wook
Published: April 2021

In a player-versus-player game such as StarCraft II, it is important to match players with others with similar skills. Studies modeling player skills were conducted, with 47.3% and 61.3% performance. In order to improve the performance, we collected 46,398 replays and compared features extracted from six sections of replays. Through the comparison of the six datasets we created, we propose a method for extracting features from a single replay. Two algorithms, k-Nearest Neighbors and Random Forest, which are most commonly used in related studies, are compared. Our research showed a outperforming accuracy of 75.3% compared to previous works. Although no direct comparison has been made with the current system, we conclude that our research can replace the placement games of five rounds.
Assessment and Integration of Renewable Energy Resources Installations with Reactive Power Compensator in Indian Utility Power System Network
Authors: Venkatesan Chandrasekaran , Kannadasan Raju , Alsharif Mohammed H. , Kim Mun-Kyeom , Nebhen Jamel
Published: April 2021

Renewable energy (RE) resource assessment is essential for planners and investors to increase its penetration capacity, and improve social and economic security. Integration of renewable power generations (RPGs) and reactive power compensators (RPCs) offer potential benefits to the existing power system network by providing a prospect for voltage control, reduction in power losses, sustainability, and reliability improvement. There are proven outcomes with these RPGs and RPCs placement in distribution systems. This work proposes a candidature location and sizing of RPGs and RPCs optimally in the Indian utility transmission power system network. The foremost purpose of this integrated operation at multiple nodes is to increase the performance of the power system concerning power loss and voltage deviation reductions, and voltage stability improvement. The loss sensitivity factor (LSF) based particle swarm optimization (PSO) technique is adapted for finding the candidature locations and sizing the RPGs and RPCs under five different configurations. Simulation outcomes display the proposed methodology can lead to extensive performance enhancement in the power system towards the sustainable development of electric energy transactions. Further, renewable resource assessment is carried out to find the viability of the candidature locations. The potential of wind and solar energy resources is assessed widely and suitable tools are used to evaluate the power extraction through RE at these selected locations. The results show that the candidature locations have great potential to evacuate the energy, which can effectively improve the existing power system technically and economically. Additionally, it is attested that the RPGs can also be utilized for power system enhancement.
Wide-Range Operation of High Step-Up DC-DC Converters with Multimode Rectifiers
Authors: Chub Andrii , Vinnikov Dmitri , Korkh Oleksandr , Jalakas Tanel , Demidova Galina
Published: April 2021

This paper discusses the essence and application specifics of the multimode rectifiers in high step-up DC-DC converters. It presents an overview of existing multimode rectifiers. Their use enables operation in the wide input voltage range needed in highly demanding applications. Owing to the rectifier mode changes, the converter duty cycle can be restricted to a range with a favorable efficiency. It is shown that the performance of such converters depends on the front-end inverter type. The study considers current- and impedance-source front-end topologies, as they are the most relevant in high step-up applications. It is explained why the full- and half-bridge implementations provide essentially different performances. Unlike the half-bridge, the full-bridge implementation shows step changes in efficiency during the rectifier mode changes, which could compromise the long-term reliability of the converter. The theoretical predictions are corroborated by experimental examples to compare performance with different boost front-end inverters.
SDN-Enabled Hybrid DL-Driven Framework for the Detection of Emerging Cyber Threats in IoT
Authors: Javeed Danish , Gao Tianhan , Khan Muhammad Taimoor
Published: April 2021

The Internet of Things (IoT) has proven to be a billion-dollar industry. Despite offering numerous benefits, the prevalent nature of IoT makes it vulnerable and a possible target for the development of cyber-attacks. The diversity of the IoT, on the one hand, leads to the benefits of the integration of devices into a smart ecosystem, but the heterogeneous nature of the IoT makes it difficult to come up with a single security solution. However, the centralized intelligence and programmability of software-defined networks (SDNs) have made it possible to compose a single and effective security solution to cope with cyber threats and attacks. We present an SDN-enabled architecture leveraging hybrid deep learning detection algorithms for the efficient detection of cyber threats and attacks while considering the resource-constrained IoT devices so that no burden is placed on them. We use a state-of-the-art dataset, CICDDoS 2019, to train our algorithm. The results evaluated by this algorithm achieve high accuracy with a minimal false positive rate (FPR) and testing time. We also perform 10-fold cross-validation, proving our results to be unbiased, and compare our results with current benchmark algorithms.
Optimization of Two-Stage IPD-(1+I) Controllers for Frequency Regulation of Sustainable Energy Based Hybrid Microgrid Network
Authors: Latif Abdul , Hussain S. M. Suhail , Das Dulal Chandra , Ustun Taha Selim
Published: April 2021

Sustainable energy based hybrid microgrids are advantageous in meeting constantly increasing energy demands. Conversely, the intermittent nature of renewable sources represents the main challenge to achieving a reliable supply. Hence, load frequency regulation by adjusting the amount of power shared between subsystems is considered as a promising research field. Therefore, this paper presents a new stratagem for frequency regulation by developing a novel two stage integral-proportional-derivative with one plus integral (IPD-(1+I)) controller for multi sources islanded microgrid system (MS-IμGS). The proposed stratagem has been tested in an MS-IμGS comprising of a wind turbine, parabolic trough, biodiesel generators, solid-oxide fuel cell, and electric water heater. The proposed model under different scenarios is simulated in MATLAB environment considering the real-time recorded wind data. A recently developed sine-cosine algorithmic technique (SCA) has been leveraged for optimal regulation of frequency in the considered microgrid. To identify the supremacy of the proposed technique, comparative studies with other classical controllers with different optimization techniques have been performed. From the comparison, it is clearly evident that, SCA-(IPD-(1+I)) controller gives better performance over other considered stratagems in terms of various time domain specific parameters, such as peak deviations (overshoot, undershoot) and settling time. Finally, the robustness of the proposed stratagem is evaluated by conducting sensitivity analysis under ±30% parametric variations and +30% load demand. The lab tests results validate the operation of the proposed system and show that it can be used to regulate the frequency in stand-alone microgrids with a high penetration of renewable energy.
FPGA-Based Controller for a Hybrid Grid-Connected PV/Wind/Battery Power System with AC Load
Authors: Allani Mohamed Yassine , Riahi Jamel , Vergura Silvano , Mami Abdelkader
Published: April 2021

The development and optimization of a hybrid system composed of photovoltaic panels, wind turbines, converters, and batteries connected to the grid, is first presented. To generate the maximum power, two maximum power point tracker controllers based on fuzzy logic are required and a battery controller is used for the regulation of the DC voltage. When the power source varies, a high-voltage supply is incorporated (high gain DC-DC converter controlled by fuzzy logic) to boost the 24 V provided by the DC bus to the inverter voltage of about 400 V and to reduce energy losses to maximize the system performance. The inverter and the LCL filter allow for the integration of this hybrid system with AC loads and the grid. Moreover, a hardware solution for the field programmable gate arrays-based implementation of the controllers is proposed. The combination of these controllers was synthesized using the Integrated Synthesis Environment Design Suite software (Version: 14.7, City: Tunis, Country: Tunisia) and was successfully implemented on Field Programmable Gate Arrays Spartan 3E. The innovative design provides a suitable architecture based on power converters and control strategies that are dedicated to the proposed hybrid system to ensure system reliability. This implementation can provide a high level of flexibility that can facilitate the upgrade of a control system by simply updating or modifying the proposed algorithm running on the field programmable gate arrays board. The simulation results, using Matlab/Simulink (Version: 2016b, City: Tunis, Country: Tunisia, verify the efficiency of the proposed solution when the environmental conditions change. This study focused on the development and optimization of an electrical system control strategy to manage the produced energy and to coordinate the performance of the hybrid energy system. The paper proposes a combined photovoltaic and wind energy system, supported by a battery acting as an energy storage system. In addition, a bi-directional converter charges/discharges the battery, while a high-voltage gain converter connects them to the DC bus. The use of a battery is useful to compensate for the mismatch between the power demanded by the load and the power generated by the hybrid energy systems. The proposed field programmable gate arrays (FPGA)-based controllers ensure a fast time response by making control executable in real time.
Adaptive Tolerant State Estimation under Model Uncertainty in Power Systems
Authors: Ma Ruizi
Published: April 2021

In this paper an adaptive tolerant estimator using singular value decomposition is proposed for a distribution network under model uncertainty in power systems. The adaptive tolerant estimator was designed with adjusted parameters and adjusted weights to overcome the limitations of model uncertainty. The estimator that reduces the measurement errors is adaptive to fast parameter changes in complicated environments. The singular value decomposition method was combined into the state estimator, which extended the over-determined cases to under-determined cases under model uncertainty. The performance of the tolerant estimator was compared with the conventional adaptive estimator, and the tolerant estimator showed accurate estimations against model uncertainty in complicated measurement environments.
Electromechanical Design of Synchronous Power Controller in Grid Integration of Renewable Power Converters to Support Dynamic Stability
Authors: Abdollahi Mostafa , Candela Jose Ignacio , Tarraso Andres , Elsaharty Mohamed Atef , Rakhshani Elyas
Published: April 2021

Nowadays, modern power converters installed in renewable power plants can provide flexible electromechanical characteristics that rely on the developed control technologies such as Synchronous Power Controller (SPC). Since high renewable penetrated power grids result in a low-inertia system, this electromechanical characteristic provides support to the dynamic stability of active power and frequency in the power generation area. This goal can be achieved through the proper tuning of virtual electromechanical parameters that are embedded in the control layers of power converters. In this paper, a novel mathematical pattern and strategy have been proposed to adjust dynamic parameters in Renewable Static Synchronous Generators controlled by SPC (RSSG-SPC). A detailed dynamic modeling was obtained for a feasible design of virtual damping coefficient and virtual moment of inertia in the electrometrical control layer of RSSG-SPC’s power converters. Mathematical solutions, modal analysis outcomes, time-domain simulation results, and real-time validations of the test in IEEE-14B benchmark confirm that the proposed method is an effective procedure for the dynamic design of RSSG-SPC to provide these dynamic stability supports in grid connection.
Feasibility of Black-Box Time Domain Modeling of Single-Phase Photovoltaic Inverters Using Artificial Neural Networks
Authors: Kaufhold Elias , Grandl Simon , Meyer Jan , Schegner Peter
Published: April 2021

This paper introduces a new black-box approach for time domain modeling of commercially available single-phase photovoltaic (PV) inverters in low voltage networks. An artificial neural network is used as a nonlinear autoregressive exogenous model to represent the steady state behavior as well as dynamic changes of the PV inverter in the frequency range up to 2 kHz. The data for the training and the validation are generated by laboratory measurements of a commercially available inverter for low power applications, i.e., 4.6 kW. The state of the art modeling approaches are explained and the constraints are addressed. The appropriate set of data for training is proposed and the results show the suitability of the trained network as a black-box model in time domain. Such models are required, i.e., for dynamic simulations since they are able to represent the transition between two steady states, which is not possible with classical frequency-domain models (i.e., Norton models). The demonstrated results show that the trained model is able to represent the transition between two steady states and furthermore reflect the frequency coupling characteristic of the grid-side current.
Mass Spectrometric Calibration Procedure for Real-Time Detection of Lighter Hydrocarbons
Authors: Mbaegbu Makuachukwu F. , Adhikari Puspa L. , Gupta Ipsita , Rowe Mathew
Published: April 2021

Determining gas compositions from live well fluids on a drilling rig is critical for real time formation evaluation. Development and utilization of a reliable mass spectrometric method to accurately characterize these live well fluids are always challenging due to lack of a robust and effectively selective instrument and procedure. The methods currently utilized need better calibration for the characterization of light hydrocarbons (C1–C6) at lower concentrations. The primary goal of this research is to develop and optimize a powerful and reliable analytical method to characterize live well fluid using a quadruple mass spectrometer (MS). The mass spectrometers currently being used in the field have issues with detection, spectra deconvolution, and quantification of analytes at lower concentrations (10–500 ppm), particularly for the lighter (<30 m/z) hydrocarbons. The objectives of the present study are thus to identify the detection issues, develop and optimize a better method, calibrate and QA/QC the MS, and validate the MS method in lab settings. In this study, we used two mass spectrometers to develop a selective and precise method to quantitatively analyze low level lighter analytes (C1–C6 hydrocarbons) with masses <75 m/z at concentrations 10–500 ppm. Our results suggest that proper mass selection like using base peaks with m/z 15, 26, 41, 43, 73, and 87, respectively, for methane, ethane, propane, butane, pentane, and hexane can help detect and accurately quantify hydrocarbons from gas streams. This optimized method in quadrupole mass spectrometer (QMS) will be invaluable for early characterization of the fluid components from a live hydrocarbon well in the field in real time.
Testing the Wear Mechanisms of the Components of Machines Used in Fossil Energy Resource Extraction
Authors: Wieczorek Andrzej N. , Jonczy Iwona , Bała Patryk , Stankiewicz Krzysztof , Staszuk Marcin
Published: April 2021

The paper presents the results of tests of wear of hardened wear-resistant steels in the presence of quartz, coal and coal-mineral abrasives of a differentiated ratios of each component and in the case without abrasives. The tests were carried out on a ring-on-ring test stand. Volume loss was the parameter used to characterize the wear level. Based on the Scanning Electron Microscopy (SEM) observations, different mechanisms of surface destruction depending on the type of abrasive were found. For the quartz abrasive, it was shown that the quartz grains cause micro-cutting, but surface cracks also appear that further transform into shallow holes. In the case of mixtures of quartz with coal, the surface was micro-scratched by quartz grains, but some of these grains were hammered into the surface, what was favoured by the presence of coal fractions. In the case of coal abrasive, traces of single cutting were observed, as well as unremoved scratches formed at the sample grinding stage. Based on the wear observation, two models of steel surface wear in the presence of coal and coal-mineral abrasives were determined.
The Systemic Approach for Estimating and Strategizing Energy Security: The Case of Ukraine
Authors: Kharazishvili Yurii , Kwilinski Aleksy , Sukhodolia Oleksandr , Dzwigol Henryk , Bobro Dmytro , Kotowicz Janusz
Published: April 2021

The current approaches to estimating the level of energy security are based on applying a comprehensive approach to selecting the factors that affect energy security and the dynamics of processes in this domain. This article reveals the application of the model of energy security estimation and strategizing based on the systemic description of energy security as an object of management: the integral system, elements and connections, functions, processes, and the system’s material. At the same time, this model is able to take into account the dynamics of technological, political, economic, and other factors operating in the country and on the global arena. The energy security estimation model developed uses a modern methodology of integrated estimation: a multiplicative form of the integrated index, a formalized definition of the safe existence limits in order to provide scientific substantiation of the threshold vector, a modified rationing method, the principal components method, and the sliding matrix method to substantiate dynamic weighting coefficients. The paper demonstrates the systemic approach application to shaping strategic goals in the energy security domain in the context of sustainable development; the trajectory of energy security development is calculated by the method of strategizing that applies the principle “future is determined by the trajectory to the future” instead of the classical forecasting “past determines the future”. In general, the article shows the possibility of unifying the process of formalizing energy security (according to the needs of the researcher: country, industry, energy network, supply chain) as an object of management, estimating energy security status, and strategizing the regulatory sphere transformation in accordance with the target values for monitoring the effectiveness of management.
Thermodynamic and Kinetic Considerations Regarding the Prospects for a Dual-Purpose Hydrogen Extraction and Separation Membrane
Authors: Sohlberg Karl
Published: April 2021

Extraction of hydrogen from hydrocarbons is a logical intermediate-term solution for the escalating worldwide demand for hydrogen. This work explores the possibility of using a single membrane to accomplish both the catalytic dehydrogenation and physical separation of hydrogen gas as a possible way to improve the efficiency of hydrogen production from hydrocarbon sources. The present analysis shows that regions of pressure/temperature space exist for which the overall process is thermodynamically spontaneous (ΔG < 0). Each step in the process is based on known physics. The rate of hydrogen production is likely to be controlled by the barrier to hydrogen abstraction, with the density of H-binding sites also playing a role. A critical materials issue will be the strength of the oxide/metal interface.
Experimental and Artificial Intelligence Modelling Study of Oil Palm Trunk Sap Fermentation
Authors: Ezzatzadegan Leila , Yusof Rubiyah , Morad Noor Azian , Shabanzadeh Parvaneh , Muda Nur Syuhana , Borhani Tohid N.
Published: April 2021

Five major operations for the conversion of lignocellulosic biomasses into bioethanol are pre-treatment, detoxification, hydrolysis, fermentation, and distillation. The fermentation process is a significant biological step to transform lignocellulose into biofuel. The interactions of biochemical networks and their uncertainty and nonlinearity that occur during fermentation processes are major problems for experts developing accurate bioprocess models. In this study, mechanical processing and pre-treatment on the palm trunk were done before fermentation. Analysis was performed on the fresh palm sap and the fermented sap to determine the composition. The analysis for total sugar content was done using high-performance liquid chromatography (HPLC) and the percentage of alcohols by volume was determined using gas chromatography (GC). A model was also developed for the fermentation process based on the Adaptive-Network-Fuzzy Inference System (ANFIS) combined with particle swarm optimization (PSO) to predict bioethanol production in biomass fermentation of oil palm trunk sap. The model was used to find the best experimental conditions to achieve the maximum bioethanol concentration. Graphical sensitivity analysis techniques were also used to identify the most effective parameters in the bioethanol process.
Transition Prediction in Incompressible Boundary Layer with Finite-Amplitude Streaks
Authors: Martín Juan Ángel , Paredes Pedro
Published: April 2021

Modulating the boundary layer velocity profile is a very promising strategy for achieving transition delay and reducing the friction of the plate. By perturbing the flow with counter-rotating vortices that undergo transient, non-modal growth, streamwise-aligned streaks are formed inside the boundary layer, which have been proved (theoretical and experimentally) to be very robust flow structures. In this paper, we employ efficient numerical methods to perform a parametric stability investigation of the three-dimensional incompressible flat-plate boundary layer with finite-amplitude streaks. For this purpose, the Boundary Region Equations (BREs) are applied to solve the nonlinear downstream evolution of finite amplitude streaks. Regarding the stability analysis, the linear three-dimensional plane-marching Parabolized Stability Equations (PSEs) concept constitutes the best candidate for this task. Therefore, a thorough parametric study is presented, analyzing the instability characteristics with respect to critical conditions of the modified incompressible zero-pressure-gradient flat-plate boundary layer, by means of finite-amplitude linearly optimal and suboptimal disturbances or streaks. The parameter space is extended from low- to high- amplitude streaks, accurately documenting the transition delay for low-amplitude streaks and the amplitude threshold for streak shear layer instability or bypass transition, which drastically displaces the transition front upstream.