Search results for: doubly connected domain

Authors: Amal A. Hassan, Faten H. Fahmy, Abd El-Shafy A. Nafeh, Hosam K. M. Youssef

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Due to the growing penetration of wind energy into the power grid, it is very important to study its interactions with the power system and to provide good control technique in order to deliver high quality power. In this paper, an intelligent control methodology is proposed for optimizing the controllers’ parameters of doubly fed induction generator (DFIG) based wind turbine generation system (WTGS). The genetic algorithm (GA) and particle swarm optimization (PSO) are employed and compared for the parameters adaptive tuning of the proposed proportional integral (PI) multiple controllers of the back to back converters of the DFIG based WTGS. For this purpose, the dynamic model of WTGS with DFIG and its associated controllers is presented. Furthermore, the simulation of the system is performed using MATLAB/SIMULINK and SIMPOWERSYSTEM toolbox to illustrate the performance of the optimized controllers. Finally, this work is validated to 33-bus test radial system to show the interaction between wind distributed generation (DG) systems and the distribution network.

Keywords: DFIG wind turine, intelligent control, distributed generation, particle swarm optimization, genetic algorithm

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Authors: Hyunseo Choi, Jaewan Hong, Seil Lee, Yang Mo Koo

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Non-oriented (NO) electrical steel is one of the most important soft magnetic materials for rotating machines. Si has usually been added to electrical steels to reduce eddy current loss by increasing the electrical resistivity. Si content more than 3.5 wt% causes cracks during cold rolling due to increase of brittleness. Al also increases the electrical resistivity of the materials as much as Si. In addition, cold workability of Fe-Al is better than Fe-Si, so that Al can be added up to 6.0 wt%. However, the effect of Al contents on magnetic properties of electrical steels has not been studied in detail. Magnetic domains of {100} grains in electrical steels, ranging from 1.85 to 6.54 wt% Al, were observed by magneto-optic Kerr microscopy. Furthermore, the correlation of magnetic domains with magnetic properties was investigated. As Al contents increased, the magnetic domain size of {100} grains decreased due to lowered domain wall energy. Reorganization of magnetic domain structure became more complex as domain size decreased. Therefore, the addition of Al to electrical steel caused hysteresis loss to increase. Anomalous loss decreased and saturated after 4.68% Al.

Keywords: electrical steel, magnetic domain structure, Al addition, core loss, rearrangement of domains

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Authors: Kay Thi Yar, Khin Mar Lar Tun

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Nowadays, social networks are so popular and widely used in all over the world. In addition, searching personal information of each person and searching connection between them (peoples’ relation in real world) becomes interesting issue in our society. In this paper, we propose a framework with three portions for exploring peoples’ relations from their connected information. The first portion focuses on the Graph database structure to store the connected data of peoples’ information. The second one proposes the graph database searching algorithm, the Modified-SoS-ACO (Sense of Smell-Ant Colony Optimization). The last portion proposes the Deductive Reasoning Algorithm to define two persons’ relationship. This study reveals the proper storage structure for connected information, graph searching algorithm and deductive reasoning algorithm to predict and analyze the personnel relationship from peoples’ relation in their connected information.

Keywords: personnel information, graph storage structure, graph searching algorithm, deductive reasoning algorithm

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Authors: Zeinabsadat Haghshenas

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Bloom’s Taxonomy has been changed during the years. The idea of this writing is about the revision that has happened in both facts and terms. It also contains case studies of using cognitive Bloom’s taxonomy in teaching geometric solids to the secondary school students, affective objectives in a creative workshop for adults and psychomotor objectives in fixing a malfunctioned refrigerator lamp. There is also pointed to the important role of classification objectives in adult education as a way to prevent memory loss.

Keywords: adult education, affective domain, cognitive domain, memory loss, psychomotor domain

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Authors: Anders Thorsén, Behrooz Sangchoolie, Peter Folkesson, Ted Strandberg

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As more parts of the power grid become connected to the internet, the risk of cyberattacks increases. To identify the cybersecurity threats and subsequently reduce vulnerabilities, the common practice is to carry out a cybersecurity risk assessment. For safety classified systems and products, there is also a need for safety risk assessments in addition to the cybersecurity risk assessment in order to identify and reduce safety risks. These two risk assessments are usually done separately, but since cybersecurity and functional safety are often related, a more comprehensive method covering both aspects is needed. Some work addressing this has been done for specific domains like the automotive domain, but more general methods suitable for, e.g., intelligent distributed grids, are still missing. One such method from the automotive domain is the Security-Aware Hazard Analysis and Risk Assessment (SAHARA) method that combines safety and cybersecurity risk assessments. This paper presents an approach where the SAHARA method has been modified in order to be more suitable for larger distributed systems. The adapted SAHARA method has a more general risk assessment approach than the original SAHARA. The proposed method has been successfully applied on two use cases of an intelligent distributed grid.

Keywords: intelligent distribution grids, threat analysis, risk assessment, safety, cybersecurity

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Authors: Naima Ikken, Abdelhadi Bouknadel, Nour-eddine Tariba Ahmed Haddou, Hafsa El Omari

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The operation of grid-connected inverters necessity a single-phase phase locked loop (PLL) is proposed in this article to accurately and quickly estimate and detect the grid phase angle. This article presents the improvement of a method of phase-locked loop. The novelty is to generate a method (PLL) of synchronizing the grid with a Notch filter based on adaptive fuzzy logic for inverter systems connected to the grid. The performance of the proposed method was tested under normal and abnormal operating conditions (amplitude, frequency and phase shift variations). In addition, simulation results with ISPM software are developed to verify the effectiveness of the proposed method strategy. Finally, the experimental test will be used to extract the result and discuss the validity of the proposed algorithm.

Keywords: phase locked loop, PLL, notch filter, fuzzy logic control, grid connected inverters

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Authors: Abul B. M. M. K. Islam, Nuria Lopez-Bigas, Elizaveta V. Benevolenskaya

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RBP2 has shown to be important for cell differentiation control through epigenetic mechanism. The main aim of the present study is genome-wide location analysis of human RBP2 isoforms that differ in a histone-binding domain by ChIPseq. It is conceivable that the larger isoform (LI) of RBP2, which contains a specific H3K4me3 interacting domain, differs from the smaller isoform (SI) in genomic location, may account for the observed diversity in RBP2 function. To distinguish the two RBP2 isoforms, we used the fact that the SI lacks the C-terminal PHD domain and hence used the antibodies detecting both RBP2 isoforms (AI) through a common central domain, and the antibodies detecting only LI but not SI, through a C-terminal PHD domain. Overall our analysis suggests that RBP2 occupies about 77 nucleotides and binds GC rich motifs of active genes, does not bind to centromere, telomere, or enhancer regions, and binding sites are conserved compare to random. A striking difference between the only-SI and only-LI is that a large number of only-SI peaks are located in CpG islands and close to TSS compared to only-LI peaks. Enrichment analysis of the related genes indicates that several oncogenic pathways and metabolic pathways/processes are significantly enriched among only-SI/AI targets, but not LI/only-LI peak’s targets.

Keywords: bioinformatics, cancer, ChIP-seq, KDM5A

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Authors: Rama Bhargava

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In the current paper, numerical simulation has been performed for the two-dimensional time dependent Pennes’ heat transfer model which is solved for irregular diseased tumor cells. An elliptic cryoprobe of varying sizes is taken at the center of the computational domain in such a manner that the location of the probe is fixed throughout the computation. The phase transition occurs due to the effect of probe with infusion of different nanoparticles Au, Al₂O₃, Fe₃O₄. The cooling performance of these nanoparticles injected at very low temperature, has been studied by implementing a hybrid FEM/EFGM method in which the whole domain is decomposed into two subdomains. The results are shown in terms of temperature profile inside the computational domain. Rate of cooling is obtained for various nanoparticles and it is observed that infusion of Au nanoparticles is very much efficient in increasing the heating rate than other nanoparticles. Such numerical scheme has direct applications where the domain is irregular.

Keywords: cryosurgery, hybrid EFGM/FEM, nanoparticles, simulation

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Authors: Abhigna Bhatt, Arnab Banerjee

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A periodic system with only a single coupling degree of freedom is called a monocoupled system. Monocoupled systems with mechanisms like mass in the mass system generates effective negative mass, mass connected with rigid links generates inertial amplification, and spring-mass connected with a rigid link generateseffective negative stiffness. In this paper, the representative unit cell is introduced, considering all three mechanisms combined. Further, the dynamic stiffness matrix of the unit cell is constructed, and the dispersion relation is obtained by applying the Bloch theorem. The frequency response function is also calculated for the finite length of periodic unit cells. Moreover, the input displacement signal is given to the finite length of periodic structure and using inverse Fourier transform to visualize the wave propagation in the time domain. This visualization explains the sudden attenuation in metamaterial due to energy dissipation by an embedded resonator at the resonance frequency. The visualization created for wave propagation is found necessary to understand the insights of physics behind the attenuation characteristics of the system.

Keywords: mono coupled system, negative effective mass, negative effective stiffness, inertial amplifier, fourier transform

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Authors: Geetha Baskaran, Andrzej Barjiela, Rong Qu

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Motivation: Nurse scheduling is a complex combinatorial optimization problem. It is also known as NP-hard. It needs an efficient re-scheduling to minimize some trade-off of the measures of violation by reducing selected constraints to soft constraints with measurements of their violations. Problem Statement: In this paper, we extend our novel approach to solve the nurse scheduling problem by transforming it through Information Granulation. Approach: This approach satisfies the rules of a typical hospital environment based on a standard benchmark problem. Generating good work schedules has a great influence on nurses' working conditions which are strongly related to the level of a quality health care. Domain transformation that combines the strengths of operation research and artificial intelligence was proposed for the solution of the problem. Compared to conventional methods, our approach involves judicious grouping (information granulation) of shifts types’ that transforms the original problem into a smaller solution domain. Later these schedules from the smaller problem domain are converted back into the original problem domain by taking into account the constraints that could not be represented in the smaller domain. An Integer Programming (IP) package is used to solve the transformed scheduling problem by expending the branch and bound algorithm. We have used the GNU Octave for Windows to solve this problem. Results: The scheduling problem has been solved in the proposed formalism resulting in a high quality schedule. Conclusion: Domain transformation represents departure from a conventional one-shift-at-a-time scheduling approach. It offers an advantage of efficient and easily understandable solutions as well as offering deterministic reproducibility of the results. We note, however, that it does not guarantee the global optimum.

Keywords: domain transformation, nurse scheduling, information granulation, artificial intelligence, simulation

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Authors: Abdelhak Djoudi, Hachemi Chekireb, El Madjid Berkouk

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The doubly fed induction generator (DFIG) received recently an important consideration in medium and high power wind energy conversion systems integration, due to its advantages compared to other generators types. The stator power sliding mode control (SPSMC) proves a great efficiency judge against other control laws and schemes. In the SPSMC laws elaborated by several authors, only the slide surface tracking conditions are elaborated using Lyapunov functions, and the boundedness of the DFIG states is never treated. Some works have validated theirs approaches by experiments results in the case of specified machines, but these verifications stay insufficient to generalize to other machines range. Adding to this argument, the DFIG states boundedness demonstration is widely suggested in goal to ensure that in the application of the SPSMC, the states evaluates within theirs tolerable bounds. Our objective in the present paper is to highlight the efficiency of the SPSMC by stability analysis. The boundedness of the DFIG states such as the stator current and rotor flux is discussed. Moreover, the states trajectories are finding using analytical proves taking into consideration the SPSMC gains.

Keywords: Doubly Fed Induction Generator (DFIG), Stator Powers Sliding Mode Control (SPSMC), lyapunov function, stability, states boundedness, trajectories mathematical proves

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Authors: Alexandra Leukauf, Alexander Schirrer, Emir Talic

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Numerical computation of wave propagation in a large domain usually requires significant computational effort. Hence, the considered domain must be truncated to a smaller domain of interest. In addition, special boundary conditions, which absorb the outward travelling waves, need to be implemented in order to describe the system domains correctly. In this work, the linear one dimensional wave equation is approximated by utilizing the Fourier Galerkin approach. Furthermore, the artificial boundaries are realized with absorbing boundary conditions. Within this work, a systematic work flow for setting up the wave problem, including the absorbing boundary conditions, is proposed. As a result, a convenient modal system description with an effective absorbing boundary formulation is established. Moreover, the truncated model shows high accuracy compared to the global domain.

Keywords: absorbing boundary conditions, boundary control, Fourier Galerkin approach, modal approach, wave equation

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Authors: Mamidi Ramakrishna Rao

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Doubly-fed induction generators (DFIG) due to their advantages like speed variation and four-quadrant operation, find its application in wind turbines. DFIG besides supplying power to the grid has to support reactive power (kvar) under grid voltage variations, should contribute minimum fault current during faults, have high efficiency, minimum weight, adequate rotor protection during crow-bar-operation from +20% to -20% of rated speed.  To achieve the optimum performance, a good electromagnetic design of DFIG is required. In this paper, a simple and heuristic global optimization – Differential Evolution has been used. Variables considered are lamination details such as slot dimensions, stack diameters, air gap length, and generator stator and rotor stack length. Two operating conditions have been considered - voltage and speed variations. Constraints included were reactive power supplied to the grid and limiting fault current and torque. The optimization has been executed separately for three objective functions - maximum efficiency, weight reduction, and grid fault stator currents. Subsequent calculations led to the conclusion that designs determined through differential evolution help in determining an optimum electrical design for each objective function.

Keywords: design optimization, performance, DFIG, differential evolution

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Authors: Shahin A. Abdel-Naby, Asad T. Hassan

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Electron-ion recombination data are needed for plasma modeling. The recombination processes include radiative recombination (RR), dielectronic recombination (DR), and trielectronic recombination (TR). When a free electron is captured by an ion with simultaneous excitation of its core, a doubly-exited intermediate state may be formed. The doubly excited state relaxes either by electron emission (autoionization) or by radiative decay (photon emission). DR process takes place when the relaxation occurs to a bound state by photon emission. Reliable laboratory astrophysics data (theory and experiment) for DR rate coefficients are needed to determine the charge state distribution in photoionized sources such as X-ray binaries and active galactic nuclei. DR rate coefficients for NIII and OIV ions are calculated using state-of-the-art multi-configuration Breit-Pauli atomic structure AUTOSTRUCTURE collisional package within the generalized collisional-radiative framework. Level-resolved calculations for RR and DR rate coefficients from the ground and metastable initial states are produced in an intermediate coupling scheme associated with Δn = 0 (2→2) and Δn = 1 (2 →3) core-excitations. DR cross sections for these ions are convoluted with the experimental electron-cooler temperatures to produce DR rate coefficients. Good agreements are found between these rate coefficients and the experimental measurements performed at the CRYRING heavy-ion storage ring for both ions.

Keywords: atomic data, atomic process, electron-ion collision, plasmas

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Authors: Rowanda Daoud Ahmed, Mansoor Abdulhak, Muhammad Azeem Afzal, Sezer Filiz, Usama Ahmad Mughal

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This paper discusses important aspects of AI in the healthcare domain. The increase of data in healthcare both in size and complexity, opens more room for artificial intelligence applications. Our focus is to review the main AI methods within the scope of the health care domain. The results of the review show that recommendations for diagnosis and recommendations for treatment, patent engagement, and administrative tasks are the key applications of AI in healthcare. Understanding the potential of AI methods in the domain of healthcare would benefit healthcare practitioners and will improve patient outcomes.

Keywords: AI in healthcare, technologies of AI, neural network, future of AI in healthcare

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Authors: Sajjad Yahaghifar

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From the end of the last century, the importance and use of renewable energy sources have gained prominence, due not only by the fossil fuels dependence reduction, but mainly by environmental reasons related to climate change and the effects to the humanity. Consequently, solar energy has been arousing interest in several countries for being a technology considered clean, with reduced environmental impact. The output power of photo voltaic (PV) arrays is always changing with weather conditions,i.e., solar irradiation and atmospheric temperature. Therefore, maximum power point tracking (MPPT) control to extract maximum power from the PV arrays at real time becomes indispensable in PV generation system. This paper Study MPPT and total harmonic distortion (THD) in the city of Tabriz, Iran with the grid-connected PV system as distributed generation.

Keywords: MPPT, THD, grid-connected, PV system

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Authors: Talal Alshammari, Nasser Alshammari, Mohamed Sedky, Chris Howard

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With the widespread adoption of the Internet-connected devices, and with the prevalence of the Internet of Things (IoT) applications, there is an increased interest in machine learning techniques that can provide useful and interesting services in the smart home domain. The areas that machine learning techniques can help advance are varied and ever-evolving. Classifying smart home inhabitants’ Activities of Daily Living (ADLs), is one prominent example. The ability of machine learning technique to find meaningful spatio-temporal relations of high-dimensional data is an important requirement as well. This paper presents a comparative evaluation of state-of-the-art machine learning techniques to classify ADLs in the smart home domain. Forty-two synthetic datasets and two real-world datasets with multiple inhabitants are used to evaluate and compare the performance of the identified machine learning techniques. Our results show significant performance differences between the evaluated techniques. Such as AdaBoost, Cortical Learning Algorithm (CLA), Decision Trees, Hidden Markov Model (HMM), Multi-layer Perceptron (MLP), Structured Perceptron and Support Vector Machines (SVM). Overall, neural network based techniques have shown superiority over the other tested techniques.

Keywords: activities of daily living, classification, internet of things, machine learning, prediction, smart home

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Authors: Phuoc Si Nguyen

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Frequency transformation with Pascal matrix equations is a method for transforming an electronic filter (analogue or digital) into another filter. The technique is based on frequency transformation in the s-domain, bilinear z-transform with pre-warping frequency, inverse bilinear transformation and a very useful application of the Pascal’s triangle that simplifies computing and enables calculation by hand when transforming from one filter to another. This paper will introduce two methods to transform a filter into a digital filter: frequency transformation from the s-domain into the z-domain; and frequency transformation in the z-domain. Further, two Pascal matrix equations are derived: an analogue to digital filter Pascal matrix equation and a digital to digital filter Pascal matrix equation. These are used to design a desired digital filter from a given filter.

Keywords: frequency transformation, bilinear z-transformation, pre-warping frequency, digital filters, analog filters, pascal’s triangle

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Authors: Nuha Mansour Alhazmi, Magda Mohamed Aly, Fardus M. Bokhari, Ahmed Bahieldin, Sherif Edris

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Out of 30 fungal isolates, 2 new isolates were proven to degrade poly-β-hydroxybutyrate (PHB). Enzyme assay for these isolates indicated the optimal environmental conditions required for depolymerase enzyme to induce the highest level of biopolymer degradation. The two isolates were basically characterized at the morphological level as Trichoderma asperellum (isolate S1), and Aspergillus fumigates (isolate S2) using standard approaches. The aim of the present study was to characterize these two isolates at the molecular level based on the highly diverged rRNA gene(s). Within this gene, two domains of the ribosome large subunit (LSU) namely internal transcribed spacer (ITS) and 26S were utilized in the analysis. The first domain comprises the ITS1/5.8S/ITS2 regions ( > 500 bp), while the second domain comprises the D1/D2/D3 regions ( > 1200 bp). Sanger sequencing was conducted at Macrogen (Inc.) for the two isolates using primers ITS1/ITS4 for the first domain, while primers LROR/LR7 for the second domain. Sizes of the first domain ranged between 594-602 bp for S1 isolate and 581-594 bp for S2 isolate, while those of the second domain ranged between 1228-1238 bp for S1 isolate and 1156-1291 for S2 isolate. BLAST analysis indicated 99% identities of the first domain of S1 isolate with T. asperellum isolates XP22 (ID: KX664456.1), CTCCSJ-G-HB40564 (ID: KY750349.1), CTCCSJ-F-ZY40590 (ID: KY750362.1) and TV (ID: KU341015.1). BLAST of the first domain of S2 isolate indicated 100% identities with A. fumigatus isolate YNCA0338 (ID: KP068684.1) and strain MEF-Cr-6 (ID: KU597198.1), while 99% identities with A. fumigatus isolate CCA101 (ID: KT877346.1) and strain CD1621 (ID: JX092088.1). Large numbers of other T. asperellum and A. fumigatus isolates and strains showed high level of identities with S1 and S2 isolates, respectively, based on the diversity of the first domain. BLAST of the second domain of S1 isolate indicated 99 and 100% identities with only two strains of T. asperellum namely TR 3 (ID: HM466685.1) and G (ID: KF723005.1), respectively. However, other T. species (ex., atroviride, hamatum, deliquescens, harzianum, etc.) also showed high level of identities. BLAST of the second domain of S2 isolate indicated 100% identities with A. fumigatus isolate YNCA0338 (ID: KP068684.1) and strain MEF-Cr-6 (ID: KU597198.1), while 99% identities with A. fumigatus isolate CCA101 (ID: KT877346.1) and strain CD1621 (ID: JX092088.1). Large numbers of other A. fumigatus isolates and strains showed high level of identities with S2 isolate. Overall, the results of molecular characterization based on rRNA diversity for the two isolates of T. asperellum and A. fumigatus matched those obtained by morphological characterization. In addition, ITS domain proved to be more sensitive than 26S domain in diversity profiling of fungi at the species level.

Keywords: Aspergillus fumigates, Trichoderma asperellum, PHB, degradation, BLAST, ITS, 26S, rRNA

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Authors: Shrabani Ghosh

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Automatic offensive language detection in social media has become a stirring task in today's NLP. Manual Offensive language detection is tedious and laborious work where automatic methods based on machine learning are only alternatives. Previous works have done sentiment analysis over social media in different ways such as supervised, semi-supervised, and unsupervised manner. Domain adaptation in a semi-supervised way has also been explored in NLP, where the source domain and the target domain are different. In domain adaptation, the source domain usually has a large amount of labeled data, while only a limited amount of labeled data is available in the target domain. Pretrained transformers like BERT, RoBERTa models are fine-tuned to perform text classification in an unsupervised manner to perform further pre-train masked language modeling (MLM) tasks. In previous work, hate speech detection has been explored in Gab.ai, which is a free speech platform described as a platform of extremist in varying degrees in online social media. In domain adaptation process, Twitter data is used as the source domain, and Gab data is used as the target domain. The performance of domain adaptation also depends on the cross-domain similarity. Different distance measure methods such as L2 distance, cosine distance, Maximum Mean Discrepancy (MMD), Fisher Linear Discriminant (FLD), and CORAL have been used to estimate domain similarity. Certainly, in-domain distances are small, and between-domain distances are expected to be large. The previous work finding shows that pretrain masked language model (MLM) fine-tuned with a mixture of posts of source and target domain gives higher accuracy. However, in-domain performance of the hate classifier on Twitter data accuracy is 71.78%, and out-of-domain performance of the hate classifier on Gab data goes down to 56.53%. Recently self-supervised learning got a lot of attention as it is more applicable when labeled data are scarce. Few works have already been explored to apply self-supervised learning on NLP tasks such as sentiment classification. Self-supervised language representation model ALBERTA focuses on modeling inter-sentence coherence and helps downstream tasks with multi-sentence inputs. Self-supervised attention learning approach shows better performance as it exploits extracted context word in the training process. In this work, a self-supervised attention mechanism has been proposed to detect hate speech on Gab.ai. This framework initially classifies the Gab dataset in an attention-based self-supervised manner. On the next step, a semi-supervised classifier trained on the combination of labeled data from the first step and unlabeled data. The performance of the proposed framework will be compared with the results described earlier and also with optimized outcomes obtained from different optimization techniques.

Keywords: attention learning, language model, offensive language detection, self-supervised learning

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Authors: Yulan Wu

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With the rapid development of social media, the issue of fake news has gained considerable prominence, drawing the attention of both the public and governments. The widespread dissemination of false information poses a tangible threat across multiple domains of society, including politics, economy, and health. However, much research has concentrated on supervised training models within specific domains, their effectiveness diminishes when applied to identify fake news across multiple domains. To solve this problem, some approaches based on domain labels have been proposed. By segmenting news to their specific area in advance, judges in the corresponding field may be more accurate on fake news. However, these approaches disregard the fact that news records can pertain to multiple domains, resulting in a significant loss of valuable information. In addition, the datasets used for training must all be domain-labeled, which creates unnecessary complexity. To solve these problems, an unsupervised domain knowledge discovery framework for fake news detection is proposed. Firstly, to effectively retain the multidomain knowledge of the text, a low-dimensional vector for each news text to capture domain embeddings is generated. Subsequently, a feature extraction module utilizing the unsupervisedly discovered domain embeddings is used to extract the comprehensive features of news. Finally, a classifier is employed to determine the authenticity of the news. To verify the proposed framework, a test is conducted on the existing widely used datasets, and the experimental results demonstrate that this method is able to improve the detection performance for fake news across multiple domains. Moreover, even in datasets that lack domain labels, this method can still effectively transfer domain knowledge, which can educe the time consumed by tagging without sacrificing the detection accuracy.

Keywords: fake news, deep learning, natural language processing, multiple domains

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Authors: Abdulkadir Magaji

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This study analyzes and compares the actual measured and simulated performance of a 3.2 kwP grid-connected photovoltaic system. The system is located at the Outdoor Facility of Government Day secondary School Katsina State, which lies approximately between coordinate of 12°15′N 7°30′E. The system consists of 14 Mono crystalline silicon modules connected in two strings of 7 series-connected modules, each facing north at a fixed tilt of 340. The data presented in this study were measured in the year 2015, where the system supplied a total of 4628 kWh to the local electric utility grid. The performance of the system was simulated using PVsyst software using measured and Meteonorm derived climate data sets (solar radiation, ambient temperature and wind speed). The comparison between measured and simulated energy yield are discussed. Although, both simulation results were similar, better comparison between measured and predicted monthly energy yield is observed with simulation performed using measured weather data at the site. The measured performance ratio in the present study shows 58.4% is higher than those reported elsewhere as compared in the study.

Keywords: performance, evaluation, grid connection, photovoltaic system

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Authors: Shu-Yu Hsu, Chen-Chien Hsu, Wei-Yen Wang

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Color segmentation is a basic and simple way for recognizing the visual markers in a robotic tracking system. In this paper, we propose a new method for color segmentation by incorporating differential evolution algorithm and connected component labeling to autonomously preset the HSV threshold of visual markers. To evaluate the effectiveness of the proposed algorithm, a ROBOTIS OP2 humanoid robot is used to conduct the experiment, where five most commonly used color including red, purple, blue, yellow, and green in visual markers are given for comparisons.

Keywords: color segmentation, differential evolution, connected component labeling, humanoid robot

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Authors: Jaimala Ghambir, Tilak Thakur, Puneet Chawla

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As per the statistical data, the Doubly-fed Induction Generator (DFIG) based wind turbine with variable speed and variable pitch control is the most common wind turbine in the growing wind market. This machine is usually used on the grid connected wind energy conversion system to satisfy grid code requirements such as grid stability, fault ride through (FRT), power quality improvement, grid synchronization and power control etc. Though the requirements are not fulfilled directly by the machine, the control strategy is used in both the stator as well as rotor side along with power electronic converters to fulfil the requirements stated above. To satisfy the grid code requirements of wind turbine, usually grid side converter is playing a major role. So in order to improve the operation capacity of wind turbine under critical situation, the intensive study of both machine side converter control and grid side converter control is necessary In this paper DFIG is modeled using power components as variables and the performance of the DFIG system is analysed under grid voltage fluctuations. The voltage fluctuations are made by lowering and raising the voltage values in the utility grid intentionally for the purpose of simulation keeping in view of different grid disturbances.

Keywords: DFIG, dynamic modeling, DPC, sag, swell, voltage fluctuations, FRT

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Authors: Xiaofei Hu, Zhiyu Cai, Weian Yao

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V-notch problem under dynamic loading condition is considered in this paper. In the time domain, the precise time domain expanding algorithm is employed, in which a self-adaptive technique is carried out to improve computing accuracy. By expanding variables in each time interval, the recursive finite element formulas are derived. In the space domain, a Symplectic Analytical Singular Element (SASE) for V-notch problem is constructed addressing the stress singularity of the notch tip. Combining with the conventional finite elements, the proposed SASE can be used to solve the dynamic stress intensity factors (DSIFs) in a simple way. Numerical results show that the proposed SASE for V-notch problem subjected to dynamic loading condition is effective and efficient.

Keywords: V-notch, dynamic stress intensity factor, finite element method, precise time domain expanding algorithm

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Authors: Isabelle Snyder, Travis Smith

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Microgrid protection presents challenges to conventional protection techniques due to the low-induced fault current. Protection relays present in microgrid applications require a combination of settings groups to adjust based on the architecture of the microgrid in islanded and grid-connected modes. In a radial system where the microgrid is at the other end of the feeder, directional elements can be used to identify the direction of the fault current and switch settings groups accordingly (grid-connected or microgrid-connected). However, with multiple microgrid connections, this concept becomes more challenging, and the direction of the current alone is not sufficient to identify the source of the fault current contribution. ORNL has previously developed adaptive relaying schemes through other DOE-funded research projects that will be evaluated and used as a baseline for this research. The four protection techniques in this study are labeled as follows: (1) Adaptive Current only Protection System (ACPS), Intentional (2) Unbalanced Control for Protection Control (IUCPC), (3) Adaptive Protection System with Communication Controller (APSCC) (4) Adaptive Model-Driven Protective Relay (AMDPR).

Keywords: adaptive relaying, microgrid protection, sequence components, islanding detection

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Authors: Qin Long, Li Xiaoge

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The Knowledge Graphs have now become a new form of knowledge representation. However, there is no consensus in regard to a plausible and definition of entities and relationships in the domain-specific knowledge graph. Further, in conjunction with several limitations and deficiencies, various domain-specific entities and relationships recognition approaches are far from perfect. Specifically, named entity recognition in Chinese domain is a critical task for the natural language process applications. However, a bottleneck problem with Chinese named entity recognition in new domains is the lack of annotated data. To address this challenge, a domain distant supervised named entity recognition framework is proposed. The framework is divided into two stages: first, the distant supervised corpus is generated based on the entity linking model of graph attention neural network; secondly, the generated corpus is trained as the input of the distant supervised named entity recognition model to train to obtain named entities. The link model is verified in the ccks2019 entity link corpus, and the F1 value is 2% higher than that of the benchmark method. The re-pre-trained BERT language model is added to the benchmark method, and the results show that it is more suitable for distant supervised named entity recognition tasks. Finally, it is applied in the computer field, and the results show that this framework can obtain domain named entities.

Keywords: distant named entity recognition, entity linking, knowledge graph, graph attention neural network

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Authors: Shagufta Tabassum, V. P. Pawar, jr., G. N. Shinde

Abstract:

The study of dielectric relaxation properties of polar liquids in the binary mixture has been carried out at 10, 15, 20 and 25 ºC temperatures for 11 different concentrations using time domain reflectometry technique. The dielectric properties of a solute-solvent mixture of polar liquids in the frequency range of 10 MHz to 30 GHz gives the information regarding formation of monomers and multimers and also an interaction between the molecules of the liquid mixture under study. The dielectric parameters have been obtained by the least squares fit method using the Debye equation characterized by a single relaxation time without relaxation time distribution.

Keywords: excess properties, relaxation time, static dielectric constant, and time domain reflectometry technique

Procedia PDF Downloads 122

Authors: J. Ranjbarn, A. Alibeigloo

Abstract:

In this paper, we present an analytical method for analysis of nano-scale spherical shell subjected to thermo-mechanical shocks based on nonlocal elasticity theory. Thermo-mechanical properties of nano shpere is assumed to be temperature dependent. Governing partial differential equation of motion is solved analytically by using Laplace transform for time domain and power series for spacial domain. The results in Laplace domain is transferred to time domain by employing the fast inverse Laplace transform (FLIT) method. Accuracy of present approach is assessed by comparing the the numerical results with the results of published work in literature. Furtheremore, the effects of non-local parameter and wall thickness on the dynamic characteristics of the nano-sphere are studied.

Keywords: nano-scale spherical shell, nonlocal elasticity theory, thermomechanical shock, dynamic response

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Authors: Ernesto Ghini

Abstract:

Workplace accidents are an unfortunate reality. However, they are also predictable and avoidable. We conducted research into a variety of legislation covering lone working, and conducted a study into the use of connected technology and how it can help improve the safety of lone workers in Latin America. We implemented quantitative research into regulations coupled with case study research into a real-life scenario that demonstrated the benefits of technology, and discuss our findings in this paper. Connected safety solutions can improve the bottom line, delivering significant return on investment in terms of improved efficiency and the avoidance of cost associated with worker injury. And, most importantly, such solutions, as demonstrated through our research, make the difference between life and death in time-critical incident situations.

Keywords: ione worker, legislation, technology, connected safety, connectivity

Procedia PDF Downloads 42