Search results for: domain decomposition

Authors: Tisciane Perpetuo e Oliveira, Dante Inga Narvaez, Marcelo Gradella Villalva

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Installations of solar photovoltaic systems have increased considerably in the last decade. Therefore, it has been noticed that monitoring of meteorological data (solar irradiance, air temperature, wind velocity, etc.) is important to predict the potential of a given geographical area in solar energy production. In this sense, the present work compares two computational tools that are capable of estimating the energy generation of a photovoltaic system through correlation analyzes of solar radiation data: PVsyst software and an algorithm based on the PVlib package implemented in MATLAB. In order to achieve the objective, it was necessary to obtain solar radiation data (measured and from a solarimetric database), analyze the decomposition of global solar irradiance in direct normal and horizontal diffuse components, as well as analyze the modeling of the devices of a photovoltaic system (solar modules and inverters) for energy production calculations. Simulated results were compared with experimental data in order to evaluate the performance of the studied methods. Errors in estimation of energy production were less than 30% for the MATLAB algorithm and less than 20% for the PVsyst software.

Keywords: energy production, meteorological data, irradiance decomposition, solar photovoltaic system

Procedia PDF Downloads 107

Authors: Michael Tupý, Jaroslav Císař, Pavel Mokrejš, Dagmar Měřínská, Alice Tesaříková-Svobodová

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The worldwide problem is that the recycled PVB is wildly stored in landfills. However, PVB have very similar chemical properties such as PVC. Moreover, both of them are used in plasticized form. Thus, the thermal properties of plasticized PVC obtained from primary production and the PVB was obtained by recycling of windshields are compared. It is carried out in order to find degradable conditions and decide if blend of PVB/PVC can be processable together. Tested PVC contained 38 % of plasticizer diisononyl phthalate (DINP) and PVB was plasticized with 28 % of triethylene glycol, bis(2-ethylhexanoate) (3GO). Thermal and thermo-oxidative decomposition of both vinyl polymers are compared such as DSC and OOT analysis. The tensile strength analysis is added.

Keywords: polyvinyl chloride, polyvinyl butyral, recycling, reprocessing, thermal analysis, decomposition

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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: 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

Procedia PDF Downloads 85

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: Sushil Kumar Saraswat, K. K. Pant

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Co metal supported on SiO2 and Al2O3 catalysts with a metal loading varied from 30 of 70 wt.% were evaluated for decomposition of methane to CO/CO2 free hydrogen and carbon nano materials. The catalytic runs were carried out from 550-800 oC under atmospheric pressure using fixed bed vertical flow reactor. The fresh and spent catalysts were characterized by BET surface area analyzer, TPR, XRD, SEM, TEM, and TG analysis. The data showed that 50% Co/Al2O3 catalyst exhibited remarkable higher activity and stability up to 10 h time-on-stream at 750 oC with respect to H2 production compared to rest of the catalysts. However, the catalytic activity and durability was greatly declined at a higher temperature. The main reason for the catalytic inhibition of Co containing SiO2 catalysts is the higher reduction temperature of Co2SiO4. TEM images illustrate that the carbon materials with various morphologies, carbon nanofibers (CNFs), helical-shaped CNFs, and branched CNFs depending on the catalyst composition and reaction temperature, were obtained. The TG data showed that a higher yield of MWCNTs was achieved over 50% Co/Al2O3 catalyst compared to other catalysts.

Keywords: carbon nanotubes, cobalt, hydrogen production, methane decomposition

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Authors: Kirill D. Semavin, Norbert S. Chilingarov, Eugene.V. Skokan

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The ionic liquids (ILs) based on imidazolium cation are well known nowadays. The changing anions and substituents in imidazolium ring may lead to different physical and chemical properties of ILs. It is important that such ILs with halogen as anion are characterized by a low thermal stability. The data about thermal stability of 1-ethyl-3-methylimidazolium chloride are ambiguous. In the works of last years, thermal stability of this IL was investigated by thermogravimetric analysis and obtained results are contradictory. Moreover, in the last study, it was shown that the observed temperature of the beginning of decomposition significantly depends on the experimental conditions, for example, the heating rate of the sample. The vapor pressure of this IL is not presented at the literature. In this study, the vapor pressure of 1-ethyl-3-methylimidazolium chloride was obtained by Knudsen effusion mass-spectrometry (KEMS). The samples of [ЕMIm]Cl (purity > 98%) were supplied by Sigma–Aldrich and were additionally dried at dynamic vacuum (T = 60 0C). Preliminary procedures with Il were derived into glove box. The evaporation studies of [ЕMIm]Cl were carried out by KEMS with using original research equipment based on commercial MI1201 magnetic mass spectrometer. The stainless steel effusion cell had an effective evaporation/effusion area ratio of more than 6000. The cell temperature, measured by a Pt/Pt−Rh (10%) thermocouple, was controlled by a Termodat 128K5 device with an accuracy of ±1 K. In first step of this study, the optimal temperature of experiment and heating rate of samples were customized: 449 K and 5 K/min, respectively. In these conditions the sample is decomposed, but the experimental measurements of the vapor pressures are possible. The thermodynamic activity of [ЕMIm]Cl is close to 1 and products of decomposition don’t affect it at firstly 50 hours of experiment. Therefore, it lets to determine the saturated vapor pressure of IL. The electronic ionization mass-spectra shows that the decomposition of [ЕMIm]Cl proceeds with two ways. Nonetheless, the MALDI mass spectra of the starting sample and residue in the cell were similar. It means that the main decomposition products are gaseous under experimental conditions. This result allows us to obtain information about the kinetics of [ЕMIm]Cl decomposition. Thus, the original KEMS-based procedure made it possible to determine the IL vapor pressure under decomposition conditions. Also, the loss of sample mass due to the evaporation was obtained.

Keywords: ionic liquids, Knudsen effusion mass spectrometry, thermal stability, vapor pressure

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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: Toufik Bensana, Medkour Mihoub, Slimane Mekhilef

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The use of vibration analysis has been established as the most common and reliable method of analysis in the field of condition monitoring and diagnostics of rotating machinery. Rolling bearings cover a broad range of rotary machines and plays a crucial role in the modern manufacturing industry. Unfortunately, the vibration signals collected from a faulty bearing are generally nonstationary, nonlinear and with strong noise interference, so it is essential to obtain the fault features correctly. In this paper, a novel numerical analysis method based on local mean decomposition (LMD) is proposed. LMD decompose the signal into a series of product functions (PFs), each of which is the product of an envelope signal and a purely frequency modulated FM signal. The envelope of a PF is the instantaneous amplitude (IA), and the derivative of the unwrapped phase of a purely flat frequency demodulated (FM) signal is the IF. After that, the fault characteristic frequency of the roller bearing can be extracted by performing spectrum analysis to the instantaneous amplitude of PF component containing dominant fault information. The results show the effectiveness of the proposed technique in fault detection and diagnosis of rolling element bearing.

Keywords: fault diagnosis, rolling element bearing, local mean decomposition, condition monitoring

Procedia PDF Downloads 360

Authors: Nor Mazlin Zahari, Lian Gan, Xuerui Mao

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The power production in wind farms and the mechanical loads on the turbines are strongly impacted by the wake of the wind turbine. Thus, there is a need for understanding and modelling the turbine wake dynamic in the wind farm and the layout optimization. Having a good wake model is important in predicting plant performance and understanding fatigue loads. In this paper, the Dynamic Mode Decomposition (DMD) was applied to the simulation data generated by a Direct Numerical Simulation (DNS) of flow around a turbine, perturbed by upstream inflow noise. This technique is useful in analyzing the wake flow, to predict its future states and to reflect flow dynamics associated with the coherent structures behind wind turbine wake flow. DMD was employed to describe the dynamic of the flow around turbine from the DNS data. Since the DNS data comes with the unstructured meshes and non-uniform grid, the interpolation of each occurring within each element in the data to obtain an evenly spaced mesh was performed before the DMD was applied. DMD analyses were able to tell us characteristics of the travelling waves behind the turbine, e.g. the dominant helical flow structures and the corresponding frequencies. As the result, the dominant frequency will be detected, and the associated spatial structure will be identified. The dynamic mode which represented the coherent structure will be presented.

Keywords: coherent structure, Direct Numerical Simulation (DNS), dominant frequency, Dynamic Mode Decomposition (DMD)

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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

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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 123

Authors: Charles Lee

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The Principal Orthogonal Decomposition (POD) technique has been used as a model reduction tool for many applications in engineering and science. In principle, one begins with an ensemble of data, called snapshots, collected from an experiment or laboratory results. The beauty of the POD technique is that when applied, the entire data set can be represented by the smallest number of orthogonal basis elements. It is the such capability that allows us to reduce the complexity and dimensions of many physical applications. Mathematical formulations and numerical schemes for the POD method will be discussed along with applications in NASA’s Deep Space Large Antenna Arrays, Satellite Image Reconstruction, Cancer Detection with DNA Microarray Data, Maximizing Stock Return, and Medical Imaging.

Keywords: reduced-order methods, principal component analysis, cancer detection, image reconstruction, stock portfolios

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Authors: J. Ranjbarn, A. Alibeigloo

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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: Saurabh Farkya, Govinda Surampudi

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Optical Character Recognition is one of the current major research areas. This paper is focussed on recognition of Devanagari script and its sound generation. This Paper consists of two parts. First, Optical Character Recognition of Devnagari handwritten Script. Second, speech synthesis of the recognized text. This paper shows an implementation of support vector machines for the purpose of Devnagari Script recognition. The Support Vector Machines was trained with Multi Domain features; Transform Domain and Spatial Domain or Structural Domain feature. Transform Domain includes the wavelet feature of the character. Structural Domain consists of Distance Profile feature and Gradient feature. The Segmentation of the text document has been done in 3 levels-Line Segmentation, Word Segmentation, and Character Segmentation. The pre-processing of the characters has been done with the help of various Morphological operations-Otsu's Algorithm, Erosion, Dilation, Filtration and Thinning techniques. The Algorithm was tested on the self-prepared database, a collection of various handwriting. Further, Unicode was used to convert recognized Devnagari text into understandable computer document. The document so obtained is an array of codes which was used to generate digitized text and to synthesize Hindi speech. Phonemes from the self-prepared database were used to generate the speech of the scanned document using concatenation technique.

Keywords: Character Recognition (OCR), Text to Speech (TTS), Support Vector Machines (SVM), Library of Support Vector Machines (LIBSVM)

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Authors: Nivine Abbas, Sergio Lagomarsino, Serena Cattari

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The resistance domain of a generally loaded rigid shallow foundation is normally represented as an interaction diagram limited by a failure surface in the three dimensional (3D) load space (N, V, M), where N is the vertical centric load component, V is the horizontal load component and M is the bending moment component. Usually, this resistance domain is constructed neglecting the foundation sliding mechanism that take place at the level of soil-foundation interface once the applied horizontal load exceeds the interface frictional resistance of the foundation. This issue is translated in the literature by the fact that the failure limit in the (2D) load space (N, V) is constructed as a parabola having an initial slope, at the center of the coordinate system, that depends, in some works, only of the soil friction angle, and in other works, has an empirical value. However, considering a given geometry of the foundation lying on a given soil type, the initial slope of the failure limit must change, for instance, when varying the roughness of the foundation surface at its interface with the soil. The present study discusses the effect of the soil-foundation interface condition on the construction of the resistance domain, and proposes a correction to be applied to the failure limit in order to overcome this effect.

Keywords: soil-foundation interface, sliding mechanism, soil shearing, resistance domain, rigid shallow foundation

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Authors: Yousef M. Al-Roomi, Kaneez Fatema Hussain

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This work evaluates the thermal degradation kinetic parameters of CaSO₄-complex isolated after the inhibition effect of maleic-anhydride based polymer (YMR-polymers). Pyrolysis experiments were carried out at four heating rates (5, 10, 15 and 20°C/min). Several analytical model-free methods were used to determine the kinetic parameters, including Friedman, Coats and Redfern, Kissinger, Flynn-Wall-Ozawa and Kissinger-Akahira–Sunose methods. The Criado model fitting method based on real mechanism followed in thermal degradation of the complex has been applied to explain the degradation mechanism of CaSO₄-complex. In addition, a simple dynamic model was proposed over two temperature ranges for successive decomposition of CaSO₄-complex which has a combination of organic and inorganic part (adsorbed polymer + CaSO₄.2H₂O scale). The model developed enabled the assessment of pre-exponential factor (A) and apparent activation-energy (Eₐ) for both stages independently using a mathematical developed expression based on an integral solution. The unique reaction mechanism approach applied in this study showed that (Eₐ₁-160.5 kJ/mole) for organic decomposition (adsorbed polymer stage-I) has been lower than Eₐ₂-388 kJ/mole for the CaSO₄ decomposition (inorganic stage-II). Further adsorbed YMR-antiscalant not only reduced the decomposition temperature of CaSO₄-complex compared to CaSO₄-blank (CaSO₄.2H₂O scales in the absence of YMR-polymer) but also distorted the crystal lattice of the organic complex of CaSO₄ precipitates, destroying their compact and regular crystal structures observed from XRD and SEM studies.

Keywords: CaSO₄-complex, maleic-anhydride polymers, thermal degradation kinetics and mechanism, XRD and SEM studies

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Authors: Ghorab Sara, Meziani Lila, Rubin Harvey Stuart

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The rapid growth of deep learning has led to intricate and resource-intensive deep neural networks widely used in computer vision tasks. However, their complexity results in high computational demands and memory usage, hindering real-time application. To address this, research focuses on model compression techniques. The paper provides an overview of recent advancements in compressing neural networks and categorizes the various methods into four main approaches: network pruning, quantization, network decomposition, and knowledge distillation. This paper aims to provide a comprehensive outline of both the advantages and limitations of each method.

Keywords: model compression, deep neural network, pruning, knowledge distillation, quantization, low-rank decomposition

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Authors: H. Samadiyeh, R. Khajavi

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A new FD-based procedure, modal finite difference method (MFDM), is proposed for seismic wave propagation modeling, in which simulation is dealt with in the modal space. The method employs eigenvalues of a characteristic matrix formed by appropriate time-space FD stencils. Since MFD runs for different modes are totally independent of each other, MFDM can easily be parallelized while considerable simplicity in parallel-algorithm is also achieved. There is no requirement to any domain-decomposition procedure and inter-core data exchange. More important is the possibility to skip processing of less-significant modes, which enables one to adjust the procedure up to the level of accuracy needed. Thus, in addition to considerable ease of parallel programming, computation and storage costs are significantly reduced. The method is qualified for its efficiency by some numerical examples.

Keywords: Finite Difference Method, Graphics Processing Unit (GPU), Message Passing Interface (MPI), Modal, Wave propagation

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Authors: E. Sener, O. Isik, E. Eroglu, U. Sahin

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The main idea is to solve the system of Maxwell’s equations in accordance with the causality principle to get the energy quantities via Airy functions in a hollow rectangular waveguide. We used the evolutionary approach to electromagnetics that is an analytical time-domain method. The boundary-value problem for the system of Maxwell’s equations is reformulated in transverse and longitudinal coordinates. A self-adjoint operator is obtained and the complete set of Eigen vectors of the operator initiates an orthonormal basis of the solution space. Hence, the sought electromagnetic field can be presented in terms of this basis. Within the presentation, the scalar coefficients are governed by Klein-Gordon equation. Ultimately, in this study, time-domain waveguide problem is solved analytically in accordance with the causality principle. Moreover, the graphical results are visualized for the case when the energy and surplus of the energy for the time-domain waveguide modes are represented via airy functions.

Keywords: airy functions, Klein-Gordon Equation, Maxwell’s equations, Surplus of energy, wave boundary operators

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Authors: W. S. Besbas, M. A. Artemi, R. M. Salman

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Content based face sketch retrieval can be used to find images of criminals from their sketches for 'Crime Prevention'. This paper investigates the problem of CBIR of face sketch images in transform domain. Face sketch images that are similar to the query image are retrieved from the face sketch database. Features of the face sketch image are extracted in the spectrum domain of a selected transforms. These transforms are Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), and Walsh Hadamard Transform (WHT). For the performance analyses of features selection methods three face images databases are used. These are 'Sheffield face database', 'Olivetti Research Laboratory (ORL) face database', and 'Indian face database'. The City block distance measure is used to evaluate the performance of the retrieval process. The investigation concludes that, the retrieval rate is database dependent. But in general, the DCT is the best. On the other hand, the WHT is the best with respect to the speed of retrieving images.

Keywords: Content Based Image Retrieval (CBIR), face sketch image retrieval, features selection for CBIR, image retrieval in transform domain

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Authors: Isara Muangthai, Lin Sue Jane

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Electricity is vital to every country’s economy in the world. For Thailand, the electricity generation sector plays an important role in the economic system, and it is the largest source of CO2 emissions. The aim of this paper is to use the decomposition analysis to investigate the key factors contributing to the changes of CO2 emissions from the electricity sector. The decomposition analysis has been widely used to identify and assess the contributors to the changes in emission trends. Our study adopted the Divisia index decomposition to identify the key factors affecting the evolution of CO2 emissions from Thailand’s thermal power sector during 2000-2011. The change of CO2 emissions were decomposed into five factors, including: Emission coefficient, heat rate, fuel intensity, electricity intensity, and economic growth. Results have shown that CO2 emission in Thailand’s thermal power sector increased 29,173 thousand tons during 2000-2011. Economic growth was found to be the primary factor for increasing CO2 emissions, while the electricity intensity played a dominant role in decreasing CO2 emissions. The increasing effect of economic growth was up to 55,924 million tons of CO2 emissions because the growth and development of the economy relied on a large electricity supply. On the other hand, the shifting of fuel structure towards a lower-carbon content resulted in CO2 emission decline. Since the CO2 emissions released from Thailand’s electricity generation are rapidly increasing, the Thailand government will be required to implement a CO2 reduction plan in the future. In order to cope with the impact of CO2 emissions related to the power sector and to achieve sustainable development, this study suggests that Thailand’s government should focus on restructuring the fuel supply in power generation towards low carbon fuels by promoting the use of renewable energy for electricity, improving the efficiency of electricity use by reducing electricity transmission and the distribution of line losses, implementing energy conservation strategies by enhancing the purchase of energy-saving products, substituting the new power plant technology in the old power plants, promoting a shift of economic structure towards less energy-intensive services and orienting Thailand’s power industry towards low carbon electricity generation.

Keywords: co2 emission, decomposition analysis, electricity generation, energy consumption

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Authors: Fethi Bouksila, Magnus Persson, Ronny Berndtsson, Akissa Bahri

Abstract:

Under salinity conditions, we evaluate the performance of Hilhost (2000) model to predict pore electrical conductivity ECp from dielectric permittivity and bulk electrical conductivity (ECa) using Time and Frequency Domain Reflectometry sensors (TDR, FDR). Using FDR_WET sensor, RMSE of ECp was 4.15 dS m-1. By replacing the standard soil parameter (K0) in Hilhost model by K0-ECa relationship, the RMSE of ECp decreased to 0.68 dS m-1. WET sensor could give similar accuracy to estimate ECp than TDR if calibrated values of K0 were used instead of standard values in Hilhost model.

Keywords: hilhost model, soil salinity, time domain reflectometry, frequency domain reflectometry, dielectric methods

Procedia PDF Downloads 106

Authors: Jinan Fiaidhi, Sabah Mohammed

Abstract:

Cross-domain learning in the medical field is a research challenge as many conditions, like in oncology imaging, use different imaging modalities. Moreover, in most of the medical learning applications, the sample training size is relatively small. Although few-shot learning (FSL) through the use of a Siamese neural network was able to be trained on a small sample with remarkable accuracy, FSL fails to be effective for use in multiple domains as their convolution weights are set for task-specific applications. In this paper, we are addressing this problem by enabling FSL to possess the ability to shift across domains by designing a two-layer FSL network that can learn individually from each domain and produce a shared features map with extra modulation to be used at the second layer that can recognize important targets from mix domains. Our initial experimentations based on mixed medical datasets like the Medical-MNIST reveal promising results. We aim to continue this research to perform full-scale analytics for testing our cross-domain FSL learning.

Keywords: Siamese neural network, few-shot learning, meta-learning, metric-based learning, thick data transformation and analytics

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Authors: Eman A. Al-Hilo, Hawraa H. Al-Waelly

Abstract:

In this work, fractal compression (FIC) technique is introduced based on using moment features to block indexing the zero-mean range-domain blocks. The moment features have been used to speed up the IFS-matching stage. Its moments ratio descriptor is used to filter the domain blocks and keep only the blocks that are suitable to be IFS matched with tested range block. The results of tests conducted on Lena picture and Cat picture (256 pixels, resolution 24 bits/pixel) image showed a minimum encoding time (0.89 sec for Lena image and 0.78 of Cat image) with appropriate PSNR (30.01dB for Lena image and 29.8 of Cat image). The reduction in ET is about 12% for Lena and 67% for Cat image.

Keywords: fractal gray level image, fractal compression technique, iterated function system, moments feature, zero-mean range-domain block

Procedia PDF Downloads 470

Authors: Nikolai Krainiukov, Boris Melnikov

Abstract:

We study the properties of maximal prefix codes. The codes have many applications in computer science, theory of formal languages, data processing and data classification. Practical application is based on the representation of the maximal prefix codes as a sequence of words in a specific order. Our approach to study uses finite state automata (so-called flower automata) for the representation of prefix codes. An important task is the decomposition of prefix codes into prime prefix codes (factors). We discuss the properties of such prefix code decompositions. A linear time algorithm is designed which find the prime decomposition. To verify the correctness of the proposed algorithms, we implemented a system computer algebra GAP.

Keywords: maximal prefix code, regular languages, flower automata, prefix code decomposing

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Authors: Anthony R. Hassan, Jacob A. Gbadeyan

Abstract:

In this paper, the analysis of a reactive hydromagnetic Poiseuille fluid flow under each of sensitized, Arrhenius and bimolecular chemical kinetics through a channel in the presence of heat source is carried out. An exothermic reaction is assumed while the concentration of the material is neglected. Adomian Decomposition Method (ADM) together with Pade Approximation is used to obtain the solutions of the governing nonlinear non – dimensional differential equations. Effects of various physical parameters on the velocity and temperature fields of the fluid flow are investigated. The entropy generation analysis and the conditions for thermal criticality are also presented.

Keywords: chemical kinetics, entropy generation, thermal criticality, adomian decomposition method (ADM) and pade approximation

Procedia PDF Downloads 428