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نتیجه جستجو - morphology

تعداد مقالات یافته شده: 68
ردیف عنوان نوع
1 The physical and mechanical properties for flexible biomass particles using computer vision
خواص فیزیکی و مکانیکی ذرات زیست توده انعطاف پذیر با استفاده از بینایی کامپیوتری-2022
The combustion and fluidization behavior of biomass depend on the physical properties (size, morphology, and density) and mechanical performances (elastic modulus, Poisson’s ratio, tensile strength and failure strain), but their quantitative models have rarely been focused in previous researchers. Hence, a static image measurement for particle physical properties is studied. Combining the uniaxial tension and digital image correlation tech- nology, the dynamic image measurement method for the mechanical properties is proposed. The results indicate that the average roundness, rectangularity, and sphericity of present biomass particles are 0.2, 0.4, and 0.16, respectively. The equivalent diameter and density obey the skewed normal distribution. The tensile strength and failure stress are sensitive to stretching rate, fiber size and orientation. The distribution intervals of elastic modulus and Poisson’s ratio are 30–600 MPa and 0.25–0.307, respectively. The stress–strain curves obtained from imaging experiments agree well with the result of finite element method. This study provides the operating parameters for the numerical simulation of particles in the fluidized bed and combustor. Furthermore, the computer vision measurement method can be extended to the investigations of fossil fuels.
keywords: ذرات زیست توده | مشخصات فیزیکی | اجرای مکانیکی | تست کشش | آزمایش تصویربرداری | بینایی کامپیوتر | Biomass particle | Physical properties | Mechanical performances | Tensile testing | Imaging experiment | Computer vision
مقاله انگلیسی
2 Artificial intelligence versus natural selection: Using computer vision techniques to classify bees and bee mimics
هوش مصنوعی در مقابل انتخاب طبیعی: استفاده از تکنیک‌های بینایی کامپیوتری برای طبقه‌بندی زنبورها و تقلیدهای زنبور عسل-2022
Many groups of stingless insects have independently evolved mimicry of bees to fool would-be predators. To investigate this mimicry, we trained artificial intelligence (AI) algorithms—specifically, computer vision—to classify citizen scientist images of bees, bumble bees, and diverse bee mimics. For detecting bees and bumble bees, our models achieved accuracies of and , respectively. As a proxy for a natural predator, our models were poorest in detecting bee mimics that exhibit both aggressive and defensive mimicry. Using the explainable AI method of class activation maps, we validated that our models learn from appropriate components within the image, which in turn provided anatomical insights. Our t-SNE plot yielded perfect within-group clustering, as well as between-group clustering that grossly replicated the phylogeny. Ultimately, the transdisciplinary approaches herein can enhance global citizen science efforts as well as investigations of mimicry and morphology of bees and other insects.
keywords: Artificial intelligence | Bioinformatics | Computing methodology | Entomology | Zoology
مقاله انگلیسی
3 Ontological Approach for Semantic Modelling of Malay Translated Qur’an
رویکرد هستی‌شناختی برای مدل‌سازی معنایی قرآن ترجمه‌شده مالایی-2022
This thesis contributes to the areas of ontology development and analysis, natural language processing (NLP), Information Retrieval (IR) and Language Resource and Corpus Development. Research in Natural Language Processing and semantic search for English has shown successful results for more than a decade. However, it is difficult to adapt those techniques to the Malay language, because its complex morphology and orthographic forms are very different from English. Moreover, limited resources and tools for computational linguistic analysis are available for Malay. In this thesis, we address those issues and challenges by proposing MyQOS,the Malay Qur’an Ontology System, a prototype ontology-based IR with semantics for representing and accessing a Malay translation of the Qur’an. This supports the development of a semantic search engine and a question answering system and provides a framework for storing and accessing a Malay language corpus and providing computational linguistics resources. The primary use of MyQOS in the current research is for creating and improving the quality and accuracy of the query mechanism to retrieve information embedded in the Malay text of the Qur’an translation. To demonstrate the feasibility of this approach, we describe a new architecture of morphological analysis for MyQOS and query algorithms based on MyQOS. Data analysis that consisted of two measures; precision and recall, where data was obtained from MyQOS Corpus conducted in three search engines. The precision and recall for semantic search are 0.8409 (84%) and 0.8043(80%), double the results of the question answer search which are 0.4971(50%) for precision and 0.6027 (60%) for recall. The semantic search gives high precision and high recall comparing the other two methods. This indicates that semantic search returns more relevant results than irrelevant ones. To conclude, this research is among research in the retrieval of the Qur’an texts in the Malay language that managed to outline state-of-the-art information retrieval system models. Thus, the use of MyQOS will help Malay readers to understand the Qur’an in better ways. Furthermore, the creation of a Malay language corpus and computational linguistics resources will benefit other researchers, especially in religious texts, morphological analysis and semantic modelling.
مقاله انگلیسی
4 A novel multi-lead ECG personal recognition based on signals functional and structural dependencies using time-frequency representation and evolutionary morphological CNN
تشخیص شخصی نوار قلب ECG مبتنی بر وابستگی های عملکردی و ساختاری سیگنالها با استفاده از نمایش فرکانس زمان و CNN مورفولوژیکی تکاملی-2021
Biometric recognition systems have been employed in many aspects of life such as security technologies, data protection, and remote access. Physiological signals, e.g. electrocardiogram (ECG), can potentially be used in biometric recognition. From a medical standpoint, ECG leads have structural and functional dependencies. In fact, precordial ECG leads view the heart from different axial angles, whereas limb leads view it from various coronal angles. This study aimed to design a personal biometric recognition system based on ECG signals by estimating these latent medical variables. To estimate functional dependencies, within-correlation and cross- correlation in time-frequency domain between ECG leads were calculated and represented in the form of extended adjacency matrices. CNN trees were then introduced through genetic programming for the automated estimation of structural dependencies in extended adjacency matrices. CNN trees perform the deep feature learning process by using structural morphology operators. The proposed system was designed for both closed-set identification and verification. It was then tested on two datasets, i.e. PTB and CYBHi, for performance evaluation. Compared with the state-of-the-art methods, the proposed method outperformed all of them.
Keywords: Biometrics | Electrocardiogram | Functional dependencies | Structural dependencies | Genetic programming | Convolutional neural networks
مقاله انگلیسی
5 Layer number dependent optical and electrical properties of CVD grown two-dimensional anisotropic WS2
خواص نوری و الکتریکی وابسته به تعداد لایه WS2 ناهمسانگرد دوبعدی رشد CVD-2021
Engineering 2D transition metal dichalcogenides with precise control over layer number enable tuning of exciting optical and electrical properties at the nanoscale level. We report controlled one-step chemical vapour deposition growth of WS2 monolayer, bilayer, and trilayer for large scale manufacturing and demonstrate layer dependent changes in their work function, photoluminescence, and electrical conductivity. Raman, photoluminescence, and fluorescence imaging revealed that the base WS2 monolayer contains alternating triangular domains with different emission properties. It is observed that bilayer and trilayer grow selectively on less luminescent facet leading to fan-like morphology for second and third layers. We have systematically demonstrated that desired growth and areal coverage of bilayer and trilayer can be achieved by controlling WO3 precursor content. Kelvin probe force microscopic studies suggest a higher work function of thicker layers as compared to the monolayer. It was found that work function increases by 0.04 eV when thickness increases from monolayer to bilayer. FET device measurement on mono and bilayer shows n-type characteristics and two-fold higher photo-current in monolayer in comparison to the bilayer. The studied thickness dependence of the work function of WS2 is vital to the fabrication of metal contacts for WS2 based electronic and optoelectronic devices.
Keywords: CVD growth | 2D materials | PL segmentation | Optoelectronics | Transition metal dichalcogenides | KPFM
مقاله انگلیسی
6 Quantitative study of starch swelling capacity during gelatinization with an efficient automatic segmentation methodology
Quantitative study of starch swelling capacity during gelatinization with an efficient automatic segmentation methodology-2021
A novel image segmentation methodology combined with optical microscopy observation was developed for qualifying starch swelling. Starch granules in the micrograph were successfully segmented based on high- precision edges extraction achieved by Canny edge detection together with mathematical morphology operation. Granules were automatically identified by computer vision and characterized by giving quantifiable area of these granules. The evolved swelling process could be generally divided into two phases. During the first phase, starch granules were only swollen up by 2.56 %, which is hard to be identified by conventional naked eye. During the following narrow temperature interval (60–66 ℃), these starch granules were detected to swell up significantly by 9.08 %. Through the granule area variable, swelling capacity was high-throughput characterized, which allows for the whole evaluation to be completed within a couple of minutes. The proposed methodology showed a high accuracy and potential as a novel technique for characterizing gelatinization.
Keywords: Gelatinization | Computer vision | Quantification | Canny detection | Mathematical morphology
مقاله انگلیسی
7 Computer vision techniques for Upper Aero-Digestive Tract tumor grading classification - Addressing pathological challenges
تکنیک های بینایی ماشین برای طبقه بندی تومورهای دستگاه هضم دستگاه گوارش فوقانی - پرداختن به چالش های آسیب شناختی-2021
Oral cancer is one of the common cancer types which scales higher in death rate every year. The con- nectivity of two different cavities like oral cavity and nasal cavity is known as Upper Aero-Digestive Tract (UADT). Both oral and nasal cavities consist of thirteen connecting sites from mouth to upper stomach. The traditional pathological analysis like manual microscopic review brings out major intra and inter- observer variability problem. A new automated system is proposed using computer vision techniques to focus and analyse major pathological problems like intra and interobserver variability problem and mis- classification of dysplasia type of tumours. The morphological behaviour of biopsy tissue samples are analysed digitally with different sites of UADT and different cancerous and non-cancerous stages. The proposed technique will play a major role in assisting the manual pathology procedure for analysing the morphology of dysplasia type of tumours and classification of tumour gradings. A method is proposed which integrates an alternate process to find the morphology of dysplasia type tumours using different image processing techniques. A state-of-the-art Force Reconstructed Particle Swarm Optimization Based SVM is proposed for UADT oral cancer classification for ten different oral cavity sites. The proposed clas- sification technique achieved 94 % accuracy.© 2021 Elsevier B.V. All rights reserved.
Keywords: FR-PSO | SVM | Classification | Cancer | UADT | Machine Learning
مقاله انگلیسی
8 Diagnosis of obstructive sleep apnea with prediction of flow characteristics according to airway morphology automatically extracted from medical images: Computational fluid dynamics and artificial intelligence approach
تشخیص آپنه انسدادی خواب با پیش بینی ویژگی های جریان با توجه به مورفولوژی راه های هوایی به طور خودکار از تصاویر پزشکی استخراج می شود: دینامیک سیالات محاسباتی و رویکرد هوش مصنوعی-2021
Background: Obstructive sleep apnea syndrome (OSAS) is being observed in an increasing number of cases. It can be diagnosed using several methods such as polysomnography. Objectives: To overcome the challenges of time and cost faced by conventional diagnostic methods, this paper proposes computational fluid dynamics (CFD) and machine-learning approaches that are derived from the upper-airway morphology with automatic segmentation using deep learning.
Method: We adopted a 3D UNet deep-learning model to perform medical image segmentation. 3D UNet prevents the feature-extraction loss that may occur by concatenating layers and extracts the anteroposterior coordination and width of the airway morphology. To create flow characteristics of the upper airway training data, we analyzed the changes in flow characteristics according to the upper-airway morphology using CFD. A multivariate Gaussian process regression (MVGPR) model was used to train the flow characteristic values. The trained MVGPR enables the prompt prediction of the aerodynamic features of the upper airway without simulation. Unlike conventional regression methods, MVGPR can be trained by considering the correlation between the flow characteristics. As a diagnostic step, a support vector machine (SVM) with predicted aerodynamic and biometric features was used in this study to classify patients as healthy or suffering from moderate OSAS. SVM is beneficial as it is easy to learn even with a small dataset, and it can diagnose various flow characteristics as factors while enhancing the feature via the kernel function. As the patient dataset is small, the Monte Carlo cross-validation was used to validate the trained model. Furthermore, to overcome the imbalanced data problem, the oversampling method was applied.
Result: The segmented upper-airway results of the high-resolution and low-resolution models present overall average dice coefficients of 0.76±0.041 and 0.74±0.052, respectively. Furthermore, the classification accuracy, sensitivity, specificity, and F1-score of the diagnosis algorithm were 81.5%, 89.3%, 86.2%, and 87.6%, respectively.
Conclusion: The convenience and accuracy of sleep apnea diagnosis are improved using deep learning and machine learning. Further, the proposed method can aid clinicians in making appropriate decisions to evaluate the possible applications of OSAS.
Keywords: Obstructive sleep apnea syndrome | Auto-segmentation | Upper-airway morphology | Computational fluid dynamics
مقاله انگلیسی
9 Electro-conductive carbon nanofibers containing ferrous sulfate for bone tissue engineering
Electro-conductive carbon nanofibers containing ferrous sulfate for bone tissue engineering-2021
The application of electroactive scaffolds can be promising for bone tissue engineering applications. In the current paper, we aimed to fabricate an electro-conductive scaffold based on carbon nanofibers (CNFs) containing ferrous sulfate. FeSO4⋅7H2O salt with different concentrations 5, 10, and 15 wt%, were blended with polyacrylonitrile (PAN) polymer as the precursor and converted to Fe2O3/CNFs nanocomposite by electrospinning and heat treatment. The characterization was conducted using SEM, EDX, XRD, FTIR, and Raman methods. The results showed that the incorporation of Fe salt induces no adverse effect on the nanofibers morphology. EDX analysis confirmed that the Fe ions are uniformly dispersed throughout the CNF mat. FTIR spectroscopy showed the interaction of Fe salt with PAN polymer. Raman spectroscopy showed that the incorporation of FeSO4⋅7H2O reduced the ID/IG ratio, indicating more ordered carbon in the synthesized nanocomposite. Electrical resistance measurement depicted that, although the incorporation of ferrous sulfate reduced the electrical conductivity, the conductive is suitable for electrical stimulation. The in vitro studies revealed that the prepared nanocomposites were cytocompatible and only negligible toxicity (less than 10%) induced by CNFs/Fe2O3 fabricated from PAN FeSO4⋅7H2O 15%. Although various nanofibrous composite fabricated with Fe NPs have been evaluated for tissue engineering applications, CNFs exhibited promising properties, such as excellent mechanical strength, biocompatibility, and electrical conductivity. These results showed that the fabricated nanocomposites could be applied as the bone tissue engineering scaffold.
Keywords: Bone tissue engineering | Electrospinning | Carbon nanofiber | Ferrous sulfate
مقاله انگلیسی
10 Microstructure, resistivity, and shear strength of electrically conductive adhesives made of silver-coated copper powder
ریزساختار، مقاومت و استحکام برشی چسب های رسانای الکتریکی ساخته شده از پودر مس با روکش نقره-2021
Electrically conductive adhesives were made using silver-coated copper powders (filler) and epoxy. Resistivity, microstructure and shear strength of prepared adhesives were studied using two-point resistance measurements, Scanning Electron Microscope (SEM) and universal tensile tests, respectively. Effect of filler concentration (70–85 wt%), silver concentration (10–50 wt%), particles morphology (flake or spherical) and addition of graphite (2–15 wt%) were investigated on prepared adhesives properties. Results showed that by increasing the filler percentage from 70 to 85 wt%, the electrical resistivity decreases from 6.2 × 10− 3 to 3 × 10− 3 Ω⋅cm. Furthermore, the electrical resistivity of adhesives is proportional to the silver content of the filler particles. Regarding the morphological effect of filler particles, it was found that by replacing 40 wt% of the flake with spherical particles in the adhesive with a filler content of 75 wt%, the electrical resistivity of the adhesive reduces from 5.6 × 10− 3 to 4.5 × 10− 3 Ω⋅cm, while the electrical resistivity of the adhesive containing 50 wt% of the spherical particles reached to 1.1 × 10− 2 Ω⋅cm. Graphite addition to adhesive formula in concentrations less than 6 wt%, slightly lowered the resistivity of the adhesives. Increasing the graphite addition from 6 to 15 wt% enhanced the electrical resistivity from 2.8 × 10− 3 to 1.7 × 10− 2 Ω⋅cm. The shear strength of the adhesives is inversely proportional to the filler percentage in the adhesives.
Keywords: Silver | Copper | Core-shell | Electrical resistivity | Conductive | Adhesive
مقاله انگلیسی
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