Search results for: deep layer

Authors: Mansour Mohieddin Ghomshei, Reza Shahi

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Laminated composite tubes with adhesively bonded joints are widely used in aerospace and automotive industries as well as oil and gas industries. In this research, adhesively tubular single lap joints subjected to torsional and hygrothermal loadings are studied using the differential quadrature method (DQM). The analysis is based on the classical shell theory. At first, an approximate closed form solution is developed by omitting the lateral deflections in the connecting tubes. Using the analytical model, the circumferential displacements in tubes and the shear stresses in the interfacing adhesive layer are determined. Then, a numerical formulation is presented using DQM in which the lateral deflections are taken into account. By using the DQM formulation, the circumferential and radial displacements in tubes as well as shear and peel stresses in the adhesive layer are calculated. Results obtained from the proposed DQM solutions are compared well with those of the approximate analytical model and those of some published references. Finally using the DQM model, parametric studies are carried out to investigate the influence of various parameters such as adhesive layer thickness, torsional loading, overlap length, tubes radii, relative humidity, and temperature.

Keywords: adhesively bonded joint, differential quadrature method (DQM), hygrothermal, laminated composite tube

Procedia PDF Downloads 302

Authors: Chao Wang

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Under rapid innovation of information technology, the media plays a very important role in the dissemination of information, and it has a totally different analogy generations face. However, the involvement of narrative images provides more possibilities of narrative text. "Images" through the process of aperture, a camera shutter and developable photosensitive processes are manufactured, recorded and stamped on paper, displayed on a computer screen-concretely saved. They exist in different forms of files, data, or evidence as the ultimate looks of events. By the interface of media and network platforms and special visual field of the viewer, class body space exists and extends out as thin as sebum layer, extremely soft and delicate with real full tension. The physical space of sebum layer of confuses the fact that physical objects exist, needs to be established under a perceived consensus. As at the scene, the existing concepts and boundaries of physical perceptions are blurred. Sebum layer physical simulation shapes the “Topical-Similarity" immersing, leading the contemporary social practice communities, groups, network users with a kind of illusion without the presence, i.e. a non-real illusion. From the investigation and discussion of literatures, digital movies editing manufacture and produce the variability characteristics of time (for example, slices, rupture, set, and reset) are analyzed. Interactive eBook has an unique interaction in "Waiting-Greeting" and "Expectation-Response" that makes the operation of image narrative structure more interpretations functionally. The works of digital editing and interactive technology are combined and further analyze concept and results. After digitization of Interventional Imaging and interactive technology, real events exist linked and the media handing cannot be cut relationship through movies, interactive art, practical case discussion and analysis. Audience needs more rational thinking about images carried by the authenticity of the text.

Keywords: sebum layer, topical and similarity, interactive technology, image narrative

Procedia PDF Downloads 389

Authors: M. Lebrini, L. Salhi, C. Deyrat, C. Roos, O. Nait-Rabah

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The paper presents a large experimental study on the corrosion of zinc in tropical soil and in the ground water at the various depths. Through this study, the corrosion rate prediction was done on the basis of two methods the electrochemical method and the gravimetric. The electrochemical results showed that the corrosion rate is more important at the depth levels 0 m to 0.5 m and 0.5 m to 1 m and beyond these depth levels, the corrosion rate is less important. The electrochemical results indicated also that a passive layer is formed on the zinc surface. The found SEM and EDX micrographs displayed that the surface is extremely attacked and confirmed that a zinc oxide layer is present on the surface whose thickness and relief increase as the contact with soil increases.

Keywords: soil corrosion, galvanized steel, electrochemical technique, SEM and EDX

Procedia PDF Downloads 128

Authors: Panomporn Wisuthseriwong, Hatairuk Tungkasen, Siyaporn Namsongsan, Chanikarn Srinark, Mayuva Youngsabanant-Areekijseree

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The objective of the present study was to evaluate the morphology of porcine cumulus-oocyte complexes (pCOCs) and follicular fluid during follicular development. The samples were obtained from local slaughterhouses in Nakorn Pathom Province, Thailand. Pigs were classified as either in the follicular phase or luteal phase. Porcine follicles (n = 3,510) were categorized as small (1-3 mm in diameters; n=2,910), medium (4-6 mm in diameters; n=530) and large (7-8 mm in diameters; n=70). Then pCOCs and follicular fluid were collected. Finally, we found that the oocytes can be categorized into intact cumulus cells layer oocyte, multi-cumulus cells layer oocyte, partial cumulus cells layer oocyte, completely denuded oocyte and degenerated oocyte. They showed high percentage of intact and multi-cumulus cells layer oocytes from small follicles (54.68%) medium follicles (69.06%) and large follicles (68.57%), which have high potential to develop into matured oocytes in vitro. Protein composition of the follicular fluid was separated by SDS-PAGE technique. The result shows that the protein molecular weight in the small and medium follicles are 23, 50, 66, 75, 92, 100, 132, 163, 225 and >225 kDa. Meanwhile, protein molecular weight in large follicles are 12, 16, 23, 50, 66, 75, 92, 100, 132, 163, 225 and >225 kDa. All proteins play an important role in promotion and regulation on development, maturation of oocytes and regulation of ovulation. We conclude that the results of discovery can be used porcine secretion proteins for supplement in IVM/IVF technology. Acknowledgements: The project was funded by a grant from Silpakorn University Research and Development Institute (SURDI) and Faculty of Science, Silpakorn University, Thailand.

Keywords: porcine follicles, porcine oocyte, follicular fluid, SDS-PAGE

Procedia PDF Downloads 258

Authors: Hassane Ben Slimane, Dennai Benmoussa, Abderrachid Helmaoui

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We have theoretically calculated the photovoltaic conversion efficiency of a graded interface CdS/CIGS solar cell, which can be experimentally fabricated. Because the conduction band discontinuity or spike in an abrupt heterojunction CdS/CIGS solar cell can hinder the separation of hole-electron by electric field, a graded interface layer is uses to eliminate the spike and reduces recombination in space charge region. This paper describes the role of the graded band gap interface layer in decreasing the performance of the heterojunction cell. By optimizing the thickness of the graded region, an improvement of conversion efficiency has been observed in comparison to the conventional CIGS system.

Keywords: heterojunction, solar cell, graded interface, CIGS

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Authors: Khoi A. Nguyen, Rodney A. Stewart, Hong Zhang

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‘Water-related energy’ is energy use which is directly or indirectly influenced by changes to water use. Informatics applying a range of mathematical, statistical and rule-based approaches can be used to reveal important information on demand from the available data provided at second, minute or hourly intervals. This study aims to combine these two concepts to improve the current water end use disaggregation problem through applying a wide range of most advanced pattern recognition techniques to analyse the concurrent high-resolution water-energy consumption data. The obtained results have shown that recognition accuracies of all end-uses have significantly increased, especially for mechanised categories, including clothes washer, dishwasher and evaporative air cooler where over 95% of events were correctly classified.

Keywords: deep learning network, smart metering, water end use, water-energy data

Procedia PDF Downloads 306

Authors: Malik Muhammad Arslan, Muneeb Ullah, Dai Shihan, Daniyal Haider, Xiaodong Yang

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Chest X-rays are instrumental in the detection and monitoring of a wide array of diseases, including viral infections such as COVID-19, tuberculosis, pneumonia, lung cancer, and various cardiac and pulmonary conditions. To enhance the accuracy of diagnosis, artificial intelligence (AI) algorithms, particularly deep learning models like Convolutional Neural Networks (CNNs), are employed. However, these deep learning models demand a substantial and varied dataset to attain optimal precision. Generative Adversarial Networks (GANs) can be employed to create new data, thereby supplementing the existing dataset and enhancing the accuracy of deep learning models. Nevertheless, GANs have their limitations, such as issues related to stability, convergence, and the ability to distinguish between authentic and fabricated data. In order to overcome these challenges and advance the detection and classification of CXR normal and abnormal images, this study introduces a distinctive technique known as DCWGAN (Diverse Conditional Wasserstein GAN) for generating synthetic chest X-ray (CXR) images. The study evaluates the effectiveness of this Idiosyncratic DCWGAN technique using the ResNet50 model and compares its results with those obtained using the traditional GAN approach. The findings reveal that the ResNet50 model trained on the DCWGAN-generated dataset outperformed the model trained on the classic GAN-generated dataset. Specifically, the ResNet50 model utilizing DCWGAN synthetic images achieved impressive performance metrics with an accuracy of 0.961, precision of 0.955, recall of 0.970, and F1-Measure of 0.963. These results indicate the promising potential for the early detection of diseases in CXR images using this Inimitable approach.

Keywords: CNN, classification, deep learning, GAN, Resnet50

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Authors: Hanjun Yun, Gun Bae, Nabin Paul, Cheolhee Moon

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Flexible OLED is an important technology for the next generation display over various kinds of applications. However, the organic materials of OLEDs degrade rapidly under the invasion of oxygen and water moisture. The degradation causes the formation of non-emitting areas which gradually suppress the device brightness, ultimately the lifetime of the device decreasing rapidly. Until now, the most suitable sealing process of the flexible OLED devices is a thin film encapsulation (TFE). However, TFE consists of a multilayer thin-film structure with organic-inorganic materials, so the cost is expensive and the process time is long. Another problem is that the blocking characteristics from the moisture and oxygen are not perfect. Therefore, the encapsulation of the flexible OLED device is a still key technical issue for the successful market entry. In this study, we are to introduce an auxiliary sealing line between the two flexible substrates. The electrode lines were formed on the substrates which have a SiNx barrier coating layer. To induce the solid phase diffusion process between the SiNx layer and the electrode lines, the electrode materials were determined as Al-Si composition. Thermal energy was supplied for both the SiNx layer and Al-Si electrode lines within the furnace to induce the interfacial bonding through the solid phase diffusion of Si. We printed a test pattern for the edge of the flexible PET substrate of 3cm*3cm size. Experimental conditions such as heating temperature, heating time were optimized to get enough adhesion strength which was estimated through the competitive bending test. Finally, OLED devices with flexible PET substrate of 3cm*3cm size were manufactured to investigate the blocking characteristics as an encapsulation layer.

Keywords: barrier, encapsulation, OLED, solid phase diffusion

Procedia PDF Downloads 237

Authors: Špela Hajduk, Sean P. Berglund, Matejka Podlogar, Goran Dražić, Fatwa F. Abdi, Zorica C. Orel, Menny Shalom

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Graphitic carbon nitride materials (g-CN) have emerged as an attractive photocatalyst and electrocatalyst for photo and electrochemical water splitting reaction, due to their environmental benignity nature and suitable band gap. Many approaches were introduced to enhance the photoactivity and electronic properties of g-CN and resulted in significant changes in the electronic and catalytic properties. Here we demonstrate the synthesis of thin and homogenous g-CN layer on highly ordered ZnO nanowire (NW) substrate by growing a seeding layer of small supramolecular assemblies on the nanowires. The new synthetic approach leads to the formation of thin g-CN layer (~3 nm) without blocking all structure. Two different deposition methods of carbon nitride were investigated and will be presented. The amount of loaded carbon nitride significantly influences the PEC activity of hybrid material and all the ZnO/g-CNx electrodes show great improvement in photoactivity. The chemical structure, morphology and optical properties of the deposited g-CN were fully characterized by various techniques as X-ray powder spectroscopy (XRD), scanning electron microscopy (SEM), focused ion beam scanning electron microscopy (FIB-SEM), high-resolution scanning microscopy (HR-TEM) and X-ray photoelectron spectroscopy (XPS).

Keywords: carbon nitride, photoanode, solar water splitting, zinc oxide

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Authors: Rahisham Abd Rahman

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In this paper, a nonlinear pollution model has been proposed to compute electric field distribution over the polymeric insulator surface under wet contaminated conditions. A 2D axial-symmetric insulator geometry, energized with 11kV was developed and analysed using Finite Element Method (FEM). A field-dependent conductivity with simplified assumptions was established to characterize the electrical properties of the pollution layer. Comparative field studies showed that simulation of dynamic pollution model results in a more realistic field profile, offering better understanding on how the electric field behaves under wet polluted conditions.

Keywords: electric field distributions, pollution layer, dynamic model, polymeric outdoor insulators, finite element method (FEM)

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Authors: Bambang Antoro, Lasito Soebari, Geoffrey de Jong, Fernandy Meiriyanto, Michael Siahaan, Eko Wibowo, Pormando Silalahi, Ruswanto, Adi Budirumantyo

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The Ertsberg East Skarn System (EESS) is located in the Ertsberg Mining District, Papua, Indonesia. EESS is a sub-vertical zone of copper-gold mineralization hosted in both diorite (vein-style mineralization) and skarn (disseminated and vein style mineralization). Deep Mill Level Zone (DMLZ) is a mining zone in the lower part of East Ertsberg Skarn System (EESS) that product copper and gold. The Deep Mill Level Zone deposit is located below the Deep Ore Zone deposit between the 3125m to 2590m elevation, measures roughly 1,200m in length and is between 350 and 500m in width. DMLZ planned start mined on Q2-2015, being mined at an ore extraction rate about 60,000 tpd by the block cave mine method (the block cave contain 516 Mt). Mineralization and associated hydrothermal alteration in the DMLZ is hosted and enclosed by a large stock (The Main Ertsberg Intrusion) that is barren on all sides and above the DMLZ. Late porphyry dikes that cut through the Main Ertsberg Intrusion are spatially associated with the center of the DMLZ hydrothermal system. DMLZ orebody hosted in diorite and skarn, both dominantly by vein style mineralization. Percentage Material Mined at DMLZ compare with current Reserves are diorite 46% (with 0.46% Cu; 0.56 ppm Au; and 0.83% EqCu); Skarn is 39% (with 1.4% Cu; 0.95 ppm Au; and 2.05% EqCu); Hornfels is 8% (with 0.84% Cu; 0.82 ppm Au; and 1.39% EqCu); and Marble 7 % possible mined waste. Correlation between Ertsberg intrusion, major structure, and vein style mineralization is important to determine characteristic orebody in DMLZ Mine. Generally Deep Mill Level Zone has 2 type of vein filling mineralization from both hosted (diorite and skarn), in diorite hosted the vein system filled by chalcopyrite-bornite-quartz and pyrite, in skarn hosted the vein filled by chalcopyrite-bornite-pyrite and magnetite without quartz. Based on orientation the stockwork vein at diorite hosted and shallow vein in skarn hosted was generally NW-SE trending and NE-SW trending with shallow-moderate dipping. Deep Mill Level Zone control by two main major faults, geologist founded and verified local structure between major structure with NW-SE trending and NE-SW trending with characteristics slickenside, shearing, gauge, water-gas channel, and some has been re-healed.

Keywords: copper-gold, DMLZ, skarn, structure

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Authors: Elisaveta Kirilova, Wilfried Becker, Jordanka Ivanova, Tatyana Petrova

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The study is a continuation of the research on the hygrothermal piezoelectric response of a smart patch/layer joint with undesirable interface defect (gap) at dynamic time harmonic mechanical and electrical load and environmental conditions. In order to find the axial displacements, shear stress and interface debond length in a closed analytical form for different positions of the interface gap, the 1D modified shear lag analysis is used. The debond length is represented as a function of many parameters (frequency, magnitude, electric displacement, moisture and temperature, joint geometry, position of the gap along the interface, etc.). Then the Genetic algorithm (GA) is implemented to find this position of the gap along the interface at which a vanishing/minimal debond length is ensured, e.g to find the most harmless position for the safe work of the structure. The illustrative example clearly shows that analytical shear-lag solutions and GA method can be combined successfully to give an effective prognosis of interface shear stress and interface delamination in patch/layer structure at combined loading with existing defects. To show the effect of the position of the interface gap, all obtained results are given in figures and discussed.

Keywords: genetic algorithm, minimal delamination, optimal gap position, shear lag solution

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Authors: Jing Lin, Zou Yiming, Goei Ronn, Li Yun, Amanda Ong Jiamin, Alfred Tok Iing Yoong

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High-entropy alloy (HEA) coatings comprise multiple (five or more) principal elements that give superior mechanical, electrical, and thermal properties. However, the current synthesis methods of HEA coating still face huge challenges in facile and controllable preparation, as well as conformal integration, which seriously restricts their potential applications. Herein, we report a controllable synthesis of conformal quinary HEA coating consisting of noble metals (Rh, Ru, Ir, Pt, and Pd) by using the atomic layer deposition (ALD) with a post-annealing approach. This approach realizes low temperature (below 200 °C), precise control (nanoscale), and conformal synthesis (over complex substrates) of HEA coating. Furthermore, the resulting quinary HEA coating shows promising potential as a platform for catalysis, exhibiting substantially enhanced electrocatalytic hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) performances as compared to other noble metal-based structures such as single metal coating or multi-layered metal composites.

Keywords: high-entropy alloy, thin-film, catalysis, water splitting, atomic layer deposition

Procedia PDF Downloads 126

Authors: Hao Li, Guy Y. Cornejo Maceda, Yiqing Li, Jianguo Tan, Marek Morzynski, Bernd R. Noack

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We propose an automated analysis of the flow control behaviour from an ensemble of control laws and associated time-resolved flow snapshots. The input may be the rich database of machine learning control (MLC) optimizing a feedback law for a cost function in the plant. The proposed methodology provides (1) insights into the control landscape, which maps control laws to performance, including extrema and ridge-lines, (2) a catalogue of representative flow states and their contribution to cost function for investigated control laws and (3) visualization of the dynamics. Key enablers are classification and feature extraction methods of machine learning. The analysis is successfully applied to the stabilization of a mixing layer with sensor-based feedback driving an upstream actuator. The fluctuation energy is reduced by 26%. The control replaces unforced Kelvin-Helmholtz vortices with subsequent vortex pairing by higher-frequency Kelvin-Helmholtz structures of lower energy. These efforts target a human interpretable, fully automated analysis of MLC identifying qualitatively different actuation regimes, distilling corresponding coherent structures, and developing a digital twin of the plant.

Keywords: machine learning control, mixing layer, feedback control, model-free control

Procedia PDF Downloads 223

Authors: Saikat Datta

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Incompressible laminar time-varying flow is investigated over a rounded backward-facing step for a triangular piston motion at the inlet of a straight channel with very high acceleration, followed by a slow deceleration experimentally and through numerical simulation. The backward-facing step is an important test-case as it embodies important flow characteristics such as separation point, reattachment length, and recirculation of flow. A sliding piston imparts two successive triangular velocities at the inlet, constant acceleration from rest, 0≤t≤t0, and constant deceleration to rest, t0≤tKeywords: laminar boundary layer separation, rounded backward facing step, separation bubble, unsteady separation, unsteady vortex flows

Procedia PDF Downloads 66

Authors: Sadia Siddiqa, Z. Khan, M. A. Hossain

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In this article, the effect of thermal radiation on mixed convection boundary layer flow of a viscous fluid along a highly heated vertical flat plate is considered with varying density and volumetric expansion coefficient. The density of the fluid is assumed to vary exponentially with temperature, however; volumetric expansion coefficient depends linearly on temperature. Boundary layer equations are transformed into convenient form by introducing primitive variable formulations. Solutions of transformed system of equations are obtained numerically through implicit finite difference method along with Gaussian elimination technique. Results are discussed in view of various parameters, like thermal radiation parameter, volumetric expansion parameter and density variation parameter on the wall shear stress and heat transfer rate. It is concluded from the present investigation that increase in volumetric expansion parameter decreases wall shear stress and enhances heat transfer rate.

Keywords: thermal radiation, mixed convection, variable density, variable volumetric expansion coefficient

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Authors: Tesfaw Belayneh Abebe

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Integrating low-altitude earth orbit (LEO) satellites and unmanned aerial vehicles (UAVs) within a non-terrestrial network (NTN) with the assistance of reconfigurable intelligent surfaces (RIS), we investigate the problem of how to enhance throughput through integrated LEO satellites and UAVs with the assistance of RIS. We propose a method to jointly optimize the associations with the LEO satellite, the 3D trajectory of the UAV, and the phase shifts of the RIS to maximize communication throughput for RIS-assisted integrated LEO satellite and UAV-enabled wireless communications, which is challenging due to the time-varying changes in the position of the LEO satellite, the high mobility of UAVs, an enormous number of possible control actions, and also the large number of RIS elements. Utilizing a multi-agent double deep Q-network (MADDQN), our approach dynamically adjusts LEO satellite association, UAV positioning, and RIS phase shifts. Simulation results demonstrate that our method significantly outperforms baseline strategies in maximizing throughput. Lastly, thanks to the integrated network and the RIS, the proposed scheme achieves up to 65.66x higher peak throughput and 25.09x higher worst-case throughput.

Keywords: integrating low-altitude earth orbit (LEO) satellites, unmanned aerial vehicles (UAVs) within a non-terrestrial network (NTN), reconfigurable intelligent surfaces (RIS), multi-agent double deep Q-network (MADDQN)

Procedia PDF Downloads 48

Authors: Chien-Kuo Chang, Yu-Hsiang Lin, Yi-Yun Tang, Min-Chiu Wu

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With the trend of technological advancement, the harm caused by power outages is substantial, mostly due to problems in the power grid. This highlights the necessity for further improvement in the reliability of the power system. In the power system, gas-insulated switches (GIS) and power cables play a crucial role. Long-term operation under high voltage can cause insulation materials in the equipment to crack, potentially leading to partial discharges. If these partial discharges (PD) can be analyzed, preventative maintenance and replacement of equipment can be carried out, there by improving the reliability of the power grid. This research will diagnose defects by identifying three different defects in GIS and three different defects in straight cable joints, for a total of six types of defects. The partial discharge data measured will be converted through phase analysis diagrams and pulse sequence analysis. Discharge features will be extracted using convolutional image processing, and three different deep learning models, CNN, ResNet18, and MobileNet, will be used for training and evaluation. Class Activation Mapping will be utilized to interpret the black-box problem of deep learning models, with each model achieving an accuracy rate of over 95%. Lastly, the overall model performance will be enhanced through an ensemble learning voting method.

Keywords: partial discharge, gas-insulated switches, straight cable joint, defect identification, deep learning, ensemble learning

Procedia PDF Downloads 78

Authors: S. Kida

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We consider the flow of an incompressible viscous fluid in a precessing sphere whose spin and precession axes are orthogonal to each other. The flow is characterized by two non-dimensional parameters, the Reynolds number Re and the Poincare number Po. For which values of (Re, Po) will the flow approach a steady state from an arbitrary initial condition? To answer it we are searching the instability boundary of the steady states in the whole (Re, Po) plane. Here, we focus the rapidly rotating and weakly precessing limit, i.e., Re >> 1 and Po << 1. The steady flow was obtained by the asymptotic expansion for small ε=Po Re¹/² << 1. The flow exhibits nearly a solid-body rotation in the whole sphere except for a thin boundary layer which develops over the sphere surface. The thickness of this boundary layer is of O(δ), where δ=Re⁻¹/², except where two circular critical bands of thickness of O(δ⁴/⁵) and of width of O(δ²/⁵) which are located away from the spin axis by about 60°. We perform the linear stability analysis of the steady flow. We assume that the disturbances are localized in the critical bands and make an expansion analysis in terms of ε to derive the eigenvalue problem for the growth rate of the disturbance, which is solved numerically. As the solution, we obtain an asymptote of the stability boundary as Po=28.36Re⁻⁰.⁸. This agrees excellently with the corresponding laboratory experiments and numerical simulations. One of the most popular instability mechanisms so far is the parametric instability, which turns out, however, not to give the correct stability boundary. The present instability is different from the parametric instability.

Keywords: boundary layer, critical band, instability, precessing sphere

Procedia PDF Downloads 154

Authors: Chien-kuo Chang, Bo-wei Wu, Yi-yun Tang, Min-chiu Wu

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This paper proposes a power equipment diagnosis system based on partial discharge (PD), which is characterized by increasing the readability of experimental data and the convenience of operation. This system integrates a variety of analysis programs of different data formats and different programming languages and then establishes a set of interfaces that can follow and expand the structure, which is also helpful for subsequent maintenance and innovation. This study shows a case of using the developed Convolutional Neural Networks (CNN) to integrate with this system, using the designed model architecture to simplify the complex training process. It is expected that the simplified training process can be used to establish an adaptive deep learning experimental structure. By selecting different test data for repeated training, the accuracy of the identification system can be enhanced. On this platform, the measurement status and partial discharge pattern of each equipment can be checked in real time, and the function of real-time identification can be set, and various training models can be used to carry out real-time partial discharge insulation defect identification and insulation state diagnosis. When the electric power equipment entering the dangerous period, replace equipment early to avoid unexpected electrical accidents.

Keywords: partial discharge, convolutional neural network, partial discharge analysis platform, adaptive deep learning

Procedia PDF Downloads 78

Authors: S. Benmebarek, S. Benmoussa, N. Benmebarek

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Natural soils are often deposited in layers. The estimation of the bearing capacity of the soil using conventional bearing capacity theory based on the properties of the upper layer introduces significant inaccuracies if the thickness of the top layer is comparable to the width of the foundation placed on the soil surface. In this paper, numerical computations using the FLAC code are reported to evaluate the two clay layers effect on the bearing capacity beneath rigid circular rough footing subject to axial static load. The computation results of the parametric study are used to illustrate the sensibility of the bearing capacity, the shape factor and the failure mechanisms to the layered strength and layered thickness.

Keywords: numerical modeling, circular footings, layered clays, bearing capacity, failure

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Authors: Christopher Baiyegunhi, Oswald Gwavava

Abstract:

The location/source of the Beattie magnetic anomaly (BMA) and interconnectivity of geologic structures at depth have been a topic of investigation for over 30 years. Up to now, no relationship between geological structures (interconnectivity of dolerite intrusions) at depth has been established. Therefore, the environmental impact of fracking the Karoo for shale gas could not be assessed despite the fact that dolerite dykes are groundwater localizers in the Karoo. In this paper, we shed more light to the unanswered questions concerning the possible location of the source of the BMA, the connectivity of geologic structures like dolerite dykes and sills at depth and this relationship needs to be established before the tectonic evolution of the Karoo basin can be fully understood and related to fracking of the Karoo for shale gas. The result of the magnetic investigation and modelling of four gravity profiles that crosses the BMA in the study area reveals that the anomaly, which is part of the Beattie magnetic anomaly tends to divide into two anomalies and continue to trend in an NE-SW direction, the dominant gravity signatures is of long wavelength that is due to a deep source/interface inland and shallows towards the coast, the average depth to the top of the shallow and deep magnetic sources was estimated to be approximately 0.6 km and 15 km, respectively. The BMA become stronger with depth which could be an indication that the source(s) is deep possibly a buried body in the basement. The bean-shaped anomaly also behaves in a similar manner like the BMA thus it could possibly share the same source(s) with the BMA.

Keywords: Beattie magnetic anomaly, magnetic sources, modelling, Karoo Basin

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Authors: Hasan Yalcin, Tugba Dursun Capar

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Deep-fat frying causes desirable as well as undesirable changes in oil and potato, and changes the quality of the oil by hydrolysis, oxidation, and polymerization. The main objective of the present study was to investigate the pre-drying effects on the quality of both frying oil and potatoes. Prior to frying, potato slices (10 mm x10 mm x 30 mm) were air- dried at 60°C for 15, 30, 45, 60, 90, and 120 mins., respectively. Potato slices without the pre-drying treatment were considered as the control variable. Potato slices were fried in sunflower oil at 180°C for 5, 10, and 13 mins. The deep-frying experiments were repeated five times using the new potato slices in the same oil without oil replenishment. Samples of the fresh oil, together with those sampled at the end of successive frying operations (1th, 3th and 5th) were removed and analysed. Moisture content, colour and oil intake of the potato and colour, peroxide value (PV), free fatty acid (FFA), fatty acid composition and viscosity of the used oil were evaluated. The effect of frying time was also examined. Results show that pre-drying treatment had a significant effect on physicochemical properties and colour parameters of potato slices and frying oil. Pre-drying considerably decreased the oil absorption. The lowest oil absorption was found for the treatment that was pre-dried for 120, and fried for 5 min. The FFA levels decreased permanently for each pre-treatment throughout the frying period. All the pre-drying treatments had reached their maximum levels of FFA by the end of the frying procedures. The PV of the control and 60 min pre-dried sample decreased after the third frying. However, the PV of other samples increased constantly throughout the frying periods. Lastly, pre-drying did not affect the fatty acid composition of frying oil considerably when compared against previously unused oil.

Keywords: air-drying, deep-fat frying, moisture content oil uptake, quality

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Authors: A. C. Rana, Abhinav Singh Rana

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Layer by layer coating of biocompatible polyelectrolytes converts the liposomes into stable version i.e 'layersomes'. This system was further used to deliver the Amphotericin B through the oral route. Extensive optimization of different process variables resulted in the formation of layersomes with the particle size of 238.4±5.1, PDI of 0.24±0.16, the zeta potential of 34.6±1.3, and entrapment efficiency of 71.3±1.2. TEM analysis further confirmed the formation of spherical particles. Trehalose (10% w/w) resulted in the formation of fluffy and easy to redisperse cake in freeze dried layersomes. Controlled release up to 50 % within 24 h was observed in the case of layersomes. The layersomes were found stable in simulated biological fluids and resulted in the 3.59 fold higher bioavailability in comparison to free Amp-B. Furthermore, the developed formulation was found to be safe in comparison to Fungizone as indicated by blood urea nitrogen (BUN) and creatinine level.

Keywords: amphotericin B, layersomes, liposomes, toxicity

Procedia PDF Downloads 527

Authors: A. Sekhar, N. Ramakrishna Raju

Abstract:

This paper is a result of ground improvement using deep vibro techniques with combination of sand and stone columns performed on a highly liquefaction susceptible site (70 to 80% sand strata and balance silt) with low bearing capacities due to high settlements located (earth quake zone V as per IS code) at Madhepura, Bihar state in northern part of India. Initially, it was envisaged with bored cast in-situ/precast piles, stone/sand columns. However, after detail analysis to address both liquefaction and improve bearing capacities simultaneously, it was analyzed the deep vibro techniques with combination of sand and stone columns is excellent solution for given site condition which may be first time in India. First after detail soil investigation, pre eCPT test was conducted to evaluate the potential depth of liquefaction to densify silty sandy soils to improve factor of safety against liquefaction. Then trail test were being carried out at site by deep vibro compaction technique with sand and stone columns combination with different spacings of columns in triangular shape with different timings during each lift of vibro up to ground level. Different spacings and timing was done to obtain the most effective spacing and timing with vibro compaction technique to achieve maximum densification of saturated loose silty sandy soils uniformly for complete treated area. Then again, post eCPT test and plate load tests were conducted at all trail locations of different spacings and timing of sand and stone columns to evaluate the best results for obtaining the required factor of safety against liquefaction and the desired bearing capacities with reduced settlements for construction of industrial structures. After reviewing these results, it was noticed that the ground layers are densified more than the expected with improved factor of safety against liquefaction and achieved good bearing capacities for a given settlements as per IS codal provisions. It was also worked out for cost-effectiveness of lightly loaded single storied structures by using deep vibro technique with sand column avoiding stone. The results were observed satisfactory for resting the lightly loaded foundations. In this technique, the most important is to mitigating liquefaction with improved bearing capacities and reduced settlements to acceptable limits as per IS: 1904-1986 simultaneously up to a depth of 19M. To our best knowledge it was executed first time in India.

Keywords: ground improvement, deep vibro techniques, liquefaction, bearing capacity, settlement

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Authors: Shiuh-Jer Huang, Chen-Zon Yan, C. K. Huang, Chun-Chien Ting

Abstract:

Since the vision system application in industrial environment for autonomous purposes is required intensely, the image recognition technique becomes an important research topic. Here, deep learning algorithm is employed in image system to recognize the industrial object and integrate with a 7A6 Series Manipulator for object automatic gripping task. PC and Graphic Processing Unit (GPU) are chosen to construct the 3D Vision Recognition System. Depth Camera (Intel RealSense SR300) is employed to extract the image for object recognition and coordinate derivation. The YOLOv2 scheme is adopted in Convolution neural network (CNN) structure for object classification and center point prediction. Additionally, image processing strategy is used to find the object contour for calculating the object orientation angle. Then, the specified object location and orientation information are sent to robotic controller. Finally, a six-axis manipulator can grasp the specific object in a random environment based on the user command and the extracted image information. The experimental results show that YOLOv2 has been successfully employed to detect the object location and category with confidence near 0.9 and 3D position error less than 0.4 mm. It is useful for future intelligent robotic application in industrial 4.0 environment.

Keywords: deep learning, image processing, convolution neural network, YOLOv2, 7A6 series manipulator

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Authors: Parisa Mansour

Abstract:

The coronavirus disease 2019 (COVID-19) was discovered and rapidly spread to various countries around the world since the end of 2019. Computed tomography (CT) images have been used as an important alternative to the time-consuming RT. PCR test. However, manual segmentation of CT images alone is a major challenge as the number of suspected cases increases. Thus, accurate and automatic segmentation of COVID-19 infections is urgently needed. Because the imaging features of the COVID-19 infection are different and similar to the background, existing medical image segmentation methods cannot achieve satisfactory performance. In this work, we try to build a deep convolutional neural network adapted for the segmentation of chest CT images with COVID-19 infections. First, we maintain a large and novel chest CT image database containing 165,667 annotated chest CT images from 861 patients with confirmed COVID-19. Inspired by the observation that the boundary of an infected lung can be improved by global intensity adjustment, we introduce a feature variable block into the proposed deep CNN, which adjusts the global features of features to segment the COVID-19 infection. The proposed PV array can effectively and adaptively improve the performance of functions in different cases. We combine features of different scales by proposing a progressive atrocious space pyramid fusion scheme to deal with advanced infection regions with various aspects and shapes. We conducted experiments on data collected in China and Germany and showed that the proposed deep CNN can effectively produce impressive performance.

Keywords: chest, COVID-19, chest Image, coronavirus, CT image, chest CT

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Authors: Wei Li, Tuo Yang

Abstract:

In order to satisfy the real-time and accurate requirements of cigarette smoke detection in complex scenes, a cigarette smoke detection technology based on the combination of deep learning and color features was proposed. Firstly, based on the color features of cigarette smoke, the suspicious cigarette smoke area in the image is extracted. Secondly, combined with the efficiency of cigarette smoke detection and the problem of network overfitting, a network model for cigarette smoke detection was designed according to YOLOV3 algorithm to reduce the false detection rate. The experimental results show that the method is feasible and effective, and the accuracy of cigarette smoke detection is up to 99.13%, which satisfies the requirements of real-time cigarette smoke detection in complex scenes.

Keywords: deep learning, computer vision, cigarette smoke detection, YOLOV3, color feature extraction

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Authors: Hyohun Kim, Dongwha Shin, Yeonseok Kim, Ji-Su Ahn, Kensuke Nakamura, Dongeun Choi, Byung-Woo Hong

Abstract:

Artefacts are commonly encountered in the imaging process of clinical computed tomography (CT) where the artefact refers to any systematic discrepancy between the reconstructed observation and the true attenuation coefficient of the object. It is known that CT images are inherently more prone to artefacts due to its image formation process where a large number of independent detectors are involved, and they are assumed to yield consistent measurements. There are a number of different artefact types including noise, beam hardening, scatter, pseudo-enhancement, motion, helical, ring, and metal artefacts, which cause serious difficulties in reading images. Thus, it is desired to remove nuisance factors from the degraded image leaving the fundamental intrinsic information that can provide better interpretation of the anatomical and pathological characteristics. However, it is considered as a difficult task due to the high dimensionality and variability of data to be recovered, which naturally motivates the use of machine learning techniques. We propose an image restoration algorithm based on the deep neural network framework where the denoising auto-encoders are stacked building multiple layers. The denoising auto-encoder is a variant of a classical auto-encoder that takes an input data and maps it to a hidden representation through a deterministic mapping using a non-linear activation function. The latent representation is then mapped back into a reconstruction the size of which is the same as the size of the input data. The reconstruction error can be measured by the traditional squared error assuming the residual follows a normal distribution. In addition to the designed loss function, an effective regularization scheme using residual-driven dropout determined based on the gradient at each layer. The optimal weights are computed by the classical stochastic gradient descent algorithm combined with the back-propagation algorithm. In our algorithm, we initially decompose an input image into its intrinsic representation and the nuisance factors including artefacts based on the classical Total Variation problem that can be efficiently optimized by the convex optimization algorithm such as primal-dual method. The intrinsic forms of the input images are provided to the deep denosing auto-encoders with their original forms in the training phase. In the testing phase, a given image is first decomposed into the intrinsic form and then provided to the trained network to obtain its reconstruction. We apply our algorithm to the restoration of the corrupted CT images by the artefacts. It is shown that our algorithm improves the readability and enhances the anatomical and pathological properties of the object. The quantitative evaluation is performed in terms of the PSNR, and the qualitative evaluation provides significant improvement in reading images despite degrading artefacts. The experimental results indicate the potential of our algorithm as a prior solution to the image interpretation tasks in a variety of medical imaging applications. This work was supported by the MISP(Ministry of Science and ICT), Korea, under the National Program for Excellence in SW (20170001000011001) supervised by the IITP(Institute for Information and Communications Technology Promotion).

Keywords: auto-encoder neural network, CT image artefact, deep learning, intrinsic image representation, noise reduction, total variation

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Authors: Melissa Chavez Portillo, Mauricio Pacio Castillo, Hector Juarez Santiesteban, Oscar Portillo Moreno

Abstract:

Chemical bath deposited PbS:Bi3+ as an electron collection layer is introduced between the silicon wafer and the Ag electrode the performance of the PbS heterojunction thin film solar thin film solar cells with 1 cm2 active area. We employed Bi-doping to transform it into an n-type semiconductor. The experimental results reveal that the cell response parameters depend critically on the deposition procedures in terms of bath temperature, deposition time. The device achieves an open-circuit voltage of 0.4 V. The simple and low-cost deposition method of PbS:Bi3+ films is promising for the fabrication.

Keywords: Bi doping, PbS, thin films, solar cell

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