Search results for: Neural Processing Element (NPE)

Authors: Boris Blostotsky, Elia Efraim

Abstract:

Testing of columns in sway mode is performed in order to determine the maximal allowable load limited by plastic deformations or their end connections and a critical load limited by columns stability. Motivation to determine accurate value of critical force is caused by its using as follow: - critical load is maximal allowable load for given column configuration and can be used as criterion of perfection; - it is used in calculation prescribed by standards for design of structural elements under combined action of compression and bending; - it is used for verification of theoretical analysis of stability at various end conditions of columns. In the present work a new non-destructive method for determination of columns critical buckling load in sway mode is proposed. The method allows performing measurements during the tests under loads that exceeds the columns critical load without losing its stability. The possibility of such loading is achieved by structure of the loading system. The system is performed as frame with rigid girder, one of the columns is the tested column and the other is additional two-hinged strut. Loading of the frame is carried out by the flexible traction element attached to the girder. The load applied on the tested column can achieve a values that exceed the critical load by choice of parameters of the traction element and the additional strut. The system lateral stiffness and the column critical load are obtained by the dynamic method. The experiment planning and the comparison between the experimental and theoretical values were performed based on the developed dependency of lateral stiffness of the system on vertical load, taking into account a semi-rigid connections of the column's ends. The agreement between the obtained results was established. The method can be used for testing of real full-size columns in industrial conditions.

Keywords: buckling, columns, dynamic method, semi-rigid connections, sway mode

Procedia PDF Downloads 299

Authors: Monika Gałwa-Widera, Anna Kwarciak–Kozłowska, Lucyna Sławik-Dembiczak

Abstract:

Issues relating to management of sewage sludge from small and medium-sized wastewater treatment plants is a vital issue, which deal with such scholars as well as those directly involved in the issue of wastewater treatment and management of sedimentary. According to the Law on Waste generating waste is responsible for such processing to the product obtained impacted on the environment minimally. In small and medium-sized wastewater treatment plants have to deal with the technology of sludge management technology is far from drying and incineration of sewage sludge. So here you can use other technologies. One of them is the composting of sewage sludge. It is a process of processing and disposal of sewage sludge that effectively their disposal. By composting, we can obtain a product that contains significant amounts of organic matter to assess the fertilizing qualities. Modifications to the ongoing process in biological reactors allow for more rapid receipt of a wholesome product. The research presented and discussed in this publication relate to assist the composting process of sewage sludge and biomass structural material in the shares of rates: 35% biomass, 55% sludge, 10% structural material using a method which involves the re-spawning batch composting physical methods leachate from the composting process.

Keywords: biomass, composting, industry, sewage sludge

Procedia PDF Downloads 425

Authors: Chetan Gupta, Ramesh Gupta

Abstract:

Unmanned aerial vehicles, of all sizes, are prime targets of the wing morphing concept as their lightweight structures demand high aerodynamic stability while traversing unsteady atmospheric conditions. In this research study, a hybrid morphing technology is developed to aid the trailing edge of the aircraft wing to alter its camber as a monolithic element rather than functioning as conventional appendages like flaps. Kinematic tailoring, actuation techniques involving shape memory alloys (SMA), piezoelectrics – individually fall short of providing a simplistic solution to the conundrum of morphing aircraft wings. On the other hand, the feature of negligible hysteresis while actuating using compliant mechanisms has shown higher levels of applicability and deliverability in morphing wings of even large aircrafts. This research paper delves into designing a wing section model with a periodic, multi-stable compliant structure requiring lower orders of topological optimization. The design is sub-divided into three smaller domains with external hyperelastic connections to achieve deflections ranging from -15° to +15° at the trailing edge of the wing. To facilitate this functioning, a hybrid actuation system by combining the larger bandwidth feature of piezoelectric macro-fibre composites and relatively higher work densities of shape memory alloy wires are used. Finite element analysis is applied to optimize piezoelectric actuation of the internal compliant structure. A coupled fluid-surface interaction analysis is conducted on the wing section during morphing to study the development of the velocity boundary layer at low Reynold’s numbers of airflow.

Keywords: compliant mechanism, hybrid morphing, piezoelectrics, shape memory alloys

Procedia PDF Downloads 295

Authors: Eugene Y. J. Aw, J. W. Koh, S. H. Chew, K. E. Chua, Lionel L. J. Ang, Algernon C. S. Hong, Danette S. E. Tan, Grace H. B. Foo, K. Q. Hong, L. M. Cheng, M. L. Leong

Abstract:

This paper presents a novel rapid soil classification technique that combines computer vision with four-probe soil electrical resistivity method and cone penetration test (CPT), to improve the accuracy and productivity of on-site classification of excavated soil. In Singapore, excavated soils from local construction projects are transported to Staging Grounds (SGs) to be reused as fill material for land reclamation. Excavated soils are mainly categorized into two groups (“Good Earth” and “Soft Clay”) based on particle size distribution (PSD) and water content (w) from soil investigation reports and on-site visual survey, such that proper treatment and usage can be exercised. However, this process is time-consuming and labour-intensive. Thus, a rapid classification method is needed at the SGs. Computer vision, four-probe soil electrical resistivity and CPT were combined into an innovative non-destructive and instantaneous classification method for this purpose. The computer vision technique comprises soil image acquisition using industrial grade camera; image processing and analysis via calculation of Grey Level Co-occurrence Matrix (GLCM) textural parameters; and decision-making using an Artificial Neural Network (ANN). Complementing the computer vision technique, the apparent electrical resistivity of soil (ρ) is measured using a set of four probes arranged in Wenner’s array. It was found from the previous study that the ANN model coupled with ρ can classify soils into “Good Earth” and “Soft Clay” in less than a minute, with an accuracy of 85% based on selected representative soil images. To further improve the technique, the soil strength is measured using a modified mini cone penetrometer, and w is measured using a set of time-domain reflectometry (TDR) probes. Laboratory proof-of-concept was conducted through a series of seven tests with three types of soils – “Good Earth”, “Soft Clay” and an even mix of the two. Validation was performed against the PSD and w of each soil type obtained from conventional laboratory tests. The results show that ρ, w and CPT measurements can be collectively analyzed to classify soils into “Good Earth” or “Soft Clay”. It is also found that these parameters can be integrated with the computer vision technique on-site to complete the rapid soil classification in less than three minutes.

Keywords: Computer vision technique, cone penetration test, electrical resistivity, rapid and non-destructive, soil classification

Procedia PDF Downloads 197

Authors: Suman Dutta, Manish Agrawal, C. S. Jog

Abstract:

Accurate computation of hydrodynamic forces on floating structures and their deformation finds application in the ocean and naval engineering and wave energy harvesting. This manuscript presents a monolithic, finite element strategy for fluid-structure interaction involving hyper-elastic solids partly submerged in an incompressible fluid. A velocity-based Arbitrary Lagrangian-Eulerian (ALE) formulation has been used for the fluid and a displacement-based Lagrangian approach has been used for the solid. The flexibility of the ALE technique permits us to treat the free surface of the fluid as a Lagrangian entity. At the interface, the continuity of displacement, velocity and traction are enforced using the mortar method. In the mortar method, the constraints are enforced in a weak sense using the Lagrange multiplier method. In the literature, the mortar method has been shown to be robust in solving various contact mechanics problems. The time-stepping strategy used in this work reduces to the generalized trapezoidal rule in the Eulerian setting. In the Lagrangian limit, in the absence of external load, the algorithm conserves the linear and angular momentum and the total energy of the system. The use of monolithic coupling with an energy-conserving time-stepping strategy gives an unconditionally stable algorithm and allows the user to take large time steps. All the governing equations and boundary conditions have been mapped to the reference configuration. The use of the exact tangent stiffness matrix ensures that the algorithm converges quadratically within each time step. The robustness and good performance of the proposed method are demonstrated by solving benchmark problems from the literature.

Keywords: ALE, floating body, fluid-structure interaction, monolithic, mortar method

Procedia PDF Downloads 266

Authors: Babesse Saad, Ameddah Djemeleddine

Abstract:

In this paper, a learning algorithm using neuronal networks to improve the roll stability and prevent the rollover in a single unit heavy vehicle is proposed. First, LQR control to keep balanced normalized rollovers, between front and rear axles, below the unity, then a data collected from this controller is used as a training basis of a neuronal regulator. The ANN controller is thereafter applied for the nonlinear side force model, and gives satisfactory results than the LQR one.

Keywords: rollover, single unit heavy vehicle, neural networks, nonlinear side force

Procedia PDF Downloads 453

Authors: Hossein Aghababaei, Sergio Vitale, Giampaolo Ferraioli

Abstract:

In the context of Synthetic Aperture Radar (SAR) data, polarization is an important source of information for Earth's surface monitoring. SAR Systems are often considered to transmit only one polarization. This constraint leads to either single or dual polarimetric SAR imaging modalities. Single polarimetric systems operate with a fixed single polarization of both transmitted and received electromagnetic (EM) waves, resulting in a single acquisition channel. Dual polarimetric systems, on the other hand, transmit in one fixed polarization and receive in two orthogonal polarizations, resulting in two acquisition channels. Dual polarimetric systems are obviously more informative than single polarimetric systems and are increasingly being used for a variety of remote sensing applications. In dual polarimetric systems, the choice of polarizations for the transmitter and the receiver is open. The choice of circular transmit polarization and coherent dual linear receive polarizations forms a special dual polarimetric system called hybrid polarimetry, which brings the properties of rotational invariance to geometrical orientations of features in the scene and optimizes the design of the radar in terms of reliability, mass, and power constraints. The complete characterization of target scattering, however, requires fully polarimetric data, which can be acquired with systems that transmit two orthogonal polarizations. This adds further complexity to data acquisition and shortens the coverage area or swath of fully polarimetric images compared to the swath of dual or hybrid polarimetric images. The search for solutions to augment dual polarimetric data to full polarimetric data will therefore take advantage of full characterization and exploitation of the backscattered field over a wider coverage with less system complexity. Several methods for reconstructing fully polarimetric images using hybrid polarimetric data can be found in the literature. Although the improvements achieved by the newly investigated and experimented reconstruction techniques are undeniable, the existing methods are, however, mostly based upon model assumptions (especially the assumption of reflectance symmetry), which may limit their reliability and applicability to vegetation and forest scenarios. To overcome the problems of these techniques, this paper proposes a new framework for reconstructing fully polarimetric information from hybrid polarimetric data. The framework uses Deep Learning solutions to augment hybrid polarimetric data without relying on model assumptions. A convolutional neural network (CNN) with a specific architecture and loss function is defined for this augmentation problem by focusing on different scattering properties of the polarimetric data. In particular, the method controls the CNN training process with respect to several characteristic features of polarimetric images defined by the combination of different terms in the cost or loss function. The proposed method is experimentally validated with real data sets and compared with a well-known and standard approach from the literature. From the experiments, the reconstruction performance of the proposed framework is superior to conventional reconstruction methods. The pseudo fully polarimetric data reconstructed by the proposed method also agree well with the actual fully polarimetric images acquired by radar systems, confirming the reliability and efficiency of the proposed method.

Keywords: SAR image, polarimetric SAR image, convolutional neural network, deep learnig, deep neural network

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Authors: Mustafa M. Donma, Orkide Donma

Abstract:

Zinc is a vital element required for growth and development. This fact makes zinc important, particularly for children. It maintains normal cellular structure and functions. This essential element appears to have protective effects against coronary artery disease and cardiomyopathy. Higher serum zinc levels are associated with lower risk of cardiovascular diseases (CVDs). There is a significant association between low serum zinc levels and heart failure. Zinc may be a potential biomarker of cardiovascular health. High sensitive cardiac troponin T (hs-cTnT) and cardiac myosin binding protein C (cMyBP-C) are new generation markers used for prediagnosis, diagnosis, and prognosis of CVDs. The aim of this study is to determine zinc as well as new generation cardiac markers profiles in children with normal body mass index (N-BMI), obese (OB), morbid obese (MO) children, and children with metabolic syndrome (MetS) findings. The association among them will also be investigated. Four study groups were constituted. The study protocol was approved by the institutional Ethics Committee of Tekirdag Namik Kemal University. Parents of the participants filled informed consent forms to participate in the study. Group 1 is composed of 44 children with N-BMI. Group 2 and Group 3 comprised 43 OB and 45 MO children, respectively. Forty-five MO children with MetS findings were included in Group 4. World Health Organization age- and sex-adjusted BMI percentile tables were used to constitute groups. These values were 15-85, 95-99, and above 99 for N-BMI, OB, and MO, respectively. Criteria for MetS findings were determined. Routine biochemical analyses, including zinc, were performed. High sensitive-cTnT and cMyBP-C concentrations were measured by kits based on enzyme-linked immunosorbent assay principle. Appropriate statistical tests within the scope of SPSS were used for the evaluation of the study data. p<0.05 was accepted as statistically significant. Four groups were matched for age and gender. Decreased zinc concentrations were measured in Groups 2, 3, and 4 compared to Group 1. Groups did not differ from one another in terms of hs-cTnT. There were statistically significant differences between cMyBP-C levels of MetS group and N-BMI as well as OB groups. There was an increasing trend going from N-BMI group to MetS group. There were statistically significant negative correlations between zinc and hs-cTnT as well as cMyBP-C concentrations in MetS group. In conclusion, inverse correlations detected between zinc and new generation cardiac markers (hs-TnT and cMyBP-C) have pointed out that decreased levels of this physiologically essential trace element accompany increased levels of hs-cTnT as well as cMyBP-C in children with MetS. This finding emphasizes that both zinc and these new generation cardiac markers may be evaluated as biomarkers of cardiovascular health during severe childhood obesity precipitated with MetS findings and also suggested as the messengers of the future risk in the adulthood periods of children with MetS.

Keywords: cardiac myosin binding protein-C, cardiovascular diseases, children, high sensitive cardiac troponin T, obesity

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Authors: Mohammadreza Hedyehzadeh, Mahdi Yousefi

Abstract:

Colon cancer is one of the most common cancer, which predicted to increase its prevalence due to the bad eating habits of peoples. Nowadays, due to the busyness of people, the use of fast foods is increasing, and therefore, diagnosis of this disease and its treatment are of particular importance. To determine the best treatment approach for each specific colon cancer patients, the oncologist should be known the stage of the tumor. The most common method to determine the tumor stage is TNM staging system. In this system, M indicates the presence of metastasis, N indicates the extent of spread to the lymph nodes, and T indicates the size of the tumor. It is clear that in order to determine all three of these parameters, an imaging method must be used, and the gold standard imaging protocols for this purpose are CT and PET/CT. In CT imaging, due to the use of X-rays, the risk of cancer and the absorbed dose of the patient is high, while in the PET/CT method, there is a lack of access to the device due to its high cost. Therefore, in this study, we aimed to estimate the tumor size and the extent of its spread to the lymph nodes using MR images. More than 1300 MR images collected from the TCIA portal, and in the first step (pre-processing), histogram equalization to improve image qualities and resizing to get the same image size was done. Two expert radiologists, which work more than 21 years on colon cancer cases, segmented the images and extracted the tumor region from the images. The next step is feature extraction from segmented images and then classify the data into three classes: T0N0، T3N1 و T3N2. In this article, the VGG-16 convolutional neural network has been used to perform both of the above-mentioned tasks, i.e., feature extraction and classification. This network has 13 convolution layers for feature extraction and three fully connected layers with the softmax activation function for classification. In order to validate the proposed method, the 10-fold cross validation method used in such a way that the data was randomly divided into three parts: training (70% of data), validation (10% of data) and the rest for testing. It is repeated 10 times, each time, the accuracy, sensitivity and specificity of the model are calculated and the average of ten repetitions is reported as the result. The accuracy, specificity and sensitivity of the proposed method for testing dataset was 89/09%, 95/8% and 96/4%. Compared to previous studies, using a safe imaging technique (MRI) and non-use of predefined hand-crafted imaging features to determine the stage of colon cancer patients are some of the study advantages.

Keywords: colon cancer, VGG-16, magnetic resonance imaging, tumor size, lymph node metastasis

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Authors: Ahmad Lutfi, Nikolas Dhega

Abstract:

The Malala deposit in northwest Sulawesi is the only known porphyry molybdenum and the only source for rhenium, occurrence in Indonesia. The neural network method produces results that correspond very closely to those of the knowledge-based fuzzy logic method and weights of evidence method. This method required data of solid geology, regional faults, airborne magnetic, gamma-ray survey data and GIS data. This interpretation of the network output fits with the intuitive notion that a prospective area has characteristics that closely resemble areas known to contain mineral deposits. Contrasts with the weights of evidence and fuzzy logic methods, where, for a given grid location, each input-parameter value automatically results in an increase in the prospective estimated. Malala District indicated molybdenum anomalies in stream sediments from in excess of 15 km2 were obtained, including the Takudan Fault as most prominent structure with striking 40̊ to 60̊ over a distance of about 30 km and in most places weakly at anomaly B, developed over an area of 4 km2, with a ‘shell’ up to 50 m thick at the intrusive contact with minor mineralization occurring in the Tinombo Formation. Series of NW trending, steeply dipping fracture zones, named the East Zone has an estimated resource of 100 Mt at 0.14% MoS2 and minimum target of 150 Mt 0.25%. The Malala porphyries occur as stocks and dykes with predominantly granitic, with fluorine-poor class of molybdenum deposits and belongs to the plutonic sub-type. Unidirectional solidification textures consisting of subparallel, crenulated layers of quartz that area separated by layers of intrusive material textures. The deuteric nature of the molybdenum mineralization and the dominance of carbonate alteration.The nature of the Stage I with alteration barren quartz K‐feldspar; and Stage II with alteration quartz‐K‐feldspar‐apatite-molybdenite veins combined with the presence of disseminated molybdenite with primary biotite in the host intrusive.

Keywords: molybdenite, Malala, porphyries, anomaly B

Procedia PDF Downloads 141

Authors: Renata Markiewicz-Żukowska, Patryk Nowakowski, Sylwia K. Naliwajko, Jakub M. Bołtryk, Katarzyna Socha, Anna Puścion-Jakubik, Jolanta Soroczyńska, Maria H. Borawska

Abstract:

Selenium (Se) is an essential element for human health. As an integral part of glutathione peroxidase, it has antioxidant, anti-inflammatory and anticancer activities. Unfortunately, Se dietary intake is often insufficient, especially in regions where the soil is low in Se. Therefore, in search for good sources of Se, the content of this element in food products should be monitored. Food product can be considered as a source of Se when its standard portion covers above 15% of recommended daily allowance. In the case of nuts, 42g is recognized as the standard portion. The aim of this study was to determine the Se content in nuts and to answer the question of whether the studied nuts can be considered as a source of Se in the diet. The material for the study consisted of 10 types of nuts (12 samples of each one): almonds, Brazil nuts, cashews, hazelnuts, macadamia nuts, peanuts, pecans, pine nuts, pistachios and walnuts. The nuts were mineralized using microwave technique (Berghof, Germany). The content of Se was determined by atomic absorption spectrometry method with electrothermal atomization in a graphite tube with Zeeman background correction (Hitachi, Japan). The accuracy of the method was verified on certified reference material: Simulated Diet D. The statistical analysis was performed using Statistica v. 13.0 software. Statistical significance was determined at p < 0.05 level. The highest content of Se was found in Brazil nuts (4566.21 ± 3393.9 µg/kg) and the lowest in almonds (36.07 ± 18.8 µg/kg). A standard portion (42g) of almonds, brazil nuts, cashews, hazelnuts, macadamia nuts, peanuts, pecans, pine nuts, pistachios and walnuts covers the recommended daily allowance for Se respectively in: 2, 192, 28, 2, 16, 7, 4, 3, 12, 6%. Brazil nuts, cashews and macadamia nuts can be considered as a good source of Se in diet.

Keywords: atomic absorption spectrometry, diet, nuts, selenium

Procedia PDF Downloads 168

Authors: Tahamina Begum, Wan Nor Azlen Wan Mohamad, Faruque Reza, Wan Rosilawati Wan Rosli

Abstract:

To assess auditory perception and cognitive function during pregnancy is necessary as the pregnant women need extra effort for attention mainly for their executive function to maintain their quality of life. This study aimed to investigate neural correlates of cognitive and behavioral processing during mid trimester pregnancy. Event-Related Potentials (ERPs) were studied by using 128-sensor net and PAS or COWA (controlled Oral Word Association), WCST (Wisconsin Card Sorting Test), RAVLTIM (Rey Auditory Verbal and Learning Test: immediate or interference recall, delayed recall (RAVLT DR) and total score (RAVLT TS) were tested for neuropsychology assessment. In total 18 subjects were recruited (n= 9 in each group; control and pregnant group). All participants of the pregnant group were within 16-27 (mid trimester) weeks gestation. Age and education matched control healthy subjects were recruited in the control group. Participants were given a standardized test of auditory cognitive function as auditory oddball paradigm during ERP study. In this paradigm, two different auditory stimuli (standard and target stimuli) were used where subjects counted silently only target stimuli with giving attention by ignoring standard stimuli. Mean differences between target and standard stimuli were compared across groups. N100 (auditory sensory ERP component) and P300 (auditory cognitive ERP component) were recorded at T3, T4, T5, T6, Cz and Pz electrode sites. An equal number of electrodes showed non-significantly shorter amplitude of N100 component (except significantly shorter at T3, P= 0.05) and non-significant longer latencies (except significantly longer latency at T5, P= 0.008) of N100 component in pregnant group comparing control. In case of P300 component, maximum electrode sites showed non-significantly higher amplitudes and equal number of sites showed non-significant shorter latencies in pregnant group comparing control. Neuropsychology results revealed the non-significant higher score of PAS, lower score of WCST, lower score of RAVLTIM and RAVLTDR in pregnant group comparing control. The results of N100 component and RAVLT scores concluded that auditory perception is mildly impaired and P300 component proved very mild cognitive dysfunction with good executive functions in second trimester of pregnancy.

Keywords: auditory perception, pregnancy, stimuli, trimester

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Authors: Marcos Valdez Alexander Junior, Igor Aguilar-Alonso

Abstract:

Malnutrition is the root of different diseases that universally affect everyone, diseases such as obesity and malnutrition. The objective of this research is to predict the calories of the food to be eaten, developing a smart mobile application to show the user if a meal is balanced. Due to the large percentage of obesity and malnutrition in Peru, the present work is carried out. The development of the intelligent application is proposed with a three-layer architecture, and for the prediction of the nutritional value of the food, the use of pre-trained models based on convolutional neural networks is proposed.

Keywords: volume estimation, calorie estimation, artificial vision, food nutrition

Procedia PDF Downloads 82

Authors: Mostafa Shahriari, Sergio Rojas, David Pardo, Angel Rodriguez- Rozas, Shaaban A. Bakr, Victor M. Calo, Ignacio Muga

Abstract:

Logging-While Drilling (LWD) is a technique to record down-hole logging measurements while drilling the well. Nowadays, LWD devices (e.g., nuclear, sonic, resistivity) are mostly used commercially for geo-steering applications. Modern borehole resistivity tools are able to measure all components of the magnetic field by incorporating tilted coils. The depth of investigation of LWD tools is limited compared to the thickness of the geological layers. Thus, it is a common practice to approximate the Earth’s subsurface with a sequence of 1D models. For a 1D model, we can reduce the dimensionality of the problem using a Hankel transform. We can solve the resulting system of ordinary differential equations (ODEs) either (a) analytically, which results in a so-called semi-analytic method after performing a numerical inverse Hankel transform, or (b) numerically. Semi-analytic methods are used by the industry due to their high performance. However, they have major limitations, namely: -The analytical solution of the aforementioned system of ODEs exists only for piecewise constant resistivity distributions. For arbitrary resistivity distributions, the solution of the system of ODEs is unknown by today’s knowledge. -In geo-steering, we need to solve inverse problems with respect to the inversion variables (e.g., the constant resistivity value of each layer and bed boundary positions) using a gradient-based inversion method. Thus, we need to compute the corresponding derivatives. However, the analytical derivatives of cross-bedded formation and the analytical derivatives with respect to the bed boundary positions have not been published to the best of our knowledge. The main contribution of this work is to overcome the aforementioned limitations of semi-analytic methods by solving each 1D model (associated with each Hankel mode) using an efficient multi-scale finite element method. The main idea is to divide our computations into two parts: (a) offline computations, which are independent of the tool positions and we precompute only once and use them for all logging positions, and (b) online computations, which depend upon the logging position. With the above method, (a) we can consider arbitrary resistivity distributions along the 1D model, and (b) we can easily and rapidly compute the derivatives with respect to any inversion variable at a negligible additional cost by using an adjoint state formulation. Although the proposed method is slower than semi-analytic methods, its computational efficiency is still high. In the presentation, we shall derive the mathematical variational formulation, describe the proposed multi-scale finite element method, and verify the accuracy and efficiency of our method by performing a wide range of numerical experiments and comparing the numerical solutions to semi-analytic ones when the latest are available.

Keywords: logging-While-Drilling, resistivity measurements, multi-scale finite elements, Hankel transform

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Authors: Marianna Maiaru, Gregory M. Odegard, Josh Kemppainen, Ivan Gallegos, Michael Olaya

Abstract:

Ceramic matrix composites (CMCs) are typically used in applications that require long-term mechanical integrity at elevated temperatures. CMCs are usually fabricated using a polymer precursor that is initially polymerized in situ with fiber reinforcement, followed by a series of cycles of pyrolysis to transform the polymer matrix into a rigid glass or ceramic. The pyrolysis step typically generates volatile gasses, which creates porosity within the polymer matrix phase of the composite. Subsequent cycles of monomer infusion, polymerization, and pyrolysis are often used to reduce the porosity and thus increase the durability of the composite. Because of the significant expense of such iterative processing cycles, new generations of CMCs with improved durability and manufacturability are difficult and expensive to develop using standard Edisonian approaches. The goal of this research is to develop a computational process-modeling-based approach that can be used to design the next generation of CMC materials with optimized material and processing parameters for maximum strength and efficient manufacturing. The process modeling incorporates computational modeling tools, including molecular dynamics (MD), to simulate the material at multiple length scales. Results from MD simulation are used to inform the continuum-level models to link molecular-level characteristics (material structure, temperature) to bulk-level performance (strength, residual stresses). Processing parameters are optimized such that process-induced residual stresses are minimized and laminate strength is maximized. The multiscale process modeling method developed with this research can play a key role in the development of future CMCs for high-temperature and high-strength applications. By combining multiscale computational tools and process modeling, new manufacturing parameters can be established for optimal fabrication and performance of CMCs for a wide range of applications.

Keywords: digital engineering, finite elements, manufacturing, molecular dynamics

Procedia PDF Downloads 84

Authors: Hossein Aghababaei, Sergio Vitale, Giampaolo ferraioli

Abstract:

In the context of Synthetic Aperture Radar (SAR) data, polarization is an important source of information for Earth's surface monitoring . SAR Systems are often considered to transmit only one polarization. This constraint leads to either single or dual polarimetric SAR imaging modalities. Single polarimetric systems operate with a fixed single polarization of both transmitted and received electromagnetic (EM) waves, resulting in a single acquisition channel. Dual polarimetric systems, on the other hand, transmit in one fixed polarization and receive in two orthogonal polarizations, resulting in two acquisition channels. Dual polarimetric systems are obviously more informative than single polarimetric systems and are increasingly being used for a variety of remote sensing applications. In dual polarimetric systems, the choice of polarizations for the transmitter and the receiver is open. The choice of circular transmit polarization and coherent dual linear receive polarizations forms a special dual polarimetric system called hybrid polarimetry, which brings the properties of rotational invariance to geometrical orientations of features in the scene and optimizes the design of the radar in terms of reliability, mass, and power constraints. The complete characterization of target scattering, however, requires fully polarimetric data, which can be acquired with systems that transmit two orthogonal polarizations. This adds further complexity to data acquisition and shortens the coverage area or swath of fully polarimetric images compared to the swath of dual or hybrid polarimetric images. The search for solutions to augment dual polarimetric data to full polarimetric data will therefore take advantage of full characterization and exploitation of the backscattered field over a wider coverage with less system complexity. Several methods for reconstructing fully polarimetric images using hybrid polarimetric data can be found in the literature. Although the improvements achieved by the newly investigated and experimented reconstruction techniques are undeniable, the existing methods are, however, mostly based upon model assumptions (especially the assumption of reflectance symmetry), which may limit their reliability and applicability to vegetation and forest scenarios. To overcome the problems of these techniques, this paper proposes a new framework for reconstructing fully polarimetric information from hybrid polarimetric data. The framework uses Deep Learning solutions to augment hybrid polarimetric data without relying on model assumptions. A convolutional neural network (CNN) with a specific architecture and loss function is defined for this augmentation problem by focusing on different scattering properties of the polarimetric data. In particular, the method controls the CNN training process with respect to several characteristic features of polarimetric images defined by the combination of different terms in the cost or loss function. The proposed method is experimentally validated with real data sets and compared with a well-known and standard approach from the literature. From the experiments, the reconstruction performance of the proposed framework is superior to conventional reconstruction methods. The pseudo fully polarimetric data reconstructed by the proposed method also agree well with the actual fully polarimetric images acquired by radar systems, confirming the reliability and efficiency of the proposed method.

Keywords: SAR image, deep learning, convolutional neural network, deep neural network, SAR polarimetry

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Authors: Ananya Gangavarapu

Abstract:

Convolutional Neural Networks (CNN) based models are providing diagnostic capabilities on par with the medical specialists in many specialty areas. However, collecting the medical data for training purposes is very challenging because of the increased regulations around data collections and privacy concerns around personal health data. The gathering of the data becomes even more difficult if the capture devices are edge-based mobile devices (like smartphones) with feeble wireless connectivity in rural/remote areas. In this paper, I would like to highlight Federated Learning approach to mitigate data privacy and security issues.

Keywords: deep learning in healthcare, data privacy, federated learning, training in distributed environment

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Authors: Seppo Sirkemaa

Abstract:

Information systems infrastructure is the basis of business systems and processes in the company. It should be a reliable platform for business processes and activities but also have the flexibility to change business needs. The development of an infrastructure that is robust, reliable, and flexible is a challenge. Understanding technological capabilities and business needs is a key element in the development of successful information systems infrastructure.

Keywords: development, information technology, networks, technology

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Authors: Christian Neunzig, Simon Fahle, Jürgen Schulz, Matthias Möller, Bernd Kuhlenkötter

Abstract:

The increasing use of deep learning applications in production is becoming a competitive advantage. Predictive quality enables the assurance of product quality by using data-driven forecasts via machine learning models as a basis for decisions on test results. The use of real Bosch production data along the value chain of hydraulic valves is a promising approach to classifying the leakage of directional valves.

Keywords: artificial neural networks, classification, hydraulics, predictive quality, deep learning

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Authors: Natalie Riley, Anita Quigley, Robert M. I. Kapsa, George W. Greene

Abstract:

As the fields of medicine and bionics grow rapidly in technological advancement, the future and success of it depends on the ability to effectively interface between the artificial and the biological worlds. The biggest obstacle when it comes to implantable, electronic medical devices, is maintaining a ‘clean’, low noise electrical connection that allows for efficient sharing of electrical information between the artificial and biological systems. Implant fouling occurs with the adhesion and accumulation of proteins and various cell types as a result of the immune response to protect itself from the foreign object, essentially forming an electrical insulation barrier that often leads to implant failure over time. Lubricin (LUB) functions as a major boundary lubricant in articular joints, a unique glycoprotein with impressive anti-adhesive properties that self-assembles to virtually any substrate to form a highly ordered, ‘telechelic’ polymer brush. LUB does not passivate electroactive surfaces which makes it ideal, along with its innate biocompatibility, as a coating for implantable bionic electrodes. It is the aim of the study to investigate LUB’s anti-fouling properties and its potential as a safe, bioinspired material for coating applications to enhance the performance and longevity of implantable medical devices as well as reducing the frequency of implant replacement surgeries. Native, bovine-derived LUB (N-LUB) and recombinant LUB (R-LUB) were applied to gold-coated mylar surfaces. Fibroblast, chondrocyte and neural cell types were cultured and grown on the coatings under both passive and electrically stimulated conditions to test the stability and anti-adhesive property of the LUB coating in the presence of an electric field. Lactate dehydrogenase (LDH) assays were conducted as a directly proportional cell population count on each surface along with immunofluorescent microscopy to visualize cells. One-way analysis of variance (ANOVA) with post-hoc Tukey’s test was used to test for statistical significance. Under both passive and electrically stimulated conditions, LUB significantly reduced cell attachment compared to bare gold. Comparing the two coating types, R-LUB reduced cell attachment significantly compared to its native counterpart. Immunofluorescent micrographs visually confirmed LUB’s antiadhesive property, R-LUB consistently demonstrating significantly less attached cells for both fibroblasts and chondrocytes. Preliminary results investigating neural cells have so far demonstrated that R-LUB has little effect on reducing neural cell attachment; the study is ongoing. Recombinant LUB coatings demonstrated impressive anti-adhesive properties, reducing cell attachment in fibroblasts and chondrocytes. These findings and the availability of recombinant LUB brings into question the results of previous experiments conducted using native-derived LUB, its potential not adequately represented nor realized due to unknown factors and impurities that warrant further study. R-LUB is stable and maintains its anti-fouling property under electrical stimulation, making it suitable for electroactive surfaces.

Keywords: anti-fouling, bioinspired, cell attachment, lubricin

Procedia PDF Downloads 109

Authors: Aghamusa Azizov

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This study investigates the correlation between solar activity and sentiment in news media coverage, using a large-scale dataset of solar activity since 1750 and over 15 million articles from "The New York Times" dating from 1851 onwards. Employing Pearson's correlation coefficient and multiple Natural Language Processing (NLP) tools—TextBlob, Vader, and DistillBERT—the research examines the extent to which fluctuations in solar phenomena are reflected in the sentiment of historical news narratives. The findings reveal that the correlation between solar activity and media sentiment is generally negligible, suggesting a weak influence of solar patterns on the portrayal of events in news media. Notably, a moderate positive correlation was observed between the sentiments derived from TextBlob and Vader, indicating consistency across NLP tools. The analysis provides insights into the historical impact of solar activity on human affairs and highlights the importance of using multiple analytical methods to understand complex relationships in large datasets. The study contributes to the broader understanding of how extraterrestrial factors may intersect with media-reported events and underlines the intricate nature of interdisciplinary research in the data science and historical domains.

Keywords: solar activity correlation, media sentiment analysis, natural language processing, historical event patterns

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Authors: S. Zhang, L. Zhang, X. Chen

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Due to the large uncertainty on the mechanical properties of FRP (fibre reinforced plastic), the reliability evaluation of FRP structures are currently receiving much attention in industry. However, possible statistical correlation between ply mechanical properties has been so far overlooked, and they are mostly assumed to be independent random variables. In this study, the statistical correlation between ply mechanical properties of uni-directional and plain weave composite is firstly analyzed by a combination of Monte-Carlo simulation and finite element modeling of the FRP unit cell. Large linear correlation coefficients between the in-plane mechanical properties are observed, and the correlation coefficients are heavily dependent on the uncertainty of the fibre volume ratio. It is also observed that the correlation coefficients related to Poisson’s ratio are negative while others are positive. To experimentally achieve the statistical correlation coefficients between in-plane mechanical properties of FRP, all concerned in-plane mechanical properties of the same specimen needs to be known. In-plane shear modulus of FRP is experimentally derived by the approach suggested in the ASTM standard D5379M. Tensile tests are conducted using the same specimens used for the shear test, and due to non-uniform tensile deformation a modification factor is derived by a finite element modeling. Digital image correlation is adopted to characterize the specimen non-uniform deformation. The preliminary experimental results show a good agreement with the numerical analysis on the statistical correlation. Then, failure probability of laminate plates is calculated in cases considering and not considering the statistical correlation, using the Monte-Carlo and Markov Chain Monte-Carlo methods, respectively. The results highlight the importance of accounting for the statistical correlation between ply mechanical properties to achieve accurate failure probability of laminate plates. Furthermore, it is found that for the multi-layer laminate plate, the statistical correlation between the ply elastic properties significantly affects the laminate reliability while the effect of statistical correlation between the ply strength is minimal.

Keywords: failure probability, FRP, reliability, statistical correlation

Procedia PDF Downloads 149

Authors: Anna Krukowska, Tomasz Klimczuk, Adriana Zaleska-Medynska

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Photoactive materials have attracted attention due to their potential application in the degradation of environmental pollutants to non-hazardous compounds in an eco-friendly route. Among semiconductor photocatalysts, tantalates such as potassium tantalate (KTaO3) is one of the excellent functional photomaterial. However, tantalates-based materials are less active under visible-light irradiation, the enhancement in photoactivity could be improved with the modification of opto-eletronic properties of KTaO3 by doping rare earth metal (Er) and further photodeposition of noble metal nanoparticles (Pt). Inclusion of rare earth element in orthorhombic structure of tantalate can generate one high-energy photon by absorbing two or more incident low-energy photons, which convert visible-light and infrared-light into the ultraviolet-light to satisfy the requirement of KTaO3 photocatalysts. On the other hand, depositions of noble metal nanoparticles on the surface of semiconductor strongly absorb visible-light due to their surface plasmon resonance, in which their conducting electrons undergo a collective oscillation induced by electric field of visible-light. Furthermore, the high dispersion of Pt nanoparticles, which will be obtained by photodeposition process is additional important factor to improve the photocatalytic activity. The present work is aimed to study the effect of photocatalytic process of the prepared Er-doped KTaO3 and further incorporation of Pt nanoparticles by photodeposition. Moreover, the research is also studied correlations between photocatalytic activity and physico-chemical properties of obtained Pt/Er-KTaO3 samples. The Er-doped KTaO3 microcomposites were synthesized by a hydrothermal method. Then photodeposition method was used for Pt loading over Er-KTaO3. The structural and optical properties of Pt/Er-KTaO3 photocatalytic were characterized using scanning electron microscope (SEM), X-ray diffraction (XRD), volumetric adsorption method (BET), UV-Vis absorption measurement, Raman spectroscopy and luminescence spectroscopy. The photocatalytic properties of Pt/Er-KTaO3 microcomposites were investigated by degradation of phenol in aqueous phase as model pollutant under visible and ultraviolet-light irradiation. Results of this work show that all the prepared photocatalysis exhibit low BET surface area, although doping of the bare KTaO3 with rare earth element (Er) presents a slight increase in this value. The crystalline structure of Pt/Er-KTaO3 powders exhibited nearly identical positions for the main peak at about 22,8o and the XRD pattern could be assigned to an orthorhombic distorted perovskite structure. The Raman spectra of obtained semiconductors confirmed demonstrating perovskite-like structure. The optical absorption spectra of Pt nanoparticles exhibited plasmon absorption band for main peaks at about 216 and 264 nm. The addition of Pt nanoparticles increased photoactivity compared to Er-KTaO3 and pure KTaO3. Summary optical properties of KTaO3 change with its doping Er-element and further photodeposition of Pt nanoparticles.

Keywords: heterogeneous photocatalytic, KTaO3 photocatalysts, Er3+ ion doping, Pt photodeposition

Procedia PDF Downloads 349

Authors: Sebastien Relave, Christophe Desrayaud, Aurelien Vilani, Alexey Sova

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Laser beam melting (LBM) is an additive manufacturing process that enables complex 3D parts to be designed. This process is now commonly employed for various applications such as chemistry or energy, requiring the use of stainless steel grades. LBM can offer comparable and sometimes superior mechanical properties to those of wrought materials. However, we observed an anisotropic microstructure which results from the process, caused by the very high thermal gradients along the building axis. This microstructure can be harmful depending on the application. For this reason, control and prediction of the microstructure are important to ensure the improvement and reproducibility of the mechanical properties. This study is focused on the 316L SS grade and aims at understanding the solidification and transformation mechanisms during process. Experiments to analyse the nucleation and growth of the microstructure obtained by the LBM process according to several conditions. These samples have been designed on different type of support bulk and lattice. Samples are produced on ProX DMP 200 LBM device. For the two conditions the analysis of microstructures, thanks to SEM and EBSD, revealed a single phase Austenite with preferential crystallite growth along the (100) plane. The microstructure was presented a hierarchical structure consisting columnar grains sizes in the range of 20-100 µm and sub grains structure of size 0.5 μm. These sub-grains were found in different shapes (columnar and cellular). This difference can be explained by a variation of the thermal gradient and cooling rate or element segregation while no sign of element segregation was found at the sub-grain boundaries. A high dislocation concentration was observed at sub-grain boundaries. These sub-grains are separated by very low misorientation walls ( < 2°) this causes a lattice of curvature inside large grain. A discussion is proposed on the occurrence of these microstructures formation, in regard of the LBM process conditions.

Keywords: selective laser melting, stainless steel, microstructure

Procedia PDF Downloads 138

Authors: Ren-Jieh Kuo, Chun-Shou Huang

Abstract:

The research of juice flavor forecasting has become more important in China. Due to the fast economic growth in China, many different kinds of juices have been introduced to the market. If a beverage company can understand their customers’ preference well, the juice can be served more attractively. Thus, this study intends to introduce the basic theory and computing process of grapes juice flavor forecasting based on support vector regression (SVR). Applying SVR, BPN and LR to forecast the flavor of grapes juice in real data, the result shows that SVR is more suitable and effective at predicting performance.

Keywords: flavor forecasting, artificial neural networks, Support Vector Regression, China

Procedia PDF Downloads 473

Authors: Nickson Kipng’etich Lang’at, Thomas Thoruwa, John Abraham, John Wanyoko

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Green tea is made from the top two leaves and buds of a shrub, Camellia sinensis, of the family Theaceae and the order Theales. The green tea leaves are picked and immediately sent to be dried or steamed to prevent fermentation. Fluid bed drying technique is a common drying method used in drying green tea because of its ease in design and construction and fluidization of fine tea particles. Major problems in this method are significant loss of chemical content of the leaf and green appearance of tea, retention of high moisture content in the leaves and bed channeling and defluidization. The energy associated with the drying technology has been shown to be a vital factor in determining the quality of green tea. As part of the implementation, prototype dryer was built that facilitated sequence of operations involving steaming, cooling, pre-drying and final drying. The major findings of the project were in terms of quality characteristics of tea leaves and energy consumption during processing. The optimal design achieved a moisture content of 4.2 ± 0.84%. With the optimum drying temperature of 100 ºC, the specific energy consumption was 1697.8 kj.Kg^-1 and evaporation rate of 4.272 x 10^-4 Kg.m^-2.s^-1. The energy consumption in a fluidized system can be further reduced by focusing on energy saving designs.

Keywords: evaporation rate, fluid bed dryer, maceration, specific energy consumption

Procedia PDF Downloads 292

Authors: Peloriadi Konstantina, Fakis Dimitris, Grammelis Panagiotis

Abstract:

Methods for carbon capture and storage (CCS) can play a key role in the reduction of industrial CO₂ emissions, especially in the cement industry, which accounts for 7% of global emissions. Cement industries around the world have committed to address this problem by reaching carbon neutrality by the year 2050. The aim of the work to be presented was to contribute to the decarbonization strategy by integrating the 1st generation oxyfuel technology in cement production plants. This technology has been shown to improve fuel efficiency while providing one of the most cost-effective solutions when compared to other capture methods. A validated simulation of the cement plant was thus used as a basis to develop an oxyfuel retrofitted cement process. The process model for the oxyfuel technology is developed on the ASPEN (Advanced System for Process Engineering) PLUSTM simulation software. This process consists of an Air Separation Unit (ASU), an oxyfuel cement plant with coal and alternative solid fuel (ASF) as feedstock, and a carbon dioxide processing unit (CPU). A detailed description and analysis of the CPU will be presented, including the findings of a literature review and simulation results, regarding the effects of flue gas impurities during operation. Acknowledgment: This research has been conducted in the framework of the EU funded AC2OCEM project, which investigates first and the second generation oxyfuel concepts.

Keywords: oxyfuel technology, carbon capture and storage, CO₂ processing unit, cement, aspen plus

Procedia PDF Downloads 168

Authors: Japinder Wadhawan, Pradeep Rajan, Alok K. Saran, Navdeep S. Sidhu, Daanvir K. Dhir

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Currently, research focus in the development of agricultural equipment and tractor parts in India is innovation and use of alternate materials like austempered ductile iron (ADI). Three-point linkage mechanism of the tractor is susceptible to unpredictable load conditions in the field, and one of the critical components vulnerable to failure is lift arm. Conventionally, lift arm is manufactured either by forging or casting (SG Iron) and main objective of the present work is to reduce the failure occurrences in the lift arm, which is achieved by changing the manufacturing material, i.e ADI, without changing existing design. Effect of four pertinent variables of manufacturing ADI, viz. austenitizing temperature, austenitizing time, austempering temperature, austempering time, was investigated using Taguchi method for design of experiments. To analyze the effect of parameters on the mechanical properties, mean average and signal-to-noise (S/N) ratio was calculated based on the design of experiments with L9 orthogonal array and the linear graph. The best combination for achieving the desired mechanical properties of lift arm is austenitization at 860°C for 90 minutes and austempering at 350°C for 60 minutes. Results showed that the developed component is having 925 MPA tensile strength, 7.8 per cent elongation and 120 joules toughness making it more suitable material for lift arm manufacturing. The confirmatory experiment has been performed and found a good agreement between predicted and experimental value. Also, the CAD model of the existing design was developed in computer aided design software, and structural loading calculations were performed by a commercial finite element analysis package. An optimized shape of the lift arm has also been proposed resulting in light weight and cheaper product than the existing design, which can withstand the same loading conditions effectively.

Keywords: austempered ductile iron, design of experiment, finite element analysis, lift arm

Procedia PDF Downloads 221

Authors: Nahia H. Sassine, Frédéric-Victor Donzé, Arnaud Bruch, Barthélemy Harthong

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Thermal Energy Storage (TES) systems are central elements of various types of power plants operated using renewable energy sources. Packed bed TES can be considered as a cost–effective solution in concentrated solar power plants (CSP). Such a device is made up of a tank filled with a granular bed through which heat-transfer fluid circulates. However, in such devices, the tank might be subjected to catastrophic failure induced by a mechanical phenomenon known as thermal ratcheting. Thermal stresses are accumulated during cycles of loading and unloading until the failure happens. For instance, when rocks are used as storage material, the tank wall expands more than the solid medium during charge process, a gap is created between the rocks and tank walls and the filler material settles down to fill it. During discharge, the tank contracts against the bed, resulting in thermal stresses that may exceed the wall tank yield stress and generate plastic deformation. This phenomenon is repeated over the cycles and the tank will be slowly ratcheted outward until it fails. This paper aims at studying the evolution of tank wall stresses over granular bed thermal cycles, taking into account both thermal and mechanical loads, with a numerical model based on the discrete element method (DEM). Simulations were performed to study two different thermal configurations: (i) the tank is heated homogeneously along its height or (ii) with a vertical gradient of temperature. Then, the resulting loading stresses applied on the tank are compared as well the response of the internal granular material. Besides the study of the influence of different thermal configurations on the storage tank response, other parameters are varied, such as the internal angle of friction of the granular material, the dispersion of particles diameters as well as the tank’s dimensions. Then, their influences on the kinematics of the granular bed submitted to thermal cycles are highlighted.

Keywords: discrete element method (DEM), thermal cycles, thermal energy storage, thermocline

Procedia PDF Downloads 392

Authors: Aman Kaur, Colin Ribton, Wamadeva Balachandaran

Abstract:

Electron beam welding due to its inherent advantages is being extensively used for material processing where high precision is required. Especially in aerospace or nuclear industries, there are high quality requirements and the cost of materials and processes is very high which makes it very important to ensure the beam quality is maintained and checked prior to carrying out the welds. Although the processes in these industries are highly controlled, however, even the minor changes in the operating parameters of the electron gun can make large enough variations in the beam quality that can result in poor welding. To measure the beam quality a simple device has been designed that can be used at high powers. The device consists of two slits in x and y axis which collects a small portion of the beam current when the beam is deflected over the slits. The signals received from the device are processed in data acquisition hardware and the dedicated software developed for the device. The device has been used in controlled laboratory environments to analyse the signals and the weld quality relationships by varying the focus current. The results showed matching trends in the weld dimensions and the beam characteristics. Further experimental work is being carried out to determine the ability of the device and signal processing software to detect subtle changes in the beam quality and to relate these to the physical weld quality indicators.

Keywords: electron beam welding, beam quality, high power, weld quality indicators

Procedia PDF Downloads 312