Search results for: deep composite kernel

Authors: Mourad Makhlouf, Bouabdellah Benaicha, Zoubir Benmaamar, Didier Villemin

Abstract:

The addition of combustion catalysts to ammonium perchlorate-based composite fuels can indeed significantly enhance their performance. In this work, a nanocomposite was synthesized using graphene oxide (GO) and hematite nanoparticles grafted onto graphene oxide as a catalyst support.To characterize the nanocomposite, several experimental techniques were employed, including Fourier-transform infrared spectroscopy (FTIR), Raman spectroscopy, and scanning electron microscopy (SEM). FTIR is useful for analyzing chemical bonding and functional groups, while Raman spectroscopy provides information about the vibrational modes of the materials. SEM allows for visualizing the surface morphology and structure.The thermal analysis of two mixtures, one based on AP/GO and the other on AP/GO-Fe2O3, was conducted with varying percentages. The results indicated that the nanocomposite GO-Fe2O3 acted as a catalyst, significantly accelerating the thermal decomposition process of AP. This catalytic effect ultimately led to an improvement in the energy performance of the composite fuel.Overall, the synthesis and characterization of the nanocomposite, as well as the thermal analysis, demonstrated the effectiveness of GO-Fe2O3 as a combustion catalyst in enhancing the performance of ammonium perchlorate-based composite fuels.

Keywords: composite propellants, ammonium perchlorate, nanocomposite, catalytic support, hematite nanoparticles, graphene oxide, thermal decomposition

Procedia PDF Downloads 25

Authors: Joy Cao, Min Zhou

Abstract:

Purpose: Acute Type A aortic dissection is a well-known cause of extremely high mortality rate. A highly accurate and cost-effective non-invasive predictor is critically needed so that the patient can be treated at earlier stage. Although various CFD approaches have been tried to establish some prediction frameworks, they are sensitive to uncertainty in both image segmentation and boundary conditions. Tedious pre-processing and demanding calibration procedures requirement further compound the issue, thus hampering their clinical applicability. Using the latest physics informed deep learning methods to establish an accurate and cost-effective predictor framework are amongst the main goals for a better Type A aortic dissection treatment. Methods: Via training a novel physics-informed deep residual network, with non-invasive 4D MRI displacement vectors as inputs, the trained model can cost-effectively calculate all these biomarkers: aortic blood pressure, WSS, and OSI, which are used to predict potential type A aortic dissection to avoid the high mortality events down the road. Results: The proposed deep learning method has been successfully trained and tested with both synthetic 3D aneurysm dataset and a clinical dataset in the aortic dissection context using Google colab environment. In both cases, the model has generated aortic blood pressure, WSS, and OSI results matching the expected patient’s health status. Conclusion: The proposed novel physics-informed deep residual network shows great potential to create a cost-effective, non-invasive predictor framework. Additional physics-based de-noising algorithm will be added to make the model more robust to clinical data noises. Further studies will be conducted in collaboration with big institutions such as Cleveland Clinic with more clinical samples to further improve the model’s clinical applicability.

Keywords: type-a aortic dissection, deep residual networks, blood flow modeling, data-driven modeling, non-invasive diagnostics, deep learning, artificial intelligence.

Procedia PDF Downloads 76

Authors: Vinayaka Gude Divya Sampath

Abstract:

The research introduces an integrated prediction model to assess the susceptibility of roads in a future flooding event. The model consists of deep learning algorithm for forecasting gauge height data and Flood Inundation Mapper (FIM) for spatial flooding. An optimal architecture for Long short-term memory network (LSTM) was identified for the gauge located on Tangipahoa River at Robert, LA. Dropout was applied to the model to evaluate the uncertainty associated with the predictions. The estimates are then used along with FIM to identify the spatial flooding. Further geoprocessing in ArcGIS provides the susceptibility values for different roads. The model was validated based on the devastating flood of August 2016. The paper discusses the challenges for generalization the methodology for other locations and also for various types of flooding. The developed model can be used by the transportation department and other emergency response organizations for effective disaster management.

Keywords: deep learning, disaster management, flood prediction, urban flooding

Procedia PDF Downloads 129

Authors: Adrià Arbués-Sangüesa, Coloma Ballester, Gloria Haro

Abstract:

Tracking sports players is a widely challenging scenario, specially in single-feed videos recorded in tight courts, where cluttering and occlusions cannot be avoided. This paper presents an analysis of several geometric and semantic visual features to detect and track basketball players. An ablation study is carried out and then used to remark that a robust tracker can be built with Deep Learning features, without the need of extracting contextual ones, such as proximity or color similarity, nor applying camera stabilization techniques. The presented tracker consists of: (1) a detection step, which uses a pretrained deep learning model to estimate the players pose, followed by (2) a tracking step, which leverages pose and semantic information from the output of a convolutional layer in a VGG network. Its performance is analyzed in terms of MOTA over a basketball dataset with more than 10k instances.

Keywords: basketball, deep learning, feature extraction, single-camera, tracking

Procedia PDF Downloads 123

Authors: Jie Zhang, Qianyu Guo, Tieyi Zhang, Zhiyong Feng, Xiaohong Li

Abstract:

With the widespread popularity of mobile devices and the development of artificial intelligence (AI), deep learning (DL) has been extensively applied in Android apps. Compared with traditional Android apps (traditional apps), deep learning based Android apps (DL-based apps) need to use more third-party application programming interfaces (APIs) to complete complex DL inference tasks. However, existing methods (e.g., FlowDroid) for detecting sensitive information leakage in Android apps cannot be directly used to detect DL-based apps as they are difficult to detect third-party APIs. To solve this problem, we design DLtrace; a new static information flow analysis tool that can effectively recognize third-party APIs. With our proposed trace and detection algorithms, DLtrace can also efficiently detect privacy leaks caused by sensitive APIs in DL-based apps. Moreover, using DLtrace, we summarize the non-sequential characteristics of DL inference tasks in DL-based apps and the specific functionalities provided by DL models for such apps. We propose two formal definitions to deal with the common polymorphism and anonymous inner-class problems in the Android static analyzer. We conducted an empirical assessment with DLtrace on 208 popular DL-based apps in the wild and found that 26.0% of the apps suffered from sensitive information leakage. Furthermore, DLtrace has a more robust performance than FlowDroid in detecting and identifying third-party APIs. The experimental results demonstrate that DLtrace expands FlowDroid in understanding DL-based apps and detecting security issues therein.

Keywords: mobile computing, deep learning apps, sensitive information, static analysis

Procedia PDF Downloads 148

Authors: Milad Abbasi

Abstract:

Carbon fiber-reinforced polymer (CFRP) composite structures are increasingly being utilized in the automotive industry due to their lightweight and specific energy absorption capabilities. Although it is impossible to predict composite mechanical properties directly using theoretical methods, various research has been conducted so far in the literature for accurate simulation of CFRP structures' energy-absorbing behavior. In this research, axial compression experiments were carried out on hand lay-up unidirectional CFRP composite tubes. The fabrication method allowed the authors to extract the material properties of the CFRPs using ASTM D3039, D3410, and D3518 standards. A neural network machine learning algorithm was then utilized to build a robust prediction model to forecast the axial compressive properties of CFRP tubes while reducing high-cost experimental efforts. The predicted results have been compared with the experimental outcomes in terms of load-carrying capacity and energy absorption capability. The results showed high accuracy and precision in the prediction of the energy-absorption capacity of the CFRP tubes. This research also demonstrates the effectiveness and challenges of machine learning techniques in the robust simulation of composites' energy-absorption behavior. Interestingly, the proposed method considerably condensed numerical and experimental efforts in the simulation and calibration of CFRP composite tubes subjected to compressive loading.

Keywords: CFRP composite tubes, energy absorption, crushing behavior, machine learning, neural network

Procedia PDF Downloads 136

Authors: Lihong Wu, Raymond Young

Abstract:

The ability to learn is one of the most critical skills in the information age. However, many students do not have a clear understanding of what learning is, what they are learning, and why they are learning. Many students study simply to pass rather than to learn something useful for their career and their life. They have a misconception about learning and a wrong attitude towards learning. This research explores student attitudes to study in management education and explores how to intercede to lead students from shallow to deeper modes of learning.

Keywords: knowledge, surface learning, deep learning, education

Procedia PDF Downloads 484

Authors: Yong Zhang

Abstract:

Light has proven to be useful in a wide range of biomedical applications such as fluorescence imaging, photoacoustic imaging, optogenetics, photodynamic therapy, photothermal therapy, and light controlled drug/gene delivery. Taking photodynamic therapy (PDT) as an example, PDT has been proven clinically effective in early lung cancer, bladder cancer, head, and neck cancer and is the primary treatment for skin cancer as well. However, clinical use of PDT is severely constrained by the low penetration depth of visible light through thick tissue, limiting its use to target regions only a few millimeters deep. One way to enhance the range is to use invisible near-infrared (NIR) light within the optical window (700–1100nm) for biological tissues, extending the depth up to 1cm with no observable damage to the intervening tissue. We have demonstrated use of NIR-to-visible upconversion fluorescent nanoparticles (UCNPs), emitting visible fluorescence when excited by a NIR light at 980nm, as a nanotransducer for PDT to convert deep tissue-penetrating NIR light to visible light suitable for activating photosensitizers. The unique optical properties of UCNPs enable the upconversion wavelength to be tuned and matched to the activation absorption wavelength of the photosensitizer. At depths beyond 1cm, however, tissue remains inaccessible to light even within the NIR window, and this critical depth limitation renders existing phototherapy ineffective against most deep-seated cancers. We have demonstrated some new treatment modalities for deep-seated cancers based on UCNP hydrogel implants and miniaturized, wirelessly powered optoelectronic devices for light delivery to deep tissues.

Keywords: upconversion, fluorescent, nanoparticle, bioimaging, photodynamic therapy

Procedia PDF Downloads 148

Authors: B. Vinod, L. J. Sudev

Abstract:

Natural fibers as reinforcement in polymer matrix material are gaining lot of attention in recent years. Natural fibers like jute, sisal, coir, hemp, banana etc. have attracted substantial importance as a potential structural material because of its attractive features along with its good mechanical properties. Cryogenic applications of natural fiber reinforced polymer composites are gaining importance. These materials need to possess good mechanical and physical properties at cryogenic temperatures to meet the high requirements by the cryogenic engineering applications. The objective of this work is to investigate the mechanical behavior of hybrid hemp/jute fibers reinforced epoxy composite material at liquid nitrogen temperature. Hybrid hemp/jute fibers reinforced polymer composite is prepared by hand lay-up method and test specimens are cut according to ASTM standards. These test specimens are dipped in liquid nitrogen for different time durations. The tensile properties, flexural properties and impact strength of the specimen are tested immediately after the specimens are removed from liquid nitrogen container. The experimental results indicate that the cryogenic treatment of the polymer composite has a significant effect on the mechanical properties of this material. The tensile properties and flexural properties of the hybrid hemp/jute fibers epoxy composite at liquid nitrogen temperature is higher than at room temperature. The impact strength of the material decreased after subjecting it to liquid nitrogen temperature.

Keywords: liquid nitrogen temperature, polymer composite, tensile properties, flexural properties

Procedia PDF Downloads 385

Authors: Nelson Bii, Christopher Ouma, John Odhiambo

Abstract:

Non-response is a potential source of errors in sample surveys. It introduces bias and large variance in the estimation of finite population parameters. Regression models have been recognized as one of the techniques of reducing bias and variance due to random non-response using auxiliary data. In this study, it is assumed that random non-response occurs in the survey variable in the second stage of cluster sampling, assuming full auxiliary information is available throughout. Auxiliary information is used at the estimation stage via a regression model to address the problem of random non-response. In particular, the auxiliary information is used via an improved Nadaraya-Watson kernel regression technique to compensate for random non-response. The asymptotic bias and mean squared error of the estimator proposed are derived. Besides, a simulation study conducted indicates that the proposed estimator has smaller values of the bias and smaller mean squared error values compared to existing estimators of finite population mean. The proposed estimator is also shown to have tighter confidence interval lengths at a 95% coverage rate. The results obtained in this study are useful, for instance, in choosing efficient estimators of the finite population mean in demographic sample surveys.

Keywords: mean squared error, random non-response, two-stage cluster sampling, confidence interval lengths

Procedia PDF Downloads 123

Authors: Saziye Ugur

Abstract:

In this study, optical, film formation, morphological and the magnetic properties of a nanocomposite system, composed of polystyrene (PS) latex polymer and core-shell magnetic nanoparticles (MNPs) is presented. Nine different mixtures were prepared by mixing of PS latex dispersion with different amount of MNPs in the range of (0- 100 wt%). PS/MNPs films were prepared from these mixtures on glass substrates by drop casting method. After drying at room temperature, each film sample was separately annealed at temperatures from 100 to 250 °C for 10 min. In order to monitor film formation process, the transmittance of these composites was measured after each annealing step as a function of MNPs content. Below a critical MNPs content (30 wt%), it was found that PS percolates into the MNPs hard phase and forms an interconnected network upon annealing. The transmission results showed above this critical value, PS latexes were no longer film forming at all temperatures. Besides, the PS/MNPs composite films also showed excellent magnetic properties. All composite films showed superparamagnetic behaviors. The saturation magnetisation (Ms) first increased up to 0.014 emu in the range of (0-50) wt% MNPs content and then decreased to 0.010 emu with increasing MNPs content. The highest value of Ms was approximately 0.020 emu and was obtained for the film filled with 85 wt% MNPs content. These results indicated that the optical, film formation and magnetic properties of PS/MNPs composite films can be readily tuned by varying loading content of MNPs nanoparticles.

Keywords: composite film, film formation, magnetic nanoparticles, ps latex, transmission

Procedia PDF Downloads 242

Authors: Pratibha Srivastava, Chithra V. J., Sudhakar S., Nitin K. D.

Abstract:

A closed-loop system comprises of a controller, a response system, and an actuating system. The controller, which is the system under test for us, excites the actuators based on feedback from the sensors in a periodic manner. The sensors should provide the feedback to the System Under Test (SUT) within a deterministic time post excitation of the actuators. Any delay or miss in the generation of response or acquisition of excitation pulses may lead to control loop controller computation errors, which can be catastrophic in certain cases. Such systems categorised as hard real-time systems that need special strategies. The real-time operating systems available in the market may be the best solutions for such kind of simulations, but they pose limitations like the availability of the X Windows system, graphical interfaces, other user tools. In this paper, we present strategies that can be used on a general purpose operating system (Bare Linux Kernel) to achieve a deterministic deadline and hence have the added advantages of a GPOS with real-time features. Techniques shall be discussed how to make the time-critical application run with the highest priority in an uninterrupted manner, reduced network latency for distributed architecture, real-time data acquisition, data storage, and retrieval, user interactions, etc.

Keywords: real time data acquisition, real time kernel preemption, scheduling, network latency

Procedia PDF Downloads 133

Authors: Min Yu, Olivier T. Picot, Theo Graves Saunders, Ivo Dlouhy, Amit Mahajan, Michael J. Reece

Abstract:

Graphene was successfully introduced into a polymer-derived silicon oxycarbide (SiOC) matrix by phase transfer of graphene oxide (GO) from an aqueous (GO dispersed in water) to an organic phase (copolymer as SiOC precursor in diethyl ether). With GO concentrations increasing up to 2 vol%, graphene-containing flakes self-assembled into a lamellar structure in the matrix leading to composite with the anisotropic property. Spark plasma sintering (SPS) was applied to densify the composites with four different GO concentrations (0, 0.5, 1 and 2 vol%) up to ~2.3 g/cm3. The fracture toughness of SiOC-2 vol% GO composites was significantly increased by ~91% (from 0.70 to 1.34 MPa·m¹/²), at the expense of a decrease in the flexural strength (from 85MPa to 55MPa), compared to SiOC-0 vol% GO composites. Moreover, the electrical conductivity in the perpendicular direction (σ┴=3×10⁻¹ S/cm) in SiOC-2 vol% GO composite was two orders of magnitude higher than the parallel direction (σ║=4.7×10⁻³ S/cm) owing to the self-assembled lamellar structure of graphene in the SiOC matrix. The composites exhibited increased electrical conductivity (σ┴) from 8.4×10⁻³ to 3×10⁻¹ S/cm, with the increasing GO content from 0.5 to 2 vol%. The SiOC-2 vol% GO composites further showed the better electrochemical performance of oxygen reduction reaction (ORR) than pure graphene, exhibiting a similar onset potential (~0.75V vs. RHE) and more positive half-wave potential (~0.6V vs. RHE).

Keywords: composite, fracture toughness, flexural strength, electrical conductivity, electrochemical performance

Procedia PDF Downloads 154

Authors: K. Shahril, A. Nizam, M. Sabri, A. Siti Rohana, H. Salmah

Abstract:

Chemical modifier (Acrylic Acid) is used as filler treatment to improve mechanical properties and swelling behavior of polypropylene/coconut fiber (PP/CF) composites by creating more adherent bonding between CF filler and PP Matrix. Treated (with chemical modifier) and untreated (without chemical modifier) composites were prepared in the formulation of 10 wt%, 20 wt%, 30 wt%, and 40 wt%. The mechanical testing indicates that composite with 10 wt% of untreated composite has the optimum value of tensile strength, and the composite with chemical modifier shows the tensile strength was increased. By increasing of filler loading, elastic modulus was increased while the elongation at brake was decreased. Meanwhile, the swelling test discerned that the increase of filler loading increased the water absorption of composites and the presence of chemical modifier reduced the equilibrium water absorption percentage.

Keywords: coconut fiber, polypropylene, acid acrylic, ethanol, chemical modifier, composites

Procedia PDF Downloads 452

Authors: Biaou Jean-Baptiste Kouchoro, Anissa Meziane, Philippe Micheau, Mathieu Renier, Nicolas Quaegebeur

Abstract:

Numerous experimental and numerical studies have shown the interest of the local defects resonance (LDR) for the Non-Destructive Testing of metallic and composite plates. Indeed, guided ultrasonic waves such as Lamb waves, which are increasingly used for the inspection of these flat structures, enable the generation of local resonance phenomena by their interaction with a damaged area, allowing the detection of defects. When subjected to a large amplitude motion, a nonlinear behavior can predominate in the damaged area. This work presents a 2D Finite Element Model of the local resonance of a 12 mm long and 5 mm deep Flat Bottom Hole (FBH) in a 6 mm thick aluminum plate under the excitation induced by an incident A0 Lamb mode. The analysis of the transient response of the FBH enables the precise determination of its resonance frequencies and the associate modal deformations. Then, a linear parametric study varying the geometrical properties of the FBH highlights the sensitivity of the resonance frequency with respect to the plate thickness. It is demonstrated that the resonance effect disappears when the ratio of thicknesses between the FBH and the plate is below 0.1. Finally, the nonlinear behavior of the FBH is considered and studied introducing geometrical (taken into account the nonlinear component of the strain tensor) nonlinearities that occur at large vibration amplitudes. Experimental analysis allows observation of the resonance effects and nonlinear response of the FBH. The differences between these experimental results and the numerical results will be commented on. The results of this study are promising and allow to consider more realistic defects such as delamination in composite materials.

Keywords: guided waves, non-destructive testing, dynamic field testing, non-linear ultrasound/vibration

Procedia PDF Downloads 123

Authors: Syed Atif Hassan, Luv Mehta, Syed Asif Hassan

Abstract:

Hit song science is a field of investigation wherein machine learning techniques are applied to music tracks in order to extract such features from audio signals which can capture information that could explain the popularity of respective tracks. Record companies invest huge amounts of money into recruiting fresh talents and churning out new music each year. Gaining insight into the basis of why a song becomes popular will result in tremendous benefits for the music industry. This paper aims to extract basic musical and more advanced, acoustic features from songs while also taking into account external factors that play a role in making a particular song popular. We use a dataset derived from popular Spotify playlists divided by genre. We use ten genres (blues, classical, country, disco, hip-hop, jazz, metal, pop, reggae, rock), chosen on the basis of clear to ambiguous delineation in the typical sound of their genres. We feed these features into three different classifiers, namely, SVM with RBF kernel, a deep neural network, and a recurring neural network, to build separate predictive models and choosing the best performing model at the end. Predicting song popularity is particularly important for the music industry as it would allow record companies to produce better content for the masses resulting in a more competitive market.

Keywords: classifier, machine learning, music tracks, popularity, prediction

Procedia PDF Downloads 641

Authors: M. Javanmard

Abstract:

This study was aimed at analyzing the effects of packaging (MAP) and preservation conditions on the packaged fresh walnut kernel quality. The central composite plan was used for evaluating the effect of oxygen (0–10%), carbon dioxide (0-10%), and temperature (4-26 °C) on qualitative characteristics of walnut kernels. Also, the response level technique was used to find the optimal conditions for interactive effects of factors, as well as estimating the best conditions of process using least amount of testing. Measured qualitative parameters were: peroxide index, color, decreased weight, mould and yeast counting test, and sensory evaluation. The results showed that the defined model for peroxide index, color, weight loss, and sensory evaluation is significant (p < 0.001), so that increase of temperature causes the peroxide value, color variation, and weight loss to increase and it reduces the overall acceptability of walnut kernels. An increase in oxygen percentage caused the color variation level and peroxide value to increase and resulted in lower overall acceptability of the walnuts. An increase in CO₂ percentage caused the peroxide value to decrease, but did not significantly affect other indices (p ≥ 0.05). Mould and yeast were not found in any samples. Optimal packaging conditions to achieve maximum quality of walnuts include: 1.46% oxygen, 10% carbon dioxide, and temperature of 4 °C.

Keywords: shelled walnut, MAP, quality, storage temperature

Procedia PDF Downloads 369

Authors: Anika Zafiah M. Rus, S. Shafizah

Abstract:

This study was developed to compare the behavior and the ability of polymer foam composites towards sound absorption test of Shorea leprosula wood (SL) of acid hydrolysis treatment with particle size < 355µm. Three different weight ratio of polyol to wood particle has been selected which are 10wt%, 15wt%, and 20wt%. The acid hydrolysis treatment is to optimize the surface interaction of a wood particle with polymer foam matrix. In addition, the acoustic characteristic of sound absorption coefficient (Į) was determined. Further treatment is to expose the polymer composite in UV irradiation by using UV-Weatherometer. Polymer foam composite of untreated shorea leprosula particle (SL-B) with respective percentage loading shows uniform pore structure as compared with treated wood particle (SL-A). As the filler percentage loading in polymer foam increases, the Į value approaching 1 for both samples. Furthermore, SL-A shows better Į value at 3500-4500 frequency absorption level(Hz), meanwhile Į value for SL-B is maximum at 4000-5000 Hz. The frequencies absorption level for both SL-B and SL-A after UV exposure was increased with the increasing of exposure time from 0-1000 hours. It is, therefore, concluded that the Į for each sound absorbing material, with or without acid hydrolysis treatment of wood particles and it’s percentages loading in polymer matrix effect the sound absorption behavior.

Keywords: polymer foam composite, sound absorption coefficient, UV-irradiation, wood

Procedia PDF Downloads 453

Authors: Souici Abdelaziz, Tehami Mohamed, Rahal Nacer, Said Mohamed Bekkouche, Berthet Jean-Fabien

Abstract:

In this paper, the influence of the concrete slab creep on the initial deformability of a bent composite beam is modelled. This deformability depends on the rate of creep. This means the rise in value of the longitudinal strain ε c(x,t), the displacement D eflec(x,t) and the strain energy E(t). The variation of these three parameters can easily affect negatively the good appearance and the serviceability of the structure. Therefore, an analytical approach is designed to control the status of the deformability of the beam at the instant t. This approach is based on the Boltzmann’s superposition principle and very particularly on the irreversible law of deformation. For this, two conditions of compatibility and two other static equilibrium equations are adopted. The two first conditions are set according to the rheological equation of Dischinger. After having done a mathematical arrangement, we have reached a system of two differential equations whose integration allows to find the mathematical expression of each generalized internal force in terms of the ability of the concrete slab to creep.

Keywords: composite section, concrete, creep, deformation, differential equation, time

Procedia PDF Downloads 368

Authors: Changshan Zhang, Zhijin Yu, Shixing Fan

Abstract:

A comprehensive hazard evaluation for coal self-heating in composite long-wall gobs is heavily dependent on computational simulation. In this study, the spatial distributions of cracks which caused significant air leakage were simulated by universal distinct element code (UDEC) simulation. Based on the main routes of air leakage and characteristics of coal self-heating, a computational fluid dynamics (CFD) modeling was conducted to model the coal spontaneous combustion dangerous area in composite long-wall gobs. The results included the oxygen concentration distributions and temperature profiles showed that the numerical approach is validated by comparison with the test data. Furthermore, under the conditions of specific engineering, the major locations where some techniques for extinguishing and preventing long-wall gob fires need to be put into practice were also examined.

Keywords: computational simulation, UDEC simulation, coal self-heating, CFD modeling, long-wall gobs

Procedia PDF Downloads 296

Authors: Amir Mabhout, Toktam Ghafarian, Amirhossein Farzin, Zahra Makki, Sajjad Alizadeh, Amirhossein Ghavi

Abstract:

The high number of Netflix subscribers makes it attractive for data scientists to extract valuable knowledge from the viewers' behavioural analyses. This paper presents a set of statistical insights into viewers' viewing history. After that, a deep learning model is used to predict the future watching behaviour of the users based on previous watching history within the Netflixlatte data pool. Netflixlatte in an aggregated and anonymized data pool of 320 Netflix viewers with a length 250 000 data points recorded between 2008-2022. We observe insightful correlations between the distribution of viewing time and the COVID-19 pandemic outbreak. The presented deep learning model predicts future movie and TV series viewing habits with an average loss of 0.175.

Keywords: data analysis, deep learning, LSTM neural network, netflix

Procedia PDF Downloads 221

Authors: Ng Mei Han, Nu'man Abdul Hadi

Abstract:

Extraction of ferulic acid from palm pressed fiber using deep eutectic solvent (DES) of choline chloride-acetic acid (ChCl-AA) and choline chloride-citric acid (ChCl-CA) are reported. Influence of water content in DES on the extraction efficiency was investigated. ChCl-AA and ChCl-CA experienced a drop in viscosity from 9.678 to 1.429 and 22.658 ± 1.655 mm2/s, respectively as the water content in the DES increased from 0 to 50 wt% which contributed to higher extraction efficiency for the ferulic acid. Between 41,155 ± 940 mg/kg ferulic acid was obtained after 6 h reflux when ChCl-AA with 30 wt% water was used for the extraction compared to 30,940 ± 621 mg/kg when neat ChCl-AA was used. Although viscosity of the DES could be improved with the addition of water, there is a threshold where the DES could tolerate the presence of water without changing its solvent behavior. The optimum condition for extraction of ferulic acid from palm pressed fiber was heating for 6 h with DES containing 30 wt% water.

Keywords: deep eutectic solvent, extraction, ferulic acid, palm fibre

Procedia PDF Downloads 70

Authors: Nurul Aini Abdul Wahab, Nor Syamim Halidin, Sayidatina Aisah Masnan, Nur Izzati Romli

Abstract:

There are many applications of principle component method for reducing the large set of variables in various fields. Fisher’s Discriminant function is also a popular tool for classification. In this research, the researcher focuses on studying the performance of Principle Component-Fisher’s Discriminant function in helping to classify rice kernels to their defined classes. The data were collected on the smells or odour of the rice kernel using odour-detection sensor, Cyranose. 32 variables were captured by this electronic nose (e-nose). The objective of this research is to measure how well a combination model, between principle component and linear discriminant, to be as a classification model. Principle component method was used to reduce all 32 variables to a smaller and manageable set of components. Then, the reduced components were used to develop the Fisher’s Discriminant function. In this research, there are 4 defined classes of rice kernel which are Aromatic, Brown, Ordinary and Others. Based on the output from principle component method, the 32 variables were reduced to only 2 components. Based on the output of classification table from the discriminant analysis, 40.76% from the total observations were correctly classified into their classes by the PC-Discriminant function. Indirectly, it gives an idea that the classification model developed has committed to more than 50% of misclassifying the observations. As a conclusion, the Fisher’s Discriminant function that was built on a 2-component from PCA (2-PC-D) is not satisfying to classify the rice kernels into its defined classes.

Keywords: classification model, discriminant function, principle component analysis, variable reduction

Procedia PDF Downloads 322

Authors: Zabeehullah, Fahim Arif, Yawar Abbas

Abstract:

Software Defined Network (SDN) is a next genera-tion networking model which simplifies the traditional network complexities and improve the utilization of constrained resources. Currently, most of the SDN based Internet of Things(IoT) environments use traditional network routing strategies which work on the basis of max or min metric value. However, IoT network heterogeneity, dynamic traffic flow and complexity demands intelligent and self-adaptive routing algorithms because traditional routing algorithms lack the self-adaptions, intelligence and efficient utilization of resources. To some extent, SDN, due its flexibility, and centralized control has managed the IoT complexity and heterogeneity but still Software Defined IoT (SDIoT) lacks intelligence. To address this challenge, we proposed a model called Deep Routing Strategy (DRS) which uses Deep Learning algorithm to perform routing in SDIoT intelligently and efficiently. Our model uses real-time traffic for training and learning. Results demonstrate that proposed model has achieved high accuracy and low packet loss rate during path selection. Proposed model has also outperformed benchmark routing algorithm (OSPF). Moreover, proposed model provided encouraging results during high dynamic traffic flow.

Keywords: SDN, IoT, DL, ML, DRS

Procedia PDF Downloads 96

Authors: Mohammad W. Dewan, Jahangir Alam, Khurshida Sharmin

Abstract:

Synthetic fibers (carbon, glass, aramid, etc.) are generally utilized to make composite materials for better mechanical and thermal properties. However, they are expensive and non-biodegradable. In the perspective of Bangladesh, jute fibers are available, inexpensive, and comprising good mechanical properties. The improved properties (i.e., low cost, low density, eco-friendly) of natural fibers have made them a promising reinforcement in hybrid composites without sacrificing mechanical properties. In this study, jute and e-glass fiber reinforced hybrid composite materials are fabricated utilizing hand lay-up followed by a compression molding technique. Room temperature cured two-part epoxy resin is used as a matrix. Approximate 6-7 mm thick composite panels are fabricated utilizing 17 layers of woven glass and jute fibers with different fiber layering sequences- only jute, only glass, glass, and jute alternatively (g/j/g/j---) and 4 glass - 9 jute – 4 glass (4g-9j-4g). The fabricated composite panels are analyzed through fiber volume calculation, tensile test, bending test, and water absorption test. The hybridization of jute and glass fiber results in better tensile, bending, and water absorption properties than only jute fiber-reinforced composites, but inferior properties as compared to only glass fiber reinforced composites. Among different fiber layering sequences, 4g-9j-4g fibers layering sequence resulted in better tensile, bending, and water absorption properties. The effect of chemical treatment on the woven jute fiber and chopped glass microfiber infusion are also investigated in this study. Chemically treated jute fiber and 2 wt. % chopped glass microfiber infused hybrid composite shows about 12% improvements in flexural strength as compared to untreated and no micro-fiber infused hybrid composite panel. However, fiber chemical treatment and micro-filler do not have a significant effect on tensile strength.

Keywords: compression molding, chemical treatment, hybrid composites, mechanical properties

Procedia PDF Downloads 145

Authors: Mohamed Osama Hassaan

Abstract:

Recent earthquakes have demonstrated the vulnerability of older reinforced concrete buildings to fail under imposed seismic loads. Accordingly, the need to strengthen existing reinforced concrete structures, mainly columns, to resist high seismic loads has increased. Conventional strengthening techniques such as using steel plates, steel angles and concrete overlay are used to achieve the required increase in strength or ductility. However, techniques using advanced composite materials are established. The column's splice zone is the most critical zone that failed under seismic loads. There are three types of splice zone failure that can be observed under seismic action, namely, Failure of the flexural plastic hinge region, shear failure and failure due to short lap splice. A lapped splice transfers the force from one bar to another through the concrete surrounding both bars. At any point along the splice, force is transferred from one bar by a bond to the surrounding concrete and also by a bond to the other bar of the pair forming the splice. The integrity of the lap splice depends on the development of adequate bond length. The R.C. columns built in seismic regions are expected to undergo a large number of inelastic deformation cycles while maintaining the overall strength and stability of the structure. This can be ensured by proper confinement of the concrete core. The last type of failure is focused in this research. There are insufficient studies that address the problem of strengthening existing reinforced concrete columns at splice zone through confinement with “advanced composite materials". Accordingly, more investigation regarding the seismic behavior of strengthened reinforced concrete columns using the new generation of composite materials such as (Carbon fiber polymer), (Glass fiber polymer), (Armiad fiber polymer).

Keywords: strengthening, columns, advanced composite materials, earthquakes

Procedia PDF Downloads 60

Authors: Chiau Yuan Lim, Hayyiratul Fatimah Mohd Zaid, Fai Kait Chong

Abstract:

Deep Eutectic Solvent (DES) is used in various applications due to its simplicity in synthesis procedure, biodegradable, inexpensive and easily available chemical ingredients. Graphene Oxide is a popular catalyst that being used in various processes due to its stacking carbon sheets in layer which theoretically rapid up the catalytic processes. In this study, choline chloride based DESs were synthesized and ChCl-PEG(1:4) was found to be the most effective DES in performing desulfurization, which it is able to remove up to 47.4% of the sulfur content in the model oil in just 10 minutes, and up to 95% of sulfur content after repeat the process for six times. ChCl-PEG(1:4) able to perform up to 32.7% desulfurization on real diesel after 6 multiple stages. Thus, future research works should focus on removing the impurities on real diesel before utilising DESs in petroleum field.

Keywords: choline chloride, deep eutectic solvent, fuel desulfurization, graphene oxide

Procedia PDF Downloads 138

Authors: Nurul Na'imy Wan, Mohamad Sazali Said, Jaharah Ab. Ghani, Rusli Othman

Abstract:

This paper presents the surface roughness of the aluminium silicon alloy (AlSi) matrix composite which has been reinforced with aluminium nitride (AlN). Experiments were conducted at various cutting speeds, feed rates, and depths of cut, according to a standard orthogonal array L27 of Taguchi method using TiN coating tool of insert. The signal-to-noise (S/N) ratio and analysis of variance are applied to study the characteristic performance of cutting speeds, feed rates and depths of cut in measuring the surface roughness during the milling operation. The surface roughness was observed using Mitutoyo Formtracer CS-500 and analyzed using the Taguchi method. From the Taguchi analysis, it was found that cutting speed of 230 m/min, feed rate of 0.4 mm/tooth, depth of cut of 0.3 mm were the optimum machining parameters using TiN coating insert.

Keywords: AlSi/AlN metal matrix composite (MMC), surface roughness, Taguchi method, machining parameters

Procedia PDF Downloads 416

Authors: Maciej Major, Izabela Major

Abstract:

The aim of this paper is a numerical analysis of three-layered block design to walls construction subjected to the dynamic load. The block consists of the layers: concrete with rubber pads in shape of crosses, space filled with air and concrete with I-shape rubber pads. The main purpose of rubber inserts embedded during the production process is additional protection against the transversal dynamic load. For the analysis, as rubber, the Zahorski hyperelastic incompressible material model was assumed. A concentrated force as dynamic load applied to the external block surface was investigated. The results for the considered block observed as the stress distribution plot were compared to the results obtained for the solid concrete block. In order to estimate the percentage damping of proposed composite, rubber-concrete block in relation to the solid block the numerical analysis with the use of finite element method based on ADINA software was performed.

Keywords: dynamics, composite, rubber, Zahorski

Procedia PDF Downloads 229

Authors: Reham K. El Sawah, N. S. M. El-Tayeb

Abstract:

This study aims to produce a polymer matrix composite reinforced with Al₂O₃ nanoparticles in order to enhance the mechanical properties of PMMA. The composite was fabricated via Friction stir processing to ensure homogenous dispersion of Al₂O₃ nanoparticles in the polymer, and the processing was submerged to prevent the sputtering of nanoparticles. The surface quality, microstructure, impact energy and hardness of the prepared samples were investigated. Good surface quality and dispersion of nanoparticles were attained through employing sufficient processing conditions. The experimental results indicated that as the percentage of nanoparticles increased, the impact energy and hardness increased, reaching 2 kJ/m2 and 14.7 HV at a nanoparticle concentration of 25%, which means that the toughness and the hardness of the polymer-ceramic produced composite is higher than unprocessed PMMA by 66% and 33% respectively.

Keywords: friction stir processing, polymer matrix nanocomposite, mechanical properties, microstructure

Procedia PDF Downloads 155