Search results for: Neural Processing Element (NPE)

Authors: Jose Gois

Abstract:

The paper presents the development of a porous porcelain frappe with thermochromic visualization for port wines, having as a partner the Institute of Vinhos do Douro and Porto. This ceramic frappe is intended to promote the cooling and maintenance of the temperature of port wines through porous ceramic materials, consisting of a porcelain composite with sawdust addition, so as to contain, on the one hand, the similar cooling properties of the terracotta and, on the other, the resistance of materials such as porcelain. The application of the thermochromic element makes it possible to see if the wine is at optimal service temperatures, allowing users to drink the wine in the ideal conditions and contributing to more efficient maintenance of the service.

Keywords: design, frappe, porcelain, porous, thermochromic

Procedia PDF Downloads 128

Authors: T. Damak, O. Kriaa, A. Baccar, M. A. Ben Ayed, N. Masmoudi

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In the last few years, Automatic Number Plate Recognition (ANPR) systems have become widely used in the safety, the security, and the commercial aspects. Forethought, several methods and techniques are computing to achieve the better levels in terms of accuracy and real time execution. This paper proposed a computer vision algorithm of Number Plate Localization (NPL) and Characters Segmentation (CS). In addition, it proposed an improved method in Optical Character Recognition (OCR) based on Deep Learning (DL) techniques. In order to identify the number of detected plate after NPL and CS steps, the Convolutional Neural Network (CNN) algorithm is proposed. A DL model is developed using four convolution layers, two layers of Maxpooling, and six layers of fully connected. The model was trained by number image database on the Jetson TX2 NVIDIA target. The accuracy result has achieved 95.84%.

Keywords: ANPR, CS, CNN, deep learning, NPL

Procedia PDF Downloads 289

Authors: Vasile Simulescu, Jakub Mondek, Miloslav Pekař

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Hyaluronic acid (HA) is an anionic glycosaminoglycan distributed throughout connective, epithelial and neural tissues. The importance of hyaluronic acid increased in the last decades. It has many applications in medicine and cosmetics. Hyaluronic acid has been used in attempts to treat osteoarthritis of the knee via injecting it into the joint. Bovine serum albumin (also known as BSA) is a protein derived from cows, which has many biochemical applications. The aim of our research work was to compare the thermal degradation of hyaluronic acid and BSA in powder and in solution, by determining changes in molar mass and conformation, by using SEC-MALLS (size exclusion chromatography -multi angle laser light scattering). The aim of our research work was to observe the degradation in powder and in solution of different molar mass hyaluronic acid samples, at different temperatures for certain periods. The degradation of the analyzed samples was mainly observed by modifications in molar mass.

Keywords: thermal degradation, hyaluronic acid, BSA, SEC-MALLS

Procedia PDF Downloads 489

Authors: M. A. Effati, M. R. Hojjati, M. Razmdideh

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Nowadays, engine developments have focused on internal combustion engine design call for increased engine power, reduced engine size and improved fuel economy, simultaneously. In this paper, a novel design for combustion engine is proposed. Two combustion chambers were designed in two sides of cylinder. Piston was designed in a way that two sides of piston would transfer heat energy due to combustion to linear motion. This motion would convert to rotary motion through the designed mechanism connected to connecting rod. Connecting rod operation was analyzed to evaluate applied stress in 3000, 4500 and 6000 rpm. Boundary conditions including generated pressure in each side of cylinder in these 3 situations was calculated.

Keywords: combustion engine, design, finite element method, modeling

Procedia PDF Downloads 488

Authors: Mohamed Yousef, Magdy Samuel, Maha El-Meligy, Taher El-Bitar

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The present work is dealing with 2% Si-steel alloy. The alloy contains 0.05% C as well as 0.85% Al. The alloy under investigation would be used for electrical transformation purposes. A heating (expansion) - cooling (contraction) dilation investigation was executed to detect the a, a+g, and g transformation temperatures at the inflection points of the dilation curve. On heating, primary a  was detected at a temperature range between room temperature and 687 ^oC. The domain of a+g was detected in the range between 687 ^oC and 746 ^oC. g phase exists in the closed g region at the range between 746 ^oC and 1043 ^oC. The domain of a phase appears again at a temperature range between 1043 and 1105 ^oC, and followed by secondary a at temperature higher than 1105 ^oC. A physical simulation of thermo-mechanical processing on the as-cast alloy was carried out. The simulation process took into consideration the hot flat rolling pilot plant parameters. The process was executed on the thermo-mechanical simulator (Gleeble 3500). The process was designed to include seven consecutive passes. The 1^st pass represents the roughing stage, while the remaining six passes represent finish rolling stage. The whole process was executed at the temperature range from 1100 ^oC to 900 ^oC. The amount of strain starts with 23.5% at the roughing pass and decreases continuously to reach 7.5 % at the last finishing pass. The flow curve of the alloy can be abstracted from the stress-strain curves representing simulated passes. It shows alloy hardening from a pass to the other up to pass no. 6, as a result of decreasing the deformation temperature and increasing of cumulative strain. After pass no. 6, the deformation process enhances the dynamic recrystallization phenomena to appear, where the z-parameter would be high.

Keywords: si- steel, hot deformability, critical transformation temperature, physical simulation, thermo-mechanical processing, flow curve, dynamic softening.

Procedia PDF Downloads 229

Authors: A. E. Mukhametov, M. T. Yerbulekova, D. R. Dautkanova, G. A. Tuyakova, G. Aitkhozhayeva

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The accumulation of heavy metals in food is a constant problem in many parts of the world. Vegetable oils are widely used, both for cooking and for processing in the food industry, meeting the main dietary requirements. One of the main chemical pollutants, heavy metals, is usually found in vegetable oils. These chemical pollutants are carcinogenic, teratogenic and immunotoxic, harmful to consumption and have a negative effect on human health even in trace amounts. Residues of these substances can easily accumulate in vegetable oil during cultivation, processing and storage. In this article, the content of the concentration of heavy metal ions in vegetable oils of Kazakhstan production is studied: sunflower, rapeseed, safflower and linseed oil. Heavy metals: arsenic, cadmium, lead and nickel, were determined in three repetitions by the method of flame atomic absorption. Analysis of vegetable oil samples revealed that the largest lead contamination (Pb) was determined to be 0.065 mg/kg in linseed oil. The content of cadmium (Cd) in the largest amount of 0.009 mg/kg was found in safflower oil. Arsenic (As) content was determined in rapeseed and safflower oils at 0.003 mg/kg, and arsenic (As) was not detected in linseed and sunflower oil. The nickel (Ni) content in the largest amount of 0.433 mg/kg was in linseed oil. The heavy metal contents in the test samples complied with the requirements of regulatory documents for vegetable oils. An assessment of the health risk of vegetable oils with a daily consumption of 36 g per day shows that all samples of vegetable oils produced in Kazakhstan are safe for consumption. But further monitoring is needed, since all these metals are toxic and their harmful effects become apparent only after several years of exposure.

Keywords: vegetable oil, sunflower oil, linseed oil, safflower oil, toxic metals, food safety, rape oil

Procedia PDF Downloads 118

Authors: Thelma Palaoag, Melanie Basa, Jezreel Mark Panilo

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Checking short-answer questions and essays, whether it may be paper or electronic in form, is a tiring and tedious task for teachers. Evaluating a student’s output require wide array of domains. Scoring the work is often a critical task. Several attempts in the past few years to create an automated writing assessment software but only have received negative results from teachers and students alike due to unreliability in scoring, does not provide feedback and others. The study aims to create an application that will be able to check short-answer questions which incorporate information extraction. Information extraction is a subfield of Natural Language Processing (NLP) where a chunk of text (technically known as unstructured text) is being broken down to gather necessary bits of data and/or keywords (structured text) to be further analyzed or rather be utilized by query tools. The proposed system shall be able to extract keywords or phrases from the individual’s answers to match it into a corpora of words (as defined by the instructor), which shall be the basis of evaluation of the individual’s answer. The proposed system shall also enable the teacher to provide feedback and re-evaluate the output of the student for some writing elements in which the computer cannot fully evaluate such as creativity and logic. Teachers can formulate, design, and check short answer questions efficiently by defining keywords or phrases as parameters by assigning weights for checking answers. With the proposed system, teacher’s time in checking and evaluating students output shall be lessened, thus, making the teacher more productive and easier.

Keywords: information extraction, short-answer question, natural language processing, application

Procedia PDF Downloads 413

Authors: Amy Y. Wang, Eko L. Yi

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Can vicarious experiences of workplace ostracism affect employees’ willingness to voice? Given the increasingly interdependent nature of the modern workplace in which employees rely on social interactions to fulfill organizational goals, workplace ostracism –the extent to which an individual perceives that he or she is ignored or excluded by others in the workplace– has garnered significant interest from scholars and practitioners alike. Extending beyond conventional studies that largely focus on the perspectives and outcomes of ostracized targets, we address the indirect effects of workplace ostracism on third-party employees embedded in the same social context. Using a social information processing approach, we propose that the ostracism of coworkers acts as political information that influences third-party employees in their decisions to engage in risky and discretionary behaviors such as employee voice. To make sense of and to navigate through experiences of workplace ostracism, we posit that both political understanding and political skill allow third party employees to minimize the risks and uncertainty of voicing. This conceptual model was tested by a study involving 154 supervisor-subordinate dyads of a publicly listed bio-technology firm located in Mainland China. Each supervisor and their direct subordinates composed of a work team; each team had a minimum of two subordinates and a maximum of four subordinates. Human resources used the master list to distribute the ID coded questionnaires to the matching names. All studied constructs were measured using existing scales proved effective in previous literature. Hypotheses were tested using Confirmatory Factor Analysis and Hierarchal Multiple Regression. All three hypotheses were supported which showed that employees were less likely to engage in voice behaviors when their coworkers reported having experienced ostracism in the workplace. Results also showed a significant three-way interaction between political understanding and political skill on the relationship between coworkers’ ostracism and employee voice, indicating that political savviness is a valuable resource in mitigating ostracism’s negative and indirect effects. Our results illustrated that an employee’s coworkers being ostracized indeed adversely impacted his or her own voice behavior. However, not all individuals reacted passively to the social context; rather, we found that politically savvy individuals – possessing both political understanding and political skill – and their voice behaviors were less impacted by ostracism in their work environment. At the same time, we found that having only political understanding or only political skill was significantly less effective in mitigating ostracism’s negative effects, suggesting a necessary duality of political knowledge and political skill in combatting ostracism. Organizational implications, recommendations, and future research ideas are also discussed.

Keywords: employee voice, organizational politics, social information processing, workplace ostracism

Procedia PDF Downloads 121

Authors: Md. Soebur Rahman, Mahbuba Begum, Raquib Ahsan

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Composite column is a structural member that uses a combination of structural steel shapes, pipes or tubes with or without reinforcing steel bars and reinforced concrete to provide adequate load carrying capacity to sustain either axial compressive loads alone or a combination of axial loads and bending moments. Composite construction takes the advantages of the speed of construction, light weight and strength of steel, and the higher mass, stiffness, damping properties and economy of reinforced concrete. The most usual types of composite columns are the concrete filled steel tubes and the partially or fully encased steel profiles. Fully encased composite column (FEC) provides compressive strength, stability, stiffness, improved fire proofing and better corrosion protection. This paper reports experimental and numerical investigations of the behaviour of concrete encased steel composite columns subjected to short-term axial load. In this study, eleven short FEC columns with square shaped cross section were constructed and tested to examine the load-deflection behavior. The main variables in the test were considered as concrete compressive strength, cross sectional size and percentage of structural steel. A nonlinear 3-D finite element (FE) model has been developed to analyse the inelastic behaviour of steel, concrete, and longitudinal reinforcement as well as the effect of concrete confinement of the FEC columns. FE models have been validated against the current experimental study conduct in the laboratory and published experimental results under concentric load. It has been observed that FE model is able to predict the experimental behaviour of FEC columns under concentric gravity loads with good accuracy. Good agreement has been achieved between the complete experimental and the numerical load-deflection behaviour in this study. The capacities of each constituent of FEC columns such as structural steel, concrete and rebar's were also determined from the numerical study. Concrete is observed to provide around 57% of the total axial capacity of the column whereas the steel I-sections contributes to the rest of the capacity as well as ductility of the overall system. The nonlinear FE model developed in this study is also used to explore the effect of concrete strength and percentage of structural steel on the behaviour of FEC columns under concentric loads. The axial capacity of FEC columns has been found to increase significantly by increasing the strength of concrete.

Keywords: composite, columns, experimental, finite element, fully encased, strength

Procedia PDF Downloads 276

Authors: Ijaz Ahmed, Anwar Zada, Muhammad Waqas, M. U. Ashraf

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We demonstrate the performance of a very efficient tagger applies on hadronically decaying top quark pairs as signal based on deep neural network algorithms and compares with the QCD multi-jet background events. A significant enhancement of performance in boosted top quark events is observed with our limited computing resources. We also compare modern machine learning approaches and perform a multivariate analysis of boosted top-pair as well as single top quark production through weak interaction at √s = 14 TeV proton-proton Collider. The most relevant known background processes are incorporated. Through the techniques of Boosted Decision Tree (BDT), likelihood and Multlayer Perceptron (MLP) the analysis is trained to observe the performance in comparison with the conventional cut based and count approach

Keywords: top tagger, multivariate, deep learning, LHC, single top

Procedia PDF Downloads 96

Authors: Damian Czubak

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The Białowieża Forest is a very valuable natural area, included in the World Natural Heritage at UNESCO, where, due to infestation by the bark beetle (Ips typographus), norway spruce (Picea abies) have deteriorated. This catastrophic scenario led to an increase in fire danger. This was due to the occurrence of large amounts of dead wood and grass cover, as light penetrated to the bottom of the stands. These factors in a dry state are materials that favour the possibility of fire and the rapid spread of fire. One of the objectives of the study was to monitor the quantitative and qualitative changes of combustible material on the permanent decay plots of spruce stands from 2012-2022. In addition, the size of the area with highly flammable vegetation was monitored and a classification of the stands of the Białowieża Forest by flammability classes was made. The key factor that determines the potential fire hazard of a forest is combustible material. Primarily its type, quantity, moisture content, size and spatial structure. Based on the inventory data on the areas of forest districts in the Białowieża Forest, the average fire load and its changes over the years were calculated. The analysis was carried out taking into account the changes in the health status of the stands and sanitary operations. The quantitative and qualitative assessment of fallen timber and fire load of ground cover used the results of the 2019 and 2021 inventories. Approximately 9,000 circular plots were used for the study. An assessment was made of the amount of potential fuel, understood as ground cover vegetation and dead wood debris. In addition, monitoring of areas with vegetation that poses a high fire risk was conducted using data from 2019 and 2021. All sub-areas were inventoried where vegetation posing a specific fire hazard represented at least 10% of the area with species characteristic of that cover. In addition to the size of the area with fire-prone vegetation, a very important element is the size of the fire load on the indicated plots. On representative plots, the biomass of the land cover was measured on an area of 10 m2 and then the amount of biomass of each component was determined. The resulting element of variability of ground covers in stands was their flammability classification. The classification developed made it possible to track changes in the flammability classes of stands over the period covered by the measurements.

Keywords: classification, combustible material, flammable vegetation, Norway spruce

Procedia PDF Downloads 78

Authors: Andrey A. Lipovskii, Alexey V. Redkov, Vyacheslav V. Rusan, Dmitry K. Tagantsev, Valentina V. Zhurikhina

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The presented research is related to the development of recently proposed technique of the formation of composite materials, like optical glass-ceramics, with predetermined structure and properties of the crystalline component. The technique is based on the control of the size and concentration of the crystalline grains using the phenomenon of glass-ceramics decrystallization (vitrification) induced by ion-exchange. This phenomenon was discovered and explained in the beginning of the 2000s, while related theoretical description was given in 2016 only. In general, the developed theory enables one to model the process and optimize the conditions of ion-exchange processing of glass-ceramics, which provide given properties of crystalline component, in particular, profile of the average size of the crystalline grains. The optimization is possible if one knows two dimensionless parameters of the theoretical model. One of them (β) is the value which is directly related to the solubility of crystalline component of the glass-ceramics in the glass matrix, and another (γ) is equal to the ratio of characteristic times of ion-exchange diffusion and crystalline grain dissolution. The presented study is dedicated to the development of experimental technique and simulation which allow determining these parameters. It is shown that these parameters can be deduced from the data on the space distributions of diffusant concentrations and average size of crystalline grains in the glass-ceramics samples subjected to ion-exchange treatment. Measurements at least at two temperatures and two processing times at each temperature are necessary. The composite material used was a silica-based glass-ceramics with crystalline grains of Li2OSiO2. Cubical samples of the glass-ceramics (6x6x6 mm3) underwent the ion exchange process in NaNO3 salt melt at 520 oC (for 16 and 48 h), 540 oC (for 8 and 24 h), 560 oC (for 4 and 12 h), and 580 oC (for 2 and 8 h). The ion exchange processing resulted in the glass-ceramics vitrification in the subsurface layers where ion-exchange diffusion took place. Slabs about 1 mm thick were cut from the central part of the samples and their big facets were polished. These slabs were used to find profiles of diffusant concentrations and average size of the crystalline grains. The concentration profiles were determined from refractive index profiles measured with Max-Zender interferometer, and profiles of the average size of the crystalline grains were determined with micro-Raman spectroscopy. Numerical simulation were based on the developed theoretical model of the glass-ceramics decrystallization induced by ion exchange. The simulation of the processes was carried out for different values of β and γ parameters under all above-mentioned ion exchange conditions. As a result, the temperature dependences of the parameters, which provided a reliable coincidence of the simulation and experimental data, were found. This ensured the adequate modeling of the process of the glass-ceramics decrystallization in 520-580 oC temperature interval. Developed approach provides a powerful tool for fine tuning of the glass-ceramics structure, namely, concentration and average size of crystalline grains.

Keywords: diffusion, glass-ceramics, ion exchange, vitrification

Procedia PDF Downloads 258

Authors: Usama Shahid

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Be it written documents, news columns, or students' essays, verifying the content can be a time-consuming task. Apart from the spelling and grammar mistakes, the proofreader is also supposed to verify whether the content included in the essay or document is relevant or not. The irrelevant content in any document or essay is referred to as off-topic text and in this paper, we will address the problem of off-topic text detection from a document using machine learning techniques. Our study aims to identify the off-topic content from a document using Echo state network model and we will also compare data with other models. The previous study uses Convolutional Neural Networks and TFIDF to detect off-topic text. We will rearrange the existing datasets and take new classifiers along with new word embeddings and implement them on existing and new datasets in order to compare the results with the previously existing CNN model.

Keywords: off topic, text detection, eco state network, machine learning

Procedia PDF Downloads 66

Authors: Jing Ma, Xindong An

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Green infrastructure, the most important aspect of improving the quality of life, has been a crucial element of the liveability measurement. With demanding of more liveable urban environment from increasing population in city area, access to green infrastructure in walking distance should be taken into consideration. This article exemplifies the study on accessibility measurement of green infrastructure in central Auckland (New Zealand), using network analysis tool on the basis of GIS, to verify the accessibility levels of green infrastructure. It analyses the overall situation of green infrastructure and draws some conclusions on the city’s different levels of accessibility according to the categories and facilities distribution, which provides valuable references and guidance for the future facility improvement in planning strategies.

Keywords: quality of life, green infrastructure, GIS, accessibility

Procedia PDF Downloads 261

Authors: Z. Babic, I. Veza, A. Balic, M. Crnjac

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The linear programming model is sometimes difficult to apply in real business situations due to its assumption of proportionality. This paper shows an example of how to use De Novo programming approach instead of linear programming. In the De Novo programming, resources are not fixed like in linear programming but resource quantities depend only on available budget. Budget is a new, important element of the De Novo approach. Two different production situations are presented: increasing costs and quantity discounts of raw materials. The focus of this paper is on advantages of the De Novo approach in the optimization of production plan for production company which produces souvenirs made from famous stone from the island of Brac, one of the greatest islands from Croatia.

Keywords: business process, De Novo programming, optimizing, production

Procedia PDF Downloads 209

Authors: Maurice Brogly, Edwige Privas, Rajesh K. Gajendran, Sophie Bistac

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Anti-sticky coatings based on perfluoroalkoxy (PFA) coatings are widely used in food processing industry especially for bread making. Their tribological performance, such as low friction coefficient, low surface energy and high heat resistance, make them an appropriate choice for anti-sticky coating application in moulds for food processing industry. This study is dedicated to evidence the transfer of contaminants from the coating due to wear and thermal ageing of the mould. The risk of contamination is induced by the damage of the coating by bread crust during the demoulding stage. The study focuses on the wear resistance and potential transfer of perfluorinated polymer from the anti-sticky coating. Friction between perfluorinated coating and bread crust is modeled by a tribological pin-on-disc test. The cellular nature of the bread crust is modeled by a polymer foam. FTIR analysis of the polymer foam after friction allow the evaluation of the transfer from the perfluorinated coating to polymer foam. Influence of thermal ageing on the physical, chemical and wear properties of the coating are also investigated. FTIR spectroscopic results show that the increase of PFA transfer onto the foam counterface is associated to the decrease of the friction coefficient. Increasing lubrication by film transfer results in the decrease of the friction coefficient. Moreover increasing the friction test parameters conditions (load, speed and sliding distance) also increase the film transfer onto the counterface. Thermal ageing increases the hydrophobic character of the PFA coating and thus also decreases the friction coefficient.

Keywords: fluorobased polymer coatings, FTIR spectroscopy, non-stick food moulds, wear and friction

Procedia PDF Downloads 299

Authors: R. Ziaie Moayed, B. Mohammadi-Haji

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Stone columns have been widely employed to improve the load-settlement characteristics of soft soils. The results of two small scale displacement control loading tests on stone columns were used in order to validate numerical finite element simulations. Additionally, a series of numerical calculations of static loading have been performed on strengthened raft footing to investigate the effects of using stone columns on bearing capacity of footings. The bearing capacity of single and group of stone columns under static loading compares with unimproved ground.

Keywords: circular raft footing, numerical analysis, validation, vertically encased stone column

Procedia PDF Downloads 293

Authors: Soo Min Kim, Soo-Won Chae, Soung-Yon Kim, Haea Lee, Ju Yong Kang, Juneyong Lee, Seung-Ho Han

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Reverse total shoulder arthroplasty (RTSA) has become an effective treatment option for cuff tear arthropathy and massive, irreparable rotator cuff tears and indications for its use are expanding. Numerous methods for optimal fixation of the glenoid component have been suggested, such as inferior overhang, inferior tilt, to maximize initial fixation and prevent glenoid component loosening. The inferior tilt fixation of a glenoid component has been suggested, which is expected to decrease scapular notching and to improve the stability of a glenoid component fixation in reverse total shoulder arthroplasty. Inferior tilt fixation of the glenoid component has been suggested, which can improve stability and, because it provides the most uniform compressive forces and imparts the least amount of tensile forces and micromotion, reduce the likelihood of mechanical failure. Another study reported that glenoid component inferior tilt improved impingement-free range of motion as well as minimized the scapular notching. Several authors have shown that inferior tilt of a glenoid component reduces scapular notching. However, controversy still exists regarding its importance in the literature. In this study the influence of inferior tilt fixation on the primary stability of a glenoid component has been investigated. Finite element models were constructed from cadaveric scapulae and glenoid components were implanted with neutral and 10° inferior tilts. Most previous biomechanical studies regarding the effect of glenoid component inferior tilt used a solid rigid polyurethane foam or sawbones block, not cadaveric scapulae, to evaluate the stability of the RTSA. Relative micromotions at the bone-glenoid component interface, and the distribution of bone stresses under the glenoid component and around the screws were analyzed and compared between neutral and 10° inferior tilt groups. Contact area between bone and screws and cut surface area of the cancellous bone exposed after reaming of the glenoid have also been investigated because of the fact that cancellous and cortical bone thickness vary depending on the resection level of the inferior glenoid bone. The greater relative micromotion of the bone-glenoid component interface occurred in the 10° inferior tilt group than in the neutral tilt group, especially at the inferior area of the bone-glenoid component interface. Bone stresses under the glenoid component and around the screws were also higher in the 10° inferior tilt group than in the neutral tilt group, especially at the inferior third of the glenoid bone surface under the glenoid component and inferior scapula. Thus inferior tilt fixation of the glenoid component may adversely affect the primary stability and longevity of the reverse total shoulder arthroplasty.

Keywords: finite element analysis, glenoid component, inferior tilt, reverse total shoulder arthroplasty

Procedia PDF Downloads 275

Authors: Nguyen The Hoang Anh, Tran Huy Hoang, Vu Tat Thang, T. T. Quyen Bui

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Mental state classification is an important step for realizing a control system based on electroencephalography (EEG) signals which could benefit a lot of paralyzed people including the locked-in or Amyotrophic Lateral Sclerosis. Considering that EEG signals are nonstationary and often contaminated by various types of artifacts, classifying thoughts into correct mental states is not a trivial problem. In this work, our contribution is that we present and realize a novel model which integrates different techniques: Independent component analysis (ICA), relative wavelet energy, and support vector machine (SVM) for the same task. We applied our model to classify thoughts in two types of experiment whether with two or three mental states. The experimental results show that the presented model outperforms other models using Artificial Neural Network, K-Nearest Neighbors, etc.

Keywords: EEG, ICA, SVM, wavelet

Procedia PDF Downloads 364

Authors: Elaine Lima Da Fonseca, Eliomar Pereira Da Silva Filho

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The modeling of areas susceptible to soil loss by hydro erosive processes consists of a simplified instrument of reality with the purpose of predicting future behaviors from the observation and interaction of a set of geoenvironmental factors. The models of potential areas for soil loss will be obtained through binary logistic regression, artificial neural networks, and support vector machines. The choice of the municipality of Colorado do Oeste in the south of the western Amazon is due to soil degradation due to anthropogenic activities, such as agriculture, road construction, overgrazing, deforestation, and environmental and socioeconomic configurations. Initially, a soil erosion inventory map constructed through various field investigations will be designed, including the use of remotely piloted aircraft, orbital imagery, and the PLANAFLORO/RO database. 100 sampling units with the presence of erosion will be selected based on the assumptions indicated in the literature, and, to complement the dichotomous analysis, 100 units with no erosion will be randomly designated. The next step will be the selection of the predictive parameters that exert, jointly, directly, or indirectly, some influence on the mechanism of occurrence of soil erosion events. The chosen predictors are altitude, declivity, aspect or orientation of the slope, curvature of the slope, composite topographic index, flow power index, lineament density, normalized difference vegetation index, drainage density, lithology, soil type, erosivity, and ground surface temperature. After evaluating the relative contribution of each predictor variable, the erosion susceptibility model will be applied to the municipality of Colorado do Oeste - Rondônia through the SPSS Statistic 26 software. Evaluation of the model will occur through the determination of the values of the R² of Cox & Snell and the R² of Nagelkerke, Hosmer and Lemeshow Test, Log Likelihood Value, and Wald Test, in addition to analysis of the Confounding Matrix, ROC Curve and Accumulated Gain according to the model specification. The validation of the synthesis map resulting from both models of the potential risk of soil erosion will occur by means of Kappa indices, accuracy, and sensitivity, as well as by field verification of the classes of susceptibility to erosion using drone photogrammetry. Thus, it is expected to obtain the mapping of the following classes of susceptibility to erosion very low, low, moderate, very high, and high, which may constitute a screening tool to identify areas where more detailed investigations need to be carried out, applying more efficient social resources.

Keywords: modeling, susceptibility to erosion, artificial intelligence, Amazon

Procedia PDF Downloads 50

Authors: Jamison Duckworth, Shankarachary Ragi

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Detection and segmentation of wildfire smoke plumes from remote sensing imagery are being pursued as a solution for early fire detection and response. Smoke plume detection can be automated and made robust by the application of artificial intelligence methods. Specifically, in this study, the deep learning approach Mask Region-based Convolutional Neural Network (RCNN) is being proposed to learn smoke patterns across different spectral bands. This method is proposed to separate the smoke regions from the background and return masks placed over the smoke plumes. Multispectral data was acquired using NASA’s Earthdata and WorldView and services and satellite imagery. Due to the use of multispectral bands along with the three visual bands, we show that Mask R-CNN can be applied to distinguish smoke plumes from clouds and other landscape features that resemble smoke.

Keywords: deep learning, mask-RCNN, smoke plumes, spectral bands

Procedia PDF Downloads 107

Authors: Maulik Parmar, Sumeet Sandhu

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The online fashion industry experiences high product return rates. Many returns are because of size/fit mismatches -the size scale on labels can vary across brands, the size parameters may not capture all fit measurements, or the product may have manufacturing defects. Warehouse quality check of garment sizes can be semi-automated to improve speed and accuracy. This paper presents an approach for automatically measuring garment sizes from a single image of the garment -using Deep Learning to learn garment keypoints. The paper focuses on the waist size measurement of jeans and can be easily extended to other garment types and measurements. Experimental results show that this approach can greatly improve the speed and accuracy of today’s manual measurement process.

Keywords: convolutional neural networks, deep learning, distortion, garment measurements, image warping, keypoints

Procedia PDF Downloads 280

Authors: Aayushi Somani, Siba P. Samal

Abstract:

Three-dimensional (3D) meshes are data structures, which store geometric information of an object or scene, generally in the form of vertices and edges. Current technology in laser scanning and other geometric data acquisition technologies acquire high resolution sampling which leads to high resolution meshes. While high resolution meshes give better quality rendering and hence is used often, the processing, as well as storage of 3D meshes, is currently resource-intensive. At the same time, web applications for data processing have become ubiquitous owing to their accessibility. For 3D meshes, the advancement of 3D web technologies, such as WebGL, WebVR, has enabled high fidelity rendering of huge meshes. However, there exists a gap in ability to stream huge meshes to a native client and browser application due to high network latency. Also, there is an inherent delay of loading WebGL pages due to large and complex models. The focus of our work is to identify the challenges faced when such meshes are streamed into and processed on hand-held devices, owing to its limited resources. One of the solutions that are conventionally used in the graphics community to alleviate resource limitations is mesh compression. Our approach deals with a two-step approach for random accessible progressive compression and its parallel implementation. The first step includes partition of the original mesh to multiple sub-meshes, and then we invoke data parallelism on these sub-meshes for its compression. Subsequent threaded decompression logic is implemented inside the Web Browser Engine with modification of WebGL implementation in Chromium open source engine. This concept can be used to completely revolutionize the way e-commerce and Virtual Reality technology works for consumer electronic devices. These objects can be compressed in the server and can be transmitted over the network. The progressive decompression can be performed on the client device and rendered. Multiple views currently used in e-commerce sites for viewing the same product from different angles can be replaced by a single progressive model for better UX and smoother user experience. Can also be used in WebVR for commonly and most widely used activities like virtual reality shopping, watching movies and playing games. Our experiments and comparison with existing techniques show encouraging results in terms of latency (compressed size is ~10-15% of the original mesh), processing time (20-22% increase over serial implementation) and quality of user experience in web browser.

Keywords: 3D compression, 3D mesh, 3D web, chromium, client-server architecture, e-commerce, level of details, parallelization, progressive compression, WebGL, WebVR

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Authors: Hiromi Kawai

Abstract:

This study investigates the nature of EFL older children’s L2 perceptive and productive abilities using classroom data, in order to find a pedagogical solution to the teaching of L2 sounds at an early stage of learning in a formal school setting. It is still inconclusive whether older children with only EFL formal school instruction at the initial stage of L2 learning are able to attain native-like perception and production in English within the very limited amount of exposure to the target language available. Based on the notion of the lack of study of EFL Japanese children’s acquisition of English segments, the researcher uses a model of L1 speech processing which was developed for investigating L1 English children’s speech and literacy difficulties using a psycholinguistic framework. The model is composed of input channel, output channel, and lexical representation, and examines how a child receives information from spoken or written language, remembers and stores it within the lexical representations and how the child selects and produces spoken or written words. Concerning language universality and language specificity in the language acquisitional process, the aim of finding any sound errors in L1 English children seemed to conform to the author’s intention to find abilities of English sounds in older Japanese children at the novice level of English in an EFL setting. 104 students in Grade 5 (between the ages of 10 and 11 years old) of an elementary school in Tokyo participated in this study. Four tests to measure their perceptive ability and three oral repetition tests to measure their productive ability were conducted with/without reference to lexical representation. All the test items were analyzed to calculate item facility (IF) indices, and correlational analyses and Structural Equation Modeling (SEM) were conducted to examine the relationship between the receptive ability and the productive ability. IF analysis showed that (1) the participants were better at perceiving a segment than producing a segment, (2) they had difficulty in auditory discrimination of paired consonants when one of them does not exist in the Japanese inventory, (3) they had difficulty in both perceiving and producing English vowels, and (4) their L1 loan word knowledge had an influence on their ability to perceive and produce L2 sounds. The result of the Multiple Regression Modeling showed that the two production tests could predict the participants’ auditory ability of real words in English. The result of SEM showed that the hypothesis that perceptive ability affects productive ability was supported. Based on these findings, the author discusses the possible explicit method of teaching English segments to EFL older children in a formal school setting.

Keywords: EFL older children, english segments, perception, production, speech processing system

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Authors: Maciej Szeląg, Stanisław Fic

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The impact of elevated temperatures on the construction materials manifests in change of their physical and mechanical characteristics. Stresses and thermal deformations that occur inside the volume of the material cause its progressive degradation as temperature increase. Finally, the reactions and transformations of multiphase structure of cementitious composite cause its complete destruction. A particularly dangerous phenomenon is the impact of thermal shock – a sudden high temperature load. The thermal shock leads to a high value of the temperature gradient between the outer surface and the interior of the element in a relatively short time. The result of mentioned above process is the formation of the cracks and scratches on the material’s surface and inside the material. The article describes the use of computer image analysis techniques to identify and assess the structure of the cluster cracks on the surfaces of modified cement pastes, caused by thermal shock. Four series of specimens were tested. Two Portland cements were used (CEM I 42.5R and CEM I 52,5R). In addition, two of the series contained metakaolinite as a replacement for 10% of the cement content. Samples in each series were made in combination of three w/b (water/binder) indicators of respectively 0.4; 0.5; 0.6. Surface cracks of the samples were created by a sudden temperature load at 200°C for 4 hours. Images of the cracked surfaces were obtained via scanning at 1200 DPI; digital processing and measurements were performed using ImageJ v. 1.46r software. In order to examine the cracked surface of the cement paste as a system of closed clusters – the dispersal systems theory was used to describe the structure of cement paste. Water is used as the dispersing phase, and the binder is used as the dispersed phase – which is the initial stage of cement paste structure creation. A cluster itself is considered to be the area on the specimen surface that is limited by cracks (created by sudden temperature loading) or by the edge of the sample. To describe the structure of cracks two stereological parameters were proposed: A ̅ – the cluster average area, L ̅ – the cluster average perimeter. The goal of this study was to compare the investigated stereological parameters with the mechanical properties of the tested specimens. Compressive and tensile strength testes were carried out according to EN standards. The method used in the study allowed the quantitative determination of defects occurring in the examined modified cement pastes surfaces. Based on the results, it was found that the nature of the cracks depends mainly on the physical parameters of the cement and the intermolecular interactions on the dispersal environment. Additionally, it was noted that the A ̅/L ̅ relation of created clusters can be described as one function for all tested samples. This fact testifies about the constant geometry of the thermal cracks regardless of the presence of metakaolinite, the type of cement and the w/b ratio.

Keywords: cement paste, cluster cracks, elevated temperature, image analysis, metakaolinite, stereological parameters

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Authors: Hassan Manouzi, Taous-Meriem Laleg-Kirati

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In this paper we study mathematically the eigenvalue problem for stochastic elliptic partial differential equation of Wick type. Using the Wick-product and the Wiener-Ito chaos expansion, the stochastic eigenvalue problem is reformulated as a system of an eigenvalue problem for a deterministic partial differential equation and elliptic partial differential equations by using the Fredholm alternative. To reduce the computational complexity of this system, we shall use a decomposition-coordination method. Once this approximation is performed, the statistics of the numerical solution can be easily evaluated.

Keywords: eigenvalue problem, Wick product, SPDEs, finite element, Wiener-Ito chaos expansion

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Authors: Danilo López, Edwin Rivas, Fernando Pedraza

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Currently, one of the major challenges in wireless networks is the optimal use of radio spectrum, which is managed inefficiently. One of the solutions to existing problem converges in the use of Cognitive Radio (CR), as an essential parameter so that the use of the available licensed spectrum is possible (by secondary users), well above the usage values that are currently detected; thus allowing the opportunistic use of the channel in the absence of primary users (PU). This article presents the results found when estimating or predicting the future use of a spectral transmission band (from the perspective of the PU) for a chaotic type channel arrival behavior. The time series prediction method (which the PU represents) used is ANFIS (Adaptive Neuro Fuzzy Inference System). The results obtained were compared to those delivered by the RNA (Artificial Neural Network) algorithm. The results show better performance in the characterization (modeling and prediction) with the ANFIS methodology.

Keywords: ANFIS, cognitive radio, prediction primary user, RNA

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Authors: Zhao Gao, Eran Edirisinghe

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The production of an accurate sketch of a suspect based on a verbal description obtained from a witness is an essential task for most criminal investigations. The criminal investigation system employs specifically trained professional artists to manually draw a facial image of the suspect according to the descriptions of an eyewitness for subsequent identification. Within the advancement of Deep Learning, Recurrent Neural Networks (RNN) have shown great promise in Natural Language Processing (NLP) tasks. Additionally, Generative Adversarial Networks (GAN) have also proven to be very effective in image generation. In this study, a trained GAN conditioned on textual features such as keywords automatically encoded from a verbal description of a human face using an RNN is used to generate photo-realistic facial images for criminal investigations. The intention of the proposed system is to map corresponding features into text generated from verbal descriptions. With this, it becomes possible to generate many reasonably accurate alternatives to which the witness can use to hopefully identify a suspect from. This reduces subjectivity in decision making both by the eyewitness and the artist while giving an opportunity for the witness to evaluate and reconsider decisions. Furthermore, the proposed approach benefits law enforcement agencies by reducing the time taken to physically draw each potential sketch, thus increasing response times and mitigating potentially malicious human intervention. With publically available 'CelebFaces Attributes Dataset' (CelebA) and additionally providing verbal description as training data, the proposed architecture is able to effectively produce facial structures from given text. Word Embeddings are learnt by applying the RNN architecture in order to perform semantic parsing, the output of which is fed into the GAN for synthesizing photo-realistic images. Rather than the grid search method, a metaheuristic search based on genetic algorithms is applied to evolve the network with the intent of achieving optimal hyperparameters in a fraction the time of a typical brute force approach. With the exception of the ‘CelebA’ training database, further novel test cases are supplied to the network for evaluation. Witness reports detailing criminals from Interpol or other law enforcement agencies are sampled on the network. Using the descriptions provided, samples are generated and compared with the ground truth images of a criminal in order to calculate the similarities. Two factors are used for performance evaluation: The Structural Similarity Index (SSIM) and the Peak Signal-to-Noise Ratio (PSNR). A high percentile output from this performance matrix should attribute to demonstrating the accuracy, in hope of proving that the proposed approach can be an effective tool for law enforcement agencies. The proposed approach to criminal facial image generation has potential to increase the ratio of criminal cases that can be ultimately resolved using eyewitness information gathering.

Keywords: RNN, GAN, NLP, facial composition, criminal investigation

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Authors: Ziwei Wang, Andrea Lucotti, Luigi Brambilla, Matteo Tommasini, Chiara Bertarelli

Abstract:

Surface-enhanced Raman Spectroscopy (SERS) is a highly sensitive detection that provides abundant information on low concentration analytes from various researching areas. Based on localized surface plasmon resonance, metal nanostructures including gold, silver and copper have been investigated as SERS substrate during recent decades. There has been increasing more attention of exploring good performance, homogenous, repeatable SERS substrates. Here, we show that electrospinning, which is an inexpensive technique to fabricate large-scale, self-standing and repeatable membranes, can be effectively used for producing SERS substrates. Nanoparticles and nanorods are added to the feed electrospinning solution to collect functionalized polymer fibrous mats. We report stable electrospun membranes as SERS substrate using gold nanorods (AuNRs) and poly(vinyl alcohol). Particularly, a post-processing crosslinking step using glutaraldehyde under acetone environment was carried out to the electrospun membrane. It allows for using the membrane in any liquid environment, including water, which is of interest both for sensing of contaminant in wastewater, as well as for biosensing. This crosslinked AuNRs/PVA membrane has demonstrated excellent performance as SERS substrate for low concentration 10-6 M Rhodamine 6G (Rh6G) aqueous solution. This post-processing for fabricating SERS substrate is the first time reported and proved through Raman imaging of excellent stability and outstanding performance. Finally, SERS tests have been applied to several analytes, and the application of AuNRs/PVA membrane is broadened by removing the detected analyte by rinsing. Therefore, this crosslinked AuNRs/PVA membrane is re-usable.

Keywords: SERS spectroscopy, electrospinning, crosslinking, composite materials

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Authors: Ola Hall, Ibrahim Wahab, Thorsteinn Rognvaldsson, Mattias Ohlsson

Abstract:

The subfield of poverty and welfare estimation that applies machine learning tools and methods on satellite imagery is a nascent but rapidly growing one. This is in part driven by the sustainable development goal, whose overarching principle is that no region is left behind. Among other things, this requires that welfare levels can be accurately and rapidly estimated at different spatial scales and resolutions. Conventional tools of household surveys and interviews do not suffice in this regard. While they are useful for gaining a longitudinal understanding of the welfare levels of populations, they do not offer adequate spatial coverage for the accuracy that is needed, nor are their implementation sufficiently swift to gain an accurate insight into people and places. It is this void that satellite imagery fills. Previously, this was near-impossible to implement due to the sheer volume of data that needed processing. Recent advances in machine learning, especially the deep learning subtype, such as deep neural networks, have made this a rapidly growing area of scholarship. Despite their unprecedented levels of performance, such models lack transparency and explainability and thus have seen limited downstream applications as humans generally are apprehensive of techniques that are not inherently interpretable and trustworthy. While several studies have demonstrated the superhuman performance of AI models, none has directly compared the performance of such models and human readers in the domain of poverty studies. In the present study, we directly compare the performance of human readers and a DL model using different resolutions of satellite imagery to estimate the welfare levels of demographic and health survey clusters in Tanzania, using the wealth quintile ratings from the same survey as the ground truth data. The cluster-level imagery covers all 608 cluster locations, of which 428 were classified as rural. The imagery for the human readers was sourced from the Google Maps Platform at an ultra-high resolution of 0.6m per pixel at zoom level 18, while that of the machine learning model was sourced from the comparatively lower resolution Sentinel-2 10m per pixel data for the same cluster locations. Rank correlation coefficients of between 0.31 and 0.32 achieved by the human readers were much lower when compared to those attained by the machine learning model – 0.69-0.79. This superhuman performance by the model is even more significant given that it was trained on the relatively lower 10-meter resolution satellite data while the human readers estimated welfare levels from the higher 0.6m spatial resolution data from which key markers of poverty and slums – roofing and road quality – are discernible. It is important to note, however, that the human readers did not receive any training before ratings, and had this been done, their performance might have improved. The stellar performance of the model also comes with the inevitable shortfall relating to limited transparency and explainability. The findings have significant implications for attaining the objective of the current frontier of deep learning models in this domain of scholarship – eXplainable Artificial Intelligence through a collaborative rather than a comparative framework.

Keywords: poverty prediction, satellite imagery, human readers, machine learning, Tanzania

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