Search results for: privacy-preserving techniques

Authors: Satish Kumar Peddapelli

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This paper presents advances in pulse width modulation techniques which refers to a method of carrying information on train of pulses and the information be encoded in the width of pulses. Pulse Width Modulation is used to control the inverter output voltage. This is done by exercising the control within the inverter itself by adjusting the ON and OFF periods of inverter. By fixing the DC input voltage we get AC output voltage. In variable speed AC motors the AC output voltage from a constant DC voltage is obtained by using inverter. Recent developments in power electronics and semiconductor technology have lead improvements in power electronic systems. Hence, different circuit configurations namely multilevel inverters have become popular and considerable interest by researcher are given on them. A fast Space-Vector Pulse Width Modulation (SVPWM) method for five-level inverter is also discussed. In this method, the space vector diagram of the five-level inverter is decomposed into six space vector diagrams of three-level inverters. In turn, each of these six space vector diagrams of three-level inverter is decomposed into six space vector diagrams of two-level inverters. After decomposition, all the remaining necessary procedures for the three-level SVPWM are done like conventional two-level inverter. The proposed method reduces the algorithm complexity and the execution time. It can be applied to the multilevel inverters above the five-level also. The experimental setup for three-level diode-clamped inverter is developed using TMS320LF2407 DSP controller and the experimental results are analysed.

Keywords: five-level inverter, space vector pulse wide modulation, diode clamped inverter, electrical engineering

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Authors: Kirstin Burger, Paul Watts, Nicole Vorster

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Green chemistry focuses on synthesis which has a low negative impact on the environment. This research focuses on synthesizing novel compounds from an all-natural Eucalyptus citriodora oil. Eight novel plasticizer compounds are synthesized and optimized using flow chemistry technology. A precursor to one novel compound can be synthesized from the lauric acid present in coconut oil. Key parameters, such as catalyst screening and loading, reaction time, temperature, residence time using flow chemistry techniques is investigated. The compounds are characterised using GC-MS, FT-IR, 1H and 13C-NMR techniques, X-ray crystallography. The efficiency of the compounds is compared to two commercial plasticizers, i.e. Dibutyl phthalate and Eastman 168. Several PVC-plasticized film formulations are produced using the bio-based novel compounds. Tensile strength, stress at fracture and percentage elongation are tested. The property of having increasing plasticizer percentage in the film formulations is investigated, ranging from 3, 6, 9 and 12%. The diastereoisomers of each compound are separated and formulated into PVC films, and differences in tensile strength are measured. Leaching tests, flexibility, and change in glass transition temperatures for PVC-plasticized films is recorded. Research objective includes using these novel compounds as a green bio-plasticizer alternative in plastic products for infants. The inhibitory effect of the compounds on six pathogens effecting infants are studied, namely; Escherichia coli, Staphylococcus aureus, Shigella sonnei, Pseudomonas putida, Salmonella choleraesuis and Klebsiella oxytoca.

Keywords: bio-based compounds, plasticizer, tensile strength, microbiological inhibition , synthesis

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Authors: Tomas Trainys, Algimantas Venckauskas

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Application of biometric features to the cryptography for human identification and authentication is widely studied and promising area of the development of high-reliability cryptosystems. Biometric cryptosystems typically are designed for patterns recognition, which allows biometric data acquisition from an individual, extracts feature sets, compares the feature set against the set stored in the vault and gives a result of the comparison. Preprocessing and fusion of biometric data are the most important phases in generating a feature vector for key generation or authentication. Fusion of biometric features is critical for achieving a higher level of security and prevents from possible spoofing attacks. The paper focuses on the tasks of initial processing and fusion of multiple representations of finger vein modality patterns. These tasks are solved by applying conventional image preprocessing methods and machine learning techniques, Convolutional Neural Network (SVM) method for image segmentation and feature extraction. An article presents a method for generating sets of biometric features from a finger vein network using several instances of the same modality. Extracted features sets were fused at the feature level. The proposed method was tested and compared with the performance and accuracy results of other authors.

Keywords: bio-cryptography, biometrics, cryptographic key generation, data fusion, information security, SVM, pattern recognition, finger vein method.

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Authors: Saad M. Darwish, Mohamed A. El-Iskandarani, Guitar M. Shawkat

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Nowadays, the amount of available multimedia data is continuously on the rise. The need to find a required image for an ordinary user is a challenging task. Content based image retrieval (CBIR) computes relevance based on the visual similarity of low-level image features such as color, textures, etc. However, there is a gap between low-level visual features and semantic meanings required by applications. The typical method of bridging the semantic gap is through the automatic image annotation (AIA) that extracts semantic features using machine learning techniques. In this paper, a multi-label image annotation system guided by Firefly and Bayesian method is proposed. Firstly, images are segmented using the maximum variance intra cluster and Firefly algorithm, which is a swarm-based approach with high convergence speed, less computation rate and search for the optimal multiple threshold. Feature extraction techniques based on color features and region properties are applied to obtain the representative features. After that, the images are annotated using translation model based on the Net Bayes system, which is efficient for multi-label learning with high precision and less complexity. Experiments are performed using Corel Database. The results show that the proposed system is better than traditional ones for automatic image annotation and retrieval.

Keywords: feature extraction, feature selection, image annotation, classification

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Authors: Theresia El Khoury, Georges Badr, Amir Hajjam El Hassani, Stéphane N’Guyen Van Ky

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The widespread adoption of machine learning and artificial intelligence across different domains can be attributed to the digitization of data over several decades, resulting in vast amounts of data, types, and structures. Thus, data processing and preparation turn out to be a crucial stage. However, applying these techniques to S1000D standard-based data poses a challenge due to its complexity and the need to preserve logical information. This paper describes SALERNO, an S1000d AppLicability dEpended pRocessiNg mOdel. This python-based model analyzes and converts the XML S1000D-based files into an easier data format that can be used in machine learning techniques while preserving the different logic and relationships in files. The model parses the files in the given folder, filters them, and extracts the required information to be saved in appropriate data frames and Excel sheets. Its main idea is to group the extracted information by applicability. In addition, it extracts the full text by replacing internal and external references while maintaining the relationships between files, as well as the necessary requirements. The resulting files can then be saved in databases and used in different models. Documents in both English and French languages were tested, and special characters were decoded. Updates on the technical manuals were taken into consideration as well. The model was tested on different versions of the S1000D, and the results demonstrated its ability to effectively handle the applicability, requirements, references, and relationships across all files and on different levels.

Keywords: aeronautics, big data, data processing, machine learning, S1000D

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Authors: Mais Nijim, Rama Devi Chennuboyina, Waseem Al Aqqad

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Advances in spatial and spectral resolution of satellite images have led to tremendous growth in large image databases. The data we acquire through satellites, radars and sensors consists of important geographical information that can be used for remote sensing applications such as region planning, disaster management. Spatial data classification and object recognition are important tasks for many applications. However, classifying objects and identifying them manually from images is a difficult task. Object recognition is often considered as a classification problem, this task can be performed using machine-learning techniques. Despite of many machine-learning algorithms, the classification is done using supervised classifiers such as Support Vector Machines (SVM) as the area of interest is known. We proposed a classification method, which considers neighboring pixels in a region for feature extraction and it evaluates classifications precisely according to neighboring classes for semantic interpretation of region of interest (ROI). A dataset has been created for training and testing purpose; we generated the attributes by considering pixel intensity values and mean values of reflectance. We demonstrated the benefits of using knowledge discovery and data-mining techniques, which can be on image data for accurate information extraction and classification from high spatial resolution remote sensing imagery.

Keywords: remote sensing, object recognition, classification, data mining, waterbody identification, feature extraction

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Authors: Mateusz Paszko, Ksenia Siadkowska

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Due to the low-altitude and relatively low-speed flight, helicopters are easy targets for actual combat assets e.g. infrared-guided missiles. Current techniques aim to increase the combat effectiveness of the military helicopters. Protection of the helicopter in flight from early detection, tracking and finally destruction can be realized in many ways. One of them is cooling hot exhaust gasses, emitting from the engines to the atmosphere in special heat exchangers. Nowadays, this process is realized in ejective coolers, where strong heat and momentum exchange between hot exhaust gases and cold air ejected from atmosphere takes place. Flow effects of air, exhaust gases; mixture of those two and the heat transfer between cold air and hot exhaust gases are given by differential equations of: Mass transportation–flow continuity, ejection of cold air through expanding exhaust gasses, conservation of momentum, energy and physical relationship equations. Calculation of those processes in ejective cooler by means of classic mathematical analysis is extremely hard or even impossible. Because of this, it is necessary to apply the numeric approach with modern, numeric computer programs. The paper discussed the general usability of the Computational Fluid Dynamics (CFD) in a process of projecting the ejective exhaust gases cooler cooperating with helicopter turbine engine. In this work, the CFD calculations have been performed for ejective-based cooler cooperating with the PA W3 helicopter’s engines.

Keywords: aviation, CFD analysis, ejective-cooler, helicopter techniques

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Authors: A. M. John-Otumu, M. M. Rahman, M. C. Onuoha, E. P. Ojonugwa

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A Convolutional Neural Network (CNN) model for predicting Lassa fever was built using Python 3.8.0 programming language, alongside Keras 2.2.4 and TensorFlow 2.6.1 libraries as the development environment in order to reduce the current high risk of Lassa fever in West Africa, particularly in Nigeria. The study was prompted by some major flaws in existing conventional laboratory equipment for diagnosing Lassa fever (RT-PCR), as well as flaws in AI-based techniques that have been used for probing and prognosis of Lassa fever based on literature. There were 15,679 blood smear microscopic image datasets collected in total. The proposed model was trained on 70% of the dataset and tested on 30% of the microscopic images in avoid overfitting. A 3x3x3 convolution filter was also used in the proposed system to extract features from microscopic images. The proposed CNN-based model had a recall value of 96%, a precision value of 93%, an F1 score of 95%, and an accuracy of 94% in predicting and accurately classifying the images into clean or infected samples. Based on empirical evidence from the results of the literature consulted, the proposed model outperformed other existing AI-based techniques evaluated. If properly deployed, the model will assist physicians, medical laboratory scientists, and patients in making accurate diagnoses for Lassa fever cases, allowing the mortality rate due to the Lassa fever virus to be reduced through sound decision-making.

Keywords: artificial intelligence, ANN, blood smear, CNN, deep learning, Lassa fever

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Authors: Fatai Olakunle Ogundele

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Road construction is increasingly common in today's world as human development expands and people increasingly rely on cars for transportation on a daily basis. The construction of a large network of roads has dramatically altered the landscape and impacted well-being in a number of deleterious ways. In addition, the road can also shift population demographics and be a source of pollution into the environment. Road construction activities normally result in changes in alteration of the soil's physical properties through soil compaction on the road itself and on adjacent areas and chemical and biological properties, among other effects. Understanding roadside soil properties that are influenced by road construction activities can serve as a basis for formulating conservation-based management strategies. Therefore, this study examined the effects of road construction on soil properties and soil organic carbon along Lagos Badagry Expressway, Lagos, Nigeria. The study adopted purposive sampling techniques and 40 soil samples were collected at a depth of 0 – 30cm from each of the identified road intersections and infrastructures using a soil auger. The soil samples collected were taken to the laboratory for soil properties and carbon stock analysis using standard methods. Both descriptive and inferential statistical techniques were applied to analyze the data obtained. The results revealed that soil compaction inhibits ecological succession on roadsides in that increased compaction suppresses plant growth as well as causes changes in soil quality.

Keywords: highway, soil properties, organic carbon, road construction, land degradation

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Authors: Hatim Laalej, Salvador Sumohano-Verdeja, Thomas McLeay

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Monitoring of tool wear in milling operations is essential for achieving the desired dimensional accuracy and surface finish of a machined workpiece. Although there are numerous statistical models and artificial intelligence techniques available for monitoring the wear of cutting tools, these techniques cannot pin point which cutting edge of the tool, or which insert in the case of indexable tooling, is worn or broken. Currently, the task of monitoring the wear on the tool cutting edges is carried out by the operator who performs a manual inspection, causing undesirable stoppages of machine tools and consequently resulting in costs incurred from lost productivity. The present study is concerned with the development of a flute tracking system to segment signals related to each physical flute of a cutter with three flutes used in an end milling operation. The purpose of the system is to monitor the cutting condition for individual flutes separately in order to determine their progressive wear rates and to predict imminent tool failure. The results of this study clearly show that signals associated with each flute can be effectively segmented using the proposed flute tracking system. Furthermore, the results illustrate that by segmenting the sensor signal by flutes it is possible to investigate the wear in each physical cutting edge of the cutting tool. These findings are significant in that they facilitate the online condition monitoring of a cutting tool for each specific flute without the need for operators/engineers to perform manual inspections of the tool.

Keywords: machining, milling operation, tool condition monitoring, tool wear prediction

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Authors: Shatha Ghareeb, Rawaa Al-Jumeily, Thar Baker

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In Abu Dhabi, there are many different education curriculums where sector of private schools and quality assurance is supervising many private schools in Abu Dhabi for many nationalities. As there are many different education curriculums in Abu Dhabi to meet expats’ needs, there are different requirements for registration and success. In addition, there are different age groups for starting education in each curriculum. In fact, each curriculum has a different number of years, assessment techniques, reassessment rules, and exam boards. Currently, students that transfer curriculums are not being placed in the right year group due to different start and end dates of each academic year and their date of birth for each year group is different for each curriculum and as a result, we find students that are either younger or older for that year group which therefore creates gaps in their learning and performance. In addition, there is not a way of storing student data throughout their academic journey so that schools can track the student learning process. In this paper, we propose to develop a computational framework applicable in multicultural countries such as UAE in which multi-education systems are implemented. The ultimate goal is to use cloud and fog computing technology integrated with Artificial Intelligence techniques of Machine Learning to aid in a smooth transition when assigning students to their year groups, and provide leveling and differentiation information of students who relocate from a particular education curriculum to another, whilst also having the ability to store and access student data from anywhere throughout their academic journey.

Keywords: admissions, algorithms, cloud computing, differentiation, fog computing, levelling, machine learning

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Authors: Alia Alghosoun, Nabil El Moçayd, Mohammed Seaid

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Modeling dam-break flows over non-flat beds requires an accurate representation of the topography which is the main source of uncertainty in the model. Therefore, developing robust and accurate techniques for reconstructing topography in this class of problems would reduce the uncertainty in the flow system. In many hydraulic applications, experimental techniques have been widely used to measure the bed topography. In practice, experimental work in hydraulics may be very demanding in both time and cost. Meanwhile, computational hydraulics have served as an alternative for laboratory and field experiments. Unlike the forward problem, the inverse problem is used to identify the bed parameters from the given experimental data. In this case, the shallow water equations used for modeling the hydraulics need to be rearranged in a way that the model parameters can be evaluated from measured data. However, this approach is not always possible and it suffers from stability restrictions. In the present work, we propose an adaptive optimal control technique to numerically identify the underlying bed topography from a given set of free-surface observation data. In this approach, a minimization function is defined to iteratively determine the model parameters. The proposed technique can be interpreted as a fractional-stage scheme. In the first stage, the forward problem is solved to determine the measurable parameters from known data. In the second stage, the adaptive control Ensemble Kalman Filter is implemented to combine the optimality of observation data in order to obtain the accurate estimation of the topography. The main features of this method are on one hand, the ability to solve for different complex geometries with no need for any rearrangements in the original model to rewrite it in an explicit form. On the other hand, its achievement of strong stability for simulations of flows in different regimes containing shocks or discontinuities over any geometry. Numerical results are presented for a dam-break flow problem over non-flat bed using different solvers for the shallow water equations. The robustness of the proposed method is investigated using different numbers of loops, sensitivity parameters, initial samples and location of observations. The obtained results demonstrate high reliability and accuracy of the proposed techniques.

Keywords: erodible beds, finite element method, finite volume method, nonlinear elasticity, shallow water equations, stresses in soil

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Authors: Hendradi Hardhienata, Tony Sumaryada, Sri Setyaningsih

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Current progress in the field of nonlinear optics has enabled precise surface characterization in semiconductor materials. Nonlinear optical techniques are favorable due to their nondestructive measurement and ability to work in nonvacuum and ambient conditions. The advance of the bond hyperpolarizability models opens a wide range of nanoscale surface investigation including the possibility to detect molecular orientation at the surface of silicon and zincblende semiconductors, investigation of electric field induced second harmonic fields at the semiconductor interface, detection of surface impurities, and very recently, study surface defects such as twin boundary in wurtzite semiconductors. In this work, we show using nonlinear optical techniques, e.g. nonlinear bond models how arbitrary polarization of the incoming electric field in Rotational Anisotropy Spectroscopy experiments can provide more information regarding the origin of the nonlinear sources in zincblende and wurtzite semiconductor structure. In addition, using hyperpolarizability consideration, we describe how the nonlinear susceptibility tensor describing SHG can be well modelled using only few parameter because of the symmetry of the bonds. We also show how the third harmonic intensity feature shows considerable changes when the incoming field polarization angle is changed from s-polarized to p-polarized. We also propose a method how to investigate surface reconstruction and defects in wurtzite and zincblende structure at the nanoscale level.

Keywords: surface characterization, bond model, rotational anisotropy spectroscopy, effective hyperpolarizability

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Authors: Khairul Bariah Sulaiman, Tajul Aris Yang

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Malaysian dried cocoa beans have been reported to have low quality flavour and are often sold at discounted prices. Various efforts have been made to improve the Malaysian beans quality. Among these efforts is introduction of the shallow box fermentation technique and pulp preconditioned through pods storage. However, after nearly four decades of the effort was done, Malaysian cocoa farmers still received lower prices for their beans. So, this study was carried out in order to assess the flavour quality of dried cocoa beans produced by shallow box fermentation techniques, combination of shallow box fermentation with pods storage and compared to dried cocoa beans obtained from Ghana. A total of eight samples of dried cocoa was used in this study, which one of the samples was Ghanaian beans (coded with no.8), while the rest were Malaysian cocoa beans with different post-harvest processing (coded with no. 1, 2, 3, 4, 5, 6 and 7). Cocoa liquor was prepared from all samples in the prescribed techniques and sensory evaluation was carried out using Quantitative Descriptive Analysis (QDA) Method with 0-10 scale by Malaysian Cocoa Board trained panelist. Sensory evaluation showed that cocoa attributes for all cocoa liquors ranging from 3.5 to 5.3, whereas bitterness was ranging from 3.4 to 4.6 and astringent attribute ranging from 3.9 to 5.5, respectively. Meanwhile, all cocoa liquors were having acid or sourness attribute ranging from 1.6 to 3.6, respectively. In general cocoa liquor prepared from sample coded no 4 has almost similar flavour profile and no significantly different at p < 0.05 with Ghana, in term of most flavour attributes as compared to the other six samples.

Keywords: cocoa beans, flavour, fermentation, shallow box, pods storage

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Authors: Masoomeh Alsadat Mirshafaei

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The present study aims to explore the effect of an 8-week aerobic training regimen combined with cinnamon extract on serum irisin and leptin levels in insulin-resistant rats. Additionally, this research leverages various machine learning (ML) and deep learning (DL) algorithms to model the complex interdependencies between exercise, nutrition, and metabolic markers, offering a groundbreaking approach to obesity and diabetes research. Forty-eight Wistar rats were selected and randomly divided into four groups: control, training, cinnamon, and training cinnamon. The training protocol was conducted over 8 weeks, with sessions 5 days a week at 75-80% VO2 max. The cinnamon and training-cinnamon groups were injected with 200 ml/kg/day of cinnamon extract. Data analysis included serum data, dietary intake, exercise intensity, and metabolic response variables, with blood samples collected 72 hours after the final training session. The dataset was analyzed using one-way ANOVA (P<0.05) and fed into various ML and DL models, including Support Vector Machines (SVM), Random Forest (RF), and Convolutional Neural Networks (CNN). Traditional statistical methods indicated that aerobic training, with and without cinnamon extract, significantly increased serum irisin and decreased leptin levels. Among the algorithms, the CNN model provided superior performance in identifying specific interactions between cinnamon extract concentration and exercise intensity, optimizing the increase in irisin and the decrease in leptin. The CNN model achieved an accuracy of 92%, outperforming the SVM (85%) and RF (88%) models in predicting the optimal conditions for metabolic marker improvements. The study demonstrated that advanced ML and DL techniques could uncover nuanced relationships and potential cellular responses to exercise and dietary supplements, which is not evident through traditional methods. These findings advocate for the integration of advanced analytical techniques in nutritional science and exercise physiology, paving the way for personalized health interventions in managing obesity and diabetes.

Keywords: aerobic training, cinnamon extract, insulin resistance, irisin, leptin, convolutional neural networks, exercise physiology, support vector machines, random forest

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Authors: S. M. Shahinul Islam, Israt Ara, Narendra Tuteja, Sreeramanan Subramaniam

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Through anther and microspore culture methods, complete homozygous plants can be produced within a year as compared to the long inbreeding method. Isolated microspore culture is one of the most important techniques for rapid development of haploid plants. The efficiency of this method is influenced by several factors such as cultural conditions, growth regulators, plant media, pretreatments, physical and growth conditions of the donor plants, pollen isolation procedure, etc. The main purpose of this study was to improve the isolated microspore culture protocol in order to increase the efficiency of embryoids, its regeneration and reducing albinisms. Under this study we have tested mainly three different microspore isolation procedures by glass rod, homozeniger and by blending and found the efficiency on gametic embryogenesis. There are three types of media viz. washing, pre-culture and induction was used. The induction medium as AMC (modified MS) supplemented by 2, 4-D (2.5 mg/l), kinetin (0.5 mg/l) and higher amount of D-Manitol (90 g/l) instead of sucrose and two types of amino acids (L-glutamine and L-serine) were used. Out of three main microspore isolation procedure by homogenizer isolation (P4) showed best performance on ELS induction (177%) and green plantlets (104%) compared with other techniques. For all cases albinisims occurred but microspore isolation from excised anthers by glass rod and homogenizer showed lesser numbers of albino plants that was also one of the important findings in this study.

Keywords: androgenesis, pretreatment, microspore culture, regeneration, albino plants, Oryza sativa

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Authors: Leticia Cervantes, Edith Garcia, Oscar Castillo

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At today, there are several control problems where the main objective is to obtain the best control in the study to decrease the error in the application. Many techniques can use to control these problems such as Neural Networks, PID control, Fuzzy Logic, Optimization techniques and many more. In this case, fuzzy logic with fuzzy system and an optimization technique are used to control the case of study. In this case, Ant Lion Optimization is used to optimize a fuzzy system to control the velocity of a simple treadmill. The main objective is to achieve the control of the velocity in the control problem using the ALO optimization. First, a simple fuzzy system was used to control the velocity of the treadmill it has two inputs (error and error change) and one output (desired speed), then results were obtained but to decrease the error the ALO optimization was developed to optimize the fuzzy system of the treadmill. Having the optimization, the simulation was performed, and results can prove that using the ALO optimization the control of the velocity was better than a conventional fuzzy system. This paper describes some basic concepts to help to understand the idea in this work, the methodology of the investigation (control problem, fuzzy system design, optimization), the results are presented and the optimization is used for the fuzzy system. A comparison between the simple fuzzy system and the optimized fuzzy systems are presented where it can be proving the optimization improved the control with good results the major findings of the study is that ALO optimization is a good alternative to improve the control because it helped to decrease the error in control applications even using any control technique to optimized, As a final statement is important to mentioned that the selected methodology was good because the control of the treadmill was improve using the optimization technique.

Keywords: ant lion optimization, control problem, fuzzy control, fuzzy system

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Authors: Kholood Baron

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Radiologic interventional studies use fluoroscopy imaging guidance to perform both diagnostic and therapeutic procedures. These could result in high radiation doses being delivered to the patients and also to the radiology team. This is due to the prolonged fluoroscopy time and the large number of images taken, even when dose-minimizing techniques and modern fluoroscopic tools are applied. Hence, these procedures are part of the everyday routine of interventional radiology doctors, assistant nurses, and radiographers. Thus, it is important to estimate the radiation exposure dose they received in order to give objective advice and reduce both patient and radiology team radiation exposure dose. The aim of this study was to find out the total radiation dose reaching the radiologist and the patient during an interventional procedure and to determine the impact of certain parameters on the patient dose. Method: The radiation dose was measured by TLD devices (thermoluminescent dosimeter; radiation dosimeter device). Physicians, patients, nurses, and radiographers wore TLDs during 12 interventional radiology procedures performed in two hospitals, Mubarak and Chest Hospital. This study highlights the need for interventional radiologists to be mindful of the radiation doses received by both patients and medical staff during interventional radiology procedures. The findings emphasize the impact of factors such as fluoroscopy duration and the number of images taken on the patient dose. By raising awareness and providing insights into optimizing techniques and protective measures, this research contributes to the overall goal of reducing radiation doses and ensuring the safety of patients and medical staff.

Keywords: dosimetry, radiation dose, interventional radiology procedures, patient radiation dose

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Authors: Armagan Gokcearslan

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The art of animation has developed very rapidly from the aspects of script, sound and music, motion, character design, techniques being used and technological tools being developed since the first years until today. Technical variety attracts a particular attention in the art of animation. Being perceived as a kind of illusion in the beginning; animations commonly used the Flash Sketch technique. Animations artists using the Flash Sketch technique created scenes by drawing them on a blackboard with chalk. The Flash Sketch technique was used by primary animation artists like Emile Cohl, Winsor McCay ande Blackton. And then tools like Magical Lantern, Thaumatrope, Phenakisticope, and Zeotrap were developed and started to be used intensely in the first years of the art of animation. Today, on the other hand, the art of animation is affected by developments in the computer technology. It is possible to create three-dimensional and two-dimensional animations with the help of various computer software. Cut-out technique is among the important techniques being used in the art of animation. Cut-out animation technique is based on the art of paper cutting. Examining cut-out animations; it is observed that they technically resemble the art of paper cutting. The art of paper cutting has a rooted history. It is possible to see the oldest samples of paper cutting in the People’s Republic of China in the period after the 2. century B.C. when the Chinese invented paper. The most popular artist using the cut-out animation technique is the German artist Lotte Reiniger. This study titled “Cut-out Animation as a Technic and Development Inside History Process” will embrace the art of paper cutting, the relationship between the art of paper cutting and cut-out animation, its development within the historical process, animation artists producing artworks in this field, important cut-out animations, and their technical properties.

Keywords: cut-out, paper art, animation, technic

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Authors: Feijoo E. Colomine Duran, Carlos E. Peñaloza Corredor

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There modeling component selection financial investment (Portfolio) a variety of problems that can be addressed with optimization techniques under evolutionary schemes. For his feature, the problem of selection of investment components of a dichotomous relationship between two elements that are opposed: The Portfolio Performance and Risk presented by choosing it. This relationship was modeled by Markowitz through a media problem (Performance) - variance (risk), ie must Maximize Performance and Minimize Risk. This research included the study and implementation of multi-objective evolutionary techniques to solve these problems, taking as experimental framework financial market equities Colombia Stock Exchange between 2009-2015. Comparisons three multiobjective evolutionary algorithms, namely the Nondominated Sorting Genetic Algorithm II (NSGA-II), the Strength Pareto Evolutionary Algorithm 2 (SPEA2) and Indicator-Based Selection in Multiobjective Search (IBEA) were performed using two measures well known performance: The Hypervolume indicator and R_2 indicator, also it became a nonparametric statistical analysis and the Wilcoxon rank-sum test. The comparative analysis also includes an evaluation of the financial efficiency of the investment portfolio chosen by the implementation of various algorithms through the Sharpe ratio. It is shown that the portfolio provided by the implementation of the algorithms mentioned above is very well located between the different stock indices provided by the Colombia Stock Exchange.

Keywords: finance, optimization, portfolio, Markowitz, evolutionary algorithms

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Authors: Vinicius Marzall, Jussanã Milograna

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The urban drainwatering remains a major challenge for most of the Brazilian cities. Not so different of the most part, Goiania, a state capital located in Midwest of the country has few legislations about the subject matter and only one registered solution of compensative techniques for drainwater. This paper clam to show some solutions which are adopted in other Brazilian cities with consolidated legislation, suggesting technics about detention tanks in a building sit. This study analyzed and compared the legislation of Curitiba, Porto Alegre e Sao Paulo, with the actual legislation and politics of Goiania. After this, were created models with adopted data for dimensioning the size of detention tanks using the envelope curve method considering synthetic series for intense precipitations and building sits between 250 m² and 600 m², with an impermeabilization tax of 50%. The results showed great differences between the legislation of Goiania and the documentation of the others cities analyzed, like the number of techniques for drainwatering applied to the reality of the cities, educational actions to awareness the population about care the water courses and political management by having a specified funds for drainwater subjects, for example. Besides, the use of detention tank showed itself practicable, have seen that the occupation of the tank is minor than 3% of the building sit, whatever the size of the terrain, granting the exit flow to pre-occupational taxes in extreme rainfall events. Also, was developed a linear equation to measure the detention tank based in the size of the building sit in Goiania, making simpler the calculation and implementation for non-specialized people.

Keywords: clean technology, legislation, rainwater management, urban drainwater

Procedia PDF Downloads 159

Authors: Christos Theoharatos, Dimitris Tsourounis, Spiros Oikonomou, Andreas Makedonas

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This paper focuses on the problem of visually inspecting and recognizing the road conditions in front of moving vehicles, targeting automotive scenarios. The goal of road inspection is to identify whether the road is slippery or not, as well as to detect possible anomalies on the road surface like potholes or body bumps/humps. Our work is based on an artificial intelligence methodology for real-time monitoring of road conditions in autonomous driving scenarios, using state-of-the-art deep convolutional neural network (CNN) techniques. Initially, the road and ego lane are segmented within the field of view of the camera that is integrated into the front part of the vehicle. A novel classification CNN is utilized to identify among plain and slippery road textures (e.g., wet, snow, etc.). Simultaneously, a robust detection CNN identifies severe surface anomalies within the ego lane, such as potholes and speed bumps/humps, within a distance of 5 to 25 meters. The overall methodology is illustrated under the scope of an integrated application (or system), which can be integrated into complete Advanced Driver-Assistance Systems (ADAS) systems that provide a full range of functionalities. The outcome of the proposed techniques present state-of-the-art detection and classification results and real-time performance running on AI accelerator devices like Intel’s Myriad 2/X Vision Processing Unit (VPU).

Keywords: deep learning, convolutional neural networks, road condition classification, embedded systems

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Authors: Mohsen Akhlaghi, Tahereh Ebrahimi

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Pricing strategy is a vital component in achieving the balance between customer value and business profitability. The aim of this study is to provide insights into the factors, techniques, and approaches involved in pricing decisions. The study utilizes a descriptive approach to discuss various aspects of pricing strategy in marketing, drawing on concepts from market research, consumer psychology, competitive analysis, and adaptability. This approach presents a comprehensive view of pricing decisions. The result of this exploration is a framework that highlights key factors influencing pricing decisions. The study examines how factors such as market positioning, product differentiation, and brand image shape pricing strategies. Additionally, it emphasizes the role of consumer psychology in understanding price elasticity, perceived value, and price-quality associations that influence consumer behavior. Various pricing techniques, including charm pricing, prestige pricing, and bundle pricing, are mentioned as methods to enhance sales by influencing consumer perceptions. The study also underscores the importance of adaptability in responding to market dynamics through regular price monitoring, dynamic pricing, and promotional strategies. It recognizes the role of digital platforms in enabling personalized pricing and dynamic pricing models. In conclusion, the study emphasizes that effective pricing strategies strike a balance between customer value and business profitability, ultimately driving sales, enhancing brand perception, and fostering lasting customer relationships.

Keywords: business, customer benefits, marketing, pricing

Procedia PDF Downloads 79

Authors: Usama Shahid, Muhammad Zunnurain Hussain

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This research paper presents a comprehensive analysis of a sentiment analysis approach that employs valence and arousal as its foundational pillars, in comparison to traditional techniques. Sentiment analysis is an indispensable task in natural language processing that involves the extraction of opinions and emotions from textual data. The valence and arousal dimensions, representing the intensity and positivity/negativity of emotions, respectively, enable the creation of four quadrants, each representing a specific emotional state. The study seeks to determine the impact of utilizing these quadrants to identify distinct emotional states on the accuracy and efficiency of sentiment analysis, in comparison to traditional techniques. The results reveal that the valence and arousal-based approach outperforms other approaches, particularly in identifying nuanced emotions that may be missed by conventional methods. The study's findings are crucial for applications such as social media monitoring and market research, where the accurate classification of emotions and opinions is paramount. Overall, this research highlights the potential of using valence and arousal as a framework for sentiment analysis and offers invaluable insights into the benefits of incorporating specific types of emotions into the analysis. These findings have significant implications for researchers and practitioners in the field of natural language processing, as they provide a basis for the development of more accurate and effective sentiment analysis tools.

Keywords: sentiment analysis, valence and arousal, emotional states, natural language processing, machine learning, text analysis, sentiment classification, opinion mining

Procedia PDF Downloads 101

Authors: Ugochukwu Ejike Akpudo, Jank-Wook Hur

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The increasing demand for improved productivity, maintainability, and reliability has prompted rapidly increasing research studies on the emerging condition-based maintenance concept- Prognostics and health management (PHM). Varieties of fuel pumps serve critical functions in several hydraulic systems; hence, their failure can have daunting effects on productivity, safety, etc. The need for condition monitoring and assessment of these pumps cannot be overemphasized, and this has led to the uproar in research studies on standard feature extraction techniques for optimized condition assessment of fuel pumps. By extracting time-based, frequency-based and the more robust time-frequency based features from these vibrational signals, a more comprehensive feature assessment (and selection) can be achieved for a more accurate and reliable condition assessment of these pumps. With the aid of emerging deep classification and regression algorithms like the locally linear embedding (LLE), we propose a method for comprehensive condition assessment of electromagnetic fuel pumps (EMFPs). Results show that the LLE as a comprehensive feature extraction technique yields better feature fusion/dimensionality reduction results for condition assessment of EMFPs against the use of single features. Also, unlike other feature fusion techniques, its capabilities as a fault classification technique were explored, and the results show an acceptable accuracy level using standard performance metrics for evaluation.

Keywords: electromagnetic fuel pumps, comprehensive feature extraction, condition assessment, locally linear embedding, feature fusion

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Authors: C. S. Seema, P. R. Prince

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The characterization of spatio-temporal inhomogeneities occurring in the ionospheric F₂ layer is remarkable since these variations are direct consequences of electrodynamical coupling between magnetosphere and solar events. The temporal and spatial variations of the F₂ layer, which occur with a period of several days or even years, mainly owe to geomagnetic and meteorological activities. The hourly F₂ layer critical frequency (foF2) over 23rd solar cycle (1996-2008) of three ionosonde stations (Wakkanai, Kokunbunji, and Okinawa) in northern hemisphere, which falls within same longitudinal span, is analyzed using continuous wavelet techniques. Morlet wavelet is used to transform continuous time series data of foF2 to a two dimensional time-frequency space, quantifying the time evolution of the oscillatory modes. The presence of significant time patterns (periodicities) at a particular time period and the time location of each periodicity are detected from the two-dimensional representation of the wavelet power, in the plane of scale and period of the time series. The mean strength of each periodicity over the entire period of analysis is studied using global wavelet spectrum. The quasi biennial, annual, semiannual, 27 day, diurnal and 12 hour variations of foF2 are clearly evident in the wavelet power spectra in all the three stations. Critical frequency oscillations with multi-day periods (2-3 days and 9 days in the low latitude station, 6-7 days in all stations and 15 days in mid-high latitude station) are also superimposed over large time scaled variations.

Keywords: continuous wavelet analysis, critical frequency, ionosphere, solar cycle

Procedia PDF Downloads 220

Authors: Kevin William Taylor

Abstract:

This work used industry-standard practices and technologies as a foundation to explore current and future advancements in modularity for 3D environmental production. Covering environmental generation, and AI-assisted generation, this study investigated how these areas will shape the industries goal to achieve full immersion within augmented reality environments. This study will explore modular environmental construction techniques utilized in large scale 3D productions. This will include the reasoning behind this approach to production, the principles in the successful development, potential pitfalls, and different methodologies for successful implementation of practice in commercial and proprietary interactive engines. A focus will be on the role of the 3D artists in the future of environmental development, requiring adaptability to new approaches, as the field evolves in response to tandem technological advancements. Industry findings and projections theorize how these factors will impact the widespread utilization of augmented reality in daily life. This will continue to inform the direction of technology towards expansive interactive environments. It will change the tools and techniques utilized in the development of environments for game, film, and VFX. This study concludes that this technology will be the cornerstone for the creation of AI-driven AR that is able to fully theme our world, change how we see and engage with one another. This will impact the concept of a virtual self-identity that will be as prevalent as real-world identity. While this progression scares or even threaten some, it is safe to say that we are seeing the beginnings of a technological revolution that will surpass the impact that the smartphone had on modern society.

Keywords: virtual reality, augmented reality, training, 3D environments

Procedia PDF Downloads 123

Authors: Rejane Maria P. da Silva, Mariana X. Milagre, João Victor de S. Araujo, Leandro A. de Oliveira, Renato A. Antunes, Isolda Costa

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The Al-Cu-Li alloys are advanced materials for aerospace application because of their interesting mechanical properties and low density when compared with conventional Al-alloys. However, Al-Cu-Li alloys are susceptible to localized corrosion. The near-surface deformed layer (NSDL) induced by the rolling process during the production of the alloy and its removal by polishing can influence on the corrosion susceptibility of these alloys. In this work, the influence of surface preparation effects on the electrochemical activity of AA2098-T351 (Al–Cu–Li alloy) was investigated using a correlation between surface chemistry, microstructure, and electrochemical activity. Two conditions were investigated, polished and as-received surfaces of the alloy. The morphology of the two types of surfaces was investigated using confocal laser scanning microscopy (CLSM) and optical microscopy. The surface chemistry was analyzed by X-ray Photoelectron Spectroscopy (XPS) and energy dispersive X-ray spectroscopy (EDS). Global electrochemical techniques (potentiodynamic polarization and EIS technique) and a local electrochemical technique (Localized Electrochemical Impedance Spectroscopy-LEIS) were used to examine the electrochemical activity of the surfaces. The results obtained in this study showed that in the as-received surface, the near-surface deformed layer (NSDL), which is composed of Mg-rich bands, influenced the electrochemical behavior of the alloy. The results showed higher electrochemical activity to the polished surface condition compared to the as-received one.

Keywords: Al-Cu-Li alloys, surface preparation effects, electrochemical techniques, localized corrosion

Procedia PDF Downloads 159

Authors: S. Visetpotjanakit, C. Khrautongkieo

Abstract:

Radiation monitoring in the environment and foodstuffs is one of the main responsibilities of Office of Atoms for Peace (OAP) as the nuclear regulatory body of Thailand. The main goal of the OAP is to assure the safety of the Thai people and environment from any radiological incidents. Various radioanalytical methods have been developed to monitor radiation and radionuclides in the environmental and foodstuff samples. To validate our analytical performance, several proficiency test exercises from the International Atomic Energy Agency (IAEA) have been performed. Here, the results of a proficiency test exercise referred to as the Proficiency Test for Tritium, Cobalt, Strontium and Caesium Isotopes in Seawater 2017 (IAEA-RML-2017-01) are presented. All radionuclides excepting ³H were analysed using various radioanalytical methods, i.e. direct gamma-ray counting for determining ⁶⁰Co, ¹³⁴Cs and ¹³⁷Cs and developed radiochemical techniques for analysing ¹³⁴Cs, ¹³⁷Cs using AMP pre-concentration technique and 90Sr using di-(2-ethylhexyl) phosphoric acid (HDEHP) liquid extraction technique. The analysis results were submitted to IAEA. All results passed IAEA criteria, i.e. accuracy, precision and trueness and obtained ‘Accepted’ statuses. These confirm the data quality from the OAP environmental radiation laboratory to monitor radiation in the environment.

Keywords: international atomic energy agency, proficiency test, radiation monitoring, seawater

Procedia PDF Downloads 172

Authors: Udayakumar Vee, Franck Quero

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Zwitterionic polymers generally have been viewed as a new class of antimicrobial and non-fouling materials. They offer a broad versatility for chemical modification and hence great freedom for accurate molecular design, which bear an equimolar number of homogenously distributed anionic and cationic groups along their polymer chains. This study explores the effectiveness of the auxetic zwitterion carboxylate/sulfonate hydrogel in the diabetic-induced mouse model. A series of silver metal-doped auxetic zwitterion carboxylate/sulfonate/vinylaniline copolymer hydrogels is designed via a 3D printer. Zwitterion monomers have been characterized by FT-IR and NMR techniques. The effect of changing the monomers and different loading ratios of Ag over zwitterion on the final hydrogel materials' antimicrobial properties and biocompatibility will be investigated in detail. The synthesized auxetic hydrogel has been characterized using a wide range of techniques to help establish the relationship between molecular level and macroscopic properties of these materials, including mechanical and antibacterial and biocompatibility and wound healing ability. This work's comparative studies and results provide new insights and guide us in choosing a better auxetic structured material for a broad spectrum of wound healing applications in the animal model. We expect this approach to provide a versatile and robust platform for biomaterial design that could lead to promising treatments for wound healing applications.

Keywords: auxetic, zwitterion, carboxylate, sulfonate, polymer, wound healing

Procedia PDF Downloads 140