Search results for: learning orientation

Authors: Samir Sarfraj Terdalkar

Abstract:

India needs to step into affordable higher education with more focus on skill development and employability. The general scenario of higher education in India revolves around two major branches of higher education ie., Engineering and Medical Sciences. These two branches still cannot be considered as affordable. Hence, skill development of each and every student beginning from the school education should emphasize on learning skills with special focus on physics and mathematics. In India, the Central Government initiated a survey based process of all higher Educational Institutes/ Universities and colleges in India. This survey/ process was – All India Survey On Higher Education (AISHE). The focus of this process was understand and Though the increase is significant, it is necessary to propagate skill and vocational education which would add to the employability factor. Similarly, there has been a significant increase in number of higher education institutes, there is need to rethink on the type of education/ curriculum offered by these institutions. In this regard, vocational education has helped to build skill sets to certain extent. There is need to bring in this vocational educational in main stream education which could be complementary for undergraduate / post graduate education. The paper focuses on different policies to bring in vocational/ skill education.

Keywords: higher education, skill, vocational, India

Procedia PDF Downloads 59

Authors: Saba Latif

Abstract:

The use of social media among university students is a topic of ongoing debate, with conflicting views on its impact on academic achievement. This study aimed to explore the relationship between social media use and academic achievement among university students and to identify factors that may contribute to positive or negative effects. The study used a mixed-methods design, including a survey of 500 university students and qualitative interviews with a subset of participants. The survey results showed that social media use was prevalent among students, with Facebook and Instagram are the most commonly used platforms. The findings also indicated a positive relationship between social media use and academic achievement, with students who reported higher levels of social media use also reporting higher GPAs. However, the qualitative interviews revealed that excessive use of social media could be a distraction that hinders academic performance, especially when students use it to procrastinate or to stay up late at night. Overall, the findings suggest that social media use can have both positive and negative effects on academic achievement among university students. Responsible and balanced use of social media, such as setting limits on usage and avoiding procrastination, may help students maximize the benefits while minimizing the risks.

Keywords: social media, university, achievement, effective, learning

Procedia PDF Downloads 38

Authors: Nur Aisyah Jalalludin, Arwindra Rizqiawan, Goro Fujita

Abstract:

In this study, we aim to demonstrate a microgrid system experimental simulation for an easy understanding of a large-scale microgrid system. This model is required for industrial training and learning environments. However, in order to create an exact representation of a microgrid system, the laboratory-scale system must fulfill the requirements of a grid-connected inverter, in which power values are assigned to the system to cope with the intermittent output from renewable energy sources. Aside from that, during changes in load capacity, the grid-connected system must be able to supply power from the utility grid side and microgrid side in a balanced manner. Therefore, droop control is installed in the inverter’s control board to maintain equal power sharing in both sides. This power control in a stand-alone condition and droop control in a grid-connected condition must be implemented in order to maintain a stabilized system. Based on the experimental results, power control and droop control can both be applied in the system by comparing the experimental and reference values.

Keywords: droop control, droop characteristic, grid-connected inverter, microgrid, power control

Procedia PDF Downloads 862

Authors: Jacquelyn Irene Eidson

Abstract:

Women who leave the workforce due to motherhood and wish to return are a valuable, untapped resource for organizations. Levinson’s theory of adult development defines life as a sequence of transitions requiring difficult decisions that prompt humans to question their identity and their self-efficacy. The experience of being a working mother and the experience of workplace mentorship have received extensive research attention. Merging the two experiences and focusing on feelings of identity and self-efficacy provides a unique and focused opportunity for learning. Through one-on-one interviews and focus group discussion with working mothers that had previously left the workforce for an extended leave due to child-rearing, a meaningful description of their experiences will be obtained. Data is currently being collected via a collaboration with state banking associations in the United States. Results from the study will enable organizations worldwide to more effectively provide mentorship opportunities built around a culture of understanding while more effectively recruiting, supporting, developing, and retaining this valuable talent pool.

Keywords: identity, mentorship, self-efficacy, working mother

Procedia PDF Downloads 165

Authors: Getaneh Berie Tarekegn

Abstract:

Wireless communication via unmanned aerial vehicles (UAVs) has drawn a great deal of attention due to its flexibility in establishing line-of-sight (LoS) communications. However, in complex urban and dynamic environments, the movement of UAVs can be blocked by trees and high-rise buildings that obstruct directional paths. With reconfigurable intelligent surfaces (RIS), this problem can be effectively addressed. To achieve this goal, accurate channel estimation in RIS-assisted UAV-enabled wireless communications is crucial. This paper proposes an accurate channel estimation model using long short-term memory (LSTM) for a multi-user RIS-assisted UAV-enabled wireless communication system. According to simulation results, LSTM can improve the channel estimation performance of RIS-assisted UAV-enabled wireless communication.

Keywords: channel estimation, reconfigurable intelligent surfaces, long short-term memory, unmanned aerial vehicles

Procedia PDF Downloads 55

Authors: Fatna Benmessaoud, Mohammed Cheikh, Vencent Velay, Vanessa Vedal, Farhad Rezai-Aria, Christine Boher

Abstract:

The global mechanical behavior of materials is strongly linked to their microstructure, especially their crystallographic texture and their grains morphology. These material aspects determine the mechanical fields character (heterogeneous or homogeneous), thus, they give to the global behavior a degree of anisotropy according the initial microstructure. For these reasons, the prediction of global behavior of materials in relationship with the microstructure must be performed with a multi-scale approach. Therefore, multi-scale modeling in the context of crystal plasticity is widely used. In this present contribution, a phenomenological elasto-viscoplastic model developed in the crystal plasticity context and finite element method are used to investigate the effects of crystallographic texture and grains sizes on global behavior of a polycrystalline equiaxed Ti-6Al-4V alloy. The constitutive equations of this model are written on local scale for each slip system within each grain while the strain and stress mechanical fields are investigated at the global scale via finite element scale transition. The beta phase of Ti-6Al-4V alloy modeled is negligible; its percent is less than 10%. Three families of slip systems of alpha phase are considered: basal and prismatic families with a burgers vector and pyramidal family with a burgers vector. The twinning mechanism of plastic strain is not observed in Ti-6Al-4V, therefore, it is not considered in the present modeling. Nine representative elementary volumes (REV) are generated with Voronoi tessellations. For each individual equiaxed grain, the own crystallographic orientation vis-à-vis the loading is taken into account. The meshing strategy is optimized in a way to eliminate the meshing effects and at the same time to allow calculating the individual grain size. The stress and strain fields are determined in each Gauss point of the mesh element. A post-treatment is used to calculate the local behavior (in each grain) and then by appropriate homogenization, the macroscopic behavior is calculated. The developed model is validated by comparing the numerical simulation results with an experimental data reported in the literature. It is observed that the present model is able to predict the global mechanical behavior of Ti-6Al-4V alloy and investigate the microstructural parameters' effects. According to the simulations performed on the generated volumes (REV), the macroscopic mechanical behavior of Ti-6Al-4V is strongly linked to the active slip systems family (prismatic, basal or pyramidal). The crystallographic texture determines which family of slip systems can be activated; therefore it gives to the plastic strain a heterogeneous character thus an anisotropic macroscopic mechanical behavior. The average grains size influences also the Ti-6Al-4V mechanical proprieties, especially the yield stress; by decreasing of the average grains size, the yield strength increases according to Hall-Petch relationship. The grains sizes' distribution gives to the strain fields considerable heterogeneity. By increasing grain sizes, the scattering in the localization of plastic strain is observed, thus, in certain areas the stress concentrations are stronger than other regions.

Keywords: microstructural parameters, multi-scale modeling, crystal plasticity, Ti-6Al-4V alloy

Procedia PDF Downloads 95

Authors: Nikolina Ribarić

Abstract:

This paper compares the readiness of chemistry teachers to use information and communication technologies in chemistry in 2018. and 2021. A survey conducted in 2018 on a sample of teachers showed that most teachers occasionally use visualization and digitization tools in chemistry teaching (65%) but feel that they are not educated enough to use them (56%). Also, most teachers do not have adequate equipment in their schools and are not able to use ICT in teaching or digital tools for visualization and digitization of content (44%). None of the teachers find the use of digitization and visualization tools useless. Furthermore, a survey conducted in 2021 shows that most teachers occasionally use visualization and digitization tools in chemistry teaching (83%). Also, the research shows that some teachers still do not have adequate equipment in their schools and are not able to use ICT in chemistry teaching or digital tools for visualization and digitization of content (14%). Advances in the use of ICT in chemistry teaching are linked to pandemic conditions and the obligation to conduct online teaching. The share of 14% of teachers who still do not have adequate equipment to use digital tools in teaching is worrying.

Keywords: chemistry, digital content, e-learning, ICT, visualization

Procedia PDF Downloads 125

Authors: Chuen-arom Janthimachai-amorn, Chirawadee Harnrittha

Abstract:

This study aims to identify the factors facilitating the knowledge management to develop the graduate study programs to achieve success and to identify the approaches in developing the graduate study programs in the Rajbhat Suansunantha University. The 10 respondents were the administrators, the faculty, and the personnel of its Graduate School. The research methodology was based on Pla-too Model of the Knowledge Management Institute (KMI) by allocating the knowledge indicators, the knowledge creation and search, knowledge systematization, knowledge processing and filtering, knowledge access, knowledge sharing and exchanges and learning. The results revealed that major success factors were knowledge indicators, evident knowledge management planning, knowledge exchange and strong solidarity of the team and systematic and tenacious access of knowledge. The approaches allowing the researchers to critically develop the graduate study programs were the environmental data analyses, the local needs and general situations, data analyses of the previous programs, cost analyses of the resources, and the identification of the structure and the purposes to develop the new programs.

Keywords: program development, knowledge management, graduate study programs, Rajbhat Suansunantha University

Procedia PDF Downloads 281

Authors: Alaa Alrakaf, Sk. Md. Mizanur Rahman

Abstract:

Since the last two decades’ Arabic natural language processing (ANLP) has become increasingly much more important. One of the key issues related to ANLP is ambiguity. In Arabic language different pronunciation of one word may have a different meaning. Furthermore, ambiguity also has an impact on the effectiveness and efficiency of Machine Translation (MT). The issue of ambiguity has limited the usefulness and accuracy of the translation from Arabic to English. The lack of Arabic resources makes ambiguity problem more complicated. Additionally, the orthographic level of representation cannot specify the exact meaning of the word. This paper looked at the diacritics of Arabic language and used them to disambiguate a word. The proposed approach of word sense disambiguation used Diacritizer application to Diacritize Arabic text then found the most accurate sense of an ambiguous word using Naïve Bayes Classifier. Our Experimental study proves that using Arabic Diacritics with Naïve Bayes Classifier enhances the accuracy of choosing the appropriate sense by 23% and also decreases the ambiguity in machine translation.

Keywords: Arabic natural language processing, machine learning, machine translation, Naive bayes classifier, word sense disambiguation

Procedia PDF Downloads 327

Authors: Shyam Mehraaj, Samanthi E. R. Siriwardana, Shehara A. K. G. H., Wanigasinghe N. T., Wandana R. A. K., Wedage C. V.

Abstract:

A presentation is a critical tool for conveying not just spoken information but also a wide spectrum of human emotions. The single most effective thing to make the presentation successful is to practice it beforehand. Preparing for a presentation has been shown to be essential for improving emotional control, intonation and prosody, pronunciation, and vocabulary, as well as the quality of the presentation slides. As a result, practicing has become one of the most critical parts of giving a good presentation. In this research, the main focus is to analyze the audio, video, and slides of the presentation uploaded by the presenters. This proposed solution is based on the Natural Language Processing and Computer Vision techniques to cater to the requirement for the presenter to do a presentation beforehand using a mobile responsive web application. The proposed system will assist in practicing the presentation beforehand by identifying the presenters’ emotions, body language, tonality, prosody, pronunciations and vocabulary, and presentation slides quality. Overall, the system will give a rating and feedback to the presenter about the performance so that the presenters’ can improve their presentation skills.

Keywords: presentation, self-evaluation, natural learning processing, computer vision

Procedia PDF Downloads 75

Authors: Azadeh Bashiri

Abstract:

Introduction: Lack of proper diagnosis and inadequate treatment of asthma leads to physical and financial complications. This study aimed to use data mining techniques and creating a neural network intelligent system for diagnosis of asthma. Methods: The study population is the patients who had visited one of the Lung Clinics in Tehran. Data were analyzed using the SPSS statistical tool and the chi-square Pearson's coefficient was the basis of decision making for data ranking. The considered neural network is trained using back propagation learning technique. Results: According to the analysis performed by means of SPSS to select the top factors, 13 effective factors were selected, in different performances, data was mixed in various forms, so the different models were made for training the data and testing networks and in all different modes, the network was able to predict correctly 100% of all cases. Conclusion: Using data mining methods before the design structure of system, aimed to reduce the data dimension and the optimum choice of the data, will lead to a more accurate system. Therefore, considering the data mining approaches due to the nature of medical data is necessary.

Keywords: asthma, data mining, Artificial Neural Network, intelligent system

Procedia PDF Downloads 243

Authors: Hossein Aghababaei, Sergio Vitale, Giampaolo Ferraioli

Abstract:

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

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

Procedia PDF Downloads 44

Authors: Khadijat T. Bamigbade, Olufade F. W. Onifade

Abstract:

The field of automatic facial expression analysis has been an active research area in the last two decades. Its vast applicability in various domains has drawn so much attention into developing techniques and dataset that mirror real life scenarios. Many techniques such as Local Binary Patterns and its variants (CLBP, LBP-TOP) and lately, deep learning techniques, have been used for facial expression recognition. However, the problem of occlusion has not been sufficiently handled, making their results not applicable in real life situations. This paper develops a simple, yet highly efficient method tagged Local Binary Pattern-Histogram of Gradient (LBP-HOG) with occlusion detection in face image, using a multi-class SVM for Action Unit and in turn expression recognition. Our method was evaluated on three publicly available datasets which are JAFFE, CK, SFEW. Experimental results showed that our approach performed considerably well when compared with state-of-the-art algorithms and gave insight to occlusion detection as a key step to handling expression in wild.

Keywords: automatic facial expression analysis, local binary pattern, LBP-HOG, occlusion detection

Procedia PDF Downloads 139

Authors: Dinesh Khanduja, Pardeep Kumar Sharma

Abstract:

Education as the most important resource in any country has multiplying effects on all facets of development in a society. The new social realities, particularly, the interplay between democratization of education; unprecedented developments in the IT sector; emergence of knowledge society, liberalization of economy, and globalization have greatly influenced the educational process of all nations. This turbulence entails upon education to undergo dramatic changes to keep up with the new expectations. Growth of entrepreneurship among Indian women is highly important for empowering them and this is highly essential for the socio-economic development of a society. Unfortunately, in India, there is poor acceptance of entrepreneurship among women as unfounded myths and fears restrain them to be enterprising. To remove these inhibitions, the education system needs to be re-engineered to make entrepreneurship more acceptable. This paper empirically analyses the results of a survey done on around 500 female graduates in North India to measure and evaluate various entrepreneurial traits present in them. A formative model has been devised in this context, which should improve the teaching-learning process in our education system, which can lead to a sustainable growth of women entrepreneurship in India.

Keywords: women empowerment, entrepreneurship, education system, women entrepreneurship, sustainable development

Procedia PDF Downloads 320

Authors: Mikkel Snorre Wilms Boysen

Abstract:

From a theoretical perspective, expertise is generally considered a precondition for creativity. The assumption is that an individual needs to master the common and accepted rules and techniques within a certain knowledge-domain in order to create something new and valuable. However, real life cases, and a limited amount of empirical studies, demonstrate that this assumption may be overly simple. In this article, this question is explored through a number of semi-experimental case studies conducted within the fields of music, technology, and youth culture. The studies indicate that, in various ways, expertise plays an important part in creative processes. However, the case studies also indicate that expertise sometimes leads to an entrenched perspective, in the sense that knowledge and experience may work as a path into the well-known rather than into the unknown. In this article, these issues are explored with reference to different theoretical approaches to creativity and learning, including actor-network theory, the theory of blind variation and selective retention, and Csikszentmihalyi’s system model. Finally, some educational aspects and implications of this are discussed.

Keywords: creativity, expertise , education, technology

Procedia PDF Downloads 295

Authors: Tholaine Matadi

Abstract:

South Africa is a democratic country with a historical record of racially-motivated marginalisation and exclusion of the majority. During the apartheid era the country was run along pieces of legislation and policies based on racial segregation. The system held a tight clamp on interracial mixing which forced people to remain in segregated areas. For example, a citizen from the Indian community could not own property in an area allocated to white people. In this way, a great majority of people were denied basic human rights. Now, there is a supreme constitution with an entrenched justiciable Bill of Rights founded on democratic values of social justice, human dignity, equality and the advancement of human rights and freedoms. The Constitution also enshrines the values of non-racialism and non-sexism. The Constitutional Court has the power to declare unconstitutional any law or conduct considered to be inconsistent with it. Now, more than two decades down the line, despite the abolition of apartheid, there is evidence that South Africa still experiences hate crimes which violate the entrenched right of vulnerable groups not to be discriminated against on the basis of race, sexual orientation, gender, national origin, occupation, or disability. To remedy this mischief parliament has responded by drafting the Prevention and Combatting of Hate Crimes and Hate Speech Bill. The Bill has been disseminated for public comment and suggestions. It is intended to combat hate crimes and hate speech based on sheer prejudice. The other purpose of the Bill is to bring South Africa in line with international human rights instruments against racism, racial discrimination, xenophobia and related expressions of intolerance identified in several international instruments. It is against this backdrop that this paper intends to analyse the impact of the Bill on the rights to human dignity, equality, and freedom. This study is significant because the Bill was highly contested and creates a huge debate. This study relies on a qualitative evaluative approach based on desktop and library research. The article recurs to primary and secondary sources. For comparative purpose, the paper compares South Africa with countries such as Australia, Canada, Kenya, Cuba, and United Kingdom which have criminalised hate crimes and hate speech. The finding from this study is that despite the Bill’s expressed positive intentions, this draft legislation is problematic for several reasons. The main reason is that it generates considerable controversy mostly because it is considered to infringe the right to freedom of expression. Though the author suggests that the Bill should not be rejected in its entirety, she notes the brutal psychological effect of hate crimes on their direct victims and the writer emphasises that a legislature can succeed to combat hate-crimes only if it provides for them as a separate stand-alone category of offences. In view of these findings, the study recommended that since hate speech clauses have a negative impact on freedom of expression it can be promulgated, subject to the legislature enacting the Prevention and Combatting of Hate-Crimes Bill as a stand-alone law which criminalises hate crimes.

Keywords: freedom of expression, hate crimes, hate speech, human dignity

Procedia PDF Downloads 141

Authors: Proud Arunrangsiwed, Sarinya Kongtieng

Abstract:

Teachers and university lecturers face an unsolved problem, which is students’ multitasking behaviors during class time, such as texting or playing a game. It is important to examine the most powerful predictor that can result in students’ educational performances. Meta-analysis was used to analyze the research articles, which were published with the keywords, multitasking, class performance, and texting. We selected 14 research articles published during 2008-2013 from online databases, and four articles met the predetermined inclusion criteria. Effect size of each pair of variables was used as the dependent variable. The findings revealed that the students’ expectancy and value on SNSs usages is the best significant predictor of their educational performances, followed by their motivation and ability in using SNSs, prior educational performances, usage behaviors of SNSs in class, and their personal characteristics, respectively. Future study should conduct a longitudinal design to better understand the effect of multitasking in the classroom.

Keywords: meta-regression analysis, social networking sites, academic Performances, multitasking, motivation

Procedia PDF Downloads 250

Authors: Aisulu Tileukulova, Zhexebay Dauren

Abstract:

Radar plays an important role because it is widely used in civil and military fields. Target detection is one of the most important radar applications. The accuracy of detecting inconspicuous aerial objects in radar facilities is lower against the background of noise. Convolutional neural networks can be used to improve the recognition of this type of aerial object. The purpose of this work is to develop an algorithm for recognizing aerial objects using convolutional neural networks, as well as training a neural network. In this paper, the structure of a convolutional neural network (CNN) consists of different types of layers: 8 convolutional layers and 3 layers of a fully connected perceptron. ReLU is used as an activation function in convolutional layers, while the last layer uses softmax. It is necessary to form a data set for training a neural network in order to detect a target. We built a Confusion Matrix of the CNN model to measure the effectiveness of our model. The results showed that the accuracy when testing the model was 95.7%. Classification of echo signals using CNN shows high accuracy and significantly speeds up the process of predicting the target.

Keywords: radar, neural network, convolutional neural network, echo signals

Procedia PDF Downloads 314

Authors: Edward Guillén, Jeisson Sánchez, Carlos Omar Ramos

Abstract:

In recent years, a wide variety of applications are developed with Support Vector Machines -SVM- methods and Artificial Neural Networks -ANN-. In general, these methods depend on intrusion knowledge databases such as KDD99, ISCX, and CAIDA among others. New classes of detectors are generated by machine learning techniques, trained and tested over network databases. Thereafter, detectors are employed to detect anomalies in network communication scenarios according to user’s connections behavior. The first detector based on training dataset is deployed in different real-world networks with mobile and non-mobile devices to analyze the performance and accuracy over static detection. The vulnerabilities are based on previous work in telemedicine apps that were developed on the research group. This paper presents the differences on detections results between some network scenarios by applying traditional detectors deployed with artificial neural networks and support vector machines.

Keywords: anomaly detection, back-propagation neural networks, network intrusion detection systems, support vector machines

Procedia PDF Downloads 314

Authors: M. Rodionov, N. Sharapova, Z. Dedovets

Abstract:

The article describes methods of preparation of future teachers that includes the entire diversity of traditional and computer-oriented methodological approaches. The authors reveal how, in the specific educational environment, a teacher can choose the most effective combination of educational technologies based on the nature of the learning task. The key conditions that determine such a choice are that the methodological approach corresponds to the specificity of the problem being solved and that it is also responsive to the individual characteristics of the students. The article refers to the training of students in the proper use of mathematical electronic tools for educational purposes. The preparation of future mathematics teachers should be a step-by-step process, building on specific examples. At the first stage, students optimally solve problems aided by electronic means of teaching. At the second stage, the main emphasis is on modeling lessons. At the third stage, students develop and implement strategies in the study of one of the topics within a school mathematics curriculum. The article also recommended the implementation of this strategy in preparation of future teachers and stated the possible benefits.

Keywords: education, methodological approaches, teacher, secondary school

Procedia PDF Downloads 142

Authors: S. Delgado, C. Cerrada, R. S. Gómez

Abstract:

This research introduces an approach to voxelizing the surfaces of triangular meshes with efficiency and accuracy. Our method leverages parallel equidistant scan-lines and introduces a Gap Detection technique to address the limitations of existing approaches. We present a comprehensive study showcasing the method's effectiveness, scalability, and versatility in different scenarios. Voxelization is a fundamental process in computer graphics and simulations, playing a pivotal role in applications ranging from scientific visualization to virtual reality. Our algorithm focuses on enhancing the voxelization process, especially for complex models and high resolutions. One of the major challenges in voxelization in the Graphics Processing Unit (GPU) is the high cost of discovering the same voxels multiple times. These repeated voxels incur in costly memory operations with no useful information. Our scan-line-based method ensures that each voxel is detected exactly once when processing the triangle, enhancing performance without compromising the quality of the voxelization. The heart of our approach lies in the use of parallel, equidistant scan-lines to traverse the interiors of triangles. This minimizes redundant memory operations and avoids revisiting the same voxels, resulting in a significant performance boost. Moreover, our method's computational efficiency is complemented by its simplicity and portability. Written as a single compute shader in Graphics Library Shader Language (GLSL), it is highly adaptable to various rendering pipelines and hardware configurations. To validate our method, we conducted extensive experiments on a diverse set of models from the Stanford repository. Our results demonstrate not only the algorithm's efficiency, but also its ability to produce 26 tunnel free accurate voxelizations. The Gap Detection technique successfully identifies and addresses gaps, ensuring consistent and visually pleasing voxelized surfaces. Furthermore, we introduce the Slope Consistency Value metric, quantifying the alignment of each triangle with its primary axis. This metric provides insights into the impact of triangle orientation on scan-line based voxelization methods. It also aids in understanding how the Gap Detection technique effectively improves results by targeting specific areas where simple scan-line-based methods might fail. Our research contributes to the field of voxelization by offering a robust and efficient approach that overcomes the limitations of existing methods. The Gap Detection technique fills a critical gap in the voxelization process. By addressing these gaps, our algorithm enhances the visual quality and accuracy of voxelized models, making it valuable for a wide range of applications. In conclusion, "Closing the Gap: Efficient Voxelization with Equidistant Scan-lines and Gap Detection" presents an effective solution to the challenges of voxelization. Our research combines computational efficiency, accuracy, and innovative techniques to elevate the quality of voxelized surfaces. With its adaptable nature and valuable innovations, this technique could have a positive influence on computer graphics and visualization.

Keywords: voxelization, GPU acceleration, computer graphics, compute shaders

Procedia PDF Downloads 41

Authors: Rajwanlop Kumpoopong, Sukit Yindeesuk, Pornchai Silarom

Abstract:

Modeling cracks in concrete is complicated by its strain-softening behavior which requires the use of sophisticated energy criteria of fracture mechanics to assure stable and convergent solutions in the finite-element (FE) analysis particularly for relatively large structures. However, for small-scale structures such as beams and slabs, a simpler approach relies on retaining some shear stiffness in the cracking plane has been adopted in literature to model the strain-softening behavior of concrete under monotonically increased loading. According to the shear retaining approach, each element is assumed to be an isotropic material prior to cracking of concrete. Once an element is cracked, the isotropic element is replaced with an orthotropic element in which the new orthotropic stiffness matrix is formulated with respect to the crack orientation. The shear transfer factor of 0.5 is used in parallel to the crack plane. The shear retaining approach is adopted in this research to model cracks in RC bridge deck with some modifications to take into account the effect of repetitive moving truck wheel loads as they cause fatigue cracking of concrete. First modification is the introduction of fatigue tests of concrete and reinforcing steel and the Palmgren-Miner linear criterion of cumulative damage in the conventional FE analysis. For a certain loading, the number of cycles to failure of each concrete or RC element can be calculated from the fatigue or S-N curves of concrete and reinforcing steel. The elements with the minimum number of cycles to failure are the failed elements. For the elements that do not fail, the damage is accumulated according to Palmgren-Miner linear criterion of cumulative damage. The stiffness of the failed element is modified and the procedure is repeated until the deck slab fails. The total number of load cycles to failure of the deck slab can then be obtained from which the S-N curve of the deck slab can be simulated. Second modification is the modification in shear transfer factor. Moving loading causes continuous rubbing of crack interfaces which greatly reduces shear transfer mechanism. It is therefore conservatively assumed in this study that the analysis is conducted with shear transfer factor of zero for the case of moving loading. A customized FE program has been developed using the MATLAB software to accomodate such modifications. The developed procedure has been validated with the fatigue test of the 1/6.6-scale AASHTO bridge deck under the applications of both fixed-point repetitive loading and moving loading presented in the literature. Results are in good agreement both experimental vs. simulated S-N curves and observed vs. simulated crack patterns. Significant contribution of the developed procedure is a series of S-N relations which can now be simulated at any desired levels of cracking in addition to the experimentally derived S-N relation at the failure of the deck slab. This permits the systematic investigation of crack propagation or deterioration of RC bridge deck which is appeared to be useful information for highway agencies to prolong the life of their bridge decks.

Keywords: bridge deck, cracking, deterioration, fatigue, finite-element, moving truck, reinforced concrete

Procedia PDF Downloads 221

Authors: Greeshma Sharma, Sushil Chandra, Vijander Singh, Alok Prakash Mittal

Abstract:

Navigational ability requires spatial representation, planning, and memory. It covers three interdependent domains, i.e. cognitive and perceptual factors, neural information processing, and variability in brain microstructure. Many attempts have been made to see the role of spatial representation in the navigational ability, and the individual differences have been identified in the neural substrate. But, there is also a need to address the influence of planning, memory on navigational ability. The present study aims to evaluate relations of aforementioned factors in the navigational ability. Total 30 participants volunteered in the study of a virtual shopping complex and subsequently were classified into good and bad navigators based on their performances. The result showed that planning ability was the most correlated factor for the navigational ability and also the discriminating factor between the good and bad navigators. There was also found the correlations between spatial memory recall and navigational ability. However, non-verbal episodic memory and spatial memory recall were also found to be correlated with the learning variable. This study attempts to identify differences between people with more and less navigational ability on the basis of planning and memory.

Keywords: memory, planning navigational ability, virtual reality

Procedia PDF Downloads 299

Authors: Juan Bojórquez, Henry E. Reyes, Edén Bojórquez, Alfredo Reyes-Salazar

Abstract:

This paper addressed the use of Artificial Intelligence to obtain the structural reliability of reinforced concrete buildings. For this purpose, artificial neuronal networks (ANN) are developed to predict seismic demand hazard curves. In order to have enough input-output data to train the ANN, a set of reinforced concrete buildings (low, mid, and high rise) are designed, then a probabilistic seismic hazard analysis is made to obtain the seismic demand hazard curves. The results are then used as input-output data to train the ANN in a feedforward backpropagation model. The predicted values of the seismic demand hazard curves found by the ANN are then compared. Finally, it is concluded that the computer time analysis is significantly lower and the predictions obtained from the ANN were accurate in comparison to the values obtained from the conventional methods.

Keywords: structural reliability, seismic design, machine learning, artificial neural network, probabilistic seismic hazard analysis, seismic demand hazard curves

Procedia PDF Downloads 162

Authors: Wongwarit Nipitwittaya

Abstract:

The objectives of this research is bringing folk song with the teaching of Western music were to examine to investigate, to compare, develop the skill, technique, knowledge of Thai folk song and to preserve folk song of Thailand to be known more widely also learn Thai culture from Thai folk song. Study by bringing Thailand folk song is widely known for learning with Western music in course brass performance. Bringing the melody of Thai folk music and changing patterns to western music notes for appropriate on brass performance. A sample was selected from brass students, using research by assessment of knowledge from test after used Thai folk song lesson. The lesson focus for scales and key signature in western music by divided into two groups, the one study by used research tools and another one used simple lesson and a collection of research until testing. The results of the study were as follows: 1. There are good development skill form research method 2. Sound recognition can be even better. The study was a qualitative research and data collection by observation.

Keywords: Thai folk song, brass instrument, key signature, western music

Procedia PDF Downloads 627

Authors: Ebere Ellison Obisike, Justina N. Adalikwu-Obisike

Abstract:

Artificial intelligence (AI) is progressively transforming health and social care. With the rapid invention of various electronic devices, machine learning, and computing systems, the use of AI istraversing many health and social care practices. In this systematic review of journal and grey literature, this study explores how the applications of AI might promote the prevention of micro and macrovascular complications in type 1 diabetic patients. This review focuses on the use of a digitized blood glucose meter and the application of insulin pumps for the effective management of type 1 diabetes in low and middle-income countries. It is projected that the applications of AI may assist individuals with type 1 diabetes to monitor and control their blood glucose level and prevent the early onset of micro and macrovascular complications.

Keywords: artificial intelligence, blood glucose meter, insulin pump, low and middle-income countries, micro and macrovascular complications, type 1 diabetes

Procedia PDF Downloads 155

Authors: Sanjay Adhikesaven

Abstract:

Workplace accidents are a critical problem that causes many deaths, injuries, and financial losses. Climate change has a severe impact on industrial workers, partially caused by global warming. To reduce such casualties, it is important to proactively find unsafe environments where injuries could occur by detecting the use of personal protective equipment (PPE) and identifying unsafe activities. Thus, we propose an industrial workplace alerting and monitoring platform to detect PPE use and classify unsafe activity in group settings involving multiple humans and objects over a long period of time. Our proposed method is the first to analyze prolonged actions involving multiple people or objects. It benefits from combining pose estimation with PPE detection in one platform. Additionally, we propose the first open-source annotated data set with video data from industrial workplaces annotated with the action classifications and detected PPE. The proposed system can be implemented within the surveillance cameras already present in industrial settings, making it a practical and effective solution.

Keywords: computer vision, deep learning, workplace safety, automation

Procedia PDF Downloads 76

Authors: Sean McCarthy, Patrice M. Ludwig, Will Watson

Abstract:

This exploratory case study explores the deployment of Social Network Analysis (SNA) in mapping community assets in an interdisciplinary, undergraduate, team-taught course focused on income insecure populations in a rural area in the US. Specifically, it analyzes how students were taught to collect data on community assets and to visualize the connections between those assets using Kumu, an SNA data visualization tool. Further, the case study shows how social network data was also collected about student teams via their written communications in Slack, an enterprise messaging tool, which enabled instructors to manage and guide student research activity throughout the semester. The discussion presents how SNA methods can simultaneously inform both community-based research and social innovation pedagogy through the use of data visualization and collaboration-focused communication technologies.

Keywords: social innovation, social network analysis, pedagogy, problem-based learning, data visualization, information communication technologies

Procedia PDF Downloads 118

Authors: Rashini Maduka, C. R. Wijesinghe, A. R. Weerasinghe

Abstract:

Drug-drug interactions (DDIs) can happen when two or more drugs are taken together. Today DDIs have become a serious health issue due to adverse drug effects. In vivo and in vitro methods for identifying DDIs are time-consuming and costly. Therefore, in-silico-based approaches are preferred in DDI identification. Most machine learning models for DDI prediction are used chemical and biological drug properties as features. However, some drug features are not available and costly to extract. Therefore, it is better to make automatic feature engineering. Furthermore, people who have diabetes already suffer from other diseases and take more than one medicine together. Then adverse drug effects may happen to diabetic patients and cause unpleasant reactions in the body. In this study, we present a model with a graph convolutional autoencoder and a graph decoder using a dataset from DrugBank version 5.1.3. The main objective of the model is to identify unknown interactions between antidiabetic drugs and the drugs taken by diabetic patients for other diseases. We considered automatic feature engineering and used Known DDIs only as the input for the model. Our model has achieved 0.86 in AUC and 0.86 in AP.

Keywords: drug-drug interaction prediction, graph embedding, graph convolutional networks, adverse drug effects

Procedia PDF Downloads 65

Authors: Erion Luga, Aksel Seitllari, Kemal Pervanqe

Abstract:

This study investigates the relation between physical and mechanical properties of concrete paving stones using neuro-fuzzy approach. For this purpose 200 samples of concrete paving stones were selected randomly from different sources. The first phase included the determination of physical properties of the samples such as water absorption capacity, porosity and unit weight. After that the indirect tensile strength test and compressive strength test of the samples were performed. İn the second phase, adaptive neuro-fuzzy approach was employed to simulate nonlinear mapping between the above mentioned physical properties and mechanical properties of paving stones. The neuro-fuzzy models uses Sugeno type fuzzy inference system. The models parameters were adapted using hybrid learning algorithm and input space was fuzzyfied by considering grid partitioning. It is concluded based on the observed data and the estimated data through ANFIS models that neuro-fuzzy system exhibits a satisfactory performance.

Keywords: paving stones, physical properties, mechanical properties, ANFIS

Procedia PDF Downloads 303