Search results for: differential learning

Authors: Amr Sadek, Abdelrahaman Al-Qahtany, Turkey Salem Al-Qahtany

Abstract:

In this study, a backpropagation artificial neural network (ANN) model has been used to predict the cooling and heating capacities of air conditioners (AC) under different conditions. Sufficiently large measurement results were obtained from the national energy-efficiency laboratories in Saudi Arabia and were used for the learning process of the ANN model. The parameters affecting the performance of the AC, including temperature, humidity level, specific heat enthalpy indoors and outdoors, and the air volume flow rate of indoor units, have been considered. These parameters were used as inputs for the ANN model, while the cooling and heating capacity values were set as the targets. A backpropagation ANN model with two hidden layers and one output layer could successfully correlate the input parameters with the targets. The characteristics of the ANN model including the input-processing, transfer, neurons-distance, topology, and training functions have been discussed. The performance of the ANN model was monitored over the training epochs and assessed using the mean squared error function. The model was then used to predict the performance of the AC under conditions that were not included in the measurement results. The optimum performance of the AC was also predicted under the different environmental conditions in Saudi Arabia. The uncertainty of the ANN model predictions has been evaluated taking into account the randomness of the data and lack of learning.

Keywords: artificial neural network, uncertainty of model predictions, efficiency of air conditioners, cooling and heating capacities

Procedia PDF Downloads 57

Authors: Rasikh Tariq, Fatima Z. Benarab

Abstract:

Evaporative coolers has a minimum potential to reach the wet-bulb temperature of intake air which is not enough to handle a large cooling load; therefore, it is not a feasible option to overcome cooling requirement of a building. The invention of Maisotsenko (M) cycle has led evaporative cooling technology to reach the sub-wet-bulb temperature of the intake air; therefore, it brings an innovation in evaporative cooling techniques. In this work, we developed a mathematical model of the Maisotsenko based air cooler by applying energy and mass balance laws on different air channels. The governing ordinary differential equations are discretized and simulated on MATLAB. The temperature and the humidity plots are shown in the simulation results. A parametric study is conducted by varying working air inlet conditions (temperature and humidity), inlet air velocity, geometric parameters and water temperature. The influence of these aforementioned parameters on the cooling effectiveness of the HMX is reported.  Results have shown that the effectiveness of the M-Cycle is increased by increasing the ambient temperature and decreasing absolute humidity. An air velocity of 0.5 m/sec and a channel height of 6-8mm is recommended.

Keywords: HMX, maisotsenko cycle, mathematical modeling, numerical simulation, parametric study

Procedia PDF Downloads 137

Authors: Panaya Sudta, Wanchalerm Patanacharoenwong, Prachya Bumrungkun

Abstract:

As With the evolution of smart grids, ensuring the reliability and efficiency of electrical systems in smart cities has become crucial. This paper proposes a distinct approach that combines advanced machine learning techniques to accurately predict electrical failures and address voltage reliability issues. This approach aims to improve the accuracy and efficiency of reliability evaluations in smart cities. The aim of this research is to develop a comprehensive predictive model that accurately predicts electrical failures and voltage reliability in smart cities. This model integrates RFM analysis, K-means clustering, and LSTM networks to achieve this objective. The research utilizes RFM analysis, traditionally used in customer value assessment, to categorize and analyze electrical components based on their failure recency, frequency, and monetary impact. K-means clustering is employed to segment electrical components into distinct groups with similar characteristics and failure patterns. LSTM networks are used to capture the temporal dependencies and patterns in customer data. This integration of RFM, K-means, and LSTM results in a robust predictive tool for electrical failures and voltage reliability. The proposed model has been tested and validated on diverse electrical utility datasets. The results show a significant improvement in prediction accuracy and reliability compared to traditional methods, achieving an accuracy of 92.78% and an F1-score of 0.83. This research contributes to the proactive maintenance and optimization of electrical infrastructures in smart cities. It also enhances overall energy management and sustainability. The integration of advanced machine learning techniques in the predictive model demonstrates the potential for transforming the landscape of electrical system management within smart cities. The research utilizes diverse electrical utility datasets to develop and validate the predictive model. RFM analysis, K-means clustering, and LSTM networks are applied to these datasets to analyze and predict electrical failures and voltage reliability. The research addresses the question of how accurately electrical failures and voltage reliability can be predicted in smart cities. It also investigates the effectiveness of integrating RFM analysis, K-means clustering, and LSTM networks in achieving this goal. The proposed approach presents a distinct, efficient, and effective solution for predicting and mitigating electrical failures and voltage issues in smart cities. It significantly improves prediction accuracy and reliability compared to traditional methods. This advancement contributes to the proactive maintenance and optimization of electrical infrastructures, overall energy management, and sustainability in smart cities.

Keywords: electrical state prediction, smart grids, data-driven method, long short-term memory, RFM, k-means, machine learning

Procedia PDF Downloads 40

Authors: Anna Sanczyk

Abstract:

Diverse students may experience different forms of discrimination. Some of the oppression students experience in schools are racism, sexism, classism, or homophobia that may affect their achievement, and teachers need to make sure they create inclusive, equitable classroom environments. The broader literature on social change in education shows that teachers who challenge oppression and want to promote equitable and transformative education face institutional, social, and political constraints. This paper discusses research on teachers’ work to create socially just and culturally inclusive classrooms and schools. The practical contribution of this literature review is that it provides a comprehensive compilation of the studies presenting teachers’ roles and efforts in affecting social change. The examination of the research on social change in education points to the urgency of teachers addressing the needs of marginalized students and resisting systemic oppression in schools. The implications of this literature review relate to the concerns that schools should provide greater advocacy for marginalized students in diverse learning contexts, and teacher education programs should prepare teachers to be active advocates for diverse students. The literature review has the potential to inform educators to enhance educational equity and improve the learning environment. This literature review illustrates teachers as agents of change in diverse classrooms and contributes to understanding various ways of taking action towards fostering more equitable and transformative education in today’s schools.

Keywords: agents of change, diversity, opression, social change

Procedia PDF Downloads 129

Authors: Rongbo Shen, Jianhua Yao, Kezhou Yan, Kuan Tian, Cheng Jiang, Ke Zhou

Abstract:

This study proposes a generative adversarial networks (GAN) framework to perform synthetic sampling in feature space, i.e., feature augmentation, to address the class imbalance problem in medical image analysis. A feature extraction network is first trained to convert images into feature space. Then the GAN framework incorporates adversarial learning to train a feature generator for the minority class through playing a minimax game with a discriminator. The feature generator then generates features for minority class from arbitrary latent distributions to balance the data between the majority class and the minority class. Additionally, a data cleaning technique, i.e., Tomek link, is employed to clean up undesirable conflicting features introduced from the feature augmentation and thus establish well-defined class clusters for the training. The experiment section evaluates the proposed method on two medical image analysis tasks, i.e., mass classification on mammogram and cancer metastasis classification on histopathological images. Experimental results suggest that the proposed method obtains superior or comparable performance over the state-of-the-art counterparts. Compared to all counterparts, our proposed method improves more than 1.5 percentage of accuracy.

Keywords: class imbalance, synthetic sampling, feature augmentation, generative adversarial networks, data cleaning

Procedia PDF Downloads 115

Authors: Takashi Kaburagi, Takamasa Komura, Yosuke Kurihara

Abstract:

Absolute pitch is the ability to identify a musical note without a reference tone. Training for absolute pitch often occurs in preschool education. It is necessary to clarify how well the trainee can make use of synesthesia in order to evaluate the effect of the training. To the best of our knowledge, there are no existing methods for objectively confirming whether the subject is using synesthesia. Therefore, in this study, we present a method to distinguish the use of color-auditory synesthesia from the separate use of color and audition during absolute pitch training. This method measures blood volume in the prefrontal cortex using functional Near-infrared spectroscopy (fNIRS) and assumes that the cognitive step has two parts, a non-linear step and a linear step. For the linear step, we assume a second order ordinary differential equation. For the non-linear part, it is extremely difficult, if not impossible, to create an inverse filter of such a complex system as the brain. Therefore, we apply a method based on a self-organizing map (SOM) and are guided by the available data. The presented method was tested using 15 subjects, and the estimation accuracy is reported.

Keywords: absolute pitch, functional near-infrared spectroscopy, prefrontal cortex, synesthesia

Procedia PDF Downloads 254

Authors: H. Özkan Gülsoy, Antonyraj Arockiasamy

Abstract:

The application of powder injection molding technology for the fabrication of metallic and non-metallic components is of growing interest as the process considerably saves time and cost. Utilizing this fabrication method, full dense components are being prepared in various sizes. In this work, our effort is focused to study the densification behavior of the parts made using different size 316L stainless steel powders. The metal powders were admixed with an adequate amount of polymeric compounds and molded as standard tensile bars. Solvent and thermal debinding was carried out followed by sintering in ultra pure hydrogen atmosphere based on the differential scanning calorimetry (DSC) cycle. Mechanical property evaluation and microstructural characterization of the sintered specimens was performed using universal Instron tensile testing machine, Vicker’s microhardness tester, optical (OM) and scanning electron microscope (SEM), energy dispersive spectroscopy (EDS), and X-ray diffraction were used. The results are compared and analyzed to predict the strength and weakness of the test conditions.

Keywords: powder injection molding, sintering, particle size, stainless steels

Procedia PDF Downloads 350

Authors: Selinda England, Ben Bodnaryk

Abstract:

With an unprecedented increase in provincial immigration and government support for greater international and culturally diverse learners, a trade/applied learning-focused polytechnic with four campuses within one Canadian province saw the need for intercultural awareness and an intercultural teaching competence strategy for faculty training. An institution-wide pre-assessment needs survey was conducted in 2018, in which 87% of faculty professed to have some/no training when working with international and/or culturally diverse learners. After researching fellow Polytechnics in Canada and seeing very little in the way of faculty support for intercultural competence, an institutional project team comprised of members from all facets of the Polytechnic was created and included: Indigenous experts, Academic Chairs, Directors, Human Resource Managers, and international/settlement subject matter experts. The project team was organized to develop and implement a new academic model focused on enriching intercultural competence among faculty. Utilizing a competency based model, the project team incorporated inclusive terminology into competency indicators and devised a four-phase proposal for implementing intercultural teacher training: a series of workshops focused on the needs of international and culturally diverse learners, including teaching strategies based on current TESOL methodologies, literature and online resources for quick access when planning lessons, faculty assessment examples and models of interculturally proficient instructors, and future job descriptions - all which promote and encourage development of specific intercultural skills. Results from a post-assessment survey (to be conducted in Spring 2020) and caveats regarding improvements and next steps will be shared. The project team believes its intercultural and inclusive teaching competency-based model is one of the first, institution-wide faculty supported initiatives within the Canadian college and Polytechnic post-secondary educational environment; it aims to become a leader in both the province and nation regarding intercultural competency training for trades, industry, and business minded community colleges and applied learning institutions.

Keywords: cultural diversity and education, diversity training teacher training, teaching and learning, teacher training

Procedia PDF Downloads 102

Authors: Muna Mitchell, Akshay Parchure, Krishna Singaraju

Abstract:

There is an increasing need for medium- to long-span steel bridges with complex geometry due to site restrictions in developed areas. One of the solutions to grade separations in congested areas is to use longer spans on skewed supports that avoid at-grade obstructions limiting impacts to the foundation. Where vertical clearances are also a constraint, continuous steel girders can be used to reduce superstructure depths. Combining continuous long steel spans on severe skews can resolve the constraints at a cost. The behavior of skewed girders is challenging to analyze and design with subsequent complexity during fabrication and construction. As a part of a corridor improvement project, Walter P Moore designed two 1700-foot side-by-side bridges carrying four lanes of traffic in each direction over a railroad track. The bridges consist of prestressed concrete girder approach spans and three-span continuous steel plate girder units. The roadway design added complex geometry to the bridge with horizontal and vertical curves combined with superelevation transitions within the plate girder units. The substructure at the steel units was skewed approximately 56 degrees to satisfy the existing railroad right-of-way requirements. A horizontal point of curvature (PC) near the end of the steel units required the use flared girders and chorded slab edges. Due to the flared girder geometry, the cross-frame spacing in each bay is unique. Staggered cross frames were provided based on AASHTO LRFD and NCHRP guidelines for high skew steel bridges. Skewed steel bridges develop significant forces in the cross frames and rotation in the girder websdue to differential displacements along the girders under dead and live loads. In addition, under thermal loads, skewed steel bridges expand and contract not along the alignment parallel to the girders but along the diagonal connecting the acute corners, resulting in horizontal displacement both along and perpendicular to the girders. AASHTO LRFD recommends a 95 degree Fahrenheit temperature differential for the design of joints and bearings. The live load and the thermal loads resulted in significant horizontal forces and rotations in the bearings that necessitated the use of HLMR bearings. A unique bearing layout was selected to minimize the effect of thermal forces. The span length, width, skew, and roadway geometry at the bridges also required modular bridge joint systems (MBJS) with inverted-T bent caps to accommodate movement in the steel units. 2D and 3D finite element analysis models were developed to accurately determine the forces and rotations in the girders, cross frames, and bearings and to estimate thermal displacements at the joints. This paper covers the decision-making process for developing the framing plan, bearing configurations, joint type, and analysis models involved in the design of the high-skew three-span continuous steel plate girder bridges.

Keywords: complex geometry, continuous steel plate girders, finite element structural analysis, high skew, HLMR bearings, modular joint

Procedia PDF Downloads 176

Authors: W. K. Zahra, M. A. El-Beltagy, R. R. Elkhadrawy

Abstract:

So many practical problems in science and technology developed over the past decays. For instance, the mathematical boundary layer theory or the approximation of solution for different problems described by differential equations. When such problems consider large or small parameters, they become increasingly complex and therefore require the use of asymptotic methods. In this work, we consider the singularly perturbed boundary value problems which contain very small parameters. Moreover, we will consider these perturbation parameters as random variables. We propose a numerical method to solve this kind of problems. The proposed method is based on an exponential spline, Shishkin mesh discretization, and polynomial chaos expansion. The polynomial chaos expansion is used to handle the randomness exist in the perturbation parameter. Furthermore, the Monte Carlo Simulations (MCS) are used to validate the solution and the accuracy of the proposed method. Numerical results are provided to show the applicability and efficiency of the proposed method, which maintains a very remarkable high accuracy and it is ε-uniform convergence of almost second order.

Keywords: singular perturbation problem, polynomial chaos expansion, Shishkin mesh, two small parameters, exponential spline

Procedia PDF Downloads 147

Authors: Jana Beresova

Abstract:

The paper presents a study carried out in 2015-2016 within the national scheme of research - VEGA 1/0106/15 based on theoretical research and empirical verification of the concept of intercultural communicative competence. It focuses on the current conception concerning target languages teaching compatible with the Common European Framework of Reference for Languages: Learning, teaching, assessment. Our research had revealed how the concept of intercultural communicative competence had been perceived by secondary-school teachers of English in Slovakia before they were intensively trained. Intensive workshops were based on the use of both authentic and instructional materials with the goal to support interculturally oriented language teaching aimed at challenging thinking. The former concept that supported the development of the students´ linguistic knowledge and the use of a target language to obtain information about the culture of the country whose language learners were learning was expanded by the meaning-making framework which views language as a typical means by which culture is mediated. The goal of the workshop was to influence English teachers to better understand the concept of intercultural communicative competence, combining theory and practice optimally. The results of the study will be presented and analysed, providing particular recommendations for language teachers and suggesting some changes in the National Educational Programme from which English learners should benefit in their future studies or professional careers.

Keywords: authentic materials, English language teaching, instructional materials, intercultural communicative competence

Procedia PDF Downloads 257

Authors: Sahithi Yarlagadda

Abstract:

The design of antenna is constrained by mathematical and geometrical parameters. Though there are diverse antenna structures with wide range of feeds yet, there are many geometries to be tried, which cannot be customized into predefined computational methods. The antenna design and optimization qualify to apply evolutionary algorithmic approach since the antenna parameters weights dependent on geometric characteristics directly. The evolutionary algorithm can be explained simply for a given quality function to be maximized. We can randomly create a set of candidate solutions, elements of the function's domain, and apply the quality function as an abstract fitness measure. Based on this fitness, some of the better candidates are chosen to seed the next generation by applying recombination and permutation to them. In conventional approach, the quality function is unaltered for any iteration. But the antenna parameters and geometries are wide to fit into single function. So, the weight coefficients are obtained for all possible antenna electrical parameters and geometries; the variation is learnt by mining the data obtained for an optimized algorithm. The weight and covariant coefficients of corresponding parameters are logged for learning and future use as datasets. This paper drafts an approach to obtain the requirements to study and methodize the evolutionary approach to automated antenna design for our past work on Vivaldi antenna as test candidate. The antenna parameters like gain, directivity, etc. are directly caged by geometries, materials, and dimensions. The design equations are to be noted here and valuated for all possible conditions to get maxima and minima for given frequency band. The boundary conditions are thus obtained prior to implementation, easing the optimization. The implementation mainly aimed to study the practical computational, processing, and design complexities that incur while simulations. HFSS is chosen for simulations and results. MATLAB is used to generate the computations, combinations, and data logging. MATLAB is also used to apply machine learning algorithms and plotting the data to design the algorithm. The number of combinations is to be tested manually, so HFSS API is used to call HFSS functions from MATLAB itself. MATLAB parallel processing tool box is used to run multiple simulations in parallel. The aim is to develop an add-in to antenna design software like HFSS, CSTor, a standalone application to optimize pre-identified common parameters of wide range of antennas available. In this paper, we have used MATLAB to calculate Vivaldi antenna parameters like slot line characteristic impedance, impedance of stripline, slot line width, flare aperture size, dielectric and K means, and Hamming window are applied to obtain the best test parameters. HFSS API is used to calculate the radiation, bandwidth, directivity, and efficiency, and data is logged for applying the Evolutionary genetic algorithm in MATLAB. The paper demonstrates the computational weights and Machine Learning approach for automated antenna optimizing for Vivaldi antenna.

Keywords: machine learning, Vivaldi, evolutionary algorithm, genetic algorithm

Procedia PDF Downloads 100

Authors: Nodelman V.

Abstract:

Despite serious difficulties in the assimilation of the conceptual system of Calculus, software in the educational process is used only occasionally, and even then, mainly for illustration purposes. The following are a few reasons: The non-trivial nature of the studied material, Lack of skills in working with software, Fear of losing time working with software, The variety of the software itself, the corresponding interface, syntax, and the methods of working with the software, The need to find suitable models, and familiarize yourself with working with them, Incomplete compatibility of the found models with the content and teaching methods of the studied material. This paper proposes an active use of the developed non-commercial software VusuMatica, which allows removing these restrictions through Broad support for the studied mathematical material (and not only Calculus). As a result - no need to select the right software, Emphasizing the unity of mathematics, its intrasubject and interdisciplinary relations, User-friendly interface, Absence of special syntax in defining mathematical objects, Ease of building models of the studied material and manipulating them, Unlimited flexibility of models thanks to the ability to redefine objects, which allows exploring objects characteristics, and considering examples and counterexamples of the concepts under study. The construction of models is based on an original approach to the analysis of the structure of the studied concepts. Thanks to the ease of construction, students are able not only to use ready-made models but also to create them on their own and explore the material studied with their help. The presentation includes examples of using VusuMatica in studying the concepts of limit and continuity of a function, its derivative, and integral.

Keywords: counterexamples, limitations and requirements, software, teaching and learning calculus, user-friendly interface and syntax

Procedia PDF Downloads 72

Authors: Aimen Mohamed Almaloul

Abstract:

The main focus of this study is investigating the interference of Arabic in the use of English prepositions by Libyan university students. Prepositions in the tests used in the study were categorized, according to their relation to Arabic, into similar Arabic and English prepositions (SAEP), dissimilar Arabic and English prepositions (DAEP), Arabic prepositions with no English counterparts (APEC), and English prepositions with no Arabic counterparts (EPAC). The subjects of the study were the first year university students of the English department, Sabrata Faculty of Arts, Azzawia University; both males and females, and they were 100 students. The basic tool for data collection was a test of English prepositions; students are instructed to fill in the blanks with the correct prepositions and to put a zero (0) if no preposition was needed. The test was then handed to the subjects of the study. The test was then scored and quantitative as well as qualitative results were obtained. Quantitative results indicated the number, percentages and rank order of errors in each of the categories and qualitative results indicated the nature and significance of those errors and their possible sources. Based on the obtained results the researcher could detect that students made more errors in the EPAC category than the other three categories and these errors could be attributed to the lack of knowledge of the different meanings of English prepositions. This lack of knowledge forced the students to adopt what is called the strategy of transfer.

Keywords: foreign language acquisition, foreign language learning, interference system, interlanguage system, mother tongue interference

Procedia PDF Downloads 365

Authors: Yilin Liao, Hewen Li, Paula McConvey

Abstract:

Speed skaters often face a risk of concussion when they fall on the ice floor and impact crash mats during practices and competitive races. Several variables, including those related to the skater, the crash mat, and the impact position (body side/head/feet impact), are believed to influence the severity of the skater's concussion. While computer simulation modeling can be employed to analyze these accidents, the simulation process is time-consuming and does not provide rapid information for coaches and teams to assess the skater's injury risk in competitive events. This research paper promotes the exploration of the feasibility of using AI techniques for evaluating skater’s potential concussion severity, and to develop a fast concussion prediction tool using artificial neural networks to reduce the risk of treatment delays for injured skaters. The primary data is collected through virtual tests and physical experiments designed to simulate skater-mat impact. It is then analyzed to identify patterns and correlations; finally, it is used to train and fine-tune the artificial neural networks for accurate prediction. The development of the prediction tool by employing machine learning strategies contributes to the application of AI methods in sports science and has theoretical involvements for using AI techniques in predicting and preventing sports-related injuries.

Keywords: artificial neural networks, concussion, machine learning, impact, speed skater

Procedia PDF Downloads 83

Authors: Charine Faith H. Lagrimas, Rommel N. Galvan, Rizalinda L. de Leon

Abstract:

An ongoing study, methyl laurate to be used as a refrigerant in an HVAC system, requires the crystallization kinetics of the said substance. Step-wise and normal forms of Avrami model parameters were used to describe the isothermal crystallization kinetics of methyl laurate at different temperatures from Differential Scanning Calorimetry (DSC) measurements. At 3 °C, parameters showed that methyl laurate exhibits a secondary crystallization. The primary crystallization occurred with instantaneous nuclei and spherulitic growth; followed by a secondary instantaneous nucleation with a lower growth of dimensionality, rod-like. At 4 °C to 6 °C, the exotherms from DSC implied that the system was under the isokinetic range. The kinetics behavior is the same which is instantaneous nucleation with one-dimensional growth. The differences for the isokinetic range temperatures are the activation energies (directly proportional to T) and nucleation rates (inversely proportional to T). From the images obtained during the crystallization of methyl laurate using an optical microscope, it is confirmed that the nucleation and crystal growth modes obtained from the optical microscope are consistent with the parameters from Avrami model.

Keywords: Avrami model, isothermal crystallization, lipids kinetics, methyl laurate

Procedia PDF Downloads 326

Authors: E. Benga, T. Tengen, A. Alugongo

Abstract:

Periodic and continuous inventory models are the two classical management tools used to handle inventories. These models have advantages and disadvantages. The implementation of both continuous (r,Q) inventory and periodic (R, S) inventory models in most manufacturing plants comes with higher cost. Such high inventory costs are due to the fact that most manufacturing plants are not flexible enough. Since demand and lead-time are two important variables of every inventory models, their effect on the flexibility of the manufacturing plant matter most. Unfortunately, these effects are not clearly understood by managers. The reason is that the decision parameters of the continuous (r, Q) inventory and periodic (R, S) inventory models are not designed to effectively deal with the issues of uncertainties such as poor manufacturing performances, delivery performance supplies performances. There is, therefore, a need to come up with a predictive and hybrid inventory model that can combine in some sense the feature of the aforementioned inventory models. A linear combination technique is used to hybridize both continuous (r, Q) inventory and periodic (R, S) inventory models. The behavior of such hybrid inventory model is described by a differential equation and then optimized. From the results obtained after simulation, the continuous (r, Q) inventory model is more effective than the periodic (R, S) inventory models in the short run, but this difference changes as time goes by. Because the hybrid inventory model is more cost effective than the continuous (r,Q) inventory and periodic (R, S) inventory models in long run, it should be implemented for strategic decisions.

Keywords: periodic inventory, continuous inventory, hybrid inventory, optimization, manufacturing plant

Procedia PDF Downloads 371

Authors: Ali Anwar, Hu Qinglei, Li Bo, Muhammad Taha Ali

Abstract:

In this paper a generic model of perturbed nonlinear systems is considered which is affected by hard backlash nonlinearity at the input. The nonlinearity is modelled by a dynamic differential equation which presents a more precise shape as compared to the existing linear models and is compatible with nonlinear design technique such as backstepping. Moreover, a novel backstepping based nonlinear control law is designed which explicitly incorporates a continuous-time adaptive backlash inverse model. It provides a significant flexibility to control engineers, whereby they can use the estimated backlash spacing value specified on actuators such as gears etc. in the adaptive Backlash Inverse model during the control design. It ensures not only global stability but also stringent transient performance with desired precision. It is also robust to external disturbances upon which the bounds are taken as unknown and traverses the backlash spacing efficiently with underestimated information about the actual value. The continuous-time backlash inverse model is distinguished in the sense that other models are either discrete-time or involve complex computations. Furthermore, numerical simulations are presented which not only illustrate the effectiveness of proposed control law but also its comparison with PID and other backstepping controllers.

Keywords: adaptive control, hysteresis, backlash inverse, nonlinear system, robust control, backstepping

Procedia PDF Downloads 452

Authors: Umm-E-Hani, Faiza Babar, Hanif Durad

Abstract:

Malware-detecting approaches suffer many limitations, due to which all anti-malware solutions have failed to be reliable enough for detecting zero-day malware. Signature-based solutions depend upon the signatures that can be generated only when malware surfaces at least once in the cyber world. Another approach that works by detecting the anomalies caused in the environment can easily be defeated by diligently and intelligently written malware. Solutions that have been trained to observe the behavior for detecting malicious files have failed to cater to the malware capable of detecting the sandboxed or protected environment. Machine learning and deep learning-based approaches greatly suffer in training their models with either an imbalanced dataset or an inadequate number of samples. AI-based anti-malware solutions that have been trained with enough samples targeted a selected feature vector, thus ignoring the input of leftover features in the maliciousness of malware just to cope with the lack of underlying hardware processing power. Our research focuses on producing an anti-malware solution for detecting malicious PE files by circumventing the earlier-mentioned shortcomings. Our proposed framework, which is based on automated feature engineering through autoencoders, trains the model over a fairly large dataset. It focuses on the visual patterns of malware samples to automatically extract the meaningful part of the visual pattern. Our experiment has successfully produced a state-of-the-art accuracy of 99.54 % over test data.

Keywords: malware, auto encoders, automated feature engineering, classification

Procedia PDF Downloads 61

Authors: Krishna Mohan Bathula, Fatou Bintou Loucoubar, FNU Kaleemunnisa, Christelle Scharff, Mark Anthony De Castro

Abstract:

Voice recognition algorithms such as automatic speech recognition and text-to-speech systems with African languages can play an important role in bridging the digital divide of Artificial Intelligence in Africa, contributing to the establishment of a fully inclusive information society. This paper proposes a Deep Learning model that can classify the user responses as inputs for an interactive voice response system. A dataset with Wolof language words ‘yes’ and ‘no’ is collected as audio recordings. A two stage Data Augmentation approach is adopted for enhancing the dataset size required by the deep neural network. Data preprocessing and feature engineering with Mel-Frequency Cepstral Coefficients are implemented. Convolutional Neural Networks (CNNs) have proven to be very powerful in image classification and are promising for audio processing when sounds are transformed into spectra. For performing voice response classification, the recordings are transformed into sound frequency feature spectra and then applied image classification methodology using a deep CNN model. The inference model of this trained and reusable Wolof voice response recognition system can be integrated with many applications associated with both web and mobile platforms.

Keywords: automatic speech recognition, interactive voice response, voice response recognition, wolof word classification

Procedia PDF Downloads 100

Authors: Sibo Lu, Zhongkai Qian

Abstract:

This study endeavors to articulate a spectrum of capitalist transformation from a politico-economic perspective, categorizing the development of capitalism into pre-modern, modern, and post-modern stages while examining the transitions between these states. It critically engages with the concepts of pre-modernity, modernity, and post-modernity, typically aligned with cultural studies, to shed light on their objective changes in the politico-economic realm. Post-modernity, in this context, is associated with the transition to high-value-added services and economic growth in ‘developed’ countries amidst globalization, framing a ‘neoliberal’ economic transformation. The paper scrutinizes the neoliberal economic shifts in the latter half of the 20th century as part of the post-modern state, arguing against viewing post-modernity merely as a cultural or subjective interpretative stance. Instead, it is posited as a concrete politico-economic phase marked by the dominance of post-Fordist systems. Through integrating insights from the political economy with continental philosophy, this research provides a nuanced analysis of capitalism's evolution, challenging conventional class analyses under the capitalist system and fostering a broader understanding of socio-economic paradigms.

Keywords: post marxism, critical theory, political economy, capitalism transformation

Procedia PDF Downloads 21

Authors: Rudolf Egger

Abstract:

This article shows some results of an ongoing project in Kosova. This project deals with the meaning of social transformation processes in the life-courses of Kosova people. One goal is to create an oral history archive in this country. In the last seven years we did some interpretative work (using narrative interviews) concerning the experiences and meanings of social changes from the perspective of life course. We want to reconstruct the individual possibilities in creating one's life in new social structures. After the terrible massacres of ethnical-territorially defined nationalism in former Yugoslavia it is the main focus to find out something about the many small daily steps which must be done, to build up a kind of “normality” in this country. These steps can be very well reconstructed by narrations, by life stories, because personal experiences are naturally linked with social orders. Each individual story is connected with further stories, in which the collective history will be negotiated and reflected. The view on the biographical narration opens the possibility to analyze the concreteness of the “individual case” in the complexity of collective history. Life stories contain thereby a kind of a transition character, that’s why they can be used for the reconstruction of periods of political transformation. For example: In the individual story we can find very clear the national or mythological character of the Albanian people in Kosova. The shown narrations can be read also as narrative lines in relation to the (re-)interpretation of the past, in which lived life is fixed into history in the so-called collective memory in Kosova.

Keywords: biographical learning, adult education, social change, post war societies

Procedia PDF Downloads 404

Authors: Muhammad Mohsin, Fawad Ahmad, Fatima Batool, Muhammad Kaab Zarrar

Abstract:

The integration of the Internet of Things (IoT) and edge computing devices offers a transformative approach to electronic waste management, particularly in the dismantling of printed circuit boards (PCBs). This paper explores how these technologies optimize operational efficiency and improve environmental sustainability by addressing challenges such as data security, interoperability, scalability, and real-time data processing. Proposed solutions include advanced machine learning algorithms for predictive maintenance, robust encryption protocols, and scalable architectures that incorporate edge computing. Case studies from leading e-waste management facilities illustrate benefits such as improved material recovery efficiency, reduced environmental impact, improved worker safety, and optimized resource utilization. The findings highlight the potential of IoT and edge computing to revolutionize e-waste dismantling and make the case for a collaborative approach between policymakers, waste management professionals, and technology developers. This research provides important insights into the use of IoT and edge computing to make significant progress in the sustainable management of electronic waste

Keywords: internet of Things, edge computing, waste PCB disassembly, electronic waste management, data security, interoperability, machine learning, predictive maintenance, sustainable development

Procedia PDF Downloads 10

Authors: L. Rakesh, T. Y. Heblekar

Abstract:

This study entails the development and optimization of a mathematical model for a floating drum biogas reactor from first principles using thermal and empirical considerations. The model was derived on the basis of mass conservation, lumped mass heat transfer formulations and empirical biogas formation laws. The treatment leads to a system of coupled nonlinear ordinary differential equations whose solution mapped four-time independent controllable parameters to five output variables which adequately serve to describe the reactor performance. These equations were solved numerically using fourth order Runge-Kutta method for a range of input parameter values. Using the data so obtained an Artificial Neural Network with a single hidden layer was trained using Levenberg-Marquardt Damped Least Squares (DLS) algorithm. This network was then fine-tuned for optimal mapping by varying hidden layer size. This fast forward model was then employed as a health score generator in the Bacterial Foraging Optimization code. The optimal operating state of the simplified Biogas reactor was thus obtained.

Keywords: biogas, floating drum reactor, neural network model, optimization

Procedia PDF Downloads 131

Authors: Zoltán Höhling, Sarah-Lu Oberschelp, Niklas Gilsdorf, Michael Wirsching, Andrea Kuhnert

Abstract:

A majority of medical students at the University of Freiburg reported stress-related psychosomatic symptoms which are often associated with their studies. International research supports these findings, as medical students worldwide seem to be at special risk for mental health problems. In some countries and institutions, psychologically based interventions that assist medical students in coping with their stressors have been implemented. It turned out that anonymity is an important aspect here. Many students fear a potential damage of reputation when being associated with mental health problems, which may be due to a high level of competitiveness in classes. Therefore, we launched an online-platform where medical students could anonymously seek help and exchange their experiences with fellow students and experts. Medical students of all semesters have access to it through the university’s learning management system (called “ILIAS”). The informative part of the platform consists of exemplary videos showing medical students (actors) who act out scenes that demonstrate the antecedents of stress-related psychosomatic disorders. These videos are linked to different expert comments, describing the exhibited symptoms in an understandable and normalizing way. The (inter-)active part of the platform consists of self-help tools (such as meditation exercises or general tips for stress-coping) and an anonymous interactive forum where students can describe their stress-related problems and seek guidance from experts and/or share their experiences with fellow students. Besides creating an immediate proposal to help affected students, we expect that competitiveness between students might be diminished and bondage improved through mutual support between them. In the initial phase after the platform’s launch, it was accessed by a considerable number of medical students. On a closer look it appeared that platform sections like general information on psychosomatic-symptoms and self-treatment tools were accessed far more often than the online-forum during the first months after the platform launch. Although initial acceptance of the platform was relatively high, students showed a rather passive way of using our platform. While user statistics showed a clear demand for information on stress-related psychosomatic symptoms and its possible remedies, active engagement in the interactive online-forum was rare. We are currently advertising the platform intensively and trying to point out the assured anonymity of the platform and its interactive forum. Our plans, to assure students their anonymity through the use of an e-learning facility and promote active engagement in the online forum, did not (yet) turn out as expected. The reasons behind this may be manifold and based on either e-learning related issues or issues related to students’ individual needs. Students might, for example, question the assured anonymity due to a lack of trust in the technological functioning university’s learning management system. However, one may also conclude that reluctance to discuss stress-related psychosomatic symptoms with peer medical students may not be solely based on anonymity concerns, but could be rooted in more complex issues such as general mistrust between students.

Keywords: e-tutoring, stress-coping, student support, online forum

Procedia PDF Downloads 372

Authors: Sultan Ghaleb Aldaihani

Abstract:

- The environment in which educational leadership takes place is becoming increasingly complex due to factors like globalization and rapid technological change. - This is creating a "leadership gap" where the complexity of the environment outpaces the ability of leaders to effectively respond. - Educational leadership involves guiding teachers and the broader school system towards improved student learning and achievement. 2. Implications of Artificial Intelligence (AI) in Educational Leadership: - AI has great potential to enhance education, such as through intelligent tutoring systems and automating routine tasks to free up teachers. - AI can also have significant implications for educational leadership by providing better information and data-driven decision-making capabilities. - Computer-adaptive testing can provide detailed, individualized data on student learning that leaders can use for instructional decisions and accountability. 3. Enhancing Decision-Making Processes: - Statistical models and data mining techniques can help identify at-risk students earlier, allowing for targeted interventions. - Probability-based models can diagnose students likely to drop out, enabling proactive support. - These data-driven approaches can make resource allocation and decision-making more effective. 4. Improving Efficiency and Productivity: - AI systems can automate tasks and change processes to improve the efficiency of educational leadership and administration. - Integrating AI can free up leaders to focus more on their role's human, interactive elements.

Keywords: Education, Leadership, Technology, Artificial Intelligence

Procedia PDF Downloads 16

Authors: Khedidja Bouhadef, Imene Said Kouadri, Omar Rahli

Abstract:

In this paper numerical investigation is performed to analyze natural convection heat transfer characteristics within a wavy-wall enclosure filled with fluid-saturated porous medium. The bottom wall which has the wavy geometry is maintained at a constant high temperature, while the top wall is straight and is maintained at a constant lower temperature. The left and right walls of the enclosure are both straight and insulated. The governing differential equations are solved by Finite-volume approach and grid generation is used to transform the physical complex domain to a computational regular space. The aim is to examine flow field, temperature distribution and heat transfer evolutions inside the cavity when Darcy number, Rayleigh number and undulations number values are varied. The results mainly indicate that the heat transfer is rather affected by the permeability and Rayleigh number values since increasing these values enhance the Nusselt number; although the exchanges are not highly affected by the undulations number.

Keywords: grid generation, natural convection, porous medium, wavy wall enclosure

Procedia PDF Downloads 251

Authors: Amnah Moghees, Saima Abbas Dar, Muniba Saeed

Abstract:

Teaching a second language to deaf learners has always been a challenge in Pakistan. Different approaches and strategies have been followed, but they have been resulted into partial or complete failure. The study aims to investigate the language problems faced by adult deaf learners of English as second language in mainstream classrooms. Moreover, the study also determines the factors which are very much involved in language teaching and learning in mainstream classes. To investigate the language problems, data will be collected through writing samples of ten deaf adult learners and ten normal ESL learners of the same class; whereas, observation in inclusive language teaching classrooms and interviews from five ESL teachers in inclusive classes will be conducted to know the factors which are directly or indirectly involved in inclusive language education. Keeping in view this study, qualitative research paradigm will be applied to analyse the corpus. The study figures out that deaf ESL learners face severe language issues such as; odd sentence structures, subject and verb agreement violation, misappropriation of verb forms and tenses as compared to normal ESL learners. The study also predicts that in mainstream classrooms there are multiple factors which are affecting the smoothness of teaching and learning procedure; role of mediator, level of deaf learners, empathy of normal learners towards deaf learners and language teacher’s training.

Keywords: deaf English language learner, empathy, mainstream classrooms, previous language knowledge of learners, role of mediator, language teachers' training

Procedia PDF Downloads 151

Authors: Louis Botha

Abstract:

Since the presentation of South Africa’s inclusive education strategy in Education White Paper 6 in 2001, very little has been accomplished in terms of its implementation. The failure to achieve the goals set by this policy document is related to teachers lacking confidence and knowledge about how to enact inclusive education, as well as challenges of inflexible curricula, limited resources in overcrowded classrooms, and so forth. This paper presents a socio-cultural approach to addressing these challenges of implementing inclusive education in the South African context. It takes its departure from the view that inclusive education has been adequately theorized and conceptualized in terms of its philosophical and ethical principles, especially in South African policy and debates. What is missing, however, are carefully theorized, practically implementable research interventions which can address the concerns mentioned above. Drawing on socio-cultural principles of learning and development and on cultural-historical activity theory (CHAT) in particular, this paper argues for the use of formative interventions which introduce appropriately constructed mediational artifacts that have the potential to initiate inclusive practices and pedagogies within South African schools and classrooms. It makes use of Vygotsky’s concept of double stimulation to show how the proposed artifacts could instigate forms of transformative agency which promote the adoption of inclusive cultures of learning and teaching.

Keywords: cultural-historical activity theory, double stimulation, formative interventions, transformative agency

Procedia PDF Downloads 215

Authors: A. Fayad, Q. Alqhazaly, T. Cinkler

Abstract:

In light of the substantial increase in end-user requirements and the incessant need of network operators to upgrade the capabilities of access networks, in this paper, the performance of the different modulation formats on eight-channels Time and Wavelength Division Multiplexing Passive Optical Network (TWDM-PON) transmission system has been examined and compared. Limitations and features of modulation formats have been determined to outline the most suitable design to enhance the data rate and transmission reach to obtain the best performance of the network. The considered modulation formats are On-Off Keying Non-Return-to-Zero (NRZ-OOK), Carrier Suppressed Return to Zero (CSRZ), Duo Binary (DB), Modified Duo Binary (MODB), Quadrature Phase Shift Keying (QPSK), and Differential Quadrature Phase Shift Keying (DQPSK). The performance has been analyzed by varying transmission distances and bit rates under different channel spacing. Furthermore, the system is evaluated in terms of minimum Bit Error Rate (BER) and Quality factor (Qf) without applying any dispersion compensation technique, or any optical amplifier. Optisystem software was used for simulation purposes.

Keywords: BER, DuoBinary, NRZ-OOK, TWDM-PON

Procedia PDF Downloads 134