Search results for: supervised machine learning algorithm

Authors: Zhihua Yu, Qi Zhang, Mingyu Zhang, Haolong Dai

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Distributed fiber-optic vibration sensors are gaining extensive attention, for the advantages of high sensitivity, accurate location, light weight, large-scale monitoring, good concealment, and etc. In this paper, a novel optical fiber distributed vibration sensing system is proposed, which is based on self-interference of Rayleigh backscattering with phase generated carrier (PGC) demodulation algorithm. Pulsed lights are sent into the sensing fiber and the Rayleigh backscattering light from a certain position along the sensing fiber would interfere through an unbalanced Michelson Interferometry (MI) to generate the interference light. An improved PGC demodulation algorithm is carried out to recover the phase information of the interference signal, which carries the sensing information. Three vibration events were applied simultaneously to different positions over 2000m sensing fiber and demodulated correctly. Experiments show that the spatial resolution of is 10 m, and the noise level of the Φ-OTDR system is about 10-3 rad/√Hz, and the signal to noise ratio (SNR) is about 30.34dB. This vibration measurement scheme can be applied at surface, seabed or downhole for vibration measurements or distributed acoustic sensing (DAS).

Keywords: fiber optics sensors, Michelson interferometry, MI, phase-sensitive optical time domain reflectometry, Φ-OTDR, phase generated carrier, PGC

Procedia PDF Downloads 172

Authors: Abdullah Abu-Tineh, Youmen Chaaban

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This study examines the development of a professional development (PD) model for teacher growth and learning that is embedded into the school context. The School based Support Program (SBSP), designed for the Qatari context, targets the practices, knowledge and skills of both school leadership and teachers in an attempt to improve student learning outcomes. Key aspects of the model include the development of learning communities among teachers, strong leadership that supports school improvement activities, and the use of research-based PD to improve teacher practices and student achievement. This paper further presents findings from an evaluation of this PD program. Based on an adaptation of Guskey’s evaluation of PD models, 100 teachers at the participating schools were selected for classroom observations and 40 took part in in-depth interviews to examine changed classroom practices. The impact of the PD program on student learning was also examined. Teachers’ practices and their students’ achievement in English, Arabic, mathematics and science were measured at the beginning and at the end of the intervention.

Keywords: initiative, professional development, school based support Program (SBSP), school reform

Procedia PDF Downloads 475

Authors: Tanja Kreitenweis

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Lifelong learning is increasingly seen as essential for coping with the rapidly changing work environment. To this end, serious games can provide convenient and straightforward access to complex knowledge for all age groups. However, learning achievements depend largely on a learner’s non-cognitive skill disposition (e.g., motivation, self-belief, playfulness, and openness). With the aim of combining the fields of serious games and non-cognitive skills, this research focuses in particular on the use of a business simulation, which conveys change management insights. Business simulations are a subset of serious games and are perceived as a non-traditional learning method. The presented objectives of this work are versatile: (1) developing a scale, which measures learners’ knowledge and skills level before and after a business simulation was played, (2) investigating the influence of non-cognitive skills on learning in this business simulation environment and (3) exploring the moderating role of team preference in this type of learning setting. First, expert interviews have been conducted to develop an appropriate measure for learners’ skills and knowledge assessment. A pretest-posttest experimental design with German management students was implemented to approach the remaining objectives. By using the newly developed, reliable measure, it was found that students’ skills and knowledge state were higher after the simulation had been played, compared to before. A hierarchical regression analysis revealed two positive predictors for this outcome: motivation and self-esteem. Unexpectedly, playfulness had a negative impact. Team preference strengthened the link between grit and playfulness, respectively, and learners’ skills and knowledge state after completing the business simulation. Overall, the data underlined the potential of business simulations to improve learners’ skills and knowledge state. In addition, motivational factors were found as predictors for benefitting most from the applied business simulation. Recommendations are provided for how pedagogues can use these findings.

Keywords: business simulations, change management, (experiential) learning, non-cognitive skills, serious games

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Authors: Teh Kean Kheng, Low Soon Yee, Burra Venkata Durga Kumar

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In 2022, as the future world becomes increasingly computer-related, more individuals are attempting to study coding for themselves or in school. This is because they have discovered the value of learning code and the benefits it will provide them. But learning coding is difficult for most people. Even senior programmers that have experience for a decade year still need help from the online source while coding. The reason causing this is that coding is not like talking to other people; it has the specific syntax to make the computer understand what we want it to do, so coding will be hard for normal people if they don’t have contact in this field before. Coding is hard. If a user wants to learn bash code with bash prompt, it will be harder because if we look at the bash prompt, we will find that it is just an empty box and waiting for a user to tell the computer what we want to do, if we don’t refer to the internet, we will not know what we can do with the prompt. From here, we can conclude that the bash prompt is not user-friendly for new users who are learning bash code. Our goal in writing this paper is to give an idea to implement a user-friendly Bash prompt in Ubuntu OS using Artificial Intelligent (AI) to lower the threshold of learning in Bash code, to make the user use their own words and concept to write and learn Bash code.

Keywords: user-friendly, bash code, artificial intelligence, threshold, semantic similarity, lexical similarity

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Authors: Vivian Guo, Yan Qu

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Online marketplaces are not only digital places where consumers buy and sell merchandise, and they are also destinations for brands to connect with real consumers at the moment when customers are in the shopping mindset. For many marketplaces, brands have been important partners through advertising. There can be, however, a risk of advertising impacting a consumer’s shopping journey if it hurts the use experience or takes the user away from the site. Both could lead to the loss of transaction revenue for the marketplace. In this paper, we present user-based methods for cannibalization control by selectively turning off ads to users who are likely to be cannibalized by ads subject to business objectives. We present ways of measuring cannibalization of advertising in the context of an online marketplace and propose novel ways of measuring cannibalization through purchase propensity and uplift modeling. A/B testing has shown that our methods can significantly improve user purchase and engagement metrics while operating within business objectives. To our knowledge, this is the first paper that addresses cannibalization mitigation at the user-level in the context of advertising.

Keywords: cannibalization, machine learning, online marketplace, revenue optimization, yield optimization

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Authors: Sinchai Poolklai

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The purpose of this research paper was aimed to examine the level of satisfaction of the faculty members who participated in the ASEAN camp, Chantaburi, Thailand. The population of this study included all the faculty members of Suan Sunandha Rajabhat University who participated in the training and activities of the ASEAN camp during March, 2014. Among a total of 200 faculty members who answered the questionnaire, the data was complied by using SPSS program. Percentage, mean and standard deviation were utilized in analyzing the data. The findings revealed that the average mean of satisfaction was 4.37, and standard deviation was 0.7810. Moreover, the mean average can be used to rank the level of satisfaction from each of the following factors: lower cost, less time consuming, faster delivery, more effective learning, and lower environment impact.

Keywords: ASEAN camp, e-learning, satisfaction, application

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Authors: Jennifer Nutton, Gayle Ployer, Ky Scott, Jenny Morgan

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Working in a circle within higher education creates a decolonial space of mutual respect, responsibility, and reciprocity that facilitates collaborative learning and deep connections among learners and instructors. This approach is beyond simply facilitating a group in a circle but opens the door to creating a sacred space connecting each member to the land, to the Indigenous peoples who have taken care of the lands since time immemorial, to one another, and to one’s own positionality. These deep connections not only center human knowledges and relationships but also acknowledges responsibilities to land. Working in a circle as a relational pedagogical praxis also disrupts institutional power dynamics by creating a space of collaborative learning and deep connections in the classroom. Inherent within circle work is to facilitate connections not just academically but emotionally, physically, culturally, and spiritually. Recent literature supports the use of online talking circles, finding that it can offer a more relational and experiential learning environment, which is often absent in the virtual world and has been made more evident and necessary since the pandemic. These deeper experiences of learning and connection, rooted in both knowledge and the land, can then be shared with openness and vulnerability with one another, facilitating growth and change. This process of beginning with the land is critical to ensure we have the grounding to obstruct the ongoing realities of colonialism. The authors, who identify as both Indigenous and non-Indigenous, as both educators and learners, reflect on their teaching and learning experiences in circle. They share a relational pedagogical praxis framework that has been successful in educating future social workers, environmental activists, and leaders in social and human services, health, legal and political fields.

Keywords: circle work, relational pedagogies, decolonization, distance education

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Authors: Thien Van Ngo

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The engineering design process is a systematic approach to solving problems. It involves identifying a problem, brainstorming solutions, prototyping and testing solutions, and evaluating the results. The engineering design process can be used to teach students how to solve problems in a creative and innovative way. The research aim of this study was to investigate the effectiveness of using the engineering design process to enhance student learning outcomes in a physics course. A mixed research method was used in this study. The quantitative data were collected using a pretest-posttest control group design. The qualitative data were collected using semi-structured interviews. The sample was 150 first-year students in the Department of Mechanical Engineering Technology at Cao Thang Technical College in Vietnam in the 2022-2023 school year. The quantitative data were collected using a pretest-posttest control group design. The pretest was administered to both groups at the beginning of the study. The posttest was administered to both groups at the end of the study. The qualitative data were collected using semi-structured interviews with a sample of eight students in the experimental group. The interviews were conducted after the posttest. The quantitative data were analyzed using independent sample T-tests. The qualitative data were analyzed using thematic analysis. The quantitative data showed that students in the experimental group, who were taught using the engineering design process, had significantly higher post-test scores on physics problem-solving than students in the control group, who were taught using the conventional method. The qualitative data showed that students in the experimental group were more motivated and engaged in the learning process than students in the control group. Students in the experimental group also reported that they found the engineering design process to be a more effective way of learning physics. The findings of this study suggest that the engineering design process can be an effective way of enhancing student learning outcomes in physics courses. The engineering design process engages students in the learning process and helps them to develop problem-solving skills.

Keywords: engineering design process, problem-solving, learning outcome of physics, students’ physics competencies, deep learning

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Authors: Margarita Mayoral-Villa, J. Klapp, L. Di G. Sigalotti, J. E. V. Guzmán

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Automated learning techniques are widely applied in the energy sector to address challenging problems from a practical point of view. To this end, we discuss the implementation of a Message Passing algorithm (MPNN)within a Graph Neural Network(GNN)to leverage the neighborhood of a set of nodes during the aggregation process. This approach enables the characterization of multiphase diffusion processes in the reservoir, such that the flow paths underlying the interconnections between multiple wells may be inferred from previously available data on flow rates and bottomhole pressures. The results thus obtained compare favorably with the predictions produced by the Reduced Order Capacitance-Resistance Models (CRM) and suggest the potential of MPNNs to enhance the robustness of the forecasts while improving the computational efficiency.

Keywords: multiphase diffusion, message passing neural network, well interconnection, interwell connectivity, graph neural network, capacitance-resistance models

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Authors: Subrato Saha, Yun-Hyun Cho

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In electric power steering (EPS), spoke type brushless ac (BLAC) motors offer distinct advantages over other electric motor types in terms torque smoothness, reliability and efficiency. This paper deals with the shape optimization of spoke type BLAC motor, in order to reduce cogging torque. This paper examines 3 steps skewing rotor angle, optimizing rotor core edge and rotor overlap length for reducing cogging torque in spoke type BLAC motor. The methods were applied to existing machine designs and their performance was calculated using finite- element analysis (FEA). Prototypes of the machine designs were constructed and experimental results obtained. It is shown that the FEA predicted the cogging torque to be nearly reduce using those methods.

Keywords: EPS, 3-Step skewing, spoke type BLAC, cogging torque, FEA, optimization

Procedia PDF Downloads 478

Authors: Panagiota Katsikouli, William Hamilton Byrne, Thomas Gammeltoft-Hansen, Tijs Slaats

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An individual who demonstrates a well-founded fear of persecution or faces real risk of being subjected to torture is eligible for asylum. In Danish law, the exact legal thresholds reflect those established by international conventions, notably the 1951 Refugee Convention and the 1950 European Convention for Human Rights. These international treaties, however, remain largely silent when it comes to how states should assess asylum claims. As a result, national authorities are typically left to determine an individual’s legal eligibility on a narrow basis consisting of an oral testimony, which may itself be hampered by several factors, including imprecise language interpretation, insecurity or lacking trust towards the authorities among applicants. The leaky ground, on which authorities must assess their subjective perceptions of asylum applicants' credibility, questions whether, in all cases, adjudicators make the correct decision. Moreover, the subjective element in these assessments raises questions on whether individual asylum cases could be afflicted by implicit biases or stereotyping amongst adjudicators. In fact, recent studies have uncovered significant correlations between decision outcomes and the experience and gender of the assigned judge, as well as correlations between asylum outcomes and entirely external events such as weather and political elections. In this study, we analyze a publicly available dataset containing approximately 8,000 summaries of asylum cases, initially rejected, and re-tried by the Refugee Appeals Board (RAB) in Denmark. First, we look for variations in the recognition rates, with regards to a number of applicants’ features: their country of origin/nationality, their identified gender, their identified religion, their ethnicity, whether torture was mentioned in their case and if so, whether it was supported or not, and the year the applicant entered Denmark. In order to extract those features from the text summaries, as well as the final decision of the RAB, we applied natural language processing and regular expressions, adjusting for the Danish language. We observed interesting variations in recognition rates related to the applicants’ country of origin, ethnicity, year of entry and the support or not of torture claims, whenever those were made in the case. The appearance (or not) of significant variations in the recognition rates, does not necessarily imply (or not) bias in the decision-making progress. None of the considered features, with the exception maybe of the torture claims, should be decisive factors for an asylum seeker’s fate. We therefore investigate whether the decision can be predicted on the basis of these features, and consequently, whether biases are likely to exist in the decisionmaking progress. We employed a number of machine learning classifiers, and found that when using the applicant’s country of origin, religion, ethnicity and year of entry with a random forest classifier, or a decision tree, the prediction accuracy is as high as 82% and 85% respectively. tentially predictive properties with regards to the outcome of an asylum case. Our analysis and findings call for further investigation on the predictability of the outcome, on a larger dataset of 17,000 cases, which is undergoing.

Keywords: asylum adjudications, automated decision-making, machine learning, text mining

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Authors: Michael Oppong, Stephanie Owusu Ansah, Daniel Ofori

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The study investigated students’ post-covid-19 experiences with e-learning platforms among undergraduate students of public universities in the Ashanti region of Ghana. The study respectively drew 289 respondents from two public universities, i.e., Kwame Nkrumah University of Science and Technology (KNUST) Business School and the Kumasi Technical University (KsTU) Business School in Ghana. Given that the population from the two public universities was fairly high, sampling had to be done. The overall population of the study was 480 students randomly sampled from the two public universities using the sampling ratio given by Alreck and Settle (2004). The population constituted 360 students from the Kwame Nkrumah University of Science and Technology (KNUST) Business School and 120 from the Kumasi Technical University Business School (KsTU). The study employed questionnaires as a data collection tool. The data gathered were 289 responses out of 480 questionnaires administered, representing 60.2%. The data was analyzed using pie charts, bar charts, percentages, and line graphs. Findings revealed that the e-learning platforms were still useful. However, the students used it on a weekly basis post-COVID-19, unlike in the COVID-19 era, where it was used daily. All other academic activities, with the exception of examinations, are still undertaken on the e-learning platforms; however, it is underutilized in the post-COVID-19 experience. The study recommends that universities should invest in infrastructure development to enable all academic activities, most especially examinations, to be undertaken using the e-learning platforms to curtail future challenges.

Keywords: e-learning platform, undergraduate students, post-COVID-19 experience, public universities

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Authors: Hai Zeng, Anqing Liu, Shuguang Dan, Ying Zhang, Yan Li, Zihang Zeng

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Children are strong; the country strong, the development of children Basketball is a strategic advantage. Common forms of basketball equipment has been difficult to meet the needs of young children teaching the game of basketball, basketball development for 3-6 years old children in the form of appropriate teaching aids is a breakthrough basketball game teaching children bottlenecks, improve teaching critical path pleasure, but also the development of early childhood basketball a necessary requirement. In this study, literature, questionnaires, focus group interviews, comparative analysis, for domestic and foreign use of 12 kinds of basketball teaching aids (cloud computing MINI basketball, adjustable basketball MINI, MINI basketball court, shooting assist paw print ball, dribble goggles, dribbling machine, machine cartoon shooting, rebounding machine, against the mat, elastic belt, ladder, fitness ball), from fun and improve early childhood shooting technique, dribbling technology, as well as offensive and defensive rebounding against technology conduct research on conversion technology. The results show that by using appropriate forms of teaching children basketball aids, can effectively improve children's fun basketball game, targeted to improve a technology, different types of aids from different perspectives enrich the connotation of children basketball game. Recommended for children of color psychology, cartoon and environmentally friendly material production aids, and increase research efforts basketball aids children, encourage children to sports teachers aids applications.

Keywords: appropriate forms of children basketball, auxiliary equipment, appli, MINI basketball, 3-6 years old children, teaching

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Authors: Dacheng Li, Bo Huang, Qinjin Han, Ming Li

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Remotely sensed imagery with the high spatial and temporal characteristics, which it is hard to acquire under the current land observation satellites, has been considered as a key factor for monitoring environmental changes over both global and local scales. On a basis of the limited high spatial-resolution observations, challenged studies called spatiotemporal fusion have been developed for generating high spatiotemporal images through employing other auxiliary low spatial-resolution data while with high-frequency observations. However, a majority of spatiotemporal fusion approaches yield to satisfactory assumption, empirical but unstable parameters, low accuracy or inefficient performance. Although the spatiotemporal fusion methodology via sparse representation theory has advantage in capturing reflectance changes, stability and execution efficiency (even more efficient when overcomplete dictionaries have been pre-trained), the retrieval of high-accuracy dictionary and its response to fusion results are still pending issues. In this paper, we employ additional image pairs (here each image-pair includes a Landsat Operational Land Imager and a Moderate Resolution Imaging Spectroradiometer acquisitions covering the partial area of Baotou, China) only into the coupled dictionary training process based on K-SVD (K-means Singular Value Decomposition) algorithm, and attempt to improve the fusion results of two existing sparse representation based fusion models (respectively utilizing one and two available image-pair). The results show that more eligible image pairs are probably related to a more accurate overcomplete dictionary, which generally indicates a better image representation, and is then contribute to an effective fusion performance in case that the added image-pair has similar seasonal aspects and image spatial structure features to the original image-pair. It is, therefore, reasonable to construct multi-dictionary training pattern for generating a series of high spatial resolution images based on limited acquisitions.

Keywords: spatiotemporal fusion, sparse representation, K-SVD algorithm, dictionary learning

Procedia PDF Downloads 246

Authors: Anshu Radha Aggarwal

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As per the Right to Education Act (RTE), 2009, every child in the age group of 6-14 years shall be admitted in a neighborhood school. All the Out of School Children identified have to be enrolled / mainstreamed in to age appropriate class and there-after be provided special training. This paper addresses issues emerging from provisions in the RTE Act that specifically refer to the enrolment of out-of school children into age appropriate classes and the requirement to provide special trainings that will enable this to take place. In the context of RTE Act, the Out-of-School Children are first enrolled in the formal school and then they are provided with Special Training through NRSTCs (Long Term / Short term basis). These centers are functioning in formal school campus itself. This paper specifies the role of special training centers (STC). It presents a re-envisioning of assessment that recognizes two principal functions of assessment, assessment for learning and assessment of learning, instead of the more familiar categories of formative, diagnostic, summative, and evaluative assessment. The use of these two functions of assessment highlights and emphasizes the role of special training centers (STC) to assess their level for giving them appropriate special training and to evaluate their improvement in learning level. Challenge of problem faced by teachers to do diagnostic assessment, including its place in the sequence of assessment procedures appropriate in identifying and addressing individual children’s learning difficulties are solved by special training centers (STC). It is important that assessment is used to identify children with learning difficulties at the earliest possible stage so that appropriate support and intervention can be put in place. So appropriate challenges with tools are presented here for their assessment at entry level and at completion level of primary children by special training centers (STC).

Keywords: right to education, assessment, challenges, out of school children

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Authors: Min Kyu Kim, Eun Young Lee, Dong Woo Son, Yoon Seok Chang

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In this paper, we introduce a flexible box erecting machine (BEM) that swiftly and automatically transforms cardboard into a three dimensional box. Recently, the parcel service and home-shopping industries have grown rapidly, and there is an increasing need for various box types to ship various products. However, workers cannot fold thousands of boxes manually in a day. As such, automatic BEMs are garnering greater attention. This study takes equipment operation into consideration as well as mechanical improvements in order to design a BEM that is able to outperform its conventional counterparts. We analyzed six dispatching rules – First In First Out (FIFO), Shortest Processing Time (SPT), Earliest Due Date (EDD), Setup Avoidance, EDD + SPT, and EDD + Setup Avoidance – to determine which one was most suitable for BEM operation. Consequently, SPT and Setup Avoidance were found to be the most critical rules, followed by EDD + Setup Avoidance, EDD + SPT, EDD, and FIFO. This hierarchy was valid for both our conventional BEM and our new flexible BEM from the viewpoint of processing time. We believe that this research can contribute to flexible BEM management, which has the potential to increase productivity and convenience.

Keywords: automation, box erecting machine, dispatching rule, setup time

Procedia PDF Downloads 348

Authors: Nuwan Kuruwitaarachchi, M. K. M. Peiris, C. N. Madawala, K. M. A. R. Perera, V. U. N Perera

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Dealing with businesses with the foreign market always took a special place in a country’s economy. Political and social factors came into play making currency rate changes fluctuate rapidly. Currency rate prediction has become an important factor for larger international businesses since large amounts of money exchanged between countries. This research focuses on comparing the accuracy of mainly three models; Autoregressive Integrated Moving Average (ARIMA), Artificial Neural Networks(ANN) and Support Vector Machines(SVM). series of data import, export, USD currency exchange rate respect to LKR has been selected for training using above mentioned algorithms. After training the data set and comparing each algorithm, it was able to see that prediction in SVM performed better than other models. It was improved more by combining SVM and SVR models together.

Keywords: ARIMA, ANN, FFNN, RMSE, SVM, SVR

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Authors: Ella Sèdé Maforikan

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Accurate land cover mapping is essential for effective environmental monitoring and natural resources management. This study focuses on assessing the classification performance of two satellite datasets and evaluating the impact of different input feature combinations on classification accuracy in the Beterou catchment, situated in the northern part of Benin. Landsat-8 and Sentinel-2 images from June 1, 2020, to March 31, 2021, were utilized. Employing the Random Forest (RF) algorithm on Google Earth Engine (GEE), a supervised classification categorized the land into five classes: forest, savannas, cropland, settlement, and water bodies. GEE was chosen due to its high-performance computing capabilities, mitigating computational burdens associated with traditional land cover classification methods. By eliminating the need for individual satellite image downloads and providing access to an extensive archive of remote sensing data, GEE facilitated efficient model training on remote sensing data. The study achieved commendable overall accuracy (OA), ranging from 84% to 85%, even without incorporating spectral indices and terrain metrics into the model. Notably, the inclusion of additional input sources, specifically terrain features like slope and elevation, enhanced classification accuracy. The highest accuracy was achieved with Sentinel-2 (OA = 91%, Kappa = 0.88), slightly surpassing Landsat-8 (OA = 90%, Kappa = 0.87). This underscores the significance of combining diverse input sources for optimal accuracy in land cover mapping. The methodology presented herein not only enables the creation of precise, expeditious land cover maps but also demonstrates the prowess of cloud computing through GEE for large-scale land cover mapping with remarkable accuracy. The study emphasizes the synergy of different input sources to achieve superior accuracy. As a future recommendation, the application of Light Detection and Ranging (LiDAR) technology is proposed to enhance vegetation type differentiation in the Beterou catchment. Additionally, a cross-comparison between Sentinel-2 and Landsat-8 for assessing long-term land cover changes is suggested.

Keywords: land cover mapping, Google Earth Engine, random forest, Beterou catchment

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Authors: Abdulmajid Mukhtar Afat

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This paper aims at introducing nondeterministic finite automata with ε value which is used to perform some operations on languages. a program is created to implement the algorithm that converts nondeterministic finite automata with ε value (ε-NFA) to deterministic finite automata (DFA).The program is written in c++ programming language. The program inputs are FA 5-tuples from text file and then classifies it into either DFA/NFA or ε -NFA. For DFA, the program will get the string w and decide whether it is accepted or rejected. The tracking path for an accepted string is saved by the program. In case of NFA or ε-NFA automation, the program changes the automation to DFA to enable tracking and to decide if the string w exists in the regular language or not.

Keywords: DFA, NFA, ε-NFA, eclose, finite automata, operations on languages

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Authors: M. R. Ghasemi, R. Ghiasi, H. Varaee

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Surrogate model has received increasing attention for use in detecting damage of structures based on vibration modal parameters. However, uncertainties existing in the measured vibration data may lead to false or unreliable output result from such model. In this study, an efficient approach based on Monte Carlo simulation is proposed to take into account the effect of uncertainties in developing a surrogate model. The probability of damage existence (PDE) is calculated based on the probability density function of the existence of undamaged and damaged states. The kriging technique allows one to genuinely quantify the surrogate error, therefore it is chosen as metamodeling technique. Enhanced version of ideal gas molecular movement (EIGMM) algorithm is used as main algorithm for model updating. The developed approach is applied to detect simulated damage in numerical models of 72-bar space truss and 120-bar dome truss. The simulation results show the proposed method can perform well in probability-based damage detection of structures with less computational effort compared to direct finite element model.

Keywords: probability-based damage detection (PBDD), Kriging, surrogate modeling, uncertainty quantification, artificial intelligence, enhanced ideal gas molecular movement (EIGMM)

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Authors: Tathiana Yesenia Coy Mondragón, Jossitt William Vargas Cruz, Cristian Leonardo Gutiérrez Gómez

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The study of cosmic rays is based on two fundamental pillars: the detection of secondary cosmic rays on the Earth's surface and the detection of the source and origin of the cascade. In addition, the constant flow of RC generates a lot of interest for study due to the incidence of various natural phenomena, which makes it relevant to characterize their incidence parameters to determine their effect not only at subsoil or terrestrial surface levels but also throughout the atmosphere. To determine the physical parameters of the primary cosmic ray, the implementation of robust algorithms capable of reconstructing the cascade from the measured values is required, with a high level of reliability. Therefore, it is proposed to build a machine learning system that will be fed from the cosmic ray simulations in CORSIKA at different energies that lie in a range [10⁹-10¹²] eV. in order to generate a trained particle and pattern recognition system to obtain greater efficiency when inferring the nature of the origin of the cascade for EAS in the atmosphere considering atmospheric models.

Keywords: CORSIKA, cosmic rays, eas, Colombia

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Authors: Vesile Evrim, Aliyu Awwal

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By the evolvement in technology, the way of expressing opinions switched the direction to the digital world. The domain of politics as one of the hottest topics of opinion mining research merged together with the behavior analysis for affiliation determination in text which constitutes the subject of this paper. This study aims to classify the text in news/blogs either as Republican or Democrat with the minimum number of features. As an initial set, 68 features which 64 are constituted by Linguistic Inquiry and Word Count (LIWC) features are tested against 14 benchmark classification algorithms. In the later experiments, the dimensions of the feature vector reduced based on the 7 feature selection algorithms. The results show that Decision Tree, Rule Induction and M5 Rule classifiers when used with SVM and IGR feature selection algorithms performed the best up to 82.5% accuracy on a given dataset. Further tests on a single feature and the linguistic based feature sets showed the similar results. The feature “function” as an aggregate feature of the linguistic category, is obtained as the most differentiating feature among the 68 features with 81% accuracy by itself in classifying articles either as Republican or Democrat.

Keywords: feature selection, LIWC, machine learning, politics

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Authors: Pinkey Yaqub

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The aim of this study was to investigate secondary (grades IX and X) students’ attitudes towards learning the English language based on the medium of instruction of the school, the gender of the students and the grade level in which they studied. A further aim was to determine students’ proficiency in the English language according to their gender, the grade level and the medium of instruction of the school. A survey was used to investigate the attitudes of secondary students towards English language learning. Simple random sampling was employed to obtain a representative sample of the target population for the research study as a comprehensive list of established English medium schools, and newly established English medium schools were available. A questionnaire ‘Attitude towards English Language Learning’ (AtELL) was adapted from a research study on Libyan secondary school students’ attitudes towards learning English language. AtELL was reviewed by experts (n=6) and later piloted on a representative sample of secondary students (n= 160). Subsequently, the questionnaire was modified - based on the reviewers’ feedback and lessons learnt during the piloting phase - and directly administered to students of grades 9 and 10 to gather information regarding their attitudes towards learning the English language. Data collection spanned a month and a half. As the data were not normally distributed, the researcher used Mann-Whitney tests to test the hypotheses formulated to investigate students’ attitudes towards learning English as well as proficiency in the language across the medium of instruction of the school, the gender of the students and the grade level of the respondents. Statistical analyses of the data showed that the students of established English medium schools exhibited a positive outlook towards English language learning in terms of the behavioural, cognitive and emotional aspects of attitude. A significant difference was observed in the attitudes of male and female students towards learning English where females showed a more positive attitude in terms of behavioural, cognitive and emotional aspects as compared to their male counterparts. Moreover, grade 10 students had a more positive attitude towards learning English language in terms of behavioural, cognitive and emotional aspects as compared to grade 9 students. Nonetheless, students of newly established English medium schools were more proficient in English as gauged by their examination scores in this subject as compared to their counterparts studying in established English medium schools. Moreover, female students were more proficient in English while students studying in grade 9 were less proficient in English than their seniors studying in grade 10. The findings of this research provide empirical evidence to future researchers wishing to explore the relationship between attitudes towards learning language and variables such as the medium of instruction of the school, gender and the grade level of the students. Furthermore, policymakers might revisit the English curriculum to formulate specific guidelines that promote a positive and gender-balanced outlook towards learning English for male and female students.

Keywords: attitude, behavioral aspect of attitude, cognitive aspect of attitude, emotional aspect of attitude

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Authors: Jae-Yong Lee, Gyu-Hyun Bae, Yun-Soo Chung, Dae-Sub Kim, Jae-Sung Bae

Abstract:

In general, fabric is heat-treated using a stenter machine in order to dry and fix its shape. It is important to shape before the heat treatment because it is difficult to revert back once the fabric is formed. To produce the product of right shape, camera type weft straightener has been applied recently to capture and process fabric images quickly. It is more powerful in determining the final textile quality rather than photo-sensor. Positioning in front of a stenter machine, weft straightener helps to spread fabric evenly and control the angle between warp and weft constantly as right angle by handling skew and bow rollers. To process this tricky procedure, the structural analysis should be carried out in advance, based on which, its control technology can be drawn. A structural analysis is to figure out the specific contact/slippage characteristics between fabric and roller. We already examined the applicability of camera type weft straightener to plain weave fabric and found its possibility and the specific working condition of machine and rollers. In this research, we aimed to explore another applicability of camera type weft straightener. Namely, we tried to figure out camera type weft straightener can be used for fabrics. To find out the optimum condition, we increased the number of rollers. The analysis is done by ANSYS software using Finite Element Analysis method. The control function is demonstrated by experiment. In conclusion, the structural analysis of weft straightener is done to identify a specific characteristic between roller and fabrics. The control of skew and bow roller is done to decrease the error of the angle between warp and weft. Finally, it is proved that camera type straightener can also be used for the special fabrics.

Keywords: camera type weft straightener, structure analysis, control, skew and bow roller

Procedia PDF Downloads 281

Authors: Ayman M. Mansour, Bilal Hawashin, Mohammed A. Mansour

Abstract:

In this paper, we propose a cooperative learning mechanism in a multi-agent intelligent system. The basic idea is that intelligent agents are capable of collaborating with one another by sharing their knowledge. The agents will start collaboration by providing their knowledge rules to the other agents. This will allow the most important and insightful detection rules produced by the most experienced agent to bubble up for the benefit of the entire agent community. The updated rules will lead to improving the agents’ decision performance. To evaluate our approach, we designed a five–agent system and implemented it using JADE and FuzzyJess software packages. The agents will work with each other to make a decision about a suspicious medical case. This system provides quick response rate and the decision is faster than the manual methods. This will save patients life.

Keywords: intelligent, multi-agent system, cooperative, fuzzy, learning

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Authors: Josselyn Mata Calidonio, Arianna I. Maddox, Kimberly Hamad-Schifferli

Abstract:

Rapid diagnostics are critical infectious disease tools that are designed to detect a known biomarker using antibodies specific to that biomarker. However, a way to detect unknown viruses has not yet been achieved in a paper test format. We describe here a route to make an adaptable paper immunoassay that can detect an unknown biomarker, demonstrating it on SARS-CoV-2 variants. The immunoassay repurposes cross-reactive antibodies raised against the alpha variant. Gold nanoparticles of two different colors conjugated to two different antibodies create a colorimetric signal, and machine learning of the resulting colorimetric pattern is used to train the assay to discriminate between variants of alpha and Omicron BA.5. By using principal component analysis, the colorimetric test patterns can pick up and discriminate an unknown that it has not encountered before, Omicron BA.1. The test has an accuracy of 100% and a potential calculated discriminatory power of 900. We show that it can be used adaptively and that it can be used to pick up emerging variants without the need to raise new antibodies.

Keywords: adaptive immunoassay, detecting unknown viruses, gold nanoparticles, paper immunoassay, repurposing antibodies

Procedia PDF Downloads 94

Authors: Foad Nazari, Seyed Mahmood Hosseini, Mohammad Hossein Abolbashari, Mohammad Hassan Abolbashari

Abstract:

The present study is concerned with the optimal design of functionally graded plates using particle swarm optimization (PSO) algorithm. In this study, meshless local Petrov-Galerkin (MLPG) method is employed to obtain the functionally graded (FG) plate’s natural frequencies. Effects of two parameters including thickness to height ratio and volume fraction index on the natural frequencies and total mass of plate are studied by using the MLPG results. Then the first natural frequency of the plate, for different conditions where MLPG data are not available, is predicted by an artificial neural network (ANN) approach which is trained by back-error propagation (BEP) technique. The ANN results show that the predicted data are in good agreement with the actual one. To maximize the first natural frequency and minimize the mass of FG plate simultaneously, the weighted sum optimization approach and PSO algorithm are used. However, the proposed optimization process of this study can provide the designers of FG plates with useful data.

Keywords: optimal design, natural frequency, FG plate, hybrid meshless method, MLPG method, ANN approach, particle swarm optimization

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Authors: Bilal Ahmed, Muhammad Rafiq, Choongjae Im

Abstract:

The tech-savvy Gen Z's transfer towards interactive concept learning is hammering the demand for online collaborative learning environments, renovating conventional education approaches. The authors propose a novel approach to enhance learning outcomes to improve retention in 3D interactive education by connecting virtual reality (VR) and non-VR devices in the classroom and distance learning. The study evaluates students' experiences with VR interconnectivity devices in human anatomy lectures using real-time 3D interactive data visualization. Utilizing the renowned "Guo & Pooles Inventory" and the "Flow for Presence Questionnaires," it used an experimental research design with a control and experimental group to assess this novel connecting strategy's effectiveness and significant potential for in-person and online educational settings during the sessions. The experimental group's interactions, engagement levels, and usability experiences were assessed using the "Guo & Pooles Inventory" and "Flow for Presence Questionnaires," which measure their sense of presence, engagement, and immersion throughout the learning process using a 5-point Likert scale. At the end of the sessions, we used the "Perceived Usability Scale" to find our proposed system's overall efficiency, effectiveness, and satisfaction. By comparing both groups, the students in the experimental group used the integrated VR environment and VR to non-VR devices, and their sense of presence and attentiveness was significantly improved, allowing for increased engagement by giving students diverse technological access. Furthermore, learners' flow states demonstrated increased absorption and focus levels, improving information retention and Perceived Usability. The findings of this study can help educational institutions optimize their technology-enhanced teaching methods for traditional classroom settings as well as distance-based learning, where building a sense of connection among remote learners is critical. This study will give significant insights into educational technology and its ongoing progress by analyzing engagement, interactivity, usability, satisfaction, and presence.

Keywords: interactive learning environments, human-computer interaction, virtual reality, computer- supported collaborative learning

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Authors: Weichen Chang

Abstract:

To create a guided teaching method for AI generative drawing design, this paper develops a set of teaching models for AI generative drawing (DCAI), which combines learning modes such as problem-solving, thematic inquiry, phenomenon-based, task-oriented, and DFC . Through the information security AI picture book learning guided programs and content, the application of participatory action research (PAR) and interview methods to explore the dual knowledge of Context and ChatGPT (DCAI) for AI to guide the development of students' AI learning skills. In the interviews, the students highlighted five main learning outcomes (self-study, critical thinking, knowledge generation, cognitive development, and presentation of work) as well as the challenges of implementing the model. Through the use of DCAI, students will enhance their consensus awareness of generative mapping analysis and group cooperation, and they will have knowledge that can enhance AI capabilities in DCAI inquiry and future life. From this paper, it is found that the conclusions are (1) The good use of DCAI can assist students in exploring the value of their knowledge through the power of stories and finding the meaning of knowledge communication; (2) Analyze the transformation power of the integrity and coherence of the story through the context so as to achieve the tension of ‘starting and ending’; (3) Use ChatGPT to extract inspiration, arrange story compositions, and make prompts that can communicate with people and convey emotions. Therefore, new knowledge construction methods will be one of the effective methods for AI learning in the face of artificial intelligence, providing new thinking and new expressions for interdisciplinary design and design education practice.

Keywords: artificial intelligence, task-oriented, contextualization, design education

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Authors: Jonathan Sohn

Abstract:

Deep neural networks (DNNs) have shown state-of-the-art performance for many applications, including computer vision, natural language processing, and speech recognition. Recently, adversarial attacks have been studied in the context of deep neural networks, which aim to alter the results of deep neural networks by modifying the inputs slightly. For example, an adversarial attack on a DNN used for object detection can cause the DNN to miss certain objects. As a result, the reliability of DNNs is undermined by their lack of robustness against adversarial attacks, raising concerns about their use in safety-critical applications such as autonomous driving. In this paper, we focus on studying the adversarial attacks and defenses on DNNs for image classification. There are two types of adversarial attacks studied which are fast gradient sign method (FGSM) attack and projected gradient descent (PGD) attack. A DNN forms decision boundaries that separate the input images into different categories. The adversarial attack slightly alters the image to move over the decision boundary, causing the DNN to misclassify the image. FGSM attack obtains the gradient with respect to the image and updates the image once based on the gradients to cross the decision boundary. PGD attack, instead of taking one big step, repeatedly modifies the input image with multiple small steps. There is also another type of attack called the target attack. This adversarial attack is designed to make the machine classify an image to a class chosen by the attacker. We can defend against adversarial attacks by incorporating adversarial examples in training. Specifically, instead of training the neural network with clean examples, we can explicitly let the neural network learn from the adversarial examples. In our experiments, the digit recognition accuracy on the MNIST dataset drops from 97.81% to 39.50% and 34.01% when the DNN is attacked by FGSM and PGD attacks, respectively. If we utilize FGSM training as a defense method, the classification accuracy greatly improves from 39.50% to 92.31% for FGSM attacks and from 34.01% to 75.63% for PGD attacks. To further improve the classification accuracy under adversarial attacks, we can also use a stronger PGD training method. PGD training improves the accuracy by 2.7% under FGSM attacks and 18.4% under PGD attacks over FGSM training. It is worth mentioning that both FGSM and PGD training do not affect the accuracy of clean images. In summary, we find that PGD attacks can greatly degrade the performance of DNNs, and PGD training is a very effective way to defend against such attacks. PGD attacks and defence are overall significantly more effective than FGSM methods.

Keywords: deep neural network, adversarial attack, adversarial defense, adversarial machine learning

Procedia PDF Downloads 179