Search results for: physics guided machine learning

Authors: Habtamu Ayenew Asegie

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Wealth, as opposed to income or consumption, implies a more stable and permanent status. Due to natural and human-made difficulties, households' economies will be diminished, and their well-being will fall into trouble. Hence, governments and humanitarian agencies offer considerable resources for poverty and malnutrition reduction efforts. One key factor in the effectiveness of such efforts is the accuracy with which low-income or poor populations can be identified. As a result, this study aims to predict a household’s wealth status using ensemble Machine learning (ML) algorithms. In this study, design science research methodology (DSRM) is employed, and four ML algorithms, Random Forest (RF), Adaptive Boosting (AdaBoost), Light Gradient Boosted Machine (LightGBM), and Extreme Gradient Boosting (XGBoost), have been used to train models. The Ethiopian Demographic and Health Survey (EDHS) dataset is accessed for this purpose from the Central Statistical Agency (CSA)'s database. Various data pre-processing techniques were employed, and the model training has been conducted using the scikit learn Python library functions. Model evaluation is executed using various metrics like Accuracy, Precision, Recall, F1-score, area under curve-the receiver operating characteristics (AUC-ROC), and subjective evaluations of domain experts. An optimal subset of hyper-parameters for the algorithms was selected through the grid search function for the best prediction. The RF model has performed better than the rest of the algorithms by achieving an accuracy of 96.06% and is better suited as a solution model for our purpose. Following RF, LightGBM, XGBoost, and AdaBoost algorithms have an accuracy of 91.53%, 88.44%, and 58.55%, respectively. The findings suggest that some of the features like ‘Age of household head’, ‘Total children ever born’ in a family, ‘Main roof material’ of their house, ‘Region’ they lived in, whether a household uses ‘Electricity’ or not, and ‘Type of toilet facility’ of a household are determinant factors to be a focal point for economic policymakers. The determinant risk factors, extracted rules, and designed artifact achieved 82.28% of the domain expert’s evaluation. Overall, the study shows ML techniques are effective in predicting the wealth status of households.

Keywords: ensemble machine learning, households wealth status, predictive model, wealth status prediction

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Authors: Mohammad H. Taheri, David J. Brown, Nasser Sherkat

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Engagement is one of the most important factors in determining successful outcomes and deep learning in students. Existing approaches to detect student engagement involve periodic human observations that are subject to inter-rater reliability. Our solution uses real-time multimodal multisensor data labeled by objective performance outcomes to infer the engagement of students. The study involves four students with a combined diagnosis of cerebral palsy and a learning disability who took part in a 3-month trial over 59 sessions. Multimodal multisensor data were collected while they participated in a continuous performance test. Eye gaze, electroencephalogram, body pose, and interaction data were used to create a model of student engagement through objective labeling from the continuous performance test outcomes. In order to achieve this, a type of continuous performance test is introduced, the Seek-X type. Nine features were extracted including high-level handpicked compound features. Using leave-one-out cross-validation, a series of different machine learning approaches were evaluated. Overall, the random forest classification approach achieved the best classification results. Using random forest, 93.3% classification for engagement and 42.9% accuracy for disengagement were achieved. We compared these results to outcomes from different models: AdaBoost, decision tree, k-Nearest Neighbor, naïve Bayes, neural network, and support vector machine. We showed that using a multisensor approach achieved higher accuracy than using features from any reduced set of sensors. We found that using high-level handpicked features can improve the classification accuracy in every sensor mode. Our approach is robust to both sensor fallout and occlusions. The single most important sensor feature to the classification of engagement and distraction was shown to be eye gaze. It has been shown that we can accurately predict the level of engagement of students with learning disabilities in a real-time approach that is not subject to inter-rater reliability, human observation or reliant on a single mode of sensor input. This will help teachers design interventions for a heterogeneous group of students, where teachers cannot possibly attend to each of their individual needs. Our approach can be used to identify those with the greatest learning challenges so that all students are supported to reach their full potential.

Keywords: affective computing in education, affect detection, continuous performance test, engagement, flow, HCI, interaction, learning disabilities, machine learning, multimodal, multisensor, physiological sensors, student engagement

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Authors: Kashif Laeeq, Zubair A. Shaikh

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The paradigm of education is drastically changing from conventional to e-learning model. Due to ease of learning with various other benefits, several educational institutions are adopting the e-learning models. Some institutions are still willing to transform their educational system on to e-learning, but due to limited resources, they are still compromising on the old traditional system. The cloud computing could be one of the best solutions to overcome this problem by providing hardware, software, and infrastructure resources with cost efficient manner. The adoption of cloud computing in education will bring revolution in this paradigm. This paper introduces various positive features of e-learning and presents a way how cloud computing technology can be provisioned e-learning model. This paper also investigates the numerous challenges and opportunities that would be observed in cloud computing adoption in e-learning domain. The concept and knowledge present in this paper may create a new direction of research in the domain of cloud-based e-learning.

Keywords: cloud-based e-learning, e-learning, cloud computing application, smart learning

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Authors: Mzamani Aaron Mabasa

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The research study examines the reality that the Language Services Directorate can be considered a language repository hub. The study postulates that multilingual education guided by language policy implementation can improve student performance and pass rate. Various documents in the form of style guides, glossaries and tutorial letters may be used to enable students to understand complex words, sentences, phrases and paragraphs when technical vocabularies are used. This paper addresses the way in which quality assurance can transform South African official languages, including Sign Language, as mandated by the Language Policy for Higher Education. The paper further emphasizes that Language Services is unique in the sense that it involves all South African officials as tools for student support and facilitation of learning. This is in line with the Constitution of the Republic of South Africa (1996) and the Unisa Language Policy of 2023, which declares the status, parity and esteem of these official languages regarding usage in formal function domains, namely education, economy, social and politics. The aim of this paper is to ensure that quality assurance is ultimately accomplished in terms of teaching and learning standards. Eventually, all South African languages can be used for official domains to achieve functional multilingualism. This paper furthermore points out that content analysis as a research instrument as far as a qualitative approach is concerned may be used as a data collection technique.

Keywords: repository, multilingualism, policy, education

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Authors: Muneer Abbad

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In search of better, traditional learning universities have expanded their ways to deliver knowledge and integrate cost effective e-learning systems. Universities’ use of information and communication technologies has grown tremendously over the last decade. To ensure efficient use of the e-learning system, this project aimed to evaluate the good and bad practices, detect errors and determine areas for further improvements in usage. This project critically evaluated the students’ perception of the e-learning system and recommended changes to improve students’ e-learning usage, through conducting questionnaire given to the students that have experience with e-learning systems. Results of the study indicated that, in general, students have favourable perceptions toward using the e-learning system. They seemed to value the resources tool and its contribution to building their knowledge more than other e-learning tools. However, they seemed to perceive a limited value from the audio or video podcasts. This study has shown that technology acceptance is the most variable, factor that contributes to students’ perception and satisfaction of the e-learning system.

Keywords: e-learning, perception, Jordan, universities

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Authors: R. Janyaphadungpong, A. Pimkhaokham

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This prospective clinical study determined the insertion torque (IT) value and monitored the changes in implant stability quotient (ISQ) values during the 12 weeks healing period from implant placement without bone graft (control group) and with bone graft using the guided bone regeneration (GBR) technique (study group). The relationship between the IT and ISQ values of the implants was also assessed. The control and study groups each consisted of 6 patients with 8 implants per group. The ASTRA TECH Implant System™ EV 4.2 mm in diameter was placed in the posterior mandibular region. In the control group, implants were placed in bone without bone graft, whereas in the study group implants were placed simultaneously with the GBR technique at favorable bone defect. IT (Ncm) of each implant was recorded when fully inserted. ISQ values were obtained from the Osstell® ISQ at the time of implant placement, and at 2, 4, 8, and 12 weeks. No difference in IT was found between groups (P = 0.320). The ISQ values in the control group were significantly higher than in the study group at the time of implant placement and at 4 weeks. There was no significant association between IT and ISQ values either at baseline or after the 12 weeks. At 12 weeks of healing, the control and study groups displayed different trends. Mean ISQ values for the control group decreased over the first 2 weeks and then started to increase. ISQ value increases were statistically significant at 8 weeks and later, whereas mean ISQ values in the study group decreased over the first 4 weeks and then started to increase, with statistical significance after 12 weeks. At 12 weeks, all implants achieved osseointegration with mean ISQ values over the threshold value (ISQ>70). These results indicated that implants, in which guided bone regeneration technique was performed during implant placement for treating favorable bone defects, were as predictable as implants placed without bone graft. However, loading in implants placed with the GBR technique for correcting favorable bone defects should be performed after 12 weeks of healing to ensure implant stability and osseointegration.

Keywords: dental implant, favorable bone defect, guided bone regeneration technique, implant stability

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Authors: F. Ruiz-Fuster

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Arnold’s redefinition of human motives can sustain a psychology of education which emphasizes the beauty of knowledge and the exercise of intellectual functions. Thus, education instead of focusing on skills and learning by doing would be centered on ‘the widest reaches of the human spirit’. One way to attain it is by developing children’s inherent interest. Arnold takes into account the fact that the desire to know is the inherent interest which leads students to explore and learn. She also emphasizes the need of exercising human functions as thinking, judging and reasoning. According to Arnold, the influence of psychological theories of motivation in education has derived in considering that all learning and school tasks should derive from children’s needs and impulses. The desire to know and the curiosity have not been considered as basic and active as any instinctive drive or basic need, so there has been an attempt to justify and understand how biological drives guide student’s learning. However, understanding motives and motivation not as a drive, an instinct or an impulse guided by our basic needs, but as a want that leads to action can help to understand, from a psychological perspective, how teachers can motivate students to learn, strengthening their desire and interest to reason and discover the whole new world of knowledge.

Keywords: academic motivation, interests, desire to know, educational psychology, intellectual functions

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Authors: Nur Sofi Hidayah, Ratih Tri Purwatiningsih

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Learning is a process attempts person to obtain a new behavior changes as a whole, as a result of his own experience in the interaction with the environment. Learning involves our brain to think, while the ability of the brain to each student's performance is different. To obtain optimal learning results then need time to learn the exact hour that the brain's performance is not too heavy. Referring to the learning system in Finland which apply 45 minutes to learn and a 15-minute break is expected to be the brain work better, with the rest of the brain, the brain will be more focused and lessons can be absorbed well. It can be concluded that learning in this way students learn with brain always fresh and the best possible use of the time, but it can make students not saturated in a lesson.

Keywords: learning, working hours brain, time efficient learning, working hours in the brain receive stimulus.

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Authors: Paula Paulino, Alzira Matias, Ana Margarida Veiga Simão

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Self-regulated learning (SRL) directs students in analyzing proposed tasks, setting goals and designing plans to achieve those goals. The literature has suggested a metacognitive strategy for goal attainment known as Mental Contrasting with Implementation Intentions (MCII). This strategy involves Mental Contrasting (MC), in which a significant goal and an obstacle are identified, and Implementation Intentions (II), in which an "if... then…" plan is conceived and operationalized to overcome that obstacle. The present study proposes to assess the MCII process and whether it promotes students’ commitment towards learning goals during school tasks in sciences subjects. In this investigation, we intended to study the MCII strategy in a systemic context of the classroom. Fifty-six students from middle school and secondary education attending a public school in Lisbon (Portugal) participated in the study. The MCII strategy was explicitly taught in a procedure that included metacognitive modeling, guided practice and autonomous practice of strategy. A mental contrast between a goal they wanted to achieve and a possible obstacle to achieving that desire was instructed, and then the formulation of plans in order to overcome the obstacle identified previously. The preliminary results suggest that the MCII metacognitive strategy, applied to the school context, leads to more sophisticated reflections, the promotion of learning goals and the elaboration of more complex and specific self-regulated plans. Further, students achieve better results on school tests and worksheets after strategy practice. This study presents important implications since the MCII has been related to improved outcomes and increased attendance. Additionally, MCII seems to be an innovative process that captures students’ efforts to learn and enhances self-efficacy beliefs during learning tasks.

Keywords: implementation intentions, learning goals, mental contrasting, metacognitive strategy, self-regulated learning

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Authors: Andy Wang

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Personalized learning refers to an educational approach that tailors instruction to meet the unique needs, interests, and abilities of each learner. This method of learning aims at providing students with a customized learning experience that is more engaging, interactive, and relevant to their personal lives. With generative AI technology, the author has developed a Personal Tutoring Bot (PTB) that supports personalized learning. The author is currently testing PTB in his EE 499 – Microelectronics Metrology course. Virtual Reality (VR) and Augmented Reality (AR) provide interactive and immersive learning environments that can engage student in online learning. This paper presents the rationale of integrating VR/AR tools in PTB and discusses challenges and solutions of incorporating VA/AR into the Personal Tutoring Bot (PTB).

Keywords: personalized learning, online education, hands-on practice, VR/AR tools

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Authors: Jayanta Kumar Ghosh, Swastik Sunil Goriwale, Himangshu Sarkar

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Groundwater is one of the most valuable natural resources that society consumes for its domestic, industrial, and agricultural water supply. Its bulk and indiscriminate consumption affects the groundwater resource. Often, it has been found that the groundwater recharge rate is much lower than its demand. Thus, to maintain water and food security, it is necessary to monitor and management of groundwater storage. However, it is challenging to estimate groundwater storage (GWS) by making use of existing hydrological models. To overcome the difficulties, machine learning (ML) models are being introduced for the evaluation of groundwater level (GWL). Thus, the objective of this research work is to develop an ML-based model for the prediction of GWL. This objective has been realized through the development of an artificial neural network (ANN) model based on hydro-gravimetry. The model has been developed using training samples from field observations spread over 8 months. The developed model has been tested for the prediction of GWL in an observation well. The root means square error (RMSE) for the test samples has been found to be 0.390 meters. Thus, it can be concluded that the hydro-gravimetric-based ANN model can be used for the prediction of GWL. However, to improve the accuracy, more hydro-gravimetric parameter/s may be considered and tested in future.

Keywords: machine learning, hydro-gravimetry, ground water level, predictive model

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Authors: Kaushik Sathupadi, Sandesh Achar

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Pervasive computing is a growing research field that aims to acknowledge human physical life routines (HPLR) based on body-worn sensors such as MEMS sensors-based technologies. The use of these technologies for human activity recognition is progressively increasing. On the other hand, personalizing human life routines using numerous machine-learning techniques has always been an intriguing topic. In contrast, various methods have demonstrated the ability to recognize basic movement patterns. However, it still needs to be improved to anticipate the dynamics of human living patterns. This study introduces state-of-the-art techniques for recognizing static and dy-namic patterns and forecasting those challenging activities from multi-fused sensors. Further-more, numerous MEMS signals are extracted from one self-annotated IM-WSHA dataset and two benchmarked datasets. First, we acquired raw data is filtered with z-normalization and denoiser methods. Then, we adopted statistical, local binary pattern, auto-regressive model, and intrinsic time scale decomposition major features for feature extraction from different domains. Next, the acquired features are optimized using maximum relevance and minimum redundancy (mRMR). Finally, the artificial neural network is applied to analyze the whole system's performance. As a result, we attained a 90.27% recognition rate for the self-annotated dataset, while the HARTH and KU-HAR achieved 83% on nine living activities and 90.94% on 18 static and dynamic routines. Thus, the proposed HPLR system outperformed other state-of-the-art systems when evaluated with other methods in the literature.

Keywords: artificial intelligence, machine learning, gait analysis, local binary pattern (LBP), statistical features, micro-electro-mechanical systems (MEMS), maximum relevance and minimum re-dundancy (MRMR)

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Authors: Nuri Balta, Ali Eryilmaz

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High-quality teachers are the key to improve student learning. Without a professional development of the teachers, the improvement of student success is difficult and incomplete. This study offers an in-service training course model for professional development of teachers (PD) entitled "teachers teaching teachers" (TTT). The basic premise of the PD program, designed for this study, was primarily aimed to increase the subject matter knowledge of high school physics teachers. The TTT course (the three hour long workshops), organized for this study, lasted for seven weeks with seventeen teachers took part in the TTT program at different amounts. In this study, the effect of the TTT program on teachers’ knowledge improvement was searched through the modern physics unit (MPU). The participating teachers taught the unit to one of their grade ten classes earlier, and they taught another equivalent class two months later. They were observed in their classes both before and after TTT program. The teachers were divided into placebo and the treatment groups. The aim of Solomon four-group design is an attempt to eliminate the possible effect of pre-test. However, in this study the similar design was used to eliminate the effect of pre teaching. The placebo group teachers taught their both classes as regular and the treatment group teachers had TTT program between the two teachings. The class observation results showed that the TTT program increased teachers’ knowledge and skills in teaching MPU. Further, participating in the TTT program caused teachers to teach the MPU in accordance with the requirements of the curriculum. In order to see any change in participating teachers’ success, an achievement test was applied to them. A large effect size (dCohen=.93) was calculated for the effect of TTT program on treatment group teachers’ achievement. The results suggest that staff developers should consider including topics, attractive to teachers, in-service training programs (a) to help teachers’ practice teaching the new topics (b) to increase the participation rate. During the conduction of the TTT courses, it was observed that teachers could not end some discussions and explain some concepts. It is now clear that teachers need support, especially when discussing counterintuitive concepts such as modern physics concepts. For this reason it is recommended that content focused PD programs be conducted at the helm of a scholarly coach.

Keywords: high school physics, in-service training course, modern physics unit, teacher professional development

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Authors: Nathenal Thomas Lambamo

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Based on the fact that more than 85% of the labor force and 90% of the export earnings are taken by agriculture in Ethiopia and it can be said that it is the backbone of the overall socio-economic activities in the country. Among the cereal crops that the agriculture sector provides for the country, wheat is the third-ranking one preceding teff and maize. In the present day, wheat is in higher demand related to the expansion of industries that use them as the main ingredient for their products. The local supply of wheat for these companies covers only 35 to 40% and the rest 60 to 65% percent is imported on behalf of potential customers that exhaust the country’s foreign currency reserves. The above facts show that the need for this crop in the country is too high and in reverse, the productivity of the crop is very less because of these reasons. Wheat disease is the most devastating disease that contributes a lot to this unbalance in the demand and supply status of the crop. It reduces both the yield and quality of the crop by 27% on average and up to 37% when it is severe. This study aims to detect the most frequent and degrading wheat diseases, Septoria and Leaf rust, using the most efficiently used subset of machine learning technology, deep learning. As a state of the art, a deep learning class classification technique called Convolutional Neural Network (CNN) has been used to detect diseases and has an accuracy of 99.01% is achieved.

Keywords: septoria, leaf rust, deep learning, CNN

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Authors: Soheila Sadeghi

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In the dynamic landscape of project management, scope changes are an inevitable reality that can significantly impact project performance. These changes, whether initiated by stakeholders, external factors, or internal project dynamics, can lead to cost overruns and schedule delays. Accurately predicting the consequences of these changes is crucial for effective project control and informed decision-making. This study aims to develop predictive models to estimate the impact of scope changes on project cost and schedule using machine learning techniques. The research utilizes a comprehensive dataset containing detailed information on project tasks, including the Work Breakdown Structure (WBS), task type, productivity rate, estimated cost, actual cost, duration, task dependencies, scope change magnitude, and scope change timing. Multiple machine learning models are developed and evaluated to predict the impact of scope changes on project cost and schedule. These models include Linear Regression, Decision Tree, Ridge Regression, Random Forest, Gradient Boosting, and XGBoost. The dataset is split into training and testing sets, and the models are trained using the preprocessed data. Cross-validation techniques are employed to assess the robustness and generalization ability of the models. The performance of the models is evaluated using metrics such as Mean Squared Error (MSE) and R-squared. Residual plots are generated to assess the goodness of fit and identify any patterns or outliers. Hyperparameter tuning is performed to optimize the XGBoost model and improve its predictive accuracy. The feature importance analysis reveals the relative significance of different project attributes in predicting the impact on cost and schedule. Key factors such as productivity rate, scope change magnitude, task dependencies, estimated cost, actual cost, duration, and specific WBS elements are identified as influential predictors. The study highlights the importance of considering both cost and schedule implications when managing scope changes. The developed predictive models provide project managers with a data-driven tool to proactively assess the potential impact of scope changes on project cost and schedule. By leveraging these insights, project managers can make informed decisions, optimize resource allocation, and develop effective mitigation strategies. The findings of this research contribute to improved project planning, risk management, and overall project success.

Keywords: cost impact, machine learning, predictive modeling, schedule impact, scope changes

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Authors: Benedict Ita, Peter Okoi

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In this work, the concept of the extended Nikiforov-Uvarov technique is discussed and employed to obtain the exact bound state energy eigenvalues and the corresponding normalized eigenfunctions of the inverse square root potential. With expressions for the exact energy eigenvalues and corresponding eigenfunctions, the expressions for the expectation values of the inverse separation-squared, kinetic energy, and the momentum-squared of the potential are presented using the Hellmann Feynman theorem. For visualization, algorithms written and implemented in Python language are used to generate tables and plots for l-states of the energy eigenvalues and some expectation values. The results obtained here may find suitable applications in areas like atomic and molecular physics, chemical physics, nuclear physics, and solid-state physics.

Keywords: Schrodinger equation, Nikoforov-Uvarov method, inverse square root potential, diatomic molecules, Python programming, Hellmann-Feynman theorem, second order differential equation, matrix algebra

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Authors: Marzieh Karimihaghighi, Carlos Castillo

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This work studies how Machine Learning (ML) may be used to increase the effectiveness of a criminal recidivism risk assessment tool, RisCanvi. The two key dimensions of this analysis are predictive accuracy and algorithmic fairness. ML-based prediction models obtained in this study are more accurate at predicting criminal recidivism than the manually-created formula used in RisCanvi, achieving an AUC of 0.76 and 0.73 in predicting violent and general recidivism respectively. However, the improvements are small, and it is noticed that algorithmic discrimination can easily be introduced between groups such as national vs foreigner, or young vs old. It is described how effectiveness and algorithmic fairness objectives can be balanced, applying a method in which a single error disparity in terms of generalized false positive rate is minimized, while calibration is maintained across groups. Obtained results show that this bias mitigation procedure can substantially reduce generalized false positive rate disparities across multiple groups. Based on these results, it is proposed that ML-based criminal recidivism risk prediction should not be introduced without applying algorithmic bias mitigation procedures.

Keywords: algorithmic fairness, criminal risk assessment, equalized odds, recidivism

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Authors: Niyati Sharma, Om Anand, Sanjeev Kumar Prasad

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Assuming future sale value for any of the organizations is one of the major essential characteristics of tactical development. Walmart Sales Forecasting is the finest illustration to work with as a beginner; subsequently, it has the major retail data set. Walmart uses this sales estimate problem for hiring purposes also. We would like to analyzing how the internal and external effects of one of the largest companies in the US can walk out their Weekly Sales in the future. Demand forecasting is the planned prerequisite of products or services in the imminent on the basis of present and previous data and different stages of the market. Since all associations is facing the anonymous future and we do not distinguish in the future good demand. Hence, through exploring former statistics and recent market statistics, we envisage the forthcoming claim and building of individual goods, which are extra challenging in the near future. As a result of this, we are producing the required products in pursuance of the petition of the souk in advance. We will be using several machine learning models to test the exactness and then lastly, train the whole data by Using linear regression and fitting the training data into it. Accuracy is 8.88%. The extra trees regression model gives the best accuracy of 97.15%.

Keywords: random forest algorithm, linear regression algorithm, extra trees classifier, mean absolute error

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Authors: Bernard Igoche Igoche, Olumuyiwa Matthew, Peter Bednar, Alexander Gegov

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This study presents an ontology-driven approach for knowledge discovery and validation from admission databases in Nigerian polytechnic institutions. The research aims to address the challenges of extracting meaningful insights from vast amounts of admission data and utilizing them for decision-making and process improvement. The proposed methodology combines the knowledge discovery in databases (KDD) process with a structural causal model (SCM) ontological framework. The admission database of Benue State Polytechnic Ugbokolo (Benpoly) is used as a case study. The KDD process is employed to mine and distill knowledge from the database, while the SCM ontology is designed to identify and validate the important features of the admission process. The SCM validation is performed using the conditional independence test (CIT) criteria, and an algorithm is developed to implement the validation process. The identified features are then used for machine learning (ML) modeling and prediction of admission status. The results demonstrate the adequacy of the SCM ontological framework in representing the admission process and the high predictive accuracies achieved by the ML models, with k-nearest neighbors (KNN) and support vector machine (SVM) achieving 92% accuracy. The study concludes that the proposed ontology-driven approach contributes to the advancement of educational data mining and provides a foundation for future research in this domain.

Keywords: admission databases, educational data mining, machine learning, ontology-driven knowledge discovery, polytechnic education, structural causal model

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Authors: Amalia Sholihati, Bambang Riyanto Trilaksono

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As an island nation, is a necessity for the Republic of Indonesia to have a capable military defense on land, sea or air that the development of military weapons such as rockets for air defense becomes very important. RKX rocket-200 is one of the guided missiles which are developed by consortium Indonesia and coordinated by LAPAN that serve to intercept the target. RKX-200 is designed to have the speed of Mach 0.5-0.9. RKX rocket-200 belongs to the category two-stage rocket that control is carried out on the second stage when the rocket has separated from the booster. The requirement for better performance to intercept missiles with higher maneuverability continues to push optimal guidance law development, which is derived from non-linear equations. This research focused on the design and implementation of a guidance system based OGL on the rocket RKX-200 while considering the limitation of rockets such as aerodynamic rocket and actuator. Guided missile control system has three main parts, namely, guidance system, navigation system and autopilot systems. As for other parts such as navigation systems and other supporting simulated on MATLAB based on the results of previous studies. In addition to using the MATLAB simulation also conducted testing with hardware-based ARM TWR-K60D100M conjunction with a navigation system and nonlinear models in MATLAB using Hardware-in-the-Loop Simulation (HILS).

Keywords: RKX-200, guidance system, optimal guidance law, Hils

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Authors: Zenaide Carvalho da Silva, Leandro Rodrigues Ferreira, Andrey Ricardo Pimentel

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Learning styles (LS) refer to the ways and forms that the student prefers to learn in the teaching and learning process. Each student has their own way of receiving and processing information throughout the learning process. Therefore, knowing their LS is important to better understand their individual learning preferences, and also, understand why the use of some teaching methods and techniques give better results with some students, while others it does not. We believe that knowledge of these styles enables the possibility of making propositions for teaching; thus, reorganizing teaching methods and techniques in order to allow learning that is adapted to the individual needs of the student. Adapting learning would be possible through the creation of online educational resources adapted to the style of the student. In this context, this article presents the structure of a learning object interface adaptation based on the LS. The structure created should enable the creation of the adapted learning object according to the student's LS and contributes to the increase of student’s motivation in the use of a learning object as an educational resource.

Keywords: adaptation, interface, learning object, learning style

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Authors: Lei Zhu, Nan Li

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Cold stamping has been widely applied in the automotive industry for the mass production of a great range of automotive panels. Predicting the springback to ensure the dimensional accuracy of the cold-stamped components is a critical step. The main approaches for the prediction and compensation of springback in cold stamping include running Finite Element (FE) simulations and conducting experiments, which require forming process expertise and can be time-consuming and expensive for the design of cold stamping tools. Machine learning technologies have been proven and successfully applied in learning complex system behaviours using presentative samples. These technologies exhibit the promising potential to be used as supporting design tools for metal forming technologies. This study, for the first time, presents a novel application of a Convolutional Neural Network (CNN) based surrogate model to predict the springback fields for variable U-shape cold bending geometries. A dataset is created based on the U-shape cold bending geometries and the corresponding FE simulations results. The dataset is then applied to train the CNN surrogate model. The result shows that the surrogate model can achieve near indistinguishable full-field predictions in real-time when compared with the FE simulation results. The application of CNN in efficient springback prediction can be adopted in industrial settings to aid both conceptual and final component designs for designers without having manufacturing knowledge.

Keywords: springback, cold stamping, convolutional neural networks, machine learning

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Authors: Ibrahim Khan, Waqas Khalid

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The Healthcare sector is known to consume a higher proportion of total energy consumption in the HVAC market owing to an excessive cooling and heating requirement in maintaining human thermal comfort in indoor conditions, catering to patients undergoing treatment in hospital wards, rooms, and intensive care units. The indoor thermal comfort conditions in selected hospitals of Islamabad, Pakistan, were measured on a real-time basis with the collection of first-hand experimental data using calibrated sensors measuring Ambient Temperature, Wet Bulb Globe Temperature, Relative Humidity, Air Velocity, Light Intensity and CO2 levels. The Experimental data recorded was analyzed in conjunction with the Thermal Comfort Questionnaire Surveys, where the participants, including patients, doctors, nurses, and hospital staff, were assessed based on their thermal sensation, acceptability, preference, and comfort responses. The Recorded Dataset, including experimental and survey-based responses, was further analyzed in the development of a correlation between operative temperature, operative relative humidity, and other measured operative parameters with the predicted mean vote and adaptive predicted mean vote, with the adaptive temperature and adaptive relative humidity estimated using the seasonal data set gathered for both summer – hot and dry, and hot and humid as well as winter – cold and dry, and cold and humid climate conditions. The Machine Learning Logistic Regression Algorithm was incorporated to train the operative experimental data parameters and develop a correlation between patient sensations and the thermal environmental parameters for which a new ML-based adaptive thermal comfort model was proposed and developed in our study. Finally, the accuracy of our model was determined using the K-fold cross-validation.

Keywords: predicted mean vote, thermal comfort, energy management, logistic regression, machine learning

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Authors: Donna H. Gabor

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This study aimed to develop and validate an electronic module in physics for first-year college students of Iloilo and find out if there would be a significant difference in the performance of students before and after using the electronic module. The e-module was composed of one topic with two sub-lessons in linear motion (kinematics). The participants of the study were classified into three groups: the subject matter experts who are physics instructors who suggested the content, physical appearance, and limitations of the e-module; the IT experts who are active both in teaching and developing computer programs; and 28 students divided into two groups, 15 in the pilot group and 13 in the final test group. A researcher created 30 items checklist form (difficulty of a sample problem, comprehension, application, and definition of terms) was prepared and validated by the experts in subject matter for gathering data. To test the difference in student performance in physics, the researcher prepared an achievement test containing 25 items, multiple choices. The findings revealed that there was an increase in the performance of students in the pretest and post-test. T-test results revealed that there was a significant difference in the test scores of the students before and after using the module which can be used as a future reference for linear motion as an additional teaching tool in physics.

Keywords: electronic module, kinematics, linear motion, physics

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Authors: Francis Ojo Ologunagba, Monday Olatunbosun Ale, Lewis A. Olutayo

Abstract:

The economic successes of commercial development of agricultural product processing depend upon the adaptability of each processing stage to mechanization. In maize processing, one of its post-harvest operations that is still facing a major challenge is dehusking. Therefore, a maize dehusking machine that could replace the prevalent traditional method of dehusking maize in developing countries, especially Nigeria was designed, constructed and tested at the Department of Agricultural and Bio-Environmental Engineering Technology, Rufus Giwa Polytechnic, Owo. The basic features of the machine are feeding unit (hopper), housing frame, dehusking unit, drive mechanism and discharge outlets. The machine was tested with maize of 50mm average diameter at 13% moisture content and 2.5mm machine roller clearance. Test results showed appreciable performance with the dehusking efficiency of 92% and throughput capacity of 200 Kg/hr at a machine speed of 400rpm. The estimated production cost of the machine at the time of construction is forty-five thousand, one hundred and eighty nairas (₦45,180) excluding the cost of the electric motor. It is therefore recommended for small and medium-scale maize farmers and processors in Nigeria.

Keywords: construction, dehusking, design, efficiency, maize

Procedia PDF Downloads 321

Authors: James P. Takona

Abstract:

In a world that is experiencing sharp digital transformations guided by digital technologies, the potential of technology to drive transformation and evolution in the higher is apparent. Higher education is facing a paradigm shift that exposes susceptibilities and threats to fully online programs in the face of post-Covid-19 trends of commodification. This historical moment is likely to be remembered as a critical turning point from analog to digital degree-focused learning modalities, where the default became the pivot point of competition between higher education institutions. Fall 2020 marks a significant inflection point in higher education as students, educators, and government leaders scrutinize higher education's price and value propositions through the new lens of traditional lecture halls versus multiple digitized delivery modes. Online education has since tiled the way for a pedagogical shift in how teachers teach and students learn. The incremental growth of online education in the west can now be attributed to the increasing patronage among students, faculty, and institution administrators. More often than not, college instructors assume paraclete roles in this learning mode, while students become active collaborators and no longer passive learners. This paper offers valuable discernments into the threats, challenges, and opportunities of a massive digital revolution in servicing degree programs. To view digital instruction and learning demands for instructional practices that revolve around collaborative work, engaging students in learning activities, and an engagement that promotes active efforts to solicit strong connections between course activities and expected learning pace for all students. Appropriate digital technologies demand instructors and students need prior solid skills. Need for the use of digital technology to support instruction and learning, intelligent tutoring offers great promise, and failures at implementing digital learning may not improve outcomes for specific student populations. Digital learning benefits students differently depending on their circumstances and background and those of the institution and/or program. Students have alternative options, access to the convenience of learning anytime and anywhere, and the possibility of acquiring and developing new skills leading to lifelong learning.

Keywords: digi̇tized learning, digital education, collaborative work, high education, online education, digitize delivery

Procedia PDF Downloads 89

Authors: T. Praveenkumar, Kulpreet Singh, Divy Bhanpuriya, M. Saimurugan

Abstract:

This study analysed the classification accuracy for gearbox faults using Machine Learning Techniques. Gearboxes are widely used for mechanical power transmission in rotating machines. Its rotating components such as bearings, gears, and shafts tend to wear due to prolonged usage, causing fluctuating vibrations. Increasing the dependability of mechanical components like a gearbox is hampered by their sealed design, which makes visual inspection difficult. One way of detecting impending failure is to detect a change in the vibration signature. The current study proposes various machine learning algorithms, with aid of these vibration signals for obtaining the fault classification accuracy of an automotive 4-Speed synchromesh gearbox. Experimental data in the form of vibration signals were acquired from a 4-Speed synchromesh gearbox using Data Acquisition System (DAQs). Statistical features were extracted from the acquired vibration signal under various operating conditions. Then the extracted features were given as input to the algorithms for fault classification. Supervised Machine Learning algorithms such as Support Vector Machines (SVM) and unsupervised algorithms such as Deep Feed Forward Neural Network (DFFNN), Deep Belief Networks (DBN) algorithms are used for fault classification. The fusion of DBN & DFFNN classifiers were architected to further enhance the classification accuracy and to reduce the computational complexity. The fault classification accuracy for each algorithm was thoroughly studied, tabulated, and graphically analysed for fused and individual algorithms. In conclusion, the fusion of DBN and DFFNN algorithm yielded the better classification accuracy and was selected for fault detection due to its faster computational processing and greater efficiency.

Keywords: deep belief networks, DBN, deep feed forward neural network, DFFNN, fault diagnosis, fusion of algorithm, vibration signal

Procedia PDF Downloads 111

Authors: Hao-Hsi Tseng, Hsin-Yun Lee, Yu-Cheng Kuo

Abstract:

In order to understand whether there is a better than the learning function of learning methods and improve the CAD Courses for enterprise’s design human resource development, this research is applied in learning practical learning computer graphics software. In this study, Revit building information model for learning content, design of two different modes of learning curriculum to learning, learning functions, respectively, and project learning. Via a post-test, questionnaires and student interviews, etc., to study the effectiveness of a comparative analysis of two different modes of learning. Students participate in a period of three weeks after a total of nine-hour course, and finally written and hands-on test. In addition, fill in the questionnaire response by the student learning, a total of fifteen questionnaire title, problem type into the base operating software, application software and software-based concept features three directions. In addition to the questionnaire, and participants were invited to two different learning methods to conduct interviews to learn more about learning students the idea of two different modes. The study found that the ad hoc short-term courses in learning, better learning outcomes. On the other hand, functional style for the whole course students are more satisfied, and the ad hoc style student is difficult to accept the ad hoc style of learning.

Keywords: development, education, human resource, learning

Procedia PDF Downloads 358

Authors: Nasser Al-Rawahi

Abstract:

The study aims at determining self-regulated learning strategies used by Omani physical education candidates to learn sport skills. The data were collected by a self-regulated learning theory questionnaire. The sample of the study comprised of 145 undergraduate physical education students enrolled in the department of physical education at the College of Education, Sultan Qaboos University. The findings of the study revealed that the most commonly used strategies for learning sport skills by Omani physical education candidate are ‘the effort learning strategies, planning learning strategies and evaluation learning strategies’. However, the reflection learning strategies, self-monitoring and self-efficacy learning strategies were revealed as the least used strategies by the PE candidates in learning and acquiring sport skills. Based on these findings, suggestions and recommendations for future research were provided.

Keywords: learning strategies, physical education candidates, self-regulated learning theory, Oman

Procedia PDF Downloads 613

Authors: Yanmeng Liu

Abstract:

This article analyzes and compares the linguistic differences between machine translation and manual translation, through a case study of the book Gone with the Wind. As an important carrier of human feeling and thinking, the literature translation poses a huge difficulty for machine translation, and it is supposed to expose distinct translation features apart from manual translation. In order to display linguistic features objectively, tentative uses of computerized and statistical evidence to the systematic investigation of large scale translation corpora by using quantitative methods have been deployed. This study compiles bilingual corpus with four versions of Chinese translations of the book Gone with the Wind, namely, Piao by Chunhai Fan, Piao by Huairen Huang, translations by Google Translation and Baidu Translation. After processing the corpus with the software of Stanford Segmenter, Stanford Postagger, and AntConc, etc., the study analyzes linguistic data and answers the following questions: 1. How does the machine translation differ from manual translation linguistically? 2. Why do these deviances happen? This paper combines translation study with the knowledge of corpus linguistics, and concretes divergent linguistic dimensions in translated text analysis, in order to present linguistic deviances in manual and machine translation. Consequently, this study provides a more accurate and more fine-grained understanding of machine translation products, and it also proposes several suggestions for machine translation development in the future.

Keywords: corpus-based analysis, linguistic deviances, machine translation, statistical evidence

Procedia PDF Downloads 141