Search results for: teaching learning based algorithm

Authors: Ramdan B. A. Koad, Ahmed F. Zobaa

Abstract:

Since the output characteristics of Photovoltaic (PV) system depends on the ambient temperature, solar radiation and load impedance, its maximum Power Point (MPP) is not constant. Under each condition PV module has a point at which it can produce its MPP. Therefore, a Maximum Power Point Tracking (MPPT) method is needed to uphold the PV panel operating at its MPP. This paper presents comparative study between the conventional MPPT methods used in (PV) system: Perturb and Observe (P&O), Incremental Conductance (IncCond), and Particle Swarm Optimization (PSO) algorithm for (MPPT) of (PV) system. To evaluate the study, the proposed PSO MPPT is implemented on a DC-DC converter and has been compared with P&O and INcond methods in terms of their tracking speed, accuracy and performance by using the Matlab tool Simulink. The simulation result shows that the proposed algorithm is simple, and is superior to the P&O and IncCond methods.

Keywords: photovoltaic systems, maximum power point tracking, perturb and observe method, incremental conductance, methods and practical swarm optimization algorithm

Procedia PDF Downloads 363

Authors: Minghui Zhang, Sirine Fkaier, Sabri Fernana, Svetlana Kiseleva, Denis Kiselev

Abstract:

The real-time identification of bacteria and fungi is difficult because they emit much weaker signals than pollen. In 2020, Plair developed Rapid-E+, which extends abilities of Rapid-E to detect smaller bioaerosols such as bacteria and fungal spores with diameters down to 0.3 µm, while keeping the similar or even better capability for measurements of large bioaerosols like pollen. Rapid-E+ enables simultaneous measurements of (1) time-resolved, polarization and angle dependent Mie scattering patterns, (2) fluorescence spectra resolved in 16 channels, and (3) fluorescence lifetime of individual particles. Moreover, (4) it provides 2D Mie scattering images which give the full information on particle morphology. The parameters of every single bioaerosol aspired into the instrument are subsequently analysed by machine learning. Firstly, pure species of microbes, e.g., Bacillus subtilis (a species of bacteria), and Penicillium chrysogenum (a species of fungal spores), were aerosolized in a bioaerosol chamber for Rapid-E+ training. Afterwards, we tested microbes under different concentrations. We used several steps of data analysis to classify and identify microbes. All single particles were analysed by the parameters of light scattering and fluorescence in the following steps. (1) They were treated with a smart filter block to get rid of non-microbes. (2) By classification algorithm, we verified the filtered particles were microbes based on the calibration data. (3) The probability threshold (defined by the user) step provides the probability of being microbes ranging from 0 to 100%. We demonstrate how Rapid-E+ identified simultaneously microbes based on the results of Bacillus subtilis (bacteria) and Penicillium chrysogenum (fungal spores). By using machine learning, Rapid-E+ achieved identification precision of 99% against the background. The further classification suggests the precision of 87% and 89% for Bacillus subtilis and Penicillium chrysogenum, respectively. The developed algorithm was subsequently used to evaluate the performance of microbe classification and quantification in real-time. The bacteria and fungi were aerosolized again in the chamber with different concentrations. Rapid-E+ can classify different types of microbes and then quantify them in real-time. Rapid-E+ enables classifying different types of microbes and quantifying them in real-time. Rapid-E+ can identify pollen down to species with similar or even better performance than the previous version (Rapid-E). Therefore, Rapid-E+ is an all-in-one instrument which classifies and quantifies not only pollen, but also bacteria and fungi. Based on the machine learning platform, the user can further develop proprietary algorithms for specific microbes (e.g., virus aerosols) and other aerosols (e.g., combustion-related particles that contain polycyclic aromatic hydrocarbons).

Keywords: bioaerosols, laser-induced fluorescence, Mie-scattering, microorganisms

Procedia PDF Downloads 96

Authors: Mehmet Savsar

Abstract:

Industrial Engineering is one of the engineering disciplines that deal with analysis, design, and improvement of systems, which include manufacturing, supply chain, healthcare, communication, and general service systems. Industrial engineers involve with comprehensive study of a given system, analysis of its interacting units, determination of problem areas, application of various optimization and operations research tools, and recommendation of solutions resulting in significant improvements. The Senior Design course in Industrial Engineering is the culmination of the Industrial Engineering Curriculum in a Capstone Design course, which fundamentally deals with systems analysis and design. The course at Kuwait University has been carefully designed with various course objectives and course outcomes in mind to achieve several program outcomes by practices and learning experiences, which are explicitly gained by systems analysis and design. The Senior Design Course is carried out in a selected industrial or service organization, with support from its engineering personnel, during a full semester by a team of students, who are usually in the last semester of their academic programs. A senior faculty member constantly administers the course to ensure that the students accomplish the prescribed objectives. Students work in groups to formulate issues and propose solutions and communicate, results in formal written and oral presentations. When the course is completed, they emerge as engineers that can be clearly identified as more mature, able to communicate better, able to participate in team work, able to see systems perspective in analysis and design, and more importantly, able to assume responsibility at entry level as engineers. The accomplishments are mainly due to real life experiences gained during the course of their design study. This paper presents methods, procedures, and experiences in teaching a Senior Design Course in Industrial Engineering Curriculum. A detailed description of the course, its role, its objectives, outcomes, learning practices, and assessments are explained in relation to other courses in Industrial Engineering Curriculum. The administration of the course, selected organizations where the course project is carried out, problems and solution tools utilized, student accomplishments and obstacles faced are presented. Issues discussed in this paper could help instructors in teaching the course as well as in clarifying the contribution of a design course to the industrial engineering education in general. In addition, the methods and teaching procedures presented could facilitate future improvements in industrial engineering curriculum.

Keywords: senior design course, industrial engineering, capstone design, education

Procedia PDF Downloads 139

Authors: Fazıl Gökgöz, Fahrettin Filiz

Abstract:

Load forecasting has become crucial in recent years and become popular in forecasting area. Many different power forecasting models have been tried out for this purpose. Electricity load forecasting is necessary for energy policies, healthy and reliable grid systems. Effective power forecasting of renewable energy load leads the decision makers to minimize the costs of electric utilities and power plants. Forecasting tools are required that can be used to predict how much renewable energy can be utilized. The purpose of this study is to explore the effectiveness of LSTM-based neural networks for estimating renewable energy loads. In this study, we present models for predicting renewable energy loads based on deep neural networks, especially the Long Term Memory (LSTM) algorithms. Deep learning allows multiple layers of models to learn representation of data. LSTM algorithms are able to store information for long periods of time. Deep learning models have recently been used to forecast the renewable energy sources such as predicting wind and solar energy power. Historical load and weather information represent the most important variables for the inputs within the power forecasting models. The dataset contained power consumption measurements are gathered between January 2016 and December 2017 with one-hour resolution. Models use publicly available data from the Turkish Renewable Energy Resources Support Mechanism. Forecasting studies have been carried out with these data via deep neural networks approach including LSTM technique for Turkish electricity markets. 432 different models are created by changing layers cell count and dropout. The adaptive moment estimation (ADAM) algorithm is used for training as a gradient-based optimizer instead of SGD (stochastic gradient). ADAM performed better than SGD in terms of faster convergence and lower error rates. Models performance is compared according to MAE (Mean Absolute Error) and MSE (Mean Squared Error). Best five MAE results out of 432 tested models are 0.66, 0.74, 0.85 and 1.09. The forecasting performance of the proposed LSTM models gives successful results compared to literature searches.

Keywords: deep learning, long short term memory, energy, renewable energy load forecasting

Procedia PDF Downloads 269

Authors: Asish O. Mathew, Lewlyn L. R. Rodrigues

Abstract:

The organizations in the knowledge economy era have recognized the importance of building knowledge assets for sustainable growth and development. In comparison to other industries, Information Technology (IT) enterprises, holds an edge in developing an effective Knowledge Management (KM) program, thanks to their in-house technological abilities. This paper tries to study the various knowledge-based incentive programs and its effect on Knowledge Sharing and Learning in the context of the Indian IT sector. A conceptual model is developed linking KM incentives, knowledge sharing, and learning. A questionnaire study is conducted to collect primary data from the knowledge workers of the IT organizations located in India. The data was analysed using Structural Equation Modeling using Partial Least Square method. The results show a strong influence of knowledge management incentives on knowledge sharing and an indirect influence on learning.

Keywords: knowledge management, knowledge management incentives, knowledge sharing, learning

Procedia PDF Downloads 482

Authors: Majid Khalili

Abstract:

This paper deals with a bi-objective hybrid no-wait flowshop scheduling problem minimizing the makespan and total weighted tardiness, in which we consider transportation times between stages. Obtaining an optimal solution for this type of complex, large-sized problem in reasonable computational time by using traditional approaches and optimization tools is extremely difficult. This paper presents a new multi-objective variable neighborhood algorithm (MOVNS). A set of experimental instances are carried out to evaluate the algorithm by advanced multi-objective performance measures. The algorithm is carefully evaluated for its performance against available algorithm by means of multi-objective performance measures and statistical tools. The related results show that a variant of our proposed MOVNS provides sound performance comparing with other algorithms.

Keywords: no-wait hybrid flowshop scheduling; multi-objective variable neighborhood algorithm; makespan; total weighted tardiness

Procedia PDF Downloads 421

Authors: Duckyong Kim, Jong Kang Park, Jong Tae Kim

Abstract:

DOA (Direction of Arrival) estimation is an important method in array signal processing and has a wide range of applications such as direction finding, beam forming, and so on. In this paper, we briefly introduce the MUSIC (Multiple Signal Classification) Algorithm, one of DOA estimation methods for analyzing several targets. Then we apply the MUSIC algorithm to the two-dimensional antenna array to analyze DOA estimation in 3D space through MATLAB simulation. We also analyze the design factors that can affect the accuracy of DOA estimation through simulation, and proceed with further consideration on how to apply the system.

Keywords: DOA estimation, MUSIC algorithm, spatial spectrum, array signal processing

Procedia PDF Downloads 382

Authors: M. Hadavi, Z. Hashemi

Abstract:

Learning strategies play an important role in the development of language skills. Vocabulary learning strategies as the backbone of these strategies have become a major part of English language teaching. This study is a comparative analysis of Vocabulary Learning Strategies (VLS) use and preference among freshmen and senior EFL medical sciences students with different fields of study. 449 students (236 freshman and 213 seniors) participated in the study. 64.6% were female and 35.4% were male. The instrument utilized in this research was a questionnaire consisting of 41 items related to the students’ approach to vocabulary learning. The items were classified under eight sections as dictionary strategies, guessing strategies, study preferences, memory strategies, autonomy, note- taking strategies, selective attention, and social strategies. The participants were asked to answer each item with a 5-point Likert-style frequency scale as follows:1) I never or almost never do this, 2) I don’t usually do this, 3) I sometimes do this, 4) I usually do this, and 5)I always or almost always do this. The results indicated that freshmen students and particularly surgical technology students used more strategies compared to the seniors. Overall guessing and dictionary strategies were the most frequently used strategies among all the learners (p=0/000). The mean and standard deviation of using VLS in the students who had no previous history of participating in the private English language classes was less than the students who had attended these type of classes (p=0/000). Female students tended to use social and study preference strategies whereas male students used mostly guessing and dictionary strategies. It can be concluded that the senior students under instruction from the university have learned to rely on themselves and choose the autonomous strategies more, while freshmen students use more strategies that are related to the study preferences.

Keywords: vocabulary leaning strategies, medical sciences, students, linguistics

Procedia PDF Downloads 455

Authors: P. Suresh, K. Gunasekaran, R. Thanigaivelan

Abstract:

Radio frequency identification (RFID) technology has been attracting considerable attention with the expectation of improved supply chain visibility for consumer goods, apparel, and pharmaceutical manufacturers, as well as retailers and government procurement agencies. It is also expected to improve the consumer shopping experience by making it more likely that the products they want to purchase are available. Recent announcements from some key retailers have brought interest in RFID to the forefront. A modified K- Means Cluster based Heuristic approach, Hybrid Genetic Algorithm (GA) - Simulated Annealing (SA) approach, Hybrid K-Means Cluster based Heuristic-GA and Hybrid K-Means Cluster based Heuristic-GA-SA for Open Loop Supply Chain Network problem are proposed. The study incorporated uniform crossover operator and combined crossover operator in GAs for solving open loop supply chain distribution network problem. The algorithms are tested on 50 randomly generated data set and compared with each other. The results of the numerical experiments show that the Hybrid K-means cluster based heuristic-GA-SA, when tested on 50 randomly generated data set, shows superior performance to the other methods for solving the open loop supply chain distribution network problem.

Keywords: RFID, supply chain distribution network, open loop supply chain, genetic algorithm, simulated annealing

Procedia PDF Downloads 172

Authors: Chieh-Ning Lan, Tzu-Shin Lin, Kun-Hao Lin

Abstract:

Chinese handwriting skill is one of the basic skills of school-age children in Taiwan, which helps them to learn most academic subjects. Differ from the alphabetic language system, Chinese written language is a logographic script with a complicated 2-dimensional character structure as a morpheme. Visuospatial ability places a great role in Chinese handwriting to maintain good proportion and alignment of these interwoven strokes. In Taiwan, school-age students faced the challenge to recognize and write down Chinese characters, especially in children with written expression difficulties (CWWDs). In this study, we developed an instructional app to help CWWDs practice Chinese handwriting skills, and we aimed to apply the mobile assisted language learning (MALL) system in clinical writing strategies. To understand the feasibility and satisfaction of this auxiliary instructional writing app, we investigated the perceive and value both from school-age students and the clinic therapists, who were the target users and the experts. A group of 8 elementary school children, as well as 8 clinic therapists, were recruited. The school-age students were asked to go through a paper-based instruction and were asked to score the visual expression based on their graphic preference; the clinic therapists were asked to watch an introductive video of this instructional app and complete the online formative questionnaire. In the results of our study, from the perspective of user interface design, school-age students were more attracted to cartoon-liked pictures rather than line drawings or vivid photos. Moreover, compared to text, pictures which have higher semantic transparency were more commonly chosen by children. In terms of the quantitative survey from clinic therapists, they were highly satisfied with this auxiliary instructional writing app, including the concepts such as visual design, teaching contents, and positive reinforcement system. Furthermore, the qualitative results also suggested comprehensive positive feedbacks on the teaching contents and the feasibility of integrating the app into clinical treatments. Interestingly, we found that clinic therapists showed high agreement in approving CWWDs’ writing ability with using orthographic knowledge; however, in the qualitative section, clinic therapists pointed out that CWWDs usually have relative insufficient background knowledge in Chinese character orthographic rules, which because it is not a key-point in conventional handwriting instruction. Also, previous studies indicated that conventional Chinese reading and writing instructions were lacked of utilizing visual-spatial arrangement strategies. Based on the sharing experiences from all participants, we concluded several interesting topics that are worth to dedicate to in the future. In this undergoing app system, improvement and revision will be applied into the system design, and will establish a better and more useful instructional system for CWWDs within their treatments; enlightened by the opinions related to learning content, the importance of orthographic knowledge in Chinese character recognition should be well discussed and involved in CWWDs’ intervention in the future.

Keywords: auxiliary instructional app, children with writing difficulties, Chinese handwriting, orthographic knowledge

Procedia PDF Downloads 177

Authors: Jindriska Svobodova, Jan Novotny

Abstract:

A teacher’s attitude to creativity plays an essential role in the thinking development of his/her students. The purpose of this study is to understand if a science teacher's personal creativity can modify his/her ability to produce various kinds of questions. This research used an education activity based on cosmic sketches and pictures by K.E. Tsiolkovsky, the founder of astronautics, to explore if any relationship between individual creativity and the asking questions skill exists. As a screening instrument, which allows an assessment of the respondent's creative potential, a common test of creative thinking was used. The results of the creativity test and the diversity of the questions are mentioned.

Keywords: science education, active learning, physics teaching, religious cosmology

Procedia PDF Downloads 234

Authors: Siwanit Autthawuttikul, Prakob Koraneekid, Sayamon Insa-ard

Abstract:

The purposes of this research were to development creatively integrated teaching skills using Information and Communication Technology (ICT) for professional teacher in schools under the education area of the basic education commission, ministry of education both schools under the office of primary education and those under The office of secondary education in eight western region provinces of Thailand. This is useful in defining a vision for the school strategy and restructuring schools in addition, teachers will have developed skills in teaching creative integrated ICT. The research methodology comprises quantitative and qualitative data collection. The Baseline Survey, focus group for discussions and then the model was developed creatively integrated teaching skills using ICT. The findings showed that 7 elements were important: (1) Academy Transformation (2) Information Technology Infrastructure (3) Personal Development (4) Supervision, Monitoring and Evaluation (5) Motivating and Rewarding (6) Important factor affecting the success of teaching integrated with ICT were knowledge, skills, attitudes and (7) The role of the individual concerned. The comparison creatively integrated teaching skills before and after participating in the overall shows that the average creatively integrated teaching skills using ICT after attending the event is 3.27, and standard deviation was 0.56, higher than before which is 2.60 and the standard deviation was 0.56. There are significant differences significant statistically level of .05. The final average score of the evaluation plan design creatively integrated teaching skills using ICT teachers' average score was 26.94 at the high levels.

Keywords: integrated curriculum, information and communications technology, teachers in the western region, schools

Procedia PDF Downloads 447

Authors: Fatima Al-Aswadi, Chan Yong

Abstract:

With the large volume of unstructured data that increases day by day on the web, the motivation of representing the knowledge in this data in the machine processable form is increased. Ontology is one of the major cornerstones of representing the information in a more meaningful way on the semantic Web. The goal of Ontology learning from text is to elicit and represent domain knowledge in the machine readable form. This paper aims to give a follow-up review on the ontology learning systems from text and some of their defects. Furthermore, it discusses how far the ontology learning process will enhance in the future.

Keywords: concept discovery, deep learning, ontology learning, semantic relation, semantic web

Procedia PDF Downloads 529

Authors: Rodrigo Aguiar, Adelino Ferreira

Abstract:

Road traffic accidents are the leading cause of unnatural death and injuries worldwide, representing a significant problem of road safety. In this context, the use of artificial intelligence with advanced machine learning techniques has gained prominence as a promising approach to predict traffic accidents. This article investigates the application of machine learning algorithms to develop traffic accident frequency prediction models. Models are evaluated based on performance metrics, making it possible to do a comparative analysis with traditional prediction approaches. The results suggest that machine learning can provide a powerful tool for accident prediction, which will contribute to making more informed decisions regarding road safety.

Keywords: machine learning, artificial intelligence, frequency of accidents, road safety

Procedia PDF Downloads 93

Authors: Ruth Nsibirano

Abstract:

Online mode of delivery in higher institutions of learning, popularly known in some circles as e-Learning or distance education is a new phenomenon that is steadily taking root in African universities but specifically at Makerere University. For slightly over a decade, the Department of Open and Distance Learning has been offering the first generation mode of distance education. In this, learning and teaching experiences were based on the use of hard copy materials circulated through postal services in a rather correspondence mode. There were more challenges to this including high dropout rates, limited support to the learners and sustainability issues. Fortunately, the Department was supported by the Norwegian Government through a NORHED grant to “leapfrog” to the fifth generation of distance education that makes more use of educational technologies and tools. The capacity of faculty staff was gradually enhanced through a series of training to handle the upgraded structure of fifth generation distance education. The trained staff was then tasked to develop modules befitting an online delivery mode, for use on the program. This paper will present attitudes, experiences of the course writers with a view of sharing the good practices that enabled them leap from e-faculty trainees to distinct online course writers. This perspective will hopefully serve as building blocks to enhance the capacity of other upcoming distance education programs in low capacity universities and also promote the uptake of e-Education on the continent and beyond. Methodologically the findings were collected through individual interviews with the 30 course writers. In addition, semi structured questionnaires were designed to collect data on the profile, challenges and lessons from the writers. Findings show that the attitudes of course writers on project supported activities are so much tagged to the returns from their committed efforts. In conclusion, therefore, it is strategically useful to assess and selectively choose which individual to nominate for involvement at the initial stages.

Keywords: distance education, online course content, staff attitudes, best practices in online learning

Procedia PDF Downloads 256

Authors: A. Kavya Sri, K. Sai Vineela, R. Vanitha, S. Rohith

Abstract:

Large pieces of ice that break from the glaciers are known as icebergs. The threat that icebergs pose to navigation, production of offshore oil and gas services, and underwater pipelines makes their detection crucial. In this project, an automated iceberg tracking method using deep learning techniques and satellite images of icebergs is to be developed. With a temporal resolution of 12 days and a spatial resolution of 20 m, Sentinel-1 (SAR) images can be used to track iceberg drift over the Southern Ocean. In contrast to multispectral images, SAR images are used for analysis in meteorological conditions. This project develops a web-based graphical user interface to detect and track icebergs using sentinel-1 images. To track the movement of the icebergs by using temporal images based on their latitude and longitude values and by comparing the center and area of all detected icebergs. Testing the accuracy is done by precision and recall measures.

Keywords: synthetic aperture radar (SAR), icebergs, deep learning, spatial resolution, temporal resolution

Procedia PDF Downloads 94

Authors: Kanyarat Bussaban, Kunyanuth Kularbphettong

Abstract:

This research aims to create a model for analysis of student behavior using Library resources based on data mining technique in case of Suan Sunandha Rajabhat University. The model was created under association rules, apriori algorithm. The results were found 14 rules and the rules were tested with testing data set and it showed that the ability of classify data was 79.24 percent and the MSE was 22.91. The results showed that the user’s behavior model by using association rule technique can use to manage the library resources.

Keywords: behavior, data mining technique, a priori algorithm, knowledge discovery

Procedia PDF Downloads 410

Authors: Maimuna Akram, Khadija Zia, Sohaib Naseer

Abstract:

Pedagogical objectives and the nature of the course content may lead instructors to take varied approaches to selecting a student for the cold call, specifically in a studio setup where students work on different projects independently and show progress work time to time at scheduled critiques. Cold-calling often proves to be an effective tool in eliciting a response without enforcing judgment onto the recipients. While there is a mixed range of behavior exhibited by students who are cold-called, a classification of responses from anxiety-provoking to inspiring may be elicited; there is a need for a greater understanding of utilizing the exchanges in bringing about fruitful and engaging outcomes of studio discussions. This study aims to unravel the dimensions of utilizing the cold-call approach in a didactic exchange within studio pedagogy. A questionnaire survey was conducted in an undergraduate class at Arts and Design School. The impact of cold calling on students’ participation was determined through various parameters, including course choice, participation frequency, students’ comfortability, and teaching methodology. After analyzing the surveys, specific classroom teachers were interviewed to provide a qualitative perspective of the faculty. It was concluded that cold-calling increases students’ participation frequency and also increases preparation for class. Around 67% of students responded that teaching methods play an important role in learning activities and students’ participation during class discussions. 84% of participants agreed that cold calling is an effective way of learning. According to research, cold-calling can be done in large numbers without making students uncomfortable. As a result, the findings of this study support the use of this instructional method to encourage more students to participate in class discussions.

Keywords: active learning, class discussion, class participation, cold calling, pedagogical methods, student engagement

Procedia PDF Downloads 41

Authors: Hanan A. Algamdi, Huda Y. Alyami

Abstract:

Learning object repositories contribute to developing educational process through its advantages; as they employ technology effectively, and use it to create new resources for effective learning, as well as they provide opportunities for collaboration in content through providing the ability for editing, modifying and developing it. This supports the relationships between communities that benefit from these repositories, and reflects positively on the content quality. Therefore, this study aims at exploring the most prominent learning topics in the 21st century, which should be included in learning object repositories, and identifying the necessary set of learning skills that the repositories should develop among today students. For conducting this study, the analytical descriptive method will be employed, and study sample will include a group of leaders, experts, and specialists in curricula and e-learning at ministry of education in Kingdom of Saudi Arabia.

Keywords: learning object, repositories, 21st century, quality

Procedia PDF Downloads 308

Authors: Valentin Père, Mathieu Milhé, Fabien Baillon, Jean-Louis Dirion

Abstract:

Microgrids offer potential solutions to meet the need for local grid stability and increase isolated networks autonomy with the integration of intermittent renewable energy production and storage facilities. In such a context, sizing production and storage for a given network is a complex task, highly depending on input data such as power load profile and renewable resource availability. This work aims at developing an operating cost computation methodology for different microgrid designs based on the use of deep reinforcement learning (RL) algorithms to tackle the optimal operation problem in stochastic environments. RL is a data-based sequential decision control method based on Markov decision processes that enable the consideration of random variables for control at a chosen time scale. Agents trained via RL constitute a promising class of Energy Management Systems (EMS) for the operation of microgrids with energy storage. Microgrid sizing (or design) is generally performed by minimizing investment costs and operational costs arising from the EMS behavior. The latter might include economic aspects (power purchase, facilities aging), social aspects (load curtailment), and ecological aspects (carbon emissions). Sizing variables are related to major constraints on the optimal operation of the network by the EMS. In this work, an islanded mode microgrid is considered. Renewable generation is done with photovoltaic panels; an electrochemical battery ensures short-term electricity storage. The controllable unit is a hydrogen tank that is used as a long-term storage unit. The proposed approach focus on the transfer of agent learning for the near-optimal operating cost approximation with deep RL for each microgrid size. Like most data-based algorithms, the training step in RL leads to important computer time. The objective of this work is thus to study the potential of Batch-Constrained Q-learning (BCQ) for the optimal sizing of microgrids and especially to reduce the computation time of operating cost estimation in several microgrid configurations. BCQ is an off-line RL algorithm that is known to be data efficient and can learn better policies than on-line RL algorithms on the same buffer. The general idea is to use the learned policy of agents trained in similar environments to constitute a buffer. The latter is used to train BCQ, and thus the agent learning can be performed without update during interaction sampling. A comparison between online RL and the presented method is performed based on the score by environment and on the computation time.

Keywords: batch-constrained reinforcement learning, control, design, optimal

Procedia PDF Downloads 128

Authors: Wen-Bao Qiao, Jean-Charles Créput

Abstract:

To accelerate the solution for large scale traveling salesman problems (TSP), a parallel 2-opt local search algorithm with simple implementation based on Graphics Processing Unit (GPU) is presented and tested in this paper. The parallel scheme is based on technique of data decomposition by dynamically assigning multiple K processors on the integral tour to treat K edges’ 2-opt local optimization simultaneously on independent sub-tours, where K can be user-defined or have a function relationship with input size N. We implement this algorithm with doubly linked list on GPU. The implementation only requires O(N) memory. We compare this parallel 2-opt local optimization against sequential exhaustive 2-opt search along integral tour on TSP instances from TSPLIB with more than 10000 cities.

Keywords: parallel 2-opt, double links, large scale TSP, GPU

Procedia PDF Downloads 634

Authors: Masaki Omata, Shumma Hosokawa

Abstract:

An experiment to verify the relationships between physiological indexes of an e-learner and the presence or absence of an operation during e-learning is described. Electroencephalogram (EEG), hemoencephalography (HEG), skin conductance (SC), and blood volume pulse (BVP) values were measured while participants performed experimental learning tasks. The results show that there are significant differences between the SC values when reading with clicking on learning materials and the SC values when reading without clicking, and between the HEG ratio when reading (with and without clicking) and the HEG ratio when resting for four of five participants. We conclude that the SC signals can be used to estimate whether or not a learner is performing an active task and that the HEG ratios can be used to estimate whether a learner is learning.

Keywords: e-learning, physiological index, physiological signal, state of learning

Procedia PDF Downloads 385

Authors: Jame O. Ezema, Odenigbo Veronica

Abstract:

The study covers the topic Information Communication and Technology (ICTs) knowledge as a way of enhancing Literacy and Lifelong learning in Nigeria. This work delved into defining of ICTs. Types of ICTs and media technologies were also mentioned. It further explained how ICTs can be strengthened and the uses of ICTs in education was duly emphasized. The paper also enumerated some side effects of ICTs on learners while the role of ICTs in enhancing literacy was explained. The study carried out strategies to use ICTs meaningfully in Literacy Programs and also emphasized the word lifelong learning in Nigeria. Some recommendations were made towards acquiring ICTs knowledge, so as to enhance Literacy and Lifelong learning in Nigeria.

Keywords: literacy, distance-learning, life-long learning for sustainable development, e-learning

Procedia PDF Downloads 508

Authors: Merey M. Sarsengeldin, Alexandr S. Kolokhmatov, Galiya Seidaliyeva, Alexandr Ozerov, Sanim T. Imatayeva

Abstract:

This series of interdisciplinary studies is an attempt to investigate and develop a scalable model of fair socioeconomic relations on the base of blockchain using positive psychology techniques and Machine Learning algorithms for data analytics. In this particular study, we use hyperinteraction approach and intuition to investigate their influence on 'wisdom of crowds' via created mobile application which was created for the purpose of this research. Along with the public blockchain and private Decentralized Autonomous Organization (DAO) which were elaborated by us on the base of Ethereum blockchain, a model of fair financial relations of members of DAO was developed. We developed a smart contract, so-called, Fair Price Protocol and use it for implementation of model. The data obtained from mobile application was analyzed by ML algorithms. A model was tested on football matches.

Keywords: blockchain, Naïve Bayes algorithm, hyperinteraction, intuition, wisdom of crowd, decentralized autonomous organization

Procedia PDF Downloads 176

Authors: Min Kyeong Cha, Ju Yeon Mun, Seong Baeg Kim

Abstract:

Recently, ‘play of learning’ became important and is emphasized as a useful learning tool. Therefore, interest in edutainment contents is growing. Storytelling is considered first as a method that improves the transmission of information and learner's interest when planning edutainment contents. In this study, we designed edutainment contents in the form of an adventure game that applies the storytelling method. This content provides questions and items constituted dynamically and reorganized learning contents through analysis of test results. It allows learners to solve various questions through effective iterative learning. As a result, the learners can reach mastery learning.

Keywords: storytelling, edutainment, mastery learning, computer operating principle

Procedia PDF Downloads 322

Authors: Blanco T. F., Fernández-López A.

Abstract:

Nowadays, education, motivated by the new demands of the 21st century, acquires a dimension that converts the skills that new generations may need into a huge and uncertain set of knowledge too broad to be entirety covered. Within this set, and as tools to reach them, we find Learning and Knowledge Technologies (LKT). Thus, in order to prepare students for an everchanging society in which the technological boom involves everything, it is essential to develop digital competence. Nevertheless LKT seems not to have found their place in the educational system. This work is aimed to go a step further in the research of the most appropriate procedures and resources for technological integration in the classroom. The main objective of this exploratory study is to analyze the didactic suitability (epistemic, cognitive, affective, interactional, mediational and ecological) for teaching and learning processes of mathematics with robotics and 3D printing. The analysis carried out is drawn from a STEAM (Science, Technology, Engineering, Art and Mathematics) project that has the Pilgrimage way to Santiago de Compostela as a common thread. The sample is made up of 25 Primary Education students (10 and 11 years old). A qualitative design research methodology has been followed, the sessions have been distributed according to the type of technology applied. Robotics has been focused towards learning two-dimensional mathematical notions while 3D design and printing have been oriented towards three-dimensional concepts. The data collection instruments used are evaluation rubrics, recordings, field notebooks and participant observation. Indicators of didactic suitability proposed by Godino (2013) have been used for the analysis of the data. In general, the results show a medium-high level of didactic suitability. Above these, a high mediational and cognitive suitability stands out, which led to a better understanding of the positions and relationships of three-dimensional bodies in space and the concept of angle. With regard to the other indicators of the didactic suitability, it should be noted that the interactional suitability would require more attention and the affective suitability a deeper study. In conclusion, the research has revealed great expectations around the combination of teaching-learning processes of mathematics and LKT. Although there is still a long way to go in terms of the provision of means and teacher training.

Keywords: 3D printing, didactic suitability, educational design, robotics

Procedia PDF Downloads 109

Authors: Mairo Musa Galadima, Phoebe Mshelia

Abstract:

In Nigeria, the national policy of education stipulates that the kindergarten primary schools and the legislature are to use the three popular Nigerian Languages namely: Hausa, Igbo and Yoruba. However, the English language seems to be preferred and this calls for this paper. Attempts were made to draw out the challenges faced by learners in understanding English speech sounds and using them to communicate effectively in English; using 5(five) selected secondary school in Bauchi. It was discover that challenges abound in the wrong use of stress and intonation, transfer of phonetic features from their first language. Others are inadequate qualified teachers and relevant materials including text-books. It is recommended that teachers of English should lay more emphasis on the teaching of supra-segmental features and should be encouraged to go for further studies, seminars and refresher courses.

Keywords: kindergarten, stress, phonetic and intonation, Nigeria

Procedia PDF Downloads 304

Authors: Felicitas Pielsticker, Christoph Pielsticker, Ingo Witzke

Abstract:

Neurological findings provide foundational results for many different disciplines. In this article we want to discuss these with a special focus on mathematics education. The intention is to make neuroscience research useful for the description of cognitive mathematical learning processes. A key issue of mathematics education is that students often behave as if their mathematical knowledge is constructed in isolated compartments with respect to the specific context of the original learning situation; supporting students to link these compartments to form a coherent mathematical society of mind is a fundamental task not only for mathematics teachers. This aspect goes hand in hand with the question if there is such a thing as abstract general mathematical knowledge detached from concrete reality. Educational Neuroscience may give answers to the question why students develop their mathematical knowledge in isolated subjective domains of experience and if it is generally possible to think in abstract terms. To address these questions, we will provide examples from different fields of mathematics education e.g. students’ development and understanding of the general concept of variables or the mathematical notion of universal proofs. We want to discuss these aspects in the reflection of functional studies which elucidate the role of specific brain regions in mathematical learning processes. In doing this the paper addresses concept formation processes of students in the mathematics classroom and how to support them adequately considering the results of (educational) neuroscience.

Keywords: brain regions, concept formation processes in mathematics education, proofs, teaching-learning processes

Procedia PDF Downloads 154

Authors: Rui Mao, Heming Ji, Xiaoyu Wang

Abstract:

Keystroke dynamics are widely used in identity recognition. It has the advantage that the individual typing rhythm is difficult to imitate. It also supports continuous authentication through the keyboard without extra devices. The existing keystroke dynamics authentication methods based on machine learning have a drawback in supporting relatively complex scenarios with massive data. There are drawbacks to both feature extraction and model optimization in these methods. To overcome the above weakness, an authentication model of keystroke dynamics based on deep learning is proposed. The model uses feature vectors formed by keystroke content and keystroke time. It ensures efficient continuous authentication by cooperating attention mechanisms with the combination of CNN and Bi-LSTM. The model has been tested with Open Data Buffalo dataset, and the result shows that the FRR is 3.09%, FAR is 3.03%, and EER is 4.23%. This proves that the model is efficient and accurate on continuous authentication.

Keywords: keystroke dynamics, identity authentication, deep learning, CNN, LSTM

Procedia PDF Downloads 162

Authors: Filip Rusak, Yulia Arzhaeva, Dadong Wang

Abstract:

Machine learning based Computer-Aided Diagnosis (CAD) is gaining much popularity in medical imaging and diagnostic radiology. However, it requires a large amount of high quality and labeled training image datasets. The training images may come from different sources and be acquired from different radiography machines produced by different manufacturers, digital or digitized copies of film radiographs, with various sizes as well as different pixel intensity distributions. In this paper, a content-aware image augmentation method is presented to deal with these variations. The results of the proposed method have been validated graphically by plotting the removed and added seams of pixels on original images. Two different chest X-ray (CXR) datasets are used in the experiments. The CXRs in the datasets defer in size, some are digital CXRs while the others are digitized from analog CXR films. With the proposed content-aware augmentation method, the Seam Carving algorithm is employed to resize CXRs and the corresponding labels in the form of image masks, followed by histogram matching used to normalize the pixel intensities of digital radiography, based on the pixel intensity values of digitized radiographs. We implemented the algorithms, resized the well-known Montgomery dataset, to the size of the most frequently used Japanese Society of Radiological Technology (JSRT) dataset and normalized our digital CXRs for testing. This work resulted in the unified off-the-shelf CXR dataset composed of radiographs included in both, Montgomery and JSRT datasets. The experimental results show that even though the amount of augmentation is large, our algorithm can preserve the important information in lung fields, local structures, and global visual effect adequately. The proposed method can be used to augment training and testing image data sets so that the trained machine learning model can be used to process CXRs from various sources, and it can be potentially used broadly in any medical imaging applications.

Keywords: computer-aided diagnosis, image augmentation, lung segmentation, medical imaging, seam carving

Procedia PDF Downloads 230