Search results for: Unsupervised feature learning.

Authors: R. K. Agrawal, Rajni Bala

Abstract:

Developing an accurate classifier for high dimensional microarray datasets is a challenging task due to availability of small sample size. Therefore, it is important to determine a set of relevant genes that classify the data well. Traditionally, gene selection method often selects the top ranked genes according to their discriminatory power. Often these genes are correlated with each other resulting in redundancy. In this paper, we have proposed a hybrid method using feature ranking and wrapper method (Genetic Algorithm with multiclass SVM) to identify a set of relevant genes that classify the data more accurately. A new fitness function for genetic algorithm is defined that focuses on selecting the smallest set of genes that provides maximum accuracy. Experiments have been carried on four well-known datasets1. The proposed method provides better results in comparison to the results found in the literature in terms of both classification accuracy and number of genes selected.

Keywords: Gene selection, genetic algorithm, microarray datasets, multi-class SVM.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2051

Authors: Qiuyu Dai, Haochong Zhang, Xiangrong Liu

Abstract:

Efficient matrix-vector multiplication with diagonal sparse matrices is pivotal in a multitude of computational domains, ranging from scientific simulations to machine learning workloads. When encoded in the conventional Diagonal (DIA) format, these matrices often induce computational overheads due to extensive zero-padding and non-linear memory accesses, which can hamper the computational throughput, and elevate the usage of precious compute and memory resources beyond necessity. The ’DIA-Adaptive’ approach, a methodological enhancement introduced in this paper, confronts these challenges head-on by leveraging the advanced parallel instruction sets embedded within Machine Learning Units (MLUs). This research presents a thorough analysis of the DIA-Adaptive scheme’s efficacy in optimizing Sparse Matrix-Vector Multiplication (SpMV) operations. The scope of the evaluation extends to a variety of hardware architectures, examining the repercussions of distinct thread allocation strategies and cluster configurations across multiple storage formats. A dedicated computational kernel, intrinsic to the DIA-Adaptive approach, has been meticulously developed to synchronize with the nuanced performance characteristics of MLUs. Empirical results, derived from rigorous experimentation, reveal that the DIA-Adaptive methodology not only diminishes the performance bottlenecks associated with the DIA format but also exhibits pronounced enhancements in execution speed and resource utilization. The analysis delineates a marked improvement in parallelism, showcasing the DIA-Adaptive scheme’s ability to adeptly manage the interplay between storage formats, hardware capabilities, and algorithmic design. The findings suggest that this approach could set a precedent for accelerating SpMV tasks, thereby contributing significantly to the broader domain of high-performance computing and data-intensive applications.

Keywords: Adaptive method, DIA, diagonal sparse matrices, MLU, sparse matrix-vector multiplication.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 216

Authors: Seyoung Kim, Jeongmin Kim, Kwang Ryel Ryu

Abstract:

A database that records average traffic speeds measured at five-minute intervals for all the links in the traffic network of a metropolitan city. While learning from this data the models that can predict future traffic speed would be beneficial for the applications such as the car navigation system, building predictive models for every link becomes a nontrivial job if the number of links in a given network is huge. An advantage of adopting k-nearest neighbor (k-NN) as predictive models is that it does not require any explicit model building. Instead, k-NN takes a long time to make a prediction because it needs to search for the k-nearest neighbors in the database at prediction time. In this paper, we investigate how much we can speed up k-NN in making traffic speed predictions by reducing the amount of data to be searched for without a significant sacrifice of prediction accuracy. The rationale behind this is that we had a better look at only the recent data because the traffic patterns not only repeat daily or weekly but also change over time. In our experiments, we build several different k-NN models employing different sets of features which are the current and past traffic speeds of the target link and the neighbor links in its up/down-stream. The performances of these models are compared by measuring the average prediction accuracy and the average time taken to make a prediction using various amounts of data.

Keywords: Big data, k-NN, machine learning, traffic speed prediction.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1372

Authors: Masahiro Taketa, Masaki Ito, Takao Kawamura, Kazunori Sugahara

Abstract:

We develop a new interface for Bus-Net which is optimized for a smartphone. We are continuing to develop the shortest path planning system of public transportation called "Bus-Net" in Tottori prefecture as web application to improve the usability of public transportation. Recent trend of computing platform, however has shifted to an advanced mobile device called a smartphone such as iPhone and Android in Japan. A smartphone has different characters with existing feature phone in terms of OS, large touche panel, and several other features. We derive a guideline to design the new interface for a smartphone to full use of the functionality. The guideline is about simplicity of user-s operation, location awareness and usability. We developed the new interface for “Bus-Net" on iPhone referring to the guideline. Due to the evaluation, the application interface we developed is better than the existing web-based interface in terms of the usability.

Keywords: Path Planning, Public Transportation, Smartphone, User Interface

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1798

Authors: M. Zerikat, S. Chekroun

Abstract:

This paper proposes an effective adaptation learning algorithm based on artificial neural networks for speed control of an induction motor assumed to operate in a high-performance drives environment. The structure scheme consists of a neural network controller and an algorithm for changing the NN weights in order that the motor speed can accurately track of the reference command. This paper also makes uses a very realistic and practical scheme to estimate and adaptively learn the noise content in the speed load torque characteristic of the motor. The availability of the proposed controller is verified by through a laboratory implementation and under computation simulations with Matlab-software. The process is also tested for the tracking property using different types of reference signals. The performance and robustness of the proposed control scheme have evaluated under a variety of operating conditions of the induction motor drives. The obtained results demonstrate the effectiveness of the proposed control scheme system performances, both in steady state error in speed and dynamic conditions, was found to be excellent and those is not overshoot.

Keywords: Electric drive, Induction motor, speed control, Adaptive control, neural network, High Performance.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2022

Authors: S. Odeh, E. Abdelghani

Abstract:

Collaborative working environments for distance education can be considered as a more generic form of contemporary remote labs. At present, the majority of existing real laboratories are not constructed to allow the involved participants to collaborate in real time. To make this revolutionary learning environment possible we must allow the different users to carry out an experiment simultaneously. In recent times, multi-user environments are successfully applied in many applications such as air traffic control systems, team-oriented military systems, chat-text tools, multi-player games etc. Thus, understanding the ideas and techniques behind these systems could be of great importance in the contribution of ideas to our e-learning environment for collaborative working. In this investigation, collaborative working environments from theoretical and practical perspectives are considered in order to build an effective collaborative real laboratory, which allows two students or more to conduct remote experiments at the same time as a team. In order to achieve this goal, we have implemented distributed system architecture, enabling students to obtain an automated help by either a human tutor or a rule-based e-tutor.

Keywords: Collaboration environment, e-tutor, multi-user environments, socio-technical system.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1479

Authors: Ifeyinwa E. Achumba, Djamel Azzi, Rinat Khusainov

Abstract:

There are three approaches to complete Bayesian Network (BN) model construction: total expert-centred, total datacentred, and semi data-centred. These three approaches constitute the basis of the empirical investigation undertaken and reported in this paper. The objective is to determine, amongst these three approaches, which is the optimal approach for the construction of a BN-based model for the performance assessment of students- laboratory work in a virtual electronic laboratory environment. BN models were constructed using all three approaches, with respect to the focus domain, and compared using a set of optimality criteria. In addition, the impact of the size and source of the training, on the performance of total data-centred and semi data-centred models was investigated. The results of the investigation provide additional insight for BN model constructors and contribute to literature providing supportive evidence for the conceptual feasibility and efficiency of structure and parameter learning from data. In addition, the results highlight other interesting themes.

Keywords: Bayesian networks, model construction, parameterlearning, structure learning, performance index, model comparison.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1721

Authors: J. P. Dubois, O. M. Abdul-Latif

Abstract:

Support Vector Machine (SVM) is a statistical learning tool that was initially developed by Vapnik in 1979 and later developed to a more complex concept of structural risk minimization (SRM). SVM is playing an increasing role in applications to detection problems in various engineering problems, notably in statistical signal processing, pattern recognition, image analysis, and communication systems. In this paper, SVM was applied to the detection of medical ultrasound images in the presence of partially developed speckle noise. The simulation was done for single look and multi-look speckle models to give a complete overlook and insight to the new proposed model of the SVM-based detector. The structure of the SVM was derived and applied to clinical ultrasound images and its performance in terms of the mean square error (MSE) metric was calculated. We showed that the SVM-detected ultrasound images have a very low MSE and are of good quality. The quality of the processed speckled images improved for the multi-look model. Furthermore, the contrast of the SVM detected images was higher than that of the original non-noisy images, indicating that the SVM approach increased the distance between the pixel reflectivity levels (detection hypotheses) in the original images.

Keywords: LS-SVM, medical ultrasound imaging, partially developed speckle, multi-look model.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1333

Authors: Mohammadali Tavakoli, Atefeh Heydari, Zuriati Ismail, Naomie Salim

Abstract:

With the increasing number of people reviewing products online in recent years, opinion sharing websites has become the most important source of customers’ opinions. Unfortunately, spammers generate and post fake reviews in order to promote or demote brands and mislead potential customers. These are notably destructive not only for potential customers, but also for business holders and manufacturers. However, research in this area is not adequate, and many critical problems related to spam detection have not been solved to date. To provide green researchers in the domain with a great aid, in this paper, we have attempted to create a highquality framework to make a clear vision on review spam-detection methods. In addition, this report contains a comprehensive collection of detection metrics used in proposed spam-detection approaches. These metrics are extremely applicable for developing novel detection methods.

Keywords: Fake reviews, Feature collection, Opinion spam, Spam detection.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2512

Authors: Vladimir Simovic, Matija Varga, Tonco Marusic

Abstract:

This research paper shows matrix technology models and examples of information systems in education (in the Republic of Croatia and in the Germany) in support of business, education (when learning and teaching) and e-learning. Here we researched and described the aims and objectives of the main process in education and technology, with main matrix classes of data. In this paper, we have example of matrix technology with detailed description of processes related to specific data classes in the processes of education and an example module that is support for the process: ‘Filling in the directory and the diary of work’ and ‘evaluation’. Also, on the lower level of the processes, we researched and described all activities which take place within the lower process in education. We researched and described the characteristics and functioning of modules: ‘Fill the directory and the diary of work’ and ‘evaluation’. For the analysis of the affinity between the aforementioned processes and/or sub-process we used our application model created in Visual Basic, which was based on the algorithm for analyzing the affinity between the observed processes and/or sub-processes.

Keywords: Designing, education management, information systems, matrix technology, process affinity.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1089

Authors: P. Phenpun, S. Wareewan

Abstract:

This qualitative study aimed to describe the opinions in relation to humanized care emerging from the volunteer activities of nursing students at Boromarajonani College of Nursing, Chonburi, Thailand. One hundred and twenty-seven second-year nursing students participated in this study. The volunteer activity model was composed of preparation, implementation, and evaluation through a learning log, in which students were encouraged to write their daily activities after completing practical training at the healthcare center. The preparation content included three main categories: service minded, analytical thinking, and client participation. The preparation process took over three days that accumulates up to 20 hours only. The implementation process was held over 10 days, but with a total of 70 hours only, with participants taking part in volunteer work activities at a healthcare center. A learning log was used for evaluation and data were analyzed using content analysis. The findings were as follows. With service minded, there were two subcategories that emerged from volunteer activities, which were service minded towards patients and within themselves. There were three categories under service minded towards patients, which were rapport, compassion, and empathy service behaviors, and there were four categories under service minded within themselves, which were self-esteem, self-value, management potential, and preparedness in providing good healthcare services. In line with analytical thinking, there were two components of analytical thinking, which were analytical skill for their works and analytical thinking for themselves. There were four subcategories under analytical thinking for their works, which were evidence based thinking, real situational thinking, cause analysis thinking, and systematic thinking, respectively. There were four subcategories under analytical thinking for themselves, which were comparative between themselves, towards their clients that leads to the changing of their service behaviors, open-minded thinking, modernized thinking, and verifying both verbal and non-verbal cues. Lastly, there were three categories under participation, which were mutual rapport relationship; reconsidering client’s needs services and providing useful health care information.

Keywords: Humanized care service, volunteer activity, nursing student, and learning log.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1565

Authors: Jürgen Roßmann, André Kupetz, Roland Wischnewski

Abstract:

An intuitive user interface for the teleoperation of mobile rescue robots is one key feature for a successful exploration of inaccessible and no-go areas. Therefore, we have developed a novel framework to embed a flexible and modular user interface into a complete 3-D virtual reality simulation system. Our approach is based on a client-server architecture to allow for a collaborative control of the rescue robot together with multiple clients on demand. Further, it is important that the user interface is not restricted to any specific type of mobile robot. Therefore, our flexible approach allows for the operation of different robot types with a consistent concept and user interface. In laboratory tests, we have evaluated the validity and effectiveness of our approach with the help of two different robot platforms and several input devices. As a result, an untrained person can intuitively teleoperate both robots without needing a familiarization time when changing the operating robot.

Keywords: Teleoperation of mobile robots, augmented reality, user interface, virtual reality.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1838

Authors: Shuo-Li Hsu, Peng-Wei Gau, I-Lin Wu, Jyh-Horng Jeng

Abstract:

For most image fusion algorithms separate relationship by pixels in the image and treat them more or less independently. In addition, they have to be adjusted different parameters in different time or weather. In this paper, we propose a region–based image fusion which combines aspects of feature and pixel-level fusion method to replace only by pixel. The basic idea is to segment far infrared image only and to add information of each region from segmented image to visual image respectively. Then we determine different fused parameters according different region. At last, we adopt artificial neural network to deal with the problems of different time or weather, because the relationship between fused parameters and image features are nonlinear. It render the fused parameters can be produce automatically according different states. The experimental results present the method we proposed indeed have good adaptive capacity with automatic determined fused parameters. And the architecture can be used for lots of applications.

Keywords: Image fusion, Region-based fusion, Segmentation, Neural network, Multi-sensor.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2247

Authors: Leandro Marcos Alves Vaz

Abstract:

The teaching of physics in Brazilian public schools emphasizes strongly the theoretical aspects of this science, showing its philosophical and mathematical basis, but neglecting its experimental character. Perhaps the lack of science laboratories explains this practice. In this work, we present a method of teaching physics using the computer. As alternatives to real experiments, we have the trials through simulators, many of which are free software available on the internet. In order to develop a study on the use of simulators in teaching, knowing the impossibility of simulations on all topics in a given subject, we combined these programs with phenomenological and/or experimental texts in order to mitigate this limitation. This study proposes the use of simulators and the debate using phenomenological/experimental texts on electrostatic theme in groups of the 3rd year of EJA (Adult and Youth Education) in order to verify the advantages of this methodology. Some benefits of the hybridization of the traditional method with the tools used were: Greater motivation of the students in learning, development of experimental notions, proactive socialization to learning, greater easiness to understand some concepts and the creation of collaborative activities that can reduce timidity of part of the students.

Keywords: Physics teaching, simulators, youth and adult education, experimentation, electrostatic.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1178

Authors: R.Sukesh Kumar, Abhisek Verma, Jasprit Singh

Abstract:

In this work a novel approach for color image segmentation using higher order entropy as a textural feature for determination of thresholds over a two dimensional image histogram is discussed. A similar approach is applied to achieve multi-level thresholding in both grayscale and color images. The paper discusses two methods of color image segmentation using RGB space as the standard processing space. The threshold for segmentation is decided by the maximization of conditional entropy in the two dimensional histogram of the color image separated into three grayscale images of R, G and B. The features are first developed independently for the three ( R, G, B ) spaces, and combined to get different color component segmentation. By considering local maxima instead of the maximum of conditional entropy yields multiple thresholds for the same image which forms the basis for multilevel thresholding.

Keywords: conditional entropy, multi-level thresholding, segmentation, two dimensional image histogram

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2990

Authors: Hamza Nejib, Okba Taouali

Abstract:

This paper presents two of the most knowing kernel adaptive filtering (KAF) approaches, the kernel least mean squares and the kernel recursive least squares, in order to predict a new output of nonlinear signal processing. Both of these methods implement a nonlinear transfer function using kernel methods in a particular space named reproducing kernel Hilbert space (RKHS) where the model is a linear combination of kernel functions applied to transform the observed data from the input space to a high dimensional feature space of vectors, this idea known as the kernel trick. Then KAF is the developing filters in RKHS. We use two nonlinear signal processing problems, Mackey Glass chaotic time series prediction and nonlinear channel equalization to figure the performance of the approaches presented and finally to result which of them is the adapted one.

Keywords: KLMS, online prediction, KAF, signal processing, RKHS, Kernel methods, KRLS, KLMS.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1045

Authors: Konstantinos C. Kartsakalis, Angeliki Skoura, Vasileios Megalooikonomou

Abstract:

The fuzzy composition of objects depicted in images acquired through MR imaging or the use of bio-scanners has often been a point of controversy for field experts attempting to effectively delineate between the visualized objects. Modern approaches in medical image segmentation tend to consider fuzziness as a characteristic and inherent feature of the depicted object, instead of an undesirable trait. In this paper, a novel technique for efficient image retrieval in the context of images in which segmented objects are either crisp or fuzzily bounded is presented. Moreover, the proposed method is applied in the case of multiple, even conflicting, segmentations from field experts. Experimental results demonstrate the efficiency of the suggested method in retrieving similar objects from the aforementioned categories while taking into account the fuzzy nature of the depicted data.

Keywords: Fuzzy Object, Fuzzy Image Segmentation, Motion Descriptors, MRI Imaging, Object-Based Image Retrieval.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2299

Authors: Andreas Heindl, Alexander K. Seewald, Martin Schepelmann, Radu Rogojanu, Giovanna Bises, Theresia Thalhammer, Isabella Ellinger

Abstract:

Bone remodeling occurs by the balanced action of bone resorbing osteoclasts (OC) and bone-building osteoblasts. Increased bone resorption by excessive OC activity contributes to malignant and non-malignant diseases including osteoporosis. To study OC differentiation and function, OC formed in in vitro cultures are currently counted manually, a tedious procedure which is prone to inter-observer differences. Aiming for an automated OC-quantification system, classification of OC and precursor cells was done on fluorescence microscope images based on the distinct appearance of fluorescent nuclei. Following ellipse fitting to nuclei, a combination of eight features enabled clustering of OC and precursor cell nuclei. After evaluating different machine-learning techniques, LOGREG achieved 74% correctly classified OC and precursor cell nuclei, outperforming human experts (best expert: 55%). In combination with the automated detection of total cell areas, this system allows to measure various cell parameters and most importantly to quantify proteins involved in osteoclastogenesis.

Keywords: osteoclasts, machine learning, ellipse fitting.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1906

Authors: Agni Stylianou-Georgiou, Eliza Pitri

Abstract:

This study is a design narrative that discusses researchers’ new learning based on changes made in pedagogies and learning opportunities in the context of a Cognitive Psychology and an Art History undergraduate course. The purpose of this study was to investigate how to encourage creative problem-based play in tertiary education engaging instructors and student-teachers in designing educational games. Course instructors modified content to encourage flexible thinking during game design problem-solving. Qualitative analyses of data sources indicated that Thinking Birds’ questions could encourage flexible thinking as instructors engaged in creative problem-based play. However, student-teachers demonstrated weakness in adopting flexible thinking during game design problem solving. Further studies of student-teachers’ shifting perspectives during different instructional design tasks would provide insights for developing the Thinking Birds’ questions as tools for creative problem solving.

Keywords: Creative problem-based play, educational games, flexible thinking, tertiary education.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 880

Authors: Sacide Güzin Mazman, Yasemin Koçak Usluel

Abstract:

Possible advantages of technology in educational context required the defining boundaries of formal and informal learning. Increasing opportunity to ubiquitous learning by technological support has revealed a question of how to discover the potential of individuals in the spontaneous environments such as social networks. This seems to be related with the question of what purposes in social networks have been being used? Social networks provide various advantages in educational context as collaboration, knowledge sharing, common interests, active participation and reflective thinking. As a consequence of these, the purpose of this study is composed of proposing a new model that could determine factors which effect adoption of social network applications for usage in educational context. While developing a model proposal, the existing adoption and diffusion models have been reviewed and they are thought to be suitable on handling an original perspective instead of using completely other diffusion or acceptance models because of different natures of education from other organizations. In the proposed model; social factors, perceived ease of use, perceived usefulness and innovativeness are determined four direct constructs that effect adoption process. Facilitating conditions, image, subjective norms and community identity are incorporated to model as antecedents of these direct four constructs.

Keywords: Adoption of innovation, educational context, social networks.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3870

Authors: Rishabh Garg, Anuja Vyas, Aamna Khan, Muhammad Azwan Tariq

Abstract:

The present study focuses on integrating Machine Learning and Genomics, hereafter termed ‘GenoLearning’, to develop Artificial Life (AL). This is achieved by leveraging gene editing to imbue genes with sequences capable of performing desired functions. To accomplish this, a specialized sub-network of Siamese Neural Network (SNN), named Transformer Architecture specialized in Sequence Analysis of Genes (TASAG), compares two sequences: the desired and target sequences. Differences between these sequences are analyzed, and necessary edits are made on-screen to incorporate the desired sequence into the target sequence. The edited sequence can then be synthesized chemically using a Computerized DNA Synthesizer (CDS). The CDS fabricates DNA strands according to the sequence displayed on a computer screen, aided by microprocessors. These synthesized DNA strands can be inserted into an ovum to initiate further development, eventually leading to the creation of an Embot, and ultimately, an H-Bot. While this study aims to explore the potential benefits of Artificial Intelligence (AI) technology, it also acknowledges and addresses the ethical considerations associated with its implementation.

Keywords: Machine Learning, Genomics, Genetronics, DNA, Transformer, Siamese Neural Network, Gene Editing, Artificial Life, H-Bot, Zoobot.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 42

Authors: Yassamina Berkane, Leïla Kloul, Yoann Demoli

Abstract:

Quality of life, environmental impact, congestion of mobility means, and infrastructures remain significant challenges for urban mobility. Solutions like car sharing, spatial redesign, eCommerce, and autonomous vehicles will likely increase the unit veh-km and the density of cars in urban traffic, thus reducing congestion. However, the impact of such solutions is not clear for researchers. Congestion arises from growing populations that must travel greater distances to arrive at similar locations (e.g., workplaces, schools) during the same time frame (e.g., rush hours). This paper first reviews the research and application cases of urban congestion methods through recent years. Rethinking the question of time, it then investigates people’s willingness and flexibility to adapt their arrival and departure times from workplaces. We use neural networks and methods of supervised learning to apply a methodology for predicting peoples’ intentions from their responses in a questionnaire. We created and distributed a questionnaire to more than 50 companies in the Paris suburb. Obtained results illustrate that our methodology can predict peoples’ intentions to reschedule their activities (work, study, commerce, etc.).

Keywords: Urban mobility, decongestion, machine learning, neural network.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 470

Authors: Zhengyong Liu, Huiqing Ying

Abstract:

GFRG(Glass Fiber Reinforced Gypsum) wall is a green product which can erect a building fast in prefabricated method, but its application to high-rise residential buildings is limited for its poor lateral stiffness. This paper has proposed a modification to GFRG walls structure to increase its lateral stiffness, which aiming to erect small high-rise residential buildings as load-bearing walls. The elastic finite element analysis to it has shown the lateral deformation feature and the distributions of the axial force and the shear force. The analysis results show that the new GFRG reinforced concrete wall can be used for small high-rise residential buildings.

Keywords: GFRG wall, lateral features, elastic analysis, residential building.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3329

Authors: Mansour Al Qubeissi, Sergei S. Sazhin, Cyril Crua, Morgan R. Heikal

Abstract:

This paper presents the application of the Discrete Component Model for heating and evaporation to multi-component biodiesel fuel droplets in direct injection internal combustion engines. This model takes into account the effects of temperature gradient, recirculation and species diffusion inside droplets. A distinctive feature of the model used in the analysis is that it is based on the analytical solutions to the temperature and species diffusion equations inside the droplets. Nineteen types of biodiesel fuels are considered. It is shown that a simplistic model, based on the approximation of biodiesel fuel by a single component or ignoring the diffusion of components of biodiesel fuel, leads to noticeable errors in predicted droplet evaporation time and time evolution of droplet surface temperature and radius.

Keywords: Heat/Mass Transfer, Biodiesel, Multi-component Fuel, Droplet, Evaporation.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2795

Authors: Hernán G. Cortés-Mora, José I. Péna-Reyes, Alfonso Herrera-Jiménez

Abstract:

This article describes the implementation experience of Project-Based Learning (PBL) in an engineering course of the Universidad Nacional de Colombia, with the aim of strengthening student skills necessary for the exercise of their profession under a sustainability framework. Firstly, we present a literature review on the education for sustainability field, emphasizing the skills and knowledge areas required for its development, as well as the commitment of the Faculty of Engineering of the Universidad Nacional de Colombia, and other engineering faculties of the country, regarding education for sustainability. This article covers the general aspects of the course, describes how students team were formed, and how their experience was during the first semester of 2017. During this period two groups of students decided to develop their course project aiming to solve a problem regarding a Non-Governmental Organization (NGO) that works with head-of-household mothers in a low-income neighborhood in Bogota (Colombia). Subsequently, we show how sustainability is involved in the course, how tools are provided to students, and how activities are developed as to strengthen their abilities, which allows them to incorporate sustainability in their projects while also working on the methodology used to develop said projects. Finally, we introduce the results obtained by the students who sent the prototypes of their projects to the community they were working on and the conclusions reached by them regarding the course experience.

Keywords: Sustainability, project based learning, engineering education, higher education for sustainability.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 816

Authors: Georgia H. Mega

Abstract:

Street art, a category of artwork displayed in public spaces, has been recognized as a potential tool for promoting reflective thinking in both adult and primary education. Educational techniques that encourage critical and creative thinking, as well as deeper reflection, have been developed and applied in educational curricula. This paper aims to explore the potential of art street in cultivating learners' reflective awareness towards multiculturalism. More specifically, two artworks displayed in public spaces have been selected: the artwork of Kleomenis Kostopoulos and the artwork of Rustam Obic. The reason of this selection is because of their strong symbolism towards multiculturalism. The street arts have been elaborated by adult (+18) and minor students (K-12) in educational settings, under the same educator’s guidance, following appropriate for each age learning techniques. Adults cultivate their reflection using Freire’s learning method, whereas minors cultivate critical thinking using visible thinking techniques from Project Zero. Through qualitative methodology (context analysis) the depth of reflection/critical thinking has been emphasized for both age groups. The case study shows that street art can play a significant role to the promotion/cultivation of deep thinking towards challenging contemporary phenomena like multiculturalism.

Keywords: Street art, observation of art works, reflective awareness, educational techniques, multiculturalism.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 78

Authors: Yoshiaki Takeda, Norifumi Aoyama, Takahiro Tanaami, Syouhei Honda, Kenta Tabata, Hiroyuki Kamata

Abstract:

In this paper, we validate crater detection in moon surface image using FLDA. This proposal assumes that it is applied to SLIM (Smart Lander for Investigating Moon) project aiming at the pin-point landing to the moon surface. The point where the lander should land is judged by the position relations of the craters obtained via camera, so the real-time image processing becomes important element. Besides, in the SLIM project, 400kg-class lander is assumed, therefore, high-performance computers for image processing cannot be equipped. We are studying various crater detection methods such as Haar-Like features, LBP, and PCA. And we think these methods are appropriate to the project, however, to identify the unlearned images obtained by actual is insufficient. In this paper, we examine the crater detection using FLDA, and compare with the conventional methods.

Keywords: Crater Detection, Fisher Linear Discriminant Analysis , Haar-Like Feature, Image Processing.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1722

Authors: Nittaya Kerdprasop, Kittisak Kerdprasop

Abstract:

Frequent patterns are patterns such as sets of features or items that appear in data frequently. Finding such frequent patterns has become an important data mining task because it reveals associations, correlations, and many other interesting relationships hidden in a dataset. Most of the proposed frequent pattern mining algorithms have been implemented with imperative programming languages such as C, Cµ, Java. The imperative paradigm is significantly inefficient when itemset is large and the frequent pattern is long. We suggest a high-level declarative style of programming using a functional language. Our supposition is that the problem of frequent pattern discovery can be efficiently and concisely implemented via a functional paradigm since pattern matching is a fundamental feature supported by most functional languages. Our frequent pattern mining implementation using the Haskell language confirms our hypothesis about conciseness of the program. The performance studies on speed and memory usage support our intuition on efficiency of functional language.

Keywords: Association, frequent pattern mining, functionalprogramming, pattern matching.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2128

Authors: M. A. S. Fahim, J. Sužiedelytė Visockienė

Abstract:

With the growing concern over air pollution (AP), it is clear that this has gained more prominence than ever before. The level of consciousness has increased and a sense of knowledge now has to be forwarded as a duty by those enlightened enough to disseminate it to others. This realization often comes after an understanding of how poor air quality indices (AQI) damage human health. The study focuses on assessing air pollution prediction models specifically for Lithuania, addressing a substantial need for empirical research within the region. Concentrating on Vilnius, it specifically examines particulate matter concentrations 10 micrometers or less in diameter (PM10). Utilizing Gaussian Process Regression (GPR) and Regression Tree Ensemble, and Regression Tree methodologies, predictive forecasting models are validated and tested using hourly data from January 2020 to December 2022. The study explores the classification of AP data into anthropogenic and natural sources, the impact of AP on human health, and its connection to cardiovascular diseases. The study revealed varying levels of accuracy among the models, with GPR achieving the highest accuracy, indicated by an RMSE of 4.14 in validation and 3.89 in testing.

Keywords: Air pollution, anthropogenic and natural sources, machine learning, Gaussian process regression, tree ensemble, forecasting models, particulate matter.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 90

Authors: Shujun Fu, Qiuqi Ruan, Wenqia Wang, Yu Li

Abstract:

Utilizing echoic intension and distribution from different organs and local details of human body, ultrasonic image can catch important medical pathological changes, which unfortunately may be affected by ultrasonic speckle noise. A feature preserving ultrasonic image denoising and edge enhancement scheme is put forth, which includes two terms: anisotropic diffusion and edge enhancement, controlled by the optimum smoothing time. In this scheme, the anisotropic diffusion is governed by the local coordinate transformation and the first and the second order normal derivatives of the image, while the edge enhancement is done by the hyperbolic tangent function. Experiments on real ultrasonic images indicate effective preservation of edges, local details and ultrasonic echoic bright strips on denoising by our scheme.

Keywords: anisotropic diffusion, coordinate transformation, directional derivatives, edge enhancement, hyperbolic tangent function, image denoising.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1892