Search results for: Machine tools

Authors: L. Major, J. M. Lackner, M. Dyner, B. Major

Abstract:

Development of new generation bio-tribological, multilayer coatings opens an avenue for fabrication of future hightech functional surfaces. In the presented work, nano-composite, Cr/CrN+[Cr/ a-C:H implanted by metallic nanocrystals] multilayer coatings have been developed for surface protection of medical tools. Thin films were fabricated by a hybrid Pulsed Laser Deposition technique. Complex microstructure analysis of nanomultilayer coatings, subjected to mechanical and biological tests, were performed by means of transmission electron microscopy (TEM). Microstructure characterization revealed the layered arrangement of Cr23C6 nanoparticles in multilayer structure. Influence of deposition conditions on bio-tribological properties of the coatings was studied. The bio-tests were used as a screening tool for the analyzed nanomultilayer coatings before they could be deposited on medical tools. Bio-medical tests were done using fibroblasts. The mechanical properties of the coatings were investigated by means of a ball-ondisc mechanical test. The micro hardness was done using Berkovich indenter. The scratch adhesion test was done using Rockwell indenter. From the bio-tribological point of view, the optimal properties had the C106_1 material.

Keywords: Bio-tribological coatings, cell-material interaction, hybrid PLD, tribology.

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Authors: Mohamed A. Khlifi, Badr M. Alshammari

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This paper discusses the general methods to saturation in the steady-state, two axis (d & q) frame models of synchronous machines. In particular, the important role of the magnetic coupling between the d-q axes (cross-magnetizing phenomenon), is demonstrated. For that purpose, distinct methods of saturation modeling of dumper synchronous machine with cross-saturation are identified, and detailed models synthesis in d-q axes. A number of models are given in the final developed form. The procedure and the novel models are verified by a critical application to prove the validity of the method and the equivalence between all developed models is reported. Advantages of some of the models over the existing ones and their applicability are discussed.

Keywords: Cross-magnetizing, models synthesis, synchronous machine, saturated modeling, state-space vectors.

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Authors: Rohit Choudhry, Kumkum Garg

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In this paper, we propose a hybrid machine learning system based on Genetic Algorithm (GA) and Support Vector Machines (SVM) for stock market prediction. A variety of indicators from the technical analysis field of study are used as input features. We also make use of the correlation between stock prices of different companies to forecast the price of a stock, making use of technical indicators of highly correlated stocks, not only the stock to be predicted. The genetic algorithm is used to select the set of most informative input features from among all the technical indicators. The results show that the hybrid GA-SVM system outperforms the stand alone SVM system.

Keywords: Genetic Algorithms, Support Vector Machines, Stock Market Forecasting.

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Authors: M. R. Homaeinezhad, E. Tavakkoli, M. Habibi, S. A. Atyabi, A. Ghaffari

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This paper describes a new supervised fusion (hybrid) electrocardiogram (ECG) classification solution consisting of a new QRS complex geometrical feature extraction as well as a new version of the learning vector quantization (LVQ) classification algorithm aimed for overcoming the stability-plasticity dilemma. Toward this objective, after detection and delineation of the major events of ECG signal via an appropriate algorithm, each QRS region and also its corresponding discrete wavelet transform (DWT) are supposed as virtual images and each of them is divided into eight polar sectors. Then, the curve length of each excerpted segment is calculated and is used as the element of the feature space. To increase the robustness of the proposed classification algorithm versus noise, artifacts and arrhythmic outliers, a fusion structure consisting of five different classifiers namely as Support Vector Machine (SVM), Modified Learning Vector Quantization (MLVQ) and three Multi Layer Perceptron-Back Propagation (MLP–BP) neural networks with different topologies were designed and implemented. The new proposed algorithm was applied to all 48 MIT–BIH Arrhythmia Database records (within–record analysis) and the discrimination power of the classifier in isolation of different beat types of each record was assessed and as the result, the average accuracy value Acc=98.51% was obtained. Also, the proposed method was applied to 6 number of arrhythmias (Normal, LBBB, RBBB, PVC, APB, PB) belonging to 20 different records of the aforementioned database (between– record analysis) and the average value of Acc=95.6% was achieved. To evaluate performance quality of the new proposed hybrid learning machine, the obtained results were compared with similar peer– reviewed studies in this area.

Keywords: Feature Extraction, Curve Length Method, SupportVector Machine, Learning Vector Quantization, Multi Layer Perceptron, Fusion (Hybrid) Classification, Arrhythmia Classification, Supervised Learning Machine.

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Authors: Jaimala Gambhir, Tilak Thakur, Puneet Chawla

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As per the statistical data, the Doubly-fed Induction Generator (DFIG) based wind turbine with variable speed and variable pitch control is the most common wind turbine in the growing wind market. This machine is usually used on the grid connected wind energy conversion system to satisfy grid code requirements such as grid stability, Fault Ride Through (FRT), power quality improvement, grid synchronization and power control etc. Though the requirements are not fulfilled directly by the machine, the control strategy is used in both the stator as well as rotor side along with power electronic converters to fulfil the requirements stated above. To satisfy the grid code requirements of wind turbine, usually grid side converter is playing a major role. So in order to improve the operation capacity of wind turbine under critical situation, the intensive study of both machine side converter control and grid side converter control is necessary In this paper DFIG is modeled using power components as variables and the performance of the DFIG system is analysed under grid voltage fluctuations. The voltage fluctuations are made by lowering and raising the voltage values in the utility grid intentionally for the purpose of simulation keeping in view of different grid disturbances.

Keywords: DFIG, dynamic modeling, DPC, sag, swell, voltage fluctuations, FRT.

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Authors: Vladimir Aleksandrovich Rogov, Ghorbani Siamak

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Determination of optimal conditions of machining parameters is important to reduce the production cost and achieve the desired surface quality. This paper investigates the influence of cutting parameters on surface roughness and natural frequency in turning of aluminum alloy AA2024. The experiments were performed at the lathe machine using two different cutting tools made of AISI 5140 and carbide cutting insert coated with TiC. Turning experiments were planned by Taguchi method L₉ orthogonal array.Three levels for spindle speed, feed rate, depth of cut and tool overhang were chosen as cutting variables. The obtained experimental data has been analyzed using signal to noise ratio and analysis of variance. The main effects have been discussed and percentage contributions of various parameters affecting surface roughness and natural frequency, and optimal cutting conditions have been determined. Finally, optimization of the cutting parameters using Taguchi method was verified by confirmation experiments.

Keywords: Turning, Cutting conditions, Surface roughness, Natural frequency, Taguchi method, ANOVA, S/N ratio.

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Authors: Amin Almasi

Abstract:

Reliability assessment and risk analysis of rotating machine rotors in various overload and malfunction situations present challenge to engineers and operators. In this paper a new analytical method for evaluation of rotor under large deformation is addressed. Model is presented in general form to include also composite rotors. Presented simulation procedure is based on variational work method and has capability to account for geometric nonlinearity, large displacement, nonlinear support effect and rotor contacting other machine components. New shape functions are presented which capable to predict accurate nonlinear profile of rotor. The closed form solutions for various operating and malfunction situations are expressed. Analytical simulation results are discussed

Keywords: Large Deformation, Nonlinear, Rotor.

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Authors: J. Fernandez de Canete, S. Gonzalez-Perez, P. del Saz-Orozco

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Neural networks offer an alternative approach both for identification and control of nonlinear processes in process engineering. The lack of software tools for the design of controllers based on neural network models is particularly pronounced in this field. SIMULINK is properly a widely used graphical code development environment which allows system-level developers to perform rapid prototyping and testing. Such graphical based programming environment involves block-based code development and offers a more intuitive approach to modeling and control task in a great variety of engineering disciplines. In this paper a SIMULINK based Neural Tool has been developed for analysis and design of multivariable neural based control systems. This tool has been applied to the control of a high purity distillation column including non linear hydrodynamic effects. The proposed control scheme offers an optimal response for both theoretical and practical challenges posed in process control task, in particular when both, the quality improvement of distillation products and the operation efficiency in economical terms are considered.

Keywords: Distillation, neural networks, software tools, identification, control.

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Authors: Sung Ho Ha, Seong Hyeon Joo, Eun Kyung Kwon

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Until recently, researchers have developed various tools and methodologies for effective clinical decision-making. Among those decisions, chest pain diseases have been one of important diagnostic issues especially in an emergency department. To improve the ability of physicians in diagnosis, many researchers have developed diagnosis intelligence by using machine learning and data mining. However, most of the conventional methodologies have been generally based on a single classifier for disease classification and prediction, which shows moderate performance. This study utilizes an ensemble strategy to combine multiple different classifiers to help physicians diagnose chest pain diseases more accurately than ever. Specifically the ensemble strategy is applied by using the integration of decision trees, neural networks, and support vector machines. The ensemble models are applied to real-world emergency data. This study shows that the performance of the ensemble models is superior to each of single classifiers.

Keywords: Diagnosis intelligence, ensemble approach, data mining, emergency department

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Authors: Jian Zheng, Yoshiyasu Takahashi, Yuichi Kobayashi, Tatsuhiro Sato

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In the study, we present a conceptual framework for developing a scheduling system that can generate self-explanatory and easy-understanding schedules. To this end, a user interface is conceived to help planners record factors that are considered crucial in scheduling, as well as internal and external sources relating to such factors. A hybrid approach combining machine learning and constraint programming is developed to generate schedules and the corresponding factors, and accordingly display them on the user interface. Effects of the proposed system on scheduling are discussed, and it is expected that scheduling efficiency and system understandability will be improved, compared with previous scheduling systems.

Keywords: Constraint programming, Factors considered in scheduling, machine learning, scheduling system.

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Authors: R. Xu, X. Zhao, X. Li, C. Kwan, C.-I Chang

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An image texture analysis and target recognition approach of using an improved image texture feature coding method (TFCM) and Support Vector Machine (SVM) for target detection is presented. With our proposed target detection framework, targets of interest can be detected accurately. Cascade-Sliding-Window technique was also developed for automated target localization. Application to mammogram showed that over 88% of normal mammograms and 80% of abnormal mammograms can be correctly identified. The approach was also successfully applied to Synthetic Aperture Radar (SAR) and Ground Penetrating Radar (GPR) images for target detection.

Keywords: Image texture analysis, feature extraction, target detection, pattern classification.

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Authors: Antonis Sideris, Elias Chlis Kalogeropoulos, Konstantia Moirogiorgou

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The present study proposes a methodology for the efficient daily management of fleet vehicles and construction machinery. The application covers the area of remote monitoring of heavy-duty vehicles operation parameters, where specific sensor data are stored and examined in order to provide information about the vehicle’s health. The vehicle diagnostics allow the user to inspect whether maintenance tasks need to be performed before a fault occurs. A properly designed machine learning model is proposed for the detection of two different types of faults through classification. Cross validation is used and the accuracy of the trained model is checked with the confusion matrix.

Keywords: Fault detection, feature selection, machine learning, predictive maintenance.

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Authors: Adrienne Kline, Jaydip Desai

Abstract:

Electroencephalogram (EEG) is a noninvasive technique that registers signals originating from the firing of neurons in the brain. The Emotiv EEG Neuroheadset is a consumer product comprised of 14 EEG channels and was used to record the reactions of the neurons within the brain to two forms of stimuli in 10 participants. These stimuli consisted of auditory and visual formats that provided directions of ‘right’ or ‘left.’ Participants were instructed to raise their right or left arm in accordance with the instruction given. A scenario in OpenViBE was generated to both stimulate the participants while recording their data. In OpenViBE, the Graz Motor BCI Stimulator algorithm was configured to govern the duration and number of visual stimuli. Utilizing EEGLAB under the cross platform MATLAB®, the electrodes most stimulated during the study were defined. Data outputs from EEGLAB were analyzed using IBM SPSS Statistics® Version 20. This aided in determining the electrodes to use in the development of a brain-machine interface (BMI) using real-time EEG signals from the Emotiv EEG Neuroheadset. Signal processing and feature extraction were accomplished via the Simulink® signal processing toolbox. An Arduino™ Duemilanove microcontroller was used to link the Emotiv EEG Neuroheadset and the right and left Mecha TE™ Hands.

Keywords: Brain-machine interface, EEGLAB, emotiv EEG neuroheadset, openViBE, simulink.

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Authors: Jaydip M. Desai, Antonio Valdevit, Arthur Ritter

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Robotic surgery is used to enhance minimally invasive surgical procedure. It provides greater degree of freedom for surgical tools but lacks of haptic feedback system to provide sense of touch to the surgeon. Surgical robots work on master-slave operation, where user is a master and robotic arms are the slaves. Current, surgical robots provide precise control of the surgical tools, but heavily rely on visual feedback, which sometimes cause damage to the inner organs. The goal of this research was to design and develop a realtime Simulink based robotic system to study force feedback mechanism during instrument-object interaction. Setup includes three VelmexXSlide assembly (XYZ Stage) for three dimensional movement, an end effector assembly for forceps, electronic circuit for four strain gages, two Novint Falcon 3D gaming controllers, microcontroller board with linear actuators, MATLAB and Simulink toolboxes. Strain gages were calibrated using Imada Digital Force Gauge device and tested with a hard-core wire to measure instrument-object interaction in the range of 0-35N. Designed Simulink model successfully acquires 3D coordinates from two Novint Falcon controllers and transfer coordinates to the XYZ stage and forceps. Simulink model also reads strain gages signal through 10-bit analog to digital converter resolution of a microcontroller assembly in real time, converts voltage into force and feedback the output signals to the Novint Falcon controller for force feedback mechanism. Experimental setup allows user to change forward kinematics algorithms to achieve the best-desired movement of the XYZ stage and forceps. This project combines haptic technology with surgical robot to provide sense of touch to the user controlling forceps through machine-computer interface.

Keywords: Haptic feedback, MATLAB, Simulink, Strain Gage, Surgical Robot.

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Authors: Melisa Yazdan Panahi

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Since, today in most countries around the world much attention is paid to planning the smallest unit in the city i.e. the residential neighborhoods to achieve sustainable urban development goals, a variety of assessment tools have been developed to assess and monitor the sustainability of new developments. One of the most reliable and widely used assessment tools is LEED-ND rating system. This paper whit the aim of assessing sustainability level of Roshdieh neighborhood in Tabriz, has introduced this rating system and applied it in the study area. The results indicate that Roshdieh has the potential of achieving the standards of sustainable neighborhoods, but the present situation is far from the ideal point.

Keywords: LEED-ND, Sustainable Neighborhood, New Developments, Tabriz.

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Authors: Kasra Majbouri Yazdi, Adel Majbouri Yazdi, Saeid Khodayi, Jingyu Hou, Wanlei Zhou, Saeed Saedy

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Fake news and false information are big challenges of all types of media, especially social media. There is a lot of false information, fake likes, views and duplicated accounts as big social networks such as Facebook and Twitter admitted. Most information appearing on social media is doubtful and in some cases misleading. They need to be detected as soon as possible to avoid a negative impact on society. The dimensions of the fake news datasets are growing rapidly, so to obtain a better result of detecting false information with less computation time and complexity, the dimensions need to be reduced. One of the best techniques of reducing data size is using feature selection method. The aim of this technique is to choose a feature subset from the original set to improve the classification performance. In this paper, a feature selection method is proposed with the integration of K-means clustering and Support Vector Machine (SVM) approaches which work in four steps. First, the similarities between all features are calculated. Then, features are divided into several clusters. Next, the final feature set is selected from all clusters, and finally, fake news is classified based on the final feature subset using the SVM method. The proposed method was evaluated by comparing its performance with other state-of-the-art methods on several specific benchmark datasets and the outcome showed a better classification of false information for our work. The detection performance was improved in two aspects. On the one hand, the detection runtime process decreased, and on the other hand, the classification accuracy increased because of the elimination of redundant features and the reduction of datasets dimensions.

Keywords: Fake news detection, feature selection, support vector machine, K-means clustering, machine learning, social media.

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Authors: Daniel I. Morariu, Lucian N. Vintan, Volker Tresp

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Text categorization is the problem of classifying text documents into a set of predefined classes. After a preprocessing step, the documents are typically represented as large sparse vectors. When training classifiers on large collections of documents, both the time and memory restrictions can be quite prohibitive. This justifies the application of feature selection methods to reduce the dimensionality of the document-representation vector. In this paper, we present three feature selection methods: Information Gain, Support Vector Machine feature selection called (SVM_FS) and Genetic Algorithm with SVM (called GA_SVM). We show that the best results were obtained with GA_SVM method for a relatively small dimension of the feature vector.

Keywords: Feature Selection, Learning with Kernels, Support Vector Machine, Genetic Algorithm, and Classification.

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Authors: Mohammad Javadi

Abstract:

Serial hierarchical support vector machine (SHSVM) is proposed to discriminate three brain tissues which are white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). SHSVM has novel classification approach by repeating the hierarchical classification on data set iteratively. It used Radial Basis Function (rbf) Kernel with different tuning to obtain accurate results. Also as the second approach, segmentation performed with DAGSVM method. In this article eight univariate features from the raw DTI data are extracted and all the possible 2D feature sets are examined within the segmentation process. SHSVM succeed to obtain DSI values higher than 0.95 accuracy for all the three tissues, which are higher than DAGSVM results.

Keywords: Brain segmentation, DTI, hierarchical, SVM.

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Authors: Daniel Morariu, Lucian N. Vintan, Volker Tresp

Abstract:

Text categorization is the problem of classifying text documents into a set of predefined classes. After a preprocessing step, the documents are typically represented as large sparse vectors. When training classifiers on large collections of documents, both the time and memory restrictions can be quite prohibitive. This justifies the application of feature selection methods to reduce the dimensionality of the document-representation vector. In this paper, three feature selection methods are evaluated: Random Selection, Information Gain (IG) and Support Vector Machine feature selection (called SVM_FS). We show that the best results were obtained with SVM_FS method for a relatively small dimension of the feature vector. Also we present a novel method to better correlate SVM kernel-s parameters (Polynomial or Gaussian kernel).

Keywords: Feature Selection, Learning with Kernels, SupportVector Machine, and Classification.

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Authors: Manisha Rathi, Thierry Chaussalet

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Hospital staff and managers are under pressure and concerned for effective use and management of scarce resources. The hospital admissions require many decisions that have complex and uncertain consequences for hospital resource utilization and patient flow. It is challenging to predict risk of admissions and length of stay of a patient due to their vague nature. There is no method to capture the vague definition of admission of a patient. Also, current methods and tools used to predict patients at risk of admission fail to deal with uncertainty in unplanned admission, LOS, patients- characteristics. The main objective of this paper is to deal with uncertainty in health system variables, and handles uncertain relationship among variables. An introduction of machine learning techniques along with statistical methods like Regression methods can be a proposed solution approach to handle uncertainty in health system variables. A model that adapts fuzzy methods to handle uncertain data and uncertain relationships can be an efficient solution to capture the vague definition of admission of a patient.

Keywords: Admission, Fuzzy, Regression, Uncertainty

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Authors: S. E. Oraby, A.M. Alaskari

Abstract:

The stochastic nature of tool life using conventional discrete-wear data from experimental tests usually exists due to many individual and interacting parameters. It is a common practice in batch production to continually use the same tool to machine different parts, using disparate machining parameters. In such an environment, the optimal points at which tools have to be changed, while achieving minimum production cost and maximum production rate within the surface roughness specifications, have not been adequately studied. In the current study, two relevant aspects are investigated using coated and uncoated inserts in turning operations: (i) the accuracy of using machinability information, from fixed parameters testing procedures, when variable parameters situations are emerged, and (ii) the credibility of tool life machinability data from prior discrete testing procedures in a non-stop machining. A novel technique is proposed and verified to normalize the conventional fixed parameters machinability data to suit the cases when parameters have to be changed for the same tool. Also, an experimental investigation has been established to evaluate the error in the tool life assessment when machinability from discrete testing procedures is employed in uninterrupted practical machining.

Keywords: Machinability, tool life, tool wear, wear variability

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Authors: Belkacem Chikhaoui, Helene Pigot

Abstract:

Cognitive models allow predicting some aspects of utility and usability of human machine interfaces (HMI), and simulating the interaction with these interfaces. The action of predicting is based on a task analysis, which investigates what a user is required to do in terms of actions and cognitive processes to achieve a task. Task analysis facilitates the understanding of the system-s functionalities. Cognitive models are part of the analytical approaches, that do not associate the users during the development process of the interface. This article presents a study about the evaluation of a human machine interaction with a contextual assistant-s interface using ACTR and GOMS cognitive models. The present work shows how these techniques may be applied in the evaluation of HMI, design and research by emphasizing firstly the task analysis and secondly the time execution of the task. In order to validate and support our results, an experimental study of user performance is conducted at the DOMUS laboratory, during the interaction with the contextual assistant-s interface. The results of our models show that the GOMS and ACT-R models give good and excellent predictions respectively of users performance at the task level, as well as the object level. Therefore, the simulated results are very close to the results obtained in the experimental study.

Keywords: HMI, interface evaluation, Analytical evaluation, cognitivemodeling, user modeling, user performance.

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Authors: Kwok Tak Kit

Abstract:

The construction industry plays an important role in environmental and carbon emissions as it consumes a huge amount of natural resources and energy. Sustainable engineering involves the process of planning, design, procurement, construction and delivery in which the whole building and construction process resulting from building and construction can be effectively and sustainability managed to achieve the use of natural resources. Implementation of sustainable technology development and innovation, adoption of the advanced construction process and facilitate the facilities management to implement the energy and waste control more accurately and effectively. Study and research in the relationship of BIM and environment assessment tools lack a clear discussion. In this paper, we will focus on the synergy of BIM technology and sustainable engineering in the AEC industry and outline the key factors which enhance the use of advanced innovation, technology and method and define the role of stakeholders to achieve zero-carbon emission toward the Paris Agreement to limit global warming to well below 2°C above pre-industrial levels. A case study of the adoption of Building Information Modeling (BIM) and environmental assessment tools in Hong Kong will be discussed in this paper.

Keywords: sustainability, sustainable engineering, BIM, LEED

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Authors: Lily Ingsrisawang, Supawadee Ingsriswang, Saisuda Somchit, Prasert Aungsuratana, Warawut Khantiyanan

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This paper presents the methodology from machine learning approaches for short-term rain forecasting system. Decision Tree, Artificial Neural Network (ANN), and Support Vector Machine (SVM) were applied to develop classification and prediction models for rainfall forecasts. The goals of this presentation are to demonstrate (1) how feature selection can be used to identify the relationships between rainfall occurrences and other weather conditions and (2) what models can be developed and deployed for predicting the accurate rainfall estimates to support the decisions to launch the cloud seeding operations in the northeastern part of Thailand. Datasets collected during 2004-2006 from the Chalermprakiat Royal Rain Making Research Center at Hua Hin, Prachuap Khiri khan, the Chalermprakiat Royal Rain Making Research Center at Pimai, Nakhon Ratchasima and Thai Meteorological Department (TMD). A total of 179 records with 57 features was merged and matched by unique date. There are three main parts in this work. Firstly, a decision tree induction algorithm (C4.5) was used to classify the rain status into either rain or no-rain. The overall accuracy of classification tree achieves 94.41% with the five-fold cross validation. The C4.5 algorithm was also used to classify the rain amount into three classes as no-rain (0-0.1 mm.), few-rain (0.1- 10 mm.), and moderate-rain (>10 mm.) and the overall accuracy of classification tree achieves 62.57%. Secondly, an ANN was applied to predict the rainfall amount and the root mean square error (RMSE) were used to measure the training and testing errors of the ANN. It is found that the ANN yields a lower RMSE at 0.171 for daily rainfall estimates, when compared to next-day and next-2-day estimation. Thirdly, the ANN and SVM techniques were also used to classify the rain amount into three classes as no-rain, few-rain, and moderate-rain as above. The results achieved in 68.15% and 69.10% of overall accuracy of same-day prediction for the ANN and SVM models, respectively. The obtained results illustrated the comparison of the predictive power of different methods for rainfall estimation.

Keywords: Machine learning, decision tree, artificial neural network, support vector machine, root mean square error.

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Authors: Fahri Benli, Sitalakshmi Venkatraman, Ye Wei, Fiona Wahr

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A flipped classroom which is a form of blended learning shifts the focus from a teacher-centered approach to a learner-centered approach. However, not all learners are ready to take the active role of knowledge and skill acquisition through a flipped classroom and they continue to delve in a passive mode of learning. This challenges educators in designing, scaffolding and facilitating in-class activities for students to have active learning experiences in a flipped classroom environment. Experiential learning theories have been employed by educators in the past in physical classrooms based on the principle that knowledge could be actively developed through direct experience. However, with more of online teaching witnessed recently, there are inherent limitations in designing and simulating an experiential learning activity for an online environment. In this paper, we explore enhancing experiential learning using smart digital tools that could be employed in a flipped classroom within a higher education setting. We present the use of smart collaborative tools online to enhance the experiential learning activity to teach higher-order cognitive concepts of business process modeling as a case study.

Keywords: Experiential learning, flipped classroom, smart software tools, online learning higher-order learning attributes.

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Authors: T. Kamalakis, I. Neokosmidis, D. Varoutas, T. Sphicopoulos

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The photonic component industry is a highly innovative industry with a large value chain. In order to ensure the growth of the industry much effort must be devoted to road mapping activities. In such activities demand and price evolution forecasting tools can prove quite useful in order to help in the roadmap refinement and update process. This paper attempts to provide useful guidelines in roadmapping of optical components and considers two models based on diffusion theory and the extended learning curve for demand and price evolution forecasting.

Keywords: Roadmapping, Photonic Components, Forecasting, Diffusion Theory.

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Authors: Ghulam Zakria, Zailin Guan , Yasser Riaz Awan, Wan Lizhi

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There are many real world problems in which parameters like the arrival time of new jobs, failure of resources, and completion time of jobs change continuously. This paper tackles the problem of scheduling jobs with random due dates on multiple identical machines in a stochastic environment. First to assign jobs to different machine centers LPT scheduling methods have been used, after that the particular sequence of jobs to be processed on the machine have been found using simple stochastic techniques. The performance parameter under consideration has been the maximum lateness concerning the stochastic due dates which are independent and exponentially distributed. At the end a relevant problem has been solved using the techniques in the paper..

Keywords: Quantity Production Flow Shop, LPT Scheduling, Stochastic Scheduling, Maximum Lateness, Random Due Dates

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Authors: Farhad Kolahan, Vahid Kayvanfar

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In this paper we address a multi-objective scheduling problem for unrelated parallel machines. In unrelated parallel systems, the processing cost/time of a given job on different machines may vary. The objective of scheduling is to simultaneously determine the job-machine assignment and job sequencing on each machine. In such a way the total cost of the schedule is minimized. The cost function consists of three components, namely; machining cost, earliness/tardiness penalties and makespan related cost. Such scheduling problem is combinatorial in nature. Therefore, a Simulated Annealing approach is employed to provide good solutions within reasonable computational times. Computational results show that the proposed approach can efficiently solve such complicated problems.

Keywords: Makespan, Parallel machines, Scheduling, Simulated Annealing

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Authors: Eti Gilad, Shosh Millet

Abstract:

In light of the technological development and its introduction into the field of education, an online course was designed in parallel to the 'conventional' course for teaching the ''Qualitative Research Methods''. This course aimed to characterize learning-teaching processes in a 'Qualitative Research Methods' course studied in two different frameworks. Moreover, its objective was to explore the difference between the culture of a physical learning environment and that of online learning. The research monitored four learner groups, a total of 72 students, for two years, two groups from the two course frameworks each year. The courses were obligatory for M.Ed. students at an academic college of education and were given by one female-lecturer. The research was conducted in the qualitative method as a case study in order to attain insights about occurrences in the actual contexts and sites in which they transpire. The research tools were open-ended questionnaire and reflections in the form of vignettes (meaningful short pictures) to all students as well as an interview with the lecturer. The tools facilitated not only triangulation but also collecting data consisting of voices and pictures of teaching and learning. The most prominent findings are: differences between the two courses in the change features of the learning environment culture for the acquisition of contents and qualitative research tools. They were manifested by teaching methods, illustration aids, lecturer's profile and students' profile.

Keywords: Face to face course, online course, qualitative research, vignettes.

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Authors: Heba Mohammed Nagy, Walid Mohamed Aly, Osama Fathy Hegazy

Abstract:

Educational  data mining  is  a  specific  data   mining field applied to data originating from educational environments, it relies on different  approaches to discover hidden knowledge  from  the  available   data. Among these approaches are   machine   learning techniques which are used to build a system that acquires learning from previous data. Machine learning can be applied to solve different regression, classification, clustering and optimization problems.

In  our  research, we propose  a “Student  Advisory  Framework” that  utilizes  classification  and  clustering  to  build  an  intelligent system. This system can be used to provide pieces of consultations to a first year  university  student to  pursue a  certain   education   track   where  he/she  will  likely  succeed  in, aiming  to  decrease   the  high  rate   of  academic  failure   among these  students.  A real case study  in Cairo  Higher  Institute  for Engineering, Computer  Science  and  Management  is  presented using  real  dataset   collected  from  2000−2012.The dataset has two main components: pre-higher education dataset and first year courses results dataset. Results have proved the efficiency of the suggested framework.

Keywords: Classification, Clustering, Educational Data Mining (EDM), Machine Learning.

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