Search results for: low/high level features

Authors: A. Ortuzar, I. Escondrillas, F. Mijangos

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There is a need for new systems that can be attached to drinking water treatment plants and have the required treatment capacity as well as the selectivity regarding components derived from anthropogenic activities. In a context of high volumes of water and low concentration of contaminants, adsorption/interchange processes are appealing since they meet the required features. Iron oxides such as siderite and molysite, which are respectively based on FeCO₃ and FeCl₃, can be found in nature. In this work, their observed performance, raw or roasted at different temperatures, as adsorbents of some inorganic anions is discussed. Roasted 1:1 FeCO₃: FeCl₃ mixture was very selective for arsenic and allowed a 100% removal of As from a 10 mg L^-1 As solution. Besides, the 1:1 FeCO₃ and FeCl₃ mixture roasted at 500 ºC showed good selectivity for, in order of preference, arsenate, bromate, phosphate, fluoride and nitrate anions with distribution coefficients of, respectively, 4200, 2800, 2500 0.4 and 0.03 L g^-1.

Keywords: Drinking water, natural adsorbent materials, removal, selectivity.

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Authors: Seongmin Yim

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The Korean government has applied preliminary feasibility study for new and huge R&D programs since 2008.The study is carried out from the viewpoints of technology, policy, and Economics. Then integrate the separate analysis and finally arrive at a definite result; whether a program is feasible or unfeasible, This paper describes the concept and method of the feasibility analysis focused on technological viability assessment for technical analysis. It consists of technology trend assessment and technology level assessment. Through the analysis, we can determine the chance of schedule delay or cost overrun occurring in the proposed plan.

Keywords: Preliminary Feasibility Study, Technological viability, Technology Trend Assessment, Technology Level Assessment

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Authors: Jeries Khoury

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The use of legendary, folkloric and religious symbols is one of the most important phenomena in modern Arabic poetry. Interestingly enough, most of the modern Arabic poetry’s pioneers were so fascinated by the biblical symbols and they managed to use many modern techniques to make these symbols adequate for their personal life from one side and fit to their Islamic beliefs from the other. One of the most famous poets to do so was al-Sayya:b. The way he employed one of these symbols ‘job’, the new features he adds to this character and the link between this character and his personal life will be discussed in this study. Besides, the study will examine the influence of al-Sayya:b on another modern poet Saadi Yusuf, who, following al-Sayya:b, used the character of Job in a special way, by mixing its features with al-Sayya:b’s personal features and in this way creating a new mixed character. A semantic, cultural and comparative analysis of the poems written by al-Sayya:b himself and the other poets who evoked the mixed image of al-Sayya:b-Job, can reveal the changes Arab poets made to the original biblical figure of Job to bring it closer to Islamic culture. The paper will make an intensive use of intertextuality idioms in order to shed light on the network of relations between three kinds of texts (indeed three ‘palimpsests’: 1- biblical- the primary text; 2- poetic- al-Syya:b’s secondary version; 3- re-poetic- Sa’di Yusuf’s tertiary version). The bottom line in this paper is that that al-Sayya:b was directly influenced by the dramatic biblical story of Job more than the brief Quranic version of the story. In fact, the ‘new’ character of Job designed by al-Sayya:b himself differs from the original one in many aspects that we can safely say it is the Sayyabian-Job that cannot be found in the poems of any other poets, unless they are evoking the own tragedy of al-Sayya:b himself, like what Saadi Yusuf did.

Keywords: Arabic poetry, intertextuality, job, meter, modernism, symbolism.

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Authors: Heroe Wijanto, Sugihartono, Suhartono Tjondronegoro, Kuspriyanto

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The use of High Order Statistics (HOS) analysis is expected to provide so many candidates of features that can be selected for pattern recognition. More candidates of the feature can be extracted using simple manipulation through a specific mathematical function prior to the HOS analysis. Feature extraction method using HOS analysis combined with Difference to the Nth-Power manipulation has been examined in application for Automatic Modulation Recognition (AMR) to perform scheme recognition of three digital modulation signal, i.e. QPSK-16QAM-64QAM in the AWGN transmission channel. The simulation results is reported when the analysis of HOS up to order-12 and the manipulation of Difference to the Nth-Power up to N = 4. The obtained accuracy rate of AMR using the method of Simple Decision obtained 90% in SNR > 10 dB in its classifier, while using the method of Voted Decision is 96% in SNR > 2 dB.

Keywords: modulation, automatic modulation recognition, feature analysis, feature manipulation.

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Authors: Ying-jie Zhang, Li-ling Ge

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This paper presents an improved image segmentation model with edge preserving regularization based on the piecewise-smooth Mumford-Shah functional. A level set formulation is considered for the Mumford-Shah functional minimization in segmentation, and the corresponding partial difference equations are solved by the backward Euler discretization. Aiming at encouraging edge preserving regularization, a new edge indicator function is introduced at level set frame. In which all the grid points which is used to locate the level set curve are considered to avoid blurring the edges and a nonlinear smooth constraint function as regularization term is applied to smooth the image in the isophote direction instead of the gradient direction. In implementation, some strategies such as a new scheme for extension of u+ and u- computation of the grid points and speedup of the convergence are studied to improve the efficacy of the algorithm. The resulting algorithm has been implemented and compared with the previous methods, and has been proved efficiently by several cases.

Keywords: Energy minimization, image segmentation, level sets, edge regularization.

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Authors: Jih-Rue Pan, Feng-Chuan Pan

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Today-s healthcare industries had become more patient-centric than profession-centric, from which the issues of quality of healthcare and the patient safety are the major concerns in the modern healthcare facilities. An unplanned extubation (UE) may be detrimental to the patient-s life, and thus is one of the major indexes of patient safety and healthcare quality. A high UE rate not only defeated the healthcare quality as well as the patient safety policy but also the nurses- morality, and job satisfaction. The UE problem in a psychiatric hospital is unique and may be a tough challenge for the healthcare professionals for the patients were mostly lacking communication capabilities. We reported with this essay a particular project that was organized to reduce the UE rate from the current 2.3% to a lower and satisfactory level in the long-term care units of a psychiatric hospital. The project was conducted between March 1st, 2011 and August 31st, 2011. Based on the error information gathered from varied units of the hospital, the team analyzed the root causes with possible solutions proposed to the meetings. Four solutions were then concluded with consensus and launched to the units in question. The UE rate was now reduced to a level of 0.17%. Experience from this project, the procedure and the tools adopted would be good reference to other hospitals.

Keywords: Unplanned extubation, patient safety, error information

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Authors: A. V. Budin, Ph. G. Rutberg, M. E. Pinchuk, A. A. Bogomaz, V. Yu. Svetova

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The investigation results of high-density hydrogen heating by high-current electric arc are presented at initial pressure from 5 MPa to 160 MPa with current amplitude up to 1.6 MA and current rate of rise 109-1011 A/s. When changing the initial pressure and current rate of rise, channel temperature varies from several electronvolts to hundreds electronvolts. Arc channel radius is several millimeters. But the radius of the discharge chamber greater than the radius of the arc channel on approximately order of magnitude. High efficiency of gas heating is caused by radiation absorption of hydrogen surrounding the arc. Current channel consist from vapor of the initiating wire. At current rate of rise of 109 A/s and relatively small current amplitude gas heating occurs due to radiation absorption in the band transparency of hydrogen by the wire vapours with photon energies less than 13.6 eV. At current rate of rise of 1011 A/s gas heating is due to hydrogen absorption of soft X-rays from discharge channel.

Keywords: High-density hydrogen heating by high-current electric arc.

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Authors: Aziah Khamis, H. Shareef

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The purpose of planned islanding is to construct a power island during system disturbances which are commonly formed for maintenance purpose. However, in most of the cases island mode operation is not allowed. Therefore distributed generators (DGs) must sense the unplanned disconnection from the main grid. Passive technique is the most commonly used method for this purpose. However, it needs improvement in order to identify the islanding condition. In this paper an effective method for identification of islanding condition based on phase space and neural network techniques has been developed. The captured voltage waveforms at the coupling points of DGs are processed to extract the required features. For this purposed a method known as the phase space techniques is used. Based on extracted features, two neural network configuration namely radial basis function and probabilistic neural networks are trained to recognize the waveform class. According to the test result, the investigated technique can provide satisfactory identification of the islanding condition in the distribution system.

Keywords: Classification, Islanding detection, Neural network, Phase space.

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Authors: Muhibul H. Bhuyan, Md. T. Hasan, Hasan Iskander

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Electrocardiographic (ECG) machine is an important equipment to diagnose heart problems. Besides, the ECG signals are used to detect many other features of human body and behavior. But it is not so cheap and simple in operation to be used in the countries like Bangladesh, where most of the people are very low income earners. Therefore, in this paper, we have tried to implement a simple and portable ECG machine. Since Arduino Uno microcontroller is very cheap, we have used it in our system to minimize the cost. Our designed system is powered by a 2-voltage level DC power supply. It provides wireless connectivity to have ECG data either in smartphone having android operating system or a PC/laptop having Windows operating system. To get the data, a graphic user interface has been designed. Android application has also been made using IDE for Android 2.3 and API 10. Since it requires no USB host API, almost 98% Android smartphones, available in the country, will be able to use it. We have calculated the heart rate from the measured ECG by our designed machine and by an ECG machine of a reputed diagnostic center in Dhaka city for the same people at the same time on same day. Then we calculated the percentage of errors between the readings of two machines and computed its average. From this computation, we have found out that the average percentage of error is within an acceptable limit.

Keywords: Low cost ECG machine, heart diseases, remote monitoring, Arduino microcontroller.

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Authors: Emanuele Stomeo, Tatiana Kalganova, Cyrille Lambert

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The evolution of logic circuits, which falls under the heading of evolvable hardware, is carried out by evolutionary algorithms. These algorithms are able to automatically configure reconfigurable devices. One of main difficulties in developing evolvable hardware with the ability to design functional electrical circuits is to choose the most favourable EA features such as fitness function, chromosome representations, population size, genetic operators and individual selection. Until now several researchers from the evolvable hardware community have used and tuned these parameters and various rules on how to select the value of a particular parameter have been proposed. However, to date, no one has presented a study regarding the size of the chromosome representation (circuit layout) to be used as a platform for the evolution in order to increase the evolvability, reduce the number of generations and optimize the digital logic circuits through reducing the number of logic gates. In this paper this topic has been thoroughly investigated and the optimal parameters for these EA features have been proposed. The evolution of logic circuits has been carried out by an extrinsic evolvable hardware system which uses (1+λ) evolution strategy as the core of the evolution.

Keywords: Evolvable hardware, genotype size, computational intelligence, design of logic circuits.

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Authors: Yogi Satrya Aryadinata, Anne Laurent, Michel Sala

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Gradual patterns have been studied for many years as they contain precious information. They have been integrated in many expert systems and rule-based systems, for instance to reason on knowledge such as “the greater the number of turns, the greater the number of car crashes”. In many cases, this knowledge has been considered as a rule “the greater the number of turns → the greater the number of car crashes” Historically, works have thus been focused on the representation of such rules, studying how implication could be defined, especially fuzzy implication. These rules were defined by experts who were in charge to describe the systems they were working on in order to turn them to operate automatically. More recently, approaches have been proposed in order to mine databases for automatically discovering such knowledge. Several approaches have been studied, the main scientific topics being: how to determine what is an relevant gradual pattern, and how to discover them as efficiently as possible (in terms of both memory and CPU usage). However, in some cases, end-users are not interested in raw level knowledge, and are rather interested in trends. Moreover, it may be the case that no relevant pattern can be discovered at a low level of granularity (e.g. city), whereas some can be discovered at a higher level (e.g. county). In this paper, we thus extend gradual pattern approaches in order to consider multiple level gradual patterns. For this purpose, we consider two aggregation policies, namely horizontal and vertical.

Keywords: Gradual Pattern.

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Authors: Elia Favarelli, Enrico Testi, Andrea Giorgetti

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Autonomous structural health monitoring (SHM) of many structures and bridges became a topic of paramount importance for maintenance purposes and safety reasons. This paper proposes a set of machine learning (ML) tools to perform automatic feature selection and detection of anomalies in a bridge from vibrational data and compare different feature extraction schemes to increase the accuracy and reduce the amount of data collected. As a case study, the Z-24 bridge is considered because of the extensive database of accelerometric data in both standard and damaged conditions. The proposed framework starts from the first four fundamental frequencies extracted through operational modal analysis (OMA) and clustering, followed by time-domain filtering (tracking). The fundamental frequencies extracted are then fed to a dimensionality reduction block implemented through two different approaches: feature selection (intelligent multiplexer) that tries to estimate the most reliable frequencies based on the evaluation of some statistical features (i.e., entropy, variance, kurtosis), and feature extraction (auto-associative neural network (ANN)) that combine the fundamental frequencies to extract new damage sensitive features in a low dimensional feature space. Finally, one-class classification (OCC) algorithms perform anomaly detection, trained with standard condition points, and tested with normal and anomaly ones. In particular, principal component analysis (PCA), kernel principal component analysis (KPCA), and autoassociative neural network (ANN) are presented and their performance are compared. It is also shown that, by evaluating the correct features, the anomaly can be detected with accuracy and an F1 score greater than 95%.

Keywords: Anomaly detection, dimensionality reduction, frequencies selection, modal analysis, neural network, structural health monitoring, vibration measurement.

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Authors: Antonio Natali, Ambra Molesini

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In modern distributed software systems, the issue of communication among composing parts represents a critical point, but the idea of extending conventional programming languages with general purpose communication constructs seems difficult to realize. As a consequence, there is a (growing) gap between the abstraction level required by distributed applications and the concepts provided by platforms that enable communication. This work intends to discuss how the Model Driven Software Development approach can be considered as a mature technology to generate in automatic way the schematic part of applications related to communication, by providing at the same time high level specialized languages useful in all the phases of software production. To achieve the goal, a stack of languages (meta-meta¬models) has been introduced in order to describe – at different levels of abstraction – the collaborative behavior of generic entities in terms of communication actions related to a taxonomy of messages. Finally, the generation of platforms for communication is viewed as a form of specification of language semantics, that provides executable models of applications together with model-checking supports and effective runtime environments.

Keywords: Interactions, specific languages, meta-models, model driven development.

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Authors: H. B. Mahmud, Syamsul Bahri, Y. W. Yee, Y. T. Yeap

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This paper reports the strength and durability properties of high strength high performance concrete incorporating rice husk ash (RHA) having high silica, low carbon content and appropriate fineness. In this study concrete containing 10%, 15% and 20% RHA as cement replacement and water to binder ratio of 0.25 were investigated. The results show that increasing amount of RHA increases the dosage of superplasticizer to maintain similar workability. Partial replacement of cement with RHA did not increase the early age compressive strength of concrete. However, concrete containing RHA showed higher compressive strength at later ages. The results showed that compressive strength of concrete in the 90-115 MPa range can be obtained at 28 curing days and the durability properties of RHA concrete performed better than that of control concrete. The water absorption of concrete incorporating 15% RHA exhibited the lowest value. The porosity of concrete is consistent with water absorption whereby higher replacement of RHA decreased the porosity of concrete. There is a positive correlation between reducing porosity and increasing compressive strength of high strength high performance concrete. The results also indicate that up to 20% of RHA incorporation could be advantageously blended with cement without adversely affecting the strength and durability properties of concrete.

Keywords: Compressive strength, durability, high performance concrete, rice husk ash.

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Authors: Saeedeh Bakhtiari, Johannes Depessemier, Stijn Hertelé, Wim De Waele

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High cycle fatigue comprising up to 10⁷ load cycles has been the subject of many studies, and the behavior of many materials was recorded adequately in this regime. However, many applications involve larger numbers of load cycles during the lifetime of machine components. In this ultra-high cycle regime, other failure mechanisms play, and the concept of a fatigue endurance limit (assumed for materials such as steel) is often an oversimplification of reality. When machine component design demands a high geometrical complexity, cast iron grades become interesting candidate materials. Grey cast iron is known for its low cost, high compressive strength, and good damping properties. However, the ultra-high cycle fatigue behavior of cast iron is poorly documented. The current work focuses on the ultra-high cycle fatigue behavior of EN-GJL-250 (GG25) grey cast iron by developing an ultrasonic (20 kHz) fatigue testing system. Moreover, the testing machine is instrumented to measure the temperature and the displacement of  the specimen, and to control the temperature. The high resonance frequency allowed to assess the  behavior of the cast iron of interest within a matter of days for ultra-high numbers of cycles, and repeat the tests to quantify the natural scatter in fatigue resistance.

Keywords: GG25, cast iron, ultra-high cycle fatigue, ultrasonic test.

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Authors: Herbert Ugrinowitsch, Suziane Peixoto dos Santos-Naves, Michele Viviene Carbinatto, Rodolfo NovellinoBenda, Go Tani

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An experiment was conducted to examine the effect of the level of performance stabilization on the human adaptability to perceptual-motor perturbation in a complex coincident timing task. Three levels of performance stabilization were established operationally: pre-stabilization, stabilization, and super-stabilization groups. Each group practiced the task until reached its level of stabilization in a constant sequence of movements and under a constant time constraint before exposure to perturbation. The results clearly showed that performance stabilization is a pre-condition for adaptation. Moreover, variability before reaching stabilization is harmful to adaptation and persistent variability after stabilization is beneficial. Moreover, the behavior of variability is specific to each measure.

Keywords: Adaptation, motor skill, perturbation, stabilization.

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Authors: Jia Xin Low, Keng Wah Choo

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This paper presents an automatic normal and abnormal heart sound classification model developed based on deep learning algorithm. MITHSDB heart sounds datasets obtained from the 2016 PhysioNet/Computing in Cardiology Challenge database were used in this research with the assumption that the electrocardiograms (ECG) were recorded simultaneously with the heart sounds (phonocardiogram, PCG). The PCG time series are segmented per heart beat, and each sub-segment is converted to form a square intensity matrix, and classified using convolutional neural network (CNN) models. This approach removes the need to provide classification features for the supervised machine learning algorithm. Instead, the features are determined automatically through training, from the time series provided. The result proves that the prediction model is able to provide reasonable and comparable classification accuracy despite simple implementation. This approach can be used for real-time classification of heart sounds in Internet of Medical Things (IoMT), e.g. remote monitoring applications of PCG signal.

Keywords: Convolutional neural network, discrete wavelet transform, deep learning, heart sound classification.

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Authors: L.C. Ciobotaru

Abstract:

In electronegative-electropositive gas mixtures plasma, at a total pressure varying in the range of ten to hundred Torr, the appearance of a quasi-mochromatization effect of the emitted radiation was reported. This radiation could be the result of the generating mechanisms at molecular level, which is the case of the excimer radiation but also at atomic level. Thus, in the last case, in (Ne+1%Ar/Xe+H2) gas mixtures plasma in a dielectric barrier discharge, this effect, called M-effect, consists in the reduction of the discharge emission spectrum practice at one single, strong spectral line with λ = 585.3 nm. The present paper is concerned with the characteristics comparative investigation of the principal reaction mechanisms involved in the quasi-monochromatization effect existence in the case of the excimer radiation, respectively of the Meffect. Also, the paper points out the role of the metastable electronegative atoms in the appearance of the monochromatization – effect at atomic level.

Keywords: Colombian forces, Direct Harpoon reaction, Monochromatization – effect, Resonant polar three-body reaction.

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Authors: S. Aghainajafizadeh, F. Heydari, H. Abbasian

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Populations of wild boar present in semi-arid of central Iran. We studied features influencing bed site selection by this species in semi-arid central steppe of Iran. Habitat features of the detected bed site were compared with randomly selected by quantifying number of habitat variables in semi- arid area in Iran. The results revealed that the most important influencing factors in bed site selection were vegetation cover, number of Artemisia sieberi, percentage cover and height of Acer cinerascens, percentage cover and height of Amygdalus scoparia. This is the first ecological study of the wild boar in a protected area of the semi desert biome of Iran. Sustainability of wild boar populations in this area dependent to shrubs of Amygdalus scoparia and Acer cinerascens for thermal and camouflage cover.

Keywords: Wild boar, Bed site selection, Yazd, Iran

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Authors: Paulo Fernando Silva Filho, Elcio Hideiti Shiguemori

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The selection of specific landmarks for an Unmanned Aerial Vehicles’ Visual Navigation systems based on Automatic Landmark Recognition has significant influence on the precision of the system’s estimated position. At the same time, manual selection of the landmarks does not guarantee a high recognition rate, which would also result on a poor precision. This work aims to develop an automatic landmark selection that will take the image of the flight area and identify the best landmarks to be recognized by the Visual Navigation Landmark Recognition System. The criterion to select a landmark is based on features detected by ORB or AKAZE and edges information on each possible landmark. Results have shown that disposition of possible landmarks is quite different from the human perception.

Keywords: Clustering, edges, feature points, landmark selection, X-Means.

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Authors: Ta-Min Hung, Tien-Lung Sun

Abstract:

Thanks to VR technology advanced, there are many researches had used VR technology to develop a training system. Using VR characteristics can simulate many kinds of situations to reach our training-s goal. However, a good training system not only considers real simulation but also considers learner-s learning motivation. So, there are many researches started to conduct game-s features into VR training system. We typically called this is a serious game. It is using game-s features to engage learner-s learning motivation. However, VR or Serious game has another important advantage. That is simulating feature. Using this feature can create any kinds of pressured environments. Because in the real environment may happen any emergent situations. So, increasing the trainees- pressure is more important when they are training. Most pervious researches are investigated serious game-s applications and learning performance. Seldom researches investigated how to increase the learner-s mental workload when they are training. So, in our study, we will introduce a real case study and create two types training environments. Comparing the learner-s mental workload between VR training and serious game.

Keywords: Intrinsic Motivation, Mental Workload, VR Training, Serious Game

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Authors: M. Jagabar Sathik, K. Ramani

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In this paper family of multilevel inverter topology with reduced number of power switches is presented. The proposed inverter can generate both even and odd level. The proposed topology is suitable for symmetric structure. The proposed symmetric inverter results in reduction of power switches, power diode and gate driver circuits and also it may further minimize the installation area and cost. To prove the superiority of proposed topology is compared with conventional topologies. The performance of this symmetric multilevel inverter has been tested by computer based simulation and prototype based experimental setup for nine-level inverter is developed and results are verified.

Keywords: Cascaded H- Bridge (CHB), Multilevel Inverter (MLI), Nearest Level Modulation (NLM), Total Harmonic Distortion (THD).

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Authors: B. Bagheri Nakhjavanlo, T. J. Ellis, P. Raoofi, J. Dehmeshki

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This paper presents a customized deformable model for the segmentation of abdominal and thoracic aortic aneurysms in CTA datasets. An important challenge in reliably detecting aortic aneurysm is the need to overcome problems associated with intensity inhomogeneities and image noise. Level sets are part of an important class of methods that utilize partial differential equations (PDEs) and have been extensively applied in image segmentation. A Gaussian kernel function in the level set formulation, which extracts the local intensity information, aids the suppression of noise in the extracted regions of interest and then guides the motion of the evolving contour for the detection of weak boundaries. The speed of curve evolution has been significantly improved with a resulting decrease in segmentation time compared with previous implementations of level sets. The results indicate the method is more effective than other approaches in coping with intensity inhomogeneities.

Keywords: Abdominal and thoracic aortic aneurysms, intensityinhomogeneity, level sets, local fitting binary.

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Authors: Dim En Nyaung, Thin Lai Lai Thein

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Due to the rapid increase of Internet, web opinion sources dynamically emerge which is useful for both potential customers and product manufacturers for prediction and decision purposes. These are the user generated contents written in natural languages and are unstructured-free-texts scheme. Therefore, opinion mining techniques become popular to automatically process customer reviews for extracting product features and user opinions expressed over them. Since customer reviews may contain both opinionated and factual sentences, a supervised machine learning technique applies for subjectivity classification to improve the mining performance. In this paper, we dedicate our work is the task of opinion summarization. Therefore, product feature and opinion extraction is critical to opinion summarization, because its effectiveness significantly affects the identification of semantic relationships. The polarity and numeric score of all the features are determined by Senti-WordNet Lexicon. The problem of opinion summarization refers how to relate the opinion words with respect to a certain feature. Probabilistic based model of supervised learning will improve the result that is more flexible and effective.

Keywords: Opinion Mining, Opinion Summarization, Sentiment Analysis, Text Mining.

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Authors: Nader Gharibnavaz, Hossein Yazdi

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If organizations like Mellat Bank want to identify its customer market completely to reach its specified goals, it can segment the market to offer the product package to the right segment. Our objective is to offer a segmentation model for Iran banking market in Mellat bank view. The methodology of this project is combined by “segmentation on the basis of four part-quality variables" and “segmentation on the basis of different in means". Required data are gathered from E-Systems and researcher personal observation. Finally, the research offers the organization that at first step form a four dimensional matrix with 756 segments using four variables named value-based, behavioral, activity style, and activity level, and at the second step calculate the means of profit for every cell of matrix in two distinguished work level (levels α1:normal condition and α2: high pressure condition) and compare the segments by checking two conditions that are 1- homogeneity every segment with its sub segment and 2- heterogeneity with other segments, and so it can do the necessary segmentation process. After all, the last offer (more explained by an operational example and feedback algorithm) is to test and update the model because of dynamic environment, technology, and banking system.

Keywords: market segmentation model, banking system, Mellat bank

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Authors: Chaiwat Tantarangsee

Abstract:

The purposes of this study are 1) to identify learning styles of university students in Bangkok, and 2) to study the frequency of the relevant instructional context of the identified learning styles. Learning Styles employed in this study are those of Honey and Mumford, which include 1) Reflectors, 2) Theorists, 3) Pragmatists, and 4) Activists. The population comprises 1383 students and 5 lecturers. Research tools are 2 questionnaires – one used for identifying students- learning styles, and the other used for identifying the frequency of the relevant instructional context of the identified learning styles. The research findings reveal that 32.30 percent - are Activists, while 28.10 percent are Theorists, 20.10 are Reflectors, and 19.50 are Pragmatists. In terms of the relevant instructional context of the identified 4 learning styles, it is found that the frequency level of the instructional context is totally in high level. Moreover, 2 lists of the context being conducted most frequently are 'Lead'in activity to review background knowledge,- and 'Information retrieval report.' And these two activities serve the learning styles of theorists and activists. It is, therefore, suggested that more instructional context supporting the activists, the majority of the population, learning best by doing, as well as emotional learning situation should be added.

Keywords: Instructional Context, Learning Styles, Learning Style Preference, and Learning Style Questionnaire.

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Authors: M A Hannan, A. Hussain, S. A. Samad, K. A. Ishak, A. Mohamed

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This paper presents a general trainable framework for fast and robust upright human face and non-human object detection and verification in static images. To enhance the performance of the detection process, the technique we develop is based on the combination of fast neural network (FNN) and classical neural network (CNN). In FNN, a useful correlation is exploited to sustain high level of detection accuracy between input image and the weight of the hidden neurons. This is to enable the use of Fourier transform that significantly speed up the time detection. The combination of CNN is responsible to verify the face region. A bootstrap algorithm is used to collect non human object, which adds the false detection to the training process of the human and non-human object. Experimental results on test images with both simple and complex background demonstrate that the proposed method has obtained high detection rate and low false positive rate in detecting both human face and non-human object.

Keywords: Algorithm, detection of human and non-human object, FNN, CNN, Image training.

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

Abstract:

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: Tayfun Gundogdu, Guven Komurgoz

Abstract:

This paper presents design, analysis and comparison of the different rotor type permanent magnet machines. The presented machines are designed as having same geometrical dimensions and same materials for comparison. The main machine parameters of interior and exterior rotor type machines including eddy current effect, torque-speed characteristics and magnetic analysis are investigated using MAXWELL program. With this program, the components of the permanent magnet machines can be calculated with high accuracy. Six types of Permanent machines are compared with respect to their topology, size, magnetic field, air gap flux, voltage, torque, loss and efficiency. The analysis results demonstrate the effectiveness of the proposed machines design methodology. We believe that, this study will be a helpful resource in terms of examination and comparison of the basic structure and magnetic features of the PM (Permanent magnet) machines which have different rotor structure.

Keywords: Motor design, Permanent Magnet, Finite-Elementmethod.

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Authors: Amro Issam Hamed Attia Ramadan, Marco Zappatore, Pasquale Balena, Antonella Longo

Abstract:

Considering the energy crisis that is hitting Europe, it becomes increasingly necessary to change energy policies to depend less on fossil fuels and replace them with energy from renewable sources. This has triggered the urge to use clean energy, not only to satisfy energy needs and fulfill the required consumption, but also to decrease the danger of climatic changes due to harmful emissions. Many countries have already started creating energy communities based on renewable energy sources. The first step to understanding energy needs in any place is to perfectly know the consumption. In this work, we aim to estimate electricity consumption for a municipality that makes up part of a rural area located in southern Italy using forecast models that allow for the estimation of electricity consumption for the next 10 years, and we then apply the same model to the province where the municipality is located and estimate the future consumption for the same period to examine whether it is possible to start from the municipality level to reach the province level when creating energy communities.

Keywords: ARIMA, electricity consumption, forecasting models, time series.

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