Search results for: spatiotemporal features

Authors: Prasanna Haddela

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Document analysis is a well matured yet still active research field, partly as a result of the intricate nature of building computational tools but also due to the inherent problems arising from the variety and complexity of human languages. Breaking down language barriers is vital in enabling access to a number of recent technologies. This paper investigates the application of document classification methods to new Sinhalese datasets. This language is geographically isolated and rich with many of its own unique features. We will examine the interpretability of the classification models with a particular focus on the use of evolved Lucene search queries generated using a Genetic Algorithm (GA) as a method of document classification. We will compare the accuracy and interpretability of these search queries with other popular classifiers. The results are promising and are roughly in line with previous work on English language datasets.

Keywords: evolved search queries, Sinhala document classification, Lucene Sinhala analyzer, interpretable text classification, genetic algorithm

Procedia PDF Downloads 116

Authors: Khalil Khan, Ikram Syed, Muhammad Ehsan Mazhar, Iran Uddin, Nasir Ahmad

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Face parts labeling is the process of assigning class labels to each face part. A face parts labeling method (FPL) which divides a given image into its constitutes parts is proposed in this paper. A database FaceD consisting of 564 images is labeled with hand and make publically available. A supervised learning model is built through extraction of features from the training data. The testing phase is performed with two semantic segmentation methods, i.e., pixel and super-pixel based segmentation. In pixel-based segmentation class label is provided to each pixel individually. In super-pixel based method class label is assigned to super-pixel only – as a result, the same class label is given to all pixels inside a super-pixel. Pixel labeling accuracy reported with pixel and super-pixel based methods is 97.68 % and 93.45% respectively.

Keywords: face labeling, semantic segmentation, classification, face segmentation

Procedia PDF Downloads 259

Authors: Saghar Heidari

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In this paper, we study the valuation problem of European continuous-installment options under Markov-modulated models with a partial differential equation approach. Due to the opportunity for continuing or stopping to pay installments, the valuation problem under regime-switching models can be formulated as coupled partial differential equations (CPDE) with free boundary features. To value the installment options, we express the truncated CPDE as a linear complementarity problem (LCP), then a finite element method is proposed to solve the resulted variational inequality. Under some appropriate assumptions, we establish the stability of the method and illustrate some numerical results to examine the rate of convergence and accuracy of the proposed method for the pricing problem under the regime-switching model.

Keywords: continuous-installment option, European option, regime-switching model, finite element method

Procedia PDF Downloads 141

Authors: Haya Salah, Srinivas Sharan

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Healthcare facilities use appointment systems to schedule their appointments and to manage access to their medical services. With the growing demand for outpatient care, it is now imperative to manage physician's time effectively. However, high variation in consultation duration affects the clinical scheduler's ability to estimate the appointment duration and allocate provider time appropriately. Underestimating consultation times can lead to physician's burnout, misdiagnosis, and patient dissatisfaction. On the other hand, appointment durations that are longer than required lead to doctor idle time and fewer patient visits. Therefore, a good estimation of consultation duration has the potential to improve timely access to care, resource utilization, quality of care, and patient satisfaction. Although the literature on factors influencing consultation length abound, little work has done to predict it using based data-driven approaches. Therefore, this study aims to predict consultation duration using supervised machine learning algorithms (ML), which predicts an outcome variable (e.g., consultation) based on potential features that influence the outcome. In particular, ML algorithms learn from a historical dataset without explicitly being programmed and uncover the relationship between the features and outcome variable. A subset of the data used in this study has been obtained from the electronic medical records (EMR) of four different outpatient clinics located in central Pennsylvania, USA. Also, publicly available information on doctor's characteristics such as gender and experience has been extracted from online sources. This research develops three popular ML algorithms (deep learning, random forest, gradient boosting machine) to predict the treatment time required for a patient and conducts a comparative analysis of these algorithms with respect to predictive performance. The findings of this study indicate that ML algorithms have the potential to predict the provider service time with superior accuracy. While the current approach of experience-based appointment duration estimation adopted by the clinic resulted in a mean absolute percentage error of 25.8%, the Deep learning algorithm developed in this study yielded the best performance with a MAPE of 12.24%, followed by gradient boosting machine (13.26%) and random forests (14.71%). Besides, this research also identified the critical variables affecting consultation duration to be patient type (new vs. established), doctor's experience, zip code, appointment day, and doctor's specialty. Moreover, several practical insights are obtained based on the comparative analysis of the ML algorithms. The machine learning approach presented in this study can serve as a decision support tool and could be integrated into the appointment system for effectively managing patient scheduling.

Keywords: clinical decision support system, machine learning algorithms, patient scheduling, prediction models, provider service time

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Authors: Kulwinder Singh Mann

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The innovative features of information system, known as Radiology Information System (RIS), for electronic medical records has shown a good impact in the hospital. The objective is to help and make their work easier; such as for a physician to access the patient’s data and for a patient to check their bill transparently. The interoperability of RIS with the other intra-hospital information systems it interacts with, dealing with the compatibility and open architecture issues, are accomplished by two novel mechanisms. The first one is the particular message handling system that is applied for the exchange of information, according to the Health Level Seven (HL7) protocol’s specifications and serves the transfer of medical and administrative data among the RIS applications and data store unit. The second one implements the translation of information between the formats that HL7 and Digital Imaging and Communication in Medicine (DICOM) protocols specify, providing the communication between RIS and Picture and Archive Communication System (PACS) which is used for the increasing incorporation of modern medical imaging equipment.

Keywords: RIS, PACS, HIS, HL7, DICOM, messaging service, interoperability, digital images

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Authors: Miljan B. Petrović, Dušan B. Petrović, Goran S. Nikolić

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This paper describes a method for AWGN (Additive White Gaussian Noise) variance estimation in noisy stochastic signals, referred to as Multiplicative-Noising Variance Estimation (MNVE). The aim was to develop an estimation algorithm with minimal number of assumptions on the original signal structure. The provided MATLAB simulation and results analysis of the method applied on speech signals showed more accuracy than standardized AR (autoregressive) modeling noise estimation technique. In addition, great performance was observed on very low signal-to-noise ratios, which in general represents the worst case scenario for signal denoising methods. High execution time appears to be the only disadvantage of MNVE. After close examination of all the observed features of the proposed algorithm, it was concluded it is worth of exploring and that with some further adjustments and improvements can be enviably powerful.

Keywords: noise, signal-to-noise ratio, stochastic signals, variance estimation

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Authors: Gábor Kovács

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Requirement for pole-changing motors emerged at the very early times of asynchronous motor design. Different solutions have been elaborated and some of them are generally used. An alternative is the so called 3 Y/3 Y pole-changing winding. This paper deals with high power application of this solution. A complete and comprehensive study is introduced, including features and design guidelines. The method presented in this paper is especially suitable for pole numbers being close to each other. The study also reveals that the method is more advantageous then the existing solutions for high power motors with 1:3 pole ratio. Using this motor, a new and complete drive supply system has been proposed as most appropriate arrangement of high power main naval propulsion drive. Further, the method makes possible to extend the pole ratio to 1:6, 1:9, 1:12, etc. At the end, the proposal is further extended to the here so far missing 1:4, 1:5, 1:7 etc. pole ratios. A complete proposal for the theoretically infinite range has been given in this way.

Keywords: induction motor, pole changing 3Y/3Y, pole phase modulation, pole changing 1:3, 1:6

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Authors: Alden Boby, Dane Brown, James Connan

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Character recognition is a well-researched topic across disciplines. Regardless, creating a solution that can cater to multiple situations is still challenging. Vehicle licence plates lack an international standard, meaning that different countries and regions have their own licence plate format. A problem that arises from this is that the typefaces and designs from different regions make it difficult to create a solution that can cater to a wide range of licence plates. The main issue concerning detection is the character recognition stage. This paper aims to create an object detection-based character recognition model trained on a custom dataset that consists of typefaces of licence plates from various regions. Given that characters have featured consistently maintained across an array of fonts, YOLO can be trained to recognise characters based on these features, which may provide better performance than OCR methods such as Tesseract OCR.

Keywords: computer vision, character recognition, licence plate recognition, object detection

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Authors: Abdel Hadi N. Ebraheim, Eman Azomi, Nefisa A. Fahmy

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This paper designs and implements a computer-aided system (CAS) to help detect and diagnose liver cirrhosis in patients with Chronic Hepatitis C. Our system reduces the required features (tests) the patient is asked to do to tests to their minimal best most informative subset of tests, with a diagnostic accuracy above 99%, and hence saving both time and costs. We use the Support Vector Machine (SVM) with cross-validation, a Multilayer Perceptron Neural Network (MLP), and a Generalized Regression Neural Network (GRNN) that employs a base of radial functions for functional approximation, as classifiers. Our system is tested on 199 subjects, of them 99 Chronic Hepatitis C.The subjects were selected from among the outpatient clinic in National Herpetology and Tropical Medicine Research Institute (NHTMRI).

Keywords: liver cirrhosis, artificial neural network, support vector machine, multi-layer perceptron, classification, accuracy

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Authors: Allan Chia, Gabriel Gervais

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The case study on FoodXervices Inc shows how businesses need to reinvent and transform themselves in order to adapt and thrive and it also features how an SME can also devote resources to CSR causes. The company, Ng Chye Mong, was set up in 1937 and it went through ups and downs and encountered several failures and successes. In the 1970’s, the management of the company was entrusted to the next generation who continued to manage and expanded the business. In early 2003, the business encountered several challenges. A pair of siblings from the next generation of the Ng family joined the business fulltime and together they set-out to transform the company into FoodXervices Inc. In 2012, they started a charity, Food Bank Singapore Pte Ltd. The authors conducted case study research involving a series of in-depth interviews with the business owner and staff. This case study is an example of how to run a business and coordinate a charity concurrently while mobilising the same resources. The uniqueness of this case is the operational synergy of both the business and charity to promote corporate responsibility causes and initiatives in Singapore.

Keywords: family-owned business, charity, corporate social responsibility, branding

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Authors: Vittorio Caffi, Maria Pignataro, Antonio Cosimo Devito, Marco Pesenti

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This paper deals with the integration of advanced digital design and modelling tools and methodologies, known as Building Information Modelling, into the traditional Academy educational programs for building engineers and architects. Nowadays, the challenge the Academy has to face is to present the new tools and their features to the pupils, making sure they acquire the proper skills in order to leverage the potential they offer also for the other courses embedded in the educational curriculum. The syllabus here presented refers to the “Drawing for building engineering”, “2D and 3D laboratory” and “3D modelling” curricula of the MSc in Building Engineering of the Politecnico di Milano. Such topics, included since the first year in the MSc program, are fundamental to give the students the instruments to master the complexity of an architectural or building engineering project with digital tools, so as to represent it in its various forms.

Keywords: BIM, BIM curricula, computational design, digital modelling

Procedia PDF Downloads 670

Authors: Andreea Cervatiuc

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This study focuses on teaching second language writing through a constructivist approach. Unlike traditional approaches to teaching second language writing, which were product-oriented and emphasized surface features of writing, such as spelling and grammar, the constructivist approach to teaching second language writing is process-oriented and fosters discovery of meaning, creativity, collaboration, and writing for an audience. Educators who take a constructivist approach to teaching second language writing create communities of writers in their classrooms, emphasize that the goal of writing is to share ideas with others, and engage their students in collaborative, creative, and authentic writing activities, such as writing conferences, group story writing, finish the story, and chain writing. The constructivist approach to teaching second language writing combines a focus on genres, scaffolding, and treating writing as a process. Through constructivist writing, students co-create knowledge and engage in meaningful dialogue with various texts and their peers. The findings of this study can have implications for applied linguists, teachers, and language learners.

Keywords: constructivist second language, writing genres, process writing, scaffolding

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Authors: B. O. Adegoke, B. O. Ola, D. T. Ademiluyi

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This paper examines the use of computer and its related health hazard among computer users in South-Western zone of Nigeria. Two hundred and eighteen (218) computer users constituted the population used to evaluate association between posture, extensive computer use and related health hazard. The instruments for the study are a questionnaire on demographics, lifestyle, body features and work ability index while mean rating, standard deviation and t test were used for data analysis. Identified health related hazard include damages to the eyesight, bad posture, arthritis, musculoskeletal disorders, headache, stress and so on. The results showed that factors such as work demand, posture, closeness to computer screen and excessive working hours on computers constitute health hazards in both old and young computer users of various gender. It is therefore recommended that total number of hours spent with computer should be monitored and controlled.

Keywords: computer-related health hazard, musculoskeletal disorders, computer usage, work ability index

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Authors: Somar Boubou, Tatsuo Narikiyo, Michihiro Kawanishi

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In this work, we address the problem of 3D object recognition with depth sensors such as Kinect or Structure sensor. Compared with traditional approaches based on local descriptors, which depends on local information around the object key points, we propose a global features based descriptor. Proposed descriptor, which we name as Differential Histogram of Normal Vectors (DHONV), is designed particularly to capture the surface geometric characteristics of the 3D objects represented by depth images. We describe the 3D surface of an object in each frame using a 2D spatial histogram capturing the normalized distribution of differential angles of the surface normal vectors. The object recognition experiments on the benchmark RGB-D object dataset and a self-collected dataset show that our proposed descriptor outperforms two others descriptors based on spin-images and histogram of normal vectors with linear-SVM classifier.

Keywords: vision in control, robotics, histogram, differential histogram of normal vectors

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Authors: Chothmal, Basant Agarwal

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Sentiment analysis means to classify a given review document into positive or negative polar document. Sentiment analysis research has been increased tremendously in recent times due to its large number of applications in the industry and academia. Sentiment analysis models can be used to determine the opinion of the user towards any entity or product. E-commerce companies can use sentiment analysis model to improve their products on the basis of users’ opinion. In this paper, we propose a new One-class Support Vector Machine (One-class SVM) based sentiment analysis model for movie review documents. In the proposed approach, we initially extract features from one class of documents, and further test the given documents with the one-class SVM model if a given new test document lies in the model or it is an outlier. Experimental results show the effectiveness of the proposed sentiment analysis model.

Keywords: feature selection methods, machine learning, NB, one-class SVM, sentiment analysis, support vector machine

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Authors: Payal Abichandani, Rishi Prakash, Paras Nath Barwal, B. K. Murthy

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Information generated from various computerization processes is a potential rich source of knowledge for its designated community. To pass this information from generation to generation without modifying the meaning is a challenging activity. To preserve and archive the data for future generations it’s very essential to prove the authenticity of the data. It can be achieved by extracting the metadata from the data which can prove the authenticity and create trust on the archived data. Subsequent challenge is the technology obsolescence. Metadata extraction and standardization can be effectively used to resolve and tackle this problem. Metadata can be categorized at two levels i.e. Technical and Domain level broadly. Technical metadata will provide the information that can be used to understand and interpret the data record, but only this level of metadata isn’t sufficient to create trustworthiness. We have developed a tool which will extract and standardize the technical as well as domain level metadata. This paper is about the different features of the tool and how we have developed this.

Keywords: digital preservation, metadata, OAIS, PDI, XML

Procedia PDF Downloads 395

Authors: Te Kīpa Kēpa B. Morgan, Oliver W. Mcmillan, Dylan N. Taute, Tumanako N. Fa'aui

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Epistemologies are ways of knowing. Indigenous Peoples are aware that they do not perceive and experience the world in the same way as others. So it is important when empowering Indigenous epistemologies, such as that of the New Zealand Māori, to also be able to represent a scientific understanding within the same analysis. A geothermal development assessment tool has been developed by adapting the Mauri Model Decision Making Framework. Mauri is a metric that is capable of representing the change in the life-supporting capacity of things and collections of things. The Mauri Model is a method of grouping mauri indicators as dimension averages in order to allow holistic assessment and also to conduct sensitivity analyses for the effect of worldview bias. R-shiny is the coding platform used for this Vision Mātauranga research which has created an expert decision support tool (DST) that combines a stakeholder assessment of worldview bias with an impact assessment of mauri-based indicators to determine the sustainability of proposed geothermal development. The initial intention was to develop guidelines for quantifying mātauranga Māori impacts related to geothermal resources. To do this, three typical scenarios were considered: a resource owner wishing to assess the potential for new geothermal development; another party wishing to assess the environmental and cultural impacts of the proposed development; an assessment that focuses on the holistic sustainability of the resource, including its surface features. Indicator sets and measurement thresholds were developed that are considered necessary considerations for each assessment context and these have been grouped to represent four mauri dimensions that mirror the four well-being criteria used for resource management in Aotearoa, New Zealand. Two case studies have been conducted to test the DST suitability for quantifying mātauranga Māori and other biophysical factors related to a geothermal system. This involved estimating mauri0meter values for physical features such as temperature, flow rate, frequency, colour, and developing indicators to also quantify qualitative observations about the geothermal system made by Māori. A retrospective analysis has then been conducted to verify different understandings of the geothermal system. The case studies found that the expert DST is useful for geothermal development assessment, especially where hapū (indigenous sub-tribal grouping) are conflicted regarding the benefits and disadvantages of their’ and others’ geothermal developments. These results have been supplemented with evaluations for the cumulative impacts of geothermal developments experienced by different parties using integration techniques applied to the time history curve of the expert DST worldview bias weighted plotted against the mauri0meter score. Cumulative impacts represent the change in resilience or potential of geothermal systems, which directly assists with the holistic interpretation of change from an Indigenous Peoples’ perspective.

Keywords: decision support tool, holistic geothermal assessment, indigenous knowledge, mauri model decision-making framework

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Authors: Avatharam Ganivada, Krishna Shah

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The Mask R-CNN is recently introduced by the team of Facebook AI Research (FAIR), which is mainly concerned with instance segmentation in images. Here, the Mask R-CNN is based on ResNet and feature pyramid network (FPN), where a single dropout method is employed. This paper provides a modified Mask R-CNN by adding multiple dropout methods into the Mask R-CNN. The proposed model has also utilized the concepts of Resnet and FPN to extract stage-wise network feature maps, wherein a top-down network path having lateral connections is used to obtain semantically strong features. The proposed model produces three outputs for each object in the image: class label, bounding box coordinates, and object mask. The performance of the proposed network is evaluated in the segmentation of every instance in images using COCO and cityscape datasets. The proposed model achieves better performance than the state-of-the-networks for the datasets.

Keywords: instance segmentation, object detection, convolutional neural networks, deep learning, computer vision

Procedia PDF Downloads 75

Authors: Sumaiya Amir Khan, Shayma Al-Sharif, Samiha Mazher, Neha Intikhab Khan

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This research paper aims to evaluate the effectiveness of sleep-tracking AI applications in smart-watches. It focuses on comparing the sleep analyses of two different smartwatch brands, Samsung and Fitbit, and measuring sleep at three different stages – REM (Rapid-Eye-Movement), NREM (Non-Rapid-Eye-Movement), and deep sleep. The methodology involves the participation of different users and analyzing their sleep data. The results reveal that although light sleep is the longest stage, deep sleep is higher than average in the participants. The study also suggests that light sleep is not uniform, and getting higher levels of deep sleep can prevent debilitating health conditions. Based on the findings, it is recommended that individuals should aim to achieve higher levels of deep sleep to maintain good health. Overall, this research contributes to the growing literature on the effectiveness of sleep-tracking AI applications and their potential to improve sleep quality.

Keywords: sleep tracking, lifestyle, accuracy, health, AI, AI features, ML

Procedia PDF Downloads 84

Authors: Reem AlKabbi, Hayat Ali, Mariam Yasser

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Nowadays, eGovernment websites are becoming indispensable for public, business, personal efficiency or even improvement of livelihoods. Using these websites, citizens undertake number of tasks that would otherwise be difficult or impossible. However, many of these websites are not accessible to all people' types including People with Disabilities (PWDs). Through Web Accessibility Guidelines, Web developers can develop Web applications that are accessible to PWDs. This research is to investigate the Accessibility of eGovernment websites in Kingdom of Bahrain. The accessibility was measured using Web Content Accessibility Guidelines (WCAG) and section 508. For the evaluation purpose, some automatic tools were used. Samples of 43 eGovernment websites were selected. The accessibility of the websites was analyzed by using several automatic evaluation tools such as Total Validator and Functional Accessibility Evaluator (FAE). The evaluation process revealed several errors according to the accessibility guidelines. This research provides few recommendations for further improvement of accessibility features of eGovernment websites based on the highlighted issues and key findings reported in this research.

Keywords: website accessibility, W3C, PWD, e-government

Procedia PDF Downloads 452

Authors: Mouhammd Alkasassbeh, Alaa Lasasmeh

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We live in an era of rapid development as a result of significant scientific growth. Like other technologies, wireless sensor networks (WSNs) are playing one of the main roles. Based on WSNs, ZigBee adds many features to devices, such as minimum cost and power consumption, and increasing the range and connect ability of sensor nodes. ZigBee technology has come to be used in various fields, including science, engineering, and networks, and even in medicinal aspects of intelligence building. In this work, we generated two main datasets, the first being based on tree topology and the second on star topology. The datasets were evaluated by three machine learning (ML) algorithms: J48, meta.j48 and multilayer perceptron (MLP). Each topology was classified into normal and abnormal (attack) network traffic. The dataset used in our work contained simulated data from network simulation 2 (NS2). In each database, the Bayesian network meta.j48 classifier achieved the highest accuracy level among other classifiers, of 99.7% and 99.2% respectively.

Keywords: IDS, Machine learning, WSN, ZigBee technology

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Authors: Rahul Ganpat Mapari, D. G. Wakde

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In this paper, a proposed approach to improve the power factor of single-phase rectifiers and to regulate the output voltage against the change in grid voltage and load is presented. This converter topology is evaluated on the basis of performance and its salient features like simplicity, low cost and high performance are discussed to analyze its applicability. The proposed control strategy is bridgeless, transformer-less and output current sensor-less and consists of only two Bi-directional IGBTs and two diodes. The voltage regulation is achieved by a simple voltage divider to communicate to a controller to control the duty cycles of PWM. A control technique and operational procedure are also developed, both theoretically and experimentally. The experimental results clearly verify the theoretical analysis from the prototype connected to grid unity.

Keywords: Active Rectifier (AC-DC), power factor, single phase, voltage regulation

Procedia PDF Downloads 582

Authors: L. O. Luengas, E. V. Morales, L. F. G. De Souza, I. S. Bott

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Duplex Stainless Steels are well known for its good mechanical properties, and corrosion resistance. However, when submitted to heating, these features can be lost since the good properties are strongly dependent on the austenite-ferrite phase ratio which has to be approximately 1:1 to keep the phase balance. In a welded joint, the transformation kinetics at the heat affected zone (HAZ) is a function of the cooling rates applied which in turn are dependent on the heat input. The HAZ is usually ferritized at these temperatures, and it has been argued that small variations of the chemical composition can play a role in the solid state transformation sequence of ferrite to austenite during cooling. The δ → γ transformation has been reported to be massive and diffusionless due to the fast cooling rate, but it is also considered a diffusion controlled transformation. The aim of this work is to evaluate the effect of different heat inputs on the HAZ of two duplex stainless steels UNS S32304 and S32750, obtained by physical simulation.

Keywords: duplex stainless steels, HAZ, microstructural characterization, physical simulation

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Authors: Chia Wei Lim, Ning Yan

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The subject of reactor design, dealing with the transformation of chemical feedstocks into more valuable products, constitutes the central idea of chemical engineering. Despite its importance, the way it is taught to chemical engineering undergraduates has stayed virtually the same over the past several decades, even as the chemical industry increasingly leans towards the use of software for the design and daily monitoring of chemical plants. As such, there has been a widening learning gap as chemical engineering graduates transition from university to the industry since they are not exposed to effective platforms that relate the fundamental concepts taught during lectures to industrial applications. While the success of technology enhanced learning (TEL) has been demonstrated in various chemical engineering subjects, TELs in the teaching of reactor design appears to focus on the simulation of reactor processes, as opposed to arguably more important ideas such as the selection and optimization of reactor configuration for different types of reactions. This presents an opportunity for us to utilize the readily available easy-to-use MATLAB App platform to create an educational tool to aid the learning of fundamental concepts of reactor design and to link these concepts to the industrial context. Here, interactive software for the learning of reactor design has been developed to narrow the learning gap experienced by chemical engineering undergraduates. Dubbed the ReactorDesign App, it enables students to design reactors involving complex design equations for industrial applications without being overly focused on the tedious mathematical steps. With the aid of extensive visualization features, the concepts covered during lectures are explicitly utilized, allowing students to understand how these fundamental concepts are applied in the industrial context and equipping them for their careers. In addition, the software leverages the easily accessible MATLAB App platform to encourage self-directed learning. It is useful for reinforcing concepts taught, complementing homework assignments, and aiding exam revision. Accordingly, students are able to identify any lapses in understanding and clarify them accordingly. In terms of the topics covered, the app incorporates the design of different types of isothermal and non-isothermal reactors, in line with the lecture content and industrial relevance. The main features include the design of single reactors, such as batch reactors (BR), continuously stirred tank reactors (CSTR), plug flow reactors (PFR), and recycle reactors (RR), as well as multiple reactors consisting of any combination of ideal reactors. A version of the app, together with some guiding questions to aid explorative learning, was released to the undergraduates taking the reactor design module. A survey was conducted to assess its effectiveness, and an overwhelmingly positive response was received, with 89% of the respondents agreeing or strongly agreeing that the app has “helped [them] with understanding the unit” and 87% of the respondents agreeing or strongly agreeing that the app “offers learning flexibility”, compared to the conventional lecture-tutorial learning framework. In conclusion, the interactive ReactorDesign App has been developed to encourage self-directed explorative learning of the subject and demonstrate the industrial applications of the taught design concepts.

Keywords: explorative learning, reactor design, self-directed learning, technology enhanced learning

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Authors: T. Mohanarajah, J. Rizk, M. Nagrial, A. Hellany

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This paper analyses numerous features of the synchronous Reluctance Motor (Syn-RM) and propose a rotor for high electrical torque, power factor & efficiency using Finite Element Method (FEM). A comprehensive analysis completed on solid rotor structure while the total thickness of the flux guide kept constant. A number of tests carried out for nine different studies to find out optimum location of the flux guide, the optimum location of multiple flux guides & optimum wall thickness between flux guides for high-performance reluctance machines. The results are concluded with the aid of FEM simulation results, the saliency ratio and machine characteristics (location, a number of barriers & wall width) analysed.

Keywords: electrical machines, finite element method, synchronous reluctance machines, variable reluctance machines

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Authors: N. Tahouni, M. Gholami, M. H. Panjeshahi

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Gas flaring is one of the most GHG emitting sources in the oil and gas industries. It is also a major way for wasting such an energy that could be better utilized and even generates revenue. Minimize flaring is an effective approach for reducing GHG emissions and also conserving energy in flaring systems. Integrating waste and flared gases into the fuel gas networks (FGN) of refineries is an efficient tool. A fuel gas network collects fuel gases from various source streams and mixes them in an optimal manner, and supplies them to different fuel sinks such as furnaces, boilers, turbines, etc. In this article we use fuel gas network model proposed by Hasan et al. as a base model and modify some of its features and add constraints on emission pollution by gas flaring to reduce GHG emissions as possible. Results for a refinery case study showed that integration of flare gas stream with waste and natural gas streams to construct an optimal FGN can significantly reduce total annualized cost and flaring emissions.

Keywords: flaring, fuel gas network, GHG emissions, stream

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Authors: Halit Tutar, Omer Eren, Oguz Parlakay

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The demand for food and energy of mankind has been increasing every passing day. Renewable energy sources have been pushed to forefront since fossil fuels will be run out in the near future and their negative effects to the environment. As in every sector, the transport sector benefits from biofuel (biogas, bioethanol and biodiesel) one of the renewable energy sources as well. The edible oil crops are used in production of biodiesel. Utilizing edible oil crops as renewable energy source may raise a debate in the view of that there is a shortage in raw material of edible oil crops in Turkey. Researches related to utilization of non-edible oil crops as biodiesel raw materials have been recently increased, and especially studies related to their vegetative production and adaptation have been accelerated in Europe. In this review edible oil crops are compared to non-edible oil crops for biodiesel production in the sense of biodiesel production, some features of non-edible oil crops and their harmful emissions to environment are introduced. The data used in this study, obtained from articles, thesis, reports relevant to edible and non edible oil crops in biodiesel.

Keywords: biodiesel, edible oil crops, environmental impacts, renewable energy

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Authors: Supanyika Sengsai, Aree Thongpukdee, Chalermchai Kanchanakachain

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The study aimed to collect morphological data of secretory structures that contribute to taxonomy of Indigofera. Detail features of trichomes occurrence in vegetative and reproductive organs of Indigofera wightii Grah. ex Wigh & Arn., a species traditionally used as source of indigo to dye “Thaisongdam” clothing were investigated. Examination through light microscopy and scanning electrom microscopy were done. Non secretory, T-shaped trichomes appeared throughout surfaces of stems, leaves, flowers and fruits. Secretory or glandular trichomes occurred in two types; one has big cylindrical head and short peduncle, distributed on adaxial surface of sepals and around the pedicel, whereas another possesses smaller cylindrical head but long peduncle. The latter was found on apical surface of immature pods. No phenolic and lipophilic compounds were detected from these glands.

Keywords: indigofera, trichome, Thaisongdam, Thailand

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Authors: Mohamed Sanad, Noha Hassan

Abstract:

A broadband resonant terminal antenna has been developed. It can be used in different MIMO arrangements such as 2x2, 4x4, 8x8, or even 12x12 MIMO configurations. The antenna covers the whole LTE and WiFi bands besides the existing 2G/3G bands (700-5800 MHz), without using any matching/tuning circuits. Matching circuits significantly reduce the efficiency of any antenna and reduce the battery life. They also reduce the bandwidth because they are frequency dependent. The antenna can be implemented in smartphone handsets, tablets, laptops, notebooks or any other terminal. It is also suitable for different IoT and vehicle applications. The antenna is manufactured from a flexible material and can be bent or folded and shaped in any form to fit any available space in any terminal. It is self-contained and does not need to use the ground plane, the chassis or any other component of the terminal. Hence, it can be mounted on any terminal at different positions and configurations. Its performance does not get affected by the terminal, regardless of its type, shape or size. Moreover, its performance does not get affected by the human body of the terminal’s users. Because of all these unique features of the antenna, multiples of them can be simultaneously used for MIMO diversity coverage in any terminal device with a high isolation and a low correlation factor between them.

Keywords: IOT, LTE, MIMO, terminal antenna, WiFi

Procedia PDF Downloads 189

Authors: Daria Beziakina, Elena Bulgakova

Abstract:

The paper deals with cross-gender and cross-linguistic comparison of pitch characteristics for Tuvinian with two other Turkic languages - Uzbek and Azerbaijani, based on the results of statistical analysis of pitch parameter values and intonation patterns used by male and female speakers. The main goal of our work is to obtain the ranges of pitch parameter values typical for Tuvinian speakers for the purpose of automatic language identification. We also propose a cross-gender analysis of declarative intonation in the poorly studied Tuvinian language. The ranges of pitch parameter values were obtained by means of specially developed software that deals with the distribution of pitch values and allows us to obtain statistical language-specific pitch intervals.

Keywords: speech analysis, statistical analysis, speaker recognition, identification of person

Procedia PDF Downloads 348