Search results for: machine harvested grapes

Authors: Khenfer-Koummich Fatima, Mesbahi Larbi, Hendel Fatiha

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This paper presents a recognition system for isolated words like robot commands. It’s carried out by Time Delay Neural Networks; TDNN. To teleoperate a robot for specific tasks as turn, close, etc… In industrial environment and taking into account the noise coming from the machine. The choice of TDNN is based on its generalization in terms of accuracy, in more it acts as a filter that allows the passage of certain desirable frequency characteristics of speech; the goal is to determine the parameters of this filter for making an adaptable system to the variability of speech signal and to noise especially, for this the back propagation technique was used in learning phase. The approach was applied on commands pronounced in two languages separately: The French and Arabic. The results for two test bases of 300 spoken words for each one are 87%, 97.6% in neutral environment and 77.67%, 92.67% when the white Gaussian noisy was added with a SNR of 35 dB.

Keywords: TDNN, neural networks, noise, speech recognition

Procedia PDF Downloads 272

Authors: Walid Louhichi, Mouhebeddine Berrima, Narjes Ben Rajed

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Elasticity is the essential property of cloud computing. As the name suggests, it constitutes the ability of a cloud system to adjust resource provisioning in relation to fluctuating workload. There are two types of elasticity operations, vertical and horizontal. In this work, we are interested in horizontal scaling, which is ensured by two mechanisms; scaling in and scaling out. Following the sizing of the system, we can adopt scaling in in the event of over-supply and scaling out in the event of under-supply. In this paper, we propose a formal model, based on colored and temporized Petri nets, for the modeling of the duplication and the removal of a virtual machine from a server. This model is based on formal Petri Nets modeling language. The proposed models are edited, verified, and simulated with two examples implemented in CPNtools, which is a modeling tool for colored and timed Petri nets.

Keywords: cloud computing, elasticity, elasticity controller, petri nets, scaling in, scaling out

Procedia PDF Downloads 137

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

Procedia PDF Downloads 465

Authors: Slim Chokri

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Load forecasting is one of the central functions in power systems operations. Electricity cannot be stored, which means that for electric utility, the estimate of the future demand is necessary in managing the production and purchasing in an economically reasonable way. A majority of the recently reported approaches are based on neural network. The attraction of the methods lies in the assumption that neural networks are able to learn properties of the load. However, the development of the methods is not finished, and the lack of comparative results on different model variations is a problem. This paper presents a new approach in order to predict the Tunisia daily peak load. The proposed method employs a computational intelligence scheme based on the Fuzzy neural network (FNN) and support vector regression (SVR). Experimental results obtained indicate that our proposed FNN-SVR technique gives significantly good prediction accuracy compared to some classical techniques.

Keywords: neural network, load forecasting, fuzzy inference, machine learning, fuzzy modeling and rule extraction, support vector regression

Procedia PDF Downloads 418

Authors: T. O. Ajewole, K. S. Olorunmiaye, D. A. Animasaun, M. Okpeku

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Drought impact on the production of food crops for the benefit of mankind cannot be overemphasized. This study shows the different kind of genes expressed at various level of drought regimes on Basella alba and rubra using a real-time PCR machine. The planting was done in the screen house while the gene expression study was carried out in the laboratory. Sandy-loamy soil was collected and four levels of drought regime was used as treatment and a control experiment was set up for the two vegetables. Drought interval of 5, 10, 15 and 20 days were used as treatments while a control experiment which was not starved of water at any point was also set up, five replicates were set up for each treatment. Stress was introduced at 12 Weeks after planting (WAP). From the result of this study, Basella alba shows the highest amplicon size of 34.6 and 52.32 for GmPCS5 and HVA1 respectively which by implication means these genes were expressed the more as the stress period interval increases.

Keywords: water stress, basella alba, basella rubra, HVA1

Procedia PDF Downloads 27

Authors: Jorge A. Ruiz-Vanoye, Ocotlán Díaz-Parra, Alejandro Fuentes-Penna, Daniel Vélez-Díaz, Edith Olaco García

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In this paper, we present the use of the discriminant analysis to select evolutionary algorithms that better solve instances of the vehicle routing problem with time windows. We use indicators as independent variables to obtain the classification criteria, and the best algorithm from the generic genetic algorithm (GA), random search (RS), steady-state genetic algorithm (SSGA), and sexual genetic algorithm (SXGA) as the dependent variable for the classification. The discriminant classification was trained with classic instances of the vehicle routing problem with time windows obtained from the Solomon benchmark. We obtained a classification of the discriminant analysis of 66.7%.

Keywords: Intelligent Transportation Systems, data-mining techniques, evolutionary algorithms, discriminant analysis, machine learning

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Authors: Unchalee Inkampa, Tuanjai Somboonwiwat

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Painting process of automobiles and automobile parts, which is a continuous process based on EDP (Electrode position paint, EDP). Through EDP, all work pieces will be continuously sent to the painting process. Work process can be divided into 2 groups based on the running time: Painting Room 1 and Painting Room 2. This leads to continuous operation. The problem that arises is waiting for workloads onto Painting Room. The grading process EDP to Painting Room is a major problem. Therefore, this paper aim to develop production sequencing method by applying EDP to painting process. It also applied fixed rate launching for painting room and earliest due date (EDD) for EDP process and swap pairwise interchange for waiting time to a minimum of machine. The result found that the developed method could improve painting reduced waiting time, on time delivery, meeting customers wants and improved productivity of painting unit.

Keywords: sequencing, mixed model lines, painting process, electrode position paint

Procedia PDF Downloads 397

Authors: Vigen Arakelian

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This paper deals with the study of devices for displacement of the mould base of blow-molding machines. The displacement of the mould in the studied case is carried out by a linear actuator, which ensures the descent of the mould base and by extension springs, which return the letter in the initial position. The aim of this paper is to study the inverse dynamics of the device for displacement of the mould base of blow-molding machines and to determine its optimum parameters for higher rate of production. In the other words, it is necessary to solve the inverse dynamic problem to find the equation of motion linking applied forces with displacements. This makes it possible to determine the stiffness coefficient of the spring to turn the mold base back to the initial position for a given time. The obtained results are illustrated by a numerical example. It is shown that applying a spring with stiffness returns the mould base of the blow molding machine into the initial position in 0.1 sec.

Keywords: design, mechanisms, dynamics, blow-molding machines

Procedia PDF Downloads 134

Authors: William Anderson, Kyle Kobold, Oleg Yakimenko

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Green microgrids using mostly renewable energy (RE) for generation, are complex systems with inherent nonlinear dynamics. Among a variety of different optimization tools there are only a few ones that adequately consider this complexity. This paper evaluates applicability of two somewhat similar optimization tools tailored for standalone RE microgrids and also assesses a machine learning tool for performance prediction that can enhance the reliability of any chosen optimization tool. It shows that one of these microgrid optimization tools has certain advantages over another and presents a detailed routine of preparing input data to simulate RE microgrid behavior. The paper also shows how neural-network-based predictive modeling can be used to validate and forecast solar power generation based on weather time series data, which improves the overall quality of standalone RE microgrid analysis.

Keywords: microgrid, renewable energy, complex systems, optimization, predictive modeling, neural networks

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Authors: Mohammad Hedar

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This paper presents selected topics from the area of high-speed electrical machine control with a focus on loss minimization. It focuses on pulse amplitude modulation (PAM) set-up in order to minimize the inrush current peak. An overview of these machines and the control topologies that have been used with these machines are reported. The critical problem that happens when controlling a high-speed electrical motor is the high current peak in the start-up process, which will cause high power-losses. The main goal of this paper is to clarify how the inrush current peak can be minimized in the start-up process. PAM control method is proposed to use in the frequency inverter, simulation results for PAM & PWM control method, and steps to improve the PAM control are reported. The simulations were performed with data for PMSM (nominal speed: 25 000 min-1, power: 3.1 kW, load: 1.2 Nm).

Keywords: control topology, frequency inverter, high-speed electrical machines, PAM, power losses, PWM

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Authors: Kazumasa Kawasaki, Katsuya Fukazawa

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Nickel-based superalloys are an important class of engineering material within the aerospace and power generation, due to their excellent combination of corrosion resistance and mechanical properties, including high-temperature applications Inconel 625 is one of such superalloys and difficult-to-machine material. In cutting of Inconel 625 superalloy with a ball end mill, the problem of adhesive wear often occurs. However, the proper cutting conditions are not known so much because of lack of study examples. In this study, the experiments using ball end mills made of carbide tools were tried to find the best cutting conditions out following qualifications. Using Inconel 625 superalloy as a work material, three kinds of experiment, with the revolution speed of 5000 rpm, 8000 rpm, and 10000 rpm, were performed under dry cutting conditions in feed speed per tooth of 0.045 mm/ tooth, depth of cut of 0.1 mm. As a result, in the case of 8000 rpm, it was successful to cut longest with the least wear.

Keywords: Inconel 625, ball end mill, carbide tool, high speed cutting, tool wear

Procedia PDF Downloads 182

Authors: Mohammadamin Hashemi, Mohammadreza Kashizadeh

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Droughts pose a significant threat to sustainable water resource management, agriculture, and socioeconomic sectors, particularly in the field of climate change. This study investigates drought simulation using rainfall-runoff modelling in the Cuneo district, Italy, over the past 60-year period. The study leverages the TUW model, a lumped conceptual rainfall-runoff model with a semi-distributed operation capability. Similar in structure to the widely used Hydrologiska Byråns Vattenbalansavdelning (HBV) model, the TUW model operates on daily timesteps for input and output data specific to each catchment. It incorporates essential routines for snow accumulation and melting, soil moisture storage, and streamflow generation. Multiple catchments' discharge data within the Cuneo district form the basis for thorough model calibration employing the Kling-Gupta Efficiency (KGE) metric. A crucial metric for reliable drought analysis is one that can accurately represent low-flow events during drought periods. This ensures that the model provides a realistic picture of water availability during these critical times. Subsequent validation of monthly discharge simulations thoroughly evaluates overall model performance. Beyond model development, the investigation delves into drought analysis using the robust Standardized Runoff Index (SRI). This index allows for precise characterization of drought occurrences within the study area. A meticulous comparison of observed and simulated discharge data is conducted, with particular focus on low-flow events that characterize droughts. Additionally, the study explores the complex interplay between land characteristics (e.g., soil type, vegetation cover) and climate variables (e.g., precipitation, temperature) that influence the severity and duration of hydrological droughts. The study's findings demonstrate successful calibration of the TUW model across most catchments, achieving commendable model efficiency. Comparative analysis between simulated and observed discharge data reveals significant agreement, especially during critical low-flow periods. This agreement is further supported by the Pareto coefficient, a statistical measure of goodness-of-fit. The drought analysis provides critical insights into the duration, intensity, and severity of drought events within the Cuneo district. This newfound understanding of spatial and temporal drought dynamics offers valuable information for water resource management strategies and drought mitigation efforts. This research deepens our understanding of drought dynamics in the Cuneo region. Future research directions include refining hydrological modelling techniques and exploring future drought projections under various climate change scenarios.

Keywords: hydrologic extremes, hydrological drought, hydrological modelling, machine learning, rainfall-runoff modelling

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Authors: Sondos M. Hammad, Sherin M. ElGokhy, Mahmoud M. Fahmy, Elsayed A. Sallam

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In this paper, an ensemble-based Support Vector Machine (SVM) classification approach is proposed. It is used for miRNA prediction. Three problems, commonly associated with previous approaches, are alleviated. These problems arise due to impose assumptions on the secondary structural of premiRNA, imbalance between the numbers of the laboratory checked miRNAs and the pseudo-hairpins, and finally using a training data set that does not consider all the varieties of samples in different species. We aggregate the predicted outputs of three well-known SVM classifiers; namely, Triplet-SVM, Virgo and Mirident, weighted by their variant features without any structural assumptions. An additional SVM layer is used in aggregating the final output. The proposed approach is trained and then tested with balanced data sets. The results of the proposed approach outperform the three base classifiers. Improved values for the metrics of 88.88% f-score, 92.73% accuracy, 90.64% precision, 96.64% specificity, 87.2% sensitivity, and the area under the ROC curve is 0.91 are achieved.

Keywords: MiRNAs, SVM classification, ensemble algorithm, assumption problem, imbalance data

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Authors: Navid Khayat

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Drained shear strength of saturated soils is fully understood. Shear strength of unsaturated soils is usually expressed in terms of soil suction. Evaluation of shear strength of compacted mixtures of sand-bentonite at optimum water content is main purpose of this research. To prepare the required samples, first, bentonite and sand are mixed in 10, 30, 50 and 70 percent by dry weight and then compacted at the proper optimum water content according to the standard proctor test. The samples were sheared in direct shear machine. Stress-strain relationship of samples indicated a ductile behavior. Most of the samples showed a dilatancy behavior during the shear and the tendency for dilatancy increased with the increase in sand proportion. The results show that with the increase in percentage of sand a decrease in cohesion intercept c' for mixtures and an increase in the angle of internal friction Φ’is observed.

Keywords: bentonite, sand, drained shear strength, cohesion intercept

Procedia PDF Downloads 298

Authors: L. P. Gomes, G. F. Araújo, Y. M. L. Cordeiro, C. T. Andrade, E. M. Del Aguila, V. M. F. Paschoalin

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The application of nanoscale materials and nanostructures is an emerging area, these since materials may provide solutions to technological and environmental challenges in order to preserve the environment and natural resources. To reach this goal, the increasing demand must be accompanied by 'green' synthesis methods. Chitosan is a natural, nontoxic, biopolymer derived by the deacetylation of chitin and has great potential for a wide range of applications in the biological and biomedical areas, due to its biodegradability, biocompatibility, non-toxicity and versatile chemical and physical properties. Chitosan also presents high antimicrobial activities against a wide variety of pathogenic and spoilage microorganisms. Ultrasonication is a common tool for the preparation and processing of polymer nanoparticles. It is particularly effective in breaking up aggregates and in reducing the size and polydispersity of nanoparticles. High-intensity ultrasonication has the potential to modify chitosan molecular weight and, thus, alter or improve chitosan functional properties. The aim of this study was to evaluate the influence of sonication intensity and time on the changes of commercial chitosan characteristics, such as molecular weight and its potential antibacterial activity against Gram-negative bacteria. The nanoparticles (NPs) were produced from two commercial chitosans, of medium molecular weight (CS-MMW) and low molecular weight (CS-LMW) from Sigma-Aldrich®. These samples (2%) were solubilized in 100 mM sodium acetate pH 4.0, placed on ice and irradiated with an ultrasound SONIC ultrasonic probe (model 750 W), equipped with a 1/2" microtip during 30 min at 4°C. It was used on constant duty cycle and 40% amplitude with 1/1s intervals. The ultrasonic degradation of CS-MMW and CS-LMW were followed up by means of ζ-potential (Brookhaven Instruments, model 90Plus) and dynamic light scattering (DLS) measurements. After sonication, the concentrated samples were diluted 100 times and placed in fluorescence quartz cuvettes (Hellma 111-QS, 10 mm light path). The distributions of the colloidal particles were calculated from the DLS and ζ-potential are measurements taken for the CS-MMW and CS-LMW solutions before and after (CS-MMW30 and CS-LMW30) sonication for 30 min. Regarding the results for the chitosan sample, the major bands can be distinguished centered at Radius hydrodynamic (Rh), showed different distributions for CS-MMW (Rh=690.0 nm, ζ=26.52±2.4), CS-LMW (Rh=607.4 and 2805.4 nm, ζ=24.51±1.29), CS-MMW30 (Rh=201.5 and 1064.1 nm, ζ=24.78±2.4) and CS-LMW30 (Rh=492.5, ζ=26.12±0.85). The minimal inhibitory concentration (MIC) was determined using different chitosan samples concentrations. MIC values were determined against to E. coli (106 cells) harvested from an LB medium (Luria-Bertani BD™) after 18h growth at 37 ºC. Subsequently, the cell suspension was serially diluted in saline solution (0.8% NaCl) and plated on solid LB at 37°C for 18 h. Colony-forming units were counted. The samples showed different MICs against E. coli for CS-LMW (1.5mg), CS-MMW30 (1.5 mg/mL) and CS-LMW30 (1.0 mg/mL). The results demonstrate that the production of nanoparticles by modification of their molecular weight by ultrasonication is simple to be performed and dispense acid solvent addition. Molecular weight modifications are enough to provoke changes in the antimicrobial potential of the nanoparticles produced in this way.

Keywords: antimicrobial agent, chitosan, green production, nanoparticles

Procedia PDF Downloads 307

Authors: Silpa Mariyam John, S. Baburaj, Prajit Geevarghese

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Diabetes accelerates pregnancy and can cause adverse effects on the fetus. Studies have shown that fetal abdominal circumference measured in ultrasound is an early parameter for the assessment of macrosomia. The objective of the study is to compare the fetal abdominal circumferences between diabetes and non-diabetic mothers. It was a comparative cross-sectional study conducted in a tertiary care hospital in Trivandrum, Kerala, with a sample size calculated as 95 for each group. All mothers taking antenatal care and delivering at the hospital were included after obtaining consent. The mothers and their newborns were divided into 2 groups (diabetic and non-diabetic). Relevant fetal biometry values were collected from medical records, and birth weight was measured by a calibrated electronic weighing machine after birth. The data were entered in MS EXCEL and analyzed. It was found that there is a significant relationship between the fetal abdominal circumference and birthweight in diabetic mothers during the first and third trimesters.

Keywords: newborn, diabetes, abdominal circumference, ultrasound

Procedia PDF Downloads 35

Authors: Patik Joslin Kenfack, Polyakov Vladimir Mikhailovich

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Machine Learning (ML) systems are trained using human generated data that could be biased by implicitly containing racist, sexist, or discriminating data. ML models learn those biases or even amplify them. Recent research in work on has begun to consider issues of fairness. The concept of fairness is extended to recommendation. A recommender system will be considered fair if it doesn’t under rank items of protected group (gender, race, demographic...). Several metrics for evaluating fairness concerns in recommendation systems have been proposed, which take pairs of items as ‘instances’ in fairness evaluation. It doesn’t take in account the fact that the fairness should be evaluated across a list of items. The paper explores a probabilistic approach that generalize pairwise metric by using a list k (listwise) of items as ‘instances’ in fairness evaluation, parametrized by k. We also explore new regularization method based on this metric to improve fairness ranking during model training.

Keywords: Fairness, Recommender System, Ranking, Listwise Approach

Procedia PDF Downloads 126

Authors: Shuen-Tai Wang, Hsi-Ya Chang

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Cloud virtualization technologies are becoming more and more prevalent, cloud users usually encounter the problem of how to access to the virtualized remote desktops easily over the web without requiring the installation of special clients. To resolve this issue, we took advantage of the HTML5 technology and developed web-based remote desktop. It permits users to access the terminal which running in our cloud platform from anywhere. We implemented a sketch of web interface following the cloud computing concept that seeks to enable collaboration and communication among users for high performance computing. Given the development of remote desktop virtualization, it allows to shift the user’s desktop from the traditional PC environment to the cloud platform, which is stored on a remote virtual machine rather than locally. This proposed effort has the potential to positively provide an efficient, resilience and elastic environment for online cloud service. This is also made possible by the low administrative costs as well as relatively inexpensive end-user terminals and reduced energy expenses.

Keywords: virtualization, remote desktop, HTML5, cloud computing

Procedia PDF Downloads 323

Authors: David J. Foran, Nhan Do, Samuel Ajjarapu, Wenjin Chen, Tahsin Kurc, Joel H. Saltz

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The large-scale data and computational requirements of investigators throughout the clinical and research communities demand an informatics infrastructure that supports both existing and new investigative and translational projects in a robust, secure environment. In some subspecialties of medicine and research, the capacity to generate data has outpaced the methods and technology used to aggregate, organize, access, and reliably retrieve this information. Leading health care centers now recognize the utility of establishing an enterprise-wide, clinical data warehouse. The primary benefits that can be realized through such efforts include cost savings, efficient tracking of outcomes, advanced clinical decision support, improved prognostic accuracy, and more reliable clinical trials matching. The overarching objective of the work presented here is the development and implementation of a flexible Intelligent Retrieval and Interrogation System (IRIS) that exploits the combined use of computational imaging, genomics, and data-mining capabilities to facilitate clinical assessments and translational research in oncology. The proposed System includes a multi-modal, Clinical & Research Data Warehouse (CRDW) that is tightly integrated with a suite of computational and machine-learning tools to provide insight into the underlying tumor characteristics that are not be apparent by human inspection alone. A key distinguishing feature of the System is a configurable Extract, Transform and Load (ETL) interface that enables it to adapt to different clinical and research data environments. This project is motivated by the growing emphasis on establishing Learning Health Systems in which cyclical hypothesis generation and evidence evaluation become integral to improving the quality of patient care. To facilitate iterative prototyping and optimization of the algorithms and workflows for the System, the team has already implemented a fully functional Warehouse that can reliably aggregate information originating from multiple data sources including EHR’s, Clinical Trial Management Systems, Tumor Registries, Biospecimen Repositories, Radiology PAC systems, Digital Pathology archives, Unstructured Clinical Documents, and Next Generation Sequencing services. The System enables physicians to systematically mine and review the molecular, genomic, image-based, and correlated clinical information about patient tumors individually or as part of large cohorts to identify patterns that may influence treatment decisions and outcomes. The CRDW core system has facilitated peer-reviewed publications and funded projects, including an NIH-sponsored collaboration to enhance the cancer registries in Georgia, Kentucky, New Jersey, and New York, with machine-learning based classifications and quantitative pathomics, feature sets. The CRDW has also resulted in a collaboration with the Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC) at the U.S. Department of Veterans Affairs to develop algorithms and workflows to automate the analysis of lung adenocarcinoma. Those studies showed that combining computational nuclear signatures with traditional WHO criteria through the use of deep convolutional neural networks (CNNs) led to improved discrimination among tumor growth patterns. The team has also leveraged the Warehouse to support studies to investigate the potential of utilizing a combination of genomic and computational imaging signatures to characterize prostate cancer. The results of those studies show that integrating image biomarkers with genomic pathway scores is more strongly correlated with disease recurrence than using standard clinical markers.

Keywords: clinical data warehouse, decision support, data-mining, intelligent databases, machine-learning.

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Authors: Keunho Park, Sunghwan Jeong

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The 4th Industrial Revolution, the next-generation industrial revolution, which is made up of convergence of information and communication technology (ICT), is no exception to the livestock industry, and various studies are being conducted to apply the livestock smart farm. In order to monitor livestock using sensors, it is necessary to drill holes in the organs such as the nose, ears, and even the stomach of the livestock to wear or insert the sensor into the livestock. This increases the stress of livestock, which in turn lowers the quality of livestock products or raises the issue of animal ethics, which has become a major issue in recent years. In this paper, we conducted a study to monitor livestock activity based on vision technology, effectively monitoring livestock activity without increasing animal stress and violating animal ethics. The movement rate was calculated based on the difference images between the frames, and the livestock activity was evaluated. As a result, the average F1-score was 96.67.

Keywords: barn monitoring, livestock, machine vision, smart farm

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Authors: Saed Khawaldeh, Mohamed Elsharnouby, Alaa Eddin Alchalabi, Usama Pervaiz, Tajwar Aleef, Vu Hoang Minh

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Taxonomic classification has a wide-range of applications such as finding out more about the evolutionary history of organisms that can be done by making a comparison between species living now and species that lived in the past. This comparison can be made using different kinds of extracted species’ data which include DNA sequences. Compared to the estimated number of the organisms that nature harbours, humanity does not have a thorough comprehension of which specific species they all belong to, in spite of the significant development of science and scientific knowledge over many years. One of the methods that can be applied to extract information out of the study of organisms in this regard is to use the DNA sequence of a living organism as a marker, thus making it available to classify it into a taxonomy. The classification of living organisms can be done in many machine learning techniques including Neural Networks (NNs). In this study, DNA sequences classification is performed using Convolutional Neural Networks (CNNs) which is a special type of NNs.

Keywords: deep networks, convolutional neural networks, taxonomic classification, DNA sequences classification

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Authors: Nasiru Aliyu, Glyn Atkinson, Nick Stannard

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With increasing demand for electric motors used in nearly all sectors of our day to day activities, which range from the motor that rotates the washing machine and dishwasher to the tens of thousands of motors used in domestic appliance. The number of applications for soft magnetic composites (SMC) material is growing significantly. This paper presents the development of a compact single sided concentrated winding axial flux PM motor using soft magnetic composite as core for reducing core losses and cost. The effects of changing the flux carrying component to pressed SMC parts are investigated based on a comprehensive understanding of the properties of the material. A 3-D finite-element analysis is performed for accurate parameter calculation. To validate the simulation, a new static test measurement was fully conducted on a prototype motor and agree with the theoretical calculations and old measured static test.

Keywords: SMC, compact development, axial field motor, 3DFA

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Authors: Ezio Bassi, Francesco Vercesi, Francesco Benzi

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The aim of this study is to evaluate the potentiality of synchronous reluctance motors for lift systems by also evaluating the vibroacoustic behaviour of the motor. Two types of synchronous machines are designed, analysed, and compared with an equivalent induction motor, which is the more common solution in such gearbox applications. The machines' performance are further improved with optimization procedures based on multiobjective optimization genetic algorithm (MOGA). The difference between the two synchronous motors consists in the rotor geometry; a symmetric and an asymmetric rotor design were investigated. The evaluation of the vibroacoustic performance has been conducted with a multi-variable model and finite element software taking into account electromagnetic, mechanical, and thermal features of the motor, therefore carrying out a multi-physics analysis of the electrical machine.

Keywords: synchronous reluctance motor, vibro-acoustic, lift systems, genetic algorithm

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Authors: Hang Lo Lee, Ki Il Song, Hee Hwan Ryu

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An accurate prediction of TBM(Tunnel Boring Machine) performance is very difficult for reliable estimation of the construction period and cost in preconstruction stage. For this purpose, the aim of this study is to analyze the evaluation process of various prediction models published since 2000 for TBM performance, and to select the optimal input parameters for the prediction model. A classification system of TBM performance prediction model and applied methodology are proposed in this research. Input and output parameters applied for prediction models are also represented. Based on these results, a statistical analysis is performed using the collected data from shield TBM tunnel in South Korea. By performing a simple regression and residual analysis utilizinFg statistical program, R, the optimal input parameters are selected. These results are expected to be used for development of prediction model of TBM performance.

Keywords: TBM performance prediction model, classification system, simple regression analysis, residual analysis, optimal input parameters

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Authors: Rosa L. Figueroa, Christopher A. Flores, Qing Zeng-Treitler

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A regular expression consists of sequence characters which allow describing a text path. Usually, in clinical research, regular expressions are manually created by programmers together with domain experts. Lately, there have been several efforts to investigate how to generate them automatically. This article presents a text classification algorithm based on regexes. The algorithm named REX was designed, and then, implemented as a simplified method to create regexes to classify Spanish text automatically. In order to classify ambiguous cases, such as, when multiple labels are assigned to a testing example, REX includes an information gain method Two sets of data were used to evaluate the algorithm’s effectiveness in clinical text classification tasks. The results indicate that the regular expression based classifier proposed in this work performs statically better regarding accuracy and F-measure than Support Vector Machine and Naïve Bayes for both datasets.

Keywords: information gain, regular expressions, smith-waterman algorithm, text classification

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Authors: Ahbdelfattah M. Khourshid, T. Elabeidi

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Friction Stir Welding (FSW), a solid state joining technique, is widely being used for joining Al alloys for aerospace, marine automotive and many other applications of commercial importance. FSW were carried out using a vertical milling machine on Al 5083 alloy pipe. These pipe sections are relatively small in diameter, 5mm, and relatively thin walled, 2mm. In this study, 5083 aluminum alloy pipe were welded as similar alloy joints using (FSW) process in order to investigate mechanical and microstructural properties .rotation speed 1400 r.p.m and weld speed 10,40,70 mm/min. In order to investigate the effect of welding speeds on mechanical properties, metallographic and mechanical tests were carried out on the welded areas. Vickers hardness profile and tensile tests of the joints as a metallurgical investigation, Optic Microscopy and Scanning Electron Microscopy (SEM) were used for base and weld zones.

Keywords: friction stir welding (FSW), Al alloys, mechanical properties, microstructure

Procedia PDF Downloads 512

Authors: Rezaei Hachesu Peyman, Oliyaee Azadeh, Salahzadeh Zahra, Alizadeh Somayyeh, Safaei Naser

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Coronary Artery Disease (CAD) is one major cause of disability in adults and one main cause of death in developed. In this study, data mining techniques including Decision Trees, Artificial neural networks (ANNs), and Support Vector Machine (SVM) analyze CAD data. Data of 4948 patients who had suffered from heart diseases were included in the analysis. CAD is the target variable, and 24 inputs or predictor variables are used for the classification. The performance of these techniques is compared in terms of sensitivity, specificity, and accuracy. The most significant factor influencing CAD is chest pain. Elderly males (age > 53) have a high probability to be diagnosed with CAD. SVM algorithm is the most useful way for evaluation and prediction of CAD patients as compared to non-CAD ones. Application of data mining techniques in analyzing coronary artery diseases is a good method for investigating the existing relationships between variables.

Keywords: classification, coronary artery disease, data-mining, knowledge discovery, extract

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Authors: J. Yang, Y. Ma, X. Zhang, S. Li, Y. Zhang

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The traditional k-means algorithm has been widely used as a simple and efficient clustering method. However, the algorithm often converges to local minima for the reason that it is sensitive to the initial cluster centers. In this paper, an algorithm for selecting initial cluster centers on the basis of minimum spanning tree (MST) is presented. The set of vertices in MST with same degree are regarded as a whole which is used to find the skeleton data points. Furthermore, a distance measure between the skeleton data points with consideration of degree and Euclidean distance is presented. Finally, MST-based initialization method for the k-means algorithm is presented, and the corresponding time complexity is analyzed as well. The presented algorithm is tested on five data sets from the UCI Machine Learning Repository. The experimental results illustrate the effectiveness of the presented algorithm compared to three existing initialization methods.

Keywords: degree, initial cluster center, k-means, minimum spanning tree

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Authors: Pengzhou Lu, Liming Xin, Payam Tavakoli, Zhonghua Lin, Roberto V. P. Ribeiro, Mitesh V. Badiwala

Abstract:

Ex vivo Heart Perfusion (EVHP) has been developed as an alternative strategy to expand cardiac donation by enabling resuscitation and functional assessment of hearts donated from marginal donors, which were previously not accepted. EVHP parameters, such as perfusion flow (PF) and perfusion pressure (PP) are crucial for optimal organ preservation. However, with the heart’s constant physiological changes during EVHP, such as coronary vascular resistance, manual control of these parameters is rendered imprecise and cumbersome for the operator. Additionally, low control precision and the long adjusting time may lead to irreversible damage to the myocardial tissue. To solve this problem, an automatic heart perfusion system was developed by applying a Human-Machine Interface (HMI) and a Programmable-Logic-Controller (PLC)-based circuit to control PF and PP. The PLC-based control system collects the data of PF and PP through flow probes and pressure transducers. It has two control modes: the RPM-flow mode and the pressure mode. The RPM-flow control mode is an open-loop system. It influences PF through providing and maintaining the desired speed inputted through the HMI to the centrifugal pump with a maximum error of 20 rpm. The pressure control mode is a closed-loop system where the operator selects a target Mean Arterial Pressure (MAP) to control PP. The inputs of the pressure control mode are the target MAP, received through the HMI, and the real MAP, received from the pressure transducer. A PID algorithm is applied to maintain the real MAP at the target value with a maximum error of 1mmHg. The precision and control speed of the RPM-flow control mode were examined by comparing the PLC-based system to an experienced operator (EO) across seven RPM adjustment ranges (500, 1000, 2000 and random RPM changes; 8 trials per range) tested in a random order. System’s PID algorithm performance in pressure control was assessed during 10 EVHP experiments using porcine hearts. Precision was examined through monitoring the steady-state pressure error throughout perfusion period, and stabilizing speed was tested by performing two MAP adjustment changes (4 trials per change) of 15 and 20mmHg. A total of 56 trials were performed to validate the RPM-flow control mode. Overall, the PLC-based system demonstrated the significantly faster speed than the EO in all trials (PLC 1.21±0.03, EO 3.69±0.23 seconds; p < 0.001) and greater precision to reach the desired RPM (PLC 10±0.7, EO 33±2.7 mean RPM error; p < 0.001). Regarding pressure control, the PLC-based system has the median precision of ±1mmHg error and the median stabilizing times in changing 15 and 20mmHg of MAP are 15 and 19.5 seconds respectively. The novel PLC-based control system was 3 times faster with 60% less error than the EO for RPM-flow control. In pressure control mode, it demonstrates a high precision and fast stabilizing speed. In summary, this novel system successfully controlled perfusion flow and pressure with high precision, stability and a fast response time through a user-friendly interface. This design may provide a viable technique for future development of novel heart preservation and assessment strategies during EVHP.

Keywords: automatic control system, biomedical engineering, ex-vivo heart perfusion, human-machine interface, programmable logic controller

Procedia PDF Downloads 154

Authors: Darren Zou

Abstract:

Forecasting the stock market is a very challenging task. Various economic indicators such as GDP, exchange rates, interest rates, and unemployment have a substantial impact on the stock market. Time series models are the traditional methods used to predict stock market changes. In this paper, a machine learning method, Bayesian Additive Regression Tree (BART) is used in predicting stock market indexes based on multiple economic indicators. BART can be used to model heterogeneous treatment effects, and thereby works well when models are misspecified. It also has the capability to handle non-linear main effects and multi-way interactions without much input from financial analysts. In this research, BART is proposed to provide a reliable prediction on day-to-day stock market activities. By comparing the analysis results from BART and with time series method, BART can perform well and has better prediction capability than the traditional methods.

Keywords: BART, Bayesian, predict, stock

Procedia PDF Downloads 106