Search results for: Ultra-precision machine tool

Authors: Ayman M. Alaskari, Samy E. Oraby, Abdulla I. Almazrouee

Abstract:

Coated tool inserts can be considered as the backbone of machining processes due to their wear and heat resistance. However, defects of coating can degrade the integrity of these inserts and the number of these defects should be minimized or eliminated if possible. Recently, the advancement of coating processes and analytical tools open a new era for optimizing the coating tools. First, an overview is given regarding coating technology for cutting tool inserts. Testing techniques for coating layers properties, as well as the various coating defects and their assessment are also surveyed. Second, it is introduced an experimental approach to examine the possible coating defects and flaws of worn multicoated carbide inserts using two important techniques namely scanning electron microscopy and atomic force microscopy. Finally, it is recommended a simple procedure for investigating manufacturing defects and flaws of worn inserts.

Keywords: AFM, Coated inserts, Defects, SEM.

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Authors: Manop Vorasri, Komson Jirapattarasilp, Sittichai Kaewkuekool

Abstract:

The objective of this research was to study factors, which were affected on surface roughness in high speed milling of hardened tool steel. Material used in the experiment was tool steel JIS SKD 61 that hardened on 60 ±2 HRC. Full factorial experimental design was conducted on 3 factors and 3 levels (3 3 designs) with 2 replications. Factors were consisted of cutting speed, feed rate, and depth of cut. The results showed that influenced factor affected to surface roughness was cutting speed, feed rate and depth of cut which showed statistical significant. Higher cutting speed would cause on better surface quality. On the other hand, higher feed rate would cause on poorer surface quality. Interaction of factor was found that cutting speed and depth of cut were significantly to surface quality. The interaction of high cutting speed associated with low depth of cut affected to better surface quality than low cutting speed and high depth of cut.

Keywords: High-speed milling, Tool steel, SKD 61 Steel, Surface roughness, Cutting speed, Feed rate, Depth of cut

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Authors: Mohd A. Mezher, Maysam F. Abbod

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Genetic Folding (GF) a new class of EA named as is introduced for the first time. It is based on chromosomes composed of floating genes structurally organized in a parent form and separated by dots. Although, the genotype/phenotype system of GF generates a kernel expression, which is the objective function of superior classifier. In this work the question of the satisfying mapping-s rules in evolving populations is addressed by analyzing populations undergoing either Mercer-s or none Mercer-s rule. The results presented here show that populations undergoing Mercer-s rules improve practically models selection of Support Vector Machine (SVM). The experiment is trained multi-classification problem and tested on nonlinear Ionosphere dataset. The target of this paper is to answer the question of evolving Mercer-s rule in SVM addressed using either genetic folding satisfied kernel-s rules or not applied to complicated domains and problems.

Keywords: Genetic Folding, GF, Evolutionary Algorithms, Support Vector Machine, Genetic Algorithm, Genetic Programming, Multi-Classification, Mercer's Rules

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Authors: Victor Bodell, Lukas Ekstrom, Somayeh Aghanavesi

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Fuel consumption (FC) is one of the key factors in determining expenses of operating a heavy-duty vehicle. A customer may therefore request an estimate of the FC of a desired vehicle. The modular design of heavy-duty vehicles allows their construction by specifying the building blocks, such as gear box, engine and chassis type. If the combination of building blocks is unprecedented, it is unfeasible to measure the FC, since this would first r equire the construction of the vehicle. This paper proposes a machine learning approach to predict FC. This study uses around 40,000 vehicles specific and o perational e nvironmental c onditions i nformation, such as road slopes and driver profiles. A ll v ehicles h ave d iesel engines and a mileage of more than 20,000 km. The data is used to investigate the accuracy of machine learning algorithms Linear regression (LR), K-nearest neighbor (KNN) and Artificial n eural n etworks (ANN) in predicting fuel consumption for heavy-duty vehicles. Performance of the algorithms is evaluated by reporting the prediction error on both simulated data and operational measurements. The performance of the algorithms is compared using nested cross-validation and statistical hypothesis testing. The statistical evaluation procedure finds that ANNs have the lowest prediction error compared to LR and KNN in estimating fuel consumption on both simulated and operational data. The models have a mean relative prediction error of 0.3% on simulated data, and 4.2% on operational data.

Keywords: Artificial neural networks, fuel consumption, machine learning, regression, statistical tests.

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Authors: Dhairya Partap Sing, Vikram Singh, Sudhir Kumar

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This investigation proposes Friction stir welding technique to solve the fusion welding problems. Objectives of this investigation are fabrication of AA7075-10%wt. Silicon carbide (SiC) aluminum metal matrix composite and optimization of optimal process parameters of friction stir welded AA7075-10%wt. SiC Composites. Composites were prepared by the mechanical stir casting process. Experiments were performed with four process parameters such as tool rotational speed, weld speed, axial force and tool geometry considering three levels of each. The quality characteristics considered is joint efficiency (JE). The welding experiments were conducted using L₂₇ orthogonal array. An orthogonal array and design of experiments were used to give best possible welding parameters that give optimal JE. The fabricated welded joints using rotational speed of 1500 rpm, welding speed (1.3 mm/sec), axial force (7 k/n) of and tool geometry (square) give best possible results. Experimental result reveals that the tool rotation speed, welding speed and axial force are the significant process parameters affecting the welding performance. The predicted optimal value of percentage JE is 95.621. The confirmation tests also have been done for verifying the results.

Keywords: Metal matrix composite, axial force, joint efficiency, rotational speed, traverse speed, tool geometry.

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Authors: Reza Meimandi Parizi, Abdul Azim Abdul Ghani

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The ever-growing usage of aspect-oriented development methodology in the field of software engineering requires tool support for both research environments and industry. So far, tool support for many activities in aspect-oriented software development has been proposed, to automate and facilitate their development. For instance, the AJaTS provides a transformation system to support aspect-oriented development and refactoring. In particular, it is well established that the abstract interpretation of programs, in any paradigm, pursued in static analysis is best served by a high-level programs representation, such as Control Flow Graph (CFG). This is why such analysis can more easily locate common programmatic idioms for which helpful transformation are already known as well as, association between the input program and intermediate representation can be more closely maintained. However, although the current researches define the good concepts and foundations, to some extent, for control flow analysis of aspectoriented programs but they do not provide a concrete tool that can solely construct the CFG of these programs. Furthermore, most of these works focus on addressing the other issues regarding Aspect- Oriented Software Development (AOSD) such as testing or data flow analysis rather than CFG itself. Therefore, this study is dedicated to build an aspect-oriented control flow graph construction tool called AJcFgraph Builder. The given tool can be applied in many software engineering tasks in the context of AOSD such as, software testing, software metrics, and so forth.

Keywords: Aspect-Oriented Software Development, AspectJ, Control Flow Graph, Data Flow Analysis

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Authors: Hamed Rahmani, Wim Groot

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The term "youth not in employment, education or training (NEET)" refers to a combination of youth unemployment and school dropout. This study investigates the variables that increase the risk of becoming NEET in Iran. A selection bias-adjusted Probit model was employed using machine learning to identify these risk factors. We used cross-sectional data obtained from the Statistical Center of Iran and the Ministry of Cooperatives Labor and Social Welfare that are taken from the labor force survey conducted in the spring of 2021. We look at years of education, work experience, housework, the number of children under the age of 6 years in the home, family education, birthplace, and the amount of land owned by households. Results show that hours spent performing domestic chores enhance the likelihood of youth becoming NEET, and years of education, years of potential work experience decrease the chance of being NEET. The findings also show that female youth born in cities were less likely than those born in rural regions to become NEET.

Keywords: NEET youth, probit, CART, machine learning, unemployment.

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Authors: Aya Salama

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Digital Twin has emerged as a compelling research area, capturing the attention of scholars over the past decade. It finds applications across diverse fields, including smart manufacturing and healthcare, offering significant time and cost savings. Notably, it often intersects with other cutting-edge technologies such as Data Mining, Artificial Intelligence, and Machine Learning. However, the concept of a Human Digital Twin (HDT) is still in its infancy and requires further demonstration of its practicality. HDT takes the notion of Digital Twin a step further by extending it to living entities, notably humans, who are vastly different from inanimate physical objects. The primary objective of this research was to create an HDT capable of automating real-time human responses by simulating human behavior. To achieve this, the study delved into various areas, including clustering, supervised classification, topic extraction, and sentiment analysis. The paper successfully demonstrated the feasibility of HDT for generating personalized responses in social messaging applications. Notably, the proposed approach achieved an overall accuracy of 63%, a highly promising result that could pave the way for further exploration of the HDT concept. The methodology employed Random Forest for clustering the question database and matching new questions, while K-nearest neighbor was utilized for sentiment analysis.

Keywords: Human Digital twin, sentiment analysis, topic extraction, supervised machine learning, unsupervised machine learning, classification and clustering.

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Authors: Jaya Mathew

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Many organizations are faced with the challenge of how to analyze and build Machine Learning models using their sensitive telemetry data. In this paper, we discuss how users can leverage the power of R without having to move their big data around as well as a cloud based solution for organizations willing to host their data in the cloud. By using ScaleR technology to benefit from parallelization and remote computing or R Services on premise or in the cloud, users can leverage the power of R at scale without having to move their data around.

Keywords: Predictive maintenance, machine learning, big data, cloud, on premise SQL, R.

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Authors: R. Alipour, F. Nadjarian, A. Alinaghizade

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Considering complexity of products, new geometrical design and investment tolerances that are necessary, measuring and dimensional controlling involve modern and more precise methods. Photo digitizing method using two cameras to record pictures and utilization of conventional method named “cloud points" and data analysis by the use of ATOUS software, is known as modern and efficient in mentioned context. In this paper, benefits of photo digitizing method in evaluating sampling of machining processes have been put forward. For example, assessment of geometrical integrity surface in 5-axis milling process and measurement of carbide tool wear in turning process, can be can be brought forward. Advantages of this method comparing to conventional methods have been expressed.

Keywords: photo digitizing, tool wear, geometrical integrity, cloud points

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Authors: Sungin Jeong

Abstract:

This paper presents a linear oscillating generator of cylindrical type for hybrid electric vehicle application. The focus of the study is the suggestion of the optimal model and the design rule of the cylindrical linear oscillating generator with permanent magnet in the back-iron translator. The cylindrical topology is achieved using equivalent magnetic circuit considering leakage elements as initial modeling. This topology with permanent magnet in the back-iron translator is described by number of phases and displacement of stroke. For more accurate analysis of an oscillating machine, it will be compared by moving just one-pole pitch forward and backward the thrust of single-phase system and three-phase system. Through the analysis and comparison, a single-phase system of cylindrical topology as the optimal topology is selected. Finally, the detailed design of the optimal topology takes the magnetic saturation effects into account by finite element analysis. Besides, the losses are examined to obtain more accurate results; copper loss in the conductors of machine windings, eddy-current loss of permanent magnet, and iron-loss of specific material of electrical steel. The considerations of thermal performances and mechanical robustness are essential, because they have an effect on the entire efficiency and the insulations of the machine due to the losses of the high temperature generated in each region of the generator. Besides electric machine with linear oscillating movement requires a support system that can resist dynamic forces and mechanical masses. As a result, the fatigue analysis of shaft is achieved by the kinetic equations. Also, the thermal characteristics are analyzed by the operating frequency in each region. The results of this study will give a very important design rule in the design of linear oscillating machines. It enables us to more accurate machine design and more accurate prediction of machine performances.

Keywords: Equivalent magnetic circuit, finite element analysis, hybrid electric vehicle, free piston engine, cylindrical linear oscillating generator

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Authors: M. Khalid Imran, Syed Masood, Milan Brandt, Sudip Bhattacharya, Jyotirmoy Mazumder

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In the present investigation, H13 tool steel has been deposited on copper alloy substrate using both CO2 and diode laser. A detailed parametric analysis has been carried out in order to find out optimum processing zone for coating defect free H13 tool steel on copper alloy substrate. Followed by parametric optimization, the microstructure and microhardness of the deposited clads have been evaluated. SEM micrographs revealed dendritic microstructure in both clads. However, the microhardness of CO2 laser deposited clad was much higher compared to diode laser deposited clad.

Keywords: CO2 laser, Diode laser, Direct Metal Deposition, Microstructure, Microhardness, Porosity.

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Authors: K. Westmoreland Bowers, Matthew W. Ragas, Jeffrey C. Neely

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The purpose of this study was to assess the value of Second Life among post-secondary instructors with experience using Second Life as an educational tool. Using Everett Rogers-s diffusion of innovations theory, survey respondents (N = 162), were divided into three adopter categories: innovators, early adopters and the early majority. Respondents were from 15 countries and 25 academic disciplines, indicating the considerable potential this innovation has to be adopted across many different borders and in many areas of academe. Nearly 94% of respondents said they plan to use Second Life again as an educational tool. However, no significant differences were found in instructors- levels of satisfaction with Second Life as an educational tool or their perceived effect on student learning across adopter categories. On the other hand, instructors who conducted class fully in Second Life were significantly more satisfied than those who used Second Life as only a small supplement to a real-world class. Overall, personal interest factors, rather than interpersonal communication factors, most influenced respondents- decision to adopt Second Life as an educational tool. In light of these findings, theoretical implications are discussed and practical suggestions are provided.

Keywords: Second Life, Virtual Worlds, Educational Technology, Diffusion of Innovations

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Authors: Vickneswaran Jeyabalan, Andrews Samraj, Loo Chu Kiong

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The noteworthy point in the advancement of Brain Machine Interface (BMI) research is the ability to accurately extract features of the brain signals and to classify them into targeted control action with the easiest procedures since the expected beneficiaries are of disabled. In this paper, a new feature extraction method using the combination of adaptive band pass filters and adaptive autoregressive (AAR) modelling is proposed and applied to the classification of right and left motor imagery signals extracted from the brain. The introduction of the adaptive bandpass filter improves the characterization process of the autocorrelation functions of the AAR models, as it enhances and strengthens the EEG signal, which is noisy and stochastic in nature. The experimental results on the Graz BCI data set have shown that by implementing the proposed feature extraction method, a LDA and SVM classifier outperforms other AAR approaches of the BCI 2003 competition in terms of the mutual information, the competition criterion, or misclassification rate.

Keywords: Adaptive autoregressive, adaptive bandpass filter, brain machine Interface, EEG, motor imaginary.

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Authors: Abdulhamit Donder, Erhan Ilhan Konukseven

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Grinding operation is performed in order to obtain desired surfaces precisely in machining process. The needed relative motion between the cutting tool and the workpiece is generally created either by the movement of the cutting tool or by the movement of the workpiece or by the movement of both of them as in our case. For all these cases, the coherence level between the movements and the interaction forces is a key influential parameter for efficient grinding. Therefore, in this work, spectral coherence analysis has been performed to investigate the coherence level between grinding interaction forces and the movement of the workpiece on our robotic-grinding experimental setup in METU Mechatronics Laboratory.

Keywords: Coherence analysis, correlation, FFT, grinding, Hanning window, machining, Piezo actuator, reverse arrangements test, spectral analysis.

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Authors: Ahmed Abdulla Ashlam, Atta Badii, Frederic Stahl

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In recent years, SQL injection attacks have been identified as being prevalent against web applications. They affect network security and user data, which leads to a considerable loss of money and data every year. This paper presents the use of classification algorithms in machine learning using a method to classify the login data filtering inputs into "SQLi" or "Non-SQLi,” thus increasing the reliability and accuracy of results in terms of deciding whether an operation is an attack or a valid operation. A method as a Web-App is developed for auto-generated data replication to provide a twin of the targeted data structure. Shielding against SQLi attacks (WebAppShield) that verifies all users and prevents attackers (SQLi attacks) from entering and or accessing the database, which the machine learning module predicts as "Non-SQLi", has been developed. A special login form has been developed with a special instance of the data validation; this verification process secures the web application from its early stages. The system has been tested and validated, and up to 99% of SQLi attacks have been prevented.

Keywords: SQL injection, attacks, web application, accuracy, database, WebAppShield.

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Authors: Damith Senanayake, Lakmal Muthugama, Laksheen Mendis, Tiroshan Madushanka

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Customer churn prediction is one of the most useful areas of study in customer analytics. Due to the enormous amount of data available for such predictions, machine learning and data mining have been heavily used in this domain. There exist many machine learning algorithms directly applicable for the problem of customer churn prediction, and here, we attempt to experiment on a novel approach by using a cognitive learning based technique in an attempt to improve the results obtained by using a combination of supervised learning methods, with cognitive unsupervised learning methods.

Keywords: Growing Self Organizing Maps, Kernel Methods, Churn Prediction.

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Authors: S. K. Aruna, S. Chitra

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This research paper presents a framework for classifying Magnetic Resonance Imaging (MRI) images for Dementia. Dementia, an age-related cognitive decline is indicated by degeneration of cortical and sub-cortical structures. Characterizing morphological changes helps understand disease development and contributes to early prediction and prevention of the disease. Modelling, that captures the brain’s structural variability and which is valid in disease classification and interpretation is very challenging. Features are extracted using Gabor filter with 0, 30, 60, 90 orientations and Gray Level Co-occurrence Matrix (GLCM). It is proposed to normalize and fuse the features. Independent Component Analysis (ICA) selects features. Support Vector Machine (SVM) classifier with different kernels is evaluated, for efficiency to classify dementia. This study evaluates the presented framework using MRI images from OASIS dataset for identifying dementia. Results showed that the proposed feature fusion classifier achieves higher classification accuracy.

Keywords: Magnetic resonance imaging, dementia, Gabor filter, gray level co-occurrence matrix, support vector machine.

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Authors: Norah Almubairik, Wadha Almattar, Amani Alqarni

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Social media applications are increasingly being used in our everyday communications. These applications utilise end-to-end encryption mechanisms which make them suitable tools for criminals to exchange messages. These messages are preserved in the volatile memory until the device is restarted. Therefore, volatile forensics has become an important branch of digital forensics. In this study, the WormHex tool was developed to inspect the memory dump files for Windows and Mac based workstations. The tool supports digital investigators by enabling them to extract valuable data written in Arabic and English through web-based WhatsApp and Twitter applications. The results confirm that social media applications write their data into the memory, regardless of the operating system running the application, with there being no major differences between Windows and Mac.

Keywords: Volatile memory, REGEX, digital forensics, memory acquisition

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Authors: Hany Osman, M. F. Baki

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We address the balancing problem of transfer lines in this paper to find the optimal line balancing that minimizes the nonproductive time. We focus on the tool change time and face orientation change time both of which influence the makespane. We consider machine capacity limitations and technological constraints associated with the manufacturing process of auto cylinder heads. The problem is represented by a mixed integer programming model that aims at distributing the design features to workstations and sequencing the machining processes at a minimum non-productive time. The proposed model is solved by an algorithm established using linearization schemes and Benders- decomposition approach. The experiments show the efficiency of the algorithm in reaching the exact solution of small and medium problem instances at reasonable time.

Keywords: Transfer line balancing, Benders' decomposition, Linearization.

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Authors: A.V.Naresh Babu, S.Sivanagaraju

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A considerable progress has been achieved in transient stability analysis (TSA) with various FACTS controllers. But, all these controllers are associated with single transmission line. This paper is intended to discuss a new approach i.e. a multi-line FACTS controller which is interline power flow controller (IPFC) for TSA of a multi-machine power system network. A mathematical model of IPFC, termed as power injection model (PIM) presented and this model is incorporated in Newton-Raphson (NR) power flow algorithm. Then, the reduced admittance matrix of a multi-machine power system network for a three phase fault without and with IPFC is obtained which is required to draw the machine swing curves. A general approach based on L-index has also been discussed to find the best location of IPFC to reduce the proximity to instability of a power system. Numerical results are carried out on two test systems namely, 6-bus and 11-bus systems. A program in MATLAB has been written to plot the variation of generator rotor angle and speed difference curves without and with IPFC for TSA and also a simple approach has been presented to evaluate critical clearing time for test systems. The results obtained without and with IPFC are compared and discussed.

Keywords: Flexible alternating current transmission system (FACTS), first swing stability, interline power flow controller (IPFC), power injection model (PIM).

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Authors: Gehad S. Kaseb, Mona F. Ahmed

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Arabic Sentiment Analysis (SA) is one of the most common research fields with many open areas. This paper proposes different pre-processing steps and a modified methodology to improve the accuracy using normal Support Vector Machine (SVM) classification. The paper works on two datasets, Arabic Sentiment Tweets Dataset (ASTD) and Extended Arabic Tweets Sentiment Dataset (Extended-ATSD), which are publicly available for academic use. The results show that the classification accuracy approaches 86%.

Keywords: Arabic, hybrid classification, sentiment analysis, tweets.

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Authors: Gabriela-Alina Dumitrel, Teodor Todinca, Carmen Holotescu, Cosmina-Mariana Militaru

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The paper presents a computational tool developed for the evaluation of technical and economic advantages of an innovative cleaning and conditioning technology of fluidized bed steam/oxygen gasifiers outlet product gas. This technology integrates into a single unit the steam gasification of biomass and the hot gas cleaning and conditioning system. Both components of the computational tool, process flowsheet and economic evaluator, have been developed under IPSEpro software. The economic model provides information that can help potential users, especially small and medium size enterprises acting in the regenerable energy field, to decide the optimal scale of a plant and to better understand both potentiality and limits of the system when applied to a wide range of conditions.

Keywords: biomass, CHP units, economic evaluation, gasification.

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Authors: Ammar Ayad Issa Al-Rifaie, Zuhreya Muazu, Maysam Ali Abdulwahid, Dermot Gleeson

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The aim of this audit was to examine the efficiency of alcohol history documentation and screening for hazardous drinkers at the Medical Admission Unit (MAU) of Northern General Hospital (NGH), Sheffield, to identify any potential for enhancing clinical practice. Data were collected from medical clerking sheets, ICE system and directly from 82 patients by three junior medical doctors using both CAGE questionnaire and AUDIT-C tool for newly admitted patients to MAU in NGH, in the period between January and March 2015. Alcohol consumption was documented in around two-third of the patient sample and this was documented fairly accurately by health care professionals. Some used subjective words such as 'social drinking' in the alcohol units’ section of the history. CAGE questionnaire was applied to only four patients and none of the patients had documented advice, education or referral to an alcohol liaison team. AUDIT-C tool had identified 30.4%, while CAGE 10.9%, of patients admitted to the NGH MAU as hazardous drinkers. The amount of alcohol the patient consumes positively correlated with the score of AUDIT-C (Pearson correlation 0.83). Re-audit is planned to be carried out after integrating AUDIT-C tool as labels in the notes and presenting a brief teaching session to junior doctors. Alcohol misuse screening is not adequately undertaken and no appropriate action is being offered to hazardous drinkers. CAGE questionnaire is poorly applied to patients and when satisfactory and adequately used has low sensitivity to detect hazardous drinkers in comparison with AUDIT-C tool. Re-audit of alcohol screening practice after introducing AUDIT-C tool in clerking sheets (as labels) is required to compare the findings and conclude the audit cycle.

Keywords: Alcohol screening, AUDIT-C, CAGE, Hazardous drinking.

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Authors: Asif Ekbal, Sivaji Bandyopadhyay

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Named Entity Recognition (NER) aims to classify each word of a document into predefined target named entity classes and is now-a-days considered to be fundamental for many Natural Language Processing (NLP) tasks such as information retrieval, machine translation, information extraction, question answering systems and others. This paper reports about the development of a NER system for Bengali and Hindi using Support Vector Machine (SVM). Though this state of the art machine learning technique has been widely applied to NER in several well-studied languages, the use of this technique to Indian languages (ILs) is very new. The system makes use of the different contextual information of the words along with the variety of features that are helpful in predicting the four different named (NE) classes, such as Person name, Location name, Organization name and Miscellaneous name. We have used the annotated corpora of 122,467 tokens of Bengali and 502,974 tokens of Hindi tagged with the twelve different NE classes 1, defined as part of the IJCNLP-08 NER Shared Task for South and South East Asian Languages (SSEAL) 2. In addition, we have manually annotated 150K wordforms of the Bengali news corpus, developed from the web-archive of a leading Bengali newspaper. We have also developed an unsupervised algorithm in order to generate the lexical context patterns from a part of the unlabeled Bengali news corpus. Lexical patterns have been used as the features of SVM in order to improve the system performance. The NER system has been tested with the gold standard test sets of 35K, and 60K tokens for Bengali, and Hindi, respectively. Evaluation results have demonstrated the recall, precision, and f-score values of 88.61%, 80.12%, and 84.15%, respectively, for Bengali and 80.23%, 74.34%, and 77.17%, respectively, for Hindi. Results show the improvement in the f-score by 5.13% with the use of context patterns. Statistical analysis, ANOVA is also performed to compare the performance of the proposed NER system with that of the existing HMM based system for both the languages.

Keywords: Named Entity (NE), Named Entity Recognition (NER), Support Vector Machine (SVM), Bengali, Hindi.

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Authors: Khaled Abduesslam. M, Mohammed Ali, Basher H Alsdai, Muhammad Nizam, Inayati

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This paper presents the voltage problem location classification using performance of Least Squares Support Vector Machine (LS-SVM) and Learning Vector Quantization (LVQ) in electrical power system for proper voltage problem location implemented by IEEE 39 bus New- England. The data was collected from the time domain simulation by using Power System Analysis Toolbox (PSAT). Outputs from simulation data such as voltage, phase angle, real power and reactive power were taken as input to estimate voltage stability at particular buses based on Power Transfer Stability Index (PTSI).The simulation data was carried out on the IEEE 39 bus test system by considering load bus increased on the system. To verify of the proposed LS-SVM its performance was compared to Learning Vector Quantization (LVQ). The results showed that LS-SVM is faster and better as compared to LVQ. The results also demonstrated that the LS-SVM was estimated by 0% misclassification whereas LVQ had 7.69% misclassification.

Keywords: IEEE 39 bus, Least Squares Support Vector Machine, Learning Vector Quantization, Voltage Collapse.

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Authors: T. C. Manjunath, C. Ardil

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This paper presents an efficient method of obtaining a straight-line motion in the tool configuration space using an articulated robot between two specified points. The simulation results & the implementation results show the effectiveness of the method.

Keywords: Bounded deviation algorithm, Straight line motion, Tool configuration space, Joint space, TCV.

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Authors: Praveen Boinee, Alessandro De Angelis, Gian Luca Foresti

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Ensemble learning algorithms such as AdaBoost and Bagging have been in active research and shown improvements in classification results for several benchmarking data sets with mainly decision trees as their base classifiers. In this paper we experiment to apply these Meta learning techniques with classifiers such as random forests, neural networks and support vector machines. The data sets are from MAGIC, a Cherenkov telescope experiment. The task is to classify gamma signals from overwhelmingly hadron and muon signals representing a rare class classification problem. We compare the individual classifiers with their ensemble counterparts and discuss the results. WEKA a wonderful tool for machine learning has been used for making the experiments.

Keywords: Ensembles, WEKA, Neural networks [NN], SupportVector Machines [SVM], Random Forests [RF].

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Authors: Ma. Rosario Concepcion O. Ang, Luis Caezar Ian K. Panganiban, Charmyne B. Mamador, Oliver Dan G. De Luna, Michael D. Bausas, Joselito P. Cruz

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PhilSHORE is a multi-site, multi-device and multicriteria decision support tool designed to support the development of tidal current energy in the Philippines. Its platform is based on Geographic Information Systems (GIS) which allows for the collection, storage, processing, analyses and display of geospatial data. Combining GIS tools with open source web development applications, PhilSHORE becomes a webGIS-based marine spatial planning tool. To date, PhilSHORE displays output maps and graphs of power and energy density, site suitability and site-device analysis. It enables stakeholders and the public easy access to the results of tidal current energy resource assessments and site suitability analyses. Results of the initial development show that PhilSHORE is a promising decision support tool for ORE project developments.

Keywords: GIS, Site Suitability Analysis, Tidal Current Energy Resource Assessment, WebGIS.

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Authors: Lari Kela, Pekka Vähäoja

Abstract:

Pressure wave velocity in a hydraulic system was determined using piezo pressure sensors without removing fluid from the system. The measurements were carried out in a low pressure range (0.2 – 6 bar) and the results were compared with the results of other studies. This method is not as accurate as measurement with separate measurement equipment, but the fluid is in the actual machine the whole time and the effect of air is taken into consideration if air is present in the system. The amount of air is estimated by calculations and comparisons between other studies. This measurement equipment can also be installed in an existing machine and it can be programmed so that it measures in real time. Thus, it could be used e.g. to control dampers.

Keywords: Bulk modulus, pressure wave, sound velocity.

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