Search results for: machine migration

Authors: Walid M. Adel, Liang Guo-Zhu

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To investigate the characterization of the mechanical properties of composite solid propellant (CSP) based on hydroxyl-terminated polybutadiene (HTPB) at different temperatures and strain rates, uniaxial tensile tests were conducted over a range of temperatures -60 °C to +76 °C and strain rates 0.000164 to 0.328084 s^-1 using a conventional universal testing machine. From the experimental data, it can be noted that the mechanical properties of AP/HTPB propellant are mainly dependent on the applied strain rate and the temperature condition. The stress-strain responses exhibited an initial yielding followed by the viscoelastic phase, which was strongly affected by the strain rate and temperature. It was found that the mechanical properties increased with both increasing strain rate and decreasing temperature. Based on the experimental tests, the master curves of the tensile properties are drawn using predetermined shift factor and the results were discussed. This work is a first step in preliminary investigation the nonlinear viscoelasticity behavior of CSP.

Keywords: AP/HTPB composite solid propellant, mechanical behavior, nonlinear viscoelastic, tensile test, master curves.

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Authors: Jia Xin Low, Keng Wah Choo

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

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

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Authors: Cheng-Hong Yang, Tsung-Mu Shih, Li-Yeh Chuang

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Serial Analysis of Gene Expression is a powerful quantification technique for generating cell or tissue gene expression data. The profile of the gene expression of cell or tissue in several different states is difficult for biologists to analyze because of the large number of genes typically involved. However, feature selection in machine learning can successfully reduce this problem. The method allows reducing the features (genes) in specific SAGE data, and determines only relevant genes. In this study, we used a genetic algorithm to implement feature selection, and evaluate the classification accuracy of the selected features with the K-nearest neighbor method. In order to validate the proposed method, we used two SAGE data sets for testing. The results of this study conclusively prove that the number of features of the original SAGE data set can be significantly reduced and higher classification accuracy can be achieved.

Keywords: Serial Analysis of Gene Expression, Feature selection, Genetic Algorithm, K-nearest neighbor method.

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Authors: K. D. Ro, J. T. Park, J. H. Kim

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This experiment was conducted in attempt of improving hydrodynamic efficiency of the propulsion mechanism by installing a spring to the wing so that the opening angle of the wing in one stroke can be changed automatically, compared to the existing method of fixed maximum opening angle in Weis-Fogh type ship propulsion mechanism. Average thrust coefficient was almost fixed with all velocity ratio with the prototype, but with the spring type, thrust coefficient increased sharply as velocity ratio increased. Average propulsive efficiency was larger with bigger opening angle in the prototype, but in the spring type, the one with smaller spring coefficient had larger value. In the range over 1.0 in velocity ratio where big thrust can be generated, spring type had more than twice of propulsive efficiency increase compared to the prototype.

Keywords: Hydraulic Machine, Propulsion Mechanism, FluidForce, Elastic Wing.

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Authors: Dim En Nyaung, Thin Lai Lai Thein

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

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

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Authors: Hyunchul Ahn, William X. S. Wong

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Corporate credit rating prediction is one of the most important topics, which has been studied by researchers in the last decade. Over the last decade, researchers are pushing the limit to enhance the exactness of the corporate credit rating prediction model by applying several data-driven tools including statistical and artificial intelligence methods. Among them, multiclass support vector machine (MSVM) has been widely applied due to its good predictability. However, heuristics, for example, parameters of a kernel function, appropriate feature and instance subset, has become the main reason for the critics on MSVM, as they have dictate the MSVM architectural variables. This study presents a hybrid MSVM model that is intended to optimize all the parameter such as feature selection, instance selection, and kernel parameter. Our model adopts genetic algorithm (GA) to simultaneously optimize multiple heterogeneous design factors of MSVM.

Keywords: Corporate credit rating prediction, feature selection, genetic algorithms, instance selection, multiclass support vector machines.

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Authors: Sheela Tiwari, R. Naresh, R. Jha

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The paper presents an investigation in to the effect of neural network predictive control of UPFC on the transient stability performance of a multimachine power system. The proposed controller consists of a neural network model of the test system. This model is used to predict the future control inputs using the damped Gauss-Newton method which employs ‘backtracking’ as the line search method for step selection. The benchmark 2 area, 4 machine system that mimics the behavior of large power systems is taken as the test system for the study and is subjected to three phase short circuit faults at different locations over a wide range of operating conditions. The simulation results clearly establish the robustness of the proposed controller to the fault location, an increase in the critical clearing time for the circuit breakers, and an improved damping of the power oscillations as compared to the conventional PI controller.

Keywords: Identification, Neural networks, Predictive control, Transient stability, UPFC.

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Authors: Ziad. Sh. Al Sarraf

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Ultrasonic metal welding has been the subject of ongoing research and development, most recently concentrating on metal joining in miniature devices, for example to allow solder-free wire bonding. As well as at the small scale, there are also opportunities to research the joining of thicker sheet metals and to widen the range of similar and dissimilar materials that can be successfully joined using this technology. This study presents the design, characterisation and test of a lateral-drive ultrasonic metal spot welding device. The ultrasonic metal spot welding horn is modelled using finite element analysis (FEA) and its vibration behaviour is characterised experimentally to ensure ultrasonic energy is delivered effectively to the weld coupon. The welding stack and fixtures are then designed and mounted on a test machine to allow a series of experiments to be conducted for various welding and ultrasonic parameters. Weld strength is subsequently analysed using tensile-shear tests. The results show how the weld strength is particularly sensitive to the combination of clamping force and ultrasonic vibration amplitude of the welding tip, but there are optimal combinations of these and also limits that must be clearly identified.

Keywords: Ultrasonic welding, vibration amplitude, welding force, weld strength.

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Authors: Stephen Akuma, Timothy Ndera

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Information seekers get “lost in hyperspace” due to the voluminous documents updated daily on the internet. Adaptive Hypermedia Systems (AHS) are used to direct learners to their target goals. One of the most common AHS designed to help information seekers to overcome the problem of information overload is the Adaptive Education Hypermedia System (AEHS). However, this paper focuses on AEHS that adopts the learning preference of high school students and deliver learning content according to this preference throughout their learning experience. The research developed a prototype system for predicting students’ learning preference from the Visual, Aural, Read-Write and Kinesthetic (VARK) learning style model and adopting the learning content suitable to their preference. The predicting strength of several classifiers was compared and we found Support Vector Machine (SVM) to be more accurate in predicting learning style based on users’ preferences.

Keywords: Hypermedia, adaptive education, learning style, lesson content, user profile, prediction, feedback, adaptive hypermedia, learning style.

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Authors: Yu Zhang, Pedro Desouza

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Since big data has become substantially more accessible and manageable due to the development of powerful tools for dealing with unstructured data, people are eager to mine information from social media resources that could not be handled in the past. Sentiment analysis, as a novel branch of text mining, has in the last decade become increasingly important in marketing analysis, customer risk prediction and other fields. Scientists and researchers have undertaken significant work in creating and improving their sentiment models. In this paper, we present a concept of selecting appropriate classifiers based on the features and qualities of data sources by comparing the performances of five classifiers with three popular social media data sources: Twitter, Amazon Customer Reviews, and Movie Reviews. We introduced a couple of innovative models that outperform traditional sentiment classifiers for these data sources, and provide insights on how to further improve the predictive power of sentiment analysis. The modeling and testing work was done in R and Greenplum in-database analytic tools.

Keywords: Sentiment Analysis, Social Media, Twitter, Amazon, Data Mining, Machine Learning, Text Mining.

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Authors: Jagath Samarabandu, Xiaoqing Liu

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Using bottom-up image processing algorithms to predict human eye fixations and extract the relevant embedded information in images has been widely applied in the design of active machine vision systems. Scene text is an important feature to be extracted, especially in vision-based mobile robot navigation as many potential landmarks such as nameplates and information signs contain text. This paper proposes an edge-based text region extraction algorithm, which is robust with respect to font sizes, styles, color/intensity, orientations, and effects of illumination, reflections, shadows, perspective distortion, and the complexity of image backgrounds. Performance of the proposed algorithm is compared against a number of widely used text localization algorithms and the results show that this method can quickly and effectively localize and extract text regions from real scenes and can be used in mobile robot navigation under an indoor environment to detect text based landmarks.

Keywords: Landmarks, mobile robot navigation, scene text, text localization and extraction.

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Authors: R.K. Behera, S.Behera

Abstract:

In a wind power generator using doubly fed induction generator (DFIG), the three-phase pulse width modulation (PWM) voltage source converter (VSC) is used as grid side converter (GSC) and rotor side converter (RSC). The standard linear control laws proposed for GSC provides not only instablity against comparatively large-signal disturbances, but also the problem of stability due to uncertainty of load and variations in parameters. In this paper, a nonlinear controller is designed for grid side converter (GSC) of a DFIG for wind power application. The nonlinear controller is designed based on the input-output feedback linearization control method. The resulting closed-loop system ensures a sufficient stability region, make robust to variations in circuit parameters and also exhibits good transient response. Computer simulations and experimental results are presented to confirm the effectiveness of the proposed control strategy.

Keywords: Doubly fed Induction Generator, grid side converter, machine side converter, dc link, feedback linearization.

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Authors: Edward Guillén, Jeisson Sánchez, Carlos Omar Ramos

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In recent years, a wide variety of applications are developed with Support Vector Machines -SVM- methods and Artificial Neural Networks -ANN-. In general, these methods depend on intrusion knowledge databases such as KDD99, ISCX, and CAIDA among others. New classes of detectors are generated by machine learning techniques, trained and tested over network databases. Thereafter, detectors are employed to detect anomalies in network communication scenarios according to user’s connections behavior. The first detector based on training dataset is deployed in different real-world networks with mobile and non-mobile devices to analyze the performance and accuracy over static detection. The vulnerabilities are based on previous work in telemedicine apps that were developed on the research group. This paper presents the differences on detections results between some network scenarios by applying traditional detectors deployed with artificial neural networks and support vector machines.

Keywords: Anomaly detection, back-propagation neural networks, network intrusion detection systems, support vector machines.

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Authors: R. Usubamatov

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The velocity of a moving point in a general path is the vector quantity, which has both magnitude and direction. The magnitude or the direction of the velocity vector can change over time as a result of acceleration that the time rate of velocity changes. Acceleration analysis is important because inertial forces and inertial torques are proportional to rectilinear and angular accelerations accordingly. The loads must be determined in advance to ensure that a machine is adequately designed to handle these dynamic loads. For planar motion, the vector direction of acceleration is commonly separated into two elements: tangential and centripetal or radial components of a point on a rotating body. All textbooks in physics, kinematics and dynamics of machinery consider the magnitude of a radial acceleration at condition when a point rotates with a constant angular velocity and it means without acceleration. The magnitude of the tangential acceleration considered on a basis of acceleration for a rotating point. Such condition of presentation of magnitudes for two components of acceleration logically and mathematically is not correct and may cause further confusion in calculation. This paper presents new analytical expressions of the radial and absolute accelerations of a rotating point with acceleration and covers the gap in theoretical study of acceleration analysis.

Keywords: acceleration analysis, kinematics of mechanisms.

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Authors: M. Shadmand

Abstract:

The curing process of insulation system for electrical machines plays a determinative role for its durability and reliability. Polar structure of insulating resin molecules and used filler of insulation system can be taken as an occasion to leverage it to enhance overall characteristics of insulation system, mechanically and electrically. The curing process regime for insulating system plays an important role for its mechanical and electrical characteristics by arranging the polymerization of chain structure for resin. In this research, the effect of electrical field application on in-curing insulating system for Global Vacuum Pressurized Impregnation (GVPI) system for traction motor was considered by performing the dissipation factor, polarization and de-polarization current (PDC) and voltage endurance (aging) measurements on sample test objects. Outcome results depicted obvious improvement in mechanical strength of the insulation system as well as higher electrical characteristics with routing and long-time (aging) electrical tests. Coming together, polarization of insulation system during curing process would enhance the machine life time. 

Keywords: Insulation system, GVPI, PDC, aging.

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Authors: Galina Mihaleva, C. Koh

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Fashion, like many other design fields, undergoes numerous evolutions throughout the ages. This paper aims to recognize and evaluate the significance of advance technology in fashion design and examine how it changes the role of modern fashion designers by modifying the creation process. It also touches on how modern culture is involved in such developments and how it affects fashion design in terms of conceptualizing and fabrication. The methodology used is through surveying the various examples of technological applications to fashion design and drawing parallels between what was achievable then and what is achievable now. By comparing case studies, existing fashion design examples and crafting method experimentations; we then spot patterns in which to predict the direction of future developments in the field. A breakdown on the elements of technology in fashion design helps us understand the driving force behind such a trend. The results from explorations in the paper have shown that there is an observed pattern of a distinct increase in interest and progress in the field of fashion technology, which leads to the birth of hybrid crafting methods. In conclusion, it is shown that as fashion technology continues to evolve, their role in clothing crafting becomes more prominent and grows far beyond the humble sewing machine.

Keywords: Fashion design, functional aesthetics, smart textiles, 3D printing.

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Authors: R. Babisaraswathi, N. Shanthi, S. S. Kiruthika

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Web search engines are designed to retrieve and extract the information in the web databases and to return dynamic web pages. The Semantic Web is an extension of the current web in which it includes semantic content in web pages. The main goal of semantic web is to promote the quality of the current web by changing its contents into machine understandable form. Therefore, the milestone of semantic web is to have semantic level information in the web. Nowadays, people use different keyword- based search engines to find the relevant information they need from the web. But many of the words are polysemous. When these words are used to query a search engine, it displays the Search Result Records (SRRs) with different meanings. The SRRs with similar meanings are grouped together based on Word Sense Disambiguation (WSD). In addition to that semantic annotation is also performed to improve the efficiency of search result records. Semantic Annotation is the process of adding the semantic metadata to web resources. Thus the grouped SRRs are annotated and generate a summary which describes the information in SRRs. But the automatic semantic annotation is a significant challenge in the semantic web. Here ontology and knowledge based representation are used to annotate the web pages.

Keywords: Ontology, Semantic Web, WordNet, Word Sense Disambiguation.

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Authors: A.M. Vural, K.C. Bayindir

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Back-to-back static synchronous compensator (BtBSTATCOM) consists of two back-to-back voltage-source converters (VSC) with a common DC link in a substation. This configuration extends the capabilities of conventional STATCOM that bidirectional active power transfer from one bus to another is possible. In this paper, VSCs are designed in quasi multi-pulse form in which GTOs are triggered only once per cycle in PSCAD/EMTDC. The design details of VSCs as well as gate switching circuits and controllers are fully represented. Regulation modes of BtBSTATCOM are verified and tested on a multi-machine power system through different simulation cases. The results presented in the form of typical time responses show that practical PI controllers are almost robust and stable in case of start-up, set-point change, and line faults.

Keywords: Flexible AC Transmission Systems (FACTS), Backto-Back Static Synchronous Compensator (BtB-STATCOM), quasi multi-pulse voltage source converter, active power transfer; voltage control.

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Authors: Parvinder S. Sandhu, Sunil Khullar, Satpreet Singh, Simranjit K. Bains, Manpreet Kaur, Gurvinder Singh

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Fault-proneness of a software module is the probability that the module contains faults. To predict faultproneness of modules different techniques have been proposed which includes statistical methods, machine learning techniques, neural network techniques and clustering techniques. The aim of proposed study is to explore whether metrics available in the early lifecycle (i.e. requirement metrics), metrics available in the late lifecycle (i.e. code metrics) and metrics available in the early lifecycle (i.e. requirement metrics) combined with metrics available in the late lifecycle (i.e. code metrics) can be used to identify fault prone modules using Genetic Algorithm technique. This approach has been tested with real time defect C Programming language datasets of NASA software projects. The results show that the fusion of requirement and code metric is the best prediction model for detecting the faults as compared with commonly used code based model.

Keywords: Genetic Algorithm, Fault Proneness, Software Faultand Software Quality.

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Authors: K. P. Oyeduntan, K. Oshinubi

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Nigeria is referred as the ‘Giant of Africa’ due to high population, land mass and large economy. However, it still trails far behind many smaller economies in the continent in terms of maritime operations. As we have seen that the maritime industry is the sparkplug for national growth, because it houses the most crucial infrastructure that generates wealth for a nation, it is worrisome that a nation with six seaports lag in maritime activities. In this research, we have studied how the Gross Domestic Product (GDP) of the maritime transport influences the Nigerian economy. To do this, we applied Simple Linear Regression (SLR), Support Vector Machine (SVM), Polynomial Regression Model (PRM), Generalized Additive Model (GAM) and Generalized Linear Mixed Model (GLMM) to model the relationship between the nation’s Total GDP (TGDP) and the Maritime Transport GDP (MGDP) using a time series data of 20 years. The result showed that the MGDP is statistically significant to the Nigerian economy. Amongst the statistical tool applied, the PRM of order 4 describes the relationship better when compared to other methods. The recommendations presented in this study will guide policy makers and help improve the economy of Nigeria.

Keywords: Economy, GDP, maritime transport, port, regression.

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Authors: Mian Tahir Aftab, Muhammad Umer, Riaz Ahmad

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This article proposes an Ant Colony Optimization (ACO) metaheuristic to minimize total makespan for scheduling a set of jobs and assign workers for uniformly related parallel machines. An algorithm based on ACO has been developed and coded on a computer program Matlab®, to solve this problem. The paper explains various steps to apply Ant Colony approach to the problem of minimizing makespan for the worker assignment & jobs scheduling problem in a parallel machine model and is aimed at evaluating the strength of ACO as compared to other conventional approaches. One data set containing 100 problems (12 Jobs, 03 machines and 10 workers) which is available on internet, has been taken and solved through this ACO algorithm. The results of our ACO based algorithm has shown drastically improved results, especially, in terms of negligible computational effort of CPU, to reach the optimal solution. In our case, the time taken to solve all 100 problems is even lesser than the average time taken to solve one problem in the data set by other conventional approaches like GA algorithm and SPT-A/LMC heuristics.

Keywords: Ant Colony Optimization (ACO), Genetic algorithms (GA), Makespan, SPT-A/LMC heuristic.

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Authors: Gee-Sern Jison Hsu

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A fusion classifier composed of two modules, one made by a hidden Markov model (HMM) and the other by a support vector machine (SVM), is proposed to recognize faces with pose variations in open-set recognition settings. The HMM module captures the evolution of facial features across a subject-s face using the subject-s facial images only, without referencing to the faces of others. Because of the captured evolutionary process of facial features, the HMM module retains certain robustness against pose variations, yielding low false rejection rates (FRR) for recognizing faces across poses. This is, however, on the price of poor false acceptance rates (FAR) when recognizing other faces because it is built upon withinclass samples only. The SVM module in the proposed model is developed following a special design able to substantially diminish the FAR and further lower down the FRR. The proposed fusion classifier has been evaluated in performance using the CMU PIE database, and proven effective for open-set face recognition with pose variations. Experiments have also shown that it outperforms the face classifier made by HMM or SVM alone.

Keywords: Face recognition, open-set identification, hidden Markov model, support vector machines.

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Authors: J. Chen, N. Hundal

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Conventional machining is a form of subtractive manufacturing, in which a collection of material-working processes utilizing power-driven machine tools are used to remove undesired material to achieve a desired geometry. This paper presents an approach for comparison between turning center and vertical machining center by optimization of cutting parameters at cylindrical workpieces leading to minimum surface roughness by using taguchi methodology. Aluminum alloy was taken to conduct experiments due to its unique high strength-weight ratio that is maintained at elevated temperatures and their exceptional corrosion resistance. During testing, the effects of the cutting parameters on the surface roughness were investigated. Additionally, by using taguchi methodology for each of the cutting parameters (spindle speed, depth of cut, insert diameter, and feed rate) minimum surface roughness for the process of turn-milling was determined according to the cutting parameters. A confirmation experiment demonstrates the effectiveness of taguchi method.

Keywords: Surface roughness, taguchi parameter design, turning center, turn-milling operations, vertical machining center.

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Authors: Damola O. Lawal, David W. Gresty, Diane E. Gan, Louise Hewitt

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This paper investigates the feasibility of using a programmable USB such as the O.MG Cable to perform a file tampering attack. Here, the O.MG Cable, an apparently harmless mobile device charger is used in an unauthorised way, to alter the content of a file (an accounts record-January_Contributions.xlsx). The aim is to determine if a forensics analyst can reliably determine who has altered the target file; the O.MG Cable or the user of the machine. This work highlights some of the traces of the O.MG Cable left behind on the target computer itself such as the Product ID (PID) and Vendor ID (ID). Also discussed is the O.MG Cable’s behaviour during the experiments. We determine if a forensics analyst could identify if any evidence has been left behind by the programmable device on the target file once it has been removed from the computer to establish if the analyst would be able to link the traces left by the O.MG Cable to the file tampering. It was discovered that the forensic analyst might mistake the actions of the O.MG Cable for the computer users. Experiments carried out in this work could further the discussion as to whether an innocent user could be punished for the unauthorised changes made by a programmable device.

Keywords: O.MG Cable, programmable USB, file tampering attack, digital evidence credibility, miscarriage of justice, cyber fraud.

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Authors: Alicia Heraz, Claude Frasson

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This paper investigates how the use of machine learning techniques can significantly predict the three major dimensions of learner-s emotions (pleasure, arousal and dominance) from brainwaves. This study has adopted an experimentation in which participants were exposed to a set of pictures from the International Affective Picture System (IAPS) while their electrical brain activity was recorded with an electroencephalogram (EEG). The pictures were already rated in a previous study via the affective rating system Self-Assessment Manikin (SAM) to assess the three dimensions of pleasure, arousal, and dominance. For each picture, we took the mean of these values for all subjects used in this previous study and associated them to the recorded brainwaves of the participants in our study. Correlation and regression analyses confirmed the hypothesis that brainwave measures could significantly predict emotional dimensions. This can be very useful in the case of impassive, taciturn or disabled learners. Standard classification techniques were used to assess the reliability of the automatic detection of learners- three major dimensions from the brainwaves. We discuss the results and the pertinence of such a method to assess learner-s emotions and integrate it into a brainwavesensing Intelligent Tutoring System.

Keywords: Algorithms, brainwaves, emotional dimensions, performance.

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Authors: A.H.M.A.Rahim

Abstract:

The impact of fixed speed squirrel cage type as well as variable speed doubly fed induction generators (DFIG) on dynamic performance of a multimachine power system has been investigated. Detailed models of the various components have been presented and the integration of asynchronous and synchronous generators has been carried out through a rotor angle based transform. Simulation studies carried out considering the conventional dynamic model of squirrel cage asynchronous generators show that integration, as such, could degrade to the AC system performance transiently. This article proposes a frequency or power controller which can effectively control the transients and restore normal operation of fixed speed induction generator quickly. Comparison of simulation results between classical cage and doubly-fed induction generators indicate that the doubly fed induction machine is more adaptable to multimachine AC system. Frequency controller installed in the DFIG system can also improve its transient profile.

Keywords: Doubly-fed generator, Induction generator, Multimachine system modeling, Wind energy systems

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Authors: Scott N. Gerard, Aliza Heching, Susann M. Keohane, Samuel S. Adams

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The world-wide population of people over 60 years of age is growing rapidly. The explosion is placing increasingly onerous demands on individual families, multiple industries and entire countries. Current, human-intensive approaches to eldercare are not sustainable, but IoT and AI technologies can help. The Knowledge Reactor (KR) is a contextual, data fusion engine built to address this and other similar problems. It fuses and centralizes IoT and System of Record/Engagement data into a reactive knowledge graph. Cognitive applications and services are constructed with its multiagent architecture. The KR can scale-up and scaledown, because it exploits container-based, horizontally scalable services for graph store (JanusGraph) and pub-sub (Kafka) technologies. While the KR can be applied to many domains that require IoT and AI technologies, this paper describes how the KR specifically supports the challenging domain of cognitive eldercare. Rule- and machine learning-based analytics infer activities of daily living from IoT sensor readings. KR scalability, adaptability, flexibility and usability are demonstrated.

Keywords: Ambient sensing, AI, artificial intelligence, eldercare, IoT, internet of things, knowledge graph.

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Authors: Fatemeh Amiri, Caro Lucas, Nasser Yazdani

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As the network based technologies become omnipresent, demands to secure networks/systems against threat increase. One of the effective ways to achieve higher security is through the use of intrusion detection systems (IDS), which are a software tool to detect anomalous in the computer or network. In this paper, an IDS has been developed using an improved machine learning based algorithm, Locally Linear Neuro Fuzzy Model (LLNF) for classification whereas this model is originally used for system identification. A key technical challenge in IDS and LLNF learning is the curse of high dimensionality. Therefore a feature selection phase is proposed which is applicable to any IDS. While investigating the use of three feature selection algorithms, in this model, it is shown that adding feature selection phase reduces computational complexity of our model. Feature selection algorithms require the use of a feature goodness measure. The use of both a linear and a non-linear measure - linear correlation coefficient and mutual information- is investigated respectively

Keywords: anomaly Detection, feature selection, Locally Linear Neuro Fuzzy (LLNF), Mutual Information (MI), liner correlation coefficient.

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Authors: Pradeep Singh, Shrish Verma

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Software fault prediction models are created by using the source code, processed metrics from the same or previous version of code and related fault data. Some company do not store and keep track of all artifacts which are required for software fault prediction. To construct fault prediction model for such company, the training data from the other projects can be one potential solution. Earlier we predicted the fault the less cost it requires to correct. The training data consists of metrics data and related fault data at function/module level. This paper investigates fault predictions at early stage using the cross-project data focusing on the design metrics. In this study, empirical analysis is carried out to validate design metrics for cross project fault prediction. The machine learning techniques used for evaluation is Naïve Bayes. The design phase metrics of other projects can be used as initial guideline for the projects where no previous fault data is available. We analyze seven datasets from NASA Metrics Data Program which offer design as well as code metrics. Overall, the results of cross project is comparable to the within company data learning.

Keywords: Software Metrics, Fault prediction, Cross project, Within project.

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Authors: Safa Boudhaouia, Mohamed Amen Gahbiche, Eliane Giraud, Wacef Ben Salem, Philippe Dal Santo

Abstract:

Multi-point forming (MPF) and asymmetric incremental forming (ISF) are two flexible processes for sheet metal manufacturing. To take advantages of these two techniques, a hybrid process has been developed: The Multipoint Incremental Forming (MPIF). This process accumulates at once the advantages of each of these last mentioned forming techniques, which makes it a very interesting and particularly an efficient process for single, small, and medium series production. In this paper, an experimental and a numerical investigation of this technique are presented. To highlight the flexibility of this process and its capacity to manufacture standard and complex shapes, several pieces were produced by using MPIF. The forming experiments are performed on a 3-axis CNC machine. Moreover, a numerical model of the MPIF process has been implemented in ABAQUS and the analysis showed a good agreement with experimental results in terms of deformed shape. Furthermore, the use of an elastomeric interpolator allows avoiding classical local defaults like dimples, which are generally caused by the asymmetric contact and also improves the distribution of residual strain. Future works will apply this approach to other alloys used in aeronautic or automotive applications.

Keywords: Incremental forming, numerical simulation, MPIF, multipoint forming.

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