Search results for: Support Vector Machine Training

Authors: G. Candel, D. Naccache

Abstract:

t-SNE is an embedding method that the data science community has widely used. It helps two main tasks: to display results by coloring items according to the item class or feature value; and for forensic, giving a first overview of the dataset distribution. Two interesting characteristics of t-SNE are the structure preservation property and the answer to the crowding problem, where all neighbors in high dimensional space cannot be represented correctly in low dimensional space. t-SNE preserves the local neighborhood, and similar items are nicely spaced by adjusting to the local density. These two characteristics produce a meaningful representation, where the cluster area is proportional to its size in number, and relationships between clusters are materialized by closeness on the embedding. This algorithm is non-parametric. The transformation from a high to low dimensional space is described but not learned. Two initializations of the algorithm would lead to two different embedding. In a forensic approach, analysts would like to compare two or more datasets using their embedding. A naive approach would be to embed all datasets together. However, this process is costly as the complexity of t-SNE is quadratic, and would be infeasible for too many datasets. Another approach would be to learn a parametric model over an embedding built with a subset of data. While this approach is highly scalable, points could be mapped at the same exact position, making them indistinguishable. This type of model would be unable to adapt to new outliers nor concept drift. This paper presents a methodology to reuse an embedding to create a new one, where cluster positions are preserved. The optimization process minimizes two costs, one relative to the embedding shape and the second relative to the support embedding’ match. The embedding with the support process can be repeated more than once, with the newly obtained embedding. The successive embedding can be used to study the impact of one variable over the dataset distribution or monitor changes over time. This method has the same complexity as t-SNE per embedding, and memory requirements are only doubled. For a dataset of n elements sorted and split into k subsets, the total embedding complexity would be reduced from O(n2) to O(n2/k), and the memory requirement from n2 to 2(n/k)2 which enables computation on recent laptops. The method showed promising results on a real-world dataset, allowing to observe the birth, evolution and death of clusters. The proposed approach facilitates identifying significant trends and changes, which empowers the monitoring high dimensional datasets’ dynamics.

Keywords: Concept drift, data visualization, dimension reduction, embedding, monitoring, reusability, t-SNE, unsupervised learning.

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Authors: M.V Rajesh, Archana R, A Unnikrishnan, R Gopikakumari, Jeevamma Jacob

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The System Identification problem looks for a suitably parameterized model, representing a given process. The parameters of the model are adjusted to optimize a performance function based on error between the given process output and identified process output. The linear system identification field is well established with many classical approaches whereas most of those methods cannot be applied for nonlinear systems. The problem becomes tougher if the system is completely unknown with only the output time series is available. It has been reported that the capability of Artificial Neural Network to approximate all linear and nonlinear input-output maps makes it predominantly suitable for the identification of nonlinear systems, where only the output time series is available. [1][2][4][5]. The work reported here is an attempt to implement few of the well known algorithms in the context of modeling of nonlinear systems, and to make a performance comparison to establish the relative merits and demerits.

Keywords: Multilayer neural networks, Radial Basis Functions, Clustering algorithm, Back Propagation training, Extended Kalmanfiltering, Mean Square Error, Nonlinear Modeling, Cramer RaoLower Bound.

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Authors: O. Kadri, M. D. Mouss, L.H. Mouss, F. Merah

Abstract:

This paper proposes a novel hybrid algorithm for feature selection based on a binary ant colony and SVM. The final subset selection is attained through the elimination of the features that produce noise or, are strictly correlated with other already selected features. Our algorithm can improve classification accuracy with a small and appropriate feature subset. Proposed algorithm is easily implemented and because of use of a simple filter in that, its computational complexity is very low. The performance of the proposed algorithm is evaluated through a real Rotary Cement kiln dataset. The results show that our algorithm outperforms existing algorithms.

Keywords: Binary Ant Colony algorithm, Support VectorMachine, feature selection, classification.

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Authors: Felipe V. Díaz, Claudio A. Mammana, Armando P. M. Guidobono, Raúl E. Bolmaro

Abstract:

Through a proper analysis of residual strain and stress distributions obtained at the surface of high speed milled specimens of AA 6082–T6 aluminium alloy, the performance of an improved indentation method is evaluated. This method integrates a special device of indentation to a universal measuring machine. The mentioned device allows introducing elongated indents allowing to diminish the absolute error of measurement. It must be noted that the present method offers the great advantage of avoiding both the specific equipment and highly qualified personnel, and their inherent high costs. In this work, the cutting tool geometry and high speed parameters are selected to introduce reduced plastic damage. Through the variation of the depth of cut, the stability of the shapes adopted by the residual strain and stress distributions is evaluated. The results show that the strain and stress distributions remain unchanged, compressive and small. Moreover, these distributions reveal a similar asymmetry when the gradients corresponding to conventional and climb cutting zones are compared.

Keywords: Residual strain, residual stress, high speed milling, indentation methods, aluminium alloys.

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Authors: Ali Y Al-Attraqchi, P. Rajeev, M. Javad Hashemi, Riadh Al-Mahaidi

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This paper presents a probabilistic incremental dynamic analysis (IDA) of a full reinforced concrete building subjected to column loss scenario for the assessment of progressive collapse. The IDA is chosen to explicitly account for uncertainties in loads and system capacity. Fragility curves are developed to predict the probability of progressive collapse given the loss of one or more columns. At a broader scale, it will also provide critical information needed to support the development of a new generation of design codes that attempt to explicitly quantify structural robustness.

Keywords: Incremental dynamic analysis, progressive collapse, structural engineering, pushdown analysis.

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Authors: Won-Jun Choi, Seung-Jun Yu, Jung-In Baik, Hyoung-Kyu Song

Abstract:

Cooperative communication scheme can be substituted for multiple-input multiple-output (MIMO) technique when it may not be able to support multiple antennas due to size, cost or hardware limitations. In other words, cooperative communication scheme is an efficient method to achieve spatial diversity without multiple antennas. For satisfaction of rising QoS, we propose a reliable cooperative communication scheme with M-QAM based Dual Carrier Modulation (M-DCM), which can increase diversity gain. Although our proposed scheme is very simple method, it gives us frequency and spatial diversity. Simulation result shows our proposed scheme obtains diversity gain more than the conventional cooperative communication scheme.

Keywords: cooperation, diversity, M-DCM, OFDM.

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Authors: Teklu Urgessa, Wookjae Maeng, Joong Seek Lee

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Application of five implementations of three data mining classification techniques was experimented for extracting important insights from tourism data. The aim was to find out the best performing algorithm among the compared ones for tourism knowledge discovery. Knowledge discovery process from data was used as a process model. 10-fold cross validation method is used for testing purpose. Various data preprocessing activities were performed to get the final dataset for model building. Classification models of the selected algorithms were built with different scenarios on the preprocessed dataset. The outperformed algorithm tourism dataset was Random Forest (76%) before applying information gain based attribute selection and J48 (C4.5) (75%) after selection of top relevant attributes to the class (target) attribute. In terms of time for model building, attribute selection improves the efficiency of all algorithms. Artificial Neural Network (multilayer perceptron) showed the highest improvement (90%). The rules extracted from the decision tree model are presented, which showed intricate, non-trivial knowledge/insight that would otherwise not be discovered by simple statistical analysis with mediocre accuracy of the machine using classification algorithms.

Keywords: Classification algorithms; data mining; tourism; knowledge discovery.

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Authors: M. Chumburidze, N. Salia, T. Namchevadze

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This work deals with case processing problems in business. The task of strategic credit requirements management of cases portfolio is discussed. The information model of credit requirements in a binary tree diagram is considered. The algorithms to solve issues of prioritizing clusters of cases in business have been investigated. An implementation of priority queues to support case management operations has been presented. The corresponding pseudo codes for the programming application have been constructed. The tools applied in this development are based on binary tree ordering algorithms, optimization theory, and business management methods.

Keywords: Credit network, case portfolio, binary tree, priority queue, stack.

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Authors: A. P. Kulkarni, P. Randive, A. R. Mache

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In today-s era of plasma and laser cutting, machines using oxy-acetylene flame are also meritorious due to their simplicity and cost effectiveness. The objective to devise a Computer controlled Oxy-Fuel profile cutting machine arose from the increasing demand for metal cutting with respect to edge quality, circularity and lesser formation of redeposit material. The System has an 8 bit micro controller based embedded system, which assures stipulated time response. A new window based Application software was devised which takes a standard CAD file .DXF as input and converts it into numerical data required for the controller. It uses VB6 as a front end whereas MS-ACCESS and AutoCAD as back end. The system is designed around AT89C51RD2, powerful 8 bit, ISP micro controller from Atmel and is optimized to achieve cost effectiveness and also maintains the required accuracy and reliability for complex shapes. The backbone of the system is a cleverly designed mechanical assembly along with the embedded system resulting in an accuracy of about 10 microns while maintaining perfect linearity in the cut. This results in substantial increase in productivity. The observed results also indicate reduced inter laminar spacing of pearlite with an increase in the hardness of the edge region.

Keywords: Computer-Control, Profile, Oxy-Fuel.

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Authors: J. Kasigwa, V. Baryamureeba, D. Williams

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The goal of admission control is to support the Quality of Service demands of real-time applications via resource reservation in IP networks. In this paper we introduce a novel Dynamic Admission Control (DAC) mechanism for IP networks. The DAC dynamically allocates network resources using the previous network pattern for each path and uses the dynamic admission algorithm to improve bandwidth utilization using bandwidth brokers. We evaluate the performance of the proposed mechanism through trace-driven simulation experiments in view point of blocking probability, throughput and normalized utilization.

Keywords: Bandwidth broker, dynamic admission control(DAC), IP networks, quality of service, real-time flows.

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Authors: Omid A. Zargar

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One of the most challenging times in operation of big industrial plant or utilities is the time that alert lamp of Bently Nevada connection in main board substation turn on and show the alert condition of machine. All of the maintenance groups usually make a lot of discussion with operation and together rather this alert signal is real or fake. This will be more challenging when condition monitoring vibrationdata shows 1X(X=current rotor frequency) in fast Fourier transform(FFT) and vibration phase trends show 90 degree shift between two non-contact probedirections with overall high radial amplitude amounts. In such situations, CM (condition monitoring) groups usually suspicious about unbalance in rotor. In this paper, four critical case histories related to SIEMENS V94.2 Gas Turbines in Iran power industry discussed in details. Furthermore, probe looseness and fake (unreal) trip in gas turbine power plants discussed. In addition, critical operation decision in alert condition in power plants discussed in details.

Keywords: Gas turbine, field balance, turbine compressors, balancing tools, balancing data collectors.

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Authors: G. Renuka Devi

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This paper presents design and analysis of fault tolerate feature of n-phase induction motor drive. The n-phase induction motor (more than 3-phases) has a number of advantages over conventional 3-phase induction motor, it has low torque pulsation with increased torque density, more fault tolerant feature, low current ripple with increased efficiency. When increasing the number of phases, it has reduced current per phase without increasing per phase voltage, resulting in an increase in the total power rating of n-phase motors in the same volume machine. In this paper, the theory of operation of a multi-phase induction motor is discussed. The detailed study of d-q modeling of n-phase induction motors is elaborated. The d-q model of n-phase (5, 6, 7, 9 and 12) induction motors is developed in a MATLAB/Simulink environment. The steady state and dynamic performance of the multi-phase induction motor is studied under varying load conditions. Comparison of 5-phase induction is presented under normal and fault conditions.

Keywords: d-q model, dynamic Response, fault tolerant feature, matlab/simulink, multi-phase induction motor, transient response.

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Authors: Mafarja Majdi, Salwani Abdullah, Najmeh S. Jaddi

Abstract:

One of the global combinatorial optimization problems in machine learning is feature selection. It concerned with removing the irrelevant, noisy, and redundant data, along with keeping the original meaning of the original data. Attribute reduction in rough set theory is an important feature selection method. Since attribute reduction is an NP-hard problem, it is necessary to investigate fast and effective approximate algorithms. In this paper, we proposed two feature selection mechanisms based on memetic algorithms (MAs) which combine the genetic algorithm with a fuzzy record to record travel algorithm and a fuzzy controlled great deluge algorithm, to identify a good balance between local search and genetic search. In order to verify the proposed approaches, numerical experiments are carried out on thirteen datasets. The results show that the MAs approaches are efficient in solving attribute reduction problems when compared with other meta-heuristic approaches.

Keywords: Rough Set Theory, Attribute Reduction, Fuzzy Logic, Memetic Algorithms, Record to Record Algorithm, Great Deluge Algorithm.

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Authors: Giovanni Luca Masala

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Pattern recognition is the research area of Artificial Intelligence that studies the operation and design of systems that recognize patterns in the data. Important application areas are image analysis, character recognition, fingerprint classification, speech analysis, DNA sequence identification, man and machine diagnostics, person identification and industrial inspection. The interest in improving the classification systems of data analysis is independent from the context of applications. In fact, in many studies it is often the case to have to recognize and to distinguish groups of various objects, which requires the need for valid instruments capable to perform this task. The objective of this article is to show several methodologies of Artificial Intelligence for data classification applied to biomedical patterns. In particular, this work deals with the realization of a Computer-Aided Detection system (CADe) that is able to assist the radiologist in identifying types of mammary tumor lesions. As an additional biomedical application of the classification systems, we present a study conducted on blood samples which shows how these methods may help to distinguish between carriers of Thalassemia (or Mediterranean Anaemia) and healthy subjects.

Keywords: Computer Aided Detection, mammary tumor, pattern recognition, dissimilarity

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Authors: R. Usubamatov, A. Usubamatova, S. Hussain

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Modern highly automated production systems faces problems of reliability. Machine function reliability results in changes of productivity rate and efficiency use of expensive industrial facilities. Predicting of reliability has become an important research and involves complex mathematical methods and calculation. The reliability of high productivity technological automatic machines that consists of complex mechanical, electrical and electronic components is important. The failure of these units results in major economic losses of production systems. The reliability of transport and feeding systems for automatic technological machines is also important, because failure of transport leads to stops of technological machines. This paper presents reliability engineering on the feeding system and its components for transporting a complex shape parts to automatic machines. It also discusses about the calculation of the reliability parameters of the feeding unit by applying the probability theory. Equations produced for calculating the limits of the geometrical sizes of feeders and the probability of sticking the transported parts into the chute represents the reliability of feeders as a function of its geometrical parameters.

Keywords: Chute-feeder, parts, reliability.

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Authors: Seyoung Kim, Jeongmin Kim, Kwang Ryel Ryu

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A database that records average traffic speeds measured at five-minute intervals for all the links in the traffic network of a metropolitan city. While learning from this data the models that can predict future traffic speed would be beneficial for the applications such as the car navigation system, building predictive models for every link becomes a nontrivial job if the number of links in a given network is huge. An advantage of adopting k-nearest neighbor (k-NN) as predictive models is that it does not require any explicit model building. Instead, k-NN takes a long time to make a prediction because it needs to search for the k-nearest neighbors in the database at prediction time. In this paper, we investigate how much we can speed up k-NN in making traffic speed predictions by reducing the amount of data to be searched for without a significant sacrifice of prediction accuracy. The rationale behind this is that we had a better look at only the recent data because the traffic patterns not only repeat daily or weekly but also change over time. In our experiments, we build several different k-NN models employing different sets of features which are the current and past traffic speeds of the target link and the neighbor links in its up/down-stream. The performances of these models are compared by measuring the average prediction accuracy and the average time taken to make a prediction using various amounts of data.

Keywords: Big data, k-NN, machine learning, traffic speed prediction.

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Authors: Amir-Massoud Bidgoli, Mehdi Naseri Parsa

Abstract:

In this paper a combined feature selection method is proposed which takes advantages of sample domain filtering, resampling and feature subset evaluation methods to reduce dimensions of huge datasets and select reliable features. This method utilizes both feature space and sample domain to improve the process of feature selection and uses a combination of Chi squared with Consistency attribute evaluation methods to seek reliable features. This method consists of two phases. The first phase filters and resamples the sample domain and the second phase adopts a hybrid procedure to find the optimal feature space by applying Chi squared, Consistency subset evaluation methods and genetic search. Experiments on various sized datasets from UCI Repository of Machine Learning databases show that the performance of five classifiers (Naïve Bayes, Logistic, Multilayer Perceptron, Best First Decision Tree and JRIP) improves simultaneously and the classification error for these classifiers decreases considerably. The experiments also show that this method outperforms other feature selection methods.

Keywords: feature selection, resampling, reliable features, Consistency Subset Evaluation.

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Authors: Ibrahim Shaker, Amr El Hossany, Moustafa Osman, Mohamed El Raey

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This paper will explore integration model between GIS–SCADA system and enclosure quantification model to approach the impact of failure-safe event. There are real demands to identify spatial objects and improve control system performance. Nevertheless, the employed methodology is predicting electro-mechanic operations and corresponding time to environmental incident variations. Open processing, as object systems technology, is presented for integration enclosure database with minimal memory size and computation time via connectivity drivers such as ODBC:JDBC during main stages of GIS–SCADA connection. The function of Geographic Information System is manipulating power distribution in contrast to developing issues. In other ward, GIS-SCADA systems integration will require numerical objects of process to enable system model calibration and estimation demands, determine of past events for analysis and prediction of emergency situations for response training.

Keywords: Air dispersion model, integration power system, SCADA systems, GIS system, environmental management.

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Authors: Tessy Badriyah, Jim S. Briggs, Dave R. Prytherch

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It is well known that Logistic Regression is the gold standard method for predicting clinical outcome, especially predicting risk of mortality. In this paper, the Decision Tree method has been proposed to solve specific problems that commonly use Logistic Regression as a solution. The Biochemistry and Haematology Outcome Model (BHOM) dataset obtained from Portsmouth NHS Hospital from 1 January to 31 December 2001 was divided into four subsets. One subset of training data was used to generate a model, and the model obtained was then applied to three testing datasets. The performance of each model from both methods was then compared using calibration (the χ2 test or chi-test) and discrimination (area under ROC curve or c-index). The experiment presented that both methods have reasonable results in the case of the c-index. However, in some cases the calibration value (χ2) obtained quite a high result. After conducting experiments and investigating the advantages and disadvantages of each method, we can conclude that Decision Trees can be seen as a worthy alternative to Logistic Regression in the area of Data Mining.

Keywords: Decision Trees, Logistic Regression, clinical outcome, risk of mortality.

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Authors: Sri Sumartiningsih

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In elevating performance in competetive sports, an athlete must continously train in achieving maximum performance,but needs to pay attention to recovery therapy, that is to recover from fatigue as well as injury.The correct recovery therapy will assist in process of recovery and helps in the training in achieving better performace. Binahong (Anredera cordifolia) was proven empirically by the locals in assisting speedy recovery from an injury.Clinical research with lab animals receiving blunt trauma injury, microscopically shown signs of: 1) redness, 2) heatiness, 3) swelling and, 4) lack of activity. There is also microscopic indication of: 1) infiltration of inflame cells (migration of cells to the trauma area), 2) Cells necrosis, 3) Congestion (as a result of dead red blood cells), 4) uedema. On administration of Binahong for 3 days, there is a significant drop of 5% in cell inflammation, 2% increase of fibroblast (cell membrance) count.Conclutin: Binahong do assist in reducing cell inflammation and increase counts of cells fibroblast. Suggestion: In helping athlete's to recover from force injury, we need study about Binahong's roots to inflammation cell and healing of injuried cell.

Keywords: Binahong, sport injury, hematoma

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Authors: Jens Friedrich, Matthias A. Gebele, Armin Lechler, Alexander Verl

Abstract:

Chatter vibrations and process instabilities are the most important factors limiting the productivity of the milling process. Chatter can leads to damage of the tool, the part or the machine tool. Therefore, the estimation and prediction of the process stability is very important. The process stability depends on the spindle speed, the depth of cut and the width of cut. In milling, the process conditions are defined in the NC-program. While the spindle speed is directly coded in the NC-program, the depth and width of cut are unknown. This paper presents a new simulation based approach for the prediction of the depth and width of cut of a milling process. The prediction is based on a material removal simulation with an analytically represented tool shape and a multi-dexel approach for the workpiece. The new calculation method allows the direct estimation of the depth and width of cut, which are the influencing parameters of the process stability, instead of the removed volume as existing approaches do. The knowledge can be used to predict the stability of new, unknown parts. Moreover with an additional vibration sensor, the stability lobe diagram of a milling process can be estimated and improved based on the estimated depth and width of cut.

Keywords: Dexel, process stability, material removal, milling.

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Authors: Parviz Alinezhad, Ali Sanati, Koorosh Naser Momtahen

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Square pipes (pipes with square cross sections) are being used for various industrial objectives, such as machine structure components and housing/building elements. The utilization of them is extending rapidly and widely. Hence, the out-put of those pipes is increasing and new application fields are continually developing. Due to various demands in recent time, the products have to satisfy difficult specifications with high accuracy in dimensions. The reshaping process design of pipes with square cross sections; however, is performed by trial and error and based on expert-s experience. In this paper, a computer-aided simulation is developed based on the 2-D elastic-plastic method with consideration of the shear deformation to analyze the reshaping process. Effect of various parameters such as diameter of the circular pipe and mechanical properties of metal on product dimension and quality can be evaluated by using this simulation. Moreover, design of reshaping process include determination of shrinkage of cross section, necessary number of stands, radius of rolls and height of pipe at each stand, are investigated. Further, it is shown that there are good agreements between the results of the design method and the experimental results.

Keywords: Circular Pipes, Square Pipes, Shear Deformation, Reshaping Process, Numerical Simulation.

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Authors: V. Prapulla, A. Mitra, R. Bhattacharjee, S. Nandi

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We present a simplified equalization technique for a π/4 differential quadrature phase shift keying ( π/4 -DQPSK) modulated signal in a multipath fading environment. The proposed equalizer is realized as a fractionally spaced adaptive decision feedback equalizer (FS-ADFE), employing exponential step-size least mean square (LMS) algorithm as the adaptation technique. The main advantage of the scheme stems from the usage of exponential step-size LMS algorithm in the equalizer, which achieves similar convergence behavior as that of a recursive least squares (RLS) algorithm with significantly reduced computational complexity. To investigate the finite-precision performance of the proposed equalizer along with the π/4 -DQPSK modem, the entire system is evaluated on a 16-bit fixed point digital signal processor (DSP) environment. The proposed scheme is found to be attractive even for those cases where equalization is to be performed within a restricted number of training samples.

Keywords: Adaptive decision feedback equalizer, Fractionally spaced equalizer, π/4 DQPSK signal, Digital signal processor.

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Authors: Hao Mi, Ming Yang, Tian-yue Yang

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Ultrasonic infrared nondestructive testing is a kind of testing method with high speed, accuracy and localization. However, there are still some problems, such as the detection requires manual real-time field judgment, the methods of result storage and viewing are still primitive. An intelligent non-destructive detection system based on embedded linux is put forward in this paper. The hardware part of the detection system is based on the ARM (Advanced Reduced Instruction Set Computer Machine) core and an embedded linux system is built to realize image processing and defect detection of thermal images. The CLAHE algorithm and the Butterworth filter are used to process the thermal image, and then the boa server and CGI (Common Gateway Interface) technology are used to transmit the test results to the display terminal through the network for real-time monitoring and remote monitoring. The system also liberates labor and eliminates the obstacle of manual judgment. According to the experiment result, the system provides a convenient and quick solution for industrial non-destructive testing.

Keywords: Remote monitoring, non-destructive testing, embedded linux system, image processing.

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Authors: Prabhudev Hosur, Rolando Carrasco

Abstract:

This paper presents the enhanced frame-based video coding scheme. The input source video to the enhanced frame-based video encoder consists of a rectangular-size video and shapes of arbitrarily-shaped objects on video frames. The rectangular frame texture is encoded by the conventional frame-based coding technique and the video object-s shape is encoded using the contour-based vertex coding. It is possible to achieve several useful content-based functionalities by utilizing the shape information in the bitstream at the cost of a very small overhead to the bitrate.

Keywords: Video coding, content-based, hyper video, interactivity, shape coding, polygon.

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Authors: Kee-Young Kwahk

Abstract:

Cognizant of the fact that enterprise systems involve organizational change and their implementation is over shadowed by a high failure rate, it is argued that there is the need to focus attention on employees- perceptions of such organizational change when explaining adoption behavior of enterprise systems. For this purpose, the research incorporates a conceptual constructo fattitude toward change that captures views about the need for organizational change. Centered on this conceptual construct, the research model includes beliefs regarding the system and behavioral intention as its consequences, and the personal characteristics of organizational commitment and perceived personal competence as its antecedents. Structural equation analysis using LISREL provides significant support for the proposed relationships. Theoretical and practical implications are discussed along with limitations.

Keywords: Sociotechnical changes, organizational change, attitude toward change, enterprise information systems.

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Authors: Oscar Fernandez-Flores, Tony Speer, Rodney Day

Abstract:

This paper identifies five key design characteristics of production scheduling software systems in printed circuit board (PCB) manufacturing. The authors consider that, in addition to an effective scheduling engine, a scheduling system should be able to process a preventative maintenance calendar, to give the user the flexibility to handle data using a variety of electronic sources, to run simulations to support decision-making, and to have simple and customisable graphical user interfaces. These design considerations were the result of a review of academic literature, the evaluation of commercial applications and a compilation of requirements of a PCB manufacturer. It was found that, from those systems that were evaluated, those that effectively addressed all five characteristics outlined in this paper were the most robust of all and could be used in PCB manufacturing.

Keywords: Decision-making, ERP, PCB, scheduling.

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Authors: Ambra Giovannelli, Erika Maria Archilei

Abstract:

The amount of the electrical power required by refrigeration systems is relevant worldwide. It is evaluated in the order of 15% of the total electricity production taking refrigeration and air-conditioning into consideration. For this reason, in the last years several energy saving techniques have been proposed to reduce the power demand of such plants. The paper deals with the development of an innovative internal recovery system for cryogenic cooling plants. Such a system consists in a Compressor-Expander Group (CEG) designed on the basis of the automotive turbocharging technology. In particular, the paper is focused on the design of the expander, the critical component of the CEG system. Due to the low volumetric flow entering the expander and the high expansion ratio, a commercial turbocharger expander wheel was strongly modified. It was equipped with a transonic nozzle, designed to have a radially inflow full admission. To verify the performance of such a machine and suggest improvements, two different set of nozzles have been designed and modelled by means of the commercial Ansys-CFX software. steady-state 3D CFD simulations of the second-generation prototype are presented and compared with the initial ones.

Keywords: Energy saving, organic fluids, radial turbine, refrigeration plant, vapor compression systems.

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Authors: Samir Abdelhamid, Noureddine Bouguechal

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We report in this paper the model adopted by our system of continuous speech recognition in Arab language SySRA and the results obtained until now. This system uses the database Arabdic-10 which is a corpus of word for the Arab language and which was manually segmented. Phonetic decoding is represented by an expert system where the knowledge base is translated in the form of production rules. This expert system transforms a vocal signal into a phonetic lattice. The higher level of the system takes care of the recognition of the lattice thus obtained by deferring it in the form of written sentences (orthographical Form). This level contains initially the lexical analyzer which is not other than the module of recognition. We subjected this analyzer to a set of spectrograms obtained by dictating a score of sentences in Arab language. The rate of recognition of these sentences is about 70% which is, to our knowledge, the best result for the recognition of the Arab language. The test set consists of twenty sentences from four speakers not having taken part in the training.

Keywords: Continuous speech recognition, lexical analyzer, phonetic decoding, phonetic lattice, vocal signal.

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Authors: Ł. Januszkiewicz, G. Bacci, H. Gierszal, M. Luise

Abstract:

In this paper channel estimation techniques are considered as the support methods for OFDM transmission systems based on Non Binary LDPC (Low Density Parity Check) codes. Standard frequency domain pilot aided LS (Least Squares) and LMMSE (Linear Minimum Mean Square Error) estimators are investigated. Furthermore, an iterative algorithm is proposed as a solution exploiting the NB-LDPC channel decoder to improve the performance of the LMMSE estimator. Simulation results of signals transmitted through fading mobile channels are presented to compare the performance of the proposed channel estimators.

Keywords: LDPC codes, LMMSE, OFDM, turbo channelestimation.

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