Search results for: tool wear prediction

Authors: Ilias Spais, George Bafas

Abstract:

Natural Language Understanding Systems (NLU) will not be widely deployed unless they are technically mature and cost effective to develop. Cost effective development hinges on the availability of tools and techniques enabling the rapid production of NLU applications through minimal human resources. Further, these tools and techniques should allow quick development of applications in a user friendly way and should be easy to upgrade in order to continuously follow the evolving technologies and standards. This paper presents a visual tool for the structuring and editing of dialog forms, the key element of driving conversation in NLU applications based on IBM technology. The main focus is given on the basic component used to describe Human – Machine interactions of that kind, the Dialogue Manager. In essence, the description of a tool that enables the visual representation of the Dialogue Manager mainly during the implementation phase is illustrated.

Keywords: Conversational Applications, Forms Dialogue Manager (FDM), Natural Language Processing, Natural Language Understanding.

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Authors: A. Faro, D. Giordano, F. Maiorana

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Data mining uses a variety of techniques each of which is useful for some particular task. It is important to have a deep understanding of each technique and be able to perform sophisticated analysis. In this article we describe a tool built to simulate a variation of the Kohonen network to perform unsupervised clustering and support the entire data mining process up to results visualization. A graphical representation helps the user to find out a strategy to optimize classification by adding, moving or delete a neuron in order to change the number of classes. The tool is able to automatically suggest a strategy to optimize the number of classes optimization, but also support both tree classifications and semi-lattice organizations of the classes to give to the users the possibility of passing from one class to the ones with which it has some aspects in common. Examples of using tree and semi-lattice classifications are given to illustrate advantages and problems. The tool is applied to classify macroeconomic data that report the most developed countries- import and export. It is possible to classify the countries based on their economic behaviour and use the tool to characterize the commercial behaviour of a country in a selected class from the analysis of positive and negative features that contribute to classes formation. Possible interrelationships between the classes and their meaning are also discussed.

Keywords: Unsupervised classification, Kohonen networks, macroeconomics, Visual data mining, Cluster interpretation.

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Authors: Azad A. Mohammed, Gulan B. Hassan

Abstract:

Analytical procedure was carried out in this paper to calculate the ultimate load capacity of reinforced concrete corbels strengthened or repaired externally with CFRP sheets. Strut and tie method and shear friction method proposed earlier for analyzing reinforced concrete corbels were modified to incorporate the effect of external CFRP sheets bonded to the corbel. The points of weakness of any method that lead to an inaccuracy, especially when overestimating test results were checked and discussed. Comparison of prediction with the test data indicates that the ratio of test / calculated ultimate load is 0.82 and 1.17 using strut and tie method and shear friction method, respectively. If the limits of maximum shear stress is followed, the calculated ultimate load capacity using shear friction method was found to underestimates test data considerably.

Keywords: Corbel, Strengthening, Strut and Tie Model, Shear Friction

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Authors: Enas M. F. El Houby, Marwa S. Hassan

Abstract:

Combined therapy using Interferon and Ribavirin is the standard treatment in patients with chronic hepatitis C. However, the number of responders to this treatment is low, whereas its cost and side effects are high. Therefore, there is a clear need to predict patient’s response to the treatment based on clinical information to protect the patients from the bad drawbacks, Intolerable side effects and waste of money. Different machine learning techniques have been developed to fulfill this purpose. From these techniques are Associative Classification (AC) and Decision Tree (DT). The aim of this research is to compare the performance of these two techniques in the prediction of virological response to the standard treatment of HCV from clinical information. 200 patients treated with Interferon and Ribavirin; were analyzed using AC and DT. 150 cases had been used to train the classifiers and 50 cases had been used to test the classifiers. The experiment results showed that the two techniques had given acceptable results however the best accuracy for the AC reached 92% whereas for DT reached 80%.

Keywords: Associative Classification, Data mining, Decision tree, HCV, interferon.

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Authors: I. Mahdavi, A. Jazayeri, M. Jahromi, R. Jafari, H. Iranmanesh

Abstract:

One of the most important problems in production planning of flexible manufacturing system (FMS) is machine tool selection and operation allocation problem that directly influences the production costs and times .In this paper minimizing machining cost, set-up cost and material handling cost as a multi-objective problem in flexible manufacturing systems environment are considered. We present a 0-1 integer linear programming model for the multiobjective machine tool selection and operation allocation problem and due to the large scale nature of the problem, solving the problem to obtain optimal solution in a reasonable time is infeasible, Paretoant colony optimization (P-ACO) approach for solving the multiobjective problem in reasonable time is developed. Experimental results indicate effectiveness of the proposed algorithm for solving the problem.

Keywords: Flexible manufacturing system, Production planning, Machine tool selection, Operation allocation, Multiobjective optimization, Metaheuristic.

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Authors: Ε. Giovanis

Abstract:

The purpose of this paper is to present two different approaches of financial distress pre-warning models appropriate for risk supervisors, investors and policy makers. We examine a sample of the financial institutions and electronic companies of Taiwan Security Exchange (TSE) market from 2002 through 2008. We present a binary logistic regression with paned data analysis. With the pooled binary logistic regression we build a model including more variables in the regression than with random effects, while the in-sample and out-sample forecasting performance is higher in random effects estimation than in pooled regression. On the other hand we estimate an Adaptive Neuro-Fuzzy Inference System (ANFIS) with Gaussian and Generalized Bell (Gbell) functions and we find that ANFIS outperforms significant Logit regressions in both in-sample and out-of-sample periods, indicating that ANFIS is a more appropriate tool for financial risk managers and for the economic policy makers in central banks and national statistical services.

Keywords: ANFIS, Binary logistic regression, Financialdistress, Panel data

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

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

Keywords: Digital Preservation, Metadata, OAIS, PDI, XML.

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Authors: M. Cuevas-González, O. Monserrat, A. Barra, C. Reyes-Carmona, R. M. Mateos, J. P. Galve, R. Sarro, M. Cantalejo, E. Peña, M. Martínez-Corbella, J. A. Luque, J. M. Azañón, A. Millares, M. Béjar, J. A. Navarro, L. Solari

Abstract:

Geohazard prone areas require continuous monitoring to detect risks, understand the phenomena occurring in those regions and prevent disasters. Satellite interferometry (InSAR) has come to be a trustworthy technique for ground movement detection and monitoring in the last few years. InSAR based techniques allow to process large areas providing high number of displacement measurements at low cost. However, the results provided by such techniques are usually not easy to interpret by non-experienced users hampering its use for decision makers. This work presents a set of tools developed in the framework of different projects (Momit, Safety, U-Geohaz, Riskcoast) and an example of their use in the Granada Coastal area (Spain) is shown. The ADA (Active Displacement Areas) tool has been developed with the aim of easing the management, use and interpretation of InSAR based results. It provides a semi-automatic extraction of the most significant ADAs through the application ADAFinder tool. This tool aims to support the exploitation of the European Ground Motion Service (EU-GMS), which will offer reliable and systematic information on natural and anthropogenic ground motion phenomena across Europe.

Keywords: Ground displacements, InSAR, natural hazards, satellite imagery.

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Authors: Marina Franulovic, Robert Basan, Bozidar Krizan

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Constitutive modeling of material behavior is becoming increasingly important in prediction of possible failures in highly loaded engineering components, and consequently, optimization of their design. In order to account for large number of phenomena that occur in the material during operation, such as kinematic hardening effect in low cycle fatigue behavior of steels, complex nonlinear material models are used ever more frequently, despite of the complexity of determination of their parameters. As a method for the determination of these parameters, genetic algorithm is good choice because of its capability to provide very good approximation of the solution in systems with large number of unknown variables. For the application of genetic algorithm to parameter identification, inverse analysis must be primarily defined. It is used as a tool to fine-tune calculated stress-strain values with experimental ones. In order to choose proper objective function for inverse analysis among already existent and newly developed functions, the research is performed to investigate its influence on material behavior modeling.

Keywords: Genetic algorithm, kinematic hardening, material model, objective function

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Authors: M. Raciti Castelli, G. Grandi, E. Benini

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This paper presents a study of laminar to turbulent transition on a profile specifically designed for wind turbine blades, the DU91-W2-250, which belongs to a class of wind turbine dedicated airfoils, developed by Delft University of Technology. A comparison between the experimental behavior of the airfoil studied at Delft wind tunnel and the numerical predictions of the commercial CFD solver ANSYS FLUENT® has been performed. The prediction capabilities of the Spalart-Allmaras turbulence model and of the γ-θ Transitional model have been tested. A sensitivity analysis of the numerical results to the spatial domain discretization has also been performed using four different computational grids, which have been created using the mesher GAMBIT®. The comparison between experimental measurements and CFD results have allowed to determine the importance of the numerical prediction of the laminar to turbulent transition, in order not to overestimate airfoil friction drag due to a fully turbulent-regime flow computation.

Keywords: CFD, wind turbine, DU91-W2-250, laminar to turbulent transition.

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Authors: Zohreh Sheikh Khozani, Wan Hanna Melini Wan Mohtar, Mojtaba Porhemmat

Abstract:

Prediction of wall shear stress in a rectangular channel, with non-homogeneous roughness distribution, was studied. Estimation of shear stress is an important subject in hydraulic engineering, since it affects the flow structure directly. In this study, the Genetic Algorithm Artificial (GAA) neural network is introduced as a hybrid methodology of the Artificial Neural Network (ANN) and modified Genetic Algorithm (GA) combination. This GAA method was employed to predict the wall shear stress. Various input combinations and transfer functions were considered to find the most appropriate GAA model. The results show that the proposed GAA method could predict the wall shear stress of open channels with high accuracy, by Root Mean Square Error (RMSE) of 0.064 in the test dataset. Thus, using GAA provides an accurate and practical simple-to-use equation.

Keywords: Artificial neural network, genetic algorithm, genetic programming, rectangular channel, shear stress.

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Authors: Muhammad Aqil, Ichiro Kita, Moses Macalinao

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Reliable water level forecasts are particularly important for warning against dangerous flood and inundation. The current study aims at investigating the suitability of the adaptive network based fuzzy inference system for continuous water level modeling. A hybrid learning algorithm, which combines the least square method and the back propagation algorithm, is used to identify the parameters of the network. For this study, water levels data are available for a hydrological year of 2002 with a sampling interval of 1-hour. The number of antecedent water level that should be included in the input variables is determined by two statistical methods, i.e. autocorrelation function and partial autocorrelation function between the variables. Forecasting was done for 1-hour until 12-hour ahead in order to compare the models generalization at higher horizons. The results demonstrate that the adaptive networkbased fuzzy inference system model can be applied successfully and provide high accuracy and reliability for river water level estimation. In general, the adaptive network-based fuzzy inference system provides accurate and reliable water level prediction for 1-hour ahead where the MAPE=1.15% and correlation=0.98 was achieved. Up to 12-hour ahead prediction, the model still shows relatively good performance where the error of prediction resulted was less than 9.65%. The information gathered from the preliminary results provide a useful guidance or reference for flood early warning system design in which the magnitude and the timing of a potential extreme flood are indicated.

Keywords: Neural Network, Fuzzy, River, Forecasting

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Authors: Nastaran Moosavi, Mohammad Mokhtari

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Seismic inversion is a technique which has been in use for years and its main goal is to estimate and to model physical characteristics of rocks and fluids. Generally, it is a combination of seismic and well-log data. Seismic inversion can be carried out through different methods; we have conducted and compared post-stack and pre- stack seismic inversion methods on real data in one of the fields in the Persian Gulf. Pre-stack seismic inversion can transform seismic data to rock physics such as P-impedance, S-impedance and density. While post- stack seismic inversion can just estimate P-impedance. Then these parameters can be used in reservoir identification. Based on the results of inverting seismic data, a gas reservoir was detected in one of Hydrocarbon oil fields in south of Iran (Persian Gulf). By comparing post stack and pre-stack seismic inversion it can be concluded that the pre-stack seismic inversion provides a more reliable and detailed information for identification and prediction of hydrocarbon reservoirs.

Keywords: Density, P-impedance, S-impedance, post-stack seismic inversion, pre-stack seismic inversion.

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Authors: A. B. Chehreh, M. Grätzel, M. Klein, J. P. Bergmann, F. Walther

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Friction stir welding (FSW) is an advantageous method in the thermal joining processes, featuring the welding of various dissimilar and similar material combinations, joining temperatures below the melting point which prevents irregularities such as pores and hot cracks as well as high strengths mechanical joints near the base material. The FSW process consists of a rotating tool which is made of a shoulder and a probe. The welding process is based on a rotating tool which plunges in the workpiece under axial pressure. As a result, the material is plasticized by frictional heat which leads to a decrease in the flow stress. During the welding procedure, the material is continuously displaced by the tool, creating a firmly bonded weld seam behind the tool. However, the mechanical properties of the weld seam are affected by the design and geometry of the tool. These include in particular microstructural and surface properties which can favor crack initiation. Following investigation compares the dynamic properties of FSW weld seams with conventional and stationary shoulder geometry based on load increase test (LIT). Compared to classical Woehler tests, it is possible to determine the fatigue strength of the specimens after a short amount of time. The investigations were carried out on a robotized welding setup on 2 mm thick EN AW 5754 aluminum alloy sheets. It was shown that an increased tensile and fatigue strength can be achieved by using the stationary shoulder concept. Furthermore, it could be demonstrated that the LIT is a valid method to describe the fatigue behavior of FSW weld seams.

Keywords: Aluminum alloy, fatigue performance, fracture, friction stir welding.

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Authors: K. S. Jasmine, R. Vasantha

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Component-Based software engineering provides an opportunity for better quality and increased productivity in software development by using reusable software components [10]. One of the most critical aspects of the quality of a software system is its performance. The systematic application of software performance engineering techniques throughout the development process can help to identify design alternatives that preserve desirable qualities such as extensibility and reusability while meeting performance objectives [1]. In the present scenario, software engineering methodologies strongly focus on the functionality of the system, while applying a “fix- it-later" approach to software performance aspects [3]. As a result, lengthy fine-tunings, expensive extra hard ware, or even redesigns are necessary for the system to meet the performance requirements. In this paper, we propose design based, implementation independent, performance prediction approach to reduce the overhead associated in the later phases while developing a performance guaranteed software product with the help of Unified Modeling Language (UML).

Keywords: Software Reuse, Component-based development, Unified Modeling Language, Software performance, Software components, Performance engineering, Software engineering.

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Authors: Jean François Chatelain, Imed Zaghbani, Joseph Monier

Abstract:

The machining of Carbon Fiber Reinforced Plastics has come to constitute a significant challenge for many fields of industry. The resulting surface finish of machined parts is of primary concern for several reasons, including contact quality and impact on the assembly. Therefore, the characterization and prediction of roughness based on machining parameters are crucial for costeffective operations. In this study, a PCD tool comprised of two straight flutes was used to trim 32-ply carbon fiber laminates in a bid to analyze the effects of the feed rate and the cutting speed on the surface roughness. The results show that while the speed has but a slight impact on the surface finish, the feed rate for its part affects it strongly. A detailed study was also conducted on the effect of fiber orientation on surface roughness, for quasi-isotropic laminates used in aerospace. The resulting roughness profiles for the four-ply orientation lay-up were compared, and it was found that fiber angle is a critical parameter relating to surface roughness. One of the four orientations studied led to very poor surface finishes, and characteristic roughness profiles were identified and found to only relate to the ply orientations of multilayer carbon fiber laminates.

Keywords: Roughness, Detouring, Composites, Aerospace

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Authors: Tahseen A. Jilani, Aqil S. Burney, C. Ardil

Abstract:

Time series models have been used to make predictions of academic enrollments, weather, road accident, casualties and stock prices, etc. Based on the concepts of quartile regression models, we have developed a simple time variant quantile based fuzzy time series forecasting method. The proposed method bases the forecast using prediction of future trend of the data. In place of actual quantiles of the data at each point, we have converted the statistical concept into fuzzy concept by using fuzzy quantiles using fuzzy membership function ensemble. We have given a fuzzy metric to use the trend forecast and calculate the future value. The proposed model is applied for TAIFEX forecasting. It is shown that proposed method work best as compared to other models when compared with respect to model complexity and forecasting accuracy.

Keywords: Quantile Regression, Fuzzy time series, fuzzy logicalrelationship groups, heuristic trend prediction.

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Authors: Alanoud Moraya Aldalan, Abdulaziz Almaleh

Abstract:

A crucial part of maintaining a customer-oriented business in the telecommunications industry is understanding the reasons and factors that lead to customer churn. Competition between telecom companies has greatly increased in recent years, which has made it more important to understand customers’ needs in this strong market. For those who are looking to turn over their service providers, understanding their needs is especially important. Predictive churn is now a mandatory requirement for retaining customers in the telecommunications industry. Machine learning can be used to accomplish this. Churn Prediction has become a very important topic in terms of machine learning classification in the telecommunications industry. Understanding the factors of customer churn and how they behave is very important to building an effective churn prediction model. This paper aims to predict churn and identify factors of customers’ churn based on their past service usage history. Aiming at this objective, the study makes use of feature selection, normalization, and feature engineering. Then, this study compared the performance of four different machine learning algorithms on the Orange dataset: Logistic Regression, Random Forest, Decision Tree, and Gradient Boosting. Evaluation of the performance was conducted by using the F1 score and ROC-AUC. Comparing the results of this study with existing models has proven to produce better results. The results showed the Gradients Boosting with feature selection technique outperformed in this study by achieving a 99% F1-score and 99% AUC, and all other experiments achieved good results as well.

Keywords: Machine Learning, Gradient Boosting, Logistic Regression, Churn, Random Forest, Decision Tree, ROC, AUC, F1-score.

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Authors: Z. Bottyán, F. Wantuch, A. Z. Gyöngyösi, Z. Tuba, K. Hadobács, P. Kardos, R. Kurunczi

Abstract:

The sensitivity of UAVs to the atmospheric effects are apparent. All the same the meteorological support for the UAVs missions is often non-adequate or partly missing. In our paper we show a new complex meteorological support system for different types of UAVs pilots, specialists and decision makers, too. The mentioned system has two important parts with different forecasts approach such as the statistical and dynamical ones. The statistical prediction approach is based on a large climatological data base and the special analog method which is able to select similar weather situations from the mentioned data base to apply them during the forecasting procedure. The applied dynamic approach uses the specific WRF model runs twice a day and produces 96 hours, high resolution weather forecast for the UAV users over the Hungary. An easy to use web-based system can give important weather information over the Carpathian basin in Central-Europe. The mentioned products can be reached via internet connection.

Keywords: Aviation meteorology, statistical weather prediction, unmanned aerial systems, WRF.

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Authors: Xiaolei Hu, Enrico Ferrera, Riccardo Tomasi, Claudio Pastrone

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Load Forecasting plays a key role in making today's and future's Smart Energy Grids sustainable and reliable. Accurate power consumption prediction allows utilities to organize in advance their resources or to execute Demand Response strategies more effectively, which enables several features such as higher sustainability, better quality of service, and affordable electricity tariffs. It is easy yet effective to apply Load Forecasting at larger geographic scale, i.e. Smart Micro Grids, wherein the lower available grid flexibility makes accurate prediction more critical in Demand Response applications. This paper analyses the application of short-term load forecasting in a concrete scenario, proposed within the EU-funded GreenCom project, which collect load data from single loads and households belonging to a Smart Micro Grid. Three short-term load forecasting techniques, i.e. linear regression, artificial neural networks, and radial basis function network, are considered, compared, and evaluated through absolute forecast errors and training time. The influence of weather conditions in Load Forecasting is also evaluated. A new definition of Gain is introduced in this paper, which innovatively serves as an indicator of short-term prediction capabilities of time spam consistency. Two models, 24- and 1-hour-ahead forecasting, are built to comprehensively compare these three techniques.

Keywords: Short-term load forecasting, smart micro grid, linear regression, artificial neural networks, radial basis function network, Gain.

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Authors: M. Karimpour, N. Elkhoury, L. Hitihamillage, S. Moridpour, R. Hesami

Abstract:

There is a necessity among rail transportation authorities for a superior understanding of the rail track degradation overtime and the factors influencing rail degradation. They need an accurate technique to identify the time when rail tracks fail or need maintenance. In turn, this will help to increase the level of safety and comfort of the passengers and the vehicles as well as improve the cost effectiveness of maintenance activities. An accurate model can play a key role in prediction of the long-term behaviour of railroad tracks. An accurate model can decrease the cost of maintenance. In this research, the rail track degradation is predicted using an autoregressive moving average with exogenous input (ARMAX). An ARMAX has been implemented on Melbourne tram data to estimate the values for the tram track degradation. Gauge values and rail usage in Million Gross Tone (MGT) are the main parameters used in the model. The developed model can accurately predict the future status of the tram tracks.

Keywords: ARMAX, Dynamic systems, MGT, Prediction, Rail degradation.

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Authors: Pavana Manvikar, G K Purohit

Abstract:

The functional performance of machined components, often, depends on surface topography, hardness, nature of stress and strain induced on the surface, etc. Invariably, surfaces of metallic components obtained by turning, milling, etc., consist of irregularities such as machining marks are responsible for the above. Surface finishing/coating processes used to produce improved surface quality/textures are classified as chip-removal and chip-less processes. Burnishing is chip-less cold working process carried out to improve surface finish, hardness and resistance to fatigue and corrosion; not obtainable by other surface coating and surface treatment processes. It is a very simple, but effective method which improves surface characteristics and is reported to introduce compressive stresses.

Of late, considerable attention is paid to post-machining, finishing operations, such as burnishing. During burnishing the micro-irregularities start to deform plastically, initially the crests are gradually flattened and zones of reduced deformation are formed. When all the crests are deformed, the valleys between the micro-irregularities start moving in the direction of the newly formed surface. The grain structure is then condensed, producing a smoother and harder surface with superior load-carrying and wear-resistant capabilities.

Burnishing can be performed on a lathe with a highly polished ball or roller type tool which is traversed under force over a rotating/stationary work piece. Often, several passes are used to obtain the work piece surface with the desired finish and hardness.

This paper presents the findings of an experimental investigation on the effect of ball burnishing parameters such as, burnishing speed, feed, force and number of passes; on surface roughness (Ra) and micro-hardness (Hv) of a 60/40 copper/zinc alloy, using a 2-level fractional factorial design of experiments (DoE). Mathematical models were developed to predict surface roughness and hardness generated by burnishing in terms of the above process parameters. A ball-type tool, designed and constructed from a high chrome steel material (HR_C=63 and Ra=0.012 µm), was used for burnishing of fine-turned cylindrical bars (0.68-0.78µm and 145Hv). They are given by,

 

Ra= 0.305-0.005X₁ - 0.0175X₂ + 0.0525X₄ + 0.0125X₁X₄ -0.02X₂X₄ - 0.0375X₃X₄

 

Hv=160.625 -2.37 5X₁ + 5.125X₂ + 1.875X₃ + 4.375X₄ - 1.625X₁X₄ + 4.375X₂X₄ - 2.375X₃X₄

 

High surface microhardness (175H_V) was obtained at 400rpm, 2passes, 0.05mm/rev and 15kgf., and high surface finish (0.20µm) was achieved at 30kgf, 0.1mm/rev, 112rpm and single pass. In other words, surface finish improved by 350% and microhardness improved by 21% compared to as machined conditions.

Keywords: Ball burnishing, surface roughness, micro-hardness.

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Authors: Tomáš Bublík, Miroslav Virius

Abstract:

This paper presents general results on the Java source code snippet detection problem. We propose the tool which uses graph and subgraph isomorphism detection. A number of solutions for all of these tasks have been proposed in the literature. However, although that all these solutions are really fast, they compare just the constant static trees. Our solution offers to enter an input sample dynamically with the Scripthon language while preserving an acceptable speed. We used several optimizations to achieve very low number of comparisons during the matching algorithm.

Keywords: AST, Java, tree matching, Scripthon, source code recognition

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Authors: Mohd Azman Abdul Latif, Erwan Basiron

Abstract:

A flaw or drift from expected operational performance in one component (NAND, PMIC, controller, DRAM, etc.) may affect the reliability of the entire Solid State Drive (SSD) system. Therefore, it is important to ensure the required quality of each individual component through qualification testing specified using standards or user requirements. Qualification testing is time-consuming and comes at a substantial cost for product manufacturers. A highly technical team, from all the eminent stakeholders is embarking on reliability prediction from beginning of new product development, identify critical to reliability parameters, perform full-blown characterization to embed margin into product reliability and establish control to ensure the product reliability is sustainable in the mass production. The paper will discuss a comprehensive development framework, comprehending SSD end to end from design to assembly, in-line inspection, in-line testing and will be able to predict and to validate the product reliability at the early stage of new product development. During the design stage, the SSD will go through intense reliability margin investigation with focus on assembly process attributes, process equipment control, in-process metrology and also comprehending forward looking product roadmap. Once these pillars are completed, the next step is to perform process characterization and build up reliability prediction modeling. Next, for the design validation process, the reliability prediction specifically solder joint simulator will be established. The SSD will be stratified into Non-Operating and Operating tests with focus on solder joint reliability and connectivity/component latent failures by prevention through design intervention and containment through Temperature Cycle Test (TCT). Some of the SSDs will be subjected to the physical solder joint analysis called Dye and Pry (DP) and Cross Section analysis. The result will be feedbacked to the simulation team for any corrective actions required to further improve the design. Once the SSD is validated and is proven working, it will be subjected to implementation of the monitor phase whereby Design for Assembly (DFA) rules will be updated. At this stage, the design change, process and equipment parameters are in control. Predictable product reliability at early product development will enable on-time sample qualification delivery to customer and will optimize product development validation, effective development resource and will avoid forced late investment to bandage the end-of-life product failures. Understanding the critical to reliability parameters earlier will allow focus on increasing the product margin that will increase customer confidence to product reliability.

Keywords: e2e reliability prediction, SSD, TCT, Solder Joint Reliability, NUDD, connectivity issues, qualifications, characterization and control.

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Authors: Anant Kumar Singh, Sunil Jha, Pulak M. Pandey

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A novel nanofinishing process using improved ball end magnetorheological (MR) finishing tool was developed for finishing of flat as well as 3D surfaces of ferromagnetic and non ferromagnetic workpieces. In this process a magnetically controlled ball end of smart MR polishing fluid is generated at the tip surface of the tool which is used as a finishing medium and it is guided to follow the surface to be finished through computer controlled 3-axes motion controller. The experiments were performed on ferromagnetic workpiece surface in the developed MR finishing setup to study the effect of finishing time on final surface roughness. The performance of present finishing process on final finished surface roughness was studied. The surface morphology was observed under scanning electron microscopy and atomic force microscope. The final surface finish was obtained as low as 19.7 nm from the initial surface roughness of 142.9 nm. The outcome of newly developed finishing process can be found useful in its applications in aerospace, automotive, dies and molds manufacturing industries, semiconductor and optics machining etc.

Keywords: Ball end MR finishing tool, Magnetorheological finishing, Nanofinishing

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Authors: Gonzalo Rubio, Eusebio Valero, Sven Lanzan

Abstract:

The design of a modern aircraft is based on three pillars: theoretical results, experimental test and computational simulations. As a results of this, Computational Fluid Dynamic (CFD) solvers are widely used in the aeronautical field. These solvers require the correct selection of many parameters in order to obtain successful results. Besides, the computational time spent in the simulation depends on the proper choice of these parameters. In this paper we create an expert system capable of making an accurate prediction of the number of iterations and time required for the convergence of a computational fluid dynamic (CFD) solver. Artificial neural network (ANN) has been used to design the expert system. It is shown that the developed expert system is capable of making an accurate prediction the number of iterations and time required for the convergence of a CFD solver.

Keywords: Artificial Neural Network, Computational Fluid Dynamics, Optimization

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Authors: Hyon I. Paek, Sang Rim Kim, Hyon A. Ryu

Abstract:

In functional linear regression, one typical problem is to reduce dimension. Compared with multivariate linear regression, functional linear regression is regarded as an infinite-dimensional case, and the main task is to reduce dimensions of functional response and functional predictors. One common approach is to adapt functional principal component analysis (FPCA) on functional predictors and then use a few leading functional principal components (FPC) to predict the functional model. The leading FPCs estimated by the typical FPCA explain a major variation of the functional predictor, but these leading FPCs may not be mostly correlated with the functional response, so they may not be significant in the prediction for response. In this paper, we propose a supervised FPCA method for a functional response model with FPCs obtained by considering the correlation of the functional response. Our method would have a better prediction accuracy than the typical FPCA method.

Keywords: Supervised, functional principal component analysis, functional response, functional linear regression.

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Authors: Arun Goel

Abstract:

Prediction of maximum local scour is necessary for the safety and economical design of the bridges. A number of equations have been developed over the years to predict local scour depth using laboratory data and a few pier equations have also been proposed using field data. Most of these equations are empirical in nature as indicated by the past publications. In this paper attempts have been made to compute local depth of scour around bridge pier in dimensional and non-dimensional form by using linear regression, simple regression and SVM (Poly & Rbf) techniques along with few conventional empirical equations. The outcome of this study suggests that the SVM (Poly & Rbf) based modeling can be employed as an alternate to linear regression, simple regression and the conventional empirical equations in predicting scour depth of bridge piers. The results of present study on the basis of non-dimensional form of bridge pier scour indicate the improvement in the performance of SVM (Poly & Rbf) in comparison to dimensional form of scour.

Keywords: Modeling, pier scour, regression, prediction, SVM (Poly & Rbf kernels).

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Authors: K. Nirmala Devi, V. Murali Bhaskaran

Abstract:

Traditional document representation for classification follows Bag of Words (BoW) approach to represent the term weights. The conventional method uses the Vector Space Model (VSM) to exploit the statistical information of terms in the documents and they fail to address the semantic information as well as order of the terms present in the documents. Although, the phrase based approach follows the order of the terms present in the documents rather than semantics behind the word. Therefore, a semantic concept based approach is used in this paper for enhancing the semantics by incorporating the ontology information. In this paper a novel method is proposed to forecast the intraday stock market price directional movement based on the sentiments from Twitter and money control news articles. The stock market forecasting is a very difficult and highly complicated task because it is affected by many factors such as economic conditions, political events and investor’s sentiment etc. The stock market series are generally dynamic, nonparametric, noisy and chaotic by nature. The sentiment analysis along with wisdom of crowds can automatically compute the collective intelligence of future performance in many areas like stock market, box office sales and election outcomes. The proposed method utilizes collective sentiments for stock market to predict the stock price directional movements. The collective sentiments in the above social media have powerful prediction on the stock price directional movements as up/down by using Granger Causality test.

Keywords: Bag of Words, Collective Sentiments, Ontology, Semantic relations, Sentiments, Social media, Stock Prediction, Twitter, Vector Space Model and wisdom of crowds.

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Authors: A. Goel, S.C. Gupta, S.K.Wasan

Abstract:

Testable software has two inherent properties – observability and controllability. Observability facilitates observation of internal behavior of software to required degree of detail. Controllability allows creation of difficult-to-achieve states prior to execution of various tests. In this paper, we describe COTT, a Controllability and Observability Testing Tool, to create testable object-oriented software. COTT provides a framework that helps the user to instrument object-oriented software to build the required controllability and observability. During testing, the tool facilitates creation of difficult-to-achieve states required for testing of difficultto- test conditions and observation of internal details of execution at unit, integration and system levels. The execution observations are logged in a test log file, which are used for post analysis and to generate test coverage reports.

Keywords: Controllability, Observability, Test Coverage and Testing Tool.

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