Search results for: real time data.
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 12976

Search results for: real time data.

10486 Exploiting Machine Learning Techniques for the Enhancement of Acceptance Sampling

Authors: Aikaterini Fountoulaki, Nikos Karacapilidis, Manolis Manatakis

Abstract:

This paper proposes an innovative methodology for Acceptance Sampling by Variables, which is a particular category of Statistical Quality Control dealing with the assurance of products quality. Our contribution lies in the exploitation of machine learning techniques to address the complexity and remedy the drawbacks of existing approaches. More specifically, the proposed methodology exploits Artificial Neural Networks (ANNs) to aid decision making about the acceptance or rejection of an inspected sample. For any type of inspection, ANNs are trained by data from corresponding tables of a standard-s sampling plan schemes. Once trained, ANNs can give closed-form solutions for any acceptance quality level and sample size, thus leading to an automation of the reading of the sampling plan tables, without any need of compromise with the values of the specific standard chosen each time. The proposed methodology provides enough flexibility to quality control engineers during the inspection of their samples, allowing the consideration of specific needs, while it also reduces the time and the cost required for these inspections. Its applicability and advantages are demonstrated through two numerical examples.

Keywords: Acceptance Sampling, Neural Networks, Statistical Quality Control.

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10485 The Effect of the Parameters of the Grinding on the Characteristics of the Deposit Phosphate Ore of Kef Es Sennoun, Djebel Onk-Tebessa, Algeria

Authors: N. Benabdeslam, N. Bouzidi, F. Atmani, R. Boucif, A. Sakhri

Abstract:

The objective of this study was to provide answers for a better understanding of the mechanisms involved during grinding. To obtain a phosphate powder, we carry out sieving - grinding circuits for each parameter influencing the process. The analysis of the average particle size of the different tests carried out served in the first place as a basis for the determination of the granulometric curve area, the characteristics and the granular coefficients, then the exploitation of the different results for the calculation of the energies consumed for the fragmentation of different ore types, the energy coefficients as well as the ability to grind. Indeed, a time of 5 to 10 minutes can be chosen as the optimal grinding time in a disc mill for a % in weight of the highest pass. However, grinding time can influence the granular characteristics of ore.

Keywords: Energy, granular characteristics, grinding, mineralogical composition, phosphate ore.

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10484 Delay-Distribution-Dependent Stability Criteria for BAM Neural Networks with Time-Varying Delays

Authors: J.H. Park, S. Lakshmanan, H.Y. Jung, S.M. Lee

Abstract:

This paper is concerned with the delay-distributiondependent stability criteria for bidirectional associative memory (BAM) neural networks with time-varying delays. Based on the Lyapunov-Krasovskii functional and stochastic analysis approach, a delay-probability-distribution-dependent sufficient condition is derived to achieve the globally asymptotically mean square stable of the considered BAM neural networks. The criteria are formulated in terms of a set of linear matrix inequalities (LMIs), which can be checked efficiently by use of some standard numerical packages. Finally, a numerical example and its simulation is given to demonstrate the usefulness and effectiveness of the proposed results.

Keywords: BAM neural networks, Probabilistic time-varying delays, Stability criteria.

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10483 A Novel Model for Simultaneously Minimising Costs and Risks in Just-in-Time Systems Using Multi-Backup Suppliers: Part 1- Modelling

Authors: Faraj El Dabee, Romeo Marian, Yousef Amer

Abstract:

Just-In-Time (JIT) is a lean manufacturing tool, which provides the benefits of efficiency, and of minimizing unnecessary costs for many organisations. However, the risks arising from these benefits have been disregarded. These risks impact on system processes disrupting the whole supply chain. This paper proposes an inventory model that can simultaneously reduce costs and risks in JIT systems. This model is developed to ascertain an optimal ordering strategy for procuring raw materials by using regular multi-external and local backup suppliers to reduce the total cost of the products, and at the same time to reduce the risks arising from this cost reduction within production systems. Some results that will be illustrated in the second part of this paper are presented.

Keywords: Lean manufacturing, Just-in-Time (JIT), production system, cost-risk reduction, inventory model, eternal supplier, local backup supplier.

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10482 Quantifying and Adjusting the Effects of Publication Bias in Continuous Meta-Analysis

Authors: N.R.N. Idris

Abstract:

This study uses simulated meta-analysis to assess the effects of publication bias on meta-analysis estimates and to evaluate the efficacy of the trim and fill method in adjusting for these biases. The estimated effect sizes and the standard error were evaluated in terms of the statistical bias and the coverage probability. The results demonstrate that if publication bias is not adjusted it could lead to up to 40% bias in the treatment effect estimates. Utilization of the trim and fill method could reduce the bias in the overall estimate by more than half. The method is optimum in presence of moderate underlying bias but has minimal effects in presence of low and severe publication bias. Additionally, the trim and fill method improves the coverage probability by more than half when subjected to the same level of publication bias as those of the unadjusted data. The method however tends to produce false positive results and will incorrectly adjust the data for publication bias up to 45 % of the time. Nonetheless, the bias introduced into the estimates due to this adjustment is minimal

Keywords: Publication bias, Trim and Fill method, percentage relative bias, coverage probability

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10481 A New Robust Stability Criterion for Dynamical Neural Networks with Mixed Time Delays

Authors: Guang Zhou, Shouming Zhong

Abstract:

In this paper, we investigate the problem of the existence, uniqueness and global asymptotic stability of the equilibrium point for a class of neural networks, the neutral system has mixed time delays and parameter uncertainties. Under the assumption that the activation functions are globally Lipschitz continuous, we drive a new criterion for the robust stability of a class of neural networks with time delays by utilizing the Lyapunov stability theorems and the Homomorphic mapping theorem. Numerical examples are given to illustrate the effectiveness and the advantage of the proposed main results.

Keywords: Neural networks, Delayed systems, Lyapunov function, Stability analysis.

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10480 Artificial Intelligence-Based Chest X-Ray Test of COVID-19 Patients

Authors: Dhurgham Al-Karawi, Nisreen Polus, Shakir Al-Zaidi, Sabah Jassim

Abstract:

The management of COVID-19 patients based on chest imaging is emerging as an essential tool for evaluating the spread of the pandemic which has gripped the global community. It has already been used to monitor the situation of COVID-19 patients who have issues in respiratory status. There has been increase to use chest imaging for medical triage of patients who are showing moderate-severe clinical COVID-19 features, this is due to the fast dispersal of the pandemic to all continents and communities. This article demonstrates the development of machine learning techniques for the test of COVID-19 patients using Chest X-Ray (CXR) images in nearly real-time, to distinguish the COVID-19 infection with a significantly high level of accuracy. The testing performance has covered a combination of different datasets of CXR images of positive COVID-19 patients, patients with viral and bacterial infections, also, people with a clear chest. The proposed AI scheme successfully distinguishes CXR scans of COVID-19 infected patients from CXR scans of viral and bacterial based pneumonia as well as normal cases with an average accuracy of 94.43%, sensitivity 95%, and specificity 93.86%. Predicted decisions would be supported by visual evidence to help clinicians speed up the initial assessment process of new suspected cases, especially in a resource-constrained environment.

Keywords: COVID-19, chest x-ray scan, artificial intelligence, texture analysis, local binary pattern transform, Gabor filter.

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10479 A Study about the Distribution of the Spanning Ratios of Yao Graphs

Authors: Maryam Hsaini, Mostafa Nouri-Baygi

Abstract:

A critical problem in wireless sensor networks is limited battery and memory of nodes. Therefore, each node in the network could maintain only a subset of its neighbors to communicate with. This will increase the battery usage in the network because each packet should take more hops to reach its destination. In order to tackle these problems, spanner graphs are defined. Since each node has a small degree in a spanner graph and the distance in the graph is not much greater than its actual geographical distance, spanner graphs are suitable candidates to be used for the topology of a wireless sensor network. In this paper, we study Yao graphs and their behavior for a randomly selected set of points. We generate several random point sets and compare the properties of their Yao graphs with the complete graph. Based on our data sets, we obtain several charts demonstrating how Yao graphs behave for a set of randomly chosen point set. As the results show, the stretch factor of a Yao graph follows a normal distribution. Furthermore, the stretch factor is in average far less than the worst case stretch factor proved for Yao graphs in previous results. Furthermore, we use Yao graph for a realistic point set and study its stretch factor in real world.

Keywords: Wireless sensor network, spanner graph, Yao Graph.

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10478 Continuous Feature Adaptation for Non-Native Speech Recognition

Authors: Y. Deng, X. Li, C. Kwan, B. Raj, R. Stern

Abstract:

The current speech interfaces in many military applications may be adequate for native speakers. However, the recognition rate drops quite a lot for non-native speakers (people with foreign accents). This is mainly because the nonnative speakers have large temporal and intra-phoneme variations when they pronounce the same words. This problem is also complicated by the presence of large environmental noise such as tank noise, helicopter noise, etc. In this paper, we proposed a novel continuous acoustic feature adaptation algorithm for on-line accent and environmental adaptation. Implemented by incremental singular value decomposition (SVD), the algorithm captures local acoustic variation and runs in real-time. This feature-based adaptation method is then integrated with conventional model-based maximum likelihood linear regression (MLLR) algorithm. Extensive experiments have been performed on the NATO non-native speech corpus with baseline acoustic model trained on native American English. The proposed feature-based adaptation algorithm improved the average recognition accuracy by 15%, while the MLLR model based adaptation achieved 11% improvement. The corresponding word error rate (WER) reduction was 25.8% and 2.73%, as compared to that without adaptation. The combined adaptation achieved overall recognition accuracy improvement of 29.5%, and WER reduction of 31.8%, as compared to that without adaptation.

Keywords: speaker adaptation; environment adaptation; robust speech recognition; SVD; non-native speech recognition

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10477 Control of Building Ventilation with CO2 Gas Sensors Based on Doped Magnesium Ferrite Nanoparticles for the Development of Construction and Infrastructure Industry

Authors: Maryam Kiani, Abdul Basit Kiani

Abstract:

To develop construction and infrastructure industry, sensors are highly desired to control building ventilation. Zinc doped magnesium ferrite nanoparticles (Z@MFO) (Zn = 0.0, 0.2, 0.3, 0.4) were prepared in this paper. Structural analyses confirmed the formation of spinel cubic nanostructures. X-Ray diffraction (XRD) data represent high reactive surface area due to small average particle size about 15 nm, which efficiently influences the gas sensing mechanism. The gas sensing property of Z@MFO for several gases was obtained by measuring the resistance as a function of different factors, such as composition and response time in air and in presence of gas. The sensitivity of spinel ferrite to CO2 at room temperature has been compared. The Z@MFO nano-structure exhibited high sensitivity represented good response time of (~1 min) to CO2, demonstrated that the material can be used in the field of gas sensors with high sensitivity and good selectivity at room temperature to control building ventilation. CO2 gas sensors play a vital role in ensuring the safety, comfort, and sustainability of modern building environments.

Keywords: MgFe2O4 nanoparticles, synthesis, gas sensing properties, X ray differentiation.

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10476 Application of Universal Distribution Factors for Real-Time Complex Power Flow Calculation

Authors: Abdullah M. Alodhaiani, Yasir A. Alturki, Mohamed A. Elkady

Abstract:

Complex power flow distribution factors, which relate line complex power flows to the bus injected complex powers, have been widely used in various power system planning and analysis studies. In particular, AC distribution factors have been used extensively in the recent power and energy pricing studies in free electricity market field. As was demonstrated in the existing literature, many of the electricity market related costing studies rely on the use of the distribution factors. These known distribution factors, whether the injection shift factors (ISF’s) or power transfer distribution factors (PTDF’s), are linear approximations of the first order sensitivities of the active power flows with respect to various variables. This paper presents a novel model for evaluating the universal distribution factors (UDF’s), which are appropriate for an extensive range of power systems analysis and free electricity market studies. These distribution factors are used for the calculations of lines complex power flows and its independent of bus power injections, they are compact matrix-form expressions with total flexibility in determining the position on the line at which line flows are measured. The proposed approach was tested on IEEE 9-Bus system. Numerical results demonstrate that the proposed approach is very accurate compared with exact method.

Keywords: Distribution Factors, Power System, Sensitivity Factors, Electricity Market.

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10475 Comparative Analysis of the Third Generation of Research Data for Evaluation of Solar Energy Potential

Authors: Claudineia Brazil, Elison Eduardo Jardim Bierhals, Luciane Teresa Salvi, Rafael Haag

Abstract:

Renewable energy sources are dependent on climatic variability, so for adequate energy planning, observations of the meteorological variables are required, preferably representing long-period series. Despite the scientific and technological advances that meteorological measurement systems have undergone in the last decades, there is still a considerable lack of meteorological observations that form series of long periods. The reanalysis is a system of assimilation of data prepared using general atmospheric circulation models, based on the combination of data collected at surface stations, ocean buoys, satellites and radiosondes, allowing the production of long period data, for a wide gamma. The third generation of reanalysis data emerged in 2010, among them is the Climate Forecast System Reanalysis (CFSR) developed by the National Centers for Environmental Prediction (NCEP), these data have a spatial resolution of 0.50 x 0.50. In order to overcome these difficulties, it aims to evaluate the performance of solar radiation estimation through alternative data bases, such as data from Reanalysis and from meteorological satellites that satisfactorily meet the absence of observations of solar radiation at global and/or regional level. The results of the analysis of the solar radiation data indicated that the reanalysis data of the CFSR model presented a good performance in relation to the observed data, with determination coefficient around 0.90. Therefore, it is concluded that these data have the potential to be used as an alternative source in locations with no seasons or long series of solar radiation, important for the evaluation of solar energy potential.

Keywords: Climate, reanalysis, renewable energy, solar radiation.

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10474 An Inverse Heat Transfer Algorithm for Predicting the Thermal Properties of Tumors during Cryosurgery

Authors: Mohamed Hafid, Marcel Lacroix

Abstract:

This study aimed at developing an inverse heat transfer approach for predicting the time-varying freezing front and the temperature distribution of tumors during cryosurgery. Using a temperature probe pressed against the layer of tumor, the inverse approach is able to predict simultaneously the metabolic heat generation and the blood perfusion rate of the tumor. Once these parameters are predicted, the temperature-field and time-varying freezing fronts are determined with the direct model. The direct model rests on one-dimensional Pennes bioheat equation. The phase change problem is handled with the enthalpy method. The Levenberg-Marquardt Method (LMM) combined to the Broyden Method (BM) is used to solve the inverse model. The effect (a) of the thermal properties of the diseased tissues; (b) of the initial guesses for the unknown thermal properties; (c) of the data capture frequency; and (d) of the noise on the recorded temperatures is examined. It is shown that the proposed inverse approach remains accurate for all the cases investigated.

Keywords: Cryosurgery, inverse heat transfer, Levenberg-Marquardt method, thermal properties, Pennes model, enthalpy method.

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10473 Explorative Data Mining of Constructivist Learning Experiences and Activities with Multiple Dimensions

Authors: Patrick Wessa, Bart Baesens

Abstract:

This paper discusses the use of explorative data mining tools that allow the educator to explore new relationships between reported learning experiences and actual activities, even if there are multiple dimensions with a large number of measured items. The underlying technology is based on the so-called Compendium Platform for Reproducible Computing (http://www.freestatistics.org) which was built on top the computational R Framework (http://www.wessa.net).

Keywords: Reproducible computing, data mining, explorative data analysis, compendium technology, computer assisted education

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10472 Exploring Value of Time, Shopping Behavior and Shopping Motivation of International Tourists in the Chatuchak Weekend Market

Authors: Chanin Yoopetch, Natcha Toemtemsab

Abstract:

The purpose of this study was to explore the demographic differences of international tourists according to three main factors, including the value of time, shopping behavior and shopping motivation. The Chatuchak Weekend Market is known as one of the biggest weekend markets in the world. Too little academic studies had been conducted in this area of weekend market, despite its growth and continuous development. In general, both domestic visitors and international tourists are attracted to the perception of cheap and bargaining prices the weekend market. However, systematic research study can provide reliable understanding of the perception of the visitors. This study focused on the group of international tourists who visited the market and aimed to provide better insights based on the differences in their demographic factors. Findings indicated that several differences in value of time, shopping behavior, and shopping motivation were identified by gender, income and age. Research implications and directions for further studies were discussed.

Keywords: Value of Time, Shopping Behavior, Shopping Motivation, Chatuchak Weekend Market.

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10471 The Lubrication Regimes Recognition of a Pressure-Fed Journal Bearing by Time and Frequency Domain Analysis of Acoustic Emission Signals

Authors: S. Hosseini, M. Ahmadi Najafabadi, M. Akhlaghi

Abstract:

The health of the journal bearings is very important in preventing unforeseen breakdowns in rotary machines, and poor lubrication is one of the most important factors for producing the bearing failures. Hydrodynamic lubrication (HL), mixed lubrication (ML), and boundary lubrication (BL) are three regimes of a journal bearing lubrication. This paper uses acoustic emission (AE) measurement technique to correlate features of the AE signals to the three lubrication regimes. The transitions from HL to ML based on operating factors such as rotating speed, load, inlet oil pressure by time domain and time-frequency domain signal analysis techniques are detected, and then metal-to-metal contacts between sliding surfaces of the journal and bearing are identified. It is found that there is a significant difference between theoretical and experimental operating values that are obtained for defining the lubrication regions.

Keywords: Acoustic emission technique, pressure fed journal bearing, time and frequency signal analysis, metal-to-metal contact.

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10470 Performance Analysis of Software Reliability Models using Matrix Method

Authors: RajPal Garg, Kapil Sharma, Rajive Kumar, R. K. Garg

Abstract:

This paper presents a computational methodology based on matrix operations for a computer based solution to the problem of performance analysis of software reliability models (SRMs). A set of seven comparison criteria have been formulated to rank various non-homogenous Poisson process software reliability models proposed during the past 30 years to estimate software reliability measures such as the number of remaining faults, software failure rate, and software reliability. Selection of optimal SRM for use in a particular case has been an area of interest for researchers in the field of software reliability. Tools and techniques for software reliability model selection found in the literature cannot be used with high level of confidence as they use a limited number of model selection criteria. A real data set of middle size software project from published papers has been used for demonstration of matrix method. The result of this study will be a ranking of SRMs based on the Permanent value of the criteria matrix formed for each model based on the comparison criteria. The software reliability model with highest value of the Permanent is ranked at number – 1 and so on.

Keywords: Matrix method, Model ranking, Model selection, Model selection criteria, Software reliability models.

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10469 Network Reconfiguration of Distribution System Using Artificial Bee Colony Algorithm

Authors: S. Ganesh

Abstract:

Power distribution systems typically have tie and sectionalizing switches whose states determine the topological configuration of the network. The aim of network reconfiguration of the distribution network is to minimize the losses for a load arrangement at a particular time. Thus the objective function is to minimize the losses of the network by satisfying the distribution network constraints. The various constraints are radiality, voltage limits and the power balance condition. In this paper the status of the switches is obtained by using Artificial Bee Colony (ABC) algorithm. ABC is based on a particular intelligent behavior of honeybee swarms. ABC is developed based on inspecting the behaviors of real bees to find nectar and sharing the information of food sources to the bees in the hive. The proposed methodology has three stages. In stage one ABC is used to find the tie switches, in stage two the identified tie switches are checked for radiality constraint and if the radilaity constraint is satisfied then the procedure is proceeded to stage three otherwise the process is repeated. In stage three load flow analysis is performed. The process is repeated till the losses are minimized. The ABC is implemented to find the power flow path and the Forward Sweeper algorithm is used to calculate the power flow parameters. The proposed methodology is applied for a 33–bus single feeder distribution network using MATLAB.

Keywords: Artificial Bee Colony (ABC) algorithm, Distribution system, Loss reduction, Network reconfiguration.

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10468 A Study of Students’ Perceptions Regarding the Effectiveness of Semester and Annual Examination System at Institute of Education and Research

Authors: Ayesha Batool, Saghir Ahmad, Abid Hussain Ch.

Abstract:

The art of the examination is probably the most difficult one in the whole range of educational practices. Semester system is the system of examination, which is set with an institute by its own teachers. Annual system is the system of examination, which is constructed and administrated by some agency outside the institute, it enables the teacher to estimate the effectiveness of the instruction, and students to estimate the progress made by them. On the other hand, semester system of examinations requires following the curriculum strictly and methods of teaching are to be employed by the choice of teachers. The main purpose of the study was to investigate university students’ perceptions regarding the effectiveness of semester system and annual system. The study was quantitative in nature. The sample consisted of 200 students. A five point Likert type scale was used to collect the data. The statistical measures like frequencies, mean, standard deviation, and One Way ANOVA test were applied to analyze the data. The major findings of the study indicated that in semester system students do not spend much time in political activities and develop their study habits. It also revealed that annual system of examination does not satisfy the educational aspirations of the students.

Keywords: Effectiveness, semester system, annual system.

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10467 Causes of Final Account Closing Delay: A Theoretical Framework

Authors: Zarabizan Zakaria, Syuhaida Ismail, Aminah Md. Yusof

Abstract:

Delay can be defined as time overrun or extension of time to complete the project. There are high possibilities that delay issues in final account closing cannot be avoided especially in construction project in Malaysia which is unique and dynamic in the terms of nature of design and technical skill. Delay in final account closing is a situation when the actual planning (time and budget allocation) of a construction project exceeds the planned schedule or on the other hand, final account closing exceeds the time and other provisions specified in the contract. The causes of delay discussed in this paper are appraised from the literature review. There are two main types of delay: excusable delay and non-excusable delay. The literature reviews on the delay in final account closing which is then translated into a theoretical framework are summarized in the context of construction players and academician perspective. It is anticipated that the finding reported in this paper could assist the planning of future strategies and guidelines of final account closing for the betterment of construction projects in Malaysia.

Keywords: Construction industry, construction contract, final account closing, delay.

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10466 Analysis of Textual Data Based On Multiple 2-Class Classification Models

Authors: Shigeaki Sakurai, Ryohei Orihara

Abstract:

This paper proposes a new method for analyzing textual data. The method deals with items of textual data, where each item is described based on various viewpoints. The method acquires 2- class classification models of the viewpoints by applying an inductive learning method to items with multiple viewpoints. The method infers whether the viewpoints are assigned to the new items or not by using the models. The method extracts expressions from the new items classified into the viewpoints and extracts characteristic expressions corresponding to the viewpoints by comparing the frequency of expressions among the viewpoints. This paper also applies the method to questionnaire data given by guests at a hotel and verifies its effect through numerical experiments.

Keywords: Text mining, Multiple viewpoints, Differential analysis, Questionnaire data

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10465 Removal of Nitrogen Compounds from Industrial Wastewater Using Sequencing Batch Reactor: The Effects of React Time

Authors: Ali W. Alattabi, Khalid S. Hashim, Hassnen M. Jafer, Ali Alzeyadi

Abstract:

This study was performed to optimise the react time (RT) and study its effects on the removal rates of nitrogen compounds in a sequencing batch reactor (SBR) treating synthetic industrial wastewater. The results showed that increasing the RT from 4 h to 10, 16 and 22 h significantly improved the nitrogen compounds’ removal efficiency, it was increased from 69.5% to 95%, 75.7 to 97% and from 54.2 to 80.1% for NH3-N, NO3-N and NO2-N respectively. The results obtained from this study showed that the RT of 22 h was the optimum for nitrogen compounds removal efficiency.

Keywords: Ammonia-nitrogen, retention time, nitrate, nitrite, sequencing batch reactor, sludge characteristics.

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10464 New Hybrid Method to Model Extreme Rainfalls

Authors: Y. Laaroussi, Z. Guennoun, A. Amar

Abstract:

Modeling and forecasting dynamics of rainfall occurrences constitute one of the major topics, which have been largely treated by statisticians, hydrologists, climatologists and many other groups of scientists. In the same issue, we propose, in the present paper, a new hybrid method, which combines Extreme Values and fractal theories. We illustrate the use of our methodology for transformed Emberger Index series, constructed basing on data recorded in Oujda (Morocco). The index is treated at first by Peaks Over Threshold (POT) approach, to identify excess observations over an optimal threshold u. In the second step, we consider the resulting excess as a fractal object included in one dimensional space of time. We identify fractal dimension by the box counting. We discuss the prospect descriptions of rainfall data sets under Generalized Pareto Distribution, assured by Extreme Values Theory (EVT). We show that, despite of the appropriateness of return periods given by POT approach, the introduction of fractal dimension provides accurate interpretation results, which can ameliorate apprehension of rainfall occurrences.

Keywords: Extreme values theory, Fractals dimensions, Peaks Over Threshold, Rainfall occurrences.

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10463 Comparison of S-transform and Wavelet Transform in Power Quality Analysis

Authors: Mohammad Javad Dehghani

Abstract:

In the power quality analysis non-stationary nature of voltage distortions require some precise and powerful analytical techniques. The time-frequency representation (TFR) provides a powerful method for identification of the non-stationary of the signals. This paper investigates a comparative study on two techniques for analysis and visualization of voltage distortions with time-varying amplitudes. The techniques include the Discrete Wavelet Transform (DWT), and the S-Transform. Several power quality problems are analyzed using both the discrete wavelet transform and S–transform, showing clearly the advantage of the S– transform in detecting, localizing, and classifying the power quality problems.

Keywords: Power quality, S-Transform, Short Time FourierTransform , Wavelet Transform, instantaneous sag, swell.

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10462 A 1H NMR-Linked PCR Modelling Strategy for Tracking the Fatty Acid Sources of Aldehydic Lipid Oxidation Products in Culinary Oils Exposed to Simulated Shallow-Frying Episodes

Authors: Martin Grootveld, Benita Percival, Sarah Moumtaz, Kerry L. Grootveld

Abstract:

Objectives/Hypotheses: The adverse health effect potential of dietary lipid oxidation products (LOPs) has evoked much clinical interest. Therefore, we employed a 1H NMR-linked Principal Component Regression (PCR) chemometrics modelling strategy to explore relationships between data matrices comprising (1) aldehydic LOP concentrations generated in culinary oils/fats when exposed to laboratory-simulated shallow frying practices, and (2) the prior saturated (SFA), monounsaturated (MUFA) and polyunsaturated fatty acid (PUFA) contents of such frying media (FM), together with their heating time-points at a standard frying temperature (180 oC). Methods: Corn, sunflower, extra virgin olive, rapeseed, linseed, canola, coconut and MUFA-rich algae frying oils, together with butter and lard, were heated according to laboratory-simulated shallow-frying episodes at 180 oC, and FM samples were collected at time-points of 0, 5, 10, 20, 30, 60, and 90 min. (n = 6 replicates per sample). Aldehydes were determined by 1H NMR analysis (Bruker AV 400 MHz spectrometer). The first (dependent output variable) PCR data matrix comprised aldehyde concentration scores vectors (PC1* and PC2*), whilst the second (predictor) one incorporated those from the fatty acid content/heating time variables (PC1-PC4) and their first-order interactions. Results: Structurally complex trans,trans- and cis,trans-alka-2,4-dienals, 4,5-epxy-trans-2-alkenals and 4-hydroxy-/4-hydroperoxy-trans-2-alkenals (group I aldehydes predominantly arising from PUFA peroxidation) strongly and positively loaded on PC1*, whereas n-alkanals and trans-2-alkenals (group II aldehydes derived from both MUFA and PUFA hydroperoxides) strongly and positively loaded on PC2*. PCR analysis of these scores vectors (SVs) demonstrated that PCs 1 (positively-loaded linoleoylglycerols and [linoleoylglycerol]:[SFA] content ratio), 2 (positively-loaded oleoylglycerols and negatively-loaded SFAs), 3 (positively-loaded linolenoylglycerols and [PUFA]:[SFA] content ratios), and 4 (exclusively orthogonal sampling time-points) all powerfully contributed to aldehydic PC1* SVs (p 10-3 to < 10-9), as did all PC1-3 x PC4 interaction ones (p 10-5 to < 10-9). PC2* was also markedly dependent on all the above PC SVs (PC2 > PC1 and PC3), and the interactions of PC1 and PC2 with PC4 (p < 10-9 in each case), but not the PC3 x PC4 contribution. Conclusions: NMR-linked PCR analysis is a valuable strategy for (1) modelling the generation of aldehydic LOPs in heated cooking oils and other FM, and (2) tracking their unsaturated fatty acid (UFA) triacylglycerol sources therein.

Keywords: Frying oils, frying episodes, lipid oxidation products, cytotoxic/genotoxic aldehydes, chemometrics, principal component regression, NMR Analysis.

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10461 Quantum-Like Approach for Deriving a Theory Describing the Concept of Interpretation

Authors: Yehuda Roth

Abstract:

In quantum theory, a system’s time evolution is predictable unless an observer performs measurement, as the measurement process can randomize the system. This randomness appears when the measuring device does not accurately describe the measured item, i.e., when the states characterizing the measuring device appear as a superposition of those being measured. When such a mismatch occurs, the measured data randomly collapse into a single eigenstate of the measuring device. This  scenario resembles the interpretation process in which the observer does not experience an objective reality but interprets it based on preliminary descriptions initially ingrained into his/her mind. This distinction is the motivation for the present study in which the collapse scenario is regarded as part of the interpretation process of the observer. By adopting the formalism of the quantum theory, we present a complete mathematical approach that describes the interpretation process. We demonstrate this process by applying the proposed interpretation formalism to the ambiguous image "My wife and mother-in-law" to identify whether a woman in the picture is young or old.

Keywords: Interpretation, ambiguous images, data reception, state matching, classification, determination.

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10460 Influence of Hydraulic Retention Time on Biogas Production from Frozen Seafood Wastewater using Decanter Cake as Anaerobic Co-digestion Material

Authors: Thaniya Kaosol, Narumol Sohgrathok

Abstract:

In this research, an anaerobic co-digestion using decanter cake from palm oil mill industry to improve the biogas production from frozen seafood wastewater is studied using Continuously Stirred Tank Reactor (CSTR) process. The experiments were conducted in laboratory-scale. The suitable Hydraulic Retention Time (HRT) was observed in CSTR experiments with 24 hours of mixing time using the mechanical mixer. The HRT of CSTR process impacts on the efficiency of biogas production. The best performance for biogas production using CSTR process was the anaerobic codigestion for 20 days of HRT with the maximum methane production rate of 1.86 l/d and the average maximum methane production of 64.6%. The result can be concluded that the decanter cake can improve biogas productivity of frozen seafood wastewater.

Keywords: anaerobic co-digestion, frozen seafood wastewater, decanter cake, biogas, hydraulic retention time

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10459 Reliability Analysis of Computer Centre at Yobe State University Using LRU Algorithm

Authors: V. V. Singh, Yusuf Ibrahim Gwanda, Rajesh Prasad

Abstract:

In this paper, we focus on the reliability and performance analysis of Computer Centre (CC) at Yobe State University, Damaturu, Nigeria. The CC consists of three servers: one database mail server, one redundant and one for sharing with the client computers in the CC (called as a local server). Observing the different possibilities of the functioning of the CC, the analysis has been done to evaluate the various popular measures of reliability such as availability, reliability, mean time to failure (MTTF), profit analysis due to the operation of the system. The system can ultimately fail due to the failure of router, redundant server before repairing the mail server and switch failure. The system can also partially fail when a local server fails. The failed devices have restored according to Least Recently Used (LRU) techniques. The system can also fail entirely due to a cooling failure of the server, electricity failure or some natural calamity like earthquake, fire tsunami, etc. All the failure rates are assumed to be constant and follow exponential time distribution, while the repair follows two types of distributions: i.e. general and Gumbel-Hougaard family copula distribution.

Keywords: Reliability, availability Gumbel-Hougaard family copula, MTTF, internet data center.

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10458 Using Automated Database Reverse Engineering for Database Integration

Authors: M. R. Abbasifard, M. Rahgozar, A. Bayati, P. Pournemati

Abstract:

One important problem in today organizations is the existence of non-integrated information systems, inconsistency and lack of suitable correlations between legacy and modern systems. One main solution is to transfer the local databases into a global one. In this regards we need to extract the data structures from the legacy systems and integrate them with the new technology systems. In legacy systems, huge amounts of a data are stored in legacy databases. They require particular attention since they need more efforts to be normalized, reformatted and moved to the modern database environments. Designing the new integrated (global) database architecture and applying the reverse engineering requires data normalization. This paper proposes the use of database reverse engineering in order to integrate legacy and modern databases in organizations. The suggested approach consists of methods and techniques for generating data transformation rules needed for the data structure normalization.

Keywords: Reverse Engineering, Database Integration, System Integration, Data Structure Normalization

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10457 An Impairment Sensitive and Reliable SR-ARQ Mechanism for Unreliable Feedback in GPRS

Authors: Mansab Ali, Muhammad Khalid Khan

Abstract:

The advances in wireless communication have opened unlimited horizons but there are some challenges as well. The Nature derived air medium between MS (Mobile Station) and BS (Base Station) is beyond human control and produces channel impairment. The impact of the natural conditions at the air medium is the biggest issue in wireless communication. Natural conditions make reliability more cumbersome; here reliability refers to the efficient recovery of the lost or erroneous data. The SR-ARQ (Selective Repeat-Automatic Repeat Request) protocol is a de facto standard for any wireless technology at the air interface with its standard reliability features. Our focus in this research is on the reliability of the control or feedback signal of the SR-ARQ protocol. The proposed mechanism, RSR-ARQ (Reliable SR-ARQ) is an enhancement of the SR-ARQ protocol that has ensured the reliability of the control signals through channel impairment sensitive mechanism. We have modeled the system under two-state discrete time Markov Channel. The simulation results demonstrate the better recovery of the lost or erroneous data that will increase the overall system performance.

Keywords: ISR-ARQ, MAA, RSR-ARQ, SAA.

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