Search results for: Core Strength Exercise Training

Authors: Anas M. Fares, A. Touqan

Abstract:

A common approach in resisting lateral forces is the use of reinforced concrete shear walls in buildings. These walls represent the main elements to resist the lateral forces due to their large strength and stiffness. However, such walls may contain many openings due to functional requirements, and this may largely affect the overall lateral stiffness of them. It is thus of prime importance to quantify the effect of openings on the dynamic performance of the shear walls. SAP2000 structural analysis program is used as a main source after verifying the results. This study is made by using linear elastic analysis. The results are compared to ASCE7-16 code empirical equations for estimating the fundamental period of shear wall structures. Finally, statistical regression is used to fit an equation for estimating the increase in the fundamental period of shear-walled regular structures due to windows openings in the walls.

Keywords: Concrete, earthquake-resistant design, finite element, fundamental period, lateral stiffness, linear analysis, modal analysis, rayleigh, SAP2000, shear wall, ASCE7-16.

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Authors: M. S. Hosseini, M. Tazzoli-Shadpour, I. Amjadi, A. A. Katbab, E. Jaefargholi-Rangraz

Abstract:

Bionanotechnology deals with nanoscopic interactions between nanostructured materials and biological systems. Polymer nanocomposites with optimized biological activity have attracted great attention. Nanoclay is considered as reinforcing nanofiller in manufacturing of high performance nanocomposites. In current study, organomodified-nanoclay with negatively charged silicate layers was incorporated into biomedical grade silicone rubber. Nanoparticle loading has been tailored to enhance cell behavior. Addition of nanoparticles led to improved mechanical properties of substrate with enhanced strength and stiffness while no toxic effects was observed. Results indicated improved viability and proliferation of cells by addition of nanofillers. The improved mechanical properties of the matrix result in proper cell response through adjustment and arrangement of cytoskeletal fibers. Results can be applied in tissue engineering when enhanced substrates are required for improvement of cell behavior for in vivo applications.

Keywords: Biocompatibility, Composite, Organomodified- Nanoclay, Proliferation

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Authors: H. Mirzaee, A. Pourzaki

Abstract:

Using strength Pulse Electrical Field (PEF) in food industries is a non-thermal process that can deactivate microorganisms and increase penetration in plant and animals tissues without serious impact on food taste and quality. In this paper designing and fabricating of a PEF generator has been presented. Pulse generation methods have been surveyed and the best of them selected. The equipment by controller set can generate square pulse with adjustable parameters such as amplitude 1-5kV, frequency 0.1-10Hz, pulse width 10-100s, and duty cycle 0-100%. Setting the number of pulses, and presenting the output voltage and current waveforms on the oscilloscope screen are another advantages of this equipment. Finally, some food samples were tested that yielded the satisfactory results. PEF applying had considerable effects on potato, banana and purple cabbage. It caused increase Brix factor from 0.05 to 0.15 in potato solution. It is also so effective in extraction color material from purple cabbage. In the last experiment effects of PEF voltages on color extraction of saffron scum were surveyed (about 6% increasing yield).

Keywords: PEF, Capacitor, Switch, IGBT

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Authors: Mohammed Mustapha Alhaji, Salawu Sadiku

Abstract:

A clay soil classified as A-7-6 and CH soil according to AASHTO and unified soil classification system respectively, was stabilized using A-3 soil (AASHTO soil classification system). The clay soil was replaced with 0%, 10%, 20%, to 100% A-3 soil, compacted at both British Standard Light (BSL) and British Standard Heavy (BSH) compaction energy levels and using Unconfined Compressive Strength (UCS) as evaluation criteria. The Maximum Dry Density (MDD) of the treated soils at both the BSL and BSH compaction energy levels showed increase from 0% to 40% A-3 soil replacement after which the values reduced to 100% replacement. The trend of the Optimum Moisture Content (OMC) with varied A-3 soil replacement was similar to that of MDD but in a reversed order. The OMC reduced from 0% to 40% A-3 soil replacement after which the values increased to 100% replacement. This trend was attributed to the observed reduction in void ratio from 0% to 40% replacement after which the void ratio increased to 100% replacement. The maximum UCS for the soil at varied A-3 soil replacement increased from 272 and 770 kN/m2 for BSL and BSH compaction energy level at 0% replacement to 295 and 795 kN/m2 for BSL and BSH compaction energy level respectively at 10% replacement after which the values reduced to 22 and 60 kN/m2 for BSL and BSH compaction energy level respectively at 70% replacement. Beyond 70% replacement, the mixtures could not be moulded for UCS test.

Keywords: A-3 soil, clay soil, pozzolanic action, stabilization.

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Authors: R. Tur, A. Yardımcı

Abstract:

Imperfect knowledge cannot be avoided all the time. Imperfections may have several forms; uncertainties, imprecision and incompleteness. When we look to classification of methods for the management of imperfect knowledge we see fuzzy set-based techniques. The choice of a method to process data is linked to the choice of knowledge representation, which can be numerical, symbolic, logical or semantic and it depends on the nature of the problem to be solved for example decision support, which will be mentioned in our study. Fuzzy Logic is used for its ability to manage imprecise knowledge, but it can take advantage of the ability of neural networks to learn coefficients or functions. Such an association of methods is typical of so-called soft computing. In this study a new method was used for the management of imprecision for collected knowledge which related to economic analysis of construction industry in Turkey. Because of sudden changes occurring in economic factors decrease competition strength of construction companies. The better evaluation of these changes in economical factors in view of construction industry will made positive influence on company-s decisions which are dealing construction.

Keywords: Fuzzy logic, decision support systems, construction industry.

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Authors: Okan Özgönenel

Abstract:

This paper presents the development of a wavelet based algorithm, for distinguishing between magnetizing inrush currents and power system fault currents, which is quite adequate, reliable, fast and computationally efficient tool. The proposed technique consists of a preprocessing unit based on discrete wavelet transform (DWT) in combination with an artificial neural network (ANN) for detecting and classifying fault currents. The DWT acts as an extractor of distinctive features in the input signals at the relay location. This information is then fed into an ANN for classifying fault and magnetizing inrush conditions. A 220/55/55 V, 50Hz laboratory transformer connected to a 380 V power system were simulated using ATP-EMTP. The DWT was implemented by using Matlab and Coiflet mother wavelet was used to analyze primary currents and generate training data. The simulated results presented clearly show that the proposed technique can accurately discriminate between magnetizing inrush and fault currents in transformer protection.

Keywords: Artificial neural network, discrete wavelet transform, fault detection, magnetizing inrush current.

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Authors: Leandi Steenkamp

Abstract:

The sectional title industry has been a part of the property landscape in South Africa for almost half a century, and plays a significant role in addressing the housing problem in the country. Stakeholders such as owners and investors in sectional title property are in most cases not directly involved in the management thereof, and place reliance on the audited annual financial statements of bodies corporate for decision-making purposes. Although the industry seems to be highly regulated, the legislation regarding accounting and auditing of sectional title is vague and ambiguous. Furthermore, there are no industry-specific auditing and accounting standards to guide accounting and auditing practitioners in performing their work and industry financial benchmarks are not readily available. In addition, financial pressure on sectional title schemes is often very high due to the fact that some owners exercise unrealistic pressure to keep monthly levies as low as possible. All these factors have an impact on the business risk as well as audit risk of bodies corporate. Very little academic research has been undertaken on the sectional title industry in South Africa from an accounting and auditing perspective. The aim of this paper is threefold: Firstly, to discuss the findings of a literature review on uncertainties, ambiguity and confusing aspects in current legislation regarding the audit of a sectional title property that may cause or increase audit and business risk. Secondly, empirical findings of risk-related aspects from the results of interviews with three groups of body corporate role-players will be discussed. The role-players were body corporate trustee chairpersons, body corporate managing agents and accounting and auditing practitioners of bodies corporate. Specific reference will be made to business risk and audit risk. Thirdly, practical recommendations will be made on possibilities of closing the audit expectation gap, and further research opportunities in this regard will be discussed.

Keywords: Assurance, audit, audit risk, body corporate, corporate governance, sectional title.

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Authors: Nicholas Milburn, Yu Liang, Dalei Wu

Abstract:

Athlete performance scoring within the climbing domain presents interesting challenges as the sport does not have an objective way to assign skill. Assessing skill levels within any sport is valuable as it can be used to mark progress while training, and it can help an athlete choose appropriate climbs to attempt. Machine learning-based methods are popular for complex problems like this. The dataset available was composed of dynamic force data recorded during climbing; however, this dataset came with challenges such as data scarcity, imbalance, and it was temporally heterogeneous. Investigated solutions to these challenges include data augmentation, temporal normalization, conversion of time series to the spectral domain, and cross validation strategies. The investigated solutions to the classification problem included light weight machine classifiers KNN and SVM as well as the deep learning with CNN. The best performing model had an 80% accuracy. In conclusion, there seems to be enough information within climbing force data to accurately categorize climbers by skill.

Keywords: Classification, climbing, data imbalance, data scarcity, machine learning, time sequence.

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Authors: Kristina Davoian, Wolfram-M. Lippe

Abstract:

Evolutionary Programming (EP) represents a methodology of Evolutionary Algorithms (EA) in which mutation is considered as a main reproduction operator. This paper presents a novel EP approach for Artificial Neural Networks (ANN) learning. The proposed strategy consists of two components: the self-adaptive, which contains phenotype information and the dynamic, which is described by genotype. Self-adaptation is achieved by the addition of a value, called the network weight, which depends on a total number of hidden layers and an average number of neurons in hidden layers. The dynamic component changes its value depending on the fitness of a chromosome, exposed to mutation. Thus, the mutation step size is controlled by two components, encapsulated in the algorithm, which adjust it according to the characteristics of a predefined ANN architecture and the fitness of a particular chromosome. The comparative analysis of the proposed approach and the classical EP (Gaussian mutation) showed, that that the significant acceleration of the evolution process is achieved by using both phenotype and genotype information in the mutation strategy.

Keywords: Artificial Neural Networks (ANN), Learning Theory, Evolutionary Programming (EP), Mutation, Self-Adaptation.

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Authors: Ashish Payal, C. S. Rai, B. V. R. Reddy

Abstract:

With the increasing use and application of Wireless Sensor Networks (WSN), need has arisen to explore them in more effective and efficient manner. An important area which can bring efficiency to WSNs is the localization process, which refers to the estimation of the position of wireless sensor nodes in an ad hoc network setting, in reference to a coordinate system that may be internal or external to the network. In this paper, we have done comparison and analysed Sigmoidal Feedforward Artificial Neural Networks (SFFANNs) and Radial Basis Function (RBF) networks for developing localization framework in WSNs. The presented work utilizes the Received Signal Strength Indicator (RSSI), measured by static node on 100 x 100 m² grid from three anchor nodes. The comprehensive evaluation of these approaches is done using MATLAB software. The simulation results effectively demonstrate that FFANNs based sensor motes will show better localization accuracy as compared to RBF.

Keywords: Localization, wireless sensor networks, artificial neural network, radial basis function, multi-layer perceptron, backpropagation, RSSI.

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Authors: W. Handoko, F. Pahlevani, V. Sahajwalla

Abstract:

Dual-phase high carbon steels (DHCS) are commonly known for their improved strength, hardness, and abrasive resistance properties due to co-presence of retained austenite and martensite at the same time. Retained austenite is a meta-stable phase at room temperature, and stability of this phase governs the response of DHCS at different conditions. This research paper studies the effect of RA stability on corrosion behaviour of high carbon steels after they have been immersed into 1.0 M NaCl solution for various times. For this purpose, two different steels with different RA stabilities have been investigated. The surface morphology of the samples before and after corrosion attack was observed by secondary electron microscopy (SEM) and atomic force microscopy (AFM), along with the weight loss and Vickers hardness analysis. Microstructural investigations proved the preferential attack to retained austenite phase during corrosion. Hence, increase in the stability of retained austenite in dual-phase steels led to decreasing the weight loss rate.

Keywords: High carbon steel, austenite stability, atomic force microscopy, corrosion.

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Authors: Pashayev A., Askerov D., C. Ardil, Sadiqov R., Abdullayev P.

Abstract:

Groundlessness of application probability-statistic methods are especially shown at an early stage of the aviation GTE technical condition diagnosing, when the volume of the information has property of the fuzzy, limitations, uncertainty and efficiency of application of new technology Soft computing at these diagnosing stages by using the fuzzy logic and neural networks methods. It is made training with high accuracy of multiple linear and nonlinear models (the regression equations) received on the statistical fuzzy data basis. At the information sufficiency it is offered to use recurrent algorithm of aviation GTE technical condition identification on measurements of input and output parameters of the multiple linear and nonlinear generalized models at presence of noise measured (the new recursive least squares method (LSM)). As application of the given technique the estimation of the new operating aviation engine D30KU-154 technical condition at height H=10600 m was made.

Keywords: Identification of a technical condition, aviation gasturbine engine, fuzzy logic and neural networks.

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Authors: M.R. Mustafa, M.H. Isa, R.B. Rezaur

Abstract:

Prediction of highly non linear behavior of suspended sediment flow in rivers has prime importance in the field of water resources engineering. In this study the predictive performance of two Artificial Neural Networks (ANNs) namely, the Radial Basis Function (RBF) Network and the Multi Layer Feed Forward (MLFF) Network have been compared. Time series data of daily suspended sediment discharge and water discharge at Pari River was used for training and testing the networks. A number of statistical parameters i.e. root mean square error (RMSE), mean absolute error (MAE), coefficient of efficiency (CE) and coefficient of determination (R2) were used for performance evaluation of the models. Both the models produced satisfactory results and showed a good agreement between the predicted and observed data. The RBF network model provided slightly better results than the MLFF network model in predicting suspended sediment discharge.

Keywords: ANN, discharge, modeling, prediction, suspendedsediment,

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Authors: Lu Si, Jie Yu, Shasha Li, Jun Ma, Lei Luo, Qingbo Wu, Yongqi Ma, Zhengji Liu

Abstract:

Instance selection (IS) technique is used to reduce the data size to improve the performance of data mining methods. Recently, to process very large data set, several proposed methods divide the training set into some disjoint subsets and apply IS algorithms independently to each subset. In this paper, we analyze the limitation of these methods and give our viewpoint about how to divide and conquer in IS procedure. Then, based on fast condensed nearest neighbor (FCNN) rule, we propose a large data sets instance selection method with MapReduce framework. Besides ensuring the prediction accuracy and reduction rate, it has two desirable properties: First, it reduces the work load in the aggregation node; Second and most important, it produces the same result with the sequential version, which other parallel methods cannot achieve. We evaluate the performance of FCNN-MR on one small data set and two large data sets. The experimental results show that it is effective and practical.

Keywords: Instance selection, data reduction, MapReduce, kNN.

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Authors: M. R. Abusrea, Shiyi Jiang, Dingding Chen, Kazuo Arakawa

Abstract:

Novel wind-lens turbine designs can augment power output. Vacuum-Assisted Resin Transfer Molding (VARTM) is used to form large and complex structures from a Carbon Fiber Reinforced Polymer (CFRP) composite. Typically, wind-lens turbine structures are fabricated in segments, and then bonded to form the final structure. This paper introduces five new adhesive joints, divided into two groups: one is constructed between dry carbon and CFRP fabrics, and the other is constructed with two dry carbon fibers. All joints and CFRP fabrics were made in our laboratory using VARTM manufacturing techniques. Specimens were prepared for tensile testing to measure joint performance. The results showed that the second group of joints achieved a higher tensile strength than the first group. On the other hand, the tensile fracture behavior of the two groups showed the same pattern of crack originating near the joint ends followed by crack propagation until fracture.

Keywords: Adhesive joints, CFRP, VARTM, resin transfer molding.

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Authors: Ekachai Phaisangittisagul, Rapeepol Chongprachawat

Abstract:

Obtaining labeled data in supervised learning is often difficult and expensive, and thus the trained learning algorithm tends to be overfitting due to small number of training data. As a result, some researchers have focused on using unlabeled data which may not necessary to follow the same generative distribution as the labeled data to construct a high-level feature for improving performance on supervised learning tasks. In this paper, we investigate the impact of the relationship between unlabeled and labeled data for classification performance. Specifically, we will apply difference unlabeled data which have different degrees of relation to the labeled data for handwritten digit classification task based on MNIST dataset. Our experimental results show that the higher the degree of relation between unlabeled and labeled data, the better the classification performance. Although the unlabeled data that is completely from different generative distribution to the labeled data provides the lowest classification performance, we still achieve high classification performance. This leads to expanding the applicability of the supervised learning algorithms using unsupervised learning.

Keywords: Autoencoder, high-level feature, MNIST dataset, selftaught learning, supervised learning.

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Authors: Olatunde I. Sekunowo, Stephen I. Durowaye, Oluwashina P. Gbenebor

Abstract:

Copper being one of the major intrinsic residual impurities in steel possesses the tendency to induce severe microstructural distortions if not controlled within certain limits. Hence, this paper investigates the effect of this element on the mechanical properties of construction steel with a view to ascertain its safe limits for effective control. The experiment entails collection of statistically scheduled samples of hot rolled profiles with varied copper concentrations in the range of 0.12-0.39 wt. %. From these samples were prepared standard test specimens subjected to tensile, impact, hardness and microstructural analyses. Results show a rather huge compromise in mechanical properties as the specimens demonstrated 54.3%, 74.2% and 64.9% reduction in tensile strength, impact energy and hardness respectively as copper content increases from 0.12 wt. % to 0.39 wt. %. The steel’s abysmal performance is due to the severe distortion of the microstructure occasioned by the development of incoherent complex compounds which weaken the pearlite reinforcing phase. It is concluded that the presence of copper above 0.22 wt. % is deleterious to construction steel performance.

Keywords: Construction steel, mechanical properties, processing method, trace elements.

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Authors: Pashayev A., Askerov D., C. Ardil, Sadiqov R., Abdullayev P.

Abstract:

Groundlessness of application probability-statistic methods are especially shown at an early stage of the aviation GTE technical condition diagnosing, when the volume of the information has property of the fuzzy, limitations, uncertainty and efficiency of application of new technology Soft computing at these diagnosing stages by using the fuzzy logic and neural networks methods. It is made training with high accuracy of multiple linear and nonlinear models (the regression equations) received on the statistical fuzzy data basis. At the information sufficiency it is offered to use recurrent algorithm of aviation GTE technical condition identification on measurements of input and output parameters of the multiple linear and nonlinear generalized models at presence of noise measured (the new recursive least squares method (LSM)). As application of the given technique the estimation of the new operating aviation engine D30KU-154 technical condition at height H=10600 m was made.

Keywords: Identification of a technical condition, aviation gasturbine engine, fuzzy logic and neural networks.

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Authors: Z. Pavlík, M. Pavlíková, P. Volfová, M. Keppert, R. Černý

Abstract:

Properties of cement pastes with fine-ground ceramics used as an alternative binder replacing Portland cement up to 20% of its mass are investigated. At first, the particle size distribution of cement and fine-ground ceramics is measured using laser analyser. Then, the material properties are studied in the early hardening period up to 28 days. The hydration process of studied materials is monitored by electrical conductivity measurement using TDR sensors. The changes of materials- structures within the hardening are observed using pore size distribution measurement. The compressive strength measurements are done as well. Experimental results show that the replacement of Portland cement by fine-ground ceramics in the amount of up to 20% by mass is acceptable solution from the mechanical point of view. One can also assume similar physical properties of designed materials to the reference material with only Portland cement as binder.

Keywords: Fine-ground ceramics, cement pastes, early age properties, mechanical properties, pore size distribution, electrical conductivity measurement.

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Authors: Adnan Mazari, Engin Akcagun, Antonin Havelka, Funda Buyuk Mazari, Pavel Kejzlar

Abstract:

This article experimentally investigates various physical properties of special fire retardant sewing threads under different sewing speeds. The aramid threads are common for sewing the fire-fighter clothing due to high strength and high melting temperature. 3 types of aramid threads with different linear densities are used for sewing at different speed of 2000 to 4000 r/min. The needle temperature is measured at different speeds of sewing and tensile properties of threads are measured before and after the sewing process respectively. The results shows that the friction and abrasion during the sewing process causes a significant loss to the tensile properties of the threads and needle temperature rises to nearly 300^oC at 4000 r/min of machine speed. The Scanning electron microscope images are taken before and after the sewing process and shows no melting spots but significant damage to the yarn. It is also found that machine speed of 2000r/min is ideal for sewing firefighter clothing for higher tensile properties and production.

Keywords: Kevlar, needle temperature, Nomex, sewing.

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Authors: F. Atheer, Al-Saoudi, Robin Kalfat, Riadh Al-Mahaidi

Abstract:

Over the last two decades, externally bonded fiber reinforced polymer (FRP) composites bonded to concrete substrates has become a popular method for strengthening reinforced concrete (RC) highway and railway bridges. Such structures are exposed to severe cyclic loading throughout their lifetime often resulting in fatigue damage to structural components and a reduction in the service life of the structure. Since experimental and numerical results on the fatigue performance of FRP-to-concrete joints are still limited, the current research focuses on assessing the fatigue performance of externally bonded FRP-to-concrete joints using a direct shear test. Some early results indicate that the stress ratio and the applied cyclic stress level have a direct influence on the fatigue life of the externally bonded FRP. In addition, a calibrated finite element model is developed to provide further insight into the influence of certain parameters such as: concrete strength, FRP thickness, number of cycles, frequency, and stiffness on the fatigue life of the FRP-toconcrete joints.

Keywords: FRP, concrete bond, control, fatigue, finite element model.

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Authors: Jehad Al Dallal

Abstract:

Attributes and methods are the basic contents of an object-oriented class. The connectivity among these class members and the relationship between the class and other classes play an important role in determining the quality of an object-oriented system. Class cohesion evaluates the degree of relatedness of class attributes and methods, whereas class coupling refers to the degree to which a class is related to other classes. Researchers have proposed several class cohesion and class coupling measures. However, the correlation between class coupling and class cohesion measures has not been thoroughly studied. In this paper, using classes of three open-source Java systems, we empirically investigate the correlation between several measures of connectivity-based class cohesion and coupling. Four connectivity-based cohesion measures and eight coupling measures are considered in the empirical study. The empirical study results show that class connectivity-based cohesion and coupling internal quality attributes are inversely correlated. The strength of the correlation depends highly on the cohesion and coupling measurement approaches.

Keywords: Object-oriented class, software quality, class cohesion measure, class coupling measure.

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Authors: Ploysai Ohama, Nattida Tumpat

Abstract:

Natural dye extracted from Caesalpinia sappan Linn. was applied to a cotton fabric and silk yarn by dyeing process. The dyestuff component of Caesalpinia sappan Linn. was extracted using water and ethanol. Analytical studies such as UV–VIS spectrophotometry and gravimetric analysis were performed on the extracts. Brazilein, the major dyestuff component of Caesalpinia sappan Linn. was confirmed in both aqueous and ethanolic extracts by UV–VIS spectrum. The color of each dyed material was investigated in terms of the CIELAB (L*, a* and b*) and K/S values. Cotton fabric dyed without mordant had a shade of reddish-brown, while those post-mordanted with aluminum potassium sulfate, ferrous sulfate and copper sulfate produced a variety of wine red to dark purple color shades. Cotton fabric and silk yarn dyeing was studied using aluminum potassium sulfate as a mordant. The observed color strength was enhanced with increase in mordant concentration.

Keywords: Natural dyes, Plant materials, Dyeing, Mordant.

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Authors: Pramod Goyal, S. K. Saxena

Abstract:

The convergence of heterogeneous wireless access technologies characterizes the 4G wireless networks. In such converged systems, the seamless and efficient handoff between different access technologies (vertical handoff) is essential and remains a challenging problem. The heterogeneous co-existence of access technologies with largely different characteristics creates a decision problem of determining the “best" available network at “best" time to reduce the unnecessary handoffs. This paper proposes a dynamic decision model to decide the “best" network at “best" time moment to handoffs. The proposed dynamic decision model make the right vertical handoff decisions by determining the “best" network at “best" time among available networks based on, dynamic factors such as “Received Signal Strength(RSS)" of network and “velocity" of mobile station simultaneously with static factors like Usage Expense, Link capacity(offered bandwidth) and power consumption. This model not only meets the individual user needs but also improve the whole system performance by reducing the unnecessary handoffs.

Keywords: Dynamic decision model, Seamless handoff, Vertical handoff, Wireless networks

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Authors: Miika Toivanen, Jouko Lampinen

Abstract:

This paper presents an online method that learns the corresponding points of an object from un-annotated grayscale images containing instances of the object. In the first image being processed, an ensemble of node points is automatically selected which is matched in the subsequent images. A Bayesian posterior distribution for the locations of the nodes in the images is formed. The likelihood is formed from Gabor responses and the prior assumes the mean shape of the node ensemble to be similar in a translation and scale free space. An association model is applied for separating the object nodes and background nodes. The posterior distribution is sampled with Sequential Monte Carlo method. The matched object nodes are inferred to be the corresponding points of the object instances. The results show that our system matches the object nodes as accurately as other methods that train the model with annotated training images.

Keywords: Bayesian modeling, Gabor filters, Online learning, Sequential Monte Carlo.

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Authors: Ramandeep S. Sidhu, Sunil Khullar, Parvinder S. Sandhu, R. P. S. Bedi, Kiranbir Kaur

Abstract:

In this paper, subtractive clustering based fuzzy inference system approach is used for early detection of faults in the function oriented software systems. This approach has been tested with real time defect datasets of NASA software projects named as PC1 and CM1. Both the code based model and joined model (combination of the requirement and code based metrics) of the datasets are used for training and testing of the proposed approach. The performance of the models is recorded in terms of Accuracy, MAE and RMSE values. The performance of the proposed approach is better in case of Joined Model. As evidenced from the results obtained it can be concluded that Clustering and fuzzy logic together provide a simple yet powerful means to model the earlier detection of faults in the function oriented software systems.

Keywords: Subtractive clustering, fuzzy inference system, fault proneness.

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Authors: Stephen Akuma, Timothy Ndera

Abstract:

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

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

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Authors: Meiyuan Zheng, Jingwu He, Yuexi Xiong

Abstract:

A universal multi-function leaf spring main landing gear was designed for light aircraft. The main landing gear combined with the leaf spring, skidding, and wheels enables it to have a good takeoff and landing performance on various grounds such as the hard, snow, grass and sand grounds. Firstly, the characteristics of different landing sites were studied in this paper in order to analyze the load of the main landing gear on different types of grounds. Based on this analysis, the structural design optimization along with the strength and stiffness characteristics of the main landing gear has been done, which enables it to have good takeoff and landing performance on different types of grounds given the relevant regulations and standards. Additionally, the impact of the skidding on the aircraft during the flight was also taken into consideration. Finally, a universal multi-function leaf spring type of the main landing gear suitable for light aircraft has been developed.

Keywords: Landing gear, multi-function, leaf spring, skidding.

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Authors: Krishnamoorthi R., Sheba Kezia Malarchelvi P. D.

Abstract:

In this paper, an image adaptive, invisible digital watermarking algorithm with Orthogonal Polynomials based Transformation (OPT) is proposed, for copyright protection of digital images. The proposed algorithm utilizes a visual model to determine the watermarking strength necessary to invisibly embed the watermark in the mid frequency AC coefficients of the cover image, chosen with a secret key. The visual model is designed to generate a Just Noticeable Distortion mask (JND) by analyzing the low level image characteristics such as textures, edges and luminance of the cover image in the orthogonal polynomials based transformation domain. Since the secret key is required for both embedding and extraction of watermark, it is not possible for an unauthorized user to extract the embedded watermark. The proposed scheme is robust to common image processing distortions like filtering, JPEG compression and additive noise. Experimental results show that the quality of OPT domain watermarked images is better than its DCT counterpart.

Keywords: Orthogonal Polynomials based Transformation, Digital Watermarking, Copyright Protection, Visual model.

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Authors: Perminderjit Singh, Sukhvir Singh

Abstract:

Total Quality Management (TQM) refers to management methods used to enhance quality and productivity in business organizations. Total Quality Management (TQM) has become a frequently used term in discussions concerning quality. Total Quality management has brought rise in demands on the organizations policy and the customers have gained more importance in the organizations focus. TQM is considered as an important management tool, which helps the organizations to satisfy their customers. In present research critical success factors includes management commitment, customer satisfaction, continuous improvement, work culture and environment, supplier quality management, training and development, employee satisfaction and product/process design are studied. A questionnaire is developed to implement these critical success factors in implementation of total quality management in Indian industry. Questionnaires filled by consulting different industrial organizations. Data collected from questionnaires is analyzed by descriptive and importance indexes. 

Keywords: Total quality management, critical success factor, employee satisfaction.

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