Search results for: Best combination of design parameters
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 8478

Search results for: Best combination of design parameters

4458 Pilot Scale Production and Compatibility Criteria of New Self-Cleaning Materials

Authors: J. Ranogajec, O. Rudic, S. Pasalic, S. Vucetic, D. Cjepa

Abstract:

The paper involves a chain of activities from synthesis, establishment of the methodology for characterization and testing of novel protective materials through the pilot production and application on model supports. It summarizes the results regarding the development of the pilot production protocol for newly developed self-cleaning materials. The optimization of the production parameters was completed in order to improve the most important functional properties (mineralogy characteristics, particle size, self-cleaning properties and photocatalytic activity) of the newly designed nanocomposite material.

Keywords: Cultural heritage. Materials compatibility. Pilot production. Self-cleaning.

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4457 Heat Treatment of Aluminum Alloy 7449

Authors: Suleiman E. Al-lubani, Mohammad E. Matarneh, Hussien M. Al-Wedyan, Ala M. Rayes

Abstract:

Aluminum alloy has an extensive range of industrial application due to its consistent mechanical properties and structural integrity. The heat treatment by precipitation technique affected the Magnesium, Silicon Manganese and copper crystals dissolved in the Aluminum alloy. The crystals dislocated to precipitate on the crystal’s boundaries of the Aluminum alloy when given a thermal energy increased its hardness. In this project various times and temperature were varied to find out the best combination of these variables to increase the precipitation of the metals on the Aluminum crystal’s boundaries which will lead to get the highest hardness. These specimens are then tested for their hardness and tensile strength. It is noticed that when the temperature increases, the precipitation increases and consequently the hardness increases. A threshold temperature value (264C0) of Aluminum alloy should not be reached due to the occurrence of recrystalization which causes the crystal to grow. This recrystalization process affected the ductility of the alloy and decrease hardness. In addition, and while increasing the temperature the alloy’s mechanical properties will decrease. The mechanical properties, namely tensile and hardness properties are investigated according to standard procedures. In this research, different temperature and time have been applied to increase hardening.The highest hardness at 100°c in 6 hours equals to 207.31 HBR, while at the same temperature and time the lowest elongation equals to 146.5.

Keywords: Aluminum alloy, recrystalization process, heat treatment, hardness properties, precipitation, intergranular breakage.

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4456 A Multi-layer Artificial Neural Network Architecture Design for Load Forecasting in Power Systems

Authors: Axay J Mehta, Hema A Mehta, T.C.Manjunath, C. Ardil

Abstract:

In this paper, the modelling and design of artificial neural network architecture for load forecasting purposes is investigated. The primary pre-requisite for power system planning is to arrive at realistic estimates of future demand of power, which is known as Load Forecasting. Short Term Load Forecasting (STLF) helps in determining the economic, reliable and secure operating strategies for power system. The dependence of load on several factors makes the load forecasting a very challenging job. An over estimation of the load may cause premature investment and unnecessary blocking of the capital where as under estimation of load may result in shortage of equipment and circuits. It is always better to plan the system for the load slightly higher than expected one so that no exigency may arise. In this paper, a load-forecasting model is proposed using a multilayer neural network with an appropriately modified back propagation learning algorithm. Once the neural network model is designed and trained, it can forecast the load of the power system 24 hours ahead on daily basis and can also forecast the cumulative load on daily basis. The real load data that is used for the Artificial Neural Network training was taken from LDC, Gujarat Electricity Board, Jambuva, Gujarat, India. The results show that the load forecasting of the ANN model follows the actual load pattern more accurately throughout the forecasted period.

Keywords: Power system, Load forecasting, Neural Network, Neuron, Stabilization, Network structure, Load.

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4455 Extended Cubic B-spline Interpolation Method Applied to Linear Two-Point Boundary Value Problems

Authors: Nur Nadiah Abd Hamid, Ahmad Abd. Majid, Ahmad Izani Md. Ismail

Abstract:

Linear two-point boundary value problem of order two is solved using extended cubic B-spline interpolation method. There is one free parameters, λ, that control the tension of the solution curve. For some λ, this method produced better results than cubic B-spline interpolation method.

Keywords: two-point boundary value problem, B-spline, extendedcubic B-spline.

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4454 Flow Transformation: An Investigation on Theoretical Aspects and Numerical Computation

Authors: Abhisek Sarkar, Abhimanyu Gaur

Abstract:

In this report we have discussed the theoretical aspects of the flow transformation, occurring through a series of bifurcations. The parameters and their continuous diversion, the intermittent bursts in the transition zone, variation of velocity and pressure with time, effect of roughness in turbulent zone, and changes in friction factor and head loss coefficient as a function of Reynolds number for a transverse flow across a cylinder have been discussed. An analysis of the variation in the wake length with Reynolds number was done in FORTRAN.

Keywords: Attractor, Bifurcation, Energy cascade, Energy spectra, Intermittence, Vortex stretching.

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4453 Hybrid Authentication System Using QR Code with OTP

Authors: Salim Istyaq

Abstract:

As we know, number of Internet users are increasing drastically. Now, people are using different online services provided by banks, colleges/schools, hospitals, online utility, bill payment and online shopping sites. To access online services, text-based authentication system is in use. The text-based authentication scheme faces some drawbacks with usability and security issues that bring troubles to users. The core element of computational trust is identity. The aim of the paper is to make the system more compliable for the imposters and more reliable for the users, by using the graphical authentication approach. In this paper, we are using the more powerful tool of encoding the options in graphical QR format and also there will be the acknowledgment which will send to the user’s mobile for final verification. The main methodology depends upon the encryption option and final verification by confirming a set of pass phrase on the legal users, the outcome of the result is very powerful as it only gives the result at once when the process is successfully done. All processes are cross linked serially as the output of the 1st process, is the input of the 2nd and so on. The system is a combination of recognition and pure recall based technique. Presented scheme is useful for devices like PDAs, iPod, phone etc. which are more handy and convenient to use than traditional desktop computer systems.

Keywords: Graphical Password, OTP, QR Codes, Recognition based graphical user authentication, usability and security.

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4452 Some Factors Affecting the Compressive Behaviour of Structural Masonry at Small Scales

Authors: A. Mohammed, T. G. Hughes

Abstract:

This paper presents part of a research into the small scale modelling of masonry. Small scale testing of masonry has been carried out by many authors, but few have attempted a systematic determination of the parameters that affect masonry at a small scale. The effect of increasing mortar strength and different sand gradings under compression were investigated. The results show masonry strength at small scale is influenced by increasing mortar strength and different sand gradings.

Keywords: Compression, masonry, models, mortar, sand gradings

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4451 Adhesive Connections in Timber: A Comparison between Rough and Smooth Wood Bonding Surfaces

Authors: Valentina Di Maria, Anton Ianakiev

Abstract:

The use OF adhesive anchors for wooden constructions is an efficient technology to connect and design timber members in new timber structures and to rehabilitate the damaged structural members of historical buildings. Due to the lack of standard regulation in this specific area of structural design, designers’ choices are still supported by test analysis that enables knowledge, and the prediction, of the structural behaviour of glued in rod joints. The paper outlines an experimental research activity aimed at identifying the tensile resistance capacity of several new adhesive joint prototypes made of epoxy resin, steel bar and timber, Oak and Douglas Fir species. The development of new adhesive connectors has been carried out by using epoxy to glue stainless steel bars into pre-drilled holes, characterised by smooth and rough internal surfaces, in timber samples. The realization of a threaded contact surface using a specific drill bit has led to an improved bond between wood and epoxy. The applied changes have also reduced the cost of the joints’ production. The paper presents the results of this parametric analysis and a Finite Element analysis that enables identification and study of the internal stress distribution in the proposed adhesive anchors.

Keywords: Glued in rod joints, adhesive anchors, timber, epoxy, rough contact surface, threaded hole shape.

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4450 A Four-Year Study of Thyroid Carcinoma in Hail Region: Increased Incidence

Authors: Laila Seada, Hanan Oreiby, Fawaz Al Rashid, Ashraf Negm

Abstract:

Background and Objective: In most areas of the world, the incidence of thyroid cancer has been increasing over the last decade, mostly due to a combination of early detection of the neoplasm resulting from sensitive procedures and increased population exposure to radiation and unrecognized carcinogens. Methods: Cases of thyroid cancer have been retrieved from the cancer registry at King Khalid Hospital during the period from August 2012 to April 2016. Age, gender and histopathologic types have been recorded. Results: Thyroid carcinoma ranked as the second most common malignancy in females (25%) after breast cancer (31%). It constituted 20.8% of all newly diagnosed cancer cases. As for males, it ranked the 4th type of malignancy after gastrointestinal cancer, lymphomas and soft tissue sarcomas. Mean age for females and males was 38.7 +/- 13.2 and 60.25 +/- 11.5 years, respectively, and the difference between the two groups was statistically significant (p value = 0.0001). Fifty-five (82%) were papillary carcinomas including 10 follicular variant of papillary (FVPC), and eight papillary micro carcinomas (PMC) and two tall cell/oncocytic variants. Follicular carcinomas constituted two (3.1%), while two (3.1%) were anaplastic, and two (3.1%) were medullary. Conclusion: Thyroid cancer incidence in Hail is ranking as the 2nd most common female malignancy similar to other regions in the Kingdom. However, this high incidence contrasts with much lower rates worldwide.

Keywords: Thyroid, Hail, papillary, micro carcinoma.

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4449 Defining a Semantic Web-based Framework for Enabling Automatic Reasoning on CIM-based Management Platforms

Authors: Fernando Alonso, Rafael Fernandez, Sonia Frutos, Javier Soriano

Abstract:

CIM is the standard formalism for modeling management information developed by the Distributed Management Task Force (DMTF) in the context of its WBEM proposal, designed to provide a conceptual view of the managed environment. In this paper, we propose the inclusion of formal knowledge representation techniques, based on Description Logics (DLs) and the Web Ontology Language (OWL), in CIM-based conceptual modeling, and then we examine the benefits of such a decision. The proposal is specified as a CIM metamodel level mapping to a highly expressive subset of DLs capable of capturing all the semantics of the models. The paper shows how the proposed mapping provides CIM diagrams with precise semantics and can be used for automatic reasoning about the management information models, as a design aid, by means of newgeneration CASE tools, thanks to the use of state-of-the-art automatic reasoning systems that support the proposed logic and use algorithms that are sound and complete with respect to the semantics. Such a CASE tool framework has been developed by the authors and its architecture is also introduced. The proposed formalization is not only useful at design time, but also at run time through the use of rational autonomous agents, in response to a need recently recognized by the DMTF.

Keywords: CIM, Knowledge-based Information Models, OntologyLanguages, OWL, Description Logics, Integrated Network Management, Intelligent Agents, Automatic Reasoning Techniques.

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4448 A Risk Assessment Tool for the Contamination of Aflatoxins on Dried Figs based on Machine Learning Algorithms

Authors: Kottaridi Klimentia, Demopoulos Vasilis, Sidiropoulos Anastasios, Ihara Diego, Nikolaidis Vasileios, Antonopoulos Dimitrios

Abstract:

Aflatoxins are highly poisonous and carcinogenic compounds produced by species of the genus Aspergillus spp. that can infect a variety of agricultural foods, including dried figs. Biological and environmental factors, such as population, pathogenicity and aflatoxinogenic capacity of the strains, topography, soil and climate parameters of the fig orchards are believed to have a strong effect on aflatoxin levels. Existing methods for aflatoxin detection and measurement, such as high-performance liquid chromatography (HPLC), and enzyme-linked immunosorbent assay (ELISA), can provide accurate results, but the procedures are usually time-consuming, sample-destructive and expensive. Predicting aflatoxin levels prior to crop harvest is useful for minimizing the health and financial impact of a contaminated crop. Consequently, there is interest in developing a tool that predicts aflatoxin levels based on topography and soil analysis data of fig orchards. This paper describes the development of a risk assessment tool for the contamination of aflatoxin on dried figs, based on the location and altitude of the fig orchards, the population of the fungus Aspergillus spp. in the soil, and soil parameters such as pH, saturation percentage (SP), electrical conductivity (EC), organic matter, particle size analysis (sand, silt, clay), concentration of the exchangeable cations (Ca, Mg, K, Na), extractable P and trace of elements (B, Fe, Mn, Zn and Cu), by employing machine learning methods. In particular, our proposed method integrates three machine learning techniques i.e., dimensionality reduction on the original dataset (Principal Component Analysis), metric learning (Mahalanobis Metric for Clustering) and K-nearest Neighbors learning algorithm (KNN), into an enhanced model, with mean performance equal to 85% by terms of the Pearson Correlation Coefficient (PCC) between observed and predicted values.

Keywords: aflatoxins, Aspergillus spp., dried figs, k-nearest neighbors, machine learning, prediction

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4447 Parametric Non-Linear Analysis of Reinforced Concrete Frames with Supplemental Damping Systems

Authors: Daniele Losanno, Giorgio Serino

Abstract:

This paper focuses on parametric analysis of reinforced concrete structures equipped with supplemental damping braces. Practitioners still luck sufficient data for current design of damper added structures and often reduce the real model to a pure damper braced structure even if this assumption is neither realistic nor conservative. In the present study, the damping brace is modelled as made by a linear supporting brace connected in series with the viscous/hysteretic damper. Deformation capacity of existing structures is usually not adequate to undergo the design earthquake. In spite of this, additional dampers could be introduced strongly limiting structural damage to acceptable values, or in some cases, reducing frame response to elastic behavior. This work is aimed at providing useful considerations for retrofit of existing buildings by means of supplemental damping braces. The study explicitly takes into consideration variability of (a) relative frame to supporting brace stiffness, (b) dampers’ coefficient (viscous coefficient or yielding force) and (c) non-linear frame behavior. Non-linear time history analysis has been run to account for both dampers’ behavior and non-linear plastic hinges modelled by Pivot hysteretic type. Parametric analysis based on previous studies on SDOF or MDOF linear frames provide reference values for nearly optimal damping systems design. With respect to bare frame configuration, seismic response of the damper-added frame is strongly improved, limiting deformations to acceptable values far below ultimate capacity. Results of the analysis also demonstrated the beneficial effect of stiffer supporting braces, thus highlighting inadequacy of simplified pure damper models. At the same time, the effect of variable damping coefficient and yielding force has to be treated as an optimization problem.

Keywords: Brace stiffness, dissipative braces, non-linear analysis, plastic hinges, reinforced concrete.

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4446 Exploration of the Communication Area of Infrared Short-Range Communication Systems for Intervehicle Communication

Authors: Wern-Yarng Shieh, Hsin-Chuan Chen, Ti-Ho Wang, Bo-Wei Chen

Abstract:

Infrared communication in the wavelength band 780- 950 nm is very suitable for short-range point-to-point communications. It is a good choice for vehicle-to-vehicle communication in several intelligent-transportation-system (ITS) applications such as cooperative driving, collision warning, and pileup-crash prevention. In this paper, with the aid of a physical model established in our previous works, we explore the communication area of an infrared intervehicle communication system utilizing a typical low-cost cormmercial lightemitting diodes (LEDs) as the emitter and planar p-i-n photodiodes as the receiver. The radiation pattern of the emitter fabricated by aforementioned LEDs and the receiving pattern of the receiver are approximated by a linear combination of cosinen functions. This approximation helps us analyze the system performance easily. Both multilane straight-road conditions and curved-road conditions with various radius of curvature are taken into account. The condition of a small car communicating with a big truck, i.e., there is a vertical mounting height difference between the emitter and the receiver, is also considered. Our results show that the performance of the system meets the requirement of aforementioned ITS applications in terms of the communication area.

Keywords: Dedicated short-range communication (DSRC), infrared communication, intervehicle communication, intelligent transportation system (ITS).

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4445 Integrating Artificial Neural Network and Taguchi Method on Constructing the Real Estate Appraisal Model

Authors: Mu-Yen Chen, Min-Hsuan Fan, Chia-Chen Chen, Siang-Yu Jhong

Abstract:

In recent years, real estate prediction or valuation has been a topic of discussion in many developed countries. Improper hype created by investors leads to fluctuating prices of real estate, affecting many consumers to purchase their own homes. Therefore, scholars from various countries have conducted research in real estate valuation and prediction. With the back-propagation neural network that has been popular in recent years and the orthogonal array in the Taguchi method, this study aimed to find the optimal parameter combination at different levels of orthogonal array after the system presented different parameter combinations, so that the artificial neural network obtained the most accurate results. The experimental results also demonstrated that the method presented in the study had a better result than traditional machine learning. Finally, it also showed that the model proposed in this study had the optimal predictive effect, and could significantly reduce the cost of time in simulation operation. The best predictive results could be found with a fewer number of experiments more efficiently. Thus users could predict a real estate transaction price that is not far from the current actual prices.

Keywords: Artificial Neural Network, Taguchi Method, Real Estate Valuation Model.

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4444 An Index for the Differential Diagnosis of Morbid Obese Children with and without Metabolic Syndrome

Authors: Mustafa M. Donma, Orkide Donma

Abstract:

Metabolic syndrome (MetS) is a severe health problem caused by morbid obesity, the severest form of obesity. The components of MetS are rather stable in adults. However, the diagnosis of MetS in morbid obese (MO) children still constitutes a matter of discussion. The aim of this study was to develop a formula, which facilitated the diagnosis of MetS in MO children and was capable of discriminating MO children with and without MetS findings. The study population comprised MO children. Age and sex-dependent body mass index (BMI) percentiles of the children were above 99. Increased blood pressure, elevated fasting blood glucose (FBG), elevated triglycerides (TRG) and/or decreased high density lipoprotein cholesterol (HDL-C) in addition to central obesity were listed as MetS components for each child. Two groups were constituted. In the first group, there were 42 MO children without MetS components. Second group was composed of 44 MO children with at least two MetS components. Anthropometric measurements including weight, height, waist and hip circumferences were performed during physical examination. BMI and homeostatic model assessment of insulin resistance (HOMA-IR) values were calculated. Informed consent forms were obtained from the parents of the children. Institutional Non-Interventional Clinical Studies Ethics Committee approved the study design. Routine biochemical analyses including FBG, insulin (INS), TRG, HDL-C were performed. The performance and the clinical utility of Diagnostic Obesity Notation Model Assessment Metabolic Syndrome Index (DONMA MetS index) [(INS/FBG)/(HDL-C/TRG)*100] was tested. Appropriate statistical tests were applied to the study data. p value smaller than 0.05 was defined as significant. MetS index values were 41.6 ± 5.1 in MO group and 104.4 ± 12.8 in MetS group. Corresponding values for HDL-C values were 54.5 ± 13.2 mg/dl and 44.2 ± 11.5 mg/dl. There was a statistically significant difference between the groups (p < 0.001). Upon evaluation of the correlations between MetS index and HDL-C values, a much stronger negative correlation was found in MetS group (r = -0.515; p = 0.001) in comparison with the correlation detected in MO group (r = -0.371; p = 0.016). From these findings, it was concluded that the statistical significance degree of the difference between MO and MetS groups was highly acceptable for this recently introduced MetS index. This was due to the involvement of all of the biochemically defined MetS components into the index. This is particularly important because each of these four parameters used in the formula is a cardiac risk factor. Aside from discriminating MO children with and without MetS findings, MetS index introduced in this study is important from the cardiovascular risk point of view in MetS group of children.

Keywords: Fasting blood glucose, high density lipoprotein cholesterol, insulin, metabolic syndrome, morbid obesity, triglycerides.

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4443 Adaptive Functional Projective Lag Synchronization of Lorenz System

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

Abstract:

This paper addresses functional projective lag synchronization of Lorenz system with four unknown parameters, where the output of the master system lags behind the output of the slave system proportionally. For this purpose, an adaptive control law is proposed to make the states of two identical Lorenz systems asymptotically synchronize up. Based on Lyapunov stability theory, a novel criterion is given for asymptotical stability of the null solution of an error dynamics. Finally, some numerical examples are provided to show the effectiveness of our results.

Keywords: Adaptive function projective synchronization, Chaotic system, Lag synchronization, Lyapunov method

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4442 Effect of Chemical Pretreatments and Dehydration Methods on Quality Characteristics of Tomato Powder and Its Storage Stability

Authors: Reihaneh Ahmadzadeh Ghavidel, Mehdi Ghiafeh Davoodi

Abstract:

Dehydration process was carried out for tomato slices of var. Avinash after giving different pre-treatments such as calcium chloride (CaCl2), potassium metabisulphite (KMS), calcium chloride and potassium metabisulphite (CaCl2 +KMS), and sodium chloride (NaCl). Untreated samples served as control. Solar drier and continuous conveyor (tunnel) drier were used for dehydration. Quality characteristics of tomato slices viz. moisture content, sugar, titratable acidity, lycopene content, dehydration ratio, rehydration ratio and non-enzymatic browning as affected by dehydration process were studied. Storage study was also carried out for a period of six months for tomato powder packed into different types of packaging materials viz. metalized polyester (MP) film and low density poly ethylene (LDPE). Changes in lycopene content and non-enzymatic browning (NEB) were estimated during storage at room temperature. Pretreatment of 5 mm thickness of tomato slices with calcium chloride in combination with potassium metabisulphite and drying using a tunnel drier with subsequent storage of product in metalized polyester bags was selected as the best process.

Keywords: Drying pretreatments, Solar drying, Tomato powder, Tunnel drying

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4441 A New Model for Economic Optimization of Water Diversion System during Dam Construction using PSO Algorithm

Authors: Saeed Sedighizadeh, Abbas Mansoori, Mohammad Reza Pirestani, Davoud Sedighizadeh

Abstract:

The usual method of river flow diversion involves construction of tunnels and cofferdams. Given the fact that the cost of diversion works could be as high as 10-20% of the total dam construction cost, due attention should be paid to optimum design of the diversion works. The cost of diversion works depends, on factors, such as: the tunnel dimensions and the intended tunneling support measures during and after excavation; quality and characterizes of the rock through which the tunnel should be excavated; the dimensions of the upstream (and downstream) cofferdams; and the magnitude of river flood the system is designed to divert. In this paper by use of the cost of unit prices for tunnel excavation, tunnel lining, tunnel support (rock bolt + shotcrete) and cofferdam fill the cost function was determined. The function is then minimized by the aid of PSO Algorithm (particle swarm optimization). It is found that the optimum diameter and the total diversion cost are directly related to the river flood discharge (Q). It has also shown that in addition to optimum diameter design discharge (Q), river length, tunnel length, is mainly a function of the ratios (not the absolute values) of the unit prices and does not depend on the overall price levels in the respective country. The results of optimization use in some of the case study lead us to significant changes in the cost.

Keywords: Diversion Tunnel, Optimization, PSO Algorithm

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4440 Powerful Laser Diode Matrixes for Active Vision Systems

Authors: Dzmitry M. Kabanau, Vladimir V. Kabanov, Yahor V. Lebiadok, Denis V. Shabrov, Pavel V. Shpak, Gevork T. Mikaelyan, Alexandr P. Bunichev

Abstract:

This article is deal with the experimental investigations of the laser diode matrixes (LDM) based on the AlGaAs/GaAs heterostructures (lasing wavelength 790-880 nm) to find optimal LDM parameters for active vision systems. In particular, the dependence of LDM radiation pulse power on the pulse duration and LDA active layer heating as well as the LDM radiation divergence are discussed.

Keywords: Active vision systems, laser diode matrixes, thermal properties, radiation divergence.

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4439 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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4438 Property of Polyurethane: from Soy-derived Phosphate Ester

Authors: Flora Elvistia Firdaus

Abstract:

Polyurethane foams (PUF) were formed by a chemical reaction of polyol and isocyanate. The polyol was manufactured by ring-opening hydrolysis of epoxidized soybean oil in the presence of phosphoric acid under varying experimental conditions. Other factors in the foam formulation such as water content and surfactant were kept constant. The effect of the amount of solvents, phosphoric acid, and their derivates in the foam formulation on the properties of polyurethane foams were studied. The properties of the material were measured via a number of parameters, which are water content of prepared polyol, polymer density and cellular structures.

Keywords: soy, polyurethane, phosporic acid

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4437 A Research on DC Voltage Offsets Generated by PWM-Controlled Inverters

Authors: Marios N. Moschakis

Abstract:

The increasing penetration of Distributed Generation and storage connected to the distribution network via PWM converters increases the possibility of a DC-component (offset) in voltage or current flowing into the grid. This occurs when even harmonics are present in the network voltage. DC-components can affect the operation and safety of several grid components. Therefore, an investigation of the way they are produced is important in order to take appropriate measures for their elimination. Further research on DC-components that appear on output voltage of converters is performed for different parameters of PWM technique and characteristics of even harmonics.

Keywords: Asymmetric even harmonics, DC-offsets, distributed generation, electric machine drive systems, power quality.

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4436 Hand Gesture Interpretation Using Sensing Glove Integrated with Machine Learning Algorithms

Authors: Aqsa Ali, Aleem Mushtaq, Attaullah Memon, Monna

Abstract:

In this paper, we present a low cost design for a smart glove that can perform sign language recognition to assist the speech impaired people. Specifically, we have designed and developed an Assistive Hand Gesture Interpreter that recognizes hand movements relevant to the American Sign Language (ASL) and translates them into text for display on a Thin-Film-Transistor Liquid Crystal Display (TFT LCD) screen as well as synthetic speech. Linear Bayes Classifiers and Multilayer Neural Networks have been used to classify 11 feature vectors obtained from the sensors on the glove into one of the 27 ASL alphabets and a predefined gesture for space. Three types of features are used; bending using six bend sensors, orientation in three dimensions using accelerometers and contacts at vital points using contact sensors. To gauge the performance of the presented design, the training database was prepared using five volunteers. The accuracy of the current version on the prepared dataset was found to be up to 99.3% for target user. The solution combines electronics, e-textile technology, sensor technology, embedded system and machine learning techniques to build a low cost wearable glove that is scrupulous, elegant and portable.

Keywords: American sign language, assistive hand gesture interpreter, human-machine interface, machine learning, sensing glove.

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4435 Shape Sensing and Damage Detection of Thin-Walled Cylinders Using an Inverse Finite Element Method

Authors: Ionel D. Craiu, Mihai Nedelcu

Abstract:

Thin-walled cylinders are often used by the offshore industry as columns of floating installations. Based on observed strains, the inverse Finite Element Method (iFEM) may rebuild the deformation of structures. Structural Health Monitoring uses this approach extensively. However, the number of in-situ strain gauges is what determines how accurate it is, and for shell structures with complicated deformation, this number can easily become too high for practical use. Any thin-walled beam member's complicated deformation can be modeled by the Generalized Beam Theory (GBT) as a linear combination of pre-specified cross-section deformation modes. GBT uses bar finite elements as opposed to shell finite elements. This paper proposes an iFEM/GBT formulation for the shape sensing of thin-walled cylinders based on these benefits. This method significantly reduces the number of strain gauges compared to using the traditional inverse-shell finite elements. Using numerical simulations, dent damage detection is achieved by comparing the strain distributions of the undamaged and damaged members. The effect of noise on strain measurements is also investigated.

Keywords: Damage detection, generalized beam theory, inverse finite element method, shape sensing.

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4434 Double Reduction of Ada-ECATNet Representation using Rewriting Logic

Authors: Noura Boudiaf, Allaoua Chaoui

Abstract:

One major difficulty that faces developers of concurrent and distributed software is analysis for concurrency based faults like deadlocks. Petri nets are used extensively in the verification of correctness of concurrent programs. ECATNets [2] are a category of algebraic Petri nets based on a sound combination of algebraic abstract types and high-level Petri nets. ECATNets have 'sound' and 'complete' semantics because of their integration in rewriting logic [12] and its programming language Maude [13]. Rewriting logic is considered as one of very powerful logics in terms of description, verification and programming of concurrent systems. We proposed in [4] a method for translating Ada-95 tasking programs to ECATNets formalism (Ada-ECATNet). In this paper, we show that ECATNets formalism provides a more compact translation for Ada programs compared to the other approaches based on simple Petri nets or Colored Petri nets (CPNs). Such translation doesn-t reduce only the size of program, but reduces also the number of program states. We show also, how this compact Ada-ECATNet may be reduced again by applying reduction rules on it. This double reduction of Ada-ECATNet permits a considerable minimization of the memory space and run time of corresponding Maude program.

Keywords: Ada tasking, ECATNets, Algebraic Petri Nets, Compact Representation, Analysis, Rewriting Logic, Maude.

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4433 Surrogate based Evolutionary Algorithm for Design Optimization

Authors: Maumita Bhattacharya

Abstract:

Optimization is often a critical issue for most system design problems. Evolutionary Algorithms are population-based, stochastic search techniques, widely used as efficient global optimizers. However, finding optimal solution to complex high dimensional, multimodal problems often require highly computationally expensive function evaluations and hence are practically prohibitive. The Dynamic Approximate Fitness based Hybrid EA (DAFHEA) model presented in our earlier work [14] reduced computation time by controlled use of meta-models to partially replace the actual function evaluation by approximate function evaluation. However, the underlying assumption in DAFHEA is that the training samples for the meta-model are generated from a single uniform model. Situations like model formation involving variable input dimensions and noisy data certainly can not be covered by this assumption. In this paper we present an enhanced version of DAFHEA that incorporates a multiple-model based learning approach for the SVM approximator. DAFHEA-II (the enhanced version of the DAFHEA framework) also overcomes the high computational expense involved with additional clustering requirements of the original DAFHEA framework. The proposed framework has been tested on several benchmark functions and the empirical results illustrate the advantages of the proposed technique.

Keywords: Evolutionary algorithm, Fitness function, Optimization, Meta-model, Stochastic method.

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4432 Prediction of Air-Water Two-Phase Frictional Pressure Drop Using Artificial Neural Network

Authors: H. B. Mehta, Vipul M. Patel, Jyotirmay Banerjee

Abstract:

The present paper discusses the prediction of gas-liquid two-phase frictional pressure drop in a 2.12 mm horizontal circular minichannel using Artificial Neural Network (ANN). The experimental results are obtained with air as gas phase and water as liquid phase. The superficial gas velocity is kept in the range of 0.0236 m/s to 0.4722 m/s while the values of 0.0944 m/s, 0.1416 m/s and 0.1889 m/s are considered for superficial liquid velocity. The experimental results are predicted using different Artificial Neural Network (ANN) models. Networks used for prediction are radial basis, generalised regression, linear layer, cascade forward back propagation, feed forward back propagation, feed forward distributed time delay, layer recurrent, and Elman back propagation. Transfer functions used for networks are Linear (PURELIN), Logistic sigmoid (LOGSIG), tangent sigmoid (TANSIG) and Gaussian RBF. Combination of networks and transfer functions give different possible neural network models. These models are compared for Mean Absolute Relative Deviation (MARD) and Mean Relative Deviation (MRD) to identify the best predictive model of ANN.

Keywords: Minichannel, Two-Phase Flow, Frictional Pressure Drop, ANN, MARD, MRD.

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4431 Flexible Wormhole-Switched Network-on-chip with Two-Level Priority Data Delivery Service

Authors: Faizal A. Samman, Thomas Hollstein, Manfred Glesner

Abstract:

A synchronous network-on-chip using wormhole packet switching and supporting guaranteed-completion best-effort with low-priority (LP) and high-priority (HP) wormhole packet delivery service is presented in this paper. Both our proposed LP and HP message services deliver a good quality of service in term of lossless packet completion and in-order message data delivery. However, the LP message service does not guarantee minimal completion bound. The HP packets will absolutely use 100% bandwidth of their reserved links if the HP packets are injected from the source node with maximum injection. Hence, the service are suitable for small size messages (less than hundred bytes). Otherwise the other HP and LP messages, which require also the links, will experience relatively high latency depending on the size of the HP message. The LP packets are routed using a minimal adaptive routing, while the HP packets are routed using a non-minimal adaptive routing algorithm. Therefore, an additional 3-bit field, identifying the packet type, is introduced in their packet headers to classify and to determine the type of service committed to the packet. Our NoC prototypes have been also synthesized using a 180-nm CMOS standard-cell technology to evaluate the cost of implementing the combination of both services.

Keywords: Network-on-Chip, Parallel Pipeline Router Architecture, Wormhole Switching, Two-Level Priority Service.

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4430 Fused Structure and Texture (FST) Features for Improved Pedestrian Detection

Authors: Hussin K. Ragb, Vijayan K. Asari

Abstract:

In this paper, we present a pedestrian detection descriptor called Fused Structure and Texture (FST) features based on the combination of the local phase information with the texture features. Since the phase of the signal conveys more structural information than the magnitude, the phase congruency concept is used to capture the structural features. On the other hand, the Center-Symmetric Local Binary Pattern (CSLBP) approach is used to capture the texture information of the image. The dimension less quantity of the phase congruency and the robustness of the CSLBP operator on the flat images, as well as the blur and illumination changes, lead the proposed descriptor to be more robust and less sensitive to the light variations. The proposed descriptor can be formed by extracting the phase congruency and the CSLBP values of each pixel of the image with respect to its neighborhood. The histogram of the oriented phase and the histogram of the CSLBP values for the local regions in the image are computed and concatenated to construct the FST descriptor. Several experiments were conducted on INRIA and the low resolution DaimlerChrysler datasets to evaluate the detection performance of the pedestrian detection system that is based on the FST descriptor. A linear Support Vector Machine (SVM) is used to train the pedestrian classifier. These experiments showed that the proposed FST descriptor has better detection performance over a set of state of the art feature extraction methodologies.

Keywords: Pedestrian detection, phase congruency, local phase, LBP features, CSLBP features, FST descriptor.

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4429 A Study on the Attractiveness of Heavy Duty Motorcycle

Authors: Kaishuan Shen, Pan Changyu, Yuhsiang Lu, Zongshao Liu, Chishxsin Chuang, Minyuan Ma

Abstract:

The culture of riding heavy motorcycles originates from advanced countries and mainly comes from Europe, North America, and Japan. Heavy duty motorcycle riders are different from people who view motorcycles as a convenient mean of transportation. They regard riding them as a kind of enjoyment and high-level taste. The activities of riding heavy duty motorcycles have formes a distinctive landscape in domestic land in Taiwan. Previous studies which explored motorcycle culture in Taiwan still focused on the objects of motorcycle engine displacement under 50 cc.. The study aims to study the heavy duty motorcycles of engine displacement over 550 cc. and explores where their attractiveness is. For finding the attractiveness of heavy duty motorcycle, the study chooses Miryoku Engineering (Preference-Based Design) approach. Two steps are adopted to proceed the research. First, through arranging the letters obtained from interviewing experts, EGM (The Evaluation Grid Method) was applied to find out the structure of attractiveness. The attractive styles are eye-dazzling, leisure, classic, and racing competitive styles. Secondarily, Quantification Theory Type I analysis was adopted as a tool for analyzing the importance of attractiveness. The relationship between style and attractive parts was also discussed. The results could contribute to the design and research development of heavy duty motorcycle industry in Taiwan.

Keywords: attractiveness, evaluation, heavy dutymotorcycle, miryoku engineering

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