Search results for: back flashover

Authors: Akif Kutlu, Merve Ermis, Nihal Eratlı, Mehmet H. Omurtag

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The objective of this study is to investigate the forced vibration analysis of a planar curved beam lying on elastic foundation by using the mixed finite element method. The finite element formulation is based on the Timoshenko beam theory. In order to solve the problems in frequency domain, the element matrices of two nodded curvilinear elements are transformed into Laplace space. The results are transformed back to the time domain by the well-known numerical Modified Durbin’s transformation algorithm. First, the presented finite element formulation is verified through the forced vibration analysis of a planar curved Timoshenko beam resting on Winkler foundation and the finite element results are compared with the results available in the literature. Then, the forced vibration analysis of a planar curved beam resting on Winkler-Pasternak foundation is conducted.

Keywords: Curved beam, dynamic analysis, elastic foundation, finite element method.

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Authors: E.Assareh, M.A. Behrang, R. Assareh, N. Hedayat

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An integrated Artificial Neural Network- Particle Swarm Optimization (PSO) is presented for analyzing global electricity consumption. To aim this purpose, following steps are done: STEP 1: in the first step, PSO is applied in order to determine world-s oil, natural gas, coal and primary energy demand equations based on socio-economic indicators. World-s population, Gross domestic product (GDP), oil trade movement and natural gas trade movement are used as socio-economic indicators in this study. For each socio-economic indicator, a feed-forward back propagation artificial neural network is trained and projected for future time domain. STEP 2: in the second step, global electricity consumption is projected based on the oil, natural gas, coal and primary energy consumption using PSO. global electricity consumption is forecasted up to year 2040.

Keywords: Particle Swarm Optimization, Artificial NeuralNetworks, Fossil Fuels, Electricity, Forecasting.

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Authors: Li-Chung Su, Chia-Yu Chen, Tzu-Yuan Lai, Sheng-Jye Hwang

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Mechanical design of the thin-film solar framed module and mounting system is important to enhance module reliability and to increase areas of applications. The stress induced by different mounting positions played a main role controlling the stability of the whole mechanical structure. From the finite element method, under the pressure from the back of module, the stress at Lc (center point of the Long frame) increased and the stresses at Center, Corner and Sc (center point of the Short frame) decreased while the mounting position was away from the center of the module. In addition, not only the stress of the glass but also the stress of the frame decreased. Accordingly it was safer to mount in the position away from the center of the module. The emphasis of designing frame system of the module was on the upper support of the Short frame. Strength of the overall structure and design of the corner were also important due to the complexity of the stress in the Long frame.

Keywords: Finite element method, Framed module, Mountingposition

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Authors: Souhir Ben Amor, Heni Boubaker, Lotfi Belkacem

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This aims of this paper is to forecast the electricity spot prices. First, we focus on modeling the conditional mean of the series so we adopt a generalized fractional -factor Gegenbauer process (k-factor GARMA). Secondly, the residual from the -factor GARMA model has used as a proxy for the conditional variance; these residuals were predicted using two different approaches. In the first approach, a local linear wavelet neural network model (LLWNN) has developed to predict the conditional variance using the Back Propagation learning algorithms. In the second approach, the Gegenbauer generalized autoregressive conditional heteroscedasticity process (G-GARCH) has adopted, and the parameters of the k-factor GARMA-G-GARCH model has estimated using the wavelet methodology based on the discrete wavelet packet transform (DWPT) approach. The empirical results have shown that the k-factor GARMA-G-GARCH model outperform the hybrid k-factor GARMA-LLWNN model, and find it is more appropriate for forecasts.

Keywords: k-factor, GARMA, LLWNN, G-GARCH, electricity price, forecasting.

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Authors: Bita Mashayekhi, Marjan Fayyazi, Parisa Sefati

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Audit quality is a popular topic in accounting and auditing research because recent decades’ financial crises reduce the reliability of financial reports to public investors and cause significant doubt about the audit profession. Therefore, doing research to identify effective factors in improving audit quality is necessary for bringing back public investors’ trust to financial statements as well as audit reports. In this study, we explore the relationship between audit rotation and audit quality. For this purpose, we employ the Duff (2009) model of audit quality to measure audit quality and use a questionnaire survey of 27 audit service quality attributes. Our results show that there is a negative relationship between auditor’s rotation and audit quality as we consider the auditor’s reputation, capability, assurance, experience, and responsiveness as surrogates for audit quality. There is no evidence for verifying a same relationship when we use the auditor’s independence and expertise for measuring audit quality.

Keywords: Audit quality, auditor’s rotation, reputation, capability, assurance, experience, responsiveness, independence, expertise.

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Authors: Ali Hussian Ali AlTimemy, Fawzi M. Al Naima

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This paper presents the prediction of kidney dysfunction using different neural network (NN) approaches. Self organization Maps (SOM), Probabilistic Neural Network (PNN) and Multi Layer Perceptron Neural Network (MLPNN) trained with Back Propagation Algorithm (BPA) are used in this study. Six hundred and sixty three sets of analytical laboratory tests have been collected from one of the private clinical laboratories in Baghdad. For each subject, Serum urea and Serum creatinin levels have been analyzed and tested by using clinical laboratory measurements. The collected urea and cretinine levels are then used as inputs to the three NN models in which the training process is done by different neural approaches. SOM which is a class of unsupervised network whereas PNN and BPNN are considered as class of supervised networks. These networks are used as a classifier to predict whether kidney is normal or it will have a dysfunction. The accuracy of prediction, sensitivity and specificity were found for each type of the proposed networks .We conclude that PNN gives faster and more accurate prediction of kidney dysfunction and it works as promising tool for predicting of routine kidney dysfunction from the clinical laboratory data.

Keywords: Kidney Dysfunction, Prediction, SOM, PNN, BPNN, Urea and Creatinine levels.

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

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

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

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Authors: Raniya Alsharif

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The color theory of good and evil is the association of colors to the omnipresent concept of good and evil, where human behavior and perception can be highly influenced by seeing black and white, making these connotations almost dangerously distinctive where they can be very hard to distinguish. This theory is a human construct that dates back to ancient Egypt and has been used since then in almost all forms of communication and expression, such as art, fashion, literature, and religious manuscripts, helping the implantation of preconceived ideas that influence behavior and society. This is a mixed-methods research that uses both surveys to collect quantitative data related to the theory and a vignette to collect qualitative data by using a scenario where participants aged between 18-25 will style two characters of good and bad characteristics with color contrasting clothes, both yielding results about the nature of the preconceived perceptions associated with ‘black and white’ and ‘good and evil’, illustrating the important role of media and communications in human behavior and subconscious, and also uncover how far this theory goes in the age of social media enlightenment.

Keywords: Color perception, interpretivism, thematic analysis, vignettes.

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Authors: Zulaiha Ali Othman, Helmi Md Rais, Abdul Razak Hamdan

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Ants are fascinating creatures that demonstrate the ability to find food and bring it back to their nest. Their ability as a colony, to find paths to food sources has inspired the development of algorithms known as Ant Colony Systems (ACS). The principle of cooperation forms the backbone of such algorithms, commonly used to find solutions to problems such as the Traveling Salesman Problem (TSP). Ants communicate to each other through chemical substances called pheromones. Modeling individual ants- ability to manipulate this substance can help an ACS find the best solution. This paper introduces a Dynamic Ant Colony System with threelevel updates (DACS3) that enhance an existing ACS. Experiments were conducted to observe single ant behavior in a colony of Malaysian House Red Ants. Such behavior was incorporated into the DACS3 algorithm. We benchmark the performance of DACS3 versus DACS on TSP instances ranging from 14 to 100 cities. The result shows that the DACS3 algorithm can achieve shorter distance in most cases and also performs considerably faster than DACS.

Keywords: Dynamic Ant Colony System (DACS), TravelingSalesmen Problem (TSP), Optimization, Swarm Intelligent.

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Authors: Wei-Yi Yang, Li-Hui Lee, Hsiao-Li Gien, Hsing-Yi Chu, Yi-Ting Chou, Der-Ming Liou

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The American Health Level Seven (HL7) Reference Information Model (RIM) consists of six back-bone classes that have different specialized attributes. Furthermore, for the purpose of enforcing the semantic expression, there are some specific mandatory vocabulary domains have been defined for representing the content values of some attributes. In the light of the fact that it is a duplicated effort on spending a lot of time and human cost to develop and modify Clinical Information Systems (CIS) for most hospitals due to the variety of workflows. This study attempts to design and develop sharing RIM-based components of the CIS for the different business processes. Therefore, the CIS contains data of a consistent format and type. The programmers can do transactions with the RIM-based clinical repository by the sharing RIM-based components. And when developing functions of the CIS, the sharing components also can be adopted in the system. These components not only satisfy physicians- needs in using a CIS but also reduce the time of developing new components of a system. All in all, this study provides a new viewpoint that integrating the data and functions with the business processes, it is an easy and flexible approach to build a new CIS.

Keywords: HL7, Reference Information Model (RIM), web service, process management.

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Authors: Vishwesh Kulkarni, Nikhil Bellarykar

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Cellular complexity stems from the interactions among thousands of different molecular species. Thanks to the emerging fields of systems and synthetic biology, scientists are beginning to unravel these regulatory, signaling, and metabolic interactions and to understand their coordinated action. Reverse engineering of biological networks has has several benefits but a poor quality of data combined with the difficulty in reproducing it limits the applicability of these methods. A few years back, many of the commonly used predictive algorithms were tested on a network constructed in the yeast Saccharomyces cerevisiae (S. cerevisiae) to resolve this issue. The network was a synthetic network of five genes regulating each other for the so-called in vivo reverse-engineering and modeling assessment (IRMA). The network was constructed in S. cereviase since it is a simple and well characterized organism. The synthetic network included a variety of regulatory interactions, thus capturing the behaviour of larger eukaryotic gene networks on a smaller scale. We derive a new set of algorithms by solving a nonlinear optimization problem and show how these algorithms outperform other algorithms on these datasets.

Keywords: Synthetic gene network, network identification, nonlinear modeling, optimization.

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Authors: Wisam K. Hussam, Ali Alfeeli, Gergory J. Sheard

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Solar panels that use photovoltaic (PV) cells are popular for converting solar radiation into electricity. One of the major problems impacting the performance of PV panels is the overheating caused by excessive solar radiation and high ambient temperatures, which degrades the efficiency of the PV panels remarkably. To overcome this issue, an aluminum heat sink was used to dissipate unwanted heat from PV cells. The dimensions of the heat sink were determined considering the optimal fin spacing that fulfils hot climatic conditions. In this study, the effects of cooling on the efficiency and power output of a PV panel were studied experimentally. Two PV modules were used: one without and one with a heat sink. The experiments ran for 11 hours from 6:00 a.m. to 5:30 p.m. where temperature readings in the rear and front of both PV modules were recorded at an interval of 15 minutes using sensors and an Arduino microprocessor. Results are recorded for both panels simultaneously for analysis, temperate comparison, and for power and efficiency calculations. A maximum increase in the solar to electrical conversion efficiency of 35% and almost 55% in the power output were achieved with the use of a heat sink, while temperatures at the front and back of the panel were reduced by 9% and 11%, respectively.

Keywords: Photovoltaic cell, natural convection, heat sink, efficiency.

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Authors: Zulaiha Ali Othman, Helmi Md Rais, Abdul Razak Hamdan

Abstract:

Ants are fascinating creatures that demonstrate the ability to find food and bring it back to their nest. Their ability as a colony, to find paths to food sources has inspired the development of algorithms known as Ant Colony Systems (ACS). The principle of cooperation forms the backbone of such algorithms, commonly used to find solutions to problems such as the Traveling Salesman Problem (TSP). Ants communicate to each other through chemical substances called pheromones. Modeling individual ants- ability to manipulate this substance can help an ACS find the best solution. This paper introduces a Dynamic Ant Colony System with threelevel updates (DACS3) that enhance an existing ACS. Experiments were conducted to observe single ant behavior in a colony of Malaysian House Red Ants. Such behavior was incorporated into the DACS3 algorithm. We benchmark the performance of DACS3 versus DACS on TSP instances ranging from 14 to 100 cities. The result shows that the DACS3 algorithm can achieve shorter distance in most cases and also performs considerably faster than DACS.

Keywords: Dynamic Ant Colony System (DACS), Traveling Salesmen Problem (TSP), Optimization, Swarm Intelligent.

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Authors: Edward Guillén, Jeisson Sánchez, Carlos Omar Ramos

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In recent years, a wide variety of applications are developed with Support Vector Machines -SVM- methods and Artificial Neural Networks -ANN-. In general, these methods depend on intrusion knowledge databases such as KDD99, ISCX, and CAIDA among others. New classes of detectors are generated by machine learning techniques, trained and tested over network databases. Thereafter, detectors are employed to detect anomalies in network communication scenarios according to user’s connections behavior. The first detector based on training dataset is deployed in different real-world networks with mobile and non-mobile devices to analyze the performance and accuracy over static detection. The vulnerabilities are based on previous work in telemedicine apps that were developed on the research group. This paper presents the differences on detections results between some network scenarios by applying traditional detectors deployed with artificial neural networks and support vector machines.

Keywords: Anomaly detection, back-propagation neural networks, network intrusion detection systems, support vector machines.

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Authors: Thanawat Chuleekiat

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Socketing of bored piles in rock is always seen as a matter of debate on construction sites between consultants and contractors. The socketing depth normally depends on the type of rock, depth at which the rock is available below the pile cap and load carrying capacity of the pile. In this paper, the review of field load test data of drilled shaft socketed in weathered limestone conducted using conventional static pile load test and dynamic pile load test was made to evaluate a unit shaft friction for the bored piles socketed in weathered limestone (weak rock). The borehole drilling data were also reviewed in conjunction with the pile test result. In addition, the back-calculated unit shaft friction was reviewed against various empirical methods for bored piles socketed in weak rock. The paper concludes with an estimated ultimate unit shaft friction from the case study in Qatar for preliminary design.

Keywords: Piled foundation, weathered limestone, shaft friction, rock socket, pile load test.

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Authors: A. J. Al-Shareef, E. A. Mohamed, E. Al-Judaibi

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Load forecasting has become in recent years one of the major areas of research in electrical engineering. Most traditional forecasting models and artificial intelligence neural network techniques have been tried out in this task. Artificial neural networks (ANN) have lately received much attention, and a great number of papers have reported successful experiments and practical tests. This article presents the development of an ANN-based short-term load forecasting model with improved generalization technique for the Regional Power Control Center of Saudi Electricity Company, Western Operation Area (SEC-WOA). The proposed ANN is trained with weather-related data and historical electric load-related data using the data from the calendar years 2001, 2002, 2003, and 2004 for training. The model tested for one week at five different seasons, typically, winter, spring, summer, Ramadan and fall seasons, and the mean absolute average error for one hour-ahead load forecasting found 1.12%.

Keywords: Artificial neural networks, short-term load forecasting, back propagation.

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Authors: Chiung-ying Lee, Chia-hua Chang

Abstract:

In July 1, 2007, Taiwan Stock Exchange (TWSE) on market observation post system (MOPS) adds a new "Financial reference database" for investors to do investment reference. This database as a warning to public offering companies listed on the public financial information and it original within eight targets. In this paper, this database provided by the indicators for the application of company financial crisis early warning model verify that the database provided by the indicator forecast for the financial crisis, whether or not companies have a high accuracy rate as opposed to domestic and foreign scholars have positive results. There is use of Logistic Regression Model application of the financial early warning model, in which no joined back-conditions is the first model, joined it in is the second model, has been taken occurred in the financial crisis of companies to research samples and then business took place before the financial crisis point with T-1 and T-2 sample data to do positive analysis. The results show that this database provided the debt ratio and net per share for the best forecast variables.

Keywords: Financial reference database, Financial early warning model, Logistic Regression.

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Authors: Isao Tomita

Abstract:

We develop a periodically-poled LiNbO3 (PPLN) device for highly-efficient third-harmonic generation (THG), where the THG efficiency is enhanced with a cavity. THG can usually be produced via χ(3)-nonlinear materials by optical pumping with very high pump-power. Instead, we here propose THG by moderate-power pumping through a specially-designed PPLN device containing only χ(2)-nonlinearity, where sum-frequency generation in the χ(2) process is employed for the mixing of a pump beam and a second-harmonic-generation (SHG) beam produced from the pump beam. The cavity is designed to increase the SHG power with dichroic mirrors attached to both ends of the device that perfectly reflect the SHG beam back to the device and yet let the pump and THG beams pass through the mirrors. This brings about a THG-power enhancement because of THG power proportional to the enhanced SHG power. We examine the THG-efficiency dependence on the mirror reflectance and show that very high THG-efficiency is obtained at moderate pump-power when compared with that of a cavity-free PPLN device.

Keywords: Cavity, periodically-poled LiNbO3, sum-frequency generation, third-harmonic generation.

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Authors: Muhammad Muhsin, Abhishek Mishra, Shreyansh Vishwakarma, K. Dasaratha Babu, Anudevi Samuel

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In an interconnected power system, any sudden small load perturbation in any of the interconnected areas causes the deviation of the area frequencies, the tie line power and voltage deviation at the generator terminals. This paper deals with the study of performance of intelligent Fuzzy Logic controllers coupled with Conventional Controllers (PI and PID) for Load Frequency Control. For analysis, an isolated single area and interconnected two area thermal power systems with and without generation rate constraints (GRC) have been considered. The studies have been performed with conventional PI and PID controllers and their performance has been compared with intelligent fuzzy controllers. It can be demonstrated that these controllers can successfully bring back the excursions in area frequencies and tie line powers within acceptable limits in smaller time periods and with lesser transients as compared to the performance of conventional controllers under same load disturbance conditions. The simulations in MATLAB have been used for comparative studies.

Keywords: Area Control Error, Fuzzy Logic, Generation rate constraint, Load Frequency, Tie line Power.

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Authors: Sabry A. El Azazy

Abstract:

Tourism plays an essential role in supporting the National Economy. Egypt was ranked as one of the most attractive touristic destinations worldwide. Tourism as a service sector affects political events and unstable conditions. Within the revolution of January 25th, 2011, tourism became below standards, and the archeological heritage sites were subject to threat. Because of the political tension and social instability, Egypt's tourism sector has drastically dropped. Currently, Egypt is working on overcoming the crisis caused by political unrest. However, it is expected to take a long time to get back to where it was, especially in terms of regaining the confidence of travelers in the country's ability to guarantee and maintain security and stability. Recently, many great projects have been done, such as; New Administrative Cairo Capital, New Suez Canal logistic project, New City of Al Alamin, New Grand Egyptian Museum, as well as other great projects that reflect positively on the tourism industry and archaeological heritage development in Egypt.

Keywords: Archaeology, archaeological heritage, attractions, national economy, political events, touristic destinations, tourism industry.

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Authors: J. Lin, T. C. Kuo, C. -Y. Gau, K. C. Liu, Y. J. Huang, J. D. Yu, Y. W. Lin

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A multi-agent type robot for disaster response in calamity scene is proposed in this paper. The proposed grouped rescue robots can perform cooperative reconnaissance and surveillance to achieve a given rescue mission. The multi-agent rescue of dual set robot consists of one master set and three slave units. The research for this rescue robot system is going to detect at harmful environment where human is unreachable, such as the building is infected with virus or the factory has hazardous liquid in effluent. As a dual set robot, with Bluetooth and communication network, the master set can connect with slave units and send information back to computer by wireless and monitor. Therefore, rescuer can be informed the real-time information in a calamity area. Furthermore, each slave robot is able to obstacle avoidance by ultrasonic sensors, and encodes distance and location by compass. The master robot can integrate every devices information to increase the efficiency of prospected and research unknown area.

Keywords: Designing of multi-agent rescue robot, development and basic experiments of master-slave type rescue robots.

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Authors: N. Phattanakulkaewmorie, T. Paseephol, A.Moongngarm

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This study investigated the effect of germination on chemical compositions, physio-chemical properties of malted (germinated) red sorghum flours and evaluated characteristics of gluten free breads from sorghum flour. Results showed that germinated sorghum flour had higher amylase activity, swelling power and solubility at 95°C, but lower in the peak, break down, final and set back viscosities than ungerminated sample (p≤0.05). Five gluten free breads made from sorghum flour blends, with different ratios of ungerminated and germinated sorghum flour, were compared for the physical properties with those made from wheat flour. Crumb hardness, cohesiveness, gumminess and chewiness of sorghum breads were found significantly higher than those of wheat bread. With increasing of ungerminated flour proportion, the bread hardness increased while the cohesiveness declined. Sorghum breads appeared red to human eyes with a*values of 10.41-15.77.Their crust and crumb colors differed significantly from those of wheat bread.

Keywords: Flour, germination, gluten free bread, sorghum.

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Authors: Darshana Thomas, Edward Wilkie, James Irvine

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The Internet of things (IoT) is currently a highly researched topic, especially within the context of the smart home. These are small sensors that are capable of gathering data and transmitting it to a server. The majority of smart home products use protocols such as ZigBee or Bluetooth Low Energy (BLE). As these small sensors are increasing in number, the need to implement these with much more capable and ubiquitous transmission technology is necessary. The high power consumption is the reason that holds these small sensors back from using other protocols such as the most ubiquitous form of communication, WiFi. Comparing the power consumption of existing transmission technologies to one with WiFi inbuilt, would provide a better understanding for choosing between these technologies. We have developed a small IoT device with WiFi capability and proven that it is much more efficient than the first protocol, 433 MHz. We extend our work in this paper and compare WiFi power consumption with the other most widely used protocol BLE. The experimental results in this paper would conclude whether the developed prototype is capable in terms of power consumption to replace the existing protocol BLE with WiFi.

Keywords: Bluetooth, internet of things, power consumption, WiFi.

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Authors: Mangesh R. Phate, V. H. Tatwawadi

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This paper is intended to develop an artificial neural network (ANN) based model of material removal rate (MRR) in the turning of ferrous and nonferrous material in a Indian small-scale industry. MRR of the formulated model was proved with the testing data and artificial neural network (ANN) model was developed for the analysis and prediction of the relationship between inputs and output parameters during the turning of ferrous and nonferrous materials. The input parameters of this model are operator, work-piece, cutting process, cutting tool, machine and the environment.

The ANN model consists of a three layered feedforward back propagation neural network. The network is trained with pairs of independent/dependent datasets generated when machining ferrous and nonferrous material. A very good performance of the neural network, in terms of contract with experimental data, was achieved. The model may be used for the testing and forecast of the complex relationship between dependent and the independent parameters in turning operations.

Keywords: Field data based model, Artificial neural network, Simulation, Convectional Turning, Material removal rate.

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Authors: Iman Mousavinejad, Reza Kazemi, , Mohsen Bayani Khaknejad

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Active Front Steering system (AFS) provides an electronically controlled superposition of an angle to the steering wheel angle. This additional degree of freedom enables a continuous and driving-situation dependent on adaptation of the steering characteristics. In an active steering system, there needs be no fixed relationship between the steering wheel and the angle of the road wheels. Not only can the effective steering ratio be varied with speed, for example, but also the road wheel angles can be controlled by a combination of driver and computer inputs. Features like steering comfort, effort and steering dynamics are optimized and stabilizing steering interventions can be performed. In contrast to the conventional stability control, the yaw rate was fed back to AFS controller and the stability performance was optimized with Sliding Mode control (SMC) method. In addition, tire uncertainties have been taken into account in SM controller to provide the control robustness. In this paper, 3-DOF nonlinear model is used to design the AFS controller and 8-DOF nonlinear model is used to model the controlled vehicle.

Keywords: Active Front Steering (AFS), Sliding Mode Control method (SMC), Yaw rate, Vehicle Stability, Robustness

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Authors: Z. RahimAlipour, A. T. Haghighat

Abstract:

detecting the deadlock is one of the important problems in distributed systems and different solutions have been proposed for it. Among the many deadlock detection algorithms, Edge-chasing has been the most widely used. In Edge-chasing algorithm, a special message called probe is made and sent along dependency edges. When the initiator of a probe receives the probe back the existence of a deadlock is revealed. But these algorithms are not problem-free. One of the problems associated with them is that they cannot detect some deadlocks and they even identify false deadlocks. A key point not mentioned in the literature is that when the process is waiting to obtain the required resources and its execution has been blocked, how it can actually respond to probe messages in the system. Also the question of 'which process should be victimized in order to achieve a better performance when multiple cycles exist within one single process in the system' has received little attention. In this paper, one of the basic concepts of the operating system - daemon - will be used to solve the problems mentioned. The proposed Algorithm becomes engaged in sending probe messages to the mandatory daemons and collects enough information to effectively identify and resolve multi-cycle deadlocks in distributed systems.

Keywords: Distributed system, distributed deadlock detectionand resolution, daemon, false deadlock.

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Authors: Jatin Kumar Choudhary, Naren Rayala, Abbas Eslami Kiasari, Fahimeh Jafari

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Great Britain has one of the safest road networks in the world. However, the consequences of any death or serious injury are devastating for loved ones, as well as for those who help the severely injured. This paper aims to analyse Great Britain's road safety situation and show the response measures for areas where the total damage caused by accidents can be significantly and quickly reduced. For the past 30 years, the UK has had a good record in reducing fatalities over the past 30 years, there is still a considerable number of road deaths. The government continues to scale back road deaths empowering responsible road users by identifying and prosecuting the parameters that make the roads less safe. This study represents an exploratory analysis with deep insights which could provide policy makers with invaluable insights into how accidents happen and how they can be mitigated. We use STATS19 data published by the UK government. Since we need more information about locations which is not provided in STATA19, we first expand the features of the dataset using OpenStreetMap and Visual Crossing. This paper also provides a discussion regarding new road safety methods.

Keywords: Road safety, data analysis, OpenStreetMap, feature expanding.

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Authors: R. Krishnamoorthi, N. Kannan

Abstract:

In this paper, a new algorithm for generating codebook is proposed for vector quantization (VQ) in image coding. The significant features of the training image vectors are extracted by using the proposed Orthogonal Polynomials based transformation. We propose to generate the codebook by partitioning these feature vectors into a binary tree. Each feature vector at a non-terminal node of the binary tree is directed to one of the two descendants by comparing a single feature associated with that node to a threshold. The binary tree codebook is used for encoding and decoding the feature vectors. In the decoding process the feature vectors are subjected to inverse transformation with the help of basis functions of the proposed Orthogonal Polynomials based transformation to get back the approximated input image training vectors. The results of the proposed coding are compared with the VQ using Discrete Cosine Transform (DCT) and Pairwise Nearest Neighbor (PNN) algorithm. The new algorithm results in a considerable reduction in computation time and provides better reconstructed picture quality.

Keywords: Orthogonal Polynomials, Image Coding, Vector Quantization, TSVQ, Binary Tree Classifier

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Authors: Robert Koch, Mario Golling, Gabi Dreo Rodosek

Abstract:

Tracing and locating the geographical location of users (Geolocation) is used extensively in todays Internet. Whenever we, e.g., request a page from google we are - unless there was a specific configuration made - automatically forwarded to the page with the relevant language and amongst others, dependent on our location identified, specific commercials are presented. Especially within the area of Network Security, Geolocation has a significant impact. Because of the way the Internet works, attacks can be executed from almost everywhere. Therefore, for an attribution, knowledge of the origination of an attack - and thus Geolocation - is mandatory in order to be able to trace back an attacker. In addition, Geolocation can also be used very successfully to increase the security of a network during operation (i.e. before an intrusion actually has taken place). Similar to greylisting in emails, Geolocation allows to (i) correlate attacks detected with new connections and (ii) as a consequence to classify traffic a priori as more suspicious (thus particularly allowing to inspect this traffic in more detail). Although numerous techniques for Geolocation are existing, each strategy is subject to certain restrictions. Following the ideas of Endo et al., this publication tries to overcome these shortcomings with a combined solution of different methods to allow improved and optimized Geolocation. Thus, we present our architecture for improved Geolocation, by designing a new algorithm, which combines several Geolocation techniques to increase the accuracy.

Keywords: IP geolocation, prosecution of computer fraud, attack attribution, target-analysis

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Authors: Ali Reza Rajabi, Shahab Rashnoei, Mojtaba Hakimzadeh, Amir Habibi

Abstract:

At any point of time, a power system operating condition should be stable, meeting various operational criteria and it should also be secure in the event of any credible contingency. Present day power systems are being operated closer to their stability limits due to economic and environmental constraints. Maintaining a stable and secure operation of a power system is therefore a very important and challenging issue. Voltage instability has been given much attention by power system researchers and planners in recent years, and is being regarded as one of the major sources of power system insecurity. Voltage instability phenomena are the ones in which the receiving end voltage decreases well below its normal value and does not come back even after setting restoring mechanisms such as VAR compensators, or continues to oscillate for lack of damping against the disturbances. Reactive power limit of power system is one of the major causes of voltage instability. This paper investigates the effects of coordinated series capacitors (SC) with static VAR compensators (SVC) on steady-state voltage stability of a power system. Also, the influence of the presence of series capacitor on static VAR compensator controller parameters and ratings required to stabilize load voltages at certain values are highlighted.

Keywords: Static VAR Compensator (SVC), Series Capacitor (SC), voltage stability, reactive power.

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