Search results for: Relative mapping error

Authors: Şemsettin Şahin, Ahmet Oğuz Aktürk, İsmail Çelik

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This study investigated the views of teachers, students and their parents on the FATIH (Movement of Enhancing Opportunities and Improving Technology) Project, which was put into service by the Ministry of National Education in cooperation with the Ministry of Transportation in Turkey in November 2010 for the purpose of increasing students’ success and planned to be completed within 5 years. The study group consisted of teachers employed in a pilot school in the province of Karaman in central Turkey included within the scope of the FATIH Project, students attending this school and parents whose children are students in that school. The research data were collected through forms developed by the researchers to determine the views of teachers, students and parents on the FATIH Project. The descriptive analysis method, one of the qualitative research methods, was used in the study. An analysis of the data revealed that a large majority of the teachers and the students believed that if computers were used to serve their set purpose, then they could make considerable contributions to education. A large majority of the parents, on the other hand, regard the use of computers in education as a great opportunity for the students. The views of the teachers, students and parents on the FATIH Project usually overlap. Most of the participants in the study pointed out that the FATIH Project was intended to use technology effectively in education. Moreover, each individual participant described their role in the FATIH Project in accordance with their relative position and stated that they could perform whatever was expected of them for the effective and efficient use and progress of the Project. The views of the participants regarding the FATIH Project vary according to the kind of the participants.

Keywords: Education, FATIH Project, technology.

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Authors: Mamta Garg

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While compressing text files is useful, compressing still image files is almost a necessity. A typical image takes up much more storage than a typical text message and without compression images would be extremely clumsy to store and distribute. The amount of information required to store pictures on modern computers is quite large in relation to the amount of bandwidth commonly available to transmit them over the Internet and applications. Image compression addresses the problem of reducing the amount of data required to represent a digital image. Performance of any image compression method can be evaluated by measuring the root-mean-square-error & peak signal to noise ratio. The method of image compression that will be analyzed in this paper is based on the lossy JPEG image compression technique, the most popular compression technique for color images. JPEG compression is able to greatly reduce file size with minimal image degradation by throwing away the least “important" information. In JPEG, both color components are downsampled simultaneously, but in this paper we will compare the results when the compression is done by downsampling the single chroma part. In this paper we will demonstrate more compression ratio is achieved when the chrominance blue is downsampled as compared to downsampling the chrominance red in JPEG compression. But the peak signal to noise ratio is more when the chrominance red is downsampled as compared to downsampling the chrominance blue in JPEG compression. In particular we will use the hats.jpg as a demonstration of JPEG compression using low pass filter and demonstrate that the image is compressed with barely any visual differences with both methods.

Keywords: JPEG, Discrete Cosine Transform, Quantization, Color Space Conversion, Image Compression, Peak Signal to Noise Ratio & Compression Ratio.

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Authors: Navdeep Goel, Salvador Gabarda

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Digital images are widely used in computer applications. To store or transmit the uncompressed images requires considerable storage capacity and transmission bandwidth. Image compression is a means to perform transmission or storage of visual data in the most economical way. This paper explains about how images can be encoded to be transmitted in a multiplexing time-frequency domain channel. Multiplexing involves packing signals together whose representations are compact in the working domain. In order to optimize transmission resources each 4 × 4 pixel block of the image is transformed by a suitable polynomial approximation, into a minimal number of coefficients. Less than 4 × 4 coefficients in one block spares a significant amount of transmitted information, but some information is lost. Different approximations for image transformation have been evaluated as polynomial representation (Vandermonde matrix), least squares + gradient descent, 1-D Chebyshev polynomials, 2-D Chebyshev polynomials or singular value decomposition (SVD). Results have been compared in terms of nominal compression rate (NCR), compression ratio (CR) and peak signal-to-noise ratio (PSNR) in order to minimize the error function defined as the difference between the original pixel gray levels and the approximated polynomial output. Polynomial coefficients have been later encoded and handled for generating chirps in a target rate of about two chirps per 4 × 4 pixel block and then submitted to a transmission multiplexing operation in the time-frequency domain.

Keywords: Chirp signals, Image multiplexing, Image transformation, Linear canonical transform, Polynomial approximation.

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Authors: Ahmed Rekik, Mourad Zribi, Ahmed Ben Hamida, Mohamed Benjelloun

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This paper presents an optimal and unsupervised satellite image segmentation approach based on Pearson system and k-Means Clustering Algorithm Initialization. Such method could be considered as original by the fact that it utilised K-Means clustering algorithm for an optimal initialisation of image class number on one hand and it exploited Pearson system for an optimal statistical distributions- affectation of each considered class on the other hand. Satellite image exploitation requires the use of different approaches, especially those founded on the unsupervised statistical segmentation principle. Such approaches necessitate definition of several parameters like image class number, class variables- estimation and generalised mixture distributions. Use of statistical images- attributes assured convincing and promoting results under the condition of having an optimal initialisation step with appropriated statistical distributions- affectation. Pearson system associated with a k-means clustering algorithm and Stochastic Expectation-Maximization 'SEM' algorithm could be adapted to such problem. For each image-s class, Pearson system attributes one distribution type according to different parameters and especially the Skewness 'β1' and the kurtosis 'β2'. The different adapted algorithms, K-Means clustering algorithm, SEM algorithm and Pearson system algorithm, are then applied to satellite image segmentation problem. Efficiency of those combined algorithms was firstly validated with the Mean Quadratic Error 'MQE' evaluation, and secondly with visual inspection along several comparisons of these unsupervised images- segmentation.

Keywords: Unsupervised classification, Pearson system, Satellite image, Segmentation.

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Authors: Farhad Asadi, S. Hossein Sadati

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This paper presents a prediction performance of feedforward Multilayer Perceptron (MLP) and Echo State Networks (ESN) trained with extended Kalman filter. Feedforward neural networks and ESN are powerful neural networks which can track and predict nonlinear signals. However, their tracking performance depends on the specific signals or data sets, having the risk of instability accompanied by large error. In this study we explore this process by applying different network size and leaking rate for prediction of nonlinear or chaotic signals in MLP neural networks. Major problems of ESN training such as the problem of initialization of the network and improvement in the prediction performance are tackled. The influence of coefficient of activation function in the hidden layer and other key parameters are investigated by simulation results. Extended Kalman filter is employed in order to improve the sequential and regulation learning rate of the feedforward neural networks. This training approach has vital features in the training of the network when signals have chaotic or non-stationary sequential pattern. Minimization of the variance in each step of the computation and hence smoothing of tracking were obtained by examining the results, indicating satisfactory tracking characteristics for certain conditions. In addition, simulation results confirmed satisfactory performance of both of the two neural networks with modified parameterization in tracking of the nonlinear signals.

Keywords: Feedforward neural networks, nonlinear signal prediction, echo state neural networks approach, leaking rates, capacity of neural networks.

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Authors: Ahmed G. Mahgoub, Dahlia H. Hafez, Mostafa A. Abu Kiefa

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The Cone Penetration Test (CPT) is a common in-situ test which generally investigates a much greater volume of soil more quickly than possible from sampling and laboratory tests. Therefore, it has the potential to realize both cost savings and assessment of soil properties rapidly and continuously. The principle objective of this paper is to demonstrate the feasibility and efficiency of using artificial neural networks (ANNs) to predict the soil angle of internal friction (Φ) and the soil modulus of elasticity (E) from CPT results considering the uncertainties and non-linearities of the soil. In addition, ANNs are used to study the influence of different parameters and recommend which parameters should be included as input parameters to improve the prediction. Neural networks discover relationships in the input data sets through the iterative presentation of the data and intrinsic mapping characteristics of neural topologies. General Regression Neural Network (GRNN) is one of the powerful neural network architectures which is utilized in this study. A large amount of field and experimental data including CPT results, plate load tests, direct shear box, grain size distribution and calculated data of overburden pressure was obtained from a large project in the United Arab Emirates. This data was used for the training and the validation of the neural network. A comparison was made between the obtained results from the ANN's approach, and some common traditional correlations that predict Φ and E from CPT results with respect to the actual results of the collected data. The results show that the ANN is a very powerful tool. Very good agreement was obtained between estimated results from ANN and actual measured results with comparison to other correlations available in the literature. The study recommends some easily available parameters that should be included in the estimation of the soil properties to improve the prediction models. It is shown that the use of friction ration in the estimation of Φ and the use of fines content in the estimation of E considerable improve the prediction models.

Keywords: Angle of internal friction, Cone penetrating test, General regression neural network, Soil modulus of elasticity.

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Authors: Victor Krebss

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Task of object localization is one of the major challenges in creating intelligent transportation. Unfortunately, in densely built-up urban areas, localization based on GPS only produces a large error, or simply becomes impossible. New opportunities arise for the localization due to the rapidly emerging concept of a wireless ad-hoc network. Such network, allows estimating potential distance between these objects measuring received signal level and construct a graph of distances in which nodes are the localization objects, and edges - estimates of the distances between pairs of nodes. Due to the known coordinates of individual nodes (anchors), it is possible to determine the location of all (or part) of the remaining nodes of the graph. Moreover, road map, available in digital format can provide localization routines with valuable additional information to narrow node location search. However, despite abundance of well-known algorithms for solving the problem of localization and significant research efforts, there are still many issues that currently are addressed only partially. In this paper, we propose localization approach based on the graph mapped distances on the digital road map data basis. In fact, problem is reduced to distance graph embedding into the graph representing area geo location data. It makes possible to localize objects, in some cases even if only one reference point is available. We propose simple embedding algorithm and sample implementation as spatial queries over sensor network data stored in spatial database, allowing employing effectively spatial indexing, optimized spatial search routines and geometry functions.

Keywords: Intelligent Transportation System, Sensor Network, Localization, Spatial Query, GIS, Graph Embedding.

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Authors: E. Cintura, M. I. Gomes

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Nowadays, sustainable development issues have a key role in the planning of the man-made environment. Ensuring this development means limiting the impact of human activity on nature. It is essential to secure healthy places and good living conditions. For these reasons, indoor air quality and building materials play a fundamental role in sustainable architectural projects. These factors significantly affect human health: they can radically change the quality of the internal environment and energy consumption. The use of natural materials such as earth has many beneficial aspects in comfort and indoor air quality. As well as advantages in the environmental impact of the construction, they ensure a low energy consumption. Since they are already present in nature, their production and use do not require a high-energy consumption. Furthermore, they have a high thermo-hygrometric capacity, being able to absorb moisture, contributing positively to indoor conditions. Indoor air quality is closely related to relative humidity. For these reasons, it can be affirmed that the use of earth materials guarantees a sustainable development and at the same time improves the health of the building users. This paper summarizes several researches that demonstrate the importance of indoor air quality for human health and how it strictly depends on the building materials used. Eco-efficient plasters are also considered: earth and ash mortar. The bibliography consulted has the objective of supporting future experimental and laboratory analyzes. It is necessary to carry on with research by the use of simulations and testing to confirm the hygrothermal properties of eco-efficient plasters and therefore their ability to improve indoor air quality.

Keywords: Hygroscopicity, hygrothermal comfort, mortar, plaster.

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Authors: Ichiro Mukai

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The purpose of this study is to investigate whether firms applying International Financial Reporting Standards (IFRS), provide high-quality and comparable earnings information that is useful for decision making of information users relative to firms applying local Generally Accepted Accounting Principles (GAAP). Focus is placed on the earnings quality of listed firms in several developed countries: Australia, Canada, France, Germany, Japan, the United Kingdom (UK), and the United States (US). Except for Japan and the US, the adoption of IFRS is mandatory for listed firms in these countries. In Japan, the application of IFRS is allowed for specific listed firms. In the US, the foreign firms listed on the US securities market are permitted to apply IFRS but the listed domestic firms are prohibited from doing so. In this paper, the differences in earnings quality are compared between firms applying local GAAP and those applying IFRS in each country and industry category, and the reasons of differences in earnings quality are analyzed using various factors. The results show that, although the earnings quality of firms applying IFRS is higher than that of firms applying local GAAP, this varies with country and industry category. Thus, even if a single set of global accounting standards is used for all listed firms worldwide, it is difficult to establish comparability of financial information among global firms. These findings imply that various circumstances surrounding firms, industries, and countries etc. influence business operations and affect the differences in earnings quality.

Keywords: Accruals, earnings quality, IFRS, information comparability.

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Authors: O. Hassanein, Sreenatha G. Anavatti, Tapabrata Ray

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The dynamics of the Autonomous Underwater Vehicles (AUVs) are highly nonlinear and time varying and the hydrodynamic coefficients of vehicles are difficult to estimate accurately because of the variations of these coefficients with different navigation conditions and external disturbances. This study presents the on-line system identification of AUV dynamics to obtain the coupled nonlinear dynamic model of AUV as a black box. This black box has an input-output relationship based upon on-line adaptive fuzzy model and adaptive neural fuzzy network (ANFN) model techniques to overcome the uncertain external disturbance and the difficulties of modelling the hydrodynamic forces of the AUVs instead of using the mathematical model with hydrodynamic parameters estimation. The models- parameters are adapted according to the back propagation algorithm based upon the error between the identified model and the actual output of the plant. The proposed ANFN model adopts a functional link neural network (FLNN) as the consequent part of the fuzzy rules. Thus, the consequent part of the ANFN model is a nonlinear combination of input variables. Fuzzy control system is applied to guide and control the AUV using both adaptive models and mathematical model. Simulation results show the superiority of the proposed adaptive neural fuzzy network (ANFN) model in tracking of the behavior of the AUV accurately even in the presence of noise and disturbance.

Keywords: AUV, AUV dynamic model, fuzzy control, fuzzy modelling, adaptive fuzzy control, back propagation, system identification, neural fuzzy model, FLNN.

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Authors: C. W. Hsu, C. N. Liang, L. Y. Ke, F. Y. Huang

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Many accidents were happened because of fast driving, habitual working overtime or tired spirit. This paper presents a solution of remote warning for vehicles collision avoidance using vehicular communication. The development system integrates dedicated short range communication (DSRC) and global position system (GPS) with embedded system into a powerful remote warning system. To transmit the vehicular information and broadcast vehicle position; DSRC communication technology is adopt as the bridge. The proposed system is divided into two parts of the positioning andvehicular units in a vehicle. The positioning unit is used to provide the position and heading information from GPS module, and furthermore the vehicular unit is used to receive the break, throttle, and othersignals via controller area network (CAN) interface connected to each mechanism. The mobile hardware are built with an embedded system using X86 processor in Linux system. A vehicle is communicated with other vehicles via DSRC in non-addressed protocol with wireless access in vehicular environments (WAVE) short message protocol. From the position data and vehicular information, this paper provided a conflict detection algorithm to do time separation and remote warning with error bubble consideration. And the warning information is on-line displayed in the screen. This system is able to enhance driver assistance service and realize critical safety by using vehicular information from the neighbor vehicles.KeywordsDedicated short range communication, GPS, Control area network, Collision avoidance warning system.

Keywords: Dedicated short range communication, GPS, Control area network, Collision avoidance warning system.

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Authors: Zhan Fengmei Yao, Hui Li, Jiahua Zhang g

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The Northeast China (NEC) was the most important agriculture areas and known as the Golden-Maize-Belt. Based on observed crop data and crop model, we design four simulating experiments and separate relative impacts and contribution under climate change, planting date shift, and varieties change as well change of varieties and planting date. Without planting date and varieties change, maize yields had no significant change trend at Hailun station located in the north of NEC, and presented significant decrease by 0.2 - 0.4 t/10a at two stations, which located in the middle and the south of NEC. With planting date change, yields showed a significant increase by 0.09 - 0.47 t/10a. With varieties change, maize yields had significant increase by 1.8~ 1.9 t/10a at Hailun and Huadian stations, but a non-significant and low increase by 0.2t /10a at Benxi located in the south of NEC. With change of varieties and planting date, yields presented a significant increasing by 0.53- 2.0 t/10a. Their contribution to yields was -25% ~ -55% for climate change, 15% ~ 35% for planting date change, and 20% ~110% for varieties change as well 30% ~135% for varieties with planting date shift. It found that change in varieties and planting date were highest yields and were responsible for significant increases in maize yields, varieties was secondly, and planting date was thirdly. It found that adaptation in varieties and planting date greatly improved maize yields, and increased yields annual variability. The increase of contribution with planting date and varieties change in 2000s was lower than in 1990s. Yields with the varieties change and yields with planting date and varieties change all showed a decreasing trend at Huadian and Benxi since 2002 or so. It indicated that maize yields increasing trend stagnated in the middle and south of NEC, and continued in the north of NEC.

Keywords: Climate change, maize yields, varieties, planting date, impacts.

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Authors: Jeries J. Abou-Hanna

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Several techniques exist for determining stress-intensity factors in linear elastic fracture mechanics analysis. These techniques are based on analytical, numerical, and empirical approaches that have been well documented in literature and engineering handbooks. However, not all techniques share the same merit. In addition to overly-conservative results, the numerical methods that require extensive computational effort, and those requiring copious user parameters hinder practicing engineers from efficiently evaluating stress-intensity factors. This paper investigates the prospects of reducing the complexity and required variables to determine stress-intensity factors through the utilization of the stress gradient and a weighting function. The heart of this work resides in the understanding that fracture emanating from stress concentration locations cannot be explained by a single maximum stress value approach, but requires use of a critical volume in which the crack exists. In order to understand the effectiveness of this technique, this study investigated components of different notch geometry and varying levels of stress gradients. Two forms of weighting functions were employed to determine stress-intensity factors and results were compared to analytical exact methods. The results indicated that the “exponential” weighting function was superior to the “absolute” weighting function. An error band +/- 10% was met for cases ranging from a steep stress gradient in a sharp v-notch to the less severe stress transitions of a large circular notch. The incorporation of the proposed method has shown to be a worthwhile consideration.

Keywords: Fracture mechanics, finite element method, stress intensity factor, stress gradient.

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Authors: Y. S. Zhou, C. R. Li, L. L. Tang, C. X. Gao, D. J. Wang, Y. Y. Guo

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Triangular trihedral corner reflector (CR) has been widely used as point target for synthetic aperture radar (SAR) calibration and image quality assessment. The additional “tip” of the triangular plate does not contribute to the reflector’s theoretical RCS and if it interacts with a perfectly reflecting ground plane, it will yield an increase of RCS at the radar bore-sight and decrease the accuracy of SAR calibration and image quality assessment. Regarding this problem, two types of CRs were manufactured. One was the hexagonal trihedral CR. It is a self-illuminating CR with relatively small plate edge length, while large edge length usually introduces unexpected edge diffraction error. The other was the triangular trihedral CR with extended bottom plate which considers the effect of ‘tip’ into the total RCS. In order to assess the performance of the two types of new CRs, flight campaign over the National Calibration and Validation Site for High Resolution Remote Sensors was carried out. Six hexagonal trihedral CRs and two bottom-extended trihedral CRs, as well as several traditional triangular trihedral CRs, were deployed. KOMPSAT-5 X-band SAR image was acquired for the performance analysis of the hexagonal trihedral CRs. C-band airborne SAR images were acquired for the performance analysis of the bottom-extended trihedral CRs. The analysis results showed that the impulse response function of both the hexagonal trihedral CRs and bottom-extended trihedral CRs were much closer to the ideal sinc-function than the traditional triangular trihedral CRs. The flight campaign results validated the advantages of new types of CRs and they might be useful in the future SAR calibration mission.

Keywords: Synthetic Aperture Radar, calibration, corner reflector, KOMPSAT-5.

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Authors: Aarati Susan Mathew, Radhika Narendra Patel, Jiji Mathew

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A retrospective study conducted at Christian Medical College (CMC) Teaching Hospital, Vellore, India on 14th August 2014 to assess the accuracy of clinically estimated foetal weight upon labour admission. Estimating foetal weight is a crucial factor in assessing maternal and foetal complications during and after labour. Medical notes of ninety-eight postnatal women who fulfilled the inclusion criteria were studied to evaluate the correlation between their recorded Estimated Foetal Weight (EFW) on admission and actual birth weight (ABW) of the newborn after delivery. Data concerning maternal and foetal demographics was also noted. Accuracy was determined by absolute percentage error and proportion of estimates within 10% of ABW. Actual birth weights ranged from 950-4080g. A strong positive correlation between EFW and ABW (r=0.904) was noted. Term deliveries (≥40 weeks) in the normal weight range (2500-4000g) had a 59.5% estimation accuracy (n=74) compared to pre-term (<40 weeks) with an estimation accuracy of 0% (n=2). Out of the term deliveries, macrosomic babies (>4000g) were underestimated by 25% (n=3) and low birthweight (LBW) babies were overestimated by 12.7% (n=9). Registrars who estimated foetal weight were accurate in babies within normal weight ranges. However, there needs to be an improvement in predicting weight of macrosomic and LBW foetuses. We have suggested the use of an amended version of the Johnson’s formula for the Indian population for improvement and a need to re-audit once implemented.

Keywords: Clinical palpation, estimated foetal weight, pregnancy, India, Johnson’s formula.

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Authors: Somayeh Komeylian

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The direction of arrival (DoA) estimation is the crucial aspect of the radar technologies for detecting and dividing several signal sources. In this scenario, the antenna array output modeling involves numerous parameters including noise samples, signal waveform, signal directions, signal number, and signal to noise ratio (SNR), and thereby the methods of the DoA estimation rely heavily on the generalization characteristic for establishing a large number of the training data sets. Hence, we have analogously represented the two different optimization models of the DoA estimation; (1) the implementation of the decision directed acyclic graph (DDAG) for the multiclass least-squares support vector machine (LS-SVM), and (2) the optimization method of the deep neural network (DNN) radial basis function (RBF). We have rigorously verified that the LS-SVM DDAG algorithm is capable of accurately classifying DoAs for the three classes. However, the accuracy and robustness of the DoA estimation are still highly sensitive to technological imperfections of the antenna arrays such as non-ideal array design and manufacture, array implementation, mutual coupling effect, and background radiation and thereby the method may fail in representing high precision for the DoA estimation. Therefore, this work has a further contribution on developing the DNN-RBF model for the DoA estimation for overcoming the limitations of the non-parametric and data-driven methods in terms of array imperfection and generalization. The numerical results of implementing the DNN-RBF model have confirmed the better performance of the DoA estimation compared with the LS-SVM algorithm. Consequently, we have analogously evaluated the performance of utilizing the two aforementioned optimization methods for the DoA estimation using the concept of the mean squared error (MSE).

Keywords: DoA estimation, adaptive antenna array, Deep Neural Network, LS-SVM optimization model, radial basis function, MSE.

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Authors: Jalil Emadi, Abbas Solemani

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Pressure waves and Water Hammer occur in a pumping system when valves are closed or opened suddenly or in the case of sudden failure of pumps. Determination of maximum water hammer is considered one of the most important technical and economical items of which engineers and designers of pumping stations and conveyance pipelines should take care. Hammer Software is a recent application used to simulate water hammer. The present study focuses on determining significance of each input parameter of the application relative to the maximum amount of water hammer estimated by the software. The study determines estimated maximum water hammer variations due to variations of input parameters including water temperature, pipe type, thickness and diameter, electromotor rpm and power, and moment of inertia of electromotor and pump. In our study, Kuhrang Pumping Station was modeled using WaterGEMS Software. The pumping station is characterized by total discharge of 200 liters per second, dynamic height of 194 meters and 1.5 kilometers of steel conveyance pipeline and transports water to Cheshme Morvarid for farmland irrigation. The model was run in steady hydraulic condition and transferred to Hammer Software. Then, the model was run in several unsteady hydraulic conditions and sensitivity of maximum water hammer to each input parameter was calculated. It is shown that parameters to which maximum water hammer is most sensitive are moment of inertia of pump and electromotor, diameter, type and thickness of pipe and water temperature, respectively.

Keywords: Pressure Wave, Water Hammer, Sensitivity Analysis, Hammer Software, Kuhrang, Cheshme Morvarid

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Authors: Talal S. Abdullah, Shahin Mehraban, Geraint Lodwig, Nicholas P. Lavery

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The defining property of Interstitial Free (IF) steels is formability, comprehensively measured using the Lankford coefficient (r-value) on uniaxial tensile test data. The contributing factors supporting this feature are grain size, orientation, and elemental additions. The processes that effectively modulate these factors are the casting procedure, hot rolling, and heat treatment. An existing methodology is well-practised in the steel industry; however, large-scale production and experimentation consume significant proportions of time, money, and material. Introducing small-scale rapid alloy prototyping (RAP) as an alternative process would considerably reduce the drawbacks relative to standard practices. The aim is to finetune the existing fundamental procedures implemented in the industrial plant to adapt to the RAP route. IF material is remelted in the 80-gram coil induction melting (CIM) glovebox. To birth small grains, maximum deformation must be induced onto the cast material during the hot rolling process. The rolled strip must then satisfy the polycrystalline behaviour of the bulk material by displaying a resemblance in microstructure, hardness, and formability to that of the literature and actual plant steel. A successful outcome of this work is that small-scale RAP can achieve target compositions with similar microstructures and statistically consistent mechanical properties which complements and accelerates the development of novel steel grades.

Keywords: Interstitial free, miniaturized tensile specimen, plastic anisotropy, rapid alloy prototyping.

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Authors: Payam Haghparast, Mikhail V. Sorin, Hakim Nesreddine

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The complex oblique shock phenomenon can be simply assumed as a normal shock at the constant area section to simulate a sharp pressure increase and velocity decrease in 1-D thermodynamic models. The assumed normal shock location is one of the greatest sources of error in ejector thermodynamic models. Most researchers consider an arbitrary location without justifying it. Our study compares the effect of normal shock place on ejector dimensions in 1-D models. To this aim, two different ejector experimental test benches, a constant area-mixing ejector (CAM) and a constant pressure-mixing (CPM) are considered, with different known geometries, operating conditions and working fluids (R245fa, R141b). In the first step, in order to evaluate the real value of the efficiencies in the different ejector parts and critical back pressure, a CFD model was built and validated by experimental data for two types of ejectors. These reference data are then used as input to the 1D model to calculate the lengths and the diameters of the ejectors. Afterwards, the design output geometry calculated by the 1D model is compared directly with the corresponding experimental geometry. It was found that there is a good agreement between the ejector dimensions obtained by the 1D model, for both CAM and CPM, with experimental ejector data. Furthermore, it is shown that normal shock place affects only the constant area length as it is proven that the inlet normal shock assumption results in more accurate length. Taking into account previous 1D models, the results suggest the use of the assumed normal shock location at the inlet of the constant area duct to design the supersonic ejectors.

Keywords: 1D model, constant area-mixing, constant pressure-mixing, normal shock location, ejector dimensions.

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Authors: Jae-Yong Lee, Gyu-Hyun Bae, Yun-Soo Chung, Dae-Sub Kim, Jae-Sung Bae

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In general, fabric is heat-treated using a stenter machine in order to dry and fix its shape. It is important to shape before the heat treatment because it is difficult to revert back once the fabric is formed. To produce the product of right shape, camera type weft straightener has been applied recently to capture and process fabric images quickly. It is more powerful in determining the final textile quality rather than photo-sensor. Positioning in front of a stenter machine, weft straightener helps to spread fabric evenly and control the angle between warp and weft constantly as right angle by handling skew and bow rollers. To process this tricky procedure, the structural analysis should be carried out in advance, based on which, its control technology can be drawn. A structural analysis is to figure out the specific contact/slippage characteristics between fabric and roller. We already examined the applicability of camera type weft straightener to plain weave fabric and found its possibility and the specific working condition of machine and rollers. In this research, we aimed to explore another applicability of camera type weft straightener. Namely, we tried to figure out camera type weft straightener can be used for fabrics. To find out the optimum condition, we increased the number of rollers. The analysis is done by ANSYS software using Finite Element Analysis method. The control function is demonstrated by experiment. In conclusion, the structural analysis of weft straightener is done to identify a specific characteristic between roller and fabrics. The control of skew and bow roller is done to decrease the error of the angle between warp and weft. Finally, it is proved that camera type straightener can also be used for the special fabrics.

Keywords: Camera type weft straightener, structure analysis, control, skew and bow roller.

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Authors: Alireza Ansariyar, Safieh Laaly

Abstract:

Over the last decades, the vehicle industry has shown increased interest in integrating autonomous, connected, and electrical technologies in vehicle design with the primary hope of improving mobility and road safety while reducing transportation’s environmental impact. Using the State of Maryland (M.D.) in the United States as a pilot study, this research investigates Connected and Autonomous Vehicles (CAVs) fuel consumption and air pollutants including Carbon Monoxide (CO), Particulate Matter (PM), and Nitrogen Oxides (NOx) and utilizes meaningful linear regression models to predict CAV’s environmental effects. Maryland transportation network was simulated in VISUM software, and data on a set of variables were collected through a comprehensive survey. The number of pollutants and fuel consumption were obtained for the time interval 2010 to 2021 from the macro simulation. Eventually, four linear regression models were proposed to predict the amount of C.O., NOx, PM pollutants, and fuel consumption in the future. The results highlighted that CAVs’ pollutants and fuel consumption have a significant correlation with the income, age, and race of the CAV customers. Furthermore, the reliability of four statistical models was compared with the reliability of macro simulation model outputs in the year 2030. The error of three pollutants and fuel consumption was obtained at less than 9% by statistical models in SPSS. This study is expected to assist researchers and policymakers with planning decisions to reduce CAV environmental impacts in M.D.

Keywords: Connected and autonomous vehicles, statistical model, environmental effects, pollutants and fuel consumption, VISUM, linear regression models.

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Authors: Hamidreza Hoshyarmanesh, Hosein Madieh, Sanju Lama, Yaser Maddahi, Garnette R. Sutherland, Kourosh Zareinia

Abstract:

This paper illustrates the concept of an algorithm to register specified markers on the neuroArm surgical manipulators, an image-guided MR-compatible tele-operated robot for microsurgery and stereotaxy. Two range-finding algorithms, namely time-of-flight and phase-shift, are evaluated for registration and supervisory control. The time-of-flight approach is implemented in a semi-field experiment to determine the precise position of a tiny retro-reflective moving object. The moving object simulates a surgical tool tip. The tool is a target that would be connected to the neuroArm end-effector during surgery inside the magnet bore of the MR imaging system. In order to apply flight approach, a 905-nm pulsed laser diode and an avalanche photodiode are utilized as the transmitter and receiver, respectively. For the experiment, a high frequency time to digital converter was designed using a field-programmable gate arrays. In the phase-shift approach, a continuous green laser beam with a wavelength of 530 nm was used as the transmitter. Results showed that a positioning error of 0.1 mm occurred when the scanner-target point distance was set in the range of 2.5 to 3 meters. The effectiveness of this non-contact approach exhibited that the method could be employed as an alternative for conventional mechanical registration arm. Furthermore, the approach is not limited by physical contact and extension of joint angles.

Keywords: 3D laser scanner, intraoperative MR imaging, neuroArm, real time registration, robot-assisted surgery, supervisory control.

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Authors: B. R. O. Omidiwura, A. F. Agboola, E. A. Iyayi

Abstract:

Pure form of L-Dopa is used to enhance muscular development, fat breakdown and suppress Parkinson disease in humans. However, the L-Dopa in mucuna seed, when present with other antinutritional factors, causes nutritional disorders in monogastric animals. Information on the utilisation of pure L-Dopa in monogastric animals is scanty. Therefore, effect of L-Dopa on growth performance and carcass characteristics in broiler chickens was investigated. Two hundred and forty one-day-old chicks were allotted to six treatments, which consisted of a positive control (PC) with standard energy (3100Kcal/Kg) and negative control (NC) with high energy (3500Kcal/Kg). The rest 4 diets were NC+0.1, NC+0.2, NC+0.3 and NC+0.4% L-Dopa, respectively. All treatments had 4 replicates in a completely randomized design. Body weight gain, final weight, feed intake, dressed weight and carcass characteristics were determined. Body weight gain and final weight of birds fed PC were 1791.0 and 1830.0g, NC+0.1% L-Dopa were 1827.7 and 1866.7g and NC+0.2% L-Dopa were 1871.9 and 1910.9g respectively, and the feed intake of PC (3231.5g), were better than other treatments. The dressed weight at 1375.0g and 1357.1g of birds fed NC+0.1% and NC+0.2% L-Dopa, respectively, were similar but better than other treatments. Also, the thigh (202.5g and 194.9g) and the breast meat (413.8g and 410.8g) of birds fed NC+0.1% and NC+0.2% L-Dopa, respectively, were similar but better than birds fed other treatments. The drum stick of birds fed NC+0.1% L-Dopa (220.5g) was observed to be better than birds on other diets. Meat to bone ratio and relative organ weights were not affected across treatments. L-Dopa extract, at levels tested, had no detrimental effect on broilers, rather better bird performance and carcass characteristics were observed especially at 0.1% and 0.2% L-Dopa inclusion rates. Therefore, 0.2% inclusion is recommended in diets of broiler chickens for improved performance and carcass characteristics.

Keywords: Broilers, Carcass characteristics, L-Dopa, performance.

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Authors: M. Govindarajan

Abstract:

One of the major developments in machine learning in the past decade is the ensemble method, which finds highly accurate classifier by combining many moderately accurate component classifiers. In this research work, new ensemble classification methods are proposed with homogeneous ensemble classifier using bagging and heterogeneous ensemble classifier using arcing and their performances are analyzed in terms of accuracy. A Classifier ensemble is designed using Radial Basis Function (RBF) and Support Vector Machine (SVM) as base classifiers. The feasibility and the benefits of the proposed approaches are demonstrated by the means of standard datasets of intrusion detection. The main originality of the proposed approach is based on three main parts: preprocessing phase, classification phase, and combining phase. A wide range of comparative experiments is conducted for standard datasets of intrusion detection. The performance of the proposed homogeneous and heterogeneous ensemble classifiers are compared to the performance of other standard homogeneous and heterogeneous ensemble methods. The standard homogeneous ensemble methods include Error correcting output codes, Dagging and heterogeneous ensemble methods include majority voting, stacking. The proposed ensemble methods provide significant improvement of accuracy compared to individual classifiers and the proposed bagged RBF and SVM performs significantly better than ECOC and Dagging and the proposed hybrid RBF-SVM performs significantly better than voting and stacking. Also heterogeneous models exhibit better results than homogeneous models for standard datasets of intrusion detection. 

Keywords: Data mining, ensemble, radial basis function, support vector machine, accuracy.

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Authors: S. Mhamdi, O. Brendel, P. Montpied, K. Ben Yahia, N. Saouyah, B. Hasnaoui, E. Dreyer

Abstract:

Leaf Life Span (LLS) is used to classify trees into two main groups: evergreen and deciduous species. It varies according to the forms of life between taxonomic groups. Co-occurrence of deciduous and evergreen oaks is common in some Mediterranean type climate areas. Nevertheless, in the Tunisian forests, there is no enough information about the functional inter-specific diversity among oak species, especially in the mixed stand marked by the simultaneous presence of Q. suber L., Q. canariensis Willd. and their hybrid (Q. afares), the latter being an endemic oak species threatened with extinction. This study has been conducted to estimate the LLS, the relative growth rate, and the count of different growth flushes of samplings in semi-controlled conditions. Our study took 17 months, with an observation's interval of 4 weeks. The aim is to characterize and compare the hybrid species to the parental ones. Differences were observed among species, both for phenology and growth. Indeed, Q. suber saplings reached higher total height and number of growth flushes then Q. canariensis, while Q. afares showed much less growth flushes than the parental species. The LLS of parental species has exceeded the duration of the experiment, but their hybrid lost all leaves on all cohorts. The short LLSs of hybrid species are in accordance with this phenology in the field, but for Q. canariensis there was a contrast with observations in the field where phenology is strictly annual. This study allowed us to differentiate the hybrid from both parental species.

Keywords: Leaf life span, growth, hybrid, evergreen, deciduous, seedlings, Q. afares Pomel, Q. suber L., Q. canariensis Willd.

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Authors: Edikan E. Akpanibah Obinichi C. Mandah Imoleayo S. Asiwaju

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In this paper, we studied the effect of supplementary premium on the optimal portfolio policy in a defined contribution (DC) pension scheme with refund of premium clauses. This refund clause allows death members’ next of kin to withdraw their relative’s accumulated wealth during the accumulation period. The supplementary premium is to help sustain the scheme and is assumed to be stochastic. We considered cases when the remaining wealth is equally distributed and when it is not equally distributed among the remaining members. Next, we considered investments in cash and equity to help increase the remaining accumulated funds to meet up with the retirement needs of the remaining members and composed the problem as a continuous time mean-variance stochastic optimal control problem using the actuarial symbol and established an optimization problem from the extended Hamilton Jacobi Bellman equations. The optimal portfolio policy, the corresponding optimal fund size for the two assets and also the efficient frontier of the pension members for the two cases was obtained. Furthermore, the numerical simulations of the optimal portfolio policies with time were presented and the effect of the supplementary premium on the optimal portfolio policy was discussed and observed that the supplementary premium decreases the optimal portfolio policy of the risky asset (equity). Secondly we observed a disparity between the optimal policies for the two cases.

Keywords: Defined contribution pension scheme, extended Hamilton Jacobi Bellman equations, optimal portfolio policies, refund of premium clauses, supplementary premium.

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Authors: Gunasekaran Raja, Ramkumar Jayaraman

Abstract:

In this paper, we consider CCL-N (Cooperative Cross Layer Network) topology based on the cross layer (both centralized and distributed) environment to form network communities. Various performance metrics related to the IEEE 802.16 networks are discussed to design CCL-N Topology. In CCL-N topology, nodes are classified as master nodes (Master Base Station [MBS]) and serving nodes (Relay Station [RS]). Nodes communities are organized based on the networking terminologies. Based on CCL-N Topology, various simulation analyses for both transparent and non-transparent relays are tabulated and throughput efficiency is calculated. Weighted load balancing problem plays a challenging role in IEEE 802.16 network. CoTS (Concurrent Transmission Scheduling) Scheme is formulated in terms of three aspects – transmission mechanism based on identical communities, different communities and identical node communities. CoTS scheme helps in identifying the weighted load balancing problem. Based on the analytical results, modularity value is inversely proportional to that of the error value. The modularity value plays a key role in solving the CoTS problem based on hop count. The transmission mechanism for identical node community has no impact since modularity value is same for all the network groups. In this paper three aspects of communities based on the modularity value which helps in solving the problem of weighted load balancing and CoTS are discussed.

Keywords: Cross layer network topology, concurrent scheduling, modularity value, network communities and weighted load balancing.

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Authors: R. E. Masto, L. C. Ram, S. K. Verma, V. A. Selvi, J. George, R. C. Tripathi, N. K. Srivastava, D. Mohanty, S. K.Jha, A. K. Sinha, A. Sinha

Abstract:

There are many sources trough which the soil get enriched and contaminated with REEs. The determination of REEs in environmental samples has been limited because of the lack of sensitive analytical techniques. Soil samples were collected from four sites including open cast coal mine, natural coal burning, coal washery and control in the coal field located in Dhanbad, India. Total concentrations of rare earth elements (REEs) were determined using the inductively coupled plasma atomic absorption spectrometry in order to assess enrichment status in the coal field. Results showed that the mean concentrations of La, Pr, Eu, Tb, Ho, and Tm in open cast mine and natural coal burning sites were elevated compared to the reference concentrations, while Ce, Nd, Sm, and Gd were elevated in coal washery site. When compared to reference soil, heavy REEs (HREEs) were enriched in open cast mines and natural coal burning affected soils, however, the HREEs were depleted in the coal washery sites. But, the Chondrite-normalization diagram showed significant enrichment for light REEs (LREEs) in all the soils. High concentration of Pr, Eu, Tb, Ho, Tm, and Lu in coal mining and coal burning sites may pose human health risks. Factor analysis showed that distribution and relative abundance of REEs of the coal washery site is comparable with the control. Eventually washing or cleaning of coal could significantly decrease the emission of REEs from coal into the environment.

Keywords: Rare earth elements, coal, soil, factor analysis

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Authors: Miguel A. Gómez , Enrique Hilario , Francisco J. Alvarez , Elena Gastiasoro , Antonia Alvarez, Juan L. Larrabe

Abstract:

Total liquid ventilation can support gas exchange in animal models of lung injury. Clinical application awaits further technical improvements and performance verification. Our aim was to develop a liquid ventilator, able to deliver accurate tidal volumes, and a computerized system for measuring lung mechanics. The computer-assisted, piston-driven respirator controlled ventilatory parameters that were displayed and modified on a real-time basis. Pressure and temperature transducers along with a lineal displacement controller provided the necessary signals to calculate lung mechanics. Ten newborn lambs (<6 days old) with respiratory failure induced by lung lavage, were monitored using the system. Electromechanical, hydraulic and data acquisition/analysis components of the ventilator were developed and tested in animals with respiratory failure. All pulmonary signals were collected synchronized in time, displayed in real-time, and archived on digital media. The total mean error (due to transducers, A/D conversion, amplifiers, etc.) was less than 5% compared to calibrated signals. Improvements in gas exchange and lung mechanics were observed during liquid ventilation, without impairment of cardiovascular profiles. The total liquid ventilator maintained accurate control of tidal volumes and the sequencing of inspiration/expiration. The computerized system demonstrated its ability to monitor in vivo lung mechanics, providing valuable data for early decision-making.

Keywords: immature lamb, perfluorocarbon, pressure-limited, total liquid ventilation, ventilator; volume-controlled

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Authors: Muhammad Aqil, Ichiro Kita, Moses Macalinao

Abstract:

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

Keywords: Neural Network, Fuzzy, River, Forecasting

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