Search results for: Road Boundary Detection

Authors: B. Rebekka, B. Malarkodi

Abstract:

This paper describes an approach to detect the transmitted signals for 2×2 Multiple Input Multiple Output (MIMO) setup using roulette wheel based ant colony optimization technique. The results obtained are compared with classical zero forcing and least mean square techniques. The detection rates achieved using this technique are consistently larger than the one achieved using classical methods for 50 number of attempts with two different antennas transmitting the input stream from a user. This paves the path to use alternative techniques to improve the throughput achieved in advanced networks like Long Term Evolution (LTE) networks.

Keywords: MIMO, ant colony optimization, roulette wheel, soft computing, LTE.

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Authors: Mamoun F. Abdel-Hafez

Abstract:

In this paper, the detection of a fault in the Global Positioning System (GPS) measurement is addressed. The class of faults considered is a bias in the GPS pseudorange measurements. This bias is modeled as an unknown constant. The fault could be the result of a receiver fault or signal fault such as multipath error. A bias bank is constructed based on set of possible fault hypotheses. Initially, there is equal probability of occurrence for any of the biases in the bank. Subsequently, as the measurements are processed, the probability of occurrence for each of the biases is sequentially updated. The fault with a probability approaching unity will be declared as the current fault in the GPS measurement. The residual formed from the GPS and Inertial Measurement Unit (IMU) measurements is used to update the probability of each fault. Results will be presented to show the performance of the presented algorithm.

Keywords: Estimation and filtering, Statistical data analysis, Faultdetection and identification.

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Authors: N. Hammid, F. Bouarab-Dahmani, T. Berkane

Abstract:

Since its appearance, the MOOC (massive open online course) is gaining more and more intention of the educational communities over the world. Apart from the current MOOCs design and purposes, the creators of MOOC focused on the importance of the connection and knowledge exchange between individuals in learning. In this paper, we present a conceptual model for massive open online blended courses where teachers over the world can collaborate and exchange their experience to get a common efficient content designed as a MOOC opened to their students to live a better learning experience. This model is based on disciplines’ concepts capitalization and the detection of the obstacles met by their students when faced with problem situations (exercises, projects, case studies, etc.). This detection is possible by analyzing the frequently of semantic errors committed by the students. The participation of teachers in the design of the course and the attendance by their students can guarantee an efficient and extensive participation (an important number of participants) in the course, the learners’ motivation and the evaluation issues, in the way that the teachers designing the course assess their students. Thus, the teachers review, together with their knowledge, offer a better assessment and efficient connections to their students.

Keywords: MOOC, Massive Open Online Courses, Online learning, E-learning, Blended learning.

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Authors: Levente Varga, Dávid Deritei, Mária Ercsey-Ravasz, Răzvan Florian, Zsolt I. Lázár, István Papp, Ferenc Járai-Szabó

Abstract:

One of the major shortcomings of widely used scientometric indicators is that different disciplines cannot be compared with each other. The issue of cross-disciplinary normalization has been long discussed, but even the classification of publications into scientific domains poses problems. Structural properties of citation networks offer new possibilities, however, the large size and constant growth of these networks asks for precaution. Here we present a new tool that in order to perform cross-field normalization of scientometric indicators of individual publications relays on the structural properties of citation networks. Due to the large size of the networks, a systematic procedure for identifying scientific domains based on a local community detection algorithm is proposed. The algorithm is tested with different benchmark and real-world networks. Then, by the use of this algorithm, the mechanism of the scientometric indicator normalization process is shown for a few indicators like the citation number, P-index and a local version of the PageRank indicator. The fat-tail trend of the article indicator distribution enables us to successfully perform the indicator normalization process.

Keywords: Citation networks, scientometric indicator, cross-field normalization, local cluster detection.

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Authors: Leili Nosrati, Alireza Nemaney Pour

Abstract:

Although e-mail is the most efficient and popular communication method, unwanted and mass unsolicited e-mails, also called spam mail, endanger the existence of the mail system. This paper proposes a new algorithm called Dynamic Weighted Majority Concept Drift Detection (DWM-CDD) for content-based filtering. The design purposes of DWM-CDD are first to accurate the performance of the previously proposed algorithms, and second to speed up the time to construct the model. The results show that DWM-CDD can detect both sudden and gradual changes quickly and accurately. Moreover, the time needed for model construction is less than previously proposed algorithms.

Keywords: Concept drift, Content-based filtering, E-mail, Spammail.

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Authors: M. Ghanbari, S. Hossainpour, G. Rezazadeh

Abstract:

In this paper, longitudinal vibration of a micro-beam in micro-scale fluid media has been investigated. The proposed mathematical model for this study is made up of a micro-beam and a micro-plate at its free end. An AC voltage is applied to the pair of piezoelectric layers on the upper and lower surfaces of the micro-beam in order to actuate it longitudinally. The whole structure is bounded between two fixed plates on its upper and lower surfaces. The micro-gap between the structure and the fixed plates is filled with fluid. Fluids behave differently in micro-scale than macro, so the fluid field in the gap has been modeled based on micro-polar theory. The coupled governing equations of motion of the micro-beam and the micro-scale fluid field have been derived. Due to having non-homogenous boundary conditions, derived equations have been transformed to an enhanced form with homogenous boundary conditions. Using Galerkin-based reduced order model, the enhanced equations have been discretized over the beam and fluid domains and solve simultaneously in order to obtain force response of the micro-beam. Effects of micro-polar parameters of the fluid as characteristic length scale, coupling parameter and surface parameter on the response of the micro-beam have been studied.

Keywords: Micro-polar theory, Galerkin method, MEMS, micro-fluid.

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Authors: Ionel D. Craiu, Mihai Nedelcu

Abstract:

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

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

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Authors: Abed Ahmed, Mehrdad Asadi, Jennifer Martay

Abstract:

Stainless steel pipelines are crucial components to transportation and storage in the oil and gas industry. However, the rise of random attacks and vandalism on these pipes for their valuable transport has led to more security and protection for incoming surface impacts. These surface impacts can lead to large global deformations of the pipe and place the pipe under strain, causing the eventual failure of the pipeline. Therefore, understanding how these surface impact loads affect the pipes is vital to improving the pipes’ security and protection. In this study, experimental test and finite element analysis (FEA) have been carried out on EN3B stainless steel specimens to study the impact behaviour. Low velocity impact tests at 9 m/s with 16 kg dome impactor was used to simulate for high momentum impact for localised failure. FEA models of clamped and deformable boundaries were modelled to study the effect of the boundaries on the pipes impact behaviour on its impact resistance, using experimental and FEA approach. Comparison of experimental and FE simulation shows good correlation to the deformable boundaries in order to validate the robustness of the FE model to be implemented in pipe models with complex anisotropic structure.

Keywords: Dynamic impact, deformable boundary conditions, finite element modeling, FEM, finite element, FE, LS-DYNA, Stainless steel pipe.

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Authors: H. Higashiyama, M. Sano, F. Nakanishi, M. Sugiyama, O. Takahashi, S. Tsukuma

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The heat island phenomenon becomes one of the environmental problems. As countermeasures in the field of road engineering, cool pavements such as water retaining pavements and solar radiation reflective pavements have been developed to reduce the surface temperature of asphalt pavements in the hot summer climate in Japan. The authors have studied on the water retaining pavements with cement–based grouting materials. The cement–based grouting materials consist of cement, ceramic waste powder, and natural zeolite. The ceramic waste powder is collected through the recycling process of electric porcelain insulators. In this study, mixing ratio between the ceramic waste powder and the natural zeolite and a type of cement for the cement–based grouting materials is investigated to measure the surface temperature of asphalt pavements in the outdoor. All of the developed cement–based grouting materials were confirmed to effectively reduce the surface temperature of the asphalt pavements. Especially, the cement–based grouting material using the ultra–rapid hardening cement with the mixing ratio of 0.7:0.3 between the ceramic waste powder and the natural zeolite reduced mostly the surface temperature by 20 °C and more.

Keywords: Ceramic waste powder, natural zeolite, road surface temperature, water retaining pavements.

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Authors: Antonis Sideris, Elias Chlis Kalogeropoulos, Konstantia Moirogiorgou

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The present study proposes a methodology for the efficient daily management of fleet vehicles and construction machinery. The application covers the area of remote monitoring of heavy-duty vehicles operation parameters, where specific sensor data are stored and examined in order to provide information about the vehicle’s health. The vehicle diagnostics allow the user to inspect whether maintenance tasks need to be performed before a fault occurs. A properly designed machine learning model is proposed for the detection of two different types of faults through classification. Cross validation is used and the accuracy of the trained model is checked with the confusion matrix.

Keywords: Fault detection, feature selection, machine learning, predictive maintenance.

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Authors: I. L. Ahmad, M. Mohamed, N. A. Ab. Ghani

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This paper presents the work of signal discrimination specifically for Electrocardiogram (ECG) waveform. ECG signal is comprised of P, QRS, and T waves in each normal heart beat to describe the pattern of heart rhythms corresponds to a specific individual. Further medical diagnosis could be done to determine any heart related disease using ECG information. The emphasis on QRS Complex classification is further discussed to illustrate the importance of it. Pan-Tompkins Algorithm, a widely known technique has been adapted to realize the QRS Complex classification process. There are eight steps involved namely sampling, normalization, low pass filter, high pass filter (build a band pass filter), derivation, squaring, averaging and lastly is the QRS detection. The simulation results obtained is represented in a Graphical User Interface (GUI) developed using MATLAB.

Keywords: ECG, Pan Tompkins Algorithm, QRS Complex, Simulation

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Authors: Seyed Mostafa Torabi

Abstract:

A Wireless sensor network (WSN) consists of a set of battery-powered nodes, which collaborate to perform sensing tasks in a given environment. Each node in WSN should be capable to act for long periods of time with scrimpy or no external management. One requirement for this independent is: in the presence of adverse positions, the sensor nodes must be capable to configure themselves. Hence, the nodes for determine the existence of unusual events in their surroundings should make use of position awareness mechanisms. This work approaches the problem by considering the possible unusual events as diseases, thus making it possible to diagnose them through their symptoms, namely, their side effects. Considering these awareness mechanisms as a foundation for highlevel monitoring services, this paper also shows how these mechanisms are included in the primal plan of an intrusion detection system.

Keywords: Awareness Mechanism, Intrusion Detection, Independent, Wireless Sensor Network

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Authors: Kasra Majbouri Yazdi, Adel Majbouri Yazdi, Saeid Khodayi, Jingyu Hou, Wanlei Zhou, Saeed Saedy

Abstract:

Fake news and false information are big challenges of all types of media, especially social media. There is a lot of false information, fake likes, views and duplicated accounts as big social networks such as Facebook and Twitter admitted. Most information appearing on social media is doubtful and in some cases misleading. They need to be detected as soon as possible to avoid a negative impact on society. The dimensions of the fake news datasets are growing rapidly, so to obtain a better result of detecting false information with less computation time and complexity, the dimensions need to be reduced. One of the best techniques of reducing data size is using feature selection method. The aim of this technique is to choose a feature subset from the original set to improve the classification performance. In this paper, a feature selection method is proposed with the integration of K-means clustering and Support Vector Machine (SVM) approaches which work in four steps. First, the similarities between all features are calculated. Then, features are divided into several clusters. Next, the final feature set is selected from all clusters, and finally, fake news is classified based on the final feature subset using the SVM method. The proposed method was evaluated by comparing its performance with other state-of-the-art methods on several specific benchmark datasets and the outcome showed a better classification of false information for our work. The detection performance was improved in two aspects. On the one hand, the detection runtime process decreased, and on the other hand, the classification accuracy increased because of the elimination of redundant features and the reduction of datasets dimensions.

Keywords: Fake news detection, feature selection, support vector machine, K-means clustering, machine learning, social media.

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Authors: Hussein Alahmer, Amr Ahmed

Abstract:

Liver cancer is one of the common diseases that cause the death. Early detection is important to diagnose and reduce the incidence of death. Improvements in medical imaging and image processing techniques have significantly enhanced interpretation of medical images. Computer-Aided Diagnosis (CAD) systems based on these techniques play a vital role in the early detection of liver disease and hence reduce liver cancer death rate.  This paper presents an automated CAD system consists of three stages; firstly, automatic liver segmentation and lesion’s detection. Secondly, extracting features. Finally, classifying liver lesions into benign and malignant by using the novel contrasting feature-difference approach. Several types of intensity, texture features are extracted from both; the lesion area and its surrounding normal liver tissue. The difference between the features of both areas is then used as the new lesion descriptors. Machine learning classifiers are then trained on the new descriptors to automatically classify liver lesions into benign or malignant. The experimental results show promising improvements. Moreover, the proposed approach can overcome the problems of varying ranges of intensity and textures between patients, demographics, and imaging devices and settings.

Keywords: CAD system, difference of feature, Fuzzy c means, Liver segmentation.

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Authors: N.H. Mvungi

Abstract:

This paper addresses control of commutation of switched reluctance (SR) motor without the use of a physical position detector. Rotor position detection schemes for SR motor based on magnetisation characteristics of the motor use normal excitation or applied current /voltage pulses. The resulting schemes are referred to as passive or active methods respectively. The research effort is in realizing an economical sensorless SR rotor position detector that is accurate, reliable and robust to suit a particular application. An effective and reliable means of generating commutation signals of an SR motor based on inductance profile of its stator windings determined using active probing technique is presented. The scheme has been validated online using a 4-phase 8/6 SR motor and an 8-bit processor.

Keywords: Position detection, rotor position, sensorless, switched reluctance, SR.

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Authors: Guorong Yan, Subhash Rakheja

Abstract:

The liquid cargo contained in a partly-filled road tank vehicle is prone to dynamic slosh movement when subjected to external disturbances. The slosh behavior has been identified as a significant factor impairing the safety of liquid cargo transportation. The laboratory experiments have been conducted for analyzing fluid slosh in partly filled tanks. The experiment results measured under forced harmonic excitations reveal the three-dimensional nature of the fluid motion and coupling between the lateral and longitudinal fluid slosh at resonance. Several spectral components are observed for the transient slosh forces, which can be associated with the excitation, resonance, and beat frequencies. The peak slosh forces and moments in the vicinity of resonance are significantly larger than those of the equivalent rigid mass. Due to the nature of coupling between sloshing fluid and vehicle body, the issue of the dynamic fluid-structure interaction is essential in the analysis of tank-vehicle dynamics. A dynamic pitch plane model of a Tridem truck incorporated the fluid slosh dynamics is developed to analyze the fluid-vehicle interaction under the straight-line braking maneuvers. The results show that the vehicle responses are highly associated with the characteristics of fluid slosh force and moment.

Keywords: Braking performance, fluid induced vibration, fluidslosh, fluid structure interaction, tank trucks, vehicle dynamics.

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Authors: Andrii Shalaginov, Katrin Franke, Xiongwei Huang

Abstract:

One of the leading problems in Cyber Security today is the emergence of targeted attacks conducted by adversaries with access to sophisticated tools. These attacks usually steal senior level employee system privileges, in order to gain unauthorized access to confidential knowledge and valuable intellectual property. Malware used for initial compromise of the systems are sophisticated and may target zero-day vulnerabilities. In this work we utilize common behaviour of malware called ”beacon”, which implies that infected hosts communicate to Command and Control servers at regular intervals that have relatively small time variations. By analysing such beacon activity through passive network monitoring, it is possible to detect potential malware infections. So, we focus on time gaps as indicators of possible C2 activity in targeted enterprise networks. We represent DNS log files as a graph, whose vertices are destination domains and edges are timestamps. Then by using four periodicity detection algorithms for each pair of internal-external communications, we check timestamp sequences to identify the beacon activities. Finally, based on the graph structure, we infer the existence of other infected hosts and malicious domains enrolled in the attack activities.

Keywords: Malware detection, network security, targeted attack.

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Authors: Rado Flajs, Miran Saje

Abstract:

We present a simple nonconforming approximation of the linear two–point boundary value problem which violates patch test requirements. Nevertheless the solutions, obtained from these type of approximations, converge to the exact solution.

Keywords: Generalized patch test, Irons' patch test, nonconforming finite element, convergence.

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Authors: V. Veeraprathap, G. S. Harish, G. Narendra Kumar

Abstract:

Uncontrolled growth of abnormal cells in the lung in the form of tumor can be either benign (non-cancerous) or malignant (cancerous). Patients with Lung Cancer (LC) have an average of five years life span expectancy provided diagnosis, detection and prediction, which reduces many treatment options to risk of invasive surgery increasing survival rate. Computed Tomography (CT), Positron Emission Tomography (PET), and Magnetic Resonance Imaging (MRI) for earlier detection of cancer are common. Gaussian filter along with median filter used for smoothing and noise removal, Histogram Equalization (HE) for image enhancement gives the best results without inviting further opinions. Lung cavities are extracted and the background portion other than two lung cavities is completely removed with right and left lungs segmented separately. Region properties measurements area, perimeter, diameter, centroid and eccentricity measured for the tumor segmented image, while texture is characterized by Gray-Level Co-occurrence Matrix (GLCM) functions, feature extraction provides Region of Interest (ROI) given as input to classifier. Two levels of classifications, K-Nearest Neighbor (KNN) is used for determining patient condition as normal or abnormal, while Artificial Neural Networks (ANN) is used for identifying the cancer stage is employed. Discrete Wavelet Transform (DWT) algorithm is used for the main feature extraction leading to best efficiency. The developed technology finds encouraging results for real time information and on line detection for future research.

Keywords: ANN, DWT, GLCM, KNN, ROI, artificial neural networks, discrete wavelet transform, gray-level co-occurrence matrix, k-nearest neighbor, region of interest.

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Authors: Varsha Shah, Patel Pritesh, Patel Sagar, PrasantaKundu, RanjanMaheshwari

Abstract:

Performance of vehicle depends on driving patterns and vehicle drive train configuration. Driving patterns depends on traffic condition, road condition and driver behavior. HEV design is carried out under certain constrain like vehicle operating range, acceleration, decelerations, maximum speed and road grades which are directly related to the driving patterns. Therefore the detailed study on HEV performance over a different drive cycle is required for selection and sizing of HEV components. A simple hardware is design to measured velocity v/s time profile of the vehicle by operating vehicle on Indian roads under real traffic conditions. To size the HEV components, a detailed dynamic model of the vehicle is developed considering the effect of inertia of rotating components like wheels, drive chain, engine and electric motor. Using vehicle model and different Indian drive cycles data, total tractive power demanded by vehicle and power supplied by individual components has been calculated.Using above information selection and estimation of component sizing for HEV is carried out so that HEV performs efficiently under hostile driving condition. Complete analysis is carried out in LABVIEW.

Keywords: BLDC motor, Driving cycle, LABVIEW Ultracapacitors, Vehicle Dynamics,

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Authors: Zhiyuan Du, Baisravan Hom Chaudhuri, Pierluigi Pisu

Abstract:

In connected vehicle systems where wireless communication is available among the involved vehicles and intersection controllers, it is possible to design an intersection coordination strategy that leads the connected and automated vehicles (CAVs) travel through the road intersections without the conventional traffic light control. In this paper, we present a distributed coordination strategy for the CAVs at multiple interconnected intersections that aims at improving system fuel efficiency and system mobility. We present a distributed control solution where in the higher level, the intersection controllers calculate the road desired average velocity and optimally assign reference velocities of each vehicle. In the lower level, every vehicle is considered to use model predictive control (MPC) to track their reference velocity obtained from the higher level controller. The proposed method has been implemented on a simulation-based case with two-interconnected intersection network. Additionally, the effects of mixed vehicle types on the coordination strategy has been explored. Simulation results indicate the improvement on vehicle fuel efficiency and traffic mobility of the proposed method.

Keywords: Connected vehicles, automated vehicles, intersection coordination systems, multiple interconnected intersections, model predictive control.

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Authors: Jie Cai, David C. Levy

Abstract:

Electronic nose (array of chemical sensors) are widely used in food industry and pollution control. Also it could be used to locate or detect the direction of the source of emission odors. Usually this task is performed by electronic nose (ENose) cooperated with mobile vehicles, but when a source is instantaneous or surrounding is hard for vehicles to reach, problem occurs. Thus a method for stationary ENose to detect the direction of the source and locate the source will be required. A novel method which uses the ratio between the responses of different sensors as a discriminant to determine the direction of source in natural wind surroundings is presented in this paper. The result shows that the method is accurate and easily to be implemented. This method could be also used in movably, as an optimized algorithm for robot tracking source location.

Keywords: Electronic nose, Nature wind situation, Source direction detection.

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Authors: Ali N. Suri, Ahmad A. Al-Makhlufi

Abstract:

In this paper the problem of buckling of plates on foundation of finite length and with different side support is studied.

The Finite Strip Method is used as tool for the analysis. This method uses finite strip elastic, foundation, and geometric matrices to build the assembly matrices for the whole structure, then after introducing boundary conditions at supports, the resulting reduced matrices is transformed into a standard Eigenvalue-Eigenvector problem. The solution of this problem will enable the determination of the buckling load, the associated buckling modes and the buckling wave length.

To carry out the buckling analysis starting from the elastic, foundation, and geometric stiffness matrices for each strip a computer program FORTRAN list is developed.

Since stiffness matrices are function of wave length of buckling, the computer program used an iteration procedure to find the critical buckling stress for each value of foundation modulus and for each boundary condition.

The results showed the use of elastic medium to support plates subject to axial load increase a great deal the buckling load, the results found are very close with those obtained by other analytical methods and experimental work.

The results also showed that foundation compensates the effect of the weakness of some types of constraint of side support and maximum benefit found for plate with one side simply supported the other free.

Keywords: Buckling, Finite Strip, Different Sides Support, Plates on Foundation.

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Authors: Watcharin Kaewapichai, Pakorn Kaewtrakulpong

Abstract:

In this paper, a method to detect multiple ellipses is presented. The technique is efficient and robust against incomplete ellipses due to partial occlusion, noise or missing edges and outliers. It is an iterative technique that finds and removes the best ellipse until no reasonable ellipse is found. At each run, the best ellipse is extracted from randomly selected edge patches, its fitness calculated and compared to a fitness threshold. RANSAC algorithm is applied as a sampling process together with the Direct Least Square fitting of ellipses (DLS) as the fitting algorithm. In our experiment, the method performs very well and is robust against noise and spurious edges on both synthetic and real-world image data.

Keywords: Direct Least Square Fitting, Ellipse Detection, RANSAC

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Authors: Davit Mirzoyan, Ararat Khachatryan

Abstract:

A process corner monitoring circuit (PCMC) is presented in this work. The circuit generates a signal, the logical value of which depends on the process corner only. The signal can be used in both digital and analog circuits for testing and compensation of process variations (PV). The presented circuit uses only metal-oxide-semiconductor (MOS) transistors, which allow increasing its detection accuracy, decrease power consumption and area. Due to its simplicity the presented circuit can be easily modified to monitor parametrical variations of only n-type and p-type MOS (NMOS and PMOS, respectively) transistors, resistors, as well as their combinations. Post-layout simulation results prove correct functionality of the proposed circuit, i.e. ability to monitor the process corner (equivalently die-to-die variations) even in the presence of within-die variations.

Keywords: Detection, monitoring, process corner, process variation.

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Authors: S. Ul. Islam, H. Rahman, W. S. Abbasi, N. Rathore

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In this research numerical simulations are performed, using the multi-relaxation-time lattice Boltzmann method, in the range 3 ≤ β = w[d] ≤ 30 at Re = 100, 200 and 300, where β the blockage ratio, w is the equispaced distance between centers of cylinders, d is the diameter of the cylinder and Re is the Reynolds number, respectively. Special attention is paid to the effect of the equispaced distance between centers of cylinders. Visualization of the vorticity contour visualization are presented for some simulation showing the flow dynamics and patterns for blockage effect. Results show that the drag and mean drag coefficients, and Strouhal number, in general, decrease with the increase of β for fixed Re. It is found that the decreasing rate of drag and mean drag coefficients and Strouhal number is more distinct in the range 3 ≤ β ≤ 15. We found that when β > 15, the blockage effect almost diminishes. Our results further indicate that the drag and mean drag coefficients, peak value of the lift coefficient, root-mean-square value of the lift and drag coefficients and the ratio between lift and drag coefficients decrease with the increase of Re. The results indicate that symmetry boundary condition have more blockage effect as compared to periodic boundary condition.

Keywords: Blockage ratio, Multi-relaxation-time lattice Boltzmann method, Square cylinder, Vortex formation.

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Authors: Ziya Uddin

Abstract:

This paper deals with the theoretical and numerical investigation of magneto hydrodynamic boundary layer flow of a nanofluid past a wedge shaped wick in heat pipe used for the cooling of electronic components and different type of machines. To incorporate the effect of nanoparticle diameter, concentration of nanoparticles in the pure fluid, nanothermal layer formed around the nanoparticle and Brownian motion of nanoparticles etc., appropriate models are used for the effective thermal and physical properties of nanofluids. To model the rotation of nanoparticles inside the base fluid, microfluidics theory is used. In this investigation ethylene glycol (EG) based nanofluids, are taken into account. The non-linear equations governing the flow and heat transfer are solved by using a very effective particle swarm optimization technique along with Runge-Kutta method. The values of heat transfer coefficient are found for different parameters involved in the formulation viz. nanoparticle concentration, nanoparticle size, magnetic field and wedge angle etc. It is found that, the wedge angle, presence of magnetic field, nanoparticle size and nanoparticle concentration etc. have prominent effects on fluid flow and heat transfer characteristics for the considered configuration.

Keywords: Heat transfer, Heat pipe, numerical modeling, nanofluid applications, particle swarm optimization, wedge shaped wick.

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Authors: Ya Lv

Abstract:

This article presents the application of the semi-analytic method (SAM) in the thermal management solution (TMS) of the energy storage system (ESS). The TMS studied in this work is fluid cooling. In fluid cooling, both effective heat conduction and heat convection are indispensable due to the heat transfer from solid to fluid. Correspondingly, an efficient TMS requires a design investigation of the following parameters: fluid inlet temperature, ESS initial temperature, fluid flow rate, working c rate, continuous working time, and materials properties. Their variation induces a change of thermal performance in the battery module, which is usually evaluated by numerical simulation. Compared to complicated computation resources and long computation time in simulation, the SAM is developed in this article to predict the thermal influence within a few seconds. In SAM, a fast prediction model is reckoned by combining numerical simulation with theoretical/empirical equations. The SAM can explore the thermal effect of boundary parameters in both steady-state and transient heat transfer scenarios within a short time. Therefore, the SAM developed in this work can simplify the design cycle of TMS and inspire more possibilities in TMS design.

Keywords: Semi-analytic method, fast prediction model, thermal influence of boundary parameters, energy storage system.

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Authors: V.S.Kale, S.R.Bhide, P.P.Bedekar, G.V.K.Mohan

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The protection of parallel transmission lines has been a challenging task due to mutual coupling between the adjacent circuits of the line. This paper presents a novel scheme for detection and classification of faults on parallel transmission lines. The proposed approach uses combination of wavelet transform and neural network, to solve the problem. While wavelet transform is a powerful mathematical tool which can be employed as a fast and very effective means of analyzing power system transient signals, artificial neural network has a ability to classify non-linear relationship between measured signals by identifying different patterns of the associated signals. The proposed algorithm consists of time-frequency analysis of fault generated transients using wavelet transform, followed by pattern recognition using artificial neural network to identify the type of the fault. MATLAB/Simulink is used to generate fault signals and verify the correctness of the algorithm. The adaptive discrimination scheme is tested by simulating different types of fault and varying fault resistance, fault location and fault inception time, on a given power system model. The simulation results show that the proposed scheme for fault diagnosis is able to classify all the faults on the parallel transmission line rapidly and correctly.

Keywords: Artificial neural network, fault detection and classification, parallel transmission lines, wavelet transform.

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Authors: Sudipta Nath, Arindam Dey, Abhijit Chakrabarti

Abstract:

This paper presents features that characterize power quality disturbances from recorded voltage waveforms using wavelet transform. The discrete wavelet transform has been used to detect and analyze power quality disturbances. The disturbances of interest include sag, swell, outage and transient. A power system network has been simulated by Electromagnetic Transients Program. Voltage waveforms at strategic points have been obtained for analysis, which includes different power quality disturbances. Then wavelet has been chosen to perform feature extraction. The outputs of the feature extraction are the wavelet coefficients representing the power quality disturbance signal. Wavelet coefficients at different levels reveal the time localizing information about the variation of the signal.

Keywords: Power quality, detection of disturbance, wavelet transform, multiresolution signal decomposition.

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