Search results for: rapid complex algorithm prototyping.

Authors: Mihai Lungu, Romulus Lungu, Lucian Grigorie

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The paper presents a new system for the automat control of the aircrafts- flight in lateral plane using the cinematic model and the dynamic inversion. Starting from the equations of the aircrafts- lateral movement, the authors use two axes systems and obtained a control law that cancels the lateral deviation of the flying objects from the runway line. This system makes the aircrafts- direction angle to follow the direction angle of the runway line. Simulations in Matlab/Simulink have been done for different aircraft-s initial points and direction angles. The inconvenience of this system is the long duration of the “transient regime". That is why this system can be used independently, but the results are not very good; thus, it can be a part (subsystem) of other systems. The main system that cancels the lateral deviation from the runway line is based on dynamic inversion and uses, as subsystem, the control system for the lateral movement using the cinematic model. Using complex Matlab/Simulink models, the authors obtained the time evolution of the direction angle and the time evolution of the aircraft lateral deviation with respect to the runway line, for different values of the initial direction angle and for different wind types. The system has a very good behavior for all initial direction angles and wind types.

Keywords: Direction angle, Dynamic inversion, Lateraldeviation, Lateral movement

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Authors: Atiqul Islam, Shamim Akhter, Tumnun E. Mursalin

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Least Development Countries (LDC) like Bangladesh, whose 25% revenue earning is achieved from Textile export, requires producing less defective textile for minimizing production cost and time. Inspection processes done on these industries are mostly manual and time consuming. To reduce error on identifying fabric defects requires more automotive and accurate inspection process. Considering this lacking, this research implements a Textile Defect Recognizer which uses computer vision methodology with the combination of multi-layer neural networks to identify four classifications of textile defects. The recognizer, suitable for LDC countries, identifies the fabric defects within economical cost and produces less error prone inspection system in real time. In order to generate input set for the neural network, primarily the recognizer captures digital fabric images by image acquisition device and converts the RGB images into binary images by restoration process and local threshold techniques. Later, the output of the processed image, the area of the faulty portion, the number of objects of the image and the sharp factor of the image, are feed backed as an input layer to the neural network which uses back propagation algorithm to compute the weighted factors and generates the desired classifications of defects as an output.

Keywords: Computer vision, image acquisition device, machine vision, multi-layer neural networks.

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Authors: C. Ardil

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This paper presents preference programming technique based multiple criteria decision making analysis for selecting a facility location for a new organization or expansion of an existing facility which is of vital importance for a decision support system and strategic planning process. The implementation of decision support systems is considered crucial to sustain competitive advantage and profitability persistence in turbulent environment. As an effective strategic management and decision making is necessary, multiple criteria decision making analysis supports the decision makers to formulate and implement the right strategy. The investment cost associated with acquiring the property and facility construction makes the facility location selection problem a long-term strategic investment decision, which rationalize the best location selection which results in higher economic benefits through increased productivity and optimal distribution network. Selecting the proper facility location from a given set of alternatives is a difficult task, as many potential qualitative and quantitative multiple conflicting criteria are to be considered. This paper solves a facility location selection problem using preference programming, which is an effective multiple criteria decision making analysis tool applied to deal with complex decision problems in the operational research environment. The ranking results of preference programming are compared with WSM, TOPSIS and VIKOR methods.

Keywords: Facility Location Selection, Multiple Criteria Decision Making, Multiple Criteria Decision Making Analysis, Preference Programming, Location Selection, WSM, TOPSIS, VIKOR

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Authors: Feng-Yi Huang, Yuan-Jye Tseng

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In the traditional concept of product life cycle management, the activities of design, manufacturing, and assembly are performed in a sequential way. The drawback is that the considerations in design may contradict the considerations in manufacturing and assembly. The different designs of components can lead to different assembly sequences. Therefore, in some cases, a good design may result in a high cost in the downstream assembly activities. In this research, an integrated design evaluation and assembly sequence planning model is presented. Given a product requirement, there may be several design alternative cases to design the components for the same product. If a different design case is selected, the assembly sequence for constructing the product can be different. In this paper, first, the designed components are represented by using graph based models. The graph based models are transformed to assembly precedence constraints and assembly costs. A particle swarm optimization (PSO) approach is presented by encoding a particle using a position matrix defined by the design cases and the assembly sequences. The PSO algorithm simultaneously performs design evaluation and assembly sequence planning with an objective of minimizing the total assembly costs. As a result, the design cases and the assembly sequences can both be optimized. The main contribution lies in the new concept of integrated design evaluation and assembly sequence planning model and the new PSO solution method. The test results show that the presented method is feasible and efficient for solving the integrated design evaluation and assembly planning problem. In this paper, an example product is tested and illustrated.

Keywords: assembly sequence planning, design evaluation, design for assembly, particle swarm optimization

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Authors: SP. Chokkalingam, K. Komathy

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Advances in the field of image processing envision a new era of evaluation techniques and application of procedures in various different fields. One such field being considered is the biomedical field for prognosis as well as diagnosis of diseases. This plethora of methods though provides a wide range of options to select from, it also proves confusion in selecting the apt process and also in finding which one is more suitable. Our objective is to use a series of techniques on bone scans, so as to detect the occurrence of rheumatoid arthritis (RA) as accurately as possible. Amongst other techniques existing in the field our proposed system tends to be more effective as it depends on new methodologies that have been proved to be better and more consistent than others. Computer aided diagnosis will provide more accurate and infallible rate of consistency that will help to improve the efficiency of the system. The image first undergoes histogram smoothing and specification, morphing operation, boundary detection by edge following algorithm and finally image subtraction to determine the presence of rheumatoid arthritis in a more efficient and effective way. Using preprocessing noises are removed from images and using segmentation, region of interest is found and Histogram smoothing is applied for a specific portion of the images. Gray level co-occurrence matrix (GLCM) features like Mean, Median, Energy, Correlation, Bone Mineral Density (BMD) and etc. After finding all the features it stores in the database. This dataset is trained with inflamed and noninflamed values and with the help of neural network all the new images are checked properly for their status and Rough set is implemented for further reduction.

Keywords: Computer Aided Diagnosis, Edge Detection, Histogram Smoothing, Rheumatoid Arthritis.

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Authors: Jongwan Eun, Sam Helwany, Lakshyana K. C.

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The United Arab Emirates (UAE) is significantly dependent on desalinated water and groundwater resource, which is expensive and highly energy intensive. Despite the scarce water resource, stagnates only 54% of the recycled water was reused in 2012, and due to the lack of infrastructure to reuse the recycled water, the portion is expected to decrease with growing water usage. In this study, an “Oasis” complex comprised of Sustainable Farming Compartments (SFC) was proposed for reusing treated wastewater. The wastewater is used to decrease the ambient temperature of the SFC via an evaporative cooler. The SFC prototype was designed, built, and tested in an environmentally controlled laboratory and field site to evaluate the feasibility and effectiveness of the SFC subjected to various climatic conditions in Abu Dhabi. Based on the experimental results, the temperature drop achieved in the SFC in the laboratory and field site were5 ̊C from 22 ̊C and 7- 15 ̊C (from 33-45 ̊C to average 28 ̊C at relative humidity < 50%), respectively. An energy simulation using TRNSYS was performed to extend and validate the results obtained from the experiment. The results from the energy simulation and experiments show statistically close agreement. The total power consumption of the SFC system was approximately three and a half times lower than that of an electrical air conditioner. Therefore, by using treated wastewater, the SFC has a promising prospect to solve Abu Dhabi’s ecological concern related to desertification and wind erosion.

Keywords: Ecological farming system, energy simulation, evaporative cooling system, treated wastewater, temperature, humidity.

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Authors: M. Vogiatzis, K. Perakis

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Spatial-explicit and up-to-date land use/land cover information is fundamental for spatial planning, land management, sustainable development, and sound decision-making. In the last decade, many satellite-derived land cover products at different spatial, spectral, and temporal resolutions have been developed, such as the European Copernicus Land Cover product. However, more efficient and detailed information for land use/land cover is required at the regional or local scale. A typical Mediterranean basin with a complex landscape comprised of various forest types, crops, artificial surfaces, and wetlands was selected to test and develop our approach. In this study, we investigate the improvement of Copernicus Land Cover product (CLC2018) using Landsat 8 and Sentinel-2 pixel-based classification based on all available existing geospatial data (Forest Maps, LPIS, Natura2000 habitats, cadastral parcels, etc.). We examined and compared the performance of the Random Forest classifier for land use/land cover mapping. In total, 10 land use/land cover categories were recognized in Landsat 8 and 11 in Sentinel-2A. A comparison of the overall classification accuracies for 2018 shows that Landsat 8 classification accuracy was slightly higher than Sentinel-2A (82,99% vs. 80,30%). We concluded that the main land use/land cover types of CLC2018, even within a heterogeneous area, can be successfully mapped and updated according to CLC nomenclature. Future research should be oriented toward integrating spatiotemporal information from seasonal bands and spectral indexes in the classification process.

Keywords: land use/land cover, random forest, Landsat-8 OLI, Sentinel-2A MSI, Corine land cover

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Authors: Fathollah Ommi, Golnaz Pourabedin, Koros Nekofa

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In recent years fuel cell vehicles are rapidly appearing all over the globe. In less than 10 years, fuel cell vehicles have gone from mere research novelties to operating prototypes and demonstration models. At the same time, government and industry in development countries have teamed up to invest billions of dollars in partnerships intended to commercialize fuel cell vehicles within the early years of the 21st century. The purpose of this study is evaluation of model and performance of fuel cell hybrid electric vehicle in different drive cycles. A fuel cell system model developed in this work is a semi-experimental model that allows users to use the theory and experimental relationships in a fuel cell system. The model can be used as part of a complex fuel cell vehicle model in advanced vehicle simulator (ADVISOR). This work reveals that the fuel consumption and energy efficiency vary in different drive cycles. Arising acceleration and speed in a drive cycle leads to Fuel consumption increase. In addition, energy losses in drive cycle relates to fuel cell system power request. Parasitic power in different parts of fuel cell system will increase when power request increases. Finally, most of energy losses in drive cycle occur in fuel cell system because of producing a lot of energy by fuel cell stack.

Keywords: Drive cycle, Energy efficiency, energy consumption, Fuel cell system.

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Authors: K. Atashgar

Abstract:

When an assignable cause(s) manifests itself to a multivariate process and the process shifts to an out-of-control condition, a root-cause analysis should be initiated by quality engineers to identify and eliminate the assignable cause(s) affected the process. A root-cause analysis in a multivariate process is more complex compared to a univariate process. In the case of a process involved several correlated variables an effective root-cause analysis can be only experienced when it is possible to identify the required knowledge including the out-of-control condition, the change point, and the variable(s) responsible to the out-of-control condition, all simultaneously. Although literature addresses different schemes to monitor multivariate processes, one can find few scientific reports focused on all the required knowledge. To the best of the author’s knowledge this is the first time that a multi task model based on artificial neural network (ANN) is reported to monitor all the required knowledge at the same time for a multivariate process with more than two correlated quality characteristics. The performance of the proposed scheme is evaluated numerically when different step shifts affect the mean vector. Average run length is used to investigate the performance of the proposed multi task model. The simulated results indicate the multi task scheme performs all the required knowledge effectively.

Keywords: Artificial neural network, Multivariate process, Statistical process control, Change point.

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Authors: Kanthida Kusonmano, Michael Netzer, Bernhard Pfeifer, Christian Baumgartner, Klaus R. Liedl, Armin Graber

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Availability of high dimensional biological datasets such as from gene expression, proteomic, and metabolic experiments can be leveraged for the diagnosis and prognosis of diseases. Many classification methods in this area have been studied to predict disease states and separate between predefined classes such as patients with a special disease versus healthy controls. However, most of the existing research only focuses on a specific dataset. There is a lack of generic comparison between classifiers, which might provide a guideline for biologists or bioinformaticians to select the proper algorithm for new datasets. In this study, we compare the performance of popular classifiers, which are Support Vector Machine (SVM), Logistic Regression, k-Nearest Neighbor (k-NN), Naive Bayes, Decision Tree, and Random Forest based on mock datasets. We mimic common biological scenarios simulating various proportions of real discriminating biomarkers and different effect sizes thereof. The result shows that SVM performs quite stable and reaches a higher AUC compared to other methods. This may be explained due to the ability of SVM to minimize the probability of error. Moreover, Decision Tree with its good applicability for diagnosis and prognosis shows good performance in our experimental setup. Logistic Regression and Random Forest, however, strongly depend on the ratio of discriminators and perform better when having a higher number of discriminators.

Keywords: Classification, High dimensional data, Machine learning

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Authors: Azam Fazelpour, Saeed R. Dehghani, Vlastimil Masek, Yuri S. Muzychka

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This study employs a method based on image analyses and structure information to detect accumulated ice on known structures. The icing of marine vessels and offshore structures causes significant reductions in their efficiency and creates unsafe working conditions. Image processing methods are used to measure ice loads automatically. Most image processing methods are developed based on captured image analyses. In this method, ice loads on structures are calculated by defining structure coordinates and processing captured images. A pyramidal structure is designed with nine cylindrical bars as the known structure of experimental setup. Unsymmetrical ice accumulated on the structure in a cold room represents the actual case of experiments. Camera intrinsic and extrinsic parameters are used to define structure coordinates in the image coordinate system according to the camera location and angle. The thresholding method is applied to capture images and detect iced structures in a binary image. The ice thickness of each element is calculated by combining the information from the binary image and the structure coordinate. Averaging ice diameters from different camera views obtains ice thicknesses of structure elements. Comparison between ice load measurements using this method and the actual ice loads shows positive correlations with an acceptable range of error. The method can be applied to complex structures defining structure and camera coordinates.

Keywords: Camera calibration, Ice detection, ice load measurements, image processing.

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Authors: Mohamed M. Ragab, Neveen A. Abdel Raoof, Reham H. Diab

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Background: Delayed onset muscle soreness (DOMS) is the most common symptom when ordinary individuals and athletes are exposed to unaccustomed physical activity, especially eccentric contraction which impairs athletic performance, ordinary people work ability and physical functioning. Multitudes of methods have been investigated to reduce DOMS. One of the valuable methods to control DOMS is repeated bout effect (RBE) as a prophylactic method. Purpose: To compare the repeated bout effect of submaximal eccentric with maximal isometric contraction on induced DOMS. Methods: Sixty normal male volunteers were assigned randomly into three equal groups: Group A (first study group): 20 subjects received submaximal eccentric contraction on non-dominant elbow flexors as a prophylactic exercise. Group B (second study group): 20 subjects received maximal isometric contraction on nondominant elbow flexors as a prophylactic exercise. Group C (control group): 20 subjects did not receive any prophylactic exercises. Maximal isometric peak torque of elbow flexors and patient related elbow evaluation (PREE) scale were measured for each subject 3 times before, immediately after, and 48 hours after induction of DOMS. Results: Post-hoc test for maximal isometric peak torque and PREE scale immediately and 48 hours after induction of DOMS revealed that group (A) and group (B) resulted in significant decrease in maximal isometric strength loss and elbow pain and disability rather than control group (C), but submaximal eccentric group (A) was more effective than maximal isometric group (B) as it showed more rapid recovery of functional strength and less degrees of elbow pain and disability. Conclusion: Both submaximal eccentric contraction and maximal isometric contraction were effective in prevention of DOMS but submaximal eccentric contraction produced a greater protective effect against muscle damage induced by maximal eccentric exercise performed 2 days later.

Keywords: Delayed onset muscle soreness, maximal isometric peak torque, patient related elbow evaluation scale, repeated bout effect.

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Authors: Vladimir Korovkin, Albert Park, Evgeny Kaganer

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Digital divide, the gap in the access to the world of digital technologies and the socio-economic opportunities that they create is an important phenomenon of the XXI century. This gap may exist between countries, regions within a country or socio-demographic groups, creating the classes of “digital have and have nots”. While the 1st-level divide (the difference in opportunities to access the digital networks) was demonstrated to diminish with time, the issues of 2nd level divide (the difference in skills and usage of digital systems) and 3rd level divide (the difference in effects obtained from digital technology) may grow. The paper offers a systemic review of literature on the measurement of the digital divide, noting the certain conceptual stagnation due to the lack of effective instruments that would capture the complex nature of the phenomenon. As a result, many important concepts do not receive the empiric exploration they deserve. As a solution the paper suggests a composite Digital Life Index, that studies separately the digital supply and demand across seven independent dimensions providing for 14 subindices. The Index is based on Internet-borne data, a distinction from traditional research approaches that rely on official statistics or surveys. The application of the model to the study of the digital divide between Russian regions and between cities in China have brought promising results. The paper advances the existing methodological literature on the 2nd level digital divide and can also inform practical decision-making regarding the strategies of national and regional digital development.

Keywords: Digital transformation, second-level digital divide, composite index, digital policy.

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Authors: Izian Abd. Karim, Kachalla Mohammed, Nora Farah A. A. Aziz, Law Teik Hua

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The economic use and ease of construction of profiled deck composite slab is marred with the complex and un-economic strength verification required for the serviceability and general safety considerations. Beside these, albeit factors such as shear span length, deck geometries and mechanical frictions greatly influence the longitudinal shear strength, that determines the ultimate strength of profiled deck composite slab, and number of methods available for its determination; partial shear and slope-intercept are the two methods according to Euro-code 4 provision. However, the complexity associated with shear behavior of profiled deck composite slab, the use of these methods in determining the load carrying capacities of such slab yields different and conflicting values. This couple with the time and cost constraint associated with the strength verification is a source of concern that draws more attentions nowadays, the issue is critical. Treating some of these known shear strength influencing factors as random variables, the load carrying capacity violation of profiled deck composite slab from the use of the two-methods defined according to Euro-code 4 are determined using reliability approach, and comparatively studied. The study reveals safety values from the use of m-k method shows good standing compared with that from the partial shear method.

Keywords: Composite slab, first order reliability method, longitudinal shear, partial shear connection, slope-intercept.

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Authors: Aleš Florian, Lenka Ševelová

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Complex sensitivity analysis of stresses in a concrete slab of the real type of rigid pavement made from recycled materials is performed. The computational model of the pavement is designed as a spatial (3D) model, is based on a nonlinear variant of the finite element method that respects the structural nonlinearity, enables to model different arrangements of joints, and the entire model can be loaded by the thermal load. Interaction of adjacent slabs in joints and contact of the slab and the subsequent layer are modeled with the help of special contact elements. Four concrete slabs separated by transverse and longitudinal joints and the additional structural layers and soil to the depth of about 3m are modeled. The thickness of individual layers, physical and mechanical properties of materials, characteristics of joints, and the temperature of the upper and lower surface of slabs are supposed to be random variables. The modern simulation technique Updated Latin Hypercube Sampling with 20 simulations is used. For sensitivity analysis the sensitivity coefficient based on the Spearman rank correlation coefficient is utilized. As a result, the estimates of influence of random variability of individual input variables on the random variability of principal stresses s1 and s3 in 53 points on the upper and lower surface of the concrete slabs are obtained.

Keywords: Concrete, FEM, pavement, sensitivity, simulation.

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Authors: Riccardo Fratini, Riccardo Santini, Jacopo Serafini, Massimo Gennaretti, Stefano Panzieri

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This paper presents a nonlinear differential model, for a three-bladed horizontal axis wind turbine (HAWT) suited for control applications. It is based on a 8-dofs, lumped parameters structural dynamics coupled with a quasi-steady sectional aerodynamics. In particular, using the Euler-Lagrange Equation (Energetic Variation approach), the authors derive, and successively validate, such model. For the derivation of the aerodynamic model, the Greenbergs theory, an extension of the theory proposed by Theodorsen to the case of thin airfoils undergoing pulsating flows, is used. Specifically, in this work, the authors restricted that theory under the hypothesis of low perturbation reduced frequency k, which causes the lift deficiency function C(k) to be real and equal to 1. Furthermore, the expressions of the aerodynamic loads are obtained using the quasi-steady strip theory (Hodges and Ormiston), as a function of the chordwise and normal components of relative velocity between flow and airfoil Ut, Up, their derivatives, and section angular velocity ε˙. For the validation of the proposed model, the authors carried out open and closed-loop simulations of a 5 MW HAWT, characterized by radius R =61.5 m and by mean chord c = 3 m, with a nominal angular velocity Ωn = 1.266rad/sec. The first analysis performed is the steady state solution, where a uniform wind Vw = 11.4 m/s is considered and a collective pitch angle θ = 0.88◦ is imposed. During this step, the authors noticed that the proposed model is intrinsically periodic due to the effect of the wind and of the gravitational force. In order to reject this periodic trend in the model dynamics, the authors propose a collective repetitive control algorithm coupled with a PD controller. In particular, when the reference command to be tracked and/or the disturbance to be rejected are periodic signals with a fixed period, the repetitive control strategies can be applied due to their high precision, simple implementation and little performance dependency on system parameters. The functional scheme of a repetitive controller is quite simple and, given a periodic reference command, is composed of a control block Crc(s) usually added to an existing feedback control system. The control block contains and a free time-delay system eτs in a positive feedback loop, and a low-pass filter q(s). It should be noticed that, while the time delay term reduces the stability margin, on the other hand the low pass filter is added to ensure stability. It is worth noting that, in this work, the authors propose a phase shifting for the controller and the delay system has been modified as e^(−(T−γk)), where T is the period of the signal and γk is a phase shifting of k samples of the same periodic signal. It should be noticed that, the phase shifting technique is particularly useful in non-minimum phase systems, such as flexible structures. In fact, using the phase shifting, the iterative algorithm could reach the convergence also at high frequencies. Notice that, in our case study, the shifting of k samples depends both on the rotor angular velocity Ω and on the rotor azimuth angle Ψ: we refer to this controller as a spatial repetitive controller. The collective repetitive controller has also been coupled with a C(s) = PD(s), in order to dampen oscillations of the blades. The performance of the spatial repetitive controller is compared with an industrial PI controller. In particular, starting from wind speed velocity Vw = 11.4 m/s the controller is asked to maintain the nominal angular velocity Ωn = 1.266rad/s after an instantaneous increase of wind speed (Vw = 15 m/s). Then, a purely periodic external disturbance is introduced in order to stress the capabilities of the repetitive controller. The results of the simulations show that, contrary to a simple PI controller, the spatial repetitive-PD controller has the capability to reject both external disturbances and periodic trend in the model dynamics. Finally, the nominal value of the angular velocity is reached, in accordance with results obtained with commercial software for a turbine of the same type.

Keywords: Wind turbines, aeroelasticity, repetitive control, periodic systems.

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Authors: Derjew Ayele Ejigu, Houde Song, Xiaojing Liu

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This work presents the particle swarm optimization trained neural network (PSO-NN) supervisory proportional integral derivative (PID) control method to monitor the pressurized water reactor (PWR) core power for safe operation. The proposed control approach is implemented on the transfer function of the PWR core, which is computed from the state-space model. The PWR core state-space model is designed from the neutronics, thermal-hydraulics, and reactivity models using perturbation around the equilibrium value. The proposed control approach computes the control rod speed to maneuver the core power to track the reference in a closed-loop scheme. The particle swarm optimization (PSO) algorithm is used to train the neural network (NN) and to tune the PID simultaneously. The controller performance is examined using integral absolute error, integral time absolute error, integral square error, and integral time square error functions, and the stability of the system is analyzed by using the Bode diagram. The simulation results indicated that the controller shows satisfactory performance to control and track the load power effectively and smoothly as compared to the PSO-PID control technique. This study will give benefit to design a supervisory controller for nuclear engineering research fields for control application.

Keywords: machine learning, neural network, pressurized water reactor, supervisory controller

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Authors: Hesham Abd Elkhalek, Sherif M. Hafez, Yasser M. El Fahham

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Allocating limited budget to maintain bridge networks and selecting effective maintenance strategies for each bridge represent challenging tasks for maintenance managers and decision makers. In Egypt, bridges are continuously deteriorating. In many cases, maintenance works are performed due to user complaints. The objective of this paper is to develop a practical and reliable framework to manage the maintenance, repair, and rehabilitation (MR&R) activities of Bridges network considering performance and budget limits. The model solves an optimization problem that maximizes the average condition of the entire network given the limited available budget using Genetic Algorithm (GA). The framework contains bridge inventory, condition assessment, repair cost calculation, deterioration prediction, and maintenance optimization. The developed model takes into account multiple parameters including serviceability requirements, budget allocation, element importance on structural safety and serviceability, bridge impact on network, and traffic. A questionnaire is conducted to complete the research scope. The proposed model is implemented in software, which provides a friendly user interface. The framework provides a multi-year maintenance plan for the entire network for up to five years. A case study of ten bridges is presented to validate and test the proposed model with data collected from Transportation Authorities in Egypt. Different scenarios are presented. The results are reasonable, feasible and within acceptable domain.

Keywords: Bridge Management Systems (BMS), cost optimization condition assessment, fund allocation, Markov chain.

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Authors: K. Rusnakova, D. Gerych, M. Stehlik

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Postural stability is a complex process involving actions of biomechanical, motor, sensory and central nervous system components. Numerous joint systems, muscles involved, the complexity of sporting movements and situations require perfect coordination of the body's movement patterns. To adapt to a constantly changing situation in such a dynamic environment as physical performance, optimal input of information from visual, vestibular and somatosensory sensors are needed. Combat soldiers are required to perform physically and mentally demanding tasks in adverse conditions, and poor postural stability has been identified as a risk factor for lower extremity musculoskeletal injury. The aim of this study is to investigate whether some personality traits are related to the performance of static postural stability among soldiers of combat troops. NEO personality inventory (NEO-PI-R) was used to identify personality traits and the Nintendo Wii Balance Board was used to assess static postural stability of soldiers. Postural stability performance was assessed by changes in center of pressure (CoP) and center of gravity (CoG). A posturographic test was performed for 60 s with eyes opened during quiet upright standing. The results showed that facets of neuroticism and conscientiousness personality traits were significantly correlated with measured parameters of CoP and CoG. This study can help for better understanding the relationship between personality traits and static postural stability. The results can be used to optimize the training process at the individual level.

Keywords: Neuroticism, conscientiousness, postural stability, combat troops.

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Authors: Nyeng P. Gyang

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Even though past, current and future trends suggest that multicore and cloud computing systems are increasingly prevalent/ubiquitous, this class of parallel systems is nonetheless underutilized, in general, and barely used for research on employing parallel Delaunay triangulation for parallel surface modeling and generation, in particular. The performances, of actual/physical and virtual/cloud multicore systems/machines, at executing various algorithms, which implement various parallelization strategies of the incremental insertion technique of the Delaunay triangulation algorithm, were evaluated. T-tests were run on the data collected, in order to determine whether various performance metrics differences (including execution time, speedup and efficiency) were statistically significant. Results show that the actual machine is approximately twice faster than the virtual machine at executing the same programs for the various parallelization strategies. Results, which furnish the scalability behaviors of the various parallelization strategies, also show that some of the differences between the performances of these systems, during different runs of the algorithms on the systems, were statistically significant. A few pseudo superlinear speedup results, which were computed from the raw data collected, are not true superlinear speedup values. These pseudo superlinear speedup values, which arise as a result of one way of computing speedups, disappear and give way to asymmetric speedups, which are the accurate kind of speedups that occur in the experiments performed.

Keywords: Cloud computing systems, multicore systems, parallel delaunay triangulation, parallel surface modeling and generation.

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Authors: Abobaker Mohammed Qasem Farhan, Shangming Yang, Mohammed Al-Nehari

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Pneumonia is a kind of lung disease that creates congestion in the chest. Such pneumonic conditions lead to loss of life due to the severity of high congestion. Pneumonic lung disease is caused by viral pneumonia, bacterial pneumonia, or COVID-19 induced pneumonia. The early prediction and classification of such lung diseases help reduce the mortality rate. We propose the automatic Computer-Aided Diagnosis (CAD) system in this paper using the deep learning approach. The proposed CAD system takes input from raw computerized tomography (CT) scans of the patient's chest and automatically predicts disease classification. We designed the Hybrid Deep Learning Algorithm (HDLA) to improve accuracy and reduce processing requirements. The raw CT scans are pre-processed first to enhance their quality for further analysis. We then applied a hybrid model that consists of automatic feature extraction and classification. We propose the robust 2D Convolutional Neural Network (CNN) model to extract the automatic features from the pre-processed CT image. This CNN model assures feature learning with extremely effective 1D feature extraction for each input CT image. The outcome of the 2D CNN model is then normalized using the Min-Max technique. The second step of the proposed hybrid model is related to training and classification using different classifiers. The simulation outcomes using the publicly available dataset prove the robustness and efficiency of the proposed model compared to state-of-art algorithms.

Keywords: CT scans, COVID-19, deep learning, image processing, pneumonia, lung disease.

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Authors: Shin-Hyung Cho, Dong-Kyu Kim, Seung-Young Kho

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Truck-involved crashes have higher crash severity than non-truck-involved crashes. There have been many studies about the frequency of crashes and the development of severity models, but those studies only analyzed the relationship between observed variables. To identify why more people are injured or killed when trucks are involved in the crash, we must examine to quantify the complex causal relationship between severity of the crash and risk factors by adopting the latent factors of crashes. The aim of this study was to develop a structural equation or model based on truck-involved and non-truck-involved crashes, including five latent variables, i.e. a crash factor, environmental factor, road factor, driver’s factor, and severity factor. To clarify the unique characteristics of truck-involved crashes compared to non-truck-involved crashes, a confirmatory analysis method was used. To develop the model, we extracted crash data from 10,083 crashes on Korean freeways from 2008 through 2014. The results showed that the most significant variable affecting the severity of a crash is the crash factor, which can be expressed by the location, cause, and type of the crash. For non-truck-involved crashes, the crash and environment factors increase severity of the crash; conversely, the road and driver factors tend to reduce severity of the crash. For truck-involved crashes, the driver factor has a significant effect on severity of the crash although its effect is slightly less than the crash factor. The multiple group analysis employed to analyze the differences between the heterogeneous groups of drivers.

Keywords: Crash severity, structural equation modeling, truck-involved crashes, multiple group analysis, crash on freeway.

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Authors: Dileep Kumar Gupta, Rajendra Prasad, Pradeep Kumar, Varun Narayan Mishra, Ajeet Kumar Vishwakarma, Prashant Kumar Srivastava

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An approach was evaluated for the retrieval of soil moisture of bare soil surface using bistatic scatterometer data in the angular range of 200 to 700 at VV- and HH- polarization. The microwave data was acquired by specially designed X-band (10 GHz) bistatic scatterometer. The linear regression analysis was done between scattering coefficients and soil moisture content to select the suitable incidence angle for retrieval of soil moisture content. The 250 incidence angle was found more suitable. The support vector regression analysis was used to approximate the function described by the input output relationship between the scattering coefficient and corresponding measured values of the soil moisture content. The performance of support vector regression algorithm was evaluated by comparing the observed and the estimated soil moisture content by statistical performance indices %Bias, root mean squared error (RMSE) and Nash-Sutcliffe Efficiency (NSE). The values of %Bias, root mean squared error (RMSE) and Nash-Sutcliffe Efficiency (NSE) were found 2.9451, 1.0986 and 0.9214 respectively at HHpolarization. At VV- polarization, the values of %Bias, root mean squared error (RMSE) and Nash-Sutcliffe Efficiency (NSE) were found 3.6186, 0.9373 and 0.9428 respectively.

Keywords: Bistatic scatterometer, soil moisture, support vector regression, RMSE, %Bias, NSE.

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Authors: H. B. Kekre, Sudeep D. Thepade

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The automatic construction of large, high-resolution image vistas (mosaics) is an active area of research in the fields of photogrammetry [1,2], computer vision [1,4], medical image processing [4], computer graphics [3] and biometrics [8]. Image stitching is one of the possible options to get image mosaics. Vista Creation in image processing is used to construct an image with a large field of view than that could be obtained with a single photograph. It refers to transforming and stitching multiple images into a new aggregate image without any visible seam or distortion in the overlapping areas. Vista creation process aligns two partial images over each other and blends them together. Image mosaics allow one to compensate for differences in viewing geometry. Thus they can be used to simplify tasks by simulating the condition in which the scene is viewed from a fixed position with single camera. While obtaining partial images the geometric anomalies like rotation, scaling are bound to happen. To nullify effect of rotation of partial images on process of vista creation, we are proposing rotation invariant vista creation algorithm in this paper. Rotation of partial image parts in the proposed method of vista creation may introduce some missing region in the vista. To correct this error, that is to fill the missing region further we have used image inpainting method on the created vista. This missing view regeneration method also overcomes the problem of missing view [31] in vista due to cropping, irregular boundaries of partial image parts and errors in digitization [35]. The method of missing view regeneration generates the missing view of vista using the information present in vista itself.

Keywords: Vista, Overlap Estimation, Rotation Invariance, Missing View Regeneration.

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Authors: Michael Gerges, Ograbe Ahiakwo, Martin Jaeger, Ahmad Asaad

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Recent advances of Building Information Modeling (BIM) especially in the Middle East have increased remarkably. Dubai has been taking a lead on this by making it mandatory for BIM to be adopted for all projects that involve complex architecture designs. This is because BIM is a dynamic process that assists all stakeholders in monitoring the project status throughout different project phases with great transparency. It focuses on utilizing information technology to improve collaboration among project participants during the entire life cycle of the project from the initial design, to the supply chain, resource allocation, construction and all productivity requirements. In view of this trend, the paper examines the extent of applying BIM in the State of Kuwait, by exploring practitioners’ perspectives on BIM, especially their perspectives on main barriers and main advantages. To this end structured interviews were carried out based on questionnaires and with a range of different construction professionals. The results revealed that practitioners perceive improved communication and mitigated project risks by encouraged collaboration between project participants. However, it was also observed that the full implementation of BIM in the State of Kuwait requires concerted efforts to make clients demanding BIM, counteract resistance to change among construction professionals and offer more training for design team members. This paper forms part of an on-going research effort on BIM and its application in the State of Kuwait and it is on this basis that further research on the topic is proposed.

Keywords: Building Information Modeling, BIM, construction industry, Kuwait.

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Authors: Paulina Pietrzyk-Kowalec

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This paper presents the phenomenon of multilingualism becoming the norm rather than the exception in the European Union. It also seeks to describe the correlation between the command of foreign languages and employability. It is evident that the challenges of today's societies when it comes to employability are more and more complex. Thus, it is one of the crucial tasks of higher education to prepare its students to face this kind of complexity, understand its nuances, and have the capacity to adapt effectively to situations that are common in corporations based in the countries belonging to the EU. From this point of view, the assessment of the impact that the command of foreign languages of European university students could have on the numerous business sectors becomes vital. It also involves raising awareness of future professionals to make them understand the importance of mastering communicative skills in foreign languages that will meet the requirements of students' prospective employers. The direct connection between higher education institutions and the world of business also allows companies to realize that they should rethink their recruitment and human resources procedures in order to take into account the importance of foreign languages. This article focuses on the objective of the multilingualism policy developed by the European Commission, which is to enable young people to master at least two foreign languages, which is crucial in their future careers. The article puts emphasis on the existence of a crucial connection between the research conducted in higher education institutions and the business sector in order to reduce current qualification gaps.

Keywords: Cross-cultural communication, employability, human resources, language attitudes, multilingualism.

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Authors: Kamal K.Bharadwaj, Rekha Kandwal

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An important structuring mechanism for knowledge bases is building clusters based on the content of their knowledge objects. The objects are clustered based on the principle of maximizing the intraclass similarity and minimizing the interclass similarity. Clustering can also facilitate taxonomy formation, that is, the organization of observations into a hierarchy of classes that group similar events together. Hierarchical representation allows us to easily manage the complexity of knowledge, to view the knowledge at different levels of details, and to focus our attention on the interesting aspects only. One of such efficient and easy to understand systems is Hierarchical Production rule (HPRs) system. A HPR, a standard production rule augmented with generality and specificity information, is of the following form Decision If < condition> Generality Specificity . HPRs systems are capable of handling taxonomical structures inherent in the knowledge about the real world. In this paper, a set of related HPRs is called a cluster and is represented by a HPR-tree. This paper discusses an algorithm based on cumulative learning scenario for dynamic structuring of clusters. The proposed scheme incrementally incorporates new knowledge into the set of clusters from the previous episodes and also maintains summary of clusters as Synopsis to be used in the future episodes. Examples are given to demonstrate the behaviour of the proposed scheme. The suggested incremental structuring of clusters would be useful in mining data streams.

Keywords: Cumulative learning, clustering, data mining, hierarchical production rules.

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Authors: Yash Pal, A.Swarup, Bhim Singh

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This paper presents a novel control strategy of a threephase four-wire Unified Power Quality (UPQC) for an improvement in power quality. The UPQC is realized by integration of series and shunt active power filters (APFs) sharing a common dc bus capacitor. The shunt APF is realized using a thee-phase, four leg voltage source inverter (VSI) and the series APF is realized using a three-phase, three leg VSI. A control technique based on unit vector template technique (UTT) is used to get the reference signals for series APF, while instantaneous sequence component theory (ISCT) is used for the control of Shunt APF. The performance of the implemented control algorithm is evaluated in terms of power-factor correction, load balancing, neutral source current mitigation and mitigation of voltage and current harmonics, voltage sag and swell in a three-phase four-wire distribution system for different combination of linear and non-linear loads. In this proposed control scheme of UPQC, the current/voltage control is applied over the fundamental supply currents/voltages instead of fast changing APFs currents/voltages, there by reducing the computational delay and the required sensors. MATLAB/Simulink based simulations are obtained, which support the functionality of the UPQC. MATLAB/Simulink based simulations are obtained, which support the functionality of the UPQC.

Keywords: Power Quality, UPQC, Harmonics, Load Balancing, Power Factor Correction, voltage harmonic mitigation, currentharmonic mitigation, voltage sag, swell

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Authors: Lutfurahman Aftab

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The Taliban’s relationships with the neighboring countries are a complex political issue that local people interpret one way, and politicians have different perceptions; therefore, it is a current issue that needs to be analyzed broadly and impartially. In this article, we investigate the Taliban’s current relationships with the neighboring countries, as well as look at the effects these relationships have on the current peace negotiations in Doha, which began on September 12, 2020. The issue of Taliban and the current peace process has turned to be the center-of-attention for most of the neighboring countries, and every country has opened new pages in their foreign policies because after the Taliban-US peace agreement, the neighboring countries are meticulously and closely observing the situation and they believe that the Taliban is on the verge to tighten their grips on the future political power of Afghanistan. Every neighboring country of Afghanistan has political, economic, and social interests in this land-locked country. The Taliban’s current role within the peace talks and anticipated future position within the Afghan government will have great political, economic, and social implications on countries in the region as they assess their foreign policies. As these countries move to form closer ties with the Taliban, the government of Afghanistan is worried that this may hinder the peace process. Afghanistan has long blamed Pakistan for sheltering the Taliban and providing safe havens for the terrorist groups, including Al Qaeda, and the recent visits of Taliban’s delegations to Islamabad, Pakistan, have raised concern among government officials in Afghanistan who believe that the Taliban is not independent in their decisions, and for every step they take, are consulting with Pakistan’s political leadership.

Keywords: peace process, USA, Afghanistan, Taliban

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Authors: N. Chegeni, M. J. Tahmasebi Birgani

Abstract:

Nowadays, in most radiotherapy departments, the commercial treatment planning systems (TPS) used to calculate dose distributions needs to be verified; therefore, quick, easy-to-use and low cost dose distribution algorithms are desirable to test and verify the performance of the TPS. In this paper, we put forth an analytical method to calculate the phantom scatter contribution and depth dose on the central axis based on the equivalent square concept. Then, this method was generalized to calculate the profiles at any depth and for several field shapes regular or irregular fields under symmetry and asymmetry photon beam conditions. Varian 2100 C/D and Siemens Primus Plus Linacs with 6 and 18 MV photon beam were used for irradiations. Percentage depth doses (PDDs) were measured for a large number of square fields for both energies, and for 45^º wedges which were employed to obtain the profiles in any depth. To assess the accuracy of the calculated profiles, several profile measurements were carried out for some treatment fields. The calculated and measured profiles were compared by gamma-index calculation. All γ–index calculations were based on a 3% dose criterion and a 3 mm dose-to-agreement (DTA) acceptance criterion. The γ values were less than 1 at most points. However, the maximum γ observed was about 1.10 in the penumbra region in most fields and in the central area for the asymmetric fields. This analytical approach provides a generally quick and fairly accurate algorithm to calculate dose distribution for some treatment fields in conventional radiotherapy.

Keywords: Dose distribution, equivalent field, asymmetric field, irregular field.

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