Search results for: multilevel modelling

Authors: Rodrigo Pacheco-Ruiz, Jonathan Adams, Felix Pedrotti

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This paper introduces the applicability of underwater photogrammetric survey within challenging conditions as the main tool to enhance and enrich the process of documenting archaeological excavation through the creation of 4D models. Photogrammetry was being attempted on underwater archaeological sites at least as early as the 1970s’ and today the production of traditional 3D models is becoming a common practice within the discipline. Photogrammetry underwater is more often implemented to record exposed underwater archaeological remains and less so as a dynamic interpretative tool.  Therefore, it tends to be applied in bright environments and when underwater visibility is > 1m, reducing its implementation on most submerged archaeological sites in more turbid conditions. Recent years have seen significant development of better digital photographic sensors and the improvement of optical technology, ideal for darker environments. Such developments, in tandem with powerful processing computing systems, have allowed underwater photogrammetry to be used by this research as a standard recording and interpretative tool. Using multi-source photogrammetry (5, GoPro5 Hero Black cameras) this paper presents the accumulation of daily (4D) underwater surveys carried out in the Early Bronze Age (3,300 BC) to Late Ottoman (17th Century AD) archaeological site of Ropotamo in the Bulgarian Black Sea under challenging conditions (< 0.5m visibility). It proves that underwater photogrammetry can and should be used as one of the main recording methods even in low light and poor underwater conditions as a way to better understand the complexity of the underwater archaeological record.

Keywords: 4D modelling, Black Sea, maritime archaeology, underwater photogrammetry, Bronze Age, low visibility.

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Authors: V. Keim, J. Spachtholz, J. Hammer

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The importance of fibre reinforced plastics continually increases due to the excellent mechanical properties, low material and manufacturing costs combined with significant weight reduction. Today, components are usually designed and calculated numerically by using finite element methods (FEM) to avoid expensive laboratory tests. These programs are based on material models including material specific deformation characteristics. In this research project, material models for short glass fibre reinforced plastics are presented to simulate the visco-elasto-plastic deformation behaviour. Prior to modelling specimens of the material EMS Grivory HTV-5H1, consisting of a Polyphthalamide matrix reinforced by 50wt.-% of short glass fibres, are characterized experimentally in terms of the highly time dependent deformation behaviour of the matrix material. To minimize the experimental effort, the cyclic deformation behaviour under tensile and compressive loading (R = −1) is characterized by isothermal complex low cycle fatigue (CLCF) tests. Combining cycles under two strain amplitudes and strain rates within three orders of magnitude and relaxation intervals into one experiment the visco-elastic deformation is characterized. To identify visco-plastic deformation monotonous tensile tests either displacement controlled or strain controlled (CERT) are compared. All relevant modelling parameters for this complex superposition of simultaneously varying mechanical loadings are quantified by these experiments. Subsequently, two different material models are compared with respect to their accuracy describing the visco-elasto-plastic deformation behaviour. First, based on Chaboche an extended 12 parameter model (EVP-KV2) is used to model cyclic visco-elasto-plasticity at two time scales. The parameters of the model including a total separation of elastic and plastic deformation are obtained by computational optimization using an evolutionary algorithm based on a fitness function called genetic algorithm. Second, the 12 parameter visco-elasto-plastic material model by Launay is used. In detail, the model contains a different type of a flow function based on the definition of the visco-plastic deformation as a part of the overall deformation. The accuracy of the models is verified by corresponding experimental LCF testing.

Keywords: Complex low cycle fatigue, material modelling, short glass fibre reinforced polyphthalamides, visco-elasto-plastic deformation.

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Authors: Roberto Iglesias, Theocharis Kyriacou, Ulrich Nehmzow, Steve Billings

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Fundamental sensor-motor couplings form the backbone of most mobile robot control tasks, and often need to be implemented fast, efficiently and nevertheless reliably. Machine learning techniques are therefore often used to obtain the desired sensor-motor competences. In this paper we present an alternative to established machine learning methods such as artificial neural networks, that is very fast, easy to implement, and has the distinct advantage that it generates transparent, analysable sensor-motor couplings: system identification through nonlinear polynomial mapping. This work, which is part of the RobotMODIC project at the universities of Essex and Sheffield, aims to develop a theoretical understanding of the interaction between the robot and its environment. One of the purposes of this research is to enable the principled design of robot control programs. As a first step towards this aim we model the behaviour of the robot, as this emerges from its interaction with the environment, with the NARMAX modelling method (Nonlinear, Auto-Regressive, Moving Average models with eXogenous inputs). This method produces explicit polynomial functions that can be subsequently analysed using established mathematical methods. In this paper we demonstrate the fidelity of the obtained NARMAX models in the challenging task of robot route learning; we present a set of experiments in which a Magellan Pro mobile robot was taught to follow four different routes, always using the same mechanism to obtain the required control law.

Keywords: Mobile robotics, system identification, non-linear modelling, NARMAX.

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Authors: F. F. Howard, I. Yakubu, C. B. Boye, J. S. Y. Kuma

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Natural and human activities, such as mining operations, expose the natural soil to adverse environmental conditions, leading to contamination of soil, groundwater, and surface water, which has negative effects on humans, flora, and fauna. Bare or partly exposed soil is most liable to fluvial erosion. This paper enumerates various methods used to identify, map, and model fluvial erosion in a mining environment. Classical, Artificial Intelligence (AI), and GIS methods have been reviewed. One of the many classical methods used to estimate river erosion is the Revised Universal Soil Loss Equation (RUSLE) model. The RUSLE model is easy to use. Its reliance on empirical relationships that may not always be applicable to specific circumstances or locations is a flaw. Other classical models for estimating fluvial erosion are the Soil and Water Assessment Tool (SWAT) and the Universal Soil Loss Equation (USLE). These models offer a more complete understanding of the underlying physical processes and encompass a wider range of situations. Although more difficult to utilise, they depend on the availability and dependability of input data for correctness. AI can help deal with multivariate and complex difficulties and predict soil loss with higher accuracy than traditional methods, and also be used to build unique models for identifying degraded areas. AI techniques have become popular as an alternative predictor for degraded environments. However, this research proposed a hybrid of classical, AI, and GIS methods for efficient and effective modelling of fluvial erosion.

Keywords: Fluvial erosion, classical methods, Artificial Intelligence, Geographic Information System.

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Authors: Essam A. Al-Bahkali, Mahir H. Es-saheb, Jonny Herwan

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In this study the elastic-plastic stress distribution in weld-bonded joint, fabricated from austenitic stainless steel (AISI 304) sheet of 1.00 mm thickness and Epoxy adhesive Araldite 2011, subjected to axial loading is investigated. This is needed to improve design procedures and welding codes, and saving efforts in the cumbersome experiments and analysis. Therefore, a complete 3-D finite element modelling and analysis of spot welded, bonded and weld-bonded joints under axial loading conditions is carried out. A comprehensive systematic experimental program is conducted to determine many properties and quantities, of the base metals and the adhesive, needed for FE modelling, such like the elastic – plastic properties, modulus of elasticity, fracture limit, the nugget and heat affected zones (HAZ) properties, etc. Consequently, the finite element models developed, for each case, are used to evaluate stresses distributions across the entire joint, in both the elastic and plastic regions. The stress distribution curves are obtained, particularly in the elastic regions and found to be consistent and in excellent agreement with the published data. Furthermore, the stresses distributions are obtained in the weld-bonded joint and display the best results with almost uniform smooth distribution compared to spot and bonded cases. The stress concentration peaks at the edges of the weld-bonded region, are almost eliminated resulting in achieving the strongest joint of all processes.

Keywords: Spot Welded, Weld-Bonded, Load-Displacement curve, Stress distribution

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Authors: U. Datta

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The main objective of this study is to find a suitable approach to monitor the land infrastructure growth over a period of time using multispectral satellite images. Bi-temporal change detection method is unable to indicate the continuous change occurring over a long period of time. To achieve this objective, the approach used here estimates a statistical model from series of multispectral image data over a long period of time, assuming there is no considerable change during that time period and then compare it with the multispectral image data obtained at a later time. The change is estimated pixel-wise. Statistical composite hypothesis technique is used for estimating pixel based change detection in a defined region. The generalized likelihood ratio test (GLRT) is used to detect the changed pixel from probabilistic estimated model of the corresponding pixel. The changed pixel is detected assuming that the images have been co-registered prior to estimation. To minimize error due to co-registration, 8-neighborhood pixels around the pixel under test are also considered. The multispectral images from Sentinel-2 and Landsat-8 from 2015 to 2018 are used for this purpose. There are different challenges in this method. First and foremost challenge is to get quite a large number of datasets for multivariate distribution modelling. A large number of images are always discarded due to cloud coverage. Due to imperfect modelling there will be high probability of false alarm. Overall conclusion that can be drawn from this work is that the probabilistic method described in this paper has given some promising results, which need to be pursued further.

Keywords: Co-registration, GLRT, infrastructure growth, multispectral, multitemporal, pixel-based change detection.

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Authors: William Xiu Shun Wong, Myungsu Lim, Yoonjin Hyun, Chen Liu, Seongi Choi, Dasom Kim, Kee-Young Kwahk, Namgyu Kim

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Recently, many users have begun to frequently share their opinions on diverse issues using various social media. Therefore, numerous governments have attempted to establish or improve national policies according to the public opinions captured from various social media. In this paper, we indicate several limitations of the traditional approaches to analyze public opinion on science and technology and provide an alternative methodology to overcome these limitations. First, we distinguish between the science and technology analysis phase and the social issue analysis phase to reflect the fact that public opinion can be formed only when a certain science and technology is applied to a specific social issue. Next, we successively apply a start list and a stop list to acquire clarified and interesting results. Finally, to identify the most appropriate documents that fit with a given subject, we develop a new logical filter concept that consists of not only mere keywords but also a logical relationship among the keywords. This study then analyzes the possibilities for the practical use of the proposed methodology thorough its application to discover core issues and public opinions from 1,700,886 documents comprising SNS, blogs, news, and discussions.

Keywords: Big data, social network analysis, text mining, topic modeling.

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Authors: Dhruvit S. Berawala, Jann R. Ursin, Obrad Slijepcevic

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Shale gas is one of the most rapidly growing forms of natural gas. Unconventional natural gas deposits are difficult to characterize overall, but in general are often lower in resource concentration and dispersed over large areas. Moreover, gas is densely packed into the matrix through adsorption which accounts for large volume of gas reserves. Gas production from tight shale deposits are made possible by extensive and deep well fracturing which contacts large fractions of the formation. The conventional reservoir modelling and production forecasting methods, which rely on fluid-flow processes dominated by viscous forces, have proved to be very pessimistic and inaccurate. This paper presents a new approach to forecast shale gas production by detailed modeling of gas desorption, diffusion and non-linear flow mechanisms in combination with statistical representation of these processes. The representation of the model involves a cube as a porous media where free gas is present and a sphere (SiC: Sphere in Cube model) inside it where gas is adsorbed on to the kerogen or organic matter. Further, the sphere is considered consisting of many layers of adsorbed gas in an onion-like structure. With pressure decline, the gas desorbs first from the outer most layer of sphere causing decrease in its molecular concentration. The new available surface area and change in concentration triggers the diffusion of gas from kerogen. The process continues until all the gas present internally diffuses out of the kerogen, gets adsorbs onto available surface area and then desorbs into the nanopores and micro-fractures in the cube. Each SiC idealizes a gas pathway and is characterized by sphere diameter and length of the cube. The diameter allows to model gas storage, diffusion and desorption; the cube length takes into account the pathway for flow in nanopores and micro-fractures. Many of these representative but general cells of the reservoir are put together and linked to a well or hydraulic fracture. The paper quantitatively describes these processes as well as clarifies the geological conditions under which a successful shale gas production could be expected. A numerical model has been derived which is then compiled on FORTRAN to develop a simulator for the production of shale gas by considering the spheres as a source term in each of the grid blocks. By applying SiC to field data, we demonstrate that the model provides an effective way to quickly access gas production rates from shale formations. We also examine the effect of model input properties on gas production.

Keywords: Sphere in Cube Grid Approach to Modelling of Shale Gas Production Using Non-Linear Flow Mechanisms

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

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

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

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Authors: Essa Alenazi, Zulfikar Adamu

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The Kingdom of Saudi Arabia (KSA) is not an exception in relying on the growth of its construction industry to support rapid population growth. However, its need for infrastructure development is constrained by low productivity levels and cost overruns caused by factors such as delays to project completion. Delays in delivering a construction project are a global issue and while theories such as Optimism Bias have been used to explain such delays, in KSA, client-related causes of delays are also significant. The objective of this paper is to develop a framework-based approach to explore how the country’s construction industry can manage and reduce delays in construction projects through building information modelling (BIM) in order to mitigate the cost consequences of such delays.  It comprehensively and systematically reviewed the global literature on the subject and identified gaps, critical delay factors and the specific benefits that BIM can deliver for the delay management.  A case study comprising of nine hospital projects that have experienced delay and cost overruns was also carried out. Five critical delay factors related to the clients were identified as candidates that can be mitigated through BIM’s benefits. These factors are: Ineffective planning and scheduling of the project; changes during construction by the client; delay in progress payment; slowness in decision making by the client; and poor communication between clients and other stakeholders. In addition, data from the case study projects strongly suggest that optimism bias is present in many of the hospital projects. Further validation via key stakeholder interviews and documentations are planned.

Keywords: BIM, client perspective, delay management, optimism bias, public sector projects.

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Authors: Akan C. Offong, E. J. Anthony, Vasilije Manovic

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A modified steady-state numerical model is developed for the electrochemical reduction of CO₂ to formic acid. The numerical model achieves a CD (current density) (~60 mA/cm²), FE-faradaic efficiency (~98%) and conversion (~80%) for CO₂ electro-reduction to formic acid in a microfluidic cell. The model integrates charge and species transport, mass conservation, and momentum with electrochemistry. Specifically, the influences of Bi-Sn based nanoparticle catalyst (on the cathode surface) at different mole fractions and 1-ethyl-3-methyl imidazolium tetra-fluoroborate ([EMIM][BF₄]) electrolyte, on CD, FE and CO₂ conversion to formic acid is studied. The reaction is carried out at a constant concentration of electrolyte (85% v/v., [EMIM][BF₄]). Based on the mass transfer characteristics analysis (concentration contours), mole ratio 0.5:0.5 Bi-Sn catalyst displays the highest CO₂ mole consumption in the cathode gas channel. After validating with experimental data (polarisation curves) from literature, extensive simulations reveal performance measure: CD, FE and CO₂ conversion. Increasing the negative cathode potential increases the current densities for both formic acid and H₂ formations. However, H₂ formations are minimal as a result of insufficient hydrogen ions in the ionic liquid electrolyte. Moreover, the limited hydrogen ions have a negative effect on formic acid CD. As CO₂ flow rate increases, CD, FE and CO₂ conversion increases.

Keywords: Carbon dioxide, electro-chemical reduction, microfluidics, ionic liquids, modelling.

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Authors: Joshi Manohar. V., Sujatha. P., Anjaneyulu K. S. R

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The range of the output power is a very important and evident limitation of two-level inverters. In order to overcome this disadvantage, multilevel inverters are introduced. Recently, Cascade H-Bridge inverters have emerged as one of the popular converter topologies used in numerous industrial applications. The modulation switching strategies such as phase shifted carrier based Pulse Width Modulation (PWM) technique and Stair case modulation with Selective Harmonic Elimination (SHE) PWM technique are generally used. NR method is used to solve highly non linear transcendental equations which are formed by SHEPWM method. Generally NR method has a drawback of requiring good initial guess but in this paper a new approach is implemented for NR method with any random initial guess. A three phase CHB 11-level inverter is chosen for analysis. MATLAB/SIMULINK programming environment and harmonic profiles are compared. Finally this paper presents a method at fundamental switching frequency with least % THDV.

Keywords: Cascade H-bridge 11- level Inverter, NR method, Phase shifted carrier based pulse width modulation (PSCPWM), Selective Harmonic Elimination Pulse Width Modulation (SHEPWM), Total Harmonic Distortion (%THDv).

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Authors: M. Moeinaddini, Z. Asadi-Shekari, Z. Sultan, M. Zaly Shah

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Happiness can be related to everything that can provide a feeling of satisfaction or pleasure. This study tries to consider the relationship between land use factors and feeling of happiness at the neighbourhood level. Land use variables (beautiful and attractive neighbourhood design, availability and quality of shopping centres, sufficient recreational spaces and facilities, and sufficient daily service centres) are used as independent variables and the happiness score is used as the dependent variable in this study. In addition to the land use variables, socio-economic factors (gender, race, marital status, employment status, education, and income) are also considered as independent variables. This study uses the Oxford happiness questionnaire to estimate happiness score of more than 300 people living in six neighbourhoods. The neighbourhoods are selected randomly from Skudai neighbourhoods in Johor, Malaysia. The land use data were obtained by adding related questions to the Oxford happiness questionnaire. The strength of the relationship in this study is found using generalised linear modelling (GLM). The findings of this research indicate that increase in happiness feeling is correlated with an increasing income, more beautiful and attractive neighbourhood design, sufficient shopping centres, recreational spaces, and daily service centres. The results show that all land use factors in this study have significant relationship with happiness but only income, among socio-economic factors, can affect happiness significantly. Therefore, land use factors can affect happiness in Skudai more than socio-economic factors.

Keywords: Neighbourhood land use, neighbourhood design, happiness, socio-economic factors, generalised linear modelling.

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Authors: Jörg Linde, Ekaterina Buyko, Robert Altwasser, Udo Hahn, Reinhard Guthke

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Reverse engineering of full-genomic interaction networks based on compendia of expression data has been successfully applied for a number of model organisms. This study adapts these approaches for an important non-model organism: The major human fungal pathogen Candida albicans. During the infection process, the pathogen can adapt to a wide range of environmental niches and reversibly changes its growth form. Given the importance of these processes, it is important to know how they are regulated. This study presents a reverse engineering strategy able to infer fullgenomic interaction networks for C. albicans based on a linear regression, utilizing the sparseness criterion (LASSO). To overcome the limited amount of expression data and small number of known interactions, we utilize different prior-knowledge sources guiding the network inference to a knowledge driven solution. Since, no database of known interactions for C. albicans exists, we use a textmining system which utilizes full-text research papers to identify known regulatory interactions. By comparing with these known regulatory interactions, we find an optimal value for global modelling parameters weighting the influence of the sparseness criterion and the prior-knowledge. Furthermore, we show that soft integration of prior-knowledge additionally improves the performance. Finally, we compare the performance of our approach to state of the art network inference approaches.

Keywords: Pathogen, network inference, text-mining, Candida albicans, LASSO, mutual information, reverse engineering, linear regression, modelling.

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Authors: A. Habibizad Navin, A. Sadighi, M. Naghian Fesharaki, M. Mirnia, M. Teshnelab, R. Keshmiri

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This paper presents a new data oriented model of image. Then a representation of it, ADBT, is introduced. The ability of ADBT is clustering, segmentation, measuring similarity of images etc, with desired precision and corresponding speed.

Keywords: Data oriented modelling, image, clustering, segmentation, classification, ADBT and image processing.

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Authors: Mohamed Elghorab, Vendra C. Madhav Rao, Jennifer X. Wen

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Turbulence modelling is still evolving, and efforts are on to improve and develop numerical methods to simulate the real turbulence structures by using the empirical and experimental information. The monotonically integrated large eddy simulation (MILES) is an attractive approach for modelling turbulence in high Re flows, which is based on the solving of the unfiltered flow equations with no explicit sub-grid scale (SGS) model. In the current work, this approach has been used, and the action of the SGS model has been included implicitly by intrinsic nonlinear high-frequency filters built into the convection discretization schemes. The MILES solver is developed using the opensource CFD OpenFOAM libraries. The role of flux limiters schemes namely, Gamma, superBee, van-Albada and van-Leer, is studied in predicting turbulent statistical quantities for a fully developed channel flow with a friction Reynolds number, Re_T = 180, and compared the numerical predictions with the well-established Direct Numerical Simulation (DNS) results for studying the wall generated turbulence. It is inferred from the numerical predictions that Gamma, van-Leer and van-Albada limiters produced more diffusion and overpredicted the velocity profiles, while superBee scheme reproduced velocity profiles and turbulence statistical quantities in good agreement with the reference DNS data in the streamwise direction although it deviated slightly in the spanwise and normal to the wall directions. The simulation results are further discussed in terms of the turbulence intensities and Reynolds stresses averaged in time and space to draw conclusion on the flux limiter schemes performance in OpenFOAM context.

Keywords: Flux limiters, MILES, OpenFOAM, turbulence structures, TVD schemes.

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Authors: M. Inmaculada Alvarez-Fernández, Celestino González-Nicieza, M. Belén Prendes-Gero, Fernando López-Gayarre

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Among many factors affecting the stability of mining excavations, rock-bursts and tremors play a special role. These dynamic loads occur practically always and have different sources of generation. The most important of them is the commonly used mining technique, which disintegrates a certain area of the rock mass not only in the area of the planned mining, but also creates waves that significantly exceed this area affecting the structural elements. In this work it is analysed the consequences of dynamic loads over the structural elements in an underground room and pillar mine to avoid roof instabilities. With this end, dynamic loads were evaluated through in situ and laboratory tests and simulated with numerical modelling. Initially, the geotechnical characterization of all materials was carried out by mean of large-scale tests. Then, drill holes were done on the roof of the mine and were monitored to determine possible discontinuities in it. Three seismic stations and a triaxial accelerometer were employed to measure the vibrations from blasting tests, establish the dynamic behaviour of roof and pillars and develop the transmission laws. At last, computer simulations by FLAC3D software were done to check the effect of vibrations on the stability of the roofs. The study shows that in-situ tests have a greater reliability than laboratory samples because of eliminating the effect of heterogeneities, that the pillars work decreasing the amplitude of the vibration around them, and that the tensile strength of a beam and depending on its span is overcome with waves in phase and delayed. The obtained transmission law allows designing a blasting which guarantees safety and prevents the risk of future failures.

Keywords: Dynamic modelling, long term instability risks, room and pillar, seismic collapse.

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Authors: Chin-Yin Chen, Chi-Cheng Cheng

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This paper aims at to develop a robust optimization methodology for the mechatronic modules of machine tools by considering all important characteristics from all structural and control domains in one single process. The relationship between these two domains is strongly coupled. In order to reduce the disturbance caused by parameters in either one, the mechanical and controller design domains need to be integrated. Therefore, the concurrent integrated design method Design For Control (DFC), will be employed in this paper. In this connect, it is not only applied to achieve minimal power consumption but also enhance structural performance and system response at same time. To investigate the method for integrated optimization, a mechatronic feed drive system of the machine tools is used as a design platform. Pro/Engineer and AnSys are first used to build the 3D model to analyze and design structure parameters such as elastic deformation, nature frequency and component size, based on their effects and sensitivities to the structure. In addition, the robust controller,based on Quantitative Feedback Theory (QFT), will be applied to determine proper control parameters for the controller. Therefore, overall physical properties of the machine tool will be obtained in the initial stage. Finally, the technology of design for control will be carried out to modify the structural and control parameters to achieve overall system performance. Hence, the corresponding productivity is expected to be greatly improved.

Keywords: Machine tools, integrated structure and control design, design for control, multilevel decomposition, quantitative feedback theory.

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Authors: M. Stewart, Ameer Al-Khaykan, J. M. Counsell

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Emissions are a consequence of electricity generation. A major option for low carbon generation, local energy systems featuring Combined Heat and Power with solar PV (CHPV) has significant potential to increase energy performance, increase resilience, and offer greater control of local energy prices while complementing the UK’s emissions standards and targets. Recent advances in dynamic modelling and simulation of buildings and clusters of buildings using the IDEAS framework have successfully validated a novel multi-vector (simultaneous control of both heat and electricity) approach to integrating the wide range of primary and secondary plant typical of local energy systems designs including CHP, solar PV, gas boilers, absorption chillers and thermal energy storage, and associated electrical and hot water networks, all operating under a single unified control strategy. Results from this work indicate through simulation that integrated control of thermal storage can have a pivotal role in optimizing system performance well beyond the present expectations. Environmental impact analysis and reporting of all energy systems including CHPV LES presently employ a static annual average carbon emissions intensity for grid supplied electricity. This paper focuses on establishing and validating CHPV environmental performance against conventional emissions values and assessment benchmarks to analyze emissions performance without and with an active thermal store in a notional group of non-domestic buildings. Results of this analysis are presented and discussed in context of performance validation and quantifying the reduced environmental impact of CHPV systems with active energy storage in comparison with conventional LES designs.

Keywords: CHPV, thermal storage, control, dynamic simulation.

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Authors: Martin Grootveld, Benita Percival, Sarah Moumtaz, Kerry L. Grootveld

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Objectives/Hypotheses: The adverse health effect potential of dietary lipid oxidation products (LOPs) has evoked much clinical interest. Therefore, we employed a ¹H NMR-linked Principal Component Regression (PCR) chemometrics modelling strategy to explore relationships between data matrices comprising (1) aldehydic LOP concentrations generated in culinary oils/fats when exposed to laboratory-simulated shallow frying practices, and (2) the prior saturated (SFA), monounsaturated (MUFA) and polyunsaturated fatty acid (PUFA) contents of such frying media (FM), together with their heating time-points at a standard frying temperature (180 ^oC). Methods: Corn, sunflower, extra virgin olive, rapeseed, linseed, canola, coconut and MUFA-rich algae frying oils, together with butter and lard, were heated according to laboratory-simulated shallow-frying episodes at 180 ^oC, and FM samples were collected at time-points of 0, 5, 10, 20, 30, 60, and 90 min. (n = 6 replicates per sample). Aldehydes were determined by ¹H NMR analysis (Bruker AV 400 MHz spectrometer). The first (dependent output variable) PCR data matrix comprised aldehyde concentration scores vectors (PC1* and PC2*), whilst the second (predictor) one incorporated those from the fatty acid content/heating time variables (PC1-PC4) and their first-order interactions. Results: Structurally complex trans,trans- and cis,trans-alka-2,4-dienals, 4,5-epxy-trans-2-alkenals and 4-hydroxy-/4-hydroperoxy-trans-2-alkenals (group I aldehydes predominantly arising from PUFA peroxidation) strongly and positively loaded on PC1*, whereas n-alkanals and trans-2-alkenals (group II aldehydes derived from both MUFA and PUFA hydroperoxides) strongly and positively loaded on PC2*. PCR analysis of these scores vectors (SVs) demonstrated that PCs 1 (positively-loaded linoleoylglycerols and [linoleoylglycerol]:[SFA] content ratio), 2 (positively-loaded oleoylglycerols and negatively-loaded SFAs), 3 (positively-loaded linolenoylglycerols and [PUFA]:[SFA] content ratios), and 4 (exclusively orthogonal sampling time-points) all powerfully contributed to aldehydic PC1* SVs (p 10^-3 to < 10^-9), as did all PC1-3 x PC4 interaction ones (p 10^-5 to < 10^-9). PC2* was also markedly dependent on all the above PC SVs (PC2 > PC1 and PC3), and the interactions of PC1 and PC2 with PC4 (p < 10^-9 in each case), but not the PC3 x PC4 contribution. Conclusions: NMR-linked PCR analysis is a valuable strategy for (1) modelling the generation of aldehydic LOPs in heated cooking oils and other FM, and (2) tracking their unsaturated fatty acid (UFA) triacylglycerol sources therein.

Keywords: Frying oils, frying episodes, lipid oxidation products, cytotoxic/genotoxic aldehydes, chemometrics, principal component regression, NMR Analysis.

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Authors: Dinesh Acharya U., N. V. Subba Reddy, Krishnamoorthi Makkithaya

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The recognition of handwritten numeral is an important area of research for its applications in post office, banks and other organizations. This paper presents automatic recognition of handwritten Kannada numerals based on structural features. Five different types of features, namely, profile based 10-segment string, water reservoir; vertical and horizontal strokes, end points and average boundary length from the minimal bounding box are used in the recognition of numeral. The effect of each feature and their combination in the numeral classification is analyzed using nearest neighbor classifiers. It is common to combine multiple categories of features into a single feature vector for the classification. Instead, separate classifiers can be used to classify based on each visual feature individually and the final classification can be obtained based on the combination of separate base classification results. One popular approach is to combine the classifier results into a feature vector and leaving the decision to next level classifier. This method is extended to extract a better information, possibility distribution, from the base classifiers in resolving the conflicts among the classification results. Here, we use fuzzy k Nearest Neighbor (fuzzy k-NN) as base classifier for individual feature sets, the results of which together forms the feature vector for the final k Nearest Neighbor (k-NN) classifier. Testing is done, using different features, individually and in combination, on a database containing 1600 samples of different numerals and the results are compared with the results of different existing methods.

Keywords: Fuzzy k Nearest Neighbor, Multiple Classifiers, Numeral Recognition, Structural features.

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Authors: N. Muthukumaran, R. Ravi

Abstract:

The Simulation based VLSI Implementation of FELICS (Fast Efficient Lossless Image Compression System) Algorithm is proposed to provide the lossless image compression and is implemented in simulation oriented VLSI (Very Large Scale Integrated). To analysis the performance of Lossless image compression and to reduce the image without losing image quality and then implemented in VLSI based FELICS algorithm. In FELICS algorithm, which consists of simplified adjusted binary code for Image compression and these compression image is converted in pixel and then implemented in VLSI domain. This parameter is used to achieve high processing speed and minimize the area and power. The simplified adjusted binary code reduces the number of arithmetic operation and achieved high processing speed. The color difference preprocessing is also proposed to improve coding efficiency with simple arithmetic operation. Although VLSI based FELICS Algorithm provides effective solution for hardware architecture design for regular pipelining data flow parallelism with four stages. With two level parallelisms, consecutive pixels can be classified into even and odd samples and the individual hardware engine is dedicated for each one. This method can be further enhanced by multilevel parallelisms.

Keywords: Image compression, Pixel, Compression Ratio, Adjusted Binary code, Golumb Rice code, High Definition display, VLSI Implementation.

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Authors: J. Urbánek Jiří, Krahulec Josef, Johanidesová Jitka, F. Urbánek Jiří

Abstract:

This paper deals with using of prevailing operation system MS Office (SmartArt...) for mathematical models, using DYVELOP (Dynamic Vector Logistics of Processes) method. It serves for crisis situations investigation and modelling within the organizations of critical infrastructure. In first part of paper, it will be introduced entities, operators, and actors of DYVELOP method. It uses just three operators of Boolean algebra and four types of the entities: the Environments, the Process Systems, the Cases, and the Controlling. The Process Systems (PrS) have five “brothers”: Management PrS, Transformation PrS, Logistic PrS, Event PrS and Operation PrS. The Cases have three “sisters”: Process Cell Case, Use Case, and Activity Case. They all need for the controlling of their functions special Ctrl actors, except ENV – it can do without Ctrl. Model´s maps are named the Blazons and they are able mathematically - graphically express the relationships among entities, actors and processes. In second part of this paper, the rich blazons of DYVELOP method will be used for the discovering and modelling of the cycling cases and their phases. The blazons need live PowerPoint presentation for better comprehension of this paper mission. The crisis management of energetic crisis infrastructure organization is obliged to use the cycles for successful coping of crisis situations. Several times cycling of these cases is necessary condition for the encompassment for both emergency events and the mitigation of organization´s damages. Uninterrupted and continuous cycling process brings for crisis management fruitfulness and it is good indicator and controlling actor of organizational continuity and its sustainable development advanced possibilities. The research reliable rules are derived for the safety and reliable continuity of energetic critical infrastructure organization in the crisis situation.

Keywords: Blazons, computational assistance, DYVELOP method, critical infrastructure.

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Authors: T.R. Sahroni, S. Sulaiman, I. Romli, M.R. Salleh, H.A. Ariff

Abstract:

The thermal expansion behaviour of silicon carbide (SCS-2) fibre reinforced 6061 aluminium matrix composite subjected to the influenced thermal mechanical cycling (TMC) process were investigated. The thermal stress has important effect on the longitudinal thermal expansion coefficient of the composites. The present paper used experimental data of the thermal expansion behaviour of a SiC/Al composite for temperatures up to 370°C, in which their data was used for carrying out modelling of theoretical predictions.

Keywords: Nonlinear, thermal, fibre reinforced, metal matrixcomposites

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Authors: Y. C. Mao, G. S. Chen

Abstract:

Bicycle configuration is not as large as those of motorcycles or automobiles, while it indeed composes a complicated dynamic system. People-s requirements on comfortability, controllability and safety grow higher as the research and development technologies improve. The shock absorber affects the vehicle suspension performances enormously. The absorber takes the vibration energy and releases it at a suitable time, keeping the wheel under a proper contact condition with road surface, maintaining the vehicle chassis stability. Suspension design for mountain bicycles is more difficult than that of city bikes since it encounters dynamic variations on road and loading conditions. Riders need a stiff damper as they exert to tread on the pedals when climbing, while a soft damper when they descend downhill. Various switchable shock absorbers are proposed in markets, however riders have to manually switch them among soft, hard and lock positions. This study proposes a novel design of the bicycle shock absorber, which provides automatic smooth tuning of the damping coefficient, from a predetermined lower bound to theoretically unlimited. An automatic quick releasing valve is involved in this design so that it can release the peak pressure when the suspension fork runs into a square-wave type obstacle and prevent the chassis from damage, avoiding the rider skeleton from injury. This design achieves the automatic tuning process by innovative plunger valve and fluidic passage arrangements without any electronic devices. Theoretical modelling of the damper and spring are established in this study. Design parameters of the valves and fluidic passages are determined. Relations between design parameters and shock absorber performances are discussed in this paper. The analytical results give directions to the shock absorber manufacture.

Keywords: Modelling, Simulation, Bicycle, Shock Absorber, Damping, Releasing Valve

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Authors: B. A. Gobin, R. K. Subramanian

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This paper gives an overview of how an OWL ontology has been created to represent template knowledge models defined in CML that are provided by CommonKADS. CommonKADS is a mature knowledge engineering methodology which proposes the use of template knowledge model for knowledge modelling. The aim of developing this ontology is to present the template knowledge model in a knowledge representation language that can be easily understood and shared in the knowledge engineering community. Hence OWL is used as it has become a standard for ontology and also it already has user friendly tools for viewing and editing.

Keywords: Ontology, OWL, Template Knowledge Models, CommonKADS

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Authors: Mohammad Bsoul, Iain Phillips, Chris Hinde

Abstract:

This paper is concerned with the design and implementation of MICOSim, an event-driven simulator written in Java for evaluating the performance of Grid entities (users, brokers and resources) under different scenarios such as varying the numbers of users, resources and brokers and varying their specifications and employed strategies.

Keywords: Grid computing, Economic Scheduling, Simulation, Event-Driven, Java.

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Authors: Abdulatif A. M. Shaban

Abstract:

Transient simulation of power electronic circuits is of considerable interest to the designer. The switching nature of the devices used permits development of specialized algorithms which allow a considerable reduction in simulation time compared to general purpose simulation algorithms. This paper describes a method used to simulate a power electronic circuits using the SIMULINK toolbox within MATLAB software. Theoretical results are presented provides the basis of transient analysis of a power electronic circuits.

Keywords: Modelling, Simulation.

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Authors: Mourtou Efstratia, Abdel-Badeeh M. Salem, Pavlidis George

Abstract:

Hierarchical high-level PNs (HHPNs) with time versions are a useful tool to model systems in a variety of application domains, ranging from logistics to complex workflows. This paper addresses an application domain which is receiving more and more attention: procedure that arranges the final inpatient charge in payment-s office and their management. We shall prove that Petri net based analysis is able to improve the delays during the procedure, in order that inpatient charges could be more reliable and on time.

Keywords: eHealth, Petri-Nets, Hospital Services, InpatientCharges, Workflow Modeling.

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Authors: Muhammad Farhan, Sayed Faruque, Amr Mohammed, Sami Osman, Omar Al-Jabari, Abdul Almojil

Abstract:

Saudi Arabia in recent years has seen drastic increase in traffic related crashes. With population of over 29 million, Saudi Arabia is considered as a fast growing and emerging economy. The rapid population increase and economic growth has resulted in rapid expansion of transportation infrastructure, which has led to increase in road crashes. Saudi Ministry of Interior reported more than 7,000 people killed and 68,000 injured in 2011 ranking Saudi Arabia to be one of the worst worldwide in traffic safety. The traffic safety issues in the country also result in distress to road users and cause and economic loss exceeding 3.7 billion Euros annually. Keeping this in view, the researchers in Saudi Arabia are investigating ways to improve traffic safety conditions in the country. This paper presents a multilevel approach to collect traffic safety related data required to do traffic safety studies in the region. Two highway corridors including King Fahd Highway 39 kilometre and Gulf Cooperation Council Highway 42 kilometre long connecting the cities of Dammam and Khobar were selected as a study area. Traffic data collected included traffic counts, crash data, travel time data, and speed data. The collected data was analysed using geographic information system to evaluate any correlation. Further research is needed to investigate the effectiveness of traffic safety related data when collected in a concerted effort.

Keywords: Crash Data, Data Collection, Traffic Safety.

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