Search results for: estimated model

Authors: M.K. Bhuyan, Aragala Jagan.

Abstract:

Current image-based individual human recognition methods, such as fingerprints, face, or iris biometric modalities generally require a cooperative subject, views from certain aspects, and physical contact or close proximity. These methods cannot reliably recognize non-cooperating individuals at a distance in the real world under changing environmental conditions. Gait, which concerns recognizing individuals by the way they walk, is a relatively new biometric without these disadvantages. The inherent gait characteristic of an individual makes it irreplaceable and useful in visual surveillance. In this paper, an efficient gait recognition system for human identification by extracting two features namely width vector of the binary silhouette and the MPEG-7-based region-based shape descriptors is proposed. In the proposed method, foreground objects i.e., human and other moving objects are extracted by estimating background information by a Gaussian Mixture Model (GMM) and subsequently, median filtering operation is performed for removing noises in the background subtracted image. A moving target classification algorithm is used to separate human being (i.e., pedestrian) from other foreground objects (viz., vehicles). Shape and boundary information is used in the moving target classification algorithm. Subsequently, width vector of the outer contour of binary silhouette and the MPEG-7 Angular Radial Transform coefficients are taken as the feature vector. Next, the Principal Component Analysis (PCA) is applied to the selected feature vector to reduce its dimensionality. These extracted feature vectors are used to train an Hidden Markov Model (HMM) for identification of some individuals. The proposed system is evaluated using some gait sequences and the experimental results show the efficacy of the proposed algorithm.

Keywords: Gait Recognition, Gaussian Mixture Model, PrincipalComponent Analysis, MPEG-7 Angular Radial Transform.

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Authors: K. D. R. Jagath-Kumara, D. D. Dias

Abstract:

The sea waves carry thousands of GWs of power globally. Although there are a number of different approaches to harness offshore energy, they are likely to be expensive, practically challenging, and vulnerable to storms. Therefore, this paper considers using the near shore waves for generating mechanical and electrical power. It introduces two new approaches, the wave manipulation and using a variable duct turbine, for intercepting very wide wave fronts and coping with the fluctuations of the wave height and the sea level, respectively. The first approach effectively allows capturing much more energy yet with a much narrower turbine rotor. The second approach allows using a rotor with a smaller radius but captures energy of higher wave fronts at higher sea levels yet preventing it from totally submerging. To illustrate the effectiveness of the first approach, the paper contains a description and the simulation results of a scale model of a wave manipulator. Then, it includes the results of testing a physical model of the manipulator and a single duct, axial flow turbine in a wave flume in the laboratory. The paper also includes comparisons of theoretical predictions, simulation results, and wave flume tests with respect to the incident energy, loss in wave manipulation, minimal loss, brake torque, and the angular velocity.

Keywords: Near-shore sea waves, Renewable energy, Wave energy conversion, Wave manipulation.

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Authors: Radhe Tado, Ashish B. Deoghare, K. M. Pandey

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The aim of this study is to estimate the effect of blood flow through the coronary artery in human heart so as to assess the coronary artery disease.Velocity, wall shear stress (WSS), strain rate and wall pressure distribution are some of the important hemodynamic parameters that are non-invasively assessed with computational fluid dynamics (CFD). These parameters are used to identify the mechanical factors responsible for the plaque progression and/or rupture in left coronary arteries (LCA) in coronary arteries.The initial step for CFD simulations was the construction of a geometrical model of the LCA. Patient specific artery model is constructed using computed tomography (CT) scan data with the help of MIMICS Research 19.0. For CFD analysis ANSYS FLUENT-14.5 is used.Hemodynamic parameters were quantified and flow patterns were visualized both in the absence and presence of coronary plaques. The wall pressure continuously decreased towards distal segments and showed pressure drops in stenotic segments. Areas of high WSS and high flow velocities were found adjacent to plaques deposition.

Keywords: Computational fluid dynamics, hemodynamics, velocity, strain rate, wall pressure, wall shear stress.

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Authors: Ziraguen O. Williams, Shield B. Lin, Fouad N. Matari, Leslie J. Quiocho

Abstract:

In a task to assist NASA in analyzing the dynamic forces caused by operational countermeasures of an astronaut’s exercise platform impacting the spacecraft, feedback delay and signal noise were added to a simulation model of an active controlled vibration isolation and stabilization system to regulate the movement of the exercise platform. Two additional simulation tools used in this study were Trick and MBDyn, software simulation environments developed at the NASA Johnson Space Center. Simulation results obtained from these three tools were very similar. All simulation results support the hypothesis that an active controlled vibration isolation and stabilization system outperforms a passive controlled system even with the addition of feedback delay and signal noise to the active controlled system. In this paper, squat exercise was used in creating excited force to the simulation model. The exciter force from squat exercise was calculated from motion capture of an exerciser. The simulation results demonstrate much greater transmitted force reduction in the active controlled system than the passive controlled system.

Keywords: Astronaut, counterweight, stabilization, vibration.

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Authors: Fazl Ullah, Rahmat Ullah

Abstract:

This paper shows a comprehensive learning focused on the optimization of gas production in shale gas reservoirs through hydraulic fracturing. Shale gas has emerged as an important unconventional vigor resource, necessitating innovative techniques to enhance its extraction. The key objective of this study is to examine the influence of fracture parameters on reservoir productivity and formulate strategies for production optimization. A sophisticated model integrating gas flow dynamics and real stress considerations is developed for hydraulic fracturing in multi-stage shale gas reservoirs. This model encompasses distinct zones: a single-porosity medium region, a dual-porosity average region, and a hydraulic fracture region. The apparent permeability of the matrix and fracture system is modeled using principles like effective stress mechanics, porous elastic medium theory, fractal dimension evolution, and fluid transport apparatuses. The developed model is then validated using field data from the Barnett and Marcellus formations, enhancing its reliability and accuracy. By solving the partial differential equation by means of COMSOL software, the research yields valuable insights into optimal fracture parameters. The findings reveal the influence of fracture length, diversion capacity, and width on gas production. For reservoirs with higher permeability, extending hydraulic fracture lengths proves beneficial, while complex fracture geometries offer potential for low-permeability reservoirs. Overall, this study contributes to a deeper understanding of hydraulic cracking dynamics in shale gas reservoirs and provides essential guidance for optimizing gas production. The research findings are instrumental for energy industry professionals, researchers, and policymakers alike, shaping the future of sustainable energy extraction from unconventional resources.

Keywords: Fluid-solid coupling, apparent permeability, shale gas reservoir, fracture property, numerical simulation.

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Authors: Mohammad Abdallah

Abstract:

Due to the constant increase in terrorist attacks, the research and engineering communities have given significant attention to building performance under explosions. This paper presents a methodology for studying and simulating the dynamic responses of steel structures during external detonations, particularly for accurately investigating the impact of incrementing charge weight on the members total behavior, resistance and failure. Prediction damage method was introduced to evaluate the damage level of the steel members based on five scenarios of explosions. Johnson–Cook strength and failure model have been used as well as ABAQUS finite element code to simulate the explicit dynamic analysis, and antecedent field tests were used to verify the acceptance and accuracy of the proposed material strength and failure model. Based on the structural response, evaluation criteria such as deflection, vertical displacement, drift index, and damage level; the obtained results show the vulnerability of steel columns and un-braced steel frames which are designed and optimized to carry dead and live load to resist and endure blast loading.

Keywords: Steel structure, blast load, terrorist attacks, charge weight, damage level.

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Authors: Youcef Bekakra, Djilani Ben attous

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In this paper, a field oriented control (FOC) induction motor drive is presented. In order to eliminate the speed sensor, an adaptation algorithm for tuning the rotor speed is proposed. Based on the Model Reference Adaptive System (MRAS) scheme, the rotor speed is tuned to obtain an exact FOC induction motor drive. The reference and adjustable models, developed in stationary stator reference frame, are used in the MRAS scheme to estimate induction rotor speed from measured terminal voltages and currents. The Integral Proportional (IP) gains speed controller are tuned by a modern approach that is the Particle Swarm Optimization (PSO) algorithm in order to optimize the parameters of the IP controller. The use of PSO as an optimization algorithm makes the drive robust, with faster dynamic response, higher accuracy and insensitive to load variation. The proposed algorithm has been tested by numerical simulation, showing the capability of driving load.

Keywords: Induction motor drive, field oriented control, model reference adaptive system (MRAS), particle swarm optimization (PSO).

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Authors: Sarantos Psycharis

Abstract:

Physical Computing, as an instructional model, is applied in the framework of the Engineering Pedagogy to teach “transversal/cross-cutting ideas” in a STEM content approach. Labview and Arduino were used in order to connect the physical world with real data in the framework of the so called Computational Experiment. Tertiary prospective engineering educators were engaged during their course and Computational Thinking (CT) concepts were registered before and after the intervention across didactic activities using validated questionnaires for the relationship between self-efficacy, computer programming, and CT concepts when STEM content epistemology is implemented in alignment with the Computational Pedagogy model. Results show a significant change in students’ responses for self-efficacy for CT before and after the instruction. Results also indicate a significant relation between the responses in the different CT concepts/practices. According to the findings, STEM content epistemology combined with Physical Computing should be a good candidate as a learning and teaching approach in university settings that enhances students’ engagement in CT concepts/practices.

Keywords: STEM, computational thinking, physical computing, Arduino, Labview, self-efficacy.

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Authors: Christian H. Sanabria-Montaña, Rodrigo Huerta-Quintanilla

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A lattice network is a special type of network in which all nodes have the same number of links, and its boundary conditions are periodic. The most basic lattice network is the ring, a one-dimensional network with periodic border conditions. In contrast, the Cartesian product of d rings forms a d-dimensional lattice network. An analytical expression currently exists for the clustering coefficient in this type of network, but the theoretical value is valid only up to certain connectivity value; in other words, the analytical expression is incomplete. Here we obtain analytically the clustering coefficient expression in d-dimensional lattice networks for any link density. Our analytical results show that the clustering coefficient for a lattice network with density of links that tend to 1, leads to the value of the clustering coefficient of a fully connected network. We developed a model on criminology in which the generalized clustering coefficient expression is applied. The model states that delinquents learn the know-how of crime business by sharing knowledge, directly or indirectly, with their friends of the gang. This generalization shed light on the network properties, which is important to develop new models in different fields where network structure plays an important role in the system dynamic, such as criminology, evolutionary game theory, econophysics, among others.

Keywords: Clustering coefficient, criminology, generalized, regular network d-dimensional.

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Authors: Sharan Badiger, Prema T. Akkasaligar, Sandeep HM, Biradar MS

Abstract:

Essential hypertension (HTN) usually clusters with other cardiovascular risk factors such as age, overweight, diabetes, insulin resistance and dyslipidemia. The target organ damage (TOD) such as left ventricular hypertrophy, microalbuminuria (MA), acute coronary syndrome (ACS), stroke and cognitive dysfunction takes place early in course of hypertension. Though the prevalence of hypertension is high in India, the relationship between microalbuminuria and target organ damage in hypertension is not well studied. This study aim at detecting MA in essential hypertension and its relation to severity of HTN, duration of HTN, body mass index (BMI), age and TOD such as HTN retinopathy and acute coronary syndrome The present study was done in 100 patients of essential hypertension non diabetics admitted to B.L.D.E.University-s Sri B.M.Patil Medical College, Bijapur, from October 2008 to April 2011. The patients underwent detailed history and clinical examination. Early morning 5 ml of urine sample was collected & MA was estimated by immunoturbidometry method. The relationship of MA with the duration & severity of HTN, BMI, age, sex and TOD's like hypertensive retinopathy, ACS was assessed by univariate analysis. The prevalence of MA in this study was found to be 63 %. In that 42% were male & 21% were female. In this study a significant association between MA and the duration of hypertension (p = 0.036) & (OR =0.438). Longer the duration of hypertension, more possibility of microalbumin in urine. Also there was a significant association between severity of hypertension and MA (p=0.045) and (OR=0.093). MA was positive in 50 (79.4%) patients out of 63, whose blood pressure was >160/100 mm Hg. In this study a significant association between MA and the grades of hypertensive retinopathy (p =0.011) and acute coronary syndrome (p = 0.041) (OR =2.805). Gender and BMI did not pose high risk for MA in this study.The prevalence of MA in essential hypertension is high in this part of the community and MA will increase the risk of developing target organ damage.Early screening of patients with essential hypertension for MA and aggressive management of positive cases might reduce the burden of chronic kidney diseases and cardiovascular diseases in the community.

Keywords: Acute coronary syndrome, Essential hypertension, Microalbuminuria, Target organ damage

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Authors: Manoj Kumar, A.K. Verma, A. Srividya

Abstract:

Time varying network induced delays in networked control systems (NCS) are known for degrading control system-s quality of performance (QoP) and causing stability problems. In literature, a control method employing modeling of communication delays as probability distribution, proves to be a better method. This paper focuses on modeling of network induced delays as probability distribution. CAN and MIL-STD-1553B are extensively used to carry periodic control and monitoring data in networked control systems. In literature, methods to estimate only the worst-case delays for these networks are available. In this paper probabilistic network delay model for CAN and MIL-STD-1553B networks are given. A systematic method to estimate values to model parameters from network parameters is given. A method to predict network delay in next cycle based on the present network delay is presented. Effect of active network redundancy and redundancy at node level on network delay and system response-time is also analyzed.

Keywords: NCS (networked control system), delay analysis, response-time distribution, worst-case delay, CAN, MIL-STD-1553B, redundancy

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Authors: Jahedul Islam Chowdhury, Bao Kha Nguyen, David Thornhill, Roy Douglas, Stephen Glover

Abstract:

Organic Rankine Cycle (ORC) is the most commonly used method for recovering energy from small sources of heat. The investigation of the ORC in supercritical condition is a new research area as it has a potential to generate high power and thermal efficiency in a waste heat recovery system. This paper presents a steady state ORC model in supercritical condition and its simulations with a real engine’s exhaust data. The key component of ORC, evaporator, is modelled using finite volume method, modelling of all other components of the waste heat recovery system such as pump, expander and condenser are also presented. The aim of this paper is to investigate the effects of mass flow rate and evaporator outlet temperature on the efficiency of the waste heat recovery process. Additionally, the necessity of maintaining an optimum evaporator outlet temperature is also investigated. Simulation results show that modification of mass flow rate is the key to changing the operating temperature at the evaporator outlet.

Keywords: Organic Rankine cycle, supercritical condition, steady state model, waste heat recovery.

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Authors: S. Khali, R. Nebbali, K. Bouhadef

Abstract:

A numerical investigation is performed for non Newtonian fluids flow between two concentric cylinders. The D2Q9 lattice Boltzmann model developed from the Bhatangar-Gross-Krook (LBGK) approximation is used to obtain the flow field for fluids obeying to the power-law model. The inner and outer cylinders rotate in the same and the opposite direction while the end walls are maintained at rest. The combined effects of the Reynolds number (Re) of the inner and outer cylinders, the radius ratio (η) as well as the power-law index (n) on the flow characteristics are analyzed for an annular space of a finite aspect ratio (Γ). Two flow modes are obtained: a primary mode (laminar stable regime) and a secondary mode (laminar unstable regime). The so obtained flow structures are different from one mode to another. The transition critical Reynolds number Re_c from the primary to the secondary mode is analyzed for the co-courant and counter-courant flows. This critical value increases as n increases. The prediction of the swirling flow of non Newtonians fluids in axisymmetric geometries is shown in the present work.

Keywords: Taylor-Couette flows, non Newtonian fluid, Lattice Boltzmann method.

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Authors: Wadii Boulila, Imed Riadh Farah, Karim Saheb Ettabaa, Basel Solaiman, Henda Ben Ghezala

Abstract:

This paper investigates the problem of tracking spa¬tiotemporal changes of a satellite image through the use of Knowledge Discovery in Database (KDD). The purpose of this study is to help a given user effectively discover interesting knowledge and then build prediction and decision models. Unfortunately, the KDD process for spatiotemporal data is always marked by several types of imperfections. In our paper, we take these imperfections into consideration in order to provide more accurate decisions. To achieve this objective, different KDD methods are used to discover knowledge in satellite image databases. Each method presents a different point of view of spatiotemporal evolution of a query model (which represents an extracted object from a satellite image). In order to combine these methods, we use the evidence fusion theory which considerably improves the spatiotemporal knowledge discovery process and increases our belief in the spatiotemporal model change. Experimental results of satellite images representing the region of Auckland in New Zealand depict the improvement in the overall change detection as compared to using classical methods.

Keywords: Knowledge discovery in satellite databases, knowledge fusion, data imperfection, data mining, spatiotemporal change detection.

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Authors: Mahmoud Hassanlourad, Mehran Naghizadehrokni, Vahid Molaei

Abstract:

In this study, the seismic behavior of a shallow tunnel with square cross section is investigated in a two layered and elastic heterogeneous environment using numerical method. To do so, FLAC finite difference software was used. Behavioral model of the ground and tunnel structure was assumed linear elastic. Dynamic load was applied to the model for 0.2 seconds from the bottom in form of a square pulse with maximum acceleration of 1 m/s². The interface between the two layers was considered at three different levels of crest, middle, and bottom of the tunnel. The stiffness of the two upper and lower layers was considered to be varied from 10 MPa to 1000 MPa. Deformation of cross section of the tunnel due to dynamic load propagation, as well as the values of axial force and bending moment created in the tunnel structure, were examined in the three states mentioned above. The results of analyses show that heterogeneity of the environment, its stratification, and positioning of the interface of the two layers with respect to tunnel height and the stiffness ratio of the two layers have significant effects on the value of bending moment, axial force, and distortion of tunnel cross-section.

Keywords: Dynamic analysis, shallow-buried tunnel, two-layered ground.

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Authors: S. Nandagopalan, N. Pradeep

Abstract:

The aim of this paper is to propose a general framework for storing, analyzing, and extracting knowledge from two-dimensional echocardiographic images, color Doppler images, non-medical images, and general data sets. A number of high performance data mining algorithms have been used to carry out this task. Our framework encompasses four layers namely physical storage, object identification, knowledge discovery, user level. Techniques such as active contour model to identify the cardiac chambers, pixel classification to segment the color Doppler echo image, universal model for image retrieval, Bayesian method for classification, parallel algorithms for image segmentation, etc., were employed. Using the feature vector database that have been efficiently constructed, one can perform various data mining tasks like clustering, classification, etc. with efficient algorithms along with image mining given a query image. All these facilities are included in the framework that is supported by state-of-the-art user interface (UI). The algorithms were tested with actual patient data and Coral image database and the results show that their performance is better than the results reported already.

Keywords: Active Contour, Bayesian, Echocardiographic image, Feature vector.

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Authors: Sh. Kianfar, A. Moheb, H. Ghaforian

Abstract:

The equilibrium, thermodynamics and kinetics of the biosorption of Cd (II) and Pb(II) by a Spore Forming Bacillus (MGL 75) were investigated at different experimental conditions. The Langmuir and Freundlich, and Dubinin-Radushkevich (D-R) equilibrium adsorption models were applied to describe the biosorption of the metal ions by MGL 75 biomass. The Langmuir model fitted the equilibrium data better than the other models. Maximum adsorption capacities q max for lead (II) and cadmium (II) were found equal to 158.73mg/g and 91.74 mg/g by Langmuir model. The values of the mean free energy determined with the D-R equation showed that adsorption process is a physiosorption process. The thermodynamic parameters Gibbs free energy (ΔG°), enthalpy (ΔH°), and entropy (ΔS°) changes were also calculated, and the values indicated that the biosorption process was exothermic and spontaneous. Experiment data were also used to study biosorption kinetics using pseudo-first-order and pseudo-second-order kinetic models. Kinetic parameters, rate constants, equilibrium sorption capacities and related correlation coefficients were calculated and discussed. The results showed that the biosorption processes of both metal ions followed well pseudo-second-order kinetics.

Keywords: biosorption, kinetics, Metal ion removal, thermodynamics

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Authors: Hamid Goharnejad, Amir Hossein Eghbali

Abstract:

Recent investigations have demonstrated the global sea level rise due to climate change impacts. In this study, climate changes study the effects of increasing water level in the strait of Hormuz. The probable changes of sea level rise should be investigated to employ the adaption strategies. The climatic output data of a GCM (General Circulation Model) named CGCM3 under climate change scenario of A1b and A2 were used. Among different variables simulated by this model, those of maximum correlation with sea level changes in the study region and least redundancy among themselves were selected for sea level rise prediction by using stepwise regression. One of models (Discrete Wavelet artificial Neural Network) was developed to explore the relationship between climatic variables and sea level changes. In these models, wavelet was used to disaggregate the time series of input and output data into different components and then ANN was used to relate the disaggregated components of predictors and input parameters to each other. The results showed in the Shahid Rajae Station for scenario A1B sea level rise is among 64 to 75 cm and for the A2 Scenario sea level rise is among 90 t0 105 cm. Furthermore, the result showed a significant increase of sea level at the study region under climate change impacts, which should be incorporated in coastal areas management.

Keywords: Climate change scenarios, sea-level rise, strait of Hormuz, artificial neural network, fuzzy logic.

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Authors: V. Agrawal, A. Sharma

Abstract:

High Strength Concrete (HSC) is defined as concrete that meets special combination of performance and uniformity requirements that cannot be achieved routinely using conventional constituents and normal mixing, placing, and curing procedures. It is a highly complex material, which makes modeling its behavior a very difficult task. This paper aimed to show possible applicability of Neural Networks (NN) to predict the slump in High Strength Concrete (HSC). Neural Network models is constructed, trained and tested using the available test data of 349 different concrete mix designs of High Strength Concrete (HSC) gathered from a particular Ready Mix Concrete (RMC) batching plant. The most versatile Neural Network model is selected to predict the slump in concrete. The data used in the Neural Network models are arranged in a format of eight input parameters that cover the Cement, Fly Ash, Sand, Coarse Aggregate (10 mm), Coarse Aggregate (20 mm), Water, Super-Plasticizer and Water/Binder ratio. Furthermore, to test the accuracy for predicting slump in concrete, the final selected model is further used to test the data of 40 different concrete mix designs of High Strength Concrete (HSC) taken from the other batching plant. The results are compared on the basis of error function (or performance function).

Keywords: Artificial Neural Networks, Concrete, prediction ofslump, slump in concrete

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Authors: S. Karabulut, A. Güllü, A. Güldas, R. Gürbüz

Abstract:

This study investigates the effects of the lead angle and chip thickness variation on surface roughness during the machining of compacted graphite iron using ceramic cutting tools under dry cutting conditions. Analytical models were developed for predicting the surface roughness values of the specimens after the face milling process. Experimental data was collected and imported to the artificial neural network model. A multilayer perceptron model was used with the back propagation algorithm employing the input parameters of lead angle, cutting speed and feed rate in connection with chip thickness. Furthermore, analysis of variance was employed to determine the effects of the cutting parameters on surface roughness. Artificial neural network and regression analysis were used to predict surface roughness. The values thus predicted were compared with the collected experimental data, and the corresponding percentage error was computed. Analysis results revealed that the lead angle is the dominant factor affecting surface roughness. Experimental results indicated an improvement in the surface roughness value with decreasing lead angle value from 88° to 45°.

Keywords: CGI, milling, surface roughness, ANN, regression, modeling, analysis.

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Authors: Zahra Ramezanpanah, Joachim Carvallo, Aurelien Rodriguez

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This paper aims to detect anomalies in streaming data using machine learning algorithms. In this regard, we designed two separate pipelines and evaluated the effectiveness of each separately. The first pipeline, based on supervised machine learning methods, consists of two phases. In the first phase, we trained several supervised models using the UNSW-NB15 data set. We measured the efficiency of each using different performance metrics and selected the best model for the second phase. At the beginning of the second phase, we first, using Argus Server, sniffed a local area network. Several types of attacks were simulated and then sent the sniffed data to a running algorithm at short intervals. This algorithm can display the results of each packet of received data in real-time using the trained model. The second pipeline presented in this paper is based on unsupervised algorithms, in which a Temporal Graph Network (TGN) is used to monitor a local network. The TGN is trained to predict the probability of future states of the network based on its past behavior. Our contribution in this section is introducing an indicator to identify anomalies from these predicted probabilities.

Keywords: Cyber-security, Intrusion Detection Systems, Temporal Graph Network, Anomaly Detection.

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Authors: T. Sakamoto, S. Kainuma

Abstract:

Firstly, in order to examine the influence of the remaining amount of the rust on the coating film durability, the accelerated deterioration tests were carried out. In order to prepare test specimens, uncoated steel plates were corroded by the Salt Spray Test (SST) prior to the accelerated deterioration tests, and then the prepared test specimens were coated by epoxy resin and phthalic acid resin each of which has different gas-barrier performance. As the result, it was confirmed that the under-film corrosion occurred in the area and the adjacency to great quantities of salt exists in the rust, and did not occurred in the specimen which was applied the epoxy resin paint after the surface preparation by the power tool. Secondly, in order to clarify the influence of the corrosive factors on the coating film durability, outdoor exposure tests were conducted for one year on actual steel bridge located at a coastal area. The tests specimens consist of coated corroded plates and the uncoated steel plates, and they were installed on the different structural members of the bridge for one year. From the test results, the uncoated steel plates which were installed on the underside of the member are easily corrosive and had highly correlation with the amount of salt in the rust. On the other hand, the most corrosive under-film steel was the vertical surface of the web plate. Thus, it was confirmed that under-film corrosion rate was not match with corrosion rate of the uncoated steel. Consequently, it is estimated that the main factors of under-film corrosion are gas-barrier property of coating film and corrosive factors such as water vapor and temperature. The salt which significantly corrodes the uncoated steel plate is not directly related to the under-film corrosion.

Keywords: Accelerated deterioration test, Coating durability, Environmental factor, Under-film corrosion.

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Authors: Hesam Sedaghat Nejad, Navid Hosseini, Arash Nikvar Hassani

Abstract:

Determination of the optimum block size with high profitability is one of the significant parameters in designation of the building stone mines. The aim of this study was to determine the optimum dimensions of building stone blocks in Delichai travertine mine of Damavand in Tehran province through combining the effective parameters proven in determination of the optimum dimensions in building stones such as the spacing of joints and gaps, extraction tools constraints with the help of modeling by Gemcom software. To this end, following simulation of the topography of the mine, the block model was prepared and then in order to use spacing joints and discontinuities as a limiting factor, the existing joints set was added to the model. Since only one almost horizontal joint set with a slope of 5 degrees was available, this factor was effective only in determining the optimum height of the block, and thus to determine the longitudinal and transverse optimum dimensions of the extracted block, the power of available loader in the mine was considered as the secondary limiting factor. According to the aforementioned factors, the optimal block size in this mine was measured as 3.4×4×7 meter.

Keywords: Building stone, optimum block size, Delichai Travertine Mine, loader power.

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Authors: Mohammad Ahmad, Sander Gersen, Erwin Wilbers

Abstract:

Carbon capture, transport and underground storage have become a major solution to reduce CO₂ emissions from power plants and other large CO₂ sources. A big part of this captured CO₂ stream is transported at high pressure dense phase conditions and stored in offshore underground depleted oil and gas fields. CO₂ is also transported in offshore pipelines to be used for enhanced oil and gas recovery. The captured CO₂ stream with impurities may contain water that causes severe corrosion problems, flow assurance failure and might damage valves and instrumentations. Thus, free water formation should be strictly prevented. The purpose of this work is to study the solubility of water in pure CO₂ and in CO₂ mixtures under real pipeline pressure (90-150 bar) and temperature operation conditions (5-35°C). A set up was constructed to generate experimental data. The results show the solubility of water in CO₂ mixtures increasing with the increase of the temperature or/and with the increase in pressure. A drop in water solubility in CO₂ is observed in the presence of impurities. The data generated were then used to assess the capabilities of two mixture models: the GERG-2008 model and the EOS-CG model. By generating the solubility data, this study contributes to determine the maximum allowable water content in CO₂ pipelines.

Keywords: Carbon capture and storage, water solubility, equation of states.

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Authors: V. Ngamaramvaranggul, S. Thenissara

Abstract:

The simulation of extrusion process is studied widely in order to both increase products and improve quality, with broad application in wire coating. The annular tube-tooling extrusion was set up by a model that is termed as Navier-Stokes equation in addition to a rheological model of differential form based on singlemode exponential Phan-Thien/Tanner constitutive equation in a twodimensional cylindrical coordinate system for predicting the contraction point of the polymer melt beyond the die. Numerical solutions are sought through semi-implicit Taylor-Galerkin pressurecorrection finite element scheme. The investigation was focused on incompressible creeping flow with long relaxation time in terms of Weissenberg numbers up to 200. The isothermal case was considered with surface tension effect on free surface in extrudate flow and no slip at die wall. The Stream Line Upwind Petrov-Galerkin has been proposed to stabilize solution. The structure of mesh after die exit was adjusted following prediction of both top and bottom free surfaces so as to keep the location of contraction point around one unit length which is close to experimental results. The simulation of extrusion process is studied widely in order to both increase products and improve quality, with broad application in wire coating. The annular tube-tooling extrusion was set up by a model that is termed as Navier-Stokes equation in addition to a rheological model of differential form based on single-mode exponential Phan- Thien/Tanner constitutive equation in a two-dimensional cylindrical coordinate system for predicting the contraction point of the polymer melt beyond the die. Numerical solutions are sought through semiimplicit Taylor-Galerkin pressure-correction finite element scheme. The investigation was focused on incompressible creeping flow with long relaxation time in terms of Weissenberg numbers up to 200. The isothermal case was considered with surface tension effect on free surface in extrudate flow and no slip at die wall. The Stream Line Upwind Petrov-Galerkin has been proposed to stabilize solution. The structure of mesh after die exit was adjusted following prediction of both top and bottom free surfaces so as to keep the location of contraction point around one unit length which is close to experimental results.

Keywords: wire coating, free surface, tube-tooling, extrudate swell, surface tension, finite element method.

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Authors: Ngono Mindzeng Terencia

Abstract:

Community based tourism, as a sustainable tourism approach, has been adopted as a tool for development among local communities in third world countries with income generation as the main driver. However, an analysis of community based tourism and development brings to light another driving force which is paramount to development strategies in the difficult conditions of third world countries: this driving force is “place revitalization”. This paper seeks to assess the relevance of “place revitalization” to the enhancement of development within the challenging context of developing countries. The research provides a community based tourism model to development in third world countries through a three step process based on awareness, mentoring and empowerment at the local level. It also tries to examine how effectively this model can address the development problems faced by the local communities of third world countries. The case study for this research is the Bamiléké region of Cameroon, the breeding ground of community based tourism initiatives and a region facing the difficulties of third world countries that are great impediments to community based tourism.

Keywords: Awareness, empowerment, local communities, mentoring, place revitalization, third world countries.

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Authors: Humayun Murshed, Shibly Abdullah

Abstract:

The research conducted in early seventies apparently assumed the existence of a universal decision model for union negotiators and furthermore tended to regard financial information as a ‘neutral’ input into a rational decision making process. However, research in the eighties began to question the neutrality of financial information as an input in collective bargaining rather viewing it as a potentially effective means for controlling the labour force. Furthermore, this later research also started challenging the simplistic assumptions relating particularly to union objectives which have underpinned the earlier search for universal union decision models. Despite the above developments there seems to be a dearth of studies in developing countries concerning the use of financial information in collective bargaining. This paper seeks to begin to remedy this deficiency. Utilising a case study approach based on two enterprises, one in the public sector and the other a multinational, the universal decision model is rejected and it is argued that the decision whether or not to use financial information is a contingent one and such a contingency is largely defined by the context and environment in which both union and management negotiators work. An attempt is also made to identify the factors constraining as well as promoting the use of financial information in collective bargaining, these being regarded as unique to the organisations within which the case studies are conducted.

Keywords: Collective Bargaining, Developing Countries, Disclosures, Financial Information.

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Authors: Zone-Ching Lin, Meng-Hua Lin, Ying-Chih Hsu

Abstract:

This paper uses quasi-steady molecular statics model and diamond tool to carry out simulation temperature rise of nanoscale orthogonal cutting single-crystal silicon. It further qualitatively analyzes temperature field of silicon workpiece without considering heat transfer and considering heat transfer. This paper supposes that the temperature rise of workpiece is mainly caused by two heat sources: plastic deformation heat and friction heat. Then, this paper develops a theoretical model about production of the plastic deformation heat and friction heat during nanoscale orthogonal cutting. After the increased temperature produced by these two heat sources are added up, the acquired total temperature rise at each atom of the workpiece is substituted in heat transfer finite difference equation to carry out heat transfer and calculates the temperature field in each step and makes related analysis.

Keywords: Quasi-steady molecular statics, Nanoscale orthogonal cutting, Finite difference, Temperature.

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Authors: Insaf Ajili, Malik Mallem, Jean-Yves Didier

Abstract:

Human motion recognition has been extensively increased in recent years due to its importance in a wide range of applications, such as human-computer interaction, intelligent surveillance, augmented reality, content-based video compression and retrieval, etc. However, it is still regarded as a challenging task especially in realistic scenarios. It can be seen as a general machine learning problem which requires an effective human motion representation and an efficient learning method. In this work, we introduce a descriptor based on Laban Movement Analysis technique, a formal and universal language for human movement, to capture both quantitative and qualitative aspects of movement. We use Discrete Hidden Markov Model (DHMM) for training and classification motions. We improve the classification algorithm by proposing two DHMMs for each motion class to process the motion sequence in two different directions, forward and backward. Such modification allows avoiding the misclassification that can happen when recognizing similar motions. Two experiments are conducted. In the first one, we evaluate our method on a public dataset, the Microsoft Research Cambridge-12 Kinect gesture data set (MSRC-12) which is a widely used dataset for evaluating action/gesture recognition methods. In the second experiment, we build a dataset composed of 10 gestures(Introduce yourself, waving, Dance, move, turn left, turn right, stop, sit down, increase velocity, decrease velocity) performed by 20 persons. The evaluation of the system includes testing the efficiency of our descriptor vector based on LMA with basic DHMM method and comparing the recognition results of the modified DHMM with the original one. Experiment results demonstrate that our method outperforms most of existing methods that used the MSRC-12 dataset, and a near perfect classification rate in our dataset.

Keywords: Human Motion Recognition, Motion representation, Laban Movement Analysis, Discrete Hidden Markov Model.

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Authors: Eric P. Jiang

Abstract:

As the Internet continues to grow at a rapid pace as the primary medium for communications and commerce and as telecommunication networks and systems continue to expand their global reach, digital information has become the most popular and important information resource and our dependence upon the underlying cyber infrastructure has been increasing significantly. Unfortunately, as our dependency has grown, so has the threat to the cyber infrastructure from spammers, attackers and criminal enterprises. In this paper, we propose a new machine learning based network intrusion detection framework for cyber security. The detection process of the framework consists of two stages: model construction and intrusion detection. In the model construction stage, a semi-supervised machine learning algorithm is applied to a collected set of network audit data to generate a profile of normal network behavior and in the intrusion detection stage, input network events are analyzed and compared with the patterns gathered in the profile, and some of them are then flagged as anomalies should these events are sufficiently far from the expected normal behavior. The proposed framework is particularly applicable to the situations where there is only a small amount of labeled network training data available, which is very typical in real world network environments.

Keywords: classification, data analysis and mining, network intrusion detection, semi-supervised learning.

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