Статьи журнала - International Journal of Engineering and Manufacturing
Все статьи: 484
Статья научная
Numerical study simplifies the challenges associated with the study of moderate and intense low oxygen Dilution (MILD) combustion. In this study, the numerical investigation of turbulent non-premixed combustion in a Delft Co-flow Burner presents, which emulates MILD combustion behaviour. MILD combustion yields high thermal and fuel efficiency along with very low emission of pollutants. Using commercial ANSYS software, this study focuses on assessing the performance of two different turbulent-chemistry interactions models: a) Eddy Dissipation Concept (EDC) with reduced chemical kinetic schemes with 22 species (DRM 22) and b) Steady Diffusion Flamelet model, which is adopted in the Probability Density Function (PDF) approach method using chemical kinetic schemes GRI mech 3.0. The results of numerical simulations are compared with available experimental data measurement and calculated by solving the k-epsilon realizable turbulence model for two different jet fuel Reynolds numbers of 4100 and 8800. It has observed that the Steady Diffusion Flamelet PDF model approach shows moderately better agreement with the predicting temperature fields of experimental data using chemical Mechanism GRI mech 3.0 than the EDC model approach with a chemical mechanism with DRM 22. However, both models perform a better understanding for predicting the velocity field with experimental data. The models also predict and capture the effects of lift-off height (ignition kernel) with increasing of fuel jet Reynolds number, Overall, despite having more computational cost, the EDC model approach with GRI mech 3.0 yields better prediction. These featured models are suitable for the application of complex industrial combustion concentrating low emission combustion.
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Numerical simulation of droplet coalescence in turbulent stream using level set method
Статья научная
In the present paper a novel numerical method for solving the problem of two-phase flow with moving interfaces in both laminar and turbulent flow regimes is developed. The developed numerical method is based on the solution of the Reynolds-Averaged Navier Stokes equations in both phases separately with appropriate boundary conditions located at the interface separating the two fluids. The solution algorithm is performed on a regular and structured two-dimensional computational grid using the control volume approach. The complex shapes as well as the geometrical quantities of the interface are determined via the level set method. The numerical method is firstly validated against the prediction of the well known flow dynamics over a circular cylinder. Further, the numerical simulation of two colliding droplets in gas flow is numerically predicted showing the important dynamics associated with the different flow regimes considered. The remarkable capability of the developed numerical method in predicting turbulent two-phase flow dynamics enables us to predict further a wide range of two-phase flow industrial and engineering applications.
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Nutritional Evaluation of Adinandra Nitida Leaves
Статья научная
In this study, the nutritional evaluation of Adinandra nitida leaves was investigated. It was found that Adinandra nitida leaves were rich in necessary amino acids and unsaturated fatty acids. The content of the main flavonoid (camellianin A) was as high as 27.57±0.92 %. These results proved the high nutritional value of Adinandra nitida leaves. It has great commercial interest in the food and phyto-pharmaceutical market.
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On a GARCH Model with Normal Scale Mixture Innovations
Статья научная
Recently, there has been a lot of interest in modeling real data with a heavy tailed distribution. A popular candidate is the so-called generalized autoregressive conditional heteroscedastic (GARCH) model. Unfortunately, the tails of normal GARCH models are not thick enough in some applications. In this paper, we propose a GARCH model with normal scale mixture innovations, the parameters estimation procedure using EM algorithm is also provided. It is shown that GARCH models with normal scale mixture innovations have tails thicker than those of normal GARCH models. Therefore, the GARCH models with normal scale mixture innovations are more capable of capturing the heavy-tailed features in real data. Shanghai Stock Market Index as a real example illustrates the results.
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Online Signature Verification Using Fully Connected Deep Neural Networks
Статья научная
Biometric systems have been used in a wide range of applications. In this paper, we have introduced an online signature verification system using deep neural network models. The proposed system is designed to be used in a production environment and has accuracies on par with the state-of-the-art signature verification methods. It authenticates much faster than most of the existing signature verification systems (less than 2 seconds). To achieve better accuracies and faster training times, a feature vector with 42 features, both static and dynamic, is obtained from the signature sample. This feature vector is fed into the user identification model, which predicts the identity of the user with about 99% accuracy and based on this prediction, the user authentication model predicts if the signature is genuine or forged for that recognized user, with about 98% accuracy. The best possible accuracy achieved by the proposed system for 40 users is 97.5% and EER about 2%. The dataset from the Signature Verification Competition 2004 (SVC2004) was used to assess the performance of the proposed system. The results show that the proposed system competes with and even outperforms existing methods.
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Open Educational Resources (OER) for Sustainable Development using Autonomic Cloud Computing System
Статья научная
Open Educational Resources (OER) are those teaching and learning materials that are available either in the public domain or under an open license. The focus of this research work is to propose a conceptual framework for design and implementation of TVET autonomic cloud-based OERs for it integration into classroom teaching and learning strategies towards sustainable development. The beauty of this proposed system is its autonomous and self-managed features. The system will have capability of including the following: laboratory activities; syllabi, homework and assignment; assessments (CBT- computer-based test), lecture notes; audio visual lectures; simulation; lesson plan; and textbooks etc. while the system will be own and maintained by Yaba college of technology Nigeria, it's services will be available for usage by any individual with interest in TVET across the globe.
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Operational Risk Research on Social Pooling Fund Under Diseases Score Settlement System
Статья научная
Objectives: To find out the inner outer risks and its influence on social pooling fund under diseases score settlement (DSS).Methods: To Use step multiple linear regression analysis, the risk factors of the fund have been screened out. The selected risk factors have been taken into BP artificial neural network (BPANN). Results: In 12,724 insured inpatients, chronic diseases accounted for 24.89%.The average medical expense per inpatient was 11,950.88RMB and per hospitalization expenditure of social pooling fund was 7,665.81RMB. The 10 variables such as age, sex, unit type, hospital level, individual pays,medicine fee, medical fee, operation fee, nurse expense, bed fee and other expense were statistically significant. Conclusion: The growing aging population, changes in disease spectrum, increasing medical costs are all risks of non-controllable running outside the system. Moral hazard and the defective design of the system belong to the system controllable risks. The results from BPANN were compatible with multiple linear regression analysis. The payment system plays an important role in health insurance [1]. Good payment can control the hospitalization expenditures in a reasonable scope, while an imperfect one can throw a monkey-wrench into the system.The diseases score settlement (DSS) is payment system of Huai’an in China. This article develops two simple models (step multiple linear regression analysis and back-propagation artificial neural network(BPANN)) to illustrate the risks both inside and outside DSS and explore the risk control function of DSS. BPANN are the most widely used networks and are considered to be the workhorse of ANNs because of its simplicity and its power to extract useful information from samples [2].Due to its strong learning ability and generalization capability, BP networks have been successfully used in forecasting some financial problems, for example, predicting stock market returns [3], loan risk warning [4] and forecasting bankruptcy firms [5].
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Статья научная
Optimal installation of electrical distribution transformers has always been a challenging task for distribution operator (DSO)s due to load variations, particularly for seasonal loads. Depending on the quality of distribution systems in different regions and countries, a considerable number of installed transformers may be oversized or have capacity lower than critical standard. In this study, IEC 60076-7 is used to calculate the temperature limitations for distribution transformer capacities and determine optimal transformer capacity for an electrical distribution substation based on the critical values and limitations given in the standard. A data logger is installed on the substation and the load data is recorded for one year. Additionally, the impact of different parameters like ambient temperature is investigated for optimal determination of transformer capacity.
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Optimal Controller Design for the System of Ball-on-sphere: The Linear Quadratic Gaussian (LQG) Case
Статья научная
Control system plays a critical function as one of the essential bedrocks of contemporary social development. Differential equations are time-based equations. The analysis of these equations according to time-domain, is what the theory of modern control is based on. It uses a state-space method which allows direct design in the time-domain. With the state-space method, many controllers can be designed optimally. LQG is one of these controllers. This controller is covered much in the literature. Despite this, not many works cover the ball-on-sphere system. Therefore, the research designed an optimal LQG controller for the system of ball-on-sphere. System dynamics were first investigated and the mathematical model was derived. After that, the system was linearized and then the state-space representation was obtained. Using this representation, the controller was designed and applied to the system for control. The control was done based on the specified desired system performance. Finally, the controller's performance was analyzed. Results obtained showed that the controller met the desired system performance. The controller satisfied the at least 80% performance requirement with θ_x is 82.35% and θ_y is 82.95% less than their respective unregulated settling times. It was also observed that minimizing the total control energy leads to maximizing the total transient energy. Another finding was that all states played role in regulating the controller to the desired system performance. Unfortunately, a settling time (of the ball's angles) of less than 1.00 sec could not be realized. The realized performance is 2.35% and 2.95% more than the desired performance in x and y directions, respectively, for the ball’s angles settling time. This research is significant because it is the first to design an LQG controller for the ball-on-sphere system. Therefore, bridging the existing gap in the literature is the value of this research.
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Статья научная
The objective for this research is to assess the optimal measurement model for cell adhesion force. The Human Umbilical Vein Endothelial Cell Line (ECV304) was cultured on a type of biomedical material, polydimethylsiloxane (PDMS). The research also studied the parameters such as alternatives of working solution, styles of PDMS substrate, driving frequency and collagen smearing etc. The result showed the cells cultured on the large area substrate with 2 mm structural spacing and the small area substrate with 100μm structural spacing have better adhesive force. It was also clear to find that large area substrates also showed faster cell growth and expansion. They are more suitable as culture substrates for the measurement of cell adhesion force. As for work solution, 2% glucose solution that has relative low conductivity and concentration has the best measurement that effectively obtained cell adhesion force.
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Optimal Segmentation Framework for Detection of Brain Anomalies
Статья научная
This work presents an enhancement in accuracy for brain disorder detection using optimal unification. The strategy for detection of segments and brain regions causing medical conditions are described. This work demonstrates the application of multilateral filter and applied watershed method with EM-GM method. The most popular existing techniques of brain tumor detection are not optimal compared to this combination of Watershed and EM-GM technique with the proposed optimal unification technique. The result is optimally unified and achieved high accuracy. The multilateral filter enhances the image edges for better segmentation using signal amplitude moderation of the pixel. In the unification process, the optimal sets of segments are divided and finest merged results are considered with the brain regions detected with anomalies. Henceforth the number of possible medical investigations will be reduced.
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Option Pricing Under Stochastic Interest Rates
Статья научная
This paper reviews the research history of option pricing, then our model assumes that the interest rate subject to a given Vasicek stochastic differential equations, using option pricing by martingale method to study the stochastic interest rate model of European option pricing and obtain the pricing formula. Finally, we compare the differences between the standard European option pricing formulas and European option pricing formula under stochastic interest rate.
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Oscillatory Behavior of a Class of Second-order Nonlinear Dynamic Equations on Time Scales
Статья научная
The paper is devoted to the oscillation of a class of second-order nonlinear dynamic equations on time scales. By developing a generalized Riccati transformation technique, we establish some oscillation criteria for all solutions of the equations. Our results improve and extend some known results in the literature.
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PID Temperature Controller Design for Shell and Tube Heat Exchanger
Статья научная
Heat exchangers are one of the most important thermal devices. Shell and tube heat exchangers are the common types of heat exchangers and sustained a wide range of operating temperature and pressure. Modeling and controlling heat exchanger system is a difficult assignment because of its nonlinearity. As the flow rates changes, the gain, time delay and time constant varies, hence causing system nonlinearity. The solution for such problems is finding acceptable mathematical model and design a controller which provides better performance indices. In this paper mathematical model (experimental or empirical based) to represent the real system and design suitable controller which remove the offset and settle fast with minimum steady state error has been proposed. To this end, system model design the Proportional-Integral-Derivative controller for shell and tube heat exchanger using Ziegler Nichols method, Cohen-coon method and Chein et al. method. Since two opposing dynamic effects are existing in the system and has a problem of dynamics of inverse response and large overshoot. Therefore, Chein et al. tuning method have better performance than that of the others. In case of Chein et al. the overshoot of 2.577 % and settling time of 63.1 s.
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PID control design for second order systems
Статья научная
The Proportional Integral Derivative (PID) controllers are most commonly used in industries to compensate several numbers of practical industrial processes by the virtue of their simplicity and robustness. Several tuning methods exist for parameter tuning of PID controller. In this paper PID control design for second order system has been done with various methods. The effectiveness of tuning methods has been compared based up on time response specifications.
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Pain Expression Recognition Based on SLPP and MKSVM
Статья научная
In this paper, a novel approach is proposed for recognizing pain expression. First of all, supervised locality preserving projections (SLPP) is adopted for extracting feature of pain expression, which can solve the problem that LPP ignores the within-class local structure using adopting prior class label information, and then multiple kernels support vector machines (MKSVM) is employed for recognizing pain expression, Compared to SVM, which can improve the interpretability of decision function and classifier performance. Experimental results are shown to demonstrate the effectiveness of the proposed method.
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Parallelization of Needleman-Wunsch Algorithm Based on Software Pipelining
Статья научная
Sequence alignment is one of the most important algorithms that analyzing massive biological information. In modern bioinformatics, it plays an important role in field of serching for similar sequences, predicting sequence information of unkown sequence, looking for specific position of sequence, predicting protein structure and so on. Needleman-Wunsch algorithm is the earliest global alignment algorithm, it gets widely application with its accuracy, however, it has a high time complexity and its speed is slower. This paper adopts software pipelining technique to optimize Needleman-Wunsch algorithm with parallelization, and OpenMP which is the industrialized standard of shared memory programming is used to parallelize it. The performance of Needlelman-Wunsch algorithm can get a great improvement with the optimization.
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Parametric Optimization of Drilling Parameters in Aluminum 6061T6 Plate to Minimize the Burr
Статья научная
In the manufacturing, process a burr has been observed during the drilling through a hole in an aluminum bar. From the view of the life of a product, minimization of the burr should be significant. So in this research main aim is to identify how input parameters: drill diameter, point angle & spindle speed influenced output parameters burr height & thickness. To execute this operation a total of 27 examinations on an Aluminum 6061T6 plate is taken. Overall research performed into two stages. In first stage, Surface response methodology is used to design two objective functions for burr height & thickness with the help of input parameters and then these two objective functions combined to construct a single objective function. In next stage improved version of elephant swarm optimization (ESWSA) algorithm is applied to get the optimum input parameters. The predicted output variable after the optimization techniques (Test 2 & Test 3) further checked with experimental result to determine the accuracy of the proposed model. In a conclusion section it is seen that the average error of drill diameter, drill point angle & spindle speed are 1.72%, 3.84% & 3.89% respectively with average RMSE is 2.56 *10^-6. For further validation of effectiveness of proposed model is also compared with the state of art techniques in the field burr minimization.
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Parametric optimization of Liquid Flow Process by ANOVA Optimized DE, PSO & GA Algorithms
Статья научная
Control of liquid level & flow are the most interest domain in process control industry. Generally process parameter of the liquid flow system is varied frequently during the operation. So the selection of the level of process parameters i.e. input variables seems to be important for achieving the optimum flow rate. In the present work focus is given on the identification of the proper combination of the input parameters in liquid flow rate process. Flow sensor output, pipe diameter, liquid conductivity & viscosity have been taken as input parameter; flow rate obtained from test is taken as response parameter. Till now several researchers have been performed various optimization methods for optimized the parameters of the process plant. But still computational time & convergence speed of the applied optimization techniques for the modelling of the nonlinear process system is still an open challenge for the modern research. In this research we proposed three evolutionary algorithms are used to optimize the process parameters of the nonlinear model implemented by ANOVA to mitigate the unbalance, convergence speed and reduce the total computational time. Overall research performed into three stage, in first phase nonlinear equation ANOVA has been used for mathematical model for the process, In second stage three evolutionary algorithms: GA, PSO & DE are applied for parametric optimization of liquid flow process to maximize the response parameter & in last phase comparative study performed on simulated results based on confirmed test & validated our proposed methodology.
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Particle Swarm Optimization With Adaptive Parameters and Boundary Constraints
Статья научная
The core idea of PSO is that each particle searches the best solution of optimization problems according to “information sharing” between surrounding particles and itself. PSO has fast convergence speed and high global search capability. For low accuracy and divergent results of elementary PSO, this paper proposes a kind of PSO with adaptive parameters and boundary constraints. Inertia weight and learning factors increase or decrease linearly with iterative process, in order that the particles search the global space in early period of the algorithm and converge towards the global optimum later. At the same time, the author sets particle boundary constraints to ensure the optimization accuracy. Theoretical analysis and numerical simulation results show the efficiency and high optimization accuracy of the designed method.
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