Search results for: global generalized minimum residual (Gl-GMRES).

Authors: Nabila Charkaoui

Abstract:

Interest in Human Consciousness has been revived in the late 20th century from different scientific disciplines. Consciousness studies involve both its understanding and its application. In this paper, a computational model of the minimum consciousness functions necessary in my point of view for Artificial Intelligence applications is presented with the aim of improving the way computations will be made in the future. In section I, human consciousness is briefly described according to the scope of this paper. In section II, a minimum set of consciousness functions is defined - based on the literature reviewed - to be modelled, and then a computational model of these functions is presented in section III. In section IV, an analysis of the model is carried out to describe its functioning in detail.

Keywords: Consciousness, perception, attention.

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Authors: Abiodun M. Aibinu, Athaur Rahman Najeeb, Momoh J. E. Salami, Amir A. Shafie

Abstract:

An alternative approach to the use of Discrete Fourier Transform (DFT) for Magnetic Resonance Imaging (MRI) reconstruction is the use of parametric modeling technique. This method is suitable for problems in which the image can be modeled by explicit known source functions with a few adjustable parameters. Despite the success reported in the use of modeling technique as an alternative MRI reconstruction technique, two important problems constitutes challenges to the applicability of this method, these are estimation of Model order and model coefficient determination. In this paper, five of the suggested method of evaluating the model order have been evaluated, these are: The Final Prediction Error (FPE), Akaike Information Criterion (AIC), Residual Variance (RV), Minimum Description Length (MDL) and Hannan and Quinn (HNQ) criterion. These criteria were evaluated on MRI data sets based on the method of Transient Error Reconstruction Algorithm (TERA). The result for each criterion is compared to result obtained by the use of a fixed order technique and three measures of similarity were evaluated. Result obtained shows that the use of MDL gives the highest measure of similarity to that use by a fixed order technique.

Keywords: Autoregressive Moving Average (ARMA), MagneticResonance Imaging (MRI), Parametric modeling, Transient Error.

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Authors: Vijay Kumar Nath, Deepika Hazarika, Anil Mahanta,

Abstract:

Discrete Cosine Transform (DCT) based transform coding is very popular in image, video and speech compression due to its good energy compaction and decorrelating properties. However, at low bit rates, the reconstructed images generally suffer from visually annoying blocking artifacts as a result of coarse quantization. Lapped transform was proposed as an alternative to the DCT with reduced blocking artifacts and increased coding gain. Lapped transforms are popular for their good performance, robustness against oversmoothing and availability of fast implementation algorithms. However, there is no proper study reported in the literature regarding the statistical distributions of block Lapped Orthogonal Transform (LOT) and Lapped Biorthogonal Transform (LBT) coefficients. This study performs two goodness-of-fit tests, the Kolmogorov-Smirnov (KS) test and the 2- test, to determine the distribution that best fits the LOT and LBT coefficients. The experimental results show that the distribution of a majority of the significant AC coefficients can be modeled by the Generalized Gaussian distribution. The knowledge of the statistical distribution of transform coefficients greatly helps in the design of optimal quantizers that may lead to minimum distortion and hence achieve optimal coding efficiency.

Keywords: Lapped orthogonal transform, Lapped biorthogonal transform, Image compression, KS test,

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Authors: I. Algul, G. Akgun, H. Kurtaran

Abstract:

Dynamic analysis of composite doubly curved panels with variable thickness subjected to different pulse types using Generalized Differential Quadrature method (GDQ) is presented in this study. Panels with variable thickness are used in the construction of aerospace and marine industry. Giving variable thickness to panels can allow the designer to get optimum structural efficiency. For this reason, estimating the response of variable thickness panels is very important to design more reliable structures under dynamic loads. Dynamic equations for composite panels with variable thickness are obtained using virtual work principle. Partial derivatives in the equation of motion are expressed with GDQ and Newmark average acceleration scheme is used for temporal discretization. Several examples are used to highlight the effectiveness of the proposed method. Results are compared with finite element method. Effects of taper ratios, boundary conditions and loading type on the response of composite panel are investigated.

Keywords: Generalized differential quadrature method, doubly curved panels, laminated composite materials, small displacement.

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Authors: Je Young Lee, Hee Sung Kim, Kwang Ho Choi, Joonhoo Lim, Sebum Chun, Hyung Keun Lee

Abstract:

This study is purposed to develop an efficient fault detection method for Global Navigation Satellite Systems (GNSS) applications based on adaptive noise covariance estimation. Due to the dependence on radio frequency signals, GNSS measurements are dominated by systematic errors in receiver’s operating environment. In the proposed method, the pseudorange and carrier-phase measurement noise covariances are obtained at time propagations and measurement updates in process of Carrier-Smoothed Code (CSC) filtering, respectively. The test statistics for fault detection are generated by the estimated measurement noise covariances. To evaluate the fault detection capability, intentional faults were added to the filed-collected measurements. The experiment result shows that the proposed method is efficient in detecting unhealthy measurements and improves GNSS positioning accuracy against fault occurrences.

Keywords: Adaptive estimation, fault detection, GNSS, residual.

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Authors: Prabhat Ranjan Prem, B.H.Bharatkumar, Nagesh R Iyer

Abstract:

A research program is conducted to evaluate the mechanical properties of Ultra High Performance Concrete, target compressive strength at the age of 28 days being more than 150 MPa. The methodology to develop such mix has been explained. The material properties, mix design and curing regime are determined. The material attributes are understood by studying the stress strain behaviour of UHPC cylinders under uniaxial compressive loading. The load –crack mouth opening displacement (cmod) of UHPC beams, flexural strength and fracture energy was evaluated using third point loading test. Compressive strength and Split tensile strength results are determined to find out the compressive and tensile behaviour. Residual strength parameters are presented vividly explaining the flexural performance, toughness of concrete.Durability studies were also done to compare the effect of fibre to that of a control mix For all the studies the Mechanical properties were evaluated by varying the percentage and aspect ratio of steel fibres The results reflected that higher aspect ratio and fibre volume produced drastic changes in the cube strength, cylinder strength, post peak response, load-cmod, fracture energy flexural strength, split tensile strength, residual strength and durability. In regards to null application of UHPC in India, an initiative is undertaken to comprehend the mechanical behaviour of UHPC, which will be vital for longer run in commercialization for structural applications.

Keywords: Ultra High Performance Concrete, Reinforcement Index, Compressive Strength, Tensile Strength, Flexural Strength, Residual Strength, Fracture Energy, Stress-Strain Relationships, Load-Crack Mouth Opening Displacement and Durability.

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Authors: Josu Arteche, Jesus Orbe

Abstract:

The log periodogram regression is widely used in empirical applications because of its simplicity, since only a least squares regression is required to estimate the memory parameter, d, its good asymptotic properties and its robustness to misspecification of the short term behavior of the series. However, the asymptotic distribution is a poor approximation of the (unknown) finite sample distribution if the sample size is small. Here the finite sample performance of different nonparametric residual bootstrap procedures is analyzed when applied to construct confidence intervals. In particular, in addition to the basic residual bootstrap, the local and block bootstrap that might adequately replicate the structure that may arise in the errors of the regression are considered when the series shows weak dependence in addition to the long memory component. Bias correcting bootstrap to adjust the bias caused by that structure is also considered. Finally, the performance of the bootstrap in log periodogram regression based confidence intervals is assessed in different type of models and how its performance changes as sample size increases.

Keywords: bootstrap, confidence interval, log periodogram regression, long memory.

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Authors: Y. F. Yang, B. T. Wang, J. J. Fan, J. Yin

Abstract:

The previous researches focused on the influence of anthropogenic greenhouse gases exerting global warming, but not consider whether desert sand may warm the planet, this could be improved by accounting for sand's physical and geometric properties. Here we show, sand particles (because of their geometry) at the desert surface form an extended surface of up to 1 + π/4 times the planar area of the desert that can contact sunlight, and at shallow depths of the desert form another extended surface of at least 1 + π times the planar area that can contact air. Based on this feature, an enhanced heat exchange system between sunlight, desert sand, and air in the spaces between sand particles could be built up automatically, which can increase capture of solar energy, leading to rapid heating of the sand particles, and then the heating of sand particles will dramatically heat the air between sand particles. The thermodynamics of deserts may thus have contributed to global warming, especially significant to future global warming if the current desertification continues to expand.

Keywords: global warming, desert sand, extended surface, heat exchange, thermodynamics

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Authors: L. Khoshnevisan, H. R. Momeni, A. Ashraf-Modarres

Abstract:

One of the robust fault detection filter (RFDF) designing method is based on sliding-mode theory. The main purpose of our study is to introduce an innovative simplified reference residual model generator to formulate the RFDF as a sliding-mode observer without any manipulation package or transformation matrix, through which the generated residual signals can be evaluated. So the proposed design is more explicit and requires less design parameters in comparison with approaches requiring changing coordinates. To the best author's knowledge, this is the first time that the sliding mode technique is applied to detect actuator and sensor faults in a real boiler. The designing procedure is proposed in a drum boiler in Synvendska Kraft AB Plant in Malmo, Sweden as a multivariable and strongly coupled system. It is demonstrated that both sensor and actuator faults can robustly be detected. Also sensor faults can be diagnosed and isolated through this method.

Keywords: Boiler, fault detection, robustness, simplified sliding-mode observer.

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Authors: Ruxandra A. Vodă

Abstract:

With the industrial revolution, global leaders must more rapidly become knowledgeable of and develop essential cross-cultural competencies to be effective. Ethnocentrism represents a hidden barrier of effective leadership and must be acknowledged and addressed proactively by global leaders. The article examines the impact of ethnocentrism in four critical areas (leadership strategy, cross-cultural competencies, intercultural communication, and adaptation to international contexts) and argues that by developing cross-cultural competencies, leaders might naturally reduce ethnocentrism levels. This paper will also offer few examples to support international managers in understanding how ethnocentrism can affect performance.

Keywords: Adaptation to intercultural contexts, cross-cultural competencies, effective leadership, ethnocentrism, global leader, intercultural communication, leadership strategy, the GLOBE Project.

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Authors: Noritaka Shigei, Kazuto Matsumoto, Yoshiki Nakashima, Hiromi Miyajima

Abstract:

Although Mobile Wireless Sensor Networks (MWSNs), which consist of mobile sensor nodes (MSNs), can cover a wide range of observation region by using a small number of sensor nodes, they need to construct a network to collect the sensing data on the base station by moving the MSNs. As an effective method, the network construction method based on Virtual Rails (VRs), which is referred to as VR method, has been proposed. In this paper, we propose two types of effective techniques for the VR method. They can prolong the operation time of the network, which is limited by the battery capabilities of MSNs and the energy consumption of MSNs. The first technique, an effective arrangement of VRs, almost equalizes the number of MSNs belonging to each VR. The second technique, an adaptive movement method of MSNs, takes into account the residual energy of battery. In the simulation, we demonstrate that each technique can improve the network lifetime and the combination of both techniques is the most effective.

Keywords: Wireless sensor network, mobile sensor node, relay of sensing data, virtual rail, residual energy.

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Authors: Meghal Desai, Jigisha Parikh

Abstract:

Aggregation behavior of sodium salicylate and sodium cumene sulfonate was studied in aqueous solution at different temperature. Specific conductivity and relative viscosity were measured at different temperature to find minimum hydrotropic concentration. The thermodynamic parameters (free energy, enthalpy and entropy) were evaluated in the temperature range of 30°C-70°C. The free energy decreased with increase in temperature. The aggregation was found to be exothermic in nature and favored by positive value of entropy.

Keywords: Hydrotropes, Enthalpy, Entropy, Free Energy, Minimum Hydrotropic Concentration.

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Authors: V. Churkin, M. Lopatin

Abstract:

The purpose of the paper is to estimate the US small wind turbines market potential and forecast the small wind turbines sales in the US. The forecasting method is based on the application of the Bass model and the generalized Bass model of innovations diffusion under replacement purchases. In the work an exponential distribution is used for modeling of replacement purchases. Only one parameter of such distribution is determined by average lifetime of small wind turbines. The identification of the model parameters is based on nonlinear regression analysis on the basis of the annual sales statistics which has been published by the American Wind Energy Association (AWEA) since 2001 up to 2012. The estimation of the US average market potential of small wind turbines (for adoption purchases) without account of price changes is 57080 (confidence interval from 49294 to 64866 at P = 0.95) under average lifetime of wind turbines 15 years, and 62402 (confidence interval from 54154 to 70648 at P = 0.95) under average lifetime of wind turbines 20 years. In the first case the explained variance is 90,7%, while in the second - 91,8%. The effect of the wind turbines price changes on their sales was estimated using generalized Bass model. This required a price forecast. To do this, the polynomial regression function, which is based on the Berkeley Lab statistics, was used. The estimation of the US average market potential of small wind turbines (for adoption purchases) in that case is 42542 (confidence interval from 32863 to 52221 at P = 0.95) under average lifetime of wind turbines 15 years, and 47426 (confidence interval from 36092 to 58760 at P = 0.95) under average lifetime of wind turbines 20 years. In the first case the explained variance is 95,3%, while in the second – 95,3%.

Keywords: Bass model, generalized Bass model, replacement purchases, sales forecasting of innovations, statistics of sales of small wind turbines in the United States.

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Authors: Pan Long, Bi Dongjie, Li Xifeng, Xie Yongle

Abstract:

The near-field synthetic aperture radar (SAR) imaging is an advanced nondestructive testing and evaluation (NDT&E) technique. This paper investigates the complex-valued signal processing related to the near-field SAR imaging system, where the measurement data turns out to be noncircular and improper, meaning that the complex-valued data is correlated to its complex conjugate. Furthermore, we discover that the degree of impropriety of the measurement data and that of the target image can be highly correlated in near-field SAR imaging. Based on these observations, A modified generalized sparse Bayesian learning algorithm is proposed, taking impropriety and noncircularity into account. Numerical results show that the proposed algorithm provides performance gain, with the help of noncircular assumption on the signals.

Keywords: Complex-valued signal processing, synthetic aperture radar (SAR), 2-D radar imaging, compressive sensing, Sparse Bayesian learning.

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Authors: Mohammed Zidane, Said Safi, Mohamed Sabri, Ahmed Boumezzough

Abstract:

This paper describes a blind algorithm, which is compared with two another algorithms proposed in the literature, for estimating of the minimum phase channel parameters. In order to identify blindly the impulse response of these channels, we have used Higher Order Statistics (HOS) to build our algorithm. The simulation results in noisy environment, demonstrate that the proposed method could estimate the phase and magnitude with high accuracy of these channels blindly and without any information about the input, except that the input excitation is identically and independent distribute (i.i.d) and non-Gaussian.

Keywords: System Identification, Higher Order Statistics, Communication Channels.

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Authors: N. W. Paul, M. Banerjee

Abstract:

Discussing the nexus between global health policy and local practices, this article addresses the recent Ebola outbreak as a role model for narrative co-constructions of epidemic risk. We will demonstrate in how far a theory-driven and methodologically rooted analysis of narrativity can help to improve mechanisms of prevention and intervention whenever epidemic risk needs to be addressed locally in order to contribute to global health. Analyzing the narrative transformation of Ebola, we will also address issues of transcultural problem-solving and of normative questions at stake. In this regard, we seek to contribute to a better understanding of a key question of global health and justice as well as to the underlying ethical questions. By highlighting and analyzing the functions of narratives, this paper provides a translational approach to refine our practices by which we address epidemic risk, be it on the national, the transnational or the global scale.

Keywords: Ebola, Epidemic Risk, Medical Ethics, Medical Humanities.

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Authors: Adnene Arbi, Chaouki Aouiti, Abderrahmane Touati

Abstract:

In this paper, we consider the uniform asymptotic stability, global asymptotic stability and global exponential stability of the equilibrium point of discrete Hopfield neural networks with delays. Some new stability criteria for system are derived by using the Lyapunov functional method and the linear matrix inequality approach, for estimating the upper bound of Lyapunov functional derivative.

Keywords: Hopfield neural networks, uniform asymptotic stability, global asymptotic stability, exponential stability.

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Authors: Morteza Talebi, Jianan Wang, Zhihua Qu

Abstract:

The security of power systems against malicious cyberphysical data attacks becomes an important issue. The adversary always attempts to manipulate the information structure of the power system and inject malicious data to deviate state variables while evading the existing detection techniques based on residual test. The solutions proposed in the literature are capable of immunizing the power system against false data injection but they might be too costly and physically not practical in the expansive distribution network. To this end, we define an algebraic condition for trustworthy power system to evade malicious data injection. The proposed protection scheme secures the power system by deterministically reconfiguring the information structure and corresponding residual test. More importantly, it does not require any physical effort in either microgrid or network level. The identification scheme of finding meters being attacked is proposed as well. Eventually, a well-known IEEE 30-bus system is adopted to demonstrate the effectiveness of the proposed schemes.

Keywords: Algebraic Criterion, Malicious Cyber-Physical Data Injection, Protection and Identification, Trustworthy Power System.

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Authors: Khalid Khan, Atique Ahmad Zafar, Mohammed A. Alnuem, Hashim Khan

Abstract:

Global Software Development (GSD) projects are passing through different boundaries of a company, country and even in other continents where time zone differs between both sites. Beside many benefits of such development, research declared plenty of negative impacts on these GSD projects. It is important to understand problems which may lie during the execution of GSD project with different time zones. This research project discussed and provided different issues related to time delays in GSD projects. In this paper, authors investigated some of the time delay factors which usually lie in GSD projects with different time zones. This investigation is done through systematic review of literature. Furthermore, the practices to overcome these delay factors which have already been reported in literature and GSD organizations are also explored through literature survey and case studies.

Keywords: Case Studies, Global Software Development, Global Software Engineering, Temporal Difference, Time Delay

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Authors: Tianchai Suksri, U-thai Sritheeravirojana, Arjin Numsomran, Viriya Kongrattana, Thongchai Werataweemart

Abstract:

A control system design with Characteristic Ratio Assignment (CRA) is proven that effective for SISO control design. But the control system design for MIMO via CRA is not concrete procedure. In this paper presents the control system design method for quadruple-tank process via CRA. By using the decentralized method for both minimum phase and non-minimum phase are made. The results from PI and PID controller design via CRA can be illustrated the validity of our approach by MATLAB.

Keywords: CRA, Quadruple-Tank.

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Authors: Kai Wang, Yanling Zu

Abstract:

By utilizing the comparison theorem and Lyapunov second method, some sufficient conditions for the permanence and global attractivity of positive periodic solution for a predator-prey model with mutual interference m ∈ (0, 1) and delays τi are obtained. It is the first time that such a model is considered with delays. The significant is that the results presented are related to the delays and the mutual interference constant m. Several examples are illustrated to verify the feasibility of the results by simulation in the last part.

Keywords: Predator-prey model, Mutual interference, Delays, Permanence, Global attractivity

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Authors: Radek Fisera, Petr Stluka

Abstract:

This paper describes a methodology for remote performance monitoring of retail refrigeration systems. The proposed framework starts with monitoring of the whole refrigeration circuit which allows detecting deviations from expected behavior caused by various faults and degradations. The subsequent diagnostics methods drill down deeper in the equipment hierarchy to more specifically determine root causes. An important feature of the proposed concept is that it does not require any additional sensors, and thus, the performance monitoring solution can be deployed at a low installation cost. Moreover only a minimum of contextual information is required, which also substantially reduces time and cost of the deployment process.

Keywords: Condition monitoring, energy baselining, fault detection and diagnostics, commercial refrigeration.

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Authors: S. Anna Durai, E. Anna Saro

Abstract:

Image Compression using Artificial Neural Networks is a topic where research is being carried out in various directions towards achieving a generalized and economical network. Feedforward Networks using Back propagation Algorithm adopting the method of steepest descent for error minimization is popular and widely adopted and is directly applied to image compression. Various research works are directed towards achieving quick convergence of the network without loss of quality of the restored image. In general the images used for compression are of different types like dark image, high intensity image etc. When these images are compressed using Back-propagation Network, it takes longer time to converge. The reason for this is, the given image may contain a number of distinct gray levels with narrow difference with their neighborhood pixels. If the gray levels of the pixels in an image and their neighbors are mapped in such a way that the difference in the gray levels of the neighbors with the pixel is minimum, then compression ratio as well as the convergence of the network can be improved. To achieve this, a Cumulative distribution function is estimated for the image and it is used to map the image pixels. When the mapped image pixels are used, the Back-propagation Neural Network yields high compression ratio as well as it converges quickly.

Keywords: Back-propagation Neural Network, Cumulative Distribution Function, Correlation, Convergence.

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Authors: Jean-Christophe Sibel, Sylvain Reynal, David Declercq

Abstract:

In the context of channel coding, the Generalized Belief Propagation (GBP) is an iterative algorithm used to recover the transmission bits sent through a noisy channel. To ensure a reliable transmission, we apply a map on the bits, that is called a code. This code induces artificial correlations between the bits to send, and it can be modeled by a graph whose nodes are the bits and the edges are the correlations. This graph, called Tanner graph, is used for most of the decoding algorithms like Belief Propagation or Gallager-B. The GBP is based on a non unic transformation of the Tanner graph into a so called region-graph. A clear advantage of the GBP over the other algorithms is the freedom in the construction of this graph. In this article, we explain a particular construction for specific graph topologies that involves relevant performance of the GBP. Moreover, we investigate the behavior of the GBP considered as a dynamic system in order to understand the way it evolves in terms of the time and in terms of the noise power of the channel. To this end we make use of classical measures and we introduce a new measure called the hyperspheres method that enables to know the size of the attractors.

Keywords: iterative decoder, LDPC, region-graph, chaos.

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Authors: Vipul M. Patel, Hemantkumar B. Mehta

Abstract:

Technological innovations in electronic world demand novel, compact, simple in design, less costly and effective heat transfer devices. Closed Loop Pulsating Heat Pipe (CLPHP) is a passive phase change heat transfer device and has potential to transfer heat quickly and efficiently from source to sink. Thermal performance of a CLPHP is governed by various parameters such as number of U-turns, orientations, input heat, working fluids and filling ratio. The present paper is an attempt to predict the thermal performance of a CLPHP using Artificial Neural Network (ANN). Filling ratio and heat input are considered as input parameters while thermal resistance is set as target parameter. Types of neural networks considered in the present paper are radial basis, generalized regression, linear layer, cascade forward back propagation, feed forward back propagation; feed forward distributed time delay, layer recurrent and Elman back propagation. Linear, logistic sigmoid, tangent sigmoid and Radial Basis Gaussian Function are used as transfer functions. Prediction accuracy is measured based on the experimental data reported by the researchers in open literature as a function of Mean Absolute Relative Deviation (MARD). The prediction of a generalized regression ANN model with spread constant of 4.8 is found in agreement with the experimental data for MARD in the range of ±1.81%.

Keywords: ANN models, CLPHP, filling ratio, generalized regression, spread constant.

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Authors: Zhaojun Wang, Zongdi Sun, Yongjie Chen, Manman Chen, Linghui Wang

Abstract:

This paper uses the analytic hierarchy process to study the execution effect of the minimum purchase price of grain in different regions and various grain crops. Firstly, for different regions, five indicators including grain yield, grain sown area, gross agricultural production, grain consumption price index, and disposable income of rural residents were selected to construct an evaluation index system. We collect data of six provinces including Hebei Province, Heilongjiang Province and Shandong Province from 2006 to 2017. Then, the judgment matrix is constructed, and the hierarchical single ordering and consistency test are carried out to determine the scoring standard for the minimum purchase price of grain. The ranking of the execution effect from high to low is: Heilongjiang Province, Shandong Province, Hebei Province, Guizhou Province, Shaanxi Province, and Guangdong Province. Secondly, taking Shandong Province as an example, we collect the relevant data of sown area and yield of cereals, beans, potatoes and other crops from 2006 to 2017. The weight of area and yield index is determined by expert scoring method. And the average sown area and yield of cereals, beans and potatoes in 2006-2017 were calculated, respectively. On this basis, according to the sum of products of weights and mean values, the execution effects of different grain crops are determined. It turns out that among the cereals, the minimum purchase price had the best execution effect on paddy, followed by wheat and finally maize. Moreover, among major categories of crops, cereals perform best, followed by beans and finally potatoes. Lastly, countermeasures are proposed for different regions, various categories of crops, and different crops of the same category.

Keywords: Analytic hierarchy process, grain yield, grain sown area, minimum grain purchase price.

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Authors: Apirak Sombat, Teerapol Saleewong, Poom Kumam, Parin Chaipunya, Wiyada Kumam, Anantachai Padcharoen, Yeol Je Cho, Thana Sutthibutpong

Abstract:

This research is aimed to study a two-step iteration process defined over a finite family of σ-asymptotically quasi-nonexpansive nonself-mappings. The strong convergence is guaranteed under the framework of Banach spaces with some additional structural properties including strict and uniform convexity, reflexivity, and smoothness assumptions. With similar projection technique for nonself-mapping in Hilbert spaces, we hereby use the generalized projection to construct a point within the corresponding domain. Moreover, we have to introduce the use of duality mapping and its inverse to overcome the unavailability of duality representation that is exploit by Hilbert space theorists. We then apply our results for σ-asymptotically quasi-nonexpansive nonself-mappings to solve for ideal efficiency of vector optimization problems composed of finitely many objective functions. We also showed that the obtained solution from our process is the closest to the origin. Moreover, we also give an illustrative numerical example to support our results.

Keywords: σ-asymptotically quasi-nonexpansive nonselfmapping, strong convergence, fixed point, uniformly convex and uniformly smooth Banach space.

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Authors: Nassereldeen A. Kabashi, Suleyman A. Muyibi. Mohammed E. Saeed., Farhana I. Yahya

Abstract:

It is important to remove manganese from water because of its effects on human and the environment. Human activities are one of the biggest contributors for excessive manganese concentration in the environment. The proposed method to remove manganese in aqueous solution by using adsorption as in carbon nanotubes (CNT) at different parameters: The parameters are CNT dosage, pH, agitation speed and contact time. Different pHs are pH 6.0, pH 6.5, pH 7.0, pH 7.5 and pH 8.0, CNT dosages are 5mg, 6.25mg, 7.5mg, 8.75mg or 10mg, contact time are 10 min, 32.5 min, 55 min, 87.5 min and 120 min while the agitation speeds are 100rpm, 150rpm, 200rpm, 250rpm and 300rpm. The parameters chosen for experiments are based on experimental design done by using Central Composite Design, Design Expert 6.0 with 4 parameters, 5 levels and 2 replications. Based on the results, condition set at pH 7.0, agitation speed of 300 rpm, 7.5mg and contact time 55 minutes gives the highest removal with 75.5%. From ANOVA analysis in Design Expert 6.0, the residual concentration will be very much affected by pH and CNT dosage. Initial manganese concentration is 1.2mg/L while the lowest residual concentration achieved is 0.294mg/L, which almost satisfy DOE Malaysia Standard B requirement. Therefore, further experiments must be done to remove manganese from model water to the required standard (0.2 mg/L) with the initial concentration set to 0.294 mg/L.

Keywords: Adsorption, CNT, DOE, Manganese, Parameters.

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Authors: Hidetoshi Konno, Akio Suzuki

Abstract:

The paper presented a transient population dynamics of phase singularities in 2D Beeler-Reuter model. Two stochastic modelings are examined: (i) the Master equation approach with the transition rate (i.e., λ(n, t) = λ(t)n and μ(n, t) = μ(t)n) and (ii) the nonlinear Langevin equation approach with a multiplicative noise. The exact general solution of the Master equation with arbitrary time-dependent transition rate is given. Then, the exact solution of the mean field equation for the nonlinear Langevin equation is also given. It is demonstrated that transient population dynamics is successfully identified by the generalized Logistic equation with fractional higher order nonlinear term. It is also demonstrated the necessity of introducing time-dependent transition rate in the master equation approach to incorporate the effect of nonlinearity.

Keywords: Transient population dynamics, Phase singularity, Birth-death process, Non-stationary Master equation, nonlinear Langevin equation, generalized Logistic equation.

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Authors: S. Montenegro, W. Hu

Abstract:

The objective of this paper is to develop a neural network-based residual generator to detect the fault in the actuators for a specific communication satellite in its attitude control system (ACS). First, a dynamic multilayer perceptron network with dynamic neurons is used, those neurons correspond a second order linear Infinite Impulse Response (IIR) filter and a nonlinear activation function with adjustable parameters. Second, the parameters from the network are adjusted to minimize a performance index specified by the output estimated error, with the given input-output data collected from the specific ACS. Then, the proposed dynamic neural network is trained and applied for detecting the faults injected to the wheel, which is the main actuator in the normal mode for the communication satellite. Then the performance and capabilities of the proposed network were tested and compared with a conventional model-based observer residual, showing the differences between these two methods, and indicating the benefit of the proposed algorithm to know the real status of the momentum wheel. Finally, the application of the methods in a satellite ground station is discussed.

Keywords: Satellite, Attitude Control, Momentum Wheel, Neural Network, Fault Detection.

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