Studies on the interactions of metal oxides and Na2 SO4 at 1100 and 1200 K in oxygen

The interaction of different metal oxides such as Co₃O₄, NiO, Al₂O₃, Cr₂O₃, Fe₂O₃ and SiO₂ with Na₂SO₄ at a temperature of 1100 and 1200 K in flowing oxygen has been studied. The thermogravimetric studies for each system were carried out as a function of Na₂SO₄ in the mixture. The presence of different constituents in the reaction products were identified by X-ray diffraction analysis and the morphologies of the reaction products were characterized using metallography and scanning electron microscopy (SEM). The formation of products was also investigated by thermodynamic computation of free energies of the reactions and the study of relevant equilibrium phase diagrams. The soluble species in the aqueous solutions of the reaction products were determined quantitatively using atomic absorption spectrophotometry. The high temperature interaction products usually contain a 3-phase structure namely, Na₂O·M₂O _x , M₂O _x and metal sulphide and/or metal sulphate. The formation of Na₂O·M₂O _x depends upon the solid state solubility of metal oxide in the molten salt at high temperatures. Under limited solubility conditions Na₂O·M₂O _x is invariably formed, but as soon as this condition is relaxed the oxide. M₂O _x , precipitates and forms a separate phase.     

Image Super Resolution and Enhancement Using E-spline

E-splines （Exponential spline） polynomials represent the best smooth transition between continuous and discrete domains. As they are constructed from convolution of exponential segments, there are many degrees of freedom to optimally choose the most convenient E-spline, suitable for a specific application. In this paper, the parameters of these E-splines were optimally chosen, to enhance the performance of image zooming and interpolation schemes. The proposed technique is based on minimizing the total variation function of the detail coefficients of the E-spline based wavelet decomposition. In zooming applications, the quality of interpolated images are further improved and sharpened by applying ICA technique to them, in order to remove any dependency. Illustrative examples are given to verify image enhancement of the proposed E-spline scheme, when compared with the existing approaches.     

A recursive algorithm for generating the equations of motion of spatial mechanical systems with application to the five-point suspension

In this paper, a recursive formulation for generating the equations of motion of spatial mechanical systems is presented. The rigid bodies are replaced by a dynamically equivalent constrained system of particles which avoids introducing any rotational coordinates. For the open-chain system, the equations of motion are generated recursively along the serial chains using the concepts of linear and angular momenta. Closed-chain systems are transformed to open-chain systems by cutting suitable kinematic joints and introducing cut-joint constraints. The formulation is used to carry out the dynamic analysis of multi-link five-point suspension. The results of the simulation demonstrate the generality and simplicity of the proposed dynamic formulation.     

Emotion space model for classifying opinions in stock message board

The online stock message is known to have impacts on the trend of the stock market. Understanding investor opinions in stock message boards is important, and the automatic classification of the investors’ opinions is one of the key methods for the issue. Traditional opinion classification methods mainly use terms and their frequency, part of speech, rule of opinions and sentiment shifters. But semantic information is ignored in term selection, and it is also hard to find the complete rules. In this paper, based on the classification of human emotions proposed by Ekman, we extend the traditional positive–negative analysis to the six important emotion states to build an extremely low dimensional emotion space model (ESM). It enables the prediction of investors’ emotions in public. Specifically, we use lexical semantic extension and correlation analysis methods to extend the scale of emotion words, which can capture more words with strong emotions for ad hoc domain, like network emotion symbols. We apply our ESM on messages of a famous stock message board TheLion. We also compare our model with traditional methods information gain and mutual information. The results show that ESM is not parameter sensitive. Besides, ESM is efficient for modeling sentiment classifying and can achieve higher classification accuracy than traditional ones.     

Numerical weather prediction revisions using the locally trained differential polynomial network

Meso-scale forecasts result from global numerical weather prediction models, which are based upon the differential equations for atmospheric dynamics that do not perfectly determine weather conditions near the ground. Statistical corrections can combine complex numerical models, based on the physics of the atmosphere to forecast the large-scale weather patterns, and regression in post-processing to clarify surface weather details according to local observations and climatological conditions. Neural networks trained with local relevant weather observations of fluctuant data relations in current conditions, entered by numerical model outcomes of the same data types, may revise its one target short-term prognosis (e.g. relative humidity or temperature) to stand for these methods. Polynomial neural networks can compose general partial differential equations, which allow model more complicated real system functions from discrete time-series observations than using standard soft-computing methods. This new neural network technique generates convergent series of substitution relative derivative terms, which combination sum can define and solve an unknown general partial differential equation, able to describe dynamic processes of the weather system in a local area, analogous to the differential equation systems of numerical models. The trained network model revises hourly-series of numerical prognosis of one target variable in sequence, applying the general differential equation solution of the correction multi-variable function to corresponding output variables of the 24-hour numerical forecast. The experimental results proved this polynomial network type can successfully revise some numerical weather prognoses after this manner.     

Certificate sharing system for secure certificate distribution in mobile environment

As mobile and Internet technologies evolve, mobile services (e.g., Internet banking, social commerce) continuously expand and diversify. In order to use these mobile services, it is essential that security services, especially distribution certificates (e.g., bank certificates), relevant to mobile devices be provided. Some approaches to providing distribution certificates between a user's mobile device and a personal computer (PC) have been proposed. However, the existing approaches do not guarantee that the certificate in the mobile devices same with the issued one from the PC, causing constraints on mobile services such as mobile phone banking and mobile commerce (M-commerce).In this paper, we propose a novel approach that shares certificates securely without modification of the existing standard certificate format between a smartphone and a PC. We also implemented the certificate sharing system (CSS) in a virtual private network (VPN). The CSS provides strong end-to-end data security for the certificate with a key size of 192-bits which is able to guarantee an expiration date of three years. It also provides strong data security on physical devices with the use of device ID. The certificate that is shared between devices is available only through the CSS's authorization process. In addition, the CSS provides a flexible and extensible system for sharing certificates in enterprise environments. The CSS module of a PC was implemented by way of a standard web language, and the CSS module of a smartphone was developed with the assistance of mobile applications with a small size of 1210KB.     

New MPPT algorithm for PV applications based on hybrid dynamical approach

This paper proposes a new Maximum Power Point Tracking (MPPT) algorithm for photovoltaic applications using the multicellular converter as a stage of power adaptation. The proposed MPPT technique has been designed using a hybrid dynamical approach to model the photovoltaic generator. The hybrid dynamical theory has been applied taking advantage of the particular topology of the multicellular converter. Then, a hybrid automata has been established to optimize the power production. The maximization of the produced solar energy is achieved by switching between the different operative modes of the hybrid automata, which is conditioned by some invariance and transition conditions. These conditions have been validated by simulation tests under different conditions of temperature and irradiance. Moreover, the performance of the proposed algorithm has been then evaluated by comparison with standard MPPT techniques numerically and by experimental tests under varying external working conditions. The results have shown the interesting features that the hybrid MPPT technique presents in terms of performance and simplicity for real time implementation.     

Integrated standalone hybrid solar PV,fuel cell and diesel generator power system for battery or supercapacitor storage systems in Khorfakkan,United Arab Emirates

Renewable energy resources play a very important rule these days to assist the conventional energy systems for doing its function in the UAE due to high greenhouse gas (GHG) emissions and energy demand. In this paper, the analysis and performance of integrated standalone hybrid solar PV, fuel cell and diesel generator power system with battery energy storage system (BESS) or supercapacitor energy storage system (SCESS) in Khorfakkan city, Sharjah were presented. HOMER Pro software was used to model and simulate the hybrid energy system (HES) based on the daily energy consumption for Khorfakkan city. The simulation results show that using SCESS as an energy storage system will help the performance of HES based on the Levelized cost of energy (LCOE) and greenhouse gas (GHG) emissions. The HES with SCESS has renewable fraction (68.1%) and 0.346 $/kWh LCOE. The HES meets the annual AC primary load of the city (13.6 GWh) with negligible electricity excess and with an unmet electrical load of 1.38%. The reduction in GHG emissions for HES with SCESS was 83.2%, equivalent to saving 814,428 gallons of diesel.     

A multiple fuzzy inference systems framework for daily stock trading with application to NASDAQ stock exchange

The aim of this study is to develop an expert system for predicting daily trading decisions in a typical financial market environment. The developed system thus employs a Multiple FISs framework consisting of three dedicated FISs for stock trading decisions, Buy, Hold and Sell respectively. As input to the Multiple FISs framework, the system takes the fundamental information of the respective companies and the historical prices of the stocks which are processed to give the technical information. The framework suggests the investor to Buy, Sell or Hold on a daily basis for a portfolio of stock taken into consideration. Experimenting the framework on selected stocks of NASDAQ stock exchange shows that including the fundamental data of the stocks as input along with the technical data significantly improves the profit return than that of the system taking only technical information as input data. Characterised as a stock market indicator, the framework performs better than some of the most popularly used technical indicators such as Moving Average Convergence/Divergence (MACD), Relative Strength Index (RSI), Stochastic Oscillator (SO) and Chaikin Oscillator (CO). The developed framework also gives better profit return compared to an existing model with similar objective.