Applying portfolio theory-based modified ABC to electricity generation mix

Portfolio theory has found its model in numerous engineering applications for optimizing the electrical generation mix of an electricity area. However, to have better performance of this theory, this paper presents a new heuristic method as known modified artificial bee colony (MABC) to portfolio optimization problem. Moreover, we consider both dis-patchable and non-dis-patchable constrains variables and energy sources. Note that the proposed MABC method uses a Chaotic Local Search (CLS) to enhance the self searching ability of the original ABC algorithm. Resulting, in this paper a portfolio theory-based MABC model that explicitly distinguishes between electricity generation (energy), installed capacity (power) and actual instantaneous power delivery is proposed. Therefore, in this model, the uncertainties of wind power and ramp-up/down constrains of traditional power plants are correctly considered in the investment cost. The numerical results show the great potential of proposed model with lowest risk on generation cost. Also, they are show that MABC approach is successful in portfolio optimization.     

An adaptive PID like controller using mix locally recurrent neural network for robotic manipulator with variable payload

Being complex, non-linear and coupled system, the robotic manipulator cannot be effectively controlled using classical proportional-integral-derivative (PID) controller. To enhance the effectiveness of the conventional PID controller for the nonlinear and uncertain systems, gains of the PID controller should be conservatively tuned and should adapt to the process parameter variations. In this work, a mix locally recurrent neural network (MLRNN) architecture is investigated to mimic a conventional PID controller which consists of at most three hidden nodes which act as proportional, integral and derivative node. The gains of the mix locally recurrent neural network based PID (MLRNNPID) controller scheme are initialized with a newly developed cuckoo search algorithm (CSA) based optimization method rather than assuming randomly. A sequential learning based least square algorithm is then investigated for the on-line adaptation of the gains of MLRNNPID controller. The performance of the proposed controller scheme is tested against the plant parameters uncertainties and external disturbances for both links of the two link robotic manipulator with variable payload (TL-RMWVP). The stability of the proposed controller is analyzed using Lyapunov stability criteria. A performance comparison is carried out among MLRNNPID controller, CSA optimized NNPID (OPTNNPID) controller and CSA optimized conventional PID (OPTPID) controller in order to establish the effectiveness of the MLRNNPID controller.     

The use of SVM-FFA in estimating fatigue life of polyethylene terephthalate modified asphalt mixtures

To predict fatigue life of Polyethylene Terephthalate (PET) modified asphalt mixture, various soft computing methods such as Genetic Programming (GP), Artificial Neural Network (ANN), and Fuzzy Logic-based methods have been employed. In this study, an application of Support Vector Machine Firefly Algorithm (SVM-FFA) is implemented to predict fatigue life of PET modified asphalt mixture. The inputs are PET percentages, stress levels and environmental temperatures. The performance of proposed method is validated against observed experiment data. The results of the prediction using SVM-FFA are then compared to those of applying ANN and GP approach and it is concluded that SVM-FFA leads to more accurate results when compared to observed experiment data.     

An effective approach for parameter determination of the digital phase-locked loop in the z-domain

In digital communication systems, typical methodologies in determining loop parameters of the digital phase-locked loop (DPLL) are based on the mapping transformation from the analog domain to the digital domain. However, such transform based algorithms are relatively complicated and not straightforward, and they also cause the problem that loop parameters are affected by the pre-detection integration time greatly. To solve these issues, an effective direct method of determining loop parameters of the second-order DPLL in the z-domain is proposed in this paper. Through ascertaining specific positions of the closed-loop system function's poles inside the right-hand side of the z-plane's unit circle, unknown parameters are calculated directly and flexibly in this method, which enables the DPLL to acquire good low-pass filtering characteristic and system stability. This novel method not only reduces the complexity of solving the parameters, but also eliminates the effect of the pre-detection integration time on loop parameters. Simulation results are provided to confirm the feasibility of the proposed method and to show that the DPLL obtained by this method achieves the similar tracking performance to the discretized PLL.     

Synthesis of a non-integer periodic function generator of a four-bar mechanism using a Haar wavelet

In this study, a synthesis method for a non-integer periodic function generator of a four-bar mechanism using a Haar wavelet is presented. Using wavelet theory, the mathematical model for the output of a four-bar mechanism is established. Next, the characteristics of the four-bar mechanism output variable are described by the wavelet feature parameters; the relationship of the wavelet feature parameters between the output and its translation or dilation is also investigated. Based on this finding, three numerical atlas databases comprising of 203,500 groups of basic dimensional types of planar four-bar mechanisms, 148,995 groups of basic dimensional types of spherical four-bar mechanism and 11,175 groups of basic dimensional types of planar slider–crank mechanism are compiled. According to the internal relationship between the wavelet feature parameters, the sizes of the objective mechanism can be obtained using fuzzy recognition. Based on a genetic algorithm, optimization of the link parameters can be realized. Three examples are presented to demonstrate the efficacy and practicality of the proposed theory.     

Evolving flexible beta basis function neural tree using extended genetic programming & Hybrid Artificial Bee Colony

In this paper, a new hybrid learning algorithm is introduced to evolve the flexible beta basis function neural tree (FBBFNT). The structure is developed using the Extended Genetic Programming (EGP) and the Beta parameters and connected weights are optimized by the Hybrid Artificial Bee Colony algorithm. This hybridization is essentially based on replacing the random Artificial Bee Colony (ABC) position with the guided Opposite-based Particle Swarm Optimization (OPSO) position. Such modification can minimize the delay which might be lead by the random position, in reaching the global solution. The performance of the proposed model is evaluated for benchmark problems drawn from time series prediction area and is compared with those of related methods.     

一种基于敏感分量融合的液压泵退化特征提取方法

液压泵退化特征提取是实现故障预测的关键环节。在液压泵性能退化过程中，其振动信号复杂度高、非线性强，难以有效地提取退化特征，为此，本文提出一种基于敏感分量融合的退化特征提取方法。采用改进局部特征尺度分解（ILCD）方法对振动信号进行分解，并利用贝叶斯信息准则与所构建的敏感因子，对内禀尺度分量进行筛选，以减少干扰分量的影响，得到敏感分量，有效抓取特征信息；在此基础上，引入离散余弦变换代替传统复合谱分析中的傅里叶变换，提出离散余弦变换-复合谱（DCS）算法，以解决信息遗漏问题，并利用DCS对敏感分量进行融合，提取复合谱熵作为退化特征，以提高对退化过程的表征能力；最后，通过对液压泵性能退化试验实测振动信号的应用分析，验证了该方法的有效性。     

Prediction of heavy oil viscosity using a radial basis function neural network

Heavy oil and extra heavy oil resources comprise about 75% of petroleum resources. The most important characteristic of heavy oils is their viscosity. Consequently, to extract and prepare these kinds of crude oil for use, great emphasis should be put on viscosity. The present study highlights the application of intelligent model named radial basis function (RBF) network optimized by genetic algorithm for estimation of diluted heavy oil viscosity in presence on kerosene. The input parameters of model were temperature and mass fraction of kerosene. The output of model was viscosity of heavy oil. Genetic algorithm was utilized to optimize the tuning parameters of RBF model. The outcomes of this study showed that the proposed model is accurate in estimation of target data.     

Effect of in-situ processing parameters on microstructure and mechanical properties of TiC particulate reinforced Al–4.5Cu alloy MMC fabricated by stir-casting technique – Optimization using grey based differential evolution algorithm

Fabrication of metal matrix composite materials by using the traditional casting process is a very promising task of developing near net shape products at a very low cost. Among these casting processes, the stir-casting technique has fine microstructures because of fast cooling, less porosity and good quality bonding among particle and the base matrix alloy. The major aim of this present paper is to fabricate titanium carbide reinforced metal matrix composite (MMC) by using micron sized activated charcoal powder, to get react with titanium material for producing TiC intermetallic particles in the Al–4.5%Cu matrix by in-situ technique so as to produce Al–4.5%Cu–5%TiC MMC, with the intention of finding the optimum processing parameter i.e. pouring temperature, stirring speed in liquid melt-stirring process, reaction time with the aim of achieving better microstructure and mechanical properties, i.e. micro-hardness, toughness and ultimate tensile strength by using an efficient grey based differential evolution algorithm. Mechanical properties of optimum processing condition are verified by doing confirmation tests. ANOVA analysis is performed to analyze the role of each factor on multiple performance characteristics and it was found that stirrer rotational speed and reaction time is playing a key role in affecting the mechanical property. Additionally, to predict the response parameters regression equations were developed and the validity of regression model is tested by plotting scatter diagram. Finally microstructure study has been performed for the optimal sample by using optical microscope.