Earticle

The research of different light source on pulse broadening causing by fiber dispersion has been theoretically investigated and simulated of optical current transformer. There is no light source spectral width to consider the impact of the results till now. The studies show that the outputs of optical current transformer are different when the different spectra width of different light sources, and our experimental results verify above conclusions. That show that the effect is small enough and the treatment of using monochromatic model to describe broad-band systems is reasonable and feasible, if the wavelength accumulation effects of the other optical parameter of sensing head are not considered. The results can provide a reference for optical current transformer practical research.

The present study aims to design a methodical model based on Analytical hierarchy Process (AHP) and Artificial Neural Network (ANN) for estimation of player price in IPL. Based on expert view several key features are chosen for cricket player price calculation in IPL twenty-20 cricket tournament. Initial weights of attributes are calculated through AHP. Dataset is prepared using Open Source Information from the internet and offline experts. Back propagation neural network trains a pre normalized performance dataset of last three years IPL statistical dataset of 226 players. Finally, our proposed methodology gives a systematic way to select the important attributes and calculate the weights based on expert opinion to measure the optimal price for a player which will help the IPL team owner to select the player according their budget and strategies. A strict tradeoff between ‘budget conscious bidding’ and ‘performance based bidding’ is thus optimized using this model.

Based on computational fluid dynamics, friction lubrication theory and hydrostatic technology, large size hydrostatic Guide-way of heavy vertical CNC lathes，which are widely used, was taken as research object. The control equation of hydrostatic guide-way’s interstitial fluid was established. By finite volume method, lubricating oil properties, boundary conditions such as constant flow inlet, outlet pressure, and wall were set. The velocity distribution of hydrostatic thrust bearing’s interstitial fluid was obtained through iterative computations. And influence rules of cavity depth on interstitial oil-film of large size hydrostatic Guide-way’s was discussed. The results reflect hydrostatic bearing’s internal fluid flow state .and provide theoretical basis for the design of practical engineering hydrostatic bearing.

Artificial fish-swarm algorithm is a realization model of the swarm intelligence optimization algorithm. It uses the optimization model of imitated nature fish for feeding from top to bottom, clusters and rear, local optimization by individual fish, achieve the purpose of global optimal values highlighted in the groups. RBFNN based on the AFSA can accurately find the optimal solution quickly and ensure the diversity of artificial fish. It is easier to find the global optimal point of optimal fish. This design uses second-order pendulum as a controlled object, using artificial fish swarm algorithm applied to the neural network training algorithms, building design of RBF Neural networks control module , verifing by Matlab simulation of actual control controller performance.

Kalman filtering is a well-established methodology used in various multi-sensor data fusion applications. In our experiment, we first obtain measurements from the accelerometer and gyroscope and fuse them using Kalman filter in an inertial measurement unit (IMU). We estimate Kalman filter output and estimation error. The affect of process noise and measurement noise on estimation error is tested. It is explored that the measurement noise has significant role to increase estimation error in the data fusion process.

Based on an analysis of the advantages and disadvantages of single-layer and multi-layer BOM structure, and combine with specific examples of products, a more practical—cross-type BOM structure method is presented. Aiming to solve the system efficiency problems brought by the BOM operation in manufacturing. Regarding the queue as a temporary table, we put forward an improved multi-layer BOM traversal algorithm, makes full use of the basic operation characteristics and setting marks in related fields queue and simplifies the complexity of the algorithm. Compared with the traditional recursive algorithm and hierarchical algorithm, the improved algorithm has the advantages of fast speed, low resource consumption, and high practicability. And the improved algorithm is verified through an example.

Some new simple control methods of modified function projective synchronization in two chaotic systems are investigated in this paper. Based on Lyapunov method, a general feedback error control law is proposed, which contains only feedback error term and easy to implement in practice. Moreover, an adaptive feedback error control scheme is proposed, in which the feedback gain can be automatically adapted to suitable constant. Numerical example is provided to show the effectiveness of proposed method.

Errors in the scanning procedures lead to uncertainties when trying to segment the scanned images. Fuzzy c-means is a clustering method that can be applied to segment images with uncertainty estimates. Bias-corrected fuzzy c-means (BCFCM) clustering compensates for two sources of uncertainty by modeling noise and bias fields during the segmentation process. In this paper, we present an approach to improve BCFCM clustering and apply it to magnetic resonance imaging (MRI) data of the human brain. Our approach is based on two variants of BCFCM clustering, the classical one and the one with distance-based weights. We improve both variants by slightly modifying their main algorithms for better bias field estimation. To evaluate the improved algorithms, we apply the algorithms to synthetic data, simulated MRI brain data, and real MRI brain data with ground truth in form of manual segmentation. All experiment results show that our improved methods outperform the original methods in both the segmentation accuracy and efficiency (the number of iterations).

Processing effect is a lot of. In this paper, the research perspective is to improve the machining efficiency and machining quality of the system. Based on the analysis of the characteristics of the edm process as the foundation, proposed a fuzzy control technology. In edm process, using fuzzy neural network control technology. According to the characteristics of the fuzzy controller, combined with the advantages of artificial neural network, to correct the adverse effect of some imperfect rules, improve the fast response ability of the control system in a reliable way.

The measurement of online service quality has become increasing important as the e-commerce companies deliver an expanding array of service through the websites. There are few quantitative studies on the online service quality assessment focused on the market in China, which is one of the developing countries with the highest online population growth. This paper is an attempt to identify the measurement of the online service quality in China, and grey approach is applied to identify the criteria of online service quality assessment. The results could be the guideline for the e-commerce companies in terms of improving their service.

Due to cost effectiveness and less electrical fault hazards, the HVDC transmission is more suitable for long distance power transmission than that for the conventional AC transmission system. Moreover the utilization of power electronics has made HVDC technology competitive and reliable in comparison to HVAC system. The controlling mechanism of the DC link (either monopolar or bipolar) is the major concern for HVDC technology. Determination of control loop for system operation needs to commensurate with the conduction period of the semiconductor switching device. Generally IGBT and MOSFET are widely used as switching device whose delay angle, advance angle and extinction angle need to be matched with the controlling mechanism of the converter and inverter section. In this paper, the controlling characteristics of a monopolar HVDC system are determined by using MATLAB/Simulink. The Simpower tool box is used for designing the controlling blocks of the HVDC system. The controlling characteristics of the monopolar HVDC system can be determined from the simulation results of the gate currents of IGBT, output voltages of converter and inverter and modulation index.

The security performance problems of broadcast channel with an eavesdropper (BCE) have been studied for many years in the field of communication. Many articles have given the meaningful results. However, almost no consequent of an outer bound on BCE has been given by previous work, especially, no significant results has been given. So this paper proposes and proves an outer bound of BCE by using information entropy, and further proves this outer bound is tight in the cut-set bound by max-flow min-cut theorem. It illustrates the outer bound is meaningful. Finally, by contrasting a variety of existing results, we can get that the outer bound in this paper include (tight) in the existing outer bound of BCE in the case of not considering the eavesdropper.

Abundance estimation is an important step of quantitative analysis of hyperspectral remote sensing data. Due to physical interpretation, sum-to-one and non-negativity constraints are generally imposed on the abundances of materials. This paper presents a geometric approach to fully constrained linear spectral unmixing using variable endmember sets for the pixels. First, an improved method for selecting per-pixel candidate endmember set is presented, which is suitable for dealing with hyperspectral image with large number of endmembers. To determine the optimal per-pixel endmember set from the entire endmembers present in the hyperspectral scene, an iterative partially constrained geometric unmixing is then performed, in which subspace projection is used for fully constrained least square estimation. The performance of the resulting unmixing algorithm is evaluated by comparison with benchmark unmixing algorithm on synthetic and real hyperspectral data.

The scarcity of automatic fire detection systems continues to be a problem that needs a serious attention in order to save human lives by preventing injuries and/or deaths. The newest innovations are continuing to use cameras and computer algorithms to analyze the visible effects of fire and its motion in their applications. As their approaches present some drawbacks when working in spatial domain, the main difficulty is still to identify objects if they do not occur at the expected position. In this paper, we present an improved fast and robust algorithm for detecting fire/smoke in a cluttered scene from a pair of cameras. The input images are first segmented according to a pre-determined disparity threshold map and the real-time disparities of fire. Binary image processing techniques are used to reject noise introduced into the segmented images through low-resolution disparity calculations which consequently can lead to the gain of clearer results. In order to reduce the false alarms, a new segmentation method used in this approach shows that segmented images using stereo-vision are more accurate than those segmented using color approach for the overall detection. The segmented images are then used for image feature extraction for a Fuzzy-neural network classifier to help the system to generate a warning in case fire/smoke is detected.

In this paper, we propose a new version of FP-Growth algorithm to find association rules. In this version, we vary the minsup value from one level to another. This variation is made in two cases: increasing and decreasing the minsup value. We performed a set of experiments to validate the usefulness of our proposition in the generation of association rules process.

Shape of granule is one of the important issues in granular computing classification problems and related to the classification accuracy, the number of granule, and the join process of two granules. A bottle up granular computing classification algorithm (BUGrC) is developed in the frame work of fuzzy lattices. Firstly, the granules are represented as 4 shapes, namely hyperdiamond granule, hypersphere granule, hypercube granule, and hyperbox granule. Secondly, the granule set is induced by the training set and the bottle up join operator. Thirdly, machine learning benchmark datasets are used to analyze and discuss the BUGrC with different shape granules.

It’s very important for environment simulation in the three-dimensional railway line selection and design, and the establishment of three-dimensional landscape model impacts the fidelity of simulation environment directly. The paper studied. Of the 3D landscape modeling methods and key technologies. The paper achieved three-dimensional modeling of railway line used bi-cubic coons model, realized line structures three-dimensional modeling by object-oriented modeling approach. Based on Visual C + + and Oracle databases, The paper established the structure model library and realized the effective management of the model, Solved the exact match and control problem between structure mode and the three-dimensional scene, Achieved the application of structure mode in three-dimensional line landscape simulation and established a three-dimensional line landscape with higher fidelity. Developed a three-dimensional landscape line dynamic environment simulation system based on the methods and theories of paper studied. Using this system, Yunnan - Tibet railway line has been designed 3D landscape modeling, Verified the correctness of the theory and algorithms of paper studied.

Bad weather conditions including haze, fog, rain etc. degrade the picture quality which causes poor visualizations. Various methods have been proposed to eradicate the effects of above conditions. Initially, polarization filters were used to restore the foggy image. Though this method estimates airlight to quite good an extent but at the same time uses multiple images therefore, cannot be applied on dynamic scenes. In addition, it has very high time complexity. To overcome the use of multiple images Dark channel prior technique was used that effectively calculates the transmission map to restore the foggy image. For this purpose it requires usage of soft matting technique that causes halo effect and has high time complexity. We are of the opinion that if transmission map is calculated using dark channel prior and airlight is estimated using polarisation filters, the resultant process will not only have lower time complexity but at the same time overcomes the drawback of multiple images. The above mentioned technique is implemented in MATLAB-9 and analysed in this paper. The results shows that usage of this hybrid approach gives quite better results.

For the application background on a guidance of audit information resource, this paper constructs the ontology of audit methods which is suitable for medical insurance in audit field, and provides information basis for the ontology-based rule reasoning, through utilizing the collected relevant information of audit methods in actual work of medical insurance. Then, the paper mainly researches the rule reasoning algorithm—forward rule rapid matching Rete algorithm in expert system. Aiming at the existing problems in Rete algorithm, the paper introduces the improved method of active hash. Finally, the paper verifies that the improved Rete algorithm can improve implementation efficiency, and has practical significance in the intelligent retrieval system.

Biogeography-based optimization (BBO) has shown excellent exploitation ability of the population information for simple-objective optimization problem. But if BBO is directly applied in multi-objective optimization problems (MOPs), optimal solution set gained by BBO has worse diversity and distribution. To overcome these shortcomings, a chaos migration operator is put forwards to improve the diversity of the population. And then based on the new chaos migration operator, Chaos biogeography multi-objective optimization algorithm (CBBMO) is proposed for MOPs. In CBBMO, the chaos migration operator and original mutation operator of BBO are applied to produce the next generation population. The archive is used to conserve the Pareto optimal solutions. The experiment results show that the proposed algorithm CBBMO is feasible and effective for MOPs.

In order to solve closed-loop logistics network design problem in the low-carbon economic environment, in this paper, queuing theory is used to calculate the amount of storage of reverse logistics nodes and interval number is used to describe the uncertainty of the logistics network, a bi-objectives logistics network design model which minimum the total cost and carbon emissions of logistics network is established, the solving idea based on interval-number multiple attribute decision theory is proposed, solving algorithm based on genetic algorithm is designed. The feasibility of the model and solution algorithm is verified through case analysis; this method can meet the low-carbon closed-loop logistics network design requirements under uncertainty conditions.

Semi or completely autonomous unmanned vehicles, remotely driven or controlled through artificial intelligence, are instrumental to foster space exploration. One of the most essential tasks of a rover is terrain traversing which requires the need of efficient suspension systems. This communication presents a suspension system giving degrees of freedom to every wheel with the help of linear actuators connected through bell crank levers. The actuation of linear actuators directly varies the height of every wheel from the chassis hence offering articulation to the rover. A control system is developed offering an algorithm for its autonomous actuation. This system proves instrumental for leveling of the chassis where any kind of slope, roll or pitch, may impute abstaining of payloads from efficient working. This was tried and tested successfully as a part of the rover developed by Team RUDRA from SRM University, INDIA (first Team from Asia and finishing at the fifth position) at University Rover Challenge 2013, held at UTAH, USA in May-June.

A new hybrid Optimization Algorithm is proposed to solve Constrained Multi-objective Optimization Problems (CMOPs). The algorithm is named BBO/DE which combines the exploitation ability of Biogeography-based Optimization (BBO) and the exploration ability of Differential Evolution (DE). Meanwhile distance measures and adaptive penalty functions are adopted to handle the constraints so that optimal solutions in the infeasible space can be searched effectively. In addition, the feasible archive is applied to store the non-dominated feasible solutions obtained so far and is updated based on crowding-distance. Experiment results demonstrate that the proposed hybrid algorithm BBO/DE can approximate the true Pareto front and has better distribution.

To improve the accuracy of digital image edge detection, an ENO nonlinear fourth-order interpolation based subpixel edge detection algorithm was proposed in this paper. A stencil was constructed through classical Canny operator, followed by processing gray images to generate gradient images. ENO nonlinear fourth-order interpolation was applied in the gradient direction of target edges, and then subpixel subdivision computation was performed to obtain subpixel accurate locations of target edges, the performance of our edge detection algorithm was compared with that of centered cubic convolution and non-centered cubic convolution based edge detection methods. Simulation results demonstrated that the ENO based interpolation detection algorithm improved the abilities of detecting image edges.

In collecting and analyzing information about recent research papers, authors, and journals with their citation and indices, we noticed that in many cases such indices fail to reflect reality and are incapable to distinguish between different authors in some cases while they clearly have different research profiles. There are many research papers that can be found in literature that discussed the behavior and limitations of the current citation or publication indices. Most of them referred to the sensitivity problem and the fact that most indices neglect some publications during index calculation. These two problems are the most important limitations that may judge index fairness. In this paper, we proposed new indices; MIExp-Index using exponential function and MIArea-Index using the concept of the area under curve (AUC) in order to enhance the sensitivity and thus the fairness of index assessment. To demonstrate our two novel indices, data of real and hypothetical authors were used to demonstrate these two approaches. The results showed that both of the proposed indices are very sensitive for each single citation that the paper receives after being published. In addition, the two indices consider all authors’ publications, even the new ones, into account.

Optimization problems, variational inequalities, minimax problems can be formulated as equilibrium problems. The iterative algorithms of fixed points are often applied to finding the solution of equilibrium problems. In this paper, we introduce a new hybrid iterative algorithm for finding a common element of the set of fixed points of asymptotically nonexpansive mappings and the set of solutions of an equilibrium problem in Hilbert spaces. Besides, an example of variational inequality problem is given to illustrate the efficiency and performance of the newly algorithm.

Localizing humans inside a home or office environment is vital for various service robot applications. This paper presents a novel method to estimate human locations using distributed laser range finders. First, the Kalman filter is employed on the laser data to produce a statistically optimal scan estimate. Next, Scan points in the foreground are then grouped using mean shift clustering algorithm. An area-divided clustering mode is introduced to ensure enough clusters to represent the contour of human. Finally, the centers of humans are estimated from the obtained clusters using incomplete ellipse fitting. Experiments are conducted to prove the robustness and efficiency of the proposed method.

This paper is investigated the low frequency noise behavior in subthreshold regime of gate-all-around silicon nanowire field effect transistors. Downscaling of multi gate structure beyond 50 nm gate length describes the quantum confinement related model. A drain current model has been described for output characteristics of silicon nanowire FET that is incorporated with velocity saturation effects and compact modeling of RF noise behavior is analyzed for gate-all-around structure. Noise performance of gate-all-around transistor is investigated at high frequency band for radio frequency RF specified application and consequently low frequency noise behavior can be analyzed using drain current model. This paper shows that noise is decreasing with frequency. Higher subthreshold, lower drain induced barrier lowering DIBL, higher on-off ratio and higher noise figure at lower frequency is achieved by gate all around configuration and comparison has been done with double gate structure.