General scheduling framework in computational Grid based on Petri net

A general scheduling framework (GSF) for independent tasks in computational Grid is proposed in this paper, which modeled by Petri net and located on the layer of Grid scheduler. Furthermore, a new mapping algorithm aimed at time and cost is designed on the basis of this framework. The algorithm uses weighted average fuzzy applicability to express the matching degree between available machines and independent tasks. Some existent heuristic algorithms are tested in GSF, and the results of simulation and comparison not only show good flexibility and adaptability of GSF, but also prove that, given a certain aim, the new algorithm can consider the factors of time and cost as a whole and its performance is higher than those mentioned algorithms.     

Dynamic services selection algorithm in Web services composition supporting cross-enterprises collaboration

Based on the deficiency of time convergence and variability of Web services selection for services composition supporting cross-enterprises collaboration, an algorithm QCDSS (QoS constraints of dynamic Web services selection) to resolve dynamic Web services selection with QoS global optimal path, was proposed. The essence of the algorithm was that the problem of dynamic Web services selection with QoS global optimal path was transformed into a multi-objective services composition optimization problem with QoS constraints. The operations of the cross and mutation in genetic algorithm were brought into PSOA (particle swarm optimization algorithm), forming an improved algorithm (IPSOA) to solve the QoS global optimal problem. Theoretical analysis and experimental results indicate that the algorithm can better satisfy the time convergence requirement for Web services composition supporting cross-enterprises collaboration than the traditional algorithms. Foundation item: Project(70631004) supported by the Key Project of the National Natural Science Foundation of China; Project(20080440988) supported by the Postdoctoral Science Foundation of China; Project(09JJ4030) supported by the Natural Science Foundation of Hunan Province, China; Project supported by the Postdoctoral Science Foundation of Central South University, China     

Short-term forecasting optimization algorithms for wind speed along Qinghai-Tibet railway based on different intelligent modeling theories

To protect trains against strong cross-wind along Qinghai-Tibet railway, a strong wind speed monitoring and warning system was developed. And to obtain high-precision wind speed short-term forecasting values for the system to make more accurate scheduling decision, two optimization algorithms were proposed. Using them to make calculative examples for actual wind speed time series from the 18th meteorological station, the results show that: the optimization algorithm based on wavelet analysis method and improved time series analysis method can attain high-precision multi-step forecasting values, the mean relative errors of one-step, three-step, five-step and ten-step forecasting are only 0.30%, 0.75%, 1.15% and 1.65%, respectively. The optimization algorithm based on wavelet analysis method and Kalman time series analysis method can obtain high-precision one-step forecasting values, the mean relative error of one-step forecasting is reduced by 61.67% to 0.115%. The two optimization algorithms both maintain the modeling simple character, and can attain prediction explicit equations after modeling calculation. Foundation item: Project(2006BAC07B03) supported by the National Key Technology R & D Program of China; Project(2006G040-A) supported by the Foundation of the Science and Technology Section of Ministry of Railway: Project(2008yb044) supported by the Foundation of Excellent Doctoral Dissertation of Central South University     

Automated retinal blood vessels segmentation based on simplified PCNN and fast 2D-Otsu algorithm

According to the characteristics of dynamic firing in pulse coupled neural network (PCNN) and regional configuration in retinal blood vessel network, a new method combined with simplified PCNN and fast 2D-Otsu algorithm was proposed for automated retinal blood vessels segmentation. Firstly, 2D Gaussian matched filter was used to enhance the retinal images and simplified PCNN was employed to segment the blood vessels by firing neighborhood neurons. Then, fast 2D-Otsu algorithm was introduced to search the best segmentation results and iteration times with less computation time. Finally, the whole vessel network was obtained via analyzing the regional connectivity. Experiments implemented on the public Hoover database indicate that this new method gets a 0.803 5 true positive rate and a 0.028 0 false positive rate on an average. According to the test results, compared with Hoover algorithm and method of PCNN and 1D-Otsu, the proposed method shows much better performance. Foundation item: Project (60872081) supported by the National Natural Science Foundation of China; Project (50051) supported by the Program for New Century Excellent Talents in University; Project (4092034) supported by the Natural Science Foundation of Beijing     

Four optimal design approaches of high-order finite-impulse response filters based on neural network

Four optimal approaches of high-order finite-impulse response(FIR) digital filters were developed for designing four types filters using neural network algorithms. The solutions were presented as parallel algorithms to approximate the desired frequency response specification. Therefore, these methods avoid matrix inversion, and make a fast calculation of the filter’s coefficients possible. The convergence theorems of these proposed algorithms were presented and proved to illustrate them stable, and the implementation of these methods was described together with some design guidelines. The simulation results show that the ripples of the designed FIR filters are significantly little in the pass-band and stop-band, and the proposed algorithms are of fast convergence.     

Time series online prediction algorithm based on least squares support vector machine

Deficiencies of applying the traditional least squares support vector machine （LS-SVM） to time series online prediction were specified. According to the kernel function matrix＇s property and using the recursive calculation of block matrix, a new time series online prediction algorithm based on improved LS-SVM was proposed. The historical training results were fully utilized and the computing speed of LS-SVM was enhanced. Then, the improved algorithm was applied to timc series online prediction. Based on the operational data provided by the Northwest Power Grid of China, the method was used in the transient stability prediction of electric power system. The results show that, compared with the calculation time of the traditional LS-SVM（75 1 600 ms）, that of the proposed method in different time windows is 40-60 ms, proposed method is above 0.8. So the improved method is online prediction. and the prediction accuracy（normalized root mean squared error） of the better than the traditional LS-SVM and more suitable for time series online prediction.     

Hybrid optimization model of product concepts

Deficiencies of applying the simple genetic algorithm to generate concepts were specified. Based on analyzing conceptual design and the morphological matrix of an excavator, the hybrid optimization model of generating its concepts was proposed, viz. an improved adaptive genetic algorithm was applied to explore the excavator concepts in the searching space of conceptual design, and a neural network was used to evaluate the fitness of the population. The optimization of generating concepts was finished through the ＂evolution - evaluation＂ iteration. The results show that by using the hybrid optimization model, not only the fitness evaluation and constraint conditions are well processed, but also the search precision and convergence speed of the optimization process are greatly improved. An example is presented to demonstrate the advantages of the orooosed method and associated algorithms.     

A bi-objective model for job-shop scheduling problem to minimize both energy consumption and makespan

The issue of reducing energy consumption for the job-shop scheduling problem in machining systems is addressed, whose dual objectives are to minimize both the energy consumption and the makespan. First, the biobjective model for the job-shop scheduling problem is proposed. The objective function value of the model represents synthesized optimization of energy consumption and makespan. Then, a heuristic algorithm is developed to locate the optimal or near optimal solutions of the model based on the Tabu search mechanism. Finally, the experimental case is presented to demonstrate the effectiveness of the proposed model and the algorithm. Foundation item: Project (50475062) supported by the National Natural Science Foundation of China; project (2004-47-19) supported by the Natural Science Foundation of Chongqing City of China     

A multi-sequential number-theoretic optimization algorithm using clustering methods

A multi-sequential number-theoretic optimization method based on clustering was developed and applied to the optimization of functions with many local extrema. Details of the procedure to generate the clusters and the sequential schedules were given. The algorithm was assessed by comparing its performance with generalized simulated annealing algorithm in a difficult instructive example and a D-optimum experimental design problem. It is shown the presented algorithm to be more effective and reliable based on the two examples. Foundation item: Project supported by the Postdoctoral Science Foundation of Central South University     

Synchronous hybrid transport network design

Delay, as an inevitable real-world phenomenon, is usually ignored in transport network design. A model of urban hybrid transport system with stochastic delay was created on the basis of the idealized public transport system design. After formulating the total trip time cost composed of accessing time in the sub-region of the city, waiting time at the public transport station, and in-vehicle time in the public transit network, the analytical properties of the total trip time cost function were investigated. The results show that in the urban hybrid transport network design, the total trip time cost reaches its approximate minimum in a δ-neighbourhood of buffer time of 1.5 min, and that through modelling optimal delay in hybrid transport system, the maximal synchronization can be achieved and operational efficiency and passenger satisfaction can be improved. The proposed modelling and analytical investigations are attempts to contribute to more realistic modelling of future idealized public transport system that involves more practical constraints. Foundation item: Project (70671008) supported by the National Natural Science Foundation of China, Project (3340-74236000003) supported by the Scientific Research Innovation Fund Project for Graduate Student of Hunan Province, China     

Investigations and practices on green manufacturing in machining systems

A machining system is a typical manufacturing system. A green manufacturing function framework of machining systems is structured to describe the traits of input, output and control elements in the system. Based on the function framework, the green manufacturing problem framework of machining systems is presented. The green manufacturing problems in machining systems are classified into three classes and related series of subclass problems. The three classes of problems in the green manufacturing problem framework are the problems of the minimization of resource consumption, the minimization of environmental discharge, and the synthesized minimization of both of them. A series of investigations and practices on green manufacturing in machining system, performed by the authors for quite a long period, are introduced in brief, such as the optimizing system for raw material cutting, the matching system for energy-saving in machining, the design of highly efficient dry hobbing machine tools, the multi-objective decision-making model for green manufacturing in machining systems, and the decision-making supporting system for green manufacturing in machining processes. Foundation item: Project (50475062) supported by the National Natural Science Foundation of China; project (47-19) supported by the Natural Science Foundation of Chongqing, China     

Period of arnold transformation and its application in image scrambling

With the security problem of image information as the background, some more properties of the period of Arnold transformation of two-dimension were studied by means of introducing a integer sequence. Some new results are obtained. Two interesting conjectures on the period of Arnold transformation are given. When making digital images scrambling by Arnold transformation, it is important to know the period of the transformation for the image. As the application of the theory, a new method for computing the periods at last are proposed. Foundation item: Project (10471020) supported by the National Natural Science Foundation; project (04JJ6028) supported by the Natural Science Foundation of Hunan Province; project (03A002) supported by the Ministry of Education of Hunan Province     

Support vector machine forecasting method improved by chaotic particle swarm optimization and its application

By adopting the chaotic searching to improve the global searching performance of the particle swarm optimization (PSO), and using the improved PSO to optimize the key parameters of the support vector machine (SVM) forecasting model, an improved SVM model named CPSO-SVM model was proposed. The new model was applied to predicting the short term load, and the improved effect of the new model was proved. The simulation results of the South China Power Market’s actual data show that the new method can effectively improve the forecast accuracy by 2.23% and 3.87%, respectively, compared with the PSO-SVM and SVM methods. Compared with that of the PSO-SVM and SVM methods, the time cost of the new model is only increased by 3.15 and 4.61 s, respectively, which indicates that the CPSO-SVM model gains significant improved effects. Foundation item: Project(70572090) supported by the National Natural Science Foundation of China     

Adaptive learning with guaranteed stability for discrete-time recurrent neural networks

To avoid unstable learning, a stable adaptive learning algorithm was proposed for discrete-time recurrent neural networks. Unlike the dynamic gradient methods, such as the backpropagation through time and the real time recurrent learning, the weights of the recurrent neural networks were updated online in terms of Lyapunov stability theory in the proposed learning algorithm, so the learning stability was guaranteed. With the inversion of the activation function of the recurrent neural networks, the proposed learning algorithm can be easily implemented for solving varying nonlinear adaptive learning problems and fast convergence of the adaptive learning process can be achieved. Simulation experiments in pattern recognition show that only 5 iterations are needed for the storage of a 15×15 binary image pattern and only 9 iterations are needed for the perfect realization of an analog vector by an equilibrium state with the proposed learning algorithm.     

Some issues on 2.5-D transient electromagnetic forward

Numerical calculation for two integral transforms in 2.5-D transient electromagnetic forward is a difficult and key task, namely, the inverse Fourier transform and the inverse Laplace transform. Some effective algorithms for them were described. Based on the known algorithms in DC resistivity on wave-number distribution and selection, we proposed a principle on how to choose the least wave-number concerning the central-loop transient electromagnetic method. First, observe the behavior of transformation function curve with regard to wave-number in Fourier domain. In the light of its asymptote, ascertain the coverage scope of wave-number. Compared with analytic solution, the least wave-number in Fourier domain can be derived. Furthermore, the Laplace numerical inversion algorithm which needs only a few Laplace variables in pure real domain was also introduced here. The procedure was applied to forward modeling on transient electromagnetic field of a vertical magnetic dipole over uniform half-space to demonstrate them effectiveness and general applicability. Foundation item: Project (40344022) supported by the National Natural Science Foundation of China     

Design and analysis of two-layer anonymous communication system

A new architecture for scalable anonymous communication system（SACS） was proposed. The users were divided into several subgroups managed by different sub-blenders, and all sub-blenders were managed by the main-blender using two layers management scheme. The identity information of members are distributed on different sub-blenders, which makes each member keep much less information and network overload greatly reduce. The anonymity and the overhead of the new scheme were analyzed and compared with that of Crowds, which shows the cost of storage and network overhead for the new scheme largely decreases while the anonymity is little degraded. The experiment results also show that the new system architecture is well scalable. The ratio of management cost of SACS to that of Crowds is about 1：25 while the value of P（1｜H1＋） only increases by 0.001-0.020, which shows that SACS keeps almost the same anonymity with Crowds.     

An efficient embedding tree matching algorithm based on metaphoric dependency syntax tree

To find out all dependency relationships in which metaphors probably exist between syntax constituents in a given sentence, a dependency tree matching algorithm oriented to Chinese metaphor processing is proposed based on a research of unordered tree inclusion matching. In this algorithm, the pattern library is composed of formalization dependency syntax trees that are derived from large-scale metaphor sentences. These kinds of metaphor sentences are saved in the pattern library in advance. The main process of this algorithm is up-down searching and bottom-up backtracking revising. The algorithm discovers potential metaphoric structures in Chinese sentences from metaphoric dependency pattern library. Finally, the feasibility and efficiency of the new matching algorithm are further testified by the results of a series of experiments on dependency pattern library. Hence, accurate dependency relationships can be achieved through this algorithm. Foundation item: Project(50474033) supported by the National Natural Science Foundation of China     

Development of magnetocaloric materials in room temperature magnetic refrigeration application in recent six years

Some magnetocaloric materials were used successfully in magnetic refrigeration application and became one of the critical parts of magnetic refrigeration technology whose delightful progresses were made worldwide in the past 30 years. At the same time, the research on giant magnetocaloric materials will accelerate the development of room temperature magnetic refrigeration. In this paper, the new theoretical and experimental investigations on magnetic materials in room temperature application were described, including Gd and its binary and ternary intermetallic compounds, Mn-based compounds, La(Fe_13−x M _x )-based compounds and manganites. Based on the analysis of hysteresis, corrosion, cost and heat process, the comparison between different families of magnetic materials was discussed. Further research of room temperature magnetic refrigerant was suggested. Foundation item: Project(50876082) supported by the National Natural Science Foundation of China; Project (IRT0746) supported by the Changjiang Scholars and Innovative Research Team in University     

Robust stability of discrete-time nonlinear system with time-delay

The robustly asymptotical stability problem for discrete-time nonlinear systems with time-delay was investigated. Positive definite matrix are constructed through Lyapunov functional. With the identity transform, property of matrix inverse and S-procedure, a new sufficient condition independent of the size of time-delay for robust stability of discrete-time nonlinear systems with time-delay is established. With Schur complement, another equivalent sufficient condition for robust stability of discrete-time nonlinear systems with time-delay is given. Finally, a sufficient condition dependent on the size of time-delay for robust stability of discrete-time nonlinear systems with time-delay is obtained. A unified approach is used to cast the robust stability problem into a convex optimization involving linear matrix inequalities. Foundation item: Project (60425310) supported by the National Natural Science Foundation of China; project (2001AA4422200) supported by the Teaching and Research Award Program for Outstanding Young Teachers in Higher Education Institutions of the Ministry of Education of China     

Synthesis and photophysical and electrochemical properties of new cyclometalated platinum complex containing oxadiazole ligand

A new cyclometalated platinum complex containing 2, 5-bis（naphthalene-1-yl）-1,3,4-oxadiazole ligand was synthesized and characterized. The UV-Vis absorptions and photoluminescent properties of the ligand and its platinum complex were investigated A characteristic metal-ligand charge transfer absorption peak at 439 nm in the UV spectrum and a strong emission peak at 625 nm in the photoluminescence spectrum were observed for this complex in dichloromethane. Cyclic voltammtry （CV） analysis shows that the EHOMO （energy level of the highest occupied molecular orbital） and ELUMO （energy level of the lowest unoccupied molecular orbital） of the platinum complex are about -5.69 and -3.25 eV, respectively, indicating that the oxadiazole-based platinum complex has a potential application in electrophosphorescent devices used as a red-emitting material.