The Chromatic Uniqueness of Bipartite Graphs K （ m, n ） - A with ｜ A ｜ = 2

The chromatically uniqueness of bipartite graphs K （m, n） - A（]A] = 2） was studied. With comparing the numbers of partitions into r color classes of two chromatically equivalent graphs, one general numerical condition guaranteeing that K（ m, n） - A （ I A ] = 2） is chromatically unique were obtained. This covers and improves the former correlative results.     

Fuzzy ARTMAP neural network for seafloor classification from multibeam sonar data

This paper presents a seafloor classification method of multibeam sonar data, based on the use of Adaptive Resonance Theory （ART） neural networks. A general ART-based neural network, Fuzzy ARTMAP, has been proposed for seafloor classification of multibeam sonar data. An evolutionary strategy was used to generate new training samples near the cluster boundaries of the neural network, therefore the weights can be revised and refined by supervised learning. The proposed method resolves the training problem for Fuzzy ARTMAP neural networks, which are applied to seafloor classification of multibeam sonar data when there are less than adequate ground-troth samples. The results were synthetically analyzed in comparison with the standard Fuzzy ARTMAP network and a conventional Bayesian classifier. The conclusion can be drawn that Fuzzy ARTMAP neural networks combining with GA algorithms can be alternative powerful tools for seafloor classification of multibeam sonar data.     

Reexploring the upper bound for the chromatic number of graphs

The upper bound of the chromatic number of simple graphs is explored. Its original idea comes from Coffman, Hakimi and Schmeichel, who recently studied the chromatic number of graphs with strong conditions. In this paper, corresponding conditions are weakened and the result proves that of Ershov and Kozhukhin's.     

New generation of multi-scale NWP system （GRAPES）： general scientific design

A new generation of numerical prediction system GRAPES （a short form of Global/Regional Assimilation and PrEdiction System） was set up in China Meteorological Administration （CMA）. This paper fo- cuses on the scientific design and preliminary results of the numerical prediction model in GRAPES, including basic idea and strategy of the general scientific design, multi-scale dynamic core, physical package configuration, architecture and parallelization of the codes. A series of numerical experiments using the real data with horizontal resolutions from 10 to 280 km and idealized experiments with very high resolution up to 100 m are conducted, giving encouraging results supporting the multi-scale application of GRAPES. The results of operational implementation of GRAPES model in some NWP centers are also presented with stress at evaluations of the capability to predict the main features of precipitation in China. Finally the issues to be dealt with for further development are discussed.     

Optimization for entransy dissipation minimization in heat exchanger

A common of two-fluid flow heat exchanger, in which the heat transfer between high- and low-temperature sides obeys Newton＇s law [q∝△（T）], is studied in this paper. By taking entransy dissipation minimization as optimization objective, the optimum parameter distributions in the heat exchanger are derived by using optimal control theory under the condition of fixed heat load. The condition corresponding to the minimum entransy dissipation is that corresponding to a constant heat flux density. Three kinds of heat exchangers, including parallel flow, condensing flow and counter-flow, are considered, and the results show that only the counter-flow heat exchanger can realize the entransy dissipation minimization in the heat transfer process. The obtained results for entransy dissipation minimization are also compared with those obtained for entropy generation minimization by numerical examples.     

Path of Building a Global City in Shanghai Based on the “Space of Flows” Theory

The "Shanghai Master Plan(2017—2035)" clearly states that building a global city with excellence has become the direction of its efforts in recent decades which requires Shanghai to become an important "global node" in the global city network structure like New York and London. Based on the "space of flow" theory, this paper uses the interlocking network model of Globalization and World Cities(GaWC) to calculate connective index of 48 cities whose level is "alpha" and above in GaWC's researches. There are still many gaps between Shanghai and other top global cities such as New York, London and Tokyo. Through the calculation of Theil Entropy, the importance ranking of the factors affecting Shanghai's status in the global city network is obtained. The results show that the proportion of foreign population, the number of sister cities, the number of headquarters of multinational corporations and the proportion of GDP in the whole country significantly affect Shanghai's status in the global city network.     

Some Probability Properties of Random Walk in Time-Random Environment

A general formulation of the stochastic model for random walk in time-random environment and an equivalent definition is established in this paper.Moreover,some basic probability relations similar to the classical case whichare very useful in the corresponding research of fractal properties are given.At the end,a typical example is provided to show the recurrence and transience.     

PBNA： An Improved Probabilistic Biological Network Alignment Method

Biological network alignment is an important research topic in the field of bioinformatics. Nowadays almost every existing alignment method is designed to solve the deterministic biological network alignment problem.However, it is worth noting that interactions in biological networks, like many other processes in the biological realm,are probabilistic events. Therefore, more accurate and better results can be obtained if biological networks are characterized by probabilistic graphs. This probabilistic information, however, increases difficulties in analyzing networks and only few methods can handle the probabilistic information. Therefore, in this paper, an improved Probabilistic Biological Network Alignment（PBNA） is proposed. Based on Iso Rank, PBNA is able to use the probabilistic information. Furthermore, PBNA takes advantages of Contributor and Probability Generating Function（PGF） to improve the accuracy of node similarity value and reduce the computational complexity of random variables in similarity matrix. Experimental results on dataset of the Protein-Protein Interaction（PPI） networks provided by Todor demonstrate that PBNA can produce some alignment results that ignored by the deterministic methods, and produce more biologically meaningful alignment results than Iso Rank does in most of the cases based on the Gene Ontology Consistency（GOC） measure. Compared with Prob method, which is designed exactly to solve the probabilistic alignment problem, PBNA can obtain more biologically meaningful mappings in less time.     

A hybrid model of a subminiature helicopter in horizontal turn

A hybrid model of a subminiature helicopter in horizontal turn is presented. This model is based on a mechanism model and its compensated neural network （NN）. First, the nonlinear dynamics of a sub-miniature helicopter is established. Through the linearization of the nonlinear dynamics on a trim point, the linear time-invariant mechanism model in horizontal turn is obtained. Then a diagonal recursive neural network is used to compensate the model error between the mechanism model and the nonlinear model, thus the hybrid model of a subminiature helicopter in horizontal turn is achieved. Simulation results show that the hybrid model has higher accuracy than the mechanism model and the obtained compensated-NN has good generalization capability.     

Support vector machine applied in QSAR modelling

Support vector machine （SVM）, partial least squares （PLS）, and Back-Propagation artificial neural network （ANN） were employed to establish QSAR models of 2 dipeptide datasets. In order to validate predictive capabilities on external dataset of the resulting models, both internal and external validations were performed. The division of dataset into both training and test sets was carried out by D-optimal design. The results showed that support vector machine （SVM） behaved well in both calibration and prediction. For the dataset of 48 bitter tasting dipeptides （BTD）, the results obtained by support vector regression （SVR） were superior to that by PLS in both calibration and prediction. When compared with BP artificial neural network, SVR showed less calibration power but more predictive capability. For the dataset of angiotensin-converting enzyme （ACE） inhibitors, the results obtained by support vector machine （SVM） regression were equivalent to those by PLS and BP artificial neural network. In both datasets, SVR using linear kernel function behaved well as that using radial basis kernel function. The results showed that there is wide prospect for the application of support vector machine （SVM） into QSAR modeling.     

Hamilton-Jacobi-Bellman equations and dynamic programming for power-optimization of a multistage heat engine system with generalized convective heat transfer law

A multistage endoreversible Carnot heat engine system operating between a finite thermal capacity high-temperature fluid reservoir and an infinite thermal capacity low-temperature environment with generalized convective heat transfer law [q∝(ΔT) m ] is investigated in this paper.Optimal control theory is applied to derive the continuous Hamilton-Jacobi-Bellman (HJB) equations,which determine the optimal fluid temperature configurations for maximum power output under the conditions of fixed initial time and fixed initial temperature of the driving fluid.Based on the universal optimization results,the analytical solution for the Newtonian heat transfer law (m=1) is also obtained.Since there are no analytical solutions for the other heat transfer laws (m≠1),the continuous HJB equations are discretized and dynamic programming algorithm is performed to obtain the complete numerical solutions of the optimization problem.The relationships among the maximum power output of the system,the process period and the fluid temperature are discussed in detail.The results obtained provide some theoretical guidelines for the optimal design and operation of practical energy conversion systems.     

Combination of Multi-class Probability Support Vector Machines for Fault Diagnosis

To deal with multi-source multi-class classification problems, the method of combining multiple multi-class probability support vector machines （MPSVMs） using Bayesian theory is proposed in this paper. The MPSVMs are designed by mapping the output of standard support vector machines into a calibrated posterior probability by using a learned sigmoid function and then combining these learned binary-class probability SVMs. Two Bayes based methods for combining multiple MPSVMs are applied to improve the performance of classification. Our proposed methods are applied to fault diagnosis of a diesel engine. The experimental results show that the new methods can improve the accuracy and robustness of fault diagnosis.     

Clustering approach based on hierarchical expansion for community detection of scientific collaboration network①

This study presents a clustering algorithm based on hierarchical expansion to solve the problem of community detection in scientific collaboration network .The characteristics of achievements infor-mation related to scientific and technological domains are analyzed , and then an ontology that repre-sents their latent collaborative relations is built to detect clusters from the collaboration network .A case study is conducted to collect a data set of research achievements in the electric vehicle field and better clustering results are obtained .A hierarchical recommendation framework that enriches the domain ontologies and retrieves more relevant information resources is proposed in the last part of this paper .This work also lays out a novel insight into the exploitation of scientific collaboration net-work to better classify achievements information .     

Sensor Network Design for Nonlinear Processes

This paper presents a method to design a cost-optimal nonredundant sensor network to observe all variables in a general nonlinear process. A mixed integer linear programming model was used to minimize the cost with data classification to check the observability of all unmeasured variables. This work is a starting point for designing sensor networks for general nonlinear processes based on various criteria, such as reliability and accuracy.     

Anomaly Detection with Artificial Immune Network

Inspired by the immune network theory, an adaptive anomaly detection paradigm based on artificial immune network, referred as APAI, is proposed. The implementation of the paradigm includes： initially, the first is to create the initial antibody network; then, through the learning of each training antigen, the antibody network is evolved and updated by the optimal antibodies. Finally, anomaly detection process is accomplished by majority vote of the k nearest neighbor antibodies in the network. The experiments used the famous Sonar Benchmark dataset in our study, which is taken from the UCI machine learning database. The obtained detection accuracy of APAI was 97.7%, which was very promising with regard to the other classification applications in the literature for this problem. In addition to its nonlinear classification properties, APAI possesses biological immune network properties such as clonal selection, immune network, and immune memory, which can be applied to pattern recognition, classification, and etc.     

Numeration and type synthesis of 3-DOF orthogonal translational parallel manipulators

Low-degree-of-freedom （Low-DOF） parallel manipulators （PMs） have drawn extensive interests, particularly in type synthesis in which two main formal approaches were established by using the reciprocal screw system theory and Lie group theory. This paper aims at numeration and type synthesis of orthogonal translational parallel manipulators （OTPMs） by resorting to an integration of the group- based method and graphical representation of the topology. For this purpose, the concept of Cartesian DOF-characteristic matrix, orig- inated from displacement subgroup and displacement submanifold, is proposed. A new approach based on the combination of the atlas of Cartesian DOF-characteristic matrix and the displacement group-theoretic method is addressed for both exhaustive classification and type synthesis of OTPMs. The proposed approach is prone to construct an orthogonal structure and is easy to realize the complete classification and exhaustive enumeration of this class of low-DOF PMs.     

Density functional theory calculation on the C―H bond insertion reaction of dibromocarbene with acetaldehyde

The insertion reaction mechanism of CBr2 with CH3CHO has been studied by using the B3LYPI6-31G（d） method. The geometries of reactions, transition state and products were completely optimized. All the energy of the species was obtained at the CCSD（T）/6-31G（d） level. All the transition state is verified by the vibrational analysis and the internal reaction coordinate （IRC） calculations. The results show that the propionaldehyde （Hp1） is the main product of CH2 insertion with CH3CHO. The calculated results indicated that all the major pathways of the reaction were obtained on the singlet potential energy surface. The singlet CBr2 not only can insert the Cα-H [reaction I（1）]） but also can react with Cβ-H [reaction l1（1）]. The statistical thermodynamics and Eyring transition state theory with Wigner correc-tion are used to study the thermodynamic and kinetic characters of I（1） and I1（1） in temperature range from 100 to 2200 K. The results show that the appropriate reaction temperature rang is 250 to 1750 K and 250 to 1600 K at 1.0 atm for I（1） and I1（1） respectively. The rate constant and equilibrium constant are distinct in the range from 250 to 1000 K so that I（1） more easily occurs, while the reactions are not selected in the temperature range of 1000-1600 K.     

Block Cipher Design:Generalized Single-Use-Algorithm Based on Chaos

The idea of using a one-time-one-key design has been widely applied in conventional cryptography.With the security theory of conventional cryptology,encryption algorithms are made public while all the secrets are encoded only in the keys.This paper applies chaos theory to conventional cryptography to develop a one-time-one-algorithm design.A general theory is given to generate the clock key,substitution box,permutation box and operational sign functions for a one-time-one-algorithm scheme.This scheme is then implemented in a system to manage the tradeoff between speed and the security of the encryption algorithm.