Applied Mechanics and Materials Vols. 713-715

Abstract: In order to enhance the operation efficiency of RSA algorithm, a new improved algorithm was suggested in this paper which made some improvements in structure and operation, and it was applied to digital signature. The experiment made comparison between a combinatorial optimization algorithm which combined SMM with index of 2^k hexadecimal algorithm and the new algorithm. It shows that the new algorithm reaches a high level in operation speed.

Abstract: The urban traffic condition is changed timely, so the traditional serial algorithm cannot satisfy the requirement of traffic scale and condition changes. Therefore, this paper proposes a DNA non-dominated sorting genetic algorithm for route optimization problem of multi-objects. First, through Pareto frontiers solution set optimization and algorithm complexity analysis, we determine the multi-objects problem to be optimized. Then we convert the problem into optimization problem of single-object fitness function, namely the elite populations optimization strategy, through which we can obtain the optimal solution of timely traffic condition.

Abstract: The purpose of this report is to investigate current existing algorithm to cluster sequential data based on hidden Markov model (HMM). Clustering is a classic technique that divides a set of objects into groups (called clusters) so that objects in the same cluster are similar in some sense. The clustering of sequential or time series data, however, draws lately more and more attention from researchers. Hidden Markov model (HMM)-based clustering of sequences is probabilistic model-based approach to clustering sequences. Generally, there are two kinds of methodologies: parametric and semi-parametric. The parametric methods make strict assumptions that each cluster is represented by a corresponding HMM, while the semi-parametric approaches relax this assumption and transform the problem to a similarity-based issue. Generally, the semi-parametric methods perform better than parametric approaches as reported by some researchers. Future research can be done in exploring new distance measures between sequences and extending current HMM-based methodologies by using other models.

Abstract: In the paper, the survival time of patients with lung cancer is inferred based on binary classification variables Logistic regression linear model and the data analysis is implemented by R software.

Abstract: In order to achieve a low cost and low exhaust pollution in logistics distribution path. In view of the shortages of existing genetic algorithm and ant colony algorithm which have the characteristics of some limitations, such as ant colony algorithm's convergence slow, easy going, the characteristics of such as genetic algorithm premature convergence in the process of path optimization, process complex, the paper proposed the improved artificial fish swarm algorithm in order to solve logistics route optimization problem. At last, through simulation experiment, the improved artificial fish swarm algorithm is proved correct and effective.

Abstract: We present a new algorithm for mining maximal frequent itemsets, MaxMining, from big transaction databases. MaxMining employs the depth-first traversal and iterative method. It re-represents the transaction database by vertical tidset format, travels the search space with effective pruning strategies which reduces the search space dramatically. MaxMining removes all the non-maximal frequent itemsets to get the exact set of maximal frequent itemsets directly, no need to enumerate all the frequent itemsets from smaller ones step by step. It backtracks to the proper ancestor directly, needless level by level, ignoring those redundant frequent itemsets. We found that MaxMining can be more effective to find all the maximal frequent itemsets from big databases than many of proposed algorithms with ordinary pruning strategies.

Abstract: In order to reflect the decision-making more scientific and democratic, modern decision problems often require the participation of multiple decision makers. In group decision making process,require the use of intuitionistic fuzzy hybrid averaging operator (IFHA) to get the final decision result.

Abstract: Smooth Support Vector Regression (SSVR) is new modified edition of traditional support vector regression for better performance. To further improve the modeling capability of SSVR, it is necessary to take into account the feature extraction based on Independent Component Analysis (ICA) before SSVR. Simulation on the example of function approximation shows that the result of SSVR based on ICA feature extraction is better than that of SSVR without ICA preprocess.

Abstract: A multi-objective optimization problem of ramp metering and dynamic route guidance is presented. The problem domain, a freeway integration control application considers the efficiency and equity of system, is formulated as a multi-objective optimization problem. The Gini coefficient is adopted in this study as an indicator of equity. The control strategy’s effect is demonstrated through its application to the simple freeway network. Analyses of simulation results using this approach show the equity of the system have a significant improvement over traditional control, especially for the case of large traffic demand. Using the multi-objective optimization approach, the Gini coefficient of the network has been reduced by 55% compared to traditional method.

Abstract: To simulate skull-CSF-brain interaction relations, a simple finite element head model is established, based on ALE (Arbitrary Lagrangian-Eulerian) and overlapping mesh methods. The responses of head under impact was simulated with this model. The numerical results are coincidence well with the experimental results conducted by Nahum et al. What’s more, it is found that the skull-brain relative displacement and brain injury may be predicted better with the ALE method.