Applied Mechanics and Materials Vol. 197

Abstract: A variable neighborhood based memetic algorithm (VNMA) is proposed to minimize makespan for a single batch processing machine in this paper. Random instances were generated to verify the effectiveness of VNMA. Comparisons are made through using a genetic algorithm (GA) addressed in the literature as a comparator method. Computational results demonstrate that VNMA outperformed the GA with respect to solutions quality and run times.

Abstract: Dynamic allocation of Radio Frequency (Hereafter called “RF”) is critical in the battlefield spectrum management. The article analyzes the conventional method of RF dynamic allocation in the battlefield environment, and set up the mathematic model of the RF dynamic allocation by using the results of spectrum detected in battlefield. It designs the algorithms with the combination of Genetic Algorithms and Tabu Search Algorithms. The simulation experiment proves the high efficiency of hybrid algorithms and it suit for solving the RF dynamic allocation problem in the battlefield environment.

Abstract: 40t semi-trailer frame is optimized by usage of topological optimization and static/dynamic analysis technology. After the optimization, The static characteristics of frame completely satisfy requirement of intensity and stiffness, and the quality is reduced by 10% compared with the original frame. The dynamic characteristics of the new frame have been improved greatly, which indicate that the optimized frame structure is more reasonable, and lay the foundation for reducing the cost and improving manufacturing efficiency.

Abstract: With the rapid development of digital medicine, improving the diagnostic accuracy for birth defects (BD) by using data mining techniques has been paid more attentions by researchers. In this paper, an automated classification technique based on Gene Expression Programming (GEP) to detect the defect infants, named Birth Defects Detection based on Gene Expression Programming (BDD-GEP) is proposed. The main contributions of this paper include: (1) proposing two contrast inequalities (CIs) for birth defects detection: the defection contrasts to normal and the normal contrasts to defection, (2) designing a new fitness function to mine the normal and defect CIs by GEP, (3) presenting a method to select useful CIs for classification, (4) implementing the BDD-GEP algorithm through combining the proposed CIs with k-Nearest Neighbor algorithm. In order to evaluate the proposed classification method, 11,897 infant samples from national center for birth defects monitoring of China were used, and the method was compared with several existing classification methods. The experimental results show that the overall detection accuracy of BDD-GEP was as high as 87.8%. Specifically, the F-measure of the detect samples was about 70.2%, and the F-measure of the normal samples was about 92.3%.

Abstract: Data Mining is the process of discovering interesting knowledge from large amounts of data stored either in databases, data warehouses, or other information repositories. From biological studies, the Yeast Proteome Database (YPD) is a model for the organization and presentation of genome-wide functional data. Accordingly, a yeast gene expression which is a unicellular DNA is selected which contains 6103 genes and the database combined with a number of related dataset to create a general dataset. DNA-binding transcriptional regulators interpret the genome’s regulatory code by binding to specific sequences to induce or repress gene expression. The gene products including RNA and protein are responsible for the development and functioning of all living membranes by 2 steps process, transcription and translation. Various transcription factors control gene transcription by binding to the promoter regions. Translation is the production of proteins from mRNA produced in transcription. In this study, out of the 169 transcription factors known to access yeast, we are considering those thought to be involved in the response of Hydrogen Peroxide (H2O2). They are 22 transcription factors. Each one is partitioned to 3 parts: TF with No H2O2, TF with Low H2O2 and TF with High H2O2. The aim of this paper was to enhance the effectiveness of the integration of hydrogen peroxide response data related to yeast gene expression data to obtain a protein response process model and to label a set of important genes related to this approach.

Abstract: The information organization and expression is a key issue for the Augmented Reality Maintenance Guiding System（ARMGS）. In this paper, we firstly build up the relationship between the aircraft maintenance manual, EBOM figure and virtual prototype data , then construct a prototype of Interactive Electronic Technical Manual (IETM). The system can convert the aircraft virtual prototype data to the required information for augmented reality maintenance guiding system, based on the information, the prototype can be rapidly and lifelikely displayed. The system proposed in this paper significantly improves the quality and effectiveness of modeling.

Abstract: For vehicle routing problem, its model is easy to state and difficult to solve. The shuffled frog leaping algorithm is a novel meta-heuristic optimization approach and has strong quickly optimal searching power. The paper applies herein this algorithm to solve the vehicle routing problem; presents a high-efficiency encoding method based on the nearest neighborhood list; improves evolution strategies of the algorithm in order to keep excellent characteristics of the best frog. This proposed algorithm provides a new idea for solving VRP.

Abstract: Working vacation queue models are well applied in the modeling and analysis of the router in optical networks. The GI/Geo/c queue with working vacations is studied in this paper. Through establishing two-dimensional Markov chain and using matrix-geometric solution method, the stability condition is derived. Adopting UL-type RG-factorization of irreducible Markov chain, the stationary distribution is given. Based on these, the probability distribution of queue-length and PGF of waiting time are obtained in the end.

Abstract: To improve the accuracy of prediction on software failure data, one combine forecasting model is proposed based on least square support vector machine (LS-SVM) and Markov chain. First, LS-SVM optimized by simulated annealing algorithm (SA) is adopted to establish the time series forecasting model on software failure data. Then, in order to improve the prediction accuracy, the prediction interval is narrowed by means of Markov chain. Finally, after applying the combination forecasting model to the predicting of one commercial software failure data, the results indicate that the model has a certain precision and reliability.

Abstract: For the characteristics of fuzziness, indeterminacy etc. in nonlinear systems, this paper, combining fuzzy inference system with neural network, Adaptive Neural Fuzzy Inference System model had been provided in the paper, ANFIS method is based on Sugeno fuzzy model and has a structure similar to neural network that tunes the parameters of the fuzzy inference system with back propagation algorithm and least - square method and can produce fuzzy rules automatically. This solutes extraction of fuzzy rules and learning of parameters of membership functions play an essential role in the design. This paper gives the simulation example of modeling a typical system with ANFIS method and good result is obtained.