Applied Mechanics and Materials Vols. 571-572

Abstract: There is no standard and sophisticated experimental teaching evaluation system in university currently even experimental teaching is an important part of practice teaching, which results in lacking of standardization and systematization during experimental teaching. Directing to the problems, this paper constructed an experimental teaching evaluation index system and proposed an evaluation method based on AHP(analytic hierarchy process) and fuzzy mathematical evaluation method, which calculates the corresponding weights for each index and evaluates teaching effectiveness using the fuzzy comprehensive evaluation method and comes to the evaluation results, finally this method was applied to evaluate the experimental teaching process in university fundamental computer course, which showed the validity and reliability of the evaluation system.

Abstract: The fuzzy comprehensive evaluation model of comfort of high-speed train was established by using Fuzzy-ANP. In this model,firstly, the weight of each multiple index was determined by employing ANP; Secondly, the fuzzy evaluation matrix of multiple index was assessed by the expert scoring method; Finally, the evaluation grade of comfort of high-speed train under multi-hierarchy was discussed by using the fuzzy comprehensive evaluation method combined with the weight and the fuzzy evaluation matrix of each multiple index . The evaluation method provided an effective theoretical support for comprehensive assessment research of comfort of high-speed train through the analysis of an example. This template explains and demonstrates how to prepare your camera-ready paper for Trans Tech Publications.

Abstract: This paper presents a method to construct a profitable portfolio of mutual funds for investors. This method comprises two stages. In the first stage, the DEA, Sharpe and Treynor indices of mutual funds and the monthly rates of return (ROR) of mutual funds are used to select a mutual fund portfolio. In the second stage, the linear regression model, the Fruit Fly Optimization Algorithm (FOA) and the General Regression Neural Network (GRNN) are used to construct a prediction model for the net asset values of each of the constituent mutual funds of the portfolio. The trade decision of a selected mutual fund is then made based on the rise or fall of its net asset value. The empirical results showed that, compared to other combinations, the combination of using Sharpe index for portfolio selection and the GRNN optimized with FOA for net asset value prediction offered the best accumulated return rate for the mutual fund portfolio investment.

Abstract: A feedforward and feedback path tracking algorithm appropriate for automatic parking is proposed in this paper. This algorithm is composed of feedforward control and feedback control. In the path tracking, vehicle position and reference path information are used to calculate the lateral and heading error of the vehicle as well as the curvature of reference path, and put these three as the system inputs, the steering angel of the vehicle as the output. The system parameters are settled by multiple tests. Experiments show that, this method has a high performance in path tracking and meets the requirements of vehicle parking in both low speed situation and narrow spaces.

Abstract: Surgeons are striving to achieve higher quality results in minimally invasive operation. Intelligent medical equipments are able to improve operation safety. Otological drill milling through a bone tissue wall is a common milling fault in ear surgery. In this paper a multi-sensor information fusion method for identifying milling faults is presented. Five surgeons experimented on calvarian bones using the intelligent otological drill. The average recognition rate of milling faults was 91%, and only 0.8% of normal millings were identified as milling faults.

Abstract: The answer extraction model has a direct impact on the performance of the Automatic Question Answering System (QA System). In this paper, an answer extraction model based on named entity recognition was presented. It mainly answers specific questions whose answers are related with the named entity. Firstly, it classified the questions according to answer types. And then it identified named entities with suitable types in the fragmented information. Finally, it got the final answer based on scores. The experiments in the paper proved that the model could accurately answer the questions provided by Text REtrieval Conference (TREC). Thus, the proposed model is easy to implement and its performance is good for specific questions.

Abstract: In the problem of mining classification rules, previous methods are too general or too over-fitting for a given dataset. In this paper, through analyzing the characteristics of concept lattice and indiscernible matrix, classification rule acquisition based on lattice was proposed. Our method includes two phase: (1) proposing incremental generating the nodes of lattice using indiscernible matrix. (2) Developing some theorems for pruning redundant rules quickly. It is shown by experimental results that our approach not only results in shorter execution times, but also avoids missing important rules than the generalization of previous known methods.

Abstract: This paper addresses the challenges in performance monitoring and degradation diagnosing of complex industrial process. A Bayesian network-based model was developed using real operation data of the system, and readings from multiple sensor sources were regarded as evidences to identify the root cause of process variations causing system degradation. An inferring process was executed with the belief network to output possible root causes of the process variation. Then, a probabilistic confidence level of controllable variables was recommended for optimizing operation. A full-scope simulator for the power plant unit is employed as an indispensable test bed for the probabilistic network model. To achieve a decision support simulation, a simulated operation database is generated through varying boundary parameters. The model was re-parameterized with the data from the simulated operation database. The simulation test results show that the model can properly represent the domain knowledge, and the decision advices are effective for such an elaborate work. The probabilistic model can tackle the problems which are indiscernible in conventional thermodynamic model-based analysis.

Abstract: The existing network selection algorithm generally considering the QoS needs of all services which carried by the terminal. Then selecting an optimal network to access to achieve the overall best of all services, However, for each individual service is not necessarily optimal. To solve the problem above, On the basis of multi-mode terminal and multi- parallel transmission of data, we propose a traffic-based network access selection algorithm, which was designed to realize independent network selection for each traffic flow by considering the network congestion and the transmission of high priority traffic and achieve adaptively network access which making the traffic efficient and reliable transmission, By which each network can be fully utilized. The Simulation results demonstrate that the algorithm can reduce the times of network selection and improve the transmission efficiency and the utilization of network. The results suggest that this kind of network algorithm is effective.

Abstract: Military communication network plays a very important role in modern warfare, so it is inevitably damaged by the adversary. In this paper, basing on the condition of the military communication network having been damaged, the demand of regulating the capabilities of some lines in the damaged network was first discussed. Then, according to this demand, the thought of regulating the capabilities of some key lines in the damaged network was explained. Finally, the main steps of a regulative arithmetic was presented. A calculated example indicates that the regulative arithmetic is with simplicity and practicality.