基于级联神经网络的蛋白质二级结构预测

为提高蛋白质二级结构预测的精度,提出一种由两层网络构成的级联神经网络模型。第1层网络采用具有差异度的5个子网构成的网络模型,对第2层网络的输入编码进行改进。对PDBSelect25中的36条蛋白质共6 122个残基进行测试,结果表明,该模型能有效预测蛋白质二级结构,其预测精度分别比SNN, DSC, PREDSATOR方法提高5.31%, 1.21%和0.92%,平均预测精度提高到69.61%。     

基于机器人群的主动传感器网络自组织的运动规划

主动传感器网络的自组织通常要求移动节点群(机器人群)通过障碍物环境移动到指定地点后, 重新调整并按预定布局组网. 在网络的自组织过程中要保证每个移动节点(机器人)与整个网络之间的连通性. 在对移动机器人的保持连通性进行优化的基础上, 提出了单步位置预测与群体势场相结合的分布式运动规划方法进行主动传感器网络的部署和重置, 证明了机器人运动控制的稳定性和网络的连通性保持, 进行了有和无障碍物环境下超过40个机器人的仿真, 结果表明该方法适用于大规模的主动传感器网络重置, 并对不同规模的网络具有可扩展性.     

A meta-heuristic approach to solve a JIT scheduling problem in hybrid flow shop

In this paper we address a hybrid flow shop scheduling problem considering the minimization of the sum of the total earliness and tardiness penalties. This problem is proven to be NP-hard, and consequently the development of heuristic and meta-heuristic approaches to solve it is well justified. So, we propose an ant colony optimization method to deal with this problem. Our proposed method has several features, including some heuristics that specifically take into account both earliness and tardiness penalties to compute the heuristic information values. The performance of our algorithm is tested by numerical experiments on a large number of randomly generated problems. A comparison with solutions performance obtained by some constructive heuristics is presented. The results show that the proposed approach performs well for this problem.     

基于CCD的垂线坐标仪的设计

针对矿井井壁变形引起的径向位移测量问题,提出了一种基于CCD的垂线坐标仪的设计方案,给出了垂线坐标仪的结构及其在井壁变形监测系统中的布置,详细介绍了垂线坐标仪的软、硬件设计。该垂线坐标仪利用CCD采集光信号并将其转换为视频信号,采用LPC932对采集的视频信号进行二值化处理及计数,并将处理结果发送至地面计算机进行分析,从而得到矿井井壁变形引起的径向位移量。测试结果验证了该垂线坐标仪的稳定性和精确性。     

基于粗糙集理论的语义Web服务匹配和发现

提出了基于粗糙集理论的语义Web服务匹配和发现机制。设计了基于粗糙集理论的语义Web服务匹配和发现算法（RSM）。有效的解决了服务匹配中不确定属性的问题。实验结果表明,采用RSM算法,能够实现用户需求和Web服务之间的准确匹配,从而克服了传统Web服务发现中查全率低、查准率差的缺点。     

Partner selection in a virtual enterprise under uncertain information about candidates

Partner selection is a major issue in the formation of a virtual enterprise. In practice, in the partner selection process, the information about the candidates and their performances are incomplete and uncertain. Vague sets theory is one of the methods used to deal with uncertain information. In this paper, a new method based on vague sets is proposed to deal with the partner selection problem in the formation of a virtual enterprise while the factors of satisfaction degree, due date, cost and the precedence of tasks are taken into account. On the basis of the agreement index of satisfaction degree, the formulated partner selection problems are interpreted so as to maximize the minimum agreement index. To solve the problem, an improved particle swarm optimization (PSO) algorithm is proposed. Finally, the simulation of a numerical example demonstrates that the method is effective.     

Investigation of Ant System parameter interactions by using design of experiments for job-shop scheduling problems

In recent years, one of the most important and promising research fields has been metaheuristics to find optimal or near-optimal solutions for NP-hard combinatorial optimization problems. Improving the quality of the solution or the solution time is basic research area on metaheuristics. Modifications of the existing ones or creation of hybrid approaches are the focus of these efforts. Another area of improving the solution quality of metaheuristics is finding the optimal combination of algorithm control parameters. This is usually done by design of experiments or one-at-a-time approach in genetic algorithms, simulated annealing and similar metaheuristics. We observe that, in studies which use Ant Colonies Optimization (ACO) as an optimization technique; the levels of control parameters are determined by some non-systematic initial experiments and the interactions of the parameters are not studied yet.In this study, the parameters of Ant System have been investigated on different sized and randomly generated job-shop scheduling problems by using design of experiments. The effects and interactions of the parameters have been interpreted with the outputs of the experiments. Referring to the statistical analysis it is observed that none of the interactions between the Ant System parameters has a significant effect on makespan value. A specific fractional experimental design is suggested instead of the full factorial design. Depending on the findings from the benchmark problems it will be a reliable approach to use the suggested design for saving time and effort in experiments without sacrificing the solution quality.     

A new autonomous celestial navigation method for the lunar rover

A secure and autonomous navigation system is needed for the lunar rover in future lunar missions in case of emergencies. Celestial navigation is a very attractive solution for long distance navigation on the Moon without the need of ground navigation aids. It only uses star altitudes, which are measured by a high accuracy star sensor and inertial measurement unit (IMU) to estimate the position of the rover. The navigational accuracy of this method depends largely on the accuracy of measurements, so the measurement errors have a great impact on the navigational performance. A new autonomous celestial navigation method for the lunar rover is presented in this paper, which uses the augmented state unscented particle filter (ASUPF) to deal with the systematic error and random error in the measurements. The validity and feasibility of this new method is tested and examined by the hardware-in-loop test. A position estimation error within 60 m is obtained. Compared to the conventional method, this method shows better navigation performance and higher adaptability to these measurement errors.     

An integrated method for finding key suppliers in SCM

Association rule is a widely used data mining technique that searches through an entire data set for rules revealing the nature and frequency of relationships or associations between data entities. Supplier selection is a significant work in supply chain management. Often, there will be thousands of potential suppliers and identifying a subset of these suppliers can be a complex process of determining a satisfactory subset based on a number of factors. In this paper, the supplier selection can be viewed as the problem of mining a large database of shipment. The proposed method incorporates the extended association rule algorithm of data mining with that of set theory to find key suppliers. This research has employed a numerical example for the integrated method to develop suitable supplier clusters. The results show that the method is effective and applicable.     

基于流形学习和SVM的Web文档分类算法

为解决Web文档分类问题,提出一种基于流形学习和SVM的Web文档分类算法。该算法利用流形学习算法LPP对训练集中的高维Web文档空间进行非线性降维,从中找出隐藏在高维观测数据中有意义的低维结构,在降维后的低维特征空间中利用乘性更新规则的优化SVM进行分类预测。实验结果表明该算法以较少的运行时间获得更高的分类准确率。