物联网技术在信息工程安全监理中的应用

通过对物联网技术的分析,提出利用无线射频识别技术,构建出一个信息工程安全监理模型,并通过改进人工神经网络,增强了系统全局最优性,并提高了对质量、进度和投资的监督,形成一个有效的信息工程安全监理机制,以保障信息工程项目顺利进行。     

Comparing ANN and GMM in a voice conversion framework

In this paper, we present a comparative analysis of artificial neural networks (ANNs) and Gaussian mixture models (GMMs) for design of voice conversion system using line spectral frequencies (LSFs) as feature vectors. Both the ANN and GMM based models are explored to capture nonlinear mapping functions for modifying the vocal tract characteristics of a source speaker according to a desired target speaker. The LSFs are used to represent the vocal tract transfer function of a particular speaker. Mapping of the intonation patterns (pitch contour) is carried out using a codebook based model at segmental level. The energy profile of the signal is modified using a fixed scaling factor defined between the source and target speakers at the segmental level. Two different methods for residual modification such as residual copying and residual selection methods are used to generate the target residual signal. The performance of ANN and GMM based voice conversion (VC) system are conducted using subjective and objective measures. The results indicate that the proposed ANN-based model using LSFs feature set may be used as an alternative to state-of-the-art GMM-based models used to design a voice conversion system.     

人工气候老化后状态调节时间对芳纶纤维拉伸测试的影响

依据GJB 8496—2015标准中的拉伸方法,对人工气候老化前及老化后不进行状态调节、状态调节1 h、状态调节24 h、状态调节48 h、状态调节96 h后的芳纶纤维的拉伸强度进行测量、比较,从而得到结论:芳纶纤维在人工气候老化后,样品的性能有一定程度下降。在刚刚结束老化后立即进行测试,性能下降达到峰值,待状态调节24 h以后,其老化后性能逐步达到一个相对稳定的状态。建议在实际测试中,人工气候老化后的样品应该在老化结束后,放入恒温恒湿间调节24 h以后再测试。     

基于演化算法的复杂机械广义优化设计实现策略研究

针对复杂机械广义优化设计过程中优化模型不易获得这一难题，利用人工神经网络的高度非线性是非线性映射能力，将优化建模转换为求取设计变量到设计方案的映射关系，构建了复杂机械广义优化柔性建模方法；引入模糊评价理论，设计了基于演化计算的优化方法，给出了优化流程图。该策略特别适用于复杂机械概念设计阶段的方案优化。     

Nature,Nurture and PDP: Preposterous Developmental Postulates?

In this article I discuss the nature/nurture debate in terms of evidence and theorizing from the field of cognitive development, and pinpoint various problems where the Connectionist framework needs to be further explored from this perspective. Evidence from normal and abnormal developmental phenotypes points to some domain-specific constraints on early learning. Yet, by invoking the dynamics of epigenesis, I avoid recourse to a strong Nativist stance and remain within the general spirit of Connectionism.     

水厂生产运行管理决策支持系统的开发

介绍了水厂生产管理决策支持系统的设计原理和方法，其中主要讲述了数据库和知识库的建立，并阐述了该系统的运行功能、管理和维护方法。     

使用人工神经网络对强力旋压有限元模拟进行参数预选

在筒形件强力旋压领域内，介绍了使用人工神经网络的手段，在MARC有限元模拟的前处理中实现参数预选的方法。作为与有限元模拟相结合的旋压智能化设计的一项关键技术，能够达到减少有限元模拟的误差和工作量的目的。     

Artificial neural network application for modeling the rail rolling process

Rail rolling process is one of the most complicated hot rolling processes. Evaluating the effects of parametric values on this complex process is only possible through modeling. In this study, the production parameters of different types of rails in the rail rolling processes were modeled with an artificial neural network (ANN), and it was aimed to obtain optimum parameter values for a different type of rail. For this purpose, the data from the Rail and Profile Rolling Mill in Kardemir Iron & Steel Works Co. (Karabük, Turkey) were used. BD1, BD2, and Tandem are three main parts of the rolling mill, and in order to obtain the force values of the 49 kg/m rail in each pass for the BD1 and BD2 sections, the force and torque values for the Tandem section, parameter values of 60, 54, 46, and 33 kg/m type rails were used. Comparing the results obtained from the ANN model and the actual field data demonstrated that force and torque values were obtained with acceptable error rates. The results of the present study demonstrated that ANN is an effective and reliable method to acquire data required for producing a new rail, and concerning the rail production process, it provides a productive way for accurate and fast decision making.     

Time series forecasting by neural networks: A knee point-based multiobjective evolutionary algorithm approach

In this paper, we investigate the problem of time series forecasting using single hidden layer feedforward neural networks (SLFNs), which is optimized via multiobjective evolutionary algorithms. By utilizing the adaptive differential evolution (JADE) and the knee point strategy, a nondominated sorting adaptive differential evolution (NSJADE) and its improved version knee point-based NSJADE (KP-NSJADE) are developed for optimizing SLFNs. JADE aiming at refining the search area is introduced in nondominated sorting genetic algorithm II (NSGA-II). The presented NSJADE shows superiority on multimodal problems when compared with NSGA-II. Then NSJADE is applied to train SLFNs for time series forecasting. It is revealed that individuals with better forecasting performance in the whole population gather around the knee point. Therefore, KP-NSJADE is proposed to explore the neighborhood of the knee point in the objective space. And the simulation results of eight popular time series databases illustrate the effectiveness of our proposed algorithm in comparison with several popular algorithms.