局部偏最小二乘回归建模参数对近红外检测结果的影响研究

报道了在局部加权(LWR)回归方法基础上,自主改进的更简单、实用的局部偏最小二乘回归(LPLS)的原理和方法。并以云南优质烤烟为实验材料,在国产光栅漫反射型近红外仪器上,研究了主成分数以及局部建模样品数对检测结果的影响。结果表明：应用交叉验证方法推荐的尼古丁组分模型主成分数并不是最优,通过适当降低主成分数可提高检测效果;局部建模样品数为30～50个时总糖、总氮、尼古丁预测准确度的提高幅度可分别达7%,14%,10%以上。该方法能有效提高近红外数学模型的预测准确度,是建立具有高度适应性近红外数学模型的有效方法。     

Reconstruction of a random phase dynamics network from observations

We consider networks of coupled phase oscillators of different complexity: Kuramoto–Daido-type networks, generalized Winfree networks, and hypernetworks with triple interactions. For these setups an inverse problem of reconstruction of the network connections and of the coupling function from the observations of the phase dynamics is addressed. We show how a reconstruction based on the minimization of the squared error can be implemented in all these cases. Examples include random networks with full disorder both in the connections and in the coupling functions, as well as networks where the coupling functions are taken from experimental data of electrochemical oscillators. The method can be directly applied to asynchronous dynamics of units, while in the case of synchrony, additional phase resettings are necessary for reconstruction.     

Neural network applied to reconstruction of complex objects based on fringe projection

The neural network has been introduced into the reconstruction of the complex object based on fringe projection. In this method, the neural network with powerful property of approximation is used to get the continuous approximate function of a discrete fringe pattern captured by an image frame grabber. The depth-related phase of the measured object modulated into the fringe pattern can be demodulated by dealing with the approximate function. Compared with the Fourier transform profilometry (FTP), in the network method, one deformed fringe pattern is needed to reconstruct the tested object, and a high spatial resolution is maintained for no filtering process. Therefore, this method performs better than FTP in the measurement of the complex object. Moreover, the network method is capable of demodulating more depth-related phase even in the case that the local shadow exists in the fringe pattern. Computer simulations and experiments validate the feasibility of this method.     

无线传感器网络多跳路由协议研究

目的：无线传感器网络发展迅速,但传感器的高能耗问题成为制约其发展的主要瓶颈,高效节能的路由协议设计成为研究热点。方法：针对目前无线传感器网络常用的LEACH路由协议存在的簇首能耗过分集中、簇首分布不均衡问题,提出了改进的路由协议EEACRA,在总结、分析LEACH路由协议现有问题的基础上,给出了EEACRA路由协议的簇首选取门限值、簇首位置调整算法和基于能量代价最小的簇间多跳路由算法的实现方法,同时给出了具体的实现EEACRA协议的工作流程和关键算法。在MATLAB环境下对LEACH路由协议和EEACRA路由协议进行了仿真,对比了不同能耗降低措施对网络能耗降低的贡献。结果：仿真结果表明EEACRA路由协议的网络稳定期较LEACH路由协议有较大的改善。结论：证明了改进的路由协议EEACRA可以有效地提高网络的稳定期。     

Revised multi-choice goal programming for multi-period,multi-stage inventory controlled supply chain model with popup stores in Guerrilla marketing

In this paper, we consider a supply chain network design problem with popup stores which can be opened for a few weeks or months before closing seasonally in a marketplace. The proposed model is multi-period and multi-stage with multi-choice goals under inventory management constraints and formulated by 0–1 mixed integer linear programming. The design tasks of the problem involve the choice of the popup stores to be opened and the distribution network design to satisfy the demand with three multi-choice goals. The first goal is minimization of the sum of transportation costs in all stages; the second is to minimization of set up costs of popup stores; and the third goal is minimization of inventory holding and backordering costs. Revised multi-choice goal programming approach is applied to solve this mixed integer linear programming model. Also, we provide a real-world industrial case to demonstrate how the proposed model works.     

利用BP神经网络光谱分类法研究肉品新鲜度

利用自行设计的一种光纤探头式生物表面光谱测试系统 ,对肉品进行测量。建立用于肉品新鲜度的自动分类和识别的 BP神经网络 ,准确率达到 93.3%。试验表明 ,利用光谱分析法测量肉品的新鲜度快捷、准确 ,可用于实际肉品检测     

基于近红外光谱技术的石油组分定量分析新方法

针对石油化工产品生产控制和质量检查的需要,为提高测定产品组成的效率,将近红外光谱法作为基础测定方法,以直馏柴油、加氢精制柴油和催化裂化柴油为校正模型的训练样本,测定其中饱和烃、胶质、单环芳烃、双环芳烃、三环芳烃和环烷烃的组成,论述了采用模糊神经网络建立校正模型测定石油化工产品组成的可行性。基于dSPACE硬件平台,用验证样本对模糊神经网络校正模型进行了检验,实验结果表明,该方法响应快、误差小、鲁棒性强,在近红外长波区内,校正样品和验证样品的均方误差小于10-6。该方法可用于石油化工产品的生产工艺研究中。     

Theoretical investigation of Mn adsorbates aside self-organised Bi nanolines on hydrogenated Si(0 0 1) surface

We have theoretically investigated the atomic structure, magnetic behaviour, and electronic properties of Mn adsorbates on hydrogen passivated self-organised Bi nanolines on the Si(0 0 1)surface. It is found that the most stable geometry for monolayer (ML) coverage of Mn is just underneath the first Si(0 0 1) surface layer. The Mn atoms in the optimised configuration are seven-fold coordinated with their neighbouring Si atoms. Total energy calculations suggest that the Mn adsorbates form a degenerate state of ferromagnetic and anti-ferromagnetic lines parallel and adjacent to the self-assembled Bi lines. The density functional band structure calculation within the local-spin density approximation shows that the ferromagnetic system behaves like a metal in both spin channels. On the other side, the anti-ferromagnetic phase exhibits a half-metallic phenomenon with semiconducting character for the majority spin channel and semi-metallic character for the minority spin channel.     

Reliable computing with unreliable components: Using separable environments to stabilize long-term information storage

How, in the face of both intrinsic and extrinsic volatility, can unconventional computing fabrics store information over arbitrarily long periods? Here, we argue that the predictable structure of many realistic environments, both natural and artificial, can be used to maintain useful categorical boundaries even when the computational fabric itself is inherently volatile and the inputs and outputs are partially stochastic. As a concrete example, we consider the storage of binary classifications in connectionist networks, although the underlying principles should be applicable to other unconventional computing paradigms. Specifically, we demonstrate that an unsupervised, activity dependent plasticity rule, AHAH (Anti-Hebbian-And-Hebbian), allows binary classifications to remain stable even when the underlying synaptic weights are subject to random noise. When embedded in environments composed of separable features, the weight vector is restricted by the AHAH rule to local attractors representing stable partitions of the input space, allowing unsupervised recovery of stored binary classifications following random perturbations that leave the system in the same basin of attraction. We conclude that the stability of long-term memories may depend not so much on the reliability of the underlying substrate, but rather on the reproducible structure of the environment itself, suggesting a new paradigm for reliable computing with unreliable components.