计算机网络系统及应用

计算机网络技术在过去的几十年里得到了快速的发展，在近十几年中由于互联网的出现，使得网络技术应用迅速深入到全世界的各个角落和社会的各个层面，资源的广泛、信息的便捷传输等功能，对人类的生产、生活产生了质的影响。     

浅谈计算机网络安全

当今人类社会越来越依赖计算机，互联网的应用也愈加广泛，而网络的开放性和自由性也引发了各种网络安全问题。如何确保网络信息的保密性、完整性和可用性，是一个亟待解决的问题。     

神经网络方法在大跨度桥梁施工控制中的应用

神经网络是一种模拟人脑结构和功能的信息处理系统，介绍网络算法基本原理及其在预应力混凝土连续刚构桥节段施工中对桥面标高偏差预测的应用。实例表明：神经网络利用实测样本的自学习功能，对桥梁施工预拱度有较好的预测精度，验证了该方法的合理性和可行性。     

人工智能的研究和应用

在计算机工程技术的飞速发展历程中，科学家一直致力于人工智能的努力研究。人工智能（artificial intelligence，ai）一直都处于计算机技术的最前沿，经历了几起几落……人工智能对于普通人来说已经不是那样的可望而不可及，它吸引了更多的研究人员为之奉献才智，几个世纪以来，人类依靠其智慧，发明了许多机器，使人类从许多体力劳动中解放出来。现在，有了电脑，电脑是一个信息处理的工具、人脑也是一个信息处理的器官。在用电脑代替人脑的部分功能，用电脑模拟思维、产生智能行为方面，取得一定的成效。19世纪以来．数理逻辑、自动机理论、控制论、信息论、仿生学、心理学、电脑等科学技术的发展，为一个新科的诞生准备了思想、理论与物质基础。在这一背景下，1956年美国的一些科学家，包括心理学家、数学家、计算机学家、信息论学家在美国一所大学举办讨论会，正式提出了人工智能（Artificial Intelligence简称A1）这一术语，开始了具有真正意义的人工智能的研究。到目前位置人工智能方面的应用已经大量的应用到了一些工作环境中，为解决普通机器需要人为操作的情况得到了极大改善，以下就谈谈人工职能的一些应用。     

基于BP神经网络的ETO产品配置设计方法

 按订单设计(engineering-to-order, ETO)的定制产品因产品族结构比较复杂，产品间结构差异较大，设计过程涉及个人经验和灵感，并大量应用人机交互处理，难以实现设计自动化、程序化。人工神经网络模仿人脑结构及智能行为，具有大规模并行处理、容错、自组织和自适应能力及联想功能，符合ETO配置设计的特点。通过对ETO定制产品需求的分析，构建并训练具有一定结构和功能的BP神经网络，训练好的网络蕴含着ETO配置设计规则和经验。实例证明了该方法的可行性。     

脑功能网络核心节点及网络稳定性研究

采用偏相关的计算方法对大脑的正电子断层发射成像建立脑区间的功能连接网络,利用小世界网络中节点度和节点介数求出大脑网络中的核心节点并对应到解剖学结构中,通过随机攻击和对核心节点的目标攻击研究网络的稳定性.结果表明,大脑网络具有明显的小世界拓扑结构,拥有较高度和较大节点介数的节点为网络的核心节点,从而得出正常人大脑的核心节点主要分布在额叶和顶叶区域,通过随机攻击和对节点中心的目标攻击得出大脑网络具有较强的鲁棒性和脆弱性.     

智能源于人、拓于工——人工智能发展的一点思考

人工智能(AI)旨在模拟人脑中信息存储和处理机制等智能行为,使机器具有一定程度的智能水平。随着互联网、大数据、云计算和深度学习等新一代信息技术的飞速发展,目前AI领域的研究和应用已经取得重要进展。本文将深入分析与AI密切相关的计算机科学、控制科学、类脑智能、人脑智能等学科或领域之间的交融与历史演进;指出神经科学、脑科学与认知科学中有关脑的结构与功能机制的研究成果,为构建智能计算模型提供了重要的启发,并从逻辑模型及系统、神经元及网络模型、视觉神经分层机制等方面,分别阐述智能的驱动与发展;最后从互联网的计算理论、AI的演算和计算的融合、类脑智能的模型和机理、AI对神经科学的推动作用、反馈计算的算法设计与控制系统的能级五个方面,对AI的发展趋势进行了展望。     

国务院关于深化“互联网+先进制造业”发展工业互联网的指导意见(选编)

正当前,全球范围内新一轮科技革命和产业变革蓬勃兴起。工业互联网作为新一代信息技术与制造业深度融合的产物,日益成为新工业革命的关键支撑和深化"互联网+先进制造业"的重要基石,对未来工业发展产生全方位、深层次、革命性影响。工业互联网通过系统构建网络、平台、安全三大功能体系,打造人、机、物全面互联的新型网络基础设施,形成智能化发展的新兴业态和应用模式,是推进制造强国和网络强国建设的重要基础,     

核磁共振成像在脑连接方面的研究

大脑是人体最复杂的器官,担负着对所有高级认知活动的管理。而脑连接技术则是一种研究人脑结构和功能的最常用的、非常有效的手段。基于核磁共振成像技术获得的数据,介绍脑连接的研究进展。     

大脑驱动燃料电池

美国麻省理工学院的研究者设计了一种可植入人脑的葡萄糖燃料电池，能够利用脑脊液产生电流。几个世纪以来，人类使用大脑的能量解决复杂谜题，创造新发明，以及奇妙的艺术设计。而现在，科学家发明了一种能够让我们利用大脑获得电能的技术。     

Novel interpretable mechanism of neural networks based on network decoupling method

The lack of interpretability of the neural network algorithm has become the bottleneck of its wide application. We propose a general mathematical framework, which couples the complex structure of the system with the nonlinear activation function to explore the decoupled dimension reduction method of high-dimensional system and reveal the calculation mechanism of the neural network. We apply our framework to some network models and a real system of the whole neuron map of Caenorhabditis elegans. Result shows that a simple linear mapping relationship exists between network structure and network behavior in the neural network with high-dimensional and nonlinear characteristics. Our simulation and theoretical results fully demonstrate this interesting phenomenon. Our new interpretation mechanism provides not only the potential mathematical calculation principle of neural network but also an effective way to accurately match and predict human brain or animal activities, which can further expand and enrich the interpretable mechanism of artificial neural network in the future.     

基于神经网络的智能诊断

人工智能与诊断理论的结合形成了智能诊断,早期发展的模拟人脑思维推理的、基于知识的专家系统以串行运行的格式进入设备诊断领域,形成了基于知识的诊断推理专家系统,国内外已有许多成熟的商品化软件系统。近几年新发展起来的人工智能的一个分支－－人工神经网络模仿人脑物理结构以其强大的并行运算和联想能力非常适合于设备诊断中状态识别,本单位研制的通用型神经网络智能诊断系统,已达到商品化水平,并已在生产线上运行。     

Ideal observer analysis for task normalization of pattern classifier performance applied to EEG and fMRI data

The application of multivariate techniques to neuroimaging and electrophysiological data has greatly enhanced the ability to detect where, when, and how functional neural information is processed during a variety of behavioral tasks. With the extension to single-trial analysis, neuroscientists are able to relate brain states to perceptual, cognitive, and motor processes. Using pattern classification methods, the neuroscientist can extract neural performance measures in a manner analogous to human behavioral performance, allowing for a consistent information content metric across measurement modalities. However, as with behavioral psychophysical performance, pattern classifier performances are a product of both the task-relevant information inherent in the brain and in the task/stimuli. Here, we argue for the use of an ideal observer framework with which the researcher can effectively normalize the observed neural performance given the task's inherent objective difficulty. We use data from a face versus car discrimination task and compare classifier performance applied to electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data with corresponding human behavior through the absolute and relative efficiency metrics. We show that confounding variables that can lead to erroneous interpretations of information content can be accounted for through comparisons to an ideal observer, allowing for more confident interpretation of the neural mechanisms involved in the task of interest. Finally, we discuss limitations of interpretation due to the transduction of indirect measures of neural activity, underlying assumptions in the optimality of the pattern classifiers, and dependence of efficiency results on signal contrast.     

Comparative analysis of Alzheimer's disease classification by CDR level using CNN,feature selection,and machine-learning techniques

Magnetic resonance imaging (MRI) of brain needs an impeccable analysis to investigate all its structure and pattern. This analysis may be a sharp visual analysis by an experienced medical professional or by a computer aided diagnosis system that can help to predict, what may be the recent condition. Similarly, on the basis of various information and technique, a system can be designed to detect whether a patient is prone to Alzheimer's disease or not. And this task of detection of abnormalities at an initial stage from brain MRI is a major challenge in the field of neurosciences. The main idea behind our research is to utilize the deep layers feature extraction benefited from deep neural network architecture, without extensive hardware resource training, and classifying the image on a basis of simple machine-learning algorithm with selected best features in order to reduce work load, classification error and hardware utilization time. We have utilized convolution neural network (CNN) layer using similar architecture like that of Alexnet with some parametric change, for the automatic extraction of features of images obtained from slice extraction of whole brain MRI whereas 13 manual features based on gray level co-occurrence matrix were also extracted to test the impact of this features on ranking. If we had only classified using CNN network, the misclassification rate was much higher. So, feature selection is achieved with feature ranking algorithms like Mutinffs, ReliefF, Laplacian and UDFS and so on and also tested with different machine-learning techniques like Support Vector Machine, K-Nearest Neighbor and Subspace Ensemble under different testing condition. The performance of the result is satisfactory with classification accuracy around 98% to 99% with 7:3 ratio of random holdout partition of training to testing image sets and also with fivefolds of cross-validation on the same set using a standardized template.     

The study of brain networks driven by steady‐state visual evoked potentials

In this article, we investigated the brain networks during the steady‐state visual evoked potential (SSVEP) task. Two questions: (1) SSVEP‐driven network structures; and (2) the relationship between SSVEP‐driven networks and stimulus frequencies were studied from a network point of view. Method of directed transfer function was applied to brain active signals recorded from electroencephalography (EEG). The resulting connectivity matrices then were converted to graphs by applying a threshold, so that graph theoretical could be used to analyze the characteristics of SSVEP‐driven networks. The results showed that network connections exist in many scalp locations beyond occipital regions. Different from the outflow areas located mainly around the parietal areas, the inflow areas had a widely distribution pattern including the frontal, temporal, and occipital areas. Furthermore, for a wide range of thresholds, with increasing frequency (7–30 Hz), the distribution of clustering coefficient and characteristic path length presented positive and negative correlation with the three parallel flicker SSVEP subsystems, respectively. The results suggested that a specific frequency may evoke certain SSVEP components more than others, and, therefore, one may generate different evoked potentials which results in different network pattern.     

Hierarchical folding of elastic membranes under biaxial compressive stress

Mechanical instabilities that cause periodic wrinkling during compression of layered materials find applications in stretchable electronics and microfabrication, but can also limit an application's performance owing to delamination or cracking under loading and surface inhomogeneities during swelling. In particular, because of curvature localization, finite deformations can cause wrinkles to evolve into folds. The wrinkle-to-fold transition has been documented in several systems, mostly under uniaxial stress. However, the nucleation, the spatial structure and the dynamics of the invasion of folds in two-dimensional stress configurations remain elusive. Here, using a two-layer polymeric system under biaxial compressive stress, we show that a repetitive wrinkle-to-fold transition generates a hierarchical network of folds during reorganization of the stress field. The folds delineate individual domains, and each domain subdivides into smaller ones over multiple generations. By modifying the boundary conditions and geometry, we demonstrate control over the final network morphology. The ideas introduced here should find application in the many situations where stress impacts two-dimensional pattern formation.     

应用概率神经网络诊断自行火炮发动机的故障

目的　研究概率神经网络模型 ,并应用于故障诊断 .方法　对基于概率统计思想和 Bayes分类规则的概率神经网络模型、网络结构、算法及其特点进行分析 ,利用其进行故障诊断 ,并提出一种优化估计平滑因子的方法 .结果　概率神经网络可很好地诊断自行火炮发动机进行中油路和气路的故障 .结论　概率神经网络在模式识别和故障诊断领域中可取得良好地应用效果     

Toward the Next Generation of Retinal Neuroprosthesis: Visual Computation with Spikes

A neuroprosthesis is a type of precision medical device that is intended to manipulate the neuronal signals of the brain in a closed-loop fashion, while simultaneously receiving stimuli from the environment and controlling some part of a human brain or body. Incoming visual information can be processed by the brain in millisecond intervals. The retina computes visual scenes and sends its output to the cortex in the form of neuronal spikes for further computation. Thus, the neuronal signal of interest for a retinal neuroprosthesis is the neuronal spike. Closed-loop computation in a neuroprosthesis includes two stages: encoding a stimulus as a neuronal signal, and decoding it back into a stimulus. In this paper, we review some of the recent progress that has been achieved in visual computation models that use spikes to analyze natural scenes that include static images and dynamic videos. We hypothesize that in order to obtain a better understanding of the computational principles in the retina, a hypercircuit view of the retina is necessary, in which the different functional network motifs that have been revealed in the cortex neuronal network are taken into consideration when interacting with the retina. The different building blocks of the retina, which include a diversity of cell types and synaptic connections—both chemical synapses and electrical synapses (gap junctions)—make the retina an ideal neuronal network for adapting the computational techniques that have been developed in artificial intelligence to model the encoding and decoding of visual scenes. An overall systems approach to visual computation with neuronal spikes is necessary in order to advance the next generation of retinal neuroprosthesis as an artificial visual system.