P比特光网络多故障定位的NP-complete问题研究

解决多故障定位的非多项式完全问题(NP-complete)在P比特级光网络中变的更加困难。计算复杂度、计算时间与网络的输入规模成指数增长关系。文章阐述已有的多故障定位算法以及协议,包括透明的故障定位算法、推理算法、深度探测算法、启发式生成树算法以及有限区域向量匹配协议(LVM)等,深入分析了P比特级光网络中多故障定位NP-complete问题的难点所在,同时提出一种基于蚁群优化进行告警的遍历和包含故障元素最少的故障集合的寻找方法。

基于图卷积网络的比特币非法交易识别方法

比特币作为匿名的加密数字资产逐渐成为部分非法地下交易的选择。为了净化金融市场、打击非法交易,需要对比特币网络中的非法交易活动进行识别。在相关工作的基础上,文章提出一种基于多层感知器与图卷积网络结合的检测比特币网络中非法交易的方法(Multi-layer Perceptrons+Graph Convolutional Network,MP-GCN)。MP-GCN使用多层感知器与图卷积网络组合,构建识别非法交易的模型。具体来说,在图卷积层之前和两层图卷积层中间用多层感知器进行辅助的特征提取,最后用线性层对非法交易进行划分。实验结果证明,MP-GCN达到的检测效果优于相同数据集的传统图卷积网络的检测效果。

动态多太比特核心光网络技术浅析

随着信息产业的大数据业务发展,传统的网络资源已经不能满足人们的要求,新一代网络体系必将发生变革并产生新一代网络(NGN),动态多太比特核心光网络(CORONET)计划是由美国提出来的新的一代网络计划,主要是寻求高度动态、多太比特核心光网络体系结构、协议、控制和管理的方案。利用波分复用(WDM)带宽资源和强大的波长路由能力,构造一个全球范围的核心光网络,即连接全球范围内主要站点的大容量、长距离基础设施。将IP over WDM的全光交换技术应用到CORONET网络系统中,采用的三次握手分布信号协议方法(3WHS)通过动态链接可以达到百毫秒级的快速建立时间并极大提高网络安全。最后,借鉴该网络计划的发展情况阐述对我们国家光网络发展所带来的启示。

如何实现特稿“买卖”中的多赢关系—以华西特稿网络的松散型市场联营模式为例

华西特稿及其网络建立以来的十多年的市场运作,用八个字来概括：＂经营得法,多方受益＂。华西特稿网络这种松散型联营模式是市场运作的一个成功范例,也是市场经济下的一种探索。它使华西都市报、华西特稿网络单位、特稿作者及读者四方都受益,形成了一个多赢的局面。本文从重金购稿、低价批发、拓展受众群、辐射全国、提升影响力等方面进行阐述,从而为特稿＂买与卖＂多赢关系的构建提供思路。

基于细胞类型特异性网络的雷公藤治疗类风湿性关节炎作用机制解析

目的:基于细胞类型特异性网络研究雷公藤治疗类风湿性关节炎(RA)的作用机制。方法:通过中药系统药理学数据库与分析平台(TCMSP)收集雷公藤的化学成分;通过Similarity Ensemble Approach数据库检索雷公藤有效成分的靶点;通过TTD数据库检索RA相关靶点;融合生化反应数据和细胞的基因表达数据,构建B淋巴细胞类型特异性网络;并基于实体语法系统对此网络进行定性推理,解析雷公藤治疗RA的作用机制。结果:发现雷公藤的9个有效成分通过18个靶点作用于21个RA相关靶点,通过调节B淋巴细胞转录因子活性、胞外信号传递、细胞活化发挥治疗RA作用;提取其中1个子网络进行分析发现,雷公藤的有效成分白桦脂酸通过抑制酪氨酸蛋白磷酸酶非受体1型(PTPN1)而抑制了RA疾病相关靶点酪氨酸蛋白激酶(SYK)的活化,从而调节RA的疾病状态。结论:雷公藤通过多种机制起到系统治疗RA的作用。

构建个体特异性异常指标探测复杂疾病网络临界点

探测复杂疾病临界点对疾病早期诊断至关重要,通过对高通量的生物分子数据的挖掘,本文提出一种结合个体特异性网络与隐马尔科夫模型的方法,构建个体特异性异常指标,以探测从相对健康期到疾病临界期的临界点。为验证该方法的有效性,将该方法分别应用在模拟数据集、肺部急性损伤数据、前列腺癌数据中,均成功在疾病恶性突变前找到其各自的临界点。信号基因的有效性和敏感性都通过生物功能分析得到了验证。

网络科技鼓动的艺术变革——读黄鸣奋教授新著《比特挑战缪斯——网络与艺术》

科技的进步往往导致新型的艺术工具的出现并从而引发艺术的变革.印刷术的发明、无线电技术的应用、电影电视的繁荣在历史上给艺术带来的全新气象有目共睹,而20世纪中叶才出现的计算机随着科技的飞速发展也迅速地给艺术注入了新的活力,尤其是当它进入网络阶段后,不仅促成了人类生活方式、生存状态的改变,而且还引起了艺术翻天覆地的变化.

根据网络状况实时压缩视频的研究及实现

针对保证帧率不变的前提下,提出根据网络状况实时压缩视频的解决方法,并给出了实时压缩算法的软件实现,同时给出了模拟仿真实验数据和实验结果。实验表明,根据网络状况动态控制小波分解次数可以实现实时压缩视频,也验证了本文提出的解决方法的正确性和可行性。

基于强化图卷积和时空循环门的区块链非法交易检测方法

区块链非法交易检测任务需要充分挖掘历史交易数据中固有的时间和空间特征。针对现有的非法交易检测方法存在误差较大的问题,提出一种基于强化图卷积和时空循环门的区块链非法交易检测方法(RGCN-SRG)。首先,利用比特币区块链历史交易数据构造交易图谱,引入一组具有不同尺寸卷积核的强化图卷积网络(RGCN),全面地提取该图谱的拓扑信息并生成特征向量;另外,考虑到区块链交易的时序特点,提出一种时空循环门结构(SRG),在传统门结构中引入图卷积运算,以提取交易图多个时空维度的依赖信息;最后,经过一个线性层和激活函数输出非法交易检测的预测结果。将提出的方法与GCN、DEDGAT、EGT以及GCN+MLP进行比较,在F 1方面,分别提高了18.4、10.7、9.3和4.9百分点;在精度方面,分别提高了11.5、11.2、7.7和3.7百分点。

Underwater Image Enhancement Based on Multi-scale Adversarial Network

In this study,an underwater image enhancement method based on multi-scale adversarial network was proposed to solve the problem of detail blur and color distortion in underwater images.Firstly,the local features of each layer were enhanced into the global features by the proposed residual dense block,which ensured that the generated images retain more details.Secondly,a multi-scale structure was adopted to extract multi-scale semantic features of the original images.Finally,the features obtained from the dual channels were fused by an adaptive fusion module to further optimize the features.The discriminant network adopted the structure of the Markov discriminator.In addition,by constructing mean square error,structural similarity,and perceived color loss function,the generated image is consistent with the reference image in structure,color,and content.The experimental results showed that the enhanced underwater image deblurring effect of the proposed algorithm was good and the problem of underwater image color bias was effectively improved.In both subjective and objective evaluation indexes,the experimental results of the proposed algorithm are better than those of the comparison algorithm.

Infrasound Event Classification Fusion Model Based on Multiscale SE-CNN and BiLSTM

The classification of infrasound events has considerable importance in improving the capability to identify the types of natural disasters.The traditional infrasound classification mainly relies on machine learning algorithms after artificial feature extraction.However,guaranteeing the effectiveness of the extracted features is difficult.The current trend focuses on using a convolution neural network to automatically extract features for classification.This method can be used to extract signal spatial features automatically through a convolution kernel;however,infrasound signals contain not only spatial information but also temporal information when used as a time series.These extracted temporal features are also crucial.If only a convolution neural network is used,then the time dependence of the infrasound sequence will be missed.Using long short-term memory networks can compensate for the missing time-series features but induces spatial feature information loss of the infrasound signal.A multiscale squeeze excitation–convolution neural network–bidirectional long short-term memory network infrasound event classification fusion model is proposed in this study to address these problems.This model automatically extracted temporal and spatial features,adaptively selected features,and also realized the fusion of the two types of features.Experimental results showed that the classification accuracy of the model was more than 98%,thus verifying the effectiveness and superiority of the proposed model.

基于NS-3的光纤入室组网及空口调度仿真

目前的光纤接入无线网方案中,光网络与无线网络独立工作,缺乏协同工作机制,导致设备间相互干扰。通过引入中心化的决策控制机制,主设备基于从设备上报的信息进行集中决策控制,实现光纤与无线网络的协同工作。在NS-3平台添加XG-PON模块,对FTTR组网进行仿真,并设置主设备控制下的空口调度策略。仿真实现NS-3中XG-PON模块的上下行数据传输和上行带宽分配功能,并对FTTR网络下的多用户空口数据传输进行调度,在不同用户数量下,整网数据吞吐量分别获得0.64%~5.66%的提升,说明中心化决策机制具有有效的调度能力。

A super-resolution reconstruction algorithm for mural images based on improved generative adversarial network

In order to solve the problem of the lack of ornamental value and research value of ancient mural paintings due to low resolution and fuzzy texture details,a super resolution(SR)method based on generative adduction network(GAN)was proposed.This method reconstructed the detail texture of mural image better.Firstly,in view of the insufficient utilization of shallow image features,information distillation blocks(IDB)were introduced to extract shallow image features and enhance the output results of the network behind.Secondly,residual dense blocks with residual scaling and feature fusion(RRDB-Fs)were used to extract deep image features,which removed the BN layer in the residual block that affected the quality of image generation,and improved the training speed of the network.Furthermore,local feature fusion and global feature fusion were applied in the generation network,and the features of different levels were merged together adaptively,so that the reconstructed image contained rich details.Finally,in calculating the perceptual loss,the brightness consistency between the reconstructed fresco and the original fresco was enhanced by using the features before activation,while avoiding artificial interference.The experimental results showed that the peak signal-to-noise ratio and structural similarity metrics were improved compared with other algorithms,with an improvement of 0.512 dB-3.016 dB in peak signal-to-noise ratio and 0.009-0.089 in structural similarity,and the proposed method had better visual effects.

Rolling bearing fault diagnosis based on data-level and feature-level information fusion

To address the limitation of single acceleration sensor signals in effectively reflecting the health status of rolling bearings,a rolling bearing fault diagnosis method based on the fusion of data-level and feature-level information was proposed.First,according to the impact characteristics of rolling bearing faults,correlation kurtosis rules were designed to guide the weight distribution of multi-sensor signals.These rules were then combined with a weighted fusion method to obtain high-quality data-level fusion signals.Subsequently,a feature-fusion convolutional neural network(FFCNN)that merges the one-dimensional(1D)features extracted from the fused signal with the two-dimensional(2D)features extracted from the wavelet time-frequency spectrum was designed to obtain a comprehensive representation of the health status of rolling bearings.Finally,the fused features were fed into a Softmax classifier to complete the fault diagnosis.The results show that the proposed method exhibits an average test accuracy of over 99.00%on the two rolling bearing fault datasets,outperforming other comparison methods.Thus,the method can be effectively utilized for diagnosing rolling bearing faults.

EFFECTIVE FEATURE ANALYSIS FOR COLOR IMAGE SEGMENTATION

An approach for color image segmentation is proposed based on the contributions of color features to segmentation rather than the choice of a particular color space. The determination of effective color features depends on the analysis of various color features from each tested color image via the designed feature encoding. It is different from the pervious methods where self organized feature map (SOFM) is used for constructing the feature encoding so that the feature encoding can self organize the effective features for different color images. Fuzzy clustering is applied for the final segmentation when the well suited color features and the initial parameter are available. The proposed method has been applied in segmenting different types of color images and the experimental results show that it outperforms the classical clustering method. The study shows that the feature encoding approach offers great promise in automating and optimizing the segmentation of color images.

Design of a road vehicle detection system based on monocular vision

In order to decrease vehicle crashes, a new rear view vehicle detection system based on monocular vision is designed. First, a small and flexible hardware platform based on a DM642 digtal signal processor （DSP） micro-controller is built. Then, a two-step vehicle detection algorithm is proposed. In the first step, a fast vehicle edge and symmetry fusion algorithm is used and a low threshold is set so that all the possible vehicles have a nearly 100% detection rate （TP） and the non-vehicles have a high false detection rate （FP）, i. e., all the possible vehicles can be obtained. In the second step, a classifier using a probabilistic neural network （PNN） which is based on multiple scales and an orientation Gabor feature is trained to classify the possible vehicles and eliminate the false detected vehicles from the candidate vehicles generated in the first step. Experimental results demonstrate that the proposed system maintains a high detection rate and a low false detection rate under different road, weather and lighting conditions.