In Vivo Albumin-Binding of a C-Functionalized Cyclam Platform for 64Cu-PET/CT Imaging in Breast Cancer Model

An improved glucose-chelator-albumin bioconjugate (GluCAB) derivative, GluCAB-2^Mal, has been synthesized and studied for in vivo ⁶⁴Cu-PET/CT imaging in breast cancer mice models together with its first-generation analogue GluCAB-1^Mal. The radioligand works on the principle of tumor targeting through the enhanced permeability and retention (EPR) effect with a supportive role played by glucose metabolism. [⁶⁴Cu]Cu-GluCAB-2^Mal (99 % RCP) exhibited high serum stability with immediate binding to serum proteins. In vivo experiments for comparison between tumor targeting of [⁶⁴Cu]Cu-GluCAB-2^Mal and previous-generation [⁶⁴Cu]Cu-GluCAB-1^Mal encompassed microPET/CT imaging and biodistribution analysis in an allograft E0771 breast cancer mouse model. Tumor uptake of [⁶⁴Cu]Cu-GluCAB-2^Mal was clearly evident with twice as much accumulation as compared to its predecessor and a tumor/muscle ratio of up to 5 after 24 h. Further comparison indicated a decrease in liver accumulation for [⁶⁴Cu]Cu-Glu-CAB-2^Mal.     

A process fault diagnosis method using multi-time scale dynamic feature extraction based on convolutional neural network

Unlike many other techniques used in process control, which are widely applied in practice and play significant roles, abnormal situation management (ASM) still relies heavily on human experience, not least because the problem of fault detection and diagnosis (FDD) has not been well addressed. In this paper, a process fault diagnosis method using multi-time scale dynamic feature extraction based on convolutional neural network (CNN) consisting of similarity measurement, variable ranking, and multi-time scale dynamic feature extraction is proposed. The CNN-based model containing the fixed multiple sampling (FMS) layer can extract dynamic characteristics of process data at different time scales. The benchmark Tennessee Eastman (TE) process is used to verify the performance of the proposed method.     

Heats and input variables selection for designing a water detection framework applicable to industrial electric arc furnaces

This paper describes the development of “heats” and input variables selection models that are incorporated into a water detection framework for an industrial steelmaking electric arc furnace (EAF). The selection models in this work are developed based on latent variable methods. The latent variable methods used in this work are multiway principal component analysis (MPCA) and multiway projection to latent structures (MPLS). The particular problems related to latent variable methods discussed in this paper include data preprocessing, including alignment, unfolding method, centering, and scaling. The outcome of the heats selection model is heats with normal operation and the outcome of the input variables selection model is variables that are highly correlated with the off-gas water vapour. The water detection framework and developed models are useful in practical settings for the prediction of water leakage and the design of appropriate fault detection and diagnosis strategies.     

一种基于雷达图表示的数值型数据的CNN分类方法

卷积神经网络（convolutional neural networks， CNN）是一种广泛用于分析视觉图像的分类方法.由于数值数据存在着非线性、耦合性等复杂的空间关系，因此基于CNN的数值型数据的研究较少.本文的目的是找到一种可行的方法，将CNN的应用领域扩展到数值数据.于是提出了一种基于雷达图表示的数值型数据的CNN分类方法（Radar-CNN）.该算法首先将数值数据表示成雷达图形式，然后将其输入CNN中构建分类模型.为了进一步研究特征尺度和序列对性能的影响，提出了两种改进算法Rank Radar-CNN和SFS Radar-CNN.为了验证所提算法的有效性，引入TE化工过程数据集进行实验测试并比较，实验结果表明Radar-CNN及其改进算法具有优异的性能.     

Chemical process fault diagnosis based on enchanted machine‐learning approach

In the chemical industry, fault diagnosis is a challenging task due to the complexity of chemical equipment. This paper proposes a machine learning‐based approach to achieve the goal of fault diagnosis. First, in order to reduce the impact of redundant features, support vector machine recursive feature elimination (SVMRFE) is used to select important features. The trained probabilistic neural network (PNN) is then used for fault diagnosis. Considering that the diagnostic performance is affected by its hidden layer element smoothing factor (σ), the modified bat algorithm (MBA) is used to optimize the PNN to obtain optimal global parameter values. The MBA adopts a better optimization mechanism than the basic algorithm and achieves excellent global convergence. It can globally optimize the smoothing factor, which effectively improves the fault diagnosis ability of the PNN. During the testing of the Tennessee Eastman (TE) process data set, we evaluate the performance of the proposed model by comparing the F₁‐score and accuracy of the different methods. The charts provided describe the fault diagnostic results and classification for the different models. The results indicate that the MBA has a better optimization ability than other traditional optimization algorithms. At the same time, the combination method proposed in this paper is also superior to others and can significantly improve the accuracy of TE process fault diagnosis.     

市电子政务外网改造故障分析及解决办法

本文从TCP/IP协议角度对市电子政务外网改造过程中,对由MTU引起故障进行讨论,分析了故障产生的原因,并给出解决方法。     

一种基于网络的设备在线故障诊断系统研究

针对生产过程中设备参数时变、非稳态特点,提出了基于网络的故障诊断系统结构,用于设备的远程监测与故障诊断。设计了TE过程与模拟电路故障实验,结果表明应用该结构对设备进行监控可以提高设备故障诊断的准确率,降低系统维修成本,提高生产效率。针对快速性问题,采用提升小波与递推最小二乘支持向量机相混合的快速故障诊断方法;通过快速算法训练最小二乘支持向量机分类器,节省了存储空间和运算时间,增加了诊断模型的适应性。根据设计要求设计了工业故障诊断研究实验平台,并在此平台上对提出的方法进行了验证。该应用平台采用C/S,B/S的混合模式,集成Matlab,力控等专业软件系统,并采用OPC,ODBC,数据库技术实现了故障诊断系统的基本功能。     

IP网络融合方式及关键技术

提出了如何将多张物理上分开的IP网络合并成一张物理网、多张逻辑网的思路,对其中的关键技术进行了分析,并对通过融合的网络承载IPv6应用进行了探讨.     

基于MPLS VPN的流量统计技术

MPLS VPN技术是未来构建VPN的发展方向,越来越受到客户和运营商的青睐.传统的基于IP的流量统计无法满足当前网络发展的需要,因此对MPLS VPN网络的流量进行分析,能够很好地规划网络并指导网络的设计,具有非常重要的意义.     

VPN技术的浅析与比较

利用公共网络来构建的私人专用网络称为虚拟私有网络（VPN）。文章首先介绍了VPN的基本概念及发展概况,然后从业务需求、安全性、可靠性、可扩展性、QoS保障等方面对基于电话网的VPN、基于传统TCP／IP网络的VPN以及基于MPLS网络的VPN三种技术依次进行了介绍,最后对这三种VPN技术的应用场合及优缺点等进行了综合比较。