Cyber Resilience through Real-Time Threat Analysis in Information Security

This paper examines how cybersecurity is developing and how it relates to more conventional information security. Although information security and cyber security are sometimes used synonymously, this study contends that they are not the same. The concept of cyber security is explored, which goes beyond protecting information resources to include a wider variety of assets, including people [1]. Protecting information assets is the main goal of traditional information security, with consideration to the human element and how people fit into the security process. On the other hand, cyber security adds a new level of complexity, as people might unintentionally contribute to or become targets of cyberattacks. This aspect presents moral questions since it is becoming more widely accepted that society has a duty to protect weaker members of society, including children [1]. The study emphasizes how important cyber security is on a larger scale, with many countries creating plans and laws to counteract cyberattacks. Nevertheless, a lot of these sources frequently neglect to define the differences or the relationship between information security and cyber security [1]. The paper focus on differentiating between cybersecurity and information security on a larger scale. The study also highlights other areas of cybersecurity which includes defending people, social norms, and vital infrastructure from threats that arise from online in addition to information and technology protection. It contends that ethical issues and the human factor are becoming more and more important in protecting assets in the digital age, and that cyber security is a paradigm shift in this regard [1].

Effects of real-time traffic information systems on traffic performance under different network structures

The effects of real-time traffic information system(RTTIS)on traffic performance under parallel,grid and ring networks were investigated.The simulation results show that the effects of the proportion of RTTIS usage depend on the road network structures.For traffic on a parallel network,the performance of groups with and without RTTIS level is improved when the proportion of vehicles using RTTIS is greater than 0 and less than 30%,and a proportion of RTTIS usage higher than 90%would actually deteriorate the performance.For both grid and ring networks,a higher proportion of RTTIS usage always improves the performance of groups with and without RTTIS.For all three network structures,vehicles without RTTIS benefit from some proportion of RTTIS usage in a system.

Modeling and Statistical Properties Research on Online Real-Time Information Transmission Network

In this paper, the model of the online real-time information transmission network, such as wechat, micro-blog, and QQ network, is proposed and built, based on the connection properties between users of the online real-time information transmission network, and combined with the local world evolving characteristics in complex network, then the statistical topological properties of the network is obtained by numerical simulation. Furthermore, we simulated the process of information transmission on the network, according to the actual characteristics of the online real-time information transmission. Statistics show that the degree distribution presents the characteristics of scale free network, presenting power law distribution, while the average path length, the average clustering coefficient and the average size of the network also has a power-law relationship, moreover, the model parameters has no effect on power-law exponent. The spread of information on the network represents obvious fluctuation scaling, reflecting the characteristics that information transmission fluctuates over time.

DuFNet:Dual Flow Network of Real-Time Semantic Segmentation for Unmanned Driving Application of Internet of Things

The application of unmanned driving in the Internet of Things is one of the concrete manifestations of the application of artificial intelligence technology.Image semantic segmentation can help the unmanned driving system by achieving road accessibility analysis.Semantic segmentation is also a challenging technology for image understanding and scene parsing.We focused on the challenging task of real-time semantic segmentation in this paper.In this paper,we proposed a novel fast architecture for real-time semantic segmentation named DuFNet.Starting from the existing work of Bilateral Segmentation Network(BiSeNet),DuFNet proposes a novel Semantic Information Flow(SIF)structure for context information and a novel Fringe Information Flow(FIF)structure for spatial information.We also proposed two kinds of SIF with cascaded and paralleled structures,respectively.The SIF encodes the input stage by stage in the ResNet18 backbone and provides context information for the feature fusionmodule.Features from previous stages usually contain rich low-level details but high-level semantics for later stages.Themultiple convolutions embed in Parallel SIF aggregate the corresponding features among different stages and generate a powerful global context representation with less computational cost.The FIF consists of a pooling layer and an upsampling operator followed by projection convolution layer.The concise component provides more spatial details for the network.Compared with BiSeNet,our work achieved faster speed and comparable performance with 72.34%mIoU accuracy and 78 FPS on Cityscapes Dataset based on the ResNet18 backbone.

Data network traffic analysis and optimization strategy of real-time power grid dynamic monitoring system for wide-frequency measurements

The application and development of a wide-area measurement system(WAMS)has enabled many applications and led to several requirements based on dynamic measurement data.Such data are transmitted as big data information flow.To ensure effective transmission of wide-frequency electrical information by the communication protocol of a WAMS,this study performs real-time traffic monitoring and analysis of the data network of a power information system,and establishes corresponding network optimization strategies to solve existing transmission problems.This study utilizes the traffic analysis results obtained using the current real-time dynamic monitoring system to design an optimization strategy,covering the optimization in three progressive levels:the underlying communication protocol,source data,and transmission process.Optimization of the system structure and scheduling optimization of data information are validated to be feasible and practical via tests.

An improved pulse coupled neural networks model for semantic IoT

In recent years,the Internet of Things(IoT)has gradually developed applications such as collecting sensory data and building intelligent services,which has led to an explosion in mobile data traffic.Meanwhile,with the rapid development of artificial intelligence,semantic communication has attracted great attention as a new communication paradigm.However,for IoT devices,however,processing image information efficiently in real time is an essential task for the rapid transmission of semantic information.With the increase of model parameters in deep learning methods,the model inference time in sensor devices continues to increase.In contrast,the Pulse Coupled Neural Network(PCNN)has fewer parameters,making it more suitable for processing real-time scene tasks such as image segmentation,which lays the foundation for real-time,effective,and accurate image transmission.However,the parameters of PCNN are determined by trial and error,which limits its application.To overcome this limitation,an Improved Pulse Coupled Neural Networks(IPCNN)model is proposed in this work.The IPCNN constructs the connection between the static properties of the input image and the dynamic properties of the neurons,and all its parameters are set adaptively,which avoids the inconvenience of manual setting in traditional methods and improves the adaptability of parameters to different types of images.Experimental segmentation results demonstrate the validity and efficiency of the proposed self-adaptive parameter setting method of IPCNN on the gray images and natural images from the Matlab and Berkeley Segmentation Datasets.The IPCNN method achieves a better segmentation result without training,providing a new solution for the real-time transmission of image semantic information.

Research on Network Platform of Information Management and Security

With the rapid development and wide application of network technology, information security issues are increasingly highlighted, received more and more attention. This article introduces the present situation of network information security, discusses the connotation of network information security, and analyzes the main threat to the security of the network information. And we separately detailed description of the data monitoring platform architecture from the data layer, network layer and presentation layer three levels, focuses on the functional structure of intelligent database platform, and puts forward to measures that ensure the safety of the platform and the internal data security. Through the design of the platform to improve the information security system has certain significance.

网络协同车间信息集成的分析

本文在分析传统的企业车间信息系统结构的基础上 ,将网络协同作业原理引入到车间信息集成系统中 。

筒并捻车间生产信息监测与管理系统的研究

为了提高纺织企业筒并捻车间的生产管理水平,对车间的生产管理现状进行了研究,通过实际需求的分析,基于LAN构建了C/S模式的系统体系结构,利用计算机网络技术、串口通信技术、数据库技术实现了系统的模块功能、数据库的设计、通信和数据采集技术,并对系统的体系结构、主要管理功能、优点以及软件设计过程中的关键技术进行了介绍。结合实际应用证明,该系统实现了从制定计划到生产过程监控,再到生产数据的各种统计分析,促进了纺织企业信息化的建设。

两级树模型无线传感网络在制造现场的应用

针对制造现场信息采集和传送的特点,采用基于Zigbee标准的两级树模型无线传感网络来实现制造现场信息的实时准确的采集和传送,开发了简洁实用的协议栈软件。最后建立了B/S架构的制造现场系统软件平台,在基于网络公共数据库的基础上实现现场信息的系统集成。

LiveWeb:A Sensorweb Portal for Sensing the World in Real-Time

A state-of-the-art sensorweb is a global observation system for varied sensory phenomena from the physical world and the cyber world. This paper presents the architecture, design, and application of a sensorweb service portal called the LiveWeb. This portal has been published on the Internet and is used by researchers, students, and also other communities. This system has been used to represent and monitor real-time physical sensor data and cyber activities from ubiquitous sources. LiveWeb meets its goal of pro- viding an efficient and robust sensor information oriented web service, enabled with real-time data repre- sentation and notification. Living in the current world with the immense magnitude of information, it is impor- tant to keep different communities updated and informed with their context specific data. There are search engines available in the Internet to find relatively static items, but not to observe the events in real-time. In addition, mostly the sensed data and events have meaning only when accompanied with corresponding geographic information. LiveWeb is an effective and efficient sensorweb service, that tries to fulfill the aforementioned requirements. The LiveWeb system consists of three main components： （a） special features of a PHP web application, （b） a Java background processing program, and （c） a database. It is a robust system and is currently running efficiently under the environment of Ubuntu 6.10, Apache 2.0, PHP 5.0, JAVA 1.60, and MySQL 1.6.

三层架构的车间实时信息网络的研究与应用

为了实现车间级的生产调度,采用基于RS485总线的三层架构车间实时信息网络对制造车间进行信息化管理。通过对车间实时信息网络的理论分析和论证,以及在某汽配公司制造车间的实际应用,发现此车间实时信息网络很好地实现了车间的生产调度,具有车间信息化成本低,抗干扰性强的特点。

四层架构的车间实时信息网络的研究与应用

为了实现车间级的生产调度,避免车间布线困难的难题,采用了基于RF433M无线通信的四层架构的车间实时信息网络对制造车间进行了信息化。通过对此车间实时信息网络的理论分析,以及在某机电公司的制造车间的实际应用中发现,此车间的实时信息网络,不但很好地实现了车间的生产调度,解决了车间布线困难的难题,而且具有车间信息化成本低、易于实现的优点。

船厂信息化车间的组态网络系统

基于国家对船舶行业提出的发展智能化工厂的要求,并结合船厂车间目前的实际情况,设计了一种船厂信息化车间的组态网络。该组态网络系统结合目前国际上先进的无线通信技术,在硬件及软件上进行优化组态,实现船厂车间对数控设备的集中控制管理,亦通过预留接口与上层高级管理系统的结合,完成了对整个船厂的信息化管理,能取得较高的经济效益。

关于制造企业车间信息集成管理系统的选型

主要讨论了在选购车间信息集成管理系统时所必须注意的几个要点,以便为设备改造时顺利地选型提供参数。

船舶智能感知车间的网络构建及系统应用

针对船舶制造现场缺乏实时感知系统、船舶制造车间未构建数据传输至工位的通信系统等问题,应用网络构建技术和信息采集与传输技术,构建船舶智能感知车间网络,实现船舶制造车间内部的“人、机、料”互联,达到数据采集的多源异构性和实时性,提高设备综合利用率和船厂管理水平。

Content-Based Image Retrieval with Feature Extraction and Rotation Invariance

Over recent years, Convolutional Neural Networks (CNN) has improved performance on practically every image-based task, including Content-Based Image Retrieval (CBIR). Nevertheless, since features of CNN have altered orientation, training a CBIR system to detect and correct the angle is complex. While it is possible to construct rotation-invariant features by hand, retrieval accuracy will be low because hand engineering only creates low-level features, while deep learning methods build high-level and low-level features simultaneously. This paper presents a novel approach that combines a deep learning orientation angle detection model with the CBIR feature extraction model to correct the rotation angle of any image. This offers a unique construction of a rotation-invariant CBIR system that handles the CNN features that are not rotation invariant. This research also proposes a further study on how a rotation-invariant deep CBIR can recover images from the dataset in real-time. The final results of this system show significant improvement as compared to a default CNN feature extraction model without the OAD.

信息化翻译教学的新视野

本文从剖析传统翻译教学模式的问题入手，论述了信息化技术条件下开展信息化翻译教学的必要性和优势，介绍了四种信息化翻译教学方法：多媒体网络教室的翻译工作坊、计算机辅助翻译的简单应用、基于语料库的翻译教学和自行设计学生自学自测课件等，并对今后的翻译教学研究做出展望。