基于Real ESRGAN的视频修复系统研究

图像修复和视频修复是计算机视觉的一项重要任务,其中图像修复又是视频修复的基础。为此,如何有效提升图像质量是实现视频质量提升的关键。传统的图像修复算法主要以样本信息为基础,通过对样本内容的扩撒来实现对破损区域的修复;由于这种方式对于图像样本有一定要求,从而制约传统图像修复技术的发展。为此,以生成新图像内容为基础的神经网络如GAN的出现,为图像修复技术转向深度学习提供方向。该课题主要以Real ESRGAN网络的图像修复技术为基础,通过对音频视频数据的隔离处理以及相同帧数据的优化和标记,构建视频修复处理流程。通过对随机视频样本的测试,并通过对单帧图片质量和视频数据流畅性与协调性的评估,该视频处理方法表现出较好的系统性能。

A CNN-Based Single-Stage Occlusion Real-Time Target Detection Method

Aiming at the problem of low accuracy of traditional target detection methods for target detection in endoscopes in substation environments, a CNN-based real-time detection method for masked targets is proposed. The method adopts the overall design of backbone network, detection network and algorithmic parameter optimisation method, completes the model training on the self-constructed occlusion target dataset, and adopts the multi-scale perception method for target detection. The HNM algorithm is used to screen positive and negative samples during the training process, and the NMS algorithm is used to post-process the prediction results during the detection process to improve the detection efficiency. After experimental validation, the obtained model has the multi-class average predicted value (mAP) of the dataset. It has general advantages over traditional target detection methods. The detection time of a single target on FDDB dataset is 39 ms, which can meet the need of real-time target detection. In addition, the project team has successfully deployed the method into substations and put it into use in many places in Beijing, which is important for achieving the anomaly of occlusion target detection.

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].

A Real-Time TCP Stream Reassembly Mechanism in High-Speed Network

With the continual growth of the variety and complexity of network crime means, the traditional packet feature matching cannot detect all kinds of intrusion behaviors completely. It is urgent to reassemble network stream to perform packet processing at a semantic level above the network layer. This paper presents an efficient TCP stream reassembly mechanism for real-time processing of high-speed network traffic. By analyzing the characteristics of network stream in high-speed network and TCP connection establishment process, several polices for designing the reassembly mechanism are built. Then, the reassembly implementation is elaborated in accordance with the policies. Finally, the reassembly mechanism is compared with the traditional reassembly mechanism by the network traffic captured in a typical gigabit gateway. Experiment results illustrate that the reassembly mechanism is efficient and can satisfy the real-time property requirement of traffic analysis system in high-speed network.

Design, Analysis and Real-time Implementation of Networked Predictive Control Systems

这份报纸用预兆的控制策略涉及设计，分析和联网的控制系统的即时实现。联网的控制系统的特征的分析被详细说明，它证明一个联网的控制系统与常规控制系统不同。完成靠近环的联网的控制系统的需要的性能，联网的预兆的控制计划被介绍。设计，稳定性分析和联网的预兆的控制系统的即时实现被学习。它被联网的预兆的控制计划能补偿随机的网络通讯延期和数据退学学生的模拟和实际实验说明，完成需要的控制性能并且有好靠近环的稳定性。

Real estate appraisal system based on GIS and BP neural network

For the inefficiency and inaccuracy of appraisal method of traditional estate appraisal theory, the real estate appraisal system based on GIS and BP neural network was established. The structure of the system was designed which includes appraisal model, trade case, GIS database and query analysis module. With the help of the L-M algorithm in MATLAB software, BP neural network was improved and the trade cases were trained, then the BP neural network which has already been trained was tested. At the same time, the BP neural and GIS were put together to construct the hedonic price estimate model. The C# and ArcGIS9.3 were used to achieve the system in VS2008. City basic geographic data and real estate related information were used as the basic data in practice. The results show that the functions of querying, adding and editing the spatial data and attribute data are achieved and also the efficiency and accuracy of real estate are improved, so that the new method of real estate is provided by the 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.

Comparing the Accuracy of Network Utilization Performance between Real Network and Simulation Model for Local Area Network (LAN)

This article presents a novel approach for the measurement and estimation of network traffic utilization between network nodes in heterogeneous environment. This research investigates performance evaluation of network interface on heterogeneous services and technologies environment. This study proposes an enhanced equation to evaluate the performance of network interface via Little Law and Queuing theories to improve the evaluation algorithm. To get accuracy results on the performance of simulation model, it measures (verify and validate) data from Local Area Network (real network environment). This project uses network management tool to capture those data and Fluke Optiview device to generate traffic. As a result, this simulation model can provide a good approximation of the real traffic observed in the real network environment. Through laboratory and field experiments, the result shows that the model via simulation is capable of approximating the performance of network utilization and traffic over heterogeneous services and techniques within a minimum error range.

Verification of Real-Time Pricing Systems Based on Probabilistic Boolean Networks

In this paper, verification of real-time pricing systems of electricity is considered using a probabilistic Boolean network (PBN). In real-time pricing systems, electricity conservation is achieved by manipulating the electricity price at each time. A PBN is widely used as a model of complex systems, and is appropriate as a model of real-time pricing systems. Using the PBN-based model, real-time pricing systems can be quantitatively analyzed. In this paper, we propose a verification method of real-time pricing systems using the PBN-based model and the probabilistic model checker PRISM. First, the PBN-based model is derived. Next, the reachability problem, which is one of the typical verification problems, is formulated, and a solution method is derived. Finally, the effectiveness of the proposed method is presented by a numerical example.

Influences of Network Delay on Quality of Experience for Soft Objects in Networked Real-Time Game with Haptic Sense

In this paper, we investigate the influences of network delay on QoE (Quality of Experience) such as the operability of haptic interface device and the fairness between players for soft objects in a networked real-time game subjectively and objectively. We handle a networked balloon bursting game in which two players burst balloons (i.e., soft objects) in a 3D virtual space by using haptic interface devices, and the players compete for the number of burst balloons. As a result, we find that the operability depends on the network delay from the local terminal to the other terminal, and the fairness is mainly dependent on the difference in network delay between the players’ terminals. We confirm that there exists a trade-off relationship between the operability and the fairness. We also see that the contribution of the fairness is larger than that of the operability to the comprehensive quality (i.e., the weighted sum of the operability and fairness). Assessment results further show that the output timing of terminals should be adjusted to the terminal which has the latest output timing to maintain the fairness when the difference in network delay between the terminals is large. In this way, the comprehensive quality at each terminal can be maintained as high as possible.

Contention-Based Beaconless Real-Time Routing Protocol for Wireless Sensor Networks

This paper presents a novel real-time routing protocol, called CBRR, with less energy consumption for wireless sensor networks (WSNs). End-to-End real-time requirements are fulfilled with speed or delay constraint at each hop through integrating the contention and neighbor table mechanisms. More precisely, CBRR maintains a neighbor table via the contention mechanism being dependent on wireless broadcast instead of beacons. Comprehensive simulations show that CBRR can not only achieve higher performance in static networks, but also work well for dynamic networks.

Multistability of delayed complex-valued recurrent neural networks with discontinuous real-imaginarytype activation functions

In this paper, the multistability issue is discussed for delayed complex-valued recurrent neural networks with discontinuous real-imaginary-type activation functions. Based on a fixed theorem and stability definition, sufficient criteria are established for the existence and stability of multiple equilibria of complex-valued recurrent neural networks. The number of stable equilibria is larger than that of real-valued recurrent neural networks, which can be used to achieve high-capacity associative memories. One numerical example is provided to show the effectiveness and superiority of the presented results.

Method for integer ambiguity resolution in GPS network RTK

A method for integer ambiguity resolution in the global positioning system （GPS） multi-reference station network real time kinematic （RTK） is proposed. First, the barycenter of the triangle of reference stations for ambiguity resolution is taken as a reference point. The satellite which has the largest elevation angle with the reference point is selected as a reference satellite. The parameters for constructing the weight matrix of carrier phase observation and the criteria for checking the correctness of integer ambiguity resolution of a network are obtained. Then, the wide ambiguity is calculated by a linear combination method of dualband observation. And the LI ambiguity is obtained by a nonionosphere combination method. The Kalman filter is introduced to refine the floating-point solution of ambiguity and estimate the real-time tropospheric delay. Finally, the cofactor matrix of ambiguity is de-correlated by Z-transformation to reduce the searching space of the integer ambiguity solution and improve the efficiency of the least-squares ambiguity decorrelation adjustment （LAMBDA） algorithm. The experimental results show that this method can reliably obtain the integer ambiguity solution among multi-reference stations with 40 epochs.

A Survey on Real-Time MAC Protocols in Wireless Sensor Networks

As wireless sensor network becomes pervasive, new requirements have been continuously emerged. How-ever, the most of research efforts in wireless sensor network are focused on energy problem since the nodes are usually battery-powered. Among these requirements, real-time communication is one of the big research challenges in wireless sensor networks because most of query messages carry time information. To meet this requirement, recently several real-time medium access control protocols have been proposed for wireless sensor networks in the literature because waiting time to share medium on each node is one of main source for end-to-end delay. In this paper, we first introduce the specific requirement of wireless sensor real-time MAC protocol. Then, a collection of recent wireless sensor real-time MAC protocols are surveyed, classified, and described emphasizing their advantages and disadvantages whenever possible. Finally we present a dis-cussion about the challenges of current wireless sensor real-time MAC protocols in the literature, and show the conclusion in the end.

基于LSTM神经网络的现地烈度实时估算模型——以JMA烈度为例

如何快速并且准确估计目标场点烈度是地震预警中的关键问题。常用基于衰减关系的场点烈度估计和基于P波信息的现地烈度估计往往存在大震烈度低估的问题。本文提出了一种基于长短时记忆神经网络(logn short-term memery,LSTM)的现地JMA烈度持续估计模型。该模型以现地观测地震动的能量、能量增长率、地震动卓越周期和震源距作为输入,以该点的最大仪器地震烈度为预测目标。选取了日本K-NET台网记录101次地震数据作为训练集,94次地震数据作为测试集,训练了现地烈度估算LSTM神经网络模型。结果表明:在采用3 s时窗长度的序列进行预测时,高估的比例为1.51%,低估的比例为4.00%;并且,随着时窗长度的增加,高估和低估的比例也在不断降低。模型对高烈度(大于等于4.5度)样本的预测时效性随震源距的增加而增加,对大震远场高烈度区域能提供20 s以上的预警时间。

Research on the Effect of Artificial Intelligence Real Estate Forecasting Using Multiple Regression Analysis and Artificial Neural Network: A Case Study of Ghana

To transition from conventional to intelligent real estate, the real estate industry must enhance its embrace of disruptive technology. Even though the real estate auction market has grown in importance in the financial, economic, and investment sectors, few artificial intelligence-based research has tried to predict the auction values of real estate in the past. According to the objectives of this research, artificial intelligence and statistical methods will be used to create forecasting models for real estate auction prices. A multiple regression model and an artificial neural network are used in conjunction with one another to build the forecasting models. For the empirical study, the study utilizes data from Ghana apartment auctions from 2016 to 2020 to anticipate auction prices and evaluate the forecasting accuracy of the various models available at the time. Compared to the conventional Multiple Regression Analysis, using artificial intelligence systems for real estate appraisal is becoming a more viable option (MRA). The Artificial Neural network model exhibits the most outstanding performance, and efficient zonal segmentation based on the auction evaluation price enhances the model’s prediction accuracy even more. There is a statistically significant difference between the two models when it comes to forecasting the values of real estate auctions.

On-Line Real Time Realization and Application of Adaptive Fuzzy Inference Neural Network

In this paper, a modeling algorithm developed by transferring the adaptive fuzzy inference neural network into an on-line real time algorithm, combining the algorithm with conventional system identification method and applying them to separate identification of nonlinear multi-variable systems is introduced and discussed.

基于BANs的通信及定位仿真实验平台

近年来,BANs在医学检测、监测和急救等领域被广泛应用,因此BANs对医学传感器的通信和定位系统的性能也提出了更高的要求,但利用实际设备直接在体内实验的不便,给WBAN中实现实时性能的测试和定位带来极大的困难,因此,开发了一个实时的性能测试硬件平台,用于BANs的通信和定位测试。在验证平台的有效性后,通过比较分析发现,不同调制方式中,低数据速率的调制具有更好的通信性能;并且体内的信道模型比穿透体的信道模型有更好的通信性能;对于体内定位,在RSS定位算法的基础上,利用调整矩阵改进最大似然算法,并应用于沿小肠传播路径中的若干点,结果有效地验证了系统的定位算法。

双鉴别器盲超分重建方法研究

图像超分变率重建方法在公共安全检测、卫星成像、医学和照片恢复等方面有着十分重要的用途。该文对基于生成对抗网络的超分辨率重建方法进行研究,提出一种基于纯合成数据训练的真实世界盲超分算法(RealESRGAN)的UNet3+双鉴别器Real-ESRGAN方法(Double Unet3+Real-ESRGAN, DU3-Real-ESRGAN)。首先,在鉴别器中引入UNet3+结构,从全尺度捕捉细粒度的细节和粗粒度的语义。其次,采用双鉴别器结构,一个鉴别器学习图像纹理细节,另一个鉴别器关注图像边缘,实现图像信息互补。在Set5, Set14, BSD100和Urban100数据集上,与多种基于生成对抗网络的超分重建方法相比,除Set5数据集外,DU3-Real-ESRGAN方法在峰值信噪比(PSNR)、结构相似性(SSIM)和无参图像考评价指标(NIQE)都优于其他方法,产生了更直观逼真的高分辨率图像。

基于数据流模型的航线网络结构可视分析方法

针对航线网络结构多维属性关联分析问题,提出一种基于数据流模型的多视图协同网络结构可视分析方法。引入数据流模型,增强多维数据分析时的交互和多视图协同效果;构建基于数据流模型的航线网络结构可视分析系统用于方法验证。该方法对比和实验结果表明,利用航线网络数据流实时交互和多视图协同分析网络结构数据,完成聚类、相关性、异常值分析任务,能更好地分析数据之间的关联关系,有助于理解航线网络的结构和特征。最后,通过以上方法分析网络结构特点与不足,提出航线网络优化建议,能更好地帮助用户完成可视分析任务,探索数据分布特征、发现多维数据之间的关联关系。