cloud

摘要： Healthcare systems nowadays depend on IoT sensors for sending data over the internet as a common practice.Encryption ofmedical images is very important to secure patient information.Encrypting these images consumes a lot of time onedge computing;therefore,theuse of anauto-encoder for compressionbefore encodingwill solve such a problem.In this paper,we use an auto-encoder to compress amedical image before encryption,and an encryption output(vector)is sent out over the network.On the other hand,a decoder was used to reproduce the original image back after the vector was received and decrypted.Two convolutional neural networks were conducted to evaluate our proposed approach:The first one is the auto-encoder,which is utilized to compress and encrypt the images,and the other assesses the classification accuracy of the image after decryption and decoding.Different hyperparameters of the encoder were tested,followed by the classification of the image to verify that no critical information was lost,to test the encryption and encoding resolution.In this approach,sixteen hyperparameter permutations are utilized,but this research discusses three main cases in detail.The first case shows that the combination of Mean Square Logarithmic Error(MSLE),ADAgrad,two layers for the auto-encoder,and ReLU had the best auto-encoder results with a Mean Absolute Error(MAE)=0.221 after 50 epochs and 75%classification with the best result for the classification algorithm.The second case shows the reflection of auto-encoder results on the classification results which is a combination ofMean Square Error(MSE),RMSprop,three layers for the auto-encoder,and ReLU,which had the best classification accuracy of 65%,the auto-encoder gives MAE=0.31 after 50 epochs.The third case is the worst,which is the combination of the hinge,RMSprop,three layers for the auto-encoder,and ReLU,providing accuracy of 20%and MAE=0.485.

摘要： Global warming and its serious consequences have attracted more and more attention,and how to deal with global warming has aroused extensive debate and research.Although many people believe that global warming is caused by human burning fossil fuels,there is still controversy in the scientific community.Therefore,the author analyzes various factors affecting global climate change,finds that the retreat of polar glaciers is the main cause of global warming,and puts forward reasonable countermeasures to prevent global warming.

摘要： The interaction between a molecular cloud and an external agent(e.g.,a supernova remnant,plasma jet,radiation,or another cloud)is a common phenomenon throughout the Universe and can significantly change the star formation rate within a galaxy.This process leads to fragmentation of the cloud and to its subsequent compression and can,eventually,initiate the gravitational collapse of a stable molecular cloud.It is,however,difficult to study such systems in detail using conventional techniques(numerical simulations and astronomical observations),since complex interactions of flows occur.In this paper,we experimentally investigate the compression of a foam ball by Taylor–Sedov blast waves,as an analog of supernova remnants interacting with a molecular cloud.The formation of a compression wave is observed in the foam ball,indicating the importance of such experiments for understanding how star formation is triggered by external agents.

摘要： A significant number of cloud storage environments are already implementing deduplication technology.Due to the nature of the cloud environment,a storage server capable of accommodating large-capacity storage is required.As storage capacity increases,additional storage solutions are required.By leveraging deduplication,you can fundamentally solve the cost problem.However,deduplication poses privacy concerns due to the structure itself.In this paper,we point out the privacy infringement problemand propose a new deduplication technique to solve it.In the proposed technique,since the user’s map structure and files are not stored on the server,the file uploader list cannot be obtained through the server’s meta-information analysis,so the user’s privacy is maintained.In addition,the personal identification number(PIN)can be used to solve the file ownership problemand provides advantages such as safety against insider breaches and sniffing attacks.The proposed mechanism required an additional time of approximately 100 ms to add a IDRef to distinguish user-file during typical deduplication,and for smaller file sizes,the time required for additional operations is similar to the operation time,but relatively less time as the file’s capacity grows.

摘要： With the emergence of Internet of Things, modern control systems have to deal with the big data from the ubiquitous information sensing devices, which is often beyond the capacity of traditional control technologies. To deal with this issue, the rapidly developing cloud computing may provide a perfect platform for big data storage and processing, controller design, and performance optimization.

摘要： Internet of Things(IoT)security is the act of securing IoT devices and networks.IoT devices,including industrial machines,smart energy grids,and building automation,are extremely vulnerable.With the goal of shielding network systems from illegal access in cloud servers and IoT systems,Intrusion Detection Systems(IDSs)and Network-based Intrusion Prevention Systems(NBIPSs)are proposed in this study.An intrusion prevention system is proposed to realize NBIPS to safeguard top to bottom engineering.The proposed NBIPS inspects network activity streams to identify and counteract misuse instances.The NBIPS is usually located specifically behind a firewall,and it provides a reciprocal layer of investigation that adversely chooses unsafe substances.Networkbased IPS sensors can be installed either in an inline or a passive model.An inline sensor is installed to monitor the traffic passing through it.The sensors are installed to stop attacks by blocking the traffic using an IoT signature-based protocol.

摘要： Aiming to meet the growing demand for observation and analysis in power systems that based on Internet of Things(IoT),machine learning technology has been adopted to deal with the data-intensive power electronics applications in IoT.By feeding previous power electronic data into the learning model,accurate information is drawn,and the quality of IoT-based power services is improved.Generally,the data-intensive electronic applications with machine learning are split into numerous data/control constrained tasks by workflow technology.The efficient execution of this data-intensive Power Workflow(PW)needs massive computing resources,which are available in the cloud infrastructure.Nevertheless,the execution efficiency of PW decreases due to inappropriate sub-task and data placement.In addition,the power consumption explodes due to massive data acquisition.To address these challenges,a PW placement method named PWP is devised.Specifically,the Non-dominated Sorting Differential Evolution(NSDE)is used to generate placement strategies.The simulation experiments show that PWP achieves the best trade-off among data acquisition time,power consumption,load distribution and privacy preservation,confirming that PWP is effective for the placement problem.

摘要： Recent years have witnessed the proliferation of Internet of Things(IoT),in which billions of devices are connected to the Internet,generating an overwhelming amount of data.It is challenging and infeasible to transfer and process trillions and zillions of bytes using the current cloud-device architecture.

摘要： Many organizations around the world use cloud computing Testing as Service(Taas)for their services.Cloud computing is principally based on the idea of on-demand delivery of computation,storage,applications,and additional resources.It depends on delivering user services through Internet connectivity.In addition,it uses a pay-as-you-go business design to deliver user services.It offers some essential characteristics including on-demand service,resource pooling,rapid elasticity,virtualization,and measured services.There are various types of virtualization,such as full virtualization,para-virtualization,emulation,〇S virtualization,and application virtualization.Resource scheduling in Taas is among the most challenging jobs in resource allocation to mandatory tasks/jobs based on the required quality of applications and projects.Because of the cloud environment,uncertainty,and perhaps heterogeneity,resource allocation cannot be addressed with prevailing policies.This situation remains a significant concern for the majority of cloud providers,as they face challenges in selecting the correct resource scheduling algorithm for a particular workload.The authors use the emergent artificial intelligence algorithms deep RM2,deep reinforcement learning,and deep reinforcement learning for Taas cloud scheduling to resolve the issue of resource scheduling in cloud Taas.

摘要： Web service(WS)presents a good solution to the interoperability of different types of systems that aims to reduce the overhead of high processing in a resource-limited environment.With the increasing demand for mobile WS(MWS),the WS discovery process has become a significant challenging point in the WS lifecycle that aims to identify the relevant MWSs that best match the service requests.This discovery process is a resource-consuming task that cannot be performed efficiently in a mobile computing environment due to the limitations of mobile devices.Meanwhile,a cloud computing can provide rich computing resources for mobile environments given its unlimited and easily scalable resources.This paper proposes a semantic WS discovery and invocation framework in mobile environments based on cloud and a relationship-aware matchmaking algorithm.The discovery algorithm enriches MWS and user requests semantically with the functional and non-functional properties of Ontology Web Language for Services,such as Quality of Web Service,device context,and user preferences.The WS repository is filtered based on logical reasoning and a parameter-based matching algorithm to minimize the matching space and improve runtime performance.The cosine similarity between the user request and services repository is then assessed to generate the most relevant WS.The relationships among concepts in the ontology are considered to improve the recall and precision ratio.After the WS discovery process,users can invoke and test these services in a mobile environment through a dynamic user interface.The interface of the invocation process is changed according to the WS description document.An application prototype is also developed to evaluate the framework based on a Cordova cross-mobile development framework.

摘要： 本申请公开了一种基于Spring Cloud的微服务灰度发布方法、装置、设备及系统，该方案针对互联网产品的前台服务和业务支撑系统的后台服务，设置了两种灰度发布策略，其中，针对前台服务根据版本进行升级的特点，设置了基于版本的灰度发布策略；针对后台服务不根据版本进行升级的特点，设置了基于区域的灰度发布策略。因此，实现了灵活适应两种系统形态下的灰度发布过程。此外，在这两种灰度发布策略中，该方案根据版本号或灰度标志位进行灰度路由，不仅可以实现网关到服务之间的灰度发布过程，还可以实现服务与服务之间的灰度发布过程，提升了方案的场景适应力，且成本较低。

摘要： 本发明涉及计算机应用技术领域，具体提供了一种实现SpringCloud动态路由的方法，在网关启动时，客户端向Spring CloudGateway发出请求，根据RouteLocator获取到RouteDefinitionLocator，并返回了多个RouteDefinitionLocator.getRouteDefinitions()的路由定义信息；如果在Gateway Handler Mapping中找到与请求相匹配的路由，会将其发送到Gateway Web Handler。Handler如FilteringWebHandler，执行路由定义中的过滤器链来将请求进行过滤，最后路由到具体的业务服务中。与现有技术相比，本发明扩展了RouteDefinitionRepository类获取路由配置信息的方式，将动态路由持久化存储。

摘要： 本发明属于飞行模拟训练技术领域，涉及一种基于5G的Cloud VR平台的航空训练系统，包括：内容层、平台层、网络层和终端层；所述内容层包括：部署于云端平台的课程开发系统及课程管理系统，所述课程开发系统用于开发及搭建VR训练场景，所述课程管理系统用于将开发及搭建的VR训练场景上传至云端平台上的云服务器；所述平台层部署GPU云服务器中，并通过视频流方式将VR训练场景传输至终端层；所述终端层包括：Cloud VR终端及与所述Cloud VR终端通信连接的移动终端；所述航空训练系统还包括：六自由度运动平台，所述六自由度运动平台用于调整VR训练场景中训练者身体的姿态，以使VR训练场景与训练者的前庭感官一致。

摘要： 本发明公开了一种基于边缘计算存储的Cloud VR视频播放系统,包括硬件设备和控制系统，所述硬件设备包括VR头盔、边缘计算储存服务器和中心计算云储存服务器(客户端主机)，所述控制系统的控制流程如下：S1.边缘计算存储服务器采用四棱锥投影方式对ERP全景图进行映射计算，S2.客户端主机实时获取并进行动态预测用户VR头盔的三维运动数据并计算出用户视角信息，最后把用户视角信息传给边缘计算存储服务器，采用基于视角和边缘存储的Cloud VR传输方案，可以在相同分辨率下将网络传输带宽降低60％以上，在带宽一定的情况下可以将用户接收的视频分辨率提高近三倍。

摘要： 本申请公开了一种基于Cloud XR的云端简化注视点渲染的方法以及装置。该方法包括在所述Cloud XR传输过程中预先配置帧图像处理参数；根据所述帧图像处理参数，计算当前帧图像中的注视点边界区域；根据所述注视点边界区域，将所述当前帧图像的单眼帧图像切分成第一预设区域与第二预设区域；根据所述单眼帧图像切分成第一预设区域与第二预设区域，对所述单眼帧图像的单眼区域沿Y轴镜像得到双眼帧图像切分结果；根据所述双眼帧图像切分结果，按照对应的预设分辨率进行图像转换。本申请解决了采用云端渲染时，回传的画面图像传输带宽占用较大的技术问题。通过本申降低了图像编码后关键帧的数据大小，降低传输带宽占用。

摘要： 本发明涉及一种基于边缘计算存储的Cloud VR数据传输方法和系统，用于向用户端设备传输全景图的图像数据，方法包括：采用四棱锥投影方式对全景图进行映射计算，将每张全景图根据视点投影划分为多个不同视场角的视频版本进行存储；获取并进行动态预测用户端设备的三维运动数据并计算用户视角信息；根据用户视角信息，选取对应的视场角的视频版本，并对该视频版本进行金字塔贴图操作，得到立体图像数据，计算视角切换相应的视点位置移动向量并向用户端设备传输立体图像数据。与现有技术相比，本发明具有可将所需带宽降低60％以上、视野范围内观看质量无损、避免了网络延迟带来的图像滞后和网络、避免了网络抖动导致的图像缺失等优点。

摘要： 本发明公开了一种基于边缘计算存储的Cloud VR视频播放系统,包括硬件设备和控制系统，所述硬件设备包括VR头盔、边缘计算储存服务器和中心计算云储存服务器(客户端主机)，所述控制系统的控制流程如下：S1.边缘计算存储服务器采用四棱锥投影方式对ERP全景图进行映射计算，S2.客户端主机实时获取并进行动态预测用户VR头盔的三维运动数据并计算出用户视角信息，最后把用户视角信息传给边缘计算存储服务器，采用基于视角和边缘存储的Cloud VR传输方案，可以在相同分辨率下将网络传输带宽降低60％以上，在带宽一定的情况下可以将用户接收的视频分辨率提高近三倍。

摘要： 本申请公开了一种用于Cloud XR的音视频及控制信息的私有传输方法及装置，其中所述方法包括与服务端握手成功之后，启动流媒体传输；接收所述流媒体传输过程中按照该传输协议规则传输的音视频文件数据包以及控制信息数据包；根据所述音视频文件数据包以及控制信息数据包，进行执行显示和/或动作。通过本申请实现了在不同XR品牌设备、硬件规格与渲染端服务器在音视频及控制数据的传输。解决了流媒体传输无法跨语言、跨平台传输的技术问题。本申请还提供了一种BSTP传输协议，用以实现私有传输。

摘要： 本申请提供一种基于spring cloud的微服务构建方法及spring cloud微服务架构。该方法包括：获取多个微服务的合并信息；将入参转换成http请求信息；其中，所述入参表示合并的所述微服务之间调用的参数；将feign组件的路由配置和所调用的controller层的路由配置关联；基于所述http请求信息，构造springmvc参数，路由到所调用的controller层方法，进而完成调用。通过上述步骤实现了在spring cloud微服务架构下对已经划分好的微服务进行合并。进而解决了现有技术中的服务划分问题，可以根据实际业务发展，计算量的变化合理的规划微服务，改变微服务的组成。

摘要： 本发明涉及一种spring‑cloud‑alibaba nacos框架集成istio的方法，属于程序控制技术领域，包括前提构建，安装并运行Nacos后，将开发符合需求的微服务打成jar包，测试并运行；再将Nacos与Istio融合，针对nacos注册中心编写适配器，将注册中心数据转换为Istio的ServiceEntry资源，并将ServiceEntry资源写入到K8S中，Pilot通过自动监听资源对象的变化并及时感知，将ServiceEntry转换为Pilot的内部服务模型，进而再下发到网格的各个SideCar中。本发明的有益效果：通过对注册中心附加适配器模式，可以很好地将Nacos与istio进行结合，降低以往代码的耦合性，从而实现Nacos做配置中心、服务注册，进一步实现使用istio提供的微服务限流、访问控制和端到端安全访问认证功能。