STPSO: Optimal Configuration for Cloud Environments

With the increasing number of resources provided by cloud environments, identifying which types of resources should be rent when deploying an application is often a difficult and error-prone process. Currently, most cloud environments offer a wide range of configurable resources, which can be combined in many different ways. Finding an appropriate configuration under cost constraints while meeting requirements is still a challenge. In this paper, software product line engineering is introduced to describe cloud environments, and configurable resources are abstracted as features with attributes. Then, a Self-Tuning Particle Swarm Optimization approach(called STPSO) is proposed to configure the cloud environment. STPSO can automatically adjust the arbitrary configuration to a valid configuration. To evaluate the performance of the proposed approach, we conduct a series of comprehensive experiments. The empirical experiment shows that our approach reduces time and provides a reliable way to find a correct and suitable cloud configuration when dealing with a significant number of resources.

Many-Objective Optimization-Based Task Scheduling in Hybrid Cloud Environments

Due to the security and scalability features of hybrid cloud architecture,it can bettermeet the diverse requirements of users for cloud services.And a reasonable resource allocation solution is the key to adequately utilize the hybrid cloud.However,most previous studies have not comprehensively optimized the performance of hybrid cloud task scheduling,even ignoring the conflicts between its security privacy features and other requirements.Based on the above problems,a many-objective hybrid cloud task scheduling optimization model(HCTSO)is constructed combining risk rate,resource utilization,total cost,and task completion time.Meanwhile,an opposition-based learning knee point-driven many-objective evolutionary algorithm(OBL-KnEA)is proposed to improve the performance of model solving.The algorithm uses opposition-based learning to generate initial populations for faster convergence.Furthermore,a perturbation-based multipoint crossover operator and a dynamic range mutation operator are designed to extend the search range.By comparing the experiments with other excellent algorithms on HCTSO,OBL-KnEA achieves excellent results in terms of evaluation metrics,initial populations,and model optimization effects.

Improved Secure Identification-Based Multilevel Structure of Data Sharing in Cloud Environments

The Cloud Computing Environment(CCE)developed for using the dynamic cloud is the ability of software and services likely to grow with any business.It has transformed the methodology for storing the enterprise data,accessing the data,and Data Sharing(DS).Big data frame a constant way of uploading and sharing the cloud data in a hierarchical architecture with different kinds of separate privileges to access the data.With the requirement of vast volumes of storage area in the CCEs,capturing a secured data access framework is an important issue.This paper proposes an Improved Secure Identification-based Multilevel Structure of Data Sharing(ISIMSDS)to hold the DS of big data in CCEs.The complex file partitioning technique is proposed to verify the access privilege context for sharing data in complex CCEs.An access control Encryption Method(EM)is used to improve the encryption.The Complexity is measured to increase the authentication standard.The active attack is protected using this ISIMSDS methodology.Our proposed ISIMSDS method assists in diminishing the Complexity whenever the user’s population is increasing rapidly.The security analysis proves that the proposed ISIMSDS methodology is more secure against the chosen-PlainText(PT)attack and provides more efficient computation and storage space than the related methods.The performance of the proposed ISIMSDS methodology provides more efficiency in communication costs such as encryption,decryption,and retrieval of the data.

Improved Chameleon Swarm Optimization-Based Load Scheduling for IoT-Enabled Cloud Environment

Internet of things(IoT)and cloud computing(CC)becomes widespread in different application domains such as business,e-commerce,healthcare,etc.The recent developments of IoT technology have led to an increase in large amounts of data from various sources.In IoT enabled cloud environment,load scheduling remains a challenging process which is applied for ensuring network stability with maximum resource utilization.The load scheduling problem was regarded as an optimization problem that is solved by metaheuristics.In this view,this study develops a new Circle Chaotic Chameleon Swarm Optimization based Load Scheduling(C3SOA-LS)technique for IoT enabled cloud environment.The proposed C3SOA-LS technique intends to effectually schedule the tasks and balance the load uniformly in such a way that maximum resource utilization can be accomplished.Besides,the presented C3SOA-LS model involves the design of circle chaotic mapping(CCM)with the traditional chameleon swarm optimization(CSO)algorithm for improving the exploration process,shows the novelty of the work.The proposed C3SOA-LS model computes an objective with the minimization of energy consumption and makespan.The experimental outcome implied that the C3SOA-LS model has showcased improved performance and uniformly balances the load over other approaches.

Design and Implementation of Novel Precision Internet Marketing Patterns under the Big Data and Cloud Environment

Nowadays most of the cloud applications process large amount of data to provide the desired results. The Internet environment, the enterprise network advertising, network marketing plan, need partner sites selected as carrier and publishers. Website through static pages, dynamic pages, floating window, AD links, take the initiative to push a variety of ways to show the user enterprise marketing solutions, when the user access to web pages, use eye effect and concentration effect, attract users through reading web pages or click the page again, let the user detailed comprehensive understanding of the marketing plan, which affects the user＇ s real purchase decisions. Therefore, we combine the cloud environment with search engine optimization technique, the result shows that our method outperforms compared with other approaches.

Reversible Data Hiding for Medical Images in Cloud Computing Environments Based on Chaotic Hénon Map

Reversible data hiding techniques are capable of reconstructing the original cover image from stego-images. Recently, many researchers have focused on reversible data hiding to protect intellectual property rights. In this paper, we combine reversible data hiding with the chaotic Henon map as an encryption technique to achieve an acceptable level of confidentiality in cloud computing environments. And, Haar digital wavelet transformation （HDWT） is also applied to convert an image from a spatial domain into a frequency domain. And then the decimal of coefficients and integer of high frequency band are modified for hiding secret bits. Finally, the modified coefficients are inversely transformed to stego-images.

GENERATION AND DEVELOPMENT OF MESOSCALE CLOUD ON HEAVY RAIN BELT ON THE PERIPHERY OF TYPHOON 9608 (Herb)

Typhoon-induced heavy rains are mostly studied from the viewpoint of upper-level westerly troughs. It is worthwhile to probe into a case where the rain is caused by tropical cyclone system, which is much heavier. During August 3 ~ 5, 1996, an unusually heavy rainstorm happened in the southwest of Hebei province. It was caused by 3 mesoscale convective cloud clusters on the periphery of a tropical cyclone other than the direct effects of a westerly trough. Generating in a weak baroclinic environment that is unstable with high energy, the cloud clusters were triggered off for development by unstable ageostrophic gravity waves in the low-level southeast jet stream on the periphery of the typhoon. There was a vertical circulation cell with horizontal scale close to 1000 km between the rainstorm area and westerly trough in northeast China. As shown in a computation of the Q vector of frontogenesis function, the circulation cell forms a mechanism of transforming energy between the area of interest and the westerly trough system farther away in northeast China. Study of water vapor chart indicates that high-latitude troughs in the northeast portion of the rain migrate to the southeast to enhance anti-cyclonic divergence in upper-level convection over the area of heavy rain and cause rain clusters, short-lived otherwise, to develop vigorously. It is acting as an amplifier in this case of unusually strong process of rain.

A Hybrid Virtual Cloud Learning Model during the COVID-19 Pandemic

The COVID-19 pandemic has affected the educational systems worldwide,leading to the near-total closures of schools,universities,and colleges.Universities need to adapt to changes to face this crisis without negatively affecting students’performance.Accordingly,the purpose of this study is to identify and help solve to critical challenges and factors that influence the e-learning system for Computer Maintenance courses during the COVID-19 pandemic.The paper examines the effect of a hybrid modeling approach that uses Cloud Computing Services(CCS)and Virtual Reality(VR)in a Virtual Cloud Learning Environment(VCLE)system.The VCLE system provides students with various utilities and educational services such as presentation slides/text,data sharing,assignments,quizzes/tests,and chatrooms.In addition,learning through VR enables the students to simulate physical presence,and they respond well to VR environments that are closer to reality as they feel that they are an integral part of the environment.Also,the research presents a rubric assessment that the students can use to reflect on the skills they used during the course.The research findings offer useful suggestions for enabling students to become acquainted with the proposed system’s usage,especially during theCOVID-19 pandemic,and for improving student achievementmore than the traditional methods of learning.

Extensive Study of Cloud Computing Technologies, Threats and Solutions Prospective

Infrastructure as a Service(IaaS)provides logical separation between data,network,applications and machines from the physical constrains of real machines.IaaS is one of the basis of cloud virtualization.Recently,security issues are also gradually emerging with virtualization of cloud computing.Different security aspects of cloud virtualization will be explored in this research paper,security recognizing potential threats or attacks that exploit these vulnerabilities,and what security measures are used to alleviate such threats.In addition,a dis-cussion of general security requirements and the existing security schemes is also provided.As shown in this paper,different components of virtualization environ-ment are targets to various attacks that in turn leads to security issues compromis-ing the whole cloud infrastructure.In this paper an overview of various cloud security aspects is also provided.Different attack scenarios of virtualization envir-onments and security solutions to cater these attacks have been discussed in the paper.We then proceed to discuss API security concerns,data security,hijacking of user account and other security concerns.The aforementioned discussions can be used in the future to propose assessment criteria,which could be useful in ana-lyzing the efficiency of security solutions of virtualization environment in the face of various virtual environment attacks.

Directional Point Net:3D Environmental Classification for Wearable Robots

A subject who wears a suitable robotic device will be able to walk in complex environments with the aid of environmental recognition schemes that provide reliable prior information of the human motion intent.Researchers have utilized 1 D laser signals and 2 D depth images to classify environments,but those approaches can face the problems of self-occlusion.In comparison,3 D point cloud is more appropriate for depicting the environments.This paper proposes a directional PointNet to directly classify the 3 D point cloud.First,an inertial measurement unit(IMU)is used to offset the orientation of point cloud.Then the directional PointNet can accurately classify the daily commuted terrains,including level ground,climbing up stairways,and walking down stairs.A classification accuracy of 98%has been achieved in tests.Moreover,the directional PointNet is more efficient than the previously used PointNet because the T-net,which is utilized to estimate the transformation of the point cloud,is not used in the present approach,and the length of the global feature is optimized.The experimental results demonstrate that the directional PointNet can classify the environments in robust and efficient manner.

基于ZigBee和云平台的大棚环境监测系统设计与实现

针对温室大棚环境的实时监测问题,利用ZigBee无线传感网络和GPRS远程传输技术,设计远程监测系统,在云端搭建服务器,采用Spring Boot服务框架,开发Android移动端APP.测试表明,系统能对温室大棚环境进行远程监测和预警,系统运行稳定、部署灵活、便利性好,有利于提高大棚农业管理水平.

中国光伏产业环境成熟度区域异质性研究

论文从政治环境、经济环境、社会文化环境、技术环境和自然环境5个维度构建了光伏产业环境成熟度评价指标体系,采用组合赋权结合云模型方法,对2020年我国各省份光伏产业环境成熟度进行评估。结果表明:(1)我国光伏产业环境成熟度仍有较大提升空间,53.33%的省份仍处于不成熟阶段。(2)光伏产业环境成熟度呈现区域聚集性,中东部地区以及太阳能资源丰富的西部地区成熟度明显高于其他地区。(3)技术环境和政治环境是推动光伏产业发展的重要因素,在发展初期,政策是主要驱动力,进入成长和成熟阶段后,政策支持力度逐渐减弱,技术推动占主导。(4)光伏产业发展存在木桶效应,区域光伏产业环境成熟度的提高需要各维度共同驱动。

变权理论-梯形云模型融合的金属矿绿色开采评价研究

如何有效评价金属矿山绿色开采水平对于发展绿色矿业非常重要。针对目前金属矿绿色开采评价方法存在评价结果单一、忽略金属矿绿色开采指标数据的随机性和模糊性的问题,研究提出变权理论-梯形云模型融合的金属矿绿色开采评价方法。首先,构建金属矿绿色开采评价指标体系,综合使用组合赋权法和变权理论得到各项指标的变权权重;其次,利用梯形云模型计算每个指标在对应等级下的隶属度,并得出隶属度评价向量;最后,结合变权理论与梯形云模型并按最大隶属度原则实现对金属矿绿色开采情况的评价。此外,以某金属矿山为实例,计算并分析其评价结果。该实例表明此方法对金属矿绿色开采情况进行评价可行,可为金属矿绿色开采评价的问题提供参考。

Cloud Computing Perceived Importance in the Middle Eastern Firms: The Cases of Jordan, Saudi Arabia and United Arab Emirates from the Operational Level

Firms need cloud computing adoption for strategic and competitive goals, generating business value, and at last gaining competitive advantage. This study reviews the literature regarding cloud computing and IT governance, and presents a research model along with its hypotheses formulation to examine the factors impacting cloud computing perceived importance in several Arab firms, specifically Jordan, Saudi Arabia and United Arab Emirates by using the integration of Technology Acceptance Model (TAM) model and Technology-Organizational-Environmental (TOE) framework as adapted from [1]. 329 returned surveys from top, middle-level IT managers, and IT employees from the operational level of the studied firms were analyzed using the structural equation modeling technique. The study found relative advantage, compatibility, complexity, organizational readiness, top management commitment, and training and education as important variables for impacting cloud computing adoption using perceived ease of use and perceived usefulness as mediating variables. The model explained 61%, 63%, and 74% of cloud computing adoption for perceived usefulness, perceived ease of use and perceived importance respectively.

可变形特征融合的无人驾驶系统三维车辆检测

针对无人驾驶系统环境感知中的车辆检测精度低的问题,提出一种基于可变形特征融合的三维车辆检测算法。首先,通过路面实况增强算法,提高收敛速度和性能;去除地面点云,减少无关点云的干扰。接着,构造可变形特征融合模块,自适应对齐不同模态数据之间的姿态和位置信息,提升多模态数据的利用效率;优化损失函数,添加对抗损失判断车辆运动的真实性,提高网络对小目标的检测精度。最后,通过训练得到网络模型的最佳权重,使用KITTI数据集进行测试,能达到较好的车辆识别效果。实验结果表明:其平均精度值为83.26%,平均检测时间为0.15 s。该算法能够快速、准确地在无人驾驶系统中对车辆进行识别。

实验室用电安全管理系统设计

为了减少高校实验室用电安全事故,设计了一套实验室用电安全管理系统,包含终端、集中器和云平台。终端检测并读取实验室各处用电信息以及环境信息,经过远距离无线电(LoRa)技术组网,集中器将各个终端的数据进行汇总,最后通过4G数据传输单元(DTU)将数据上传至云平台。实验员可以在移动端或PC端进行数据监测,控制实验室相关设备。该系统能够让实验员更好地实时掌握整个实验室的用电状态和实验环境,有效降低实验室事故发生率。

云智慧电商物流环境生态链设计及实证分析

随着市场需求的快速变化和消费者期望的不断提升,传统的电商物流系统面临着如何高效处理大量数据、快速响应市场变化、提高物流效率,以及如何在此过程中考虑生态环境的可持续发展等问题。云计算和智慧物流技术的快速发展,为解决这些问题提供了新的可能性。云计算通过其强大的数据处理能力,可以优化物流决策过程,智慧物流则通过智能化的资源配置和运作,提高物流系统的效率和灵活性。环境生态链理论的引入为电商物流提供了一种全新的视角,强调在追求经济效益的同时,考虑环境保护和资源的可持续发展及利用。文章将探讨云智慧电商物流环境生态链的设计及其实证分析,以期进行环境生态链实践的探索与践行,为环境工程、物流工程等相关人员提供参考。

热带云雾林植物功能性状对环境变化的年际响应

为了研究植物功能性状对环境变化的响应有助于理解植物对环境的适应策略,以海南热带雨林国家公园霸王岭片区的热带云雾林为研究对象,在2011年及2021年对21个20 m×20 m固定样地内DBH≥5 cm木本植物进行群落学调查,测定个体、物种和群落水平的植物功能性状及环境因子,分析植物功能性状和环境条件的变化,探讨环境因子对功能性状的影响规律.结果表明:(1)与2011年相比,2021年热带云雾林土壤全氮(TN)、有效氮(AN)含量显著增加,土壤有机质(SOM)、全磷(TP)、有效磷(AP)含量显著减少.空气最高温呈上升趋势,平均空气相对湿度、最低空气相对湿度呈下降趋势.对环境因子进行主成分分析,2011年负荷值较大的环境因子分别为土壤全氮、土壤全磷、土壤有效氮及最低空气相对湿度,2021年负荷值较大的环境因子为土壤有机质、土壤全磷、最高空气温度及最低空气相对湿度;(2)与2011年相比,2021年植物叶绿素含量和叶片厚度在个体水平显著增大,比叶面积显著减少;叶片厚度、叶绿素含量、茎全氮含量、叶片全氮含量在物种水平显著增大,茎可溶性糖含量、叶全磷含量显著减少;叶绿素含量、叶片厚度、茎全氮含量、茎全磷含量、叶全氮含量在群落水平显著增大,茎密度、茎可溶性糖含量、叶全磷含量显著减小;(3)多元逐步回归分析发现,土壤养分含量(如土壤有机质、全磷含量、有效氮含量等)和气候因子(如最高空气温度、最低空气相对湿度等)共同影响植物个体、物种及群落水平功能性状变化,土壤养分因子作用较大,土壤磷胁迫的加剧是影响各个群落组织水平功能性状变化关键因子.

花卉储运环境监测系统设计与实现

以STM32L4系列芯片作为主芯片,设计花卉储运环境监测系统,该系统能实时监测环境温湿度,并通过Wi-Fi将监测数据上传至阿里云平台。实验结果表明,该系统能够实现温湿度数据的实时、远程采集,可通过阿里云平台对数据进行管理,并可通过移动端APP进行数据查询,可为花卉储运环境监测提供技术支持。

Zigbee的阅览室环境监控系统设计与实现

为有效监控图书馆阅览室温湿度、一氧化碳、光照等参数,本文提出了一种基于Zigbee无线传感网技术的阅览室环境监控系统。系统基于物联网“感知层、网络层、应用层”三层体系来设计整体框架,采用CC2530微控制器来设计Zigbee的终端节点和网关节点模块,构建具有数据显示、策略设置、传感器设置、执行器设置等功能的阅览室环境监控云平台。模拟测试的结果表明,系统可有效实现阅览室温湿度和控制灯光照明的自动调节,具备实时推送天气信息、监控火警的功能,为阅览室环境监控提供了低成本、易部署的一种可行性解决方案。