Virtual Cloud Research Articles

Energy saving slot allocation-based multicast routing in cloud wireless mesh network

Many techniques of cloud computing are affected by surge load and time issues. This proposed method for virtual cloud planning utilise simple protocols in a single or multiple data centres. A new load balanced amortised multi-scheduling algorithm (LBAM) is proposed for assigning the task to cloud based on active load in the cloud environment. The proposed system calculates the multiple attribute weight for each workplace and introduces an application source that changes in application virtualisation environment. The system calculates the cloud data weight based upon the allocation of data and its consequence based on the processing efficiency of the cloud machine. This approach works effectively in most configurations, also virtual machines hold the ability to process load with high pace and efficient memory management. A detailed comparative analysis is made with the existing methods namely SLA, FPGGA and DJS algorithms. The obtained results indicated that the LBAM model is superior to compared methods under several aspects.

GRAPHICAL TOOLS TO THE RESCUE. HIGH RESOLUTION AERIAL PHOTOGRAPHY FOR THE LOCATION OF MISSING PERSONS

Since the COVID-19 pandemic changed the rules of the game in many work envi-ronments, adaptation has been a key factor. Restrictions such as social distance or confinement made searches for missing persons more difficult. Although mo-bility limitations led to a decrease in the number of disappearances, they did not stop completely and search teams had to adapt to the new scenario that prevent-ed, for example, the participation of volunteers. For these reasons, it was decided to implement a tool that has been in development for years, based on the Collab-oration Agreement signed in 2015 between the Servicio de Prevención y Extinción de Incendios de la Diputación Provincial de Zaragoza (SPEI-DPZ) and the Uni-versity of Zaragoza. The tool is called Virtual Cloud Search and Rescue (VC-SAR) and is still under development by the authors of this communication. The following is a basic description of the tool and how it was applied in a real case, in which a missing person was being searched. The device used made it possible to obtain about 5000 images of 44.7 megapixels, covering an area of over 2000 hectares. Thanks to the searches carried out, more than 100 pieces of evidence were found over the course of four days, which could be collected for on-site in-vestigation by the Navarra Fire Service. All this was possible thanks to the collab-oration of 5 different fire departments, belonging to three provinces, making pos-sible the participation of a total of 52 searchers from different locations. Keywords: Graphical tools; Aerial images; Search and rescue; Drones.

Estimating fractional cover of saltmarsh vegetation species in coastal wetlands in the Yellow River Delta, China using ensemble learning model

Saltmarshes in coastal wetlands provide important ecosystem services. Satellite remote sensing has been widely used for mapping and classification of saltmarsh vegetation, however, medium-spatial-resolution satellite datasets such as Landsat-series imagery may induce mixed pixel problems over saltmarsh landscapes which are spatially heterogeneous. Sub-pixel fractional cover estimation of saltmarsh vegetation at species level are required to better understand the distribution and canopy structure of saltmarsh vegetation. In this study, we presented an approach framework for estimating and mapping the fractional cover of major saltmarsh species in the Yellow River Delta, China based on time series Landsat 8 Operational Land Imager data. To solve the problem that the coastal area is frequently covered by clouds, we adopted the recently developed virtual image-based cloud removal (VICR) algorithm to reconstruct missing image values under the cloud/cloud shadows over the time series Landsat imagery. Then, we developed an ensemble learning model (ELM), which incorporates Random Forest Regression (RFR), K-Nearest Neighbor Regression (KNNR) and Gradient Boosted Regression Tree (GBRT) based on temporal-spectral features derived from the time-series cloudless images to estimate the fractional cover of major vegetation types, i.e., Phragmites australis, Suaeda salsa and the invasive species, Spartina alterniflora. High spatial resolution imagery acquired by the Unmanned Aerial Vehicle and Gaofen-6 satellites were used for reference sample collections. The results showed that our approach successfully estimated the fractional cover of each saltmarsh species (average of R-square:0.891, RMSE: 7.48%). Through four scenarios of experiments, we found that the ELM is advantageous over each individual model. When the images during key months were absent, cloud removal for the Landsat images considerably improved the estimation accuracies. In the study area, Spartina alterniflora covers the largest area (5753.97 ha), followed by Phragmites australis with spatial extent area of 4208.4 ha and Suaeda salsa of 1984.41 ha. The average fractional cover of S. alterniflora was 58.45%, that of P. australis was 51.64% and that of S.salsa was 51.64%.

Multi-obstacle detection and tracking algorithms for the marine environment based on unsupervised learning

Nowadays, more than in the past, research is focused on the study and development of enabling technologies to achieve the goal of autonomous navigation. The investigations cover all industrial fields: automotive, aerospace, and maritime. The first level to be developed to achieve the goal of autonomous vehicles is obstacle detection and consecutively object tracking in real-time. The most important results were obtained in the automotive sector, where the available financial resources are more significant than the others. Indeed, several methods based on trained neural networks are used to detect the obstacles. Usually, the neural networks are trained by using large datasets of LiDAR point clouds and images. Unfortunately, it is impossible to emulate this approach in the marine sector because there are no large datasets for training a neural network. For such a reason, this paper aims to present multi-object tracking based on unsupervised learning. The proposed method is tailored for the challenging marine environment, and it is suitable for the detection and tracking of both fixed and moving obstacles. This paper presents a low-computational method for multi-object tracking based on unsupervised learning, and it is discussed and analysed step by step, indicating the pros and weaknesses. Moreover, the tracking has been tested on both experimental LiDAR point clouds and virtual LiDAR point clouds created employing a tailored virtual scenario. The results are reported and discussed.

Construction and Application of Waterway Engineering Design Virtual Simulation Cloud System Based on Outcome-Based Education

Hydraulic design is automatically inherent in hydraulic engineering courses, conventional teaching of the Waterway Engineering Design course tends to have limitations such as low participation, poor interactivity, disconnection between theoretical and experimental training, and restriction of experimental design by time and space. To address these needs, a virtual simulation cloud system of Waterway Engineering Design is developed based on outcome-based education. Taking real engineering projects as prototypes, this system adopts virtual reality technology and cloud platform to simulate the scene structure and instrument function with high precision. The multi-model, integrational teaching expands the experimental content, enhances the interactivity of the design process, and provides a high-quality, immersive online learning experience for students. Since its application, the Waterway Engineering Design Virtual Simulation Cloud System has received good feedback from both teachers and students. During the Covid-19 epidemic, it provided significant support for experiments and teaching of the Waterway Engineering Design course and became a pivotal supplement to the existing teaching system. The Waterway Engineering Design Virtual Simulation Cloud System adheres to the "student-centered" teaching principle, builds up students' ability for independent learning and engineering practice, and facilitates their personal development and training for excellent engineers.

SCAN-VS-BIM FOR REAL-TIME PROGRESS MONITORING OF BRIDGE CONSTRUCTION PROJECT

Abstract. New transport infrastructure construction can stimulate the growth of economy as well as improving the public citizen welfare. However, with the rising number of mega infrastructure projects, the low project performance, such as project delay and cost escalation, are challenging the traditional Architecture, Engineering, and Construction (AEC) industry. Traditional Construction progress monitoring methods rely on manual data collecting and paperwork reporting which can be labor-intensive, time-consuming and error-prone. Therefore, it is necessary for the involved stakeholders to introduce advanced technologies which facilitates assessing the construction performance automatically and ensures the projects to be delivered on time. The application of building information modeling (BIM) provides involved parties an accurate understandable single source of truth that can improve the interoperability of project information. Nevertheless, current ‘Scan-to-BIM’ workflow cannot support the demand for real-time data analysis and status reporting. This paper presents a semi-automatic construction progress monitoring framework that evaluating the project performance of the infrastructure in real-time. It introduces Hausdorff distance which transmits the 3-D geometrical information contained by as-built point cloud to virtual point cloud directly, to avoid the drawbacks of space partitioning algorithms. The Poisson surface reconstruction utilizing volume as criterion to improve the robustness of progress determination. In addition, the application of 2D polygon fitting provides a potentially feasible method to identify the installation of pre-cast components of the bridge construction. The results indicate that the proposed framework can effectively monitor the geometric increment of road bridge construction project.

An Anomaly Detection Algorithm Based on Ensemble Learning for 5G Environment.

With the advent of the digital information age, new data services such as virtual reality, industrial Internet, and cloud computing have proliferated in recent years. As a result, it increases operator demand for 5G bearer networks by providing features such as high transmission capacity, ultra-long transmission distance, network slicing, and intelligent management and control. Software-defined networking, as a new network architecture, intends to increase network flexibility and agility and can better satisfy the demands of 5G networks for network slicing. Nevertheless, software-defined networking still faces the challenge of network intrusion. We propose an abnormal traffic detection method based on the stacking method and self-attention mechanism, which makes up for the shortcoming of the inability to track long-term dependencies between data samples in ensemble learning. Our method utilizes a self-attention mechanism and a convolutional network to automatically learn long-term associations between traffic samples and provide them to downstream tasks in sample embedding. In addition, we design a novel stacking ensemble method, which computes the sample embedding and the predicted values of the heterogeneous base learner through the fusion module to obtain the final outlier results. This paper conducts experiments on abnormal traffic datasets in the software-defined network environment, calculates precision, recall and F1-score, and compares and analyzes them with other algorithms. The experimental results show that the method designed in this paper achieves 0.9972, 0.9996, and 0.9984 in multiple indicators of precision, recall, and F1-score, respectively, which are better than the comparison methods.

An integrated risk assessment methodology based on fuzzy TOPSIS and cloud inference for urban polyethylene gas pipelines

Due to the superior performance of polyethylene (PE) gas pipes, an increasing number of PE pipelines are being used in urban areas. However, PE gas pipelines are basically located in densely populated areas with high consequences. Once a leak occurs, combustion and explosion accidents can easily happen causing a major threat to human life and property. Furthermore, the assessment results of the common risk assessment methods often have great uncertainty and deviation. It is difficult to accurately identify the main indicators that need priority management. To address these problems, this paper proposes a novel methodology by integrating a fuzzy TOPSIS model and cloud inference for conducting risk assessments of urban PE gas pipelines. The developed methodology is composed of two branches. In the first branch, a rapid risk assessment model for PE pipelines is established to determine whether there are some special emergency situations, which can be directly identified as high risk. In the second branch, a detailed risk assessment process is adopted to obtain the final result, which mainly includes risk factor analysis, risk status assessment and risk classification. Among them, risk factor analysis is used to establish a risk index system and develop a risk index management strategy by means of mathematical statistics and the fuzzy TOPSIS analysis method. The risk status assessment is used to determine the final risk value based on the virtual cloud and fault tree analysis, which is mainly realized by the triangular fuzzy number, structural entropy weight method and backward cloud transformation algorithm. Finally, the risk assessment results of PE gas pipelines are clearly presented in the form of cloud inference, and the final risk classification is determined. The case study shows that the pipeline in this section is at the medium risk level, and the management of time-related indicators and third-party damage is given priority. The result is consistent with the actual situation, which verifies the effectiveness and practicability of the methodology.

Virtual cloud computing–based and 3D multi-physics simulation for local oxygen starvation in PEM fuel cell

In this paper, the effects of load and air-flow change on a serpentine multi-channel proton-exchange membrane fuel cell (PEMFC) were studied through numerical simulation and cloud computing technology. For this purpose, load change, air-flow change pattern and initial value, as well as its lag time to the load change, were considered. The transient response of local oxygen distribution to load change and flow change was investigated, and the local oxygen starvation (LOS) behavior observed. Detailed results are presented to show the transient response of the cell in terms of average oxygen concentration and water content distribution. This work shows that the local oxygen starvation process depends on factors such as load changes and air-flow rate of change, which can be alleviated or eliminated by adjusting air flow and pattern. It is also shown that simulation efficiency can be improved by cloud computing technology. Finally, the validity of the numerical model proposed in this paper is verified by the comparison between the model results and experimental data.

Investigation of the critical success factors in the implementation of the lean industry 4.0 in manufacturing supply chain: an ISM approach

PurposeThe purpose of this paper is to develop an interpretive structural modeling (ISM) model to investigate the critical success factors (CSF) and the extent of CSF's influence in the implementation of lean industry 4.0 in manufacturing supply chain.Design/methodology/approachThe study has been carried out with the help of the latest literature followed by brainstorming sessions with experts. The experts were the managers from the industries, assistant professors, and research scholars from academia working in this domain. Finally, a structured model is formed using ISM methodology for the analysis of the CSFs followed by matrice d'impacts croisés multiplication appliquée á un classment (MIAMAC) Analysis for the validation of the model.FindingsThe study identifies robotics, virtual and augmented reality and cloud computing as the main CSFs which are responsible to drive all the identified CSFs. However the CSF professional training and development (PTD) has been identified as the weakest driver but having the highest dependent power.Research limitations/implicationsThe study has included nine CSFs and the contextual relationships between the CSFs are based on the knowledge and experience of the experts, which may be biased. Moreover, the paper has covered the ISM approach, and the same thing can be validated using the fuzzy-ISM and other multi-criteria decision-making (MCDM) techniques.Originality/valueThis investigation of the CSFs in the lean industry 4.0 is original and the identified CSFs are the result of the literature reviews and an extensive discussion from the experts. The paper uses the complete experience of the respective experts to make this work more effective and original.

Virtual Machine Migration-Based Intrusion Detection System in Cloud Environment Using Deep Recurrent Neural Network

Cloud system attracts users with the desired features, and in the meanwhile, cloud system may experience various security issues. An effective intrusion detection system is offered by the proposed Sail Fish Dolphin Optimization-based Deep Recurrent Neural Network (SFDO-based Deep RNN), which is utilized to identify anomalies in the cloud architecture. The developed SFDO is formed by integrating Sail Fish Optimizer (SFO) and Dolphin Echolocation (DE) algorithm. Virtual Machine (VM) migration and cloud data management are accomplished using ChicWhale algorithm. Some of the attribute features are collected from the cloud model such that these features are grouped using Fuzzy C-Means (FCM) clustering. The feature fusion process is carried out by a Deep RNN classifier that has been trained using the specified SFDO technique in order to achieve the intrusion detection mechanism. The approach with the lowest error value is thought to be the best approach for intruder detection, according to the fitness function. Using the BoT-IoT dataset, the proposed method’s accuracy, detection rate (DR), and false-positive rate (FPR) were assessed. The results showed that it outperformed the previous methods with values of 0.9614, 0.9648, and 0.0429, respectively.

Vision occlusion solution for line-structured light measurement system based on a plane mirror

To solve the problem of the measurement information loss caused by occlusion of the field of view in line-structured light measurement, a vision occlusion solution of the line-structured light measurement system based on a plane mirror is proposed in this work. In the system, a symmetrical structure between real and virtual images was created by the mirror. A checkerboard with a light stripe and its mirror image were captured by the camera in an image. The centers of the light stripe and its mirror image were corrected by the vanishing point constraint, and a calibration method of the free plane target was used to calibrate the real and virtual light planes. The real and virtual point clouds of the object were obtained by scanning with a linear guide. According to the ideal reflection model of the mirror, the real and virtual point clouds of the object were transformed into a common coordinate system, and an iterative closest point (ICP) algorithm was used to realize fine registration. As a result, the occluded parts can be compensated for each other. Compared with a standard ball, the mean/maximum absolute error of the measurement was 0.2553/0.3276 mm. The experimental results revealed that the proposed method can obtain the complete surface of the object, thus effectively solving the occlusion problem in the line-structured light measurement, and reducing the loss of the measurement information, which can meet the industrial measurement requirements.

Where Is Research in the Era of Electronic Health Records?

IntroductionData extraction from electronic health records (EHRs) for use in clinical research continues to be labor-intensive and to offer little benefit over traditional paper chart reviews. This is largely due to poor integration of EHR systems with hospital process flow, which still relies heavily on traditional paperwork as a means of documentation. DiscussionNew methods in data collection through mobile applications have streamlined data entry through better data standardization and improved overall data quality. However, mobile applications address only a portion of the problem. Data entry errors and legacy integration will continue to be an issue when there are changes between practitioners with different EHR systems. The combination of a mobile application with the cloud platform has been applied in multiple specialties to monitor recovery and patient-reported outcomes. ConclusionMobile applications along with a virtual cloud environment to host data provide a reasonable solution for consolidating patient data and can accelerate population research.

An Efficient LiDAR Point Cloud Map Coding Scheme Based on Segmentation and Frame-Inserting Network.

In this article, we present an efficient coding scheme for LiDAR point cloud maps. As a point cloud map consists of numerous single scans spliced together, by recording the time stamp and quaternion matrix of each scan during map building, we cast the point cloud map compression into the point cloud sequence compression problem. The coding architecture includes two techniques: intra-coding and inter-coding. For intra-frames, a segmentation-based intra-prediction technique is developed. For inter-frames, an interpolation-based inter-frame coding network is explored to remove temporal redundancy by generating virtual point clouds based on the decoded frames. We only need to code the difference between the original LiDAR data and the intra/inter-predicted point cloud data. The point cloud map can be reconstructed according to the decoded point cloud sequence and quaternion matrices. Experiments on the KITTI dataset show that the proposed coding scheme can largely eliminate the temporal and spatial redundancies. The point cloud map can be encoded to 1/24 of its original size with 2 mm-level precision. Our algorithm also obtains better coding performance compared with the octree and Google Draco algorithms.

E-Commerce Website Hosting Using AWS Cloud Services

The primary extent of this task is to comprehend how to best use different distributed computing administrations to get great execution for less expense. Amazon surrenders a marvelous arrangement of web helps that enables fashioners to make dynamic and generous applications. Conveying on AWS can save you time, money and work diverged from building and staying aware of additional customary frameworks. Amazon Web Services offers a wide plan of overall cloud-based things including process, limit, informational collections, assessment, coordinating, flexible, engineer gadgets, the board contraptions, IoT, security, and try applications: on- demand, open quite promptly, with pay-all the more just as expenses emerge esteeming. From data warehousing to association instruments, lists to content transport, more than 200 AWS organizations are available. New organizations can be provisioned quickly, without the blunt capital expense. Today, AWS gives a significantly trustworthy, flexible, negligible cost establishment stage in the cloud that controls a colossal number of associations in 190 countries all over the planet. This licenses adventures, new organizations, little and medium-sized associations and clients in the public region to get to the construction blocks they need to answer quickly to changing business essentials. Amazon EC2 (Elastic Compute Cloud), VPC (Virtual Private Cloud), IAM (Identity And Access Management), Cloud Watch, CDN (Content Delivery Network) and Route 53 are used here since it is very popular in giving extraordinary disseminated figuring organizations. In this report, I'll tell you, step by step, through the procedure of using the AWS Marketplace to game plan and produce another AWS Cloud server. In this task we furthermore used progression gadgets like GIT and Jenkins for robotization.

Operational safety assessment of straddle-type monorail vehicle system based on cloud model and improved CRITIC method

This paper proposes an improved critic method and establishes an operational safety assessment method for straddle-type monorail vehicle system based on cloud model and improved critic method. Firstly, according to the structure and function of straddle- type monorail vehicle system, the reliability index system of straddle-type monorail vehicle is established, and the weight value of reliability evaluation index is determined by improved critic method. Then, the cloud model expression of reliability evaluation index of straddle-type monorail vehicle system is obtained by virtual cloud. finally, similarity analysis is conducted between the new cloud of straddle-type monorail vehicle system reliability comprehensive index and the cloud model of reliability evaluation set, so as to judge that the comprehensive cloud model of straddle-type monorail vehicle system can be used to evaluate the operation status of “main functions” of the system. The analysis shows that the reliability evaluation method of straddle-type monorail vehicle system based on cloud model and improved critic method can correctly describe the daily operation of straddle-type monorail vehicle system in Chongqing, which provides a method reference for reliability evaluation of straddle-type monorail vehicle system.

Self-Reporting Technique-Based Clinical-Trial Service Platform for Real-Time Arrhythmia Detection

The analysis of the electrocardiogram (ECG) is critical for the diagnosis of arrhythmias. Recent advances in information and communications technology (ICT) have led to the development of wearable ECG devices and arrhythmia-detection algorithms. This study aimed to develop an ICT-based clinical trial service platform using a self-reporting technique for real-time arrhythmia detection. To establish a clinical-trial service platform, a mobile application (app), a demilitarized zone (DMZ), an internal network, and Amazon web services virtual private cloud (AWS-VPC) were developed. The ECG data acquired by a wearable device were transmitted to the mobile app, which collected the participants’ self-reported information. The mobile app transmitted raw ECG and self-reported data to the AWS-VPC and DMZ, respectively. In the AWS-VPC, the live-streaming and playback-reviewer services were operational to display the currently and previously acquired ECG data to clinicians through the web client. All the measured data were transmitted to the internal network, in which the arrhythmia-detection algorithm was executed and all the data were saved. The self-reporting technique and arrhythmia-detection algorithm are the key elements of this platform. In particular, subjective information of participants can be easily collected using a self-reporting technique. These features are particularly of critical importance for treating painless, sparsely occurring arrhythmias.

3D Printing in the Context of Cloud Manufacturing

3D printing has drawn tremendous attention in Industry 4.0. Nowadays, with the ever-increasing consumers’ requests for 3D printing services, there lies a big challenge to eliminate the unbalanced demands and supplies of 3D printing resources in a geographically distributed environment. Cloud manufacturing, as a newly emerged service-oriented manufacturing paradigm, provides a promising way to overcome the challenge by integrating 3D printing into the cloud-based context, which may flourish the development of smart networks of virtual 3D printing cloud and also promote a novel 3D printing service business model to achieve mass customization. This paper presents a comprehensive study of 3D printing in cloud manufacturing environment. Two typical models of 3D printing, i.e. primary 3D printing cloud model and advanced 3D printing cloud model are presented and compared. The system architecture design of a 3D printing cloud platform is proposed to support the latter model. Furthermore, a series of key enabling technologies are presented based on the proposed system architecture. Finally, a prototype of 3D printing cloud platform is deployed with a case study of an aircraft engine part. The research will provide valuable theoretical and practical references to the future development of 3D printing clouds.

Virtual structured-light three-dimensional point cloud compression with geometric reshaping

Converting three-dimensional (3D) point cloud data to two-dimensional (2D) image based on virtual structured-light system is a popular method in 3D data compression because the image format is easier to process by the exiting storing and transmitting methods. When this method is used in the 3D point cloud compression, the quantization error is introduced during the coordinate mapping. To solve this problem, a virtual structured-light 3D point cloud compression algorithm based on geometric reshaping is presented. And a least-square system parameter optimization method is proposed to further improve the data decoding accuracy. In the proposed method, the 3D spatial coordinates are reshaped to a 2D matrix first, and then the 2D matrix is stored as a “Holoimage” by using the parameter optimized virtual structured-light system. The quantization error introduced by coordinate mapping between X and Y coordinates of the 3D point cloud and the 2D image pixel coordinates is suppressed, so the decoding accuracy is improved. In addition, the geometric information of the 3D point cloud is hidden synchronously when the 3D point cloud is compressed, which is of great significance in the copyright protection of the point cloud data. Experiments verify the effectiveness of the proposed algorithm. The decoding root mean square error (RMSE) of the proposed method is decreased by 79.86% on average compared with the traditional one under the same compression ratio, and the compression ratio of the proposed method is 1.96 times bigger than the traditional one under the similar decoding accuracy.

Automatic Extrinsic Calibration of 3D LIDAR and Multi-Cameras Based on Graph Optimization.

In recent years, multi-sensor fusion technology has made enormous progress in 3D reconstruction, surveying and mapping, autonomous driving, and other related fields, and extrinsic calibration is a necessary condition for multi-sensor fusion applications. This paper proposes a 3D LIDAR-to-camera automatic calibration framework based on graph optimization. The system can automatically identify the position of the pattern and build a set of virtual feature point clouds, and can simultaneously complete the calibration of the LIDAR and multiple cameras. To test this framework, a multi-sensor system is formed using a mobile robot equipped with LIDAR, monocular and binocular cameras, and the pairwise calibration of LIDAR with two cameras is evaluated quantitatively and qualitatively. The results show that this method can produce more accurate calibration results than the state-of-the-art method. The average error on the camera normalization plane is 0.161 mm, which outperforms existing calibration methods. Due to the introduction of graph optimization, the original point cloud is also optimized while optimizing the external parameters between the sensors, which can effectively correct the errors caused during data collection, so it is also robust to bad data.