Open Cloud Services Research Articles

Low-Cost Industrial IoT System for Wireless Monitoring of Electric Motors Condition

Condition monitoring of industrial equipment has become a critical aspect in Industry 4.0. This paper shows the design, implementation and testing of a low-cost Industrial Internet of Things (IIoT) system designed to monitor electric motors in real-time. This system can be used to detect operating anomalies and paves the way for building predictive maintenance models. The system is built using low-cost hardware components (wireless multi-sensor modules and single-board computers as gateways), open-source software and open cloud services, where all the relevant information is stored. The module collects real-time vibration data from electric motors. Vibration analyses in the temporal and frequency domains were carried out in both modules and gateways to compare their capabilities. This approach is also a springboard to using edge/fog computing to save cloud resources. A system prototype has been tested in the laboratory and in an industrial dairy plant. The results show that the proposed system can be used for continuous monitoring of any rotatory machine with similar accuracy to professional monitoring devices but at a significantly lower cost.

Architecture-Based Reliability-Sensitive Criticality Measure for Fault-Tolerance Cloud Applications

The widespread adoption of service computing allows software to be developed by outsourcing open cloud services (i.e., SOAP-based or RESTful Web APIs) through mashup or service composition techniques. Fault tolerance for the purpose of assuring the stable execution for cloud-based software (or CBS) application has attracted great attention in coping with a loosely coupled CBS operating under dynamic and uncertain running environments. It is too expensive to rent massively redundant cloud services for CBS fault tolerance application. To reduce budget but guarantee the effectiveness of CBS fault tolerance, identifying critical components within a CBS composite system is of significant importance. We integrate CBS composite system architecture analysis and reliability sensitivity analysis approaches and propose an Architecture-based Reliability-sensitive Criticality Measure (or ARCMeas) method in this paper. We verify ARCMeas application through a cost-effective fault tolerance CBS by presenting a particle swarm optimization (PSO)-based cost-effective fault tolerance strategy determination (or PSO-CFTD) algorithm. Experimental results suggest the effectiveness of the approach.

Measuring Public Transit Accessibility Based On Google Direction API

Background: Accessibility is considered as an important indicator for the public transit service level. Transit accessibility is generally evaluated by its distance to transit access points such as bus stops and metro stations, and methods of measuring the access distances to transit points have been relying heavily on geographic data of transit facilities, built environments and pedestrian routes. Data collecting and analyzing are tedious for researchers in conventional approaches. As the application of cloud computing is on the rise, open services provided by Google Cloud Platform may simplify the procedure of accessibility measurement if the outputs of the open computing services could be validated. Aims and Objectives: This paper aims to develop a method of measuring public transit accessibility based on Google Direction API rather than local data analyzing. A mechanism of API (Application Program Interface) probing is introduced. In a case study, the metropolitan area of Beijing was sliced into gridded spaces, with transit access distance of each cell space calculated by Google Direction API. The access distances in the API feedbacks were compared with transit access numbers in each cell area in order to validate the method with their correlation coefficient. Results and Conclusion: It was found that Google Direction API generally gave shorter access distances in cell areas with more public access points. The conclusion is that open cloud services such as Google Direction API may serve as alternative solutions to public transit accessibility measurement. Transit researchers and agencies may take advantage of such open API services to avoid the tediousness of collecting and processing geographic data sets on transit facilities.

Associative white matter connecting the dorsal and ventral posterior human cortex.

Historically, the primary focus of studies of human white matter tracts has been on large tracts that connect anterior-to-posterior cortical regions. These include the superior longitudinal fasciculus (SLF), the inferior longitudinal fasciculus (ILF), and the inferior fronto-occipital fasciculus (IFOF). Recently, more refined and well-understood tractography methods have facilitated the characterization of several tracts in the posterior of the human brain that connect dorsal-to-ventral cortical regions. These include the vertical occipital fasciculus (VOF), the posterior arcuate fasciculus (pArc), the temporo-parietal connection (TP-SPL), and the middle longitudinal fasciculus (MdLF). The addition of these dorso-ventral connective tracts to our standard picture of white matter architecture results in a more complicated pattern of white matter connectivity than previously considered. Dorso-ventral connective tracts may play a role in transferring information from superior horizontal tracts, such as the SLF, to inferior horizontal tracts, such as the IFOF and ILF. We present a full anatomical delineation of these major dorso-ventral connective white matter tracts (the VOF, pArc, TP-SPL, and MdLF). We show their spatial layout and cortical termination mappings in relation to the more established horizontal tracts (SLF, IFOF, ILF, and Arc) and consider standard values for quantitative features associated with the aforementioned tracts. We hope to facilitate further study on these tracts and their relations. To this end, we also share links to automated code that segments these tracts, thereby providing a standard approach to obtaining these tracts for subsequent analysis. We developed open source software to allow reproducible segmentation of the tracts: https://github.com/brainlife/Vertical_Tracts . Finally, we make the segmentation method available as an open cloud service on the data and analyses sharing platform brainlife.io. Investigators will be able to access these services and upload their data to segment these tracts.

The open diffusion data derivatives, brain data upcycling via integrated publishing of derivatives and reproducible open cloud services

We describe the Open Diffusion Data Derivatives (O3D) repository: an integrated collection of preserved brain data derivatives and processing pipelines, published together using a single digital-object-identifier. The data derivatives were generated using modern diffusion-weighted magnetic resonance imaging data (dMRI) with diverse properties of resolution and signal-to-noise ratio. In addition to the data, we publish all processing pipelines (also referred to as open cloud services). The pipelines utilize modern methods for neuroimaging data processing (diffusion-signal modelling, fiber tracking, tractography evaluation, white matter segmentation, and structural connectome construction). The O3D open services can allow cognitive and clinical neuroscientists to run the connectome mapping algorithms on new, user-uploaded, data. Open source code implementing all O3D services is also provided to allow computational and computer scientists to reuse and extend the processing methods. Publishing both data-derivatives and integrated processing pipeline promotes practices for scientific reproducibility and data upcycling by providing open access to the research assets for utilization by multiple scientific communities.

МОДЕЛІ ВИКОРИСТАННЯ ЕЛЕМЕНТІВ ДИСТАНЦІЙНОГО НАВЧАННЯ В ШКОЛІ

The article presents three models of the use of elements of distance learning at school. All models partially or fully implement the training, interaction and collaboration of the participants in the educational process. The first model is determined by the use of open cloud services and Web 2.0 for the implementation of certain educational and managerial tasks of the school. The second model uses support for learning management and content creation. The introduction of the second model is possible with the development of the IT infrastructure of the school, the training of teachers for the use of distance learning technologies, the creation of electronic educational resources. The third model combines the use of Web 2.0 technologies and training and content management systems. Models of the use of elements of distance learning are presented of the results of regional research experimental work of schools.

A Semantic-Driven Knowledge Representation Model for the Materials Engineering Application

A Materials Engineering Application (MEA) has been presented as a solution for the problems of materials design, solutions simulation, production and processing, and service evaluation. Large amounts of data are generated in the MEA distributed and heterogeneous environment. As the demand for intelligent engineering information applications increases, the challenge is to effectively organize these complex data and provide timely and accurate on-demand services. In this paper, based on the supporting environment of Open Cloud Services Architecture (OCSA) and Virtual DataSpace (VDS), a new semantic-driven knowledge representation model for MEA information is proposed. Faced with the MEA constantly changing user requirements, this model elaborates the semantic representation of data, services and their relationships to support the construction of domain knowledge ontology. Then, based on the ontology modeling in VDS, the semantic representations of association mapping, rule-based reasoning, and evolution tracking are analyzed to support MEA knowledge acquisition. Finally, an application example of knowledge representation in the field of materials engineering is given to illustrate the proposed model, and some experimental comparisons are discussed for evaluating and verifying the effectiveness of this method.

Key Technology Research on Open Architecture for The Sharing of Heterogeneous Geographic Analysis Models

Abstract. In recent years, the increasing development of cloud computing technologies laid critical foundation for efficiently solving complicated geographic issues. However, it is still difficult to realize the cooperative operation of massive heterogeneous geographical models. Traditional cloud architecture is apt to provide centralized solution to end users, while all the required resources are often offered by large enterprises or special agencies. Thus, it's a closed framework from the perspective of resource utilization. Solving comprehensive geographic issues requires integrating multifarious heterogeneous geographical models and data. In this case, an open computing platform is in need, with which the model owners can package and deploy their models into cloud conveniently, while model users can search, access and utilize those models with cloud facility. Based on this concept, the open cloud service strategies for the sharing of heterogeneous geographic analysis models is studied in this article. The key technology: unified cloud interface strategy, sharing platform based on cloud service, and computing platform based on cloud service are discussed in detail, and related experiments are conducted for further verification.

An Effective User Behavior Modeling Approach for Data Services in the Field of Materials Engineering

New challenges about data services have arisen, especially consider the impact of user behavior. For dealing with the problem of distributed heterogeneous data sharing and satisfying the demands of data service, the complex association and dynamic changes should be tracked timely. Thereby it has become increasingly important to build a data services framework based on the user behavior analysis. In this paper, we propose an effective user behavior modeling approach based on the Open Cloud Service Architecture (OCSA) to manage the domain scientific data. Firstly, we build the data services framework in the cloud environment, and describe the related theories and conceptual model about this framework. Based on this, we elaborate the definition and classification of user behavior. Secondly, we construct the User Behavior Model (UBM) by tracking the user operation behavior respectively from the time dimension and the space dimension. Finally, we designed and realized a behavior-oriented Materials Scientific Data Sharing Service Platform. Through the description and comparison of application case, the validity of user behavior modeling method is verified.