Cloud Computing Technology Research Articles

Towards digital twin trains: implementing a cloud-based framework for railway vehicle dynamics simulation

ABSTRACT The digital twin technology holds great promise in driving the railway industry into a new era of digital intelligence. By creating a dynamic, personalized digital replica of a physical train, it has the potential to enable a range of advanced applications, including predictive maintenance, adaptive control, and early fault detection. However, substantial challenges exist that hinder the effective implementation of the digital twin concept for trains. To resolve this bottleneck issue, in this paper, these challenges are first identified and thoroughly examined, with a specific focus on those within the realm of railway vehicle dynamics simulation. A novel cloud-based simulation framework for the development of railway vehicle dynamics simulation software is then proposed to tackle the identified challenges. The framework makes use of cloud computing and cloud-based visualization technology to offer a scalable and flexible solution for railway vehicle dynamics simulation. Using the framework as its foundation, a cloud platform for constructing digital twin trains is also proposed and presented, with a detailed explanation of its architecture and functionalities. In addition, CTTSIM, a cloud computing-aided train-track system dynamics real-time simulation software, is developed as an illustrative application case of the proposed framework. An evaluation is undertaken to demonstrate the feasibility and utility of both CTTSIM and the proposed framework. This work provides a practical and effective solution for using vehicle dynamics simulation for constructing digital twin trains, representing a step towards the full implementation of the digital twin concept in the railway industry.

Evaluating cloud computing in construction projects to avoid project delay

The revolutionary impact of Cloud Computing (CC) technologies has been felt across multiple industries for an extended period. Although the construction industry is expanding over time, the role of innovation in construction is significant. However, not all stakeholders know about adopting and implementing CC for better project delivery. Hence, this paper aims to assist construction industry stakeholders in understanding the current challenges associated with using CC in the sector. The relevant literature review was conducted through a systematic data acquisition method to identify the factors in applying CC in the construction industry. The primary aim is to improve understanding of the current state of CC and its use to reduce delays in research and construction operations and to enhance safety in such projects. A survey approach was utilized in this study with the application of SPSS analysis. The outcome shows that CC may potentially improve overall industrial efficiency by gathering and leveraging data to increase productivity throughout the process, from conception to execution to exploitation. The challenges in access to CC can emerge due to various factors, including challenges in internet connectivity, economic considerations, availability of servers and data management. Furthermore, a conceptual framework has been proposed to provide summarised and organised key factors for CC application in construction projects to tackle project delays. However, the overall evaluation of factors of CC brings some encouraging and positive output which may transform the construction industry significantly.

Research on DCT shift strategy for various driving style based on “driver-vehicle-cloud” machine learning

In order to solve the problem that the DCT static shift strategy cannot adapt to the difference in driving style, the driving style identification model based on multi-dimensional data mining and intelligent algorithm heavily depends on vehicle terminal data storage and calculation, an intelligent shift strategy based on “driver-vehicle-cloud” cooperative control is proposed. Firstly, the dynamic model of the DCT vehicle is analyzed, the primary shift schedule is calculated, and a method to adaptively modify the shifting schedule of DCT according to driving style is proposed. Then, many vehicle driving data are collected, cleaned, and reconstructed by wavelet denoising and other methods, and a driving style database with 80-dimensional features is constructed. Five essential features are selected by the ReliefF method, and the driving style recognition model is constructed by combining random forest, support vector machine, naive Bayesian, and other algorithms. Finally, the support vector machine model with the highest precision is selected, and the “driver-vehicle-cloud” collaborative control system is deployed using cloud computing and vehicle-cloud collaborative technology. The experiment car test shows that the system can identify the driver’s driving style in real time and realize the differential shift schedule and driving experience of DCT.

Development and key technologies for architectural design and technical solutions of CIM platform: A review

The integration of building information modeling (BIM), geographic information systems (GIS), 5G, IOT, cloud computing, big data, artificial intelligence, and other technologies in urban management has become a new and rapidly developing trend in recent years. However, most of the existing research on the construction of city information modeling (CIM) platforms based on the integration of BIM and GIS technologies focuses on integration technologies, and there is a lack of theories, methods, and standards for further data analysis and mathematical modeling. This paper analyzes and summarizes the actual situation of the application of recent research papers in the BIM field and the application of CIM platform pilot projects, reviews the application of BIM and GIS technologies in smart cities, and discusses future trends. For example, the construction of a basic platform for smart cities requires the establishment of a three-dimensional visual CIM platform with functions such as the planning review, architectural design plan review, construction drawing review, completion acceptance filing. A smart city is built by analyzing the cyber security risks faced during the construction. The technical, management, and operational measures of the security system provide guidance and reference for the secure construction of smart cities. This involves promoting the reform of the construction work system driven by data resources obtained from government information, and the establishment a new digital government construction management system through the “separation of management and transportation.” These analyses highlight the evolution of the CIM platform construction. The results indicate that the use of key technology integration in the construction of a CIM platform requires a system theory that goes beyond integration technology and mathematical modeling methods for a detailed application. It involves analyzing the deep integration of key technologies and business processes in the CIM platform.

Research on the transformation of news media industries in China and Russia in the era of intelligent media

The subject of this study is the era of smart media, which will have a significant impact on the news media ecosystem. It will lead to another industrial transformation and change in the news industry. The convergence of traditional and new media is made possible by 5G technology, cloud computing and big data technologies. The COVID-19 pandemic has accelerated the pace of media analytics, digitisation and platformisation. In addition, recent years have seen a surge in the development of artificial intelligence, which has been applied to various fields such as military, healthcare, retirement, etc. The development of ChatGPT has been an important signal of intelligent change in the media industry. The news media industry is undergoing a transformation that is already well underway. The research methodology is comprehensive and includes: descriptive method, comparative data analysis, statistical analysis and content analysis. The novelty of the research is the analysis of intellectual media paradigms in China and Russia. The nature of media interaction between the two countries, as well as the trajectory of global innovative technologies are investigated. Trends related to the development of digitalisation and AI in the following areas are described in detail: digitalisation of operations, news production, development of media platforms and AI technologies, and media content digitalisation processes. A prospective analysis is made of the need for international specialists with both humanitarian and technical competences. The digital and intellectual transformation of news media can take the cooperation between the two countries to a new historical level. In addition, media cooperation between China and Russia can help stabilise the world situation and provide a favourable ecological environment for international media.

Cloud-magnetic resonance imaging system: In the era of 6G and artificial intelligence

Magnetic resonance imaging (MRI) plays an important role in medical diagnosis, generating petabytes of image data annually in large hospitals. This voluminous data stream requires a significant amount of network bandwidth and extensive storage infrastructure. Additionally, local data processing demands substantial manpower and hardware investments. Data isolation across different healthcare institutions hinders cross-institutional collaboration in clinics and research. In this work, we anticipate an innovative MRI system and its four generations that integrate emerging distributed cloud computing, 6G bandwidth, edge computing, federated learning, and blockchain technology. This system is called Cloud-MRI, aiming at solving the problems of MRI data storage security, transmission speed, artificial intelligence (AI) algorithm maintenance, hardware upgrading, and collaborative work. The workflow commences with the transformation of k-space raw data into the standardized Imaging Society for Magnetic Resonance in Medicine Raw Data (ISMRMRD) format. Then, the data are uploaded to the cloud or edge nodes for fast image reconstruction, neural network training, and automatic analysis. Then, the outcomes are seamlessly transmitted to clinics or research institutes for diagnosis and other services. The Cloud-MRI system will save the raw imaging data, reduce the risk of data loss, facilitate inter-institutional medical collaboration, and finally improve diagnostic accuracy and work efficiency.

Measuring Vital Signs such as Temperature, BPM, Humidity using different IOT Sensors and Designing a Mobile Application using Firebase

Abstract: This research describes the creation of an Internet of Things-based smart health surveillance system that allows for the real-time data tracking of a person's vital signs. An ESP8266 Node MCU microcontroller is integrated with temperature, humidity, and heart rate sensors in the system to gather information on the user's body temperature, ambient humidity, and heart rate. A specially created Android mobile application shows the gathered data, which is wirelessly sent to a Firebase cloud database. With the use of the mobile app, users can easily create individualized profiles, view vital sign data, and get warnings. Early health issue diagnosis and proactive illness management are aided by successful implementation, which shows that lowcost IoT devices, cloud computing, and mobile technologies may all be used for effective remote health monitoring

The State of The Knowledge of Cloud Computing Technology for Manufacturing Automation Systems

The manufacturing sector is grappling with the need to adapt to rapid technological changes, and leveraging cloud computing is becoming crucial for staying competitive and resilient. The presentation of knowledge in this work focuses on highlighting the specific challenges and opportunities in integrating cloud technologies into manufacturing systems. It aims to answer critical questions such as how cloud computing can enhance manufacturing automation, solve problems, and benefit to the industry. The methodology employed in this research takes a comprehensive and top-down approach, aligning and exploring the practical aspects of implementing these X-as-a-Service (XaaS) model in manufacturing setups. The research also acknowledges the shift from legacy Distributed Numerical Control (DNC) systems to modern solutions like MTConnect and Open Platform Communication (OPC) for data exchange in automated manufacturing systems. Emphasizing the important of data collection and real-time monitoring, the study highlights the role of Industrial Internet of Things (IoT) sensors deployed at various points of manufacturing system components (machine tools, spindles, cutting tools, production units, etc.). These sensors capture real-time production and condition data, enabling informed decision-making in manufacturing systems. This research not only presents the latest knowledge but also offers insights into the challenges, strategies, and methodologies involved in the successful integration of cloud-based technology into manufacturing automation systems. It also aims to serve as a valuable resource for manufacturers, researchers, and industry professionals navigating the transformative journey toward cloud-powered manufacturing.

Study on Comparative Analysis of Production of Dry Fruit Economic Forest Products Based on Big Data

The analysis of economic forest products production in Shandong Province mainly focuses on economic forest production and output value as well as economic forest plantation area, capital and labour input. By analysing the above, the basic situation of economic forest products production in Shandong Province can be understood on a macro level, laying the foundation for the subsequent study of the relevant data of the farmers, and at the same time providing a reference for the relevant management. There is an obvious gap between the production value of economic forest products in Shandong Province and that of the whole country. By combining modern information technologies such as 3S, Internet of Things, artificial intelligence, big data, cloud computing and other modern information technologies, we have established an integrated modern forest cultivation technology system ranging from the intelligent evaluation of forest terrain conditions, ecological environment simulation and simulation to the precise monitoring and diagnosis of forest growth, intelligent equipment and variable irrigation and fertilisation, etc. It is of great practical significance to expand the scale of production value of economic forest products in Shandong Province and narrow the gap with the national average level by carrying out research on the production and management efficiency of economic forest products in Shandong Province.

Integration of Cloud Computing and Wearable Technology for Enhanced Interactivity

The emergence of wearable computing has revolutionized the way we interact with technology, blurring the lines between the physical and digital worlds. In this research, we explore the dynamic interaction between cloud technology and wearable computing, a synergy that is reshaping the landscape of personal technology and data management. The study delves into how cloud comput-ing provides a powerful platform for wearable devices, enabling enhanced data processing capabili-ties, storage, and ubiquitous access to information. We investigate the various applications of this interaction, ranging from health monitoring to augmented reality, emphasizing the transformative impact on everyday life and various industries. The research also addresses the challenges inherent in this integration, such as data security, privacy concerns, and the need for robust, low-latency communication networks. Through a comprehensive analysis of current trends and future prospects, this study highlights the potential of cloud-assisted wearable technology in creating more personal-ized, efficient, and interconnected experiences. The findings suggest that the convergence of cloud technology and wearable computing not only offers significant benefits in terms of functionality and user experience but also poses critical considerations for data governance and ethical implications in an increasingly connected world.

Hospital Medical Behaviour Supervision and Operational Efficiency Evaluation Method based based on Big Data Platform

The application of cloud computing and big data core technologies and concepts to medical informatization can improve its flexibility and efficiency, and achieve the overall deployment and intensive management of the system. In the era of big data, the use of information management platform can optimize and standardize clinical diagnosis and treatment process, improve the quality and efficiency of diagnosis and treatment services, and improve the quality and level of scientific research, which meets the requirements of medical reform for fine management of hospitals and precise medical treatment in the era of evidence-based medicine. This paper introduces the development and application of big data analysis in the field of information technology. Through the research on the supervision of hospital medical service behavior, the top-level design is used to standardize the content of medical behavior supervision, and the effectiveness of supervision is discussed to achieve the purpose of reducing clinical paperwork. Based on the needs, the medical service behavior supervision system is proposed and constructed. Strengthen hospital medical behavior supervision through hospital big data analysis and knowledge base system support. This system can provide management with an effective monitoring and management tool, enabling them to promptly identify and solve problems that arise in medical services. By analyzing a large amount of medical data, hospitals can help predict the trend of disease occurrence in advance, thereby taking preventive and control measures in advance, and reducing the occurrence and spread of diseases. Through deep learning and analysis of patient data, personalized treatment plans can be provided for each patient to improve treatment effectiveness.

Secure and fine-grained access control of electronic health record using SAPCP-ABE technique in cloud

This study explores how Electronic Health Records (EHR) might be transformed in the context of the rapid improvements in cloud computing and IoT technology. But worries about sensitive data security and access management when it moves to large cloud provider networks surface. Even if they are secure, traditional encryption techniques sometimes lack the granularity needed for effective data protection. We suggest the Secure Access Policy – Ciphertext Policy – Attribute-based Encryption (SAPCP-ABE) algorithm as a solution to this problem. This method ensures that only authorized users may access the necessary data while facilitating fine-grained encrypted data exchange. The three main phases of SAPCP-ABE are retrieval and decoding, where the system verifies users’ access restrictions, secure outsourcing that prioritizes critical attributes, and an authenticity phase for early authentication. Performance tests show that SAPCP-ABE is a better scheme than earlier ones, with faster encryption and decryption speeds of 5 and 5.1 seconds for 512-bit keys, respectively. Security studies, numerical comparisons, and implementation outcomes demonstrate our suggested approach’s efficacy, efficiency, and scalability.

Preface: 3rd International Conference on Software, Data Processing and Information Technology (SDPIT 2024)

The 2024 3rd International Conference on Software, Data Processing and Information Technology (SDPIT 2024) is a leading conference held at Washington DC, USA during February 17-18, 2024. It encourages communication and collaboration, hoping to provide an international platform for its participants to present, share, and learn the latest advance and development of software engineering, data processing, cloud computation, mathematical modeling and information technology. Academics, researchers, and students from across the world are all welcomed to exchange findings and ideas, to draw further inspirations in these research fields. It's a great opportunity to deepen your understanding in the related topics. Everyone interested in these fields were welcomed to join the conference and to give comments and raise questions to the speeches and presentations. We are hoping we could see each other again next year. Each submission to SDPIT 2024 was reviewed by at least two program committee members. We would like to thank the program committee members and external reviewers for their hard work in reviewing and selecting papers. Hopefully, all participants and other interested readers can benefit scientifically from the proceedings and find it rewarding in the process. Chairman of Conference Committee Washington DC, USA

Music Curriculum Research Using a Large Language Model, Cloud Computing and Data Mining Technologies

This paper presents a method to enhance the scientific nature of the music curriculum model by integrating a large language model, cloud computing and data mining technology for the analysis of the music teaching curriculum model. To maintain the integrity of the mixing matrix while employing the frequency hopping frequency, the paper suggests dividing the mixing matrix into a series of sub-matrices along the vertical time axis. This approach transforms wideband music signal processing into a narrowband processing problem. Additionally, two hybrid matrix estimation algorithms are proposed in this paper using underdetermined conditions. Furthermore, utilizing the estimated mixing matrix and the detected time-frequency support domain, the paper employs the subspace projection algorithm for underdetermined blind separation of music signals in the time-frequency domain. This procedure, along with the integration of the estimated direction of arrival (DoA), enables the completion of frequency-hopping network station music signal sorting. Extensive simulation teaching demonstrates that the music curriculum model proposed in this paper, based on a large language model, cloud computing and data mining technologies, significantly enhances the quality of modern music teaching.

Preface

The advent of the fourth information revolution has made the digital economy a major driver of energy transition and low-carbon development. The overall architecture of smart energy is based on intelligence as the core, and based on infrastructure platforms such as intelligent cloud and IoT, as well as AI middle platform, Data middle office ether, etc. And with the help of cloud computing, artificial intelligence, big data, blockchain, digital twins and other technologies, smart energy can promote the intelligent transformation of energy enterprises ultimately.Against such background, the 2023 2nd International Conference on Smart Energy and Electrical Engineering (SEEE 2023) was held via hybrid form during December 22nd to 24th, 2023 in Malaysia. It was attended by about 50 researchers, scientists, engineers, and students working in the fields of smart energy and electrical engineering.The aims of SEEE 2023 are:To create an academic forum for sharing, communication and research at the international level, so that participants can be informed of the latest research directions, contents and results of smart energy and electrical engineering.To provide approaches to the main problems facing smart energy and electrical engineering, creating spaces that encourage discussion and joint development of research, technology development, and innovation.The excellent papers selected form the submissions were approved for presentation at the Conference and publication in this Conference Proceedings. Among the submissions are some submitted from abroad, but are unfortunately not accepted after rigorous review processes.There was an enabling environment for discussion and exchange of ideas, during which topics such as Core Technology of Intelligent Energy Equipment, Smart Transmission and Distribution, Power Grid Dispatching and Automatic Control, Power Systems and Energy, Electrical Theory and New Technology, etc. were presented. Some of the important issues were the sharing and discussion of Wireless Power Transfer and its Application on Electric Vehicle Wireless Charging by Prof. Yiming Zhang (Fuzhou University, China), Corona Loss Characteristics of EHV/UHV AC Transmission Lines by A. Prof. Shilong Huang (North China Electric Power University, China), Integrated Renewable Energy System based on Hydrogen by Prof. Mohan Lal Kolhe (University of Agder, Norway), etc.As in the last meeting, SEEE 2023 was again a space for the integration of undisputed knowledge and sharing of peer experiences within the specific areas of smart energy and electrical engineering. Both conference and presentation sessions were attended by many conference attendees, allowing closer relationships, both academic, as well as scientific and personal. The cordiality and good treatment of the attendees promoted a climate of friendship, which we could enjoy during all the development of the conference agenda.We would like to extend an enthusiastic round of thanks to all our conference authors and speakers for their excellent contributions. We are indebted to the Technical Program Committee for their efforts in making and pushing the conference program. Thanks also to the members of Journal of Physics: Conference Series for making the publication of SEEE 2023’s Proceedings possible.List of Committee Member is available in this pdf.

Implementation of Cloud Run and Cloud Storage as REST API Service on OutfitHub Application

The development of Cloud Computing technology has progressed rapidly in recent years especially with the emergence of Google Cloud Services (GCR) which has become one of the leading cloud service providers. This research focuses on the OutfitHub application, which plays a role in assisting users in determining clothing styles using a personalized recommendation system. In developing this application, the research seeks to implement cloud computing services to improve application performance. The purpose of this research is to implement Cloud Computing, especially Cloud run and Cloud Storage services as Rest API in the Outfithub application. By implementing these two services, it is expected that there is no need to pay attention to the problem of Storage needs that are growing at any time and no need to worry about the need for server configuration because both of these things will be fully done by GCR. Implementing Cloud Computing will provide a variety of benefits in addition to those previously mentioned, such as: being able to access data from anywhere and at any time. This implementation is expected to be able to run OutfitHub applications in a Cloud environment in a serverless computing manner without requiring the design of unnecessary virtual machines.

Improving live migration efficiency in QEMU: An eBPF-based paravirtualized approach

Virtual Machines are the key technology in cloud computing. In order to upgrade, repair or service the physical machine where a Virtual Machine is hosted, a common practice is to live-migrate the Virtual Machine to a different server. This involves copying all the guest memory over the network, which may take a non-negligible amount of time. In this work, we propose a technique to speed up the migration time by reducing the amount of guest memory to be transferred with the help of the guest OS. In particular, during live-migration, an eBPF program is injected in the guest kernel to obtain, and send to the Virtual Machine Monitor, the list of guest page frames that are currently unused. The VMM can then safely skip these pages during the copy. We have integrated this technique in the live-migration implementation of QEMU (Bellard, 2005), and we show the effects of our work in some experiments comparing the results against the QEMU default implementation.

Cross‐network service recommendation in smart cities

SummaryNowadays, Internet of Things, artificial intelligence, cloud computing, and other revolutionary technologies (e.g., edge and fog computing) have become the pillar of smart cities. These latter make users' lives easier, thanks to a wide variety of smart services offered in different dimensions (e.g., smart living, smart mobility, smart economy, smart governance). However, the rapid adoption of smart services by users and the full servicelization of several cities around the world is faced with two major issues: the lack of knowledge regarding smart services' capacities (e.g., features, contextual requirements, pricing models, privacy policies, provisioning terms, etc.), and the lack of unified rating and quantification of smart services' QoS behavior. Indeed, interested users often exploit traditional search tools (e.g., Web search engines, social networks) to find and rate the needed services. This behavior has scattered the smart services' usage data (e.g., users contexts, ratings) across multiple providers platforms, which makes the search task beyond the capacity of users and, even, other service providers. Although recommender systems are a natural solution to exempt users from exploring the huge space of the offered smart services, current recommendation approaches for smart city environments are unable to deliver correct recommendations. In fact, they have been initially designed to single‐network settings (a single service repository), while smart services' consumers often are involved in multiple provider platforms. To the best of our knowledge, there exists no approach that treated smart service recommendation across multiple information networks. Therefore, the goal of this paper is to propose a cross‐network recommender system for smart cities. We first model the multiplex network of smart services' providers as a multirelational fuzzy lattice family thanks to fuzzy relational concept analysis (fuzzy RCA), which is a powerful mathematical method for data analysis and clustering. We also use the concept of anchor users to connect providers networks via the users involved in more than one provider platform. Guided by anchors' cross‐network relations, we compute the similarity between users and we define algorithms for exploring the smart services' information network, i.e. lattice family. Extensive experiments have proved the effectiveness of cross‐network recommendation and the quality of produced recommendations, compared to state‐of‐the‐art single‐network recommendation.

Physics-informed machine learning in cyber-attack detection and resilient control of chemical processes

With the integration of internet of things (IoT) devices, cloud computing, and other digital technologies into chemical processes, the complexity and stealthiness of cyber-attacks have increased. To mitigate the impact of sensor cyber-attacks in chemical processes, this work presents a framework that develops physics-informed machine learning (PIML)-based detectors and resilient controllers for improving closed-loop performance of nonlinear system under cyber-attacks. The PIML detector is constructed through a customized loss function that integrates the domain knowledge of cyber-attacks into the training process. Additionally, upon detection of attacks, a knowledge-guided extended Kalman filter is developed to provide estimated states for resilient control prior to replacement by redundant sensors. A chemical process example is used to illustrate the application of the proposed PIML-based detection and resilient control methods to handle cyber-attacks.

Application of SDN-IP hybrid network multicast architecture in Commercial Aerospace Data Center

Abstract The increasing amount of massive data generated by commercial spacecraft in orbit puts forward higher and higher requirements for the stability, reliability, and computing power of computer systems for commercial aerospace data center. Data center computer systems are gradually transforming from X86 architecture and IP network model to a platform model with cloud computing and software-defined network (SDN) technology. This article proposes a new network architecture based on a unicast/multicast protocol for data interaction between the SDN and IP network. There are three main contributions of this article. The first is that the architecture proposed in this article aims to reduce end-to-end transmission latency and packet loss. The second is to improve the flexibility of system configuration and precise control when the SND controller state changes. The third is to verify the feasibility of deploying network architecture in a real data center environment.