System Architecture Framework Research Articles

Cyber-Physical Distributed Intelligent Motor Fault Detection.

This research paper explores the realm of fault detection in distributed motors through the vision of the Internet of electrical drives. This paper aims at employing artificial neural networks supported by the data collected by the Internet of distributed devices. Cross-verification of results offers reliable diagnosis of industrial motor faults. The proposed methodology involves the development of a cyber-physical system architecture and mathematical modeling framework for efficient fault detection. The mathematical model is designed to capture the intricate relationships within the cyber-physical system, incorporating the dynamic interactions between distributed motors and their edge controllers. Fast Fourier transform is employed for signal processing, enabling the extraction of meaningful frequency features that serve as indicators of potential faults. The artificial neural network based fault detection system is integrated with the solution, utilizing its ability to learn complex patterns and adapt to varying motor conditions. The effectiveness of the proposed framework and model is demonstrated through experimental results. The experimental setup involves diverse fault scenarios, and the system's performance is evaluated in terms of accuracy, sensitivity, and false positive rates.

Hybrid Recommender System for Tourism Based on Big Data and AI

With the advancement of the Internet, technology, and communication channels, the generation of tourist data has significantly increased across various sectors such as hotels, restaurants, transportation, heritage sites, tourist events, and activities. This surge is particularly notable with the rise of Online Travel Agencies (OTAs). However, the sheer volume of options provided to tourists by web search engines or specialized tourism websites often overwhelms them, burying relevant results in a sea of information "noise." This inundation hinders or slows down the decision-making process. To alleviate this issue and aid tourists in trip planning by facilitating the discovery of relevant information, numerous recommender systems have emerged. This article offers an overview of the diverse recommendation approaches employed in the tourism domain. Through this investigation, we propose an architecture and conceptual framework for a tourism recommender system founded on a hybrid recommendation approach. This system surpasses merely suggesting a list of tourist attractions tailored to individual preferences. Instead, it functions as a comprehensive trip planner, crafting detailed itineraries comprising a variety of tourism resources, customized for specific visit durations. KeyWords: Internet Development, Tourist Data Production, Trip Planning, Conceptual Framework.

An Advanced Driver Information System at Critical Points in the Multimodal Traffic Network

Enhancing traffic safety is one of the fundamental objectives of Intelligent Transport Systems (ITS), and it aligns closely with the principles of sustainable transport. Due to specific differences in infrastructure, vehicles, and users’ behavior, places where different modes of traffic intersect are recognized as critical points of the traffic system, making them crucial areas for the implementation of Sustainable Urban Mobility Plans (SUMPs). The SUMPs aim to create urban mobility that is both environmentally friendly and safe for all users. The continuous development and widespread adoption of innovative ITS technologies have paved the way for a system that can provide drivers with real-time information about both immediate and potential dangers at these critical points. This paper presents a comprehensive review of prior research conducted in the field, investigating the impact of information systems on drivers’ behavior, various detection and communication solutions that can be effectively integrated into such a system, and a brief overview of the models and solutions that have been developed to warn drivers in a similar context. A review of the literature found that warning systems have a significant impact on driver behavior, which contributes to increased traffic safety. Furthermore, there are numerous solutions applicable to a multimodal environment. Yet, they mostly refer either to autonomous vehicles or require an additional unit of infrastructure for communication, which is not realistically applicable to the current state of traffic in most countries of the world. This paper proposes a system architecture framework for future research that would take advantage of widely available technologies and make the system accessible to different users in a multimodal environment.

ENHANCING ENTERPRISE ENERGY MANAGEMENT WITH IOT-BASED MONITORING SYSTEMS

This article offers an in-depth exploration of how IoT technology can revolutionize energy management within enterprises. It provides a comprehensive elucidation of the system's architectural framework, spotlighting its innate capacity for real-time data collection, intricate analysis, and responsive control. The discussion extends to the seamless integration of IoT-enabled sensors, cutting-edge data analytics, and adaptive machine learning algorithms, all orchestrated to orchestrate optimal energy consumption strategies.A pivotal facet of this study underscores the paramount importance of sustainability and cost-effectiveness in the contemporary enterprise landscape. It underscores how the proposed IoT-based system assumes the role of a catalyst in realizing sustainability objectives. It achieves this by vigilant monitoring of energy utilization, waste minimization, and meticulous resource allocation. Moreover, this article presents a panorama of tangible outcomes by examining real-world deployments and substantiated case studies of the IoT-driven energy supply monitoring system. It effectively illustrates the multifaceted benefits, which encompass substantial energy conservation, streamlined operational efficiency, and a markedly reduced carbon footprint.In conclusion, this research accentuates the transformative potential of IoT technology in the realm of enterprise energy supply monitoring. It proffers a comprehensive, forward-looking solution for businesses to not only elevate their sustainability quotient but also optimize their operational prowess, thus contributing substantively to a greener, more efficient future.

On the role of future HLA-based simulation in designing safety into space vehicles

This paper discusses High Level Architecture (HLA) based simulation in the context of designing safety into space vehicles. Distributed simulation plays an important role to fuse the two worlds of safety on the one hand and cost effectiveness on the other hand. HLA represents a simulation system architecture framework standard and focuses on interoperability and reusability of simulation components. The article analyzes the impact of the usage of the future HLA version called HLA 4 on space vehicle design. New possibilities with an increased level of loose component coupling in combination with the establishment of a-priori interoperability by using the Space Reference Federation Object Model (SpaceFOM) standard are presented.

Accessible satellite data decision support systems for Yurok Tribe forest management

Local decisionmakers managing forests for sequestering carbon and mitigating disturbance face increasing pressures from climate change, while needing to derive revenue from forests resources and balance diverse stakeholder concerns. Decision support systems (DSS) that use freely available satellite data are effective tools for forest management decision-making, due to the broad spatial coverage and temporal consistency of this data. However, the literature demonstrates that many DSS developed by researchers incorporating satellite data are often not utilized by decision makers. This is in part due to a lack of stakeholder engagement in DSS development. Here, we employ an intentional stakeholder engagement process to develop an accessible DSS incorporating satellite data. This process is applied with the Yurok Tribe – an Indigenous community in California – towards the goal of managing forests for carbon sequestration. We use system architecture framework (SAF) to translate stakeholder objectives into targeted analyses and the environment-vulnerability-decision-technology (EVDT) integrated modeling framework to incorporate contextual local infrastructure and land ownership information alongside satellite data analyses. Satellite data analyses classify tree cover and track Normalized Difference Vegetation Index (NDVI) over time. We compare and show that trends in our analysis agree with independent global tree cover and biomass datasets. These analyses meet the Tribe's objective for tracking forest trends in Yurok carbon sequestration projects, while also detecting undesirable conifer encroachment on culturally-important prairies traditionally managed as open landscapes. Additionally, integration of community infrastructure data alongside satellite-derived drought, forest fire, and smoke density information via EVDT meets another Yurok Tribe objective of identifying negatively impacted Tribal areas for aid allocation. User feedback sessions with participating Yurok Tribe employees assess the prototype DSS and show 80% of users providing high ratings (≥75%) for information accuracy, relevancy, and sufficiency, demonstrating that our DSS development process overcomes previous gaps to DSS use and satellite data accessibility to augment Yurok Tribe forest management decision making.

PPSDM Business Process Analysis Using the CIMOSA Framework

As the organizing body for human resource development, as well as being the only center for human resource development for the oil and gas sub-sector industry, PPSDM MIGAS hopes to be able to improve the competence of oil and gas workers in Indonesia. In this process it is necessary to have knowledge related to the business processes that are carried out so that the process and direction to be achieved can be seen to develop human resources in the oil and gas sector. The purpose of this study is to obtain an overview of the business processes carried out by PPSDM MIGAS. In this study, the computer integrated manufacturing for pen system architecture (CIMOSA) framework is used as an approach to find out the details of business processes. The research methodology is carried out by means of observation and descriptive studies. From the results of this study, detailed information on each business process in PPSDM MIGAS has been obtained. Starting from managing processes, core business processes, to support processes, all have been carried out by PPSDM MIGAS. Overall, from the analysis of the PPSDM MIGAS business process mapping using the CIMOSA framework, it can be said that the existing business processes are clear and well directed. It is hoped that in the future PPSDM MIGAS can continue to develop services in order to produce superior and competitive human resources in the oil and gas sector.

Learning-Based Resource Allocation for Backscatter-Aided Vehicular Networks

Heterogeneous backscatter networks are emerging as a promising solution to address the proliferating coverage and capacity demands of next-generation vehicular networks. However, despite its rapid evolution and significance, the optimization aspect of such networks has been overlooked due to their complexity and scale. Motivated by this discrepancy in the literature, this work sheds light on a novel learning-based optimization framework for heterogeneous backscatter vehicular networks. More specifically, the article presents a resource allocation and user association scheme for large-scale heterogeneous backscatter vehicular networks by considering a collaboration centric spectrum sharing mechanism. In the considered network setup, multiple network service providers (NSPs) own the resources to serve several legacy and backscatter vehicular users in the network. For each NSP, the legacy vehicle user operates under the macro cell, whereas, the backscatter vehicle user operates under small private cells using leased spectrum resources. A joint power allocation, user association, and spectrum sharing problem has been formulated with an objective to maximize the utility of NSPs. In order to overcome challenges of high dimensionality and non-convexity, the problem is divided into two subproblems. Subsequently, a reinforcement learning and a supervised deep learning approach have been used to solve both subproblems in an efficient and effective manner. To evaluate the benefits of the proposed scheme, extensive simulation studies are conducted and a comparison is provided with benchmark techniques. The performance evaluation demonstrates the utility of the presented system architecture and learning-based optimization framework.

Mobile Edge Computing in Space-Air-Ground Integrated Networks: Architectures, Key Technologies and Challenges

Space-air-ground integrated networks (SAGIN) provide seamless global coverage and cross-domain interconnection for the ubiquitous users in heterogeneous networks, which greatly promote the rapid development of intelligent mobile devices and applications. However, for mobile devices with limited computation capability and energy budgets, it is still a serious challenge to meet the stringent delay and energy requirements of computation-intensive ubiquitous mobile applications. Therefore, in view of the significant success in ground mobile networks, the introduction of mobile edge computing (MEC) in SAGIN has become a promising technology to solve the challenge. By deploying computing, cache, and communication resources in the edge of mobile networks, SAGIN MEC provides both low latency, high bandwidth, and wide coverage, substantially improving the quality of services for mobile applications. There are still many unprecedented challenges, due to its high dynamic, heterogeneous and complex time-varying topology. Therefore, efficient MEC deployment, resource management, and scheduling optimization in SAGIN are of great significance. However, most existing surveys only focus on either the network architecture and system model, or the analysis of specific technologies of computation offloading, without a complete description of the key MEC technologies for SAGIN. Motivated by this, this paper first presents a SAGIN network system architecture and service framework, followed by the descriptions of its characteristics and advantages. Then, the MEC deployment, network resources, edge intelligence, optimization objectives and key algorithms in SAGIN are discussed in detail. Finally, potential problems and challenges of MEC in SAGIN are discussed for future work.

An open system architecture framework for interoperability

The interoperability of systems is a critical factor for firms to make informed operational and strategic decisions and achieve a competitive edge in the marketplace. As a result, open systems that have a higher level of interoperability with secured and stable operations have significant relevance in today's global economy. Interoperability is accomplished through appropriate system architecture and design. Thus, to achieve the open system interoperability, this paper proposes a framework that looks at the system architecture at various levels of abstraction/implementation and identifies the required attributes at each of these levels. This framework can be used as a reference to analyse and determine interoperability requirements at all levels and prioritise the required aspects of interoperability.

Inclusive Design of Earth Observation Decision Support Systems for Environmental Governance: A Case Study of Lake Nokoué

Earth Observation (EO) data can enhance understanding of human-environmental systems for the creation of climate data services, or Decision Support Systems (DSS), to improve monitoring, prediction and mitigation of climate harm. However, EO data is not always incorporated into the workflow for decision-makers for a multitude of reasons including awareness, accessibility and collaboration models. The purpose of this study is to demonstrate a collaborative model that addresses historical power imbalances between communities. This paper highlights a case study of a climate harm mitigation DSS collaboration between the Space Enabled Research Group at the MIT Media Lab and Green Keeper Africa (GKA), an enterprise located in Benin. GKA addresses the management of an invasive plant species that threatens ecosystem health and economic activities on Lake Nokoué. They do this through a social entrepreneurship business model that aims to advance both economic empowerment and environmental health. In demonstrating a Space Enabled-GKA collaboration model that advances GKA's business aims, this study first considers several popular service and technology design methods and offer critiques of each method in terms of their ability to address inclusivity in complex systems. These critiques lead to the selection of the Systems Architecture Framework (SAF) as the technology design method for the case study. In the remainder of the paper, the SAF is applied to the case study to demonstrate how the framework coproduces knowledge that would inform a DSS with Earth Observation data. The paper offers several practical considerations and values related to epistemology, data collection, prioritization and methodology for performing inclusive design of climate data services.

AgroSupportAnalytics: A Cloud-based Complaints Management and Decision Support System for Sustainable Farming in Egypt

Sustainable Farming requires up-to-date advice on crop diseases, patterns, and adequate prevention actions to face developing circumstances. Currently, in developing countries like Egypt, farmers’ access to such information is extremely limited due to the agriculture support being either not available, inconsistent, or unreliable. The presented Cloud-based Complaints Management and Decision Support System for Sustainable Farming in Egypt, named as AgroSupportAnalytics, aims to resolve the problem of both the lack of support and advice for farmers, and the inconsistencies in doing so by current manual approach provided by agricultural experts. Key contribution is the development of an automated complaint management and decision support strategy, on the basis of extensive research on requirement analysis tailored for Egypt. The solution is grounded on the application of knowledge discovery and analysis on agricultural data and farmers’ complaints, deployed on a Cloud platform, to provide farming stakeholders in Egypt with timely and suitable support. This paper presents the overall system architectural framework along with the information and storage services, which have been based on the requirements specifications phases of the project along with the historical data sets of past 10 year of farmers complaints and enquiries in Egypt.

Trends in Architecture and Middleware of Radio Telescope Control System

The control system is the central control unit of the radio telescope. It is used to monitor, control, coordinate, and manage software and hardware systems so as to satisfy the requirements of high-precision control in astronomical observation of radio telescope. The control system architecture is the foundation for the implementation of the control system, which determines the stability, scalability, and maintainability of the control system. Furthermore, the architecture design of the control system is closely geared towards the technological development of radio telescope and computer software architecture. In this article, we analyze the characteristic of the control system of a radio telescope in various steps and discuss the development of their architecture and middleware framework. System architecture and middleware framework of control system also serve as a useful reference for the design of other radio telescope control systems.

RETRACTED: Auxiliary research on difficult aerobics exercise training based on fpga and movement recognition technology

Abstract Based on case instruction, traditional training systems are based on past rivalries’ case studies and preparing to give strength preparing to Aerobics, uncommon games. The preparation results can't be assessed shrewdly and precisely, and the exhibition of investigation and dynamic examination is poor. To take care of this issue, the reason for unique high impact exercise estimates dependent on human-made consciousness is to understand a preparation system that focuses on Aerobics, the strength and the quality of particular sports. It can be achieved by researching Convolutional Neural Networks (CNN) model systems, such as optimizations and other intelligent features such as an intelligent Convolutional Neural Network (CNN) decision making. The design system architecture framework includes sensors, receivers, databases, and analysis modules. The system's core chip is the human-computer interaction FPGA control module to provide comprehensive training in impressive aerobics intensity quality. The information-gathering module is used for gathering information about gauges, sports and strength training as a language. Trainee information management and training result statistics and query execution through the information management module. Some system software includes a software configuration diagram of the system and booting and landing the system. Through the work cycle of the investigation module, the power of specific vigorous exercise sports is dissected. Test results show that the planned framework can give ongoing, standard strength preparing for Aerobics, explicit activities and improve preparing productivity.

RETRACTED ARTICLE: Research on core strength training of aerobics based on artificial intelligence and sensor network

The traditional training system based on case teaching is according to the analysis of past competitions and training cases to carry out the strength training of aerobics special movements. The training results cannot be evaluated intelligently and accurately, and the performance of dynamic analysis is poor. To address this problem, the core training system of strength quality of aerobics special movements based on artificial intelligence is designed to realize the intelligent training of the strength quality of aerobics special movements. Through the study of fuzzy paradigm system, intelligent functions such as optimization and decision-making of intelligent fuzzy network are realized. The design system architecture framework includes the modules of sensor, receiver, database, and analysis decision. The core chip of the system is the main control module of Atmega1280 MCU for man-machine interaction, so as to realize the comprehensive training of the strength quality of aerobics special movements. Information collection module is used to collect information on strength training information such as instrument, movement, and language. The problem of phase distortion in signal transmission process is processed by FIR filter. Through information management module, trainee information management and training results statistics and queries are implemented. In the system software part, the system software structure diagram and system startup and landing procedure are given. By analyzing the working process of the module, the strength of aerobics special movements is analyzed. Experimental results show that the designed system can achieve real-time and stable strength training for aerobics special movements and improve training efficiency.

Customizing Academic Knowledge Management System Architecture Framework based on Enterprise Architecture

This work is centered on making sure that it fosters out the purpose of knowledge management systems framework for the sake of making use understand how best it is used in terms of capturing, utilizing and transferring knowledge from one entity to another. The literature review as well as the document analysis were both used in combination for the purpose of making sure that all the interviews that were used were better expounded in different platforms such as stakeholder, faculty and administrator. Further, the academic knowledge management system Architectural framework had to be utterly customized on matters to do with the intelligence concept, Debrose and the Enterprise Architecture. It is through the use of this framework that then functionality of the organization is categorized in to three namely; the people, technology as well as the application that is required for use. Further, it is true that there are some benefits that are linked with the use of this kind of framework that are easily accessed on the available knowledge and helps in decision making process for the entire administration and the knowledge repository creational matters.

Digital twin-based cyber physical production system architectural framework for personalized production

Personalized production is a manufacturing concept relevant to the fourth industrial revolution, which can satisfy various customer needs inexpensively. There are three main hurdles to the efficient implementation of this concept: access, cost, and performance. This paper proposes a digital twin-based cyber physical production system (CPPS) architectural framework that overcomes the performance hurdle. The proposed architectural framework comprises five services that are solutions to the performance hurdle of personalized production, and it operates on information based on the proposed product, process, plan, plant, and resource (P4R) information model. This information model is manufacturing abstraction for personalized production and is presented on a detailed level with object-orientation and the “type and instance” concept. Whereas previous studies on the digital twin concept considered one or several facilities and focused on the development of independent applications, this study focuses on the digital twin as a core technological element of the entire system and analyzes CPPS design and its operation from the system-of-systems perspective. The application of the digital twin-based CPPS to a micro smart factory (MSF) provides an advanced solution for the personalized production of various products. An average makespan improvement of ~ 26.87% was achieved using the implemented CPPS services in the MSF.

Development of Scalable On-Line Anomaly Detection System for Autonomous and Adaptive Manufacturing Processes

Factories of the future are foreseen to evolve into smart factories with autonomous and adaptive manufacturing processes. However, the increasing complexity of the network of manufacturing processes is expected to complicate the rapid detection of process anomalies in real time. This paper proposes an architecture framework and method for the implementation of the Scalable On-line Anomaly Detection System (SOADS), which can detect process anomalies via real-time processing and analyze large amounts of process execution data in the context of autonomous and adaptive manufacturing processes. The design of this system architecture framework entailed the derivation of standard subsequence patterns using the PrefixSpan algorithm, a sequential pattern algorithm. The anomalies of the real-time event streams and derived subsequence patterns were scored using the Smith-Waterman algorithm, a sequence alignment algorithm. The excellence of the proposed system was verified by measuring the time for deriving subsequence patterns and by obtaining the anomaly scoring time from large event logs. The proposed system succeeded in large-scale data processing and analysis, one of the requirements for a smart factory, by using Apache Spark streaming and Apache Hbase, and is expected to become the basis of anomaly detection systems of smart factories.

Enterprise architecture within railway systems engineering

Owing to the complexity of the railway system, introducing new technologies or upgrading existing systems requires a whole system approach, ensuring effective integration and alignment with business needs. Enterprise architecture has been used to address this complexity, but there are currently no standardised enterprise architecture frameworks for the railway. The authors define an approach for building a whole railway system architecture framework by identifying architectural needs, analysing existing frameworks, and developing a proposed solution. The case study to support this work, taken from the GB Digital Railway programme, is an application of the proposed solution for producing requirements.

Advanced Animal Track-&-Trace Supply-Chain Conceptual Framework: An Internet of Things Approach

Abstract Information and data transmission, as well as total visibility in terms of supply chain track-&-trace, is seriously lacking in current industrial logistics-&-supply-chain management activities. This paper proposes the application of RFIDs and the Internet of things’ enterprise systems architecture to, propose a low-cost feasible cloud solution. This solution would keep track of health history, birth records, ownership history and track location of each animal by using an electronic solar-powered tag instead of the current passive ear tags. This paper employs GPRS, GSM and the Google Earth function along with RFIDs to design an enhanced proposed enterprise system-architecture framework in the form of a business process model. Various areas such as e-health, e-commerce, and cloud-based manufacturing have been revolutionized by discoveries in digitized practices. Advancements in the Internet of things led to the advent of asset tracking systems, condition monitoring and various intelligent systems that exchange data over the cloud. Farmers are very vulnerable to stock theft, straying animals and high business risks due to poor animal health management practices. Existing tracking systems have high ownership costs since some of them rely on GSM network providers whose services are costly for farmers. Innovations in digitized systems continue to add more value to businesses and individuals across broad range sectors. Authors of this paper propose a low-cost intelligent animal tracking system for an African free-ranging environment. Economies worldwide are ever becoming digital and animal husbandry practices stand to benefit from this digitisation approach.