Architecture Of Management System Research Articles

Big Data and Machine Learning-Based Iot Models for Sustainable Energy Prediction

Integrating Big Data and Internet of Things (IoT) platforms is the focus of this research, which aims to improve energy management. The problem statement is centered on the potential for development through advanced technologies and the inefficiencies in traditional energy management methods. The objectives are to analyze energy consumption patterns, develop an innovative Home Energy Management System (HEMS) architecture, and offer energy-saving solutions. Synthetic energy consumption data is generated, normalized, and divided into training and testing sets from a methodological perspective. K-nearest neighbors, Decision Trees, Support Vector Regression, and Random Forest are the machine learning models trained and evaluated. The Random Forest model outperforms other models in terms of the accuracy of its predictions of energy consumption. The integration of renewable energy sources with cutting-edge technology to revolutionize energy management practices is the essence of novelty. In conclusion, this investigation underscores the importance of utilizing advanced technologies to promote sustainable energy management, providing practitioners and policymakers with practical insights.

Artificial intelligence and IoT driven system architecture for municipality waste management in smart cities: A review

Numerous devices, including sensors, RF ID, and other types of smart devices, have been developed as a result of the Artificial Intelligence (AI), and Internet of Things (IoT) revolution. Urban areas can become smart by monitoring and collecting data about their surroundings through the deployment of technologies with powerful computational capabilities and those that are converted into intelligent things. Waste management is among the most significant issues in smart cities, a rise in metropolitan regions and faster increases in population are the main reasons. When it comes to gathering data about waste management, intelligent services can serve as the front line. Waste management with IoT support is a common example of a service offered by smart cities. Various duties, like gathering, processing, and use of waste in appropriate facilities, are included in waste management. The present study proposed an updated waste management system architecture design after reviewing existing artificial intelligence and IoT-based waste management systems and automation in smart cities. The proposed system architecture deals with the automation of municipality trash in smarter urban areas, using IoT technology and sending notification messages based on sensor data relating to the dustbin state, such as full or empty. The notifications are sent simultaneously to the municipality office and the waste carrier vehicle driver, so that waste can be emptied on time. The proposed system architecture represents a scalable and adaptable model for municipalities that aim to transform their waste collection processes and play a key step in minimizing municipality waste in smart cities. By deploying this proposed system architecture with smart sensors and IoT devices, municipalities can monitor waste levels to ensure that bins are emptied when it is necessary. This reduces the frequency of waste collection, lowers fuel consumption, and minimizes operational costs. The Route optimization algorithms further enhance efficiency by determining the most efficient paths for waste collection trucks, so they can reduce travel time and fuel emissions.

Research and Design of Intelligent Parking Management System based on the YOLO Algorithm

Given the difficulty in managing parking lots and inefficient utilization of parking spaces at university and college campuses, this paper designs an intelligent campus parking management system with functions such as license plate recognition (LPR), online parking space reservations, campus parking navigation, and mobile app payment through the parking space detection technology based on You Only Look Once (YOLO) algorithm, Internet of Things (IoT) technology, and cloud platform technology. This system obtains information relating to license plates, parking spaces, etc. from sensing nodes with the IoT technology and transmits the collected information through NB-IoT technology to a cloud platform for storage and management to facilitate information exchange between hardware and software. This paper describes the architecture of the intelligent parking management system, parking space detection technology based on the YOLOv4 algorithm, and hardware and software design of the system. The paper proposes an effective solution to parking problems facing colleges and universities. The paper concludes that the intelligent campus parking management system based on the YOLOv4 algorithm can prevent the haphazard parking of vehicles and traffic congestion at open campuses and therefore has important value for application and popularization.

HabSim-HMS: A Systems Testbed to Investigate Situational Awareness for Extraterrestrial Habitation

Deep-space habitation presents unique challenges in operating complex control-based systems in distant and disruptive environments far from Earth. The capability to handle unforeseen situations and manage system health is crucial for resilient operations in deep space. Trusted health management strategies operational on the International Space Station (ISS) heavily rely on ground control support. These strategies must adapt to cope with sizable communication delays imposed by the infrastructure limitations or light distances that separate ground control and the habitat. Alternative approaches that increase reliance on artificial intelligence-based onboard autonomy are constrained by limited onboard computing and risks associated with human-system integration. There is an increased need to study the design and architecture of the health management system (HMS) of such complex systems to address autonomy challenges for deep-space habitation. This paper presents a model-based HMS testbed developed at the Resilient Extra-Terrestrial Habitats Institute that is part of the HabSim, a system-of-systems simulator of a smart habitat. The testbed is designed to facilitate performance assessment of critical health management functions essential to enable fault-management autonomy, situational awareness, and interface-based mission control. In this paper, the HMS testbed is motivated and placed within HabSim, the architecture of its components is detailed, and the testbed features are demonstrated through an illustrative example. The lessons learned in developing this testbed that may be useful for others seeking to solve similar problems are also discussed.

Software architecture for implementing a personal budget manager with artificial intelligence

The rapid adoption of artificial intelligence (AI) in consumer markets has led to the need for extensive research into its impact on consumer behavior, particularly in how it exploits cognitive biases. This raises ethical questions and highlights the need to balance such influences with AI systems that help consumers, not manipulate them. The development and implementation of an intelligent financial management system, which not only automates financial processes, but also integrates AI-driven tools to support better financial decision-making, contributes to solving these problems. The work contains a description of the development of the architecture of an intelligent financial management system, which is aimed at solving problems related to financial illiteracy, the complexity of financial markets, and the impact of cognitive biases on financial decision-making. The system provides for the use of modern technologies of artificial intelligence and machine learning to automate financial processes and provide users with personalized recommendations. The development of a system based on microservice architecture requires the use of certain architectural patterns that ensure efficiency, reliability and scalability, so the work was based on a critical analysis of modern architectural paradigms, such as microservice architecture and Event-Driven Architecture. The implementation of AI supports ethical guidelines that prioritize transparency, fairness and consumer autonomy, i.e. ensure that the AI ​​system is free from bias, actions can be easily interpreted by users. The methodology of this research is based on an interdisciplinary approach that combines the analysis of architectural solutions, methods of machine learning and artificial intelligence with methods of ensuring the reliability and scalability of software systems.

Adaptive and Scalable Database Management with Machine Learning Integration: A PostgreSQL Case Study

The increasing complexity of managing modern database systems, particularly in terms of optimizing query performance for large datasets, presents significant challenges that traditional methods often fail to address. This paper proposes a comprehensive framework for integrating advanced machine learning (ML) models within the architecture of a database management system (DBMS), with a specific focus on PostgreSQL. Our approach leverages a combination of supervised and unsupervised learning techniques to predict query execution times, optimize performance, and dynamically manage workloads. Unlike existing solutions that address specific optimization tasks in isolation, our framework provides a unified platform that supports real-time model inference and automatic database configuration adjustments based on workload patterns. A key contribution of our work is the integration of ML capabilities directly into the DBMS engine, enabling seamless interaction between the ML models and the query optimization process. This integration allows for the automatic retraining of models and dynamic workload management, resulting in substantial improvements in both query response times and overall system throughput. Our evaluations using the Transaction Processing Performance Council Decision Support (TPC-DS) benchmark dataset at scale factors of 100 GB, 1 TB, and 10 TB demonstrate a reduction of up to 42% in query execution times and a 74% improvement in throughput compared with traditional approaches. Additionally, we address challenges such as potential conflicts in tuning recommendations and the performance overhead associated with ML integration, providing insights for future research directions. This study is motivated by the need for autonomous tuning mechanisms to manage large-scale, heterogeneous workloads while answering key research questions, such as the following: (1) How can machine learning models be integrated into a DBMS to improve query optimization and workload management? (2) What performance improvements can be achieved through dynamic configuration tuning based on real-time workload patterns? Our results suggest that the proposed framework significantly reduces the need for manual database administration while effectively adapting to evolving workloads, offering a robust solution for modern large-scale data environments.

Optimization Analysis of Comprehensive Budget Management from the Perspective of Digital Restructuring: Taking a Water Company as an Example

This article takes water enterprises as the research object, exploring the organic combination of comprehensive budget management under the trend of digitalization and digital technology in the new era, as well as the process of management system reform. Introduce the current status of its budget management work, comprehensively analyze the architecture and operation mode of the current comprehensive budget management system of water enterprises, and evaluate and analyze the overall situation and shortcomings of its budget management system based on financial data such as budget formulation and execution. From the perspective of digital restructuring and upgrading of comprehensive budget management work, corresponding countermeasures are proposed to make up for the shortcomings and suggestions for the future development of the enterprise’s comprehensive budget.

Experimental study and performance evaluation of R744 thermal management systems with enhanced parallel cooling for electric vehicles

As worldwide environmental concerns grow, R744 (CO2) has become one of the preferred refrigerants in electric vehicle (EV) thermal management system due to its environmentally friendly characteristics. This study presents the development and evaluation of an Enhanced Parallel Cooling (EPC) system, a novel advancement designed to optimize the performance of R744 thermal management systems in EVs. A series of experiments are carried out to investigate the effects of key operating variables such as the coolant flow rate, and air velocity on the system efficiency. Meanwhile, Response Surface Methodology (RSM) is employed to determine the optimal control contour map under various environmental conditions. The maximum cooling capacity of the EPC system in supercharging mode is experimentally evaluated. Furthermore, a comparative analysis of driving range improvement under the Urban Dynamometer Driving Schedule (UDDS) and the Highway Fuel Economy Test (HWFET) cycles is carried out. With the help of the Series Cooling Unit (SCU) in the EPC system, the maximum cooling capacity increases by 27.8 % to 9.89 kW, and the driving range increases by up to 14.6 km. The findings underscore the potential of the innovative R744 thermal management system architecture to improve operational efficiency and extend the driving range of EVs.

Numerical study on heat dissipation of double layer enhanced liquid cooling plate for lithium battery module

The thermal management system's architecture is crucial for lithium batteries' efficiency and financial viability, predominantly influencing their security and longevity. We conceptualized a double-layer enhanced LCP, meticulously crafted to augment the heat dissipation capabilities of the battery assembly. This novel design targets the reduction of peak temperatures and pressure drops, fostering an even internal temperature profile. Comprehensively evaluate the performance of different settings of the coolant cooling system, and the monitoring points are strategically set in horizontal and vertical directions. Analyzing the data, it becomes evident that the system's vertical orientation significantly affects the battery module's peak temperature and temperature variation. Furthermore, extensive investigations were conducted on the impact of layout, coolant flow rate, and inlet temperature on thermal dissipation. Our findings indicate that the double-layer enhanced liquid cooling plates outperform conventional designs. There is a substantial 2.41 % decrease in the peak battery module temperature and an 80.6 % reduction in voltage decline. Operating at a flow rate of 10 g per second and an entry temperature of 25 °C, our liquid cooling system demonstrates superior cooling efficiency with minimal power consumption. It proficiently restrains the maximum battery module temperature below 30.43 °C and restricts temperature variance to merely 3.48 °C. The suggested approach in this research contributes to improved thermal equilibrium, diminishes temperature disparities and pressure concerns, thereby fostering the technological advancement of electric vehicle's liquid cooling systems.

A Model-Based Strategy for Active Balancing and SoC and SoH Estimations of an Automotive Battery Management System

This paper presents a novel integrated control architecture for automotive battery management systems (BMSs). The primary focus is on estimating the state of charge (SoC) and the state of health (SoH) of a battery pack made of sixteen parallel-connected modules (PCMs), while actively balancing the system. A key challenge in this architecture lies in the interdependence of the three algorithms, where the output of one influences the others. To address this control problem and obtain a solution suitable for embedded applications, the proposed algorithms rely on an equivalent circuit model. Specifically, the SoCs of each module are computed by a bank of extended Kalman filters (EKFs); with respect to the SoH functionality, the internal resistances of the modules are estimated via a linear filtering approach, while the capacities are computed through a total least squares algorithm. Finally, a model predictive control (MPC) was employed for the active balancing. The proposed controller was calibrated with Samsung INR18650-20R lithium-ion cells data. The control system was validated in a simulation environment through typical automotive dynamic scenarios, in the presence of measurement noise, modeling uncertainties, and battery degradation.

Architecture of the Information Security Incident Management System at Oil and Gas Enterprises

Architecture of the Information Security Incident Management System at Oil and Gas Enterprises

Analysis and Design of Integration Model for API Management and CI/CD at Directorate General of Taxation

The Directorate General of Taxes (DGT) currently utilizes Application Programming Interface (API) to enhance efficiency in tax data exchange with external parties. DGT is facing challenges due to the rising number of published APIs and the increasing connections from external parties to the DGT system, which necessitates a speedy API issuance process. The objective of this research is to assist the Directorate General of Taxation (DGT) in developing an integrated API management system with Continuous Integration/Continuous Deployment (CI/CD). The system design process is conducted using the Standards and Architectures for E-Government Application (SAGA) framework, encompassing Enterprise Viewpoint, Technology Viewpoint, Computational Viewpoint, Information Viewpoint, and Engineering Viewpoint. A qualitative method is employed, including interviews to gain insights into the existing issues. Additionally, information regarding systems and technologies is documented for gap analysis. The results of this analysis are then utilized to design the architecture of the API management system, applications, and technologies. This research yields a model of the API management system integrated with CI/CD at DGT. The model is developed using 3Scale and Jenkins software. Following validation, the API management system at DGT operates effectively with three DGT API systems and three API users.

Advancing geological modelling and geodata management: a web-based system with AI assessment in Singapore

ABSTRACT This paper outlines the development and deployment of the Geodata Modelling and Management System (GEM2S) in Singapore. GEM2S offers a sophisticated web-based platform for visualising and interacting with 3D geological models of Singapore. Beyond basic visualisation and interaction, GEM2S provides advanced features for geodata management, including the functionality to upload new borehole data with AI assessment and to integrate geophysical results. The paper begins by elucidating the data processing procedures, which involve extracting data from electronic site investigation records. Additionally, common data errors for cleaning and validation purposes are summarised. Subsequently, the paper presents the 3D geological modelling process and results. The architecture of the web-GIS geodata management system is detailed, outlining the data flow and functions for interactive queries. Furthermore, the paper investigates two AI-based prototypes for quality assessment of newly acquired borehole data. Incorporating virtual boreholes generated from the geophysics effectively enhances the quality of the resulting geological model. GEM2S can serve as a comprehensive and user-friendly platform, making it a valuable system for underground project design and a foundational database for future subsurface planning in Singapore.

Solar–Hydrogen Storage System: Architecture and Integration Design of University Energy Management Systems

As a case study on sustainable energy use in educational institutions, this study examines the design and integration of a solar–hydrogen storage system within the energy management framework of Kangwon National University’s Samcheok Campus. This paper provides an extensive analysis of the architecture and integrated design of such a system, which is necessary given the increasing focus on renewable energy sources and the requirement for effective energy management. This study starts with a survey of the literature on hydrogen storage techniques, solar energy storage technologies, and current university energy management systems. In order to pinpoint areas in need of improvement and chances for progress, it also looks at earlier research on solar–hydrogen storage systems. This study’s methodology describes the system architecture, which includes fuel cell integration, electrolysis for hydrogen production, solar energy harvesting, hydrogen storage, and an energy management system customized for the needs of the university. This research explores the energy consumption characteristics of the Samcheok Campus of Kangwon National University and provides recommendations for the scalability and scale of the suggested system by designing three architecture systems of microgrids with EMS Optimization for solar–hydrogen, hybrid solar–hydrogen, and energy storage. To guarantee effective and safe functioning, control strategies and safety considerations are also covered. Prototype creation, testing, and validation are all part of the implementation process, which ends with a thorough case study of the solar–hydrogen storage system’s integration into the university’s energy grid. The effectiveness of the system, its effect on campus energy consumption patterns, its financial sustainability, and comparisons with conventional energy management systems are all assessed in the findings and discussion section. Problems that arise during implementation are addressed along with suggested fixes, and directions for further research—such as scalability issues and technology developments—are indicated. This study sheds important light on the viability and efficiency of solar–hydrogen storage systems in academic environments, particularly with regard to accomplishing sustainable energy objectives.

Optimization of thermal management system architecture in hydrogen engine employing improved genetic algorithm

Decarbonization in the aeronautical industry is a daunting challenge due to the lack of sustainable aviation fuel. Hydrogen offers a potential option for this problem, given that it is an energy-dense fuel capable of reducing climate impact. Along with it, the liquid hydrogen preheating and thermal protection bring inevitable safety issues. Currently, how to construct the optimal thermal management system (TMS) architecture and further clarify its principles is still unclear. Thus, this study compares different TMS architectures through optimized genetic algorithms (GA), and firstly points out the excellent architecture as well as its operating parameters. Strikingly, our results reveal the optimized algorithm can significantly improve efficiency and precision of the optimizing processes. Benefiting from adaptive population strategy and elite preservation strategy, optimized GA saves half the time compared to conventional GA, and the variance is 60 % smaller than that of conventional GA. Using optimized GA, the series-parallel scheme is determined as the best solution and it exhibits better advantages on lightweight. In addition, results also show the leading factor is air/helium heat exchanger area. In conclusion, we hope this work can provide guidance for the optimal GA, contribute to decarbonization, and provide more ideas for the optimization of hydrogen TMS in the future.

ADVANCED METHODS FOR MAINTAINING AND MANAGING THE LIFE CYCLE OF CLOUD ENVIRONMENTS: SURVEY

Resource management is a fundamental concept in cloud computing and virtualization, encompassing the allocation, release, coordination, and monitoring of cloud resources to optimize efficiency. The complexity arises from the virtualized, heterogeneous, and multi-user nature of these resources. Effective governance is challenging due to uncertainty, large-scale infrastructures, and unpredictable user states. This paper presents a comprehensive taxonomy of resource management technologies, offering a detailed analysis of design architecture, virtualization, and cloud deployment models, along with capabilities, objectives, methods, and mechanisms. In a cloud computing environment, deploying application-based resource management techniques necessitates understanding the system architecture and deployment model. This paper explores centralized and distributed resource management system architectures, providing a review of effective resource management techniques for both, accompanied by a comparative analysis. The evolution of cloud computing from a centralized to a distributed paradigm is examined, emphasizing the shift towards distributed cloud architectures to harness the computing power of smart connected devices at the network edge. These architectures address challenges like latency, energy consumption, and security, crucial for IoT-based applications. The literature proposes various methods for distributed resource management, aligning with the distributed nature of these architectures. Resource management in cloud computing involves discovery, provisioning, allocation, and monitoring functions, with sub-functions like mapping and scheduling. Integrated approaches to consolidation and resource management have been explored in numerous studies. This paper summarizes and analyzes existing research on resource management functions, focusing on identification, provisioning, allocation planning, and monitoring, based on their objectives and methods.

Research and evaluation of the arrangement of heat exchangers in electric vehicle thermal management systems for CO2 refrigerant

The application of transcritical CO2 cycle in the thermal management system of electric vehicles has become increasingly prevalent, but the existing thermal management system architecture has not been optimized to specifically enhance the performance of CO2 refrigerant. This paper analyzes the impact of various heat exchanger arrangements on CO2 thermal management system and evaluates system configurations based on a new performance index, cycle coefficient of performance. The effects of air temperature, ambient temperature, and heat load on system performance are conducted with a high precision model. The chiller is connected in parallel-series with indoor heat exchangers, resulting in a maximum coefficient of performance improvement of 27.89% at an indoor air inlet temperature of 10 °C, and a maximum coefficient of performance improvement of 21.69% at an ambient temperature of 5 °C, under simultaneous heating conditions. The radiator is placed on the windward of gas cooler, leading to a maximum coefficient of performance increase of 4.85% in heating mode, but a maximum coefficient of performance decrease of 9.84% in cooling mode. The evaluation of these configurations across different operating cycles reveals that the parallel-series connection is well-suited for the new European driving cycle, showing an 18.4% improvement in cycle coefficient of performance compared to the series-parallel connection. The windward arrangement of radiator proves effective for highway fuel economy test cycle in winter and the new European driving cycle in summer. This study provides effective instruction for configuration design and evaluation of transcritical CO2 vehicle thermal management systems.

Novel Architecture of Energy Management Systems Based on Deep Reinforcement Learning in Microgrid

Novel Architecture of Energy Management Systems Based on Deep Reinforcement Learning in Microgrid

Leveraging Deep Q-Learning to maximize consumer quality of experience in smart grid

The smart grid system has addressed the problems of traditional power grid by not only meeting energy demand in real-time but also limiting its wastage. The two key objectives of a smart grid system are to ensure a higher Quality of Experience (QoE) for consumers and to reduce consumer costs using dynamically varying pricing concepts. However, these two objective parameters oppose each other as maximizing the consumer QoE requires the availability of sufficient electric power at any given time, which in turn increases power purchase cost. In this paper, we introduce an efficient power management system architecture of a smart grid and develop an Optimal Energy Allocation and Prediction system based on Deep Q-Leaning, namely OEAP-DQL, that brings a trade-off between the two. The developed OEAP-DQL system is a multi-objective linear programming (MOLP) problem that predicts consumer electricity demand by exploiting four different weighted and regressive moving average forecasting methods in the action space to accurately capture dynamically varying customer demand behaviors. Furthermore, iterative exploitation of multiple learning methods decreases forecasting error and intelligent stored power management helps the OEAP-DQL smart grid operator (SGO) to enhance its profit. The results of our simulation experiments show that the OEAP-DQL system outperforms the state-of-the-art works in terms of QoE and cost.

Universal Data Specification and Real-time Data Streaming Architecture for Cloud-based Battery Management Systems

Universal Data Specification and Real-time Data Streaming Architecture for Cloud-based Battery Management Systems