Intelligent System Architecture Research Articles

Design of an intelligent system for enhancing urban social resilience

This paper considers a comprehensive approach to the use of intelligent systems in the context of smart cities, which is aimed at increasing their social sustainability under the conditions of growing urbanization and globalization. Cities face challenges related to the need to optimize the management of urban resources and improve the quality of life of residents, which requires innovative approaches to planning and the use of advanced technologies. The proposed intelligent system architecture, integrating six modules such as quality of life modeling, socio-economic analysis, intelligent tourism, environmental monitoring, legal interpretation, and misinformation detection, has demonstrated a 25–40 % performance improvement depending on the module. The effectiveness of the proposed system is explained by the use of advanced algorithms of machine learning and data analysis, which makes it possible to reduce the time of solving critical tasks and increase the adaptability of the city infrastructure to future challenges. Owing to the integration of intelligent systems into city management, cities gain the ability to respond more effectively to current and projected social and environmental challenges, significantly improving the quality of life and environmental sustainability. The proposed system could be implemented in cities of different sizes and configurations, contributing to long-term socio-economic development and environmental sustainability. Effective implementation of the system reduces city management costs by up to 30 %, while reducing CO2 emissions by 10–15 %, which is important in the context of combating climate change

Intelligent Data Governance Frameworks : A Technical Overview

This comprehensive article explores the transformative potential of AI-driven data governance frameworks in addressing modern data management challenges. The article examines the limitations of traditional governance methods in the face of exponential data growth, regulatory complexities, and diverse data sources. It delves into the architecture, core components, and benefits of intelligent governance systems that leverage advanced AI technologies such as machine learning, natural language processing, and reinforcement learning. The article highlights significant improvements in operational efficiency, compliance accuracy, and data quality achieved through AI-driven solutions. Furthermore, it investigates future directions, including the integration of explainable AI, federated learning, and blockchain technology, while acknowledging the challenges that need to be addressed for widespread adoption.

Comprehensive Task Optimization Architecture for Urban UAV-Based Intelligent Transportation System

This paper tackles the problem of resource sharing and dynamic task assignment in a task scheduling architecture designed to enable a persistent, safe, and energy-efficient Intelligent Transportation System (ITS) based on multi-rotor Unmanned Aerial Vehicles (UAVs). The addressed task allocation problem consists of heterogenous pick-up and delivery tasks with time deadline constraints to be allocated to a heterogenous fleet of UAVs in an urban operational area. The proposed architecture is distributed among the UAVs and inspired by market-based allocation algorithms. By exploiting a multi-auctioneer behavior for allocating both delivery tasks and re-charge tasks, the fleet of UAVs is able to (i) self-balance the utilization of each drone, (ii) assign dynamic tasks with high priority within each round of the allocation process, (iii) minimize the estimated energy consumption related to the completion of the task set, and (iv) minimize the impact of re-charge tasks on the delivery process. A risk-aware path planner sampling a 2D risk map of the operational area is included in the allocation architecture to demonstrate the feasibility of deployment in urban environments. Thanks to the message exchange redundancy, the proposed multi-auctioneer architecture features improved robustness with respect to lossy communication scenarios. Simulation results based on Monte Carlo campaigns corroborate the validity of the approach.

Formalization of Elements and Control Schemes in Intelligent System Models

The purpose of the work is to formalize the concepts and principles that form the basis of the mathematical model for controlling the life cycles of intelligent systems. The controlling elements formal description is part of a universal model of such systems. This model part is important for implementing complex intelligent systems with a large amount and diversity of knowledge flows and knowledge processing schemes performed simultaneously. The other two areas of this model are associated with a unified logical-algebraic description of the concept of knowledge representation formalism, as well as concepts related to the architecture of intelligent systems. Formalization of ideas about control in intelligent systems is carried out according to the principle of a hierarchy of controlling elements and processes. The elements of the mathematical model that implements this principle correspond to event-controlled automata with infinite memory and associated algorithms for the automata simulation in specific systems. The objectives of the research include the development of formats for describing the hierarchy of diagrams and assemblies of control elements in a universal model of an intelligent system, and diagrams of algorithms for the functioning of intelligent systems based on the processing of model descriptions. Models of intelligent system control elements hierarchy and execution algorithms for the sets of activated control elements for the implementation time of life cycles of intelligent systems are described by extending the special ontological structures. These structures implement customizations of universal control model

A System Architecture for Early Wilt Detection in Hydroponic Crops: An Implementation and Assessment

Abstract Hydroponic farming systems have a high risk of failure if the disaster is not mitigated and responded to quickly. Hydroponic plants only rely on mineral water that flows through their roots as a source of life; thus, any malfunction will have an immediate impact on the plant. If plant wilting is not recognized and not treated quickly, the plant will experience stress and have the potential to fail. To ensure that major disasters do not occur, an early warning mechanism is needed that can provide disaster information to farmers. In this study, an intelligent system architecture was built to detect premature wilt in hydroponic vegetable plants. It combines the capabilities offered by the OV2640 image sensor and ESP32-S system-on-chip, the Internet of Things, deep learning based on convolutional neural networks, and cloud computing to create a robust and low-cost remote plant condition monitoring solution. As a proof-of-concept, a prototype system is built, and performance tests are carried out to find out how robust and effective the proposed design is. Based on the results of the prototype test, it can be seen that the system built can run properly with an accuracy of wilting recognition of up to 90.90%, which was tested on samples of vertically grown mustard plants in a hydroponic greenhouse.

Optimized Feature Selection for DDoS Attack Recognition and Mitigation in SD-VANETs

Vehicular Ad-Hoc Networks (VANETs) are pivotal to the advancement of intelligent transportation systems (ITS), enhancing safety and efficiency on the road through secure communication networks. However, the integrity of these systems is severely threatened by Distributed Denial-of-Service (DDoS) attacks, which can disrupt the transmission of safety-critical messages and put lives at risk. This research paper focuses on developing robust detection methods and countermeasures to mitigate the impact of DDoS attacks in VANETs. Utilizing a combination of statistical analysis and machine learning techniques (i.e., Autoencoder with Long Short-Term Memory (LSTM), and Clustering with Classification), the study introduces innovative approaches for real-time anomaly detection and system resilience enhancement. Emulation results confirm the effectiveness of the proposed methods in identifying and countering DDoS threats, significantly improving (i.e., 94 percent anomaly detection rate) the security posture of a high mobility-aware ad hoc network. This research not only contributes to the ongoing efforts to secure VANETs against DDoS attacks but also lays the groundwork for more resilient intelligent transportation systems architectures.

A universal knowledge model and cognitive architectures for prototyping AGI

A universal knowledge model and cognitive architectures for prototyping AGI

Integrating FHIR and UMLS in an Intelligent Tutoring System.

Our novel Intelligent Tutoring System (ITS) architecture integrates HL7 Fast Healthcare Interoperability Resources (FHIR) for data exchange and Unified Medical Language System (UMLS) codes for content mapping.

The Evolving Technological Framework and Emerging Trends in Electrical Intelligence within Nuclear Power Facilities

This paper thoroughly explores the feasibility of integrating a variety of intelligent electrical equipment and smart maintenance technologies within nuclear power plants to enhance the currently limited level of intelligence of these systems and better support operational and maintenance tasks. Initially, this paper outlines the demands and challenges of intelligent electrical systems in nuclear power plants, highlighting the current state of development of intelligent electrical systems, including new applications of artificial intelligence and big data technologies in power grid companies, such as intelligent defect recognition through image recognition, intelligence-assisted inspections, and intelligent production commands. This paper then provides a detailed introduction to the architecture of intelligent electrical equipment, encompassing the smart electrical equipment layer, the smart control system layer, and the cloud platform layer. It discusses the intelligentization of medium- and low-voltage electrical equipment, such as smart circuit breakers, smart switchgear, and low-voltage distribution systems, emphasizing the importance of intelligentization in improving the safety, reliability, and maintenance efficiency of medium- and low-voltage distribution equipment in nuclear power plants. Furthermore, this paper addresses issues in the intelligentization of nuclear power plant electrical systems, such as information silos, the inefficiency of traditional manual inspection processes, and the lack of comprehensive intelligent design and evaluation standards, proposing corresponding solutions. Additionally, this paper presents the trends in intelligent operation and maintenance technology and applications, including primary and secondary fusion technology, intelligent patrol system architecture, intelligent inspection based on non-destructive testing, and a comprehensive solution based on inspection robots. The application of these technologies aids in achieving automated inspection, real-time monitoring, and the intelligent diagnosis of electrical equipment in nuclear power plants. Finally, this paper proposes basic principles for the development of intelligent electrical systems in nuclear power plants, including intelligent architecture, the evolutionary path, and phased goals and key technologies. It emphasizes the gradual transition from automation to digitization and then to intelligentization and presents a specific implementation plan for the intelligentization of the electrical systems in nuclear power plants. This paper concludes with a summary of short-term and long-term goals for improving the performance of nuclear power plant electrical systems through intelligent technologies and prospects for the application of intelligent technologies in the operation and maintenance of nuclear power plants in the future.

Research on the Intelligent System Architecture and Control Strategy of Mining Robot Crowds

Despite the pressure of carbon emissions and clean energy, coal remains the economic backbone of many developing countries due to its abundant resources and widespread distribution. The stable supply of coal is also vital for the global economy and remains irreplaceable in the future global energy structure. China has been a major contributor to annual coal output, accounting for nearly 50% worldwide since 2014. However, despite implementing intelligent coal mining technology, China’s coal mining industry still employs over 1.5 million underground miners, posing significant safety risks associated with underground mining operations. Therefore, the introduction of coal mining robots in underground mines is an urgently needed scientific and technological solution for upgrading China’s and even the world’s coal energy industry. The working face needs a shearer, hydraulic support, a scraper conveyor, and other equipment for coordination. The deep integration of intelligent technology with factors such as “humans, machines, the environment, and management” in the workplace is the core content of intelligent coal mines. This paper puts forward an advanced framework for robot technology systems in coal mining, including single robots, robotized equipment, robot crowds, and unmanned systems. The framework clarifies the common key technologies of coal mining robot research and development and the cross-integration with new technologies such as 5G, the industrial internet, big data, artificial intelligence, and digital twins to improve the autonomous and intelligent application of coal mining robots. By establishing a scientific and complete standard system for coal mining robots, we aim to achieve the customized research and development and standardized production of various types of robot. A specific analysis is conducted on the research progress of common key technologies such as the explosion-proof design, mechanical system innovation, power drive, intelligent sensing, positioning and navigation, and underground communication of coal mining robots. The current research and application status of various types of coal mining robots in China are summarized. A new direction for future coal mining robot research and development is proposed. Robotic mining systems should be promoted to enhance the overall intelligence level and efficiency of mining equipment. To develop human–machine environment-integrated robots to improve the autonomy and collaboration level of coal mining robots, the digital twinning of the entire mine robot system should be accelerated; the normalized operation level of coal mine robots should be improved; research on coal mining robots, shield support robots, and transportation robots should be performed; intelligence should be achieved in fully mechanized mining faces; and equipment shield support for fully mechanized mining faces should be provided. The practical process of implementing coal mining robotization is summarized in this paper, and the technical and engineering feasibility of the coal mining machine population is verified.

Architectures of modern intelligent systems: synergy of cybernetics and symbolic artificial intelligence, tools and technologies for the developmentof intelligent systems

Architectures of modern intelligent systems: synergy of cybernetics and symbolic artificial intelligence, tools and technologies for the developmentof intelligent systems

Fundamentals for formalizing the problem of information analysis architectures of intelligent training systems

Formulation of the problem. An analysis of the construction of information systems was carried out and the requirements for the appearance of intelligent training systems were determined. Model support for the analysis of an intelligent training system (ITS) is based on the use of modeling methods, their integration with existing methods and tools, providing dynamic modeling of the functional and structural properties of its information architecture. Target. To propose a new approach to formalizing the process of developing an ITS information architecture. Results. From the analysis of the content of educational and training tasks, general patterns have been identified that make it possible to apply system-forming principles to represent ITS, which comprehensively characterize the information process of modeling its information architecture based on the functional (F), structural (S) and information (I) aspects . The principles represent hierarchical categories, each of which reflects one of the levels of information interaction of models that make up the functional, structural and information levels of the interprogram interface, taking into account their level-by-level interaction. Each aspect of the consideration of models is characterized by content that generally reflects one of the aspects of the modeling object. In general, the principles reflect the paradigm of three-level information interaction of information objects, which uses for analysis existing approaches to modeling information architecture in the interests of implementing the invariant process of ITS functioning in relation to its pragmatic field. Information interaction between information objects represents the implementation of functional, structural and information relationships between objects and in their structure. Information relations in an ITS are determined by the presence of open ISF interfaces among information objects that form its information structure. The integrated model of these structures is a dynamic model of ITS, since informational and structural properties determine the conditions, and functional properties determine the nature of possible actions. Since the interaction levels are hierarchically coupled, the dynamic ITS model has a hierarchical structure. The implementation of interaction of information objects through ISF interfaces determines the transition of ITS to simulated states. The ITS information architecture model is presented in the form of a modular-cluster network structure. It is based on the principle of stratification of ITS into functional information models that implement the introduced aspects of the analysis of information interaction with grouping into clusters, reflecting the functional and structural properties of its information architecture. Practical significance. The functional and structural properties of the ITS information architecture were formalized on a variety of models (modules), their interfaces and information relationships, ensuring the development of a formal formulation of the problem of analyzing the ITS information architecture.

РАЗВИТИЕ АРХИТЕКТУРЫ ИНТЕЛЛЕКТУАЛЬНЫХ ТРАНСПОРТНЫХ СИСТЕМ

The paper analyzes the role of a complete architecture of intelligent transport systems (ITS) in ensuring their effectiveness. Many countries or groups of countries have ITS architecture with successful results of its application in practice. It is shown that the ITS architecture is constantly being updated and this is a characteristic trend. The paper proposes new approaches to the formation of the architecture of the integration platform within ITS. The scheme of organisation of data flows in different loops of software tools of the integration platform is developed. It is proposed to separate the control loops proper transport system data and identification data, which describe a partisipant of the ITS (digital object) with the expansion of capabilities in a cooperative ITS. It is possible to organise linked chains of identification of ITS digital objects, to participate in the formation of network structures for ITS work processes, describing the behaviour of digital objects during ITS processes and the behaviour of end users in accordance with the properties of network structures.

Processing method for multi-source data fabric system based on intelligent system architecture

Currently, the data management of power enterprises faces the need to analyze data sources from multiple places. However, traditional multi-source data fabric systems have problems such as low analysis efficiency and high error rates, which brings great inconvenience to the data analysis of power enterprises. In order to improve the accuracy and efficiency of data analysis in data structure systems, the intelligent system architecture is applied to the construction of source data structure systems. The main modules are data collection, data matching, data integration, and data analysis. This article uses simulated annealing genetic algorithm to perform high-performance calculations on system timing data, thus achieving data matching. This article conducted data level data integration, feature level data integration, and decision level data integration. The access survey method was used to analyze the current data management problems faced by power companies. The evaluation and analysis of general multi-source data fabric systems and multi-source data fabric systems based on intelligent system architecture were conducted using the evaluation panel evaluation method. The analysis results showed that the operational convenience of the multi-source data fabric system based on intelligent system architecture could reach 60%–80%, which greatly improved compared to general multi-source data fabric systems; the information sharing of multi-source data fabric systems based on intelligent system architecture was greatly improved; the data processing efficiency of general multi-source data fabric systems was much lower than that of multi-source data fabric systems based on intelligent system architecture; however, the symmetry of data collection and matching in the multi-source data fabric system based on intelligent system architecture was slightly insufficient, and further improvement was still needed. In order to benefit more power companies through the intelligent system architecture based multi-source data fabric system, it was necessary to strengthen the management of data collection and matching symmetry.

АРХИТЕКТУРА КООПЕРАТИВНЫХ ИНТЕЛЛЕКТУАЛЬНЫХ ТРАНСПОРТНЫХ СИСТЕМ

This paper presents methods for developing the logical architecture of cooperative intelligent transportation systems (C-ITS) within the context of the Chinese national ITS architecture. A process-oriented approach has been applied, allowing the identification and optimization of interactions between various functional domains of C-ITS, which is critically important for achieving the goals of increasing safety, controllability, and efficiency in modern transportation systems. The structural levels of the logical architecture are analyzed, including various functional domains, ensuring full integration and cooperation among the participants of the transportation process. The main focus is on defining and relating the functions, data flows, and interactions of subsystems within C-ITS, which improves the management, safety, and efficiency of intelligent transportation systems.

АНАЛИЗ МЕТОДОВ РАЗРАБОТКИ АРХИТЕКТУРЫ ИНТЕЛЛЕКТУАЛЬНЫХ ТРАНСПОРТНЫХ СИСТЕМ

This paper analyses various methods of developing the architecture of intelligent transportion systems (ITS). It is shown that ITS architecture is one of the main conditions determining the success of ITS design and creation, because ITS architecture describes the needs of ITS par-ticipants, functions, data flows and subsystems interaction. The main directions of ITS architecture development are given in the article. Two main approaches to ITS architecture development are analysed: process-oriented and object-oriented. The results of comparison of these methods by such parameters as method of analysis, updating and maintenance, complexity of logical architecture, complexity of physical architecture are given.

ОСОБЕННОСТИ МЕТОДОВ РАЗРАБОТКИ АРХИТЕКТУРЫ ИНТЕЛЛЕКТУАЛЬНЫХ ТРАНСПОРТНЫХ СИСТЕМ В КИТАЙСКОЙ НАРОДНОЙ РЕСПУБЛИКЕ

This paper considers the development steps and key points of China's intelligent transportation system architecture, and, at the same time, explains the transformation between the stages and the integration of the logical architecture, thus forming a data flow diagram of the logical architecture. The physical architecture is formed on the basis of the logical architecture, and provides the basis for the proposed application system with Chinese characteristics.

Data-Driven Approach to Curriculum Analysis

The choice of an educational program is momentous in young people's lives. Given the shortage of time after exams, applicants usually do not have time to analyze possible educational tracks. Furthermore, it requires a thorough study of learning plans. This research addresses the problem proposing the algorithm to data-driven curriculum analysis based on natural language processing of course names or competences listed in learning plans. Moreover, the intelligent software system architecture is described. The method is tested on the curricula scraped from university websites. In order to store the content a data warehouse has been developed. At this time, there are few studies on this topic. The existing ones are either on the early stages of development or scarce on implementation details. They are briefly discussed in this paper.

Medical decision support in the light of interactive granular computing: Lessons from the Ovufriend project

Medical decision support in the light of interactive granular computing: Lessons from the Ovufriend project

An architecture for the dynamic adaptation of an intelligent multi-tutoring system

The articulation between machine tutoring and human tutoring remains a productive research within in the context of Intelligent Tutoring Systems (ITS), particularly in the context of e-learning where the dropout rate is high. We explore an innovative approach, the automation of tutoring as it is done in the classroom to respond to the difficulties of the learner. We propose a generic Intelligent Multi-Tutoring System (IMTS) architecture composed of two modules COMES and MAT. The Communication Entry Service (COMES) module manages communications between the IMTS and a Learning Management System (LMS). The module Multi-Agent Tutoring (MAT) is the multi-agent system developed with JADE, which allows the dynamic adaptation of tutoring (Machine, Peer, Teacher) according to the profile of the learner. We offer a configurable system to customize tutoring to the individual needs of each learner. It can be grafted onto any learning platform, making it multidisciplinary and easy to integrate into existing learning environments. The teacher will be able to devote more time to learners which need greater his intervention.The peer will develop human and relational qualities linked to their know-how, transversal skills sought by recruiters. To validate this architecture, we provide an application and results that integrate the elements of the described model. The results of the experiment prove the feasibility and reliability of our approach.