Integration Of Intelligent Systems Research Articles

VLC performance in underground vehicular tunnels

In recent years, the integration of intelligent transportation systems (ITS) has gained popularity as a means of enhancing the safety of roadways and underground tunnels and reducing traffic congestion. Given the fact that conventional radio frequency (RF) communication systems are vulnerable to significant limitations as a result of a variety of factors, including signal attenuation and interference, which affect their application, the emerging visible light communication (VLC) technology is an exciting potential candidate for facilitating wireless access in such environments. This study investigates the deployment of VLC systems in underground vehicular tunnels involving a handover strategy based on the software-defined network (SDN) approach, with the objective of addressing the fundamental challenges faced by communication systems in such scenarios. The Optisystem software is used to simulate and investigate the performance of the proposed system, which is based on orthogonal frequency division multiplexing (OFDM) technology in both line-of-sight (LOS) and non-line-of-sight (NLOS) conditions. The simulated scenario is capable of achieving a data rate of 10 Gbps within a link range of 3 meters in the LOS approach. In the NLOS propagation model, a data rate of 2 Gbps can be attained without any error. The simulation results reveal a particular perspective on the viability of VLC systems in improving the communication infrastructure in underground vehicular tunnels and promoting efficient tunnel operations. The evaluation of the simulated system is conducted based on bit error rate (BER), signal-to-noise ratio (SNR), and the constellation diagram.

A study investigating the integrated application of intelligent construction and assembly building based on BIM technology

The construction industry, is facing the inevitable trend of digital transformation in the new wave of technology. The general production and construction build cycle is long and on-site construction activities is associated with a number of environmental issues. Additionally, the construction progress points and management are not clear, which presents a challenge for the industry. Consequently, the objective of this article is to integrate and apply a range of technologies, including BIM (Building Information Modeling), cloud computing, big data, Internet of Things, 5G, RPA (Robotic Process Automation), and others, to on-site operation control by integrating them. This integration will facilitate the analysis of the considerable advantages of intelligent construction and assembly building in its application. The conference center of the Qionglai Talent Service Exchange Center will be used as a case study to demonstrate the potential of these technologies. The paper will also propose new views and ideas for addressing the challenges currently faced by the construction industry. The research findings indicate that the integration of multiple intelligent construction systems and assembly components enhances construction progress, simplifies construction processes, and minimizes construction-related pollution. This evidence supports the viability of efficient, intelligent, and sustainable building design and construction methodologies, and provides a robust foundation for the transformation and advancement of the industry.

Design concept of intelligent integrated control system for neutral beam injection

Abstract Due to the specificity of neutral beam injection (NBI) system, the control of its actual physical system is achieved by the integrated control system (ICS). The current NBI ICS adopts a distributed design structure to balance the system load, which is also the mainstream system design and architecture model of ICS around the world. However, from a practical point of view, the distributed system architecture is not perfect. In the long run, upgrading the system intelligence and automation capabilities is an inevitable choice for the future, so it is necessary to explore ways to transform and upgrade the ICS. At present, Internet of Things (IoT), as an important part of the new generation information technology, can realize the control of everything through data exchange. This is because on the one hand, IoT has wide compatibility and powerful scenario-based capabilities, it not only has the advantages and characteristics of distributed design, but also can pull the NBI subsystems into the same level scenario and lay the foundation for the further construction of digital NBI; on the other hand, the intervention of artificial intelligence makes IoT have some new typical characteristics such as intelligent sensing, ubiquitous connectivity, precise control, digital modeling, real-time analysis and iterative optimization, which is enough to pull the current NBI ICS into a new intelligent control era. Finally, it is worth mentioning that due to its inherent design structure and functional characteristics, ICS tends to be broadly generic, so it is not exclusively used for NBI operation in nuclear fusion, and it can provide some insight into other application areas.

Analysis of Signalized Roundabouts with Intelligent Transportation Systems - The Case of Gaziantep Junction 40 (Shell)

This study presents an analysis aimed at optimizing traffic flow at the Shell Intersection in Gaziantep. Due to increasing population and vehicle numbers, traffic problems in the city are growing, especially at central intersections. Using PTV-Vissim micro-simulation software, the current situation was analyzed and alternative scenarios were developed. Performance analyses were conducted on models created using field observations and peak hour traffic counts. The intersection, currently at service level "F", is targeted to be elevated to service levels "B" and "C" through the proposed Single Point intersection design and optimized signal plans. Results indicate that the proposed changes could reduce average vehicle delay by 85-93% and increase average speed by 195-239%. This study aims to contribute to the improvement of urban traffic management through the integration of intelligent transportation systems and geometric arrangements.

An Analysis on Intelligent Systems for Remote Sensing Satellite Image Processing and Classification

The integration of intelligent systems to the remote sensing satellite image processing and classification has greatly changed. This paper offers a synthesis of the subject, with respect to intelligent systems’ contribution to the improvement of these processes’ accuracy and speed. Accuracy of different methods such as machine learning algorithms, artificial neural networks, and deep learning techniques in the extraction of information from satellite image comprehension is considered a research interest. The presented problems and open issues are data complexity, feature extraction, and classification accuracy over the data, along with new methods in enhancing the intelligent systems to minimize those problems. It expands knowledge of intelligent systems’ contribution to remote sensing applications by outlining how these advancements have influenced the progression of image analysis for the given research goals. This research work gives the summary of our research by outlining the techniques used in the study, the problems solved, and the general outcomes of incorporating intelligent systems in the area of remote sensing and satellite image analysis. Accuracy analysis results for the SVM based methodology with spatial-spectral features include 90% of accuracy, 88% of the precision, and 90% of the F1-score, which in turn makes it easy to make sound decisions when using satellite imagery in different fields like agriculture, urban development, and environment.

Hybrid PVP/Battery/Fuel Cell Wireless Charging Stations Using High-Frequency Optimized Inverter Technology for Electric Vehicles

The design and integration of intelligent energy management systems in hybrid electric vehicle (EV) charging stations, leveraging industry 4.0 and renewable energy sources, is crucial for advancing sustainability, efficiency, and technological development. The innovative hybrid EV charging station described in this study uses a combination of fuel cells, batteries, and solar panels that run at 14 amps a piece at 240 volts. The system consists of five essential components that work together to transfer power wirelessly: an EV battery bank, a boost converter, an HF inverter, transfer coils, and a power supply. Two crucial phases make up the optimization process. In phase 1, the boost converter’s maximum power point is tracked and optimized to generate the most power possible by varying the duty cycle between 10% and 90%. In phase 2, the HF uses a class ϕ2 inverter at 30 MHz to synchronize with the resonant frequency of wireless power transfer coils. Zero-voltage switching is used by a digital signal processor card to carry out control for effective operations. By utilizing hybrid sources to optimize power transmission, this design improves the sustainability of EV charging options.

Intelligent smelting process, management system: Efficient and intelligent management strategy by incorporating large language model

In the steelmaking industry, enhancing production cost-effectiveness and operational efficiency requires the integration of intelligent systems to support production activities. Thus, effectively integrating various production modules is crucial to enable collaborative operations throughout the entire production chain, reducing management costs and complexities. This paper proposes, for the first time, the integration of Vision-Language Model (VLM) and Large Language Model (LLM) technologies in the steel manufacturing domain, creating a novel steelmaking process management system. The system facilitates data collection, analysis, visualization, and intelligent dialogue for the steelmaking process. The VLM module provides textual descriptions for slab defect detection, while LLM technology supports the analysis of production data and intelligent question-answering. The feasibility, superiority, and effectiveness of the system are demonstrated through production data and comparative experiments. The system has significantly lowered costs and enhanced operational understanding, marking a critical step toward intelligent and cost-effective management in the steelmaking domain.

MANAGEMENT OF INNOVATIVE DEVELOPMENT OF ENTERPRISES IN THE SMART ECONOMY

In the contemporary economic landscape, the concept of a smart economy has emerged as a critical framework for fostering innovation and enhancing the competitiveness of enterprises. This article explores the management of innovative development within enterprises operating in the smart economy, characterized by the integration of digital technologies, intelligent systems, and data-driven decision-making processes. Through a comprehensive examination of existing literature and a mixed-methods research approach, this study analyzes the strategies, tools, and practices essential for enterprises to thrive in a continuously evolving digital environment. The article provides a comparative analysis of domestic and foreign research, highlighting the practical focus of Ukrainian scholars on implementing innovative technologies and managing processes, contrasted with the theoretical perspectives of international researchers on competitive strategies and open innovation. A detailed comparison between the development of the smart economy in Ukraine and Europe is presented, showcasing key metrics, achievements, and challenges. Furthermore, the discussion addresses the positive and negative features of the smart economy, such as enhanced efficiency and productivity, innovation and competitiveness, and issues like digital divide and cybersecurity concerns. The study offers recommendations for improving the mechanisms of managing smart economy development, emphasizing investments in digital infrastructure, promoting digital literacy, fostering public-private partnerships, and ensuring inclusive access. Finally, the article proposes an adaptive mechanism for managing innovative development, incorporating external environment analysis, organizational flexibility, human capital investment, open innovation, and advanced information technology solutions. These insights and recommendations aim to guide enterprises in effectively harnessing innovation to achieve sustainable success in the smart economy.

AI-driven empowerment of international higher education and international networking: A case study of Moroccan universities

In the rapidly evolving 21st century, characterized by technological advances, the integration of Artificial Intelligence (AI) and intelligent systems has become increasingly important. This research focuses on the role of AI and intelligent systems in strengthening higher education and networking in an international context, with a specific focus on Morocco. It is undeniable that the advent of Internet communication technologies has facilitated networking, especially in the international educational context. This paper presents an analysis of three Moroccan universities, shedding light on the impact of international networking on academic and professional experiences, especially for doctoral students in Morocco. This paper is based on a mixed method research with a focus on primary data collection in Morocco. This research aims to provide valuable insights and recommendations for fostering a robust educational ecosystem in the context of international collaboration. Some of the findings of this paper highlight that, despite some limitations, AI and intelligent systems could potentially be used as important tools to create innovative learning materials. AI is becoming essential for predicting student needs and behavior, as well as for enabling international higher education and international perspectives within universities.

Artificial neural network based on strong track and square root UKF for INS/GNSS intelligence integrated system during GPS outage

When INS/GNSS (inertial navigation system/global navigation satellite system) integrated system is applied, it will be affected by the insufficient number of visible satellites, and even the satellite signal will be lost completely. At this time, the positioning error of INS accumulates with time, and the navigation accuracy decreases rapidly. Therefore, in order to improve the performance of INS/GNSS integration during the satellite signals interruption, a novel learning algorithm for neural network has been presented and used for intelligence integrated system in this article. First of all, determine the input and output of neural network for intelligent integrated system and a nonlinear model for weighs updating during neural network learning has been established. Then, the neural network learning based on strong tracking and square root UKF (unscented Kalman filter) is proposed for iterations of the nonlinear model. In this algorithm, the square root of the state covariance matrix is used to replace the covariance matrix in the classical UKF to avoid the filter divergence caused by the negative definite state covariance matrix. Meanwhile, the strong tracking coefficient is introduced to adjust the filter gain in real-time and improve the tracking capability to mutation state. Finally, an improved calculation method of strong tracking coefficient is presented to reduce the computational complexity in this algorithm. The results of the simulation test and the field-positioning data show that the proposed learning algorithm could improve the calculation stability and robustness of neural network. Therefore, the error accumulation of INS/GNSS integration is effectively compensated, and then the positioning accuracy of INS/GNSS intelligence integrated system has been improved.

Application and Exploration of Artificial Intelligence in Teaching and Learning in Private Colleges and Universities

The rapid advancement of artificial intelligence technology across various sectors has sparked profound transformation in the field of education, particularly in the realms of pedagogy and administration within private higher education institutions. The discourse delves into the specific applications of AI educational aids in both classroom instruction and post-class learning, encompassing intelligent tutoring systems, virtual laboratories, and personalized learning recommendation systems, among others. Furthermore, it addresses the utilization of AI-driven question-answering systems, automated homework grading systems, and speech recognition technology. In terms of educational administration, it investigates the integration of intelligent student information systems, learning trajectory tracking systems, as well as course resource recommendation systems and teaching evaluation systems. The aim of the article is to furnish educators with valuable insights to enhance teaching quality and administrative efficiency, thereby promoting the intelligent development of private higher education institutions.

Towards real-time earthquake forecasting in Chile: Integrating intelligent technologies and machine learning

This paper proposes an innovative approach towards real-time earthquake forecasting in Chile by integrating intelligent technologies and machine learning methods. Earthquakes pose significant risks to communities and infrastructure in Chile, making accurate and timely forecasting crucial for disaster preparedness and mitigation. Traditional forecasting methods have limitations in providing real-time insights into seismic activity. In contrast, intelligent technologies such as artificial intelligence (AI) and machine learning offer promising avenues for enhancing prediction accuracy and speed. A new earthquake forecasting method using a modified clustering approach LMSCAN(Local Maxima-based Spatio-Cluster Analysis Network) and enhanced neural network time series analysis LSTM-IC (Long Short Term Memory Inverse Correlation) is presented in this paper. This neural network-based technology has been utilized to forecast earthquakes in the Chile region, one of the countries with the largest seismic activity. This study explores the integration of intelligent systems with machine learning algorithms to analyze seismic data and predict earthquake occurrences in Chile. By leveraging historical seismic data, sensor networks, and advanced predictive models, our approach aims to provide timely warnings and insights into seismic events, thereby improving disaster response and resilience. The proposed framework holds the potential to revolutionize earthquake forecasting by enabling real-time monitoring and proactive measures to safeguard communities and infrastructure in Chile and beyond. The remarkable 95 % accuracy achieved by this model is a testament to its exceptional learning process, which sets it apart from other models. Its ability to learn and adapt to new data is unparalleled, allowing it to forecast incredibly precisely and produce highly reliable results.

A framework for integrating intelligent transportation systems with smart city infrastructure

As cities continue to face the increasing demands of urban transportation and the need for sustainable mobility solutions, the integration of intelligent transportation systems (ITS) with smart city infrastructure emerges as a promising approach. This paper presents a novel framework for integrating ITS with smart city infrastructure, aiming to address the challenges of urban transportation and promote sustainable mobility. The framework is developed through a comprehensive literature review, case studies, and stakeholder interviews, providing significant insights into the integration process. Our research outlines the key components of smart city infrastructure that can be integrated with ITS, highlights the benefits of integration, and identifies the challenges and barriers that need to be addressed. Additionally, we propose and apply evaluation methods to assess the effectiveness of ITS integration with smart city infrastructure. The results demonstrate the novelty and significance of this framework, as it significantly reduces traffic congestion, improves air quality, and enhances citizen satisfaction. This paper contributes to the existing literature by providing a comprehensive approach to integrating ITS with smart city infrastructure, offering a transformative solution for urban transportation challenges.

Comparative Study of Machine Learning Algorithms for Traffic Sign Recognition

The increasing integration of intelligent transportation systems has brought forth a critical need for accurate and efficient traffic sign detection algorithms. In this research, we conduct a comprehensive comparative analysis of four prominent convolutional neural network (CNN) architectures—LeNet, ResNet, VGG, and a standard CNN model—for their performance in traffic sign detection. The evaluation is conducted on a diverse dataset, utilizing the German Traffic Signs Recognition Benchmark (GTSRB). Our study encompasses an in-depth examination of accuracy metrics and performance under varying conditions, such as challenging lighting, occlusion, and environmental complexities. The results demonstrate nuanced differences in accuracy and computational efficiency among the algorithms. LeNet exhibits notable improvements in real-time processing, while ResNet showcases enhanced accuracy, and VGG demonstrates robustness in challenging conditions. The findings provide valuable insights into the strengths and weaknesses of each algorithm, aiding researchers and practitioners in selecting the most suitable model for specific traffic sign detection applications. This research contributes to advancing the field of computer vision in intelligent transportation systems, with implications for enhancing road safety and efficiency. Key Words: traffic sign recognition, deep learning, Res-Net, VGG19, CNN, LeNet.

Superhydrophobic Triboelectric Structural Materials Enabled by Hierarchical Spatial Assembly

AbstractNon‐contact intelligent sensing technology is a key player in the data acquisition and environmental monitoring framework of the Internet of Everything, laying the groundwork for the realization of highly integrated intelligent network systems. However, environmental factors continue to be a major challenge in the process of real‐time monitoring and feedback of objects, constraining the stability and accuracy of the sensing signals. In this study, a hierarchical spatial assembly strategy is implemented to effectively integrate an intermediate energy storage layer with superhydrophobic surfaces (CA = 162 ± 2.5°), resulting in the development of a moisture‐resistant triboelectric structural materials with stable monitoring and tracking capabilities. The moisture‐resistant self‐powered sensor constructed from this triboelectric structural material operates effectively in extreme humidity conditions (99% RH) with a full‐scale output retention rate of 95.2%, and is capable of accurately sensing human activity from the distance of 3 m. Importantly, the sensor can also effectively detect vehicle states during operation and perform real‐time monitoring of the vehicle's surrounding environment. This study not only overcomes the long‐standing challenges of accuracy and stability in high humidity for sensors but also advances the development of sustainable, interconnected intelligent systems for the Internet of Everything in extreme environmental conditions.

Advancing autonomy through lifelong learning: a survey of autonomous intelligent systems.

The combination of lifelong learning algorithms with autonomous intelligent systems (AIS) is gaining popularity due to its ability to enhance AIS performance, but the existing summaries in related fields are insufficient. Therefore, it is necessary to systematically analyze the research on lifelong learning algorithms with autonomous intelligent systems, aiming to gain a better understanding of the current progress in this field. This paper presents a thorough review and analysis of the relevant work on the integration of lifelong learning algorithms and autonomous intelligent systems. Specifically, we investigate the diverse applications of lifelong learning algorithms in AIS's domains such as autonomous driving, anomaly detection, robots, and emergency management, while assessing their impact on enhancing AIS performance and reliability. The challenging problems encountered in lifelong learning for AIS are summarized based on a profound understanding in literature review. The advanced and innovative development of lifelong learning algorithms for autonomous intelligent systems are discussed for offering valuable insights and guidance to researchers in this rapidly evolving field.

WAYS TO REDUCE THE IMPACT OF PULSATIONS IN LOCAL POWER SUPPLY ROBOTIC SYSTEMS

An analysis of the changes in the flow of pulsation transforming the energy-dynamic indicators of on-board power electronics systems has shown that there are several options for solving this problem.
 The article outlines the main options for the current problem of pulsation in the energy systems of autonomous electrical systems, such as UAVs, robotic complexes, and other systems. The effectiveness of changes in pulsation is examined, in addition to directly assessing the effectiveness of stagnation strategies and methods. Also in the statistics, the optimal technical solutions for specific operating minds were identified.
 The prospects and future directives have been predicted based on the proposals for further research in the sphere of changes in pulsation in local life systems.
 In the future, it is predicted and supported by the development of technology and innovation in this country.
 To sum up, we can say that the statistics have analyzed the changes in pulsation in robotic local power supply systems, which is one of the key directions in the field of electrical engineering and electronics. Increased attention to this problem is due to the need for assured stability, reliability and greater energy efficiency of electronic systems. Research in this area is aimed at developing more comprehensive methods and strategies for reducing noise and pulsation in electrical systems.
 Assessing the effectiveness of stagnant methods, promoting the development of new technologies and innovation, as well as developing promising directions in this field opens the way to creating more stable and adaptive living systems. Replacement of advanced electronic filters, voltage stabilizers, selection of new materials and components, integration of intelligent energy management systems and optimization of essential energy resources local power supply systems – the whole purpose is to directly conduct further investigations.
 The growing demand for stable and efficient electronic systems is driven by current research and the introduction of new technologies to change pulsation. Technological developments and scientific research in this area open up broad prospects for reducing the power supply and ensuring the stability of the functioning of modern electronic devices.

Analog and Mixed-signal Integrated Circuits of 5Gand 6G Data Communication

The complex analog and mixed-signal transceivers where overall system performance is often limited by the weakest performing subsystem. While analog and mixed-signal integrated circuits have significantly advanced, the future of 5G and 6G transceiver design could be accelerated by including artificial intelligence. In this combination, analog integrated circuit design and operation would harness machine learning to identify, characterize, and act upon variations and anomalies in system performance. Focusing on 5G and 6G, this paper investigates solutions for a unified intelligent integrated transceiver: a conceptual combination of a traditional analog subsystem, a supporting digital subsystem that enables artificial intelligence, and dedicated feedback circuitry or sensors that monitor performance, efficiency, or reliability. Active and passive components and propagation channels are reviewed based on their merits of introducing intelligence. Holistically and for broader applicability, the paper conceptualizes and coins the notion of an ‘‘intelligent integrated system (IIS)’’, which brings forward a novel unified vision and approach toward context-aware subsystems that dynamically interact with ambient and varying operating conditions. To demonstrate viability, the paper concatenates a select set of measurement results.

Future of precision manufacturing: Integrating advanced metrology and intelligent monitoring for process optimization

Precision manufacturing is undergoing a transformative evolution fueled by the integration of advanced metrology techniques and intelligent monitoring systems. This abstract explores the future trajectory of precision manufacturing through the convergence of these technologies, focusing on their synergistic role in process optimization. Advanced metrology techniques, including high-resolution imaging, laser scanning, and non-contact surface measurement, provide unprecedented levels of accuracy and detail in capturing dimensional data. These techniques enable manufacturers to precisely analyze component geometry, surface finish, and tolerances, facilitating the production of parts with exceptional precision and quality. Moreover, the integration of metrology within the manufacturing process allows for real-time feedback, enabling rapid adjustments and corrections to ensure adherence to design specifications. Intelligent monitoring systems complement advanced metrology by continuously collecting data from various sensors embedded within manufacturing equipment. These systems utilize artificial intelligence (AI) and machine learning algorithms to analyze vast amounts of data in real-time, detecting anomalies, predicting equipment failures, and optimizing process parameters. By leveraging data-driven insights, manufacturers can enhance production efficiency, minimize downtime, and reduce scrap rates. The synergy between advanced metrology and intelligent monitoring extends beyond quality control to encompass holistic process optimization. Through the seamless integration of these technologies, manufacturers can achieve unparalleled levels of precision, efficiency, and flexibility in their operations. For instance, real-time metrology feedback combined with AI-driven monitoring enables adaptive manufacturing processes that dynamically adjust parameters based on changing environmental conditions or material properties. Furthermore, the future of precision manufacturing lies in the adoption of a digital twin approach, where virtual replicas of physical manufacturing systems are created and synchronized with real-time data. This enables predictive maintenance, virtual prototyping, and simulation-based optimization, leading to significant cost savings and accelerated innovation cycles. The future of precision manufacturing hinges on the integration of advanced metrology and intelligent monitoring technologies. By harnessing the synergies between these innovations, manufacturers can achieve unprecedented levels of precision, efficiency, and agility, driving forward the evolution of manufacturing in the digital era.

Optimization of Information Technology Through Intelligent System Integration : Comprehensive Exploration

In the era of digital transformation, intelligent system integration is the main capital for optimizing the use of information technology. This article provides a comprehensive exploration to explore how intelligent system integration forms a strong foundation for changing the outlook of information technology. The main focus of this article is utilization in operational efficiency, smarter decision making, and product and service innovation. The basic concept of intelligent system integration is explained by providing an explanation of how artificial intelligence and information technology can interact synergistically. This article also discusses the role of artificial intelligence in improving the functionality of information systems and its positive impact on decision making. The benefits of intelligent system integration in increasing operational efficiency can be explained through the automation of tasks performed, increased productivity through faster data processing, and reduced human errors. This article also shows how intelligent system integration supports better decision making through in-depth and accurate data analysis. Additionally, this article discusses how intelligent system integration drives innovation in products and services. As input for future development, this article provides an in-depth overview of intelligent system integration trends that contribute to the optimization of information technology, by opening the door to an era of innovation and greater efficiency.