Integration Of Intelligent Systems Research Articles

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.

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.

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.

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.

Investigation and development of scheduling and networking technologies for small cells based on optical transmission networks

In addition to the power dispatching department, other related departments and management agencies are also interested in power grid data. These developments coincide with an increase in the number of plants and stations, a dramatic increase in the amount of power system data, and an increase in the difficulty of managing and analyzing the data. Thus, a key area of current power information system research is how to analyze and process the massive amounts of grid operation data to guarantee the safe and effective implementation of power production while also being able to support decision-making for power production and power scheduling. In light of this, this paper builds a complete number distribution system on top of the current scheduling program-controlled exchange, hoping to inherit the high reliability and stability of the scheduling switching network. It also introduces the IPoE technology networking and offers a technical foundation for the state management of the equipment. Finally, it uses the optical transmission network scheduling telephone networking technology as its foundation, based on a thorough analysis of the current state of the domestic power dispatching management system. In order to improve the quality of power grid management, it is helpful in the development of an organic and unified integrated intelligent system, the creation of an ideal and seamless information flow, and the eventual realization of the integration and standardization of the production plan, the production process, and the sale of electricity.

Introducing fine grained energy consumption variables into a public passenger transport simulation in SUMO

Improving energy efficiency in public passenger transport is key to achieving sustainability goals. In many developing countries, public passenger transport relies heavily on fossil fuels, which necessitates a strong focus on reducing emissions from energy consumption. Surprisingly, few studies have addressed this issue in the context of daily operations, with most research focusing on operational and infrastructural changes. This paper aims to fill this gap by introducing detailed energy consumption variables into a public transport simulation model using SUMO, a tool known for its seamless integration with Python. The development of two Python scripts underpins this endeavour. The first script facilitates the automatic generation of scenarios, allowing the manipulation of parameters such as station spacing, line length, passenger capacity and waiting times, among others. The second script is dedicated to estimating energy consumption for each scenario. Together, these tools allow us to calculate average energy consumption for different scenarios and modes of public transport. As a result, they provide valuable insights for the formulation of public policies aimed at saving energy in different public transport environments and lay the foundation for the integration and development of intelligent transport systems and simulation-based digital twins.

Toward microfluidic continuous-flow and intelligent downstream processing of biopharmaceuticals.

Biopharmaceuticals have emerged as powerful therapeutic agents, revolutionizing the treatment landscape for various diseases, including cancer, infectious diseases, autoimmune and genetic disorders. These biotherapeutics pave the way for precision medicine with their unique and targeted capabilities. The production of high-quality biologics entails intricate manufacturing processes, including cell culture, fermentation, purification, and formulation, necessitating specialized facilities and expertise. These complex processes are subject to rigorous regulatory oversight to evaluate the safety, efficacy, and quality of biotherapeutics prior to clinical approval. Consequently, these drugs undergo extensive purification unit operations to achieve high purity by effectively removing impurities and contaminants. The field of personalized precision medicine necessitates the development of novel and highly efficient technologies. Microfluidic technology addresses unmet needs by enabling precise and compact separation, allowing rapid, integrated and continuous purification modules. Moreover, the integration of intelligent biomanufacturing systems with miniaturized devices presents an opportunity to significantly enhance the robustness of complex downstream processing of biopharmaceuticals, with the benefits of automation and advanced control. This allows seamless data exchange, real-time monitoring, and synchronization of purification steps, leading to improved process efficiency, data management, and decision-making. Integrating autonomous systems into biopharmaceutical purification ensures adherence to regulatory standards, such as good manufacturing practice (GMP), positioning the industry to effectively address emerging market demands for personalized precision nano-medicines. This perspective review will emphasize on the significance, challenges, and prospects associated with the adoption of continuous, integrated, and intelligent methodologies in small-scale downstream processing for various types of biologics. By utilizing microfluidic technology and intelligent systems, purification processes can be enhanced for increased efficiency, cost-effectiveness, and regulatory compliance, shaping the future of biopharmaceutical production and enabling the development of personalized and targeted therapies.

Development and Evaluation of an Intelligent Control System for Sustainable and Efficient Energy Management

This paper presents a comprehensive study on the integration of Intelligent Control Systems in the global industrial sector, focusing on enhancing energy management through the synergy of Supervisory Control and Data Acquisition (SCADA), Machine Learning (ML), and Digital Twin technologies. We elaborate on a novel ICS architecture designed to optimize energy consumption, reduce operational costs, and minimize environmental impacts. Our system leverages SCADA for real-time monitoring and control, ML algorithms for predictive analytics and optimization, and Digital Twin technology for advanced simulation and operational efficiency. The implementation of the system in a mid-scale industrial facility demonstrated significant improvements: a 15% reduction in energy consumption, an 18% decrease in peak energy demand, a 30% reduction in CO2 emissions, and a 15% reduction in operational downtime, with predictive accuracy standing at 90%. These results underline the potential of integrating advanced digital technologies in industrial energy management, offering a scalable model for sustainable and efficient industrial practices. Future work will explore broader applications and the incorporation of emerging technologies to further enhance the system's capabilities and applicability in diverse industrial settings.

Smart Home Automation

Home automation, fueled by advancements in technology, has evolved into a transformative paradigm, reshaping the way we interact with and manage our living spaces. This paper explores the multifaceted landscape of home automation, investigating the integration of smart devices, sensors, and intelligent control systems to create residences that are not only interconnected but also adaptive and responsive to the needs of their occupants. With a focus on enhancing convenience, energy efficiency, and security, home automation technologies offer innovative solutions for everyday tasks. This abstract delves into the fundamental components of home automation, examining the integration of Internet of Things (IoT) devices, artificial intelligence, and user interfaces. It also explores the challenges and considerations associated with widespread adoption, including issues of privacy, interoperability, and standardization. As the field continues to evolve, this research contributes insights into the current state of home automation, its impact on daily living, and the potential for future development in creating intelligent, user-centric living environments.

A Comprehensive Review on Object Detectors for Urban Mobility on Smart Traffic Management

This comprehensive review explores the landscape of object detectors in the context of urban mobility for smart traffic management. With the increasing complexity of urban environments and the integration of intelligent transportation systems, the demand for accurate and efficient object detection algorithms has surged. This paper provides a thorough examination of state-of-the-art object detectors, evaluating their performance, strengths, and limitations in the specific context of urban mobility. The review encompasses a wide range of detectors, including traditional computer vision methods and modern deep learning approaches, discussing their applicability to real-world urban traffic scenarios. By synthesizing insights from diverse methodologies, this review aims to guide researchers, practitioners, and policymakers in selecting suitable object detectors for enhancing smart traffic management systems in urban settings.

An In-Depth Analysis of Smart Service Systems: Exploring Concepts and Overcoming Challenges

This comprehensive survey delves into the realm of Smart Service Systems, aiming to provide an in-depth analysis of its fundamental concepts and address the challenges faced in its implementation. Smart Service Systems represent a transformative paradigm that leverages cutting-edge technologies, such as artificial intelligence, Internet of Things, and cloud computing, to enhance the efficiency, effectiveness, and personalization of service delivery. The paper explores the key components and underlying principles that form the backbone of these systems, shedding light on their potential applications and benefits across various domains. However, the realization of Smart Service Systems comes with a set of formidable challenges that need to be tackled. These challenges encompass technical, ethical, legal, and societal aspects, among others. By examining and understanding these obstacles, the paper aims to propose potential strategies and solutions to overcome them. The analysis is further enriched with case studies and real-world examples to provide valuable insights for researchers, practitioners, and policymakers. In conclusion, this survey serves as a comprehensive resource for the exploration of Smart Service Systems, illuminating the path towards harnessing their full potential while addressing the complexities they present. It is hoped that this work will inspire further research and foster advancements in this burgeoning field, ultimately leading to the widespread adoption and integration of intelligent service systems for a more efficient and sustainable future.

Intelligent Wireless Sensor Network for Gas Classification Using Machine Learning

Systems based on wireless sensor networks convey a prevailing tool to monitor data in complex environments. In this work, we present an intelligent integrated system that monitors various crucial environmental parameters, and applies machine learning to classify multiple gases. The system is a complete, sophisticated, interactive multiple parameters-observing, with two-way communication via wireless mobile network. It consists of a number of buoys distributed throughout the sea and integrated with a suite of sensors capable of measuring a range of essential air and underwater parameters. Four well-known machine learning algorithms are implemented and compared, namely multilayer perceptron, Naïve Bayes, logistic regression, and support vector machines. We present a thorough investigation and deep analysis to evaluate the system performance. The experimental results show that the system achieves a correct classification rate of 92.66%, with a weighted average precision, recall, F-score, and MMC of 0.93, 0.93, 0.926, and 0.91, respectively.

Development and Prospect of Intelligent Air-to-Ground Precision Strike Munition

Intelligent munition, which integrates networking, big data, deep learning and other artificial intelligence (AI) technologies, is increasingly become the key to military reforms and combats in the future. Therefore, this study defines the connotations and features of intelligent air-to-ground precision strike munition, and provides an analysis on the development status and trend of such munition at home and abroad from the aspects of single intelligence and swarm intelligence. By drawing experience from the development of the U.S. army, this paper elaborates on the ways of establishing the technological system of intelligent air-to-ground precision strike munition from three aspects, namely, single intelligent technology, system integration and operational application. The study is expected to promote the research and development of the next generation of air-to-ground precision strike munition with intelligent features including perception, learning, decision-making and collaboration.