Development Of Intelligent System Research Articles

Efficient electrocardiogram generation based on cardiac electric vector simulation model

This study introduces a novel Cardiac Electric Vector Simulation Model (CEVSM) to address the computational inefficiencies and low fidelity of traditional electrophysiological models in generating electrocardiograms (ECGs). Our approach leverages CEVSM to efficiently produce reliable ECG samples, facilitating data augmentation essential for the computer-aided diagnosis of myocardial infarction (MI). Significantly, experimental results show that our model dramatically reduces computation time compared to conventional models, with the self-adapting regression transformation matrix method (SRTM) providing clear advantages. SRTM not only achieves high fidelity in ECG simulations but also ensures exceptional consistency with the gold standard method, greatly enhancing MI localization accuracy by data augmentation. These advancements highlight the potential of our model to generate dependable ECG training samples, making it highly suitable for data augmentation and significantly advancing the development and validation of intelligent MI diagnostic systems. Furthermore, this study demonstrates the feasibility of applying life system simulations in the training of medical big models.

인공지능과 인권

Human rights and artificial intelligence are two important concepts that interact with each other in today's rapidly developing digital age. How will artificial intelligence technology affect human rights that are spreading throughout people's lives? This question has become an increasing focus of attention. Artificial intelligence is the field that enables computer systems to simulate human-like thinking and decision-making capabilities. However, some of the problems that arise from artificial intelligence may have a direct impact on human rights. The interaction between human rights and artificial intelligence is a complex problem. While artificial intelligence technology has the potential to harm people and human rights, it also has great potential to be used to protect and promote human rights. Therefore, artificial intelligence technology must be developed and used while observing human rights through an approach supported by an ethical and legal framework. In this paper, after reviewing human rights issues such as artificial intelligence and freedom of expression, artificial intelligence and freedom of privacy, artificial intelligence and bias, hate, discrimination, artificial intelligence and defamation, artificial intelligence and copyright, we will discuss the issue of legal response to artificial intelligence. Regarding the relationship between the state and science and technology, the Constitution stipulates that “the state shall endeavor to develop the national economy through innovation of science and technology and the development of information and manpower (Article 127), and the rights of authors, inventors, and scientists are protected by law (Article 22).” It declares that the state will actively plan and form for the promotion of science and technology and lead in a certain direction. In relation to these constitutional provisions, the establishment and implementation of science and technology policies can be seen as an important duty given to the Republic of Korea as a democratic welfare state. It can be said that the main legislative duty is to achieve a balance between the value of promoting the development and utilization of artificial intelligence systems and the appropriate response to the risks that artificial intelligence can pose. To achieve a balance between artificial intelligence and human rights, it is necessary to comply with ethical values, use transparent and responsible systems, update legal regulations, and adopt an approach that guarantees stakeholder participation. By doing so, we will be able to build a future where human rights are protected while taking advantage of the potential benefits of artificial intelligence.

Future of Medical Equipment Technology

Advances in manufacturing and digital information technology have sparked the Industrial Revolution 4.0. UDIs and 6G telemedicine networks are reshaping the future of healthcare technology as part of the Fourth Industrial Revolution (4IR), which is now underway. With the help of UDIs, device recalls and post-market surveillance may be done more accurately and efficiently, and studies using UDI data can be conducted to help doctors make better clinical decisions. The development of intelligent healthcare systems is currently a hot topic, and wireless connectivity is a critical component. The goal of the 6G vision is to transform the globe into a secure and completely linked digital society through the use of communication technologies with intelligence. Stentrode, digital pacemaker, insulin pumps and coronary intervention with drug-eluting stents are some of the new methods that will improve human life. As technology continues to evolve, so will our methods of self-care. In the future, patients will continue to seek out companies that use technological innovation in medicine to empower them to take charge of their own health. The healthcare system is evolving to become more proactive, personalised, and easy for you, which makes it easier than ever to live longer and perform better.

Drowsiness Detection using CNN

Abstract: This research introduces a Driver Drowsiness Detection system employing Convolutional Neural Networks (CNN). The system analyses real-time facial features from in-vehicle cameras to determine a driver's alertness level. Trained on diverse datasets, the CNN model demonstrates high accuracy in identifying drowsiness signs, making it suitable for real-world deployment. This system contributes to road safety by providing timely alerts to prevent accidents caused by driver fatigue. As road safety remains a critical concern, the development of intelligent systems to mitigate driver-related risks has become imperative.

Bibliometric Analysis using Vos Viewer with Publish or Perish of Intelligent Tutoring System in Private Universities

The objective of this study is to analyze the development of intelligent tutoring systems in private universities. We conducted the analysis using bibliometric methods, utilizing the Publish or Perish and VOSviewer applications. Data was obtained by using the publish or perish application with the keyword "intelligent tutoring system in private university" from the Google Scholar database from 2019 to 2024. According to search results, the number of research papers has decreased from 117 to 23 from 2020 to 2024. Mapping using VOSviewer application produces three types of visualization, namely network, overlay, and density visualization. In its conclusion, this research notes a decrease in the number of studies discussing in private universities since 2020, but still shows great potential for development by other researchers.

Revolutionizing Traffic Management: Advanced Machine Learning Techniques for Accurate Traffic Flow Prediction

In modern urban¬¬¬¬¬ transportation systems, efficient traffic flow prediction is of paramount importance to optimize traffic management, reduce congestion, and improve the overall commuting experience. This research report examines the application of state-of-the-art machine learning algorithms to accurately predict traffic flow patterns. By leveraging historical traffic data, weather conditions, time of day, and various other relevant features, our proposed model exhibits significant predictive capabilities. We study the effectiveness of various machine learning techniques, such as neural networks, decision trees, and ensemble methods, in capturing the complex dynamics of traffic flows. Through extensive experiments and validation using real datasets, we demonstrate the superiority of our approach compared to traditional methods. Ultimately, this research will contribute to the further development of intelligent transportation systems, paving the way for more efficient and sustainable urban mobility solutions

Research on the Influence of Teacher-Student Education Expectation Consistency on Students’ Physical Exercise: Based on Polynomial Regression and Response Surface Analysis

Students' physical exercise has important practical significance in enhancing students' physical health and promoting the health construction. Based on the expectation effect theory, this study explores the influence of teacher-student expectation consistency on students' physical exercise from a binary perspective. Using China Education Tracking Survey (CEPS) data, through polynomial regression and response surface analysis, it is found that: (1) There are four combinations of teacher-student educational expectation. Compared with the combination of "high-low", "low-high" and "low-low", when teacher-student educational expectation is consistent with "high-high", students' cognitive ability and students' physical exercise are higher; (2) Cognitive ability mediates the relationship between teacher-student educational expectation consistency and students' physical exercise; (3) The class atmosphere moderates the consistency of teacher-student education expectation and cognitive ability. This study employs computer technology to validate the impact of the teacher-student duality interaction on students' physical exercise through polynomial regression and response surface analysis. Through the design and development of intelligent auxiliary education system, teachers can gain better insight into students' educational expectations while promoting their engagement in physical exercise.

Research and Application of Large Model-Based Intelligent Customer Service System

With the rapid development of artificial intelligence technology, intelligent customer service systems have been widely used. This paper addresses the limitations of traditional intelligent customer service systems, such as limited language understanding ability, narrow knowledge coverage, and insufficient personalized service. It proposes an intelligent customer service system design scheme based on the RAG model. The scheme leverages the powerful language understanding and generation capabilities of large models, combined with dialogue management and knowledge base retrieval enhancement techniques, to build an efficient and intelligent customer service system. This paper introduces the overall architecture of the system, the design and implementation of each module, and comprehensively evaluates the system through experiments. The experimental results show that the system can provide accurate and fluent customer service, significantly improving customer satisfaction. The research in this paper provides new ideas and references for the development of intelligent customer service systems.

Application of Internet-of-Things Wireless Communication Technology in Agricultural Irrigation Management: A Review

The integration of Internet-of-Things technology with traditional agricultural irrigation is a crucial factor in the advancement of traditional agricultural irrigation towards smart irrigation. Despite the widespread use of conventional irrigation methods in many areas, they lead to the significant wastage of both human and water resources. Therefore, the development of energy-saving and efficient intelligent irrigation systems through the application of Internet-of-Things technology and wireless communication technology is the way forward. This paper summarizes the common wireless communication technologies in the agricultural Internet of Things: Fifth-generation, WiFi, ZigBee, LoRa, and NB-IoT. The research status of the above wireless communication technology in agricultural irrigation management is discussed, and the agricultural irrigation management example using the above wireless communication technology is also presented. The advantages and limitations of the application of the above wireless communication technology in agricultural irrigation management are sorted out. Finally, this paper analyzes the challenges of data security issues, data fusion problems, intelligent irrigation system costs, power and energy problems, and system equipment failures faced by the use of IoT wireless communication technology in agricultural irrigation management. This review aims to assist researchers and users in choosing the most suitable wireless communication technology for diverse applications.

Intelligent Power Management of Electric vehicle with onboard PV by ANN-based Model Predictive Control

This paper presents a novel configuration of Hybrid Electric Vehicle (HEV) with fuel cell as primary source and an on-board Photovoltaic (PV) array as secondary source. It incorporates an advanced Model Predictive Control (MPC) system that utilizes Artificial Neural Network (ANN) technology to control induction motor of electric vehicle. The system has three main parts: a fuel cell, an electrolyzer, and a PV array. Each part has a different job in supplying power to the vehicle. The PV array functions to supplement the fuel cell by providing power when ample irradiation is available. When the car isn't being used, the extra power from the solar panels goes to the electrolyzer. There, it turns into chemical energy, making hydrogen. This hydrogen is stored in the onboard hydrogen tank for future use. To fulfil the voltage needs of both the motor and energy sources, a quadratic bidirectional buck-boost converter (QBBC) is utilized. This converter efficiently manages the voltage output while ensuring compatibility with various energy sources. The system undergoes comprehensive analysis under different irradiance and speed conditions to evaluate its performance and efficiency. To make the solar power system more efficient and cost-effective, a Maximum Power Point Tracking (MPPT) algorithm is used. This algorithm helps get the maximum power from the PV system. An improved incremental conductance algorithm is used for this, making sure it accurately finds the best power point. The control strategy incorporates both outer voltage and inner current control, to effectively control the DC output voltage of the QBBC. The vehicle's drive system employs an indirect vector-controlled induction motor. To regulate the motor's speed effectively, an advanced ANN-MPC system is used, providing precise control and high efficiency. This paper introduces a novel approach for predictive torque control of AC machines, eliminating the need for weighting factors. An ANN estimator is used to accurately expect the motor speed, which improves the overall performance of the control system. By using MATLAB/SIMULINK to test how well the proposed Electric Vehicle (EV) setup works and make sure it's effective in real-life situations. Through this comprehensive approach, the paper contributes to advancing the development of efficient and intelligent electric vehicle systems..

Computer-aided mobility solutions: Machine learning innovations to secure smart urban transportation

This study introduces a groundbreaking hybrid deep learning Intrusion Detection System (IDS) tailored to improve the security of Electric Vehicle (EV) charging processes in the context of computer-aided mobility solutions for smart urban transportation. The proposed system ingeniously integrates XGBoost and Convolutional Neural Network (CNN) technologies, leveraging their combined strengths to fortify the resilience of EV charging infrastructure. In response to the demand for transparency in decision-making, our approach incorporates Explainable AI Techniques, specifically employing Shapley Additive exPlanations (SHAP) values. This not only enables the IDS to identify anomalous behavior but also provides clear insights into the features influencing the detection process. The transparency achieved is pivotal for fostering stakeholder trust, ensuring regulatory compliance, and facilitating well-informed decision-making within the dynamic landscape of smart urban transportation. A sophisticated mechanism is implemented to enable the model to autonomously adjust and learn from evolving charging patterns over time. This adaptive capability ensures the IDS remains effective in the face of changing charging behaviors, contributing to the resilience of the overall smart urban transportation system. This research not only propels the field of cybersecurity in smart urban transportation but also establishes a robust framework for the development of intelligent systems. These systems play a critical role in securing essential processes while providing interpretable insights for stakeholders in the realm of computer-aided mobility solutions.

Participation versus scale: Tensions in the practical demands on participatory AI

Ongoing calls from academic and civil society groups and regulatory demands for the central role of affected communities in development, evaluation, and deployment of artificial intelligence systems have created the conditions for an incipient “participatory turn” in AI. This turn encompasses a wide number of approaches — from legal requirements for consultation with civil society groups and community input in impact assessments, to methods for inclusive data labeling and co-design. However, more work remains in adapting the methods of participation to the scale of commercial AI. In this paper, we highlight the tensions between the localized engagement of community-based participatory methods, and the globalized operation of commercial AI systems. Namely, the scales of commercial AI and participatory methods tend to differ along the fault lines of (1) centralized to distributed development; (2) calculable to self-identified publics; and (3) instrumental to intrinsic perceptions of the value of public input. However, a close look at these differences in scale demonstrates that these tensions are not irresolvable but contingent. We note that beyond its reference to the size of any given system, scale serves as a measure of the infrastructural investments needed to extend a system across contexts. To scale for a more participatory AI, we argue that these same tensions become opportunities for intervention by offering case studies that illustrate how infrastructural investments have supported participation in AI design and governance. Just as scaling commercial AI has required significant investments, we argue that scaling participation accordingly will require the creation of infrastructure dedicated to the practical dimension of achieving the participatory tradition’s commitment to shifting power.

Multisource‐multitarget cooperative positioning using probability hypothesis density filter in internet of vehicles

AbstractAccurate positioning of intelligent connected vehicle (ICV) is a key element for the development of cooperative intelligent transportation system. In vehicular networks, lots of state‐related measurements, especially the mutual measurements between ICVs, are shared. It is an advisable strategy to fuse these measurements for a more robust positioning. In this context, an innovative framework, referred to as multisource‐multitarget cooperative positioning (MMCP) is presented. In MMCP, ICVs are local information source, that upload both the states of ICVs estimated by on‐board sensors and the relative vectors between surrounding objects and vehicles to a fusion centre. In the fusion centre, ICVs are selected as the global targets, and the relative vectors are converted into global measurements. Then, the MMCP is modelled into a multi‐target tracking problem with specific targets. This paper proposes a low complexity Gaussian mixture probability hypothesis density (GM‐PHD‐LC) filter to match and fuse the global measurements to further improve the estimation of ICVs. The evaluation results show that our GM‐PHD‐LC can provide 10 Hz positioning services in urban area, and significantly improve the positioning accuracy compared to the standalone global navigation satellite system.

Research on PSO‐SVM base wine grade recognition based on Max‐Relevance and Min‐Redundancy feature selection

SummaryIn response to the challenges inherent in Baijiu classification, characterized by ambiguity and limited methodologies, this study introduces a novel framework for comprehensive Baijiu brewing process management. By integrating mathematical models and machine learning algorithms, our aim is to standardize and enhance the accuracy of the Baijiu brewing process. Through a meticulous analysis of 13 input features using the MRMR algorithm, we identified key variables that significantly influence base wine grades. Subsequently, we compared the efficacy of support vector machine (SVM) algorithms under three kernel functions for base wine grading classification. Additionally, we developed an enhanced particle swarm‐support vector machine (IPSO‐SVM) algorithm, which incorporates nonlinearly decreasing inertia weights to enhance global and local search capabilities. Evaluation metrics, including accuracy and F1‐SCORE, were employed to assess the model's performance. Our experimental results demonstrate that IPSO‐SVM significantly improves convergence speed and accuracy, achieving a classification accuracy of 98.31% on the test set with an F1‐SCORE of 98.18%. Furthermore, experimental tests conducted at a prominent strong‐flavored wine enterprise in China validated the model's effectiveness, revealing no significant difference in grading quality between model and manual Baijiu classification, with a mean error below 1%. This study presents a pioneering approach to digitize and standardize Baijiu classification processes, laying theoretical foundations for the development of intelligent Baijiu classification systems.

Utilizing Polarization Diversity in GBSAR Data-Based Object Classification.

In recent years, the development of intelligent sensor systems has experienced remarkable growth, particularly in the domain of microwave and millimeter wave sensing, thanks to the increased availability of affordable hardware components. With the development of smart Ground-Based Synthetic Aperture Radar (GBSAR) system called GBSAR-Pi, we previously explored object classification applications based on raw radar data. Building upon this foundation, in this study, we analyze the potential of utilizing polarization information to improve the performance of deep learning models based on raw GBSAR data. The data are obtained with a GBSAR operating at 24 GHz with both vertical (VV) and horizontal (HH) polarization, resulting in two matrices (VV and HH) per observed scene. We present several approaches demonstrating the integration of such data into classification models based on a modified ResNet18 architecture. We also introduce a novel Siamese architecture tailored to accommodate the dual input radar data. The results indicate that a simple concatenation method is the most promising approach and underscore the importance of considering antenna polarization and merging strategies in deep learning applications based on radar data.

Development and Application of Big Data-Driven Intelligent Evaluation System for Physical and Aesthetic Education

Big data driven intelligence evaluation system for physical and aesthetic education are used to leverage vast amounts of data to provide more objective, nuanced, and personalized assessments of student performance and progress. This paper proposes a hybrid approach for development of big data driven intelligence evaluation system for physical and aesthetic education. The proposed hybrid approach is the combined performance of both the Dual attention graph convolution network (DAGCN) and Weibull time to event Recurrent Neural Network (WRNN).Commonly it is named as WRNN-DAGCN technique. The major objective of the proposed approach is to provide for development of big data driven intelligence evaluation system for physical and aesthetic education. WRNN is design to enhancing the efficiency of physical training. Enhance the physical training from the WRNN by using the DAGCN. By then, the MATLAB/Simulink working platform has the suggested model implemented, and the present processes are used to calculate the execution. The proposed method shows better results in all existing methods. The accuracy level of proposed BDEPA-WRNN-DAGCN approach is 98% that is higher than the other existing methods. From the result, it is conclude that the proposed approach based error rate is less compared to existing techniques.

Multi-Source Information Fusion Based DLaaS for Traffic Flow Prediction

Traffic flow prediction is the key to transportation safety and efficiency. The advance in machine learning and deep learning has promoted the development of intelligent transportation systems. For example, the emergence of Deep Learning as a Service (DLaaS) has benefitted researchers a lot in dealing with large scale dataset and complex deep learning algorithms. In traffic forecasting, despite the success of deep learning-based models, there are still shortcomings, such as inadequate use of temporal and spatial traffic information, and indirect modeling of dependencies in traffic data. To address these challenges, we learn the transportation network in the form of a graph, and use graph wavelet as a key component to extract well-positioned features from the graph based on the transportation network. Compared with graph convolution, graph wavelets are very flexible and do not need to specify adjacent regions in the topological graph structure for feature extraction. At the same time, we propose to combine the multi-information fusion traffic control and guidance collaborative neural network and the results obtained are better than the benchmark algorithms. The results by comparison with several baseline methods show that our proposed method can outperform all the baseline methods.

Design and development of intelligent unmanned construction system for open-pit mine

China’s huge mineral outputs support the national economy and people’s livelihood. However, the mining operation is rough and the safety accident happens frequently. Consequently, the demand for unmanned mining operations is urgent, which is of great significance to safe production. Facing the complex operation scenarios of open-pit mines, an unmanned construction system framework is proposed based on intelligent construction machinery. On this basis, an unmanned excavation subsystem is designed and developed based on the hydraulic excavator, and an unmanned transportation subsystem is designed and developed based on the mining truck. Equipped with advanced sensors and controllers, the unmanned excavation subsystem and the unmanned transportation subsystem possess sensing, decision-making, and control abilities by using AI algorithms. The experiment of the unmanned construction systems is successfully conducted on the open-pit mine test site. This study provides equipment and technical support for improving the intelligent level of mine construction and ensuring safe production for the open-pit mine.

A Novel Medium Access Control Strategy for Heterogeneous Traffic in Wireless Body Area Networks

So far, Wireless Body Area Networks (WBANs) have played a pivotal role in driving the development of intelligent healthcare systems with broad applicability across various domains. Each WBAN consists of one or more types of sensors that can be embedded in clothing, attached directly to the body, or even implanted beneath an individual's skin. These sensors typically serve a single application. However, the traffic generated by each sensor may have distinct requirements. This diversity necessitates a dual approach: tailored treatment based on the specific needs of each traffic type and the fulfillment of application requirements, such as reliability and timeliness. Nevertheless, the presence of energy constraints and the unreliable nature of wireless communications make QoS provisioning under such networks a non-trivial task. In this context, the current paper introduces a novel Medium Access Control (MAC) strategy for the regular traffic applications of WBANs, designed to significantly enhance efficiency when compared to the established MAC protocols IEEE 802.15.4 and IEEE 802.15.6, with a particular focus on improving reliability, timeliness, and energy efficiency.

World Industrial Revolutions and the Development of Artificial Intelligence System

World Industrial Revolutions and the Development of Artificial Intelligence System