Intelligent Management Research Articles

A human-centered intelligent platform for holographic control and management of 3D printers

Under the wave of emerging human-centric concepts such as Industry 5.0 and Human-Centered Intelligent Manufacturing, Mixed Reality (MR) has emerged as a pivotal enabling technology with profound impacts on human–machine interaction. Simultaneously, the affordability of Fused Deposition Modeling (FDM) 3D printers has expanded their accessibility beyond industrial applications to homes and public spaces. However, the operational and management complexities of different 3D printers and their varying human–machine interfaces (HMI) present challenges for users, especially beginners. In this paper, we presented a human-centered intelligent platform for FDM 3D printer control and management enabled by Mixed Reality. A flexible system architecture is developed in the paper with two essential communication protocols, MQTT and WebSocket, for stable data transfer. In addition, a task-oriented HMI is explored on the MR headset to simplify the operation procedure with little training effort and allow parallel supervision of multiple machines. By leveraging the advantages of our architecture, real-time monitoring, failure detection, and hologram visualization were able to be integrated into our platform for intelligent operation and management. Importantly, the flexibility of the architecture via a holographic interface enables our platform to easily expand to various machining operations in the future.

Federating cross-domain BIM-based knowledge graph

Despite decades of the digitization of the construction industry, interoperability challenges prevail. One of which is the lack of sufficient integration between domain-specific BIM models. Therefore, the paper proposes a novel methodology for federating cross-domain BIM-based knowledge graphs. The approach leverages the integration of multiple models into a single knowledge graph, aiming to bridge the existing gaps in data exchange and enhance the semantic richness of building information models. Through a detailed exploration of existing data exchange mechanisms, popular ontologies, and schema limitations, this research addresses the interoperability dilemma. A practical demonstration, encapsulated in the “LBD-Online-Merge” demo application, validates the methodology’s efficacy in real-world scenarios, illustrating its potential to revolutionize building operations management and data preparation for the operational phase of a building’s lifecycle. This study significantly contributes to the academic and practical discourse by providing a robust framework for achieving a more integrated, intelligent building information management ecosystem, setting a new benchmark for future research and applications in constructing semantically rich digital twins of the built environment.

Safety hazard inspection system during typhoon warning period to improve the disaster resistance ability of road and bridge construction site

Typhoons are frequent in coastal cities, and the vulnerability of road and bridge construction sites is high, but existing studies have not focused on the safety management of road and bridge construction sites under typhoon disasters. Traditional manual safety inspections suffer from problems such as low efficiency, high subjectivity, and incomplete coverage. Therefore, this study aims to develop an intelligent safety management system for road and bridge construction sites during the typhoon warning period, in order to achieve rapid and automated typhoon prevention and control. In this study, the automatic identification algorithm of potential safety hazards was developed based on the MATLAB platform, and then the system design of the MATLAB GUI was carried out, which ultimately forms the automatic safety hazard inspection and measure enquiry system during the typhoon warning period for road and bridge construction sites (RBS-TY). The system can realize the detection of vertical deformation, safety distance and safety height by using the point cloud data of the object to be measured. According to the identified safety hazards, it will automatically retrieve the database of typhoon safety measures in the road and bridge construction site and provide the countermeasures in the GUI interface. Experimentally verified results show that this system can assist safety management personnel to quickly guide typhoon prevention and control work during the 24-h typhoon warning period, improve the efficiency of safety management, and help to promote the development of intelligent typhoon emergency response technology at road and bridge construction sites.

OVERVIEW ON IMPLEMENTATION OF SMART GRID TECHNOLOGY

The Elevated energy consumption and a growing need for power in commercial, residential, and industrial settings have drawn researchers to explore novel solutions for the grid of the future. This review provides an overview on smart grid technology and its historical development from its conceptual beginnings to its today real-world implementation (intelligent networks). The review then analyzes the multifaceted impacts of smart grids such as social impact, user impact, security impact and environmental impacts. We also delve into their positive performance metrics, including enhanced reliability, efficiency, and resilience, with specific examples of improved power quality and increased renewable energy integration. Finally, the various challenges and opportunities that smart grids offer. We explore cutting-edge advancements like artificial intelligence, edge computing, & blockchain technology, showcasing their potential to overcome existing barriers and pave the way for a new era of intelligent energy management.

OPTIMIZATION OF HEAT CONSUMPTION IN CENTRAL HEATING SYSTEMS AT COMMERCIAL FACILITIES

Rising energy prices and stricter environmental regulations are exacerbating the problem of inefficient heat consumption in the commercial sector. Central heating systems of office buildings, shopping malls, and industrial complexes are often characterized by excessive thermal energy consumption, which leads to significant economic losses and a negative impact on the natural environment. The purpose of the study is to study the directions and trends of optimization in the field under consideration (taking into account modern technological capabilities, and economic factors). There are disagreements in the professional community about the priority of measures to improve energy efficiency: some experts prefer improving the thermal insulation of buildings, others — the introduction of intelligent control systems, and others — the integration of renewable energy sources. The present study demonstrates that the greatest effect is achieved with a systematic approach combining various initiatives. The conclusion is formulated that in the current conditions and the future, a comprehensive application of modern thermal insulation materials, highly efficient heating equipment, intelligent management mechanisms, and alternative energy sources is required. The article is of interest to engineers-designers of heating systems, energy managers of commercial facilities, specialists in energy efficiency of buildings, and heads of companies interested in optimizing operating costs and improving the environmental friendliness of their real estate.

70 - “Digital Twin” Intelligent Management of Large Medical Equipment Based On Internet of Things Platform

70 - “Digital Twin” Intelligent Management of Large Medical Equipment Based On Internet of Things Platform

Well control technology and digital intelligent management practice in Zhaodong Oilfield

Abstract Zhao-dong Oilfield is an offshore cooperation project in the mode of “international operation and PetroChina control”, adheres to the principle of “safety and environmental protection first, equal emphasis on production and efficiency”, practices the well control management concept of the whole life cycle, and consolidates the foundation of well control management by continuously optimizing the system and system, adheres to the integrated design of geology and engineering to control the source risk, implements fine trajectory control and anti-collision management to control the process risk, and The digital and intelligent production monitoring system is applied to control dynamic risks and realize the safe, efficient and sustainable development of Zhao-dong Oilfield.

Data Mining Technology for Smart Campus in Behavior Association Analysis of College Students

The data in smart campuses is complex and massive, with insufficient utilization, and existing data processing methods have many limitations. Therefore, in order to improve the efficiency of data processing in universities and assist in student management, a data processing method integrating cluster analysis and association rule mining is proposed. The proposed method is divided into two parts. Firstly, an improved K-Means model based on information entropy and density optimization is constructed for clustering analysis of student consumption, learning, and other data; Secondly, use the improved Mapping Apriori to obtain the correlation between student grades, consumption records, and learning behavior. The clustering results on student consumption data show that the average accuracy of ED-K-Means clustering is 97.41%, which is 12.8%, 8.5% and 4.0% higher than the comparison algorithm. The result of the correlation between consumption level and achievement shows that when the amount of consumption is less than 1500 yuan, the student’s achievement is directly proportional to the amount of consumption. Therefore, the proposed method can effectively mine and analyze student behavior data, which has important practical significance for intelligent management in universities.

Emotional Intelligence and Teacher Productivity and Performance in Public Elementary Schools: An Input for a School-Based Intervention Program

Emotional intelligence (EI) is crucial to every individual's well-being. It is a significant factor in establishing positive relationships, especially in the post-pandemic era. Teachers play vital roles in education as they are the front liners who directly interact with learners, parents, and stakeholders. Therefore, they should have a strong EI to employ constructive approaches in managing their school environment. Therefore, educational leaders and school administrators must recognize how personal challenges impact teachers' emotional intelligence, particularly during the pandemic, by establishing reliable support mechanisms that foster a positive learning atmosphere. These resources can help teachers manage personal issues and empower them to support their students effectively. The study analyzed the impact of Emotional Intelligence on teachers’ productivity and performance as input for a school-based intervention program. This descriptive research utilized a quantitative correlation approach. A simple random sampling technique was employed to determine the population sample size. The questionnaire was sent to 655 target respondents from two sub-offices of the Division of Laguna, Department of Education, Philippines, through two methods: via a Google link or using paper and pen, with the permission of their school heads. The questionnaire was sent to the target participants in two ways: through a Google link or through paper-and-pen with the permission of the participants’ school heads. The results revealed that the respondents demonstrated high emotional intelligence but average stress management. On the other hand, they were both at high levels in terms of productivity and performance. The relationship between emotional intelligence and teachers’ productivity exhibited moderate correlations, while there was a strong correlation between Emotional Intelligence and Teaching Performance. Finally, some demographic factors influenced the relationship between emotional intelligence (EI) and Teaching Performance. Most school interventions focus on skill development, curriculum development, teaching methodologies, or student support systems, and Emotional Intelligence is often neglected. Providing emotional support to teachers is important for recognizing how their emotional states impact their effectiveness and the learning environment. This support can help to continually enhance productivity and performance, leading to better learning outcomes.

AI-enhanced fault-tolerant control and security in transportation and logistics systems: addressing physical and cyber threats

Transportation and logistics systems are becoming increasingly complex and critical to modern infrastructure. This paper proposes a novel AI-enhanced fault-tolerant control framework to address the dual challenges of physical malfunctions and cyber threats. By leveraging advanced machine learning algorithms and real-time data analytics, the proposed methodology aims to enhance the reliability, safety, and security of transportation and logistics systems. This research explores the foundations and practical implementations of AI-driven anomaly detection, predictive maintenance, and autonomous response systems. The findings demonstrate significant improvements in system resilience and robustness, making a substantial contribution to the field of intelligent transportation management.

AI-powered decision making for road safety optimization under probabilistic linguistic Sugeno-Weber aggregation information

Traffic accidents, a global concern, pose threats to lives and carry substantial economic and societal burdens. This paper explores the innovative integration of artificial intelligence (AI) into the ambiance of Multi Criteria Decision Making (MCDM) to address the challenge. We investigate AI's potential to enhance road safety globally, utilizing data analysis, predictive modeling, and intelligent traffic management systems. Our goal is to revolutionize accident prevention by optimizing decision-making processes and promoting safer and more efficient road environments. In the realm of information aggregation and fusion, growing interest among researchers has centered on probabilistic linguistic expression sets, which adeptly aggregate uncertain data. This article's primary aim is to explore methodologies for aggregating information within a probabilistic linguistic environment. To achieve this goal, we have introduced procedural laws based on the Sugeno-Weber (SW) framework for handling probabilistic linguistic term elements (PLTEs), rooted in both the product and sum of SW operations. Consequently, we have crafted a range of probabilistic linguistic aggregation techniques, encompassing the probabilistic linguistic SW Average (PLSWA) and Geometric (PLSWG), followed by weighted as well as ordered aggregation operators like PLSWWA and PLSWWG, PLSWOWA and PLSWOWG. By harnessing SW τ-norm and τ-conorm, we have developed versatile aggregation tools that facilitate information reinforcement. Through the utilization of proposed operators, we have presented strategies for effectively integrating probabilistic linguistic term sets (PLTs) in the context of MCDM. We have compared our suggested procedures with the TOPSIS approach and elucidated the diverse features inherent to these operators.

Estrategias de Gestión para la Optimización del Tránsito Urbano en el Cantón La Concordia: Desafíos y Oportunidades

This article analyzes the impact of traffic congestion in La Concordia, Ecuador, from an administrative perspective, and proposes solutions based on a review of existing literature. The study is based on the growing problem of urbanization and insufficient transportation planning, which has caused significant problems in urban traffic, affecting both the quality of life of citizens and the economic productivity of the region. The objective of the study is to propose viable administrative strategies that can reduce vehicular congestion, based on previous experiences in Latin American cities with similar characteristics. In methodological terms, a qualitative literature review was carried out to analyze successful cases in the implementation of sustainable mobility policies, prioritizing those studies that present solutions for medium-sized developing cities. The results of the analysis highlight the importance of implementing intelligent traffic management systems, improving public transport and encouraging the use of non-motorized means of transport, such as bicycles and pedestrian routes. The discussion highlights that, despite technological and administrative advances, there are significant challenges such as lack of funding and social resistance to the use of public transport. The conclusions suggest that the implementation of these solutions must be accompanied by a cultural change and a greater commitment from local authorities to ensure their long-term effectiveness.

Strategic Management Practices in Multinational Corporations: A Comparative Study

This paper explores strategic management practices within multinational corporations (MNCs) through a comparative study of different regional and industrial contexts. The research aims to identify key strategies that drive success and sustainability in a globalized business environment. Utilizing a mixed-method approach, the study combines quantitative analysis of financial performance metrics with qualitative insights from interviews with senior executives. The findings highlight the significance of adaptive strategies, cultural intelligence, and robust risk management frameworks. Additionally, the study uncovers regional variations in strategic priorities, emphasizing the need for MNCs to tailor their approaches to local market conditions. The paper concludes with recommendations for practitioners to enhance strategic alignment and operational effectiveness across diverse geographical landscapes.

The integration of Artificial Intelligence in demand forecasting and inventory management in the United States

The integration of Artificial Intelligence in demand forecasting and inventory management in the United States has been examined in this paper. Demand forecasting and inventory management are two of critical areas in supply chain management, which is a veritable tool for promoting industrialization, manufacturing capabilities, and customers satisfaction. The use of AI in form of robotics, machine learning, deep learning, and predictive analytics, among others in all aspects of supply chain operations is gaining ground by the day. The integration of AI into the supply chain process can sustain multi-billion dollars trades in the United States, by reducing the cost of production and distribution, reducing human errors causing inaccurate demand forecasts, return shipment and cancellations of orders, etc. The challenges relating to the use of AI in demand forecasting and inventory management such as high cost of installation and maintenance, data privacy violations, requirement of skilled personnel, which are limited in global supply, and employees’ resistance to change were also identified. The outlook of relationship between Artificial Intelligence and supply chain management looks hopeful, brighter, and encouraging. This will be made possible by continuous development of AI capabilities and reducing the challenges of its widespread integration.

Reinforcement Learning-Based Adaptive Optimal Control Model for Signal Light in Intelligent Transportation Systems

Day-to-day mobility among the population has increased with economic growth. Smart cities are renovated with advanced technologies to admire modern life in which intelligent transportation becomes highly focused. Because the traffic signal control systems are fixed at a constant time. They split the traffic signal into predetermined intervals and function inefficiently; they result in long wait times, waste fuel and increased carbon emissions. This research study introduces a novel technique for traffic light management to reduce the uncertainties in the system. A dynamic and intelligent traffic light adaptive optimal management system (DITLAOCS) is implemented in this research. It does this by modifying the traffic signal duration in run time and using real-time traffic data as input. Furthermore, the proposed DITLAOCS executes based on three modes: fairness mode (FM), priority mode (PM) and emergent mode (EM). In fairness mode (FM), all vehicles are prioritized equally, while vehicles in different categories receive varying priority levels. Emergency vehicles, on the other hand, receive the highest priority. Furthermore, a fuzzy inference method based on traffic data is shown to choose one mode out of three (FM, PM and EM). This model uses deep reinforcement learning to switch traffic lights in three different phases (red, green and yellow). We evaluated and accurately simulated DITLAOCS on the Shaanxi city map in China using Simulation of Urban MObility (SUMO), an open-source simulator. The simulation results illustrate the efficiency of DITLAOCS when compared to other cutting-edge algorithms on several performance measures.

Research on Construction of Knowledge Base for MRI Equipment Maintenance Based on Knowledge Graph

To improve the level of modern hospital management and establish a scientific and effective medical equipment maintenance guarantee system, this paper proposes the construction of a dynamic interactive fault repair knowledge base system for magnetic resonance imaging (MRI) equipment based on knowledge graph technology. This innovative application of intelligent management for large medical equipment involves collecting and organizing actual maintenance data. The paper constructs the medical equipment maintenance knowledge base, with MRI equipment as a case study, and initially implements functions such as fault information retrieval, technical guidance, and training and learning for MRI equipment. These functions aim to provide an objective decision-making basis for the maintenance and management of large medical equipment, enhance the efficiency of fault repair and maintenance, and elevate the construction level of intelligent hospitals.

Research on Digital Technology of Paleontological Skeletal Specimens and Its Application in Museum Display

Digital technology plays an increasingly important role in the display of paleontological skeletal specimens. Through technologies such as high-precision scanning and virtual reality, viewers can immerse themselves in the living environment of ancient organisms and experience unprecedented interaction. In this process, there are also technical challenges, such as the fragility of specimens, the balance between scientific and educational display content, and so on. In order to fully leverage the advantages of digital technology, museums need to strengthen technological research and innovation, enhance audience education and guidance, and focus on cross platform cooperation and intelligent management. In the future, with the continuous advancement of technology, personalized and customized display experiences will become possible, bringing audiences richer and more vivid knowledge of paleontology. The application of digital technology not only enhances the display effect of paleontological specimens, but also injects new vitality into paleontological research and knowledge dissemination. Thoroughly studying the digital technology of paleontological skeletal specimens and its application in museum exhibitions is of great significance for promoting the development of paleontological research and enhancing public scientific literacy.

Tourist attraction reviews based on deep learning Sentiment Analysis System

In recent years, with the rapid development of information technology, online travel reviews have become a key source of information for tourists to understand destinations. In the face of massive and unstructured travel review data, this paper innovatively proposes a sentiment analysis system for tourist attraction reviews based on deep learning. The system cleverly uses the BERT pre-trained model, and combines Bi-LSTM and attention mechanism to significantly improve the accuracy of sentiment classification. In order to verify its effectiveness, this paper conducts experiments on the evaluation data of scenic spots in Hubei Province. The experimental results are very impressive through multi-dimensional indicators such as accuracy, precision, recall, and F1-score. The accuracy rate is as high as 93.5%, the accuracy rate is 94.1% for positive reviews, the accuracy rate for negative reviews is 92.8%, the recall rate is 92.1%, and the F1-score is 92.8%. This fully shows that the system can accurately identify the emotional tendency of tourist attraction reviews, provide a strong basis for scenic spot management, help it adjust service strategies in a timely manner, and also assist tourists to make more informed decisions, providing a reliable technical reference for the digital transformation and intelligent management of the tourism industry.

Industrial IoT-Based Energy Monitoring System: Using Data Processing at Edge

Edge-assisted IoT technologies combined with conventional industrial processes help evolve diverse applications under the Industrial IoT (IIoT) and Industry 4.0 era by bringing cloud computing technologies near the hardware. The resulting innovations offer intelligent management of the industrial ecosystems, focusing on increasing productivity and reducing running costs by processing massive data locally. In this research, we design, develop, and implement an IIoT and edge-based system to monitor the energy consumption of a factory floor’s stationary and mobile assets using wireless and wired energy meters. Once the edge receives the meter’s data, it stores the information in the database server, followed by the data processing method to find nine additional analytical parameters. The edge also provides a master user interface (UI) for comparative analysis and individual UI for in-depth energy usage insights, followed by activity and inactivity alarms and daily reporting features via email. Moreover, the edge uses a data-filtering technique to send a single wireless meter’s data to the cloud for remote energy and alarm monitoring per project scope. Based on the evaluation, the edge server efficiently processes the data with an average CPU utilization of up to 5.58% while avoiding measurement errors due to random power failures throughout the day.

Deep Learning for Water Quality Prediction—A Case Study of the Huangyang Reservoir

Water quality prediction is a fundamental prerequisite for effective water resource management and pollution prevention. Accurate predictions of water quality information can provide essential technical support and strategic planning for the protection of water resources. This study aims to enhance the accuracy of water quality prediction, considering the temporal characteristics, variability, and complex nature of water quality data. We utilized the LTSF-Linear model to predict water quality at the Huangyang Reservoir. Comparative analysis with three other models (ARIMA, LSTM, and Informer) revealed that the Linear model outperforms them, achieving reductions of 8.55% and 10.51% in mean square error (MSE) and mean absolute error (MAE), respectively. This research introduces a novel method and framework for predicting hydrological parameters relevant to water quality in the Huangyang Reservoir. These findings offer a valuable new approach and reference for enhancing the intelligent and sustainable management of the reservoir.