International Journal of Engineering Intelligent Systems

Remote Monitoring System Based on the Internet of Things and Monitoring Method for Design of Construction Machinery

2025-10-18T21:57:16-07:00

The monitoring of construction machinery is a major safety issue, and the prevention of accidents is something that concerns anyone responsible for the security of construction machinery. How to establish a safe, reliable, stable and scalable monitoring system for construction machinery is a crucial issue. In recent years, as the concept of the Internet ofThings (IoT) has gradually been applied to remote monitoring technology, an increasing number of applications have emerged for the remote monitoring of construction machinery, making it a key research direction. Remote monitoring can be considered as either "monitoring" or "control". Of these, "monitoring" is remote monitoring, which can be the monitoring of the environment or the monitoring of the computer system and the network devices. To address the issue of construction machinery safety, this current study used
the IoT positioning algorithm to determine the impact that remote monitoring systems have on safety in the construction of a large-scale building safety and, so that the relationship between remote monitoring and construction machinery design. Typically, the positioning algorithm uses various information sources, such as visible light sources, infrared sources, microwave sources, and landform fluctuations to produce images. Experimental results indicated that in normal environments, the parameters of payload, security processing, load density and transmission scheduling were improved. The RTT value of data transmission was tested and analyzed on UDP and SMS channels. Factors such as channel delay, security encapsulation, data
compression, channel type and queue parameters were analyzed. The validity, reliability and safety of the remote monitoring system for large-scale building monitoring were verified, indicating that the remote monitoring system and monitoring method using the Internet of Things can be applied to, and improved, the design of construction machinery.

Keywords: construction machinery, Internet of Things (IoT), remote monitoring system, monitoring method, positioning algorithm

Cite As

L. Han, L. Gao, S. Feng, F. Qu, B. Song, "Remote Monitoring System Based on the Internet of Things and Monitoring Method
for Design of Construction Machinery", Engineering Intelligent Systems, vol. 33 no. 5, pp. 485-498, 2025.

Collaborative Operation of Artificial Intelligence and Mechanical Arm Based on Transformer-XL

2025-10-18T22:05:59-07:00

In response to the problems of insufficient flexibility, slow response speed, and poor long-term dependency processing ability in traditional mechanical arm collaborative operations, this article combined Transformer-XL and deep Q-learning (DQL) to improve the adaptability and decision-making efficiency of mechanical arms in complex dynamic environments, thereby achieving more efficient and accurate collaborative operations. Firstly, the collected sensor data can be cleaned and normalized to improve the effectiveness of model training. Secondly, building deep learning models based on the Transformer-XL architecture can improve the ability to capture long-term dependency relationships. Then, based on the DQL algorithm, the decision-making process of the mechanical arm in dynamic environments can be optimized. Finally, the output of Transformer-XL can be combined with DQL to form an efficient collaborative control strategy. The experimental results show that compared with traditional control methods, DQL (combined with Transformer-XL) exhibits significant advantages in various task environments. In more complex dynamic obstacle avoidance tasks, the decision time is only 220 milliseconds, while maintaining a success rate of 88%. It demonstrates faster response speed and higher success rate in complex dynamic environments. At the same time, it still maintains a success rate of 60% with a delay of 90 seconds, demonstrating stronger adaptability in dynamic environments compared to traditional control strategies. The outstanding performance in high dynamic environments also validates the reliability of the research content.

Keywords: Mechanical Arm; Transformer-XL Architecture; Deep Q-learning; Normalization Processing; Control Strategy

Cite As

W. Zhang, M. Xia, B. Yang, "Collaborative Operation of Artificial Intelligence and Mechanical Arm Based on Transformer-XL",
Engineering Intelligent Systems, vol. 33 no. 5, pp. 499-511, 2025.

Consumer Preference Analysis and Marketing Strategy in Digital Economy Based on Deep Learning

2025-10-18T22:11:32-07:00

With the rapid development of the digital economy, consumer behavior has become both complex and diverse. The traditional marketing model has difficulty meeting the increasingly personalized needs of modern consumers. How to comprehensively understand consumer preferences and implement accurate and effective marketing strategies has become a problem for enterprises. Through deep learning technology, this study analyzes the changing trend of consumer preferences, explores data-driven personalized marketing strategies, and provides theoretical support and practical guidance for enterprises wishing to develop more efficient marketing programs. The preference characteristics of consumers in the context of digital economy are mined using a method that combines a deeplearning model with big data analysis. Alargeamountofconsumerbehaviordatais collected and analyzed, and a consumer preference prediction model is constructed. Furthermore, the role of technologies, such as digital marketing tools and personalized recommendation systems, in enhancing consumer satisfaction and increasing corporate income is discussed. The research results show that the personalized characteristics of consumer preferences are obvious, and deep learning technology can effectively capture the differences among consumergroups, and improve the effectiveness of marketing strategies through accurate personalized recommendations. Marketing strategies based on consumer preferences have higher conversion rates than traditional methods, and can encourage consumer participation and strengthen brand loyalty. The research also found that the use of technologies in the digital economy plays can improve marketing efficiency and optimizing resource allocation. This study provides a theoretical basis for enterprises wishing to develop personalized marketing strategies, and provides practical guidance for the application of deep learning in digital marketing. As digital technologies continue to advance, future research is expected to optimize models and strategies to help companies gain an edge in the increasingly competitive market.

Keywords: Consumer preference; deep learning; digital economy; personalized marketing; recommendation system

Cite As

D. Cai, "Consumer Preference Analysis and Marketing Strategy in Digital Economy Based on Deep Learning",
Engineering Intelligent Systems, vol. 33 no. 5, pp. 513-524, 2025.

Design of Vehicle Structural Intelligent Systems Stability and Safety Control System Based on Genetic Algorithm

2025-10-24T21:51:52-07:00

In the field of vehicle structural stability and safety control, traditional optimization methods lack sufficient global search capability and flexibility, making it difficult to simultaneously consider the conflicts and nonlinear relationships between various objectives in complex and multi-objective dynamic environments. In response to these issues, this study used a control system design method based on the improved SPEA2 (Strength Pareto Evolutionary Algorithm 2) to optimize suspension system parameters and power allocation strategies through a combination of elite strategy and Pareto ranking. By constructing a multi-objective optimization model, suspension stiffness, body inclination angle, and power distribution ratio were used as optimization variables, and then a dynamic weight update mechanism was introduced to address target conflicts under complex operating conditions. The experimental results showed the method proposed in this study controlled the yaw rate of the vehicle within 1.4 rad/s during sharp turns, reduced the wheel slip rate to 6.0% on slippery roads, and shortened the power response time to 2.4 seconds. This method effectively improves the dynamic performance of vehicles in complex environments, strengthening the robustness of the system and enhancing its adaptability.

Keywords: genetic algorithm optimization, vehicle stability control, safety control system, multi-objective optimization, pareto-based ranking, suspension system design

Cite As

X. Feng, " Design of Vehicle Structural Intelligent Systems Stability and Safety Control System Based on Genetic Algorithm",
Engineering Intelligent Systems, vol. 33 no. 5, pp. 525-534, 2025.

Dynamic Panel Data Estimation of the Impact of Digital Finance on Technological Innovation Performance of Chinese Enterprises Based on BP Neural Network Analysis

2025-10-24T21:58:35-07:00

Given the rapidly evolving field of digital finance, this study used a combination of back propagation (BP) network analysis and dynamic panel data estimation to investigate the impact of digital finance on the performance of Chinese enterprises in regard to scientific and technological innovation. The data was sourced from two open datasets: Quandl and Crunchbase. After cleaning and standardizing the data, a dynamic panel data model was constructed and analyzed using a BP neural network (BPNN).Through an evaluation of the BPNN model’s dynamic adaptability index, characteristic importance consistency index, and innovation contribution index, this study determined the relationship between digital finance and enterprise’s scientific and technical innovation performance. The results showed that for the two datasets, the dynamic adaptability indexes of BPNN model were above 82% and 83% respectively, indicating that the model has strong adaptability to data changes in different time periods. The consistency index of feature importance was above 86%, indicating the stability of the model when judging the importance of different input features. The findings demonstrate that the BPNN model has clear advantages when analyzing the relationship between digital finance and the scientific and technological innovation performance of enterprises. It can accurately capture the complex relationship between them and provide a reference for enterprises and policy makers. However, there are several limitations such as limited data set selection and incomplete evaluation indicators. Overall, the innovation contribution index is above 81% and 86%, which highlights the unique contribution of digital finance-related characteristics to the performance prediction of scientific and technological innovation of enterprises. By means of innovative evaluation indicators, the study also comprehensively analyzes the effect of applying the BPNN model in this field, thereby providing a valuable reference for subsequent research and practice.

Keywords: digital finance; scientific and technological innovation; back propagation network; dynamic adaptability; consistency of feature importance.

Cite As

H. Zhang, "Dynamic Panel Data Estimation of the Impact of Digital Finance on Technological Innovation Performance of Chinese Enterprises Based on BP Neural Network Analysis", Engineering Intelligent Systems, vol. 33 no. 5, pp. 535-544, 2025.

High-precision Motion Capture System Based on 3D Space Animation Scene Construction Based on Optical Fiber Communication Network

2025-10-24T22:04:38-07:00

Motion capture technology has advanced quickly in recent years thanks to the rapid development of sensor technology, the invention of inertial navigation, and the ongoing development of hardware configurations for measurement and computation. With the advancement of communication technology, optical fiber communication networks are now the most widely used form of communication, and they are fiercely competitive with other forms of communication. Consequently, the combination of a motion capture system and an optical fiber communication network offer several potential applications. For instance, the electronic optical motion capture technology and the virtual reality technology can be combined to produce a very realistic character animation effect in the creation of large-scale 3D games. Human motion capture technology is widely used in the film and television animation industry to make the characters more realistic. In this research, a high-precision motion capture system based on a three dimensional space animation scenario using an optical fiber communication network is proposed. The system was built using the technology of the optical fiber communication network and motion capture. An experiment was conducted to determine the accuracy of the motion capture system. The experimental findings indicate that without a high-speed optical fiber in the time domain, the average motion capture accuracy was 46.7%. The addition of high-speed optical fiber time domain improved the average accuracy of the motion capture by 2.5%, and improved the performance of each joint point capture.

Keywords: motion capture system, optical fiber communication network, three-dimensional space, animation scene

Cite As

J. Li, "High-precision Motion Capture System Based on 3D Space Animation Scene Construction Based on Optical Fiber Communication Network", Engineering Intelligent Systems, vol. 33 no. 5, pp. 545-553, 2025.

Improving the Effectiveness of English Cross-Cultural Business Communication Based on Fuzzy Logic

2025-10-24T22:09:54-07:00

With the advancement of globalization, cross-cultural business communication has become an important factor for the success of enterprises. Communication between people from different cultural backgrounds can lead to misunderstandings, conflicts, and difficulties in conveying information accurately. These issues can affect the international development of enterprises. In order to improve the effectiveness of cross-cultural communication, this paper builds a new cross-cultural model based on fuzzy logic theory to evaluate the effectiveness of communication. The fuzzy reasoning method is used to comprehensively analyze and optimize communication efficiency by taking into account cultural fitness, communication success rate, language articulation and other variables. Firstly, the communication data for different cultural backgrounds are collected, and the fuzzy logic model is used to predict and evaluate the effectiveness of cross-cultural communication. The model’s effectiveness in handling uncertainty and ambiguity from cultural differences is validated by comparing the model-predicted communication effectiveness scores with observed outcomes from real cross cultural business interactions, operationalized as communication success rate, clarity/accuracy of information transfer, cultural adaptability ratings, and participant satisfaction. The results show that the fuzzy logic model can accurately indicate the complex factors in cross-cultural communication, and provide theoretical support and practical guidance for improving communication efficiency.

Keywords: cross-cultural communication; fuzzy logic; communication effectiveness; cultural differences; business communication

Cite As

X. Zhang, "Improving the Effectiveness of English Cross-Cultural Business Communication Based on Fuzzy Logic", Engineering Intelligent Systems, vol. 33 no. 5, pp. 555-564, 2025.

Integration and Effect Analysis of Artificial Intelligence in Intercultural Communication English Teaching

2025-10-24T22:15:11-07:00

Traditional English teaching methods are often limited to the imparting of language knowledge, and neglect the cultivation of students’ intercultural communication skills. In recent years, the rapid development of artificial intelligence (AI) technology has brought new opportunities to the field of education. In language teaching, the application ofAI technology can support the cultivation of cross-cultural communication competence. This study aims to explore theeffect of applying AI to intercultural communication English teaching, and combineAI technology with intercultural communication teaching to improve students’ intercultural understanding and communication ability. Using experimental research methods, an experimental group and a control group were set up to compare the differences between AI-assisted teaching and traditional teaching methods in improving intercultural communication ability. The application of specific AI tools in the classroom, the improvement of students’ intercultural communication ability after using AI tools, and the evaluation of teachers regarding the teaching effect of AI are studied. By means of both quantitative and qualitative analysis, the outcome of using AI for intercultural communication English teaching is evaluated. According to the results, the students in the experimental group showed significant improvement in cross-cultural communication ability, and were better than the students in the control group in terms of cultural adaptability, cross-cultural understanding and communication confidence. Generally, teachers believed that AI tools can increase classroom interaction and increase students’ motivation to learn and participate, confirming the effectiveness and potential of using AI for the teaching of English. This study provides a new perspective and methodology for intercultural communication English teaching, and proposes a teaching model that combines AI technology with traditional teaching methods. This study can provide a valuable reference for the future application of AI in the field of education.

Keywords: artificial intelligence; cross-cultural communication; English teaching; teaching mode; cultural adaptability

Cite As

N. Wang, "Integration and Effect Analysis of Artificial Intelligence in Intercultural Communication English Teaching",
Engineering Intelligent Systems, vol. 33 no. 5, pp. 565-579, 2025.

Intelligent Diagnosis Algorithm for Bearing Faults Based on Artificial Intelligence Deep Learning

2025-10-24T22:20:47-07:00

The performance and dependability of mechanical equipment have always been greatly impacted by bearing failure, which has long been a regular issue. The manual monitoring and analysis that is often required by the traditional Bearing Fault Diagnosis (abbreviated as BFD for convenience) procedures is inefficient and prone to error. In order to address this issue, this study examines the BFD method based onArtificial Intelligence (AI) Deep Learning (DL) and create a BFD model that maximizes BFD efficiency and accuracy by utilizing CNN-ETR (Convolutional Neural Networks-Extreme Randomized Trees Regression) under DL technology. The research results indicated that under the same other conditions, for three different types of faults, the diagnostic time of the experimental group was below 2.5 seconds, while the diagnostic time of the control group was between 2.5 seconds and 5 seconds. The diagnostic time of the experimental group was significantly lower than that of the control group, indicating a positive relationship between DL and the efficiency of the BFD algorithm.

Keywords: intelligent diagnosis algorithm for bearing faults; deep learning; artificial intelligence; diagnosis time; energy consumption

Cite As

Z. Han, C. Zong, "Intelligent Diagnosis Algorithm for Bearing Faults Based on Artificial Intelligence Deep Learning",
Engineering Intelligent Systems, vol. 33 no. 5, pp. 581-589, 2025.

Student Management and Career Guidance in Schools Through Data Analysis

2025-10-24T22:26:09-07:00

This paper analyzed the learning status of sophomore students at the North China University of Science and Technology using the random forest (RF) algorithm, and the employment direction of graduates from the same university using the association rule algorithm. The results indicated that the random forest (RF) algorithm performed best when the number of decision trees was set to 70. Among the factors influencing learning status, the factor with the highest relative importance was “final exam score”, followed by “midterm exam score” and “online learning time”. The association rule algorithm was effective in mining the rules that impact the employment direction of graduates.

Keywords: random forest, association rule, learning status, career guidance

Cite As

H. Chen, X. Zhang, " Student Management and Career Guidance in Schools Through Data Analysis",
Engineering Intelligent Systems, vol. 33 no. 5, pp. 591-595, 2025.

The Impact of Employees’ Mental Health Status on Performance Based on Data Mining

2025-10-24T22:31:06-07:00

Withtheintensification ofglobal competition, employee mental health issues have increasingly become akey factor affecting corporate performance. A large multinational technology company A has more than 20,000 employees worldwide. It was found that about 40% of employees had experienced varying degrees of mental health challenges, such as anxiety and depression, in the past year. To meet this challenge, Company A launched the “Psychological Capital Improvement Program", which aims to evaluate and improve employees’ mental health through data mining technology and psychological models, and improve job satisfaction and performance. The project team first conducted demand research and technology selection, chose suitable data mining tools, and established a multi-source data collection platform, integrating information such as mental health questionnaires, behavioral logs, and physiological signals. Through multiple linear regression, LSTM model and cluster analysis, the complex relationship between mental health status and performance was revealed. Based on these analysis results, personalized management strategies were formulated, such as interventions to stimulate work motivation and support mental health. Ultimately, through continuous monitoring and optimization of processes, Company A significantly improved the mental health level and work efficiency of its employees, while also promoting the growth of innovation capabilities. The success of this project not only brought significant economic benefits and social value to Company A, but also provided valuable experience for other companies in terms of mental health management.

Keywords: mental health management, data mining, personalized intervention, performance improvement, LSTM

Cite As

F. He, "The Impact of Employees’ Mental Health Status on Performance Based on Data Mining",
Engineering Intelligent Systems, vol. 33 no. 5, pp. 597-606, 2025.