Application Of Intelligent System Research Articles

CAnDOIT: Causal Discovery with Observational and Interventional Data from Time Series

The study of cause and effect is of the utmost importance in many branches of science, but also for many practical applications of intelligent systems. In particular, identifying causal relationships in situations that include hidden factors is a major challenge for methods that rely solely on observational data for building causal models. This article proposes CAnDOIT, a causal discovery method to reconstruct causal models using both observational and interventional time‐series data. The use of interventional data in the causal analysis is crucial for real‐world applications, such as robotics, where the scenario is highly complex and observational data alone are often insufficient to uncover the correct causal structure. Validation of the method is performed initially on randomly generated synthetic models and subsequently on a well‐known benchmark for causal structure learning in a robotic manipulation environment. The experiments demonstrate that the approach can effectively handle data from interventions and exploit them to enhance the accuracy of the causal analysis. A Python implementation of CAnDOIT is developed and is publicly available on GitHub: https://github.com/lcastri/causalflow.

Multifunctional Triboelectric Metamaterials with Unidirectional Charge Transfer Channels for Linear Mechanical Motion Energy Harvesting

AbstractConventional triboelectric generators (TEGs) have been developed to mainly harvest the energy of linear mechanical motions and convert it usually into oscillating or pulsive, but not sustainable, electrical outputs. In this study, unidirectional charge transfer mechanisms are introduced to develop a triboelectric mechanical metamaterial (TMM) with sustainable electrical outputs. Density functional theory and experimental analyses demonstrate three minimum necessary components of TMMs fabricated by only two triboelectric materials (i.e., copper and PTFE) to generate sustainable electrical outputs. Under a wide range of compression‐tension strain of ±50%, the maximum open‐circuit voltage, short‐circuit current, and volumetric power density are 3860 V, 8 µA, and 365.3 kW m−3, respectively. Different from most conventional cellular solids, the mechanical energy dissipation of the TMMs increases quadratically with the unit cell number. High volumetric electromechanical efficiency is achieved when the unit cell is miniaturized. In addition to energy harvesting and mechanical energy dissipation, TMMs can sense the displacement by counting the number of distinctive peaks in the short‐circuit charge transfer or reaction force versus time curves. The extreme electromechanical functionalities of the TMMs facilitate their applications in intelligent suspension systems, miniaturized green power sources, and self‐sensing energy harvesters.

Natural helical carboxymethyl cellulose fibers-based actuator for smart controller applications.

Water-responsive polymers have received great attention in the field of smart actuators due to their mechanical response to humidity without energy consumption. Natural polymers-based actuators are competitive because of their green, sustainable, and biocompatible characteristics. In this paper, we report a natural twisting carboxymethyl cellulose (CMC) fiber-based actuator (TCFA) with remarkable performance under water and moist stimulation. The water-responsive CMC fibers were prepared using wet spinning and twisting. Owing to the helical structure, good water absorption, and swelling performance, the CMC fiber exhibits excellent rotational motion with a rotation speed of 4615 rpm and recovery speed of 1538.4 rpm under water stimulation. In addition, the actuator revolutions remained almost constant after more than 254 cycles, thus demonstrating the excellent cyclic stability of this untwisting recovery process. Moreover, the CMC fiber shows application in a smart rainy curtain, smart crane, and concentration detection for alcohol solution. We offer an easy and scalable method to obtain a CMC fiber-based actuator with low cost, sustainability as well as excellent driving performance. Our work inspires a new idea in the design of cellulose-derived actuators and their application in future intelligent systems.

The Gap Between Trustworthy AI Research and Trustworthy Software Research: A Tertiary Study

With the increasing application and complexity of Artificial Intelligence (AI) systems, the trustworthiness of AI has garnered widespread attention across various fields. An AI system is a specific type of software system with unique trustworthiness requirements due to its distinctive characteristics in data and algorithms. Our objective is to investigate the state-of-the-art in trustworthy AI and trustworthy software separately and to analyze the connections and gaps between them. To this end, we conducted a tertiary study, which is a systematic literature review of existing secondary studies. These secondary studies are divided into two groups: one focuses on trustworthy AI and the other on trustworthy software. We developed frameworks for both trustworthy AI and trustworthy software, summarized the definitions of quality attributes in a structured format, and analyzed the similarities of these attributes between the two areas. Additionally, we created a swimlane diagram illustrating trustworthy practices throughout the development life-cycle and in relation to specific quality attributes. Researchers in these two areas originate from distinct research communities, leading to a significant gap between the trustworthiness of AI and software. However, we believe that existing research on trustworthy software can effectively address some gaps in trustworthy AI research, and we have identified evidence of connections between the two areas.

Application and optimisation of intelligent control system for home robots

Abstract. As a significant part of the smart home, intelligent home robots can provide a variety of convenient home services such as automatic cleaning, elderly care, entertainment interaction, etc., which are highly valued and favoured by the majority of home users, and the market scale is expanding. However, the design and optimization of the control system represents a crucial technical challenge to truly realize the intelligence and autonomy of home robots. This paper presents a comprehensive analysis of the design and optimization of automatic control systems for intelligent home robots, which aims to improve the performance and user experience of intelligent home robots and better meet the needs of families. Through collection and analysis of the research on domestic robots and their control technology ,the core technical elements of the control system are summarized and refined, and the hardware structure and software framework of the control system are analyzed. The research results provide valuable insights for improving the performance and capabilities of home robot control systems to better meet the diverse needs and expectations of home users. These innovations provide a solid foundation for advancing the development of intelligent robots in the home. Future trends in control systems may include enhanced perceptual capabilities and decision-making intelligence to enable robots to better understand and meet the needs of home users.

Design and Application of Intelligent Recommender Systems in Vocational Rehabilitation

With the rapid development of information technology, the application of intelligent recommender systems in various fields has become more and more extensive, and vocational rehabilitation, as a field focusing on personalized services, faces the challenge of how to effectively use big data and intelligent algorithms to enhance the rehabilitation effect. This paper discusses the design principles and key technologies of intelligent recommender system in vocational rehabilitation, combining data-driven recommendation algorithms with users' personalized needs to achieve accurate recommendation of vocational rehabilitation programs. Through the optimization of system architecture and functional implementation, the intelligent recommender system shows remarkable effects in practical application, effectively improving the decision-making efficiency and user satisfaction in the rehabilitation process. Finally, this paper gives an outlook on the future development of the intelligent recommendation system in the field of vocational rehabilitation, and proposes possible research directions and application expansion paths.

Rethinking prediction-based video anomaly detection from local–global normality perspective

Video anomaly detection (VAD) has been intensively studied for years because of its potential applications in intelligent video systems. Prediction-based VAD methods have gained a lot of attention in recent years due to their simplicity and effectiveness, which learn normality through a frame prediction tasks. However, we find that they often consider only local or global normality in the temporal dimension thus are difficult to generalize for various scenarios. Specifically, some of them focus on learning local spatiotemporal representations from consecutive frames to enhance the representation for normal events while the other methods are devoted to memorizing prototypical normal patterns of whole training videos to weaken the generalization for anomalies. In this paper, we rethink these two types of methods and find that the former methods are difficult to adapt to simple scenarios due to powerful representation allows these methods to represent some anomalies and causes miss detection while the latter struggle with complex scenarios due to the limitations for diverse normal patterns and causes false alarm. To verify our discoveries, we design a two-branch model, Local–Global Normality Network (LGN-Net), to simultaneously learn local and global normality. We verified the effect of local and global normality on the VAD model by ablation study and find that the model can be more adaptive to different scenarios when accounting for both normality. The code is available online: https://github.com/Myzhao1999/LGN-Net.

Design and Application of Intelligent Building Environment Monitoring System Based on Wireless Sensor Network

Design and Application of Intelligent Building Environment Monitoring System Based on Wireless Sensor Network

Expert consensus on the clinical application of efficient intelligent chromosomal karyotyping precise auxiliary diagnosis system

Chromosomal karyotyping analysis has been considered as the gold standard for cytogenetic diagnosis and an effective measure for preventing birth defects. However, conventional karyotyping analysis relies heavily on manual recognition, which is time-consuming and labor-intensive. The application of an efficient intelligent chromosomal karyotyping precise auxiliary diagnosis system in clinical practice can significantly reduce the analysis time and greatly improve the efficiency, increase the detection rate for low-percentage mosaicisms, and promote homogenization between laboratories. This can effectively enhance the capacity and level of cytogenetic diagnosis. With the continuous application of such system in the field of karyotyping analysis, a substantial amount of clinical application data and experience has been accumulated. This consensus has been formulated after multiple rounds of discussion by experts from the clinical application collaboration group of the efficient intelligent chromosomal karyotyping precise auxiliary diagnosis system. It aims to provide a reference for peers to better utilize intelligent auxiliary diagnosis systems during chromosomal karyotyping analysis and promote the standardized development of karyotyping analysis technology.

Retraction Note: Application of intelligent football training system based on IoT optical imaging and sensor data monitoring

Retraction Note: Application of intelligent football training system based on IoT optical imaging and sensor data monitoring

Research on the Intelligent System of English Teaching from the Perspective of Intercultural Education Based on Deep Learning

In today’s globalized world, cross-cultural communication skills are crucial for English learners. However, traditional English teaching often overlooks cultural differences among students, leading to difficulties in practical communication. The development of deep learning technology has provided new possibilities for English teaching, which can analyze students’ cultural background and learning needs through intelligent systems, and provide more personalized teaching plans. This study focuses on the application of intelligent system in English teaching from the perspective of intercultural education. By analyzing the characteristics of cross-cultural education and the advantages of deep learning technology, an intelligent system model of English teaching based on deep learning is proposed. The model can provide individualized English teaching programs according to students’ cultural backgrounds, learning needs and learning progress. A recommendation algorithm based on a cross-cultural education model comparison is proposed. For each student, retrieve similar teaching cases from the database based on their cultural background and learning needs. Compare the similarity between current students and cases in the database using similarity calculation algorithms. Based on the sustainable development teaching model of college English, the MAP algorithm is analyzed. The experimental results show that the intelligent English teaching system based on deep learning has a remarkable effect on improving students’ English achievement and intercultural communication ability. The model is trained and tested by the measured data. The error of the test results is less than 5%. The results of the experiment prove that the construction of a Chinese college English sustainable development education model can effectively enhance China’s cultural soft power.

A DEEP LEARNING ENSEMBLE APPROACH FOR X-RAY IMAGE CLASSIFICATION

The application of deep learning-based intelligent systems for X-ray imaging in various settings, including transportation, customs inspections, and public security, to identify hidden or prohibited objects are discussed in this study. In busy environments, x-ray inspections face challenges due to time limitations and a lack of qualified personnel. Deep learning algorithms can automate the imaging process, enhancing object detection and improving safety. This study uses a dataset of 5094 x-ray images of laptops with hidden foreign circuits and normal ones, training 11 deep learning algorithms with the 10-fold cross-validation method. The predictions of deep learning models selected based on the 70% threshold value have been combined using a meta-learner. ShuffleNet has the highest individual performance with 83.56%, followed by InceptionV3 at 81.30%, Darknet19 at 78.92%, DenseNet201 at 77.70% and Xception at 71.26%. Combining these models into an ensemble achieved a remarkable classification success rate of 85.97%, exceeding the performance of any individual model. The ensemble learning approach provides a more stable prediction output, reducing standard deviation among folds as well. This research highlights the potential for safer and more effective X-ray inspections through advanced machine learning techniques.

Research and Application of Intelligent Pedestrian Traffic Light System

This study aims to develop an intelligent pedestrian traffic light system that utilizes artificial intelligence and visual learning technology to optimize the intelligent management of traffic lights, in order to improve traffic efficiency, reduce traffic congestion, reduce the incidence of traffic accidents, and provide people with a safer and more convenient travel environment. The research team uses high-definition cameras to capture pedestrians and vehicles in real-time, and uses computer vision technology for pedestrian and vehicle detection and feature extraction. Through age classification and feature extraction, a population transit time model was constructed, and the model was optimized to improve prediction accuracy. The model was applied to traffic management for decision-making. In addition, the research also involves traffic flow analysis and real-time adjustment mechanisms, as well as the construction and application of pedestrian and vehicle models. Discover traffic patterns through data mining techniques and develop corresponding traffic management strategies. The construction and optimization adjustments of the predictive model support the implementation of the decision control model, which is based on data-driven and algorithmic optimization. The effectiveness of the model has been verified through simulation experiments and on-site testing. Finally, machine learning was used to recognize pedestrians, vehicles, and movement directions, and deep learning was applied to construct a prediction model, achieving intelligent control of pedestrian traffic lights.

Research and Application of Intelligent Pedestrian Traffic Light System

This study aims to develop an intelligent pedestrian traffic light system that utilizes artificial intelligence and visual learning technology to optimize the intelligent management of traffic lights, in order to improve traffic efficiency, reduce traffic congestion, reduce the incidence of traffic accidents, and provide people with a safer and more convenient travel environment. The research team uses high-definition cameras to capture pedestrians and vehicles in real-time, and uses computer vision technology for pedestrian and vehicle detection and feature extraction. Through age classification and feature extraction, a population transit time model was constructed, and the model was optimized to improve prediction accuracy. The model was applied to traffic management for decision-making. In addition, the research also involves traffic flow analysis and real-time adjustment mechanisms, as well as the construction and application of pedestrian and vehicle models. Discover traffic patterns through data mining techniques and develop corresponding traffic management strategies. The construction and optimization adjustments of the predictive model support the implementation of the decision control model, which is based on data-driven and algorithmic optimization. The effectiveness of the model has been verified through simulation experiments and on-site testing. Finally, machine learning was used to recognize pedestrians, vehicles, and movement directions, and deep learning was applied to construct a prediction model, achieving intelligent control of pedestrian traffic lights.

Artificial Intelligence Technology on Layered Water Injection in Oilfield Development Process

Since the 1960s, researchers have worked to advance water flooding technology to address challenges like high viscosity, low fluidity, and depleting reservoirs, aiming to prevent oil fields from becoming unproductive. The integration of artificial intelligence (AI), computer vision, and advanced algorithms like BP neural networks has recently revolutionized this field. These technological advancements have upgraded water injection methodologies, overcoming past limitations and enabling real-time monitoring and dynamic control of water injection into different oil layers. This 'intelligent layering' ensures optimized water management, enhancing overall recovery rates. This overview highlights the progression of water injection techniques, critiques traditional methods' shortcomings, and delves into the cutting-edge applications of AI-driven intelligent layering systems. It serves as a valuable guide for oil industry stakeholders, equipment manufacturers, and research institutions seeking to refine water injection practices and boost hydrocarbon extraction efficiency.

Information exchange and knowledge discovery for additive manufacturing digital thread: a comprehensive literature review

ABSTRACT With the information integration and intelligent embedding of Additive Manufacturing (AM) in intelligent industrial system applications, the information interoperability and knowledge discovery are becoming an infrangible trend that provide efficient data and knowledge-driven approaches for intelligent AM supports. STEP-NC standards in the specific AM digital thread are recognized as a potential means that can be used to explore multi-source data and knowledge analysis to support AM information exchange and knowledge discovery. As a comprehensive review of the related literatures, AM digital thread has deeply been discussed to analyse the information exchange and knowledge discovery for STEP-NC implementations. Furthermore, the STEP-NC for CNC has been reviewed to extend the possibility of STEP-NC for AM with ontology-based knowledge discovery by loads of literatures. In order to deeply explore the integration of STEP-compliant data transfer and knowledge discovery, ontology-based modelling approach and STEP-NC data transfer can be further discussed by combining the application of OntoSTEP-NC to review the literatures of data and knowledge-driven AM supports.

Field Evaluation and Application of Intelligent Quality Control Systems

During road construction, the accuracy of compaction work is critical for the structural stability and maintenance of the road. Although the plate load test (PLT) is commonly used for quality inspections, it is impractical to test every section due to time and cost constraints. Therefore, simpler testing methods are being extensively developed. This study compared quality inspection results using the commonly used PLT, the relatively simple dynamic cone penetrometer test (DCPT), the lightweight deflectometer (LWD) test, and an intelligent quality control system equipped with accelerometer and global positioning system (GPS) sensors in intelligent compaction (IC) rollers. The results showed a strong correlation between the conventional tests (PLT, DCPT, and LWD) and the values obtained from the intelligent quality control system. The correlation analysis between the intelligent quality control system and PLT, LWDT, and DCPT yielded R-square values of 0.69, 0.91, and 0.95, respectively, indicating significantly high correlations. The implementation of intelligent quality management systems in earthwork construction projects will facilitate a thorough verification of the compaction quality throughout all construction segments, ensuring consistent compaction across the project. By enabling real-time data acquisition and analysis, these systems differ markedly from traditional methods, reducing the frequency and necessity of manual inspections. This approach not only streamlines construction processes, but also enhances operational efficiency. As a result, integrating these intelligent systems is anticipated to significantly increase productivity by optimizing the workflow and resource utilization in earthwork construction.

An Effective Local Search Algorithm for Flexible Job Shop Scheduling in Intelligent Manufacturing Systems

As one of the most classical scheduling problems, flexible job shop scheduling problems (FJSP) find widespread applications in modern intelligent manufacturing systems. However, the majority of meta-heuristic methods for solving FJSP in the literature are population-based evolutionary algorithms, which are complex and time-consuming. In this paper, we propose a fast effective single-solution based local search algorithm with an innovative adaptive weighting technique (AWLS) for solving FJSP. The adaptive weighting technique assigns weights to each operation and adaptively updates them during the exploration. AWLS integrates a Tabu Search strategy and the adaptive weighting technique to smooth the landscape of the search space and enhance the exploration diversity. Computational experiments on 313 well-known benchmark instances demonstrate that AWLS is highly competitive with state-of-the-art algorithms in terms of both solution quality and computational efficiency, despite of its simplicity. Specifically, AWLS improves the previous best-known results in the literature on 33 instances and match the best-known results on the remaining ones except for only one under the same time limit of up to 300 s. As a strongly Non-deterministic Polynomia (NP)-hard problem which has been extensively studied for nearly half a century, breaking the records on these classic instances is an arduous task. Nevertheless, AWLS establishes new records on 8 challenging instances whose previous best records were established by a state-of-the-art meta-heuristic algorithm and a famous industrial solver.

Practical Application of Intelligent Vision Measurement System Based on Deep Learning

To comprehensively assess the true visual function of clinical dry eye patients and the comprehensive impact of blinking characteristics on functional vision of the human eye, an intelligent vision measurement system has been designed and developed to detect and analyze blinks from the side. The system employs deep learning keypoint recognition technology to analyze eyelid features from a lateral perspective. It presents the data of identified key points for the upper and lower eyelids in a line chart format and annotates the trough of each blink. By setting benchmark values, the system automatically calculates the proportion of complete and incomplete blinks in the tested individuals. The results indicate that the system is stable in performance and accurate in measurement, successfully achieving the anticipated design objectives. It thereby provides reliable technical support for future clinical applications.

Retraction Note: Medical intelligent system application in schizophrenia treatment satisfaction based on neural networks

Retraction Note: Medical intelligent system application in schizophrenia treatment satisfaction based on neural networks