Search Results (80)

With the rise of digital twin technology, the application of digital twin technology to industrial automation provides a new direction for the digital transformation of the global smart manufacturing industry. In order to further improve production efficiency, as well as realize enterprise digital empowerment, this paper takes a welding robot arm as the research object and constructs a welding robot arm digital twin system. Using three-dimensional modeling technology and model rendering, the welding robot arm digital twin simulation environment was built. Parent–child hierarchy and particle effects were used to truly restore the movement characteristics of the robot arm and the welding effect, with the help of TCP communication and Bluetooth communication to realize data transmission between the virtual segment and the physical end. A variety of UI components were used to design the human–machine interaction interface of the digital twin system, ultimately realizing the data-driven digital twin system. Finally, according to the digital twin maturity model constructed by Prof. Tao Fei’s team, the system was scored using five dimensions and 19 evaluation factors. After testing the system, we found that the combination of digital twin technology and automation is feasible and achieves the expected results. Full article

Quantum technologies are increasingly pervasive, underpinning the operation of numerous electronic, optical and medical devices. Today, we are also witnessing rapid advancements in quantum computing and communication. However, access to quantum technologies in computation remains largely limited to professionals in research organisations and high-tech industries. This paper demonstrates how traditional neural networks can be transformed into neuromorphic quantum models, enabling anyone with a basic understanding of undergraduate-level machine learning to create quantum-inspired models that mimic the functioning of the human brain—all using a standard laptop. We present several examples of these quantum machine learning transformations and explore their potential applications, aiming to make quantum technology more accessible and practical for broader use. The examples discussed in this paper include quantum-inspired analogues of feedforward neural networks, recurrent neural networks, Echo State Network reservoir computing, and Bayesian neural networks, demonstrating that a quantum approach can both optimise the training process and equip the models with certain human-like cognitive characteristics. Full article

Safe and reliable lunar landings are crucial for future exploration of the Moon. The regolith ejected by a lander’s rocket exhaust plume represents a significant obstacle in achieving this goal. It prevents spacecraft from reliably utilizing their navigation sensors to monitor their trajectory and spot emerging surface hazards as they near the surface. As part of NASA’s 2024 Human Lander Challenge (HuLC), the team at the University of Michigan developed an innovative concept to help mitigate this issue. We developed and implemented a machine learning (ML)-based sensor fusion system, ARC-LIGHT, that integrates sensor data from the cameras, lidars, or radars that landers already carry but disable during the final landing phase. Using these data streams, ARC-LIGHT will remove erroneous signals and recover a useful detection of the surface features to then be used by the spacecraft to correct its descent profile. It also offers a layer of redundancy for other key sensors, like inertial measurement units. The feasibility of this technology was validated through development of a prototype algorithm, which was trained on data from a purpose-built testbed that simulates imaging through a dusty environment. Based on these findings, a development timeline, risk analysis, and budget for ARC-LIGHT to be deployed on a lunar landing was created. Full article

The era of openBIM is ongoing, and the open standards IDS (Information Delivery Specification) and bSDD (BuildingSMART Data Dictionary) are significantly impacting the automation of information exchange and verification in projects, using predefined data, enabling quick updates and combining it with other data. IDS and bSDD complement the widely used open IFC (Industry Foundation Classes) format, which solves the issue of purchasing both the appropriate hardware and software to work with native files from different sources. As a result, external assignments or internal tasks have the potential to precisely define the desired product, speeding up the entire process carried out according to the BIM (Building Information Modeling) methodology, reducing the number of questions about ambiguous requirements, and eliminating the need for continuous feedback on the model. Both files can be used on the developer’s side as an attachment to BIM documents, as well as on the construction site or during the bidding process. Digital IDS and bSDD files can be interpreted not only by humans but also by machines, bringing added value and usability. An identified research gap is the lack of a clear procedure for applying the mentioned standards, and thus, the common problem of purchasing software to check the quality of the model for information content. This article demonstrates the possibility of creating IDS and bSDD files in tools based on filling in specific fields and their interrelation, as well as their practical use in the process of verifying the information content of BIM models. By adopting open standards, teams can improve communication, increase productivity, and ensure continuity in data exchanges. Full article

In recent years, novel findings have progressively and promisingly supported the potential role of Artificial intelligence (AI) in transforming the management of various neoplasms, including hepatocellular carcinoma (HCC). HCC represents the most common primary liver cancer. Alarmingly, the HCC incidence is dramatically increasing worldwide due to the simultaneous “pandemic” spreading of metabolic dysfunction-associated steatotic liver disease (MASLD). MASLD currently constitutes the leading cause of chronic hepatic damage (steatosis and steatohepatitis), fibrosis, and liver cirrhosis, configuring a scenario where an HCC onset has been reported even in the early disease stage. On the other hand, HCC represents a serious plague, significantly burdening the outcomes of chronic hepatitis B (HBV) and hepatitis C (HCV) virus-infected patients. Despite the recent progress in the management of this cancer, the overall prognosis for advanced-stage HCC patients continues to be poor, suggesting the absolute need to develop personalized healthcare strategies further. In this “cold war”, machine learning techniques and neural networks are emerging as weapons, able to identify the patterns and biomarkers that would have normally escaped human observation. Using advanced algorithms, AI can analyze large volumes of clinical data and medical images (including routinely obtained ultrasound data) with an elevated accuracy, facilitating early diagnosis, improving the performance of predictive models, and supporting the multidisciplinary (oncologist, gastroenterologist, surgeon, radiologist) team in opting for the best “tailored” individual treatment. Additionally, AI can significantly contribute to enhancing the effectiveness of metabolomics–radiomics-based models, promoting the identification of specific HCC-pathogenetic molecules as new targets for realizing novel therapeutic regimens. In the era of precision medicine, integrating AI into routine clinical practice appears as a promising frontier, opening new avenues for liver cancer research and treatment. Full article

As the world’s population continues to rise while the agricultural workforce declines, farmers are increasingly challenged to meet the growing food demand. Strawberries grown in the U.S. are especially threatened by such stipulations, as the cost of labor for such a delicate crop remains the bulk of the total production costs. Autonomous systems within the agricultural sector have enormous potential to catalyze the labor and land expansions required to meet the demands of feeding an increasing population, as well as heavily reducing the amount of food waste experienced in open fields. Our team is working to enhance robotic solutions for strawberry production, aiming to improve field processes and better replicate the efficiency of human workers. We propose a modular configuration that includes a Delta X parallel robot and a pneumatically powered end effector designed for precise strawberry harvesting. Our primary focus is on optimizing the design of the end effector and validating its high-speed actuation capabilities. The prototype of the presented end effector achieved high success rates of 94.74% in simulated environments and 100% in strawberry fields at Farias Farms, even when tasked to harvest in the densely covered conditions of the late growing season. Using an off-the-shelf robotic configuration, the system’s workspace has been validated as adequate for harvesting in a typical two-plant-per-row strawberry field, with the hardware itself being evaluated to harvest each strawberry in 2.8–3.8 s. This capability sets the stage for future enhancements, including the integration of the machine vision processes such that the system will identify and pick each strawberry within 5 s. Full article

The rapid integration of Internet of Things (IoT) systems in various sectors has escalated security risks due to sophisticated multilayer attacks that compromise multiple security layers and lead to significant data loss, personal information theft, financial losses etc. Existing research on multilayer IoT attacks exhibits gaps in real-world applicability, due to reliance on outdated datasets with a limited focus on adaptive, dynamic approaches to address multilayer vulnerabilities. Additionally, the complete reliance on automated processes without integrating human expertise in feature selection and weighting processes may affect the reliability of detection models. Therefore, this research aims to develop a Semi-Automated Intrusion Detection System (SAIDS) that integrates efficient feature selection, feature weighting, normalisation, visualisation, and human–machine interaction to detect and identify multilayer attacks, enhancing mitigation strategies. The proposed framework managed to extract an optimal set of 13 significant features out of 64 in the Edge-IIoT dataset, which is crucial for the efficient detection and classification of multilayer attacks, and also outperforms the performance of the KNN model compared to other classifiers in binary classification. The KNN algorithm demonstrated an average accuracy exceeding 94% in detecting several multilayer attacks such as UDP, ICMP, HTTP flood, MITM, TCP SYN, XSS, SQL injection, etc. Full article

With the development of 3D bioprinting and the creation of innovative biocompatible materials, several new approaches have brought advantages to patients and surgical teams. Increasingly more bone defects are now treated using 3D-bioprinted prostheses and implementing new solutions relies on the ability of engineers and medical teams to identify methods of anchoring 3D-printed prostheses and to reveal the potential influence of bioactive materials on surrounding tissues. In this paper, we described why limb salvage surgery based on 3D bioprinting is a reliable and effective alternative to amputations, and why this approach is considered the new standard in modern medicine. The preliminary results of 3D bioprinting in one of the most challenging fields in surgery are promising for the future of machine-based medicine, but also for the possibility of replacing various parts from the human body with bioactive-based constructs. In addition, besides the materials and constructs that are already tested and applied in the human body, we also reviewed bioactive materials undergoing in vitro or in vivo testing with great potential for human applications in the near future. Also, we explored the recent advancements in clinically available 3D-bioprinted constructs and their relevance in this field. Full article

The efficient classification of body position is crucial for monitoring infants’ motor development. It may fast-track the early detection of developmental issues related not only to the acquisition of motor milestones but also to postural stability and movement patterns. In turn, this may facilitate and enhance opportunities for early intervention that are crucial for promoting healthy growth and development. The manual classification of human body position based on video recordings is labour-intensive, leading to the adoption of Inertial Motion Unit (IMU) sensors. IMUs measure acceleration, angular velocity, and magnetic field intensity, enabling the automated classification of body position. Many research teams are currently employing supervised machine learning classifiers that utilise hand-crafted features for data segment classification. In this study, we used a longitudinal dataset of IMU recordings made in the lab in three different play activities of infants aged 4–12 months. The classification was conducted based on manually annotated video recordings. We found superior performance of the CatBoost Classifier over the Random Forest Classifier in the task of classifying five positions based on IMU sensor data from infants, yielding excellent classification accuracy of the Supine (97.7%), Sitting (93.5%), and Prone (89.9%) positions. Moreover, using data ablation experiments and analysing the SHAP (SHapley Additive exPlanations) values, the study assessed the importance of various groups of features from both the time and frequency domains. The results highlight that both accelerometer and magnetometer data, especially their statistical characteristics, are critical contributors to improving the accuracy of body position classification. Full article

This review provides a comprehensive analysis of the transformative role of artificial intelligence (AI) in predicting and preventing sports injuries across various disciplines. By exploring the application of machine learning (ML) and deep learning (DL) techniques, such as random forests (RFs), convolutional neural networks (CNNs), and artificial neural networks (ANNs), this review highlights AI’s ability to analyze complex datasets, detect patterns, and generate predictive insights that enhance injury prevention strategies. AI models improve the accuracy and reliability of injury risk assessments by tailoring prevention strategies to individual athlete profiles and processing real-time data. A literature review was conducted through searches in PubMed, Google Scholar, Science Direct, and Web of Science, focusing on studies from 2014 to 2024 and using keywords such as ‘artificial intelligence’, ‘machine learning’, ‘sports injury’, and ‘risk prediction’. While AI’s predictive power supports both team and individual sports, its effectiveness varies based on the unique data requirements and injury risks of each, with team sports presenting additional complexity in data integration and injury tracking across multiple players. This review also addresses critical issues such as data quality, ethical concerns, privacy, and the need for transparency in AI applications. By shifting the focus from reactive to proactive injury management, AI technologies contribute to enhanced athlete safety, optimized performance, and reduced human error in medical decisions. As AI continues to evolve, its potential to revolutionize sports injury prediction and prevention promises further advancements in athlete health and performance while addressing current challenges. Full article

Wireless Body Area Networks (WBANs) are pivotal in health care and wearable technologies, enabling seamless communication between miniature sensors and devices on or within the human body. These biosensors capture critical physiological parameters, ranging from body temperature and blood oxygen levels to real-time electrocardiogram readings. However, WBANs face significant challenges during and after deployment, including energy conservation, security, reliability, and failure vulnerability. Sensor nodes, which are often battery-operated, expend considerable energy during sensing and transmission due to inherent spatiotemporal patterns in biomedical data streams. This paper provides a comprehensive survey of data-driven approaches that address these challenges, focusing on device placement and routing, sampling rate calibration, and the application of machine learning (ML) and statistical learning techniques to enhance network performance. Additionally, we validate three existing models (statistical, ML, and coding-based models) using two real datasets, namely the MIMIC clinical database and biomarkers collected from six subjects with a prototype biosensing device developed by our team. Our findings offer insights into strategies for optimizing energy efficiency while ensuring security and reliability in WBANs. We conclude by outlining future directions to leverage approaches to meet the evolving demands of healthcare applications. Full article

Natural disasters pose significant threats to human life and property, exacerbated by their sudden onset and increasing frequency. This paper conducts a comprehensive bibliometric review to explore robust methodologies for post-disaster building damage assessment and reconnaissance, focusing on the integration of advanced data collection technologies and computational techniques. The objectives of this study were to assess the current landscape of methodologies, highlight technological advancements, and identify significant trends and gaps in the literature. Using a structured approach for data collection, this review analyzed 370 journal articles from the Scopus database from 2014 to 2024, emphasizing recent developments in remote sensing, including satellite and UAV technologies, and the application of machine learning and deep learning for damage detection and analysis. Our findings reveal substantial advancements in data collection and analysis techniques, underscoring the critical role of machine learning and remote sensing in enhancing disaster damage assessments. The results are significant as they highlight areas requiring further research and development, particularly in data fusion techniques, real-time processing capabilities, model generalization, UAV technology enhancements, and training for the rescue team. These areas are crucial for improving disaster management practices and enhancing community resilience. The application of our research is particularly relevant in developing more effective emergency response strategies and in informing policy-making for disaster-prepared social infrastructure planning. Future research should focus on closing the identified gaps and leveraging cutting-edge technologies to advance the field of disaster management. Full article

We develop a new theory of knowledge with mathematics and a broad-based series of case studies to seek a better understanding of what constitutes knowledge in the field and its value for autonomous human–machine teams facing uncertainty in the open. Like humans, as teammates, artificial intelligence (AI) machines must be able to determine what constitutes the usable knowledge that contributes to a team’s success when facing uncertainty in the field (e.g., testing “knowledge” in the field with debate; identifying new knowledge; using knowledge to innovate), its failure (e.g., troubleshooting; identifying weaknesses; discovering vulnerabilities; exploitation using deception), and feeding the results back to users and society. It matters not whether a debate is public, private, or unexpressed by an individual human or machine agent acting alone; regardless, in this exploration, we speculate that only a transparent process advances the science of autonomous human–machine teams, assists in interpretable machine learning, and allows a free people and their machines to co-evolve. The complexity of the team is taken into consideration in our search for knowledge, which can also be used as an information metric. We conclude that the structure of “knowledge”, once found, is resistant to alternatives (i.e., it is ordered); that its functional utility is generalizable; and that its useful applications are multifaceted (akin to maximum entropy production). Our novel finding is the existence of Shannon holes that are gaps in knowledge, a surprising “discovery” to only find Shannon there first. Full article

This study proposes a guidance strategy based on human–machine transition (HMT) for cooperative adaptive cruise control (CACC) truck platoon actuator failures. Existing research on the CACC platoon mainly focuses on upper-level planning and rarely considers platoon planning failures caused by actuator failures. This study proposes that the truck in the platoon creates sufficient space on the target lane through HMT when the actuator fails, thereby promoting lane changes for the entire team. The effectiveness of the proposed strategy is evaluated using the Simulation of Urban Mobility (SUMO) simulation. The results demonstrate that under conditions ensuring the normal operation of traffic flow, this guidance strategy enhances the platoon’s lane-changing capability. In addition, this strategy exhibits stronger robustness and efficiency in different traffic densities. This guidance strategy provides valuable insights into improving the driving efficiency of CACC truck platoons in complex road environments. Full article