Search Results (21,208)

Despite advances in Convolutional Neural Networks (CNNs) for intelligent fault diagnosis in CNC machine tools, bearing fault diagnosis in CNC feed systems remains challenging, particularly in multi‑scale feature extraction and generalization across operating conditions. This study introduces an enhanced multi-scale feature network (MSFN) that addresses these limitations through three integrated modules designed to extract critical fault features from vibration signals. First, a Soft-Scale Denoising (S2D) module forms the backbone of the MSFN, capturing multi-scale fault features from input signals. Second, a Multi-Scale Adaptive Feature Enhancement (MS-AFE) module based on long-range weighting mechanisms is developed to enhance the extraction of periodic fault features. Third, a Dynamic Sequence–Channel Attention (DSCA) module is incorporated to improve feature representation across channel and sequence dimensions. Experimental results on two datasets demonstrate that the proposed MSFN achieves high diagnostic accuracy and exhibits robust generalization across diverse operating conditions. Moreover, ablation studies validate the effectiveness and contributions of each module. Full article

Automatic component recognition plays a crucial role in intelligent ship manufacturing, but existing methods suffer from low recognition accuracy and high computational cost in industrial scenarios involving small samples, component stacking, and diverse categories. To address the requirements of shipbuilding industrial applications, a Triplet Spatial Reconstruction Attention (TSA) mechanism that combines threshold-based feature separation with triplet parallel processing is proposed, and a lightweight You Only Look Once Ship (YOLO-Ship) detection network is constructed. Unlike existing attention mechanisms that focus on either spatial reconstruction or channel attention independently, the proposed TSA integrates triplet parallel processing with spatial feature separation–reconstruction techniques to achieve enhanced target feature representation while significantly reducing parameter count and computational overhead. Experimental validation on a small-scale actual ship component dataset demonstrates that the improved network achieves 88.7% mean Average Precision (mAP), 84.2% precision, and 87.1% recall, representing improvements of 3.5%, 2.2%, and 3.8%, respectively, compared to the original YOLOv8n algorithm, requiring only 2.6 M parameters and 7.5 Giga Floating-point Operations per Second (GFLOPs) computational cost, achieving a good balance between detection accuracy and lightweight model design. Future research directions include developing adaptive threshold learning mechanisms for varying industrial conditions and integration with surface defect detection capabilities to enhance comprehensive quality control in intelligent manufacturing systems. Full article

The integration of machine learning (ML) into industrial automation is fundamentally reshaping how manufacturing systems are monitored, inspected, and optimized. By applying machine learning to real-time sensor data and operational histories, advanced models enable proactive fault prediction, intelligent inspection, and dynamic process control—directly enhancing system reliability, product quality, and efficiency. This review explores the transformative role of ML across three key domains: Predictive Maintenance (PdM), Quality Control (QC), and Process Optimization (PO). It also analyzes how Digital Twin (DT) and Edge AI technologies are expanding the practical impact of ML in these areas. Our analysis reveals a marked rise in deep learning, especially convolutional and recurrent architectures, with a growing shift toward real-time, edge-based deployment. The paper also catalogs the datasets used, the tools and sensors employed for data collection, and the industrial software platforms supporting ML deployment in practice. This review not only maps the current research terrain but also highlights emerging opportunities in self-learning systems, federated architectures, explainable AI, and themes such as self-adaptive control, collaborative intelligence, and autonomous defect diagnosis—indicating that ML is poised to become deeply embedded across the full spectrum of industrial operations in the coming years. Full article

Directed Energy Deposition is an additive manufacturing subgroup that uses a laser beam to melt the wire or powder to create a melt pool. In the current study, a scientometric analysis has been carried out to analyze the contribution of countries, publication type analysis, distribution of publications over the years, keywords analysis, author analysis, cited journal, categories, institutes of publication, and report the practical implications. Firstly, the database was extracted from the Web of Science and then post-processed with CiteSpace 6.2.R4 and VOSviewer 1.6.20 software. Afterward, the associated results had been extracted and reported. It was found that China is the leader according to publication, followed by the USA and Germany, which mostly published their achievements in article and proceeding paper formats, which are increasing annually. According to the keywords, additive manufacturing, Laser Metal Deposition, and fabrication are the most commonly used. Based on the CiteSapce and VOSviewer results, Lin, Xin and Huang, Weidong are the authors with the highest publication rates. In addition, Additive Manufacturing, Materials & Design, and Materials Science and Engineering: A are the most cited journals, and regarding the categories, materials science, multidisciplinary, applied physics, and manufacturing engineering are the most commonly used DED processes. Northwestern Polytechnical University, Fraunhofer Gesellschaft, and the United States Department of Energy (DOE) have performed the most research in the field of DED. Full article

The accurate prediction of significant wave height presents a complex yet vital challenge in the fields of ocean engineering. This capability is essential for disaster prevention, fostering sustainable development and deepening our understanding of various scientific phenomena. We explore the development of a comprehensive predictive methodology for wave height prediction by integrating novel Takagi–Sugeno–Kang fuzzy models within a multiresolution analysis framework. The multiresolution analysis emerges via wavelets, since they are prominent models characterised by their inherent multiresolution nature. The maximal overlap discrete wavelet transform is utilised to generate the detail and resolution components of the time series, resulting from this multiresolution analysis. The novelty of the proposed model lies on its hybrid training approach, which combines least squares with AdaBound, a gradient-based algorithm derived from the deep learning literature. Significant wave height prediction is studied as a time series problem, hence, the appropriate inputs to the model are selected by developing a surrogate-based wrapped algorithm. The developed wrapper-based algorithm, employs Bayesian optimisation to deliver a fast and accurate method for feature selection. In addition, we introduce a projection step, to further refine the approximation capabilities of the resulting predictive system. The proposed methodology is applied to a real-world time series pertaining to spectral wave height and obtained from the Poseidon operational oceanography system at the Institute of Oceanography, part of the Hellenic Center for Marine Research. Numerical studies showcase a high degree of approximation performance. The predictive scheme with the projection step yields a coefficient of determination of $0.9991$, indicating a high level of accuracy. Furthermore, it outperforms the second-best comparative model by approximately $49\%$ in terms of root mean squared error. Comparative evaluations against powerful artificial intelligence models, using regression metrics and hypothesis test, underscore the effectiveness of the proposed methodology. Full article

Long-range underwater gliders (LRUGs) have emerged as essential platforms for sustained and autonomous observation in deep and remote marine environments. This paper provides a comprehensive review of their developmental status, performance characteristics, and application progress. Emphasis is placed on two critical enabling technologies that fundamentally determine endurance: lightweight, pressure-resistant hull structures and high-efficiency buoyancy-driven propulsion systems. First, the role of carbon fiber composite pressure hulls in enhancing energy capacity and structural integrity is examined, with attention to material selection, fabrication methods, compressibility compatibility, and antifouling resistance. Second, the evolution of buoyancy control systems is analyzed, covering the transition to hybrid active–passive architectures, rapid-response actuators based on smart materials, thermohaline energy harvesting, and energy recovery mechanisms. Based on this analysis, the paper identifies four key technical challenges and proposes strategic research directions, including the development of ultralight, high-strength structural materials; integrated multi-mechanism antifouling technologies; energy-optimized coordinated buoyancy systems; and thermally adaptive glider platforms. Achieving a system architecture with ultra-long endurance, enhanced energy efficiency, and robust environmental adaptability is anticipated to be a foundational enabler for future long-duration missions and globally distributed underwater glider networks. Full article

Artificial intelligence technologies, particularly machine learning and computer vision, are being increasingly utilized to preserve, restore, and create immersive virtual experiences with cultural artifacts and sites, thus aiding in conserving cultural heritage and making it accessible to a global audience. This paper examines the performance of Generative Adversarial Networks (GAN), especially Style-Based Generator Architecture (StyleGAN), as a deep learning approach for producing realistic images of Egyptian monuments. We used Sigmoid loss for Language–Image Pre-training (SigLIP) as a unique image–text alignment system to guide monument generation through semantic elements. We also studied truncation methods to regulate the generated image noise and identify the most effective parameter settings based on architectural representation versus diverse output creation. An improved discriminator design that combined noise addition with squeeze-and-excitation blocks and a modified MinibatchStdLayer produced 27.5% better Fréchet Inception Distance performance than the original discriminator models. Moreover, differential evolution for latent-space optimization reduced alignment mistakes during specific monument construction tasks by about 15%. We checked a wide range of truncation values from 0.1 to 1.0 and found that somewhere between 0.4 and 0.7 was the best range because it allowed for good accuracy while retaining many different architectural elements. Our findings indicate that specific model optimization strategies produce superior outcomes by creating better-quality and historically correct representations of diverse Egyptian monuments. Thus, the developed technology may be instrumental in generating educational and archaeological visualization assets while adding virtual tourism capabilities. Full article

Machine learning and artificial intelligence have transformed fault detection and maintenance strategies for industrial machinery. This study applies well-established data-driven techniques to a rarely explored industrial application—the condition monitoring of high-precision paper cutting machines—enhancing condition-based maintenance to improve operational efficiency, safety, and cost-effectiveness. A key element of the proposed approach is the integration of an infrared pyrometer into vibration monitoring, utilizing accelerometer data to evaluate the state of health of machinery. Unlike traditional fault detection studies that focus on extreme degradation states, this work successfully identifies subtle deviations from optimal, which even expert technicians struggle to detect. Building on a feasibility study conducted with Tecnau SRL, a comprehensive diagnostic system suitable for industrial deployment is developed. Endurance tests pave the way for continuous monitoring under various operating conditions, enabling real-time industrial diagnostic applications. Multi-scale signal analysis highlights the significance of transient and steady-state phase detection, improving the effectiveness of real-time monitoring strategies. Despite the physical similarity of the classified states, simple time-series statistics combined with machine learning algorithms demonstrate high sensitivity to early-stage deviations, confirming the reliability of the approach. Additionally, a systematic analysis to downgrade acquisition system specifications identifies cost-effective sensor configurations, ensuring the feasibility of industrial implementation. Full article

Achieving sustainable energy goals requires efficient integration of renewables like wind energy. Doubly fed induction generator (DFIG)-based wind turbine systems (WTSs) operate efficiently across a range of speeds, making them well-suited for modern renewable energy systems. However, sudden wind speed variations can cause power oscillations, rotor speed fluctuations, and voltage instability. Traditional proportional–integral (PI) controllers struggle with such nonlinear, rapidly changing scenarios. A control approach utilizing support vector regression (SVR) is proposed for the DFIG wind turbine system. The SVR controller manages both active and reactive power by simultaneously controlling the rotor- and grid-side converters (RSC and GSC). Simulations under a sudden wind speed variation from 10 to 12 m per second show the SVR approach reduces settling time significantly (up to 70.3%), suppresses oscillations in rotor speed, torque, and power output, and maintains over 97% DC-link voltage stability. These improvements enhance power quality, reliability, and system performance, demonstrating the SVR controller’s superiority over conventional PI methods for variable-speed wind energy systems. Full article

Background and Objectives: Clostridioides difficile infection (CDI) poses a major public health problem worldwide. Materials and Methods: In this retrospective study, we included 274 patients with CDI, who were hospitalized in Internal Medicine Departments in Evangelismos General Hospital in Athens, Greece, during the past decade. Demographic, clinical and laboratory parameters of the patients were recorded. Statistical analysis revealed an association between older age and mortality as well as heart failure and mortality among patients with CDI. Results: Notably, WBC (white blood count), neutrophils, NLR (neutrophil-to-lymphocyte ratio), dNLR (derived NLR), SII (systemic immune–inflammation index) and hs-CRP (high-sensitivity C-reactive protein) demonstrated a positive association with mortality, whereas serum albumin levels and PNR (platelet-to-neutrophil ratio) exhibited an inverse relationship with mortality. We propose that the aforementioned biomarkers may be used as prognostic parameters regarding mortality from CDI. Conclusions: Large scale studies among patients with CDI with the advent of AI (artificial intelligence) may incorporate demographic, clinical and laboratory features into prognostic scores to further characterize the global CDI threat. Full article

This research examines the compounding risks of global environmental change, including climate change, environmental law, biodiversity loss, and pollution, which threaten the stability of economic systems worldwide. While digital technology and global governance regulation are increasingly being proposed as solutions, their synergistic potential in advancing economic sustainability has been less explored. How can these technologies mitigate environmental risks while promoting sustainable and equitable development, aligning with the Sustainable Development Goals? We analyze policy global environmental data from the World Bank and the United Nations, as well as literature reviews on digital interventions, artificial intelligence, and smart databases. Global environmental change presents economic stability and rule of law threats, and innovative governance responses are needed. This study evaluates the potential for digital technology to be leveraged to enhance climate resilience and regulatory systems and address key implementation, equity, and policy coherence deficits. Policy recommendations for aligning economic development trajectories with planetary boundaries emphasize that proactive digital governance integration is indispensable for decoupling growth from environmental degradation. However, fragmented governance and unequal access to technologies undermine scalability. Successful experiences demonstrate that integrated policies, combining incentives, data transparency, and multilateral coordination, deliver maximum economic and environmental co-benefits, matching digital innovation with good governance. We provide policymakers with an action plan to leverage technology as a multiplier of sustainability, prioritizing inclusive governance structures to address implementation gaps and inform legislation. Full article

This paper presents the design and implementation of a high-precision vehicle detection and classification system for toll stations on national highways in Colombia, leveraging LiDAR-based 3D point cloud processing and supervised machine learning. The system integrates a multi-sensor architecture, including a LiDAR scanner, high-resolution cameras, and Doppler radars, with an embedded computing platform for real-time processing and on-site inference. The methodology covers data preprocessing, feature extraction, descriptor encoding, and classification using Support Vector Machines. The system supports eight vehicular categories established by national regulations, which present significant challenges due to the need to differentiate categories by axle count, the presence of lifted axles, and vehicle usage. These distinctions affect toll fees and require a classification strategy beyond geometric profiling. The system achieves $89.9\%$ overall classification accuracy, including $96.2\%$ for light vehicles and $99.0\%$ for vehicles with three or more axles. It also incorporates license plate recognition for complete vehicle traceability. The system was deployed at an operational toll station and has run continuously under real traffic and environmental conditions for over eighteen months. This framework represents a robust, scalable, and strategic technological component within Intelligent Transportation Systems and contributes to data-driven decision-making for road management and toll operations. Full article

In this paper, a novel online detection system is designed to enhance accuracy and operational efficiency in the outbound logistics of automotive components after production. The system consists of a scanning portal system and an improved YOLOv12-based detection algorithm which captures images of automotive parts passing through the scanning portal in real time. By integrating deep learning, the system enables real-time monitoring and identification, thereby preventing misdetections and missed detections of automotive parts, in this way promoting intelligent automotive part recognition and detection. Our system introduces the A2C2f-SA module, which achieves an efficient feature attention mechanism while maintaining a lightweight design. Additionally, Dynamic Space-to-Depth (Dynamic S2D) is employed to improve convolution and replace the stride convolution and pooling layers in the baseline network, helping to mitigate the loss of fine-grained information and enhancing the network’s feature extraction capability. To improve real-time performance, a GFL-MBConv lightweight detection head is proposed. Furthermore, adaptive frequency-aware feature fusion (Adpfreqfusion) is hybridized at the end of the neck network to effectively enhance high-frequency information lost during downsampling, thereby improving the model’s detection accuracy for target objects in complex backgrounds. On-site tests demonstrate that the system achieves a comprehensive accuracy of 97.3% and an average vehicle detection time of 7.59 s, exhibiting not only high precision but also high detection efficiency. These results can make the proposed system highly valuable for applications in the automotive industry. Full article

Multi-vessel formation shipping demonstrates significant potential for enhancing maritime transportation efficiency and economy. However, existing route planning systems inadequately address the unique challenges of formations, where traditional methods fail to integrate global optimality, local dynamic obstacle avoidance, and formation coordination into a cohesive system. Global planning often neglects multi-ship collaborative constraints, while local methods disregard vessel maneuvering characteristics and formation stability. This paper proposes GLFM, a three-layer hierarchical framework (global optimization–local adjustment-formation collaboration module) for intelligent route planning of transport ship formations. GLFM integrates an improved multi-objective A* algorithm for global path optimization under dynamic meteorological and oceanographic (METOC) conditions and International Maritime Organization (IMO) safety regulations, with an enhanced Artificial Potential Field (APF) method incorporating ship safety domains for dynamic local obstacle avoidance. Formation, structural stability, and coordination are achieved through an improved leader–follower approach. Simulation results demonstrate that GLFM-generated trajectories significantly outperform conventional routes, reducing average risk level by 38.46% and voyage duration by 12.15%, while maintaining zero speed and period violation rates. Effective obstacle avoidance is achieved, with the leader vessel navigating optimized global waypoints and followers maintaining formation structure. The GLFM framework successfully balances global optimality with local responsiveness, enhances formation transportation efficiency and safety, and provides a comprehensive solution for intelligent route optimization in multi-constrained marine convoy operations. Full article

The rapid growth of electric vehicles (EVs) and new energy systems has put lithium-ion batteries at the center of the clean energy change. Nevertheless, to achieve the best battery performance, safety, and sustainability in many changing circumstances, major innovations are needed in Battery Management Systems (BMS). This review paper explores how artificial intelligence (AI) and digital twin (DT) technologies can be integrated to enable the intelligent BMS of the future. It investigates how powerful data approaches such as deep learning, ensembles, and models that rely on physics improve the accuracy of predicting state of charge (SOC), state of health (SOH), and remaining useful life (RUL). Additionally, the paper reviews progress in AI features for cooling, fast charging, fault detection, and intelligible AI models. Working together, cloud and edge computing technology with DTs means better diagnostics, predictive support, and improved management for any use of EVs, stored energy, and recycling. The review underlines recent successes in AI-driven material research, renewable battery production, and plans for used systems, along with new problems in cybersecurity, combining data and mass rollout. We spotlight important research themes, existing problems, and future drawbacks following careful analysis of different up-to-date approaches and systems. Uniting physical modeling with AI-based analytics on cloud-edge-DT platforms supports the development of tough, intelligent, and ecologically responsible batteries that line up with future mobility and wider use of renewable energy. Full article