Search Results (765)

Detecting available road space is a fundamental task for autonomous driving vehicles, requiring robust image feature extraction methods that operate reliably across diverse sensor-captured scenarios. However, existing approaches process each input independently without leveraging Accumulated Experiential Knowledge (AEK), limiting their adaptability and reliability. In order to explore the impact of AEK, we introduce MemRoadNet, a Memory-Augmented (MA) semantic segmentation framework that integrates human-inspired cognitive architectures with deep-learning models for free road space detection. Our approach combines an InternImage-XL backbone with a UPerNet decoder and a Human-like Memory Bank system implementing episodic, semantic, and working memory subsystems. The memory system stores road experiences with emotional valences based on segmentation performance, enabling intelligent retrieval and integration of relevant historical patterns during training and inference. Experimental validation on the KITTI road, Cityscapes, and R2D benchmarks demonstrates that our single-modality RGB approach achieves competitive performance with complex multimodal systems while maintaining computational efficiency and achieving top performance among single-modality methods. The MA framework represents a significant advancement in sensor-based computer vision systems, bridging computational efficiency and segmentation quality for autonomous driving applications. Full article

The demand for organ transplantation continues to rise worldwide, intensifying the gap between supply and demand and driving research in tissue engineering (TE). Bioprinting, particularly light-based vat photopolymerization (VP) methods such as digital light processing (DLP), has emerged as a promising strategy to fabricate complex, cell-compatible tissue constructs with high precision. In this study, we developed an open-source, bottom-up DLP bioprinter designed to provide a cost-effective and modular alternative to commercial systems. The device was built from commercially available components and custom-fabricated parts, with tolerance allocation and deviation analyses applied to ensure structural reliability. Mechanical and optical subsystems were modeled and validated, and the control architecture was implemented on the Arduino platform with a custom Python-based graphical interface. The system achieved a theoretical Z-axis resolution of 1 $\mathsf{\mu }$m and a vertical travel range of 50 mm, with accuracy and repeatability comparable to research-grade bioprinters. Initial printing trials using polyethylene glycol diacrylate (PEGDA) hydrogels demonstrated high-fidelity microfluidic constructs with adequate dimensional precision. Collectively, these results validate the functionality of the proposed system and highlight its potential as a flexible, precise, and cost-effective platform that is also easy to customize to advance the democratization of biofabrication in TE. Full article

Frequent failures in wind turbines underscore the critical need for accurate and efficient online monitoring and early warning systems to detect abnormal conditions. Given the complexity of monitoring numerous components individually, subsystem-level monitoring emerges as a practical and effective alternative. Among all subsystems, the gearbox is particularly critical due to its high failure rate and prolonged downtime. However, achieving both efficiency and accuracy in gearbox condition monitoring remains a significant challenge. To tackle this issue, we present a novel adaptive condition monitoring method specifically for wind turbine gearbox. The approach begins with adaptive feature selection based on correlation analysis, through which a quantitative indicator is defined. With the utilization the selected features, graph-based data representations are constructed, and a self-supervised contrastive residual graph neural network is developed for effective data mining. For online monitoring, a health index is derived using distance metrics in a multidimensional feature space, and statistical process control is employed to determine failure thresholds. This framework enables real-time condition tracking and early warning of potential faults. Validation using SCADA data and maintenance records from two wind farms demonstrates that the proposed method can issue early warnings of abnormalities 30 to 40 h in advance, with anomaly detection accuracy and F1 score both exceeding 90%. This highlights its effectiveness, practicality, and strong potential for real-world deployment in wind turbine monitoring applications. Full article

We present a large-scale neuromodulation system-on-chip (SoC) that integrates a 128-channel neural recording and 32-channel stimulation ASIC designed for a wide range of neuroscientific applications. Each recording channel achieves low-noise performance (~4 μV_rms) with a configurable bandwidth of 0.05 Hz–7.5 kHz and supports 16-bit digitization with scalable sampling rates up to 30 kS/s. To enhance signal quality, the ASIC includes an adjustable digital high-pass filter and a fast-settling function for rapid recovery from stimulation artifacts. SoC also incorporates on-chip electrode-impedance measurements as a built-in safety feature by reusing the recording channels. The stimulation subsystem generates current-controlled monopolar biphasic pulses with a high compliance voltage of ±6 V using standard low-voltage (1.8 V/3.3 V) CMOS devices. Each of the 32 stimulation channels provides arbitrary 9-bit programmable waveforms and dual current modes (4 μA/bit and 8 μA/bit), supporting both fine-resolution microstimulation and high-current applications such as spinal-cord and deep-brain stimulation. On-chip charge-balancing switches in each channel further ensure safe and reliable stimulation delivery. The SoC supports digital communication via a standard SPI with both 3.3 V CMOS and low-voltage differential signaling options and integrates all required analog references and low-dropout regulators. The prototype was fabricated in a standard 180 nm CMOS process, occupying 31.92 mm² (equivalently, 0.2 mm² per recording-and-stimulation channel), and was fully validated through benchtop measurements and in vitro experiments. Full article

VDI/VDE 2206 introduces the “V-Model”, a standard in the field of automotive development that uses systems engineering to derive requirements for (sub-)systems and components based on vehicle characteristics. These characteristics, which are directly experienced by drivers, are crucial in the concept phase, where virtual methods are increasingly applied. Regarding the battery electric vehicle’s energy storage, commonly a lithium-ion battery, vehicle metrics, especially for charging, range, and longitudinal dynamics, are of particular relevance. This publication will demonstrate a method to derive the requirements for the battery system based on those metrics. The core of the method is a static battery model, which considers the needed effects and dependencies in order to adequately represent the defined vehicle metrics, e.g., the battery’s open-circuit voltage and internal resistance. This paper also discusses the necessity of the relevant effects and dependencies and also why some of them can be ignored at this particular vehicle development stage. The result is a consistent method for requirement definition, from vehicle level to battery system level, applicable in the concept phase of the vehicle development process. Full article

The paper presents a hybrid intelligent expert diagnostic system (HIESD) of power transformer (PT) subsystems realized on the basis of integration of measuring and computing hardware and software complexes into a single functional architecture. HIESD performs online diagnostics of four main subsystems of PT: 1—insulating (liquid and solid insulation); 2—electromagnetic (windings, magnetic conductor); 3—voltage regulation; and 4—high-voltage inputs. Computational complexes and modules of the system are connected with the real object of power grids, 110/10 kV substation, which interact with each other and contain a relational database of retrospective offline data of the PT “life cycle” (including test and measurement results), supplemented by online monitoring data of the main subsystems, corrected by high-precision test measurements; analytical complex, in which the work of calculation modules of the operational state of PT subsystems is supplemented by predictive analytics and machine learning modules; and a knowledge base, sections of which are regularly updated and supplemented. The system architecture is tested at industrial facilities in terms of online transformer diagnostics based on dissolved gas analysis (DGA) data. Additionally, a theoretical model of diagnostics based on the electromagnetic characteristics of the transformer, which takes into account distorted and nonlinear modes of its operation, is presented. The scientific significance of the work consists of the presentation of the following new provisions: Methodology and algorithm for diagnostics of electromagnetic parameters of ST, taking into account nonlinearity and non-sinusoidality of winding currents and voltages; formation of optimal client–service architecture of training models of hybrid system based on the processes of data storage and management; and modification of the moth–flame algorithm to optimize the smoothing coefficient in the process of training a probabilistic neural network Full article

A novel closed loop visual positioning system, termed YOLO–LaserGalvo (YLGS), is proposed for precise localization of welding torch tips in industrial welding automation. The proposed system integrates a monocular camera, an infrared laser distance sensor with a galvanometer scanner, and a customized deep learning detector based on an improved YOLOv11 model. In operation, the vision subsystem first detects the approximate image location of the torch tip using the YOLOv11-based model. Guided by this detection, the galvanometer steers the IR laser beam to that point and measures the distance to the torch tip. The distance feedback is then fused with the vision coordinates to compute the precise 3D position of the torch tip in real-time. Under complex illumination, the proposed YLGS system exhibits superior robustness compared with color-marker and ArUco baselines. Experimental evaluation shows that the system outperforms traditional color-marker and ArUco-based methods in terms of accuracy, robustness, and processing speed. This marker-free method provides high-precision torch positioning without requiring structured lighting or artificial markers. Its pedagogical implications in engineering education are also discussed. Potential future work includes extending the method to full 6-DOF pose estimation and integrating additional sensors for enhanced performance. Full article

General-purpose graphics computing on processing units (GPGPUs) face significant performance limitations due to memory access latencies, particularly when traditional memory hierarchies and thread-switching mechanisms prove insufficient for complex access patterns in data-intensive applications such as machine learning (ML) and scientific computing. This paper presents a novel hardware design for a memory prefetching subsystem targeted at DDR (Double Data Rate) memory in GPGPU architectures. The proposed prefetching subsystem features a modular architecture comprising multiple parallel prefetching engines, each handling distinct memory address ranges with dedicated data buffers and adaptive stride detection algorithms that dynamically identify recurring memory access patterns. The design incorporates robust system integration features, including context flushing, watchdog timers, and flexible configuration interfaces, for runtime optimization. Comprehensive experimental validation using real-world workloads examined critical design parameters, including block sizes, prefetch outstanding limits, and throttling rates, across diverse memory access patterns. Results demonstrate significant performance improvements with average memory access latency reductions of up to 82% compared to no-prefetch baselines, and speedups in the range of 1.240–1.794. The proposed prefetching subsystem successfully enhances memory hierarchy efficiency and provides practical design guidelines for deployment in production GPGPU systems, establishing clear parameter optimization strategies for different workload characteristics. Full article

Ensuring food safety in complex supply chains requires evaluation frameworks that integrate multiple indicators, account for their interdependencies, and incorporate historical performance. This study proposes a novel RM–Shapley–FAHP framework that combines the Fuzzy Analytic Hierarchy Process, Shapley value contribution analysis, and a reputation decay mechanism to construct a dynamic, multi-year assessment model. The framework evaluates six governance subsystems, mitigates indicator redundancy, and links past performance to current risk posture. Applied to a leading food enterprise over three years, the method demonstrated superior consistency, interpretability, and operational relevance compared to FAHP, entropy weighting, and equal-weight baselines. The results demonstrate that RM–Shapley–FAHP framework effectively supports balanced development in food safety governance by capturing temporal dynamics and interdependencies, offering interpretable and operationally relevant guidance for decision makers. In future work, this framework may be extended with machine learning to improve adaptability for multi-dimensional and time-series evaluations, noted here as a research prospect rather than a present contribution. Full article

The analysis of time-resolved S₁–S_n absorption spectra in the 0–500 ps range, together with quantum-chemical calculations, uncovered a photorecoordination reaction for the following complexes of CD6 (a bis(aza-18-crown-6)-containing dienone (ketocyanine dye) with a central cyclohexanone fragment): CD6·(Mⁿ⁺)₂ (M = Ba²⁺, Sr²⁺, Ca²⁺, K⁺). This process takes place over hundreds of fs and involves an “axial-to-equatorial” conformational change, with the solvation shell undergoing rearrangement as well. The characteristic photorecoordination times were found to correlate with the stability constants of the complexes. The lifetimes for the fluorescent states of CD6 and its complexes, namely CD6·(Mⁿ⁺)₂ (M = Ba²⁺, Sr²⁺, Ca²⁺, K⁺), are different; ergo, there is no photoejection of crowned cations into the solution. The calculated conformational profiles in the ground and excited states indicate the presence of an energy barrier in this process. A general photorelaxation pathway is suggested for CD6·(Mⁿ⁺)₂ metal complexes (M = Ba²⁺, Sr²⁺, Ca²⁺, K⁺). The coordination of cations via the carbonyl moiety in the dye molecule promotes photorecoordination of metal cations in the cavities of the azacrown ether fragment. Photorecoordination times were found to correlate with the degree of conjugation between the lone pairs in the N atoms of the aza-18-crown-6 ether and the π subsystem in the dye molecules (established for the CD4–CD6 metal–dye complex series, where CD4 and CD5 are related dyes with central cyclobutanone and cyclopentanone fragments, respectively). Full article

Precision manufacturing requires handling multi-physics coupling during processing, where digital twin and AI technologies enable rapid robot programming under customized requirements. However, heterogeneous data sources, diverse domain models, and rapidly changing demands pose significant challenges to digital twin system integration. To overcome these limitations, this paper proposes a digital twin modeling strategy based on a metamodel and a virtual–real fusion architecture, which unifies models between the virtual and physical domains. Within this framework, subsystems achieve rapid integration through ontology-driven knowledge configuration, while ROS provides the execution environment for establishing robot manufacturing digital twin scenarios. A case study of a robot shaping system demonstrates that the proposed architecture effectively addresses heterogeneous data association, model interaction, and application customization, thereby enhancing the adaptability and intelligence of precision manufacturing processes. Full article

The article evaluates the relationships between the individual actors of the Quintuple Helix model and sustainability across EU countries. The model is based on the idea that innovation arises from the collaboration of five key subsystems: government, industry (economy), academia, civil society, and natural capital. Various studies have been conducted to assess the development of the EU’s sustainability goals based on the Triple Helix approach from different perspectives and from the view of the Quintuple Helix. However, we see a gap in the research in that key aspects of the success of these models in the EU have not been examined in terms of their mutual relationships. Therefore, this paper focuses on examining Quintuple Helix systems in the EU, eco-innovation, and sustainability using cluster and correlation analysis. Based on the results, we can infer that Finland, Denmark, Sweden, Austria, and Luxembourg are among the leading EU countries in applying the Quintuple Helix model and promoting sustainability through collaborative innovation processes. The most significant contributions to sustainability within this model come primarily from ecological innovations, intellectual capital, and governance. Full article

Recent research has focused on energy recovery and storage technologies. One of the materials allowing the recovery of dissipated energy is the piezoelectric material (PE). These functional materials perform reversible energy conversion, transforming electrical energy into mechanical and vice versa. In this study, we investigate the recovery of vibratory energy in vehicle suspension systems—energy traditionally dissipated by conventional shock absorbers—using piezoelectric materials to capture this wasted energy and redirect it to the vehicle’s auxiliary power supply network. We propose an integrated electromechanical model incorporating piezoelectric actuators in parallel with the suspension mechanism. The collected energy is processed and stored for later use in powering accessories such as windows and mirrors. The idea is to integrate renewable energy sources to optimize the performance of the vehicle. We proposed a Multiphysics model of the system under a software used to this type of modeling (Simcenter AMESim v1610_student). The simulation results of the system and its various sub-systems are presented for studying the piezo-actuator response to reduce consumption and increase energy performance in a vehicle. These findings will undergo experimental validation in the project’s subsequent phase. Full article

Fluoride-containing wastewater poses a significant environmental challenge, especially in the mineral processing sector. This study applies a life cycle assessment (LCA) to evaluate an electrocoagulation-based treatment process, integrating biogas-derived CO₂ for pH regulation and cogeneration of electricity, using the Egalitarian perspective, which is the most precautionary that takes into account the longest time frame and impact types that are not yet fully established but for which some indication is available. The LCA considered five subsystems: electrocoagulation, pH adjustment, sedimentation, pumping, and sludge transport, across three operational scenarios. Scenario 1 (S1) employed hydrochloric acid for pH control, Scenario 2 (S2) used biogas exclusively for pH regulation, and Scenario 3 (S3) combined biogas-based pH adjustment with power generation. Results showed an environmental impact ranking of S3 < S1 < S2, with S3 reducing overall impacts from 12.5 Pt to 6.4 Pt compared to S1. The electrocoagulation unit was the dominant contributor to environmental burdens; however, in S3, the pH adjustment subsystem delivered a net environmental benefit through surplus electricity generation. Additionally, sludge reuse as a raw material for brick production, implemented in all scenarios, further mitigated impacts. Human health emerged as the most affected endpoint, driven mainly by toxicity (carcinogenic and non-carcinogenic), climate change potential, marine ecotoxicity, and particulate matter formation. These findings highlight the benefits of integrating biogas utilization and sludge valorization into industrial wastewater management strategies. Full article

This study presents a multivariate regression-based analysis aimed at quantifying the influence of key vibration-generating components in two types of grain combines—C110H (with straw walker) and CASE IH (axial flow)—on the operator’s seat (OS). Using triaxial accelerometers, vibrational measurements were performed under both stationary and operational working mode. RMS acceleration values were recorded for major subsystems (engine, threshing unit, chassis, chopper/header) and processed via multiple linear regression. The models generated for each combine and axis (Ox, Oy, Oz) revealed high coefficients of determination (R² > 0.85), confirming the linear model’s validity. Influence maps and standardized coefficients were used to rank the sources of vibration. Results indicate that the straw walker dominates vibration transmission in the C110H, while the header and threshing system are more significant in the CASE IH. The findings support the development of predictive algorithms for real-time vibration monitoring and ergonomic improvements in combine design. Moreover, the proposed methodology provides a cost-effective diagnostic tool for early fault detection, targeted maintenance, and the long-term reduction of operator fatigue and injury risks. Full article