Search Results (1,949)

This paper examines a 24 kVA three-phase inverter supplied by a set of four synchronous Super-Lift Luo DC–DC converters (ISSLLC) connected in parallel to a common DC link. The converters boost a 28 V DC input to approximately 400 V, which then feeds a 115 V, 400 Hz inverter intended for aircraft electrical systems. The overall system was modeled analytically, simulated using PLECS, and validated in real time on a Typhoon HIL platform. In both simulation and HIL, the DC link remained low ripple, and the inverter delivered well-balanced three-phase output voltages. The measured total harmonic distortion was 0.77%, and the power factor was close to unity, staying within MIL-STD-704F limits. The agreement between the simulation and HIL results confirms the precision and real-time feasibility of the proposed ISSLLC-based inverter for aerospace and other high-gain, high-efficiency applications. Full article

To address the computational-efficiency bottlenecks of Hybrid A* and its bidirectional variant in long-distance parking and narrow-slot scenarios, an improved bidirectional Hybrid A* algorithm is presented. First, the cohesion cost is reformulated in a vector-space representation. Distance and heading-consistency terms are evaluated using dot products, which eliminates trigonometric operations and reduces the overhead of node evaluation. Second, an RS (Reeds–Shepp) cost template is constructed on a sparse grid of key nodes. Neighborhood costs are approximated with Euclidean-distance correction. In addition, a geometry reachability-based trigger is designed for analytic RS connections to avoid redundant analytic linking and unnecessary RS curve computations. Third, a KD-tree spatial index is introduced to accelerate nearest-neighbor queries in the Voronoi potential field, and vehicle corner coordinates are updated in a vectorized manner to improve the efficiency of potential-field evaluation. Simulation results in parallel and perpendicular parking show that, compared with the baseline bidirectional Hybrid A* algorithm, RS computations are reduced by 98.7% and 97.8%, respectively, while total planning time is shortened by 63.2% and 57.5%, with stable path quality. These results indicate that the proposed method effectively mitigates the dominant computational costs of bidirectional Hybrid A* in complex parking tasks and improves the efficiency and real-time performance of automatic parking path planning. Full article

Photovoltaic (PV) module manufacturers typically provide electrical parameters at Standard Test Conditions (STCs), while real operation is strongly influenced by irradiance and module temperature. This paper presents a MATLAB-based numerical model that simulates the operation of a real PV string composed of 19 series-connected modules (8.645 kWp). Each module has 72 PV cells arranged as two parallel cell groups, each group consisting of 36 cells connected in series, and a rated maximum power of 455 Wp. The validated string belongs to a PV site comprising 140 modules (63.7 kWp); however, the experimental verification is intentionally performed at the string level to avoid aggregation effects. The model implements the single-diode equivalent circuit and computes the I–V and P–V characteristics at any time step, using 500 points for curve plotting. Measured irradiance and module temperature are used as inputs, while DC voltage, current, and power recorded at the inverter MPPT are used as reference quantities. Model performance is evaluated over two representative operating days, and the verification includes point-by-point comparisons between measured and simulated electrical quantities at 25 operating points, with accuracy quantified through MAE, RMSE, and MRE. The obtained errors are: MAE—power 92.94 W (1.08% of maximum power), voltage 8.33 V (1.07% of maximum voltage), current 0.12 A (1.17% of maximum current); RMSE—power 111.78 W (1.29%), voltage 10.15 V (1.30%), current 0.15 A (1.46%); and MRE—power 2.6%, voltage 1.18%, current 2.57%. These results indicate close agreement between simulated and measured string behavior under the tested conditions, supporting the use of the proposed approach for string-level performance analysis and diagnostic assessment when irradiance and temperature are available from measurements or scenario inputs, without extending conclusions to plant-level generalization beyond the validated subsystem. Photovoltaic panel manufacturers usually provide electrical parameters in a single operating condition, but in reality, PV cells operate under very diverse weather conditions. For this reason, the manufacturer’s information is not sufficient to determine the performance of PV plants. Full article

The widespread connectivity of the Industrial Internet of Things (IIoT) improves the efficiency and functionality of connected devices. However, it also raises serious concerns about cybersecurity threats. Implementing an effective intrusion detection system (IDS) for IIoT is challenging due to heterogeneous data, high feature dimensionality, class imbalance, and the risk of data leakage during evaluation. This paper presents a leakage-safe hybrid intrusion detection framework that combines text-based and numerical network flow features in an IIoT environment. Each network flow is converted into a short text description and encoded using a frozen Large Language Model (LLM) called the Bidirectional Encoder Representations from Transformers (BERT) model to obtain fixed semantic embeddings, while numerical traffic features are standardized in parallel. To improve class separation, class prototypes are computed in Principal Component Analysis (PCA) space, and cosine similarity scores for these prototypes are added to the feature set. Class imbalance is handled only in the training data using the Synthetic Minority Over-sampling Technique (SMOTE). A Random Forest (RF) is used to select the top features, followed by a Histogram-based Gradient Boosting (HGB) classifier for final prediction. The proposed framework is evaluated on the Edge-IIoTset and ToN_IoT datasets and achieves promising results. Empirically, the framework attains 98.19% accuracy on Edge-IIoTset and 99.15% accuracy on ToN_IoT, indicating robust, leakage-safe performance. Full article

In complex urban traffic scenes, reliable pedestrian trajectory prediction is essential for Automated and Connected Electric Vehicles (ACEVs) and active safety systems. Despite recent progress, many existing approaches still suffer from limited long-term prediction accuracy, redundant temporal features, and high computational cost, which restricts their deployment on vehicles with constrained onboard resources. To address these issues, this paper presents a lightweight trajectory prediction framework named BiTraP-DGF. The model adopts parallel Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) temporal encoders to extract motion information at different time scales, allowing both short-term motion changes and longer-term movement tendencies to be captured from observed trajectories. A conditional variational autoencoder (CVAE) with a bidirectional GRU decoder is further employed to model multimodal uncertainty, where forward prediction is combined with backward goal estimation to guide trajectory generation. In addition, a gated sparse attention mechanism is introduced to suppress irrelevant temporal responses and focus on informative time segments, thereby reducing unnecessary computation. Experimental results on the JAAD dataset show that BiTraP-DGF consistently outperforms the BiTraP-NP baseline. For a prediction horizon of 1.5 s, CADE is reduced by 20.9% and CFDE by 22.8%. These results indicate that the proposed framework achieves a practical balance between prediction accuracy and computational efficiency for autonomous driving applications. Full article

Accurate small object detection in unmanned aerial vehicle (UAV) imagery is fundamental to numerous safety-critical applications, including intelligent transportation, urban surveillance, and disaster assessment. However, extreme scale compression, dense object distributions, and complex backgrounds severely constrain the feature representation capability of existing detectors, leading to degraded reliability in real-world deployments. To overcome these limitations, we propose FDE-YOLO, a lightweight yet high-performance detection framework built upon YOLOv11 with three complementary architectural innovations. The Fine-Grained Detection Pyramid (FGDP) integrates space-to-depth convolution with a CSP-MFE module that fuses multi-granularity features through parallel local, context, and global branches, capturing comprehensive small target information while avoiding computational overhead from layer stacking. The Dynamic Detection Fusion Head (DDFHead) unifies scale-aware, spatial-aware, and task-aware attention mechanisms via sequential refinement with DCNv4 and FReLU activation, adaptively enhancing discriminative capability for densely clustered targets in complex scenes. The EdgeSpaceNet module explicitly fuses Sobel-extracted boundary features with spatial convolution outputs through residual connections, recovering edge details typically lost in standard operations while reducing parameter count via depthwise separable convolutions. Extensive experiments on the VisDrone2019 dataset demonstrate that FDE-YOLO achieves 53.6% precision, 42.5% recall, 43.3% mAP50, and 26.3% mAP50:95, surpassing YOLOv11s by 2.8%, 4.4%, 4.1%, and 2.8% respectively, with only 10.25 M parameters. The proposed approach outperforms UAV-specialized methods including Drone-YOLO and MASF-YOLO while using significantly fewer parameters (37.5% and 29.8% reductions respectively), demonstrating superior efficiency. Cross-dataset evaluations on UAV-DT and NWPU VHR-10 further confirm strong generalization capability with 1.6% and 1.5% mAP50 improvements respectively, validating FDE-YOLO as an effective and efficient solution for reliable UAV-based small object detection in real-world scenarios. Full article

Rolling bearings are crucial components in CNC machine tool spindles, and their health condition directly affects machining precision and operational reliability. To address the significant challenges of bearing fault diagnosis in industrial environments, this paper proposes an adaptive shapelet-based deep learning model for bearing fault diagnosis. The proposed model integrates three key components: (1) an adaptive multi-scale shapelet extraction module for discriminative pattern learning, (2) a gated parallel CNN with depthwise separable convolutions for multi-scale spatial feature extraction, (3) an enhanced bidirectional long short-term memory network with residual connections for temporal dependency modeling. A composite loss function combining cross-entropy, supervised contrastive learning, and multi-scale consistency regularization is employed for training. To simulate real-world industrial noise conditions, Gaussian, uniform, and impulse noise were injected into the signals. Experiments conducted on the CWRU and IMS datasets demonstrate that, compared with state-of-the-art methods, the proposed approach achieves stronger noise robustness, higher fault classification accuracy, and more stable performance under severe noise contamination. Full article

Accurate wind power forecasting is critical for enhancing the operational efficiency and stability of electrical power grids. Conventional single-variable signal decomposition forecasting methods ignore the coupling relationship between wind power and multiple meteorological data, thus limiting prediction accuracy. This study proposes an accurate and fast short-term wind power prediction approach based on series-core fusion technology considering multiple meteorological data. In the data preprocessing stage, the multivariate variational mode decomposition (MVMD) algorithm decomposes wind power and meteorological variables into the same predefined number of frequency-aligned intrinsic mode functions (IMFs), thereby enhancing feature representation and improving forecasting accuracy via a more comprehensive and detailed dataset representation. During the training stage, the series-core fused time series (SOFTS) model establishes the connection among wind power channel and other meteorological variable channels for each IMF, achieving fast convergence through its streamlined and parallel structure. In the forecasting stage, the final wind power prediction is generated by the reconstruction of all IMFs. Furthermore, we conducted a comprehensive performance evaluation by comparing the proposed MVMD-SOFTS model with eight alternative models, including the CNN model, the TCN model, the LSTM model, the GRU model, the Transformer model, the SOFTS model, the CEEMDAN-SOFTS model, and the VMD-SOFTS model. The results indicate that MVMD-SOFTS outperformed all other models, demonstrating its effectiveness in capturing the multifaceted relationships in wind power forecasting. Full article

The integration of unmanned aerial vehicles (UAVs), autonomous vehicles, and advanced satellite systems in sixth-generation (6G) networks is poised to redefine next-generation communications as well as next-generation intelligent transportation systems. This paper examines the convergence of UAVs, CubeSats, and terrestrial infrastructures that comprise the framework of Space–Aerial–Ground Integrated Networks (SAGINs) as vital enablers of the International Mobile Telecommunications (IMT)-2030 standards. This paper examines the role of UAVs in providing flexible and quickly deployable airborne connectivity. It also discusses how CubeSats enhance global coverage through low-latency relaying and resilient backhaul links from low Earth orbit (LEO). Additionally, the paper highlights how terrestrial systems contribute high-capacity, densely concentrated communication layers that support various end-user applications. By examining their interoperability and coordinated resource allocation, the paper underscores that the seamless interaction of SAGIN nodes is essential for achieving the ultra-reliable, intelligent, and pervasive communication capabilities envisioned by IMT-2030. As 6G aims for ultra-low latency, high reliability, and massive connectivity, UAVs and CubeSats emerge as key enablers for extending coverage and capacity, particularly in remote and dense urban regions. Furthermore, the role of large language models (LLMs) is explored for intelligent network management and real-time data optimization, while quantum communication is analyzed for ensuring security and minimizing latency. The integration of LLMs into quantum-enhanced edge intelligence for SAGINs represents an emerging research frontier for adaptive, high-throughput, and context-aware decision-making. By exploiting quantum-assisted parallelism and entanglement-based optimization, LLMs enhance the processing efficiency of multimodal data across space, aerial, and terrestrial nodes. This paper further investigates distributed quantum inference and multimodal sensor data fusion to enable resilient, self-optimizing communication systems comprising a high volume of data traffic, which is a critical bottleneck in the global connectivity transition. LLMs are envisioned as cognitive control centers capable of generating semantic representations for mission-critical communications that enhance energy efficiency, reliability, and adaptive learning at the edge. The findings of the survey reveal that quantum-enhanced LLMs overcome challenges pertaining to bandwidth allocation, dynamic routing, and interoperability in existing classical communication systems. Overall, quantum-empowered LLMs significantly assist intelligent, autonomous, and immersive communications in SAGIN, while enabling secure, privacy-preserving communication. Full article

The performance characteristics of wind-induced energy harvesting were experimentally investigated using a flexible polyvinylidene fluoride (PVDF) energy harvester with a two-stage, parallel, and vertically aligned configuration. Ten PVDF film modules were serially connected to form a single set, and four identical sets were assembled into three different array configurations—2 × 2, 4 × 1, and 1 × 4—to systematically examine the effects of array geometry and vortex interaction on power generation performance. Experiments were conducted at wind speeds ranging from 1 to 3 m/s. At a wind speed of 3 m/s, the 2 × 2 array configuration achieved an average charging voltage of 2.895 V and a total output power of 0.731 W after 600 s, corresponding to approximately 3.3-fold and 4.2-fold increases, respectively, compared with those of the 4 × 1 (0.224 W) and 1 × 4 (0.176 W) configurations. Furthermore, the uniformity index (U = 0.701), vortex amplification index (G = 0.663), and array efficiency (η = 0.789) demonstrate that the 2 × 2 configuration provides the most uniform and efficient energy distribution among the tested configurations. These results indicate that the proposed two-stage parallel funnel-type PVDF energy harvester with a 2 × 2 array configuration is an effective design for high-efficiency energy harvesting, even under low wind speed conditions. Full article

As the demand for clean water grows, the strategic management of water resources has become increasingly critical. However, the depletion of these resources is being accelerated by anthropogenic pollutants and resultant internal pipe corrosion within distribution networks. Conventional water treatment methods are characterized by high energy consumption, rendering them impractical in environments lacking a continuous external power supply. Consequently, innovative, self-sustained technologies for simultaneously monitoring fluid conditions and purifying water are a necessity. In this work, we present a water-driven triboelectric nanogenerator (W-TENG) used for energy harvesting and water-quality monitoring within pipe networks. Composed of a silicone rubber tube and aluminum electrodes, the optimized W-TENG achieved an open-circuit voltage of 58 V, short-circuit current of 1.1 µA, and 59.5 mW/m² at a 10 MΩ load. The W-TENG distinguishes pH levels and liquid types based on electrical outputs. Notably, a parallel connection of two W-TENGs enhanced electrical energy by 214% compared to the sum of two units. As an application, a self-powered electrochemical deposition was conducted and copper ions were successfully removed using energy stored in a 1 mF capacitor. These results indicate that the W-TENG is expected to be utilized as a self-powered platform for simultaneous water purification and real-time infrastructure monitoring. Full article

Distributed Denial-of-Service (DDoS) attacks remain the most pervasive and operationally disruptive cyber threat and are routinely weaponized in interstate conflict (e.g., Russia–Ukraine and Stuxnet). Although attack-chain models are standard for Advanced Persistent Threat (APT) analysis, they have seldom been applied to DDoS, which is often framed as a single-step volumetric assault. However, ubiquitous intelligence and ambient connectivity increasingly enable DDoS campaigns to unfold as multi-stage operations rather than isolated floods. In parallel, large language models (LLMs) create new opportunities to strengthen traditional DDoS defenses through richer contextual understanding. Reviewing incidents from 2019 to 2024, we propose a three-phase DDoS attack chain—preparation, development, and execution—that captures contemporary tactics and their dependencies on novel hardware, network architectures, and application protocols. We classify these patterns, contrast them with conventional DDoS, survey current defenses (anycast and scrubbing, BGP Flowspec, programmable data planes, adaptive ML detection, API hardening), and outline research directions in cross-layer telemetry, adversarially robust learning, automated mitigation orchestration, and cooperative takedown. Full article

Phase unwrapping is the decisive factor for achieving dimensional accuracy in phase-shifting profilometry, yet unavoidable phase jumps occur at discontinuities. Existing dual-frequency heterodyne techniques suffer from a narrow measurement range and overly coarse projected fringes due to grating superposition requirements, leading to large errors when scanning objects with hole-like features. To address these issues, this paper proposes an edge-oriented phase-unwrapping error-compensation method based on fringe projection phase profilometry. First, the wrapped phase of the measured object is acquired via phase-shifting profiling. The wrapped phase map is then smoothed at multiple scales using Gaussian filters, and parallel Canny edge detection combined with phase gradient thresholding is applied to comprehensively capture both coarse and fine discontinuities. Morphological closing fills in breakpoints, followed by skeleton thinning and connectivity reconstruction to generate an edge band of defined width. Within this band, edge-preserving smoothing is performed using guided filtering or bilateral filtering, and the result is fused with the original phase through Gaussian weighting based on the distance to the skeleton. Finally, an ordered multi-frequency heterodyne unwrapping restores the absolute phase, maximally preserving true discontinuities while effectively correcting noise and detection errors. Experiments show that this method overcomes edge-induced phase jumps—with jump-error correction rates exceeding 96.7%—exhibits strong noise resilience under various conditions, and achieves measurement precision better than 0.06 mm. Full article

Conventional multi-channel ultrasonic motor (USM) drive systems commonly adopt a one-motor–one-driver architecture, in which each drive channel requires an independent isolated power supply and inverter stage. As the number of motors increases, the system volume and structural complexity grow significantly. To address this issue, this paper proposes a shared DC-bus multi-channel drive architecture for traveling-wave USM. In the proposed scheme, multiple half-bridge power stages are connected in parallel to a common high-voltage DC-bus to achieve centralized energy supply and distributed driving. A DC-side midpoint reference network is introduced to establish an AC voltage reference under a unipolar DC supply, while an independent series matching inductor is employed in each channel to shape the half-bridge output into a quasi-sinusoidal motor-terminal voltage through resonant filtering. Based on the equivalent electrical model of the USM, a unified analytical model is established to analyze the voltage formation mechanism under shared DC-bus conditions. Time-domain simulations and experimental tests are carried out on a two-channel prototype operating at a 150 V DC-bus and a 40 kHz switching frequency. The results demonstrate stable quasi-sinusoidal output voltages, preserved phase consistency, and limited inter-channel coupling during parallel operation. Compared with conventional independent-supply solutions, the proposed architecture achieves an approximately 27% reduction in overall system volume for a three-motor configuration, demonstrating good scalability for compact multi-channel USM drive systems. Full article

Intergroup communication (IC) serves as a critical arena in which narratives, worldviews, and group behaviors are expressed, confronted, and translated into concrete communicative practices. Within this unique space of interaction, divergent narratives may remain rigid and unchanging, manifesting as parallel monologues that coexist without genuine engagement. Yet, under certain conditions, such communication can also open the door to dynamic processes of mutual challenge, development, and transformation. This narrative literature review aims to strengthen the growing connection between the scholarship on narratives in societies embroiled in intractable conflict and the well-established research tradition on intergroup contact. Specifically, it seeks to enhance our understanding of the interplay between narratives, behaviors, and communication practices in both face-to-face (FTF) and online contexts of IC. While the discussion includes broader global perspectives, the primary case study centers on the ongoing conflict and communicative interactions between Israeli Jews and Palestinians. Full article