Search Results (967)

Patterned fabrics are characterized by strong periodic and symmetric structures, and defect detection in such materials is essentially the task of identifying local disruptions of global texture symmetry. Conventional low-rank decomposition methods separate defect-free regions as low-rank and defects as sparse components, yet singular value decomposition (SVD)-based formulations inevitably lose structural details, hindering faithful recovery of symmetric background patterns. Autoencoder (AE)-based reconstruction provides nonlinear modeling capacity but tends to over-reconstruct defective areas, thereby reducing the separability between anomalies and symmetric textures. To address these challenges, this study proposes WOT-AE (Weighted Optimal Transport Autoencoder), a unified framework that exploits the inherent symmetry of patterned fabrics for robust defect detection. The framework integrates three key components: (1) AE-based low-rank modeling, which replaces SVD to preserve fine-grained repetitive patterns; (2) weighted sparse isolation guided by pixel-level priors, which suppresses false positives in symmetric but defect-free regions; and (3) optimal transport alignment in the encoder feature space, which enforces distributional consistency of symmetric textures while allowing deviations caused by asymmetric defects. Through extensive experiments on benchmark patterned fabric datasets, WOT-AE demonstrates superior performance over six state-of-the-art methods, achieving more accurate detection of symmetry-breaking defects with improved robustness. Full article

Three-dimensional path planning in emergency logistics is a complex optimization problem, particularly challenging because it requires considering conflicting objectives such as flight time, energy consumption, and obstacle avoidance. Unlike most urban logistics research, this study examines emergency delivery path planning in mountainous environments during natural disasters. One of the most effective approaches to this problem is to employ multi-objective evolutionary algorithms. However, while multi-objective genetic algorithms can handle multiple conflicting objectives, they struggle when dealing with complex constraints. This paper proposes a multi-objective genetic optimization method, Adaptive Crossover-Mutation Multi-Objective Genetic Optimization (ACM-NSGA-II), based on the classic NSGA-II framework. Inspired by the principle of symmetry, this method dynamically adjusts the mutation and crossover rates based on population diversity to maintain a balanced exploration–exploitation trade-off. When population diversity is low, the mutation rate is increased to promote exploration of the solution space; when population diversity is high, the crossover rate is increased to promote better information exchange. The algorithm maintains symmetry by gradually adjusting the step size, balancing adaptability and stability. To address the obstacle avoidance problem, we introduced a dynamic path repair strategy that respects the symmetry of no-fly zone boundaries and terrain features, ensuring the safety and efficiency of Unmanned Aerial Vehicles. This algorithm jointly optimizes three objectives: safety cost, flight time, and energy consumption. The algorithm was tested in a mountainous environment model simulating a remote area. In experiments, ACM-NSGA-II was compared with several mainstream evolutionary algorithms. The Pareto set and hypervolume metrics of each method were recorded and statistically analyzed at a 5% significance level. The results show that ACM-NSGA-II outperforms the baseline algorithms in terms of diversity, convergence, and feasibility. Specifically, compared with the traditional NSGA-II, ACM-NSGA-II improved the average hypervolume metric by 53.39% and reduced the average flight time by 24.26%. ACM-NSGA-II also demonstrated significant advantages over other popular standard algorithms. Experimental results show that it can effectively solve the path planning challenge of emergency logistics Unmanned Aerial Vehicles in mountainous environments. Full article

In radio frequency (RF) systems, the mixer is a critical component for achieving frequency conversion in electromagnetic sensor backends. This paper proposes a mixer design methodology aimed at improving noise figure and conversion gain specifically for sensor signal processing applications. This design employs a process incorporating high-quality bipolar junction transistors (BJTs) and adopts a mixer-first architecture instead of a conventional low noise amplifier (LNA). By optimizing the layout and symmetry of the BJTs, the input impedance can be flexibly adjusted, thereby simplifying the receiver front-end while simultaneously improving local oscillator (LO) feedthrough. Design and simulation were completed using Advanced Design System (ADS) 2020 software. Simulation results demonstrate that the proposed mixer exhibits significant advantages in suppressing noise and interference while enhancing conversion gain, making it particularly suitable for electromagnetic sensor backend applications. Full article

Dust storms have a significant impact on the Martian atmosphere and climate. Previous studies have found that regional and global dust storms mainly occur in the Mars perihelion season. However, an anomalous spring regional dust storm occurred in the aphelion season of Martian year 35 (MY 35). The occurrence and evolution of this new type of large dust storm and its impact on the Martian atmosphere are not yet fully understood. Using Mars Climate Sounder (MCS) dust observations, this study investigates the evolutionary characteristics of the MY 35 anomalous spring storm during its pre-storm, onset, expansion, and decay phases, by comparing it with other types of regional dust storms. The evolution of the MY 35 anomalous spring dust storm is more similar to that of the MY 35 C storm, showing north–south mirror symmetry relative to the equator, suggesting that the two storms may have similar evolutionary mechanisms. Additionally, we analyze the effects of the anomalous MY 35 storm on the atmospheric thermal and dynamical structures using a combination of MCS temperature observations and LMD-GCM wind simulation results. Eastward winds in the high latitudes of both hemispheres and westward winds in the low-to-mid latitudes are significantly enhanced during the storm, corresponding to the change in the atmospheric thermal structure and the global circulation. Finally, we performed a preliminary analysis of changes in the wind field during the spring dust storm in the Chryse and Utopia plains, which are two potential landing areas for China’s Tianwen-3 Mars sample-return mission. The vertical profiles of the simulated horizonal wind in the two plains show that, during the E storm peak time, the change in daily mean wind speed is significant above 20 km, but relatively small in the atmospheric boundary layer below ~5 km. Within the boundary layer, the horizontal wind speed shows remarkable diurnal variation, remaining relatively low during the midday hours (10:00 a.m. to 4:00 p.m.). These results can provide necessary environmental parameters related to spring dust storms for China’s Tianwen-3 mission. Full article

Rolling bearings constitute critical rotating components within rolling mill equipment. Production efficiency and the operational safety of the whole mechanical system are directly governed by their operational health state. To address the dual challenges of the over-reliance of conventional diagnostic methods on expert experience and the scarcity of fault samples in industrial scenarios, we propose a virtual–physical data fusion-optimized intelligent fault diagnosis framework. Initially, a dynamics-based digital twin model for rolling bearings is developed by leveraging their geometric symmetry. It is capable of generating comprehensive fault datasets through parametric adjustments of bearing dimensions and operational environments in virtual space. Subsequently, a symmetry-informed architecture is constructed, which integrates multi-scale convolutional neural networks with attention mechanisms and bidirectional gated recurrent units (MCNN-AT-BiGRU). This architecture enables spatiotemporal feature extraction and enhances critical fault characteristics. The experimental results demonstrate 99.5% fault identification accuracy under single operating conditions. It maintains stable performance under low SNR conditions. Furthermore, the framework exhibits superior generalization capability and transferability across the different bearing types. Full article

Accurate heavy-duty vehicle load estimation is crucial for transportation and environmental regulation, yet current methods lack precision in data accuracy and practicality for field implementation. We propose a Self-Supervised Reconstruction Heterogeneous Graph Convolutional Network (SSR-HGCN) for load estimation using On-Board Diagnostics (OBD) data. The method integrates physics-constrained heterogeneous graph construction based on vehicle speed, acceleration, and engine parameters, leveraging graph neural networks’ information propagation mechanisms and self-supervised learning’s adaptability to low-quality data. The method comprises three modules: (1) a physics-constrained heterogeneous graph structure that, guided by the symmetry (invariance) of physical laws, introduces a structural asymmetry by treating kinematic and dynamic features as distinct node types to enhance model interpretability; (2) a self-supervised reconstruction module that learns robust representations from noisy OBD streams without extensive labeling, improving adaptability to data quality variations; and (3) a multi-layer feature extraction architecture combining graph convolutional networks (GCNs) and graph attention networks (GATs) for hierarchical feature aggregation. On a test set of 800 heavy-duty vehicle trips, SSR-HGCN demonstrated superior performance over key baseline models. Compared with the classical time-series model LSTM, it achieved average improvements of 20.76% in RMSE and 41.23% in MAPE. It also outperformed the standard graph model GraphSAGE, reducing RMSE by 21.98% and MAPE by 7.15%, ultimately achieving < 15% error for over 90% of test samples. This method provides an effective technical solution for heavy-duty vehicle load monitoring, with immediate applications in fleet supervision, overloading detection, and regulatory enforcement for environmental compliance. Full article

This review article explores the theory of control sets for linear control systems defined on two-dimensional Lie groups, with a focus on the plane ${\mathbb{R}}^2$ and the affine group $Af{f}_{+}\left(2\right)$. We systematically summarize recent advances, emphasizing how the geometric and algebraic structures inherent in low-dimensional Lie groups influence the formation, shape, and properties of control sets—maximal regions where controllability is maintained. Control sets with non-empty interiors are of particular interest as they characterize regions where the system can be steered between states via bounded inputs. The review highlights key results concerning the existence, uniqueness, and boundedness of these sets, including criteria based on the Ad-rank condition and orbit analysis. We also underscore the central role of the symmetry properties of Lie groups, which facilitate the systematic classification and description of control sets, linking the abstract mathematical framework to concrete, physically motivated applications. To illustrate the practical relevance of the theory, we present examples from mechanics, motion planning, and neuroscience, demonstrating how control sets naturally emerge in diverse domains. Overall, this work aims to deepen the understanding of controllability regions in low-dimensional Lie group systems and to foster future research that bridges geometric control theory with applied problems. Full article

This work investigates the stability analysis of linear control systems defined on Lie groups, with a particular focus on low-dimensional cases. Unlike their Euclidean counterparts, such systems evolve on manifolds with non-Euclidean geometry, where trajectories respect the group’s intrinsic symmetries. Stability notions, such as inner asymptotic, inner, and input–output (BIBO) stability, are studied. The qualitative behavior of solutions is shown to depend critically on the spectral decomposition of derivations associated with the drift, and on the algebraic structure of the underlying Lie algebra. We study two classes of examples in detail: Abelian and solvable two-dimensional Lie groups, and the three-dimensional nilpotent Heisenberg group. These settings, while mathematically tractable, retain essential features of non-commutativity, geometric non-linearity, and sub-Riemannian geometry, making them canonical models in control theory. The results highlight the interplay between algebraic properties, invariant submanifolds, and trajectory behavior, offering insights applicable to robotic motion planning, quantum control, and signal processing. Full article

At present, the Beetle Antennae Search (BAS) algorithm has achieved remarkable success in image segmentation. However, when dealing with some complex image segmentation problems, particularly in the context of instance segmentation, which aims to identify and delineate each distinct object of interest, even within the same semantic class, there are problems such as poor optimization performance, slow convergence speed, and low stability. Therefore, to address the challenges of instance segmentation, an improved image segmentation model is proposed, and a novel BAS algorithm called the Crossover and Mutation Beetle Antennae Search (CMBAS) algorithm is designed to optimize it. The core of our approach treats instance segmentation as a sophisticated clustering problem, where each cluster center corresponds to a unique object instance. Firstly, an improved intra-class distance based on fuzzy membership weighting is designed to enhance the compactness of individual instances. Secondly, to quantify the genetic potential of individuals through their fitness performance, CMBAS uses an adaptive crossover rate mechanism based on fitness ranking and establishes a ranking-driven crossover probability allocation model. Thirdly, to guide individuals to evolve towards excellence, CMBAS uses a strategy for individual mutation of longicorn beetle antennae based on DE/current-to-best/1. Furthermore, the symmetry-aware adaptive crossover and mutation operations enhance the balance between exploration and exploitation, leading to more robust and consistent instance-level segmentation results. Experimental results on five typical benchmark functions demonstrate that CMBAS achieves superior accuracy and stability compared to the BAGWO, BAS, GWO, PSO, GA, Jaya, and FA algorithms. In image segmentation applications, CMBAS exhibits exceptional instance segmentation performance, including an enhanced ability to distinguish between adjacent or overlapping objects of the same class, resulting in smoother and more continuous instance boundaries, clearer segmented targets, and excellent convergence performance. Full article

With the rapid advancement of mobile edge computing and Internet of Things (IoT) technologies, device-to-device (D2D) cooperative computing has garnered significant attention due to its low latency and high resource utilization efficiency. However, workflow task scheduling in D2D networks poses considerable challenges, such as severe heterogeneity in device resources and complex inter-task dependencies, which may result in low resource utilization and inefficient scheduling, ultimately breaking the computational symmetry—a balanced state of computational resource allocation among terminal devices and load balance across the network. To address these challenges and restore system-level symmetry, a novel workflow task scheduling method tailored for D2D cooperative environments is proposed. First, a Non-dominated Sorting Genetic Algorithm (NSGA) is employed to optimize the allocation of computational resources across terminal devices, maximizing the overall computing capacity while achieving a symmetrical and balanced resource distribution. A scoring mechanism and a normalization strategy are introduced to accurately assess the compatibility between tasks and processors, thereby enhancing resource utilization during scheduling. Subsequently, task priorities are determined based on the calculation of each task’s Shapley value, ensuring that critical tasks are scheduled preferentially. Finally, a hybrid algorithm integrating Q-learning with Asynchronous Advantage Actor–Critic (A3C) is developed to perform precise and adaptive task scheduling, improving system load balancing and execution efficiency. Extensive simulation results demonstrate that the proposed method outperforms state-of-art methods in both energy consumption and response time, with improvements of 26.34% and 29.98%, respectively, underscoring the robustness and superiority of the proposed method. Full article

Value-added services (VASs) are widely used to incentivize user adoption in the platform economy. While considering the symmetry of cross-network externalities of a platform, i.e., suppliers and manufacturers exert balanced and mutually reinforcing influences on each other’s participation, this study develops a stylized game model to investigate platforms’ optimal bilateral user pricing decisions and VAS provision strategies, such as outsourcing to a third-party service provider (Model OS) or in-house provision (Model PS). Then, the platform’s and the third-party service provider’s optimal pricing decisions are derived, and the equilibrium results are compared. The findings demonstrate that a platform should implement Model PS when the outsourced VAS cost coefficient is sufficiently high or the outsourced VAS quality and cost coefficient are low concurrently. Only when the outsourced VAS quality is relatively high and cost coefficient is in a low range should a platform choose Model OS. Additionally, to address the problem of declines in supply chain members’ profits caused by investment in low-quality outsourced VASs (VAS utility provided by a third party exceeds the specific value 1.38), this study also proposes a feasible VAS cost-sharing contract (Model CS) to incentivize the third-party provider to provide investment in high-quality VASs. The contract design can achieve a “win-win” outcome when the sharing ratio is at a moderate rate (especially a range from 0.291 to 0.5) and the outsourced VAS cost coefficient meets suitable thresholds. Full article

Air source heat pumps (ASHPs) have become a promising alternative to conventional heating and cooling systems, making accurate performance prediction increasingly important. This study presents a comparative analysis of Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) models for evaluating the ASHP performance under varying ambient conditions, examining the symmetry or asymmetry of prediction behavior across cold and hot regimes. Two experimental campaigns were carried out in a controlled climate room: the first primarily covering moderate to high temperatures ($-3 °\mathrm{C}$ to $36 °\mathrm{C}$), and the second mainly covering negative and low ambient temperatures ($-16 °\mathrm{C}$ to $18 °\mathrm{C}$). Performance data were collected to capture system behavior under diverse thermal conditions, making predictions more challenging. Both models were optimized, ANFIS through grid partitioning and ANN via architecture selection. Results demonstrate that ANN models achieved a superior overall accuracy, with mean absolute errors of 0.061 to 0.064 for cold and hot ambient conditions, respectively, showing a particularly strong performance under cold conditions. ANFIS demonstrated remarkable robustness in low-temperature predictions, maintaining less than 3% deviation across variations in water inlet temperature. Both approaches revealed temperature-dependent characteristics: cold-condition modeling required more complex architectures but yielded higher precision, whereas warm-condition modeling performed reliably with simpler configurations but showed slightly reduced accuracy. Full article

Few-Shot Semantic Segmentation (FSS) faces significant challenges in modeling complex backgrounds and maintaining prediction consistency due to limited training samples. Existing methods oversimplify backgrounds as single negative classes and rely solely on pixel-level alignments. To address these issues, we propose a symmetry-aware superpixel-enhanced FSS framework with a symmetric dual-branch architecture that explicitly models the superpixel region-graph in both the support and query branches. First, top–down cross-layer fusion injects low-level edge and texture cues into high-level semantics to build a more complete representation of complex backgrounds, improving foreground–background separability and boundary quality. Second, images are partitioned into superpixels and aggregated into “superpixel tokens” to construct a Region Adjacency Graph (RAG). Support-set prototypes are used to initialize query-pixel predictions, which are then projected into the superpixel space for cross-image prototype alignment with support superpixels. We further perform message passing/energy minimization on the RAG to enhance intra-region consistency and boundary adherence, and finally back-project the predictions to the pixel space. Lastly, by aggregating homogeneous semantic information, we construct robust foreground and background prototype representations, enhancing the model’s ability to perceive both seen and novel targets. Extensive experiments on the PASCAL-$5^i$ and COCO-${20}^i$ benchmarks demonstrate that our proposed model achieves superior segmentation performance over the baseline and remains competitive with existing FSS methods. Full article

Furosemide (FUR) is a loop diuretic widely used in pediatric care. However, no standardized oral liquid formulation exists due to degradation concerns, particularly the formation of furosemide-related compound B (FUR-B). This study aimed to develop and validate the HPLC method for the simultaneous quantification of FUR, FUR-B, methylparaben (MP), and propylparaben (PP) in pediatric extemporaneous oral solutions. Chromatographic separation was achieved using a Symmetry^® C18 column (4.6 × 250 mm, 5 µm) with a mobile phase of 0.1% acetic acid in water and acetonitrile (60:40, v/v) at 1.0 mL/min of flow with injection volume at 10 µL. Detection at 272 nm provided optimal sensitivity, especially for low concentrations of FUR-B. Forced degradation confirmed baseline separation of FUR from its degradation products. The condition showed high linearity (R² > 0.995), accuracy (recoveries 98.2–101.0%), and precision (RSD ≤ 2%). Robustness and ruggedness tests under varied conditions, analysts, and intra-day yielded consistent performance. Application to extemporaneous formulations showed that refrigeration (2–8 °C) retained initial composition, while elevated temperatures (30 °C and 40 °C) promoted FUR degradation, with FUR-B increasing to 6.84% after 90 days and greater MP and PP degradation. This validated method offers a reliable analytical tool for monitoring chemical changes and supporting quality control of pediatric FUR extemporaneous formulations. Full article

The integration of AI-driven optimization into Electronic Design Automation (EDA) enables smarter and more adaptive circuit design, where symmetry and asymmetry play key roles in balancing performance, robustness, and manufacturability. This work presents a model-driven optimization methodology for sizing low-phase-noise LC voltage-controlled oscillators (VCOs) at 5 GHz, targeting Wi-Fi, 5G, and automotive radar applications. The approach uses the EKV transistor model for analytical CMOS device characterization and applies a diverse set of metaheuristic algorithms for both single-objective (phase noise minimization) and multi-objective (joint phase noise and power) optimization. A central focus is on how symmetry—embedded in the complementary cross-coupled LC-VCO topology—and asymmetry—introduced by parasitics, mismatch, and layout constraints—affect optimization outcomes. The methodology implicitly captures these effects during simulation-based optimization, enabling design-space exploration that is both symmetry-aware and robust to unavoidable asymmetries. Implemented in CMOS 180 nm technology, the approach delivers designs with improved phase noise and power efficiency while ensuring manufacturability. Yield analysis confirms that integrating symmetry considerations into metaheuristic-based optimization enhances performance predictability and resilience to process variations, offering a scalable, AI-aligned solution for high-performance analog circuit design within EDA workflows. Full article