Search Results (1,677)

To address the precision and motion-smoothing challenges in path planning for spherical workpieces without Computer-Aided Design (CAD) models, this paper proposes a robust point-cloud-driven framework. Conventional Principal Component Analysis (PCA) alignment suffers from centroid shift errors due to asymmetric data loss from light-absorbing surface features. To solve this, a RANSAC-compensated hybrid PCA algorithm is developed to decouple position and orientation estimation, ensuring stable coordinate alignment despite incomplete data. Furthermore, to resolve the geometric collapse and kinematic jitter caused by traditional planar slicing in high-curvature polar regions, a spherical latitudinal equiangular conical slicing algorithm is introduced. By aligning the slicing planes with the sphere’s radial geometry, the method preserves topological accuracy while maintaining an optimal point density for smooth robotic execution. Experimental results on rubber ball groove processing demonstrate a repeat positioning accuracy of 0.09 mm and a feature coverage of 95.21%. This research provides a scientifically rigorous and computationally efficient solution for the automated processing of complex spherical surfaces. Full article

Against the backdrop of high-proportion renewable energy grid integration, modeling accuracy for substations incorporating wind and solar power is critical. Traditional modeling methods rely on theoretical parameters and lack sufficient accuracy. This study uses the 154 kV/23 kV Yeonggwang Substation in Jeollanam-do, South Korea (connected to three wind farms and three solar power plants, with 35 Micro-Phasor Measurement Unit (μPMU) measurement points deployed) as a case study. It investigates three-phase detailed modeling using Power System Computer Aided Design (PSCAD) and μPMU data-driven calibration. Based on substation topology and equipment parameters, a simulation model encompassing main transformers, transmission lines, renewable energy units, and loads was established. A hierarchical calibration system of “data preprocessing—parameter identification—iterative correction” was constructed, employing an iterative optimization strategy of “main grid layer—renewable energy layer—load layer.” A multi-objective optimization function centered on voltage, current, and power was developed. Verification results show that after calibration, the mean relative error rates (MRE) for voltage, current, active power and reactive power are 2.46%, 2.57%, 2.52% and 3.96% respectively, with mean error reduction rates (MERRs) of 80%, 82.75%, 81.33%, and 74.94% compared to pre-calibration values. The uniqueness of the calibration method proposed in this study lies in its use of actual μPMU measurement data to drive PSCAD model parameter calibration, achieving precise matching with the actual characteristics of the substation. This provides a reference method for modeling and digital twin construction of similar substations, demonstrating significant engineering application value. Full article

The rapid growth of electric vehicle (EV) deployment has created a strong demand for charging systems capable of handling higher power levels while preserving grid stability and maintaining satisfactory energy quality. In this work, a fast-charging architecture for 400 V battery systems is developed using a Vienna rectifier on the AC front end and a DC–DC buck converter on the DC stage. To enhance the performance of this topology, two complementary control techniques are combined: the Crystal Structure Algorithm (CryStAl), used for offline optimization of switching behavior, and a Random Decision Forest (RDF) model, employed for real-time adaptation to operating conditions. A clear, step-oriented derivation of the converter state–space equations is included to support controller design and ensure reproducibility. This control framework improves the key performance indices, including Total Harmonic Distortion (THD), ripple suppression, efficiency, and power factor correction. Specifically, the Vienna rectifier works on input current shaping and enhances the power quality, while the buck converter maintains a constant DC output appropriate for reliable battery charging. The simulation studies show that the combined CryStAl–RDF approach outperforms the conventional PI- and Particle Swarm Optimization (PSO)-based controllers. The proposed method achieves THD less than 2%, conversion efficiency higher than 97.5%, and a power factor close to unity. The voltage and current ripples are also significantly reduced, which justifies the extended life of the batteries and reliable charging performance. Overall, the results portray the potential of the combined metaheuristic optimization with machine learning-based decision techniques to enhance the behavior of power electronic converters for EV fast-charging applications. The proposed control method offers a practical and scalable route for next-generation EV charging infrastructure. Full article

Diskless computing nodes in cluster supercomputers require massive deployment procedure of operating systems software for their booting. Most previous studies for massive deployment of large files focus on developing flexible and scalable mechanisms in hidden or changeable network topologies, but the topology-optimal deployment has been rarely investigated in fixed open network topologies. In this article, we investigate the topology-optimal deployment method designed for a specific cluster supercomputer with the fat-tree topology. We examine possible deployment approaches and propose the optimal peer-to-peer deployment method. The proposed deployment method minimizes the number of peer-to-peer transmission steps by fully utilizing resources of the fat-tree topology and avoids contention between peer-to-peer communications. Simulation results show that the proposed method completes deployment at least nine times faster than the baseline method. Full article

This paper deals with the control of two five-phase permanent magnet synchronous motors (PMSMs), which are connected in series and operating at different speeds and torques. The topology under study is intended for use in an electrical naval propulsion system. The backstepping control strategy, which uses the Lyapunov stability concept, is employed to control the speed of the two machines considering the series connection of the PMSM stator windings. A comparative study, with respect to classical Vector Control (VC) using PI regulators, is provided to demonstrate the robustness of the proposed control strategies in both healthy and faulty conditions. Typically, dual PMSMs in series cannot operate in the degraded mode in the event of faults. This study optimizes their operation by adapting to such modes, including faults caused by symmetrical parameter changes or by an asymmetrical High Resistance Connection (HRC) in the stator windings, thereby ensuring continuity of service. The HRC is investigated and verified in one stator phase, in two adjacent stator phases and in two non-adjacent stator phases, as well as in a symmetrical HRC fault across all phases. Matlab-based simulation results validate the control design to achieve the desired performance and prove the effectiveness and the asymptotic stability of backstepping control for two series-connected 5-Ф PMSMs, thereby providing redundancy for the naval electric propulsion system. Full article

Phononic band gap crystals are characterized by periodic scatterers embedded within a matrix, which enable precise modulation of acoustic or elastic waves. Conventional optimization prioritizes bandwidth maximization, yet practical engineering often requires band gaps at specified frequencies. This requirement creates a significant design challenge. To this end, we develop a topology optimization strategy capable of maximizing elastic wave band gaps around prescribed target frequencies. The approach utilizes Material-Field Series Expansion (MFSE) for unit cell representation and a gradient-free Kriging-based algorithm to tackle the complex optimization problems. This strategy is systematically applied to optimize the band gaps of out-of-plane, in-plane, and complete wave modes, and is further extended to more complex scenarios involving dual-target frequencies. A variety of numerical results demonstrate the method’s effectiveness in engineering phononic crystals for bespoke frequency specifications. Full article

Geogrids significantly enhance the soil matrix stability and foundation bearing capacity. Despite the development of numerous geogrid configurations, their geometric design has not yet been systematically optimized. The design of geogrid aperture geometry aims to maximize geogrid performance while maintaining material efficiency. Nevertheless, topology optimized geogrid designs remain underexplored, particularly regarding the influence of aperture shape on interface shear behavior. To address this gap, this study developed SIMP-based variable density topology optimization models for three types of tensile geogrid structures: uniaxial, biaxial, and triaxial geogrid. The effects of key model parameters on the optimization results are examined, resulting in new geogrid geometries optimized primarily to minimize compliance, achieving weight reductions of 7%, 10%, and 12%, respectively. Subsequently, FLAC3D was used for tensile performance analysis, while coupled PFC3D–FLAC3D was employed for interfacial friction performance analysis. In FLAC3D, numerical simulations demonstrated that the topologically optimized geogrid outperformed conventional ones in both tensile resistance and strain distribution. Consequently, conventional biaxial and triaxial geogrids, along with their topologically optimized versions, were chosen for further analysis. Pull-out interface simulations of these geogrids were conducted using the coupled discrete element–finite difference method (PFC3D–FLAC3D) to investigate the influence of geogrid aperture shape and aperture ratio on the soil–geogrid interface. The results indicate that the reinforcement efficiency of the topologically optimized biaxial and triaxial geogrids was 10% and 8% higher, respectively, than that of the conventional geogrids. Taking the biaxial geogrid as an example, a comprehensive comparison of performance parameters between the conventional and topology-optimized versions revealed that the optimized design achieved a 10% reduction in weight. Simultaneously, it reduced stress concentration at critical locations by approximately 60% and increased the interface pull-out resistance by 20%. These findings demonstrate that the new topologically optimized geogrid exhibits significant potential for further promotion and application in practical engineering. Full article

The joint optimization of computing resources and network routing constitutes a central challenge in Computing First Networks (CFNs). However, existing research has predominantly focused on computation offloading decisions, whereas the cooperative optimization of computing power and network routing remains underexplored. Therefore, this study investigates the joint routing optimization problem within the CFN framework. We first propose a computing resource scheduling architecture for CFN, termed SICRSA, which integrates Software-Defined Networking (SDN) and Information-Centric Networking (ICN). Building upon this architecture, we further introduce an ICN-based hierarchical naming scheme for computing services, design a computing service request packet format that extends the IP header, and detail the corresponding service request identification process and workflow. Furthermore, we propose Computing-Aware Routing via Graph and Long-term Dependency Learning (CRGLD), a Graph Neural Network (GNN), and Long Short-Term Memory (LSTM)-based routing optimization algorithm, within the SICRSA framework to address the computing-aware routing (CAR) problem. The algorithm incorporates a decision-making framework grounded in spatiotemporal feature learning, thereby enabling the joint and coordinated selection of computing nodes and transmission paths. Simulation experiments conducted on real-world network topologies demonstrate that CRGLD enhances both the quality of service and the intelligence of routing decisions in dynamic network environments. Moreover, CRGLD exhibits strong generalization capability when confronted with unfamiliar topologies and topological changes, effectively mitigating the poor generalization performance typical of traditional Deep Reinforcement Learning (DRL)-based routing models in dynamic settings. Full article

This paper presents an artificial intelligence (AI)-assisted multi-objective topology optimization of a 48 V interior permanent magnet synchronous motor (PMSM) intended for mid-drive e-bike applications. The machine features a 48-slot, 8-pole stator–rotor combination with $\Delta$-shaped three buried magnets per pole, and is coupled to a multi-stage gearbox that adapts its high-speed, low-torque output to a human-scale crank speed. The design problem simultaneously maximizes average torque and efficiency while minimizing torque ripple by varying key stator slot dimensions and magnet geometries. A modular MATLAB–ANSYS Maxwell framework is developed in which finite element simulations are driven by a Bayesian optimization (BO) loop augmented by a large language model (LLM) with retrieval-augmented generation (RAG). The LLM acts as a memory-based agent that proposes candidates, shapes Gaussian Process priors, and incorporates natural language rules expressing qualitative design knowledge. Two AI-assisted trials are compared against a multi-objective Artificial Hummingbird Algorithm benchmark, RAG + BO with and without natural language input. All three methods converge to a similar Pareto region with average torque around 5.4–5.7 Nm, torque ripple of approximately 12.8–14.2%, and efficiency near 93.3–93.6%, suitable for geared e-bike drives. The LLM-guided trial achieves this performance with a 20.1% reduction in simulation expenses relative to the BO baseline and by about 48% compared to the Artificial Hummingbird Algorithm. The results demonstrate that integrating LLM guidance into Bayesian optimization improves sample efficiency while providing interpretable design trends for PMSM topologies tailored for light electric vehicles. Full article

Heteroatom functionalization of graphene is an effective strategy for designing more sustainable lithium-ion battery electrodes, as it can tune both interfacial adhesion and the electronic features of the carbon lattice. In this work, we investigated the interfacial compatibility between three graphene sheets—pristine graphene, graphene doped with one nitrogen atom (Graphene–N), and graphene doped with one sulfur atom (Graphene–S)—and three lignocellulosic binders (carboxymethylcellulose (CMC); coniferyl alcohol (LcnA); and sinapyl alcohol (LsiA)) using density functional theory (DFT). Geometries were optimized using CAM-B3LYP and M06-2X in combination with the LANL2DZ basis set, while ωB97X-D/LANL2DZ was employed for dispersion-consistent single-point refinements. The computed adsorption energies indicate that all binder–surface combinations are thermodynamically favorable within the present finite-model framework (ΔE_int ≈ −22.6 to −31.1 kcal·mol⁻¹), with LSiA consistently showing the strongest stabilization across surfaces. Nitrogen doping produces a modest but systematic strengthening of adsorption relative to pristine graphene for all binders and is accompanied by electronic signatures consistent with localized donor/basic sites while preserving the delocalized π framework. In contrast, sulfur doping yields a more binder-dependent response: it maintains strong stabilization for LSiA but weakens LCnA relative to pristine/N-doped sheets, consistent with an S-induced local distortion/polarizability pattern that can alter optimal π–π registry depending on the adsorption geometry. A combined interpretation of adsorption energies, electronic descriptors (including ΔE_gap as a model-dependent HOMO–LUMO separation), and topological analyses (AIM, ELF, LOL, and MEP) supports that Graphene–N provides the best overall balance between electronic continuity and chemically active interfacial sites, whereas Graphene–S can enhance localized anchoring but introduces more heterogeneous, lone-pair–dominated domains that may partially perturb electronic connectivity. Full article

Efficient and non-destructive extraction of clay minerals is fundamental for shale oil and gas reservoir evaluation and enrichment mechanism studies. However, traditional manual extraction experiments face bottlenecks such as low efficiency and reliance on operator experience, which limit their scalability and adaptability to intelligent research demands. To address this, this paper proposes an intelligent experimental scheduling system for clay mineral extraction based on deep reinforcement learning. First, the complex experimental process is deconstructed, and its core scheduling stages are abstracted into a Flexible Job Shop Scheduling Problem (FJSP) model with resting time constraints. Then, a scheduling agent based on the Proximal Policy Optimization (PPO) algorithm is developed and integrated with an improved Heterogeneous Graph Neural Network (HGNN) to represent the relationships among operations, machines, and constraints. This enables effective capture of the complex topological structure of the experimental environment and facilitates efficient sequential decision-making. To facilitate future practical applicability, a four-layer system architecture is proposed, comprising the physical equipment layer, execution control layer, scheduling decision layer, and interactive application layer. A digital twin module is designed to bridge the gap between theoretical scheduling and physical execution. This study focuses on validating the core scheduling algorithm through realistic simulations. Simulation results demonstrate that the proposed HGNN-PPO scheduling method significantly outperforms traditional heuristic rules (FIFO, SPT), meta-heuristic algorithms (GA), and simplified reinforcement learning methods (PPO-MLP). Specifically, in large-scale problems, our method reduces the makespan by over 9% compared to the PPO-MLP baseline, and the algorithm runs more than 30 times faster than GA. This highlights its superior performance and scalability. This study provides an effective solution for intelligent scheduling in automated chemical laboratory workflows and holds significant theoretical and practical value for advancing the intelligentization of experimental sciences, including shale oil and gas research. Full article

The global energy transition to renewable energy sources requires a rigorous assessment of the environmental impacts of all system components, including power electronics converters (PECs), which play a critical role in adapting generated energy to grid and load requirements. This paper presents a comprehensive comparative assessment of conventional PECs used in renewable energy systems, with a focus on DC-AC, DC-DC, and AC-DC converters. The study combines life cycle assessment (LCA) with the Circular Energy Sustainability Index (CESI) to evaluate both environmental performance and material circularity. The LCA is conducted using a functional unit defined as a representative converter, within consistent system boundaries that encompass material extraction, manufacturing, and end-of-life stages. This approach enables comparability among converter topologies but introduces limitations related to the exclusion of application-specific design optimizations, such as maximum efficiency, spatial constraints, and thermal management. CESI is subsequently applied as a decision-support tool to rank converter technologies according to sustainability and circularity criteria. The results reveal substantial differences among converter types: the controlled rectifier exhibits the lowest environmental impact and the highest circularity score (95.3%), followed by the uncontrolled rectifier (69.3%), whereas the inverter shows the highest environmental burden and the lowest circularity performance (38.6%), primarily due to its higher structural complexity and the material and manufacturing intensity associated with its switching architecture. Full article

With the continuous evolution of CMOS technology towards deep sub-micron and advanced nodes, the challenges of analog integrated circuits (ICs) in performance, design efficiency, and reliability are increasingly prominent. The traditional design methods that rely on manual experience and repeated simulations are no longer able to meet the requirements of complex systems for high performance, high robustness, and fast iteration. In this research, the efficient and robust optimization design methods for analog ICs are systematically reviewed. Firstly, the representative efficient design methods on topology synthesis, parameter optimization, and transfer learning are studied. In addition, with the advancement of technology and the reduction of power supply voltage, parasitic effects and the influence of the external environment on circuits can no longer be ignored. Thus, the robust optimization design methods that consider process, voltage, temperature (PVT), and parasitic effects are further investigated. Then, the advantages and limitations of different methods on design efficiency, performance, and reliability are compared and discussed. Finally, an outlook on the development trend of the efficient and robust design methods for analog ICs is provided, which can provide a reference for subsequent research and engineering applications. Full article

As a critical component of demolition robots, the rotary joint supports the entire manipulator arm and operates under severe loading conditions, rendering it highly susceptible to fatigue failure. To address this challenge, topology optimization is integrated into the structural design to simultaneously enhance fatigue life and achieve lightweighting. In this work, multiple working conditions of the demolition robot are considered and analyzed to identify the extreme operating condition. By extracting the resultant stress on the rotary joint from the assembled structure under the extreme condition, an equivalent model of the independent rotary joint is established. Given that topology optimization based on the original structure could not yield a usable structure, two topology optimization strategies based on resetting the design space are proposed, including topology optimization based on the partially filled design space and topology optimization within the fully filled design space. By performing topology optimization under different schemes, the optimized rotary joint models are reconstructed through geometric fusion. Numerical results demonstrate that the optimized rotary joints exhibit significant improvements in fatigue performance, with fatigue life doubled compared to the original design. Concurrently, the structural mass is effectively reduced. This proposed method achieves the dual objectives of fatigue life enhancement and lightweight design. Furthermore, the results reveal that resetting the design space when topology optimization fails to obtain a usable structure yields superior topology optimization outcomes, providing a valuable new insight for future structural optimization design processes in similar engineering scenarios. Full article