Search Results (98)

Under hull–propulsor coupling conditions, the geometric shape of an unmanned underwater vehicle (UUV) can significantly affect the inflow conditions of the aft rim-driven thruster (RDT) and, consequently, its propulsive performance. However, the number of UUV shape design parameters is relatively large, and their influences on the propulsive efficiency of the RDT differ markedly. If an equal-weight search strategy is still adopted for optimization, the computational cost will increase and the optimization efficiency will be reduced. To address this issue, this paper proposes an efficient global-sensitivity-information-driven sequential surrogate-based optimization method for the shape optimization design of the UUV, with the aim of improving the propulsive efficiency of the RDT corresponding to the self-propulsion equilibrium state under the cruise condition. Based on the hull–propulsor coupled numerical model of the UUV and RDT, the proposed method obtains the propulsive efficiency of the RDT at the self-propulsion point under the cruise condition by solving the self-propulsion equilibrium condition. On this basis, Sobol global sensitivity analysis is performed using the Kriging surrogate model to quantitatively evaluate the influence of the UUV shape design parameters on the propulsive efficiency of the RDT. Then, the global sensitivity information is mapped into optimization weights. Based on this, the minimum of surrogate prediction (MSP) and expected improvement (EI) sampling criteria are introduced. In this way, a surrogate model sequential optimization method driven by global sensitivity information is developed. The optimization results show that, after optimizing the UUV external shape, the propulsive efficiency of the RDT under the cruise condition is increased by 22.83%, thereby verifying the effectiveness of the proposed method. Full article

Integrated aerial platforms (IAPs), composed of multiple unmanned aerial vehicles (UAVs), can perform tasks such as aerial grasping and cooperative manipulation. In this paper, we introduce and design an IAP with joint-driven active deformation capability. During deformation and tasks such as aerial grasping, configuration-dependent variations in inertia and the center of mass (CoM) challenge control stability. To address this issue, a geometric model reference adaptive control (MRAC) scheme is developed on $\mathrm{SO}\left(3\right)$ to ensure robust and decoupled control under these time-varying conditions. The almost global stability of the closed-loop system is rigorously established through Lyapunov-based analysis and verified in simulations. The advantages of the proposed controller are further validated through real-world deformation experiments on a self-developed prototype, which successfully performs aerial grasping and assembly tasks. Full article

Stationary energy storage (SES) is increasingly needed to integrate variable renewable generation and improve consumer self-consumption, but technology choices involve associated trade-offs between cost, efficiency, and life-cycle impacts. This study evaluates the role of second-life lithium-ion (Li-ion) batteries repurposed from electric vehicles for stationary applications, compared to lead-acid (Pb-acid) batteries and power-to-hydrogen-to-power (PtH₂P) systems. We develop an optimization-based sizing and dispatch framework using measured PV–load profiles and hourly market electricity prices, and evaluate performance per 1 MWh delivered to the load over a 10-year life cycle. Economic performance is quantified through discounted cash flows equal to levelized cost of storage (LCOS), while environmental performance is assessed through life-cycle metrics with explicit representation of recycling and second-life credits. In addition to global warming potential (GWP), the analysis considers additional resource and impact metrics, as well as key operational efficiency metrics, including bidirectional consumption efficiency, autonomy, and share of self-consumption/export of photovoltaic systems. Scenario and sensitivity analyses examine the impact of policy and financial parameters, in particular feed-in tariff remuneration and discount rate, on the comparative ranking of technologies. The results highlight how circular economy pathways, especially second-life distribution for Li-ion batteries and high end-of-life recovery for lead-acid batteries, have a significant impact on the life-cycle burden for delivered energy, while market-driven conditions for dispatching and export activities shape economic outcomes. Overall, the proposed workflow provides a transparent, circularity-aware basis for selecting stationary storage technologies associated with photovoltaic systems, under realistic operational constraints. Full article

To address the core challenges in air-to-ground target re-identification (ReID), including network focus on invalid background information, poor adaptability to nonlinear feature distribution, and insufficient cross-domain generalization, this paper proposes a novel air-to-ground ReID framework based on Neighborhood Feature Centralization Attention (NFCA). On the basis of Coordinate Attention, the framework introduces a parameter-free Neighborhood Feature Centralization mechanism to build a lightweight attention module, which enhances cross-feature semantic interaction and suppresses background noise while retaining precise position encoding. It achieves end-to-end direct optimization of sample pair similarity through binary cross-entropy loss, eliminating the proxy task bias of traditional classification loss and adapting to the nonlinear structure of feature space. A multi-source data-driven training strategy is constructed by fusing ReID datasets and general classification datasets, which expands the coverage of feature space and narrows the distribution gap between training data and real air-to-ground scenarios without additional manual annotation. Experiments show that the proposed method achieves leading mAP values on the self-developed UAV air-to-ground dataset JC-1, the public person ReID dataset Market-1501, and the public vehicle ReID dataset VehicleID. Sufficient statistical validation, ablation experiments and cross-domain tests verify the advancement, reliability and generalization of the proposed method in complex air-to-ground scenarios. Full article

To explore the evolutionary dynamics of green product markets under bounded rationality, this study develops a tripartite evolutionary game model involving the government, passenger vehicle enterprises, and consumers, using China’s new energy vehicle (NEV) market as a case study. By integrating system dynamics with real-world data and policies, the paper simulates strategy evolution paths and identifies equilibrium conditions. The results show a unique evolutionarily stable strategy: the government refrains from regulation, enterprises actively produce NEVs, and consumers actively purchase green products. The government’s strategy is primarily influenced by enterprises, while enterprises’ strategy is mainly driven by consumers. Numerical analysis reveals that when the premium payment ratio of green products (price difference relative to conventional vehicles) is controlled between 27.27% and 31.82%, the market evolves most rapidly toward the ideal equilibrium. Furthermore, when the additional positive benefit ratio of green consumption falls below 36.36%, market formation and development are severely hindered; raising this ratio to 40.91% yields significant promotion effects, beyond which marginal benefits diminish. These findings provide quantitative benchmarks for policy design and strategic decision-making to foster self-sustaining green product markets. Full article

We propose a Dual-Channel assisted Music Emotion Perception and Visualization (DC-MEPV) framework designed for ambient lighting in intelligent vehicle cockpits, addressing the increasing demand for advanced human–machine interaction in the automotive industry. This framework consists of three main components: the Multi-Scale Feature Extraction Block (MSFEB), the Global Sequence Modeling Block (GSMB), and the Emotional Color Visualization Algorithm (ECV-Algo). The MSFEB extracts valence and arousal (V-A) features from dual channels at multiple temporal scales, with each channel employing a hybrid neural network architecture to capture multi-scale emotional representations. The GSMB integrates positional encoding, bidirectional long short-term memory (BiLSTM) networks, and multi-head self-attention mechanisms to dynamically model global emotional sequences. The ECV algorithm utilizes personalized emotion–color association rules to achieve expressive emotion-driven lighting visualization based on a continuous mapping from emotion space to color space. We conducted comprehensive comparison and ablation experiments to evaluate the model’s emotion perception performance, and designed three metrics to evaluate the quality of the generated visualizations. The model outperformed other networks in both comparative and ablation experiments. Additionally, the generated lights demonstrated strong performance in terms of CIEDE2000 variation rates, unique color ratios, and joint histogram entropy. DC-MEPV achieved excellent performance in emotion perception and visualizations on the DEAM and PMEmo datasets. Full article

Subcutaneous administration has become an increasingly important route for delivering protein therapeutics, driven by patient convenience and the growing use of self-administration devices. However, conventional subcutaneous injection systems are typically limited to injection volumes of approximately 1–2 mL, posing significant formulation challenges for protein drugs requiring high therapeutic doses. Monoclonal antibodies (mAbs), for example, often require concentrations exceeding 100 mg/mL to enable subcutaneous delivery, which introduces challenges related to limited solubility, elevated viscosity, and an increased risk of physical and chemical instability. Therefore, high-concentration protein suspensions have emerged as a promising formulation strategy to overcome these limitations and enable subcutaneous administration of high-dose proteins. In such systems, therapeutic protein solid particles are suspended in vehicles in which they are insoluble, giving rise to unique considerations related to particle properties, protein stability, and suspension behaviors such as viscosity, injectability, and sedimentation. Accordingly, multiple particle production approaches have been explored to enable the development of ultra-high-concentration protein suspensions (>200 mg/mL). This review article aims to provide a comprehensive overview of particle formation techniques and the relationships between key particle properties and suspension performance attributes relevant to the development of high-concentration protein suspensions for injectable applications, as well as future directions in this field. Full article

Autonomous cleaning robots operating in semi-structured dynamic environments must execute task-oriented motions while safely interacting with surrounding agents. These agents include pedestrians, vehicles, and other robots. In such environments (e.g., interaction-rich campus environments), reliable self-decision-making requires anticipating the future motions of surrounding agents rather than relying solely on reactive obstacle avoidance. This paper presents a trajectory prediction-enabled self-decision-making framework for autonomous cleaning robots in campus environments. A learning-based multi-agent trajectory prediction model is trained offline using public benchmarks and real-world operational data to capture typical interaction patterns in corridor-following, edge-cleaning, and intersection scenarios. The predicted trajectories are then incorporated as forward-looking priors into the robot’s online decision-making and planning process, enabling prediction-aware yielding, detouring, and task continuation decisions. The proposed framework is evaluated using real-world data-driven scenario reconstruction on a high-fidelity simulation platform that incorporates realistic vehicle dynamics and heterogeneous traffic participants. This evaluation focuses on short-horizon prediction performance and its impact on downstream decision-making stability. The results show that integrating trajectory prediction into the decision-making loop leads to more stable motion behavior and fewer abrupt adjustments in interaction scenarios. Under short-term prediction horizons, the evaluation results show that the proposed model achieves ADERate and FDERate exceeding 90% under predefined error thresholds, while lane-change prediction accuracy remains around 79%. In addition, the robot maintains stable speed tracking with only minor fluctuations under medium-density traffic conditions. Full article

The compressor in automotive air-conditioning systems consumes a significant fraction of the vehicle’s energy, thereby reducing driving range. Consequently, developing more efficient compressor operation is essential for improving overall thermal management. Nano-enhanced lubricants have emerged as a promising passive strategy to reduce compressor power consumption, enhance thermodynamic performance, and improve tribological behavior by minimizing friction and wear. This review critically examines existing nano-lubricant research with a focus on automotive compressor and system-level performance, friction and wear reduction mechanisms, and the influence of nanoparticle type and concentration on lubricant thermo-physical properties. The analysis reveals that nano-lubricants consistently enhance compressor operation by lowering discharge temperature and reducing power consumption, while improving coefficient of performance and cooling capacity. However, these benefits have been validated primarily under cooling-mode conditions and predominantly for reciprocating-piston compressors. Tribological studies further demonstrate substantial reductions in coefficient of friction and surface roughness, with improved anti-wear characteristics compared to virgin lubricants. Four principal mechanisms—rolling, polishing, protective-film formation, and self-repairing—have been identified as contributors to these enhancements. Nevertheless, most tribological investigations rely on simplified test rigs that do not fully represent the complex contact, loading, and thermal environments inside actual automotive compressors. This review underscores the need for system-level, mechanism-driven, and compressor-architecture-specific investigations covering both cooling and heating modes of automotive air-conditioning operation. The insights presented aim to guide future development of reliable, durable, and refrigerant-compatible nano-lubricant technologies for next-generation automotive air-conditioning systems. Full article

Accurate state of health (SOH) assessment is the cornerstone for ensuring the safety, reliability, and lifecycle value prediction of electric vehicles. While extensive research has demonstrated the significant advantages of data-driven approaches in SOH evaluation, the vast majority of work still relies on standardized test data obtained under laboratory conditions. These ideal conditions, including complete charge–discharge cycles and constant temperatures, are often unattainable in real-world operation where EV batteries face highly irregular driving patterns, fragmented charging segments, and unpredictable environmental disturbances. This paper provides a comprehensive and systematic overview of data-driven SOH assessment based on real-vehicle data, aiming to address the current research gap in unified laboratory-to-vehicle transfer frameworks. This paper first reviews existing SOH evaluation methodologies and highlights the challenges encountered when transitioning to real-world vehicle data. It delves into core technical challenges and solutions across the entire real-world SOH assessment chain, closely examining the complex characteristics of real-world data. The paper thoroughly evaluates the role of cutting-edge paradigms including weakly supervised, self-supervised, and transfer learning in mitigating label scarcity. We summarize a unified evaluation framework tailored for real-world scenarios: Vehicles-Out, Time-Rolling, Domain-Stratified (VTDS). This framework aims to systematically assess models’ generalization limits and engineering deployability across vehicles, time, and operating conditions. This work provides systematic guidance for researchers and practitioners, advancing data-driven SOH evaluation methods from theoretical research to engineering applications. Full article

We present PIRATE (Precision Imaging Real-time Autonomous Tracker and Explorer), a fully autonomous unmanned surface vehicle designed to enable self-operating data collection and persistent tracking of mobile underwater targets through the tight integration of acoustic localization, onboard visual perception, and closed-loop navigation. PIRATE employs a single mobile acoustic receiver to estimate target position using time-difference-of-arrival (TDoA) measurements acquired at different times and locations through planned autonomous motion and uses these estimates to drive adaptive vehicle behavior and activate fine-grained visual sensing in real time. This architecture enables sustained target-driven operation, in which navigation, acoustic monitoring, and visual processing are dynamically coordinated based on mission context and localization uncertainty. The system integrates real-time AI-based visual detection and tracking with automatic mission control, allowing visual perception to operate opportunistically within an acoustically guided tracking loop rather than as a standalone sensing modality. Field experiments in a shallow-water environment demonstrate reliable autonomous navigation, single-receiver acoustic localization with meter-scale accuracy, and stable onboard visual inference under sustained operation. By enabling coupled acoustic tracking and onboard visual perception in a fully autonomous surface platform free of external infrastructure, PIRATE provides a practical foundation for fine-scale behavioral observation, adaptive marine monitoring, and long-duration studies of mobile underwater organisms. We demonstrate this advantage with two possible applications. Full article

The Internet of Things (IoT) has transformed modern life through interconnected devices enabling automation across diverse environments. However, its reliance on legacy network architectures has introduced significant security vulnerabilities and efficiency challenges—for example, when Datagram Transport Layer Security (DTLS) encrypts transport-layer communications to protect IoT traffic, it simultaneously blinds intermediate proxies that need to inspect message contents for protocol translation and caching, forcing a fundamental trade-off between security and functionality. This paper presents an architectural solution based on the Recursive InterNetwork Architecture (RINA) to address these issues. We analyze current IoT network stacks, highlighting their inherent limitations—particularly how adding security at one layer often disrupts functionality at others, forcing a detrimental trade-off between security and performance. A central principle underlying our approach is the role of structural symmetry in RINA’s design. Unlike the heterogeneous, protocol-specific layers of TCP/IP, RINA exhibits recursive self-similarity: every Distributed IPC Facility (DIF), regardless of its position in the network hierarchy, instantiates identical mechanisms and offers the same interface to layers above. This architectural symmetry ensures predictable, auditable behavior while enabling policy-driven asymmetry for context-specific security enforcement. By embedding security within each layer and allowing flexible layer arrangement, RINA mitigates common IoT attacks and resolves persistent issues such as the inability of Performance Enhancing Proxies to operate on encrypted connections. We demonstrate RINA’s applicability through use cases spanning smart homes, healthcare monitoring, autonomous vehicles, and industrial edge computing, showcasing its adaptability to both RINA-native and legacy device integration. Our mixed-methods evaluation combines qualitative architectural analysis with quantitative experimental validation, providing both theoretical foundations and empirical evidence for RINA’s effectiveness. We also address emerging trends including AI-driven security and massive IoT scalability. This work establishes a conceptual foundation for leveraging recursive symmetry principles to achieve secure, efficient, and scalable IoT ecosystems. Full article

The transition to renewable energy systems and electric mobility depends on the effectiveness, reliability, and durability of lithium-ion battery technology. Accurate modeling and control of battery systems are essential to ensure safety, efficiency, and cost-effectiveness in electric vehicles and grid storage. In engineering and materials science, battery models depend on physical parameters such as capacity, energy, state of charge (SOC), internal resistance, power, and self-discharge rate. These parameters are affected by measurement uncertainty. Despite the widespread use of lithium-ion cells, few studies quantify how measurement uncertainty propagates to derived battery parameters and affects predictive modeling. This study quantifies how uncertainty in voltage, current, and temperature measurements reduces the accuracy of derived parameters used for simulation and control. This work presents a comprehensive uncertainty analysis of 18650 format lithium-ion cells with nickel cobalt aluminum oxide (NCA), nickel manganese cobalt oxide (NMC), and lithium iron phosphate (LFP) cathodes. It applies the law of error propagation to quantify uncertainty in key battery parameters. The main result shows that small variations in voltage, current, and temperature measurements can produce measurable deviations in internal resistance and SOC. These findings challenge the common assumption that such uncertainties are negligible in practice. The results also highlight a risk for battery management systems that rely on these parameters for control and diagnostics. The results show that propagated uncertainty depends on chemistry because of differences in voltage profiles, kinetic limitations, and temperature sensitivity. This observation informs cell selection and testing for specific applications. Improved quantification and control of measurement uncertainty can improve model calibration and reduce lifetime and cost risks in battery systems. These results support more robust diagnostic strategies and more defensible warranty thresholds. This study shows that battery testing and modeling should report and propagate measurement uncertainty explicitly. This is important for data-driven and physics-informed models used in industry and research. Full article

Community energy systems are expected to play an increasingly important role in the decarbonization of the residential sector, but their operation depends on how different electricity and heat storage technologies are configured and used. Existing studies typically examine storage options in isolation, limiting the comparability of their operational roles. This study addresses this gap by developing a decision-support framework that enables a consistent, operation-focused comparison of battery energy storage, hydrogen storage, and electric-vehicle-based storage within a unified community-scale hybrid energy system. The model represents electricity and heat balances in a hub formulation that couples photovoltaic and wind generation, a gas engine, an electric boiler, thermal and electrical storage units, hydrogen conversion and storage, and an aggregated fleet of electric vehicles. It is applied to a stylized Polish residential community using local demand, generation potential, and electricity price data. A set of single-technology and multi-technology scenarios is analyzed to compare how storage portfolios affect self-sufficiency, self-consumption, grid exchanges, and operating costs under current electricity market conditions. The results show that battery and electric vehicle storage primarily provide short-term flexibility and enable price-driven arbitrage, as reflected in the highest contribution of battery discharge to the electricity supply structure (5.6%) and systematic charging of BES and EVs during low-price hours, while hydrogen storage supports intertemporal shifting by charging in multi-hour surplus periods, reaching a supply share of 1.4% at the expense of substantial conversion losses. Moreover, the findings highlight fundamental trade-offs between cost-optimal, price-responsive operation and autonomy-oriented indicators such as self-sufficiency and self-consumption, showing how these depend on the composition of storage portfolios. The proposed framework, therefore, provides decision support for both technology selection and the planning and regulatory assessment of community energy systems under contemporary electricity market conditions. Full article

Accurate estimation of the State-of-Health (SOH) is essential for the reliable operation of lithium-ion batteries in electric vehicles and energy storage systems. However, conventional data-driven models often lack interpretability and show limited robustness under non-linear aging conditions. In this study, a physics-informed Transformer model is proposed for long-term SOH estimation by incorporating physically interpretable, degradation-sensitive indicators into a self-attention framework. Incremental Capacity Analysis (ICA)-derived features and thermal-gradient indicators are used as auxiliary inputs to provide physics-consistent inductive bias, enabling the model to focus on degradation-relevant regions of the charging trajectory. The proposed approach is validated using four lithium-ion battery cells exhibiting diverse aging behaviors, including severe non-linear capacity fade. Experimental results demonstrate that the proposed model consistently outperforms an LSTM baseline, achieving an RMSE below 1.5% even for the most degraded cell. Furthermore, attention map analysis reveals that the model autonomously emphasizes voltage regions associated with electrochemical phase transitions, providing clear physical interpretability. These results indicate that the proposed physics-informed Transformer offers a robust and explainable solution for battery health monitoring under practical aging conditions. Full article