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Vertical Flight Society Technical Papers - Coming Soon to Mobilus

Vertical Flight Society papers coming soon to SAE Mobilus. In partnership with the Vertical Flight Society (VFS), SAE Mobilus will be hosting more than 17,000 technical papers and proceedings from VFS. Click here to see a sample of the content covering decades of rotorcraft and vertical flight technologies.

Important Update – Accessibility Enhancements on SAE Mobilus

We’re pleased to share that SAE Mobilus has recently completed a comprehensive set of accessibility improvements across the platform. As a result, Mobilus is now aligned with WCAG 2.2 Level AA standards in most key categories. These updates are part of our ongoing commitment to ensuring the platform is accessible to all users. Enhancements include improvements to keyboard navigation, screen reader compatibility, visual contrast, error handling, and overall usability across core areas of the site. An updated Voluntary Product Accessibility Template (VPAT v2.5 – March 2026) is now available, providing a detailed overview of our accessibility conformance. You can access the VPAT directly within the Help section. Accessibility is a continuous effort. We will continue to evaluate and improve the platform to better support all users and meet evolving standards. If you have any feedback or experience any accessibility issues, we welcome your input. - The SAE Mobilus Team

SAE AI Chat — Update and What's Next

* Important Update - SAE AI chatbot Beta program * The SAE AI Chatbot Beta program - Phase 1 will end on March 31, 2026. Following this date, the AI chatbot capabilities will be temporarily unavailable as we begin development of a significantly enhanced version. We sincerely thank all Phase 1 Beta participants for their engagement and valuable feedback - your insights have played an important role in shaping the next phase of this experience. The upcoming version of the SAE AI Chatbot will expand content coverage beyond current standards, incorporating additional critical resources from our digital library. It will also be made available to a broader audience across Mobilus. Want to be part of what's next? Register your interest here to receive updates and be considered for early access. For additional information, visit the FAQs. — The Mobilus Team

SAE International Digitization and AI Policy

SAE International prohibits the entry of SAE Content into any form of Artificial Intelligence (AI) tools, such as ChatGPT, OpenAI, or Microsoft Copilot. Additionally, creating derivatives of SAE Content using AI is also prohibited without express written permission from SAE International. In the case of such use, SAE International will suspend access to SAE Content, and further legal action will be considered.

Welcome to the new SAE Mobilus®

Welcome to the new era of SAE Mobilus®, redesigned with a modern, intuitive web interface featuring enhanced search capabilities to help you stay informed and easily discover content. Login today: To access content within your subscription and make the most of the platform, please log into your subscription and your personal My Library (sign up is free) located in the top right corner of the screen. Key Features Intuitive User Interface: We've listened to our customers and tied this to the best practices in the industry to offer an improved, intuitive, and desirable website experience for our users. Improved In-app Discoverability: Enhanced search and expanded menu featuring search suggestions that allow for quick discovery of content. Announcements: Stay up to date on new features through a dedicated announcements page. Most recent announcements also get featured on the homepage. Enhanced Browse capabilities: Browse through over 200,000 publications that are now classified into categories commonly used across the industry. Deep-dive further into any topic or sub-topic of interest to make the most for your corporate projects or research. Smart Automation: "Meta Tags" on each result will help you find content related to your search. My Library: My dashboard is rebranded as My Library with powerful features to help you make the most out of the platform Mobile Friendly: Upgraded navigation on mobile devices Training Available SAE’s Customer Success team is dedicated to delivering the best platform experience possible. Through monthly trainings and short, on-demand video tutorials, find answers to your questions here: SAE Mobilus Spotlight training - Available monthly, hosted live by SAE’s Customer Success team Mobilus Minute – 2-minute videos, available on-demand Need some troubleshooting help? Access our FAQs by visiting our Help section: https://saemobilus.sae.org/help/home Resources to help you navigate the new platform are available here, where you can find links to quick start guides and video tutorials on features like My Library, Announcements, Saved Search, Login and new features. Have feedback that you'd like to share with us? Please submit your feedback to the Mobilus team through the blue Knowledge bot in the bottom right hand corner of the new platform.

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Recent SAE Edge™ Research Reports

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Wing-in-Ground Effect Vehicles: From Modern Military and Commercial Development to Global Disaster Response

EPR202501711/24/2025

The wing-in-ground effect (WIG) vehicle represents a significant advancement in aerodynamics and vehicle design, leveraging the ground effect phenomenon to enhance lift and reduce drag when flying close to the surface. This unique capability allows WIG vehicles to achieve higher payloads, longer range, and greater fuel efficiency compared to traditional aircraft, making them an attractive option for modern military and global disaster response applications. Wing-in-Ground Effect Vehicles: From Modern Military and Commercial Development to Global Disaster Response discusses future disaster response, logistics, and military applications for WIG vehicles, including the ongoing development of aerospace and transportation technology. Relavant advancements in materials and propulsion systems holds promise for further enhancing WIG performance and operational range. Additionally, cost-effective and powerful flight computers with various types of mission-enabling sensor suites from the

Sustainable Circular Future Mobility: Environmental Impact of Next-gen Vehicles

EPR20250159/22/2025

The next generation of mobility, driven by shared, driverless, connected, and electrified vehicles, holds strong potential to advance sustainability through lower emissions and improved resource efficiency. However, critical questions remain regarding their true environmental impact, including battery lifecycle management, material consumption, and circular manufacturing practices. Sustainable Circular Future Mobility: Environmental Impact of Next-gen Vehicles explores these unresolved issues, focusing on the shift from internal combustion to electric vehicles, supply chain challenges, regulatory gaps, and the operational realities of sustainable productization. It also critically examines the risks of greenwashing, the need for consistent standards, and the role of intersectoral collaboration—with energy, urban planning, information and communications technologies, and waste management sectors—in building resilient, scalable solutions. The report provides strategic recommendations and

Next-gen AI for Aerospace Engineering

EPR20250169/22/2025

Despite growing investments, the widespread adoption and scalable deployment of generative artificial intelligence (AI) remains a challenge due to data trustworthiness, regulatory uncertainty, interpretability, and ethical governance. The need to accelerate automation and maintain the human-in-the-loop demonstrates broader questions of responsibility and transparency. Next-gen AI for Aerospace Engineering investigates the transformative role of GenAI within aerospace engineering, examining its shift from conventional workflows toward more AI-driven solutions in design, manufacturing, and maintenance. It emphasizes GenAI’s emerging ability to automate repetitive mundane tasks, reduce design complexity, and optimize engineering pipelines. The report underscores the need for validation methods that must align AI-generated outputs with physics-informed models, integration with legacy engineering tools (e.g., computational fluid dynamics, finite element analysis, digital twins), and

Small, Mobile, and Autonomous Agricultural Robots

EPR20250128/13/2025

The automation of labor-intensive picking and planting operations is having an immediate impact in the agricultural indutry. In its simplest form, robotic automation can reduce the labor and soil disturbance while enabling organic soil cover and increasing species diversification through precision approaches to planting, weeding, and spraying. With this, pesticides and fertilizers can be applied in a more targeted way, and with machinery visiting fields more frequently, earlier and more targeted intervention can occur before pests become established. Small, Mobile, and Autonomous Agricultural Robots identifies issues that need to be resolved fo for this technology to thrive, including improving methods of acquiring and labeling training data to facilitate more accurate models for specific applications. It also discusses concepts such as general-purpose mechanical platforms for use as carriers of agricultural automation systems with high stability, positional accuracy, and variable

Safety Impacts of Active Safety and Driving Automation Features

EPR20250138/13/2025

Advanced technologies that assist the human driver or reduce (or even eliminate) the human driver’s role are becoming increasingly prevalent in new light-duty vehicles used by the general public. These technologies are divided between Active Safety features that monitor the human driver and vehicle motion and act intermittently to mitigate and avoid crashes, and Driving Automation features that assume some or all of the dynamic driving task from the human driver. Both types of technologies have the potential to reduce injuries and save lives by reducing the frequency and/or severity of crashes. Safety Impacts of Active Safety and Driving Automation Features addresses the current capabilities and future potential for Active Safety and Driving Automation features to reduce crash frequency and severity and provides an overview of the state of the industry for both types of features, including current deployments, trends, and anticipated rollouts. Gaps in knowledge, unsettled issues, and

Hybrid Data- and Physical Model-driven Safe and Intelligent Motion Planning and Control for Autonomous Vehicles

EPR20250147/18/2025

Autonomous vehicle motion planning and control are vital components of next-generation intelligent transportation systems. Recent advances in both data- and physical model-driven methods have improved driving performance, yet current technologies still fall short of achieving human-level driving in complex, dynamic traffic scenarios. Key challenges include developing safe, efficient, and human-like motion planning strategies that can adapt to unpredictable environments. Data-driven approaches leverage deep neural networks to learn from extensive datasets, offering promising avenues for intelligent decision-making. However, these methods face issues such as covariate shift in imitation learning and difficulties in designing robust reward functions. In contrast, conventional physical model-driven techniques use rigorous mathematical formulations to generate optimal trajectories and handle dynamic constraints. Hybrid Data- and Physical Model-Driven Safe and Intelligent Motion Planning and

Product Assurance in the Age of Artificial Intelligence

EPR20250115/21/2025

Driven by the vast consumer marketplace, the electronics megatrend has reshaped nearly every sector of society. The advancements in semiconductors and software, originally built to serve consumer demand, are now delivering significant value to non-consumer industries. Today, electronics are making inroads into traditionally conservative, safety-critical sectors such as automotive and aerospace. In doing so, electronics—now further propelled by artificial intelligence—are disrupting the functional safety architectures of these cyber-physical systems. Electronics have created the world of cyber-physical systems, raising broader concerns about the broader category of product assurance. Product Assurance in the Age of Artificial Intelligence continues the work of previous SAE Edge Research Reports in examining open research challenges arising from this shift, particularly in automotive systems, as core electronic technologies (e.g., the combination of software and communications) have even

Edge Artificial Intelligence in Connected, Cooperative and Automated Mobility

EPR20250095/20/2025

With many stakeholders involved, and major investments supporting it, the advancements in automated driving (AD) are undoubtedly there. Generally speaking, the motivation for advancing AD is driver convenience and road safety. Regarding the development of AD, original equipment manufacturers, technology start-ups, and AD systems developers have taken different approaches for automated vehicles (AVs). Some manufacturers are on the path toward stand-alone vehicles, mostly relying on onboard sensors and intelligence. On the other hand, the connected, cooperative, and automated mobility (CCAM) approach relies on additional communication and information exchange to ensure safe and secure operation. CCAM holds great potential to improve traffic management, road safety, equity, and convenience. In both approaches, there are increasingly large amounts of data generated and used for AD functions in perception, situational awareness, path prediction, and decision-making. The use of artificial

Leveraging Advanced Data Technologies for Smart Traffic Management

EPR20250085/20/2025

The transportation industry is transforming with the integration of advanced data technologies, edge devices, and artificial intelligence (AI). Intelligent transportation systems (ITS) are pivotal in optimizing traffic flow and safety. Central to this are transportation management centers, which manage transportation systems, traffic flow, and incident responses. Leveraging Advanced Data Technologies for Smart Traffic Management explores emerging trends in transportation data, focusing on data collection, aggregation, and sharing. Effective data management, AI application, and secure data sharing are crucial for optimizing operations. Integrating edge devices with existing systems presents challenges impacting security, cost, and efficiency. Ultimately, AI in transportation offers significant opportunities to predict and manage traffic conditions. AI-driven tools analyze historical data and current conditions to forecast future events. The importance of multidisciplinary approaches and

Space-based Solar Power for Instantaneously Dispatchable Renewable Power on Earth

EPR20250054/24/2025

Recent advances are reducing the cost of space launch, high specific power solar cells, and the production of satellite systems. Modular architectures with no moving parts and distributed power systems would minimize assembly and maintenance costs. Together, this may enable space-based solar power to provide decarbonized dispatchable power at a lower cost than equivalent technologies such as nuclear power stations. Space-based Solar Power for Instantaneously Dispatchable Renewable Power on Earth discusses the advances in emerging technologies, like thin film solar cells, reusable launch vehicles, and mass-produced modular satellite systems that would make economic space power feasible. Click here to access the full SAE EDGETM Research Report portfolio.

Latest Journal Issues

Browse All 16

Passenger Vehicle Systems

Electrified Vehicles

Advances and Current Practices in Mobility

Sustainable Transportation, Energy, Environment, & Policy

Connected and Automated Vehicles

Transportation Cybersecurity and Privacy

Vehicle Dynamics, Stability, and NVH

Transportation Safety

Alternative Powertrains

Commercial Vehicles

Aerospace

Engines

Materials and Manufacturing

Fuels and Lubricants

Passenger Cars - Mechanical Systems

Recent Books

Browse All 720

Variable Compression Systems for Future Engines and Fuels

R-5534/30/2026

Variable Compression Systems for Future Engines and Fuels explores variable compression ratio (VCR): one of the most promising—and historically elusive—advancements in internal combustion engine (ICE) technology. Long recognized for its thermodynamic benefits, VCR has challenged engineers for decades due to its mechanical complexity. Today, as the global mobility landscape demands ever higher efficiency, lower emissions, and greater fuel flexibility, VCR has emerged as a viable and transformative solution. This book delivers a comprehensive and authoritative examination of VCR technology, quantifying its efficiency and CO2-reduction potential while surveying the wide range of mechanisms conceived, developed, and tested over more than a century of engine innovation. By combining historical insight with modern analysis, the authors reveal the remarkable ingenuity of past and present engine designers—and demonstrate that VCR is not only feasible, but increasingly essential. Positioned

Aerospace Standards Index - 2026

ASIN20262/25/2026

This valuable resource lists all Aerospace Standards (AS), Aerospace Recommended Practices (ARP), Aerospace Information Reports (AIR), and Aerospace Resource Documents (ARD) published by SAE. Each listing includes title, subject, document number, key words, new and revised documents, and DODISS-adopted documents. AMS Index - Now Available!

Business Reliability Growth for Automotive Engineering, Volume 4

R-5522/17/2026

In a world where every business process is under pressure to perform faster, safer, and more reliably, this book delivers a powerful roadmap for sustained operational excellence. Centered on the proven methodology of Design of Experiments (DOE), it shows how organizations can move beyond reactive problem-solving to systematic reliability growth. From well-defined standard operating practices to management-level decision-making, the book connects strategy, data, and execution to create repeatable, measurable results across the enterprise. Readers are guided through practical, real-world applications of DOE, from selecting the right factors and levels to executing robust experiments, analyzing outcomes, validating solutions, and continuously monitoring performance. Each chapter translates complex statistical and engineering concepts into actionable business value, helping teams improve quality, reduce waste, and increase return on investment. Key capabilities explored in this book

January 2026 AMS Index

AMSJAN-20261/24/2026

Fundamentals of Sustainable Aviation Fuels

R-5471/22/2026

As global energy demands rise and environmental challenges intensify, the aviation industry faces a critical need to redefine how it powers the skies. Fundamentals of Sustainable Aviation Fuels presents a comprehensive, research-driven exploration of the transition from fossil-based fuels to renewable alternatives that promise to reshape the future of air transportation. This book bridges the gap between academic knowledge and industrial application, offering a detailed overview of the science, technology, and policy driving the adoption of Sustainable Aviation Fuels (SAFs). From understanding feedstocks and conversion pathways to evaluating production technologies, emissions performance, and life-cycle impacts, the text equips readers with a holistic understanding of SAFs within today’s global energy landscape. Across five insightful chapters, readers will find a clear roadmap: the evolution and importance of SAFs; production methods and technological foundations; environmental

SAE International’s Dictionary of ADAS and Connected Vehicles

R-5591/20/2026

The convergence of Advanced Driver Assistance Systems (ADAS) and connected vehicle technologies is ushering in a transformative era of automotive innovation—one that is fundamentally enhancing vehicle safety, efficiency, reliability, and the overall driving experience. SAE International’s Dictionary of ADAS and Connected Vehicles stands as the definitive reference for this rapidly evolving domain, meticulously compiled to clarify and standardize the language shaping modern mobility. Inside, readers will find clear, authoritative definitions encompassing the full spectrum of technologies that enable connected and automated driving—including vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and vehicle-to-everything (V2X) communication systems. This comprehensive resource translates complex engineering terminology into accessible language, enabling engineers, researchers, policymakers, educators, and enthusiasts to share a common technical foundation. Reflecting the latest

Safety, ADAS, and Cybersecurity: Vol. 4

EPRCOMPV01202512/29/2025

Electrification: Vol. 3

EPRCOMPV04202512/29/2025

Infrastructure Enablers for Automation: Vol. 4

EPRCOMPV02202512/29/2025

Automated Vehicles: Development, Testing, and Validation: Vol. 4

EPRCOMPV03202512/29/2025

Recently Published

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Advanced Composites Prepreg - Nominal 250 °F (121 °C) Cure - 12K Tow Carbon Fiber and Epoxy Resin, Plain Weave Fabric

AMS3914B (Current)

5/15/2026

The intent of this specification is for the procurement of plain weave fabric epoxy prepreg product with 250 °F (121 °C) cure for aerospace applications; therefore, no qualification or equivalency threshold values are provided. Users that intend to conduct a new material qualification or equivalency program must refer to the production quality assurance section (see 4.3).

AMS P17 Polymer Matrix Composites Committee

Design of a Modular High-Power High-Repetition-Rate Semiconductor Pulsed Laser Power Supply

2026-99-0716

5/15/2026

μsμs

Huang, De, Lu, Jiawei, Yang, Zhiqing, Xv, Ziyi, Xing, Hui

Research on Waste Heat Recovery Characteristics of Electric Drive System in Hybrid Electric Light-Duty Trucks

2026-99-0722

5/15/2026

Aimed at the high energy consumption for battery heating of a light hybrid truck in low-temperature winter, this paper proposes an optimized battery thermal management scheme based on motor waste heat and PTC cooperation. Then it verifies its energy-saving performance based on multi-condition simulation and testing. Taking the constant-speed condition at -5°C as an example, firstly, the accuracy of the battery thermal management model is verified by comparative simulation and test. Then, based on the verified model, the battery thermal management model is simulated under typical winter conditions at 0°C and 5°C. The analysis results show that, when the battery temperature is raised from the initial state to a certain target, the energy consumption of the motor waste heat-assisted PTC heating scheme is obviously less than that of PTC heating. The energy saving rates are 33.137% at -5°C, 32.45% at 0°C, and 32.56% at 5°C, respectively. The research results have proved that the effective

Meng, Shun, Zhang, Dong, Zhang, Yu, Zhang, Chunyu, Yao, Mingyao, Qiu, Liang, Qian, Yejian

Crowd Counting Network for Risk Early Warning in Exhibition Scenarios

2026-99-0705

5/15/2026

As a densely populated public place, exhibitions feature spatial layouts with multi-area linkage and instantaneous crowd flow mutations. Thus, developing a crowd flow early warning system adapted to exhibition dynamics is a key focus at the public safety and smart exhibitions to avoid risks like local congestion-induced stampedes. In general, two core challenges in exhibition crowd counting: 1) Key dynamic gathering information is hidden in high frequency components, but no correlation mechanism between frequency components and scene has been established; 2) Instant crowd gatherings cause high-frequency local density mutations, leading to time delays and spatial ambiguity of dynamic signals. To solve these, we propose a novel Crowd Counting Network for Risk Early Warning in Exhibition Scenarios with two core modules: 1) A bidirectional feature filtering module optimizes frequency information through low-frequency suppression to reduce redundancy and high-frequency activation to

Zhang, Jin, Zhang, Wanyue, Yuan, Jingjing, Chen, Zhen, Gu, Dazhi

Research on Performance Prediction Methods for Centrifugal Refrigeration Compressors in Data Centers

2026-99-0725

5/15/2026

As the “digital brain” and core foundational support for the development of intelligent transportation and connected vehicles, the performance of data centers directly determines the operational capability of intelligent transportation systems. In the process of advancing the vehicle-road-cloud collaborative architecture, the demand for high-performance computing power in data centers has experienced explosive growth. The substantial increase in computing tasks has posed severe challenges to thermal management, making efficient and reliable cooling systems an indispensable core component. Centrifugal compressor water-cooling units are the mainstream cooling solution for large-capacity scenarios, and their design optimization is crucial for improving the energy efficiency and performance of the entire cooling system. This paper proposes a one-dimensional performance prediction method for centrifugal compressors based on an empirical loss model, and realizes the iterative calculation of

Zhu, Minhao, Jiang, Bin, Li, Min, Zeng, Zihui, Gu, Yunhui

Linear Trajectory Planning of Hydrogen Storage Tank with Unequal Polar Holes

2026-99-0718

5/15/2026

In this paper, the design and process research of uniform filling linear trajectory for filament wound hydrogen storage tank with unequal polar holes are carried out. Firstly, by optimizing the slip coefficient, the winding angles of the left and right heads are smoothly and continuously transitioned to the cylindrical section. We study the necessary conditions for achieving the central angle of uniform filling, and calculate the tangent points of the trajectory line based on the continuous fraction principle. Meanwhile, the slip coefficients at the left and right ends that satisfy stable winding and uniform covering are determined. Based on the equal contour constraint conditions, we analyze the motion trajectory equation of the four-axis winding machine and convert it into the corresponding machine code for actual winding operations. Experimental results show that stable winding of fibers on the surface of the unequal-polar-hole mandrel is achieved, and uniform filling and winding

Chen, Baosen, Fu, Jianhui, Cao, Xuewen, Yu, Libin

Wavelet-Based Spatio-Temporal Fusion Network for Electromyographic Decoding in Bionic Hand Control Systems

2026-99-0744

5/15/2026

Surface electromyography (EMG) signals are essential for facilitating intuitive interactions between humans and bionic hands. However, their inherent non-stationarity, low signal-to-noise ratio, and significant inter-individual variability present considerable obstacles to precise decoding. To overcome these challenges, this study proposes a novel recognition framework combining wavelet packet decomposition and a dynamic graph convolutional-Transformer model. The process starts with multi-layer wavelet packet decomposition and adaptive threshold denoising, effectively removing noise while retaining critical signal features. Subsequently, a dynamic graph convolutional network is employed to capture spatial interactions among multi-channel electrodes, and a Transformer encoder models long-term temporal dependencies within the signals. By integrating these methods, the model generates a fused feature representation that incorporates both spatial and temporal correlations. Experimental

Huang, Rui, Zhao, Yue, Yang, Penghua, Zhu, Jintao, Xiong, Xibei

Research on Parameterization Method and System Application of Mining Supports Based on Modularization

2026-99-0715

5/15/2026

The design process of mining supports is often complicated due to their intricate structure and numerous dimensional dependencies, leading to a cumbersome modeling process and low design efficiency. To address these challenges, this paper introduces a parametric design system for mining supports built on the SolidWorks platform. The system integrates modular design concepts, module-matching principles, dimension-driven techniques, and API development. By adopting a modular assembly modeling approach, the system offers an efficient solution for managing the dimensional relationships between the various components of mining supports. Additionally, the system supports adaptive processing of 2D engineering drawings, facilitating the rapid design and manufacturing of mining supports. Engineering case studies demonstrate that this system enhances the design efficiency of mining supports by over 90%, significantly shortening the product development cycle, ensuring product quality, and

Rui, Lichao, Song, Jiahao, Yang, Zhiqing, Li, Helong, Ding, Lijian

Cabbage Cleaning Device Based on Fischertechnik Model

2026-99-0734

5/15/2026

This article focuses on the problem of high labor cost, low processing efficiency and poor automation of the existing equipment in the postharvest processing of Chinese cabbage. It will design and produce an automated Chinese cabbage processing method called Smart Fresh Pack. Root removal, leaf removal, washing, loading, weighing, packaging and labeling functions were integrated, and smart dexterous intelligence was applied to core concepts and this can be used in the bulk production scenario of supermarkets in the city and countryside Compared with traditional assembly line equipment, obvious advantages in terms of structure, function and processing capacity: Key innovations include: Low-pressure air jet cleaning replaces water washing, which prevents a second contamination and weighing error due to surface moisture; pneumatic gripper and multi-DOF robotic arms combine to package and dynamically weigh simultaneously, streamlining these tasks; machine vision relies on an SSD

Chen, Yuhui, Zhang, Yixuan, Ruan, Jia, Zhu, Huayun, He, Lianzheng, Zhao, Ping

Local LLM Based Knowledge Graph in Power Domain Using Graph-Schema Reasoning and Retrieval-Augmented Generation

2026-99-0756

5/15/2026

In recent years, large language models (LLMs) have shown great potential in many domains. However, their application in professional domains is often limited by problems like erroneous outputs and hallucinatory responses. Therefore, we present a framework that combines knowledge graphs (KGs) with local LLMs. The framework utilizes the factual information in KGs to improve the initial output of the LLMs, thereby reducing the factual errors in inference. In this paper, a domain knowledge graph is automatically constructed using textual data from the power industry. The KG contains 149,732 entities and 139,280 relationships. The proposed method is tested on EleQA, a public Q&A dataset of electricity regulations. Compared with the LLM-only baseline, the knowledge-graph-enhanced model achieves an improvement of 32.42%. Moreover, the framework shows strong adaptability and performs well on various LLMs. Our framework improves the accuracy and utility of large language models in the power

Chen, Ruiduan, Lin, Shizhong, Shao, Zhan, Cui, Shicheng, Li, Xingyu, Luo, He

Optimal Power Sharing and Voltage Restoration in DC Microgrids Based on Adaptive Droop Control

2026-99-0707

5/15/2026

The stable operation of islanded DC microgrids is conditioned by two essential objectives. One is to maintain the bus voltage at its nominal value, and this can ensure system stability. The other is to achieve cost-effective power allocation among distributed generation units, which guarantees economic efficiency. These two objectives are often conflicting. Adding droop control to the voltage and current dual closed-loop control can achieve primary current sharing. However, it inevitably introduces steady-state voltage deviations on the DC bus and results in inflexible or not optimal power sharing. To resolve these inherent limitations, this paper proposes a innovative distributed secondary control strategy. The method is designed to meet both requirements within a unified framework. In the primary control layer, it uses adaptive droop gains to optimize power distribution in real time based on changing load requirements which enables distributed generation units to achieve cost

Sun, Wei, She, Dunjun, Yu, Jinzhu, Yuan, Weibo, Peng, Bo, Zheng, Yingchun

Pattern-Guided Implicit Sentiment Analysis: Knowledge-Augmented Prompting with Aspect-Sentiment Joint Extraction for Automotive User Feedback

2026-99-0753

5/15/2026

Implicit sentiment analysis of automotive user feedback is crucial for understanding user opinions. Automotive user feedback often express opinions in an indirect way and are accompanied by a dense array of industry terms. Therefore, without costly fine-tuning, both aspect identification and sentiment analysis are rather difficult. We propose a Pattern-Guided pipeline for implicit sentiment analysis to achieve the joint extraction of aspect and sentiment. This pipeline first performs Pattern Anchoring, mapping colloquial expressions and slang to the standardized vehicle component knowledge system. Then, using Knowledge-Augmented Prompting, these domain rules are injected into well-designed prompt templates. In this pipeline, the large language model (LLM) is applied to output JSON records suitable for comprehending, including aspects, sentiments, confidence levels, and brief reasons. To enhance stability, we employ an improved prompt and consistency-driven confidence fusion to generate

Chang, Gengjia, Deng, Zuxing, Ma, Aonan, Yao, Jiangqi, Li, Xiaojian, Li, Ling

Time Series Causal Discovery Based on Data Augmentation and Causal Validation

2026-99-0703

5/15/2026

Causal discovery within time series is crucial for revealing the actual causal mechanisms in dynamic systems, and it has major impacts in various fields like economics, healthcare, and climate science. Even though it’s important, accurately figuring out causal relationships from observational temporal data is still quite a difficult task. Traditional Granger causality based methods are often limited by noise sensitivity, large amount of data, and the inability to distinguish between real causality and false correlation caused by hidden factors. In order to solve these problems, this paper presents CausalAugVeri, which is a new algorithm that cleverly mixes data augmentation with causal verification to make causal discovery more solid and precise. This work has three main points: First, we carefully check that using convolutional data augmentation techniques can greatly improve how well time series predictions work, giving a steadier base for detecting Granger causality. Second, the

Yang, Jing, Chen, Xiaotao, Qin, Xuanli, Xu, Xianjun, Hu, Zhangxiang

International Conference on the New Energy and Intelligent Vehicles

2026-99-0700

5/15/2026

TOC

Tobolski, Sue

Semantic-Driven Mathematical Modeling and Adaptive Refactoring for Logistics Optimization: A Language-Model Orchestrated Optimization Pipeline

2026-99-0750

5/15/2026

While large language models (LLMs) offer a convenient natural language interface for logistics optimization problems, it remains challenging to directly generate reliable mathematical models and executable code from unstructured text requirements. LLMs tend to produce invalid constraints or syntactically incorrect code. In addition, traditional logistics optimization methods lack the flexibility to adjust warehouse rules or operational goals without manual expert intervention. To address these issues, we propose LOOP (a Language-Model Orchestrated Optimization Pipeline), which automatically translates natural-language requirements into optimization algorithm code while retaining the rigor of classical models and solvers. LOOP leverages task-specific agents to construct accurate mathematical models and adopts a difference-driven code generation approach. First, it synchronizes model changes into executable code via semantic mapping and ensemble difference analysis. Second, it

Ding, Ruiqing, Li, Qianying, Li, Xiaojian

Fatigue Resistance of High-Pressure Hydrogen Storage Vessels with Aluminum Liner

2026-99-0731

5/15/2026

Although carbon fiber-reinforced aluminum-lined hydrogen storage vessels (Type III) exhibit outstanding specific strength and specific stiffness, the constraints imposed by their design parameters on fatigue performance and ultimate load-bearing capacity remain incompletely elucidated. We propose a fatigue life prediction method for high-pressure vessels that couples progressive damage in the fiber composite with cumulative damage in the metallic liner, aimed at forecasting the fatigue performance of Type III pressure vessels under cyclic loading. Furthermore, a finite element analysis systematically investigates the influence of key design parameters, for nominal pressure, liner diameter and liner thickness, on fatigue performance and ultimate load-bearing capacity. Results indicate that fatigue life significantly decreases with increasing nominal pressure and liner diameter, with nominal pressure exerting a more pronounced effect. Notably, altering the autoclave pressure alone cannot

Bi, Zhihai, Zhang, Qian

An Anthropomorphic Trajectory Optimization Research for Autonomous Vehicles Considering Omnidirectional Dynamics

2026-99-0733

5/15/2026

The rapid development of autonomous driving technology has brought emerging opportunities to optimize the omnidirectional vehicle driving performance. However, its compliance with driving habits directly determines its social acceptance. Therefore, how to balance consistency between performance improvement and driving habits has become an important bottleneck restricting the rapid promotion of autonomous driving technology. Manual driving vehicles mostly focus on the safety of both longitudinal and lateral movements, and cannot cope with the vertical movement, let alone the performance of economy, comfort, and efficiency. In this context, this paper proposes an anthropomorphic trajectory optimization method incorporating vehicle omnidirectional dynamic characteristics and corresponding driving habits. Firstly, this paper explores vehicle dynamic characteristics in longitudinal, lateral, and vertical directions, and reveals the coupling effect of motion states during driving

Liao, Peng, Zhang, Defeng, Ning, Donghong, Li, Sijia, Wang, Tao

Secondary Control Method for Parallel DC-DC Converters under FDI Attacks Based on Sliding Mode Observer

2026-99-0708

5/15/2026

Currently, with the continuous development of electric vehicles, DC microgrids have attracted widespread attention due to their flexible access methods and high energy transmission efficiency. However, since the distributed secondary control of DC microgrids relies on information exchange through communication networks, false data injection (FDI) attacks on these networks may cause control algorithms to fail, leading to voltage deviations, output current imbalance, and in severe cases, system instability. This study focuses on DC microgrids based on parallel DC–DC buck converters and proposes a distributed secondary control strategy based on a sliding mode observer to address FDI attacks. By treating the system's FDI attack signals as an extended state, an extended sliding mode observer is designed to track the attack signals. Based on the observed attacks, a control algorithm is proposed that compensates the control inputs through the observer, ensuring proportional sharing of bus

Sun, Wei, Chen, Jing, Yu, Jinzhu, Yuan, Weibo, Peng, Bo, Lin, Fei

Design of a Novel Intelligent Monitoring Platform for Low and Medium Altitudes

2026-99-0739

5/15/2026

This paper presents the design of a novel intelligent monitoring platform for low and medium altitudes, aiming to offer a new solution for the development of intelligent equipment operating in this airspace. Current monitoring tasks are primarily performed by fixed-wing and multi-rotor UAVs, but these platforms face significant technical bottlenecks in flight endurance and monitoring precision. This research aims to address these deficiencies. The platform is based on a small-scale unmanned airship featuring a semi-rigid, hybrid lift-body structure. Improvements were made upon the traditional ellipsoidal hull; the hull profile was optimized using a geometric superposition method, introducing an aerodynamic camber line with a maximum camber (m) of 4% to enhance aerodynamic performance at small angles of attack. In terms of its energy system, the platform is powered by a purely electric energy system composed of solar panels and batteries; solar energy is used during the day, while

Song, Ziang, Gao, Wenxuan, Cao, Xiaochuan, Zheng, Xing, Zhao, Chong

CF-CoT: Enhancing Causal Reasoning in LLMs through Counterfactual Scenarios

2026-99-0728

5/15/2026

Causal reasoning is the task to identify causal relations between a pair of events in a given context. However, causal reasoning in natural language remains a challenging task for large language models (LLMs), since they tend to mix correlation and causality and exhibit bias in their reasoning, especially by mistaking temporal proximity for causal relations. The problem is exacerbated by the models’ propensity to generate spurious justifications that confuses co-occurrence rather than actual causal relationships. Although CoT prompting has shown effectiveness in enhancing multi-step reasoning, it is prone to hallucination and spurious inferences, which generally dampens their capability to provide correct causal explanations. The variant of CoT, CoT-SC, is a more promising attempt at yielding consistent outputs by randomly sampling multiple reasoning paths, and voting for the most probable answer. However, for its implementation, CoT-SC also demands expensive computations. The

Yang, Jiaoyun, Qi, Botao, Liu, Li, Li, Lian, An, Ning

Advanced Composites Prepreg - Nominal 250 °F (121 °C) Cure - Carbon Fiber and Epoxy Resin, Unidirectional Tape

AMS3960B (Current)

5/15/2026

The intent of this specification is for the procurement of carbon fiber epoxy prepreg product with 250 °F (121 °C) cure for aerospace applications; therefore, no qualification or equivalency threshold values are provided. Users that intend to conduct a new material qualification or equivalency program must refer to the production quality assurance section (see 4.3).

AMS P17 Polymer Matrix Composites Committee

Standard Practice for Force Protection and Survivability

SAE1008A (Current)

5/15/2026

According to SAE6906, Force Protection and Survivability (FPS) is the Human Systems Integration (HSI) domain that facilitates system operation and personnel safety during and after exposure to hostile situations or environments. Force protection refers to all preventive measures taken to mitigate hostile actions against Department of Defense (DoD) and Department of Homeland Security (DHS) (e.g., U.S. Coast Guard, Customs and Border Patrol, Immigration and Customs Enforcement, etc.) personnel. Survivability denotes the capability of the system and/or personnel manning the system to avoid or withstand man-made hostile environments without suffering an abortive impairment of his/her ability to accomplish its designated mission. Damage due to enemy or fratricidal action, or even equipment failure, will endanger the warfighters' well-being and place them into a life-threatening situation.

G-45 Human Systems Integration

Rolling Element Bearing Hardness Test

ARP5483/2B (Current)

5/15/2026

This method outlines the standard procedure for testing the hardness of bearing components. Bearings covered by this test method shall be any rolling element bearing used in airframe control.

ACBG Rolling Element Bearing Committee

FITTING, BULKHEAD END - THREADED COUPLING, RIGID, SELF-BONDING, DESIGN STANDARD

AS5948A (Current)

5/15/2026

G-3, Aerospace Couplings, Fittings, Hose, Tubing Assemblies

Permanent or Fixed Place Flight Deck Head-Up Displays

ARP4102/8B (Current)

5/15/2026

This SAE Aerospace Recommended Practice (ARP) provides criteria for the design, installation, operation, and training aspects of head-up display (HUD) systems in transport category aircraft, with emphasis on pilot interface and operational requirements. The recommendations apply to permanently installed (including stowable) HUDs that display primary flight information, including those integrating enhanced flight vision system (EFVS) imagery. The intent is to ensure HUDs are designed and used in a manner that improves pilot situational awareness and flight technical performance across all phases of flight, up to and including low-visibility operations. While technical design standards (optical performance, hardware specs, etc.) are defined in documents like ARP5288 and AS8055, this document focuses on pilot usage considerations and human factors. HUD systems addressed here are typically designed to support a fail-passive operational concept applicable to Category III instrument approach

S-7 Flight Deck Handling Qualities Stds for Trans Aircraft

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