Safety assessment methods for avionics software system

Nowadays, the avionics software has been becoming more and more critical for both civil and military aircraft. However, the software may become crazy sometimes and may cause the catastrophic result if any failure in software. Therefore, the software safety assessment is not only crucial to the specific software, but also for the system and aircraft. Although there are some industry standards as guidelines for development of software system, applications of these standards to practical software systems are still challenged and hard to operate in practice. This thesis tries to solve this problem. After analyses and summaries of the system safety assessment process and existing software safety assessment process in different fields, research wants to propose the systematic and comprehensive software safety assessment process and method for avionics software. The thesis presents the research process, and proposes one suitable avionics software safety assessment process. Meanwhile, thesis uses a real functional block in flight management system as a case study, and then conducts the software safety requirement assessment based on the proposed software safety assessment method. After analysis the result of case study, this proposed software safety assessment process and methods can quickly and correctly identify the software design errors. So, this analysis can use to prove the feasibility and validity of this proposed software safety assessment process and methods, which will help engineers modify every software design errors at the early stage in order to guarantee the software safety

Generative AI-aided Optimization for AI-Generated Content (AIGC) Services in Edge Networks

As Metaverse emerges as the next-generation Internet paradigm, the ability to efficiently generate content is paramount. AI-Generated Content (AIGC) offers a promising solution to this challenge. However, the training and deployment of large AI models necessitate significant resources. To address this issue, we introduce an AIGC-as-a-Service (AaaS) architecture, which deploys AIGC models in wireless edge networks, ensuring ubiquitous access to AIGC services for Metaverse users. Nonetheless, a key aspect of providing personalized user experiences requires the careful selection of AIGC service providers (ASPs) capable of effectively executing user tasks. This selection process is complicated by environmental uncertainty and variability, a challenge not yet addressed well in existing literature. Therefore, we first propose a diffusion model-based AI-generated optimal decision (AGOD) algorithm, which can generate the optimal ASP selection decisions. We then apply AGOD to deep reinforcement learning (DRL), resulting in the Deep Diffusion Soft Actor-Critic (D2SAC) algorithm, which achieves efficient and effective ASP selection. Our comprehensive experiments demonstrate that D2SAC outperforms seven leading DRL algorithms. Furthermore, the proposed AGOD algorithm has the potential for extension to various optimization problems in wireless networks, positioning it a promising approach for the future research on AIGC-driven services in Metaverse. The implementation of our proposed method is available at: https://github.com/Lizonghang/AGOD

Sparks of GPTs in Edge Intelligence for Metaverse: Caching and Inference for Mobile AIGC Services

Aiming at achieving artificial general intelligence (AGI) for Metaverse, pretrained foundation models (PFMs), e.g., generative pretrained transformers (GPTs), can effectively provide various AI services, such as autonomous driving, digital twins, and AI-generated content (AIGC) for extended reality. With the advantages of low latency and privacy-preserving, serving PFMs of mobile AI services in edge intelligence is a viable solution for caching and executing PFMs on edge servers with limited computing resources and GPU memory. However, PFMs typically consist of billions of parameters that are computation and memory-intensive for edge servers during loading and execution. In this article, we investigate edge PFM serving problems for mobile AIGC services of Metaverse. First, we introduce the fundamentals of PFMs and discuss their characteristic fine-tuning and inference methods in edge intelligence. Then, we propose a novel framework of joint model caching and inference for managing models and allocating resources to satisfy users' requests efficiently. Furthermore, considering the in-context learning ability of PFMs, we propose a new metric to evaluate the freshness and relevance between examples in demonstrations and executing tasks, namely the Age of Context (AoC). Finally, we propose a least context algorithm for managing cached models at edge servers by balancing the tradeoff among latency, energy consumption, and accuracy

A Unified Framework for Guiding Generative AI with Wireless Perception in Resource Constrained Mobile Edge Networks

With the significant advancements in artificial intelligence (AI) technologies and powerful computational capabilities, generative AI (GAI) has become a pivotal digital content generation technique for offering superior digital services. However, directing GAI towards desired outputs still suffer the inherent instability of the AI model. In this paper, we design a novel framework that utilizes wireless perception to guide GAI (WiPe-GAI) for providing digital content generation service, i.e., AI-generated content (AIGC), in resource-constrained mobile edge networks. Specifically, we first propose a new sequential multi-scale perception (SMSP) algorithm to predict user skeleton based on the channel state information (CSI) extracted from wireless signals. This prediction then guides GAI to provide users with AIGC, such as virtual character generation. To ensure the efficient operation of the proposed framework in resource constrained networks, we further design a pricing-based incentive mechanism and introduce a diffusion model based approach to generate an optimal pricing strategy for the service provisioning. The strategy maximizes the user's utility while enhancing the participation of the virtual service provider (VSP) in AIGC provision. The experimental results demonstrate the effectiveness of the designed framework in terms of skeleton prediction and optimal pricing strategy generation comparing with other existing solutions

Generative AI-empowered Simulation for Autonomous Driving in Vehicular Mixed Reality Metaverses

In the vehicular mixed reality (MR) Metaverse, the distance between physical and virtual entities can be overcome by fusing the physical and virtual environments with multi-dimensional communications in autonomous driving systems. Assisted by digital twin (DT) technologies, connected autonomous vehicles (AVs), roadside units (RSU), and virtual simulators can maintain the vehicular MR Metaverse via digital simulations for sharing data and making driving decisions collaboratively. However, large-scale traffic and driving simulation via realistic data collection and fusion from the physical world for online prediction and offline training in autonomous driving systems are difficult and costly. In this paper, we propose an autonomous driving architecture, where generative AI is leveraged to synthesize unlimited conditioned traffic and driving data in simulations for improving driving safety and traffic efficiency. First, we propose a multi-task DT offloading model for the reliable execution of heterogeneous DT tasks with different requirements at RSUs. Then, based on the preferences of AV's DTs and collected realistic data, virtual simulators can synthesize unlimited conditioned driving and traffic datasets to further improve robustness. Finally, we propose a multi-task enhanced auction-based mechanism to provide fine-grained incentives for RSUs in providing resources for autonomous driving. The property analysis and experimental results demonstrate that the proposed mechanism and architecture are strategy-proof and effective, respectively

Research on the Protective Effect of Twin-groyne Arrangement on Riverbank

A curved channel with intersecting streams can be easily scoured by incoming flow, and the concave bank is badly damaged. This research showed that the twin-groyne could effectively adjust and optimize the flow velocity distribution, change the shape of the free water surface of the bend, prevent erosion, and promote silting on the concave bank, and it could provide a scouring and silting effect on the convex bank. When the spacing of twin-groyne was increased to more than four times the body length of the single-groyne (spur dike), the protective effect on the concave bank was weakened, and the scouring and silting effect of the convex bank was reduced. Excessive spacing of the twin-groyne could cause local erosion damage to the concave bank. When the distance exceeded the theoretical optimum, it was equivalent to the effect of single-groyne. With the increase in the submergence degree, the velocity of the concave bank decreased first and then increased, while the velocity of convex bank decreased continuously. The protective effect of a non-submerged twin-groyne with a dam spacing of four times the body length of the single-groyne was better than that of other conditions, and it is recommended to be used in practice

Unleashing the Power of Edge-Cloud Generative AI in Mobile Networks: A Survey of AIGC Services

Artificial Intelligence-Generated Content (AIGC) is an automated method for generating, manipulating, and modifying valuable and diverse data using AI algorithms creatively. This survey paper focuses on the deployment of AIGC applications, e.g., ChatGPT and Dall-E, at mobile edge networks, namely mobile AIGC networks, that provide personalized and customized AIGC services in real time while maintaining user privacy. We begin by introducing the background and fundamentals of generative models and the lifecycle of AIGC services at mobile AIGC networks, which includes data collection, training, finetuning, inference, and product management. We then discuss the collaborative cloud-edge-mobile infrastructure and technologies required to support AIGC services and enable users to access AIGC at mobile edge networks. Furthermore, we explore AIGCdriven creative applications and use cases for mobile AIGC networks. Additionally, we discuss the implementation, security, and privacy challenges of deploying mobile AIGC networks. Finally, we highlight some future research directions and open issues for the full realization of mobile AIGC networks

Perforating scleral vessels adjacent to myopic choroidal neovascularization achieved a poor outcome after intravitreal anti-VEGF therapy

BackgroundThis study aimed to summarize the features of perforating scleral vessels (PSVs) in patients with myopic choroidal neovascularization (CNV) (mCNV) using optical coherence tomography angiography (OCTA) and to identify the associations with the response after intravitreal anti-vascular endothelial growth factor (anti-VEGF) therapy.MethodsA consecutive series of naïve patients who had mCNV and received intravitreal anti-VEGF therapy with a follow-up duration of 12 months or more were enrolled. The prevalence, location, and branches of PSVs were analyzed. Projection-resolved OCTA (PR-OCTA) was used to analyze the neovascular signals between CNV and PSVs. Best corrected visual acuity (BCVA) and central macular thickness (CMT) were measured. The proportion of CMT change relative to baseline was used to assess therapeutic response.ResultsA total of 44 eyes from 42 patients with mCNV were enrolled. PSVs were identified in 41 out of 44 eyes. Branches were identified in the PSVs of 24 eyes (57.14%), and 20 eyes did not have PSV branches (47.62%). In eight eyes (18.18%), PSVs were adjacent to mCNV, and in 36 eyes (81.82%), PSVs were not adjacent to mCNV. After anti-VEGF therapy for mCNV, BCVA increased (F = 6.119, p < 0.001) and CMT decreased (F = 7.664, p < 0.001). In the eyes where PSVs were adjacent to mCNV, BCVA improvements (F = 7.649, p = 0.009) were poor, and changes in CMT were small.ConclusionThe eyes with PSVs adjacent to mCNV showed poor therapeutic responses after intravitreal anti-VEGF therapy

Neutrino Physics with JUNO

The Jiangmen Underground Neutrino Observatory (JUNO), a 20 kton multi-purposeunderground liquid scintillator detector, was proposed with the determinationof the neutrino mass hierarchy as a primary physics goal. It is also capable ofobserving neutrinos from terrestrial and extra-terrestrial sources, includingsupernova burst neutrinos, diffuse supernova neutrino background, geoneutrinos,atmospheric neutrinos, solar neutrinos, as well as exotic searches such asnucleon decays, dark matter, sterile neutrinos, etc. We present the physicsmotivations and the anticipated performance of the JUNO detector for variousproposed measurements. By detecting reactor antineutrinos from two power plantsat 53-km distance, JUNO will determine the neutrino mass hierarchy at a 3-4sigma significance with six years of running. The measurement of antineutrinospectrum will also lead to the precise determination of three out of the sixoscillation parameters to an accuracy of better than 1\%. Neutrino burst from atypical core-collapse supernova at 10 kpc would lead to ~5000inverse-beta-decay events and ~2000 all-flavor neutrino-proton elasticscattering events in JUNO. Detection of DSNB would provide valuable informationon the cosmic star-formation rate and the average core-collapsed neutrinoenergy spectrum. Geo-neutrinos can be detected in JUNO with a rate of ~400events per year, significantly improving the statistics of existing geoneutrinosamples. The JUNO detector is sensitive to several exotic searches, e.g. protondecay via the $p\to K^++\bar\nu$ decay channel. The JUNO detector will providea unique facility to address many outstanding crucial questions in particle andastrophysics. It holds the great potential for further advancing our quest tounderstanding the fundamental properties of neutrinos, one of the buildingblocks of our Universe