基于OODA环的网络化防空体系作战效能评估

以网络化防空体系作战效能评估为对象开展研究,针对效能评估过程中概念梳理不清、指标体系不科学、指标权重不合理等问题,系统梳理网络化防空体系OODA环模型,分析网络化防空体系作战流程。运用德尔菲法制定动态的效能评估指标体系,采用层次分析法确定评估指标体系指标权重。构建灰色模糊评估模型,对网络化防空体系作战效能进行综合评估,并用实例验证该方法和模型的有效性。该方法可为构建网络化防空体系,评估防空体系作战效能提供方法和参考。

面向复杂决策的OODA环:智能赋能与认知增强

传统OODA环理论存在认知粒度低、单向循环、单一入口等缺点,无法满足未来以智能化、网络化、体系化为特征的多域联合作战需求,尚不具备在以跨域协同、体系聚优等为代表的复杂决策环境中应用的能力。通过梳理分析OODA环理论发展历程及演进路线,确定了面向复杂决策的OODA环理论的重点是突出认知环节与决策环节在整个OODA环中的作用;通过分析智能技术的赋能方式,构建了智能态势认知与智能复杂决策框架,并将这两个框架嵌入认知粒度提高的OODA环,形成面向复杂决策的智能CT-OODA环理论;基于Cynefin理论说明了复杂决策问题的环境分类方法,阐述了在不同复杂程度决策环境下智能CT-OODA环的运行方式,以及如何通过OODA的决策循环实现决策环境改变的动力学;提出了面向复杂决策的智能OODA及其具体结构和运行方式,并且提出了分类复杂决策环境的方法,为未来人机融合态势认知与复杂决策提供参考。

基于OODA环的低空防御装备体系作战效能评估

针对低空防御装备体系存在功能结构复杂、信息交互多、任务抛面大、新装融合不确定等因素导致装备体系作战效能评估容易产生误差的问题,根据装备和任务特点建立低空防御装备战技指标评价体系,采用OODA环理论构建作战网络评估模型,借鉴信息熵和ADC相关概念计算网络边和节点的自信息量,通过多路作战环信息熵的聚合得到装备体系作战效能,最后对新型低空防御装备体系作战效能实例进行评估,分别得出高能激光武器、高能率微波、电子压制、蜂群对抗、粒子束等装备加入低空防御体系后对小型无人机类目标、巡飞弹类、蜂群类目标的作战效能,给出低空防御装备体系建设相关建议。

基于OODA环的作战体系能力演化分析

传统的指标聚合方法虽然能够评估层次化评估指标体系,但却无法有效分析指标间复杂关联关系。为适应体系能力动态演化的评估需求,提出了一种基于观察—判断—决策—行动(OODA)环的作战体系能力演化分析方法。首先,构建了作战体系的体系能力评估框架;然后,基于OODA环理论,结合网络层次分析法,构建了作战体系的网络化指标体系;最后,结合时间演化分析建立了作战体系能力的动态分析模型。应用实例分析可知,该方法适用于作战体系的体系能力动态分析研究。

基于OODA环的杀伤网节点重要性评估

基于网络信息体系的联合作战中,破坏敌杀伤网、降效敌杀伤体系作战效能,要求能够识别敌杀伤网中关键节点。基于OODA环理论及杀伤链概念建模,利用节点删除法,通过网络作战能力及网络循环效率两项指标的下降程度评估杀伤网节点的重要性,克服传统评估方法在指标计算中存在冗余的问题,提高评估精度。采用该方法评估某作战演练中敌方杀伤网节点的重要性,精准识别指挥决策节点的枢纽作用,验证方法的有效性,并对杀伤网节点的重要性进行评估排序,为选择进攻要点提供依据。

OODAFlow:面向智能无人系统的流式数据处理框架

智能无人系统是一种能够在复杂环境中自主进行实时推理、决策和制定行动方案的计算系统。智能无人系统实现实时决策的关键在于对流式数据的实时处理,然而随着人工智能技术和传感器技术的快速发展,智能无人系统需要处理的数据规模不断增长,数据类型变得更加复杂。面对不断增长的数据处理性能需求,智能无人系统需要一个充分优化的专用流式数据处理框架来提升其数据处理性能。针对该问题,本文提出了一种面向智能无人系统的流式数据处理框架OODAFlow,该框架将智能无人系统的硬件特征和智能计算任务的数据特征与观察-判断-决策-行动(OODA)模型思想相融合,实现了OODA任务创建、任务调度、资源调度等功能,能够实现对智能无人系统异构资源的调度和智能计算任务的处理。本文在智能无人系统上搭建了一套OODA任务处理系统,验证了所提OODAFlow框架的可行性。通过提出的图像预处理过程优化、流水线优化以及判断节点并行加速优化等方法,提高了系统的数据吞吐性能和资源利用率。无人机智能控制任务的实验表明,采用本文提出的OODAFlow框架后,智能无人系统的数据处理性能提升了73倍。

智能感知在防空反导OODA环中的应用

在未来智能化战争中,瞬息万变的战场态势对防空反导武器系统的探测感知和指挥决策提出了新的挑战。本文聚焦智能感知技术赋能未来防空反导作战装备,剖析了智能感知概念及内涵,围绕防空反导“观察、定位、决策、行动”(OODA)环以及典型作战流程,梳理感知对抗形态,主要包括预警与反预警、探测与反探测、跟踪与反跟踪、干扰与反干扰;揭示了智能感知对抗的核心科学问题是特征的隐匿与识别,给出了感知对抗技术在OODA环中的主要应用场景;最后,提出了提高防空反导装备智能感知对抗水平所需的关键技术发展建议。

一种基于OODA循环的作战能力评估模型

以作战能力为对象,OODA循环为参考、马尔科夫过程为工具,提出了一种基于OODA循环的作战能力评估模型。在模型准备上,借鉴OODA循环思想,从一般意义将作战任务的执行过程界定为顺序相连的循环程序。在模型建立上,视每运行一次OODA循环即在理论上执行了一次作战任务,多次循环后将形成关于作战任务完成程度的样本空间,从随机事件角度建立了马尔科夫过程模型。在模型解算上,给出了侦察感知、作战决策、指挥控制、火力打击能力和目标出现概率的解析解,依据作战能力的时变图和对比图,提出了相关评估建议。

基于LDA主题模型的GitHub Actions工作流项目推荐算法

在CI/CD实践中,自动化已成为软件开发实践中的一种规范。GitHub引入GitHub Actions为软件维护者提供自动化的持续集成工作流方案,尽管其为开发者提供了诸多便利,GitHub社区也提供了许多第三方的GitHub Actions服务,但仍然只有极少的项目在使用。为了满足开发人员对工作流自动化的需求,减少非开发任务工作量,提出一种基于隐含狄利克雷分布(LDA)主题模型和Jensen-Shannon距离的GitHub Actions工作流项目推荐算法。通过对GitHub Actions存储库的README文件进行主题建模,将GitHub的事件描述文本和用户输入作为模型输入,为正在开发的代码仓库推荐GitHub Actions服务。将该推荐模型与标准的基于余弦相似度的方法进行比较的实验结果表明,该方法能有效改善开源软件仓库的推荐精度。

Workout Action Recognition in Video Streams Using an Attention Driven Residual DC-GRU Network

Regular exercise is a crucial aspect of daily life, as it enables individuals to stay physically active, lowers thelikelihood of developing illnesses, and enhances life expectancy. The recognition of workout actions in videostreams holds significant importance in computer vision research, as it aims to enhance exercise adherence, enableinstant recognition, advance fitness tracking technologies, and optimize fitness routines. However, existing actiondatasets often lack diversity and specificity for workout actions, hindering the development of accurate recognitionmodels. To address this gap, the Workout Action Video dataset (WAVd) has been introduced as a significantcontribution. WAVd comprises a diverse collection of labeled workout action videos, meticulously curated toencompass various exercises performed by numerous individuals in different settings. This research proposes aninnovative framework based on the Attention driven Residual Deep Convolutional-Gated Recurrent Unit (ResDCGRU)network for workout action recognition in video streams. Unlike image-based action recognition, videoscontain spatio-temporal information, making the task more complex and challenging. While substantial progresshas been made in this area, challenges persist in detecting subtle and complex actions, handling occlusions,and managing the computational demands of deep learning approaches. The proposed ResDC-GRU Attentionmodel demonstrated exceptional classification performance with 95.81% accuracy in classifying workout actionvideos and also outperformed various state-of-the-art models. The method also yielded 81.6%, 97.2%, 95.6%, and93.2% accuracy on established benchmark datasets, namely HMDB51, Youtube Actions, UCF50, and UCF101,respectively, showcasing its superiority and robustness in action recognition. The findings suggest practicalimplications in real-world scenarios where precise video action recognition is paramount, addressing the persistingchallenges in the field. TheWAVd dataset serves as a catalyst for the development ofmore robust and effective fitnesstracking systems and ultimately promotes healthier lifestyles through improved exercise monitoring and analysis.

Abnormal Action Recognition with Lightweight Pose Estimation Network in Electric Power Training Scene

Electric power training is essential for ensuring the safety and reliability of the system.In this study,we introduce a novel Abnormal Action Recognition(AAR)system that utilizes a Lightweight Pose Estimation Network(LPEN)to efficiently and effectively detect abnormal fall-down and trespass incidents in electric power training scenarios.The LPEN network,comprising three stages—MobileNet,Initial Stage,and Refinement Stage—is employed to swiftly extract image features,detect human key points,and refine them for accurate analysis.Subsequently,a Pose-aware Action Analysis Module(PAAM)captures the positional coordinates of human skeletal points in each frame.Finally,an Abnormal Action Inference Module(AAIM)evaluates whether abnormal fall-down or unauthorized trespass behavior is occurring.For fall-down recognition,three criteria—falling speed,main angles of skeletal points,and the person’s bounding box—are considered.To identify unauthorized trespass,emphasis is placed on the position of the ankles.Extensive experiments validate the effectiveness and efficiency of the proposed system in ensuring the safety and reliability of electric power training.

基于OODA环的合成旅装备保障效能评估

对合成旅装备保障流程进行分析,论文创建了基于OODA环的装备保障效能评估指标体系,以ADC法为基础,采用AHP法和熵权法对评价指标进行组合赋权,通过模糊综合评判法求解装备保障能力,最终计算得出装备保障整体效能。最后通过实例分析,验证了评估模型的有效性,对分析研究合成旅装备保障提供有益借鉴。

Unveiling health rights:A call to action for sex workers'HIV care in the Philippines

We are writing in response to the article titled“Addressing the needs and rights of sex workers for HIV healthcare services in the Philippines”[1].The article calls for attention on the significant challenges faced by sex workers in the Philippines in accessing HIV healthcare.We appreciate the article’s effort to examine these issues in depth.We would like to present a constant flow of thoughts in this letter while highlighting the positive aspects,potential obstacles,and additional points that contribute to this ongoing discussion.

Impact of Action Observation Therapy along with Usual Physiotherapy Intervention of Individual with Alzheimer’s Disease

Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by cognitive impairments in the initial stage, which lead to severe cognitive dysfunction in the later stage. Action observation therapy (AOT) is a multisensory cognitive rehabilitation technique where the patient initially observes the actions and then tries to perform. The study aimed to examine the impact of AOT along with usual physiotherapy interventions to reduce depression, improve cognition and balance of a patient with AD. A 67 years old patient with AD was selected for this study because the patient has been suffering from depression, dementia, and physical dysfunction along with some other health conditions like diabetes and hypertension. Before starting intervention, a baseline assessment was done through the Beck Depression Inventory (BDI) tool, the Mini-Cog Scale, and the Berg Balance Scale (BBS). The patient received 12 sessions of AOT along with usual physiotherapy interventions thrice a week for four weeks, which included 45 minutes of each session. After four weeks of intervention, the patient demonstrated significant improvement in depression, cognition, and balance, whereas the BDI score declined from moderate 21/63 to mild 15/63 level of depression. The Mini-Cog score improved from 2/5 to 4/5, and the BBS score increased from 18/56 to 37/56. It is concluded that AOT along with usual physiotherapy intervention helps to reduce depression, improve cognition and balance of people with AD.

基于“OODA环”优化的卫星导航定位安全与对抗应用研究

卫星导航定位设备的“生存”环境日益复杂、严峻,各类电子对抗、干扰欺骗十分激烈,人为干扰已成为卫星导航定位安全与对抗的重点。针对传统手段无法有效应对干扰复杂性、全面性、系统性的不足,本文在分析“OODA环”理论和实践转化的基础上,结合复杂环境下“干扰-抗干扰”的整体对抗过程,优化“OODA环”运行,将其与卫星导航定位安全与对抗应用进行耦合,提出基于“环境感知-筛选隔离-检测识别-效能评估-策略规划-对抗反制-回访反馈-再感知”的卫星导航定位安全与对抗不定向循环链路,并对拓展、映射的七个环节及关键技术进行分析、论证。通过引入“循环”和“不定向”两个概念,不仅实现了周期性外循环和部分环节内循环的嵌套迭代和导向性传输的运行机制;而且优化了内循环的自由度,使得各环节互联且独立、循环但不定向,有效增强了卫星导航定位安全与对抗的鲁棒性。本文的设计思路可加快实现循环链路的动态闭环,提高对抗的整体效能,在实践应用和技术论证中具有一定的参考价值。

Abnormal Action Detection Based on Parameter-Efficient Transfer Learning in Laboratory Scenarios

Laboratory safety is a critical area of broad societal concern,particularly in the detection of abnormal actions.To enhance the efficiency and accuracy of detecting such actions,this paper introduces a novel method called TubeRAPT(Tubelet Transformer based onAdapter and Prefix TrainingModule).Thismethod primarily comprises three key components:the TubeR network,an adaptive clustering attention mechanism,and a prefix training module.These components work in synergy to address the challenge of knowledge preservation in models pretrained on large datasets while maintaining training efficiency.The TubeR network serves as the backbone for spatio-temporal feature extraction,while the adaptive clustering attention mechanism refines the focus on relevant information.The prefix training module facilitates efficient fine-tuning and knowledge transfer.Experimental results demonstrate the effectiveness of TubeRAPT,achieving a 68.44%mean Average Precision(mAP)on the CLA(Crazy LabActivity)small-scale dataset,marking a significant improvement of 1.53%over the previous TubeR method.This research not only showcases the potential applications of TubeRAPT in the field of abnormal action detection but also offers innovative ideas and technical support for the future development of laboratory safety monitoring technologies.The proposed method has implications for improving safety management systems in various laboratory environments,potentially reducing accidents and enhancing overall workplace safety.

BCCLR:A Skeleton-Based Action Recognition with Graph Convolutional Network Combining Behavior Dependence and Context Clues

In recent years,skeleton-based action recognition has made great achievements in Computer Vision.A graph convolutional network(GCN)is effective for action recognition,modelling the human skeleton as a spatio-temporal graph.Most GCNs define the graph topology by physical relations of the human joints.However,this predefined graph ignores the spatial relationship between non-adjacent joint pairs in special actions and the behavior dependence between joint pairs,resulting in a low recognition rate for specific actions with implicit correlation between joint pairs.In addition,existing methods ignore the trend correlation between adjacent frames within an action and context clues,leading to erroneous action recognition with similar poses.Therefore,this study proposes a learnable GCN based on behavior dependence,which considers implicit joint correlation by constructing a dynamic learnable graph with extraction of specific behavior dependence of joint pairs.By using the weight relationship between the joint pairs,an adaptive model is constructed.It also designs a self-attention module to obtain their inter-frame topological relationship for exploring the context of actions.Combining the shared topology and the multi-head self-attention map,the module obtains the context-based clue topology to update the dynamic graph convolution,achieving accurate recognition of different actions with similar poses.Detailed experiments on public datasets demonstrate that the proposed method achieves better results and realizes higher quality representation of actions under various evaluation protocols compared to state-of-the-art methods.

General Optimal Trajectory Planning:Enabling Autonomous Vehicles with the Principle of Least Action

This study presents a general optimal trajectory planning(GOTP)framework for autonomous vehicles(AVs)that can effectively avoid obstacles and guide AVs to complete driving tasks safely and efficiently.Firstly,we employ the fifth-order Bezier curve to generate and smooth the reference path along the road centerline.Cartesian coordinates are then transformed to achieve the curvature continuity of the generated curve.Considering the road constraints and vehicle dynamics,limited polynomial candidate trajectories are generated and smoothed in a curvilinear coordinate system.Furthermore,in selecting the optimal trajectory,we develop a unified and auto-tune objective function based on the principle of least action by employing AVs to simulate drivers’behavior and summarizing their manipulation characteristics of“seeking benefits and avoiding losses.”Finally,by integrating the idea of receding-horizon optimization,the proposed framework is achieved by considering dynamic multi-performance objectives and selecting trajectories that satisfy feasibility,optimality,and adaptability.Extensive simulations and experiments are performed,and the results demonstrate the framework’s feasibility and effectiveness,which avoids both dynamic and static obstacles and applies to various scenarios with multi-source interactive traffic participants.Moreover,we prove that the proposed method can guarantee real-time planning and safety requirements compared to drivers’manipulation.

HgaNets:Fusion of Visual Data and Skeletal Heatmap for Human Gesture Action Recognition

Recognition of human gesture actions is a challenging issue due to the complex patterns in both visual andskeletal features. Existing gesture action recognition (GAR) methods typically analyze visual and skeletal data,failing to meet the demands of various scenarios. Furthermore, multi-modal approaches lack the versatility toefficiently process both uniformand disparate input patterns.Thus, in this paper, an attention-enhanced pseudo-3Dresidual model is proposed to address the GAR problem, called HgaNets. This model comprises two independentcomponents designed formodeling visual RGB (red, green and blue) images and 3Dskeletal heatmaps, respectively.More specifically, each component consists of two main parts: 1) a multi-dimensional attention module forcapturing important spatial, temporal and feature information in human gestures;2) a spatiotemporal convolutionmodule that utilizes pseudo-3D residual convolution to characterize spatiotemporal features of gestures. Then,the output weights of the two components are fused to generate the recognition results. Finally, we conductedexperiments on four datasets to assess the efficiency of the proposed model. The results show that the accuracy onfour datasets reaches 85.40%, 91.91%, 94.70%, and 95.30%, respectively, as well as the inference time is 0.54 s andthe parameters is 2.74M. These findings highlight that the proposed model outperforms other existing approachesin terms of recognition accuracy.

Adjustment mechanism of blasting dynamic-static action in the water decoupling charge

Water decoupling charge blasting excels in rock breaking,relying on its uniform pressure transmission and low energy dissipation.The water decoupling coefficients can adjust the contributions of the stress wave and quasi-static pressure.However,the quantitative relationship between the two contributions is unclear,and it is difficult to provide reasonable theoretical support for the design of water decoupling blasting.In this study,a theoretical model of blasting fracturing partitioning is established.The mechanical mechanism and determination method of the optimal decoupling coefficient are obtained.The reliability is verified through model experiments and a field test.The results show that with the increasing of decoupling coefficient,the rock breaking ability of blasting dynamic action decreases,while quasi-static action increases and then decreases.The ability of quasi-static action to wedge into cracks changes due to the spatial adjustment of the blast hole and crushed zone.The quasi-static action plays a leading role in the fracturing range,determining an optimal decoupling coefficient.The optimal water decoupling coefficient is not a fixed value,which can be obtained by the proposed theoretical model.Compared with the theoretical results,the maximum error in the model experiment results is 8.03%,and the error in the field test result is 3.04%.