According to the rapid development of drone technologies, drones are widely used in many applications including military domains. In this paper, a novel situation-aware DRL- based autonomous nonlinear drone mobility control algorithm in cyber-physical loitering munition applications. On the battlefield, the design of DRL-based autonomous control algorithm is not straightforward because real-world data gathering is generally not available. Therefore, the approach in this paper is that cyber-physical virtual environment is constructed with Unity environment. Based on the virtual cyber-physical battlefield scenarios, a DRL-based automated nonlinear drone mobility control algorithm can be designed, evaluated, and visualized. Moreover, many obstacles exist which is harmful for linear trajectory control in real-world battlefield scenarios. Thus, our proposed autonomous nonlinear drone mobility control algorithm utilizes situation-aware components those are implemented with a Raycast function in Unity virtual scenarios. Based on the gathered situation-aware information, the drone can autonomously and nonlinearly adjust its trajectory during flight. Therefore, this approach is obviously beneficial for avoiding obstacles in obstacle-deployed battlefields. Our visualization-based performance evaluation shows that the proposed algorithm is superior from the other linear mobility control algorithms.

Affect understanding capability is essential for social robots to autonomously interact with a group of users in an intuitive and reciprocal way. However, the challenge of multi-person affect understanding comes from not only the accurate perception of each user's affective state (e.g., engagement) but also the recognition of the affect interplay between the members (e.g., joint engagement) that presents as complex, but subtle, nonverbal exchanges between them. Here we present a novel hybrid framework for identifying a parent-child dyad's joint engagement by combining a deep learning framework with various video augmentation techniques. Using a dataset of parent-child dyads reading storybooks together with a social robot at home, we first train RGB frame- and skeleton-based joint engagement recognition models with four video augmentation techniques (General Aug, DeepFake, CutOut, and Mixed) applied datasets to improve joint engagement classification performance. Second, we demonstrate experimental results on the use of trained models in the robot-parent-child interaction context. Third, we introduce a behavior-based metric for evaluating the learned representation of the models to investigate the model interpretability when recognizing joint engagement. This work serves as the first step toward fully unlocking the potential of end-to-end video understanding models pre-trained on large public datasets and augmented with data augmentation and visualization techniques for affect recognition in the multi-person human-robot interaction in the wild.

While witnessing the noisy intermediate-scale quantum (NISQ) era and beyond, quantum federated learning (QFL) has recently become an emerging field of study. In QFL, each quantum computer or device locally trains its quantum neural network (QNN) with trainable gates, and communicates only these gate parameters over classical channels, without costly quantum communications. Towards enabling QFL under various channel conditions, in this article we develop a depth-controllable architecture of entangled slimmable quantum neural networks (eSQNNs), and propose an entangled slimmable QFL (eSQFL) that communicates the superposition-coded parameters of eS-QNNs. Compared to the existing depth-fixed QNNs, training the depth-controllable eSQNN architecture is more challenging due to high entanglement entropy and inter-depth interference, which are mitigated by introducing entanglement controlled universal (CU) gates and an inplace fidelity distillation (IPFD) regularizer penalizing inter-depth quantum state differences, respectively. Furthermore, we optimize the superposition coding power allocation by deriving and minimizing the convergence bound of eSQFL. In an image classification task, extensive simulations corroborate the effectiveness of eSQFL in terms of prediction accuracy, fidelity, and entropy compared to Vanilla QFL as well as under different channel conditions and various data distributions.

Accurately extracting driving events is the way to maximize computational efficiency and anomaly detection performance in the tire frictional nose-based anomaly detection task. This study proposes a concise and highly useful method for improving the precision of the event extraction that is hindered by extra noise such as wind noise, which is difficult to characterize clearly due to its randomness. The core of the proposed method is based on the identification of the road friction sound corresponding to the frequency of interest and removing the opposite characteristics with several frequency filters. Our method enables precision maximization of driving event extraction while improving anomaly detection performance by an average of 8.506%. Therefore, we conclude our method is a practical solution suitable for road surface anomaly detection purposes in outdoor edge computing environments.

在自主驾驶环境中，同时保证实时和准确的对象检测至关重要。但是，现有的对象检测神经网络系统的特征是计算时间和准确性之间的权衡，因此必须优化这种权衡。幸运的是，在许多自动驾驶环境中，图像以连续的形式出现，提供了使用光流的机会。在本文中，我们利用光流估计来提高对象检测神经网络的性能。此外，我们提出了一个lyapunov优化框架，以实现稳定性的时间平均性能最大化。它可以自适应地确定是否使用光流程适合动态车辆环境，从而确保车辆的队列稳定性和同时的时间平均最高性能。为了验证关键思想，我们使用各种对象检测神经网络和光流估计网络进行数值实验。此外，我们通过Yolov3微小和Flownet2-S展示了可自配置的稳定检测，它们分别是实时对象检测网络和光流估计网络。在演示中，我们提出的框架将准确性提高了3.02％，检测到的对象数量增加了59.6％，并且用于计算功能的队列稳定性。

位移是评估结构条件的重要测量，但是与传感器安装和测量精度相关的困难通常会阻碍其现场测量。为了克服常规位移测量的缺点，由于其遥感功能和准确性，已经实施了基于计算机视觉（CV）的方法。本文提出了一种非目标结构位移测量的策略，该策略利用简历来避免在结构上安装目标的需求，同时使用结构性光对位移进行校准。所提出的称为Lavolution的系统使用四个等距的结构光的光束计算了相机在结构方面的相对位置，并获得了一个比例因子，以将像素运动转换为结构位移。设计了四个结构光束的夹具，并提出了相应的对齐过程。提出了一种使用设计的夹具来计算尺度因子的方法，并通过数值模拟和实验室规模实验验证了并验证。为了确认所提出的位移测量过程的可行性，进行了摇桌和全尺寸桥梁的实验，并将提出方法的精度与参考激光多普勒振动仪进行比较。

尽管量子至高无上尚未到来，但最近在实用量子计算的迫在眉睫的时代，人们对​​确定量子机学习的潜力（QML）的兴趣越来越大。由此激励，在本文中，我们基于具有两个单独的可训练参数的单独维度的量子神经网络（QNN）的独特特征重新设计多代理增强学习（MARL）：影响输出Qubit状态和极点参数的角度参数：与输出测量基础相关。我们提出了将这种二元训练性作为元学习能力，我们提出了量子元marl（QM2ARL），该量子元MARL（QM2ARL）首先应用角度训练进行元学习，然后进行极点训练，以进行几次射击或局部QNN培训。为了避免过度拟合，我们在角度训练期间开发了一种将噪声注入到极域中的角度正则化技术。此外，通过将极点作为每个受过训练的QNN的内存地址利用，我们介绍了极点内存的概念，允许仅使用两参数极点值保存和加载经过训练的QNN。从理论上讲，我们证明了角度到极正则化下的角度训练的收敛性，并通过模拟证实了QM2ARL在获得高奖励和快速收敛方面的有效性，以及在快速适应时间变化环境中的极点记忆。

最近的深度学习模型在言语增强方面已经达到了高性能。但是，获得快速和低复杂模型而没有明显的性能降解仍然是一项挑战。以前的知识蒸馏研究对言语增强无法解决这个问题，因为它们的输出蒸馏方法在某些方面不符合语音增强任务。在这项研究中，我们提出了基于特征的蒸馏多视图注意转移（MV-AT），以在时域中获得有效的语音增强模型。基于多视图功能提取模型，MV-AT将教师网络的多视图知识传输到学生网络，而无需其他参数。实验结果表明，所提出的方法始终提高瓦伦蒂尼和深噪声抑制（DNS）数据集的各种规模的学生模型的性能。与基线模型相比，使用我们提出的方法（一种用于有效部署的轻巧模型）分别使用了15.4倍和4.71倍（FLOPS），与具有相似性能的基线模型相比，Many-S-8.1GF分别达到了15.4倍和4.71倍。

量子联合学习（QFL）最近受到了越来越多的关注，其中量子神经网络（QNN）集成到联邦学习（FL）中。与现有的静态QFL方法相反，我们在本文中提出了可靠的QFL（SLIMQFL），这是一个动态QFL框架，可以应对时变的通信通道和计算能量限制。通过利用QNN的独特性质，可以分别训练并动态利用其角度参数，从而使其可行。模拟结果证实了SLIMQFL比香草QFL更高的分类精度，尤其是在较差的通道条件下。

多年来，为各种对象检测任务开发了数据集。海事域中的对象检测对于船舶的安全和导航至关重要。但是，在海事域中，仍然缺乏公开可用的大规模数据集。为了克服这一挑战，我们提出了Kolomverse，这是一个开放的大型图像数据集，可在Kriso（韩国研究所和海洋工程研究所）的海事域中进行物体检测。我们收集了从韩国21个领土水域捕获的5,845小时的视频数据。通过精心设计的数据质量评估过程，我们从视频数据中收集了大约2,151,470 4K分辨率的图像。该数据集考虑了各种环境：天气，时间，照明，遮挡，观点，背景，风速和可见性。 Kolomverse由五个类（船，浮标，渔网浮标，灯塔和风电场）组成，用于海上对象检测。该数据集的图像为3840美元$ \ times $ 2160像素，据我们所知，它是迄今为止最大的公开数据集，用于海上域中的对象检测。我们进行了对象检测实验，并在几个预训练的最先进的架构上评估了我们的数据集，以显示我们数据集的有效性和实用性。该数据集可在：\ url {https://github.com/maritimedataset/kolomverse}中获得。