通过主动学习（AL）获取最具代表性示例，可以通过最大限度地减少图像级或像素 - 明智的注释的努力来使许多数据相关的计算机视觉任务受益。在本文中，我们提出了一种新颖的协作Panoptic-Cable活动学习框架（CPRAL）来解决语义细分任务。对于最初用像素 - WISE注释采样的一小批图像，我们采用Panoptic信息来最初选择未标记的样本。考虑到分段数据集中的类别不平衡，我们导入区域高斯注意模块（RGA）以实现语义偏置选择。该子集通过投票熵突出显示，然后由高斯内核参加，以最大化偏置区域。我们还提出了一个上下文标签扩展（CLE），以提高区域注释，具有语境关注指导。通过协作语义 - 不可知的Panoptic匹配和区域缺陷的选择和延伸，我们的CPRAL可以在标签努力和性能之间取得平衡，并妥协语义分布。我们对城市景观和BDD10K数据集进行了广泛的实验，并表明CPRAL以令人印象深刻的结果和较少的标记比例优于尖端方法。

Reinforcement learning-based (RL-based) energy management strategy (EMS) is considered a promising solution for the energy management of electric vehicles with multiple power sources. It has been shown to outperform conventional methods in energy management problems regarding energy-saving and real-time performance. However, previous studies have not systematically examined the essential elements of RL-based EMS. This paper presents an empirical analysis of RL-based EMS in a Plug-in Hybrid Electric Vehicle (PHEV) and Fuel Cell Electric Vehicle (FCEV). The empirical analysis is developed in four aspects: algorithm, perception and decision granularity, hyperparameters, and reward function. The results show that the Off-policy algorithm effectively develops a more fuel-efficient solution within the complete driving cycle compared with other algorithms. Improving the perception and decision granularity does not produce a more desirable energy-saving solution but better balances battery power and fuel consumption. The equivalent energy optimization objective based on the instantaneous state of charge (SOC) variation is parameter sensitive and can help RL-EMSs to achieve more efficient energy-cost strategies.

Most semantic communication systems leverage deep learning models to provide end-to-end transmission performance surpassing the established source and channel coding approaches. While, so far, research has mainly focused on architecture and model improvements, but such a model trained over a full dataset and ergodic channel responses is unlikely to be optimal for every test instance. Due to limitations on the model capacity and imperfect optimization and generalization, such learned models will be suboptimal especially when the testing data distribution or channel response is different from that in the training phase, as is likely to be the case in practice. To tackle this, in this paper, we propose a novel semantic communication paradigm by leveraging the deep learning model's overfitting property. Our model can for instance be updated after deployment, which can further lead to substantial gains in terms of the transmission rate-distortion (RD) performance. This new system is named adaptive semantic communication (ASC). In our ASC system, the ingredients of wireless transmitted stream include both the semantic representations of source data and the adapted decoder model parameters. Specifically, we take the overfitting concept to the extreme, proposing a series of ingenious methods to adapt the semantic codec or representations to an individual data or channel state instance. The whole ASC system design is formulated as an optimization problem whose goal is to minimize the loss function that is a tripartite tradeoff among the data rate, model rate, and distortion terms. The experiments (including user study) verify the effectiveness and efficiency of our ASC system. Notably, the substantial gain of our overfitted coding paradigm can catalyze semantic communication upgrading to a new era.

The high emission and low energy efficiency caused by internal combustion engines (ICE) have become unacceptable under environmental regulations and the energy crisis. As a promising alternative solution, multi-power source electric vehicles (MPS-EVs) introduce different clean energy systems to improve powertrain efficiency. The energy management strategy (EMS) is a critical technology for MPS-EVs to maximize efficiency, fuel economy, and range. Reinforcement learning (RL) has become an effective methodology for the development of EMS. RL has received continuous attention and research, but there is still a lack of systematic analysis of the design elements of RL-based EMS. To this end, this paper presents an in-depth analysis of the current research on RL-based EMS (RL-EMS) and summarizes the design elements of RL-based EMS. This paper first summarizes the previous applications of RL in EMS from five aspects: algorithm, perception scheme, decision scheme, reward function, and innovative training method. The contribution of advanced algorithms to the training effect is shown, the perception and control schemes in the literature are analyzed in detail, different reward function settings are classified, and innovative training methods with their roles are elaborated. Finally, by comparing the development routes of RL and RL-EMS, this paper identifies the gap between advanced RL solutions and existing RL-EMS. Finally, this paper suggests potential development directions for implementing advanced artificial intelligence (AI) solutions in EMS.

准确的交通状况预测为车辆环境协调和交通管制任务提供了坚实的基础。由于道路网络数据在空间分布中的复杂性以及深度学习方法的多样性，有效定义流量数据并充分捕获数据中复杂的空间非线性特征变得具有挑战性。本文将两种分层图池方法应用于流量预测任务，以减少图形信息冗余。首先，本文验证了流量预测任务中层次图池方法的有效性。分层图合并方法与其他基线在预测性能上形成鲜明对比。其次，应用了两种主流分层图池方法，节点群集池和节点下降池，用于分析流量预测中的优势和弱点。最后，对于上述图神经网络，本文比较了不同图网络输入对流量预测准确性的预测效应。分析和汇总定义图网络的有效方法。

近年来，在运输电气化方面取得了重大进展。作为主要的储能设备，锂离子电池（LIB）已受到广泛关注。准确地预测健康状况（SOH）不仅可以缓解用户对电池寿命的焦虑，而且还可以为电池管理提供重要信息。本文提出了一种基于视觉变压器（VIT）模型的SOH的预测方法。首先，预定义电压范围的离散充电数据用作输入数据矩阵。然后，电池的循环特征是由VIT捕获的，可以获得可以获得全局特征，并且通过将循环特征与完整连接（FC）层相结合来获得SOH。同时，引入了转移学习（TL），并根据目标任务电池的早期周期数据进一步微调基于源任务电池训练的预测模型，以提供准确的预测。实验表明，与现有的深度学习方法相比，我们的方法可以获得更好的特征表达，从而可以实现更好的预测效果和传递效果。

经典的交流范式专注于准确地通过嘈杂的渠道传输位，而香农理论则对可靠通信速率提供了基本的理论限制。在这种方法中，位平均对待，并且通信系统忽略了这些位传达或如何使用的含义。可以预见的是，对智力和简洁性的未来沟通将发挥主导作用，连接的智能代理的扩散需要对编码传输范式进行根本性的重新思考，以支持地平线上的新通信形态。最近的“语义通信”概念提供了有希望的研究方向。将语义指南注入编码传输设计以实现语义感知通信，这表现出了进一步突破性和可靠性的巨大潜力。本文阐明了语义引导的源和频道编码作为语义通信的传输范式，该传输范式可以利用数据语义的多样性和无线通道多样性，以增强整个系统性能。我们介绍一般的系统体系结构和关键技术，并指出有关此主题的一些开放问题。

流量预测在智能运输系统中交通控制和调度任务的实现中起着重要作用。随着数据源的多元化，合理地使用丰富的流量数据来对流量流中复杂的时空依赖性和非线性特征进行建模是智能运输系统的关键挑战。此外，清楚地评估从不同数据中提取的时空特征的重要性成为一个挑战。提出了双层 - 空间时间特征提取和评估（DL -STFEE）模型。 DL-STFEE的下层是时空特征提取层。流量数据中的空间和时间特征是通过多画图卷积和注意机制提取的，并生成了空间和时间特征的不同组合。 DL-STFEE的上层是时空特征评估层。通过高维自我注意力发项机制产生的注意力评分矩阵，空间特征组合被融合和评估，以便获得不同组合对预测效应的影响。在实际的流量数据集上进行了三组实验，以表明DL-STFEE可以有效地捕获时空特征并评估不同时空特征组合的重要性。

交通速度预测是许多有价值应用程序的关键，由于其各种影响因素，它也是一项具有挑战性的任务。最近的工作试图通过各种混合模型获得更多信息，从而提高了预测准确性。但是，这些方法的空间信息采集方案存在两级分化问题。建模很简单，但包含很少的空间信息，或者建模是完整的，但缺乏灵活性。为了基于确保灵活性引入更多空间信息，本文提出了IRNET（可转让的交叉点重建网络）。首先，本文将相交重建为与相同结构的虚拟交集，从而简化了道路网络的拓扑结构。然后，将空间信息细分为交叉信息和交通流向的序列信息，并通过各种模型获得时空特征。第三，一种自我发项机制用于融合时空特征以进行预测。在与基线的比较实验中，不仅预测效应，而且转移性能具有明显的优势。

本地化和导航是基本的机器人任务，需要准确，最新的地图才能完成这些任务，众包数据可检测地图更改，提出了吸引人的解决方案。收集和处理众包数据需要低成本的传感器和算法，但是现有的方法依赖于昂贵的传感器或计算昂贵的算法。此外，没有现有数据集来评估点云更改检测。因此，本文提出了一个使用低成本传感器（如立体声摄像机和IMU）来检测点云图中的变化的新型框架。此外，我们创建了一个数据集和相应的指标，借助高保真模拟器虚幻引擎4.实验表明，我们的视觉框架可以有效地检测数据集中的变化。