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631.
四足机器人灵巧运动技能的生成一直受到机器人研究者们的广泛关注,其中空中翻滚运动既能展现四足机器人运动的灵活性又具有一定的实用价值.近年来,深度强化学习方法为四足机器人的灵巧运动提供了新的实现思路,利用该方法得到的闭环神经网络控制器具有适应性强、稳定性高等特点.本文在绝影Lite机器人上使用基于模仿专家经验的深度强化学习方法,实现了仿真环境中四足机器人的后空翻动作学习,并进一步证明了设计的后空翻闭环神经网络控制器相比于开环传统位置控制器具有适应性更高的特点. 相似文献
632.
为了以低成本、高时空分辨率进行大雾天气监测,提出一种利用无线通信链路进行基于深度学习的大雾天气监测方法。由于信道中不同浓度的大雾天气在信号中留有的特征不同,采集了4种不同浓度大雾下的无线电信号,建立无线电大雾天气监测数据集;通过在传统ResNet50网络中引入注意力机制并进行特征融合,得到改进后的A-ResNet50模型。利用A-ResNet50网络提取接收信号中留有的不同浓度大雾天气的特征,对四类不同浓度大雾天气进行分类识别,达到监测大雾天气的目的。所提方法在建立的数据集上进行了验证,相较于其他传统分类算法,本方法性能最优,最终识别准确率达到86.18 %,结果证明了该方法的可行性和有效性。 相似文献
633.
针对大气层内高速机动目标的拦截问题,提出了一种基于双延迟深度确定性策略梯度(TD3)算法的深度强化学习制导律,它直接将交战状态信息映射为拦截弹的指令加速度,是一种端到端、无模型的制导策略。首先,将攻防双方的交战运动学模型描述为适用于深度强化学习算法的马尔科夫决策过程,之后通过合理地设计算法训练所需的交战场景、动作空间、状态空间和网络结构,并引入奖励函数整形和状态随机初始化,构建了完整的深度强化学习制导算法。仿真结果表明:与比例导引和增强比例导引两种方案相比,深度强化学习制导策略在脱靶量更小的同时能够降低对中制导精度的要求;具有良好的鲁棒性和泛化能力,并且计算负担较小,具备在弹载计算机上运行的条件。 相似文献
634.
635.
《中国航空学报》2023,36(3):16-29
Geometric and working condition uncertainties are inevitable in a compressor, deviating the compressor performance from the design value. It’s necessary to explore the influence of geometric uncertainty on performance deviation under different working conditions. In this paper, the geometric uncertainty influences at near stall, peak efficiency, and near choke conditions under design speed and low speed are investigated. Firstly, manufacturing geometric uncertainties are analyzed. Next, correlation models between geometry and performance under different working conditions are constructed based on a neural network. Then the Shapley additive explanations (SHAP) method is introduced to explain the output of the neural network. Results show that under real manufacturing uncertainty, the efficiency deviation range is small under the near stall and peak efficiency conditions. However, under the near choke conditions, efficiency is highly sensitive to flow capacity changes caused by geometric uncertainty, leading to a significant increase in the efficiency deviation amplitude, up to a magnitude of ?3.6%. Moreover, the tip leading-edge radius and tip thickness are two main factors affecting efficiency deviation. Therefore, to reduce efficiency uncertainty, a compressor should be avoided working near the choke condition, and the tolerances of the tip leading-edge radius and tip thickness should be strictly controlled. 相似文献
636.
《Advances in Space Research (includes Cospar's Information Bulletin, Space Research Today)》2023,71(7):2978-2989
In recent years, deep learning (DL) methods have proven their efficiency for various computer vision (CV) tasks such as image classification, natural language processing, and object detection. However, training a DL model is expensive in terms of both complexities of the network structure and the amount of labeled data needed. In addition, the imbalance among available labeled data for different classes of interest may also adversely affect the model accuracy. This paper addresses these issues using a new convolutional neural network (CNN) based architecture. The proposed network incorporates both spatial and spectral information that combines two sub-networks: spatial-CNN and spectral-CNN. The spectral-CNN extracts spectral information, while spatial-CNN captures spatial information. Moreover, to make the features more robust, a multiscale spatial CNN architecture is introduced using different kernels. The final feature vector is formed by concatenating the outputs obtained from both spatial-CNN and spectral-CNN. To address the data imbalance problem, a generative adversarial network (GAN) was used to generate data for the underrepresented class. Finally, relatively a shallower network architecture was used to reduce the number of parameters in the network and improve the processing speed. The proposed model was trained and tested on Senitel-2 images for the classification of the debris-covered glacier. The results showed that the proposed method is well-suited for mapping and monitoring debris-covered glaciers at a large scale with high classification accuracy. In addition, we compared the proposed method with conventional machine learning approaches, support vector machine (SVM), random forest (RF) and multilayer perceptron (MLP). 相似文献
637.
In terms of multiple temporal and spatial scales, massive data from experiments, flow field measurements, and high-fidelity numerical simulations have greatly promoted the rapid development of fluid mechanics. Machine Learning(ML) provides a wealth of analysis methods to extract potential information from a large amount of data for in-depth understanding of the underlying flow mechanism or for further applications. Furthermore, machine learning algorithms can enhance flow information and automat... 相似文献
638.
Multi-beam antenna and beam hopping technologies are an effective solution for scarce satellite frequency resources. One of the primary challenges accompanying with Multi-Beam Satellites(MBS) is an efficient Dynamic Resource Allocation(DRA) strategy. This paper presents a learning-based Hybrid-Action Deep Q-Network(HADQN) algorithm to address the sequential decision-making optimization problem in DRA. By using a parameterized hybrid action space,HADQN makes it possible to schedule the beam patte... 相似文献
639.
《中国航空学报》2023,36(8):422-453
An on-machine measuring (OMM) system with a laser displacement sensor (LDS) is designed for measuring free-form surfaces of hypersonic aircraft’s radomes. To improve the measurement accuracy of the OMM system, a novel Iteratively Automatic machine learning Boosted hand-eye Calibration (IABC) method is proposed. Both the hand-eye relationship and LDS measurement errors can be calibrated in one calibration process without any hardware changes via IABC. Firstly, a new objective function is derived, containing analytical parameters of the hand-eye relationship and LDS errors. Then, a hybrid calibration model composed of two kernels is proposed to solve the objective function. One kernel is the analytical kernel designed for solving analytical parameters. Another kernel is the automatic machine learning (AutoML) kernel designed to model LDS errors. The two kernels are connected with stepwise iterations to find the best calibration results. Compared with traditional methods, hand-eye experiments show that IABC reduces the calibration RMSE by about 50%. Verification experiments show that IABC reduces the measurement deviations by about 25%-50% and RMSEs within 40%. Even when the training data are obviously less than the test data, IABC performs well. Experiments demonstrate that IABC is more accurate than traditional hand-eye methods. 相似文献
640.