Sea fog detection based on unsupervised domain adaptation

Sea fog detection with remote sensing images is a challenging task. Driven by the different image characteristics between fog and other types of clouds, such as textures and colors, it can be achieved by using image processing methods. Currently, most of the available methods are datadriven and relying on manual annotations. However, because few meteorological observations and buoys over the sea can be realized, obtaining visibility information to help the annotations is difficult. Considering t...     

高校贫困生资格认定的实证分析

我国现阶段构建高校贫困生资助体系时,应采取与市场经济中"效益优先,兼顾公平"的指导思想,做到"教育公平优先".由于各方的强烈呼吁,国家从2007年下半年开始加大了对贫困学生的资助力度,而具体落实到实际操作方法时,以达到资助公平为目标,其前提是必须准确的界定贫困生身份,使真正的贫困生获得必要的资助,以最大限度的实现教育公平.     

基于增量式孤立森林的液体火箭发动机异常检测方法

为解决液体火箭发动机故障标签缺失条件下流数据无监督检测问题,以及满足不同发动机台次和不同工况的自适应检测需求,基于增量学习思想,提出了基于增量式孤立森林的异常检测算法。设计了多工况流数据检测条件下的在线更新策略、异常分数表达式,并通过更新停止策略避免故障数据对模型的污染。利用多台次试车数据对该模型进行验证,并与传统方法进行比较,结果表明,该算法能够对样本异常程度进行量化评价,能够有效检测早期缓变故障,其F₁指标较原始孤立森林算法提高了43%,检测及时性优于红线算法和自适应阈值算法。     

Locally generalised multi-agent reinforcement learning for demand and capacity balancing with customised neural networks

Reinforcement Learning (RL) techniques are being studied to solve the Demand and Capacity Balancing (DCB) problems to fully exploit their computational performance. A locally generalised Multi-Agent Reinforcement Learning (MARL) for real-world DCB problems is proposed. The proposed method can deploy trained agents directly to unseen scenarios in a specific Air Traffic Flow Management (ATFM) region to quickly obtain a satisfactory solution. In this method, agents of all flights in a scenario form a multi-agent decision-making system based on partial observation. The trained agent with the customised neural network can be deployed directly on the corresponding flight, allowing it to solve the DCB problem jointly. A cooperation coefficient is introduced in the reward function, which is used to adjust the agent’s cooperation preference in a multi-agent system, thereby controlling the distribution of flight delay time allocation. A multi-iteration mechanism is designed for the DCB decision-making framework to deal with problems arising from non-stationarity in MARL and to ensure that all hotspots are eliminated. Experiments based on large-scale high-complexity real-world scenarios are conducted to verify the effectiveness and efficiency of the method. From a statistical point of view, it is proven that the proposed method is generalised within the scope of the flights and sectors of interest, and its optimisation performance outperforms the standard computer-assisted slot allocation and state-of-the-art RL-based DCB methods. The sensitivity analysis preliminarily reveals the effect of the cooperation coefficient on delay time allocation.     

基于正则化多核判别分析的航空发动机滚动轴承早期故障融合诊断方法

针对基于机匣测点信号的航空发动机滚动轴承早期故障诊断问题,提出了一种基于正则化多核判别分析的融合诊断方法.该方法首先提取多种类型的滚动轴承故障特征;然后采用相同的一组核参数为不同类型的特征分别构造一组核矩阵,并将所有核矩阵组合在一起;最后通过求解一个半无限线性规划得到该组核矩阵关于正则化核判别分析的目标函数的最优线性组合系数,进一步采用该系数计算所有核矩阵的线性组合,从而实现多种类型特征信息的融合.实验结果表明:该方法诊断正确率与采用单一类型特征诊断的最高正确率相比提高了9.25%,同时可以避免核矩阵需要人工选择的问题,从而进一步提高了故障诊断的自动化水平.      

Trajectory Tracking Control for a GMM Actuator Based on a Heuristic ILC Method

A heuristic iterative learning control （ILC） method is presented and applied to the trajectory tracking control of a giant magnetostrictive material （GMM） actuator. A GMM actuator is used as experimental equipment for micro-displacement trajectory tracking control. The advantage of the presented approach lies in quitting the model of the GMM actuator. The experimental results attest to the high efficiency of the presented method for the micro-displacement trajectory tracking control.     

基于学习的图像智能适配显示技术

为适应不同的显示分辨率,出现了各式各样的图像适配显示(IR)的方法.提出了基于图像列的一种快速适配显示方法.在处理过程中,首先,计算一个原始图像的重要性图;其次,根据图像每列的重要性程度为其分配一个比例因子,对不同图像而言,应对比例因子设置不同的上限才可以得到较好的结果;最后,提出通过机器学习方法计算出不同图像的上限,从而可以高效率地得到理想的结果.根据每一列的比例因子采用像素融合的方式处理图片得到目标分辨率.本方法是基于列实现的,其复杂度低、便于计算;设置每列系数的上限控制了图像重要部分的宽度,从而减少了不连贯,处理结果更为自然.      

柔性铰接板振动测量及迭代学习主动控制

大型柔性板状结构广泛应用于航空航天领域,这种结构特性极易引起低频振动,针对这一问题以柔性铰接板为研究对象,探究了柔性铰接板在受外部激励作用下弯曲模态的振动特性.为此搭建了一套用于测控柔性铰接板振动特性的实验平台,采用了立体视觉和压电陶瓷传感器分别检测柔性铰接板的振动信息,以此信息作反馈,采用了变遗忘因子的迭代学习控制方法对柔性铰接板的振动进行主动控制,同时以PD控制的实验效果作为对比.实验结果表明,不论是大幅值的快速衰减还是小幅值的残余振动抑制,迭代学习控制效果都优于PD控制.     

Modeling M(3000)F2 based on extreme learning machine

This paper presents an novel extreme learning machine (ELM)-based prediction model for the ionospheric propagation factor M(3000)F2 at Darwin station (12.4°S, 131.5°E; −44.5°dip) in Australia. The proposed ELM model is trained with hourly daily values of M(3000)F2 from the period 1998–2014 except 2001 and 2009. The hourly daily values of 2001 (high solar activity) and 2009 (low solar activity) are used for validating the prediction accuracy. The proposed ELM for modeling M(3000)F2 can achieve faster training process and similar testing accuracy compared with backward propagation neural network (BPNN). In addition, the performance of the ELM is verified by comparing the predicted values of M(3000)F2 with observed values and the international reference ionosphere (IRI −2016) model predicted values. Based on the error differences (the root mean square error (RMSE) and the M(3000)F2 percentage improvement values M(3000)F2_IMP(%)), the result demonstrates the effectiveness of the ELM model compared with the IRI-2016 model at hourly, daily, monthly, and yearly in high (2001) and low (2009) solar activity years. The ELM also shows good agreement with observations compared with the IRI during disturbed magnetic activity.     

空间机器人操作：一种多任务学习视角

利用空间机器人辅助、代替航天员完成在轨服务操作是近年的技术发展趋势。基于学习的空间机器人操作以深度神经网络为控制器载体,对非结构化太空环境适应能力强,在高轨、地外、深空等场景具有良好应用前景。目前,无论是空间机器人操作,还是地面机器人操作,多数研究只关注单一任务学习问题。立足一种多任务学习新视角,针对空间机器人操作面临的多任务适应性要求高、精细化要求高、不确定性强问题,首先分析了在轨服务的多样化任务需求。其次,全面综述了机器人操作多任务学习算法与应用相关工作,分析了开展空间机器人操作多任务学习的难点挑战,给出了关键技术发展建议。相关关键技术的突破将有助于提升空间机器人系统的自主性、鲁棒性,进而助力中国在轨服务技术向无人全自主方向推进。