交叉口多时段控制输入源优化研究

为了克服多时段控制模型数据输入源连续型数据离散化选取的随意性与经验性,提出一种基于传感网与人工智能理论相结合的交叉口多时段控制深度注意力递归网络输入源选取优化方法。利用Synchro与Sumo仿真评价功能模块对数据输入源进行标准化处理。以已标记好的控制方案中起始时间等关键属性作为模型输入,同时以最优数据输入源选取点为数据输出构建模型。通过对整个模型输入层、中间层、输出层和优化方法进行仿真实现,并以某城市实际交通流量数据为测试数据进行评价对比分析。结果表明,该创新模型与传统50%位、80%位取值法相比,信号配时方案更加精准高效,交叉口全天总延误时间有效降低。

A survey of deep learning-based visual question answering

With the warming up and continuous development of machine learning,especially deep learning,the research on visual question answering field has made significant progress,with important theoretical research significance and practical application value.Therefore,it is necessary to summarize the current research and provide some reference for researchers in this field.This article conducted a detailed and in-depth analysis and summarized of relevant research and typical methods of visual question answering field.First,relevant background knowledge about VQA(Visual Question Answering)was introduced.Secondly,the issues and challenges of visual question answering were discussed,and at the same time,some promising discussion on the particular methodologies was given.Thirdly,the key sub-problems affecting visual question answering were summarized and analyzed.Then,the current commonly used data sets and evaluation indicators were summarized.Next,in view of the popular algorithms and models in VQA research,comparison of the algorithms and models was summarized and listed.Finally,the future development trend and conclusion of visual question answering were prospected.

基于改进DRSN的航空发动机故障风险预警模型

航空发动机属于多发性故障机械,运用先进的计算训练方法可有效地实现准确的风险预警分析,为发动机的运维指导提供参考。在发动机故障风险预警征兆数据集中提取多变量时间序列样本,将样本矩阵化,转换为灰度图样本。预处理并增强图像数据样本,热编码化序列样本标签。深度残差收缩网络(deep residual shrinkage network,DRSN)中融入深度注意力机制与带有阈值的残差收缩块,获取高判别性特征,实现软阈值化。结合长短时记忆神经网络层与多个隐层,改进DRSN模型,使用主成分分析重构特征与主元提取,累积可解释方差贡献率为93.7%。对潜在20种故障征兆识别、分类并预警,训练精确度为96.1%。提出了改进DRSN航空发动机故障风险预警模型,与其他算法相比有较强的鲁棒性,预警正确率至少提高4.4%。