考虑换道意图的LSTM-AdaBoost车辆轨迹预测模型

不合理的车辆的换道行为是导致交通事故发生的主要原因之一,提前预知换道车辆的轨迹并及时做出相应调整有助于减少事故的发生。针对换道车辆轨迹预测问题,采用将深度学习和集成学习相结合的轨迹预测方法,并考虑了换道意图的影响。建立连续隐马尔可夫模型对车辆进行换道意图检测,提前判别车辆的换道状态,并输入至相应的轨迹预测模型中;将LSTM（long short term memory）作为AdaBoost算法（adaptive boosting）的基预测器,建立LSTM-AdaBoost模型,在多个基预测器同时进行轨迹预测的基础上,通过训练调整各个基预测器的权重并将结果加权集成,提升预测模型的精度和稳定性;通过NSGIM（next generation simulation）数据集对模型进行训练和测试,结果显示意图预测模型在变道前一秒的准确率在90%以上,LSTM-AdaBoost集成轨迹预测模型与单一的LSTM模型相比精度和稳定性显著提升,且预测结果中异常数据更少,具有较好的稳定性;同时预测对比结果也表明增加意图预测模块有助于提升换道轨迹预测的精度。     

Optimal PID control for hovering stabilization of quadcopter using long short term memory

Drones are a type of unmanned aerial vehicle. They use several rotors to control their flight motion and stabilize their attitude. This study aims to determine the optimal proportional-integral–differential (PID) gain values that can stabilize a quadcopter with four rotors quickly when its attitude is disturbed. Generally, expert knowledge and a great deal of time and money are required to obtain the PID gain values to stabilize the attitude of a drone. However, in this study, long short-term memory (LSTM), which is a type of neural network algorithm, was used to evaluate the flight motions based on PID gain values without expert knowledge of quadcopters (quadcopter flight motion, PID control algorithms, and quadcopter expert experience). To obtain the optimal values of the PID gain for stabilizing the attitude of a drone, a PID simulator algorithm was developed in this study. The developed algorithm used dynamic equations of motion for the drones. Simulations were used to acquire the drone stabilization data, and a back-propagation neural network was applied to establish an approximate model. Subsequently, the non-dominated sorting genetic algorithm-II was used to obtain the optimal PID gain values that could restore the attitude of the drone quickly. The drone motion data obtained using the simulations were used as the LSTM training data, and the optimal PID gain values obtained using the genetic algorithm were used by the LSTM to predict the motion. To verify the results, a drone was constructed, and the LSTM and dynamic simulation values were compared with the drone experimental values using an experimental device that allowed the motion of the drone to be examined. The results indicated that the motions resulting from the optimal values and the experimental results were in agreement.     

结合LSTM与CNN的野外车辆声信号分类

针对野外环境下微声传感器采集的小型轮式车、大型轮式车和履带车3种车辆声信号受风噪影响严重、分类性能较低的问题,提出了一种长短时记忆网络（LSTM）与多尺度、多层次特征融合卷积神经网络（CNN）相结合的分类算法——野外车辆识别算法（FVNet）。该算法先采用一层LSTM网络提取声信号的时序特征,充分利用声信号的长时依赖关系;再用CNN并行提取多尺度特征,避免网络加深过程中特征的流失;引入通道注意力机制进行多尺度和多层次特征融合,增强多尺度、多层次关键特征信息;最后在相同数据集上进行验证。实验结果表明,FVNet算法对3种车辆的总识别率可达94.95%,与传统方法相比,其总识别率提高了14.61%,取得了较好的分类效果。     

基于LSTM网络的序列标注中文分词法

当前主流的中文分词方法是基于字标注的传统机器学习的方法。但传统机器学习方法需要人为地从中文文本中配置并提取特征,存在词库维度高且仅利用CPU训练模型时间长的缺点。针对以上问题,进行了研究提出基于LSTM（Long Short-Term Memory）网络模型的改进方法,采用不同词位标注集并加入预先训练的字嵌入向量（character embedding）进行中文分词。在中文分词评测常用的语料上进行实验对比,结果表明：基于LSTM网络模型的方法能得到比当前传统机器学习方法更好的性能;采用六词位标注并加入预先训练的字嵌入向量能够取得相对最好的分词性能;而且利用GPU可以大大缩短深度神经网络模型的训练时间;LSTM网络模型的方法也更容易推广并应用到其他自然语言处理（NLP）中序列标注的任务。     

大坝变形的XGBoost-LSTM变权组合预测模型及应用

为了实现更高精度的大坝变形预报,提出了一种大坝变形的XGBoost-LSTM变权组合预测模型,即首先引入XGBoost模型和LSTM模型对大坝变形分别进行分析预测,然后采用变权组合方法将二者的分析预测结果进行有机融合,进而得到最终预测结果。以某混凝土重力坝为例,首先通过与随机森林、ELMAN以及逐步回归分析各模型的对比研究,论证了XGBoost与LSTM应用于大坝变形预测的优越性;进一步地,XGBoost与LSTM的变权组合预测效果相较于各单一模型取得了较大程度的提升,且相较于二者的等值赋权组合提升优势更加显著,变形预测结果与工程实际情况更加吻合,具有较好的适用性和可推广价值。     

嵌入指针网络的深度循环神经网络模型求解作业车间调度问题

提出了一种数据驱动的作业车间调度算法,训练样本来源于基准实例和部分实际生产数据,通过特征函数来构建样本的特征数据并进行归一化处理,标签数据由调度任务和相应的调度规则的映射关系构成,以LSTM模型为主框架,在模型中嵌入指针网络,将当前序列中概率最大的工件优先进入缓冲区,提高了神经网络的训练速度和质量,采用训练后的模型对新问题进行求解。结果证明了所构建模型的有效性,同时为求解作业车间调度问题提供了新思路。     

基于深度学习分位数回归模型的风电功率概率密度预测

针对风电功率预测问题,在现有预测方法和概率性区间预测的基础上,提出基于深度学习分位数回归的风电功率概率预测方法。该方法采用Adam随机梯度下降法在不同分位数条件下对长短期记忆神经网络(LSTM)的输入、遗忘、记忆、输出参数进行估计,得出未来200 h内各个时刻风电功率的概率密度函数。根据美国PJM网上的风电功率实际数据的仿真结果表明,所提方法不仅能得出较为精确的点预测结果,而且能够获得风电功率完整的概率密度函数预测结果。与神经网络分位数回归相比,其精度更高,且在同等置信度下的预测区间范围更小。     

基于ARIMA LSTM组合模型的楼宇短期负荷预测方法研究

在楼宇短期负荷预测中,针对单一预测模型难以充分学习负荷时间序列中的特性问题,提出了一种基于自回归差分移动平均-长短期记忆神经网络(ARIMA-LSTM)组合模型的楼宇负荷预测方法。首先,根据灰色关联度选取相似日时间序列数据为训练样本;然后,利用ARIMA模型预测负荷,并将原始数据和ARIM A预测数据之间的误差视为非线性分量;最后,通过LSTM神经网络对误差序列进行校正,得到楼宇短期负荷的最终预测值。通过对上海市某楼宇的预测效果分析,并将其与ARIMA模型、LSTM模型和ARIMASVM组合模型进行对比,验证了所提方法能够有效控制预测误差,提高楼宇负荷预测精度。     

基于双向LSTM的误植域名滥用检测方法

当前,误植域名检测主要以计算域名对之间的编辑距离为基础,未能充分挖掘域名的上下文信息,且对短域名的检测易产生大量的假阳性结果。采集域名相关信息进行判定虽然有助于提高检测效果,却会引入较大的额外开销.本文采用了基于域名字符串的轻量级检测策略,并引入双向长短时记忆模型（LSTM,Long Short-Term Memory）来充分利用域名上下文,提升检测效果.本文还设计了面向域名的局部敏感哈希函数,以提高在大规模域名集合上进行误植域名检测的速度.在大量真实数据集上的实验结果表明,本文的工作改进了基于编辑距离检测方法的不足,能够有效地进行误植域名滥用检测.     

Deep Neural Architecture for Recovering Dropped Pronouns in Korean

Pronouns are frequently dropped in Korean sentences, especially in text messages in the mobile phone environment. Restoring dropped pronouns can be a beneficial preprocessing task for machine translation, information extraction, spoken dialog systems, and many other applications. In this work, we address the problem of dropped pronoun recovery by resolving two simultaneous subtasks: detecting zero-pronoun sentences and determining the type of dropped pronouns. The problems are statistically modeled by encoding the sentence and classifying types of dropped pronouns using a recurrent neural network (RNN) architecture. Various RNN-based encoding architectures were investigated, and the stacked RNN was shown to be the best model for Korean zero-pronoun recovery. The proposed method does not require any manual features to be implemented; nevertheless, it shows good performance.