State of Health Estimation of Lithium-Ion Batteries Using Support Vector Regression and Long Short-Term Memory

Lithium-ion batteries are the most widely accepted type of battery in the electric vehicle industry because of some of their positive inherent characteristics. However, the safety problems associated with inaccurate estimation and prediction of the state of health of these batteries have attracted wide attention due to the adverse negative effect on vehicle safety. In this paper, both machine and deep learning models were used to estimate the state of health of lithium-ion batteries. The paper introduces the definition of battery health status and its importance in the electric vehicle industry. Based on the data preprocessing and visualization analysis, three features related to actual battery capacity degradation are extracted from the data. Two learning models, SVR and LSTM were employed for the state of health estimation and their respective results are compared in this paper. The mean square error and coefficient of determination were the two metrics for the performance evaluation of the models. The experimental results indicate that both models have high estimation results. However, the metrics indicated that the SVR was the overall best model.

Navigation jamming signal recognition based on long short-term memory neural networks

This paper introduces the time-frequency analyzed long short-term memory(TF-LSTM) neural network method for jamming signal recognition over the Global Navigation Satellite System(GNSS) receiver. The method introduces the long shortterm memory(LSTM) neural network into the recognition algorithm and combines the time-frequency(TF) analysis for signal preprocessing. Five kinds of navigation jamming signals including white Gaussian noise(WGN), pulse jamming, sweep jamming, audio jamming, and spread spectrum jamming are used as input for training and recognition. Since the signal parameters and quantity are unknown in the actual scenario, this work builds a data set containing multiple kinds and parameters jamming to train the TF-LSTM. The performance of this method is evaluated by simulations and experiments. The method has higher recognition accuracy and better robustness than the existing methods, such as LSTM and the convolutional neural network(CNN).

Multi-head attention-based long short-term memory model for speech emotion recognition

To fully make use of information from different representation subspaces,a multi-head attention-based long short-term memory(LSTM)model is proposed in this study for speech emotion recognition(SER).The proposed model uses frame-level features and takes the temporal information of emotion speech as the input of the LSTM layer.Here,a multi-head time-dimension attention(MHTA)layer was employed to linearly project the output of the LSTM layer into different subspaces for the reduced-dimension context vectors.To provide relative vital information from other dimensions,the output of MHTA,the output of feature-dimension attention,and the last time-step output of LSTM were utilized to form multiple context vectors as the input of the fully connected layer.To improve the performance of multiple vectors,feature-dimension attention was employed for the all-time output of the first LSTM layer.The proposed model was evaluated on the eNTERFACE and GEMEP corpora,respectively.The results indicate that the proposed model outperforms LSTM by 14.6%and 10.5%for eNTERFACE and GEMEP,respectively,proving the effectiveness of the proposed model in SER tasks.

Device Anomaly Detection Algorithm Based on Enhanced Long Short-Term Memory Network

The problems in equipment fault detection include data dimension explosion,computational complexity,low detection accuracy,etc.To solve these problems,a device anomaly detection algorithm based on enhanced long short-term memory(LSTM)is proposed.The algorithm first reduces the dimensionality of the device sensor data by principal component analysis(PCA),extracts the strongly correlated variable data among the multidimensional sensor data with the lowest possible information loss,and then uses the enhanced stacked LSTM to predict the extracted temporal data,thus improving the accuracy of anomaly detection.To improve the efficiency of the anomaly detection,a genetic algorithm(GA)is used to adjust the magnitude of the enhancements made by the LSTM model.The validation of the actual data from the pumps shows that the algorithm has significantly improved the recall rate and the detection speed of device anomaly detection,with the recall rate of 97.07%,which indicates that the algorithm is effective and efficient for device anomaly detection in the actual production environment.

基于SARIMA‑LSTM模型的航空旅客运输市场需求分析与预测

市场需求预测是航空公司开展生产活动的前提,科学合理的预测结果能为航空公司降低成本、提高效益。首先,选取影响航空旅客运输市场需求的因素,并对其进行相关性分析;其次,采用季节性差分自回归移动平均(SARIMA)模型和长短期记忆(LSTM)网络模型,对航空旅客运输市场需求量进行特征分析,构建了基于SARIMA模型、LSTM网络模型的组合预测(SARIMA⁃LSTM)模型,提高市场需求时间序列预测的精度;最后,以北京市航空运输市场为例,分析结果显示,SARIMA⁃LSTM组合模型的预测准确性高于单一模型,对于市场需求的预测准确率较高。

利用长短期记忆网络LSTM对赤道太平洋海表面温度短期预报

海表面温度作为海洋中一个最重要的变量,对全球气候、海洋生态等有很大的影响,因此十分有必要对海表面温度(SST)进行预报。深度学习具备高效的数据处理能力,但目前利用深度学习对整个赤道太平洋的SST短期预报及预报技巧的研究仍较少。本文基于最优插值海表面温度(OISST)的日平均SST数据,利用长短期记忆(LSTM)网络构建了未来10天赤道太平洋(10°S~10°N,120°E~80°W)SST的逐日预报模型。LSTM预报模型利用1982~2010年的观测数据进行训练,2011~2020年的观测数据作为初值进行预报和检验评估。结果表明:赤道太平洋东部地区预报均方根误差(RMSE)大于中、西部,东部预报第1天RMSE为0.6℃左右,而中、西部均小于0.3℃。在不同的年际变化位相,预报RMSE在拉尼娜出现时期最大,正常年份次之,厄尔尼诺时期最小,RMSE在拉尼娜时期比在厄尔尼诺时期可达20%。预报偏差整体表现为东正、西负。相关预报技巧上,中部最好,可预报天数基本为10天以上,赤道冷舌附近可预报天数为4~7天,赤道西边部分地区可预报天数为3天。预报模型在赤道太平洋东部地区各月份预报技巧普遍低于西部地区,相比较而言各区域10、11月份预报技巧最低。总的来说,基于LSTM构建的SST预报模型能很好地捕捉到SST在时序上的演变特征,在不同案例中预报表现良好。同时该预报模型依靠数据驱动,能迅速且较好地预报未来10天以内的日平均SST的短期变化。

基于CEEMDAN-GSA-LSTM和SVR的光伏功率短期区间预测

针对光伏输出功率存在间歇性和波动性的问题,提出一种光伏功率短期区间预测模型.首先,该模型采用自适应噪声完备集合经验模态分解将历史光伏出力数据分解为不同的分量并按照其与赤纬角、时角等时序特征量的相关性定义为时序分量和随机分量.其次,分别使用经过引力搜索算法优化的长短期记忆神经网络和支持向量回归模型对时序分量和随机分量进行预测.再次,叠加时序分量和随机分量的预测结果得到点预测结果.然后,对误差进行Johnson变换及正态分布建模后得到光伏功率区间预测结果.最后,利用算例验证该模型的有效性.结果表明:在不同天气情况下,上述模型比现有预测模型精度更高,具有较好的鲁棒性,能够基于预测值提供较为精准的置信区间.

A Complex Fuzzy LSTM Network for Temporal-Related Forecasting Problems

Time-stamped data is fast and constantly growing and it contains significant information thanks to the quick development ofmanagement platforms and systems based on the Internet and cutting-edge information communication technologies.Mining the time series data including time series prediction has many practical applications.Many new techniques were developed for use with various types of time series data in the prediction problem.Among those,this work suggests a unique strategy to enhance predicting quality on time-series datasets that the timecycle matters by fusing deep learning methods with fuzzy theory.In order to increase forecasting accuracy on such type of time-series data,this study proposes integrating deep learning approaches with fuzzy logic.Particularly,it combines the long short-termmemory network with the complex fuzzy set theory to create an innovative complex fuzzy long short-term memory model(CFLSTM).The proposed model adds a meaningful representation of the time cycle element thanks to a complex fuzzy set to advance the deep learning long short-term memory(LSTM)technique to have greater power for processing time series data.Experiments on standard common data sets and real-world data sets published in the UCI Machine Learning Repository demonstrated the proposedmodel’s utility compared to other well-known forecasting models.The results of the comparisons supported the applicability of our proposed strategy for forecasting time series data.

A multi-source information fusion layer counting method for penetration fuze based on TCN-LSTM

When employing penetration ammunition to strike multi-story buildings,the detection methods using acceleration sensors suffer from signal aliasing,while magnetic detection methods are susceptible to interference from ferromagnetic materials,thereby posing challenges in accurately determining the number of layers.To address this issue,this research proposes a layer counting method for penetration fuze that incorporates multi-source information fusion,utilizing both the temporal convolutional network(TCN)and the long short-term memory(LSTM)recurrent network.By leveraging the strengths of these two network structures,the method extracts temporal and high-dimensional features from the multi-source physical field during the penetration process,establishing a relationship between the multi-source physical field and the distance between the fuze and the target plate.A simulation model is developed to simulate the overload and magnetic field of a projectile penetrating multiple layers of target plates,capturing the multi-source physical field signals and their patterns during the penetration process.The analysis reveals that the proposed multi-source fusion layer counting method reduces errors by 60% and 50% compared to single overload layer counting and single magnetic anomaly signal layer counting,respectively.The model's predictive performance is evaluated under various operating conditions,including different ratios of added noise to random sample positions,penetration speeds,and spacing between target plates.The maximum errors in fuze penetration time predicted by the three modes are 0.08 ms,0.12 ms,and 0.16 ms,respectively,confirming the robustness of the proposed model.Moreover,the model's predictions indicate that the fitting degree for large interlayer spacings is superior to that for small interlayer spacings due to the influence of stress waves.

Real-time UAV path planning based on LSTM network

To address the shortcomings of single-step decision making in the existing deep reinforcement learning based unmanned aerial vehicle(UAV)real-time path planning problem,a real-time UAV path planning algorithm based on long shortterm memory(RPP-LSTM)network is proposed,which combines the memory characteristics of recurrent neural network(RNN)and the deep reinforcement learning algorithm.LSTM networks are used in this algorithm as Q-value networks for the deep Q network(DQN)algorithm,which makes the decision of the Q-value network has some memory.Thanks to LSTM network,the Q-value network can use the previous environmental information and action information which effectively avoids the problem of single-step decision considering only the current environment.Besides,the algorithm proposes a hierarchical reward and punishment function for the specific problem of UAV real-time path planning,so that the UAV can more reasonably perform path planning.Simulation verification shows that compared with the traditional feed-forward neural network(FNN)based UAV autonomous path planning algorithm,the RPP-LSTM proposed in this paper can adapt to more complex environments and has significantly improved robustness and accuracy when performing UAV real-time path planning.

Track correlation algorithm based on CNN-LSTM for swarm targets

The rapid development of unmanned aerial vehicle(UAV) swarm, a new type of aerial threat target, has brought great pressure to the air defense early warning system. At present, most of the track correlation algorithms only use part of the target location, speed, and other information for correlation.In this paper, the artificial neural network method is used to establish the corresponding intelligent track correlation model and method according to the characteristics of swarm targets.Precisely, a route correlation method based on convolutional neural networks (CNN) and long short-term memory (LSTM)Neural network is designed. In this model, the CNN is used to extract the formation characteristics of UAV swarm and the spatial position characteristics of single UAV track in the formation,while the LSTM is used to extract the time characteristics of UAV swarm. Experimental results show that compared with the traditional algorithms, the algorithm based on CNN-LSTM neural network can make full use of multiple feature information of the target, and has better robustness and accuracy for swarm targets.

基于LSTM-AEKF算法的锂离子电池SOC估计

针对扩展卡尔曼滤波(Extended Kalman filter,EKF)算法与长短期记忆网络(Long Short-Term Memory,LSTM)不能准确估计锂离子电池荷电状态(State of Charge,SOC)的问题,本文提出了一种基于二阶戴维宁(Thevenin)的等效电路模型,采用自适应扩展卡尔曼滤波(Adaptve Extended Kalman filter,AEKF)与LSTM相结合的SOC估计算法,即LSTM-AEKF算法。在二阶RC等效电路模型的基础上建立整数阶模型,并采用EKF算法辨识模型参数,采用LSTM-AEKF算法估计SOC,与AEKF算法、LSTM算法进行比较。根据马里兰大学公开数据集进行测试,结果表明,与传统方法相比,LSTM-AEKF算法估计SOC的平均绝对误差(Mean Absolute Error,MAE)与均方根误差(Root Mean Square Error,RMSE)分别下降了1.23%、1.5%,基于二阶RC模型的LSTM-AEKF算法可以有效估计SOC。

基于LASSO回归和QRLSTM的来水预测方法研究

精准的河流断面来水流量预测对于水资源配置管理、洪水预警和防灾减灾、生态保护和水力发电工程规划有着重要意义。为了提高单一来水流量预测模型的预测精度,采用LASSO回归算法结合分位数回归长短期记忆神经网络(QRLSTM)以及核密度估计(KDE)算法,提出了一种来水流量预测方法(LASSO-QRLSTM)。首先采用LASSO回归从高维来水特征向量中提取关键的解释变量,以降低解释变量与被解释变量之间非线性关系的复杂程度;接着建立QRLSTM来水流量预测模型,以获得不同分位点下的分位数预测值;进而利用KDE拟合概率密度函数,获得未来的来水流量可能值以及相应的概率,得出最终预测结果。将提出的模型应用于广东省西江关键断面和高要水文站的来水流量预测,并与LASSO-QRNN、LASSO-GBDT、QRLSTM、QRNN、GBDT模型进行对比。结果表明:(1)结合LASSO回归的混合预测模型预测效果均好于单一的QRLSTM、QRNN、GBDT模型。(2)提出的LASSO-QRLSTM模型在对思贤滘断面流量预测中的RMSE为1 804.270 m^(3)/s,NSE值达0.973;在概率性指标方面,LASSO-QRLSTM模型的连续分级概率评分(CRPS)和弹球损失(PL)值分别为842.618和465.964,各项评价指标均为最佳,在对比模型中表现出最好的预测效果,特别是在极值处具有更好的拟合效果和更窄的概率预测区间,表现出该模型在河流来水流量预测中的独特优势。(3)在后续对高要水文站来水流量的预测中,其预测性能得到进一步验证,展现出良好的适应性和稳定性。研究成果可为精准的水文预测和水资源优化配置提供参考。

基于VMD-LSTM-SVR的IGBT寿命特征时间序列预测

绝缘栅双极型晶体管(IGBT)失效是变频器等电力电子设备故障的主要原因,精确预测其寿命是解决该问题的方法之一,这对寿命预测模型的准确性和可靠性提出了更高要求。关断瞬态尖峰电压(Vce,peak)可以反映IGBT的老化状态,首先通过变分模态分解(VMD)技术将Vce,peak构成的时间序列分解为趋势序列和波动序列,再利用长短期记忆(LSTM)网络的时间序列特征提取优势和支持向量机回归(SVR)的非线性求解能力,建立VMD-LSTM-SVR组合模型,提升模型的预测性能。模型预测对比实验结果表明,VMD-LSTM-SVR模型提升了IGBT寿命特征时间序列预测能力,与其他模型相比,该模型的预测精度指标均方根误差下降至0.0411 V,决定系数提升至0.75111。

Deep Learning-Based Stock Price Prediction Using LSTM Model

The stock market is a vital component of the broader financial system,with its dynamics closely linked to economic growth.The challenges associated with analyzing and forecasting stock prices have persisted since the inception of financial markets.By examining historical transaction data,latent opportunities for profit can be uncovered,providing valuable insights for both institutional and individual investors to make more informed decisions.This study focuses on analyzing historical transaction data from four banks to predict closing price trends.Various models,including decision trees,random forests,and Long Short-Term Memory(LSTM)networks,are employed to forecast stock price movements.Historical stock transaction data serves as the input for training these models,which are then used to predict upward or downward stock price trends.The study’s empirical results indicate that these methods are effective to a degree in predicting stock price movements.The LSTM-based deep neural network model,in particular,demonstrates a commendable level of predictive accuracy.This conclusion is reached following a thorough evaluation of model performance,highlighting the potential of LSTM models in stock market forecasting.The findings offer significant implications for advancing financial forecasting approaches,thereby improving the decision-making capabilities of investors and financial institutions.

基于SARIMA‑LSTM组合的机场起降量短时预测方法

机场起降量短时预测方法是根据空中交通流量管理需求,对机场未来24小时时间跨度内起降量情况进行预测。首先,构建了基于季节性差分自回归移动平均(SARIMA)和长短期记忆神经网络(LSTM)的机场起降量预测模型;然后,根据误差倒数法确定组合预测权重以期得到更好的预测效果;最后,使用天津滨海机场进行实例验证,以机场起降量的小时数据建立了SARIMA(0,1,7)×(0,1,1)_(24)和LSTM模型,并分别以0.600和0.400的权重建立了组合预测模型。验证结果显示,组合模型的预测指标R2达到0.904,较反向传播(BP)神经网络等其他单一模型预测性能更佳。

基于LSTM-SVR模型的航空旅客出行指数预测

航空旅客出行的情况对民用航空机场建设与运营具有重大意义。定义了一种航空旅客出行指数,运用机器学习方法对航空旅客出行指数进行预测,克服了单一预测模型精度的不足。提出一种将长短期记忆网络(LSTM)与支持向量回归(SVR)相结合的航空旅客出行指数组合预测模型,并对预测结果集进行聚类分析。以上海机场航空旅客数据为实证,验证了LSTM-SVR组合预测模型可行性与有效性,实验结果显示:LSTM-SVR组合预测模型较传统单一预测模型具有更高的精度;同时,LSTM-SVR组合预测模型与其他组合预测模型相比也有较明显优势。此外,基于K-均值算法对航空旅客出行指数进行聚类分析并给出评级,此举为机场运营管理及旅客出行提供一定的决策支持。

基于ARIMA与LSTM的新冠肺炎网络关注度趋势研究

为有效监控和管理新型冠状病毒肺炎(COVID-19)引起的网络舆情,基于自回归移动平均(ARIMA)模型和长短期记忆(LSTM)神经网络预测和分析舆情数据,利用百度指数收集全国及武汉市网民对COVID-19的关注度数值,形成时间序列数据,并构建舆情模型;对舆情模型进行参数估计、模型诊断和模型评价。结果表明:此疫情的网络舆情前驱期为4天,爆发期为7天,波动期为14天,消退期为32天,到达峰值的时间为13天;该模型可较好地模拟COVID-19网络舆情关注度的变化趋势,且局部地区的数据拟合模型预测效果优于全国数据拟合模型。

股票信息挖掘与LSTM预测

由于受到经济环境、政治政策、市场新闻等多种因素的影响,使得预测股票动态变得极具挑战性。研究了5种常用的预测股价变动的预测方法,通过逐步增加模型的输入维度进行预测分析。首先,建立5种优化的预测模型——基于时间序列的自回归平均模型(ARMA)、灰色预测模型(GM(1,1))、BP神经网络模型(BPNN)、基于改进网格寻优算法的支持向量回归(SVR)模型、基于Tensorflow的长短时记忆神经网络模型(LSTM),研究单一维度的模型输入,即,将各股票的收盘价作为这5种模型的输入。通过实验验证,发现基于LSTM的效果明显优于其他传统机器学习算法。然后,增加模型的输入维度进行研究,即,将影响股价的13个指标作为LSTM模型的输入来预测股价,所得的模型在训练集上的均方误差为0.1438。最后,进一步增加模型的输入维度,即,通过新闻数据挖掘提取14个特征,再结合13个股价指标,以这27个维度特征作为LSTM模型的输入来预测股价,所得的模型在训练集上的均方误差为0.1045。通过实验验证得出,所采用的输入27个维度的方法,比输入13个维度在预测问题上表现得更稳健。

一种基于SARIMA-LSTM模型的电网主机负载预测方法

随着智能电网的不断发展,如何提高对信息设备运行状态的预测准确率以及设置适应数据变化的动态阈值区间是电网IT运维面临的巨大挑战。为了解决这些问题,提出了组合时间序列预测模型(SARIMA-LSTM),即在传统周期性ARIMA模型(SARIMA)的基础上,引入深度学习领域的LSTM模型,并摒弃了过去精度低、效果差的误差拟合方法,使用误差自回归方法来补偿预测结果。该模型可以学习到传统ARIMA模型无法捕捉到的误差波动规律,解决其无法预测非线性数据的问题。实验结果表明,在实际预测电网内存负载数据时,与ARIMA模型和SAIRIMA模型相比,SARIMA-LSTM模型可以实现更高的预测精度。