Prediction and Analysis of Elevator Traffic Flow under the LSTM Neural Network

Elevators are essential components of contemporary buildings, enabling efficient vertical mobility for occupants. However, the proliferation of tall buildings has exacerbated challenges such as traffic congestion within elevator systems. Many passengers experience dissatisfaction with prolonged wait times, leading to impatience and frustration among building occupants. The widespread adoption of neural networks and deep learning technologies across various fields and industries represents a significant paradigm shift, and unlocking new avenues for innovation and advancement. These cutting-edge technologies offer unprecedented opportunities to address complex challenges and optimize processes in diverse domains. In this study, LSTM (Long Short-Term Memory) network technology is leveraged to analyze elevator traffic flow within a typical office building. By harnessing the predictive capabilities of LSTM, the research aims to contribute to advancements in elevator group control design, ultimately enhancing the functionality and efficiency of vertical transportation systems in built environments. The findings of this research have the potential to reference the development of intelligent elevator management systems, capable of dynamically adapting to fluctuating passenger demand and optimizing elevator usage in real-time. By enhancing the efficiency and functionality of vertical transportation systems, the research contributes to creating more sustainable, accessible, and user-friendly living environments for individuals across diverse demographics.

Massive Files Prefetching Model Based on LSTM Neural Network with Cache Transaction Strategy

In distributed storage systems,file access efficiency has an important impact on the real-time nature of information forensics.As a popular approach to improve file accessing efficiency,prefetching model can fetches data before it is needed according to the file access pattern,which can reduce the I/O waiting time and increase the system concurrency.However,prefetching model needs to mine the degree of association between files to ensure the accuracy of prefetching.In the massive small file situation,the sheer volume of files poses a challenge to the efficiency and accuracy of relevance mining.In this paper,we propose a massive files prefetching model based on LSTM neural network with cache transaction strategy to improve file access efficiency.Firstly,we propose a file clustering algorithm based on temporal locality and spatial locality to reduce the computational complexity.Secondly,we propose a definition of cache transaction according to files occurrence in cache instead of time-offset distance based methods to extract file block feature accurately.Lastly,we innovatively propose a file access prediction algorithm based on LSTM neural network which predict the file that have high possibility to be accessed.Experiments show that compared with the traditional LRU and the plain grouping methods,the proposed model notably increase the cache hit rate and effectively reduces the I/O wait time.

Sensitivity analysis of regional rainfall-induced landslide based on UAV photogrammetry and LSTM neural network

Rainfall stands out as a critical trigger for landslides,particularly given the intense summer rainfall experienced in Zheduotang,a transitional zone from the southwest edge of Sichuan Basin to Qinghai Tibet Plateau.This area is characterized by adverse geological conditions such as rock piles,debris slopes and unstable slopes.Furthermore,due to the absence of historical rainfall records and landslide inventories,empirical methods are not applicable for the analysis of rainfall-induced landslides.Thus we employ a physically based landslide susceptibility analysis model by using highprecision unmanned aerial vehicle(UAV)photogrammetry,field boreholes and long short term memory(LSTM)neural network to obtain regional topography,soil properties,and rainfall parameters.We applied the Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability(TRIGRS)model to simulate the distribution of shallow landslides and variations in porewater pressure across the region under different rainfall intensities and three rainfall patterns(advanced,uniform,and delayed).The landslides caused by advanced rainfall pattern mostly occurred in the first 12 hours,but the landslides caused by delayed rainfall pattern mostly occurred in the last 12 hours.However,all the three rainfall patterns yielded landslide susceptibility zones categorized as high(1.16%),medium(8.06%),and low(90.78%).Furthermore,total precipitation with a rainfall intensity of 35 mm/h for 1 hour was less than that with a rainfall intensity of 1.775 mm/h for 24hours,but the areas with high and medium susceptibility increased by 3.1%.This study combines UAV photogrammetry and LSTM neural networks to obtain more accurate input data for the TRIGRS model,offering an effective approach for predicting rainfall-induced shallow landslides in regions lacking historical rainfall records and landslide inventories.

LSTM Based Neural Network Model for Anomaly Event Detection in Care-Independent Smart Homes

This study introduces a long-short-term memory(LSTM)-based neural network model developed for detecting anomaly events in care-independent smart homes,focusing on the critical application of elderly fall detection.It balances the dataset using the Synthetic Minority Over-sampling Technique(SMOTE),effectively neutralizing bias to address the challenge of unbalanced datasets prevalent in time-series classification tasks.The proposed LSTM model is trained on the enriched dataset,capturing the temporal dependencies essential for anomaly recognition.The model demonstrated a significant improvement in anomaly detection,with an accuracy of 84%.The results,detailed in the comprehensive classification and confusion matrices,showed the model’s proficiency in distinguishing between normal activities and falls.This study contributes to the advancement of smart home safety,presenting a robust framework for real-time anomaly monitoring.

Time-varying parameters estimation with adaptive neural network EKF for missile-dual control system

In this paper, a filtering method is presented to estimate time-varying parameters of a missile dual control system with tail fins and reaction jets as control variables. In this method, the long-short-term memory(LSTM) neural network is nested into the extended Kalman filter(EKF) to modify the Kalman gain such that the filtering performance is improved in the presence of large model uncertainties. To avoid the unstable network output caused by the abrupt changes of system states,an adaptive correction factor is introduced to correct the network output online. In the process of training the network, a multi-gradient descent learning mode is proposed to better fit the internal state of the system, and a rolling training is used to implement an online prediction logic. Based on the Lyapunov second method, we discuss the stability of the system, the result shows that when the training error of neural network is sufficiently small, the system is asymptotically stable. With its application to the estimation of time-varying parameters of a missile dual control system, the LSTM-EKF shows better filtering performance than the EKF and adaptive EKF(AEKF) when there exist large uncertainties in the system model.

Sensory Data Prediction Using Spatiotemporal Correlation and LSTM Recurrent Neural Network

The Wireless Sensor Networks(WSNs)are widely utilized in various industrial and environmental monitoring applications.The process of data gathering within the WSN is significant in terms of reporting the environmental data.However,it might occur that certain sensor node malfunctions due to the energy draining out or unexpected damage.Therefore,the collected data may become inaccurate or incomplete.Focusing on the spatiotemporal correlation among sensor nodes,this paper proposes a novel algorithm to predict the value of the missing or inaccurate data and predict the future data in replacement of certain nonfunctional sensor nodes.The Long-Short-Term-Memory Recurrent Neural Network(LSTM RNN)helps to more accurately derive the time-series data corresponding to the sets of past collected data,making the prediction results more reliable.It is observed from the simulation results that the proposed algorithm provides an outstanding data gathering efficiency while ensuring the data accuracy.

基于LSTM模型的股票价格预测

股票市场的价格波动被视为经济发展的晴雨表。对股票价格的精准预测一直是众多研究学者努力的方向。随着人工智能技术与大数据技术的不断应用与发展以及疫情防控期间国内经济变化和国际形势变换给股价带来的巨大波动,如何对股价进行精准预测变得越来越重要。本文根据股票市场的特点和LSTM(Long Short-Term Memory)递归神经网络的特性,对浦发银行(600000)股价进行预测。实验结果表明,LSTM模型预测股价,结果误差小,精准度高,具有良好的预测效果。

Forecasting method of monthly wind power generation based on climate model and long short-term memory neural network

Predicting wind power gen eration over the medium and long term is helpful for dispatchi ng departme nts,as it aids in constructing generation plans and electricity market transactions.This study presents a monthly wind power gen eration forecast!ng method based on a climate model and long short-term memory(LSTM)n eural n etwork.A non linear mappi ng model is established between the meteorological elements and wind power monthly utilization hours.After considering the meteorological data(as predicted for the future)and new installed capacity planning,the monthly wind power gen eration forecast results are output.A case study shows the effectiveness of the prediction method.

基于LSTM网络的单台仪器地震烈度预测模型

烈度是地震预警系统的关键产出.如何实现快速预测目标场址的地震烈度是地震预警方法技术研究中的核心问题.本文提出了一种基于长短时记忆神经网络(Long Short-Term Memory,LSTM)的单台仪器地震烈度的预测模型(LSTM-Ⅰ).该模型以一个台站观测到地震动参数的时间序列特征为输入,实现动态预测该台站可能遭受的最大烈度.选取了日本K-NET台网记录的102次地震的5103条强震加速度记录训练了神经网络,利用89次地震的3781条数据检验了模型的泛化能力.利用准确率、漏报率以及误报率三个评价指标评价了LSTM-Ⅰ模型的性能.结果表明,当采用P波触发后3 s的序列进行预测时,模型出现漏报的概率为46.78%,出现误报的概率为1.25%;当采用P波触发后10 s的序列进行预测时,模型出现漏报的概率大幅降低到17.6%,出现误报的概率降低到1.14%.结果表明LSTM-Ⅰ模型很好把握住了时间序列中蕴含的特征.进一步基于LSTM-Ⅰ模型评估了Ⅵ度下台站所能提供的预警时间.本文模型能够提供的预警时间与P-S波到时差接近,说明LSTM-Ⅰ模型具有较高的时效性.

Prediction of surface subsidence in Changchun City based on LSTM network

Monitoring and predicting of urban surface subsidence are important for urban disaster prevention and mitigation.In this paper,the Long Short-Term Memory(LSTM)network was used to predict the surface subsidence process of Changchun City from 2018 to 2020 based on PS-InSAR monitoring data.The results show that the prediction error of 57.89% of PS points in the LSTM network was less than 1mm with the average error of 1.8 mm and the standard deviation of 2.8 mm.The accuracy and reliability of the prediction were better than regression analysis,time series analysis and grey model.

基于CNN-LSTM的大坝变形组合预测模型研究

为了提高大坝变形预测模型精度和泛化能力,建立了一种基于卷积神经网络(Convolutional neural networks,CNN)与深度学习长短期记忆(Long short-term memory,LSTM)神经网络的组合预测模型CNN-LSTM。该模型先利用CNN提取大坝变形监测时间序列的特征,再利用LSTM生成特征描述,该模型精度高、泛化能力强。以柏叶口水库混凝土面板堆石坝为例,经过CNN-LSTM模型计算,将模型变形预测值与原型监测资料进行对比,再与LSTM模型及CNN模型的预测结果进行对比。结果表明,CNN-LSTM模型预测值最接近监测资料实测结果。

基于SO-LSTM的立柱液压系统故障诊断方法研究

针对目前无法快速、准确地诊断矿用立柱液压系统故障等问题,在建立仿真模型分析单一故障机制的基础上,基于优化算法提出多种故障诊断方法。将立柱物理模块与立柱液压系统模块相结合,建立立柱液压系统仿真模型;基于Simulink分析单一故障的影响,基于蛇优化LSTM神经网络建立诊断模型;最后,根据实际数据进行模型的实例验证。结果表明:蛇优化LSTM模型对液压立柱故障仿真数据识别率达到99.5%,对液压立柱故障真实数据识别率达到97%,与模型仿真数据的预测精度仅相差2.5%,预测精度较高,达到了预期目标。

基于改进INFO-Bi-LSTM模型的SO_(2)排放质量浓度预测

针对火电机组SO_(2)排放质量浓度的影响因素众多,难以准确预测的问题,提出一种改进向量加权平均(weighted mean of vectors,INFO)算法与双向长短期记忆(bi-directional long short term memory,Bi-LSTM)神经网络相结合的预测模型(改进INFO-Bi-LSTM模型)。采用Circle混沌映射和反向学习产生高质量初始化种群,引入自适应t分布提升INFO算法跳出局部最优解和全局搜索的能力。选取改进INFO-Bi-LSTM模型和多种预测模型对炉内外联合脱硫过程中4种典型工况下的SO_(2)排放质量浓度进行预测,将预测结果进行验证对比。结果表明:改进INFO算法的寻优能力得到提升,并且改进INFO-Bi-LSTM模型精度更高,更加适用于SO_(2)排放质量浓度的预测,可为变工况下的脱硫控制提供控制理论支撑。

基于CNN‑LSTM‑SE的心电图分类算法研究

心血管疾病是我国死亡率较高的疾病之一,通过观察心电图来判断心电信号是否出现异常能够对心血管疾病进行预防和筛查。由于心电图数据规模大且繁杂,临床医护人员在心电图筛查时,工作负担大且容易出现误诊或漏诊的情况。为了提高心电图的筛查效率、减少医护人员的压力,提出了一种基于卷积神经网络、长短期记忆神经网络和SE网络的心电图分类算法模型(CNN-LSTM-SE),该模型将心电图分成5种不同的类别。主要研究内容包括:选用MIT-BIH心律失常数据集作为心电信号的数据来源,使用巴特沃斯带通滤波器对心电信号进行去噪处理,通过Z-score方法对心电信号进行标准化处理,利用独热编码方法对心电信号标签进行编码,最后使用处理后的心电数据对所提算法模型进行训练和测试。实验结果表明:所提模型相较于其它模型,能够有效提高心电图分类的准确性,在实验数据集上的分类准确率达到99.1%。

基于CNN-LSTM的水泥熟料f-CaO预测模型

水泥熟料中游离氧化钙(f-CaO)含量的传统人工离线检测缺乏时效性,不利于生产指导。针对离线检测的滞后问题和软测量模型中f-CaO含量与辅助变量的时序匹配问题,提出了一种基于卷积神经网络(convolutional neural network,CNN)和长短时记忆(long short-term memory,LSTM)神经网络的f-CaO含量预测模型。首先,利用滑动窗口截取辅助变量的区间数据;然后,采用CNN提取区间数据的时序特征;之后,构建LSTM神经网络模型;最后,控制截取辅助变量的延迟时间和间隔时间,根据模型预测拟合度提取辅助变量的最优时序特征。仿真结果表明,所提模型提高了水泥熟料中f-CaO含量的预测精度。

基于LSTM神经网络的烟丝水分恒定控制系统设计

在烟丝加工过程中,水分分布受到温度、湿度多个因素的影响,控制系统无法准确反映整体水分情况;为全面提高加工型香烟的质量水平,设计基于LSTM神经网络的烟丝水分恒定控制系统;部署Profibus控制总线,并在线路体系中连接水分检测仪与水分恒定器,完成烟丝水分恒定控制系统的硬件设计;在系统软件设计方面,构建LSTM神经网络单元,根据烟叶吸湿能力分析条件,求解具体的水分分布模型,实现基于LSTM神经网络的烟丝水分模型建模;分别计算烟叶出口湿度与出口温度,并联合传递函数逼近参量与恒定时滞参数,完成对控制参数的整定处理,再联合相关应用部件,实现基于LSTM神经网络的烟丝水分恒定控制系统设计;实验结果表明,LSTM神经网络模型作用下,生丝含水量被稳定控制在13%~18%数值之间,不会因水分过量问题而导致香烟质量水平无法达到实际加工标准。

基于PSO-LSTM的重载铁路车轨桥系统随机振动响应预测方法

在车桥系统随机振动分析中,随机动力响应是评价行车安全性的关键因素之一,而现有的响应计算方法存在耗时长、成本高的问题。能够快速准确预测车-轨-桥系统的动力响应对重载铁路桥梁的状态评估和运维养维具有重要意义。本文提出了一种基于粒子群优化(Particle Swarm Optimization,PSO)长短期记忆(Long Short-term Memory,LSTM)神经网络模型的重载车桥系统随机振动响应预测方法。该方法以车桥随机参数与轨道随机不平顺激励为输入,以桥梁动力响应为输出构造代理模型。首先,基于商业软件MATLAB平台构建PSO-LSTM网络模型;其次,通过建立的车-轨-桥系统随机振动分析模型计算初始样本集对应的随机动态响应,并进行模型训练,同时利用PSO算法优化LSTM结构参数;最后,使用训练好的PSO-LSTM模型对桥梁动态响应进行预测。为了验证本算法的优越性和鲁棒性,以朔黄重载铁路实测数据为例,对比本算法与BP(Back Propagation)神经网络、GRU(Gated Recurrent Unit)神经网络和LSTM神经网络的预测效率,并讨论不同车速下的预测情况,开展本模型与实测数据及有限元分析数据的对比分析。研究结果表明:在PSO优化下,LSTM模型预测结果得到一定的改善,PSO-LSTM模型拟合相关性系数可以达到0.97,其他评价误差值也均小于BP神经网络、GRU神经网络模型,本文模型可更高效准确地预测桥梁随机动力响应,可为进一步发展车-轨-桥系统随机振动响应预测理论提供技术支持。

一种融合GA和LSTM的边坡变形预测优化网络模型及其应用

考虑到BP神经网络模型忽略边坡监测数据存在的时间相关性,以及LSTM模型由于超参数选择存在主观性而易陷入局部最优等问题,提出一种基于遗传算法和长短期记忆网络(GA-LSTM)相结合的边坡变形预测模型,以发挥遗传算法全局搜索能力和LSTM预测时序数据的优势。以海明矿业露天采场边坡为研究对象,分别采用BP神经网络模型、LSTM网络模型以及GA-LSTM网络模型对边坡监测点GNSS49变形进行预测分析,并对比各模型达到收敛条件的时间。结果表明,GA-LSTM模型与其他模型达到同一收敛条件的时间差异不大,GA-LSTM模型的拟合准确度在0.1~0.2 mm,是LSTM神经网络模型的5~7倍,是BP神经网络模型的10~20倍,具有较高的精度和稳定性,其预测值与实际监测数据基本一致,可为矿山边坡的安全生产、管理以及决策控制提供科学依据。

基于VMD-SSA-LSTM考虑刀具磨损的数控铣床切削功率预测模型研究

传统的切削过程功率获取需要基于复杂的切削功率模型且很少考虑刀具磨损的影响,针对此设计了一种基于变分模态分解(VMD)、麻雀搜索算法(SSA)、长短时记忆(LSTM)神经网络的考虑刀具磨损的数控铣床切削功率预测模型,该模型无需解构数控铣床运行过程的能耗机理,基于一次性的历史实验数据即可实现数控铣床切削过程功率的高精度预测。首先,采用人工智能机器视觉技术对刀具磨损图片进行分析处理,获取刀具磨损图像的数字化特征,从而得到刀具最大磨损量;然后,建立基于VMD-SSA-LSTM考虑刀具磨损的数控铣床切削功率预测模型,利用VMD对数控铣床运行数据进行分解,采用SSA算法对LSTM神经网络超参数进行寻优,并将分解出的铣床运行数据分量输入到LSTM神经网络中,接着将每个分量的预测值相加,得到切削功率预测值;最后以面铣加工为例,将所提出的预测模型与BP神经网络、LSTM神经网络和传统模型进行对比分析,验证了所提模型的有效性和优越性。