Research on Plant Species Identification Based on Improved Convolutional Neural Network

Plant species recognition is an important research area in image recognition in recent years.However,the existing plant species recognition methods have low recognition accuracy and do not meet professional requirements in terms of recognition accuracy.Therefore,ShuffleNetV2 was improved by combining the current hot concern mechanism,convolution kernel size adjustment,convolution tailoring,and CSP technology to improve the accuracy and reduce the amount of computation in this study.Six convolutional neural network models with sufficient trainable parameters were designed for differentiation learning.The SGD algorithm is used to optimize the training process to avoid overfitting or falling into the local optimum.In this paper,a conventional plant image dataset TJAU10 collected by cell phones in a natural context was constructed,containing 3000 images of 10 plant species on the campus of Tianjin Agricultural University.Finally,the improved model is compared with the baseline version of the model,which achieves better results in terms of improving accuracy and reducing the computational effort.The recognition accuracy tested on the TJAU10 dataset reaches up to 98.3%,and the recognition precision reaches up to 93.6%,which is 5.1%better than the original model and reduces the computational effort by about 31%compared with the original model.In addition,the experimental results were evaluated using metrics such as the confusion matrix,which can meet the requirements of professionals for the accurate identification of plant species.

Proton exchange membrane fuel cells modeling based on artificial neural networks

To understand the complexity of the mathematical models of a proton exchange membrane fuel cell (PEMFC) and their shortage of practical PEMFC control, the PEMFC complex mechanism and the existing PEMFC models are analyzed, and artificial neural networks based PEMFC modeling is advanced. The structure, algorithm, training and simulation of PEMFC modeling based on improved BP networks are given out in detail. The computer simulation and conducted experiment verify that this model is fast and accurate, and can be used as a suitable operational model for PEMFC real-time control.

A New Searching Strategy for the Lost Plane Based on RBF Neural Network Model and Global Optimization Model

In this paper, we construct two models for the searching task for a lost plane. Model 1 determines the searching area. We predict the trajectory of floats generated after the disintegration of the plane by using RBF neural network model, and then determine the searching area according to the trajectory. With the pass of time, the searching area will also be constantly moving along the trajectory. Model 2 develops a maritime search plan to achieve the purpose of completing the search in the shortest time. We optimize the searching time and transform the problem into the 0-1 knapsack problem. Solving this problem by improved genetic algorithm, we can get the shortest searching time and the best choice for the search power.

Predicting formation lithology from log data by using a neural network

In order to increase drilling speed in deep complicated formations in Kela-2 gas field, Tarim Basin, Xinjiang, west China, it is important to predict the formation lithology for drilling bit optimization. Based on the conventional back propagation （BP） model, an improved BP model was proposed, with main modifications of back propagation of error, self-adapting algorithm, and activation function, also a prediction program was developed. The improved BP model was successfully applied to predicting the lithology of formations to be drilled in the Kela-2 gas field.

Two-Phase Rate Adaptation Strategy for Improving Real-Time Video QoE in Mobile Networks

With the popularity of smart handheld devices, mobile streaming video has multiplied the global network traffic in recent years. A huge concern of users' quality of experience(Qo E) has made rate adaptation methods very attractive. In this paper, we propose a two-phase rate adaptation strategy to improve users' real-time video Qo E. First, to measure and assess video Qo E, we provide a continuous Qo E prediction engine modeled by RNN recurrent neural network. Different from traditional Qo E models which consider the Qo E-aware factors separately or incompletely, our RNN-Qo E model accounts for three descriptive factors(video quality, rebuffering, and rate change) and reflects the impact of cognitive memory and recency. Besides, the video playing is separated into the initial startup phase and the steady playback phase, and we takes different optimization goals for each phase: the former aims at shortening the startup delay while the latter ameliorates the video quality and the rebufferings. Simulation results have shown that RNN-Qo E can follow the subjective Qo E quite well, and the proposed strategy can effectively reduce the occurrence of rebufferings caused by the mismatch between the requested video rates and the fluctuated throughput and attains standout performance on real-time Qo E compared with classical rate adaption methods.

Chinese News Text Classification Based on Convolutional Neural Network

With the explosive growth of Internet text information,the task of text classification is more important.As a part of text classification,Chinese news text classification also plays an important role.In public security work,public opinion news classification is an important topic.Effective and accurate classification of public opinion news is a necessary prerequisite for relevant departments to grasp the situation of public opinion and control the trend of public opinion in time.This paper introduces a combinedconvolutional neural network text classification model based on word2vec and improved TF-IDF:firstly,the word vector is trained through word2vec model,then the weight of each word is calculated by using the improved TFIDF algorithm based on class frequency variance,and the word vector and weight are combined to construct the text vector representation.Finally,the combined-convolutional neural network is used to train and test the Thucnews data set.The results show that the classification effect of this model is better than the traditional Text-RNN model,the traditional Text-CNN model and word2vec-CNN model.The test accuracy is 97.56%,the accuracy rate is 97%,the recall rate is 97%,and the F1-score is 97%.

3D laser scanning strategy based on cascaded deep neural network

A 3D laser scanning strategy based on cascaded deep neural network is proposed for the scanning system converted from 2D Lidar with a pitching motion device. The strategy is aimed at moving target detection and monitoring. Combining the device characteristics, the strategy first proposes a cascaded deep neural network, which inputs 2D point cloud, color image and pitching angle. The outputs are target distance and speed classification. And the cross-entropy loss function of network is modified by using focal loss and uniform distribution to improve the recognition accuracy. Then a pitching range and speed model are proposed to determine pitching motion parameters. Finally, the adaptive scanning is realized by integral separate speed PID. The experimental results show that the accuracies of the improved network target detection box, distance and speed classification are 90.17%, 96.87% and 96.97%, respectively. The average speed error of the improved PID is 0.4239°/s, and the average strategy execution time is 0.1521 s.The range and speed model can effectively reduce the collection of useless information and the deformation of the target point cloud. Conclusively, the experimental of overall scanning strategy show that it can improve target point cloud integrity and density while ensuring the capture of target.

基于CNN-GRU-ISSA-XGBoost的短期光伏功率预测

针对光伏功率随机性及波动性大,单一预测模型往往难以准确分析历史数据波动规律,从而导致预测精度不高的问题,提出一种基于卷积神经网络-门控循环单元(CNN-GRU)和改进麻雀搜索算法(ISSA)优化的极限梯度提升(XGBoost)模型的短期光伏功率预测组合模型.首先去除历史数据中的异常值并对其进行归一化处理,利用主成分分析法(PCA)进行特征选取,以便更好地识别影响光伏功率的关键因素.然后采用CNN网络提取数据的空间特征,再经过GRU网络提取时间特征,针对XGBoost模型手动配置参数困难、随机性大的问题,利用ISSA对模型超参数寻优.最后对两种方法预测的结果用误差倒数法减小误差的同时对权重进行更新,得到新的预测值,从而完成对光伏功率的预测.实验结果表明,所提出的CNN-GRU-ISSA-XGBoost组合模型具有更强的适应性和更高的精度.

汽车冷却风扇噪声主动控制仿真及声品质评价

以汽车冷却风扇噪声为研究对象,基于采集的噪声生成具有不同音调特性的模拟噪声,利用改进的音调度模型计算音调度,使用等级评分法对生成的模拟噪声进行主观评价实验。根据音调特性十分明显的旋转噪声频率与风扇转速密切相关的特点,提出以风扇转速信号来构造次级声源参考信号进行主动噪声控制。最后建立并联型灰色神经网络模型并检验降噪效果。结果表明,冷却风扇噪声的音调特性是影响感知烦恼的重要因素之一,通过有源噪声控制能有效降低窄带噪声峰值从而降低音调度。引入并联型灰色神经网络进行预测的平均相对误差为2.86%,降噪后烦恼度有较好的改善。

基于卷积神经网络的入侵昆虫识别研究

现有昆虫相关识别算法识别种类较少,缺少针对数量庞大种类众多的入侵昆虫分类识别算法,难以为入侵昆虫综合系统的识别功能提供稳定高效的技术支持。该研究对31类入侵昆虫图像进行数据采集,并对图像数据进行处理与数据集划分,基于四种卷积神经网络模型DenseNet121、MobileNetV3、ResNet101和ShuffleNet对其进行训练测试分析讨论。结果表明,在入侵昆虫综合识别系统识别功能后台算法应用上,MobileNetV3表现出更好的综合性能。根据MobileNetV3模型现有缺陷和模型特性,对MobileNetV3模型指定瓶颈层的注意力机制和激活函数进行改进,改进后模型的准确率为92.8%,单张测试集图像的平均识别时间0.012 s,相较于原MobileNetV3模型分别提高0.5%、缩短15.2%,可以很好满足多昆虫识别分类需求。

基于低风速功率修正和损失函数改进的超短期风电功率预测

风电功率具有较强的波动性和随机性。为进一步提升风电功率的预测精度,提出一种基于低风速功率修正和损失函数改进的超短期风电功率预测模型。该模型采用卷积神经网络、自注意力机制和双向门控循环单元捕获风电功率序列的长期时序依赖关系。为了解决低风速下待风状态神经网络难以精确拟合的问题,模型通过预测风速并结合当前时段的风电功率对低风速段的预测功率进行修正。针对参数训练的稳定性问题,模型通过改进预测策略和共享权重,引入一种多元非线性的损失函数来提取序列间的关联性。结果表明,所提模型在多项误差指标中均优于对比模型,能够有效提升超短期风电功率的预测效果。

病毒入侵下无线网络覆盖漏洞感知模型设计

为了及时发现病毒入侵风险,保证无线网络安全,设计一种基于改进遗传神经网络的无线网络覆盖漏洞感知模型。考虑多径衰落效应,构建无线网络信号传播模型,计算信号传输路径损耗,采用地理信息系统获取可视化无线网络覆盖图;融合贝叶斯理论和属性攻击图,通过后验概率更新静态贝叶斯攻击图属性状态,实现覆盖漏洞动态风险评估;使用D-S证据理论融合多源数据,运用蚁群算法实施数据寻优,以过往与现阶段网络安全状态为基础,建立无线网络覆盖漏洞感知模型,凭借改进遗传神经网络优化模型感知准确度。仿真结果表明,所提方法在不同节点密度与感知半径下均具备良好的漏洞感知精度及效率,可广泛应用于现实场景。

基于BP-ANN与RBF-ANN的钢筋与混凝土黏结强度预测模型研究

为研究神经网络对钢筋与混凝土黏结强度的预测能力以及神经网络的输出性能,基于大量的试验数据,提出一种基于改进神经网络的变形钢筋与混凝土黏结强度预测模型,对混凝土结构的研究与实际工程应用均有着重要的意义。收集290组黏结锚固试验数据,引入基于反向传播人工神经网络(BP-ANN)与径向基函数神经网络(RBF-ANN)算法,揭示混凝土强度、保护层厚度、钢筋直径、锚固长度及配箍率对变形钢筋与混凝土黏结性能的影响规律,建立基于改进神经网络算法的钢筋与混凝土黏结强度预测模型。对比分析不同数据预处理方法和训练神经元个数对建议模型预测结果的影响,评估各经典模型与建议模型的预测精度和离散性,提出临界锚固长度计算公式。结果表明:BP-ANN预测值与试验值比值的均值、标准差及变异系数分别为1.009、0.188、0.86,其预测精度略高于RBF-ANN;建议模型能够更准确、更稳定地预测钢筋与混凝土的黏结强度,该方法为解决钢筋与混凝土黏结问题提供了新思路。

基于改进PCA-BP神经网络模型的海宁市需水预测

需水预测是地区水资源规划中的重要部分,对于实现水资源合理有序开发,保障社会经济的可持续发展有重要的指导意义。采用改进PCA-BP神经网络模型对影响需水量的9个影响因子进行降维处理,并分别以海宁市2001~2014、2015~2020年数据作为训练样本和检验样本完成模型训练,其中,综合灰色预测模型GM(1,1)对降维后的影响因子独立预测,从而预测海宁市规划年需水量,并与传统定额法的需水预测结果进行对比分析。结果表明,人口、GDP、居民生活用水量、城镇公共用水量为影响海宁市需水量的主要因子;通过构建改进PCA-BP神经网络模型得到的2025、2030、2035年需水结果,比传统定额法更为真实、合理,进一步证实了预测模型的合理性,可为海宁市未来水资源规划提供指导。

基于MISSA-CNN-BiLSTM模型的尾矿坝位移预测

为应对尾矿坝位移预测所面临的复杂情况和精度要求,提出一种基于多算法耦合的尾矿坝位移动态预测模型。首先,基于时间序列分解模型将累计位移分为趋势项和周期项,利用高斯回归时间序列预测模型预测趋势项位移;然后,运用不同Copula函数研究诱发因素与周期项位移的整体相关性,鉴于周期项位移影响因素多样性与强非线性的特点,采用多策略融合的改进麻雀搜索算法改进麻雀搜索算法(MISSA)-卷积神经网络(CNN)-双向长短期记忆(BiLSTM)模型预测周期项位移;最后,将高斯回归趋势项位移预测值和MISSA-CNN-BiLSTM周期项位移预测值叠加。结果表明:尾矿坝累积位移预测值与实测值基本一致,预测结果相关性系数R为0.996,均方根误差(RMSE)为0.13 mm,建立的MISSA-CNN-BiLSTM多算法耦合模型预测精度较高,且能较好地预测尾矿坝位移的阶跃型变化。

基于改进神经网络的船舶航行速度估计数学模型

船舶航行速度直接影响船舶航行安全与路径规划效果,为此建立基于改进神经网络的船舶航行速度估计数学模型,提升船舶航行速度估计效果。利用位置编码机制,编码处理风速与波高等船舶航向速度相关数据;在编解码卷积神经网络内,引入自注意力机制,得到改进编解码卷积神经网络;利用编码后的船舶航速训练数据,训练改进神经网络,建立船舶航行速度估计数学模型;在该模型内,输入编码后的船舶航速测试数据,通过自注意力机制,提取船舶航速数据特征,并结合自注意力蒸馏剔除冗余特征;通过全连接层处理船舶航速数据特征,输出船舶航行速度估计结果。实验证明,该模型可有效提取船舶航速数据特征;该模型可精准估计船舶航行速度;在不同浪向下,该模型船舶航行速度估计的决定系数均较高,即估计精度较高。

小波降噪及改进遗传算法的BP神经网络在基坑变形中的组合应用

以某市人民医院基坑工程为例,针对实测数据建立实测数据结合BP神经网络预测模型,小波降噪结合BP神经网络模型和小波降噪结合改进遗传算法优化的BP神经网络模型,并利用误差分析理论对基坑变形数据预测效果评价。结果表明:对比3种模型实际处理、预测数据能力,实测数据结合BP神经网络模型预测精度在1%-4%之间,小波降噪结合BP神经网络模型预测精度1%-2%之间,小波降噪结合改进遗传算法优化的BP神经网络模型预测精度在1%以内,小波降噪结合改进遗传算法优化的BP神经网络模型的预测准确率最高。针对基坑变形监测,小波降噪结合改进遗传算法优化的BP神经网络模型具有更高预测精度,可为类似工程提供实际参考。

基于HHT的电气设备宽频阻抗参数估算方法

为了提高电气设备宽频阻抗参数估算准确性,有效抑制谐波,研究基于HHT的电气设备宽频阻抗参数估算方法。获取电气设备电阻信号,利用经验模态分解方法将电阻信号分解为IMF分量;对IMF分量进行希尔伯特变换,提取希尔伯特谱,得出IMF分量的频率值;利用改进神经网络算法搭建映射关系模型,获得宽频阻抗参数估算结果。测试结果表明,利用该方法估算出的宽频阻抗参数进行谐波抑制后的总谐波失真数值较小,提高了估算准确性,解决了谐波抑制效果不佳的问题。

时间序列预测模型发展趋势分析

时间序列预测是当前研究的主要方向之一,能有效解决工业、交通等领域的工艺指标和客流量预测问题。通过分析现有的时间序列模型,可以预测未来的发展趋势。首先,分析现有时间序列预测模型的结构,以了解其技术实现方式;其次,根据技术手段的不同,将模型分为优化提升类和创新类;最后,讨论了时间序列预测模型的未来发展趋势。

基于改进SSA-BP神经网络的钠硫电池拆解刀具温度预测模型研究

钠硫电池中含有大量的高纯度钠,在自动化拆解过程中存在燃烧、爆炸等安全风险。针对钠硫电池在车削拆解时存在的安全性问题,提出一种改进SSA-BP神经网络算法来预测刀具加工的最高温度。利用ABAQUS软件计算出刀具加工的实时温度,通过电池拆解实验验证仿真数据的可靠性;然后以仿真温度数据建立样本,利用Tent混沌映射对SSA-BP神经网络算法进行优化,建立刀具温度仿真预测模型。实验结果表明:该仿真预测模型收敛速度快,鲁棒性强,模型误差小。