Multi-Innovation Gradient Iterative Locally Weighted Learning Identification for A Nonlinear Ship Maneuvering System

This paper explores a highly accurate identification modeling approach for the ship maneuvering motion with fullscale trial. A multi-innovation gradient iterative(MIGI) approach is proposed to optimize the distance metric of locally weighted learning(LWL), and a novel non-parametric modeling technique is developed for a nonlinear ship maneuvering system. This proposed method’s advantages are as follows: first, it can avoid the unmodeled dynamics and multicollinearity inherent to the conventional parametric model; second, it eliminates the over-learning or underlearning and obtains the optimal distance metric; and third, the MIGI is not sensitive to the initial parameter value and requires less time during the training phase. These advantages result in a highly accurate mathematical modeling technique that can be conveniently implemented in applications. To verify the characteristics of this mathematical model, two examples are used as the model platforms to study the ship maneuvering.

Arc Grounding Fault Identification Using Integrated Characteristics in the Power Grid

Arc grounding faults occur frequently in the power grid with small resistance grounding neutral points.The existing arc fault identification technology only uses the fault line signal characteristics to set the identification index,which leads to detection failure when the arc zero-off characteristic is short.To solve this problem,this paper presents an arc fault identification method by utilizing integrated signal characteristics of both the fault line and sound lines.Firstly,the waveform characteristics of the fault line and sound lines under an arc grounding fault are studied.After that,the convex hull,gradient product,and correlation coefficient index are used as the basic characteristic parameters to establish fault identification criteria.Then,the logistic regression algorithm is employed to deal with the reference samples,establish the machine discrimination model,and realize the discrimination of fault types.Finally,simulation test results and experimental results verify the accuracy of the proposed method.The comparison analysis shows that the proposed method has higher recognition accuracy,especially when the arc dissipation power is smaller than 2×10^(3) W,the zero-off period is not obvious.In conclusion,the proposed method expands the arc fault identification theory.

Automated detection and identification of white-backed planthoppers in paddy fields using image processing

A survey of the population densities of rice planthoppers is important for forecasting decisions and efficient control. Tra- ditional manual surveying of rice planthoppers is time-consuming, fatiguing, and subjective. A new three-layer detection method was proposed to detect and identify white-backed planthoppers （WBPHs, Sogatella furcifera （Horvath）） and their developmental stages using image processing. In the first two detection layers, we used an AdaBoost classifier that was trained on a histogram of oriented gradient （HOG） features and a support vector machine （SVM） classifier that was trained on Gabor and Local Binary Pattern （LBP） features to detect WBPHs and remove impurities. We achieved a detection rate of 85.6% and a false detection rate of 10.2%. In the third detection layer, a SVM classifier that was trained on the HOG features was used to identify the different developmental stages of the WBPHs, and we achieved an identification rate of 73.1%, a false identification rate of 23.3%, and a 5.6% false detection rate for the images without WBPHs. The proposed three-layer detection method is feasible and effective for the identification of different developmental stages of planthoppers on rice plants in paddy fields.

Abnormal user identification based on XGBoost algorithm

The eXtreme gradient boosting(XGBoost)algorithm is used to identify abnormal users.Firstly,the raw data were cleaned.Then user power characteristics were extracted from different aspects.Finally,the XGBoost classifier was used to identify the abnormal users respectively in the balanced sample set and the unbalanced sample set.In contrast,under the same characteristics,the k-nearest neighbor(KNN)classifier,back-propagation(BP)neural network classifier and random forest classifier were used to identify the abnormal users in the two samples.The experimental results show that the XGBoost classifier has higher recognition rate and faster running speed.Especially in the imbalanced data sets,the performance improvement is obvious.

Breaking chaotic shift key communication via adaptive key identification

This paper proposes an adaptive parameter identification method for breaking chaotic shift key communication from the transmitted signal in public channel. The sensitive dependence property of chaos on parameter mismatch is used for chaos adaptive synchronization and parameter identification. An index function about the synchronization error is defined and conjugate gradient method is used to minimize the index function and to search the transmitter＇s parameter （key）. By using proposed method, secure key is recovered from transmitted signal generated by low dimensional chaos and hyper chaos switching communication. Multi-parameters can also be identified from the transmitted signal with noise.

Image Generation of Tomato Leaf Disease Identification Based on Small-ACGAN

Plant diseases have become a challenging threat in the agricultural field.Various learning approaches for plant disease detection and classification have been adopted to detect and diagnose these diseases early.However,deep learning entails extensive data for training,and it may be challenging to collect plant datasets.Even though plant datasets can be collected,they may be uneven in quantity.As a result,the problem of classification model overfitting arises.This study targets this issue and proposes an auxiliary classifier GAN(small-ACGAN)model based on a small number of datasets to extend the available data.First,after comparing various attention mechanisms,this paper chose to add the lightweight Coordinate Attention(CA)to the generator module of Auxiliary Classifier GANs(ACGAN)to improve the image quality.Then,a gradient penalty mechanism was added to the loss function to improve the training stability of the model.Experiments show that the proposed method can best improve the recognition accuracy of the classifier with the doubled dataset.On AlexNet,the accuracy was increased by 11.2%.In addition,small-ACGAN outperformed the other three GANs used in the experiment.Moreover,the experimental accuracy,precision,recall,and F1 scores of the five convolutional neural network(CNN)classifiers on the enhanced dataset improved by an average of 3.74%,3.48%,3.74%,and 3.80%compared to the original dataset.Furthermore,the accuracy of MobileNetV3 reached 97.9%,which fully demonstrated the feasibility of this approach.The general experimental results indicate that the method proposed in this paper provides a new dataset expansion method for effectively improving the identification accuracy and can play an essential role in expanding the dataset of the sparse number of plant diseases.

Angular velocity dynamics identification of small unmanned helicopter using fuzzy model

Attitude identification method for unmanned helicopter based on fuzzy model at hovering is presented. The dynamical attitude model is considered as basis for attitude control and it is very complex. To reduce the complexity of model, nonlinear model of unmanned helicopter with unknown parameters are to be determined by fuzzy system first and then derivative based gradient method is used to identify unknown parameters of model. Gradient method is used due to ability that fuzzy system is not necessarily to be linear in parameters, therefore all fuzzy sets for input and output can be adjusted. The validity of the proposed model was verified using experimental data obtained by the commercially available flight simulator X-Plane. The simulation results showed high accuracy of the modeling method and attitude dynamics data matched well with experimental data.

Fingerprint Identification by Artificial Neural Network

Biometric techniques require critical operations of digital processing for identification of individuals. In this context, this paper aims to develop a system for automatic processing of fingerprint identification by their minutiae using Artificial Neural Networks （ANN）, which reveals to be highly effective. The ANN method implemented is a based on Multi-Layer Perceptron （MLP） model, which utilizes the algorithm of retro-propagation of gradient during the learning process. In such a process, the mean square error generated represents the specific parameter for the identification phase by comparing a fingerprint taken from a crime scene with those of a reference database.

Classification and Identification of Nuclear, Biological or Chemical Agents Taken from Remote Sensing Image by Using Neural Network

In the context of new risks and threats associated to nuclear, biological and chemical （NBC） attacks, and given the shortcomings of certain analytical methods such as principal component analysis （PCA）, a neural network approach seems to be more accurate. PCA consists in projecting the spectrum of a gas collected from a remote sensing system in, firstly, a three-dimensional space, then in a two-dimensional one using a model of Multi-Layer Perceptron based neural network. It adopts during the learning process, the back propagation algorithm of the gradient, in which the mean square error output is continuously calculated and compared to the input until it reaches a minimal threshold value. This aims to correct the synaptic weights of the network. So, the Artificial Neural Network （ANN） tends to be more efficient in the classification process. This paper emphasizes the contribution of the ANN method in the spectral data processing, classification and identification and in addition, its fast convergence during the back propagation of the gradient.

Bridge damage identification based on convolutional autoencoders and extreme gradient boosting trees

To enhance the accuracy and efficiency of bridge damage identification,a novel data-driven damage identification method was proposed.First,convolutional autoencoder(CAE)was used to extract key features from the acceleration signal of the bridge structure through data reconstruction.The extreme gradient boosting tree(XGBoost)was then used to perform analysis on the feature data to achieve damage detection with high accuracy and high performance.The proposed method was applied in a numerical simulation study on a three-span continuous girder and further validated experimentally on a scaled model of a cable-stayed bridge.The numerical simulation results show that the identification errors remain within 2.9%for six single-damage cases and within 3.1%for four double-damage cases.The experimental validation results demonstrate that when the tension in a single cable of the cable-stayed bridge decreases by 20%,the method accurately identifies damage at different cable locations using only sensors installed on the main girder,achieving identification accuracies above 95.8%in all cases.The proposed method shows high identification accuracy and generalization ability across various damage scenarios.

A stochastic gradient-based two-step sparse identification algorithm for multivariate ARX systems

We consider the sparse identification of multivariate ARX systems, i.e., to recover the zero elements of the unknown parameter matrix. We propose a two-step algorithm, where in the first step the stochastic gradient (SG) algorithm is applied to obtain initial estimates of the unknown parameter matrix and in the second step an optimization criterion is introduced for the sparse identification of multivariate ARX systems. Under mild conditions, we prove that by minimizing the criterion function, the zero elements of the unknown parameter matrix can be recovered with a finite number of observations. The performance of the algorithm is testified through a simulation example.

集中供热系统水力热力耦合特性辨识方法研究

集中供热系统的运行能效主要受其水力热力特性的影响,换热站作为连接热源、热网和热用户的枢纽,是分析系统水力热力特性的关键,站内设备的长期运行性能衰减会导致供热系统整体能耗升高、能效下降,因此有必要对站内设备的运行性能进行诊断.基于换热站实测运行数据,分别采用最小二乘法和梯度下降对站内主要设备包括换热器、循环泵和调节阀的性能曲线进行了辨识,从而构建了集中供热系统的水力热力特性计算方法,全面分析了某典型换热站主要设备性能衰减情况和能耗水平,对主要设备运行性能进行了评估,并对设备维护的经济性和碳减排效果进行了计算.结果表明:换热站内主要设备性能存在不同程度衰减,其中换热器传热系数由1.00 kW/(m^(2)·K)下降至0.64 kW/(m^(2)·K),循环泵标况扬程由22.4 m下降至17.3 m,调节阀流通能力由183.0 t/h下降至82.4 t/h,且阀门整体开度较低,以上问题导致了系统整体能耗的增加;为消除气候变化对集中供热系统能耗分析的影响,结合度日数和实际供热面积,对系统能耗进行评价,与标准年相比,测试年采暖季同期平均热耗和电耗分别增加了0.86 GJ/(104 m^(2)·℃·d)和2.38 kW·h/(104 m^(2)·℃·d);对主要设备进行清洗和更换后,集中供热系统采暖季诊断时间内的同期累计电耗可降低16.3%,全生命周期内CO_(2)排放量可减少128.5 t.

双线性参数系统的极大似然递推参数估计

研究具有自回归滑动平均噪声的双线性参数系统,该系统结构复杂,噪声项的研究更具普遍意义。为实现系统参数的在线辨识,采用梯度搜索方法,推导了双线性参数系统的随机梯度算法。对于出现的未知项,基于递阶辨识原理使用其估计值进行替代。极大似然估计方法基于概率论,具有良好的一致性、渐近正态性和可用性,在引入极大似然估计方法后得到了相应的极大似然随机梯度算法,为进一步减小有色噪声对参数估计精度的影响,结合多新息辨识理论,将标量单新息扩展为多新息向量,研究了具有自回归滑动平均噪声的双线性参数系统的极大似然多新息随机梯度参数估计方法,并进行了仿真验证。

基于复杂度追踪的模态参数识别方法对比研究

复杂度追踪(complexity pursuit, CP)是求解振动信号盲源分离(blind source separation, BSS)问题的一类经典方法。用复杂度追踪估计解混矩阵主要有基于源信号复杂度计算的梯度下降(complexity pursuit-gradient descent, CP-GD)算法和基于时间可预测度的广义特征值分解(temporal predictability-generalized eigenvalue decomposition, TP-GED)算法。当前,这两种算法的关联性与算法性能尚缺乏研究,因此对这两种算法的等价性和计算性能进行了研究。首先,给出CP-GD和TP-GED两种算法的具体理论及算法流程;其次,利用二、三自由度振动系统直观地展示并对比解混向量对应的源信号复杂度及可预测度的变化规律;最后,通过对多工况下多自由度系统的模态参数识别算例,对比研究两种算法的精度及计算量。研究结果表明:在低阻尼比及高信噪比条件下,两种方法得到的解混矩阵是相同的;考虑到计算信号复杂度和梯度下降较为耗时,CP-GD算法计算代价要高于TP-GED算法。

基于故障旁路特征的电网电弧接地故障辨识

中性点有效接地供配电系统中电弧接地故障频发,而已有的故障检测方法在电弧耗散功率较小,零休时长较短时存在不足,导致检测准确性偏低。针对此问题,采用故障旁路零序电流特征进行故障辨识。首先,探究了电弧接地故障下故障旁路零序电流的形成机理,揭示了电弧故障特征向故障旁路信号的传递作用。然后,采用梯度积及相关系数指标,描述故障旁路零序电流信号特征,建立辨识判据,实现故障类型判别。最后,仿真结果及实验结果验证了所提方法的有效性。对比分析表明,其相比于已有方法,该方法具有较高的识别精度。特别是在电弧耗散功率小于2×103 W、零休特征不明显的故障场景中,该方法更具识别优势。所提方法拓展了电弧故障辨识手段,可用于预防电弧故障引发的电气火灾。

基于联邦学习的玉米叶片病害识别方法

联邦学习可利用分布式数据实现模型共享训练,无需本地数据上传继而可保证数据资产安全,但数据异构导致本地模型产生漂移而难以有效聚合全局模型。为此,本文提出了一种基于联邦学习的分布式病害识别方法G-FedAvg。针对各用户间数据种类缺失异构导致模型泛化性减弱的问题,通过改进损失函数梯度更新策略,提升用户模型学习捕获全局泛化信息能力;针对数据特征差异导致模型过度拟合,通过自监督预训练,缓解因其所致性能下降。试验以玉米叶片病害识别为导向,并进一步评估病害程度,其结果表明,改进算法G-FedAvg在无需数据上传前提下,取得了与集中学习模型近乎一致的识别性能;与传统联邦学习相比,G-FedAvg的识别准确率与收敛速度有效提升,准确率波动显著降低。因此,所提算法G-FedAvg可有效联合参与用户利用其本地数据完成分布式学习,实现对玉米叶片病害的精准识别。

川东飞仙关组鲕粒滩岩性识别及其分布特征

【目的】为解决针对川东海槽南段西侧、台内等地区飞仙关组岩性变化不明确等问题。【方法】综合利用岩心、薄片、钻录井等多元地质数据对飞仙关组岩性类型及特征进行研究,提出以机器学习为基础的岩性测井智能识别方法,解决了老区岩性精细识别的技术难题,揭示了区内飞仙关组鲕粒滩岩性、分布及演化规律。【结论与结果】(1)飞仙关组主要由泥岩、泥晶灰岩、泥质灰岩、鲕粒灰岩、鲕粒云岩、泥晶云岩、膏质云岩、膏岩等岩性组成;(2)对比发现,改进的梯度提升决策树算法即随机梯度提升决策树(SGBDT)构建岩性模型优于其他算法,更适合碳酸盐岩复杂岩性识别;(3)鲕粒灰岩集中发育于开江—梁平海槽以南地区的飞一段—飞三段时期,鲕粒云岩集中发育于飞二段时期且分布分散;(4)区内鲕粒滩分布差异明显,飞一段时期主要发育于台地古地貌高点和台地边缘,飞二段时期多发育台缘鲕粒滩,少量发育台内古地貌高点鲕滩和点滩,飞三段时期主要发育台内点滩。

基于前后逐段逼近的含多分支配电网单相接地故障测距方法

随着配电网的发展,T接分支的大量接入使得配电网结构复杂,传统测距算法通常忽略分支,测距精度变低,研究含多分支配电网单相接地故障测距具有重要意义。为此,提出一种基于前后逐段逼近的含多分支配电网单相接地故障精确测距方法。首先,分析分布参数模型,根据零序电压、电流关系,提出故障区段判别系数,判别最小故障区段。其次,利用最小故障区段两端的零序电压、电流建立故障测距函数,采用梯度下降法求解精确故障点。最后,在PSCAD仿真平台上对所述方法进行验证。结果表明,所提方法可确定故障最小区段,精确计算出故障点,并具有较强的耐受过渡电阻能力,能较好适应高渗透率分布式电源的接入。