Prognostic Kalman Filter Based Bayesian Learning Model for Data Accuracy Prediction

Data is always a crucial issue of concern especially during its prediction and computation in digital revolution. This paper exactly helps in providing efficient learning mechanism for accurate predictability and reducing redundant data communication. It also discusses the Bayesian analysis that finds the conditional probability of at least two parametric based predictions for the data. The paper presents a method for improving the performance of Bayesian classification using the combination of Kalman Filter and K-means. The method is applied on a small dataset just for establishing the fact that the proposed algorithm can reduce the time for computing the clusters from data. The proposed Bayesian learning probabilistic model is used to check the statistical noise and other inaccuracies using unknown variables. This scenario is being implemented using efficient machine learning algorithm to perpetuate the Bayesian probabilistic approach. It also demonstrates the generative function for Kalman-filer based prediction model and its observations. This paper implements the algorithm using open source platform of Python and efficiently integrates all different modules to piece of code via Common Platform Enumeration (CPE) for Python.     

Software Defect Prediction Based on Non-Linear Manifold Learning and Hybrid Deep Learning Techniques

Software defect prediction plays a very important role in software quality assurance, which aims to inspect as many potentially defect-prone software modules as possible. However, the performance of the prediction model is susceptible to high dimensionality of the dataset that contains irrelevant and redundant features. In addition, software metrics for software defect prediction are almost entirely traditional features compared to the deep semantic feature representation from deep learning techniques. To address these two issues, we propose the following two solutions in this paper: (1) We leverage a novel non-linear manifold learning method - SOINN Landmark Isomap (SLIsomap) to extract the representative features by selecting automatically the reasonable number and position of landmarks, which can reveal the complex intrinsic structure hidden behind the defect data. (2) We propose a novel defect prediction model named DLDD based on hybrid deep learning techniques, which leverages denoising autoencoder to learn true input features that are not contaminated by noise, and utilizes deep neural network to learn the abstract deep semantic features. We combine the squared error loss function of denoising autoencoder with the cross entropy loss function of deep neural network to achieve the best prediction performance by adjusting a hyperparameter. We compare the SL-Isomap with seven state-of-the-art feature extraction methods and compare the DLDD model with six baseline models across 20 open source software projects. The experimental results verify that the superiority of SL-Isomap and DLDD on four evaluation indicators.     

Robust Prediction of the Bandwidth of Metamaterial Antenna Using Deep Learning

The design of microstrip antennas is a complex and time-consuming process, especially the step of searching for the best design parameters. Meanwhile, the performance of microstrip antennas can be improved using metamaterial, which results in a new class of antennas called metamaterial antenna. Several parameters affect the radiation loss and quality factor of this class of antennas, such as the antenna size. Recently, the optimal values of the design parameters of metamaterial antennas can be predicted using machine learning, which presents a better alternative to simulation tools and trial-and-error processes. However, the prediction accuracy depends heavily on the quality of the machine learning model. In this paper, and benefiting from the current advances in deep learning, we propose a deep network architecture to predict the bandwidth of metamaterial antenna. Experimental results show that the proposed deep network could accurately predict the optimal values of the antenna bandwidth with a tiny value of mean-square error (MSE). In addition, the proposed model is compared with current competing approaches that are based on support vector machines, multi-layer perceptron, K-nearest neighbors, and ensemble models. The results show that the proposed model is better than the other approaches and can predict antenna bandwidth more accurately.     

基于深度学习的软钢力学特性

目的针对机械工程中软钢材料在大塑性拉伸载荷下力学特性分析的问题,提出一种基于深度学习的分析方法来预测其力学特性。方法首先对软钢材料不同台阶角度展开拉伸实验,并将采集到的实验数据利用智能技术进行预测分析。实验模型设计为双层结构,第1层结构采用共享全连接层特征输入,第2层使用极端随机树和长短时记忆网络做联合深度训练,并对训练结果经过激活函数计算后统一输出。采用联合训练模型在实验测试集上能较好地反映出应变与应力的变化趋势、速度和数值关系。结果实验结果显示,利用联合训练模型比单一ET和LSTM预测技术在拟合效果上分别提高了28.3%和63.5%。结论利用新模型取得较好的预测效果,这为分析金属阻尼器大塑性拉伸载荷下软钢材料力学特性的分析提供了重要的参考。     

基于机器学习的电喷印精度预测方法研究

目的 节省电流体喷射打印精度预测的时间和解决电流体工艺参数的选择问题,达到提高电流体打印的质量和效率的目的。方法 为了对电流体喷射打印精度进行预测,提出有限元模型与机器学习相结合的方法。基于线性回归、支持向量回归和神经网络等机器学习算法建立4种参数与射流直径的关系模型。结果 算法结果表明:支持向量回归和神经网络预测模型的决定系数R²能达到0.9以上,表示模型可信度高;支持向量回归和神经网络预测模型指标都比线性回归预测模型的小。结论 机器学习算法可对电喷印打印精度进行有效预测,预测效率提高了十几倍,节省了精度预测的时间。     

Intelligent Feature Selection with Deep Learning Based Financial Risk Assessment Model

Due to global financial crisis, risk management has received significant attention to avoid loss and maximize profit in any business. Since the financial crisis prediction (FCP) process is mainly based on data driven decision making and intelligent models, artificial intelligence (AI) and machine learning (ML) models are widely utilized. This article introduces an intelligent feature selection with deep learning based financial risk assessment model (IFSDL-FRA). The proposed IFSDL-FRA technique aims to determine the financial crisis of a company or enterprise. In addition, the IFSDL-FRA technique involves the design of new water strider optimization algorithm based feature selection (WSOA-FS) manner to an optimum selection of feature subsets. Moreover, Deep Random Vector Functional Link network (DRVFLN) classification technique was applied to properly allot the class labels to the financial data. Furthermore, improved fruit fly optimization algorithm (IFFOA) based hyperparameter tuning process is carried out to optimally tune the hyperparameters of the DRVFLN model. For enhancing the better performance of the IFSDL-FRA technique, an extensive set of simulations are implemented on benchmark financial datasets and the obtained outcomes determine the betterment of IFSDL-FRA technique on the recent state of art approaches.     

基于改进深度信念网络训练的冷轧轧制力预报

在带钢冷轧过程中,轧制力预报精度直接决定板带材的轧制精度以及产品质量.传统的基于单隐层的神经网络建模方法结构简单,对复杂函数的表达能力与泛化能力都受到一定制约;轧制现场环境复杂,数据测量存在噪声干扰,都会直接影响预报精度.针对这些问题,提出一种基于非监督学习的改进深度信念网络预测模型.深层网络的构建以及去噪机制的引入可...     

Probabilistic Representation and Inverse Design of Metamaterials Based on a Deep Generative Model with Semi‐Supervised Learning Strategy

The research of metamaterials has achieved enormous success in the manipulation of light in a prescribed manner using delicately designed subwavelength structures, so‐called meta‐atoms. Even though modern numerical methods allow for the accurate calculation of the optical response of complex structures, the inverse design of metamaterials, which aims to retrieve the optimal structure according to given requirements, is still a challenging task owing to the nonintuitive and nonunique relationship between physical structures and optical responses. To better unveil this implicit relationship and thus facilitate metamaterial designs, it is proposed to represent metamaterials and model the inverse design problem in a probabilistically generative manner, enabling to elegantly investigate the complex structure–performance relationship in an interpretable way, and solve the one‐to‐many mapping issue that is intractable in a deterministic model. Moreover, to alleviate the burden of numerical calculations when collecting data, a semisupervised learning strategy is developed that allows the model to utilize unlabeled data in addition to labeled data in an end‐to‐end training. On a data‐driven basis, the proposed deep generative model can serve as a comprehensive and efficient tool that accelerates the design, characterization, and even new discovery in the research domain of metamaterials, and photonics in general.     

基于长短期记忆神经网络的风力发电功率预测方法

风力发电过程具有较强的随机性,导致风力发电功率的预测准确度不高。针对上述问题,提出了一种融合深度学习算法的风力发电功率预测方法。以历史风力发电功率数据作为输入,建立风力发电功率预测模型,实现对未来一个时间刻度的风力发电功率预测。算例结果表明,与传统时序预测方法相比,基于长短期记忆神经网络的风力发电功率预测结果在各项指标中误差更小,验证了上述方法在风力发电功率预测中的可行性和有效性,提升了风力发电功率预测的准确性。     

Human Action Recognition Based on Supervised Class-Specific Dictionary Learning with Deep Convolutional Neural Network Features

Human action recognition under complex environment is a challenging work. Recently, sparse representation has achieved excellent results of dealing with human action recognition problem under different conditions. The main idea of sparse representation classification is to construct a general classification scheme where the training samples of each class can be considered as the dictionary to express the query class, and the minimal reconstruction error indicates its corresponding class. However, how to learn a discriminative dictionary is still a difficult work. In this work, we make two contributions. First, we build a new and robust human action recognition framework by combining one modified sparse classification model and deep convolutional neural network (CNN) features. Secondly, we construct a novel classification model which consists of the representation-constrained term and the coefficients incoherence term. Experimental results on benchmark datasets show that our modified model can obtain competitive results in comparison to other state-of-the-art models.