Multi-layer perceptron-based data-driven multiscale modelling of granular materials with a novel Frobenius norm-based internal variable

One objective of developing machine learning(ML)-based material models is to integrate them with well-established numerical methods to solve boundary value problems(BVPs).In the family of ML models,recurrent neural networks(RNNs)have been extensively applied to capture history-dependent constitutive responses of granular materials,but these multiple-step-based neural networks are neither sufficiently efficient nor aligned with the standard finite element method(FEM).Single-step-based neural networks like the multi-layer perceptron(MLP)are an alternative to bypass the above issues but have to introduce some internal variables to encode complex loading histories.In this work,one novel Frobenius norm-based internal variable,together with the Fourier layer and residual architectureenhanced MLP model,is crafted to replicate the history-dependent constitutive features of representative volume element(RVE)for granular materials.The obtained ML models are then seamlessly embedded into the FEM to solve the BVP of a biaxial compression case and a rigid strip footing case.The obtained solutions are comparable to results from the FEM-DEM multiscale modelling but achieve significantly improved efficiency.The results demonstrate the applicability of the proposed internal variable in enabling MLP to capture highly nonlinear constitutive responses of granular materials.

Dynamic Multi-Layer Perceptron for Fetal Health Classification Using Cardiotocography Data

Fetal health care is vital in ensuring the health of pregnant women and the fetus.Regular check-ups need to be taken by the mother to determine the status of the fetus’growth and identify any potential problems.To know the status of the fetus,doctors monitor blood reports,Ultrasounds,cardiotocography(CTG)data,etc.Still,in this research,we have considered CTG data,which provides information on heart rate and uterine contractions during pregnancy.Several researchers have proposed various methods for classifying the status of fetus growth.Manual processing of CTG data is time-consuming and unreliable.So,automated tools should be used to classify fetal health.This study proposes a novel neural network-based architecture,the Dynamic Multi-Layer Perceptron model,evaluated from a single layer to several layers to classify fetal health.Various strategies were applied,including pre-processing data using techniques like Balancing,Scaling,Normalization hyperparameter tuning,batch normalization,early stopping,etc.,to enhance the model’s performance.A comparative analysis of the proposed method is done against the traditional machine learning models to showcase its accuracy(97%).An ablation study without any pre-processing techniques is also illustrated.This study easily provides valuable interpretations for healthcare professionals in the decision-making process.

Enhancing Healthcare Data Security and Disease Detection Using Crossover-Based Multilayer Perceptron in Smart Healthcare Systems

The healthcare data requires accurate disease detection analysis,real-timemonitoring,and advancements to ensure proper treatment for patients.Consequently,Machine Learning methods are widely utilized in Smart Healthcare Systems(SHS)to extract valuable features fromheterogeneous and high-dimensional healthcare data for predicting various diseases and monitoring patient activities.These methods are employed across different domains that are susceptible to adversarial attacks,necessitating careful consideration.Hence,this paper proposes a crossover-based Multilayer Perceptron(CMLP)model.The collected samples are pre-processed and fed into the crossover-based multilayer perceptron neural network to detect adversarial attacks on themedical records of patients.Once an attack is detected,healthcare professionals are promptly alerted to prevent data leakage.The paper utilizes two datasets,namely the synthetic dataset and the University of Queensland Vital Signs(UQVS)dataset,from which numerous samples are collected.Experimental results are conducted to evaluate the performance of the proposed CMLP model,utilizing various performancemeasures such as Recall,Precision,Accuracy,and F1-score to predict patient activities.Comparing the proposed method with existing approaches,it achieves the highest accuracy,precision,recall,and F1-score.Specifically,the proposedmethod achieves a precision of 93%,an accuracy of 97%,an F1-score of 92%,and a recall of 92%.

Recommendation System Based on Perceptron and Graph Convolution Network

The relationship between users and items,which cannot be recovered by traditional techniques,can be extracted by the recommendation algorithm based on the graph convolution network.The current simple linear combination of these algorithms may not be sufficient to extract the complex structure of user interaction data.This paper presents a new approach to address such issues,utilizing the graph convolution network to extract association relations.The proposed approach mainly includes three modules:Embedding layer,forward propagation layer,and score prediction layer.The embedding layer models users and items according to their interaction information and generates initial feature vectors as input for the forward propagation layer.The forward propagation layer designs two parallel graph convolution networks with self-connections,which extract higher-order association relevance from users and items separately by multi-layer graph convolution.Furthermore,the forward propagation layer integrates the attention factor to assign different weights among the hop neighbors of the graph convolution network fusion,capturing more comprehensive association relevance between users and items as input for the score prediction layer.The score prediction layer introduces MLP(multi-layer perceptron)to conduct non-linear feature interaction between users and items,respectively.Finally,the prediction score of users to items is obtained.The recall rate and normalized discounted cumulative gain were used as evaluation indexes.The proposed approach effectively integrates higher-order information in user entries,and experimental analysis demonstrates its superiority over the existing algorithms.

基于Perceptron建立某车型焊钉及白车身在线检测体系

以北京奔驰汽车有限公司206车型为基础,基于Perceptron就螺柱焊钉位置尺寸、车身尺寸和覆盖件尺寸在线自动检测以及相应的精度控制进行了研究,规划并建立焊钉位置尺寸在线测量点76个,普通测量特征1373个测量点;规划并实施焊钉在线测量方案以及后端、Z1和Z2.3在线测量方案;完成在线检测工位测量工装优化改进,达成了基于Perceptron的V206焊钉及白车身在线检测体系建设,并实现了工时和成本的节约。

Automatic Sentimental Analysis by Firefly with Levy and Multilayer Perceptron

The field of sentiment analysis(SA)has grown in tandem with the aid of social networking platforms to exchange opinions and ideas.Many people share their views and ideas around the world through social media like Facebook and Twitter.The goal of opinion mining,commonly referred to as sentiment analysis,is to categorise and forecast a target’s opinion.Depending on if they provide a positive or negative perspective on a given topic,text documents or sentences can be classified.When compared to sentiment analysis,text categorization may appear to be a simple process,but number of challenges have prompted numerous studies in this area.A feature selection-based classification algorithm in conjunction with the firefly with levy and multilayer perceptron(MLP)techniques has been proposed as a way to automate sentiment analysis(SA).In this study,online product reviews can be enhanced by integrating classification and feature election.The firefly(FF)algorithm was used to extract features from online product reviews,and a multi-layer perceptron was used to classify sentiment(MLP).The experiment employs two datasets,and the results are assessed using a variety of criteria.On account of these tests,it is possible to conclude that the FFL-MLP algorithm has the better classification performance for Canon(98%accuracy)and iPod(99%accuracy).

基于Re-Perceptron-CRF的规范类文本分词研究

通过Re-Perceptron-CRF组合方法,利用规范类文档特点,对关键词进行切分。分别采取Viterbi、Perceptron、CRF和Re-Perceptron-CRF 4种算法分别对规范类文本进行分词研究。具体为基于句法分析对规范类文本使用正则表达式进行标准化处理,得到适合分析的预处理文本,并通过Perceptron与CRF的双重算法返回各自的最优结果。实验表明,Re-Perceptron-CRF算法明显提高分词效果,在准确率和召回率上均有良好表现,其准确率和召回率分别达到94.36%和97.02%。该方法为规范类文本中文分词相关工作提供一定的研究思路,为后续应用提供好的数据支撑。但由于数据量较小,该方法仅适用于特定领域,如建筑检测领域。

基于Perceptron的非线性滑模控制

利用Ｐｅｒｃｅｐｔｒｏｎ的两类模式分类特性对非线性滑动模态方程的Ｌｙａｐｕｎｏｖ函数进行训练，以求得非线性系统的切换函数，并进行滑模控制器设计，为非线性滑模控制系统的综合设计提供了一条新的途径，并应用于车削系统中．

Prediction of flyrock distance induced by mine blasting using a novel Harris Hawks optimization-based multi-layer perceptron neural network

In mining or construction projects,for exploitation of hard rock with high strength properties,blasting is frequently applied to breaking or moving them using high explosive energy.However,use of explosives may lead to the flyrock phenomenon.Flyrock can damage structures or nearby equipment in the surrounding areas and inflict harm to humans,especially workers in the working sites.Thus,prediction of flyrock is of high importance.In this investigation,examination and estimation/forecast of flyrock distance induced by blasting through the application of five artificial intelligent algorithms were carried out.One hundred and fifty-two blasting events in three open-pit granite mines in Johor,Malaysia,were monitored to collect field data.The collected data include blasting parameters and rock mass properties.Site-specific weathering index(WI),geological strength index(GSI) and rock quality designation(RQD)are rock mass properties.Multi-layer perceptron(MLP),random forest(RF),support vector machine(SVM),and hybrid models including Harris Hawks optimization-based MLP(known as HHO-MLP) and whale optimization algorithm-based MLP(known as WOA-MLP) were developed.The performance of various models was assessed through various performance indices,including a10-index,coefficient of determination(R^(2)),root mean squared error(RMSE),mean absolute percentage error(MAPE),variance accounted for(VAF),and root squared error(RSE).The a10-index values for MLP,RF,SVM,HHO-MLP and WOA-MLP are 0.953,0.933,0.937,0.991 and 0.972,respectively.R^(2) of HHO-MLP is 0.998,which achieved the best performance among all five machine learning(ML) models.

A Perceptron Algorithm for Forest Fire Prediction Based on Wireless Sensor Networks

Forest fire prediction constitutes a significant component of forestmanagement. Timely and accurate forest fire prediction will greatly reduce property andnatural losses. A quick method to estimate forest fire hazard levels through knownclimatic conditions could make an effective improvement in forest fire prediction. Thispaper presents a description and analysis of a forest fire prediction methods based onmachine learning, which adopts WSN (Wireless Sensor Networks) technology andperceptron algorithms to provide a reliable and rapid detection of potential forest fire.Weather data are gathered by sensors, and then forwarded to the server, where a firehazard index can be calculated.

Prediction of Logistics Demand via Least Square Method and Multi-Layer Perceptron

To implement the prediction of the logistics demand capacity of a certain region,a comprehensive index system is constructed,which is composed of freight volume and other eight relevant economic indices,such as gross domestic product(GDP),consumer price index(CPI),total import and export volume,port's cargo throughput,total retail sales of consumer goods,total fixed asset investment,highway mileage,and resident population,to form the foundation for the model calculation.Based on the least square method(LSM)to fit the parameters,the study obtains an accurate mathematical model and predicts the changes of each index in the next five years.Using artificial intelligence software,the research establishes the logistics demand model of multi-layer perceptron(MLP)neural network,makes an empirical analysis on the logistics demand of Quanzhou City,and predicts its logistics demand in the next five years,which provides some references for formulating logistics planning and development strategy.

Static Digits Recognition Using Rotational Signatures and Hu Moments with a Multilayer Perceptron

This paper presents two systems for recognizing static signs (digits) from American Sign Language (ASL). These systems avoid the use color marks, or gloves, using instead, low-pass and high-pass filters in space and frequency domains, and color space transformations. First system used rotational signatures based on a correlation operator;minimum distance was used for the classification task. Second system computed the seven Hu invariants from binary images;these descriptors fed to a Multi-Layer Perceptron (MLP) in order to recognize the 9 different classes. First system achieves 100% of recognition rate with leaving-one-out validation and second experiment performs 96.7% of recognition rate with Hu moments and 100% using 36 normalized moments and k-fold cross validation.

Preliminary Biometrics of ECG Signal Based on Temporal Organization through the Implementation of a Multilayer Perceptron Neural Network

The attributes of the ECG signal signifying the unique electrical properties of the heart offer the opportunity to expand the realm of biometrics, which pertains the identification of an individual based on physical characteristics. The temporal organization of the ECG signal offers a basis for composing a machine learning feature set. The four attributes of the feature set are derived through software automation enabled by Python. These four attributes are the temporal differential of the P wave maximum and T wave maximum relative to the R wave maximum and the Q wave minimum and S wave minimum relative to the R wave maximum. The multilayer perceptron neural network was applied and evaluated in terms of classification accuracy and time to develop the model. Superior performance was achieved with respect to a reduced feature set considering only the temporal differential of the P wave maximum and T wave maximum relative to the R wave maximum by comparison to all four attributes applied to the feature set and the temporal differential of the Q wave minimum and S wave minimum relative to the R wave maximum. With these preliminary findings and the advent of portable and wearable devices for the acquisition of the ECG signal, the temporal organization of the ECG signal offers robust potential for the field of biometrics.

Digital modulation classification using multi-layer perceptron and time-frequency features

Considering that real communication signals corrupted by noise are generally nonstationary, and timefrequency distributions are especially suitable for the analysis of nonstationary signals, time-frequency distributions are introduced for the modulation classification of communication signals： The extracted time-frequency features have good classification information, and they are insensitive to signal to noise ratio （SNR） variation. According to good classification by the correct rate of a neural network classifier, a multilayer perceptron （MLP） classifier with better generalization, as well as, addition of time-frequency features set for classifying six different modulation types has been proposed. Computer simulations show that the MLP classifier outperforms the decision-theoretic classifier at low SNRs, and the classification experiments for real MPSK signals verify engineering significance of the MLP classifier.

Fast lossless color image compression method using perceptron

The technique of lossless image compression plays an important role in image transmission and storage for high quality. At present, both the compression ratio and processing speed should be considered in a real-time multimedia system. A novel lossless compression algorithm is researched. A low complexity predictive model is proposed using the correlation of pixels and color components. In the meantime, perceptron in neural network is used to rectify the prediction values adaptively. It makes the prediction residuals smaller and in a small dynamic scope. Also a color space transform is used and good decorrelation is obtained in our algorithm. The compared experimental results have shown that our algorithm has a noticeably better performance than traditional algorithms. Compared to the new standard JPEG-LS, this predictive model reduces its computational complexity. And its speed is faster than the JPEG-LS with negligible performance sacrifice.

Building up Multi－Layered Perceptrons as Classifier System for Decision Support

This paper focuses on some application issues in m.multi-layered perceptrons researches. The following problem areas are discussed: (1) the classification capability of multi-layered perceptrons; (2) theself-configuration algorithm for facilitating the design of the neural nets' structure;and,finally (3) the application of the fast BP algorithm to speed up the learning procedure. Some experimental results with respect to the application of multi-layered perceptrons as classifier systems in the comprehensive evaluation of Chinese large cities are presented.

ERROR RESPONSE AND ROBUSTNESS OF A CLASS OF MULTILAYERED PERCEPTRONS WITH THRESHOLD FUNCTIONS

In this paper, based on a stochastic mode! for inputs and weights, and in view of the disturbance of correlative and large input and weight errors, a general algorithm to obtain the output error characteristics of a class of multilayered perceptrons with threshold functions is proposed by using statistical approach. Furthermore, the formula to calculate the robustness of the networks is also given. The result of computer simulation indicates the correctness of the algorithm.

简单Perceptron学习算法的收敛性

当输入是无穷集或区域时，通过构造一个上鞅，本文证明了简单Ｐｅｒｃｅｐｔｒｏｎ学习算法的收敛性。

Identification of low-resistivity-low-contrast pay zones in the feature space with a multi-layer perceptron based on conventional well log data

In the early exploration of many oilfields,low-resistivity-low-contrast(LRLC)pay zones are easily overlooked due to the resistivity similarity to the water zones.Existing identification methods are model-driven and cannot yield satisfactory results when the causes of LRLC pay zones are complicated.In this study,after analyzing a large number of core samples,main causes of LRLC pay zones in the study area are discerned,which include complex distribution of formation water salinity,high irreducible water saturation due to micropores,and high shale volume.Moreover,different oil testing layers may have different causes of LRLC pay zones.As a result,in addition to the well log data of oil testing layers,well log data of adjacent shale layers are also added to the original dataset as reference data.The densitybased spatial clustering algorithm with noise(DBSCAN)is used to cluster the original dataset into 49 clusters.A new dataset is ultimately projected into a feature space with 49 dimensions.The new dataset and oil testing results are respectively treated as input and output to train the multi-layer perceptron(MLP).A total of 3192 samples are used for stratified 8-fold cross-validation,and the accuracy of the MLP is found to be 85.53%.

Medical Image Compression Method Using Lightweight Multi-Layer Perceptron for Mobile Healthcare Applications

As video compression is one of the core technologies required to enable seamless medical data streaming in mobile healthcare applications,there is a need to develop powerful media codecs that can achieve minimum bitrates while maintaining high perceptual quality.Versatile Video Coding(VVC)is the latest video coding standard that can provide powerful coding performance with a similar visual quality compared to the previously developed method that is High Efficiency Video Coding(HEVC).In order to achieve this improved coding performance,VVC adopted various advanced coding tools,such as flexible Multi-type Tree(MTT)block structure which uses Binary Tree(BT)split and Ternary Tree(TT)split.However,VVC encoder requires heavy computational complexity due to the excessive Ratedistortion Optimization(RDO)processes used to determine the optimalMTT block mode.In this paper,we propose a fast MTT decision method with two Lightweight Neural Networks(LNNs)using Multi-layer Perceptron(MLP),which are applied to determine the early termination of the TT split within the encoding process.Experimental results show that the proposed method significantly reduced the encoding complexity up to 26%with unnoticeable coding loss compared to the VVC TestModel(VTM).