A MACHINE VISION SYSTEM FOR INSPECTING WOOD SURFACE DEFECTS BY USING NEURAL NETWORK

With the development of wood industry, the processing of wood products becomemore significant. This paper discusses the developmen of machine vision system used to inspect andclassny the various types of defects of wood suxface. The surface defeds means the variations ofcolour and textUre. The machine vision system is to dated undesirable 'defecs' that can appear onthe surface of rough wood lwnber. A neural network was used within the Blackboard framework fora labeling verification step of the high-level recognition module of vision system. The system hasbere successfully tested on a number of boards from several different species.

Nonlinear model predictive control based on support vector machine and genetic algorithm

This paper presents a nonlinear model predictive control(NMPC) approach based on support vector machine(SVM) and genetic algorithm(GA) for multiple-input multiple-output(MIMO) nonlinear systems.Individual SVM is used to approximate each output of the controlled plant Then the model is used in MPC control scheme to predict the outputs of the controlled plant.The optimal control sequence is calculated using GA with elite preserve strategy.Simulation results of a typical MIMO nonlinear system show that this method has a good ability of set points tracking and disturbance rejection.

TYRE DYNAMICS MODELLING OF VEHICLE BASED ON SUPPORT VECTOR MACHINES

Various methods of tyre modelling are implemented from pure theoretical to empirical or semi-empirical models based on experimental results. A new way of representing tyre data obtained from measurements is presented via support vector machines （SVMs）. The feasibility of applying SVMs to steady-state tyre modelling is investigated by comparison with three-layer backpropagation （BP） neural network at pure slip and combined slip. The results indicate SVMs outperform the BP neural network in modelling the tyre characteristics with better generalization performance. The SVMsqyre is implemented in 8-DOF vehicle model for vehicle dynamics simulation by means of the PAC 2002 Magic Formula as reference. The SVMs-tyre can be a competitive and accurate method to model a tyre for vehicle dynamics simuLation.

An Ophthalmic Evaluation of Central Serous Chorioretinopathy

Nowadays in the medicalfield,imaging techniques such as Optical Coherence Tomography(OCT)are mainly used to identify retinal diseases.In this paper,the Central Serous Chorio Retinopathy(CSCR)image is analyzed for various stages and then compares the difference between CSCR before as well as after treatment using different application methods.Thefirst approach,which was focused on image quality,improves medical image accuracy.An enhancement algorithm was implemented to improve the OCT image contrast and denoise purpose called Boosted Anisotropic Diffusion with an Unsharp Masking Filter(BADWUMF).The classifier used here is tofigure out whether the OCT image is a CSCR case or not.150 images are checked for this research work(75 abnormal from Optical Coherence Tomography Image Retinal Database,in-house clinical database,and 75 normal images).This article explicitly decides that the approaches suggested aid the ophthalmologist with the precise retinal analysis and hence the risk factors to be minimized.The total precision is 90 percent obtained from the Two Class Support Vector Machine(TCSVM)classifier and 93.3 percent is obtained from Shallow Neural Network with the Powell-Beale(SNNWPB)classifier using the MATLAB 2019a program.

Deep Learning Applied to Computational Mechanics:A Comprehensive Review,State of the Art,and the Classics

Three recent breakthroughs due to AI in arts and science serve as motivation:An award winning digital image,protein folding,fast matrix multiplication.Many recent developments in artificial neural networks,particularly deep learning(DL),applied and relevant to computational mechanics(solid,fluids,finite-element technology)are reviewed in detail.Both hybrid and pure machine learning(ML)methods are discussed.Hybrid methods combine traditional PDE discretizations with ML methods either(1)to help model complex nonlinear constitutive relations,(2)to nonlinearly reduce the model order for efficient simulation(turbulence),or(3)to accelerate the simulation by predicting certain components in the traditional integration methods.Here,methods(1)and(2)relied on Long-Short-Term Memory(LSTM)architecture,with method(3)relying on convolutional neural networks.Pure ML methods to solve(nonlinear)PDEs are represented by Physics-Informed Neural network(PINN)methods,which could be combined with attention mechanism to address discontinuous solutions.Both LSTM and attention architectures,together with modern and generalized classic optimizers to include stochasticity for DL networks,are extensively reviewed.Kernel machines,including Gaussian processes,are provided to sufficient depth for more advanced works such as shallow networks with infinite width.Not only addressing experts,readers are assumed familiar with computational mechanics,but not with DL,whose concepts and applications are built up from the basics,aiming at bringing first-time learners quickly to the forefront of research.History and limitations of AI are recounted and discussed,with particular attention at pointing out misstatements or misconceptions of the classics,even in well-known references.Positioning and pointing control of a large-deformable beam is given as an example.

不同土壤对甘蔗入土切割负载压力影响的研究

针对甘蔗收获机入土切割系统负载压力的预测适应性差、准确性低的问题,通过正交试验探究在不同土壤类型下切割系统的负载压力与入土切割深度、土壤含水率、甘蔗密度及土壤硬度等因素之间的关系并对各影响因素的显著性进行排序;根据试验结果搭建基于BP神经网络的负载切割压力的预测模型并进行验证。试验及验证结果表明:各土壤中入土深度、土壤含水率、甘蔗密度对切割系统负载压力影响显著,红壤的土壤硬度影响显著,而冲积壤的入土深度与土壤含水率交互作用影响较大;预测验证得出黄壤、红壤、冲击壤的平均相对误差分别为1.81%、3.46%、3.79%。研究成果可为提高甘蔗收获机入土切割负载压力预测控制系统的适应性、可靠性提供数据支持和理论依据,对其实际应用具有一定参考价值。

Improvement of the prediction accuracy of polar motion using empirical mode decomposition

Previous studies revealed that the error of pole coordinate prediction will significantly increase for a prediction period longer than 100 days, and this is mainly caused by short period oscillations. Empirical mode decomposition （EMD）, which is increasingly popular and has advantages over classical wavelet decomposition, can be used to remove short period variations from observed time series of pole co- ordinates. A hybrid model combing EMD and extreme learning machine （ELM）, where high frequency signals are removed and processed time series is then modeled and predicted, is summarized in this paper. The prediction performance of the hybrid model is compared with that of the ELM-only method created from original time series. The results show that the proposed hybrid model outperforms the pure ELM method for both short-term and long-term prediction of pole coordinates. The improvement of prediction accuracy up to 360 days in the future is found to be 24.91% and 26.79% on average in terms of mean absolute error （MAE） for the xp and yp components of pole coordinates, respectively.

Design and implementation of gasifier flame detection system based on SCNN

Flame detection is a research hotspot in industrial production,and it has been widely used in various fields.Based on the ignition and combustion video sequence,this paper aims to improve the accuracy and unintuitive detection results of the current flame detection methods of gasifier and industrial boiler.A furnace flame detection model based on support vector machine convolutional neural network(SCNN)is proposed.This algorithm uses the advantages of neural networks in the field of image classification to process flame burning video sequences which needs detailed analysis.Firstly,the support vector machine(SVM)with better small sample classification effect is used to replace the Softmax classification layer of the convolutional neural network(CNN)network.Secondly,a Dropout layer is introduced to improve the generalization ability of the network.Subsequently,the area,frequency and other important parameters of the flame image are analyzed and processed.Eventually,the experimental results show that the flame detection model designed in this paper is more accurate than the CNN model,and the accuracy of the judgment on the flame data set collected in the gasifier furnace reaches 99.53%.After several ignition tests,the furnace flame of the gasifier can be detected in real time.

Forecasting and optimal probabilistic scheduling of surplus gas systems in iron and steel industry

To make full use of the gas resource, stabilize the pipe network pressure, and obtain higher economic benefits in the iron and steel industry, the surplus gas prediction and scheduling models were proposed. Before applying the forecasting techniques, a support vector classifier was first used to classify the data, and then the filtering was used to create separate trend and volatility sequences. After forecasting, the Markov chain transition probability matrix was introduced to adjust the residual. Simulation results using surplus gas data from an iron and steel enterprise demonstrate that the constructed SVC-HP-ENN-LSSVM-MC prediction model prediction is accurate, and that the classification accuracy is high under different conditions. Based on this, the scheduling model was constructed for surplus gas operating, and it has been used to investigate the comprehensive measures for managing the operational probabilistic risk and optimize the economic benefit at various working conditions and implementations. It has extended the concepts of traditional surplus gas dispatching systems, and provides a method for enterprises to determine optimal schedules.

INTELLIGENT TOOLS FOR PREDICTING ANXIETY OF ALZHEIMER'S PATIENTS

Objective To predict the incidence of anxiety in Alzheimer’s disease (AD) patients by using machine-learning models. Methods A large randomized controlled clinical trial was analyzed in this study, which involved AD patients and caregivers from 6 different sites in the United States. The incidence of anxiety in AD patients was predicted by backpropagation artificial neural networks and several machine learning models, including Bayesian Networks, logistic regression, ADTree, J48, and Decision table. Results Among all models for predicting the incidence of anxiety in AD patients, the artificial neural network with respectively 6 and 3 neurons in the first and second hidden layers achieved the highest predictive accuracy of 85.56 %. The decision tree revealed three main risk factors: "caregiver experiencing psychological distress", "caregiver suffering from chronic disease or cancer", and "lack of professional care service". Conclusion The unique ability of artificial neural networks on classifying nonlinearly separable problems may substantially benefit the prediction, prevention and early intervention of anxiety in Alzheimer’s patients. Decision tree has the double efficacy of predicting the incidence and discovering the risk factors of anxiety in Alzheimer’s patients. More resources should be provided to caregivers to improve their mental and physical health, and more professional care services should be adopted by Alzheimer’s families.

基于特征结构不变性思想的自适应在线神经网络算法

针对传统的采用遗忘因子的在线学习方法难以实时精确地跟踪数据所发生的漂移问题,利用在线数据通常所具有的特征结构不变特性,提升在线学习模型的自适应能力.结合在线离散化和在线聚类技术,追踪和刻画数据的特征结构,并在聚类结构中,采用一种类似深度森林算法中的特征构造策略来提取辅助的在线结构特征.通过整合样本的原始特征和额外提取的结构特征共同动态地训练并更新在线神经网络模型,采用在线序列极限学习机算法作为在线神经网络的训练算法,通过8个基准的在线数据集验证算法的有效性、可行性和优越性.实验结果表明:文中算法可很好地追踪数据所发生的概念漂移,并具有较强的自适应性.

Prediction of effluent concentration in a wastewater treatment plant using machine learning models

Of growing amount of food waste, the integrated food waste and waste water treatment was regarded as one of the efficient modeling method. However, the load of food waste to the conventional waste treatment process might lead to the high concentration of total nitrogen（T-N） impact on the effluent water quality. The objective of this study is to establish two machine learning models-artificial neural networks（ANNs） and support vector machines（SVMs）, in order to predict 1-day interval T-N concentration of effluent from a wastewater treatment plant in Ulsan, Korea. Daily water quality data and meteorological data were used and the performance of both models was evaluated in terms of the coefficient of determination（R^2）, Nash-Sutcliff efficiency（NSE）, relative efficiency criteria（d rel）. Additionally, Latin-Hypercube one-factor-at-a-time（LH-OAT） and a pattern search algorithm were applied to sensitivity analysis and model parameter optimization, respectively. Results showed that both models could be effectively applied to the 1-day interval prediction of T-N concentration of effluent. SVM model showed a higher prediction accuracy in the training stage and similar result in the validation stage.However, the sensitivity analysis demonstrated that the ANN model was a superior model for 1-day interval T-N concentration prediction in terms of the cause-and-effect relationship between T-N concentration and modeling input values to integrated food waste and waste water treatment. This study suggested the efficient and robust nonlinear time-series modeling method for an early prediction of the water quality of integrated food waste and waste water treatment process.

Multifunctional Sitting Posture Detector Based on Face Tracking

To reduce the vision problems caused by improper sitting posture,the research group used Raspberry Pi as the main controller for a multifunctional sitting posture detector with functions such as sitting posture detection,face positioning,cloud monitoring,etc.UUsing tech-nologies or algorithms such as machine vision and convolutional neural networks,our design can realize the user’s sitting posture error detec-tion,such as left,right,low head position,or forward body position with alarming,so that the user can maintain the appropriate sitting posture.