破碎机教学模型的设计及其教学实践

教学模型可有效增加感性认识,增强学生对机器内部结构、机械运行原理等的理解与掌握.本文以颚式和圆锥式一体式破碎机教学模型为例,介绍了其设计、制造及组装技术.通过对实物机器的缩小与简化,并利用钳工、数控加工、3D打印、上色处理等技术,实际构建了包括偏心机构、连杆机构、齿轮传动、带传动和弹簧机构、控制电路等于一体的破碎机教学模型,具有外形精美、造价低廉、内部结构清晰,可实现参数调整、机构运动及机械失效过程的演示等特点,在机械类科目的课堂教学实践中发挥了较好效果.

Research on Multi-Modal Time Series Data Prediction Method Based on Dual-Stage Attention Mechanism

The production data in the industrialfield have the characteristics of multimodality,high dimensionality and large correlation differences between attributes.Existing data prediction methods cannot effectively capture time series and modal features,which leads to prediction hysteresis and poor prediction stabil-ity.Aiming at the above problems,this paper proposes a time-series and modal fea-tureenhancementmethodbasedonadual-stageself-attentionmechanism(DATT),and a time series prediction method based on a gated feedforward recurrent unit(GFRU).On this basis,the DATT-GFRU neural network with a gated feedforward recurrent neural network and dual-stage self-attention mechanism is designed and implemented.Experiments show that the prediction effect of the neural network prediction model based on DATT is significantly improved.Compared with the traditional prediction model,the DATT-GFRU neural network has a smaller aver-age error of model prediction results,stable prediction performance,and strong generalization ability on the three datasets with different numbers of attributes and different training sample sizes.

A Novel Evaluation Strategy to Artificial Neural Network Model Based on Bionics

With the continuous deepening of Artificial Neural Network(ANN)research,ANN model structure and function are improving towards diversification and intelligence.However,the model is more evaluated from the pros and cons of the problem-solving results and the lack of evaluation from the biomimetic aspect of imitating neural networks is not inclusive enough.Hence,a new ANN models evaluation strategy is proposed from the perspective of bionics in response to this problem in the paper.Firstly,four classical neural network models are illustrated:Back Propagation(BP)network,Deep Belief Network(DBN),LeNet5 network,and olfactory bionic model(KIII model),and the neuron transmission mode and equation,network structure,and weight updating principle of the models are analyzed qualitatively.The analysis results show that the KIII model comes closer to the actual biological nervous system compared with other models,and the LeNet5 network simulates the nervous system in depth.Secondly,evaluation indexes of ANN are constructed from the perspective of bionics in this paper:small-world,synchronous,and chaotic characteristics.Finally,the network model is quantitatively analyzed by evaluation indexes from the perspective of bionics.The experimental results show that the DBN network,LeNet5 network,and BP network have synchronous characteristics.And the DBN network and LeNet5 network have certain chaotic characteristics,but there is still a certain distance between the three classical neural networks and actual biological neural networks.The KIII model has certain small-world characteristics in structure,and its network also exhibits synchronization characteristics and chaotic characteristics.Compared with the DBN network,LeNet5 network,and the BP network,the KIII model is closer to the real biological neural network.