Prediction of viscosity of ternary tin-based lead-free solder melt using BP neural network

Wu, Meirong; Lv, Bailin

doi:10.1108/ssmt-02-2019-0005

Cited by 5 publications

(2 citation statements)

References 26 publications

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“…The basic idea of SVM [28] is to define the optimal linear hyperplane and reduce the algorithm of the optimal linear hyperplane to a convex optimization problem. BP neural network [29] is a kind of multilayer feedforward neural network trained according to the error back propagation algorithm, which has the ability of arbitrary complex pattern classification and excellent multi-dimensional function mapping.…”

Section: Model and Evaluation Methodsmentioning

confidence: 99%

In-situ soil texture classification and physical clay content measurement based on multi-source information fusion

Meng

Yang

Ren

et al. 2023

International Journal of Agricultural and Biological Engineering

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Soil texture is one of the most important soil characteristics that affect soil properties. Rapid acquisition of soil texture information is of great significance for accurate farmland management. Traditional soil texture analysis methods are relatively complicated and cannot meet the requirements of temporal and spatial resolution. This research introduced a self-developed vehicle-mounted in-situ soil texture detection system, which can predict the type of soil texture and the particle composition of the texture, and obtain real-time data during the measurement process without preprocessing the soil samples. The detection system is mainly composed of a conductivity measuring device, a camera, an auxiliary mechanical structure, and a control system. The soil electrical conductivity (ECa) and the texture features extracted from the surface image were input into the embedded model to realize real-time texture analysis. In order to find the best model suitable for the detection system, measurements were carried out in three test fields in Northeast and North China to compare the performance of different models applied to the detection system. The results showed that for soil texture classification, ExtraTrees performed best, with Precision, Recall, and F1 all being 0.82. For particle content of soil texture prediction, the R 2 of ExtraTrees was 0.77, and RMSE and MAPE were 74.72 and 39.58. It was observed that ECa, Moment of inertia, and Entropy had larger weights in the drawn model influence weight map, and they are the main contributors to predicting soil texture. These results showed the potential of the vehicle-mounted in-situ soil texture detection system, which can provide a basis for fast, cost-effective, and efficient soil texture analysis.

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Section: Model and Evaluation Methodsmentioning

confidence: 99%

In-situ soil texture classification and physical clay content measurement based on multi-source information fusion

Meng

Yang

Ren

et al. 2023

International Journal of Agricultural and Biological Engineering

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“…Artificial neural networks have been widely used at present [1] [2]. The third generation of artificial neural network --spiking neural network [3] (SNN) has also become a topic of great interest to researchers [4], because they are more biologically reasonable than the previous two generations of neural networks.…”

Section: Introductionmentioning

confidence: 99%

SDenseNet-An Improved DenseNet Model for Spiking Neural Networks

Guo,

Wang

2023

J. Phys.: Conf. Ser.

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In order to port DenseNet to a spiking neural network, its activation function must be modified to spiking neurons. In addition, the direct conversion model suffers from the inability to transmit non-peak sequences across layers and a sluggish training pace. A deep convolutional spiking neural network (DCSNN) architecture called SDenseNet has been designed in order to address these problems. The first step is to adjust the connection sequence of each module layer in the network to ensure that the output of each layer is the spiking sequence; Next, convolutional kernels of different sizes are used in parallel in the Transition layer to extract different features and spliced along the length of the channel in an attempt to build up the performance of the feature extraction. The framework suggested in this paper has demonstrated improvement in benchmark indicators such as training speed, accuracy rate, and mean loss when compared to the framework directly converted to the spiking neural network by training the two publicly image data sets named CIFAR-10 and CIFAR-100.

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