scholarly journals Optimal-Setting for Ore and Water Feeding in Grinding Process Based on Improved Case-Based Reasoning

2021 ◽  
Vol 11 (13) ◽  
pp. 5835
Author(s):  
Bingyu Liu ◽  
Dezhi Hao ◽  
Xianwen Gao ◽  
Dingsen Zhang
Keyword(s):  
Experimental Result ◽  
Case Based Reasoning ◽  
Grinding Process ◽  
Nsga Ii ◽  
Generative Adversarial Network ◽  
Operating Variables ◽  
Adversarial Network ◽  
First Case ◽  
Optimal Setting ◽  
Case Based

The grinding product particle size is the most crucial operational index of mineral grinding processes. The size and consistency of the product directly affects the subsequent dressing and sintering. In this paper, a novel expert system is proposed for guiding the operating variables to keep the product stable with the wildly varying ore properties. First, case-based reasoning (CBR) is introduced to describe the whole grinding process with the historical data and expert experience. Second, the generative adversarial network (GAN) is employed to extend the raw data to enhance the flexibility of CBR. Moreover, the weights of different features in CBR is optimized by improved non-dominated sorting genetic algorithm II (NSGA-II). Finally, the proposed method is validated by a set of actual data collected from a Chinese dressing plant. The experimental result demonstrates the effectiveness of the proposed method.

scholarly journals A Triangular Similarity Measure for Case Retrieval in CBR and Its Application to an Agricultural Decision Support System

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4605 ◽  
Author(s):  
Zhai ◽  
Ortega ◽  
Castillejo ◽  
Beltran
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Similarity Measure ◽  
Support System ◽  
Similarity Measures ◽  
Experimental Result ◽  
Case Based Reasoning ◽  
Case Retrieval ◽  
And Performance ◽  
Case Based

Case-based reasoning has been a widely-used approach to assist humans in making decisions through four steps: retrieve, reuse, revise, and retain. Among these steps, case retrieval plays a significant role because the rest of processes cannot proceed without successfully identifying the most similar past case beforehand. Some popular methods such as angle-based and distance-based similarity measures have been well explored for case retrieval. However, these methods may match inaccurate cases under certain extreme circumstances. Thus, a triangular similarity measure is proposed to identify commonalities between cases, overcoming the drawbacks of angle-based and distance-based measures. For verifying the effectiveness and performance of the proposed measure, case-based reasoning was applied to an agricultural decision support system for pest management and 300 new cases were used for testing purposes. Once a new pest problem is reported, its attributes are compared with historical data by the proposed triangular similarity measure. Farmers can obtain quick decision support on managing pest problems by learning from the retrieved solution of the most similar past case. The experimental result shows that the proposed measure can retrieve the most similar case with an average accuracy of 91.99% and it outperforms the other measures in the aspects of accuracy and robustness.

scholarly journals Scribble-to-Painting Transformation with Multi-Task Generative Adversarial Networks

2019 ◽  
Author(s):  
Jinning Li ◽  
Yexiang Xue
Keyword(s):  
Semantic Segmentation ◽  
Neural Nets ◽  
Experimental Result ◽  
Generative Adversarial Networks ◽  
Neural Net ◽  
Generative Adversarial Network ◽  
Style Transfer ◽  
Adversarial Network ◽  
Classical Image ◽  
Artistic Images

We propose the Dual Scribble-to-Painting Network (DSP-Net), which is able to produce artistic paintings based on user-generated scribbles. In scribble-to-painting transformation, a neural net has to infer additional details of the image, given relatively sparse information contained in the outlines of the scribble. Therefore, it is more challenging than classical image style transfer, in which the information content is reduced from photos to paintings. Inspired by the human cognitive process, we propose a multi-task generative adversarial network, which consists of two jointly trained neural nets -- one for generating artistic images and the other one for semantic segmentation. We demonstrate that joint training on these two tasks brings in additional benefit. Experimental result shows that DSP-Net outperforms state-of-the-art models both visually and quantitatively. In addition, we publish a large dataset for scribble-to-painting transformation.

scholarly journals Thyroid Nodule Classification in Ultrasound Images by Fusion of Conventional Features and Res-GAN Deep Features

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Yuan Hang
Keyword(s):  
Thyroid Nodule ◽  
Early Stage ◽  
Histogram Equalization ◽  
Image Features ◽  
Experimental Result ◽  
Laplacian Operator ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Histograms Of Oriented Gradients ◽  
Number Of Patients

In spite of the gargantuan number of patients affected by the thyroid nodule, the detection at an early stage is still a challenging task. Thyroid ultrasonography (US) is a noninvasive, inexpensive procedure widely used to detect and evaluate the thyroid nodules. The ultrasonography method for image classification is a computer-aided diagnostic technology based on image features. In this paper, we illustrate a method which involves the combination of the deep features with the conventional features together to form a hybrid feature space. Several image enhancement techniques, such as histogram equalization, Laplacian operator, logarithm transform, and Gamma correction, are undertaken to improve the quality and characteristics of the image before feature extraction. Among these methods, applying histogram equalization not only improves the brightness and contrast of the image but also achieves the highest classification accuracy at 69.8%. We extract features such as histograms of oriented gradients, local binary pattern, SIFT, and SURF and combine them with deep features of residual generative adversarial network. We compare the ResNet18, a residual convolutional neural network with 18 layers, with the Res-GAN, a residual generative adversarial network. The experimental result shows that Res-GAN outperforms the former model. Besides, we fuse SURF with deep features with a random forest model as a classifier, which achieves 95% accuracy.

Case-based reasoning

1994 ◽  
Vol 9 (1) ◽  
pp. 61-64 ◽  
Author(s):  
David B. Leake
Keyword(s):  
Cognitive Model ◽  
Human Case ◽  
Case Based Reasoning ◽  
Reasoning Process ◽  
First Case ◽  
Psychological Evidence ◽  
Theoretical Foundations ◽  
Case Based

Case-based reasoning (CBR) systems reason from experience: they solve new problems by retrieving relevant prior cases and adapting them to fit new situations. In 1988 the first case-based reasoning workshop, sponsored by DARPA, identified theoretical foundations and fundamental issues for case-based reasoning research. Since then, much investigation has examined the CBR process itself, the validity of CBR as a cognitive model, and the application of CBR technology. The results of that work include refinements in theories of the case-based reasoning process, psychological evidence for human case-based reasoning, and the fielding of over 100 CBR applications.

scholarly journals Integrated Modeling and Intelligent Control Methods of Grinding Process

2013 ◽  
Vol 2013 ◽  
pp. 1-15 ◽  
Author(s):  
Jie-sheng Wang ◽  
Na-na Shen ◽  
Shi-feng Sun
Keyword(s):  
Intelligent Control ◽  
Control Method ◽  
Soft Sensor ◽  
Integrated Modeling ◽  
Case Based Reasoning ◽  
Grinding Process ◽  
Control Methods ◽  
Set Point ◽  
Self Tuning ◽  
Case Based

The grinding process is a typical complex nonlinear multivariable process with strongly coupling and large time delays. Based on the data-driven modeling theory, the integrated modeling and intelligent control method of grinding process is carried out in the paper, which includes the soft-sensor model of economic and technique indexes, the optimized set-point model utilizing case-based reasoning, and the self-tuning PID decoupling controller. For forecasting the key technology indicators (grinding granularity and mill discharge rate of grinding process), an adaptive soft-sensor modeling method based on wavelet neural network optimized by the improved shuffled frog leaping algorithm (ISFLA) is proposed. Then, a set point optimization control strategy of grinding process based on case-based reasoning (CBR) method is adopted to obtain the optimized velocity set-point of ore feed and pump water feed in the grinding process controlled loops. Finally, a self-tuning PID decoupling controller optimized is used to control the grinding process. Simulation results and industrial application experiments clearly show the feasibility and effectiveness of control methods and satisfy the real-time control requirements of the grinding process.

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