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基于CNN的自动平板电脑分类使用振动控制碗料器与螺旋扭矩优化.

Kicheol Yoon1, Sangyun Lee2, Junha Park3

  • 1Gachon Biomedical Convergence Institute, Gachon University Gil Medical Center, Incheon 21565, Republic of Korea.

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概括

这项研究引入了一种药物分类系统,该系统结合了卷积神经网络 (CNN) 训练和旋转降药技术. 该系统在使用优化料参数对102种药物类型进行分类时,达到88.8%的准确率.

关键词:
在CNN的培训中,CNN的培训.一个碗料器料器.摄像机拍摄 拍摄 摄像机拍摄放下盒子 放下盒子药丸的分类 药丸的分类

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科学领域:

  • 制药技术 制药技术 制药技术
  • 人工智能在医学中的应用
  • 机器人和自动化 机器人和自动化

背景情况:

  • 准确的药物鉴定对于患者的安全和有效治疗至关重要.
  • 现有的药物分类方法可能缺乏效率或精度.
  • 需要自动化系统来处理不断增加的药品品种和数量.

研究的目的:

  • 开发和评估一种自动化药物分类系统.
  • 将卷积神经网络 (CNN) 技术与旋转降落药丸相结合.
  • 为了优化碗料器的性能,以实现稳定的药片处理和分类.

主要方法:

  • 在102种药物类型中捕获了4080个药丸的图像.
  • 训练了一个卷积神经网络 (CNN) 用于基于图像的分类.
  • 使用了具有优化参数 (电压,扭矩,PWM,倾斜角度,振动振幅和频率) 的碗料器.
  • 在特定的工作条件下进行性能测试 (5V,20rpm,20%PWM,1.5mm振动幅度).

主要成果:

  • 使用CNN模型实现了88.8%的分类准确度.
  • 经过证明,稳定,顺序的药丸运动,没有损失或凝聚,具有优化的料参数.
  • 碗料器结构成功地承受了高达75°的斜角,以实现精确的对齐.

结论:

  • 拟议的系统有效地使用CNN和旋转降落药物的药物分类.
  • 优化的碗料参数对于可靠的药片处理和系统性能至关重要.
  • 这种自动化方法为准确和高效的药物分类提供了有希望的解决方案.