Semantic Modelling and Machine Learning Analysis of a Billion+ Electronic Components Products to Support Supply Chain Optimization

由于消费电子产品中的许多公司正在转向接近最佳的供应链管理，因此处理数字 ** 内容（例如，包含数字数据的电子元件图像，伴随有例如电子元件的目录信息 ** 表格和/或图表的单元）仍然需要巨大的人力干预，这可能导致显著的 ** 成本，而对数字化的准确性没有任何保证。因此，寻求创新的解决方案来提供软件服务，通过机器智能对电子元件进行语义建模，最大限度地减少供应链优化中的人为干预。该项目的目标是为数字/扫描目录中超过10亿个电子元件开发语义模型，消除分析过程中的人为干预，并通过在传统人工神经网络中引入许多隐藏的感知器层，即通过深度学习，优化这些非结构化电子元件库的供应链控制。

Since many companies in consumer electronics are moving to nearly-optimal supply chain management, processing of digital**content such as electronic component images containing numeric data accompanied with units such as catalogue information**tables and/or charts of electronic components, still require tremendous human effort intervention, potentially leading to significant**costs without any assurance on the accuracy of the digitization. Therefore, innovative solutions are sought to offer software**services that can semantically model the electronic components through machine intelligence by minimizing the human**intervention in the supply chain optimization.**The objective of this project is to develop a semantic model for over a billion electronic components in digital/scanned catalogues,**eliminate the human intervention in the analysis process and optimize supply chain control for these unstructured electronic**component libraries by introducing many hidden perceptron layers in the conventional artificial neural networks, namely by deep**learning.