摘要：Artificial neural networks have dramatically improved the performance of many machine-learning applications such as image recognition and natural language processing. However, the electronic hardware implementations of the above-mentioned tasks are facing performance ceiling because Moore’s Law is slowing down. In this article, we propose an optical neural network architecture based on optical scattering units to implement deep learning tasks with fast speed, low power consumption and small *** optical scattering units allow light to scatter back and forward within a small region and can be optimized through an inverse design method. The optical scattering units can implement high-precision stochastic matrix multiplication with mean squared error < 10-4 and a mere 4*4 um2 ***, an optical neural network framework based on optical scattering units is constructed by introducing "Kernel Matrix", which can achieve 97.1% accuracy on the classic image classification dataset MNIST.

摘要：研究设计了一种水质监测与预测系统,旨在提高水质管理效率。为此,采用了基于CNN-LSTM模型的方法。该模型利用CNN提取水质传感器数据的空间特征,并将输出序列传递给LSTM网络,以捕捉时序性信息。这种结合CNN和LSTM的模型既保持了高效性又具有简单的结构,非常适合部署至STM32单片机,在实现传感器数据采集、STM32的人工智能处理、本地显示以及数据上传至乐为物联云平台的过程中,该设计可以提高水质管理的效率。

摘要：辐照技术已广泛应用于工业、农业、医学、环保等领域,其在医疗废物消毒处理方面具有较大的应用潜力。对我国医疗废物的来源、类别、产生量和危害进行介绍,并对现有医疗废物处理技术的原理、优缺点和运行费用进行简要论述。在阐述辐照技术消毒原理及应用现状的基础上,以某辐照消毒处理医疗废物项目为例,详细介绍了工艺流程、设计参数、投资运行成本和调试运行效果。调试运行结果表明:相比于现有消毒技术,辐照消毒技术具有消毒彻底、处理效率高、无有害物质残留、操作简单和运行成本低等优点。

摘要：医疗废水消毒灭菌是疾病预防中极其重要的一环。针对目前传统医疗废水处理技术存在的不足和局限性,设计研发了电子束耦合传统工艺处理医疗废水的新工艺。结合电子束组合工艺处理某传染病医院医疗废水的实际运行情况,研究了电子束组合工艺对医疗废水中化学需氧量(Chemical Oxygen Demand COD)、氨氮等常规污染物和粪大肠菌群、肠道病毒等特征污染物降解和灭活的影响。长期运行结果表明:医疗废水经电子束组合工艺处理出水COD小于60 mg/L,氨氮小于8 mg/L,粪大肠菌群小于100 MNP/L,沙门氏菌和志贺氏菌均未检出,所有指标均符合《医疗机构水污染物排放标准》(GB 18466—2005)。电子束处理医疗废水效率高、效果好,在医疗机构废水处理方面有广大的应用前景。