Influences of chip serration on micro-topography of machined surface in high-speed cutting

Saw-tooth chip changes from macroscopically continuous ribbon to separated segments with the increase of cutting speed. The aim of this study is to find the correlations between chip morphology and machined surface micro-topography at different chip serration stages encountered in high speed cutting. High strength alloy steel AerMet100 was employed in orthogonal cutting experiments to obtain chips at different serration stages and corresponding machined surfaces. The chips and machined surfaces obtained were then examined with optical microscope (OM), scanning electron microscope (SEM), and white light interferometer (WLI). The result shows that chip serration causes micro-waves on machined surface, which increases machined surface roughness. However, wave amplitudes (surface roughness) at different serration stages are different. The principal factor influencing wave amplitude is the thickness of the sawed segment (tooth) of saw-tooth chip. With cutting parameters in this study, surface roughness contributed by chip serration ranges from 0.39 μm to 1.85 μm. This may bring on serious problems in the case of trying to replace grinding with high-speed cutting in rough machining. Some suggestions have been proposed to control the chip serration-caused surface roughness in high-speed cutting based on the results of the current study.     

Operational time-series data modeling via LSTM network integrating principal component analysis based on human experience

Today’s information technologies involve increasingly intelligent systems, which come at the cost of increasingly complex equipment. Modern monitoring systems collect multi-measuring-point and long-term data which make equipment health prediction a “big data” problem. It is difficult to extract information from such condition monitoring data to accurately estimate or predict health statuses. Deep learning is a powerful tool for big data processing that is widely utilized in image and speech recognition applications, and can also provide effective predictions in industrial processes. This paper proposes the Long Short-term Memory Integrating Principal Component Analysis based on Human Experience (HEPCA-LSTM), which uses operational time-series data for equipment health prognostics. Principal component analysis based on human experience is first conducted to extract condition parameters from the condition monitoring system. The long short-term memory (LSTM) framework is then constructed to predict the target status. Finally, a dynamic update of the prediction model with incoming data is performed at a certain interval to prevent any model misalignment caused by the drifting of relevant variables. The proposed model is validated on a practical case and found to outperform other prediction methods. It utilizes a powerful deep learning analysis method, the LSTM, to fully process big condition monitoring series data; it effectively extracts the features involved with human experience and takes dynamic updates into consideration.     

Smart firefighting construction in China: Status,problems, and reflections

“Smart firefighting” construction as a part of the “smart city” has been a concern of the public security and fire agencies at all levels. In this study, the status, problems, and reflections of “smart firefighting” construction in China are discussed. A recent survey indicates that China has launched its smart firefighting construction and initially created a new perspective on its regional smart firefighting work based on three main aspects: intelligent disaster perception by Internet of Things (IoT) construction, intelligent disaster prevention by big data construction, and intelligent disaster disposal by emergency rescue platform construction. However, the current smart firefighting construction in China still has some prominent problems such as the data interconnectivity and normalized management of various platforms, the extensibility of smart firefighting platforms, and the intelligent level of smart firefighting researches, which need to be solved urgently. Therefore, we argue that smart firefighting construction in China should establish data interconnectivity, industrial normalized management, 2D/3D geographic information interaction and extension, high-integration fire protection theory, and many other aspects in the near future and truly realize firefighting visualization and efficient data applications for 4D time space. This study could provide valuable reference for smart firefighting and smart city construction.     

基于MF-R和AWS密钥管理机制的物联网健康监测大数据分析系统

带有传感器的可穿戴式医疗设备不断生成大量数据，由于数据的复杂性，难以通过处理和分析大数据来找到有价值的决策信息。为了解决这个问题，提出了一种新的物联网体系结构，用于存储和处理医疗应用的可扩展传感器数据（大数据）。所提出的架构主要由两个子架构组成：Meta Fog重定向（MF-R）架构和AWS密钥管理机制。MF-R架构使用Apache Pig和Apache HBase等大数据技术来收集和存储不同传感器设备生成的传感器数据，并利用卡尔曼滤波消除噪声。AWS密钥管理机制使用密钥管理方案，目的是保护云中的数据，防止未经授权的访问。当数据存储在云中时，所提出的系统能够使用随机梯度下降算法和逻辑回归来开发心脏病的预测模型。仿真实验表明，和其他几种算法相比，提出的算法具有更小的误差，且在吞吐量、准确度等方面具有一定的优越性。     

Development and monitoring of an integrated bioprocess for production of a potential malaria vaccine with Pichia pastoris

An engineered variant of the yeast Pichia pastoris was used to produce the potential malaria vaccine D1M1. This paper describes the implementation of an expanded bed adsorption chromatography step into a sequential cultivation process of the yeast in order to link protein expression, cell release, and product capture of the secreted protein in a fully automated plant. The process is operated with a semi-continuous strategy for an integrated production. Process improvements have been achieved using design of experiments. Resulting purities of product achieved up to 87% with recoveries of 51% in a single downstream operation. Furthermore, a multivariate analysis of historical batch data was used to develop a golden batch model for process monitoring.     

一款紧凑增强型可变阻抗电磁带隙结构

基于传统AI-EBG结构,提出了一种小尺寸的增强型电磁带隙结构,实现了从0.5~9.4 GHz的宽频带-40 dB噪声抑制深度,且下截止频率减少到数百MHz,可有效抑制多层PCB板间地弹噪声。文中同时研究了EBG结构在高速电路应用时的信号完整性问题,使用差分信号方案可改善信号完整性。     

“人工智能+”背景下的数据安全与隐私保护探析

随着大数据和云计算的技术的深入应用，人工智能时代的机器学习和深度学习更需要日益增长的数据，因此数据安全与隐私保护变得更加迫切。本文介绍人工智能的定义以及特征，探究数据安全和隐私保护现状，分析数据安全和隐私保护面临的诸多问题，并提出在人工智能时代对数据安全和隐私保护的措施。     

基于DSP的多元数据同步采集与存储系统的设计与实现

从硬件和软件两个角度出发,介绍基于DSP的多元数据同步采集与存储系统的组成、工作模式以及功能的测试。系统主要由上位机和数字采集与存储单元组成,其中数字采集与存储单元的硬件部分包括电源模块,值班电路模块,数据采集模块,数据存储模块,时钟同步模块。系统采用DSP作为中央处理芯片,利用经过同步后的秒脉冲作为触发信号,实现同步数据采集。以CF卡作为存储介质,实现数据自容式存储。软件部分实现自检、同步、数据采集存储功能。经过测试,系统工作稳定,功能正常,同步精度在100ns以内。     

Identification of digital technologies and digitalisation trends in the mining industry

Digitalisation in mining refers to the use of computerised or digital devices or systems and digitised data that are to reduce costs, improve business productivity, and transform mining practices. However, it remains increasingly difficult for mining companies to decide which digital technologies are most relevant to their needs and individual mines. This paper provides an overview of digital technologies currently relevant to mining companies as presented and discussed by mining journals, the media and insight reports of leading consultancy agencies. Relevant technologies were systematically identified using text-mining techniques, and network analyses established the relations between significant technologies. Results demonstrated that currently 107 different digital technologies are pursued in the mining sector. Also, an analysis of the actual implementation of digital technologies in 158 active surface and underground mines reveals a limited uptake of digital technologies in general and that the uptake increases with the run-of-mine production. Large-scale mining operations appear to select and apply digital technologies suitable to their needs, whereas operations with lower production rates do not implement the currently available digital technologies to the same extent. These minor producers may require other digital transformation solutions tailored to their capabilities and needs and applicable to their scale of operations.