基于TMS320F2801 DSP新型电压监测仪的设计

针对目前普遍使用的电压监测仪在运行中存在的问题,从硬件及电气回路分析不足之处,提出基于TMS320F2801 DSP的改进型电压监测仪。     

3G技术在地震网络系统的安全应用价值探析

从地震的发生频率上来看,我国是一个多发的国家。同时在地震中由于受到各种灾害而引起的损失也是最严重的。所以,对地震的信号进行实时性监测并完成信号数据的快速传输必须要应用先进的技术手段。文章主要介绍的就是目前应用十分广泛的3G技术在地震的网络系统中的安全应用。     

重症监护病房紧急状况预警算法

重症监护病房中的病人身体状况通常很不稳定,常出现各种需要医护人员介入治疗的紧急状况。由于医疗资源有限,医护人员可能无法及时发现并处理这些紧急状况,给病人的存活率带来严重的负面影响。如果可以预测这些紧急状况的发生,并及时通知相关医护人员,将大大提高病人的存活率。常见重症监护病房紧急状况包括突然死亡、败血症、肺部感染、急性低血压、以及器官衰竭等。紧急状况预警建模主要采用病人的长时间生命体征监测数据,预测在一定时间之后发生某种紧急状况的可能性。预警模型所采用的监测数据分为静态数据、事件数据和时间序列数据等三类。静态数据具有容易采集、但预测准确性偏低的特点。事件数据或时间序列数据、以及多种类型数据的混合数据对于紧急状况预警模型的预测性能的提高有重要作用,将会获得更广泛的应用。     

电视监控系统云台控制器设计

电视监控系统是一种典型的分布式计算机信号采集系统。为了提高监控的范围和效果,云台控制器的优劣直接影响监控的范围和效果,因此对云台控制器的设计具有重要的意义。本系统采用串型通信,通过Modbus协议把控制信号输入单片机,经过对AT89S52单片机的编程,从而对八路继电器输出进行控制。系统不仅结构简单,而且还可以方便的对镜头、防护罩的控制进行扩展,功能强大,可以广泛应用于电视监控系统中的云台控制。     

试析环境监测信息系统安全及防御

信息化建设在环境监测行业中日渐成熟,对于该行业的稳步提升有着不可或缺的作用,然而由于该行业信息化建设处于起步阶段,尤其是在应用的信息系统在安全问题上还需强化,基于目前现状,列举环境监测领域信息系统出现的一些突出问题,并对其中的原因进行分析,同时提出了环境监测信息系统安全问题的防御措施。     

神华乌海能源公司安全监控系统联网建设解读

神华乌海能源公司(以下简称"公司")在安全监控联网系统的开发过程中,针对可能出现的各种问题,提出了综合性的联网解决方案。同时又通过强有力的管理手段,保障了系统的稳定可靠运行,使系统达到了预期的应用效果,提升了公司的安全管理水平。     

智能建筑弱电技术的研究

通过对智能建筑弱电技术中的电线/电缆导管的敷设、电气照明安装、工程中的冲突以及弱电系统的施工计划等问题的研究,总结了智能建筑弱电技术中的综合布线系统、计算机网络系统、子系统间接口方面、母线的安装、弱电设备功能以及配电装置等技术问题的解决要点。所述内容对智能建筑整体运行和安全管理以及电气施工安装的质量监控等具有较强的参考价值。     

Toward in-situ flaw detection in laser powder bed fusion additive manufacturing through layerwise imagery and machine learning

Process monitoring in additive manufacturing may allow components to be certified cheaply and rapidly and opens the possibility of healing defects, if detected. Here, neural networks (NNs) and convolutional neural networks (CNNs) are trained to detect flaws in layerwise images of a build, using labeled XCT data as a ground truth. Multiple images were recorded after each layer before and after recoating with various lighting conditions. Classifying networks were given a single image or multiple images of various lighting conditions for training and testing. CNNs demonstrated significantly better performance than NNs across all tasks. Furthermore, CNNs demonstrated improved generalizability, i.e., the ability to generalize to more diverse data than either the training or validation data sets. Specifically, CNNs trained on high-resolution layerwise images from one build showed minimal loss in performance when applied to data from an independent build, whereas the performance of the NNs degraded significantly. CNN accuracy was also demonstrated to be a function of flaw size, suggesting that smaller flaws may be produced by mechanisms that do not alter the surface morphology of the build plate. CNNs demonstrated accuracies of 93.5 % on large (>200 μm) flaws when testing and training on components from the same build and accuracies of 87.3 % when testing on a previously unseen build. Finally, evidence linking the formation of large lack-of-fusion defects to the presence of process ejecta is presented.     

Intelligent tool wear monitoring based on parallel residual and stacked bidirectional long short-term memory network

Effective tool wear monitoring (TWM) is essential for accurately assessing the degree of tool wear and for timely preventive maintenance. Existing data-driven monitoring methods mainly rely on complex feature engineering, which reduces the monitoring efficiency. This paper proposes a novel TWM model based on a parallel residual and stacked bidirectional long short-term memory (PRes–SBiLSTM) network. First, a parallel residual network (PResNet) is used to extract the multi-scale local features of sensor signals adaptively. Subsequently, a stacked bidirectional long short-term memory (SBiLSTM) network is used to obtain the time-series features related to the tool wear characteristics. Finally, the predicted tool wear value is outputted through a fully connected network. A smoothing correction method is applied to improve the prediction accuracy. The proposed model is experimentally verified to have a high prediction accuracy without sacrificing its generalization ability. A TWM system framework based on the PRes–SBiLSTM network is proposed, which has a certain reference value for TWM in actual industrial environments.     

Building a right digital twin with model engineering

In recent years, the concept of digital twin (DT) is attracting more and more attention from researchers and engineers. But there is still no consensus on what a right DT is. On one hand, some common models are renamed as DTs. On the other hand, some DTs extremely pursue ‘the same’ as physical objects, which bring unnecessary complexities to them. In this paper, we try to answer two questions from the point of view of model engineering: how to define a right digital twin, and how to build a right digital twin. The concept and related technologies of model engineering are introduced. Some basic principles and a set of metrics for a right DT are given. An evolutionary concurrent modeling method for DT (ECoM4DT) is proposed not only inheriting the theory from classic M&S methods but also highlighting the characteristics of DT compared with traditional models to systemically guide the DT modeling process.