基于虚拟仪器的装备ATS系统硬件平台设计与实现

在对现有维修检测设备存在不足分析的基础上,结合武器装备的维修技术保障需求,利用分块设计思想,采用虚拟仪器、人工智能、计算机通讯与控制等技术,搭建了基于虚拟仪器的装备自动测试系统硬件平台整体架构,在对系统各功能模块原理进行分析之后,对系统主控电路、电磁兼容性进行了设计。采用虚拟仪器的装备自动测试系统硬件平台,具有智能化、通用化、可视化的特点,通过试验结果分析,测试系统硬件平台能够实现装备的性能检测和故障诊断,测试数据符合任务要求,能满足各级修理机构维修测试需求。     

基于20-sim的控制类课程虚拟实验平台研究

本文针对目前控制类课程实验教学的特点及现状,提出了一种虚拟实验平台的设计方案。将20-sim软件与自动控制类课程实验教学相结合,建立数学模型与元件,元件与系统之间的有机联系,并通过实际案例阐述虚拟平台在实验教学中的具体应用。该实验平台具有开放性,便于开发综合性、自主性实验,同时也有助于学生创新能力的培养。     

“电力电子器件”课件仿真教学实践

本文从实践教学与人才培养的角度出发,借助于计算机虚拟仿真技术探讨了“电力电子器件”课程的教学方法与手段。采用虚拟仿真软件Silvaco,以半控型晶闸管和全控型功率MOSFET为代表,介绍了课堂教学中实时演示电力电子器件工作过程的实例。通过虚拟仿真教学实践,加深了学生对电力电子器件运行机理的理解。     

同步发电机的虚拟电动势相量及其应用

本文将dq0坐标下的d轴作为横轴,q轴作为纵轴的方式来定义电动势相量,引入了稳态电动势和暂态电动势相量,与次暂态电动势相量具有统一的表达形式;同时,引入了虚拟的稳态电动势、暂态电动势和次暂态电动势的概念,它们同样具有统一的表达形式。在同步发电机的电磁功率表达式中,通过使用统一的虚拟电动势,说明凸极机的电磁功率表达与使用统一稳态和暂态电动势的隐极机一样,具有简洁的表达方式。     

双余度发动机状态监测技术的研究与应用

在飞机、舰艇、装甲、汽车电子控制系统中采用双余度发动机状态监测技术,能够有效提高飞机、舰艇、装甲、汽车运行的安全性和可靠性,以某型涡轴发动机为研究对象,对双余度发动机状态监测技术进行了深入研究,研制了双余度发动机状态监测系统,介绍了系统的总体设计、软硬件设计、余度设计,虚拟仪表设计,并在双余度发动机状态监测系统的设计中引入了嵌入式PC/104模块;为了使状态监测系统具有更好的扩展性和适应性,将系统设计成可以在两种方式下工作:机载运行方式和地面试车方式,通过这两种方式对发动机状态进行自动监测,为发动机的状态趋势分析、故障诊断和视情维修提供科学的依据;采用双余度发动机状态监测技术研制的某型涡轴发动机状态实时监测系统经过了大量的地面试验和某型直升机上试飞试验,功能、性能满足要求,目前该系统已在某型直升机上得到应用。     

基于虚拟变形法的供水管网漏损定位方法

针对供水管网的漏损探测与定位问题,采用虚拟变形法对管网各参数进行计算,通过漏损影响矩阵和流量影响矩阵来表征管网的状态,并提出了漏损探测定位的优化计算模型.由于管网漏损点数量事先难以预先获取,提出了基于贝叶斯理论的管网漏损探测、定位模型,并结合Matlab和Epanet编制了相应的优化计算程序,数值仿真结果验证了方法的有效性和可行性.     

全媒体环境下媒体无人机应急报道平台的设想

随着计算机网络技术的日益成熟,无人机在新闻报道领域的创新应用越来越多.从无人机的基本应用入手,通过分析无人机与新闻报道的融合应用与演进,提出了其未来在云计算、大数据技术支撑下,将与多维采集、三维重建、虚拟现实、全媒体交互创新融合的构想,使视觉从二维走向三维、使演播室与现场环境智能匹配,并试图通过其工作流程与系统设计方面的分析,从技术角度阐述未来媒体无人机在应急报道中的发展方向.     

WinDock: structure-based drug discovery on Windows-based PCs

In recent years, virtual database screening using high-throughput docking (HTD) has emerged as a very important tool and a well-established method for finding new lead compounds in the drug discovery process. With the advent of powerful personal computers (PCs), it is now plausible to perform HTD investigations on these inexpensive PCs. To make HTD more accessible to a broad community, we present here WinDock, an integrated application designed to help researchers perform structure-based drug discovery tasks under a uniform, user friendly graphical interface for Windows-based PCs. WinDock combines existing small molecule searchable three-dimensional (3D) libraries, homology modeling tools, and ligand-protein docking programs in a semi-automatic, interactive manner, which guides the user through the use of each integrated software component. WinDock is coded in C++.     

Discovery of Monoacylglycerol Lipase (MAGL) Inhibitors Based on a Pharmacophore-Guided Virtual Screening Study

Monoacylglycerol lipase (MAGL) is an important enzyme of the endocannabinoid system that catalyzes the degradation of the major endocannabinoid 2-arachidonoylglycerol (2-AG). MAGL is associated with pathological conditions such as pain, inflammation and neurodegenerative diseases like Parkinson’s and Alzheimer’s disease. Furthermore, elevated levels of MAGL have been found in aggressive breast, ovarian and melanoma cancer cells. Due to its different potential therapeutic implications, MAGL is considered as a promising target for drug design and the discovery of novel small-molecule MAGL inhibitors is of great interest in the medicinal chemistry field. In this context, we developed a pharmacophore-based virtual screening protocol combined with molecular docking and molecular dynamics simulations, which showed a final hit rate of 50% validating the reliability of the in silico workflow and led to the identification of two promising and structurally different reversible MAGL inhibitors, VS1 and VS2. These ligands represent a valuable starting point for structure-based hit-optimization studies aimed at identifying new potent MAGL inhibitors.     

Multiscale Virtual Screening Optimization for Shotgun Drug Repurposing Using the CANDO Platform

Drug repurposing, the practice of utilizing existing drugs for novel clinical indications, has tremendous potential for improving human health outcomes and increasing therapeutic development efficiency. The goal of multi-disease multitarget drug repurposing, also known as shotgun drug repurposing, is to develop platforms that assess the therapeutic potential of each existing drug for every clinical indication. Our Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun multitarget repurposing implements several pipelines for the large-scale modeling and simulation of interactions between comprehensive libraries of drugs/compounds and protein structures. In these pipelines, each drug is described by an interaction signature that is compared to all other signatures that are subsequently sorted and ranked based on similarity. Pipelines within the platform are benchmarked based on their ability to recover known drugs for all indications in our library, and predictions are generated based on the hypothesis that (novel) drugs with similar signatures may be repurposed for the same indication(s). The drug-protein interactions used to create the drug-proteome signatures may be determined by any screening or docking method, but the primary approach used thus far has been BANDOCK, our in-house bioanalytical or similarity docking protocol. In this study, we calculated drug-proteome interaction signatures using the publicly available molecular docking method Autodock Vina and created hybrid decision tree pipelines that combined our original bio- and chem-informatic approach with the goal of assessing and benchmarking their drug repurposing capabilities and performance. The hybrid decision tree pipeline outperformed the two docking-based pipelines from which it was synthesized, yielding an average indication accuracy of 13.3% at the top10 cutoff (the most stringent), relative to 10.9% and 7.1% for its constituent pipelines, and a random control accuracy of 2.2%. We demonstrate that docking-based virtual screening pipelines have unique performance characteristics and that the CANDO shotgun repurposing paradigm is not dependent on a specific docking method. Our results also provide further evidence that multiple CANDO pipelines can be synthesized to enhance drug repurposing predictive capability relative to their constituent pipelines. Overall, this study indicates that pipelines consisting of varied docking-based signature generation methods can capture unique and useful signals for accurate comparison of drug-proteome interaction signatures, leading to improvements in the benchmarking and predictive performance of the CANDO shotgun drug repurposing platform.