基于C/S架构电动汽车数据管理系统的开发

电动汽车示范运营考核是大规模推广电动汽车的基础性工作,对相关政策的制定以及促进电动汽车的全面应用意义重大。针对电动汽车运营考核的各项参数要求,利用Visual Basic 6.0和Access数据库,开发电动汽车运营考核数据管理系统。该系统实现了电动汽车数据的收发,数据的存储﹑显示,历史数据图表的显示等功能,为电动汽车的研究开发以及运行工况分析提供了有力的帮助。     

基于SQL Server数据库的三维模型存取研究与实现

主要介绍将三维模型存储到SQL Server数据库中,及其用VC＋＋去读取数据库中的三维模型信息并显示。     

Tributary use and large-scale movements of grass carp in Lake Erie

Infrequent captures of invasive, non-native grass carp (Ctenopharyngodon idella) have occurred in Lake Erie over the last 30+ years, with recent evidence suggesting wild reproduction in the lake’s western basin (WB) is occurring. Information on grass carp movements in the Laurentian Great Lakes is lacking, but an improved understanding of large-scale movements and potential areas of aggregation will help inform control strategies and risk assessment if grass carp spread to other parts of Lake Erie and other Great Lakes. Twenty-three grass carp captured in Lake Erie’s WB were implanted with acoustic transmitters and released. Movements were monitored with acoustic receivers deployed throughout Lake Erie and elsewhere in the Great Lakes. Grass carp dispersed up to 236 km, with approximately 25% of fish dispersing greater than 100 km from their release location. Mean daily movements ranged from <0.01 to 2.49 km/day, with the highest daily averages occurring in the spring and summer. The Sandusky, Detroit, and Maumee Rivers, and Plum Creek were the most heavily used WB tributaries. Seventeen percent of grass carp moved into Lake Erie’s central or eastern basins, although all fish eventually returned to the WB. One fish emigrated from Lake Erie through the Huron-Erie Corridor and into Lake Huron. Based on our results, past assessments may have underestimated the potential for grass carp to spread in the Great Lakes. We recommend focusing grass carp control efforts on Sandusky River and Plum Creek given their high use by tagged fish, and secondarily on Maumee and Detroit Rivers.     

Electronic locking devices based on microcontrollers and chaotic maps using Model-Matching Control

In this work a real-time communication system using Arduino® microcontrollers, applied to electronic locking devices, is implemented. Model-Matching Control is used to achieve synchronization between transmitter and receiver Arduino® microcontrollers using only one transmission channel. Model-Matching Control is fed with dynamics from both Arduinos. Transmitter Arduino® is used also to generate in real-time an encrypted chaotic code key based on the Chen map. Receiver Arduino® recover in real-time the chaotic code key that is a binary signal key of an electronic locking device and where wireless communication is made between the two Arduinos using Bluetooth modules. System evaluation in terms of performance, randomness, and time complexity, are shown, as well as experimental results and some discussions are presented.     

Investigating the validity of subjective workload rating (NASA TLX) and subjective situation awareness rating (SART) for cognitively complex human–machine work

Subjective workload and situation awareness measures, such as the NASA task load index (TLX) and the situational awareness rating technique (SART), are frequently used in human–system evaluation. However, the interpretation of these ratings is debated. In this study, empirical evidence for the measures' theoretical assumptions was investigated by comparing operators' ratings collected immediately after performing a scenario and ratings collected after operators' acquisition through a video review of the scenario, knowledge of actual system states. Eighteen licensed control room operators participated in the simulator study, running 12 relatively challenging scenarios. It was found that the interpretation of TLX items involving introspection remained stable after operators acquired factual scenario knowledge, while the interpretation of items involving the perception of external events, such as situation awareness and performance, depended on the operators' scenario knowledge. The result shows that operators’ ratings could discriminate between mental effort, performance, frustration, and situation awareness. No clear evidence for the SART index as a measure of situation awareness was found. Instead, a subjective situation awareness measure developed for this study was distinct from workload and related to operator performance, showing that this type of measure warrants future investigation of its validity. The study findings help in developing measurement procedures and interpreting subjective measures. Finally, the study reveals that informing operators about the scenario can provide useful subjective ratings of situation awareness and performance. Future research should include procedures for how to inform participants adequately and efficiently in subjective assessments.     

Conceptual digital twin modeling based on an integrated five-dimensional framework and TRIZ function model

Digital twin represents a fusion of the informational and physical domains, to bridge the material and virtual worlds. Existing methods of digital twin modeling are mainly based on modular representation, which limits guidance of the modeling process. Such methods do not consider the components or operational rules of the digital twin in detail, thereby preventing designers from applying these methods in their fields. With the increasing application of digital twin to various engineering fields, an effective method of modeling a multi-dimensional digital twin at the conceptual level is required. To such an end, this paper presents a method for the conceptual modeling of a digital twin based on a five-dimensional digital twin framework to represent the complex relationship between digital twin objects and their attributes. The proposed method was used to model the digital twin of an intelligent vehicle at the concept level.     

Making costly manufacturing smart with transfer learning under limited data: A case study on composites autoclave processing

The integration of advanced manufacturing processes with ground-breaking Artificial Intelligence methods continue to provide unprecedented opportunities towards modern cyber-physical manufacturing processes, known as smart manufacturing or Industry 4.0. However, the “smartness” level of such approaches closely depends on the degree to which the implemented predictive models can handle uncertainties and production data shifts in the factory over time. In the case of change in a manufacturing process configuration with no sufficient new data, conventional Machine Learning (ML) models often tend to perform poorly. In this article, a transfer learning (TL) framework is proposed to tackle the aforementioned issue in modeling smart manufacturing. Namely, the proposed TL framework is able to adapt to probable shifts in the production process design and deliver accurate predictions without the need to re-train the model. Armed with sequential unfreezing and early stopping methods, the model demonstrated the ability to avoid catastrophic forgetting in the presence of severely limited data. Through the exemplified industry-focused case study on autoclave composite processing, the model yielded a drastic (88%) improvement in the generalization accuracy compared to the conventional learning, while reducing the computational and temporal cost by 56%.     

网络安全管理

就网络安全管理的几个方面进行了简述,提出了按照不同维度对网络安全管理进行深入管理和保护的思路和方法。     

网络信息安全预警监控系统改对与实现

着眼于内网安全,从地址管理、进程状态跟踪、安全软件更新、攻击行为检测、主干线路监测评估等方面建立了一套完整的内网安全预警监控机制,并提出了技术实现方法,有效解决了目前内网安全管理问题,综合提升了网络信息安全预警防护能力。     

便携式数控系统ARM监控平台的设计

在数控系统中,通常需要利用串口或者CAN总线进行数据监控,针对满足其数据采集与发送及存储的技术要求,提出一种便携式的数控系统监控平台设计方法;基于Freescale的ARM芯片MCIMX357与Linux操作系统,利用外围接口电路,实现大容量数据存储、串口多模式通信、CAN总线数据监控,同时附加人机友好界面,利用配套电源管理方案实现低功耗便携功能;通过测试表明设计合理可靠,功耗为3.5W,串口通信频率为115200bps,CAN通信速度为500K,同时该设计成果成功应用于我单位某型数控系统中。