教学过程化监控管理系统的设计

针对高校网络课程教学中出现的问题,根据教育技术学的相关理论,如协作学习、情境学习等,提出了教学过程化监控管理系统的设计方案。通过该系统的监控、管理、评价和反馈功能的相互作用,来提升高校网络课程的利用率,提高学习者自主学习的兴趣和学习效率,进而提高高校的教学管理效率及教学质量。

三维设计过程可视化监控的原理与实践

为完善船舶三维设计体系，提高设计和管理水平，开展了对产品设计过程进行动态跟踪和实时监控的研究。在建立的三维设计监控平台上，实现了对多型军民产品的并行、实时、主动式可视化监控。最后针对新技术、新需求的出现，提出了今后的发展趋势。

依托“互联网+”构建网络化教学新模式

随着科技的不断发展,网络化的教育教学改革已引起教育工作者的重视。基于此,文章提出了在高等教育教学的课堂教学课程群资源共享、学术活动交流互动等两个方面依托"互联网+"构建网络化教学新模式,促进丰富的、多层次的、融合的教学资源共享,促进师生之间实时的交流互动。

财经类专业信息技术教育改革的研究

信息技术日新月异的发展对当今财经行业产生了深远的影响。推广信息技术、培养适应信息时代需要的高素质财经人才是高等财经教育的首要目标,但是现在财经专业的信息技术教育相对滞后和缺少,本文就加强财经类专业信息技术教育改革提出了总体思想和解决方法.以探寻培养财经类高素质人才的有效途径。

Multivariate Statistical Process Monitoring and Control: Recent Developments and Applications to Chemical Industry

Multivariate statistical process monitoring and control (MSPM&C) methods for chemical process monitoring with statistical projection techniques such as principal component analysis (PCA) and partial least squares (PLS) are surveyed in this paper. The four-step procedure of performing MSPM&C for chemical process, modeling of processes, detecting abnormal events or faults, identifying the variable(s) responsible for the faults and diagnosing the source cause for the abnormal behavior, is analyzed. Several main research directions of MSPM&C reported in the literature are discussed, such as multi-way principal component analysis (MPCA) for batch process, statistical monitoring and control for nonlinear process, dynamic PCA and dynamic PLS, and on-line quality control by inferential models. Industrial applications of MSPM&C to several typical chemical processes, such as chemical reactor, distillation column, polymerization process, petroleum refinery units, are summarized. Finally, some concluding remarks and future considerations are made.

Investigation of Dynamic Multivariate Chemical Process Monitoring

Chemical process variables are always driven by random noise and disturbances. The closed-loop con-trol yields process measurements that are auto and cross correlated. The influence of auto and cross correlations on statistical process control (SPC) is investigated in detail by Monte Carlo experiments. It is revealed that in the sense of average performance, the false alarms rates (FAR) of principal component analysis (PCA), dynamic PCA are not affected by the time-series structures of process variables. Nevertheless, non-independent identical distribution will cause the actual FAR to deviate from its theoretic value apparently and result in unexpected consecutive false alarms for normal operating process. Dynamic PCA and ARMA-PCA are demonstrated to be inefficient to remove the influences of auto and cross correlations. Subspace identification-based PCA (SI-PCA) is proposed to improve the monitoring of dynamic processes. Through state space modeling, SI-PCA can remove the auto and cross corre-lations efficiently and avoid consecutive false alarms. Synthetic Monte Carlo experiments and the application in Tennessee Eastman challenge process illustrate the advantages of the proposed approach.

Fault diagnosis of chemical processes based on partitioning PCA and variable reasoning strategy

Fault detection and identification are challenging tasks in chemical processes, the aim of which is to decide out of control samples and find fault sensors timely and effectively. This paper develops a partitioning principal component analysis(PPCA) method for process monitoring. A variable reasoning strategy is proposed and applied to recognize multiple fault variables. Compared with traditional process monitoring methods, the PPCA strategy not only reflects the local behavior of process variation in each model(each direction of principal components),but also improves the monitoring performance through the combination of local monitoring results. Then, a variable reasoning strategy is introduced to locate fault variables. Unlike the contribution plot, this method locates normal and fault variables effectively, and gives initiatory judgment for ambiguous variables. Finally, the effectiveness of the proposed process monitoring and fault variable identification schemes is verified through a numerical example and TE chemical process.

Statistical Monitoring of Chemical Processes Based on Sensitive Kernel Principal Components

The kernel principal component analysis （KPCA） method employs the first several kernel principal components （KPCs）, which indicate the most variance information of normal observations for process monitoring, but may not reflect the fault information. In this study, sensitive kernel principal component analysis （SKPCA） is proposed to improve process monitoring performance, i.e., to deal with the discordance of T2 statistic and squared prediction error SVE statistic and reduce missed detection rates. T2 statistic can be used to measure the variation di rectly along each KPC and analyze the detection performance as well as capture the most useful information in a process. With the calculation of the change rate of T2 statistic along each KPC, SKPCA selects the sensitive kernel principal components for process monitoring. A simulated simple system and Tennessee Eastman process are employed to demonstrate the efficiency of SKPCA on online monitoring. The results indicate that the monitoring performance is improved significantly.

Effects of Mechanical Impacts on Hatchability of Broiler Breeders

Transporting hatching eggs from Hungary and incubating them abroad revealed lower hatchability compared to when the eggs were incubated in Hungary. Following transport, there were higher embryo losses and, notably, more malformed embryos. The aim of these initial trials was to determine if a testing device （crazy fit massage machine （CFM machine）） was able to replicate and model the mechanical impacts experienced during transport and reproduce the reduction in hatchability and increase the level of malformed embryos as observed in commercial practice. Tinytag~ high sensitivity shock and vibration loggers were used to monitor the impacts under field and trial conditions. Applying single 10 min treatments on the CFM machine, which used the same frequency （10-30 Hz） as the eggs experience under field conditions, induced the negative effect of transport, and lower hatching results were experienced. Three trials were conducted. Treated eggs in Trials 1 and 2 received automatically and periodical changing vibration in a range between 10-30 Hz for 10 min while in Trial 3 two different levels of impact were applied at 20 Hz and 30 Hz, respectively. Hatchability decreased due to the treatment significatly only in Trial 3. Significant differences were also detected in early dead levels in Trials 2 and 3 and the occurrences of malformation in Trials 1 and 3. All these results are in accordance with the field experience. Thus, the trials which examined the equipment were able to produce mechanical impacts that were repeatable in order to set up statistically reliable trials on hatching eggs.