完备ARRs集的生成方法及传感器优化配置应用

广泛应用于故障诊断和传感器优化、分析、证实的解析冗余关系(Analytical redundancy relations,ARRs)缺乏系统、有效的方法来产生完备ARRs集,为此,提出了一种逐次消元法。该方法以系统元关系(Primary relations,PRs)为基础,通过若干次循环消元过程,生成了完备ARRs集,同时生成了对应的假定特征矩阵(Hypothetical signature matrix,HSM);基于HSM,把传感器优化配置问题映射为一个特殊的0-1整数规划模型,并用分支定界法求解该模型。应用表明,该方法能在不降低故障检测率、隔离率的前提下减少传感器数目,降低了测试代价,对故障诊断中的传感器配置问题有借鉴意义。     

A unified method for optimal arbitrary pole placement

We consider the classic problem of pole placement by state feedback. We offer an eigenstructure assignment algorithm to obtain a novel parametric form for the pole-placing feedback matrix that can deliver any set of desired closed-loop eigenvalues, with any desired multiplicities. This parametric formula is then exploited to introduce an unconstrained nonlinear optimisation algorithm to obtain a feedback matrix that delivers the desired pole placement with optimal robustness and minimum gain. Lastly we compare the performance of our method against several others from the recent literature.     

Dynamic replica placement and selection strategies in data grids— A comprehensive survey

Data replication techniques are used in data grid to reduce makespan, storage consumption, access latency and network bandwidth. Data replication enhances data availability and thereby increases the system reliability. There are two steps involved in data replication, namely, replica placement and replica selection. Replica placement involves identifying the best possible node to duplicate data based on network latency and user request. Replica selection involves selecting the best replica location to access the data for job execution in the data grid. Various replica placement and selection algorithms are available in the literature. These algorithms measure and analyze different parameters such as bandwidth consumption, access cost, scalability, execution time, storage consumption and makespan. In this paper, various replica placement and selection strategies along with their merits and demerits are discussed. This paper also analyses the performance of various strategies with respect to the parameters mentioned above. In particular, this paper focuses on the dynamic replica placement and selection strategies in the data grid environment.     

Increasing urea-N efficiency for transplanted lowland rice by pneumatic injection: Yield and economics at the farm level

To increase the fertilizer-N efficiency in lowland rice (Oryza sativa L.) cultivation, new management practices are needed. Main cause of the present low efficiency is the low N recovery by plants, as a considerable part of the N applied is lost; deep placement techniques improve the recovery. A pneumatic injector, with which urea prills can be point-placed at a depth of 5–10 cm in paddy soils, was tested in 38 on-farm trials in 1989/90, mostly during the wet season. The experiments, located in Africa and Asia, focussed on differences in grain yield between conventional methods of broadcasting urea and injection by the pneumatic injector, at recommended N-rates. The study shows that the pneumatic injector is effective as a tool to improve the N fertilizer efficiency. The average yield increases per region, resulting from the use of the injector, ranged from about 250 to 1300 kg grain ha^–1. The value of the yield increase would allow most farmers to recover the costs of the injector within one season, even if labour was hired to carry out the injections. The average labour requirement of the injector was 40 hours ha^–1. In Indonesia, injection of prilled urea gave yields similar to those obtained with urea briquettes.     

Multivariable adaptive control: A survey

Adaptive control is a control methodology capable of dealing with uncertain systems to ensure desired control performance. This paper provides an overview of some fundamental theoretical aspects and technical issues of multivariable adaptive control, and a thorough presentation of various adaptive control schemes for multi-input–multi-output systems, literature reviews on adaptive control foundations and multivariable adaptive control methods, and related technical problems. It covers some basic concepts and issues such as certainty equivalence, stability, tracking, robustness, and parameter convergence. It discusses some of the most important topics of adaptive control: plant uncertainty parametrization, stable controller adaptation, and design conditions for different adaptive control schemes. The paper also presents a detailed study of well-developed multivariable model reference adaptive control theory and design techniques. It provides an introduction to multivariable adaptive pole placement and adaptive nonlinear control, and it concludes by identifying some open research problems.     

多传感器集中式增量卡尔曼滤波融合算法

在单个传感器的状态估计系统中,标准的增量卡尔曼滤波方法可以有效消除量测系统误差。对于多传感器情况,标准算法失效。针对该问题,提出了多传感器集中式增量卡尔曼滤波融合算法,即:增量卡尔曼滤波的扩维融合算法和增量卡尔曼滤波的序贯融合算法。在标准增量卡尔曼滤波算法的基础上,结合扩维融合和序贯融合的思想来实现多传感器数据的融合。实验结果表明,当存在量测系统误差时,提出的集中式融合算法与传统的集中式融合算法相比,提高了滤波精度,并且能够成功地消除量测系统误差。     

适用于智能传感器系统的SVM集成研究

以支持向量机(SVM)为代表的人工智能技术在智能传感器系统中得到了广泛的应用,但传统的SVM有"灾难性遗忘"现象,即会遗忘以前学过的知识,并且不能增量学习新的数据,这已无法满足智能传感器系统实时性的要求。而Learn++算法能够增量地学习新来的数据,即使新来数据属于新的类,也不会遗忘已经学习到的旧知识。为了解决上述问题,提出了一种基于壳向量算法的Learn++集成方法。实验结果表明:该算法不但具有增量学习的能力,而且在保证分类精度的同时,提高了训练速度,减小了存储规模,可以满足当下智能传感器系统在线学习的需求。     

高精密恒温系统在NDIR气体分析仪中的应用

当前基于非分光红外（NDIR）技术的气体分析仪设计过程中存在很多干扰源,这些干扰源对检测精度影响的轻重程度各有不同,其中,红外池中,温度的稳定性对气体的吸收影响大,此外,对于特定的吸收气体,恒温点同样重要。设计了一种基于增量式PID算法的高精度温控系统,选择对气体吸收影响最小的恒温点,并控温范围为±0.1℃,相比现有同类温控系统,精度提高近10倍。通过试验比较不控温情况下的结果和应用在本恒温系统中的测试结果表明：高精度恒温系统下,NDIR气体分析仪的信噪比得到了明显改善。该成果将直接应用到国家＂十二·五＂计划项目中。     

Ammonia volatilization from urea as affected by the addition of iron pyrites and method of application

Laboratory incubations were conducted to determine the ammonia (NH₃) loss from urea as affected by the addition of coarse and ground (fine) pyrites at 1:1, 1:2 and 1:5 urea: pyrite (w/w) ratios and methods of application (surfaceapplication, incorporation and placement). Coarse pyrites (>-2mm) were not effective in reducing NH₃ loss from urea when surface applied even at the highest ratio of pyrite (15.9% vs 18.7% without pyrite). Ground pyrites (0.1–0.25 mm), in 1:1 ratio, had about 5% less NH₃ loss than the urea alone treatment. Higher ratios of pyrites reduced NH₃ loss much more. Ammonia losses were the most with surface-applied urea (18.9%) and the least (13.5%) when placed (2.5 cm) below the soil surface. Addition of ground pyrite to surface-applied urea (1:1 ratio) decreased the loss to 13.2%. Urea+pyrite placed below the soil surface had the least loss (9.8%). Results indicate that combined application of urea and fine pyrite could reduce NH₃ loss.     

基于资源分配网络的小数据集并行集成学习方法

机器学习领域中,如何在小规模的训练数据集上获得一个具有稳定的高计算精度的算法模型,一直以来都是一个棘手而富有挑战的问题。从算法模型出发,提出了一种基于扩展卡尔曼滤波器的资源分配网络并行集成学习方法。该集成系统由多个带有扩展卡尔曼滤波器的资源分配网络（RANEKF）组成,并且每个RANEKF子网的输入由原始数据集中的输入经过随机权值的修正得到。通过和其他神经网络构成的集成学习算法的实验对比,发现提出的方法在小训练集上拥有更高的计算精度和稳定性。