Subjects: Environmental Sciences, Resource Sciences >> Basic Disciplines of Environmental Science and Technology submitted time 2020-06-12 Cooperative journals: 《干旱区研究》
Abstract：为探明覆膜滴灌条件下，作物根区土壤水、盐运移规律及其对“土壤-微生物及酶-根系”交互作用的影响，进一步提高水肥利用效率和完善精确灌溉制度，本文以温室番茄为研究对象，采用Field TDR 200对根区土壤水盐运移进行动态监测，研究常规滴灌和覆膜滴灌对水盐运移、根系、土壤微生物及酶活性的影响，分析根区土壤环境因子、土壤微生物及酶、根长密度的交互作用。结果表明：覆膜滴灌土壤水分迁移速率显著低于常规滴灌，水分分布相对均匀，且测定范围内土壤含水率≥灌水下限（22%）土壤面积为常规滴灌5倍（P<0.05）；局部盐分聚集速率减小50%，降低局部盐分聚集度；显著提高土壤温度和降低土壤pH；根区表层土壤根长密度为常规滴灌的12.8~28.5倍。这些环境因子的改变，进一步增强“土壤-微生物及酶-根系”交互作用，土壤脲酶活性提高20.83%~30.61%，磷酸酶活性提高76.92%~84.61%。因此，覆膜滴灌比常规滴灌更具水土资源利用效率提升潜力，相关农艺措施需进一步精细和完善，这可为提高干旱区设施农业水土资源利用效率提供支持。
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-10-11 Cooperative journals: 《计算机应用研究》
Abstract： There are many operating modes in modern industrial production processes, and there is a strong correlation between data sequences. Traditional SVDD as a single-mode static fault detection algorithm, it is difficult to ensure the accuracy and real-time performance of multi-mode dynamic process fault detection. In order to solve this problem, this paper propose a weighted dynamic SVDD monitoring method (NND-DWSVDD) base on nearest neighbor difference . First, use NND to eliminate the data multimodal structure and ensure that the process data obeys the unimodal distribution; then, introduce the dynamic method for the differentially processed data and add weights to highlight useful information. Finally, establish a monitoring model by using the SVDD method to achieve online monitoring. NND-DWSVDD improves the multi-modal dynamic process fault detection rate. For multimodal dynamic process fault detection, NND-DWSVDD does not require multi-model modeling, and only need a single model. It meet single-modal fault detection requirements. Through multi-modal numerical example and semiconductor production process data to validate the effectiveness of the method.
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-05-24 Cooperative journals: 《计算机应用研究》
Abstract： Aiming to improve the fault detection ability of FD-KNN in the nonlinear and multimodal process, this paper proposes a k nearest neighbors fault detection method based on varimax rotation (Rot-KNN) . First, implement varimax rotation in observed data set to obtain an orthogonal space. Next, implement FD-KNN in the new orthogonal space to detect faults. At last, propose a fault diagnosis strategy based on contribution chart. A nonlinear simulation example and the Tennessee Eastman (TE) processes of a typical nonlinear industrial process prove that the method is effective for the latent variable fault diagnosis. The experimental results indicate that the proposed method outperforms the PCA, FD-KNN and PC-KNN.
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-05-20 Cooperative journals: 《计算机应用研究》
Abstract： Aiming at non-Gaussian and multi-mode characteristics exist in industrial process data, this paper proposed a fault detection of multi-model batch process method based on statistics difference LPP. Firstly, the method of statistical pattern analysis was applied to the batch process training data set to calculate the mean and variance of statistical process variables, and turned the uneven-length batches into equal-length statistics. It could ensure that the statistics pattern approximately obeyed the Gaussian distribution. Then it used the difference algorithm to transform the multi-mode into single mode. Finally, it used the LPP algorithm to reduce dimension and extract feature, and calculated the T2 statistic of the sample. And it used the kernel density estimation to determine the control limit. The new test sample data projected onto the LPP model after statistics difference processing. Then it calculated the T2 statistics of the new data and compared them with the control limit for fault detection. Finally, the simulation results of the semiconductor process data show that this algorithm has the best fault detection effect, and demonstrate the effectiveness of the proposed algorithm.
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-04-12 Cooperative journals: 《计算机应用研究》
Abstract： In order to improve the ability of fault detection and classification, this paper proposed PCA based on probability density for fault detection of multimodal processes. It established PCA model for training data of each mode, and calculated the control limits and matching coefficients of each model. It calculated the unified control limit of each mode according to the matching coefficients. For a new data, it determined its mode by the probability density. It projected the new data to PCA model of the corresponding mode and calculated the unified statistics. It performed fault detection of multimodal processes by comparing the statistics with control limit. We applied the method to a numerical example and the semiconductor process. Simulation results show that the proposed algorithm has high accuracy in classification and fault detection of multimodal processes.
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