The PVC Stripping Process Predictive Control Based on the Implicit Algorithm

According to the nonlinear and parameters time-varying characteristics of stripper temperature control system, the PVC stripping process Generalized Predictive Control based on implicit algorithm is proposed. Firstly, supporting vector machine is adopted to dynamically modelize for the stripper temperature; Secondly, combining with real-time model linearized of nonlinear model, a predictive model is linearized for real-time online correction. Then, the implicit algorithm is used for optimal control law. Finally, the simulation results show that the algorithm has excellent validity and robustness of temperature control of the stripper

Learning the Network of Graphs for Graph Neural Networks

Graph neural networks (GNNs) have achieved great success in many scenarios with graph-structured data. However, in many real applications, there are three issues when applying GNNs: graphs are unknown, nodes have noisy features, and graphs contain noisy connections. Aiming at solving these problems, we propose a new graph neural network named as GL-GNN. Our model includes multiple sub-modules, each sub-module selects important data features and learn the corresponding key relation graph of data samples when graphs are unknown. GL-GNN further obtains the network of graphs by learning the network of sub-modules. The learned graphs are further fused using an aggregation method over the network of graphs. Our model solves the first issue by simultaneously learning multiple relation graphs of data samples as well as a relation network of graphs, and solves the second and the third issue by selecting important data features as well as important data sample relations. We compare our method with 14 baseline methods on seven datasets when the graph is unknown and 11 baseline methods on two datasets when the graph is known. The results show that our method achieves better accuracies than the baseline methods and is capable of selecting important features and graph edges from the dataset. Our code will be publicly available at \url{https://github.com/Looomo/GL-GNN}

Design of Enterprise Production and Sales measures and Forecast Information System Based on WEB

The enterprise supply of raw materials and production, sales, product information integration as a research object, on the basis of J2EE architecture and the Spring framework, building Web Based Enterprise production and distribution and prediction of multi-tier architecture ERP system. First, use spring+hibernate implementation of MVC design mode ; and, with mvc mode implementation of J2EE Multi - Application System schema, development function perfect enterprise sales management system ; last, use move average law, and Trend Forecast law, and index smooth law, and line type the most Xiaoping method and curve type the most Xiaoping method Five kind of algorithm average to sales accurate real - time forecast, decision sector can developed accurate production plan to effective guidance production job. DOI : http://dx.doi.org/10.11591/telkomnika.v12i6.547

Rolling bearing fault diagnosis of PSO-LSSVM based on CEEMD entropy fusion

Taking aim at the nonstationary nonlinearity of the rolling bearing vibration signal, a rolling bearing fault diagnosis method based on the entropy fusion feature of complementary ensemble empirical mode decomposition (CEEMD) is proposed in combination with information fusion theory. First, CEEMD of the vibration signal of the rolling bearing is performed. Then the signal is decomposed into the sum of several intrinsic mode functions (IMFs), and the singular entropy, energy entropy, and permutation entropy are obtained for the IMFs with fault features. Second, the feature extraction method of entropy fusion is proposed, and the three entropy data obtained are input into kernel principal component analysis (KPCA) for feature fusion and dimensionality reduction to obtain complementary features. Finally, the extracted features are imported into the particle swarm optimization (PSO) algorithm to optimize the least-squares support vector machine (LSSVM) for fault classification. Through experimental verification, the proposed method can be used for roller bearing fault diagnosis.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Remote Monitoring System Based on GPRS Technique for Polymerizer

In order to the make the production of PVC safety and efficiency, a remote monitoring system based on GPRS technique for PVC polymerizer was introduced in this paper, the overall structure of the device was designed, and the dissertation presented a new communication protocol of the communications terminal, monitoring center, and client-side based on CDT communication convention and polling communication convention, the advantages of which are integrated and disadvantages improved and in the design of monitoring and management system, using the structs+spring+hibernate architecture develop remote monitoring software, realized the real-time data display, remote control, historical data query, and other functions. Thus efficient and reliable data transmission was achieved, the remote dynamic monitoring on the real-time operation date and faults for different sets of PVC polymerizers was also realized

Robust strong tracking unscented Kalman filter for non‐linear systems with unknown inputs

Abstract This paper proposes a state estimation approach ‘robust strong tracking unscented Kalman filter with unknown inputs’ that can be applied to non‐linear systems with unknown inputs. Specifically, the non‐linear state and measurement equations are linearised by statistical linearisation. Then, the estimation equation of the unknown input is derived based on the weighted least squares method. The multiple suboptimal fading factor is introduced into a priori error covariance matrix to improve the tracking ability for the inaccuracy of the system model and the abrupt change of state variables caused by unknown inputs. Finally, based on the unbiased minimum variance estimation, the unbiased state estimation and the error covariance matrix are derived. Singular value decomposition is performed on the error covariance matrix to improve the stability of the algorithm. Simulated results validate the effectiveness of the proposed method

Effect of Ozone Micro-Nano-Bubbles Treatment on “Green” and the Mechanism in Soybean Sprout

In order to explore the effect of ozone micro-nano-bubbles (Ozone MNBs) on “green” and the regulative mechanism in soybean sprout, this study took soybean sprout as the experimental material, treated with 4 mg/L Ozone MNBs and stored in white LED condition. Physical quality, synthesizing and decomposing of chlorophyll (enzyme activity and substance) were measured in soybean sprout. Compared with control group, 4 mg/L Ozone MNBs treatment could significantly inhibit the “green”, enhance the activities of chlorophyllase (Chlase), chlorophyll degrading peroxidase (Chl-POX), Mg-dechelatase (MD) and pheophytinase (PPH). And it decreased the levels of precursors in chlorophyll synthesis [δ-aminolevulinic acid (ALA) and Urogen Ⅲ], chlorophyll, chlorophyll a and chlorophyll b. Additionally, it declined the content of ADP, ATP, NADP+ and NADPH in soybean sprout. Thus, 4 mg/L Ozone MNBs treatment affected the substance and enzyme activity of synthesizing and decomposing of chlorophyll, effectively hindered “green” in soybean sprout under white LED

Robust adaptive control for greenhouse climate using neural networks

This paper presents a general framework for robust adaptive neural network (NN)-based feedback linearization controller design for greenhouse climate system. The controller is based on the well-known feedback linearization, combined with radial basis functions NNs, which allows the feedback linearization technique to be used in an adaptive way. In addition, a robust sliding mode control is incorporated to deal with the bounded disturbances and the approximation errors of NNs. As a result, an inherently nonlinear robust adaptive control law is obtained, which not only provides fast and accurate tracking of varying set-points, but also guarantees asymptotic tracking even if there are inherent approximation errors. Copyright © 2010 John Wiley & Sons, Ltd.Xiaoli Luan, Peng Shi and Fei Li

Bottom-up saliency detection for attention determination

10.1007/s00138-011-0372-6Machine Vision and Applications241103-116MVAP

Skin-like cryogel electronics from suppressed-freezing tuned polymer amorphization

Abstract The sole situation of semi-crystalline structure induced single performance remarkably limits the green cryogels in the application of soft devices due to uncontrolled freezing field. Here, a facile strategy for achieving multifunctionality of cryogels is proposed using total amorphization of polymer. Through precisely lowering the freezing point of precursor solutions with an antifreezing salt, the suppressed growth of ice is achieved, creating an unusually weak and homogenous aggregation of polymer chains upon freezing, thereby realizing the tunable amorphization of polymer and the coexistence of free and hydrogen bonding hydroxyl groups. Such multi-scale microstructures trigger the integrated properties of tissue-like ultrasoftness (Young’s modulus <10 kPa) yet stretchability, high transparency (~92%), self-adhesion, and instantaneous self-healing (<0.3 s) for cryogels, along with superior ionic-conductivity, antifreezing (−58 °C) and water-retention abilities, pushing the development of skin-like cryogel electronics. These concepts open an attractive branch for cryogels that adopt regulated crystallization behavior for on-demand functionalities