Improved fault detection in nonlinear chemical processes using WKPCA-SVDD

Qingchao Jiang,Xuefeng Yan 한국화학공학회 2014 Korean Journal of Chemical Engineering Vol.31 No.11

Conventional kernel principal component analysis (KPCA) does not always perform well for nonlinearprocess monitoring because the beneficial information for fault detection may be submerged under the retained kernelprincipal components (KPCs). To overcome this deficiency, an adaptively weighted KPCA integrated with supportvector data description (WKPCA-SVDD) monitoring method is proposed. In WKPCA-SVDD, the importance of eachKPC is evaluated online by the change rate of T2statistic and then distinguished weighting values are set on the KPCs. The behaviors of all KPCs are comprehensively evaluated by the SVDD technique. Since the beneficial informationis highlighted, the monitoring performance of the statistic in the dominant subspace can be improved. The proposedWKPCA-SVDD is applied to both a numerical process and the complicated Tennessee Eastman benchmark process. Monitoring results have indicated the efficiency of the WKPCA-SVDD method.

Design and Implementation of Smart Home System Based on ZigBee Technology

Qingchao Gong,Guangming Li,Yong Pang 보안공학연구지원센터 2014 International Journal of Smart Home Vol.8 No.6

The system of smart home can provide a kind of easier, ordered and effective life style to human, and must be the development tendency for future inhabitancy mode. Therefore, a smart home system solution is proposed in this paper. Firstly, this paper analyzes the characteristics of ZigBee technology, device types, network topologies and ZigBee protocol specification. Secondly, a design scheme of smart home system based on ZigBee technology is put forward and discussed. Then, the hardware design of ZigBee nodes based on CC2530 chip is given in detail. And, embedded programming software in each ZigBee node and user management platform software are implemented respectively using IAR and Microsoft Visual C++ 6.0. Finally, system test results show that the smart home system implemented above can be served as practical application reliably.

Screening and Application of Bacillus Strains Isolated from Nonrhizospheric Rice Soil for the Biocontrol of Rice Blast

Yuexia Sha,Qingchao Zeng,Shuting Sui 한국식물병리학회 2020 Plant Pathology Journal Vol.36 No.3

Rice blast, caused by Magnaporthe oryzae, is one of the most destructive rice diseases worldwide. The aim of this study was to screen bacterial isolates to efficiently prevent the occurrence of rice blast. A total of 232 bacterial isolates were extracted from nonrhizospheric rice soil and were screened for antifungal activity against M. oryzae using a leaf segment assay. Strains S170 and S9 showed significant antagonistic activity against M. oryzae in vitro and in leaf disk assays, and controlled M. oryzae infection under greenhouse conditions. The results showed that strains S170 and S9 could effectively control rice leaf blast and panicle neck blast after five spray treatments in field. This suggested that the bacterial strains S170 and S9 were valuable and promising for the biocontrol of rice disease caused by M. oryzae. Based on 16S rDNA, and gyrA and gyrB gene sequence analyses, S170 and S9 were identified as Bacillus amyloliquefaciens and B. pumilus, respectively. The research also demonstrated that B. amyloliquefaciens S170 and B. pumilus S9 could colonize rice plants to prevent pathogenic infection and evidently suppressed plant disease caused by 11 other plant pathogenic fungi. This is the first study to demonstrate that B. amyloliquefaciens and B. pumilus isolated from nonrhizospheric rice soil are capable of recolonizing internal rice stem tissues.

Fault detection in nonlinear chemical processes based on kernel entropy component analysis and angular structure

Xuefeng Yan,Qingchao Jiang,Zhaomin Lv,Meijin Guo 한국화학공학회 2013 Korean Journal of Chemical Engineering Vol.30 No.6

Considering that kernel entropy component analysis (KECA) is a promising new method of nonlinear data transformation and dimensionality reduction, a KECA based method is proposed for nonlinear chemical process monitoring. In this method, an angle-based statistic is designed because KECA reveals structure related to the Renyi entropy of input space data set, and the transformed data sets are produced with a distinct angle-based structure. Based on the angle difference between normal status and current sample data, the current status can be monitored effectively. And,the confidence limit of the angle-based statistics is determined by kernel density estimation based on sample data of the normal status. The effectiveness of the proposed method is demonstrated by case studies on both a numerical process and a simulated continuous stirred tank reactor (CSTR) process. The KECA based method can be an effective method for nonlinear chemical process monitoring.

A Study on Rotor Profiles Design for a Novel Twinscrew Kneader

Jing Wei,Qingchao Sun,Xujian Sun,Wei Sun 한국정밀공학회 2013 International Journal of Precision Engineering and Vol. No.

The profile curve design method and the mathematical model of conjugate surfaces of twin-screw kneader used for high viscosity material mixing, transportation and malaxating are established according to the principle of gear engagement. A novel twin-screw kneader and the equations of the profile curves of the rotors as well as the rotor design are proposed. The working principles of the twin screw kneader are analyzed and numerical simulations for dynamic characteristics of the flow field are carried out Threedimensional,isothermal and steady state numerical analysis of a non-Newtonian fluid are presented, based on computational fluid dynamics (CFD) theory, and the characteristics under the conditions of different speeds and center distances are studied, such as pressure distributions, velocity fields, the maximum shear stresses and the mass flow rates. The results provide a theoretical basis for performance predictions and reasonable design for the novel twin-screw kneader.

Improvement on LEACH-C Protocol of Wireless Sensor Network (LEACH-CC)

Zhenfu Ma,Guangming Li,Qingchao Gong 보안공학연구지원센터 2016 International Journal of Future Generation Communi Vol.9 No.2

Wireless Sensor Network (WSN) has been considered as a promising method for reliably monitoring civil and military environment in hazardous or dangerous conditions. Usually, sensor webs consisting of nodes with limited power and limited wireless communications are deployed to collect useful information from the field. Due to these reasons, how to improve energy efficiency is a critical issue for WSN protocols. The Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol puts forward an algorithm where sensor nodes are built into clusters to fuse data before transmitting to Base Station (BS). An improved version of LEACH, called Low-Energy Adaptive Clustering Hierarchy-centralized (LEACH-C), is presented, where the central Base Station (BS) executes the clustering to improve energy efficiency. This paper presents an improvement of LEACH-C based on power, called Low-Energy Adaptive Clustering Hierarchy-central constrained (LEACH-CC), which balances energy distribution of network by means of changing range of nodes being cluster head. Stimulation results show that LEACH-CC can improve system lifetime over its comparatives.

Fault detection and identification using a Kullback-Leibler divergence based multi-block principal component analysis and bayesian inference

Bei Wang,Xuefeng Yan,Qingchao Jiang 한국화학공학회 2014 Korean Journal of Chemical Engineering Vol.31 No.6

Considering the huge number of variables in plant-wide process monitoring and complex relationships(linear, nonlinear, partial correlation, or independence) among these variables, multivariate statistical process monitoring(MSPM) performance may be deteriorated especially by the independent variables. Meanwhile, whether related variableskeep high concordance during the variation process is still a question. Under this circumstance, a multi-block technologybased on mathematical statistics method, Kullback-Leibler Divergence, is proposed to put the variables having similarstatistical characteristics into the same block, and then build principal component analysis (PCA) models in each lowdimensionalsubspace. Bayesian inference is also employed to combine the monitoring results from each sub-blockinto the final monitoring statistics. Additionally, a novel fault diagnosis approach is developed for fault identification. The superiority of the proposed method is demonstrated by applications on a simple simulated multivariate processand the Tennessee Eastman benchmark process.

The Feasibility of Composted Agricultural Castoffs as Substitute for Peat in Pot Flower Production

Zhang Qixiang,Liu Qingchao,Wang Kuiling,Liu Qinghua,Pan Huitang,Liang Shule 한국원예학회 2006 한국원예학회 기타간행물 Vol.- No.-

Improvement of Pristinamycin Production by Genome Shuffling and Medium Optimization for Streptomyces Pristinaespiralis

Bo Xu,Zhihua Jin,Qingchao Jin,Ninghui Li,Peilin Cen 한국생물공학회 2009 Biotechnology and Bioprocess Engineering Vol.14 No.2

To isolate an improved pristinamycin producing strain of Streptomyces pristinaespiralis, the technique of Genome shuffling was used which resulted in a high-yield recombinant G 3-56 strain. Strain G 3-56 yielded 322 ± 17 mg/L of pristinamycin which was 11.4-fold higher than that of the initial strain and 3.7-fold higher than strain UN-78 which previously had the highest yield of pristinamycin. The genetic characteristics of the recombinant G 3-56 strain was stable as revealed by our subculture experiments. The optimal production medium was determined using the orthogonal matrix method. Under the optimal medium conditions, the maximum yield of pristinamycin was 412 mg/L with about 1.24-fold higher than the original medium. To isolate an improved pristinamycin producing strain of Streptomyces pristinaespiralis, the technique of Genome shuffling was used which resulted in a high-yield recombinant G 3-56 strain. Strain G 3-56 yielded 322 ± 17 mg/L of pristinamycin which was 11.4-fold higher than that of the initial strain and 3.7-fold higher than strain UN-78 which previously had the highest yield of pristinamycin. The genetic characteristics of the recombinant G 3-56 strain was stable as revealed by our subculture experiments. The optimal production medium was determined using the orthogonal matrix method. Under the optimal medium conditions, the maximum yield of pristinamycin was 412 mg/L with about 1.24-fold higher than the original medium.

Virtual Network Embedding with Multi-attribute Node Ranking Based on TOPSIS

( Shuiqing Gong ),( Jing Chen ),( Siyi Zhao ),( Qingchao Zhu ) 한국인터넷정보학회 2016 KSII Transactions on Internet and Information Syst Vol.10 No.2

Network virtualization provides an effective way to overcome the Internet ossification problem. As one of the main challenges in network virtualization, virtual network embedding refers to mapping multiple virtual networks onto a shared substrate network. However, existing heuristic embedding algorithms evaluate the embedding potential of the nodes simply by the product of different resource attributes, which would result in an unbalanced embedding. Furthermore, ignoring the hops of substrate paths that the virtual links would be mapped onto may restrict the ability of the substrate network to accept additional virtual network requests, and lead to low utilization rate of resource. In this paper, we introduce and extend five node attributes that quantify the embedding potential of the nodes from both the local and global views, and adopt the technique for order preference by similarity ideal solution (TOPSIS) to rank the nodes, aiming at balancing different node attributes to increase the utilization rate of resource. Moreover, we propose a novel two-stage virtual network embedding algorithm, which maps the virtual nodes onto the substrate nodes according to the node ranks, and adopts a shortest path-based algorithm to map the virtual links. Simulation results show that the new algorithm significantly increases the long-term average revenue, the long-term revenue to cost ratio and the acceptance ratio.