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To accurately determine the leakage source location and strength during gas leakage accidents, this study compares the concentration obtained from the diffusion model with that measured by the sensor and proposes an improved gray wolf optimization algorithm for leakage source location. This algorithm introduces two improvement strategies. First, a nonlinear convergence factor is introduced to balance the global and local searches of the algorithm. Second, a reverse learning operation is performed on the three individuals with the worst fitness in the contemporary population. The results showed that the location results based on the improved gray wolf optimization algorithm exhibited high accuracy and stability, could quickly and accurately locate the leakage source, and provided data support for emergency disposal of accidents. [ABSTRACT FROM AUTHOR]

Abstract Background Staphylococcus aureus, particularly methicillin-resistant S. aureus (MRSA), is an important cause of pyogenic skin and soft tissue infections (SSTIs). The aim of present study is to investigate the molecular characteristic of Staphylococcus aureus isolates isolated from the pus samples from the patients with purulent skin and soft tissue infections in Wenzhou, China. Methods Between December 2002 and June 2008, a total of 111 nonduplicate S. aureus isolates were collected from the pus samples of the patients with SSTIs in a teaching hospital in Wenzhou, China. All the tested isolates were confirmed as S. aureus using a Staph SPA agglutination kit, Gram's stain and a Vitek-60 microbiology analyzer. The homology among the tested isolates was determined by pulsed-field gel electrophoresis (PFGE). Multilocus sequence typing (MLST) was used to determine the sequence types (STs) of the selected isolates. The genotypes of SCCmec were determined by a multiplex PCR in the MRSA isolates. Panton-Valentine leukocidin (PVL) genes and mecA were also determined by another multiplex PCR. Results Among the 111 S. aureus isolates, 48 and 63 isolates were community-acquired and hospital-acquired respectively. Sixty isolates were confirmed as MRSA harboring mecA detected by PCR. A total of 32 PFGE clonal types were obtained by PFGE, with 10 predominant patterns (types A to J). Twenty-five different STs including ST398 and three novel STs were found among 51 selected isolates. The main STs were ST239, ST1018, ST59, ST7 and ST88. Of 60 MRSA isolates, SCCmec II, III, IV and SCCmec V were found in three, 50, three and two isolates, respectively. The positive rates of PVL genes in overall isolates, HA-isolates, CA-isolates, MRSA isolates and MSSA isolates were 23.4% (26/111), 20.6% (13/63), 27.1% (13/48), 21.7% (13/60) and 25.5% (13/51), respectively. Eight (33.3%, 8/24) of 24 CA-MRSA isolates and 5 (13.9%, 5/36) of 36 HA-MRSA isolates were positive for PVL genes. ST239-MRSA-SCCmecIII and ST1018-MRSA-SCCmecIII clones were found to be main clones and spread between community and hospital. Conclusion S. aureus isolates causing SSTIs showed considerable molecular heterogeneity and harbored high prevalence of PVL genes. Clonal spread was responsible for the dissemination of the isolates of S. aureus associated with SSTIs.

Byline: Zeng-Qiang Chen, Jia-Xiang Zhao, Zhu-Zhi Yuan An Active Disturbances Rejection Controller (ADRC) is presented for a class of single-input-single-output non-linear systems with a hysteresis non-linearity represented by the Preisach model. The designed ADRC controller is composed of the non-linearity tracking differentiator, the extended state observer and the non-linearity PID controller. It can estimate disturbances and compensate according to the information between the input and the output of the controlled object. Thus it automatically realises the suppression of disturbance. The simulation result indicates that the ADRC controller with a set of invariable parameters can maintain that the non-linear systems with a hysteresis non-linearity have ideal tracking effect.

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To meet the demands of the modern power system for satisfactory operation and control, here, a novel data‐driven control strategy is proposed to solve the load frequency control (LFC) problems of power systems, with complete convergence analysis. This data‐based LFC approach is designed based on the simultaneous perturbation stochastic approximation (SPSA) method and neural network ensemble. The data‐based controller is constructed using a function approximator, which is fixed as a neural network. Being the control parameters, the connection weights of the neural network controller are updated at each iteration step. In order to improve the overall control accuracy and get more stable control performance, the idea of neural network ensemble is introduced for the data‐based controller structure design. The proposed data‐based controller takes past and current system information as input and generates a control signal that can affect future system performance as output, and during the whole process, it is not necessary to build mathematical model for the controlled plant. A one‐area LFC problem with system parametric uncertainties as well as a typical two‐area LFC problem have been introduced for simulation tests, and the feasibility and effectiveness of this newly proposed data‐based LFC strategy is well revealed through simulation results. [ABSTRACT FROM AUTHOR]

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)