Novel Adaptive Memory Event-Triggered-Based Fuzzy Robust Control for Nonlinear Networked Systems via the Differential Evolution Algorithm

This article mainly investigates the fuzzy optimization robust control issue for nonlinear networked systems characterized by the interval type-2(IT2)fuzzy technique under a differential evolution algorithm.To provide a more reasonable utilization of the constrained communication channel,a novel adaptive memory event-triggered(AMET)mechanism is developed,where two event-triggered thresholds can be dynamically adjusted in the light of the current system information and the transmitted historical data.Sufficient conditions with less conservative design of the fuzzy imperfect premise matching(IPM)controller are presented by introducing the Wirtinger-based integral inequality,the information of membership functions(MFs)and slack matrices.Subsequently,under the IPM policy,a new MFs intelligent optimization technique that takes advantage of the differential evolution algorithm is first provided for IT2 TakagiSugeno(T-S)fuzzy systems to update the fuzzy controller MFs in real-time and achieve a better system control effect.Finally,simulation results demonstrate that the proposed control scheme can obtain better system performance in the case of using fewer communication resources.

Analysis of Supply Chain Sustainability Drivers in the Oil and Gas Industry under Covid-19 Pandemic

The supply chain of many industries,including Oil and Gas,was significantly affected by the disruption caused by the Covid pandemic.This,in turn,had a knock-on effect on other industries around the globe.Sustaining the impact of the disruption posed a major challenge for the industry.This study contributes to the existing literature by identifying and analyzing the most significant drivers that affected the sustainability of the Oil and Gas supply chain during the Covid pandemic.Fifteen drivers were identified based on an extensive literature review and a survey conducted with experts working in the Oil and Gas industry.Multi-criteria decision-making methodologies were used to analyze these drivers.The analysis from the fuzzy analytical hierarchy process found that the most important drivers for the sustainability of the Oil and gas supply chain during the pandemic were"Risk management capacity","Government regulation"and"Health and safety of employees".On the other hand,the driver"Community Pressure"was found to be of the least importance.Furthermore,the study integrated the results of the fuzzy analytical hierarchy process with the fuzzy technique for order of preference by similarity to ideal solution to calculate the supply chain sustainability index.A case example was demonstrated to rank the industries based on such calculations.This study can support the governmental institutions in benchmarking the Oil and Gas industry based on its sustainability index.Additionally,the outcomes of the study will help industrial decision makers prioritize the drivers the company should focus and devise strategies based on the priority to improve the sustainability of their supply chain during severe disruption.This will be crucial as the World health organization has cautioned that the world may encounter another pandemic in the near future.

Research on Fuzzy Information Retrieval

The information retrieval is one of the common operations in computer information systems. This paper proposes a kind of information retrieval method based on fuzzy set theory.

Automatic Identification of Storm Cells Using Doppler Radars

Three storm automatic identification algorithms for Doppler radar are discussed. The WSR-88D Build 7.0 （B7SI） tests the intensity and continuity of the objective echoes by multiple-prescribed thresholds to build 3D storms, and when storms are merging, splitting, or clustered closely, the detection errors become larger. The B9SI algorithm is part of the Build 9.0 Radar Products Generator of the WSR-88D system. It uses multiple thresholds of reflectivity, newly designs the techniques of cell nucleus extraction and closestorms processing, and therefore is capable of identifying embedded cells in multi-cellular storms. The strong area components at a long distance are saved as 2D storms. However, the B9SI cannot give information on the convection strength of storm, because texture and gradient of reflectivity are not calculated and radial velocity data are not used. To overcome this limitation, the CSI （Convective Storm Identification） algorithm is designed in this paper. By using the fuzzy logic technique, and under the condition that the levels of the seven reflectivity thresholds of B9SI are lowered, the CSI processes the radar base data and the output of B9SI to obtain the convection index of storm. Finally, the CSI is verified with the case of a supercell occurring in Guangzhou on 11 August 2004. The computational and analysis results show that the two rises of convection index matched well with a merging growth and strong convergent growth of the supercell, and the index was 0.744 when the supercell was the strongest, and then decreased. Correspondingly, the height of the maximum reflectivity, detected by the radar also reduced, and heavy rain also occurred in a large-scale area.