Propagation characteristics from meteorological drought to agricultural drought over the Heihe River Basin, Northwest China

In the context of global warming,drought events occur frequently.In order to better understanding the process and mechanism of drought occurrence and evolution,scholars have dedicated much attention on drought propagation,mainly focusing on drought propagation time and propagation probability.However,there are relatively few studies on the sensitivities of drought propagation to seasons and drought levels.Therefore,we took the Heihe River Basin(HRB)of Northwest China as the case study area to quantify the propagation time and propagation probability from meteorological drought to agricultural drought during the period of 1981–2020,and subsequently explore their sensitivities to seasons(irrigation and non-irrigation seasons)and drought levels.The correlation coefficient method and Copula-based interval conditional probability model were employed to determine the drought propagation time and propagation probability.The results determined the average drought propagation time as 8 months in the whole basin,which was reduced by 2 months(i.e.,6 months)on average during the irrigation season and prolonged by 2 months(i.e.,10 months)during the non-irrigation season.Propagation probability was sensitive to both seasons and drought levels,and the sensitivities had noticeable spatial differences in the whole basin.The propagation probability of agricultural drought at different levels generally increased with the meteorological drought levels for the upstream,midstream,and southern downstream regions of the HRB.Lesser agricultural droughts were more likely to be triggered during the irrigation season,while severer agricultural droughts were occurred mostly during the non-irrigation season.The research results are helpful to understand the characteristics of drought propagation and provide a scientific basis for the prevention and control of droughts.This study is of great significance for the rational planning of local water resources and maintaining good ecological environment in the HRB.

Leakage Current Estimation of CMOS Circuit with Stack Effect

Leakage current of CMOS circuit increases dramatically with the technologyscaling down and has become a critical issue of high performance system. Subthreshold, gate andreverse biased junction band-to-band tunneling (BTBT) leakages are considered three maindeterminants of total leakage current. Up to now, how to accurately estimate leakage current oflarge-scale circuits within endurable time remains unsolved, even though accurate leakage modelshave been widely discussed. In this paper, the authors first dip into the stack effect of CMOStechnology and propose a new simple gate-level leakage current model. Then, a table-lookup basedtotal leakage current simulator is built up according to the model. To validate the simulator,accurate leakage current is simulated at circuit level using popular simulator HSPICE forcomparison. Some further studies such as maximum leakage current estimation, minimum leakage currentgeneration and a high-level average leakage current macromodel are introduced in detail.Experiments on ISCAS85 and ISCAS89 benchmarks demonstrate that the two proposed leakage currentestimation methods are very accurate and efficient.

Fuzzy-Expert Diagnostics for Detecting and Locating Internal Faults in Three Phase Induction Motors

Internal faults in three phase induction motors can result in serious performance degradation and eventual system failures if not properly detected and treated in time. Artificial intelligence techniques, the core of soft-computing, have numerous advantages over conventional fault diagnostic approaches; therefore, a soft-computing system was developed to detect and diagnose electric motor faults. The fault diagnostic system for three-phase induction motors samples the fault symptoms and then uses a fuzzy-expert forward inference model to identify the fault. This paper describes how to define the membership functions and fuzzy sets based on the fault symptoms and how to construct the hierarchical fuzzy inference nets with the propagation of probabilities concerning the uncertainty of faults. The designed hierarchical fuzzy inference nets efficiently detect and diagnose the fault type and exact location in a three phase induction motor. The validity and effectiveness of this approach is clearly shown from obtained testing results.