A Global Maximum Error Controller Based Nonlinearity Aware TPWL Method for Reducing Nonlinear Systems

Abstract

Model order reduction is a common practice to reduce large order systems so that their simulation and control become easy. Nonlinearity aware trajectory piecewise linear is a variation of trajectory piecewise linearization technique of order reduction that is used to reduce nonlinear systems. With this scheme, the reduced approximation of the system is generated by weighted sum of the linearized and reduced sub-models obtained at certain linearization points on the system trajectory. This scheme uses dynamically inspired weight assignment that makes the approximation nonlinearity aware. Just as weight assignment, the process of linearization points selection is also important for generating faithful approximations. This article uses a global maximum error controller based linearization points selection scheme according to which a state is chosen as a linearization point if the error between a current reduced model and the full order nonlinear system reaches a maximum value. A combination that not only selects linearization points based on an error controller but also assigns dynamic inspired weights is shown in this article. The proposed scheme generates approximations with higher accuracies. This is demonstrated by applying the proposed method to some benchmark nonlinear circuits including RC ladder network and inverter chain circuit and comparing the results with the conventional schemes.     

Formation of layered structure on bismuth-borate glass surface

X-ray diffraction analysis shows that the transformation during heat treatment from amorphous to crystalline phases of bismuth-borate glass samples takes place in sequences. After a short heat treatment, 5 min at 550 °C, a layered structure with a preferred orientation of crystallites on the surface is observed. After a long heat treatment, 8 h at the same temperature, normal polycrystalline bulk samples are obtained.     

A Novel DUF569 Gene Is a Positive Regulator of the Drought Stress Response in Arabidopsis

In the last two decades, global environmental change has increased abiotic stress on plants and severely affected crops. For example, drought stress is a serious abiotic stress that rapidly and substantially alters the morphological, physiological, and molecular responses of plants. In Arabidopsis, several drought-responsive genes have been identified; however, the underlying molecular mechanism of drought tolerance in plants remains largely unclear. Here, we report that the “domain of unknown function” novel gene DUF569 (AT1G69890) positively regulates drought stress in Arabidopsis. The Arabidopsis loss-of-function mutant atduf569 showed significant sensitivity to drought stress, i.e., severe wilting at the rosette-leaf stage after water was withheld for 3 days. Importantly, the mutant plant did not recover after rewatering, unlike wild-type (WT) plants. In addition, atduf569 plants showed significantly lower abscisic acid accumulation under optimal and drought-stress conditions, as well as significantly higher electrolyte leakage when compared with WT Col-0 plants. Spectrophotometric analyses also indicated a significantly lower accumulation of polyphenols, flavonoids, carotenoids, and chlorophylls in atduf569 mutant plants. Overall, our results suggest that novel DUF569 is a positive regulator of the response to drought in Arabidopsis.     

A spatio‐temporal water quality assessment of the Beas and Sutlej Rivers at the Harike Wetland: A Ramsar site in Punjab,India

Lakes play a vital role in regulating water storage, flow of river water, and ultimately maintaining a balanced ecosystem. Spatial and temporal variations in physicochemical parameters of water in Harike Wetland, a Ramsar site in the northwestern state of Punjab, India, were studied. This study was conducted on a monthly basis from January to December 2015. The water quality was studied at ten locations from sites 1 to 10 upstream, central and downstream from Harike Lake for ten physicochemical parameters, including temperature, pH, electrical conductivity, turbidity, dissolved oxygen concentration biological oxygen demand, nitrate and phosphate concentrations and salinity. The findings of this study revealed that, except for temperature and pH, all parameters exhibited relatively higher values for the Sutlej River, compared with the Beas River, with sampling sites 5 to site 7 exhibiting intermediate results. The mean seasonal temperature variations ranged from 16.9 to 26.6 °C, the pH from 7.7 to 8.2, electrical conductivity from 223 to 303 μS cm^?1 and TDS concentration from 148.7 to 180.4 ppm. Correlation analysis was conducted to assess the relations between the variables. The electrical conductivity exhibited a high positive correlation with salinity and biological oxygen demand, whereas it correlated negatively with the dissolved oxygen concentration. Box and whisker plots were also plotted for the study results to better examine the data distribution.     

Geospatial technology applied to spatiotemporal assessment of Harike Wetland,Punjab

The Harike Wetland situated in Punjab is a Ramsar site and a wetland of national importance. The present study was undertaken to assess the spatiotemporal dynamics of the wetland on the basis of geospatial technology and ground‐based studies. Landsat images for the years 2002 and 2014 were acquired from the United States Geological Survey and classified digitally to generate landuse/land cover maps involving four classes (water, grassland (including water hyacinth), agriculture, built‐up (settlement), barren land). The total area of the Harike Wetland was found to be 8023.68 ha. Water sampling at eleven sites was carried out and evaluated for physicochemical parameters. The water quality at several sampling points was found to be severely degraded. Change detection analysis revealed the submerged area (area under water) and grassland (including water hyacinth) had decreased over the past 12 years, whereas that area under agriculture and built‐up land has increased, indicating a shrinkage in the total wetland area. The present study also indicated that the near‐infrared band is a good indicator of water quality parameters, as indicated by the significant positive correlation between the near‐infrared band and relevant water parameters. Because the wetland is important from both an ecological perspective and economic perspective, regular monitoring is recommended, for which geospatial technology has proven to be very useful.     

Memory effect of locally ordered α-phase in the melting and phase transformation behavior of β-isotactic polypropylene

Structural changes in β-isotactic polypropylene (β-iPP) during the heating were studied by means of differential scanning calorimetry and real-time in situ X-ray diffraction using a synchrotron source. Crystalline phase transformation and the memory effect caused by residual nuclei of α-iPP were observed during the heating of β-iPP. The memory effect observed in β-iPP during heating and crystallization is believed to be due to the existence of locally ordered α-from in the melt. The effect of local α-form order was probed by studying the behavior under heating of samples with a range of thermal histories. Samples were heated above the equilibrium melting temperature of iPP to remove all residual local order and the memory effect associated with this local order. The samples crystallized isothermally at different temperatures exhibited a significantly different melting and phase transformation behavior during heating. β-iPP is found to be an excellent material for the study of polymorphism, phase transformations, and characteristic memory effects in semicrystalline polymers.     

A statistical framework for few-shot action recognition

Along with the exponential growth of online video creation platforms such as Tik Tok and Instagram, state of the art research involving quick and effective action/gesture recognition remains crucial. This work addresses the challenge of classifying short video clips, using a domain-specific feature design approach, capable of performing significantly well using as little as one training example per action. The method is based on Gunner Farneback’s dense optical flow (GF-OF) estimation strategy, Gaussian mixture models, and information divergence. We first aim to obtain accurate representations of the human movements/actions by clustering the results given by GF-OF using K-means method of vector quantization. We then proceed by representing the result of one instance of each action by a Gaussian mixture model. Furthermore, using Kullback-Leibler divergence (KL-divergence), we attempt to find similarities between the trained actions and the ones in the test videos. Classification is done by matching each test video to the trained action with the highest similarity (a.k.a lowest KL-divergence). We have performed experiments on the KTH and Weizmann Human Action datasets using One-Shot and K-Shot learning approaches, and the results reveal the discriminative nature of our proposed methodology in comparison with state-of-the-art techniques.

     

Experimental characterization of electrostatically actuated in-plane bending of microcantilevers

Experimental validation of numerical models developed by the authors to predict the static behaviour of microelectrostatic actuators is described. Cantilever microbeams, currently used in connection with RF-MEMS and micro-scale material testing were analysed. A set of microcantilevers, bending in the plane of the wafer, i.e. in the same plane as the profiling system’s target, was tested. This differs from the popular case of out-of-plane microbeams, usually studied in the literature. Geometry nonlinearity caused by large deflection of the microbeam was investigated and nonlinear coupled formulation of electromechanical equilibrium was performed. Coupled-field analysis was implemented using the Finite Element Method (FEM), to predict displacements and pull-in voltage measured by Fogale Zoomsurf 3D, subsequently plotting the displacement-versus-voltage curve to complete model validation. FEM nonlinear analysis, based on iterative approach with mesh morphing, and FEM non-incremental approach, including a special element proposed by the authors, are compared to the linear solution and to experimental results. Geometry nonlinearity appears relevant in microbeam modelling and requires a nonlinear solution of the coupled problem. Investigative work, which compared the results of 2D and 3D models to experimental data, revealed that some three dimensional effects are significant in model validation, but the 2D approach may be effective in predicting static behaviour provided that at least a microbeam thickness equivalent is adopted.     

Multidimensional analogs of the kendall equation for priority queueing systems: computational aspects

An analogy between celebrated Kendall equation for busy periods in the system M|GI|1 and analytical results for busy periods in the priority systemsM _r |GI _r |1 is drawn. These results can be viewed as generalizations of the functional Kendall equation. The methodology and algorithms of numerical solution of recurrent functional equations which appear in the analysis of such queueing systems are developed. The efficiency of the algorithms is achieved by acceleration of the numerical procedure of solving the classical Kendall equation. An algorithm of calculation of the system workload coefficient calculation is given.