Floods are common and recurring natural hazards which damages is the destruction for society. Several regions of the world with different climatic conditions face the challenge of floods in different magnitudes. Here we estimate flood susceptibility based on Analytical neural network (ANN), Deep learning neural network (DLNN) and Deep boost (DB) algorithm approach. We also attempt to estimate the future rainfall scenario, using the General circulation model (GCM) with its ensemble. The Representative concentration pathway (RCP) scenario is employed for estimating the future rainfall in more an authentic way. The validation of all models was done with considering different indices and the results show that the DB model is most optimal as compared to the other models. According to the DB model, the spatial coverage of very low, low, moderate, high and very high flood prone region is 68.20%, 9.48%, 5.64%, 7.34% and 9.33% respectively. The approach and results in this research would be beneficial to take the decision in managing this natural hazard in a more efficient way.
The Journal of Supercomputing - Recommender systems play an important role in dealing with the problems caused by the great and growing amount of information, and the collaborative filtering method... 相似文献
Thermoelectric properties of two antiperovskites SbNCa3 and BiNCa3 are calculated using first principles calculations. High values of Seebeck coefficients are observed for these materials. Electrical and thermal conductivities are also calculated. Increase in thermal conductivity and decrease in electrical conductivity are found with increasing temperature. The maximum values of thermal conductivity are 92×1014 W/m K s and 88×1014 W/m K s for SbNCa3 and BiNCa3 respectively at a temperature of 900 K. The peak values of 5×1020/Ω m s and 5.2×1020/Ω m s are achieved for n-type SbNCa3 and BiNCa3 respectively at a temperature of 300 K. Figure of merit is achieved for these materials at room temperature which shows that these materials can be useful for thermoelectric devices and alternative energy sources. 相似文献
Neural networks (NNs) are extensively used in modelling, optimization, and control of nonlinear plants. NN-based inverse type point prediction models are commonly used for nonlinear process control. However, prediction errors (root mean square error (RMSE), mean absolute percentage error (MAPE) etc.) significantly increase in the presence of disturbances and uncertainties. In contrast to point forecast, prediction interval (PI)-based forecast bears extra information such as the prediction accuracy. The PI provides tighter upper and lower bounds with considering uncertainties due to the model mismatch and time dependent or time independent noises for a given confidence level. The use of PIs in the NN controller (NNC) as additional inputs can improve the controller performance. In the present work, the PIs are utilized in control applications, in particular PIs are integrated in the NN internal model-based control framework. A PI-based model that developed using lower upper bound estimation method (LUBE) is used as an online estimator of PIs for the proposed PI-based controller (PIC). PIs along with other inputs for a traditional NN are used to train the PIC to predict the control signal. The proposed controller is tested for two case studies. These include, a chemical reactor, which is a continuous stirred tank reactor (case 1) and a numerical nonlinear plant model (case 2). Simulation results reveal that the tracking performance of the proposed controller is superior to the traditional NNC in terms of setpoint tracking and disturbance rejections. More precisely, 36% and 15% improvements can be achieved using the proposed PIC over the NNC in terms of IAE for case 1 and case 2, respectively for setpoint tracking with step changes.
The Arithmetic and Logic Unit (ALU) is a combination circuit that performs a number of arithmetic and logical operations within a microprocessor. The demand for faster and compact ALUs makes it desirable to test the ALU in conjunction with pre-design parts prior to manufacture. This may be accomplished in a process using CAD and SPICE simulation software. Our purpose is to realize a method for importing a layout drawn in Tanner L-edit and simulated in T-Spice into PSpice which is referred to as software talking. To do so we use an eight-function instruction set called Complimentary Metal Oxide Semiconductor Arithmetic and Logic Unit (CMOS ALU) which is laid out in Tanner L-edit and produces an extracted net-list which is simulated in T-Spice. An ALU equivalent design is then modeled in PSpice for further testing with pre-manufactured parts of the PSpice library. 相似文献
Soft-grain materials such as clays and other colloidal pastes share the common feature of being composed of grains that can undergo large deformations without rupture. For the simulation of such materials, we present two alternative methods: (1) an implicit formulation of the material point method (MPM), in which each grain is discretized as a collection of material points, and (2) the bonded particle model (BPM), in which each soft grain is modeled as an aggregate of rigid particles using the contact dynamics method. In the MPM, a linear elastic behavior is used for the grains. In order to allow the aggregates in the BPM to deform without breaking, we use long-range center-to-center attraction forces between the primary particles belonging to each grain together with steric repulsion at their contact points. We show that these interactions lead to a plastic behavior of the grains. Using both methods, we analyze the uniaxial compaction of 2D soft granular packings. This process is nonlinear and involves both grain rearrangements and large deformations. High packing fractions beyond the jamming state are reached as a result of grain shape change for both methods. We discuss the stress-strain and volume change behavior as well as the evolution of the connectivity of the grains. Similar textures are observed at large deformations although the BPM requires higher stress than the MPM to reach the same level of packing fraction. 相似文献
The buckling and vibration of thick rectangular nanoplates is analyzed in this article. A graphene sheet is theoretically assumed and modeled as a nanoplate in this study. The two-variable refined plate theory (RPT) is applied to obtain the differential equations of the nanoplate. The theory accounts for parabolic variation of transverse shear stress through the thickness of the plate without using a shear correction factor. Besides, the analysis is based on the nonlocal theory of elasticity to take the small-scale effects into account. For the first time, the finite strip method (FSM) based on RPT is employed to study the vibration and buckling behavior of nanoplates and graphene sheets. Hamilton’s principle is employed to obtain the differential equations of the nanoplate. The stiffness, stability and mass matrices of the nanoplate are formed using the FSM. The displacement functions of the strips are evaluated using continuous harmonic function series which satisfy the boundary conditions in one direction and a piecewise interpolation polynomial in the other direction. A matrix eigenvalue problem is solved to find the free vibration frequency and buckling load of the nanoplates subjected to different types of in-plane loadings including the uniform and nonuniform uni-axial and biaxial compression. Comparison studies are presented to verify the validity and accuracy of the proposed nonlocal refined finite strip method. Furthermore, a number of examples are presented to investigate the effects of various parameters (e.g., boundary conditions, nonlocal parameter, aspect ratio, type of loading) on the results. 相似文献
The metal nanoparticles, due to interesting features such as electrical, optical, chemical and magnetic properties, have been investigated repeatedly. Also, the mentioned nanoparticles have specific uses in terms of their antibacterial activity. The biosynthesis method is more appropriate than the chemical method for producing the nanoparticles because it does not need any special facilities; it is also economically affordable. In the current study, the silver nanoparticles (AgNPs) were obtained by using a very simple and low‐cost method via Glaucium corniculatum (L.) Curtis plant extract. The characteristics of the AgNPs were investigated using techniques including: X‐ray diffraction, transmission electron microscopy (TEM), scanning electron microscopy (SEM), and Fourier transform infrared spectroscopy. The SEM and TEM images showed that the nanoparticles had a spherical shape, and the mean diameter of them was 53.7 and 45 nm, respectively. The results of the disc diffusion test used for measuring the anti‐bacterial activity of the synthesised nanoparticles indicated that the formed nanoparticles possessed a suitable anti‐bacterial activity.Inspec keywords: silver, nanoparticles, antibacterial activity, nanomedicine, nanofabrication, X‐ray diffraction, transmission electron microscopy, scanning electron microscopy, Fourier transform infrared spectraOther keywords: green synthesis, silver nanoparticles, Glaucium corniculatum Curtis extract, antibacterial activity, metal nanoparticles, biosynthesis method, X‐ray diffraction, transmission electron microscopy, scanning electron microscopy, Fourier transform infrared spectroscopy, SEM, TEM, spherical shape, disc diffusion test, Ag相似文献