Averaged model of modular multilevel converter in rotating DQ frame.

This paper proposes an average model of Modular Multilevel Converter (MMC) in rotating DQ frame. The proposed MMC model has a modular structure and can be linked with other power elements such as AC and DC subsystems. Modelling in DQ frame has numerous advantages over traditional ABC frame in terms of simulation speed and convenience for linearization. The main challenge of developing DQ model of MMC is to deal with the multiplication terms of dynamic equations of MMC. To overcome this complexity, a generic form is first introduced for each product variable mathematical equations of the average MMC model in ABC frame and then the multiplication results are transferred to DQ frame after ignoring the higher harmonics. The detailed model and the proposed DQ average model are implemented in PSCAD/EMTDC. The simulation results of the two models show very good matching which in turn confirms the accuracy of the proposed model. Also, the DQ average model is considerably faster than the detailed and even ABC average models

ANALYZING THE PERFORMANCE OF CARRY TREE ADDERS BASED ON FPGA’S

In this paper carry tree adders are known to have the best performance in VLSI designs. However, this performance advantage does not translate directly into FPGA implementations due to constraints on logic block configurations and routing overhead. This paper investigates three types of carry-tree adders (the Kogge-Stone, sparse Kogge-Stone, and spanning tree adder) and compares them to the simple Ripple Carry Adder (RCA) and Carry Skip Adder (CSA). These designs of varied bit-widths were implemented on a Xilinx Spartan 3E FPGA and delay measurements were made with a high-performance logic analyzer. Due to the presence of a fast carry-chain, the RCA designs exhibit better delay performance up to 128 bits. The carry-tree adders are expected to have a speed advantage over the RCA as bit widths approach 256

Inference in Probabilistic Logic Programs Using Lifted Explanations

In this paper, we consider the problem of lifted inference in the context of Prism-like probabilistic logic programming languages. Traditional inference in such languages involves the construction of an explanation graph for the query that treats each instance of a random variable separately. For many programs and queries, we observe that explanations can be summarized into substantially more compact structures introduced in this paper, called "lifted explanation graph". In contrast to existing lifted inference techniques, our method for constructing lifted explanations naturally generalizes existing methods for constructing explanation graphs. To compute probability of query answers, we solve recurrences generated from the lifted graphs. We show examples where the use of our technique reduces the asymptotic complexity of inference

Nonlinear Micropolar Beam and Plate Theories with Applications to Lattice Core Sandwich Structures and Dual Mesh Control Domain Method for Structural Elements

In the first part of the dissertation we develop nonlinear beam and plate theories based on micropolar elasticity and formulate the corresponding finite element models. The developed non-linear beam and plate finite element models are then used to analyze the bending of lattice core sandwich beams and plates that are modeled as equivalent- ingle layer beams or plates based on micropolar elasticity. The rapid growth of manufacturing technologies has enabled the design and development of materials whose microstructure can be architected to achieve desired functionality. Lattice core sandwich structures are among such architected materials whose microstructure is the order of few centimeters. Modeling these structures with complete geometric details can be computationally expensive. Hence, efforts are made to model such structures as equivalent-single layer beams or plates with non- lassical continuum theories like micropolar elasticity. One such methodology to construct equivalent-single layer beams of web-core lattice beams is described and extended to other core structures. The second part of this dissertation deals with formulation of a novel numerical method, named Dual Mesh Control Domain Method (DMCDM), for functionally graded structural elements; namely beams and plates. For the past few decades finite element method has been the dominant numerical method for analysis of solids and structures while finite volume method has been dominant in the field of fluid dynamics. Both the methods have their strengths and weaknesses. For example, representing a system as a collection of connected finite elements often results in a discontinuous representation of the gradients of the solution, unless so-called C-continuity is used. However, finite element method retains the concept of duality between the secondary and primary variables of the problem and thereby simplify the process of applying boundary conditions. On the other hand, although finite volume method involves fictitious nodes at the boundary control volumes and thereby complicating the application of boundary conditions, it satisfies the integrals of governing equations (with out any weight functions) on control volumes and calculates secondary variables on the interfaces of the control volume where they are uniquely defined. Considering these observations, Professor J. N. Reddy has recently proposed a novel numerical method named Dual Mesh Control Domain Method (DMCDM). It incorporates the best features of both finite element method and finite volume method by using two different meshes. A primal mesh for interpolating the primary variables and dual mesh for satisfy the governing equations in integral form without weight functions. The details of this method and its application to structural elements is discussed in detail

Metagenomics and bio-engineering of chitin and chitosan modifying enzymes for biotechnological applications

Chitin ist nach Cellulose das am häufigsten in der Natur vorkommende Biopolymer. Chitosan ist die vollständige oder teilweise deacetylierte Version des Chitins. Chitin und Chitosan modifizierende Enzyme (CCME) finden als Werkzeuge zur Erzeugung spezieller Chitosane für die Biotechnologie und Biomedizin Anwendung. In dieser Arbeit wurde sowohl mithilfe einer metagenomischen Genbank als auch mittels mikrobieller Direktisolaten nach neuen CCMEs gesucht. Als Ausgangsmaterial für diese beiden Ansätze dienten Bodenproben, die über mehr als zehn Jahre in Kontakt mit Chitin und Chitosan waren. CCME kodierende Gene aus Bacillus spp. wurden in E. coli-Stämmen heterolog exprimiert und die entsprechenden Enzyme aufgereinigt. Eine Inkubation von Chitosanpolymeren verschiedener Acetylierungsgrade (50%, 35% und 10%) mit den aufgereinigten Enzymen generierte Mischungen aus Chitin- und Chitosan-Oligomeren, die vielversprechende Elicitor- und Priming-Effekte auf pflanzliche Zellkulturen hatten

ANNOYED-RESIDENT CONTACT CONTROL(CATCC) MODEL FOR COMPUTER STANDARD SPECIFICATION AND VERIFICATION

A completely new system architecture to treat fine grain RDF sections in a wide range. New data recruitment strategies to participate in the identification of relevant data segments. In this document, we describe RpCl, a distributed data management system and RDF for this cloud. Unlike the previous approach, RpCl administers a physiological analysis of the state information and the schema before dividing the information. The device maintains a sliding window that tracks the current good reputation of the workload, as well as relevant statistics on the number of connections to be made and the limits of criminalization. The machine combines the future representation by summarizing the RDF, which contains a local horizontal division of the triangles in a distributed network structure in the network. One important thing is a vital indicator in RpCl that uses a lexical tree to parse incoming or literal URIs and assign a distinguished number key value. The implementation of such data using classical techniques or the division of the graph using simple traditional algorithms leads to extremely inefficient distributions, as well as to a greater number of connections. Many RDF systems are based on hash defragmentation, as well as distributions, distributions and distributed connections. The Grape Network system was one of the first systems to carry out this decentralized management of RDF. In this document, we describe the structure of RpCl, its basic data structure, as well as the new algorithms that we use to divide and distribute data. We produce an integral vision of RpCl that shows that our product is usually two sizes faster than modern systems in standard workloads

Modular multilevel converter modulation using fundamental switching selective harmonic elimination method.

This paper address the issue of low order harmonics in a modular multilevel converter (MMC). Using fundamental switching selective harmonic elimination (SHE), the control angles are calculated from nonlinear equations by Newton-Raphson method. The selective harmonic elimination equations are solved in such a way that the first switching angle is used to control the magnitude of the fundamental voltage and the remaining angles are used to eliminate the lowest odd, non-triplen harmonics components as they dominate the total harmonic distortion of the converter. The concept is validated using a 9-level detailed model of MMC in PSCAD/EMTDC®. The simulation result shows a good agreement with theoretical analysis and in comparison with conventional sinusoidal pulse width modulation (SPWM), the proposed method, eliminates low order harmonics, leading to a low total harmonic distortion

StressNet: a spatial-spectral-temporal deformable attention-based framework for water stress classification in maize

In recent years, monitoring the health of crops has been greatly aided by deploying highthroughput crop monitoring techniques that integrate remotely captured imagery and deep learning techniques. Most methods rely mainly on the visible spectrum for analyzing the abiotic stress, such as water deficiency in crops. In this study, we carry out experiments on maize crop in a controlled environment of different water treatments. We make use of a multispectral camera mounted on an Unmanned Aerial Vehicle for collecting the data from the tillering stage to the heading stage of the crop. A pre-processing pipeline, followed by the extraction of the Region of Interest from orthomosaic is explained. We propose a model based on a Convolution Neural Network, added with a deformable convolutional layer in order to learn and extract rich spatial and spectral features. These features are further fed to a weighted Attention-based Bi-Directional Long Short-Term Memory network to process the sequential dependency between temporal features. Finally, the water stress category is predicted using the aggregated Spatial-Spectral-Temporal Characteristics. The addition of multispectral, multi-temporal imagery significantly improved accuracy when compared with mono-temporal classification. By incorporating a deformable convolutional layer and Bi-Directional Long Short-Term Memory network with weighted attention, our proposed model achieved best accuracy of 91.30% with a precision of 0.8888 and a recall of 0.8857. The results indicate that multispectral, multi-temporal imagery is a valuable tool for extracting and aggregating discriminative spatial-spectral-temporal characteristics for water stress classification