Modular Scheme Research Articles

A Modular Implementation Scheme for Nonsingleton Type-2 Fuzzy Logic Systems With Input Uncertainties

Using nonsingleton (NS) input fuzzifiers along with type-2 fuzzy systems to handle uncertainties in inputs has received some attention in recent years. However, the NS fuzzification schemes proposed so far have a limited impact because they are restricted to a particular kind of fuzzy sets only. This paper proposes a modular implementation scheme for NS type-2 fuzzy logic systems with input uncertainties. The proposed implementation scheme constitutes a generalized fuzzification which is independent of the forms/shapes of the fuzzy sets, i.e., both the NS fuzzifiers and the membership functions. To investigate the effectiveness of the proposed scheme, type-2 fuzzy logic controllers for three different applications, airplane altitude control, obstacle avoidance for a mobile robot, and a wall following robot, are developed. Additionally, for mapping NS fuzzifiers for real sensors, a sensory noise pattern recognition stage is also developed. Despite encountering various kinds of membership functions and input uncertainties along these three applications, the proposed scheme is able to successfully deal with all three applications. Moreover, the performance results in all three application setups show that NS fuzzification can improve the robustness of type-2 fuzzy logic systems against input uncertainties.

Ideal composite modular secret sharing schemes

The paper deals with modular secret sharing schemes for certain non-threshold access structures. It is shown that based on compositions of ideal threshold modular schemes it is possible to build an ideal scheme for compartment access structures, as well as some of the more common structures, which were called composite.

PolyTop++: an efficient alternative for serial and parallel topology optimization on CPUs & GPUs

This paper presents the PolyTop++, an efficient and modular framework for parallel structural topology optimization using polygonal meshes. It consists of a C++ and CUDA (a parallel computing model for GPUs) alternative implementations of the PolyTop code by Talischi et al. (Struct Multidiscip Optim 45(3):329---357 2012b). PolyTop++ was designed to support both CPU and GPU parallel solutions. The software takes advantage of the C++ programming language and the CUDA model to design algorithms with efficient memory management, capable of solving large-scale problems, and uses its object-oriented flexibility in order to provide a modular scheme. We describe our implementation of different solvers for the finite element analysis, including both direct and iterative solvers, and an iterative `matrix-free' solver; these were all implemented in serial and parallel modes, including a GPU version. Finally, we present numerical results for problems with about 40 million degrees of freedom both in 2D and 3D.

Design Assistant Decision Model for Parafoil Precise Airdrop

Decision capacity and command level is essential for accurate aerial delivery of parafoil. In this paper, we according to the system theory and the modular scheme, put forward the parafoil precision airdrop aided decision model consist ofaircraft flight route model,parafoil maneuvering performance model,high altitude wind field characteristic model, landing terrain environment model, the enemy threat situation model, complex electromagnetic environment simulation mode. Andwe focuses on the aircraft flight route model, landing terrain environment model and the simulation model of high altitude wind. The model can providecertain auxiliary decision information to airdrop commander; can also be used to other specific precision airdrop work.

ONLINE DISTRIBUTED SOURCE LOCALIZATION FROM EEG/MEG DATA

Electroencephalography (EEG) and Magnetoencephalography (MEG) provide insight into neuronal processes in the brain in a real-time scale. This renders these modalities particularly interesting for online analysis methods, e.g. to visualize brain activity in real-time. Brain activity can be modeled in terms of a source distribution found by solving the bioelectromagnetic inverse problem, e.g. using linear source reconstruction methods. Such methods are particularly suitable to be used on modern highly parallel processing systems, such as widely available graphic processing units (GPUs). We present a system that, according to its modular scheme, can be configured in a very flexible way using graphical building blocks. Different preprocessing algorithms together with a linear source reconstruction method can be used for online analysis. The algorithms use both CPU and GPU resources. We tested our system in a simulation and in a realistic experiment.

Double phase modular steganography with the help of error images

Many steganographic schemes based on popular least significant bit (LSB) embedding have been proposed. The embedding procedure is totally independent of pixel values due to the fact that only the LSB has to be altered. Other methods either randomly increment/decrement the pixel value or use a function to do so. Because of this, the maximum embedding capacity cannot exceed 1 bit per pixel (bpp). State-of-the-art steganographic schemes, such as edge adaptive (EA) and highly undetectable stego (HUGO), aim at such LSB-based approaches with the help of LSB-matching algorithms and, unlike classical steganography, are more concerned about the undetectability level of the stego image rather than the peak signal-to-noise ratio (PSNR) of the stego images. There is a trade-off between detectability and the embedding rate of the stego images. The larger the embedding rate, the higher the chance of detectability, and the opposite is also true. On the other hand, classical steganographic approaches aim for maximum embedding capacity (say 4 bpp) while their main ability is to provide greater PSNR values rather than undetectability. In this work, a novel modular steganographic scheme is proposed in a spatial domain that takes advantage of error images resulting from applying an image quality factor (the same as the ones used in JPEG compression) in order to find the pixels where the secret data could be embedded. We show that our proposed method is less detectable compared to the EA method. The proposed method is almost as undetectable as HUGO. The detectability level is evaluated by the most recent state-of-the-art steganalysis attack, called ensemble classifiers, with second-order subtractive pixel adjacency model features given as their input. In addition, the proposed method can embed up to 4 bpp, yet maintain a high PSNR value.

Distributed MPC for Green Thermally Comfortable Buildings Based on an Electro-thermal Modular Approach

Abstract The aim of the paper is to present a demand-side management approach that uses a distributed model-based predictive controller to provide indoor thermal comfort in buildings with a limited green energy resource. The overall system predicts the indoor environmental conditions for buildings with different plans that are modeled using an electro-thermal modular scheme. For control purposes, this modular scheme allows an easy modeling of buildings with different plans where adjacent areas can thermally interact. The control system selects the most appropriate actions to satisfy the comfort and power constraints. In a distributed coordinated environment, the control uses multiple dynamically coupled agents (one for each subsystem/zone/house) aiming to achieve satisfaction of available energy coupling constraints. The distributed environment is simulated with two houses with different plans, one house with two divisions and the other with one. The results validate the proposed methodology in terms of both thermal comfort and energy savings.

Optimized Reconfigurable Control Design for Aircraft using Genetic Algorithm

In this study, we propose a Genetic Algorithm (GA) based modular reconfigurable control scheme for an over-actuated non-linear aircraft model. The reconfiguration of the flight controller is achieved for the case of control surface faults/failures using a separate control distribution algorithm without modifying the base-line control law. The baseline Multi-Input Multi-Output (MIMO) Linear Quadratic Regulator (LQR) is optimized using GA to produce desired moment commands. Then, a GA based weighted pseudo-inverse method is used for effective distribution of commands between redundant control surfaces. Control surface effectiveness levels are used to redistribute the control commands to healthy actuators when a fault or failure occurs. Simulation results using ADMIRE aircraft model show the satisfactory performance in accommodating different faults, which confirm the efficiency of optimized reconfigurable design strategy.

A modular, operator‐splitting scheme for fluid–structure interaction problems with thick structures

SUMMARYWe present an operator‐splitting scheme for fluid–structure interaction (FSI) problems in hemodynamics, where the thickness of the structural wall is comparable to the radius of the cylindrical fluid domain. The equations of linear elasticity are used to model the structure, while the Navier–Stokes equations for an incompressible viscous fluid are used to model the fluid. The operator‐splitting scheme, based on the Lie splitting, separates the elastodynamics structure problem from a fluid problem in which structure inertia is included to achieve unconditional stability. We prove energy estimates associated with unconditional stability of this modular scheme for the full nonlinear FSI problem defined on a moving domain, without requiring any sub‐iterations within time steps. Two numerical examples are presented, showing excellent agreement with the results of monolithic schemes. First‐order convergence in time is shown numerically. Modularity, unconditional stability without temporal sub‐iterations, and simple implementation are the features that make this operator‐splitting scheme particularly appealing for multi‐physics problems involving FSI. Copyright © 2013 John Wiley & Sons, Ltd.

Research and Design on Inspection Robot for High-Voltage Transmission Lines

The paper introduces a kind of inspection robot for high-voltage transmission lines from two parts:Mechanical structure and control system. Three arms is adopted in the mechanical structure, while the control system used a hierarchical modular control scheme is designed to a kind of three-layer control structure including the ground monitoring layer, the task decision layer and the action execution layer. The PC/104 and AT89C51 is used for the main control computer, and the MX-servo is used for the power drive components. Except for describing the robots working process of crossing obstacles in detail, the paper also introduces the responsibilities, hardware components, software design process of the control system. From the simulation experiment on motion control system using Matlab/Simulink, we can conclude that the motion control system of the robot has high-stability and reliability and meet the design requirements.

Design of Single-Phase Multi-Function Energy Meter

Taking into account the demands of multi-function meter with high reliability and low cost, a single-phase multi-function energy meter is designed based on the MSP430FE427. There is a multi-parameter measurement, multi-way communication and other functions for the MCU. Hardware circuit and software flow of the system are introduced. The current and voltage design, memory design and communication anti-interface design are described in detail in hardware circuit. Modular design scheme is adopted in the software design. Each module is relatively independent, so the program is easy to modify. It indicates that the energy measurement circuit owns high accuracy through analysis and comparison of test results.

Module Matching Method Based on the Gray Theory

The matching method is used to select the most similar modular design scheme from the history product database. The existed methods are not efficient enough. A new matching method based on the gray theory was presented. The absolute correlation degree and the relative correlation degree between the target modular product and the history product were calculated respectively, and finally the comprehensive correlative degree was gotten, the most similar historical product was matched. In the end, the approach is illustrated with a hydraulic turbine case.

Overload Avoidance Model using Optimal Placement of Virtual Machines in Cloud Data Cetres

Cloud data centres improve CPU utilization of their servers (physical machines or PMs) through Virtualization (virtual machines or VMs). Over virtualised and under virtualized PMs suffer performance degradation and power dissipation respectively. This work presents a stochastic modular scheme for allocating VM requests to a PM by avoiding overloading of PM and keeping the global load characteristics under specified QoS goal. The proposed approach categorizes PMs into three groups (Under Load, Normal Load, Over Load) in a way that minimizes number of PMs in Under Load and Over Load groups and maximizes number of PMs in Normal Load group. We compute VM request rejection probability, response time, service time and number of PMs that are overload or under loaded for evaluating the performance of our model. The results show that these parameters do not degrade with increasing arrival rate. Thus the proposed model is simple yet efficient approach for VM placement problem.

A scheme of a modular plasma-opening-switch-based generator of megaampere currents for experiments with Z-pinches

A modular scheme of a megaampere current pulse generator on the plasma opening switch (POS) basis with insulation of the POS interelectrode gap by a magnetic field created by an external source is proposed for experiments with wire liners and other applications. The POS is fed from independent modules that are switched on in parallel. On the basis of an НК-50-3 (50 kV, 3 μF) capacitor, a supply module with a current through the equivalent part of the POS of up to 200 kA is designed and tested. The limiting POS parameters and the maximum permissible number of modules are determined, and circuit designs of the basic units of the generator are presented.

Increasing the Flux Measurement Range of an RF-SQUID Resonant Detection Circuit Using the Robust Symmetrical Number System

The design and simulation of a new low temperature Niobium radio frequency superconducting quantum interference devices (RF-SQUID) flux measurement architecture concept that uses N = 3 RF-SQUID rings (or channels) to significantly extend the range of the flux measurement capability beyond ±Φ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> /4 is presented. A resonant detection method based on a robust symmetrical number system (RSNS) preprocessing technique is shown to provide a large increase in the flux measurement range while producing a high-resolution representation of the input magnetic field. The RSNS preprocessing is a modular scheme in which a modulus number of comparators is used at the output of each RF SQUID. The number of comparators with logic 1 in each channel represents the integer values within each RSNS modulus sequence. When considered together, the integers within each sequence change one at a time at the next code position, resulting in an integer Gray code property. We show that the RSNS preprocessing has the feature that the maximum nonlinearity is less than a least significant bit.

On Deploying Scan Chains for Data Storage in Test Compression Environment

In this study the authors show how the interface between automatic test equipment (ATE) and on-chip decompression logic can be improved by a smart reuse of the scan chains. Storing the parent patterns of a modular decompression scheme in groups of scan chains avoids multiple loads from the ATE and thus reduces the test time. The presented algorithm for scan chain selection allows a flexible bandwidth management while preserving encoding efficiency and fault coverage.

Development of experimental structure and influence of high CO2 concentration in maize cro

Maize is a C4 plant that shows few or no response to high [CO2]. Thus, this study aimed to analyze the photosynthetic rate and yield of maize under high [CO2] and develop open-top chambers (OTC) to create an atmosphere enriched with CO2. The experiment was conducted between October 2008 and March 2009. The OTCs were developed in modular scheme. Measurement of photosynthetic rates, transpiration, stomata conductance, grain yield and dry matter were performed. The experimental design was randomized blocks with four replications and three treatments: P1 - plants grown in OTC with 700 ppm [CO2], P2 - plants grown in OTC with environmental [CO2], and P3 - control, cultivated in open field. The results were analyzed by ANOVA and Tukey's test (Pr&lt; 0.05). The chambers can reduce by 25% the photosynthetically active radiation and increase the air and leaf temperatures. Plants under high [CO2] (P1) showed the highest photosynthetic rates and the lowest stomata conductance and transpiration. The total weight of grains (g) and dry mass of shoots (g) showed no increases for P1, despite their higher photosynthetic rates.

Design and implementation of the highly-reliable, low-cost housekeeping system in the ZDPS-1A pico-satellite

The ZDPS-1A pico-satellite designed in Zhejiang University with a mass of 3.5 kg and a power consumption of less than 3.5 W is the smallest satellite in China up to now. The housekeeping system (HKS) is the core part of ZDPS-1A. The reliability of HKS has an important influence on the safety of the satellite. Traditional fault-tolerant methods do not apply to ZDPS-1A due to such pico-satellite characteristics as light weight, compactness in size, energy saving, and high integration. This paper deals with a highly-reliable, low-cost design for HKS using industrial devices. The reliable strategies of HKS include a dual modular redundancy scheme, CPU warm backup, a static triple modular redundancy scheme, and two-level watchdogs. Recursive experiments, special tests, and environmental tests show that this system meets the design target. This design has already been applied to ZDPS-1A, which was launched to execute in-orbit tasks on Sept. 22, 2010. To date, the satellite has been in a proper state for more than 15 months.

Modular neural-SVM scheme for speech emotion recognition using ANOVA feature selection method

The speech signal consists of linguistic information and also paralinguistic one such as emotion. The modern automatic speech recognition systems have achieved high performance in neutral style speech recognition, but they cannot maintain their high recognition rate for spontaneous speech. So, emotion recognition is an important step toward emotional speech recognition. The accuracy of an emotion recognition system is dependent on different factors such as the type and number of emotional states and selected features, and also the type of classifier. In this paper, a modular neural-support vector machine (SVM) classifier is proposed, and its performance in emotion recognition is compared to Gaussian mixture model, multi-layer perceptron neural network, and C5.0-based classifiers. The most efficient features are also selected by using the analysis of variations method. It is noted that the proposed modular scheme is achieved through a comparative study of different features and characteristics of an individual emotional state with the aim of improving the recognition performance. Empirical results show that even by discarding 22% of features, the average emotion recognition accuracy can be improved by 2.2%. Also, the proposed modular neural-SVM classifier improves the recognition accuracy at least by 8% as compared to the simulated monolithic classifiers.

Cu‐Catalyzed Enantioselective 1,4‐Additions of Aryl‐Grignard Reagents to Cyclohexenone in the Presence of TADDOL‐Derived Phosphane‐Phosphite Ligands

AbstractAsymmetric conjugate additions (1,4‐additions) of aryl‐Grignard reagents to cyclohex‐2‐enone, currently a more or less unsolved challenge, were investigated. For this purpose, a small library of phenol‐derived chiral phosphane‐phosphite ligands containing TADDOL‐ or BINOL‐based phosphite moieties was evaluated. These ligands are easily prepared by a short modular scheme previously developed in this laboratory. Two particularly powerful ligands (4a and 4b, both TADDOL‐derived and each possessing a bulky tert‐butyl substituent ortho to the phosphite group) were identified. Conditions were optimized with use of the addition of (4‐methoxyphenyl)magnesium bromide to cyclohexenone as a standard reaction system. Under optimized conditions [CuBr·SMe2 (4 mol‐%), ligand 4a (6 mol‐%), 2‐methyl‐THF, –78 °C, slow addition of Grignard reagent] the 1,4‐product was obtained with high enantioselectivity (up to 95 % ee) and good regioselectivity (r.r. = 90:10). The scope of the method was probed with different aryl‐Grignard reagents. It was found that reagents with electron‐donating substituents in meta‐ or para‐positions performed particularly well, whereas the presence of F or CF3 substituents led to decreased ee values. Only ortho‐substituted aryl‐Grignard reagents did not give rise to useful results. A series of phosphane‐phosphite ligands were also tested in the Rh‐catalyzed 1,4‐addition of phenylboronic acid to cyclohexenone, but enantioselectivities did not exceed 70 % ee in this case.