Fuzzy logic controller and cascade inverter for direct torque control of IM

In this paper, a five-level cascaded H-bridge multilevel inverters topology is applied on induction motor control known as direct torque control (DTC) strategy. More inverter states can be generated by a five-level inverter which improves voltage selection capability. This paper also introduces two different control methods to select the appropriate output voltage vector for reducing the torque and flux error to zero. The first is based on the conventional DTC scheme using a pair of hysteresis comparators and look up table to select the output voltage vector for controlling the torque and flux. The second is based on a new fuzzy logic controller using Sugeno as the inference method to select the output voltage vector by replacing the hysteresis comparators and lookup table in the conventional DTC, to which the results show more reduction in torque ripple and feasibility of smooth stator current. By using Matlab/Simulink, it is verified that using five-level inverter in DTC drive can reduce the torque ripple in comparison with conventional DTC, and further torque ripple reduction is obtained by applying fuzzy logic controller. The simulation results have also verified that using a fuzzy controller instead of a hysteresis controller has resulted in reduction in the flux ripples significantly as well as reduces the total harmonic distortion of the stator current to below 4 %.     

Mechanical characterization and modeling stress relaxation behavior of acrylic–polyurethane-based graft-interpenetrating polymer networks

The stress relaxation behavior of acrylic–polyurethane (PU)-based graft-interpenetrating polymer networks (IPNs) was characterized via dynamic mechanical analysis (DMA) and modeled using finite element method (FEM) analysis. Stress relaxation of glassy IPN specimens was experimentally studied under flexural testing, while rubbery IPN specimens were tested in tension. The effects of varying the styrene content in the acrylic copolymer phase, compatibility of the two phases in IPNs, and changing the concentration of acrylic copolymer and PU were studied. A higher percentage of styrene content resulted in higher homogeneity of IPN specimens, and decrease in initial modulus for acrylic copolymer specimens. Additionally, glassy IPN specimens with 90% styrene shows resistance to relaxation as high as acrylic copolymer samples. Experimental results were used to develop a numerical model to study stress relaxation response of specimens. While polymer systems have been studied computationally, numerical modeling of IPN systems is still in its infancy. A three-dimensional FEM model was developed using the Generalized Maxwell model and four-term Prony series constants, which were extracted from the stress relaxation experiments. With four terms in the Prony series, a good match was observed between experimental observations and results from the FEM model.     

Concept learning games

In this paper, we intend to have a game theoretic study on the concept learning problem in a multi-agent system. Concept learning is a very essential and well-studied domain of machine learning when it is studied under the characteristics of a multi-agent system. The most important reasons are the partiality of the environment perception for any agent and also the communication holdbacks, resulting into a deep need for a collaborative protocol in favor of multi-agent transactions. Here we wish to investigate multi-agent concept learning with the help of its components, thoroughly with a game theoretic taste, esp. on the pre-learning processes. Based on two standard notations, we address the non-unanimity of concepts, classification of objects, voting and communicating protocol, and also the learning itself. In such a game of concept learning, we consider a group of agents, communicating and consulting to upgrade their ontologies based on their conceptualizations of the environment. For this purpose, we investigate the problem in two separate and standard distinctions of game theory study, cooperation and competition. Several solution concepts and innovative ideas from the multi-agent realm are used to produce an approach that contains the reasoning process of the agents in this system. Some experimentations come at the end to show the functionality of our approach. These experimentations come distinctly for both cooperative and competitive views.     

A Fuzzy Logic-Based Method for Replica Placement in the Peer to Peer Cloud Using an Optimization Algorithm

The Peer to Peer-Cloud (P2P-Cloud) is a suitable alternative to distributed cloud-based or peer-to-peer (P2P)-based content on a large scale. The P2P-Cloud is used in many applications such as IPTV, Video-On-Demand, and so on. In the P2P-Cloud network, overload is a common problem during overcrowds. If a node receives many requests simultaneously, the node may not be able to respond quickly to user requests, and this access latency in P2P-Cloud networks is a major problem for their users. The replication method in P2P-Cloud environments reduces the time to access and uses network bandwidth by making multiple data copies in diverse locations. The replication improves access to the information and increases the reliability of the system. The data replication's main problem is identifying the best possible placement of replica data nodes based on user requests for data access time and an NP-hard optimization problem. This paper proposes a new replica replacement to improve average access time and replica cost using fuzzy logic and Ant Colony Optimization algorithm. Ants can find the shortest path to discover the optimal node to place the duplicate file with the least access time latency. The fuzzy module evaluates the historical information of each node to analyze the pheromone value per iteration. The fuzzy membership function is also used to determine each node's degree based on the four characteristics. The simulation results showed that the access time and replica cost are improved compared to other replica replacement algorithms.

     

Colloidal Quantum Dot Infrared Lasers Featuring Sub-Single-Exciton Threshold and Very High Gain

The use of colloidal quantum dots (CQDs) as a gain medium in infrared laser devices has been underpinned by the need for high pumping intensities, very short gain lifetimes, and low gain coefficients. Here, PbS/PbSSe core/alloyed-shell CQDs are employed as an infrared gain medium that results in highly suppressed Auger recombination with a lifetime of 485 ps, lowering the amplified spontaneous emission (ASE) threshold down to 300 µJ cm⁻², and showing a record high net modal gain coefficient of 2180 cm⁻¹. By doping these engineered core/shell CQDs up to nearly filling the first excited state, a significant reduction of optical gain threshold is demonstrated, measured by transient absorption, to an average-exciton population-per-dot 〈N^th〉_g of 0.45 due to bleaching of the ground state absorption. This in turn have led to a fivefold reduction in ASE threshold at 〈N^th〉_ASE = 0.70 excitons-per-dot, associated with a gain lifetime of 280 ps. Finally, these heterostructured QDs are used to achieve near-infrared lasing at 1670 nm at a pump fluences corresponding to sub-single-exciton-per-dot threshold (〈N^th〉_Las = 0.87). This work brings infrared CQD lasing thresholds on par to their visible counterparts, and paves the way toward solution-processed infrared laser diodes.     

A parameter-tuned genetic algorithm for the resource investment problem with discounted cash flows and generalized precedence relations

A resource investment problem with discounted cash flows (RIPDCF) is a project-scheduling problem in which (a) the availability levels of the resources are considered decision variables and (b) the goal is to find a schedule such that the net present value of the project cash flows optimizes. In this paper, the RIPDCF in which the activities are subject to generalized precedence relations is first modeled. Then, a genetic algorithm (GA) is proposed to solve this model. In addition, design of experiments and response surface methodology are employed to both tune the GA parameters and to evaluate the performance of the proposed method in 240 test problems. The results of the performance analysis show that the efficiency of the proposed GA method is relatively well.     

Disturbance and recovery of trunk mechanical and neuromuscular behaviours following prolonged trunk flexion: influences of duration and external load on creep-induced effects

Trunk flexion results in adverse mechanical effects on the spine and is associated with a higher incidence of low back pain. To examine the effects of creep deformation on trunk behaviours, participants were exposed to full trunk flexion in several combinations of exposure duration and external load. Trunk mechanical and neuromuscular behaviours were obtained pre- and post-exposure and during recovery using sudden perturbations. Intrinsic trunk stiffness decreased with increasing flexion duration and in the presence of the external load. Recovery of intrinsic stiffness required more time than the exposure duration and was influenced by exposure duration. Reflexive trunk responses increased immediately following exposure but recovered quickly (～2.5 min). Alterations in reflexive trunk behaviour following creep deformation exposures may not provide adequate compensation to allow for complete recovery of concurrent reductions in intrinsic stiffness, which may increase the risk of injury due to spinal instability. STATEMENT OF RELEVANCE: An increased risk of low back injury may result from flexion-induced disturbances to trunk behaviours. Such effects, however, appear to depend on the type of flexion exposure, and have implications for the design of work involving trunk flexion.     

Multi-product multi-chance-constraint stochastic inventory control problem with dynamic demand and partial back-ordering: A harmony search algorithm

In this paper, a multiproduct inventory control problem is considered in which the periods between two replenishments of the products are assumed independent random variables, and increasing and decreasing functions are assumed to model the dynamic demands of each product. Furthermore, the quantities of the orders are assumed integer-type, space and budget are constraints, the service-level is a chance-constraint, and that the partial back-ordering policy is taken into account for the shortages. The costs of the problem are holding, purchasing, and shortage. We show the model of this problem is an integer nonlinear programming type and to solve it, a harmony search approach is used. At the end, three numerical examples of different sizes are given to demonstrate the applicability of the proposed methodology in real world inventory control problems, to validate the results obtained, and to compare its performances with the ones of both a genetic and a particle swarm optimization algorithms.     

Multiple kernel learning with gaussianity measures

Kernel methods are known to be effective for nonlinear multivariate analysis. One of the main issues in the practical use of kernel methods is the selection of kernel. There have been a lot of studies on kernel selection and kernel learning. Multiple kernel learning (MKL) is one of the promising kernel optimization approaches. Kernel methods are applied to various classifiers including Fisher discriminant analysis (FDA). FDA gives the Bayes optimal classification axis if the data distribution of each class in the feature space is a gaussian with a shared covariance structure. Based on this fact, an MKL framework based on the notion of gaussianity is proposed. As a concrete implementation, an empirical characteristic function is adopted to measure gaussianity in the feature space associated with a convex combination of kernel functions, and two MKL algorithms are derived. From experimental results on some data sets, we show that the proposed kernel learning followed by FDA offers strong classification power.