Tuning fuzzy PD and PI controllers using reinforcement learning

In this paper, we propose a new auto-tuning fuzzy PD and PI controllers using reinforcement Q-learning (QL) algorithm for SISO (single-input single-output) and TITO (two-input two-output) systems. We first, investigate the design parameters and settings of a typical class of Fuzzy PD (FPD) and Fuzzy PI (FPI) controllers: zero-order Takagi–Sugeno controllers with equidistant triangular membership functions for inputs, equidistant singleton membership functions for output, Larsen’s implication method, and average sum defuzzification method. Secondly, the analytical structures of these typical fuzzy PD and PI controllers are compared to their classical counterpart PD and PI controllers. Finally, the effectiveness of the proposed method is proven through simulation examples.     

A new and accurate fault location algorithm for combined transmission lines using Adaptive Network-Based Fuzzy Inference System

This paper presents a new and accurate algorithm for locating faults in a combined overhead transmission line with underground power cable using Adaptive Network-Based Fuzzy Inference System (ANFIS). The proposed method uses 10 ANFIS networks and consists of 3 stages, including fault type classification, faulty section detection and exact fault location. In the first part, an ANFIS is used to determine the fault type, applying four inputs, i.e., fundamental component of three phase currents and zero sequence current. Another ANFIS network is used to detect the faulty section, whether the fault is on the overhead line or on the underground cable. Other eight ANFIS networks are utilized to pinpoint the faults (two for each fault type). Four inputs, i.e., the dc component of the current, fundamental frequency of the voltage and current and the angle between them, are used to train the neuro-fuzzy inference systems in order to accurately locate the faults on each part of the combined line. The proposed method is evaluated under different fault conditions such as different fault locations, different fault inception angles and different fault resistances. Simulation results confirm that the proposed method can be used as an efficient means for accurate fault location on the combined transmission lines.     

Towards the performance evaluation of 4th generation wireless communication standards: LTE versus Mobile WiMAX

Advances in wireless communication protocols and networking toward support of the next generation of mobile and radio broadband technologies have contributed to a strong competition among various telecommunication standards in particular Long Term Evolution (LTE) and Mobile Worldwide Interoperability for Microwave Access (WiMAX). In this paper, we provide an in‐depth comparison analysis of LTE and Mobile WiMAX at the physical (PHY) layer by studying the most similar PHY configuration scenarios for these two technologies. Our study includes a throughput analysis of downlink (DL) and uplink (UL) transmissions in time division duplex with the least overhead possible and different antenna schemes as well as modulation and code rates. This study also performs an overhead analysis in both protocols to provide a more in‐depth understanding of the PHY layer capacity in various PHY layer configurations. Our simulation results generally show higher performance for LTE in both DL and UL transmission with 7 Mbps in DL and 5 Mbps in UL, when using one antenna port. However, by increasing the number of antennas for multiple‐input/multiple‐output configurations, the results illustrate a reduction in the performance of LTE compared to Mobile WiMAX. This arises from the increase in reference signal overhead in LTE from 4.7% in single‐input/single‐output (SISO) to 14.28% in 4 × 4 multiple‐input/multiple‐output (MIMO). Copyright © 2013 John Wiley & Sons, Ltd.     

Dynamically vulcanized polypropylene/ethylene‐propylene diene monomer/organoclay nanocomposites: Effect of mixing sequence on structural,rheological, and mechanical properties

Dynamically vulcanized thermoplastic elastomer (TPE) nanocomposites based on polypropylene (PP), ethylene‐propylene diene monomer (EPDM) and cloisite 15A were prepared via direct melt mixing in a co‐rotating twin‐screw extruder. The mixing process was carried out with optimized processing parameters (barrel temperature = 180°C; screw speed = 150 rpm; and feeding rate = 0.2 kg/hr). The formulation used to prepare the nanocomposites was fixed to 75/20/5 (PP/EPDM/Cloisite^©15A), expressed in mass fraction. Effect of mixing sequence on the properties of vulcanized and unvulcanized (TPE) nanocomposites prepared under similar conditions was investigated using X‐ray diffraction (XRD) and a tensile testing machine. Results showed that the sequence of mixing does affect the properties of final TPE nanocomposites. Accordingly, nanocomposite samples prepared through mixing the preblended PP/clay masterbatch with EPDM phase, show better clay dispersion within the polymer matrix. J. VINYL ADDIT. TECHNOL., 22:320–325, 2016. © 2014 Society of Plastics Engineers     

The use of biomass and stillage recycle in conventional ethanol fermentation

Modifications of the conventional ethanol fermentation process using biomass and stillage recycle were investigated and the advantages of the technique were studied. The biomass recycle method resulted in a reduction in the use of approximately 8% raw material (molasses). The recycle of 15–70% of stillage (dealcoholised) from a previous fermentation was also tested successfully. Generally the results of these experiments revealed 13–47% less water consumption and consequently about 13–47% reduction of stillage volume, which was more economical for further treatments.     

The glass transition temperature of nitrated polystyrene/poly(acrylic acid) blends

Low-molecular-weight polystyrene was nitrated to different levels. The nitrated polystyrene was blended with different molecular weights of poly(acrylic acid), PAA. The glass transition temperatures (T_g) for the mixtures were investigated by differential scanning calorimetry. A single T_g was observed for all blends, indicating single-phase blends. In general, it was found that the T_g increases with molecular weight of PAA. The T_g values of the blends showed a positive deviation from the linear average T_g as a result of strong hydrogen bonding between the segments of the component polymers. The observed T_g values were not adequately represented by simple predictive equations or by single-parameter fitting equations. However, two-parameter fitting equations gave a reasonable representation of the data.     

Recent Developments in Predicting Impact and Shock Sensitivities of Energetic Materials （英）

Empirical, quantum mechanical and artificial neural network methods are three usual methods in recent years that were used to predict sensitivity of different classes of high explosives. Some recent developments in predicting sensitivity by various methods are reviewed and discussed for various classes of energetic materials.     

Metabolites and Genes behind Cardiac Metabolic Remodeling in Mice with Type 1 Diabetes Mellitus

Metabolic remodeling is at the heart of diabetic cardiomyopathy. High glycemic fluctuations increase metabolic stress in the type 1 diabetes mellitus (T1DM) heart. There is a lack of understanding on how metabolites and genes affect metabolic remodeling in the T1DM heart. We hypothesize that differential expression of metabolic genes and metabolites synergistically influence metabolic remodeling preceding T1DM cardiomyopathy. To test our hypothesis, we conducted high throughput analysis of hearts from adult male hyperglycemic Ins2^+/− (Akita) and littermate normoglycemic Ins2^+/+ (WT) mice. The Akita mouse is a spontaneous, genetic model of T1DM that develops increased levels of consistent glycemic variability without the off-target cardiotoxic effects present in chemically- induced models of T1DM. After validating the presence of a T1DM phenotype, we conducted metabolomics via LC-MS analysis and genomics via next-generation sequencing in left ventricle tissue from the Akita heart. Ingenuity Pathway Analyses revealed that 108 and 30 metabolic pathways were disrupted within the metabolomics and genomics datasets, respectively. Notably, a comparison between the two analyses showed 15 commonly disrupted pathways, including ketogenesis, ketolysis, cholesterol biosynthesis, acetyl CoA hydrolysis, and fatty acid biosynthesis and beta-oxidation. These identified metabolic pathways predicted by the differential expression of metabolites and genes provide the foundation for understanding metabolic remodeling in the T1DM heart. By limited experiment, we revealed a predicted disruption in the metabolites and genes behind T1DM cardiac metabolic derangement. Future studies targeting these genes and metabolites will unravel novel therapies to prevent/improve metabolic remodeling in the T1DM heart.