Intelligent Learning Algorithms for Active Vibration Control

YesThis correspondence presents an investigation into the comparative performance of an active vibration control (AVC) system using a number of intelligent learning algorithms. Recursive least square (RLS), evolutionary genetic algorithms (GAs), general regression neural network (GRNN), and adaptive neuro-fuzzy inference system (ANFIS) algorithms are proposed to develop the mechanisms of an AVC system. The controller is designed on the basis of optimal vibration suppression using a plant model. A simulation platform of a flexible beam system in transverse vibration using a finite difference method is considered to demonstrate the capabilities of the AVC system using RLS, GAs, GRNN, and ANFIS. The simulation model of the AVC system is implemented, tested, and its performance is assessed for the system identification models using the proposed algorithms. Finally, a comparative performance of the algorithms in implementing the model of the AVC system is presented and discussed through a set of experiments

Intelligent Active Vibration Control for a Flexible Beam System

YesThis paper presents an investigation into the development of an intelligent active vibration control (AVC) system. Evolutionary Genetic algorithms (GAs) and Adaptive Neuro-Fuzzy Inference system (ANFIS) algorithms are used to develop mechanisms of an AVC system, where the controller is designed on the basis of optimal vibration suppression using the plant model. A simulation platform of a flexible beam system in transverse vibration using finite difference (FD) method is considered to demonstrate the capabilities of the AVC system using GAs and ANFIS. MATLAB GA tool box for GAs and Fuzzy Logic tool box for ANFIS function are used for AVC system design. The system is then implemented, tested and its performance assessed for GAs and ANFIS based design. Finally a comparative performance of the algorithm in implementing AVC system using GAs and ANFIS is presented and discussed through a set of experiments

Strong enhancement of Jc in binary and alloyed in-situ MgB2 wires by a new approach: Cold high pressure densification

Cold high pressure densification (CHPD) is presented as a new way to substantially enhance the critical current density of in situ MgB2 wires at 4.2 and 20 K at fields between 5 and 14 T. The results on two binary MgB2 wires and an alloyed wire with 10 wt.% B4C are presented The strongest enhancement was measured at 20K, where cold densification at 1.85 GPa on a binary Fe/MgB2 wire raised both Jcpara and Jcperp by more than 300% at 5T, while Birr was enhanced by 0.7 T. At 4.2K, the enhancement of Jc was smaller, but still reached 53% at 10 T. After applying pressures up to 6.5 GPa, the mass density dm of the unreacted (B+Mg) mixture inside the filaments reached 96% of the theoretical density. After reaction under atmospheric pressure, this corresponds to a highest mass density df in the MgB2 filaments of 73%. After reaction, the electrical resistance of wires submitted to cold densification was found to decrease, reflecting an improved connectivity. A quantitative correlation between filament mass density and the physical properties was established. Monofilamentary rectangular wires with aspect ratios a/b < 1.25 based on low energy ball milled powders exhibited very low anisotropy ratios, Gamma = Jcpara/Jcperp being < 1.4 at 4.2 K and 10T. The present results can be generalized to alloyed MgB2 wires, as demonstrated on a wire with B4C additives. Based on the present data, it follows that cold densification has the potential of further improving the highest Jcpara and Jcperp values reported so far for in situ MgB2 tapes and wires with SiC and C additives. Investigations are under work in our laboratory to determine whether the densification method CHPD can be applied to longer wire or tape lengths.Comment: Submitted to Superconductors Science and Technolog

Obesity and Hypertension in Students of Jahangirnagar University: Alarming Issues

The prevalence of obesity and hypertension (HTN) in university students of Bangladesh has not reported yet. Considering the proper health maintenance of this population in mind, the study was aimed to determine the prevalence of obesity and HTN as well as relationship among them in the students of a residential university of Bangladesh, Jahangirnagar University. This descriptive cross sectional study included 500 randomly selected students (250 males and 250 females). Participants completed a questionnaire on physical activity, sedentary behaviour, dietary factors, smoking and family history of obesity, HTN, and coronary artery disease. Blood pressure and anthropometric parameters such as height, weight, waist and hip circumferences were measured following standard procedure. The Statistical analyses were performed using the software SPSS.The prevalence of overweight was 25% (31.1% males, 15.6% females) and obesity 7.2% (9.4% males, 4% females). Pre-HTN was found at 27.1% (38% males, 11.2% females) and HTN at 2.2% (3.3% males, 0.4% females). A high rate of smoking, sedentary behavior, physical inactivity, excessive consumption of unhealthy food, and caffeine-rich drinks was also observed. Significant correlation was found between parameters of obesity and HTN. High prevalence of pre-HTN in males and central obesity in females were found which is immediately needed to control for better health maintenance of this population

Monotone Grid Drawings of Planar Graphs

A monotone drawing of a planar graph $G$ is a planar straight-line drawing of $G$ where a monotone path exists between every pair of vertices of $G$ in some direction. Recently monotone drawings of planar graphs have been proposed as a new standard for visualizing graphs. A monotone drawing of a planar graph is a monotone grid drawing if every vertex in the drawing is drawn on a grid point. In this paper we study monotone grid drawings of planar graphs in a variable embedding setting. We show that every connected planar graph of $n$ vertices has a monotone grid drawing on a grid of size $O(n)\times O(n^2)$, and such a drawing can be found in O(n) time

Scheduling of tasks in multiprocessor system using hybrid genetic algorithms

This paper presents an investigation into the optimal scheduling of realtime tasks of a multiprocessor system using hybrid genetic algorithms (GAs). A comparative study of heuristic approaches such as `Earliest Deadline First (EDF)¿ and `Shortest Computation Time First (SCTF)¿ and genetic algorithm is explored and demonstrated. The results of the simulation study using MATLAB is presented and discussed. Finally, conclusions are drawn from the results obtained that genetic algorithm can be used for scheduling of real-time tasks to meet deadlines, in turn to obtain high processor utilization

Quasiquartet CEF ground state with possible quadrupolar ordering in the tetragonal compound YbRu2_{2}Ge2_{2}

e have investigated the magnetic properties of YbRu$_{2}$Ge$_{2}$ by means of magnetic susceptibility $\chi$(T), specific heat C(T) and electrical resistivity $\rho$(T) measurements performed on flux grown single crystals. The Curie-Weiss behavior of $\chi$(T) along the easy plane, the large magnetic entropy at low temperatures and the weak Kondo like increase in $\rho$(T) proves a stable trivalent Yb state. Anomalies in C(T), $\rho$(T) and $\chi$(T) at T$_{0}$ = 10.2 K, T$_{1}$ = 6.5 K and T$_{2}$ = 5.7 K evidence complex ordering phenomena, T$_{0}$ being larger than the highest Yb magnetic ordering temperature found up to now. The magnetic entropy just above T$_{0}$ amounts to almost Rln4, indicating that the crystal electric field (CEF) ground state is a quasiquartet instead of the expected doublet. The behavior at T$_{0}$ is rather unusual and suggest that this transition is related to quadrupolar ordering, being a consequence of the CEF quasiquartet ground state. The combination of a quasiquartet CEF ground state, a high ordering temperature, and the relevance of quadrupolar interactions makes YbRu$_{2}$Ge$_{2}$ a rather unique system among Yb based compounds.Comment: 11 pages, 5 figure, submitted to PRB rapi

GA-based learning algorithms to identify fuzzy rules for fuzzy neural networks

Identification of fuzzy rules is an important issue in designing of a fuzzy neural network (FNN). However, there is no systematic design procedure at present. In this paper we present a genetic algorithm (GA) based learning algorithm to make use of the known membership function to identify the fuzzy rules form a large set of all possible rules. The proposed learning algorithm initially considers all possible rules then uses the training data and the fitness function to perform ruleselection. The proposed GA based learning algorithm has been tested with two different sets of training data. The results obtained from the experiments are promising and demonstrate that the proposed GA based learning algorithm can provide a reliable mechanism for fuzzy rule selection