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Journal Cover International Journal of Convergence Computing
  [1 followers]  Follow
    
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 2048-9129 - ISSN (Online) 2048-9137
   Published by Inderscience Publishers Homepage  [423 journals]
  • Prediction of flow regime using ANN for air-water flow through small
           diameter tubes in horizontal plane
    • Authors: Nirjhar Bar, Manindra Nath Biswas, Sudip Kumar Das
      Pages: 107 - 129
      Abstract: Artificial neural network (ANN) modelling for the classifications of flow regimes in air-water flow through 1 mm to 5 mm tubes is presented. Two hundred eighteen data points based on the experimental investigation in 3 and 4 mm tubes and 2,114 data points from various experimental results collected from the literature for air-water two-phase flow through tubes having small diameter have been used. Five different artificial neural network training algorithms have been used to predict the flow regime. The ANN model based on radial basis function gives slightly better predictability over the other networks.
      Keywords: artificial neural networks; ANNs; flow regime prediction; multilayer perceptron; backpropagation; Levenberg-Marquardt; radial basis function; RBF; support vector machines; SVM; principal component analysis; PCA; transfer function; air-water flow; small dia
      Citation: International Journal of Convergence Computing, Vol. 2, No. 2 (2016) pp. 107 - 129
      PubDate: 2017-02-01T23:20:50-05:00
      DOI: 10.1504/IJCONVC.2016.082022
      Issue No: Vol. 2, No. 2 (2017)
       
  • GAPWA: genetic algorithm using pair wise alignment method for solving
           MSA problem
    • Authors: Rohit Kumar Yadav, Haider Banka
      Pages: 130 - 143
      Abstract: In bioinformatics, multiple sequence alignment is an NP-hard problem. Hence, bio-inspired techniques can be used to approximate the solution. In the current study, a hybrid genetic algorithm has been proposed to solve multiple sequence alignment problems i.e., termed as GAPWA. In which, two new mechanism have been proposed. First one is for initial population generation, and another for crossover and mutation operation. In the initial population generation, Needleman Wunsch pair wise method is adopted. In the second mechanism, modified crossover and mutation operation is used. In the performance analysis, GAPWA is compared with some of the well-known existing methods such as PRRP, DIALIGN, HMMT, SB-PIMA, GA, SAGA, and RBT-GA on a number of benchmark datasets from Bali-base 2.0. The obtained results show that the proposed approach GAPWA achieves better solutions compared to existing approaches in most of the cases.
      Keywords: multiple sequence alignment; MSA; genetic algorithms; GAs; pair-wise alignment; PWA; dynamic programming; bioinformatics; sequences
      Citation: International Journal of Convergence Computing, Vol. 2, No. 2 (2016) pp. 130 - 143
      PubDate: 2017-02-01T23:20:50-05:00
      DOI: 10.1504/IJCONVC.2016.082023
      Issue No: Vol. 2, No. 2 (2017)
       
  • Adaptive proportional derivative controller using fuzzy logic
    • Authors: Ritu Rani De Maity, Rajani K. Mudi
      Pages: 144 - 160
      Abstract: Design of an improved fuzzy rule-based auto-tuning PD controller has been done with conventional Ziegler-Nichols PID controller. It has been observed that for the very common system in industry, the integrating system, the classical Ziegler-Nichols tuned PID controller (ZNPID) gives excessively large value of overshoot and settling time. Here, the proposed controller is a fuzzy auto-tuning PD (FAPD) controller. The value of the derivative gain of the conventional PD controller is continuously updated by a factor α. The value of α is determined depending on the process trend, i.e., normalised value of error (e) and change of error (Δe) and 49 or nine fuzzy if then rules. In FAPD, the updating factor 'α' continuously updates the value of derivative gain to provide an overall good performance during set point change and load disturbance. To study the effectiveness of the proposed controller FAPD has been tested and compared with other PID controllers for different integrating systems with varying dead time.
      Keywords: PID control; PD control; Ziegler-Nichols tuning; auto-tuning; adaptive control; controller design; fuzzy control; fuzzy logic
      Citation: International Journal of Convergence Computing, Vol. 2, No. 2 (2016) pp. 144 - 160
      PubDate: 2017-02-01T23:20:50-05:00
      DOI: 10.1504/IJCONVC.2016.082025
      Issue No: Vol. 2, No. 2 (2017)
       
  • Design and analysis of an energy efficient and compact two-dimensional
           two-dot one-electron quantum-dot cellular automata-based ripple carry
           adder
    • Authors: Kakali Datta, Debarka Mukhopadhyay, Paramartha Dutta
      Pages: 161 - 182
      Abstract: Quantum-dot cellular automata (QCA) is a post-CMOS upcoming technology in the nanotechnology domain. Design of different QCA-based circuits is on the top agenda of research community to overcome the limitations of CMOS counterpart in respect of off-state leakage current, energy and power efficiency, and minimum dimension requirement of fabrication. Accordingly, in the present scope, we are trying to explore a design and analysis of a two-dot one-electron QCA-based circuit for ripple carry adder. In course of our detailed description of our article, we have tried to justify the effectiveness of the QCA-based circuit in comparison to CMOS-based counterpart. Our analysis includes derivation of energy and power requirement for such a two-dimensional two-dot one-electron QCA circuit-related issues. Moreover, it will be evident that the proposed architecture is compact if the cells used are square in shape.
      Keywords: quantum dot cellular automata; QCA; Coulomb's principle; full adders; circuit design; ripple carry adders; quantum dots; energy efficiency; nanotechnology
      Citation: International Journal of Convergence Computing, Vol. 2, No. 2 (2016) pp. 161 - 182
      PubDate: 2017-02-01T23:20:50-05:00
      DOI: 10.1504/IJCONVC.2016.082034
      Issue No: Vol. 2, No. 2 (2017)
       
  • Agronomic-meteorological model for weather forecasting to predict the
           rainfall using machine learning techniques
    • Authors: Baghavathi Priya Sankaralingam, Usha Sarangapani
      Pages: 183 - 192
      Abstract: Weather forecasting is essential and a challenging area for predicting rainfall. As the climatic conditions are changing dramatically, accurate prediction of atmospheric conditions is difficult. Various machine learning techniques like support vector machine (SVM) classification and regression can be applied to predict rainfall using various parameters like mean temperature, wind speed, mean dew point, minimum and maximum temperature, precipitation level, snow depth and wind gust. The proposed model for weather forecasting uses support vector machine classification and regression. This technique can be applied on weather dataset in order to predict accurate precipitation which is most useful for agricultural purpose. These results can be effectively used by agricultural sector. This useful information is given to the farmer for increasing their agricultural growth which leads to better productivity.
      Keywords: weather forecasting; support vector machines; SVM classification; regression; agriculture; agronomic-meteorological models; agronomy; meteorology; rainfall prediction; precipitation levels; machine learning; modelling
      Citation: International Journal of Convergence Computing, Vol. 2, No. 2 (2016) pp. 183 - 192
      PubDate: 2017-02-01T23:20:50-05:00
      DOI: 10.1504/IJCONVC.2016.082035
      Issue No: Vol. 2, No. 2 (2017)
       
 
 
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