Fuzzy logic systems have been widely used for controlling nonlinear and complex dynamic systems by programming heuristic knowledge. But these systems are computationally complex and resource intensive. This paper presents a technique of development and porting of a fuzzy logic approximation PID controller (FLAC) in an automatic cruise control (ACC) system. ACC is a highly nonlinear process and its control is trivial due to the large change in parameters. Therefore, a suitable controller based on heuristic knowledge will be easy to develop and provide an effective solution. But the major problem with employing fuzzy logic controller (FLC) is its complexity. Moreover, the designing of Rulebase requires efficient heuristic knowledge about the system which is rarely found. Therefore, in this paper, a novel rule extraction process is used to derive a FLAC. This controller is then ported on a C6748 DSP hardware with timing and memory optimization. Later, it is seamlessly connected to a network to support remote reconfigurability. A performance analysis is drawn based on processor-in loop test with Simulink model of a cruise control system for vehicle.

Pallab Maji

Sarat Kumar Patra

Kamalakanta Mahapatra

Design and Implementation of Fuzzy Approximation PI Controller for Automatic Cruise Control System

A single hidden layer feedforward neural network (SLFN) with online sequential extreme learning machine (OSELM) algorithm has been introduced and applied in many regression problems successfully. However, using SLFN with OSELM as black-box for nonlinear system identification may lead to building models for the identified plant with inconsistency responses from control perspective. The reason can refer to the random initialization procedure of the SLFN hidden node parameters with OSELM algorithm. In this paper, a single hidden layer feedforward wavelet network (WN) is introduced with OSELM for nonlinear system identification aimed at getting better generalization performances by reducing the effect of a random initialization procedure.

Dhiadeen Mohammed Salih

Samsul Bahari Mohd Noor

Mohammad Hamiruce Merhaban

Raja Mohd Kamil

Wavelet Network: Online Sequential Extreme Learning Machine for Nonlinear Dynamic Systems Identification

The optimal planning (sizing and siting) of the distributed generations (DGs) by using butterfly-PSO/BF-PSO technique to investigate the impacts of load models is presented in this work. The validity of the evaluated results is confirmed by comparing with well-known Genetic Algorithm (GA) and standard or conventional particle swarm optimization (PSO). To exhibit its compatibility in terms of load management, an impact of different load models on the size and location of DG has also been presented in this work. The fitness evolution function explored is the multiobjective function (FMO), which is based on the three significant indexes such as active power loss, reactive power loss, and voltage deviation index. The optimal solution is obtained by minimizing the multiobjective fitness function using BF-PSO, GA, and PSO technique. The comparison of the different optimization techniques is given for the different types of load models such as constant, industrial, residential, and commercial load models. The results clearly show that the BF-PSO technique presents the superior solution in terms of compatibility as well as computation time and efforts both. The algorithm has been carried out with 15-bus radial and 30-bus mesh system.

Aashish Kumar Bohre

Ganga Agnihotri

Manisha Dubey

Shilpa Kalambe

Impacts of the Load Models on Optimal Planning of Distributed Generation in Distribution System

In machine transliteration, it is common that the transliterated names in the target language come from multiple language origins. A conventional maximum likelihood based single model can not deal with this issue very well and often suffers from overfitting. In this paper, we exploit a coupled Dirichlet process mixture model (cDPMM) to address overfitting and names multiorigin cluster issues simultaneously in the transliteration sequence alignment step over the name pairs. After the alignment step, the cDPMM clusters name pairs into many groups according to their origin information automatically. In the decoding step, in order to use the learned origin information sufficiently, we use a cluster combination method (CCM) to build clustering-specific transliteration models by combining small clusters into large ones based on the perplexities of name language and transliteration model, which makes sure each origin cluster has enough data for training a transliteration model. On the three different Western-Chinese multiorigin names corpora, the cDPMM outperforms two state-of-the-art baseline models in terms of both the top-1 accuracy and mean F-score, and furthermore the CCM significantly improves the cDPMM.

Chunyue Zhang

Tiejun Zhao

Tingting Li

A Dirichlet Process Mixture Based Name Origin Clustering and Alignment Model for Transliteration

Human gaze is not directed to the same part of an image when lighting conditions change. Current saliency models do not consider
light level analysis during their bottom-up processes. In this paper, we
introduce a new saliency model which better mimics physiological and
psychological processes of our visual attention in case of free-viewing task
(bottom-up process). This model analyzes lighting conditions with the
aim of giving different weights to color wavelengths. The resulting saliency
measure performs better than a lot of popular cognitive approaches.

Makiese Mibulumukini

Pop-Out: A New Cognitive Model of Visual Attention That Uses Light Level Analysis to Better Mimic the Free-Viewing Task of Static Images

Majority of Indian languages have originated from two language families, namely, Indo-European
and Dravidian. Therefore, certain kind of similarity among languages of a particular family can
be expected to exist. Also, languages spoken in neighboring regions show certain similarity
since there happens to be a lot of intermingling between population of neighboring regions. This
paper develops a technique to measure similarity among Indian languages in a novel way, using
language verification framework. Four verification systems are designed for each language. 
Acceptance of one language as another, which relates to false acceptance in language verification
framework, is used as a measure of similarity. If language A shows false acceptance more than a
predefined threshold with language B, in at least three out of the four systems, then languages A and B are considered to be similar in this work. It is expected that the languages belonging to the same family should manifest their similarity in experimental results. Also, similarity
between neighboring languages should be detected through experiments. Any deviation from
such fact should be due to specific linguistic or historical reasons. This work analyzes any such
scenario.

Debapriya Sengupta

Goutam Saha

Study on Similarity among Indian Languages Using Language Verification Framework

Artificial neural network (ANN) theory is emerging as an alternative to conventional statistical methods in modeling nonlinear functions. The popular Cox proportional hazard model falls short in modeling survival data with nonlinear behaviors. ANN is a good alternative to the Cox PH as the proportionality of the hazard assumption and model relaxations are not required. In addition, ANN possesses a powerful capability of handling complex nonlinear relations within the risk factors associated with survival time. In this study, we present a comprehensive comparison of two different approaches of utilizing ANN in modeling smooth conditional hazard probability function. We use real melanoma cancer data to illustrate the usefulness of the proposed ANN methods. We report some significant results in comparing the survival time of male and female melanoma patients.

Taysseer Sharaf

Chris P. Tsokos

Two Artificial Neural Networks for Modeling Discrete Survival Time of Censored Data

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Advances in Artificial Intelligence