Forthcoming articles


International Journal of Intelligent Systems Design and Computing


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International Journal of Intelligent Systems Design and Computing (7 papers in press)


Regular Issues


  • Adaptive Backstepping Control for a Class of MIMO Uncertain Underactuated Systems with Input Constraints   Order a copy of this article
    by Ajay Kulkarni 
    Abstract: This paper presents a backstepping methodology basedrnadaptive controller scheme for a class of multi-input multi-outputrn(MIMO) uncertain underactuated systems in presence of actuatorrnconstraints. To develop a feasible controller scheme for multi-inputrnmulti-output underactuated systems, (n − p + 1) dimensions of thernn dimensional configuration space are stabilized by using onerndimension of the input space. This control term is developed byrnapplying hierarchical methodology whereas as remaining p − 1 inputrndimensions are assigned as dedicated control terms to solve therncontrol problem of remaining dimensions of the configuration space.rnBackstepping technique is used to develop the classical control termrnwhereas wavelet networks are used to approximate the uncertainrndynamics as well as to compensate the nonlinear effects of actuatorrnsaturation. A robust control term is used to attenuate thernapproximation error to a prescribed level. Uniform ultimaternboundedness (UUB) stability of the closed loop system is verified inrnthe Lyapunov sense. Simulation results illustrate the effectiveness ofrntheoretical development.
    Keywords: Underactuated systems; hierarchical controlrnstructure; backstepping control; wavelet neural network,rnactuator saturation.

    by Sitaram Soni, Pritee Khanna, Puneet Tandon 
    Abstract: The early phase of the aesthetic concept generation involves the tacit knowledge of the experts. There is general lack of formal models to capture and use this knowledge, as it is difficult to externalize, capture, express and reuse. This paper contributes to the development of formal models and an application framework for the knowledge involved in early phase aesthetic concept generation of industrial products. The models are based on four axioms. These axioms are used to develop two models; aesthetic design complex (ADC) and action grammar. These models are used to develop a design learning and generation framework. Soft computing techniques are used to capture, reuse and externalize the tacit aesthetic design knowledge. The tacit design is expressed as heuristics, which are validated by human based evaluation. The developed prototype framework shows that such a computational support is possible to aesthetic design process, practice and education.
    Keywords: design for aesthetics; knowledge based system; cognitive process; action grammar.

  • Prediction and Estimation of Civil Construction Cost using Linear Regression and Neural Network   Order a copy of this article
    by Nagaraj Dharwadkar, Sphurti Arage 
    Abstract: Adequate construction cost estimation is a main factor in any type of construction projects. Forecasting cost of construction projects can be considered as a difficult task. In order to forecast the cost of the civil construction projects, we have used the Ordinary Least Square Regression (OLSR) model and Multi-Layer Perceptron (MLP) in our proposed model. The performance of the proposed model is analysed on the data of the 12 years of schedule rates of construction projects in Pune region of India. The experiment shows 91% to 97% of accuracy in prediction using Ordinary Least Square Regression model. Similarly, we have conducted series of experiments on Multi-layer Perceptron model with different activation functions. It was observed that the Multi-layer Perceptron model with softplus activation function can be able to predict the project cost of the civil constructions with accuracy of 91 % to 98%. Thus it shows that the prediction of cost using Multi-Layer Perceptron model gives higher accuracy than the Ordinary Least Square Regression model.
    Keywords: Construction Cost estimation; Ordinary Least Square Regression (OLSR); Multi-layer Perceptron (MLP); Activation functions; Root Mean Square Error (RMSE); Mean Absolute Percentage Error (MAPE).

  • Some Statistical Aspects of Children with Disabilities in Assam, India   Order a copy of this article
    by Jumi Kalita 
    Abstract: Analysis of data plays a very important role in describing a data set from various angles of interest. It digs out different characteristics intrinsic in the data set. This paper analyses the occurrences of disability in children for genderwise distribution, rural-urban distribution, and probes any probable relationship of mothers age with occurrence of disability among newborns. The influence of the parameters like birth-cry, birth-weight, mothers health during pregnancy and severe health problem of the children within a short period after the birth in determining the disability types are analyzed through multinomial logistic regression. Analysis of data relating to children with disability may be useful in predicting disability and taking precautionary measures.
    Keywords: Disability; chi-square test; multi-nomial logistic regression.

  • An Effective Frame Based High Frequency Speech Transposition By Using Neural Network   Order a copy of this article
    by Prashant Patil, Arun Mittra, Vijay Chourasia 
    Abstract: This paper investigate Design methodology & performance of neural network based frequency transposition algorithm for hearing aid users. High frequency hearing loss associated with hearing disabled person is promising issue for research. Frequency compression & frequency transposition schemes are key solution to overcome high frequency hearing loss. Neural approach to frequency transposition makes algorithm more sensitive, accurate and specific towards processing. Proposed Neural Network frequency Transposition (NNFT) algorithm is based on framing of speech into feature vector for NN with comprehensive training & processing .Parameter to set in NNFT algorithm was calculated by evaluative study. Using this algorithm Marathi alphabets, words, confusing words are efficiently classified. Classification will improve acceptance & rejection rate for FT processing. Validation & testing result of algorithm shows improvement in sensitivity, accuracy, specificity of NNFT method compared to FT method.
    Keywords: Neural Network; Frequency transposition; Speech frames; FFT; Hearing loss; Sensitivity.

  • Ontology Based Approach for Document Semantic Similarity Using Concept Map   Order a copy of this article
    by Poonam Chahal, Manjeet Singh 
    Abstract: Ontology plays an important role in the process of semantic similarity computation. To extract the relevant and important information from a given document it is necessary to understand the semantic associated with the document. This understanding of semantic information comes through the concepts representing the words that are present in the given documents. These concepts and relationships between these concepts are used to construct the concept map which is the first step in the construction of ontology of a document. However, to extract the concepts and their relationships, the document set of words needs to be considered. These set of words further represents the concepts which are then used in the document ontology construction process. Finally, the matching process of constructed document ontology is applied to find the semantic similarity between any two given documents. However, it can be possible that a concept present in different ontologies may give different meaning depending upon the domain of the document. In our approach of document similarity, we provide a novel method of construction of document ontology based on concept map for computation of similarity between any two given documents.
    Keywords: Concept Map; Ontology; Concepts; Relationships; Semantic; Similarity.

  • Hardware and Software Load Power Control in Smart Home applications Based on Taguchi Optimization Technique   Order a copy of this article
    by Yasmina Azzougui, Abdelmadjid Recioui 
    Abstract: Power control is one of the concerns in smart grid implementation. The balance between the supplied power and the demand must be maintained so that blackouts are avoided. Smart meters play an important role in establishing this balance. On the other hand, power control implementation is a challenge as one would have to find the right mix between the hardware and the software parts. The purpose of this work is to optimize and implement a small power control system of home appliances. The objective of the optimization is to reschedule some tasks if the power demand exceeds a certain peak level. The optimization is based on Taguchi method which is known of its robustness and relatively fast convergence. The system casts a real life situation and can be considered as a small-scale prototype that can be extended to larger systems.
    Keywords: Load side management; Software; Hardware; Optimization; Taguchi method.