SAI - 2013

Accepted Papers

Towards An Ontology for UML State Machines
Youcef Belgueliel , Mustapha Bourahla and Mourad Brik, University of M'sila , Algeria
ABSTRACT
Ontology is a conceptual model that is used to represent the concepts in a domain and relationship between the concepts. It can be used for sharing and reuse of knowledge that allows humans and machines to exchange diverse information. UML state machines used to describe the behavior of a software systems. This article aims at to provide a solution for representing UML state machine model as an ontology expressed in OWL. a method proposed is a conceptualization of UML state machine and its operation, in order to check its consistency and its conformity. we chose OWL to represent formally our state machine augmented with SWRL rules to represent the dynamic aspect of the operation system and SPARQL to query our ontology.
A Model for Classification of Information for Better Knowledge Creation
Nahid Hashemian Bojnord and Sasan Nobakht, Khayyam Higher Educational Institute, Iran
ABSTRACT
In knowledge hierarchy model the importance of information in knowledge creation has been highlighted. In this paper, by focus on knowledge hierarchy model, a model has been developed for classification of information. Two features have been used in the proposed model. The first feature is information volume and the second feature is information diversity. This model categorizes the information into four major categories: similar and high volume information; diverse and high volume information; similar and little information; diverse and little information. In this paper, for each types of information, the conditions for better knowledge creation has been discussed.
Performance Comparison Between the Original Forms of Biogeography-Based Optimization Algorithms
Ali R. Alroomi, Fadhel A. Albasri and Jawad H. Talaq, University of Bahrain, Bahrain
ABSTRACT
Biogeography-based optimization (BBO) is a new population-based evolutionary algorithm and one of meta-heuristic algorithms. This technique is based on an old mathematical study that explains the geographical distribution of biological organisms. The first original form of BBO was introduced in 2008 and known as a partial migration based BBO. Few months later, BBO was re-introduced again with additional three other forms and known as single, simplified partial, and simplified single migration based BBOs. Then a lot of modifications were employed to enhance the performance of BBO. However, the literature lacks the explanations and the reasons on which the modifications are based on. This paper tries to clarify this issue by making a comparison between the four original BBO algorithms forms through a variety of benchmark functions with different dimensions and complexities. The results show that both single and simplified single migration based BBOs are faster, but have less accuracy as compared to the others. The comparison between the partial and the simplified partial migration based BBOs shows that the preference depends on the population size, problem�s complexity and dimensions and the values of the upper and lower side constraints. The partial migration model wins when these factors, except population size, are increased, and vice versa for the simplified partial migration model. The results can be used as a foundation and a first step of modification for enhancing any proposed modification on BBO including the existing modifications that are described in literature.
Exploring Translation Mode to Scientific Discourses Under the Framework of Information Dualism¹
Lei Xiaofeng, Liu Meiyan and Tian Jianguo, Northwestern Polytechnical University, China
ABSTRACT
As a practical branch of English, scientific English has always played important roles such as interflowing technological information, accelerating scientific exchanges, broadening and deepening (bi/) multilateral cooperation across the world. Due to various reasons, however, problems with the translation of scientific discourses are seriously present, which is inconducive to the introduction of advanced western science and technology into China and vice versa. Under the framework of information dualism, the author, in this paper, has designed a practical, easy-to-handle translation mode to scientific discourses, which proves to be of great help to scientific workers in acquiring scientific information, and accordingly improving their work efficiency.
Essential Modifications on Biogeography-Based Optimization Algorithm
Ali R. Alroomi, Fadhel A. Albasri and Jawad H. Talaq, University of Bahrain, Bahrain
ABSTRACT
Biogeography-based optimization (BBO) is a new population-based evolutionary algorithm and is based on an old theory of island biogeography that explains the geographical distribution of biological organisms. BBO was introduced in 2008 and then a lot of modifications were employed to enhance its performance. This paper proposes two modifications; firstly, modifying the probabilistic selection process of the migration and mutation stages to give a fairly randomized selection for all the features of the islands. Secondly, the clear duplication process after the mutation stage is sized to avoid any corruption on the suitability index variables. The obtained results through wide variety range of test functions with different dimensions and complexities proved that the BBO performance can be enhanced effectively without using any complicated form of the immigration and emigration rates. This essential modification has to be considered as an initial step for any other modification.
Assessment of Human Capacity based on Conjugate Gradient Technique using Artificial Neural Network
Abhay Saxena and H.H. Pranav Pandya, Dev Sanskriti University, India
ABSTRACT
In this paper, a new view is being introduced, to automatically assess the human capacity based on personal and family background, social and moral values and professional attitude. The study is focused on the development of the artificial neural network model for the assessment of human capacity based on the organizational performances. The ANN model is developed using Scaled Gradient Conjugate Back propagation (SCG), with a single hidden layer and sigmoid activation functions in MATLAB. The output variable is the human capacity. The final result of ANN model for human capacity is discussed, however the modeling results showed that there was excellent agreement between the experimental data and predicted values, with very good performance, fewer parameters, a shorter calculation time. The model will provide a new supportive tool for HR Department along with a time independent data frame of expert's knowledge. The model might be an alternative method for quality assessment of human capacity with organizational performance.
A Customized Flocking Algorithm for Swarms of Sensors Tracking A Swarm of Targets.
Anupam Shukla, Gaurav Ojha, Sachin Acharya and Shubham Jain, ABV-Indian Institute of Information Technology and Management, India
ABSTRACT
Wireless mobile sensor networks (WMSNs) are groups of mobile sensing agents with multi-modal sensing capabilities that communicate over wireless networks. WMSNs have more flexibility in terms of deployment and exploration abilities over static sensor networks. Sensor networks have a wide range of applications in security and surveillance systems, environmental monitoring, data gathering for network-centric healthcare systems, monitoring seismic activities and atmospheric events, tracking traffic congestion and air pollution levels, localization of autonomous vehicles in intelligent transportation systems, and detecting failures of sensing, storage, and switching components of smart grids.
A Multi-Stream Optimization for Noise Robust Distributed Speech Recognition Using Subspace-Based Speech Enhancement
M.R.L. Daalache, D. Addou and M. Boudraa, University of Sciences and Technology, Algeria
ABSTRACT
The paradigm multi-stream has been shown to result in features combined that can help to increase the robustness of distributed speech recognition (DSR) in the mobile communications. In this paper, we employs a combination of post proceeded Mel-cepstral coefficients (MFCCs) and line spectral frequencies features (LSFs) projected in linear discriminate analysis (LDA) space. The experiments performed on the Aurora 2.0 database using multi-condition training set show that, even with fewer parameters, the proposed front-end provides comparable recognition results to the standard ETSI WI008 advanced front-end, nowadays available in the vocal commands of the GSM mobile communications, while achieving higher accuracy when the signal-to-noise ratio (SNR) is very low.
A New Fuzzy Logic Based Space Vector Modulation Approach on Direct Torque Controlled Induction Motors
Fatih Korkmaz, Ismail Topaloglu and Hayati Mamur, Cankırı karatekin university, Turkey
ABSTRACT
The induction motors are indispensable motor types for industrial applications due to its well-known advantages. Therefore, many kind of control scheme are proposed for induction motors over the past years and direct torque control has gained great importance inside of them due to fast dynamic torque response behavior and simple control structure. This paper suggests a new approach on the direct torque controlled induction motors, Fuzzy logic based space vector modulation, to overcome disadvantages of conventional direct torque control like high torque ripple. In the proposed approach, optimum switching states are calculated by fuzzy logic controller and applied by space vector pulse width modulator to voltage source inverter. In order to test and compare the proposed DTC scheme with conventional DTC scheme simulations, in Matlab/Simulink, have been carried out in different speed and load conditions. The simulation results showed that a significant improvement in the dynamic torque and speed responses when compared to the conventional DTC scheme.
Assessment and Improvement Performance of Gas Transmission Refineries Based on Various Decision Styles by an Adaptive Intelligent Algorithm
Ali Azadeh¹, Fereshteh valianpour², Morteza Saberi3 and Saeed Bahrami4, ^1,2University of Tehran, Iran, ³Curtin University of Technology, Perth, Australia, Iran and ⁴Amirkabir University of Technology, Iran
ABSTRACT
This study proposes an intelligent algorithm for assessment and improvement performance of gas transmission refinery based on various decision styles. To achieve the objectives of this study, a standard questionnaire with respect to decision styles and ISO standards is completed by operators. Decision style related questions were selected based on Driver taxonomy of human decision making approach. Decisive, hierarchical, flexible and integrated were standard categorization of human decision style. The six categories of indicators which are related to decision styles and effectiveness of ISO systems (ISO18000, ISO 14000 and ISO 9000 are used as inputs and outputs respectively. The efficiency of ANN is examined against ANFIS by use of mean absolute percentage error (MAPE). The results concluded that ANN provides better solutions than ANFIS. Therefore, an effectiveness of ISO systems with respect to various decision styles has been ranked via ANN. Moreover, conventional regression approaches are applied to verify and validate the results of the intelligent algorithm. The proposed approach is applied to operators of 13 units in an actual gas transmission refinery in Iran to show its applicability and superiority. Results of this research will be effectively useable for managers because will lead to improve system performance by using the best operator for critical posts.
Semantic integration for automatic ontology Mapping
S. Amrouch¹ and S. Mostefai ²,¹ Messiah University, Algeria. and ² Mentoury University, Algeria
ABSTRACT
In the last decade, ontologies have played a key technology role for information sharing and agents interoperability in different application domains. In semantic web domain, ontologies are efficiently used to face the great challenge of representing the semantics of data, in order to bring the actual web to its full power and hence, achieve its objective. However, using ontologies as common and shared vocabularies requires a certain degree of interoperability between them. To confront this requirement, mapping ontologies is a solution that is not to be avoided. Indeed, ontology mapping build a meta layer that allows different applications and information systems to access and share their informations, of course, after resolving the different forms of syntactic, semantic and lexical mismatches. In the contribution presented in this paper, we have integrated the semantic aspect based on an external lexical resource, wordNet, to design a new algorithm for fully automatic ontology mapping. This fully automatic character features the main difference of our contribution with regards to the most of the existing semi-automatic algorithms of ontology mapping, such as Chimaera, Prompt, Onion, Glue, etc. To better enhance the performances of our algorithm, the mapping discovery stage is based on the combination of two sub-modules. The former analysis the concept's names and the later analysis their properties. Each one of these two sub-modules is itself based on the combination of lexical and semantic similarity measures.



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Home Paper Submission Program Committee Accepted Papers Workshops Contact Us AIRCC	Accepted Papers Towards An Ontology for UML State Machines Youcef Belgueliel , Mustapha Bourahla and Mourad Brik, University of M'sila , Algeria ABSTRACT Ontology is a conceptual model that is used to represent the concepts in a domain and relationship between the concepts. It can be used for sharing and reuse of knowledge that allows humans and machines to exchange diverse information. UML state machines used to describe the behavior of a software systems. This article aims at to provide a solution for representing UML state machine model as an ontology expressed in OWL. a method proposed is a conceptualization of UML state machine and its operation, in order to check its consistency and its conformity. we chose OWL to represent formally our state machine augmented with SWRL rules to represent the dynamic aspect of the operation system and SPARQL to query our ontology. A Model for Classification of Information for Better Knowledge Creation Nahid Hashemian Bojnord and Sasan Nobakht, Khayyam Higher Educational Institute, Iran ABSTRACT In knowledge hierarchy model the importance of information in knowledge creation has been highlighted. In this paper, by focus on knowledge hierarchy model, a model has been developed for classification of information. Two features have been used in the proposed model. The first feature is information volume and the second feature is information diversity. This model categorizes the information into four major categories: similar and high volume information; diverse and high volume information; similar and little information; diverse and little information. In this paper, for each types of information, the conditions for better knowledge creation has been discussed. Performance Comparison Between the Original Forms of Biogeography-Based Optimization Algorithms Ali R. Alroomi, Fadhel A. Albasri and Jawad H. Talaq, University of Bahrain, Bahrain ABSTRACT Biogeography-based optimization (BBO) is a new population-based evolutionary algorithm and one of meta-heuristic algorithms. This technique is based on an old mathematical study that explains the geographical distribution of biological organisms. The first original form of BBO was introduced in 2008 and known as a partial migration based BBO. Few months later, BBO was re-introduced again with additional three other forms and known as single, simplified partial, and simplified single migration based BBOs. Then a lot of modifications were employed to enhance the performance of BBO. However, the literature lacks the explanations and the reasons on which the modifications are based on. This paper tries to clarify this issue by making a comparison between the four original BBO algorithms forms through a variety of benchmark functions with different dimensions and complexities. The results show that both single and simplified single migration based BBOs are faster, but have less accuracy as compared to the others. The comparison between the partial and the simplified partial migration based BBOs shows that the preference depends on the population size, problem�s complexity and dimensions and the values of the upper and lower side constraints. The partial migration model wins when these factors, except population size, are increased, and vice versa for the simplified partial migration model. The results can be used as a foundation and a first step of modification for enhancing any proposed modification on BBO including the existing modifications that are described in literature. Exploring Translation Mode to Scientific Discourses Under the Framework of Information Dualism¹ Lei Xiaofeng, Liu Meiyan and Tian Jianguo, Northwestern Polytechnical University, China ABSTRACT As a practical branch of English, scientific English has always played important roles such as interflowing technological information, accelerating scientific exchanges, broadening and deepening (bi/) multilateral cooperation across the world. Due to various reasons, however, problems with the translation of scientific discourses are seriously present, which is inconducive to the introduction of advanced western science and technology into China and vice versa. Under the framework of information dualism, the author, in this paper, has designed a practical, easy-to-handle translation mode to scientific discourses, which proves to be of great help to scientific workers in acquiring scientific information, and accordingly improving their work efficiency. Essential Modifications on Biogeography-Based Optimization Algorithm Ali R. Alroomi, Fadhel A. Albasri and Jawad H. Talaq, University of Bahrain, Bahrain ABSTRACT Biogeography-based optimization (BBO) is a new population-based evolutionary algorithm and is based on an old theory of island biogeography that explains the geographical distribution of biological organisms. BBO was introduced in 2008 and then a lot of modifications were employed to enhance its performance. This paper proposes two modifications; firstly, modifying the probabilistic selection process of the migration and mutation stages to give a fairly randomized selection for all the features of the islands. Secondly, the clear duplication process after the mutation stage is sized to avoid any corruption on the suitability index variables. The obtained results through wide variety range of test functions with different dimensions and complexities proved that the BBO performance can be enhanced effectively without using any complicated form of the immigration and emigration rates. This essential modification has to be considered as an initial step for any other modification. Assessment of Human Capacity based on Conjugate Gradient Technique using Artificial Neural Network Abhay Saxena and H.H. Pranav Pandya, Dev Sanskriti University, India ABSTRACT In this paper, a new view is being introduced, to automatically assess the human capacity based on personal and family background, social and moral values and professional attitude. The study is focused on the development of the artificial neural network model for the assessment of human capacity based on the organizational performances. The ANN model is developed using Scaled Gradient Conjugate Back propagation (SCG), with a single hidden layer and sigmoid activation functions in MATLAB. The output variable is the human capacity. The final result of ANN model for human capacity is discussed, however the modeling results showed that there was excellent agreement between the experimental data and predicted values, with very good performance, fewer parameters, a shorter calculation time. The model will provide a new supportive tool for HR Department along with a time independent data frame of expert's knowledge. The model might be an alternative method for quality assessment of human capacity with organizational performance. A Customized Flocking Algorithm for Swarms of Sensors Tracking A Swarm of Targets. Anupam Shukla, Gaurav Ojha, Sachin Acharya and Shubham Jain, ABV-Indian Institute of Information Technology and Management, India ABSTRACT Wireless mobile sensor networks (WMSNs) are groups of mobile sensing agents with multi-modal sensing capabilities that communicate over wireless networks. WMSNs have more flexibility in terms of deployment and exploration abilities over static sensor networks. Sensor networks have a wide range of applications in security and surveillance systems, environmental monitoring, data gathering for network-centric healthcare systems, monitoring seismic activities and atmospheric events, tracking traffic congestion and air pollution levels, localization of autonomous vehicles in intelligent transportation systems, and detecting failures of sensing, storage, and switching components of smart grids. A Multi-Stream Optimization for Noise Robust Distributed Speech Recognition Using Subspace-Based Speech Enhancement M.R.L. Daalache, D. Addou and M. Boudraa, University of Sciences and Technology, Algeria ABSTRACT The paradigm multi-stream has been shown to result in features combined that can help to increase the robustness of distributed speech recognition (DSR) in the mobile communications. In this paper, we employs a combination of post proceeded Mel-cepstral coefficients (MFCCs) and line spectral frequencies features (LSFs) projected in linear discriminate analysis (LDA) space. The experiments performed on the Aurora 2.0 database using multi-condition training set show that, even with fewer parameters, the proposed front-end provides comparable recognition results to the standard ETSI WI008 advanced front-end, nowadays available in the vocal commands of the GSM mobile communications, while achieving higher accuracy when the signal-to-noise ratio (SNR) is very low. A New Fuzzy Logic Based Space Vector Modulation Approach on Direct Torque Controlled Induction Motors Fatih Korkmaz, Ismail Topaloglu and Hayati Mamur, Cankırı karatekin university, Turkey ABSTRACT The induction motors are indispensable motor types for industrial applications due to its well-known advantages. Therefore, many kind of control scheme are proposed for induction motors over the past years and direct torque control has gained great importance inside of them due to fast dynamic torque response behavior and simple control structure. This paper suggests a new approach on the direct torque controlled induction motors, Fuzzy logic based space vector modulation, to overcome disadvantages of conventional direct torque control like high torque ripple. In the proposed approach, optimum switching states are calculated by fuzzy logic controller and applied by space vector pulse width modulator to voltage source inverter. In order to test and compare the proposed DTC scheme with conventional DTC scheme simulations, in Matlab/Simulink, have been carried out in different speed and load conditions. The simulation results showed that a significant improvement in the dynamic torque and speed responses when compared to the conventional DTC scheme. Assessment and Improvement Performance of Gas Transmission Refineries Based on Various Decision Styles by an Adaptive Intelligent Algorithm Ali Azadeh¹, Fereshteh valianpour², Morteza Saberi3 and Saeed Bahrami4, ^1,2University of Tehran, Iran, ³Curtin University of Technology, Perth, Australia, Iran and ⁴Amirkabir University of Technology, Iran ABSTRACT This study proposes an intelligent algorithm for assessment and improvement performance of gas transmission refinery based on various decision styles. To achieve the objectives of this study, a standard questionnaire with respect to decision styles and ISO standards is completed by operators. Decision style related questions were selected based on Driver taxonomy of human decision making approach. Decisive, hierarchical, flexible and integrated were standard categorization of human decision style. The six categories of indicators which are related to decision styles and effectiveness of ISO systems (ISO18000, ISO 14000 and ISO 9000 are used as inputs and outputs respectively. The efficiency of ANN is examined against ANFIS by use of mean absolute percentage error (MAPE). The results concluded that ANN provides better solutions than ANFIS. Therefore, an effectiveness of ISO systems with respect to various decision styles has been ranked via ANN. Moreover, conventional regression approaches are applied to verify and validate the results of the intelligent algorithm. The proposed approach is applied to operators of 13 units in an actual gas transmission refinery in Iran to show its applicability and superiority. Results of this research will be effectively useable for managers because will lead to improve system performance by using the best operator for critical posts. Semantic integration for automatic ontology Mapping S. Amrouch¹ and S. Mostefai ²,¹ Messiah University, Algeria. and ² Mentoury University, Algeria ABSTRACT In the last decade, ontologies have played a key technology role for information sharing and agents interoperability in different application domains. In semantic web domain, ontologies are efficiently used to face the great challenge of representing the semantics of data, in order to bring the actual web to its full power and hence, achieve its objective. However, using ontologies as common and shared vocabularies requires a certain degree of interoperability between them. To confront this requirement, mapping ontologies is a solution that is not to be avoided. Indeed, ontology mapping build a meta layer that allows different applications and information systems to access and share their informations, of course, after resolving the different forms of syntactic, semantic and lexical mismatches. In the contribution presented in this paper, we have integrated the semantic aspect based on an external lexical resource, wordNet, to design a new algorithm for fully automatic ontology mapping. This fully automatic character features the main difference of our contribution with regards to the most of the existing semi-automatic algorithms of ontology mapping, such as Chimaera, Prompt, Onion, Glue, etc. To better enhance the performances of our algorithm, the mapping discovery stage is based on the combination of two sub-modules. The former analysis the concept's names and the later analysis their properties. Each one of these two sub-modules is itself based on the combination of lexical and semantic similarity measures.
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