This paper studies the existing approaches in optimal integrated control, computation and communication
problems. It concentrates on joint optimization problems aimed at finding communication/computation
policy and control signal. Different aspects including computational complexity, convexity, proposed
methods to find optimum and other issues related to control performance are studied and compared for different approaches.
Analysis and Enhancements of Leader Elections algorithms in Mobile Ad Hoc Net...IDES Editor
Mobile Ad Hoc networks (MANET), distinct from
traditional distributed systems, are dynamic and selforganizing
networks. MANET requires a leader to coordinate
and organize tasks. The challenge is to have the right election
algorithm that chooses the right leader based on various
factors in MANET. In this paper, we analyze four leader
election algorithms used in mobile Ad Hoc Networks. Factors
considered in our analysis are time complexity, message
complexity, assumptions considered, fault tolerance and
timing model. Our proposed enhancements include recovered
nodes inquiring about the current leader and the use of
candidates during election to reduce the overhead of starting
a new election session. In addition, better election criteria
specific to MANET, such as battery life and signal strength,
are proposed. Our evaluation and discussion shows that the
proposed enhancements are effective. The analysis can be
used as a reference for system designers in choosing the right
election algorithm for MANET.
Spectral opportunity selection based on the hybrid algorithm AHP-ELECTRETELKOMNIKA JOURNAL
Due to an ever-growing demand for spectrum and the fast-paced developmentof wireless applications, technologies such as cognitive radio enablethe efficient use of the spectrum. The objective of the present article is todesign an algorithm capable of choosing the best channel for data transmission.It uses quantitative methods that can modify behavior by changing qualityparameters in the channel. To achieve this task, a hybrid decision-makingalgorithm is designed that combinesanalytical hierarchy process(AHP)algorithms and adjusts the weights of each channel parameter, using a prioritytable. TheElimination Et Choix Tranduisant La Realité(ELECTRE)algorithm processes the information from each channel through a weightmatrix and then delivers the most favorable result for the transmitted data. Theresults reveal that the hybrid AHP-ELECTRE algorithm has a suitableperformance, which improves the throughput rate by 14% compared to similaralternatives.
A SIMPLE PROCESS TO SPEED UP MACHINE LEARNING METHODS: APPLICATION TO HIDDEN ...cscpconf
An intrinsic problem of classifiers based on machine learning (ML) methods is that their
learning time grows as the size and complexity of the training dataset increases. For this reason, it is important to have efficient computational methods and algorithms that can be applied on large datasets, such that it is still possible to complete the machine learning tasks in reasonable time. In this context, we present in this paper a simple process to speed up ML methods. An unsupervised clustering algorithm is combined with Expectation, Maximization (EM) algorithm to develop an efficient Hidden Markov Model (HMM) training. The idea of the proposed process consists of two steps. The first one involves a preprocessing step to reduce the number of training instances with any information loss. In this step, training instances with similar inputs are clustered and a weight factor which represents the frequency of these instances is assigned to each representative cluster. In the second step, all formulas in theclassical HMM training algorithm (EM) associated with the number of training instances are modified to include the weight factor in appropriate terms. This process significantly accelerates HMM training while maintaining the same initial, transition and emission probabilities matrixes as those obtained with the classical HMM training algorithm. Accordingly, the classification accuracy is preserved. Depending on the size of the training set, speedups of up to 2200 times is possible when the size is about 100.000 instances. The proposed approach is not limited to training HMMs, but it can be employed for a large variety of MLs methods.
Application of a merit function based interior point method to linear model p...Zac Darcy
This paper present
s
robust linear model predictive control (MPC) technique for small scale linear MPC
problems. The quadratic programming (QP) problem arising in linear MPC is solved using primal dual
interior point method
.
We present
a me
rit function based on a path following strategy
to calculate the step
length
α
, which
forces the convergence of feasible iterates
. The algorithm globally converges to the optimal
solution
of the QP p
roblem while strictly following
the
inequality
constraint
s.
The linear system in the QP
problem is solved using LDL
T
factorizatio
n based linear solver which reduces the computational cost of
linear system to a certain extent
.
We implement this method for
a
linear MPC problem of undamped
oscillator. With the help
of a Kalman filter observer, we show that the MPC design is robust to the external
disturbances and integrated white noise.
Software size estimation at early stages of project development holds great significance to meet
the competitive demands of software industry. Software size represents one of the most
interesting internal attributes which has been used in several effort/cost models as a predictor
of effort and cost needed to design and implement the software. The whole world is focusing
towards object oriented paradigm thus it is essential to use an accurate methodology for
measuring the size of object oriented projects. The class point approach is used to quantify
classes which are the logical building blocks in object oriented paradigm. In this paper, we
propose a class point based approach for software size estimation of On-Line Analytical
Processing (OLAP) systems. OLAP is an approach to swiftly answer decision support queries
based on multidimensional view of data. Materialized views can significantly reduce the
execution time for decision support queries. We perform a case study based on the TPC-H
benchmark which is a representative of OLAP System. We have used a Greedy based approach
to determine a good set of views to be materialized. After finding the number of views, the class
point approach is used to estimate the size of an OLAP System The results of our approach are
validated.
Software size estimation at early stages of project development holds great significance to meet the competitive demands of software industry. Software size represents one of the most
interesting internal attributes which has been used in several effort/cost models as a predictor of effort and cost needed to design and implement the software. The whole world is focusing
towards object oriented paradigm thus it is essential to use an accurate methodology for measuring the size of object oriented projects. The class point approach is used to quantify classes which are the logical building blocks in object oriented paradigm. In this paper, we propose a class point based approach for software size estimation of On-Line Analytical
Processing (OLAP) systems. OLAP is an approach to swiftly answer decision support queries based on multidimensional view of data. Materialized views can significantly reduce the
execution time for decision support queries. We perform a case study based on the TPC-H benchmark which is a representative of OLAP System. We have used a Greedy based approach
to determine a good set of views to be materialized. After finding the number of views, the class point approach is used to estimate the size of an OLAP System The results of our approach are validated.
Analysis and Enhancements of Leader Elections algorithms in Mobile Ad Hoc Net...IDES Editor
Mobile Ad Hoc networks (MANET), distinct from
traditional distributed systems, are dynamic and selforganizing
networks. MANET requires a leader to coordinate
and organize tasks. The challenge is to have the right election
algorithm that chooses the right leader based on various
factors in MANET. In this paper, we analyze four leader
election algorithms used in mobile Ad Hoc Networks. Factors
considered in our analysis are time complexity, message
complexity, assumptions considered, fault tolerance and
timing model. Our proposed enhancements include recovered
nodes inquiring about the current leader and the use of
candidates during election to reduce the overhead of starting
a new election session. In addition, better election criteria
specific to MANET, such as battery life and signal strength,
are proposed. Our evaluation and discussion shows that the
proposed enhancements are effective. The analysis can be
used as a reference for system designers in choosing the right
election algorithm for MANET.
Spectral opportunity selection based on the hybrid algorithm AHP-ELECTRETELKOMNIKA JOURNAL
Due to an ever-growing demand for spectrum and the fast-paced developmentof wireless applications, technologies such as cognitive radio enablethe efficient use of the spectrum. The objective of the present article is todesign an algorithm capable of choosing the best channel for data transmission.It uses quantitative methods that can modify behavior by changing qualityparameters in the channel. To achieve this task, a hybrid decision-makingalgorithm is designed that combinesanalytical hierarchy process(AHP)algorithms and adjusts the weights of each channel parameter, using a prioritytable. TheElimination Et Choix Tranduisant La Realité(ELECTRE)algorithm processes the information from each channel through a weightmatrix and then delivers the most favorable result for the transmitted data. Theresults reveal that the hybrid AHP-ELECTRE algorithm has a suitableperformance, which improves the throughput rate by 14% compared to similaralternatives.
A SIMPLE PROCESS TO SPEED UP MACHINE LEARNING METHODS: APPLICATION TO HIDDEN ...cscpconf
An intrinsic problem of classifiers based on machine learning (ML) methods is that their
learning time grows as the size and complexity of the training dataset increases. For this reason, it is important to have efficient computational methods and algorithms that can be applied on large datasets, such that it is still possible to complete the machine learning tasks in reasonable time. In this context, we present in this paper a simple process to speed up ML methods. An unsupervised clustering algorithm is combined with Expectation, Maximization (EM) algorithm to develop an efficient Hidden Markov Model (HMM) training. The idea of the proposed process consists of two steps. The first one involves a preprocessing step to reduce the number of training instances with any information loss. In this step, training instances with similar inputs are clustered and a weight factor which represents the frequency of these instances is assigned to each representative cluster. In the second step, all formulas in theclassical HMM training algorithm (EM) associated with the number of training instances are modified to include the weight factor in appropriate terms. This process significantly accelerates HMM training while maintaining the same initial, transition and emission probabilities matrixes as those obtained with the classical HMM training algorithm. Accordingly, the classification accuracy is preserved. Depending on the size of the training set, speedups of up to 2200 times is possible when the size is about 100.000 instances. The proposed approach is not limited to training HMMs, but it can be employed for a large variety of MLs methods.
Application of a merit function based interior point method to linear model p...Zac Darcy
This paper present
s
robust linear model predictive control (MPC) technique for small scale linear MPC
problems. The quadratic programming (QP) problem arising in linear MPC is solved using primal dual
interior point method
.
We present
a me
rit function based on a path following strategy
to calculate the step
length
α
, which
forces the convergence of feasible iterates
. The algorithm globally converges to the optimal
solution
of the QP p
roblem while strictly following
the
inequality
constraint
s.
The linear system in the QP
problem is solved using LDL
T
factorizatio
n based linear solver which reduces the computational cost of
linear system to a certain extent
.
We implement this method for
a
linear MPC problem of undamped
oscillator. With the help
of a Kalman filter observer, we show that the MPC design is robust to the external
disturbances and integrated white noise.
Software size estimation at early stages of project development holds great significance to meet
the competitive demands of software industry. Software size represents one of the most
interesting internal attributes which has been used in several effort/cost models as a predictor
of effort and cost needed to design and implement the software. The whole world is focusing
towards object oriented paradigm thus it is essential to use an accurate methodology for
measuring the size of object oriented projects. The class point approach is used to quantify
classes which are the logical building blocks in object oriented paradigm. In this paper, we
propose a class point based approach for software size estimation of On-Line Analytical
Processing (OLAP) systems. OLAP is an approach to swiftly answer decision support queries
based on multidimensional view of data. Materialized views can significantly reduce the
execution time for decision support queries. We perform a case study based on the TPC-H
benchmark which is a representative of OLAP System. We have used a Greedy based approach
to determine a good set of views to be materialized. After finding the number of views, the class
point approach is used to estimate the size of an OLAP System The results of our approach are
validated.
Software size estimation at early stages of project development holds great significance to meet the competitive demands of software industry. Software size represents one of the most
interesting internal attributes which has been used in several effort/cost models as a predictor of effort and cost needed to design and implement the software. The whole world is focusing
towards object oriented paradigm thus it is essential to use an accurate methodology for measuring the size of object oriented projects. The class point approach is used to quantify classes which are the logical building blocks in object oriented paradigm. In this paper, we propose a class point based approach for software size estimation of On-Line Analytical
Processing (OLAP) systems. OLAP is an approach to swiftly answer decision support queries based on multidimensional view of data. Materialized views can significantly reduce the
execution time for decision support queries. We perform a case study based on the TPC-H benchmark which is a representative of OLAP System. We have used a Greedy based approach
to determine a good set of views to be materialized. After finding the number of views, the class point approach is used to estimate the size of an OLAP System The results of our approach are validated.
Failure prediction of e-banking application system using adaptive neuro fuzzy...IJECEIAES
Problems often faced by IT operation unit is the difficulty in determining the cause of the failure of an incident such as slowing access to the internet banking url, non-functioning of some features of m-banking or even the cessation of the entire e-banking service. The proposed method to modify ANFIS with Fuzzy C-Means Clustering (FCM) approach is applied to detect four typical kinds of faults that may happen in the e-banking system, which are application response times, transaction per second, server utilization and network performance. Input data is obtained from the e-banking monitoring results throughout 2017 that become data training and data testing. The study shows that an ANFIS modeling with FCM optimized input has a RMSE 0.006 and increased accuracy by 1.27% compared to ANFIS without FCM optimization.
High dimensionality reduction on graphical dataeSAT Journals
Abstract In spite of the fact that graph embedding has been an intense instrument for displaying data natural structures, just utilizing all elements for data structures revelation may bring about noise amplification. This is especially serious for high dimensional data with little examples. To meet this test, a novel effective structure to perform highlight determination for graph embedding, in which a classification of graph implanting routines is given a role as a slightest squares relapse issue. In this structure, a twofold component selector is acquainted with normally handle the component cardinality at all squares detailing. The proposed strategy is quick and memory proficient. The proposed system is connected to a few graph embedding learning issues, counting administered, unsupervised and semi supervised graph embedding. Key Words:Efficient feature selection, High dimensional data, Sparse graph embedding, Sparse principal component analysis, Subproblem Optimization.
International Journal on Web Service Computing (IJWSC)ijwscjournal
A Knowledge Management (KM) System plays a crucial role in every industry as well as in Higher Learning Institutions. A RESTful resource is anything that is addressable over the Web. The resources can be accessed and transferred between clients and server. The resources can be accessed and transferred
between clients and servers. Based on our earlier research works, we have developed a comprehensive KM System framework, evaluation method, mult-dimensional metric model and useful metrics which are helpful to assess any given knowledge management system. In this proposed work, we first describe the actual implementation steps for building the KM System metric database using the multi-dimensional metric model. Secondly we describe the approaches for designing a multi-dimensional Restful Resources and Web Services using the mutli-dimensional metric model and demonstrate how the KM system can be ranked and rated for its effectiveness using WAM and RESTful Resources.
Integrative Model for Quantitative Evaluation of Selection Telecommunication ...TELKOMNIKA JOURNAL
This paper analyzes the weight of impact factors on selection the antenna places for mobile
telecommunication system in Jordan. The new technique plays a lead role in divided area and selects the
place of antennas' sites. The main objective of this research is to minimize the antenna numbers in order
to reduce the cost. Research follows flowcharting categories and stages as: The first stage aim to classify
the effective factors on the: signal radius, better position of antenna from candidate points, reserved area,
and non-preferring position. The second stage focuses on finding the effective weight of these factors on
the decision. The third stage suggest the new proposed approach by implement the MCLP and P-center
problems in linear function. The last stage has the pseudo code for the proposed approach, where the
proposed approach provides the solution that helps the planners in telecommunication industry and in
related government agencies make informed position of the antennas.
Trajectory Control With MPC For A Robot Manipülatör Using ANN ModelIJMER
In this study, in a computer the dynamic motion modelling of manipulator and control of
trajectory with an algorithm this has been tested. First after dynamic motion simulation of manipulator
has been made MPC Control. The result in this study we can observe that computed torque method gives
better results than MPC methods. So in trajectory control it is approved of using computed torque
method. In last part of this study the results are estimated forward development are exemined and
suggested. The model predictive control (MPC) technique for an articulated robot with n joints is
introduced in this paper. The proposed MPC control action is conceptually different with the trajectory
robot control methods in that the control action is determined by optimising a performance index over
the time horizon. A neural network (NN) is used in this paper as the predictive model.
Application Of Analytic Hierarchy Process And Artificial Neural Network In Bi...IJARIDEA Journal
Abstract— An appropriate decision to bid initiates all bid preparation steps. Selective bidding will reduce the number of proposals to be submitted by the contractor and saves tender preparation time which can be utilized for refining the estimated cost. Usually in industrial engineering applications final decision will be based on the evaluation of many alternatives. This will be a very difficult problem when the criteria are expressed in different units or the pertinent data are not easily quantifiable. This paper emphasizes on the use of Analytic Hierarchy Process(AHP) for analyzing the risk degree of each factor, so that decision the can be taken accordingly in deciding an appropriate bid.AHP helps to decide the best solution from various selection criteria.The study also focuses on suggesting a much broader applicability of AHP and ANN techniques on decisions of bidding.
Keywords— Analytic Hierarchy Process(AHP), Artificial Neural Network(ANN), Consistency Index(CI),
Consistency Ratio(CR), Random Index(RI), Risk degree.
Testing the performance of the power law process model considering the use of...IJCSEA Journal
Within the class of non-homogeneous Poisson process (NHPP) models and as a result of the simplicity of
the mathematical computations of the Power Law Process (PLP) model and the attractive physical
explanation of its parameters, this model has found considerable attention in repairable systems literature.
In this article, we conduct the investigation of new estimation approach, the regression estimation
procedure, on the performance of the parametric PLP model. The regression approach for estimating the
unknown parameters of the PLP model through the mean time between failure (TBF) function is evaluated
against the maximum likelihood estimation (MLE) approach. The results from the regression and MLE
approaches are compared based on three error evaluation criteria in terms of parameter estimation and its
precision, the numerical application shows the effectiveness of the regression estimation approach at
enhancing the predictive accuracy of the TBF measure.
Building a new CTL model checker using Web Servicesinfopapers
Florin Stoica, Laura Stoica, Building a new CTL model checker using Web Services, Proceeding The 21th International Conference on Software, Telecommunications and Computer Networks (SoftCOM 2013), At Split-Primosten, Croatia, 18-20 September, pp. 285-289, 2013
DOI=10.1109/SoftCOM.2013.6671858 http://dx.doi.org/10.1109/SoftCOM.2013.6671858
Automatically Estimating Software Effort and Cost using Computing Intelligenc...cscpconf
In the IT industry, precisely estimate the effort of each software project the development cost
and schedule are count for much to the software company. So precisely estimation of man
power seems to be getting more important. In the past time, the IT companies estimate the work
effort of man power by human experts, using statistics method. However, the outcomes are
always unsatisfying the management level. Recently it becomes an interesting topic if computing
intelligence techniques can do better in this field. This research uses some computing
intelligence techniques, such as Pearson product-moment correlation coefficient method and
one-way ANOVA method to select key factors, and K-Means clustering algorithm to do project
clustering, to estimate the software project effort. The experimental result show that using
computing intelligence techniques to estimate the software project effort can get more precise
and more effective estimation than using traditional human experts did
Harmony Search Algorithmic Rule for Optimum Allocation and Size of Distribute...Dr. Amarjeet Singh
Various benefits earned by desegregation Distributed
Generation (DG) in distribution systems. Such advantages are
often achieved and increased if DGs area unit optimally sized
and placed within the systems. The current work presents
distribution generation (DG) allocation strategy with objective
of up node voltage and minimizes power loss of radial
distribution systems victimization improved multi objective
harmony search algorithmic program (IMOHS).IMOHS
algorithmic program uses sensitivity analysis for distinctive
the optimum locations of distribution generation units, the
successively reduces the real power loss and improves the
voltage profile in distribution system. The target is to scale
back active power losses to minimum, whereas to attain
voltage profiles within the network in needed and determined
limit. Within the gift work the optimum decigram placement
and size drawback is developed as a multi-objective
improvement drawback to attain the above mentioned
situation.
An IEEE 33-node and IEEE 69-node radial
distribution check systems are wont to show the effectiveness
of the Improved multi objective Harmony Search rule
(IMOHS). The results obtained from the IMOHS methodology
shows that vital loss reduction is feasible mistreatment
multiple optimum sized decigram units. It’s shown that the
IMOHS methodology provides higher ends up in comparisons
thereto obtained mistreatment alternative optimization ways
like GA and PSO.
CONTINUOUSLY IMPROVE THE PERFORMANCE OF PLANNING AND SCHEDULING MODELS WITH P...Alkis Vazacopoulos
Continuously improving the accuracy and precision of planning and scheduling models is not new; unfortunately it is not institutionalized in practice. The intent of this paper is to highlight a relatively simple approach to historize or memorize past and present actual planning and scheduling data collected into what we call the past rolling horizon (PRH). The PRH is identical to the future rolling horizon (FRH) used in hierarchical production planning and model predictive control to manage omnipresent uncertainty in the model and data. Instead of optimizing future decisions such as throughputs, operating-modes and conditions we now optimize or estimate key model parameters. Although bias-updating using a single time-sample of data is common practice in advanced process control and optimization to incorporate “parameter” feedback, this is only realizable for real-time applications with comprehensive measurement systems. Proposed in this paper is the use of multiple synchronous or asynchronous time-samples in the past in conjunction with simultaneous reconciliation and regression to update a subset of the model parameters on a past rolling horizon basis to improve the performance of planning and scheduling models.
The International Journal of Database Management Systems (IJDMS) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the database management systems & its applications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on understanding Modern developments in this filed, and establishing new collaborations in these areas.
The International journal of Multimedia & Its Applications (IJMA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Multimedia & its applications. The journal focuses on all technical and practical aspects of Multimedia and its applications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on understanding recent developments this arena, and establishing new collaborations in these areas.
Failure prediction of e-banking application system using adaptive neuro fuzzy...IJECEIAES
Problems often faced by IT operation unit is the difficulty in determining the cause of the failure of an incident such as slowing access to the internet banking url, non-functioning of some features of m-banking or even the cessation of the entire e-banking service. The proposed method to modify ANFIS with Fuzzy C-Means Clustering (FCM) approach is applied to detect four typical kinds of faults that may happen in the e-banking system, which are application response times, transaction per second, server utilization and network performance. Input data is obtained from the e-banking monitoring results throughout 2017 that become data training and data testing. The study shows that an ANFIS modeling with FCM optimized input has a RMSE 0.006 and increased accuracy by 1.27% compared to ANFIS without FCM optimization.
High dimensionality reduction on graphical dataeSAT Journals
Abstract In spite of the fact that graph embedding has been an intense instrument for displaying data natural structures, just utilizing all elements for data structures revelation may bring about noise amplification. This is especially serious for high dimensional data with little examples. To meet this test, a novel effective structure to perform highlight determination for graph embedding, in which a classification of graph implanting routines is given a role as a slightest squares relapse issue. In this structure, a twofold component selector is acquainted with normally handle the component cardinality at all squares detailing. The proposed strategy is quick and memory proficient. The proposed system is connected to a few graph embedding learning issues, counting administered, unsupervised and semi supervised graph embedding. Key Words:Efficient feature selection, High dimensional data, Sparse graph embedding, Sparse principal component analysis, Subproblem Optimization.
International Journal on Web Service Computing (IJWSC)ijwscjournal
A Knowledge Management (KM) System plays a crucial role in every industry as well as in Higher Learning Institutions. A RESTful resource is anything that is addressable over the Web. The resources can be accessed and transferred between clients and server. The resources can be accessed and transferred
between clients and servers. Based on our earlier research works, we have developed a comprehensive KM System framework, evaluation method, mult-dimensional metric model and useful metrics which are helpful to assess any given knowledge management system. In this proposed work, we first describe the actual implementation steps for building the KM System metric database using the multi-dimensional metric model. Secondly we describe the approaches for designing a multi-dimensional Restful Resources and Web Services using the mutli-dimensional metric model and demonstrate how the KM system can be ranked and rated for its effectiveness using WAM and RESTful Resources.
Integrative Model for Quantitative Evaluation of Selection Telecommunication ...TELKOMNIKA JOURNAL
This paper analyzes the weight of impact factors on selection the antenna places for mobile
telecommunication system in Jordan. The new technique plays a lead role in divided area and selects the
place of antennas' sites. The main objective of this research is to minimize the antenna numbers in order
to reduce the cost. Research follows flowcharting categories and stages as: The first stage aim to classify
the effective factors on the: signal radius, better position of antenna from candidate points, reserved area,
and non-preferring position. The second stage focuses on finding the effective weight of these factors on
the decision. The third stage suggest the new proposed approach by implement the MCLP and P-center
problems in linear function. The last stage has the pseudo code for the proposed approach, where the
proposed approach provides the solution that helps the planners in telecommunication industry and in
related government agencies make informed position of the antennas.
Trajectory Control With MPC For A Robot Manipülatör Using ANN ModelIJMER
In this study, in a computer the dynamic motion modelling of manipulator and control of
trajectory with an algorithm this has been tested. First after dynamic motion simulation of manipulator
has been made MPC Control. The result in this study we can observe that computed torque method gives
better results than MPC methods. So in trajectory control it is approved of using computed torque
method. In last part of this study the results are estimated forward development are exemined and
suggested. The model predictive control (MPC) technique for an articulated robot with n joints is
introduced in this paper. The proposed MPC control action is conceptually different with the trajectory
robot control methods in that the control action is determined by optimising a performance index over
the time horizon. A neural network (NN) is used in this paper as the predictive model.
Application Of Analytic Hierarchy Process And Artificial Neural Network In Bi...IJARIDEA Journal
Abstract— An appropriate decision to bid initiates all bid preparation steps. Selective bidding will reduce the number of proposals to be submitted by the contractor and saves tender preparation time which can be utilized for refining the estimated cost. Usually in industrial engineering applications final decision will be based on the evaluation of many alternatives. This will be a very difficult problem when the criteria are expressed in different units or the pertinent data are not easily quantifiable. This paper emphasizes on the use of Analytic Hierarchy Process(AHP) for analyzing the risk degree of each factor, so that decision the can be taken accordingly in deciding an appropriate bid.AHP helps to decide the best solution from various selection criteria.The study also focuses on suggesting a much broader applicability of AHP and ANN techniques on decisions of bidding.
Keywords— Analytic Hierarchy Process(AHP), Artificial Neural Network(ANN), Consistency Index(CI),
Consistency Ratio(CR), Random Index(RI), Risk degree.
Testing the performance of the power law process model considering the use of...IJCSEA Journal
Within the class of non-homogeneous Poisson process (NHPP) models and as a result of the simplicity of
the mathematical computations of the Power Law Process (PLP) model and the attractive physical
explanation of its parameters, this model has found considerable attention in repairable systems literature.
In this article, we conduct the investigation of new estimation approach, the regression estimation
procedure, on the performance of the parametric PLP model. The regression approach for estimating the
unknown parameters of the PLP model through the mean time between failure (TBF) function is evaluated
against the maximum likelihood estimation (MLE) approach. The results from the regression and MLE
approaches are compared based on three error evaluation criteria in terms of parameter estimation and its
precision, the numerical application shows the effectiveness of the regression estimation approach at
enhancing the predictive accuracy of the TBF measure.
Building a new CTL model checker using Web Servicesinfopapers
Florin Stoica, Laura Stoica, Building a new CTL model checker using Web Services, Proceeding The 21th International Conference on Software, Telecommunications and Computer Networks (SoftCOM 2013), At Split-Primosten, Croatia, 18-20 September, pp. 285-289, 2013
DOI=10.1109/SoftCOM.2013.6671858 http://dx.doi.org/10.1109/SoftCOM.2013.6671858
Automatically Estimating Software Effort and Cost using Computing Intelligenc...cscpconf
In the IT industry, precisely estimate the effort of each software project the development cost
and schedule are count for much to the software company. So precisely estimation of man
power seems to be getting more important. In the past time, the IT companies estimate the work
effort of man power by human experts, using statistics method. However, the outcomes are
always unsatisfying the management level. Recently it becomes an interesting topic if computing
intelligence techniques can do better in this field. This research uses some computing
intelligence techniques, such as Pearson product-moment correlation coefficient method and
one-way ANOVA method to select key factors, and K-Means clustering algorithm to do project
clustering, to estimate the software project effort. The experimental result show that using
computing intelligence techniques to estimate the software project effort can get more precise
and more effective estimation than using traditional human experts did
Harmony Search Algorithmic Rule for Optimum Allocation and Size of Distribute...Dr. Amarjeet Singh
Various benefits earned by desegregation Distributed
Generation (DG) in distribution systems. Such advantages are
often achieved and increased if DGs area unit optimally sized
and placed within the systems. The current work presents
distribution generation (DG) allocation strategy with objective
of up node voltage and minimizes power loss of radial
distribution systems victimization improved multi objective
harmony search algorithmic program (IMOHS).IMOHS
algorithmic program uses sensitivity analysis for distinctive
the optimum locations of distribution generation units, the
successively reduces the real power loss and improves the
voltage profile in distribution system. The target is to scale
back active power losses to minimum, whereas to attain
voltage profiles within the network in needed and determined
limit. Within the gift work the optimum decigram placement
and size drawback is developed as a multi-objective
improvement drawback to attain the above mentioned
situation.
An IEEE 33-node and IEEE 69-node radial
distribution check systems are wont to show the effectiveness
of the Improved multi objective Harmony Search rule
(IMOHS). The results obtained from the IMOHS methodology
shows that vital loss reduction is feasible mistreatment
multiple optimum sized decigram units. It’s shown that the
IMOHS methodology provides higher ends up in comparisons
thereto obtained mistreatment alternative optimization ways
like GA and PSO.
CONTINUOUSLY IMPROVE THE PERFORMANCE OF PLANNING AND SCHEDULING MODELS WITH P...Alkis Vazacopoulos
Continuously improving the accuracy and precision of planning and scheduling models is not new; unfortunately it is not institutionalized in practice. The intent of this paper is to highlight a relatively simple approach to historize or memorize past and present actual planning and scheduling data collected into what we call the past rolling horizon (PRH). The PRH is identical to the future rolling horizon (FRH) used in hierarchical production planning and model predictive control to manage omnipresent uncertainty in the model and data. Instead of optimizing future decisions such as throughputs, operating-modes and conditions we now optimize or estimate key model parameters. Although bias-updating using a single time-sample of data is common practice in advanced process control and optimization to incorporate “parameter” feedback, this is only realizable for real-time applications with comprehensive measurement systems. Proposed in this paper is the use of multiple synchronous or asynchronous time-samples in the past in conjunction with simultaneous reconciliation and regression to update a subset of the model parameters on a past rolling horizon basis to improve the performance of planning and scheduling models.
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OPTIMIZATION APPROACHES IN OPTIMAL INTEGRATED CONTROL, COMPUTATION AND COMMUNICATION SYSTEMS
1. International Journal of Control Theory and Computer Modeling (IJCTCM) Vol.6, No.4, October 2016
DOI : 10.5121/ijctcm.2016.6401 1
OPTIMIZATION APPROACHES IN OPTIMAL
INTEGRATED CONTROL, COMPUTATION AND
COMMUNICATION SYSTEMS
M.M. Share Pasand
Department of Electrical and Electronics Engineering, Faculty of Electrical, Mechanical
and Civil Engineering, Standard Research Institute, Iran
ABSTRACT
This paper studies the existing approaches in optimal integrated control, computation and communication
problems. It concentrates on joint optimization problems aimed at finding communication/computation
policy and control signal. Different aspects including computational complexity, convexity, proposed
methods to find optimum and other issues related to control performance are studied and compared for
different approaches.
KEYWORDS
Integrated Control and Communication, Networked Control Systems, Constrained Optimization.
1. INTRODUCTION
Data communication networks are utilized in most of modern control systems, as a mean to
transmit measurement/control data between plant(s) and controller(s). Control systems utilizing
communication networks are first referred to as integrated control and communication systems
(ICCS). [1-3] The main characteristic of an ICCS is that they require data transmission for
desirable performance using limited communication resources. An ICCS could be a distributed
control system including distributed sensors/actuators controlled by a single controller or a group
of controllers which is known as a Networked Control System (NCS). Also an embedded single
loop control system with a limited communication channel or computation capacity which
requires additional measures to be considered, is an ICCS. [7] surveys issues arises in an ICCS
when integrating control and communication. Control methods used in this environment are
studied and surveyed in [8-11]. A survey of network-induced problems which affect control
systems is reported in [12].
Another group of control systems which are similar to ICCS in several aspects are integrated
control and computation systems. These systems control a group of plants using limited
computational (rather than communication) resources. In these applications, a single processor is
shared among several control tasks or have to multi-task between a single control application and
some other tasks. Adaptive and on-line optimal control algorithms as well as estimation problems
require significant computational overhead. In these applications, processor time should be
scheduled in an efficient manner to yield proper control performance. Examples could be found in
[4-6].
Therefore, control systems are emerging into control, computation and communication systems in
which an efficient control algorithm should account for communication and computation
resources. In a more general framework, one may refer to theses systems as Integrated Control,
Computation and Communication Systems (ICCCS). Figure 1, depicts the scope of ICCC
problems.
2. International Journal of Control Theory and Computer Modeling (IJCTCM) Vol.6, No.4, October 2016
2
Figure 1: Scope of ICCC Problems
The objective of an ICCC problem is to find a control algorithm alongside with management
policies for computational and communication resources to yield best possible performance.
There are different approaches in tackling the ICCC problem. In several applications, both
communication and computation constraints lead to or could be assumed to result in similar
problems (i.e. delays, outdated information, etc.) therefore most of previous works concentrated
on only one aspect. In networked control systems, communication resources are more important
than computational ones and therefore control and communication are considered in the problem
and computational issues are neglected [1,2,13-17], While [4-6] and references therein consider
only computational limitations assuming that communication channel is perfect or not present.
2. ICCC PROBLEM
The aim of an ICCCS is to compute a control input, a communication policy (which consists of
bit rate selection [5] or medium access scheduling [13,14,18-21] in contention-free networks
and/or methods to confine bandwidth usage [22,23] in contention-based networks [17]), which (in
contention-free cases) results in a communication sequence determining which node is to be
granted medium access and/or a computation policy which schedules different computational
tasks to be performed. This is an integrated problem in nature as it includes interacting objectives.
Typical objectives include quadratic forms of state and control inputs as [5,13] and network
induced error of output [45] or state [29]. Communication and computation limitations may be
implemented as constraints of optimization problem as in [13] , incorporated into state space
model itself [17] or directly added to the quadratic objective as communication or computation
costs [51].
2.1. Approaches to ICCC Problem
There are three approaches to solve ICCC problems. These approaches may be regarded as either
modelling or solving approaches.
The first approach is to solve control problem with regard to communication system situation
referred to as communication aware control [24] and could be found for instance in [25-28].
Communication aware control methods require an estimation of network traffic or delay to amend
control policy respectively. Communication aware approach is best fitted to applications utilizing
contention-based networks. This is due to the fact that a contention-based network may not
provide constant available bandwidth and may suffer from packet losses or transmission delays as
a result of varying workload [30,31], therefore it has a time-varying status which is required to be
considered when control input is determined. In this approach, a control algorithm may be
required to reduce its communication in order to save bandwidth. Examples include interrupt
based control [49], event driven control [50] and joint LQG control of [51] which adds a
communication cost to the traditional quadratic cost function.
3. International Journal of Control Theory and Computer Modeling (IJCTCM) Vol.6, No.4, October 2016
3
The second approach is to find and pre-compute an appropriate communication policy based on
the plant dynamics and/or other measures, in offline manner. This method may be regarded as
control aware communication [24] in contrast to the first approach. In this approach,
communication sequences are chosen based on control objectives. Then control inputs are
computed online and applied via the communication sequence. This approach is appropriate for
contention-free networks with the ability to pre-determine which nodes are to be granted medium
access. Examples include state aware feedback scheduling [24] used in [18-22,29]. The offline
communication sequence should be chosen in such a way that provides a minimum level of
appropriateness. For example communication sequences which preserve controllability or
observability of the NCS as discussed in [17,29,32-34].
The third approach is to integrate both problems to find optimal/sub-optimal solutions in online or
semi-online manner as in [4,5,13,16,20]. This approach may be utilized when abundant
computational resources are present and communication constraints are restrictive, affecting
control performance and stability in an adverse manner. In this method,
computation/communication and control policies are determined via solving a joint optimization
problem.
Communication decisions may be made online or offline. Online decisions on communication
policy includes selection of communication sequences, selection of sampling intervals,
determination of quantization thresholds, etc. On the other hand, control input computation is
always online due to the dynamic nature of feedback control. Table 1, illustrates the
aforementioned approaches.
Table 1: Approaches in ICCC
2.2. Computational Complexity
Although there are simple methods in the literature like [35], the first approach usually requires
the minimization of predicted cost functions [25-28]. As plant state vector is not known as a
priori, optimizations could not be performed offline and therefore this approach is not appropriate
for systems with fast responses and/or low computational power. To the best of the authors
knowledge, it is the only existing approach in contention-based paradigm.
The second approach is appropriate for most applications in contention-free paradigm as it pre-
computes communication sequences to fulfil communication constraints. This method may not be
optimal with regard to performance or bandwidth usage, however it is simple and
computationally efficient.
The third approach for solving ICCC problem usually consists of optimization problems with
complexity of high orders. Usually numerical methods should be used to solve these problems.
[13] could be referred for an experimental and simulation example.
4. International Journal of Control Theory and Computer Modeling (IJCTCM) Vol.6, No.4, October 2016
4
3. COMPARISON OF EXISTING METHODS
In this section, existing approaches are studied and compared with regard to computation
complexity, type of optimization problems and control performance considerations.
3.1 Optimal Integrated Control and Scheduling
In [13], a moving horizon quadratic cost function is introduced to represent control performance
and stability concerns. Scheduling is incorporated in the model as a set of constraints forming a
hybrid model predictive control problem (HPC) consisting of quadratic cost function of
continuous time variables (control signal vectors) and constraints of continuous and integer
variables.
3.1.1. Optimization Problem
The optimization problem could be stated as follows [13]:
min +
. :
+ 1 = " + #
$% ∈ '0,1*
$%
+
%
≤ -
% − % − 1 = $% % − $% % − 1
0 =
/
/
(1)
This model leads to a Mixed Integer Quadratic Programming (MIQP) with a convex cost function
and nonlinear (multiplicative) constraints. Note that by relaxing integer constraint $% ∈ '0,1*
into 0 ≤ $% ≤ 1, (namely QP relaxation) incorporating all variable in a time frame of N, and
defining a new variable vector as 0 = 1 $ 2 ,this problem could be reformulated
to form a standard Quadratically Constrained Quadratic Programming (QCQP) problem. The
third constraint is indeed a linear inequality and the fourth could be restated as a quadratic
equality constraint. After finding the optimum variable, scheduling sequence could be derived as
the following, leading to a sub-optimal solution.
$3% = 4
0 $% ≤ 0.5
1 $% > 0.5
/
3.1.2. Computational Complexity
Optimization problem (1), should be solved by extending variable vectors for all times within the
prediction horizon (N). This enlarges the problem as × 29 variables (all % , $% , =
0, … ). For a fixed set of scheduling variables, the problem is a QP which could be solved using
simple analytical and numerical methods. Therefore, (1) is of combinatorial complexity as it
include solving a QP of size × 9 (a predictive optimization with 9 variables and a horizon of
) for all possible combinations of $% considering (1.3). Number of possible combinations
for scheduling variables could be computed from figure 1:
5. International Journal of Control Theory and Computer Modeling (IJCTCM) Vol.6, No.4, October 2016
5
Figure 2. Communication Sequence
which is ;
9
-
< possible combinations. Assuming linear complexity for QP problems, the
computation time for a complete search over all possible communication sequences will be:
;
9
-
< × =>? 2 9
=>? @ represents computational complexity of a constrained quadratic programming problem
with dimension l. Increasing the prediction horizon will enlarge dimensions in an exponential
manner. Unfortunately increasing the horizon is the most common way in predictive approaches
to guarantee stability or enhance stability robustness [36]. In fact formal proofs for stability of
MPCs is only proposed for infinite horizon cases. [37,13]. For a simple case of two inputs and a
unity bandwidth network, a horizon of 10, results in 1024 combinations which is barely practical
to be computed within each sample time.
To reduce computational overhead, one may restrict communication sequences to a few proper
choices. For this purpose a simplified method namely OPP is proposed by [13]. OPP restricts the
search space of communication sequences to m. In other words, a constant periodic
communication sequence is formed and the algorithm only chooses the starting point. Therefore
number of combinations reduce from ;
9
-
< to 9.
3.1.3. Existing Methods for Solution
MIQP problems are usually solved via one of the common approaches known as Cutting plane
methods, decomposition methods, logic-based methods and branch and bound methods [38-40].
Branch and bound methods are known to be the most efficient approaches for convex MIQP[41]
and used in [13]. Branch and Bound methods, usually use QP relaxation as mentioned in section
A.1. In this method, ordinary constrained QP problems are derived via relaxing the integer
constraints to interval constraints. The created ordinary QP problems are solved in the nodes. As
the method makes progress down in the tree, fixed integer variables are eliminated from the
problem. This means that the number of optimization variables in the relaxed sub-problems
decreases by one for each level passed on the way down the tree. [40] For non-convex MIQP
problems, spatial branch and bound methods could be used. MATLAB, CPLEX and YALMIP are
of the existing software to solve this type of problem. [42]
3.1.4. Other Issues
Note that (1) should be performed on-line during each period, this makes the algorithm agile,
capable of yielding high performance in presence of disturbances and adaptability with respect to
model changes. However implementing (1) is computationally impractical. There is no
guarantee for stability unless for infinite horizon[13]. OPP is simpler and still better than
static scheduling [13]. Also note that (1) greedily demands for bandwidth usage as there
is no constraint on the frequency of input updates.
6. International Journal of Control Theory and Computer Modeling (IJCTCM) Vol.6, No.4, October 2016
6
3.2. Optimal Offline Periodic Scheduling
In [29], the problem of scheduling a set of actuators belonging to a set of processes is
investigated. The aim is to find optimal LQ gain for each process to implement a state feedback
controller as well as finding optimal communication logics. This is a special case of the general
problem in which sensors and actuators are subject to communication constraints and plants are
not necessarily disjoint.
3.2.1. Optimization Problem
A quadratic cost function is derived via sampling of a continuous time cost function. The
optimization problem could be state as follows:
min A%B%
+
%
. . C
∈ D
9 ≤ E ≤ E+FG
/
2)(
D
is the set of possible p-periodic communication sequences for sensors. The cost function is a
weighted sum of maximum deviation of each process quadratic cost function from the optimal
cost function derived via solving a Ricatti equation. i.e.:
B% ≜ max
‖G‖L
∑
1
E N
D
NO
− 1 (3)
NO
is the positive definite solution to continuous time Ricatti equation of the original continuous
time system and N is the solution to Ricatti equation associated with the following LQ
problem.
min +
. : 4
+ 1 = " + #
0 =
/
/
4)(
This problem consists of two optimizations. (2) is a combinatorial optimization problem and
could be restated as a Binary Linear Programming by defining the elements of sequence S as
optimization variables, if all possible sequences are admitted. (4) is a quadratic non-constrained
problem having an analytical solution based on Ricatti equation. [17] utilized Genetic Algorithms
and Particle swarm optimization to compute the optimal communication sequence. Then it
compared its methods with that of [29] via simulations.
3.2.2. Computational Complexity
Due to combinatorial nature of (2), it becomes exponentially complicated when the period of
communication sequence increases. (2) requires searching through a set of ∑ 2PDQRS
P + each time
solving a LQ problem of dimensions equal to original system dimensions. Computation
complexity will be:
7. International Journal of Control Theory and Computer Modeling (IJCTCM) Vol.6, No.4, October 2016
7
=T> U 2P
DQRS
P +
=T> U is the computational complexity of a Linear Quadratic Control problem which is usually
performed by solving a Ricatti equation. Note that if E+FG is not known or fixed as a priori,
computations may grow until a feasible sequence is achieved. [29] (3) and (4) could be performed
off-line or semi-online to result in periodic communication sequences of period p. Therefore
computational complexity is not a concern as it is in [13].
3.2.3. Existing Methods for Solution
[17] used GA and PSO to search for the optimal communication sequence. [29] uses a simpler but
less accurate method. In [17] the results are compared.
3.2.4. Other Issues
(2) is only appropriate for sensor scheduling and only applies to a situation in which a number of
plants are scheduled via a shared bus. When the plants have interaction (i.e. several output
channels of a single MIMO process are to be scheduled) or when the network bandwidth is more
than unity, the formulation should be extended.
3.3. Optimal Sample Period Assignment
[5] proposes a method for on-line sample period assignment in a feedback scheduling time
window to control several plants connected to a shared processor. Sample time assignment is in
fact an inherent way to schedule network access or assign bandwidth proportions to nodes.
Therefore this situation is similar to that of case 3.2.However [5] assumes that both sensors and
actuators of each plant have the same sampling frequency.
3.3.1. Optimization Problem
The cost function is the sum of individual quadratic cost functions for each plant. It could
also be a weighted sum of all cost functions. There are constraints regarding
schedulability and minimum sampling intervals. It is desired to find sample intervals for
each plant in order to minimize the overall cost function.
min V%
+
%
ℎ … ℎX
(5)
. . Y
Z%
ℎ%
+
%
≤ [D − []
ℎ+%X ≤ ℎ% ≤ ℎ+FG ^ = 1, . . 9
/
(5.1)
(5.2)
[D is the utilization set point of processor and [] is the utilization occupied by overhead
computations. Z% is the execution time of control task associated with ith
plant.[5].
The problem is convex with regard to _`
if (5.2) is relaxed. Therefore one may relax (5.2) and
solve (5) as a QCQP. After solving the problem, if the resultant ℎ% does not fulfill (5.2), it should
be replaced by ℎ+%X/ ℎ+FG. Note that (5.2) could be extended to n constraints each of which
associated with each plant. (i.e. ℎ%
+%X ≤ ℎ% ≤ ℎ%
+FG
)
8. International Journal of Control Theory and Computer Modeling (IJCTCM) Vol.6, No.4, October 2016
8
3.3.2. Computational Complexity
(5) should be solved every a]b
step which is similar to prediction horizon N in [13]. N contrast
to methods investigated in sections A and B, this problem is computationally simple and consists
of a near-standard optimization problem. The problem is not of a combinatorial complexity and
increasing a]b
does not adversely affect the complexity.
[5] proposed to form a pre-computed table (table 1, [5]) of cost functions and sample periods to
save processor utilization. The table is computed off-line and is used as a look up, during on-line
optimization. Linear search is applied to compute the look up table which seems sufficient though
other search methods could be utilized. The optimization problem is of order = 9c (linear
order) in which 9 is the number of plants (control tasks) and c is the number of possible
sampling periods for each plants.
3.3.3. Existing Methods for Solution
Solving (5) does not need complicated methods and is simple enough to be implemented on
embedded systems and simple firmware.
3.3.4. Other Issues
Although (5) is simple, it can’t be directly implemented to tackle problems as for section A or
3.2.[5] aims to find sampling intervals for control tasks which is scheduling of the processor time
while A and B are seeking to schedule network access. Network access schedules could not be
pre-emptive. Also information of states (and even output) are not always available.
A common drawback of methods introduced in sections B and C is that they consider a number of
disjoint plants and it is assumed that network bandwidth is enough to almost instantly transmit
each plant information to/from controller. This contradicts with many realistic situations in which
many interacting input/output channels are to be scheduled to have access to network. Also it is
shown in previous works (e.g. [13]) that the insertion of a shared network results in interactions
between disjoint plants.
Another limitation of (5) is that it relies on the concept of sampling interval which assumes that
all sensors and actuators of a specific plant are sampled via the same sample time. In practice it
may be desired for an output channel to be sampled more frequently than the other.
3.4. Integrated Control and Scheduling Based on Network Induced Error
[44] considers the relative network induced error of state as the cost function, which is similar to
the idea of Maximum Error First (MLF) of [45] but uses relative state error rather than absolute
output error.
3.4.1. Optimization Problem
The optimization problem is as follows:
min
| % − e% |
|e% |
+
%
. . Y
Z%
ℎ%
+
%
+
#%
ℎ%
≤ 9 2 /+
− 1
ℎ% ≤ ℎ+FG − #% ^ = 1, . . 9
/
6)(
(6.1)
9. International Journal of Control Theory and Computer Modeling (IJCTCM) Vol.6, No.4, October 2016
9
e% is the updated state vector existing within the controller while % is the actual state
vector for ith
plant. #% is the worst case time during which a signal may be blocked from being
transmitted. (6.1) is the schedulability criteria for non-preemptive rate monotonic scheduling.
This problem is non-convex as e% depends on sample time and is incorporated in a non-
convex manner into (6). (6.1) is a convex constraint with regard to _`
and (6.2) is convex in ℎ%.
(This issue could be resolved using the technique discussed in section 3.3.)
3.4.2. Computational Complexity
The computational complexity of this problem is similar to that of section 3.3. Especially when a
look-up table is pre-computed, the problem becomes simpler. This problem is semi-online similar
to that of section 3.3.
D.3. Existing methods for solution
[44] used Genetic Algorithm to solve (6). Methods similar to those discussed in section C could
also be utilized.
3.4.4. Other Issues
From a control point of view, (6) is not the best performance index to be minimized. A small
relative error guarantees that communication medium is providing fresh data but it does not
guarantee that control performance is desirable. In fact, (6) relies on the inherent assumption that
paying more attention to the plant with enlarged network induced error will cause better
performance. This depends on the effectiveness of control algorithm implemented in the
controller. Approaches similar to [44] are frequently discussed in the literature. Examples include
[45]-[48].
4. CONCLUSIONS
Integrated approaches for communication and control usually lead to combinatorial optimization
problems and have to be simplified in order to be performed on-line. Among existing methods,
LQR approach of [5] is the most promising as it is computationally simple however it does not
account for shared network which is an important subject. The approach taken in [13] is based on
realistic assumptions on network bandwidth limitations and interactions between different
input/output channels ,however it leads to complicated combinatorial optimization which takes
significant time to be performed.
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AUTHORS
M.M. Share Pasand was born in 1984,
engineering from Sharif and KNT Universities.
Research Institute. His research interest
theory
International Journal of Control Theory and Computer Modeling (IJCTCM) Vol.6, No.4, October 2016
Shou Yu. "Feedback scheduling of model-based networked control systems
with flexible workload." International Journal of Automation and Computing 5.4 (2008): 389
Camacho, Eduardo F., and Carlos Bordons Alba. Model predictive control. Springer Science
Mayne, David Q., et al. "Constrained model predictive control: Stability and optimality." Automatica
Löfberg, Johan. "YALMIP: A toolbox for modeling and optimization in MATLAB." Computer Aided
l Systems Design, 2004 IEEE International Symposium on. IEEE, 2004.
Dua, Vivek, Nikolaos A. Bozinis, and Efstratios N. Pistikopoulos. "A multiparametric programming
integer quadratic engineering problems." Computers & Chemical Engin
Axehill, Daniel. "Integer quadratic programming for control and communication." (2008).
G. C. Goodwin, M. M. Seron, and J. A. De Doná.” Constrained Control and Estimation
Optimisation Approach”, Springer Verlag, 2005.
Bliek1ú, Christian, Pierre Bonami, and Andrea Lodi. "Solving Mixed-Integer Quadratic Programming
CPLEX: a progress report." (2014).
Boyd, Stephen, and Lieven Vandenberghe. Convex optimization. Cambridge university press,
chun Zhang, and Ya-zhi Jing. "An integrated control and scheduling
optimization method of networked control systems." Journal of Electronic Science and Technology of
Walsh, Gregory C., Hong Ye, and Linda G. Bushnell. "Stability analysis of networked control
systems." Control Systems Technology, IEEE Transactions on 10.3 (2002): 438-446.
Branicky, Michael S., Stephen M. Phillips, and Wei Zhang. "Scheduling and feedback co
control systems." Decision and Control, 2002, Proceedings of the 41st IEEE Conference
Park, Hong Seong, et al. "A scheduling method for network-based control systems." Control Systems
Technology, IEEE Transactions on 10.3 (2002): 318-330.
Zhao, Y. B., G. P. Liu, and D. Rees. "Integrated predictive control and scheduling co
networked control systems." IET Control Theory & Applications 2.1 (2008): 7-15.
Varsakelis, Dmitris, and Panganamala R. Kumar. "Interrupt-based feedback control over a
shared communication medium." Decision and Control, 2002, Proceedings of the 41st IEEE
Conference on. Vol. 3. IEEE, 2002.
Heemels, W. P. M. H., J. H. Sandee, and P. P. J. Van Den Bosch. "Analysis of event
controllers for linear systems." International journal of control 81.4 (2008): 571-590.
Molin, Adam, and Sandra Hirche. "On LQG joint optimal scheduling and control under
communication constraints." Decision and Control, 2009 held jointly with the 2009 28th Chinese
Control Conference. CDC/CCC 2009. Proceedings of the 48th IEEE Conference on. IEEE, 2009.
was born in 1984, received his BSc. and MSc. degrees in control
KNT Universities. He is a research faculty at Standard
Research Institute. His research interest includes networked control and linear systems
International Journal of Control Theory and Computer Modeling (IJCTCM) Vol.6, No.4, October 2016
11
based networked control systems
with flexible workload." International Journal of Automation and Computing 5.4 (2008): 389-394.
Camacho, Eduardo F., and Carlos Bordons Alba. Model predictive control. Springer Science &
Mayne, David Q., et al. "Constrained model predictive control: Stability and optimality." Automatica
Löfberg, Johan. "YALMIP: A toolbox for modeling and optimization in MATLAB." Computer Aided
Dua, Vivek, Nikolaos A. Bozinis, and Efstratios N. Pistikopoulos. "A multiparametric programming
integer quadratic engineering problems." Computers & Chemical Engineering
Axehill, Daniel. "Integer quadratic programming for control and communication." (2008).
G. C. Goodwin, M. M. Seron, and J. A. De Doná.” Constrained Control and Estimation – An
Integer Quadratic Programming
Boyd, Stephen, and Lieven Vandenberghe. Convex optimization. Cambridge university press, 2004.
zhi Jing. "An integrated control and scheduling
optimization method of networked control systems." Journal of Electronic Science and Technology of
and Linda G. Bushnell. "Stability analysis of networked control
Branicky, Michael S., Stephen M. Phillips, and Wei Zhang. "Scheduling and feedback co-design for
control systems." Decision and Control, 2002, Proceedings of the 41st IEEE Conference
based control systems." Control Systems
Zhao, Y. B., G. P. Liu, and D. Rees. "Integrated predictive control and scheduling co-design for
based feedback control over a
shared communication medium." Decision and Control, 2002, Proceedings of the 41st IEEE
Heemels, W. P. M. H., J. H. Sandee, and P. P. J. Van Den Bosch. "Analysis of event-driven
Molin, Adam, and Sandra Hirche. "On LQG joint optimal scheduling and control under
9 28th Chinese
Control Conference. CDC/CCC 2009. Proceedings of the 48th IEEE Conference on. IEEE, 2009.