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Margarita Razgon; Alireza Mousavi. Correction: Razgon, M., et al. Relaxed Rule-Based Learning for Automated Predictive Maintenance: Proof of Concept. Algorithms 2020, 13, 219. Algorithms 2021, 14, 86 .
AMA StyleMargarita Razgon, Alireza Mousavi. Correction: Razgon, M., et al. Relaxed Rule-Based Learning for Automated Predictive Maintenance: Proof of Concept. Algorithms 2020, 13, 219. Algorithms. 2021; 14 (3):86.
Chicago/Turabian StyleMargarita Razgon; Alireza Mousavi. 2021. "Correction: Razgon, M., et al. Relaxed Rule-Based Learning for Automated Predictive Maintenance: Proof of Concept. Algorithms 2020, 13, 219." Algorithms 14, no. 3: 86.
Milling operations in various production processes are among the most important factors in determining the quality, stability, and consumption of energy. Optimizing and stabilizing the milling process is a non-linear multivariable control problem. In specific processes that deal with natural materials (e.g., cement, pulp and paper, beverage brewery and water/wastewater treatment industries). A novel data-driven approach utilizing real-time monitoring control technology is proposed for the purpose of optimizing the grinding of cement processing. A combined event modeling for feature extraction and the fully connected deep neural network model to predict the coarseness of cement particles is proposed. The resulting prediction allows a look ahead control strategy and corrective actions. The proposed solution has been deployed in a number of cement plants around the world. The resultant control strategy has enabled the operators to take corrective actions before the coarse return increases, both in autonomous and manual mode. The impact of the solution has improved efficiency resource use by 10% of resources, the plant stability, and the overall energy efficiency of the plant.
Morad Danishvar; Sebelan Danishvar; Francisco Souza; Pedro Sousa; Alireza Mousavi. Coarse Return Prediction in a Cement Industry’s Closed Grinding Circuit System through a Fully Connected Deep Neural Network (FCDNN) Model. Applied Sciences 2021, 11, 1361 .
AMA StyleMorad Danishvar, Sebelan Danishvar, Francisco Souza, Pedro Sousa, Alireza Mousavi. Coarse Return Prediction in a Cement Industry’s Closed Grinding Circuit System through a Fully Connected Deep Neural Network (FCDNN) Model. Applied Sciences. 2021; 11 (4):1361.
Chicago/Turabian StyleMorad Danishvar; Sebelan Danishvar; Francisco Souza; Pedro Sousa; Alireza Mousavi. 2021. "Coarse Return Prediction in a Cement Industry’s Closed Grinding Circuit System through a Fully Connected Deep Neural Network (FCDNN) Model." Applied Sciences 11, no. 4: 1361.
This paper is concerned with the Tobit Kalman filtering problem for a class of discrete time-varying systems subject to censored observations, integral measurements and probabilistic sensor failures under the Round-Robin protocol (RRP). The censored observations are characterized by the Tobit observation model, the integral measurements are described as functions of system states over a certain time interval required for data acquisition, and the sensor failures are governed by a set of uncorrelated random variables. The RRP is employed to decide the transmission sequence of sensors in order to alleviate undesirable data collisions. By resorting to the augmentation technique and the orthogonality projection principle, a protocol-based Tobit Kalman filter (TKF) is developed with the coexistence of integral measurements and sensor failures that lead to a couple of augmentation-induced terms. Moreover, the performance of the proposed filter is analyzed through examining the statistical property of the error covariance of the state estimation. Further analysis shows the existence of self-propagating upper and lower bounds on the estimation error covariance. A case study on ballistic roll rate estimation is presented to illustrate the efficacy of the developed filter.
Hang Geng; Zidong Wang; Lei Zou; Alireza Mousavi; Yuhua Cheng. Protocol-Based Tobit Kalman Filter Under Integral Measurements and Probabilistic Sensor Failures. IEEE Transactions on Signal Processing 2020, 69, 546 -559.
AMA StyleHang Geng, Zidong Wang, Lei Zou, Alireza Mousavi, Yuhua Cheng. Protocol-Based Tobit Kalman Filter Under Integral Measurements and Probabilistic Sensor Failures. IEEE Transactions on Signal Processing. 2020; 69 (99):546-559.
Chicago/Turabian StyleHang Geng; Zidong Wang; Lei Zou; Alireza Mousavi; Yuhua Cheng. 2020. "Protocol-Based Tobit Kalman Filter Under Integral Measurements and Probabilistic Sensor Failures." IEEE Transactions on Signal Processing 69, no. 99: 546-559.
A dynamic health indicator based on regressive event-tracker algorithm is proposed to accurately interpret the condition of critical components of machine tools in a production system and to predict their potential sudden breakdown based on future trends. Through sensors/actuators data acquisition, the algorithm predicts the causal links between various monitored parameters of the system and offers a diagnosis of the health state of the system. A safety and operational robustness regime determines the acceptable thresholds of the operational boundaries of the electro-mechanical components of the machines. The proposed model takes into account the possibilities of sensor values being a piecewise-linear models or a pair of exponential functions with restricted model parameters, which can predict the runs-to-failure or remaining useful life until a safety threshold. The events caused by sensors passing through sub levels of safety threshold are used as a re-enforcement learning for the models. Each remaining useful life estimation diagnosis and prognosis analysis can be conducted on individual or an interconnected network of components within a machine. The overall health indicator based on individual useful life estimation is calculated by deriving the weights from event-clustering algorithm. The work can be extended to a network of machines representing a process. The outcome of the continuously learning real-time condition monitoring modus-operandi is to accurately measure the remaining useful life of the network of critical components of a machine.
Veerendra C. Angadi; Ali Mousavi; Diego Bartolomé; Matteo Tellarini; Matteo Fazziani. Causal Modelling for Predicting Machine Tools Degradation in High Speed Production Process. IFAC-PapersOnLine 2020, 53, 271 -275.
AMA StyleVeerendra C. Angadi, Ali Mousavi, Diego Bartolomé, Matteo Tellarini, Matteo Fazziani. Causal Modelling for Predicting Machine Tools Degradation in High Speed Production Process. IFAC-PapersOnLine. 2020; 53 (3):271-275.
Chicago/Turabian StyleVeerendra C. Angadi; Ali Mousavi; Diego Bartolomé; Matteo Tellarini; Matteo Fazziani. 2020. "Causal Modelling for Predicting Machine Tools Degradation in High Speed Production Process." IFAC-PapersOnLine 53, no. 3: 271-275.
The proposed work describes a dynamic regression based event-tracker for high speed production process. The methodology discussed is a causal system and provides trends and estimations of the sensors based on a flexible regression model of the historical sensor values. A safety threshold is defined that provides a boundary of the tolerant working for the regime condition of production. This threshold is used as a reference to calculate the remaining useful life of the critical component. The estimated remaining useful life is compared with the Weibull reliability analysis. The proposed methodology provides a remaining useful life of ∼ 10 weeks for the thermal regulator use-case when compared to ∼ 9 weeks for Weibull analysis. The overestimation of the methodology is discussed and along with the alternative methodology. The sensitivity analysis is conducted on the noise and training periods are studied for better prediction.
Veerendra C. Angadi; Alireza Mousavi; Diego Bartolomé; Matteo Tellarini; Matteo Fazziani. Regressive Event-Tracker: A Causal Prediction Modelling of Degradation in High Speed Manufacturing. Procedia Manufacturing 2020, 51, 1567 -1572.
AMA StyleVeerendra C. Angadi, Alireza Mousavi, Diego Bartolomé, Matteo Tellarini, Matteo Fazziani. Regressive Event-Tracker: A Causal Prediction Modelling of Degradation in High Speed Manufacturing. Procedia Manufacturing. 2020; 51 ():1567-1572.
Chicago/Turabian StyleVeerendra C. Angadi; Alireza Mousavi; Diego Bartolomé; Matteo Tellarini; Matteo Fazziani. 2020. "Regressive Event-Tracker: A Causal Prediction Modelling of Degradation in High Speed Manufacturing." Procedia Manufacturing 51, no. : 1567-1572.
In this paper we propose a novel approach of rule learning called Relaxed Separate-and- Conquer (RSC): a modification of the standard Separate-and-Conquer (SeCo) methodology that does not require elimination of covered rows. This method can be seen as a generalization of the methods of SeCo and weighted covering that does not suffer from fragmentation. We present an empirical investigation of the proposed RSC approach in the area of Predictive Maintenance (PdM) of complex manufacturing machines, to predict forthcoming failures of these machines. In particular, we use for experiments a real industrial case study of a machine which manufactures the plastic bottle. We compare the RSC approach with a Decision Tree (DT) based and SeCo algorithms and demonstrate that RSC significantly outperforms both DT based and SeCo rule learners. We conclude that the proposed RSC approach is promising for PdM guided by rule learning.
Margarita Razgon; Alireza Mousavi. Relaxed Rule-Based Learning for Automated Predictive Maintenance: Proof of Concept. Algorithms 2020, 13, 219 .
AMA StyleMargarita Razgon, Alireza Mousavi. Relaxed Rule-Based Learning for Automated Predictive Maintenance: Proof of Concept. Algorithms. 2020; 13 (9):219.
Chicago/Turabian StyleMargarita Razgon; Alireza Mousavi. 2020. "Relaxed Rule-Based Learning for Automated Predictive Maintenance: Proof of Concept." Algorithms 13, no. 9: 219.
Gene expression programming (GEP) is a data driven evolutionary technique that is well suited to correlation mining of system components. With the rapid development of industry 4.0, the number of components in a complex industrial system has increased significantly with a high complexity of correlations. As a result, a major challenge in employing GEP to solve system engineering problems lies in computation efficiency of the evolution process. To address this challenge, this paper presents EGEP, an event tracker enhanced GEP, which filters irrelevant system components to ensure the evolution process to converge quickly. Furthermore, we introduce three theorems to mathematically validate the effectiveness of EGEP based on a GEP schema theory. Experiment results also confirm that EGEP outperforms the GEP with a shorter computation time in an evolution.
Zhengwen Huang; Maozhen Li; Alireza Mousavi; Morad Danishva; Zidong Wang. EGEP: An Event Tracker Enhanced Gene Expression Programming for Data Driven System Engineering Problems. IEEE Transactions on Emerging Topics in Computational Intelligence 2019, 3, 117 -126.
AMA StyleZhengwen Huang, Maozhen Li, Alireza Mousavi, Morad Danishva, Zidong Wang. EGEP: An Event Tracker Enhanced Gene Expression Programming for Data Driven System Engineering Problems. IEEE Transactions on Emerging Topics in Computational Intelligence. 2019; 3 (2):117-126.
Chicago/Turabian StyleZhengwen Huang; Maozhen Li; Alireza Mousavi; Morad Danishva; Zidong Wang. 2019. "EGEP: An Event Tracker Enhanced Gene Expression Programming for Data Driven System Engineering Problems." IEEE Transactions on Emerging Topics in Computational Intelligence 3, no. 2: 117-126.
An improved method for the real time sensitivity analysis in large scale complex systems is proposed in this paper. The method borrows principles from the event tracking of interrelated causal events and deploys clustering methods to automatically measure the relevance and contribution made by each input event data (ED) on system outputs. The ethos of the proposed event modeling (EM) technique is that the behavior or the state of a system is a function of the knowledge acquired about events occurring in the system and its wider operational environment. As such it builds on the theoretical and the practical foundation for the engineering of knowledge and data in modern and complex systems. The proposed EM platform EventiC filters noncontributory ED sources and has the potential to include information that was initially thought irrelevant or simply not considered at the design stage. The real-time ability to group and rank relevant input-output ED in order of its importance and relevance will not only improve the data quality, but leads to an improved higher level of mathematical formulization in the modern complex systems. The contribution of the approach to systems' modeling is in the automation of data analysis, control, and plant process modeling. EventiC has been validated as the monitoring and the control system for a cement factory. In addition to the previously known parameters, the proposed EventiC identified new influential parameters that were previously unknown. It also filtered 18% of the input data without compromising the data quality or the integrity. The solution has improved the quality of input variable selection and simplify plant control strategies.
Morad Danishvar; Alireza Mousavi; Peter Broomhead. EventiC: A Real-Time Unbiased Event-Based Learning Technique for Complex Systems. IEEE Transactions on Systems, Man, and Cybernetics: Systems 2018, 50, 1649 -1662.
AMA StyleMorad Danishvar, Alireza Mousavi, Peter Broomhead. EventiC: A Real-Time Unbiased Event-Based Learning Technique for Complex Systems. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 2018; 50 (5):1649-1662.
Chicago/Turabian StyleMorad Danishvar; Alireza Mousavi; Peter Broomhead. 2018. "EventiC: A Real-Time Unbiased Event-Based Learning Technique for Complex Systems." IEEE Transactions on Systems, Man, and Cybernetics: Systems 50, no. 5: 1649-1662.
Gene expression programming (GEP) is a data driven evolutionary technique that well suits for correlation mining. Parallel GEPs are proposed to speed up the evolution process using a cluster of computers or a computer with multiple CPU cores. However, the generation structure of chromosomes and the size of input data are two issues that tend to be neglected when speeding up GEP in evolution. To fill the research gap, this paper proposes three guiding principles to elaborate the computation nature of GEP in evolution based on an analysis of GEP schema theory. As a result, a novel data engineered GEP is developed which follows closely the generation structure of chromosomes in parallelization and considers the input data size in segmentation. Experimental results on two data sets with complementary features show that the data engineered GEP speeds up the evolution process significantly without loss of accuracy in data correlation mining. Based on the experimental tests, a computation model of the data engineered GEP is further developed to demonstrate its high scalability in dealing with potential big data using a large number of CPU cores.
Zhengwen Huang; Maozhen Li; Christos Chousidis; Alireza Mousavi; Changjun Jiang; Ali Mousavi. Schema Theory-Based Data Engineering in Gene Expression Programming for Big Data Analytics. IEEE Transactions on Evolutionary Computation 2017, 22, 792 -804.
AMA StyleZhengwen Huang, Maozhen Li, Christos Chousidis, Alireza Mousavi, Changjun Jiang, Ali Mousavi. Schema Theory-Based Data Engineering in Gene Expression Programming for Big Data Analytics. IEEE Transactions on Evolutionary Computation. 2017; 22 (5):792-804.
Chicago/Turabian StyleZhengwen Huang; Maozhen Li; Christos Chousidis; Alireza Mousavi; Changjun Jiang; Ali Mousavi. 2017. "Schema Theory-Based Data Engineering in Gene Expression Programming for Big Data Analytics." IEEE Transactions on Evolutionary Computation 22, no. 5: 792-804.
The application of classical control technique for optimising teaching and learning process is introduced and validated using a case study in a typical higher education environment. The building blocks of the experiment consist of: (1) a real-time online surveying tool, representing the system input; (2) the classroom as the plant; (3) the methods and the dynamics of teaching–learning as the process; (4) the technique for measuring the levels of satisfaction towards meeting the learning and teaching objectives of the particular class session representing the transfer function; and (5) the results from the transfer function determining the levels of satisfaction, represent the output. A feedback loop highlighting the sources of dissatisfaction provides the lecturer and the students the necessary information for adaptive measures to achieve the desired levels of teaching and learning objectives. To set out the appropriate parameters for determining the quality of teaching–learning in the class room, quality function deployment, principles are deployed. This approach allowed us to systematically breakdown the key attributes of the teaching and learning process. We conclude that by deploying the appropriate data acquisition mechanisms at appropriate intervals, the teaching and knowledge delivery process can be adopted to achieve the desired learning objectives. Even though student performance and attrition rates are outside the scope of this study, a separate future study the results could be linked to student performance and attrition rates.
A Mousavi; C Mares; Tj Stonham. Continuous feedback loop for adaptive teaching and learning process using student surveys. International Journal of Mechanical Engineering Education 2015, 43, 247 -264.
AMA StyleA Mousavi, C Mares, Tj Stonham. Continuous feedback loop for adaptive teaching and learning process using student surveys. International Journal of Mechanical Engineering Education. 2015; 43 (4):247-264.
Chicago/Turabian StyleA Mousavi; C Mares; Tj Stonham. 2015. "Continuous feedback loop for adaptive teaching and learning process using student surveys." International Journal of Mechanical Engineering Education 43, no. 4: 247-264.
The purpose of this research is twofold: first, to undertake a thorough appraisal of existing Input Variable Selection (IVS) methods within the context of time-critical and computation resource-limited dimensionality reduction problems; second, to demonstrate improvements to, and the application of, a recently proposed time-critical sensitivity analysis method called EventTracker to an environment science industrial use-case, i.e., sub-surface drilling. Producing time-critical accurate knowledge about the state of a system (effect) under computational and data acquisition (cause) constraints is a major challenge, especially if the knowledge required is critical to the system operation where the safety of operators or integrity of costly equipment is at stake. Understanding and interpreting, a chain of interrelated events, predicted or unpredicted, that may or may not result in a specific state of the system, is the core challenge of this research. The main objective is then to identify which set of input data signals has a significant impact on the set of system state information (i.e. output). Through a cause-effect analysis technique, the proposed technique supports the filtering of unsolicited data that can otherwise clog up the communication and computational capabilities of a standard supervisory control and data acquisition system. The paper analyzes the performance of input variable selection techniques from a series of perspectives. It then expands the categorization and assessment of sensitivity analysis methods in a structured framework that takes into account the relationship between inputs and outputs, the nature of their time series, and the computational effort required. The outcome of this analysis is that established methods have a limited suitability for use by time-critical variable selection applications. By way of a geological drilling monitoring scenario, the suitability of the proposed EventTracker Sensitivity Analysis method for use in high volume and time critical input variable selection problems is demonstrated.
S. Tavakoli; Alireza Mousavi; Stefan Poslad. Input variable selection in time-critical knowledge integration applications: A review, analysis, and recommendation paper. Advanced Engineering Informatics 2013, 27, 519 -536.
AMA StyleS. Tavakoli, Alireza Mousavi, Stefan Poslad. Input variable selection in time-critical knowledge integration applications: A review, analysis, and recommendation paper. Advanced Engineering Informatics. 2013; 27 (4):519-536.
Chicago/Turabian StyleS. Tavakoli; Alireza Mousavi; Stefan Poslad. 2013. "Input variable selection in time-critical knowledge integration applications: A review, analysis, and recommendation paper." Advanced Engineering Informatics 27, no. 4: 519-536.
In this paper, the fabrication of thin film transistor based on randomized network of single walled carbon nano tubes (SWCNTs-TFT) is presented. The randomized network is obtained by deposition of dispersed SWCNTs on the substrate with a novel technique combining vacuum filtration and silanization of substrate. This approach, which is compatible with all kind of substrates, allows a fabrication process at room temperature that is capable to overcome the high temperature procedure for CNTs deposition. The drain and source electrodes of the TFT are based on an interdigitated electrode (IDE) with 8 μm channel length and 3mm channel width. The obtained device shows output performance with an apparent mobility of 40.7 cm 2 /Vs, current density 0.05 μA/μm and ION /IOFF ratio 2×103. A comparison of the model describing the SWCNTs-TFT with that of (metal-oxide-semiconductor) MOS-like device confirms a p-type behavior. The proposed approach can be easily transformed to large areas leading to a suitable use in low cost industrial application.
Alireza Mousavi; Patrizia Lamberti; Vincenzo Tucci; Veit Wagner. Feasible industrial fabrication of thin film transistor based on randomized network of single walled carbon nanotubes. ASMC 2013 SEMI Advanced Semiconductor Manufacturing Conference 2013, 18 -23.
AMA StyleAlireza Mousavi, Patrizia Lamberti, Vincenzo Tucci, Veit Wagner. Feasible industrial fabrication of thin film transistor based on randomized network of single walled carbon nanotubes. ASMC 2013 SEMI Advanced Semiconductor Manufacturing Conference. 2013; ():18-23.
Chicago/Turabian StyleAlireza Mousavi; Patrizia Lamberti; Vincenzo Tucci; Veit Wagner. 2013. "Feasible industrial fabrication of thin film transistor based on randomized network of single walled carbon nanotubes." ASMC 2013 SEMI Advanced Semiconductor Manufacturing Conference , no. : 18-23.
The aim of this paper is to propose an analytical model for predicting the collective capability of a group of individuals that are assigned to a job. This collective applied capability in future can be used as a predictor of performance or success of teams that have been given an assignment. In this context capability is defined as the application of a set of inherent and acquired resources and the level of their utilization to complete a job. These resources are classified into three categories Enablers, Preferences and past Attainments. The collective capability is therefore inferred from the interrelationship between the members with respect to their Diversity, Homophily, and their past Experiences/Attainments working in teams. By reviewing the relevant literature a basic definition for capability is provided. Also to introduce a method for measuring collective capability, the existing literature on analysis of social networks and methods of interpreting and modeling the dynamics of human networks are briefly discussed. Such a modeling tool enables managers and decision makers to measure and compare different group formations with respect to their capability and use this capability index as a predictor of future performance. Companies and project managers will be able to implement special team building policies and strategies to maximize their capabilities to ensure better outcomes.
Ehsan Hosseini; Alireza Mousavi; Elham Hosseini. On the capability of human networks. 2013 IEEE International Systems Conference (SysCon) 2013, 200 -203.
AMA StyleEhsan Hosseini, Alireza Mousavi, Elham Hosseini. On the capability of human networks. 2013 IEEE International Systems Conference (SysCon). 2013; ():200-203.
Chicago/Turabian StyleEhsan Hosseini; Alireza Mousavi; Elham Hosseini. 2013. "On the capability of human networks." 2013 IEEE International Systems Conference (SysCon) , no. : 200-203.
Ontology alignment facilitates exchange of knowledge among heterogeneous data sources. Many approaches to ontology alignment use multiple similarity measures to map entities between ontologies. However, it remains a key challenge in dealing with uncertain entities for which the employed ontology alignment measures produce conflicting results on similarity of the mapped entities. This paper presents OARS, a rough-set based approach to ontology alignment which achieves a high degree of accuracy in situations where uncertainty arises because of the conflicting results generated by different similarity measures. OARS employs a combinational approach and considers both lexical and structural similarity measures. OARS is extensively evaluated with the benchmark ontologies of the ontology alignment evaluation initiative (OAEI) 2010, and performs best in the aspect of recall in comparison with a number of alignment systems while generating a comparable performance in precision.
Sadaqat Jan; Maozhen Li; Hamed Al-Raweshidy; Alireza Mousavi; Man Qi. Dealing With Uncertain Entities in Ontology Alignment Using Rough Sets. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 2012, 42, 1600 -1612.
AMA StyleSadaqat Jan, Maozhen Li, Hamed Al-Raweshidy, Alireza Mousavi, Man Qi. Dealing With Uncertain Entities in Ontology Alignment Using Rough Sets. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews). 2012; 42 (6):1600-1612.
Chicago/Turabian StyleSadaqat Jan; Maozhen Li; Hamed Al-Raweshidy; Alireza Mousavi; Man Qi. 2012. "Dealing With Uncertain Entities in Ontology Alignment Using Rough Sets." IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 42, no. 6: 1600-1612.
A Hybrid Mutation Particle Swarm Optimisation (HMPSO) technique for improved estimation of Available Transfer Capability (ATC) as a decision criterion is proposed in this paper. First, this is achieved by comparing a typical application of the Particle Swarm Optimisation (PSO) technique with conventional Genetic Algorithm (GA) methods. Next, a multi-objective optimisation problem concerning optimal installation and capacity allocation of Flexible AC Transmission Systems (FACTSs) devices is presented and demonstrated. Modern heuristic techniques such as PSO have been demonstrated to be suitable approaches in solving non-linear power system problems. The outcome of this research further demonstrates that with better utilisation of FACTS devices, it is possible to improve transmission capabilities. The motivation of this research is a direct consequence of the deregulation of electricity industries and power markets worldwide. The current deregulated environment provides transmission systems operators (TSOs) with more options when procuring transmission services. The effectiveness of the proposed algorithm is demonstrated across a range of case studies, and the results are validated through analyses conducted on IEEE 30-bus and 57-bus test systems.
H. Farahmand; M. Rashidinejad; A. Mousavi; A.A. Gharaveisi; M.R. Irving; Gareth Taylor. Hybrid Mutation Particle Swarm Optimisation method for Available Transfer Capability enhancement. International Journal of Electrical Power & Energy Systems 2012, 42, 240 -249.
AMA StyleH. Farahmand, M. Rashidinejad, A. Mousavi, A.A. Gharaveisi, M.R. Irving, Gareth Taylor. Hybrid Mutation Particle Swarm Optimisation method for Available Transfer Capability enhancement. International Journal of Electrical Power & Energy Systems. 2012; 42 (1):240-249.
Chicago/Turabian StyleH. Farahmand; M. Rashidinejad; A. Mousavi; A.A. Gharaveisi; M.R. Irving; Gareth Taylor. 2012. "Hybrid Mutation Particle Swarm Optimisation method for Available Transfer Capability enhancement." International Journal of Electrical Power & Energy Systems 42, no. 1: 240-249.
This paper reports on a critical review of the Importance-Performance Analysis (IPA) method using three different measures: customer self-stated importance, regression analysis and regression analysis with dummy variables. The study confirms that product attribute importance is an antecedent of attribute performance. The importance of product attributes from the customers point of view may change with the fluctuation in product performance level. The results of this research help in measuring the impact of service attribute performance on customer satisfaction, which in turn can help companies to identify the product attributes that have higher returns for the business. [Received 9 October 2009; Revised 4 April 2010; Accepted 4 April 2010]
Vahid Pezeshki; Alireza Mousavi. Measuring the importance of product attributes and its implication in resource allocation. International Journal of Manufacturing Research 2012, 7, 86 .
AMA StyleVahid Pezeshki, Alireza Mousavi. Measuring the importance of product attributes and its implication in resource allocation. International Journal of Manufacturing Research. 2012; 7 (1):86.
Chicago/Turabian StyleVahid Pezeshki; Alireza Mousavi. 2012. "Measuring the importance of product attributes and its implication in resource allocation." International Journal of Manufacturing Research 7, no. 1: 86.
This paper introduces a new technology platform that improves the efficiency and effectiveness of simulation modelling projects. A recently developed platform that integrates data acquisition management platform (primary models) and post simulation performance analysis models (synthesis) is described. The use of real-time discrete event simulation modelers as a vehicle is proposed. In recent years we have suggested a number of solutions to integrate shopfloor data with higher level information systems. All these solutions lacked two key capabilities. Firstly, the solutions were not capable of interacting with data acquisition systems without expert interference in determining the quality and quantity of input signals. Therefore, connecting input signals to key performance indicators (i.e. simulation parameters) was extremely challenging and error prone. Secondly, from health workers? and plant managers? perspective, simulation results (e.g. resource utilization, waiting times, work-in-process, etc.) did not correspond to industry performance metrics. SIMMON is proposed here to address these two problems.
Alireza Mousavi; Alexander Komashie; Siamak Tavakoli. Simulation-based real-time performance monitoring (simmon): A platform for manufacturing and healthcare systems. Proceedings of the 2011 Winter Simulation Conference (WSC) 2011, 600 -611.
AMA StyleAlireza Mousavi, Alexander Komashie, Siamak Tavakoli. Simulation-based real-time performance monitoring (simmon): A platform for manufacturing and healthcare systems. Proceedings of the 2011 Winter Simulation Conference (WSC). 2011; ():600-611.
Chicago/Turabian StyleAlireza Mousavi; Alexander Komashie; Siamak Tavakoli. 2011. "Simulation-based real-time performance monitoring (simmon): A platform for manufacturing and healthcare systems." Proceedings of the 2011 Winter Simulation Conference (WSC) , no. : 600-611.
This paper introduces a platform for online Sensitivity Analysis (SA) that is applicable in large scale real-time data acquisition (DAQ) systems. Here, we use the term real-time in the context of a system that has to respond to externally generated input stimuli within a finite and specified period. Complex industrial systems such as manufacturing, healthcare, transport, and finance require high-quality information on which to base timely responses to events occurring in their volatile environments. The motivation for the proposed EventTracker platform is the assumption that modern industrial systems are able to capture data in real-time and have the necessary technological flexibility to adjust to changing system requirements. The flexibility to adapt can only be assured if data is succinctly interpreted and translated into corrective actions in a timely manner. An important factor that facilitates data interpretation and information modeling is an appreciation of the affect system inputs have on each output at the time of occurrence. Many existing sensitivity analysis methods appear to hamper efficient and timely analysis due to a reliance on historical data, or sluggishness in providing a timely solution that would be of use in real-time applications. This inefficiency is further compounded by computational limitations and the complexity of some existing models. In dealing with real-time event driven systems, the underpinning logic of the proposed method is based on the assumption that in the vast majority of cases changes in input variables will trigger events. Every single or combination of events could subsequently result in a change to the system state. The proposed event tracking sensitivity analysis method describes variables and the system state as a collection of events. The higher the numeric occurrence of an input variable at the trigger level during an event monitoring interval, the greater is its impact on the final analysis of the system state. Experiments were designed to compare the proposed event tracking sensitivity analysis method with a comparable method (that of Entropy). An improvement of 10 percent in computational efficiency without loss in accuracy was observed. The comparison also showed that the time taken to perform the sensitivity analysis was 0.5 percent of that required when using the comparable Entropy-based method.
S. Tavakoli; Alireza Mousavi; P. Broomhead. Event Tracking for Real-Time Unaware Sensitivity Analysis (EventTracker). IEEE Transactions on Knowledge and Data Engineering 2011, 25, 348 -359.
AMA StyleS. Tavakoli, Alireza Mousavi, P. Broomhead. Event Tracking for Real-Time Unaware Sensitivity Analysis (EventTracker). IEEE Transactions on Knowledge and Data Engineering. 2011; 25 (2):348-359.
Chicago/Turabian StyleS. Tavakoli; Alireza Mousavi; P. Broomhead. 2011. "Event Tracking for Real-Time Unaware Sensitivity Analysis (EventTracker)." IEEE Transactions on Knowledge and Data Engineering 25, no. 2: 348-359.
The aim of this paper is to propose a novel computer-based product classification, component detection and tracking for demanufacturing and disassembly process. This is achieved by introducing a series of automated and sequential product scanning, component identification, image analysis and sorting – leading to the development of a bill of material (BOM). The produced BOM can then be associated with the relevant disassembly/demanufacture proviso. The proposed integrated image sorting and product classification (ISPC) approach can be considered as a step forward in automation of demanufacturing activities. The ISPC model proposed in this paper utilises and builds on the state-of-the-art technology and current body of research in computer-integrated demanufacturing and remanufacturing (CIDR). An appraisal of the latest research material and the factors that inhibit CIDR methods inpractice are presented. A novel solution for the integration of imaging and material identification techniques toovercome some of the existing shortcomings of automated recycling processes is proposed in this paper. The proposed product scanning and component detection ISPC software consists of four distinct models: the repertory database, the search engine, the product-attributes updater and the image sorting and classification algorithm. The software framework that integrates the four components is presented in this paper. Finally, an overall assessment of applying ISPC at various stages of CIDR processes concludes the article.
Funbi Emmanuel Simolowo; Ali Mousavi; P. O. Adjapong; Alireza Mousavi. A computer-based product classification and component detection for demanufacturing processes. International Journal of Computer Integrated Manufacturing 2011, 24, 900 -914.
AMA StyleFunbi Emmanuel Simolowo, Ali Mousavi, P. O. Adjapong, Alireza Mousavi. A computer-based product classification and component detection for demanufacturing processes. International Journal of Computer Integrated Manufacturing. 2011; 24 (10):900-914.
Chicago/Turabian StyleFunbi Emmanuel Simolowo; Ali Mousavi; P. O. Adjapong; Alireza Mousavi. 2011. "A computer-based product classification and component detection for demanufacturing processes." International Journal of Computer Integrated Manufacturing 24, no. 10: 900-914.
In this paper, we propose a scheme for Edge Detection in Digital Image based on the Residue Numbers System which leads to performance of the integration circuits with high speeds and high security and low power for Digital Image Processing. In this method, Edge Detection in Digital Images performs based on convolution operation between pixel values and applicable mask and also saving of obtained values of edges in the LUT memories and threshold operations by using values in Look-Up table. All operations in this method perform in Residue Numbers System. Then we design circuits for edge detection and of decimal to residue conversion and vice versa which in is used Matlab software and also VLSl tools for simulation. Preliminary simulation results show that the proposed scheme has the ability of edge detection of digital images.
Davar Kheirandish Taleshmekaeil; Hekmat Mohamamdzadeh; Alireza Mousavi. Using Residue Number System for Edge Detection in Digital Images processing. 2011 IEEE 3rd International Conference on Communication Software and Networks 2011, 249 -253.
AMA StyleDavar Kheirandish Taleshmekaeil, Hekmat Mohamamdzadeh, Alireza Mousavi. Using Residue Number System for Edge Detection in Digital Images processing. 2011 IEEE 3rd International Conference on Communication Software and Networks. 2011; ():249-253.
Chicago/Turabian StyleDavar Kheirandish Taleshmekaeil; Hekmat Mohamamdzadeh; Alireza Mousavi. 2011. "Using Residue Number System for Edge Detection in Digital Images processing." 2011 IEEE 3rd International Conference on Communication Software and Networks , no. : 249-253.