Md Meftahul Ferdaus | Nanyang Technological University (original) (raw)

Papers by Md Meftahul Ferdaus

Proceedings of the 31st ACM International Conference on Information & Knowledge Management

Traditional oversampling methods are well explored for binary and multi-class imbalanced datasets... more Traditional oversampling methods are well explored for binary and multi-class imbalanced datasets. In most cases, the data space is adapted for oversampling the imbalanced classes. It leads to various issues like poor modelling of the structure of the data, resulting in data overlapping between minority and majority classes that lead to poor classification performance of minority class(es). To overcome these limitations, we propose a novel data oversampling architecture called Structure Preserving Variational Learning (SPVL). This technique captures an uncorrelated distribution among classes in the latent space using an encoder-decoder framework. Hence, minority samples are generated in the latent space, preserving the structure of the data distribution. The improved latent space distribution (oversampled training data) is evaluated by training an MLP classifier and testing with unseen test dataset. The proposed SPVL method is applied to various benchmark datasets with i) binary and multi-class imbalance data, ii) high-dimensional data and, iii) large or small-scale data. Extensive experimental results demonstrated that the proposed SPVL technique outperforms the state-of-the-art counterparts. CCS CONCEPTS • Computing methodologies → Neural networks.

Proceedings of the 30th ACM International Conference on Information & Knowledge Management, 2021

Traditional oversampling methods are generally employed to handle class imbalance in datasets. Th... more Traditional oversampling methods are generally employed to handle class imbalance in datasets. This oversampling approach is independent of the classifier; thus, it does not offer an end-to-end solution. To overcome this, we propose a three-player adversarial game-based end-to-end method, where a domain-constraints mixture of generators, a discriminator, and a multi-class classifier are used. Rather than adversarial minority oversampling, we propose an adversarial oversampling (AO) and a data-space oversampling (DO) approach. In AO, the generator updates by fooling both the classifier and discriminator, however, in DO, it updates by favoring the classifier and fooling the discriminator. While updating the classifier, it considers both the real and synthetically generated samples in AO. But, in DO, it favors the real samples and fools the subset class-specific generated samples. To mitigate the biases of a classifier towards the majority class, minority samples are over-sampled at a fractional rate. Such implementation is shown to provide more robust classification boundaries. The effectiveness of our proposed method has been validated with high-dimensional, highly imbalanced and large-scale multi-class tabular datasets. The results as measured by average class specific accuracy (ACSA) clearly indicate that the proposed method provides better classification accuracy (improvement in the range of 0.7% to 49.27%) as compared to the baseline classifier. CCS CONCEPTS • Computing methodologies → Learning paradigms; Supervised learning by classification.

2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2018

Nowadays, the application of fully autonomous system like rotary wing unmanned air vehicles (UAVs... more Nowadays, the application of fully autonomous system like rotary wing unmanned air vehicles (UAVs) is increasing sharply. Due to the complex nonlinear dynamics a huge research interest is witnessed in developing learning machine based intelligent, self-organizing evolving controller for these vehicles notably to address the system's dynamic characteristics. In this work, such an evolving controller namely Generic-controller (Gcontroller) is proposed to control the altitude of a rotary wing UAV namely hexacopter. This controller can work with very minor expert domain knowledge. The evolving architecture of this controller is based on an advanced incremental learning algorithm namely Generic Evolving Neuro-Fuzzy Inference System (GENEFIS). The controller does not require any offline training, since it starts operating from scratch with an empty set of fuzzy rules, and then add or delete rules on demand. The adaptation laws for the consequent parameters are derived from the sliding mode control (SMC) theory. The Lyapunov theory is used to guarantee the stability of the proposed controller. In addition, an auxiliary robustifying control term is implemented to obtain a uniform asymptotic convergence of tracking error to zero. Finally, the G-controller's performance evaluation is observed through the altitude tracking of a UAV namely hexacopter for various trajectories.

Procedia Computer Science, 2018

In this paper, a big data analytic framework is introduced for processing high-frequency data str... more In this paper, a big data analytic framework is introduced for processing high-frequency data stream. This framework architecture is developed by combining an advanced evolving learning algorithm namely Parsimonious Network Fuzzy Inference System (PANFIS) with MapReduce parallel computation, where PANFIS has the capability of processing data stream in large volume. Big datasets are learnt chunk by chunk by processors in MapReduce environment and the results are fused by rule merging method, that reduces the complexity of the rules. The performance measurement has been conducted, and the results are showing that the MapReduce framework along with PANFIS evolving system helps to reduce the processing time around 22 percent in average in comparison with the PANFIS algorithm without reducing performance in accuracy.

Information Sciences, 2019

There exists an increasing demand for a flexible and computationally efficient controller for mic... more There exists an increasing demand for a flexible and computationally efficient controller for micro aerial vehicles (MAVs) due to a high degree of environmental perturbations. In this work, an evolving neuro-fuzzy controller, namely Parsimonious Controller (PAC) is proposed. It features fewer network parameters than conventional approaches due to the absence of rule premise parameters. PAC is built upon a recently developed evolving neuro-fuzzy system known as parsimonious learning machine (PALM) and adopts new rule growing and pruning modules derived from the approximation of bias and variance. These rule adaptation methods have no reliance on user-defined thresholds, thereby increasing the PAC's autonomy for real-time deployment. PAC adapts the consequent parameters with the sliding mode control (SMC) theory in the single-pass fashion. The boundedness and convergence of the closed-loop control system's tracking error and the controller's consequent parameters are confirmed by utilizing the LaSalle-Yoshizawa theorem. Lastly, the controller's efficacy is evaluated by observing various trajectory tracking performance from a bio-inspired flapping wing micro aerial vehicle (BI-FWMAV) and a rotary wing micro aerial vehicle called hexacopter. Furthermore, it is compared to three distinctive controllers. Our PAC outperforms the linear PID controller and feedforward neural network (FFNN) based nonlinear adaptive controller. Compared to its predecessor, G-controller, the tracking accuracy is comparable, but the PAC incurs significantly fewer parameters to attain similar or better performance than the G-controller.

Artificial Intelligence Review, 2018

In recent times, technological advancement boosts the desire of utilizing the autonomous Unmanned... more In recent times, technological advancement boosts the desire of utilizing the autonomous Unmanned Aerial Vehicle (UAV) in both civil and military sectors. Among various UAVs, the ability of rotary wing UAVs (RUAVs) in vertical takeoff and landing, to hover and perform quick maneuvering attract researchers to develop models fully autonomous control framework. The majority of first principle techniques in modeling and controlling RUAV face challenges in incorporating and handling various uncertainties. Recently various fuzzy and neuro-fuzzy based intelligent systems are utilized to enhance the RUAV's modeling and control performance. However, the majority of these fuzzy systems are based on batch learning methods, have static structure, and cannot adapt to rapidly changing environments. The implication of Evolving Intelligent System based model-free data-driven techniques can be a smart option since they can adapt their structure and parameters to cope with sudden changes in the behavior of RUAVs real-time flight. They work in a single pass learning fashion which is suitable for online real-time deployment. In this paper, state of the art of various fuzzy systems from the basic fuzzy system to evolving fuzzy system, their application in a RUAV namely quadcopter with existing limitations, and possible opportunities are analyzed. Besides, a variety of first principle techniques to control the quadcopter, their impediments, and conceivable solution with recently employed evolving fuzzy controllers are reviewed.

Journal of Artificial Intelligence and Soft Computing Research, 2019

Advanced and accurate modelling of a Flapping Wing Micro Air Vehicle (FW MAV) and its control is ... more Advanced and accurate modelling of a Flapping Wing Micro Air Vehicle (FW MAV) and its control is one of the recent research topics related to the field of autonomous MAVs. Some desiring features of the FW MAV are quick flight, vertical take-off and landing, hovering, and fast turn, and enhanced manoeuvrability contrasted with similar-sized fixed and rotary wing MAVs. Inspired by the FW MAV’s advanced features, a four-wing Nature-inspired (NI) FW MAV is modelled and controlled in this work. The Fuzzy C-Means (FCM) clustering algorithm is utilized to construct the data-driven NIFW MAV model. Being model free, it does not depend on the system dynamics and can incorporate various uncertainties like sensor error, wind gust etc. Furthermore, a Takagi-Sugeno (T-S) fuzzy structure based adaptive fuzzy controller is proposed. The proposed adaptive controller can tune its antecedent and consequent parameters using FCM clustering technique. This controller is employed to control the altitude o...

IEEE Transactions on Fuzzy Systems, 2019

Journal of Zhejiang University-SCIENCE A, 2017

In recent years, magnetorheological (MR) fluid technology has received much attention and consequ... more In recent years, magnetorheological (MR) fluid technology has received much attention and consequently has shown much improvement. Its adaptable nature has led to rapid growth in such varied engineering applications as the base isolation of civil structures, vehicle suspensions, and several bio-engineering mechanisms through its implementation in different MR fluid base devices, particularly in MR dampers. The MR damper is an advanced application of a semi-active device which performs effectively in vibration reduction due to its control ability in both on and off states. The MR damper has the capacity to generate a large damping force, with comparatively low power consumption, fast and flexible response, and simplicity of design. With reference to the huge demand for MR dampers, this paper reviews the advantages of these semi-active systems over passive and active systems, the versatile application of MR dampers, and the fabrication of the configurations of various MR dampers, and provides an overview of various MR damper models. To address the increasing adaptability of the MR dampers, their latest design optimization and advances are also presented. Because of the tremendous interest in self-powered and energy-saving technologies, a broad overview of the design of MR dampers for energy harvesting and their modeling is also incorporated in this paper.

IEEE Transactions on Fuzzy Systems, 2019

Data stream has been the underlying challenge in the age of big data because it calls for real-ti... more Data stream has been the underlying challenge in the age of big data because it calls for real-time data processing with the absence of a retraining process and/or an iterative learning approach. In realm of fuzzy system community, data stream is handled by algorithmic development of self-adaptive neurofuzzy systems (SANFS) characterized by the single-pass learning mode and the open structure property which enables effective handling of fast and rapidly changing natures of data streams. The underlying bottleneck of SANFSs lies in its design principle which involves a high number of free parameters (rule premise and rule consequent) to be adapted in the training process. This figure can even double in the case of type-2 fuzzy system. In this work, a novel SANFS, namely parsimonious learning machine (PALM), is proposed. PALM features utilization of a new type of fuzzy rule based on the concept of hyperplane clustering which significantly reduces the number of network parameters because it has no rule premise parameters. PALM is proposed in both type-1 and type-2 fuzzy systems where all of which characterize a fully dynamic rule-based system. That is, it is capable of automatically generating, merging and tuning the hyperplane-based fuzzy rule in the single pass manner. Moreover, an extension of PALM, namely recurrent PALM (rPALM), is proposed and adopts the concept of teacher-forcing mechanism in the deep learning literature. The efficacy of PALM has been evaluated through numerical study with six real-world and synthetic data streams from public database and our own real-world project of autonomous vehicles. The proposed model showcases significant improvements in terms of computational complexity and number of required parameters against several renowned SANFSs, while attaining comparable and often better predictive accuracy.

International Journal of Electronics and Electrical Engineering, 2014

Rainfall measurement system is a useful tool in weather measurement system. In tropical country s... more Rainfall measurement system is a useful tool in weather measurement system. In tropical country such as Malaysia, rainfall measurement for agricultural and weather forecasting system is very important. Samplingrelated tipping-bucket (TB) rain gauge measurements have significant errors for short time estimation. In this paper, a stable electronic rain gauge system with comprehensive data manipulation is designed and developed. This project used two major systems (a) tipping bucket with potentiometer and (b) capacitive sensor. Data is recorded and analyzed before the output is displayed. The system measures rainfalls accurately, saves the data reading and shows specific calculated for display.  Index Terms-rain gauge, tipping bucket, electronics rain gauge

2015 10th Asian Control Conference (ASCC), 2015

Polymer is obtained by a process called polymerization which is actually a chemical reaction to c... more Polymer is obtained by a process called polymerization which is actually a chemical reaction to combine monomer molecules together. In this work a suitable model and control system was proposed for obtaining the maximum production yield of industrial grade polymer. Significant process parameters specifically system temperature (0C), reaction pressure (bar), and raw materials ratio (%) were selected as process controlling inputs. Differential pressure (DP) obtained from a series of experiments were fitted into a quadratic polynomial model by applying multiple regression analysis. By analyzing the response of three dimensional surface plot, contour plot and soling the regression model equation, the optimal process conditions are obtained. To optimize the interactions among the quadratic polynomial models Center Composite Design (CCD) technique under RSM was used. The results showed the best conditions or the optimized values for initial temperature, system pressure to initiate reaction and monomer gas ratio. Further the maximum predicted value of DP was also obtained at same conditions. At last the trail experiments are evaluated by using the optimum conditions to achieve the higher DP.

ABSTRACT The Padma Multipurpose Bridge Design Project comprises a new fixed crossing of the Padma... more ABSTRACT The Padma Multipurpose Bridge Design Project comprises a new fixed crossing of the Padma River, of approximately 6.15 km long. In order to complement the construction of the Project, it is mandatory to plan and to develop road, railway, transmission and telecommunication networks to connect with the south western part of the country. The main purpose of this study is to harvest energy and assesses the cumulative environmental impact that has been caused by river training, construction and development works. The impacts are classified in few categories-land acquisition and resettlement, transport and economy. Energy harvesting though solar panel and speed breaker is proposed to install for sake of economic and environmental improvement. Here, the probable environmental impacts, both positive and negative are properly analyzed which represents a clear picture of the environmental impact scenario of any multipurpose bridge construction.

IOP Conference Series: Materials Science and Engineering, 2013

IOP Conference Series: Materials Science and Engineering, 2013

The paper describes a technique to develop a web based financial system, following latest technol... more The paper describes a technique to develop a web based financial system, following latest technology and business needs. In the development of web based application, the user friendliness and technology both are very important. It is used ASP .NET MVC 4 platform and SQL 2008 server for development of web based financial system. It shows the technique for the entry system and report monitoring of the application is user friendly. This paper also highlights the critical situations of development, which will help to develop the quality product.

IOP Conference Series: Materials Science and Engineering, 2013

IOP Conference Series: Materials Science and Engineering, 2013

Springer, 2019

In recent times, technological advancement boosts the desire of utilizing the autonomous Unmanned... more In recent times, technological advancement boosts the desire of utilizing the autonomous Unmanned Aerial Vehicle (UAV) in both civil and military sectors. Among various UAVs, the ability of rotary wing UAVs (RUAVs) in vertical takeoff and landing, to hover and perform quick maneuvering attract researchers to develop models fully autonomous control framework. The majority of first principle techniques in modeling and controlling RUAV face challenges in incorporating and handling various uncertainties. Recently various fuzzy and neuro-fuzzy based intelligent systems are utilized to enhance the RUAV's modeling and control performance. However, the majority of these fuzzy systems are based on batch learning methods, have static structure, and cannot adapt to rapidly changing environments. The implication of Evolving Intelligent System based model-free data-driven techniques can be a smart option since they can adapt their structure and parameters to cope with sudden changes in the behavior of RUAVs real-time flight. They work in a single pass learning fashion which is suitable for online real-time deployment. In this paper, state of the art of various fuzzy systems from the basic fuzzy system to evolving fuzzy system, their application in a RUAV namely quadcopter with existing limitations, and possible opportunities are analyzed. Besides, a variety of first principle techniques to control the quadcopter, their impediments, and conceivable solution with recently employed evolving fuzzy controllers are reviewed.

Elsevier, 2019

Until now the majority of the neuro and fuzzy modeling and control approaches for rotary wing Unm... more Until now the majority of the neuro and fuzzy modeling and control approaches for rotary wing Unmanned Aerial Vehicles (UAVs), such as the quadrotor, have been based on batch learning techniques, therefore static in structure, and cannot adapt to rapidly changing environments. Implication of Evolving Intelligent System (EIS) based model-free data-driven techniques in fuzzy system are good alternatives, since they are able to evolve both their structure and parameters to cope with sudden changes in behavior, and performs perfectly in a single pass learning mode which is suitable for online real-time deployment. The Metacognitive Scaffolding Learning Machine (McSLM) is seen as a generalized version of EIS since the metacognitive concept enables the what-to-learn, how-to-learn, and when-to-learn scheme, and the scaffolding theory realizes a plug-and-play property which strengthens the online working principle of EISs. This paper proposes a novel online identification scheme, applied to a quadrotor using real-time experimental flight data streams based on McSLM, namely Metacognitive Scaffolding Interval Type 2 Recurrent Fuzzy Neural Network (McSIT2RFNN). Our proposed approach demonstrated significant improvements in both accuracy and complexity against some renowned existing variants of the McSLMs and EISs.

Recently, the quadcopter configuration is becoming prevalent for both civilian and military appli... more Recently, the quadcopter configuration is becoming prevalent for both civilian and military applications, and research is continuing to improve its controllability. However, most the control methodologies depend on accurate system models which are derived from six degrees of freedom nonlinear system dynamics and they generally do not consider realistic outdoor perturbations and uncertainties. As a solution, model-free data-driven system identification and controlling of quadcopter have been refined in this paper. This nonlinear multiple input-multiple output system identification is proliferated using a fuzzy clustering model. The accuracy of the identification is seen to very high. The results of a PID controller used in conjunction with the identified fuzzy model is also presented. The PID controller with the fuzzy identified model is seen to precisely follow the commanded signals.

Proceedings of the 31st ACM International Conference on Information & Knowledge Management

Traditional oversampling methods are well explored for binary and multi-class imbalanced datasets... more Traditional oversampling methods are well explored for binary and multi-class imbalanced datasets. In most cases, the data space is adapted for oversampling the imbalanced classes. It leads to various issues like poor modelling of the structure of the data, resulting in data overlapping between minority and majority classes that lead to poor classification performance of minority class(es). To overcome these limitations, we propose a novel data oversampling architecture called Structure Preserving Variational Learning (SPVL). This technique captures an uncorrelated distribution among classes in the latent space using an encoder-decoder framework. Hence, minority samples are generated in the latent space, preserving the structure of the data distribution. The improved latent space distribution (oversampled training data) is evaluated by training an MLP classifier and testing with unseen test dataset. The proposed SPVL method is applied to various benchmark datasets with i) binary and multi-class imbalance data, ii) high-dimensional data and, iii) large or small-scale data. Extensive experimental results demonstrated that the proposed SPVL technique outperforms the state-of-the-art counterparts. CCS CONCEPTS • Computing methodologies → Neural networks.

Proceedings of the 30th ACM International Conference on Information & Knowledge Management, 2021

Traditional oversampling methods are generally employed to handle class imbalance in datasets. Th... more Traditional oversampling methods are generally employed to handle class imbalance in datasets. This oversampling approach is independent of the classifier; thus, it does not offer an end-to-end solution. To overcome this, we propose a three-player adversarial game-based end-to-end method, where a domain-constraints mixture of generators, a discriminator, and a multi-class classifier are used. Rather than adversarial minority oversampling, we propose an adversarial oversampling (AO) and a data-space oversampling (DO) approach. In AO, the generator updates by fooling both the classifier and discriminator, however, in DO, it updates by favoring the classifier and fooling the discriminator. While updating the classifier, it considers both the real and synthetically generated samples in AO. But, in DO, it favors the real samples and fools the subset class-specific generated samples. To mitigate the biases of a classifier towards the majority class, minority samples are over-sampled at a fractional rate. Such implementation is shown to provide more robust classification boundaries. The effectiveness of our proposed method has been validated with high-dimensional, highly imbalanced and large-scale multi-class tabular datasets. The results as measured by average class specific accuracy (ACSA) clearly indicate that the proposed method provides better classification accuracy (improvement in the range of 0.7% to 49.27%) as compared to the baseline classifier. CCS CONCEPTS • Computing methodologies → Learning paradigms; Supervised learning by classification.

2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2018

Nowadays, the application of fully autonomous system like rotary wing unmanned air vehicles (UAVs... more Nowadays, the application of fully autonomous system like rotary wing unmanned air vehicles (UAVs) is increasing sharply. Due to the complex nonlinear dynamics a huge research interest is witnessed in developing learning machine based intelligent, self-organizing evolving controller for these vehicles notably to address the system's dynamic characteristics. In this work, such an evolving controller namely Generic-controller (Gcontroller) is proposed to control the altitude of a rotary wing UAV namely hexacopter. This controller can work with very minor expert domain knowledge. The evolving architecture of this controller is based on an advanced incremental learning algorithm namely Generic Evolving Neuro-Fuzzy Inference System (GENEFIS). The controller does not require any offline training, since it starts operating from scratch with an empty set of fuzzy rules, and then add or delete rules on demand. The adaptation laws for the consequent parameters are derived from the sliding mode control (SMC) theory. The Lyapunov theory is used to guarantee the stability of the proposed controller. In addition, an auxiliary robustifying control term is implemented to obtain a uniform asymptotic convergence of tracking error to zero. Finally, the G-controller's performance evaluation is observed through the altitude tracking of a UAV namely hexacopter for various trajectories.

Procedia Computer Science, 2018

In this paper, a big data analytic framework is introduced for processing high-frequency data str... more In this paper, a big data analytic framework is introduced for processing high-frequency data stream. This framework architecture is developed by combining an advanced evolving learning algorithm namely Parsimonious Network Fuzzy Inference System (PANFIS) with MapReduce parallel computation, where PANFIS has the capability of processing data stream in large volume. Big datasets are learnt chunk by chunk by processors in MapReduce environment and the results are fused by rule merging method, that reduces the complexity of the rules. The performance measurement has been conducted, and the results are showing that the MapReduce framework along with PANFIS evolving system helps to reduce the processing time around 22 percent in average in comparison with the PANFIS algorithm without reducing performance in accuracy.

Information Sciences, 2019

There exists an increasing demand for a flexible and computationally efficient controller for mic... more There exists an increasing demand for a flexible and computationally efficient controller for micro aerial vehicles (MAVs) due to a high degree of environmental perturbations. In this work, an evolving neuro-fuzzy controller, namely Parsimonious Controller (PAC) is proposed. It features fewer network parameters than conventional approaches due to the absence of rule premise parameters. PAC is built upon a recently developed evolving neuro-fuzzy system known as parsimonious learning machine (PALM) and adopts new rule growing and pruning modules derived from the approximation of bias and variance. These rule adaptation methods have no reliance on user-defined thresholds, thereby increasing the PAC's autonomy for real-time deployment. PAC adapts the consequent parameters with the sliding mode control (SMC) theory in the single-pass fashion. The boundedness and convergence of the closed-loop control system's tracking error and the controller's consequent parameters are confirmed by utilizing the LaSalle-Yoshizawa theorem. Lastly, the controller's efficacy is evaluated by observing various trajectory tracking performance from a bio-inspired flapping wing micro aerial vehicle (BI-FWMAV) and a rotary wing micro aerial vehicle called hexacopter. Furthermore, it is compared to three distinctive controllers. Our PAC outperforms the linear PID controller and feedforward neural network (FFNN) based nonlinear adaptive controller. Compared to its predecessor, G-controller, the tracking accuracy is comparable, but the PAC incurs significantly fewer parameters to attain similar or better performance than the G-controller.

Artificial Intelligence Review, 2018

In recent times, technological advancement boosts the desire of utilizing the autonomous Unmanned... more In recent times, technological advancement boosts the desire of utilizing the autonomous Unmanned Aerial Vehicle (UAV) in both civil and military sectors. Among various UAVs, the ability of rotary wing UAVs (RUAVs) in vertical takeoff and landing, to hover and perform quick maneuvering attract researchers to develop models fully autonomous control framework. The majority of first principle techniques in modeling and controlling RUAV face challenges in incorporating and handling various uncertainties. Recently various fuzzy and neuro-fuzzy based intelligent systems are utilized to enhance the RUAV's modeling and control performance. However, the majority of these fuzzy systems are based on batch learning methods, have static structure, and cannot adapt to rapidly changing environments. The implication of Evolving Intelligent System based model-free data-driven techniques can be a smart option since they can adapt their structure and parameters to cope with sudden changes in the behavior of RUAVs real-time flight. They work in a single pass learning fashion which is suitable for online real-time deployment. In this paper, state of the art of various fuzzy systems from the basic fuzzy system to evolving fuzzy system, their application in a RUAV namely quadcopter with existing limitations, and possible opportunities are analyzed. Besides, a variety of first principle techniques to control the quadcopter, their impediments, and conceivable solution with recently employed evolving fuzzy controllers are reviewed.

Journal of Artificial Intelligence and Soft Computing Research, 2019

Advanced and accurate modelling of a Flapping Wing Micro Air Vehicle (FW MAV) and its control is ... more Advanced and accurate modelling of a Flapping Wing Micro Air Vehicle (FW MAV) and its control is one of the recent research topics related to the field of autonomous MAVs. Some desiring features of the FW MAV are quick flight, vertical take-off and landing, hovering, and fast turn, and enhanced manoeuvrability contrasted with similar-sized fixed and rotary wing MAVs. Inspired by the FW MAV’s advanced features, a four-wing Nature-inspired (NI) FW MAV is modelled and controlled in this work. The Fuzzy C-Means (FCM) clustering algorithm is utilized to construct the data-driven NIFW MAV model. Being model free, it does not depend on the system dynamics and can incorporate various uncertainties like sensor error, wind gust etc. Furthermore, a Takagi-Sugeno (T-S) fuzzy structure based adaptive fuzzy controller is proposed. The proposed adaptive controller can tune its antecedent and consequent parameters using FCM clustering technique. This controller is employed to control the altitude o...

IEEE Transactions on Fuzzy Systems, 2019

Journal of Zhejiang University-SCIENCE A, 2017

In recent years, magnetorheological (MR) fluid technology has received much attention and consequ... more In recent years, magnetorheological (MR) fluid technology has received much attention and consequently has shown much improvement. Its adaptable nature has led to rapid growth in such varied engineering applications as the base isolation of civil structures, vehicle suspensions, and several bio-engineering mechanisms through its implementation in different MR fluid base devices, particularly in MR dampers. The MR damper is an advanced application of a semi-active device which performs effectively in vibration reduction due to its control ability in both on and off states. The MR damper has the capacity to generate a large damping force, with comparatively low power consumption, fast and flexible response, and simplicity of design. With reference to the huge demand for MR dampers, this paper reviews the advantages of these semi-active systems over passive and active systems, the versatile application of MR dampers, and the fabrication of the configurations of various MR dampers, and provides an overview of various MR damper models. To address the increasing adaptability of the MR dampers, their latest design optimization and advances are also presented. Because of the tremendous interest in self-powered and energy-saving technologies, a broad overview of the design of MR dampers for energy harvesting and their modeling is also incorporated in this paper.

IEEE Transactions on Fuzzy Systems, 2019

Data stream has been the underlying challenge in the age of big data because it calls for real-ti... more Data stream has been the underlying challenge in the age of big data because it calls for real-time data processing with the absence of a retraining process and/or an iterative learning approach. In realm of fuzzy system community, data stream is handled by algorithmic development of self-adaptive neurofuzzy systems (SANFS) characterized by the single-pass learning mode and the open structure property which enables effective handling of fast and rapidly changing natures of data streams. The underlying bottleneck of SANFSs lies in its design principle which involves a high number of free parameters (rule premise and rule consequent) to be adapted in the training process. This figure can even double in the case of type-2 fuzzy system. In this work, a novel SANFS, namely parsimonious learning machine (PALM), is proposed. PALM features utilization of a new type of fuzzy rule based on the concept of hyperplane clustering which significantly reduces the number of network parameters because it has no rule premise parameters. PALM is proposed in both type-1 and type-2 fuzzy systems where all of which characterize a fully dynamic rule-based system. That is, it is capable of automatically generating, merging and tuning the hyperplane-based fuzzy rule in the single pass manner. Moreover, an extension of PALM, namely recurrent PALM (rPALM), is proposed and adopts the concept of teacher-forcing mechanism in the deep learning literature. The efficacy of PALM has been evaluated through numerical study with six real-world and synthetic data streams from public database and our own real-world project of autonomous vehicles. The proposed model showcases significant improvements in terms of computational complexity and number of required parameters against several renowned SANFSs, while attaining comparable and often better predictive accuracy.

International Journal of Electronics and Electrical Engineering, 2014

Rainfall measurement system is a useful tool in weather measurement system. In tropical country s... more Rainfall measurement system is a useful tool in weather measurement system. In tropical country such as Malaysia, rainfall measurement for agricultural and weather forecasting system is very important. Samplingrelated tipping-bucket (TB) rain gauge measurements have significant errors for short time estimation. In this paper, a stable electronic rain gauge system with comprehensive data manipulation is designed and developed. This project used two major systems (a) tipping bucket with potentiometer and (b) capacitive sensor. Data is recorded and analyzed before the output is displayed. The system measures rainfalls accurately, saves the data reading and shows specific calculated for display.  Index Terms-rain gauge, tipping bucket, electronics rain gauge

2015 10th Asian Control Conference (ASCC), 2015

Polymer is obtained by a process called polymerization which is actually a chemical reaction to c... more Polymer is obtained by a process called polymerization which is actually a chemical reaction to combine monomer molecules together. In this work a suitable model and control system was proposed for obtaining the maximum production yield of industrial grade polymer. Significant process parameters specifically system temperature (0C), reaction pressure (bar), and raw materials ratio (%) were selected as process controlling inputs. Differential pressure (DP) obtained from a series of experiments were fitted into a quadratic polynomial model by applying multiple regression analysis. By analyzing the response of three dimensional surface plot, contour plot and soling the regression model equation, the optimal process conditions are obtained. To optimize the interactions among the quadratic polynomial models Center Composite Design (CCD) technique under RSM was used. The results showed the best conditions or the optimized values for initial temperature, system pressure to initiate reaction and monomer gas ratio. Further the maximum predicted value of DP was also obtained at same conditions. At last the trail experiments are evaluated by using the optimum conditions to achieve the higher DP.

ABSTRACT The Padma Multipurpose Bridge Design Project comprises a new fixed crossing of the Padma... more ABSTRACT The Padma Multipurpose Bridge Design Project comprises a new fixed crossing of the Padma River, of approximately 6.15 km long. In order to complement the construction of the Project, it is mandatory to plan and to develop road, railway, transmission and telecommunication networks to connect with the south western part of the country. The main purpose of this study is to harvest energy and assesses the cumulative environmental impact that has been caused by river training, construction and development works. The impacts are classified in few categories-land acquisition and resettlement, transport and economy. Energy harvesting though solar panel and speed breaker is proposed to install for sake of economic and environmental improvement. Here, the probable environmental impacts, both positive and negative are properly analyzed which represents a clear picture of the environmental impact scenario of any multipurpose bridge construction.

IOP Conference Series: Materials Science and Engineering, 2013

IOP Conference Series: Materials Science and Engineering, 2013

The paper describes a technique to develop a web based financial system, following latest technol... more The paper describes a technique to develop a web based financial system, following latest technology and business needs. In the development of web based application, the user friendliness and technology both are very important. It is used ASP .NET MVC 4 platform and SQL 2008 server for development of web based financial system. It shows the technique for the entry system and report monitoring of the application is user friendly. This paper also highlights the critical situations of development, which will help to develop the quality product.

IOP Conference Series: Materials Science and Engineering, 2013

IOP Conference Series: Materials Science and Engineering, 2013

Springer, 2019

In recent times, technological advancement boosts the desire of utilizing the autonomous Unmanned... more In recent times, technological advancement boosts the desire of utilizing the autonomous Unmanned Aerial Vehicle (UAV) in both civil and military sectors. Among various UAVs, the ability of rotary wing UAVs (RUAVs) in vertical takeoff and landing, to hover and perform quick maneuvering attract researchers to develop models fully autonomous control framework. The majority of first principle techniques in modeling and controlling RUAV face challenges in incorporating and handling various uncertainties. Recently various fuzzy and neuro-fuzzy based intelligent systems are utilized to enhance the RUAV's modeling and control performance. However, the majority of these fuzzy systems are based on batch learning methods, have static structure, and cannot adapt to rapidly changing environments. The implication of Evolving Intelligent System based model-free data-driven techniques can be a smart option since they can adapt their structure and parameters to cope with sudden changes in the behavior of RUAVs real-time flight. They work in a single pass learning fashion which is suitable for online real-time deployment. In this paper, state of the art of various fuzzy systems from the basic fuzzy system to evolving fuzzy system, their application in a RUAV namely quadcopter with existing limitations, and possible opportunities are analyzed. Besides, a variety of first principle techniques to control the quadcopter, their impediments, and conceivable solution with recently employed evolving fuzzy controllers are reviewed.

Elsevier, 2019

Until now the majority of the neuro and fuzzy modeling and control approaches for rotary wing Unm... more Until now the majority of the neuro and fuzzy modeling and control approaches for rotary wing Unmanned Aerial Vehicles (UAVs), such as the quadrotor, have been based on batch learning techniques, therefore static in structure, and cannot adapt to rapidly changing environments. Implication of Evolving Intelligent System (EIS) based model-free data-driven techniques in fuzzy system are good alternatives, since they are able to evolve both their structure and parameters to cope with sudden changes in behavior, and performs perfectly in a single pass learning mode which is suitable for online real-time deployment. The Metacognitive Scaffolding Learning Machine (McSLM) is seen as a generalized version of EIS since the metacognitive concept enables the what-to-learn, how-to-learn, and when-to-learn scheme, and the scaffolding theory realizes a plug-and-play property which strengthens the online working principle of EISs. This paper proposes a novel online identification scheme, applied to a quadrotor using real-time experimental flight data streams based on McSLM, namely Metacognitive Scaffolding Interval Type 2 Recurrent Fuzzy Neural Network (McSIT2RFNN). Our proposed approach demonstrated significant improvements in both accuracy and complexity against some renowned existing variants of the McSLMs and EISs.

Recently, the quadcopter configuration is becoming prevalent for both civilian and military appli... more Recently, the quadcopter configuration is becoming prevalent for both civilian and military applications, and research is continuing to improve its controllability. However, most the control methodologies depend on accurate system models which are derived from six degrees of freedom nonlinear system dynamics and they generally do not consider realistic outdoor perturbations and uncertainties. As a solution, model-free data-driven system identification and controlling of quadcopter have been refined in this paper. This nonlinear multiple input-multiple output system identification is proliferated using a fuzzy clustering model. The accuracy of the identification is seen to very high. The results of a PID controller used in conjunction with the identified fuzzy model is also presented. The PID controller with the fuzzy identified model is seen to precisely follow the commanded signals.