Vincent Akpan | Aristotle University of Thessaloniki (original) (raw)

Papers by Vincent Akpan

Pure and Applied Physics, 2015

Three mathematical model structures, namely: ARMAX, OE and a SSIF are first formulated followed b... more Three mathematical model structures, namely: ARMAX, OE and a SSIF are first formulated followed by the formulation of their respective model predictors for the model identification and prediction of power transmission and distribution within Akure and its environs. A total of 51,350 data samples from the Power Holding Company of Nigeria were collected for thirteen different parameters that influences the evaluation and analysis in the case study area. The performances of these three model predictors are validated by one-step and five-step ahead prediction methods as well as the Akaike’s final prediction error (AFPE) estimates. The results obtained from the application of these three model structures and their predictors for the modeling and prediction of power transmission and distribution as well as the validation results show that the OE model predictor outperforms the ARMAX and SSIF model predictors with much smaller prediction errors, good prediction and tracking capabilities an...

Abstract—In this paper, the intelligent algorithm (IA) that is capable of adapting to dynamical t... more Abstract—In this paper, the intelligent algorithm (IA) that is capable of adapting to dynamical tropical weather conditions is proposed based on fuzzy logic techniques. The IA effectively interacts with the quality of service (QoS) criteria irrespective of the dynamic tropical weather to achieve improvement in the satellite links. To achieve this, an adaptive network-based fuzzy inference system (ANFIS) has been adopted. The algorithm is capable of interacting with the weather fluctuation to generate appropriate improvement to the satellite QoS for efficient services to the customers. 5-year (2012-2016) rainfall rate of one-minute integration time series data has been used to derive fading based on ITU-R P. 618-12 propagation models. The data are obtained from the measurement undertaken by the Communication Research Group (CRG), Physics Department, Federal University of Technology, Akure, Nigeria. The rain attenuation and signal-to-noise ratio (SNR) were derived for frequency betwee...

American Journal of Intelligent Systems, 2014

A novel technique for the nonlinear modeling and online prediction of incoming influent character... more A novel technique for the nonlinear modeling and online prediction of incoming influent characteristics of an activated sludge wastewater treatment (AS-WWTP) is presented in this paper. The nonlinear modelling and online prediction in the presence of disturbances is achieved using an online adaptive recursive least squares (ARLS) algorithm to the nonlinear model identification formulated in this paper. The performance of the proposed ARLS algorithm is compared with the so-called incremental backpropagation (INCBP) which is also an online identification. These two algorithms are validated by one-step, five-step ahead prediction methods as well as the Akaike's method to estimate the final prediction error (AFPE) of the regularized criterion. Furthermore, the validation results show the superior performance of the proposed ARLS algorithm in terms of much smaller prediction errors when compared to the INCBP algorithm. The results from the incoming influent characteristics predictions show three scenarios, namely: high toxic, low toxic and acceptable toxic levels of the incoming influent. The proposed techniques and algorithms can be adapted and deployed for the modeling and prediction of an incoming influent (sewage) for industrial WWTP management systems.

American Journal of Intelligent Systems, 2014

This paper presents the formulation and application of an online adaptive recursive least squares... more This paper presents the formulation and application of an online adaptive recursive least squares (ARLS) algorithm to the nonlinear model identification of the five biological reactor units of an activated sludge wastewater treatment (AS-WWTP). The performance of the proposed ARLS algorithm is compared with the so-called incremental backpropagation (INCBP) which is also an online identification. The algorithms are validated by one-step and five-step ahead prediction methods. The performance of the two algorithms is assessed by using the Akaike's method to estimate the final prediction error (AFPE) of the regularized criterion. Furthermore, the validation results show the superior performance of the proposed ARLS algorithm in terms of much smaller prediction errors when compared to the INCBP algorithm. Keywords Activated sludge wastewater treatment plant (AS-WWTP), Adaptive recursive least squares (ARLS), Artificial neural network (ANN), Benchmark simulation model No. 1 (BSM #1), Biological reactors, Effluent tank, Incremental backpropagation (INCBP), Nonlinear model identification, Nonlinear neural network autoregressive moving average with exogenous input (NNARMAX) model, Secondary settler and clarifier

American Journal of Intelligent Systems, 2014

... terminal configurations onto both beams. The beams are then used as cantilevers and subjected... more ... terminal configurations onto both beams. The beams are then used as cantilevers and subjected to the same load conditions. The values of the moduli of elasticity (MOE) for the two beams are determined. The output voltage of the ...

International Letters of Chemistry, Physics and Astronomy, 2015

Quantum Monte Carlo (QMC) calculations of the electric dipole moment and ground-state total energ... more Quantum Monte Carlo (QMC) calculations of the electric dipole moment and ground-state total energy of hydrazine (N 2 H 4) molecule using CASINO-code have been carried-out by employing the VMC and DMC techniques. The optimization of the Slater-Jastrow trial wavefunction was done using variance-minimization scheme. The simulations require that the configurations must evolve on the time scale of the electronic motion, and after equilibration, the estimated effective time-step be obtained. In this study, the electric dipole moment of N 2 H 4 molecule was calculated using only the DMC technique; and a value of 2.0D which is in good agreement with the experimental value of 1.85D was obtained. On the other hand, the ground-state total energy of N 2 H 4 molecule was calculated using both VMC and DMC methods. It was observed that the result obtained from the VMC technique agrees very-well with the best theoretical value [17], but the DMC technique gave a ground-state total energy lower than all other theoretical values in literature, suggesting that the DMC result of-111.842774 ± 0.00394 a.u. may be the exact ground-state total energy of hydrazine molecule. The calculated values of electric dipole moment and ground-state total energy in this work are compared with the available experimental values and the values reported by different workers. Reasonably good agreement has been obtained between them in the required order of chemical accuracy.

This paper presents a novel and efficient hardware/software co-design techniques for the developm... more This paper presents a novel and efficient hardware/software co-design techniques for the development of high-performance embedded processor system targeting field programmable gate arrays (FPGAs). Some very important and critical design considerations for developing FPGA embedded processor systems are first presented. Next, the architectures of the IBM hard-core PowerPC™440 and the Xilinx soft-core MicroBlaze™ processors are introduced together with comprehensive techniques for FPGA embedded processor systems design. Then, two embedded processor systems are designed, implemented on Virtex-5 FX70T ML507 FPGA development board, tested and their performances are evaluated on an industry-standard FPGA benchmark DMIPs (Dhrystone million instructions per second). The two embedded processors are based on: 1) the IBM PowerPC™440 hard processor core and 2) the Xilinx MicroBlaze™ soft processor core. Experimental results have shown that the IBM hard-core PowerPC™440 embedded processor system out-performs the Xilinx the soft-core MicroBlaze™ embedded processor system in terms of FPGA device consumptions and their maximum operating frequency for the DMIPs benchmark implementation. The DMIPs benchmark performance results indicate that the embedded processor system are highly optimized and can be deployed for the development of real-time embedded processor systems. Finally, a brief conclusion and some discussions on future directions are given.

This article presents a comprehensive and efficient model-based technique on how algorithms can b... more This article presents a comprehensive and efficient model-based technique on how algorithms can be developed, synthesized, modeled, pre-verified and implemented on embedded processors platforms which consist of a personal computer and a field programmable gate array (FPGA). To illustrate the proposed technique a new adaptive matrix inversion algorithm is proposed and used. The algorithm is first implemented as a synthesizable streamingloop floating-point MATLAB programs. The MATALAB programs are then synthesized using Xilinx AccelDSP to generate a System Generator block model equivalent of the MATLAB programs. Using the generated System Generator block model, the Xilinx System Generator for DSP is then employed to develop a complete System Generator hardware model of the adaptive matrix inversion algorithm. A FPGA-in-the-loop co-simulation and pre-verification using a generated hardware co-simulation block model is carried out for performance comparison. Next, an embedded MicroBlaze™ processor system is designed, tested and imported into a System Generator hardware model of the adaptive matrix inversion algorithm inside MATLAB/ Simulink environment; and a complete FPGA-in-the-loop implementation is performed. The FPGA-in-the-loop simulation results are presented. Conclusions drawn from the study are given together with some discussions and directions for further work.

The evolution of field programmable gate arrays (FPGAs) as custom-computing machines for digital ... more The evolution of field programmable gate arrays (FPGAs) as custom-computing machines for digital signal processing (DSP), real-time embedded and reconfigurable systems development, embedded processors, and as co-processors for application specific integrated circuit (ASIC) prototyping has led to the emergence of several modeling and design methodologies among which are the register transfer level (RTL) and electronic system level (ESL). Moreover, due to the vast number of FPGA manufacturers and third-party partners support tools; the choice of FPGA selection can become a challenging task. This paper: 1) discusses industry-standard FPGA system design methodologies; 2) discusses the modeling and development of FPGA-based embedded systems design based on the ESL design and verification methodologies from a higher level of abstraction view point rather than RTL; and 3) presents a complete framework for an embedded processor system design, synthesis, implementation, simulation and verification targeting an FPGA. Finally, a brief conclusion is given with some discussions on future directions.

This dissertation deals with the development of two new neural network-based model identification... more This dissertation deals with the development of two new neural network-based model identification algorithms and two new model predictive control (MPC) algorithms which are combined to form model-based adaptive control strategies. Also, two new computer platforms for the implementation of these algorithms and their corresponding strategies are proposed. The overall strategies consist of an online model identification part and a model-based predictive control part. The proposed algorithms for the model identification are practically two new algorithms for training a dynamic feedforward neural network (DFNN) which will be considered to comprise the model of a nonlinear dynamic system. The proposed training algorithms are: the adaptive recursive least squares (ARLS) algorithm and the modified Levenberg-Marquardt algorithm (MLMA). The proposed algorithms for the predictive control parts are: the adaptive generalized predictive control (AGPC) and the nonlinear adaptive model predictive control (NAMPC). The two real-time platforms for the implementation of the combined operation of identification and predictive control algorithms with the purpose of forming an adaptive control strategy are: a service-oriented architecture (SOA) cluster network based on the device profile for web services (DPWS) and a Xilinx's Virtex-5 FPGA (field programmable gate array) development board. The proposed control strategies have been applied to control three applications, namely: the fluidized bed furnace reactor (FBFR) of the steam deactivation unit (SDU) used for preparing catalyst for evaluation in a fluid catalytic cracking (FCC) pilot plant; an activated sludge wastewater treatment plant (AS-WWTP) in accordance with the European wastewater treatment standards; and the auto-pilot control unit of a nonlinear F-16 fighter aircraft. The neural network models for these three applications were validated using one-step, five-step and tenstep ahead prediction simulations as well as with the Akaike's final prediction error (AFPE) estimate algorithm. Also, the performances of the proposed ARLS and MLMA algorithms were compared with the backprogation with momentum (BPM) and incremental backpropagation (INCBP) algorithms. Also the performances of the PID control of the identified model of the FBFR process by means of the ARLS and the MLMA network training algorithms versus the PID control of the first principles model of the same process. The AGPC and NAMPC control of the considered applications when model identification is performed by the ARLS and MLMA algorithms were implemented on a conventional mulitcore personal computer (PC) and SOA cluster of muticore PCs. Hardware-in-the-loop simulations have been performed by linking the PC or SOA implementations with MATLAB simulations of the processes. An AGPC implementation with neural networks trained using the MLMA algorithm has been made on a Xilinx Virtex-5 FPGA. The hardware-in-the-loop simulations have shown that the proposed algorithms and their SOA or FPGA implementations can have execution times shorter than other algorithms which present similar performance. Therefore, they render themselves more appropriate compared to other algorithms for use in the control of processes requiring shorter sampling time for stable operations. Acknowledgement ii ACKNOWLEDGEMENT My sincere appreciation and gratitude goes to my project supervisor, Professor George Hassapis, who conceived and supervised the work contained in this dissertation. I also thank him for his technical and financial supports, encouragements and fatherly roles throughout the course of this work. I will always remain grateful to him for his advice, suggestions, intuitive comments, patience and untiring efforts in reading through my manuscripts with necessary corrections from conception through algorithm developments, problem formulations, implementations, and several simulations and analyses which have resulted in this dissertation. I also thank Professor Alkiviadis Hatzopoulos and Associate Professor Loukas Petrou for their co-supervisory roles in this work. My sincere thanks to Associate Professor Loukas Petrou for his technical supports and comments as well as his efforts and time devoted to this work from inception to completion. I specially acknowledge and thank the Greek State Scholarships' Foundation (I.K.Y.) that provided the scholarship as well as the major funding for this research. I also thank the Federal Government of Nigeria for their financial support towards the Bilateral Educational Agreement with I.K.Y. and the Federal University of Technology, Akure-Nigeria for their financial supports which has made this scholarship a reality leading to the successful completion of my doctorate degree programme. My acknowledgment also goes to Ambassador of Nigeria to Greece, His Excellency (Dr.) Etim U. Uyie for his love, care and financial assistance. My special thanks go to the Staff of the School of Electrical and Computer Engineering, AUTH, Greece. I gratefully acknowledge Dr. Simeonidis Andreas for his comments and encouragements, and to Mr. George Voukalis for his technical assistance. I also wish to thank my colleagues at the Laboratory of Computer Systems Architecture: Maria Koukourli, Ioakeim Samaras, Babis Serenis, Manos Tsardoulias and Nikos Sismanis for their technical support, comments and contributions towards the successful completion of this project. I am highly indebted to my mother Mrs. Cecilia Andrew Akpan; my mother-in-law Mrs. Titilayo Nathaniel Oyewo, and my siblings Justine, Sylvester, Emmanuel and Justina for their sacrifices and prayers. Words are not enough to thank my wife, Mrs. Rachael Oyenike Vincent-Akpan, for all her sacrifices, financial support, prayers, and encouragements throughout the period of this study. Just know that I love you. Finally, I am most grateful to God Almighty for His infinite mercy, divine grace and sound health.

Pure and Applied Physics, 2015

American Journal of Intelligent Systems, 2014

International Letters of Chemistry, Physics and Astronomy, 2015