To comply with growing demand for high effluent quality of Domestic Wastewater Treatment Plant (WWTP), a simple and reliable prediction model is thus needed. The wastewater treatment technology considered in this paper is an Aerobic Granular Sludge (AGS). The AGS systems are fundamentally complex due to uncertainty and non-linearity of the system makes it hard to predict. This paper presents model predictions and optimization as a tool in predicting the performance of the AGS. The input-output data used in model prediction are (COD, TN, TP, AN, and MLSS). After feature analysis, the prediction of the models using Support Vector Machine (SVM) and Feed-Forward Neural Network (FFNN) are developed and compared. The simulation of the model uses the experimental data obtained from Sequencing Batch Reactor under hot temperature of 50˚C. The simulation results indicated that the SVM is preferable to FFNN and it can provide a useful tool in predicting the effluent quality of WWTP.
A major challenge in hydrological modelling is to identification of optimal
parameter set of different data, catchment characteristics and objectives. Although, the
identification of optimal parameter set is difficult because of conceptual hydrological
models contain more number of parameters and accuracy also depends upon all the
relevant number of parameters influencing in a model. This identification process
cannot estimate directly and therefore it measured based on calibrating the model
which minimizing an objective function. Here, the objective function can depend upon
the sensitivity of model parameters and calibration of model. In this paper, we proposed
the Emulator Based Optimization (EBO) for reducing number of runs and improving
conceptual model efficiency. Where, emulator models are used to represent the
response surface of the simulation models and it can play a valuable role for
optimization. In this study evaluates EBO for calibrating of SWAT hydrological model
with following steps like input design, simulation model, emulator modelling,
convergence criteria and validation. The results show that EBO calibrates the model
with high accuracy and it captured the observed model with consuming less time. This
study helps for decision making, planning and designing of water resources.
The main challenges to achieving a reliable model which can predict well the process are the
nonlinearities associated with many biological and biochemical processes in the system. Artificial intelligent
approaches revolved as better alternative in predicting the system. Typical measured variables for effluent
quality of wastewater treatment plant are pH, and mixed liquor suspended solids (MLSS). This paper
presents an adaptive neuro-fuzzy inference system (ANFIS) and feed-forward neural network (FFNN)
modeling applied to the domestic plant of the Bunus regional sewage treatment plant. ANFIS and feedforward
neural network techniques as nonlinear function approximators have demonstrated the capability
of predicting nonlinear behaviour of the system. The data for the period of two years and nine months
sampled weekly (140 week samples) were collected and used for this study. Simulation studies showed
that the prediction capability of the ANFIS model is somehow better than that of the FFNN model. The
ANFIS model may serves as a valuable prediction tool for the plant.
Battery Remaining Useful life Forecast Applied in Unmanned Aerial Vehicle.IJERA Editor
The batteries has been widely used as an energy storage system for unmanned aerial vehicles (UAVs). To avoid faults in these systems (UAVs), which are powered by batteries, there are several approaches to forecast the Remaining Useful Life (RUL) of the batteries. In this work, it's proposed the use of a model based on Extended Kalman Filter (EKF) to estimate the RUL in Lithium-Ion batteries. The database used was available at NASA's repository in 2015.
Sewer systems are used to convey sewage and/or storm water to sewage treatment
plants for disposal by a network of buried sewer pipes, gutters, manholes and pits.
Unfortunately, the sewer pipe deteriorates with time leading to the collapsing of the
pipe with traffic disruption or clogging of the pipe causing flooding and
environmental pollution. Thus, the management and maintenance of the buried pipes
are important tasks that require information about the changes of the current and
future sewer pipes conditions. In this research, the study was carried on in Baghdad,
Iraq and two deteriorations model’s multinomial logistic regression and neural
network deterioration model NNDM are used to predict sewers future conditions. The
results of the deterioration models' application showed that NNDM gave the highest
overall prediction efficiency of 93.6% by adapting the confusion matrix test, while
multinomial logistic regression was inconsistent with the data. The error in prediction
of related model was due to its inability to reflect the dependent variable (condition
classes) ordered nature.
A major challenge in hydrological modelling is to identification of optimal
parameter set of different data, catchment characteristics and objectives. Although, the
identification of optimal parameter set is difficult because of conceptual hydrological
models contain more number of parameters and accuracy also depends upon all the
relevant number of parameters influencing in a model. This identification process
cannot estimate directly and therefore it measured based on calibrating the model
which minimizing an objective function. Here, the objective function can depend upon
the sensitivity of model parameters and calibration of model. In this paper, we proposed
the Emulator Based Optimization (EBO) for reducing number of runs and improving
conceptual model efficiency. Where, emulator models are used to represent the
response surface of the simulation models and it can play a valuable role for
optimization. In this study evaluates EBO for calibrating of SWAT hydrological model
with following steps like input design, simulation model, emulator modelling,
convergence criteria and validation. The results show that EBO calibrates the model
with high accuracy and it captured the observed model with consuming less time. This
study helps for decision making, planning and designing of water resources.
The main challenges to achieving a reliable model which can predict well the process are the
nonlinearities associated with many biological and biochemical processes in the system. Artificial intelligent
approaches revolved as better alternative in predicting the system. Typical measured variables for effluent
quality of wastewater treatment plant are pH, and mixed liquor suspended solids (MLSS). This paper
presents an adaptive neuro-fuzzy inference system (ANFIS) and feed-forward neural network (FFNN)
modeling applied to the domestic plant of the Bunus regional sewage treatment plant. ANFIS and feedforward
neural network techniques as nonlinear function approximators have demonstrated the capability
of predicting nonlinear behaviour of the system. The data for the period of two years and nine months
sampled weekly (140 week samples) were collected and used for this study. Simulation studies showed
that the prediction capability of the ANFIS model is somehow better than that of the FFNN model. The
ANFIS model may serves as a valuable prediction tool for the plant.
Battery Remaining Useful life Forecast Applied in Unmanned Aerial Vehicle.IJERA Editor
The batteries has been widely used as an energy storage system for unmanned aerial vehicles (UAVs). To avoid faults in these systems (UAVs), which are powered by batteries, there are several approaches to forecast the Remaining Useful Life (RUL) of the batteries. In this work, it's proposed the use of a model based on Extended Kalman Filter (EKF) to estimate the RUL in Lithium-Ion batteries. The database used was available at NASA's repository in 2015.
Sewer systems are used to convey sewage and/or storm water to sewage treatment
plants for disposal by a network of buried sewer pipes, gutters, manholes and pits.
Unfortunately, the sewer pipe deteriorates with time leading to the collapsing of the
pipe with traffic disruption or clogging of the pipe causing flooding and
environmental pollution. Thus, the management and maintenance of the buried pipes
are important tasks that require information about the changes of the current and
future sewer pipes conditions. In this research, the study was carried on in Baghdad,
Iraq and two deteriorations model’s multinomial logistic regression and neural
network deterioration model NNDM are used to predict sewers future conditions. The
results of the deterioration models' application showed that NNDM gave the highest
overall prediction efficiency of 93.6% by adapting the confusion matrix test, while
multinomial logistic regression was inconsistent with the data. The error in prediction
of related model was due to its inability to reflect the dependent variable (condition
classes) ordered nature.
Neural Network Model Development with Soft Computing Techniques for Membrane ...IJECEIAES
Membrane bioreactor employs an efficient filtration technology for solid and liquid separation in wastewater treatment process. Development of membrane filtration model is significant as this model can be used to predict filtration dynamic which is later utilized in control development. Most of the available models only suitable for monitoring purpose, which are too complex, required many variables and not suitable for control system design. This work focusing on the simple time seris model for membrane filtration process using neural network technique. In this paper, submerged membrane filtration model developed using recurrent neural network (RNN) train using genetic algorithm (GA), inertia weight particle swarm optimization (IWPSO) and gravitational search algorithm (GSA). These optimization algorithms are compared in term of its accuracy and convergent speed in updating the weights and biases of the RNN for optimal filtration model. The evaluation of the models is measured using three performance evaluations, which are mean square error (MSE), mean absolute deviation (MAD) and coefficient of determination (R2). From the results obtained, all methods yield satisfactory result for the model, with the best results given by IW-PSO.
DEVELOPING THE OPTIMIZED OCEAN CURRENT STRENGTHENING DESALINATION SEMI-PERMEA...ijbesjournal
Alongside improvements in desalination operation and development of new technologies, problems of weakened counter current and global warming have emerged. Therefore, our study suggests a new desalination model, based on the experimental Support Vector Machine (SVM) algorithm, for semipermeable membrane separation. First, the reverse osmosis (RO) process used semi-permeable membrane and osmotic pressure to remove the solutes dissolved in seawater and obtain pure freshwater. The desalination process also applied MSF and MED, which are the best technologies developed through elimination of various problems that were previously experienced. This research is directed towards suggesting a model that can effectively create the semi-permeable membrane used in the desalination process. To efficiently prevent a counter current and safely obtain the water resources, an innovative technology is suggested by applying Genetic Algorithm (GA) to the SVM model for the semi-p
Fault detection of feed water treatment process using PCA-WD with parameter o...ISA Interchange
Feed water treatment process (FWTP) is an essential part of utility boilers; and fault detection is expected for its reliability improvement. Classical principal component analysis (PCA) has been applied to FWTPs in our previous work; however, the noises of T2 and SPE statistics result in false detections and missed detections. In this paper, Wavelet denoise (WD) is combined with PCA to form a new algorithm, (PCA- WD), where WD is intentionally employed to deal with the noises. The parameter selection of PCA-WD is further formulated as an optimization problem; and PSO is employed for optimization solution. A FWTP, sustaining two 1000 MW generation units in a coal-fired power plant, is taken as a study case. Its operation data is collected for following verification study. The results show that the optimized WD is effective to restrain the noises of T2 and SPE statistics, so as to improve the performance of PCA-WD algorithm. And, the parameter optimization enables PCA-WD to get its optimal parameters in an auto- matic way rather than on individual experience. The optimized PCA-WD is further compared with classical PCA and sliding window PCA (SWPCA), in terms of four cases as bias fault, drift fault, broken line fault and normal condition, respectively. The advantages of the optimized PCA-WD, against classical PCA and SWPCA, is finally convinced with the results.
Data-driven adaptive predictive control for an activated sludge processjournalBEEI
Data-driven control requires no information of the mathematical model of the controlled process. This paper proposes the direct identification of controller parameters of activated sludge process. This class of data-driven control calculates the predictive controller parameters directly using subspace identification technique. By updating input-output data using receding window mechanism, the adaptive strategy can be achieved. The robustness test and stability analysis of direct adaptive model predictive control are discussed to realize the effectiveness of this adaptive control scheme. The applicability of the controller algorithm to adapt into varying kinetic parameters and operating conditions is evaluated. Simulation results show that by a proper and effective excitation of direct identification of controller parameters, the convergence and stability of the implicit predictive model can be achieved.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Optimized robust fuzzy sliding mode control for efficient wastewater treatmen...IAESIJAI
Wastewater treatment plants (WWTPs) are plagued by nonlinearities, uncertainties, and disturbances that degrade control performance and may even lead to severe instability. The WWTP control issue has received a lot of research and development during the last several decades. One well-known way of designing a resilient control system is called sliding mode control (SMC). The SMC's greatest strength lies in its innate resistance to disturbances and uncertainty. Incorporating fuzzy SMC would eliminate the chattering effect, the primary drawback of traditional sliding-mode controller, without sacrificing robustness against parametric uncertainties, modeling errors, and variable dynamic loads. This article discusses the hybridization of fuzzy logic with sliding mode control to provide highly excellent stability and accuracy in a control system. As a means of optimizing the fuzzy SMC, the gradient-free optimization technique known as the Jaya algorithm is investigated. By repeatedly altering a population of individual solutions, this population-based method can deal with both limited and unbounded optimization issues.
Hydrological Calibration in the Mount Lofty Ranges using Source Paramenter Es...eWater
The catchments of the Mount Lofty Ranges (MLR) provide a crucial water resource for the rural and urban community of Adelaide. The Source Catchments model is one planning tool used to assess current and future water, sediment and nutrient yields from the catchment. The modelling is critical for future planning to ensure a safe and reliable water supply is maintained.
Linking PEST and Source Catchments for hydrology calibration was an efficient and repeatable method for calibration. Future work to improve the process could include calibrating with other rainfall runoff models such as Sacramento which better account for groundwater losses or explicit representation of groundwater in the model.
This project has focused solely on rainfall and runoff. It has not considered how external impacts (e.g. climate change) may change catchment hydrological characteristics or constituent characteristics such as EMC/DWC values. Future projects through the Goyder Water Research Institute aim to address some of these
issues. This project may produce data which will inform further work in this respect.
Turbidity is a measure of water quality. Excessive turbidity poses a threat to health and causes pollution. Most of the available mathematical models of water treatment plants do not capture turbidity. A reliable model is essential for effective removal of turbidity in the water treatment plant. This paper presents a comparison of Hammerstein Wiener and neural network technique for estimating of turbidity in water treatment plant. The models were validated using an experimental data from Tamburawa water treatment plant in Kano, Nigeria. Simulation results demonstrated that the neural network model outperformed the Hammerstein-Wiener model in estimating the turbidity. The neural network model may serve as a valuable tool for predicting the turbidity in the plant.
Fredrick Ishengoma - A Novel Design of IEEE 802.15.4 and Solar Based Autonomo...Fredrick Ishengoma
The recently advancement in Wireless Sensor Network (WSN) technology has brought new distributed sensing applications such as water quality monitoring. With sensing capabilities and using parameters like pH, conductivity and temperature, the quality of water can be known. This paper proposes a novel design based on IEEE 802.15.4 (Zig-Bee protocol) and solar energy called Autonomous Water QualityMonitoring Prototype (AWQMP). The prototype is designed to use ECHERP routing protocol and Adruino Mega 2560, an open-source electronic prototyping platform for data acquisition. AWQMP is expected to give real time data acquirement and to reduce the cost of manual water quality monitoring due to its autonomous characteristic. Moreover, the proposed prototype will help to study the behavior of aquatic animals in deployed water bodies.
Due to environmental concern and certain constraint on building a new power plant, renewable energy particularly distributed generation photovoltaic (DGPV) has becomes one of the promising sources to cater the increasing energy demand of the power system. Furthermore, with appropriate location and sizing, the integration of DGPV to the grid will enhance the voltage stability and reduce the system losses. Hence, this paper proposed a new algorithm for DGPV optimal location and sizing of a transmission system based on minimization of Fast Voltage Stability Index (FVSI) with considering the system constraints. Chaotic Mutation Immune Evolutionary Programming (CMIEP) is developed by integrating the piecewise linear chaotic map (PWLCM) in the mutation process in order to increase the convergence rate of the algorithm. The simulation was applied on the IEEE 30 bus system with a variation of loads on Bus 30. The simulation results are also compared with Evolutionary Programming (EP) and Chaotic Evolutionary Programming (CEP) and it is found that CMIEP performed better in most of the cases.
Identification and real time position control of a servo-hydraulic rotary act...ISA Interchange
This paper presents a new intelligent approach for adaptive control of a nonlinear dynamic system. A modified version of the brain emotional learning based intelligent controller (BELBIC), a bio-inspired algorithm based upon a computational model of emotional learning which occurs in the amygdala, is utilized for position controlling a real laboratorial rotary electro-hydraulic servo (EHS) system. EHS systems are known to be nonlinear and non-smooth due to many factors such as leakage, friction, hysteresis, null shift, saturation, dead zone, and especially fluid flow expression through the servo valve. The large value of these factors can easily influence the control performance in the presence of a poor design. In this paper, a mathematical model of the EHS system is derived, and then the parameters of the model are identified using the recursive least squares method. In the next step, a BELBIC is designed based on this dynamic model and utilized to control the real laboratorial EHS system. To prove the effectiveness of the modified BELBIC’s online learning ability in reducing the overall tracking error, results have been compared to those obtained from an optimal PID controller, an auto-tuned fuzzy PI controller (ATFPIC), and a neural network predictive controller (NNPC) under similar circumstances. The results demonstrate not only excellent improvement in control action, but also less energy consumption.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Performance Analysis of Faults Detection in Wind Turbine Generator Based on H...chokrio
Electrical energy production based on wind power has become the most popular renewable resources in the recent years because it gets reliable clean energy with minimum cost. The major challenge for wind turbines is the electrical and the mechanical failures which can occur at any time causing prospective breakdowns and damages and therefore it leads to machine downtimes and to energy production loss. To circumvent this problem, several tools and techniques have been developed and used to enhance fault detection and diagnosis to be found in the stator current signature for wind turbines generators. Among these methods, parametric or super-resolution frequency estimation methods, which provides typical spectrum estimation, can be useful for this purpose. Facing on the plurality of these algorithms, a comparative performance analysis is made to evaluate robustness based on different metrics: accuracy, dispersion, computation cost, perturbations and faults severity. Finally, simulation results in MATLAB with most occurring faults indicate that ESPRIT and R-MUSIC algorithms have high capability of correctly identifying the frequencies of fault characteristic components, a performance ranking had been carried out to demonstrate the efficiency of the studied methods in faults detecting.
Square transposition: an approach to the transposition process in block cipherjournalBEEI
The transposition process is needed in cryptography to create a diffusion effect on data encryption standard (DES) and advanced encryption standard (AES) algorithms as standard information security algorithms by the National Institute of Standards and Technology. The problem with DES and AES algorithms is that their transposition index values form patterns and do not form random values. This condition will certainly make it easier for a cryptanalyst to look for a relationship between ciphertexts because some processes are predictable. This research designs a transposition algorithm called square transposition. Each process uses square 8 × 8 as a place to insert and retrieve 64-bits. The determination of the pairing of the input scheme and the retrieval scheme that have unequal flow is an important factor in producing a good transposition. The square transposition can generate random and non-pattern indices so that transposition can be done better than DES and AES.
Hyper-parameter optimization of convolutional neural network based on particl...journalBEEI
Deep neural networks have accomplished enormous progress in tackling many problems. More specifically, convolutional neural network (CNN) is a category of deep networks that have been a dominant technique in computer vision tasks. Despite that these deep neural networks are highly effective; the ideal structure is still an issue that needs a lot of investigation. Deep Convolutional Neural Network model is usually designed manually by trials and repeated tests which enormously constrain its application. Many hyper-parameters of the CNN can affect the model performance. These parameters are depth of the network, numbers of convolutional layers, and numbers of kernels with their sizes. Therefore, it may be a huge challenge to design an appropriate CNN model that uses optimized hyper-parameters and reduces the reliance on manual involvement and domain expertise. In this paper, a design architecture method for CNNs is proposed by utilization of particle swarm optimization (PSO) algorithm to learn the optimal CNN hyper-parameters values. In the experiment, we used Modified National Institute of Standards and Technology (MNIST) database of handwritten digit recognition. The experiments showed that our proposed approach can find an architecture that is competitive to the state-of-the-art models with a testing error of 0.87%.
More Related Content
Similar to Performance comparison of SVM and ANN for aerobic granular sludge
Neural Network Model Development with Soft Computing Techniques for Membrane ...IJECEIAES
Membrane bioreactor employs an efficient filtration technology for solid and liquid separation in wastewater treatment process. Development of membrane filtration model is significant as this model can be used to predict filtration dynamic which is later utilized in control development. Most of the available models only suitable for monitoring purpose, which are too complex, required many variables and not suitable for control system design. This work focusing on the simple time seris model for membrane filtration process using neural network technique. In this paper, submerged membrane filtration model developed using recurrent neural network (RNN) train using genetic algorithm (GA), inertia weight particle swarm optimization (IWPSO) and gravitational search algorithm (GSA). These optimization algorithms are compared in term of its accuracy and convergent speed in updating the weights and biases of the RNN for optimal filtration model. The evaluation of the models is measured using three performance evaluations, which are mean square error (MSE), mean absolute deviation (MAD) and coefficient of determination (R2). From the results obtained, all methods yield satisfactory result for the model, with the best results given by IW-PSO.
DEVELOPING THE OPTIMIZED OCEAN CURRENT STRENGTHENING DESALINATION SEMI-PERMEA...ijbesjournal
Alongside improvements in desalination operation and development of new technologies, problems of weakened counter current and global warming have emerged. Therefore, our study suggests a new desalination model, based on the experimental Support Vector Machine (SVM) algorithm, for semipermeable membrane separation. First, the reverse osmosis (RO) process used semi-permeable membrane and osmotic pressure to remove the solutes dissolved in seawater and obtain pure freshwater. The desalination process also applied MSF and MED, which are the best technologies developed through elimination of various problems that were previously experienced. This research is directed towards suggesting a model that can effectively create the semi-permeable membrane used in the desalination process. To efficiently prevent a counter current and safely obtain the water resources, an innovative technology is suggested by applying Genetic Algorithm (GA) to the SVM model for the semi-p
Fault detection of feed water treatment process using PCA-WD with parameter o...ISA Interchange
Feed water treatment process (FWTP) is an essential part of utility boilers; and fault detection is expected for its reliability improvement. Classical principal component analysis (PCA) has been applied to FWTPs in our previous work; however, the noises of T2 and SPE statistics result in false detections and missed detections. In this paper, Wavelet denoise (WD) is combined with PCA to form a new algorithm, (PCA- WD), where WD is intentionally employed to deal with the noises. The parameter selection of PCA-WD is further formulated as an optimization problem; and PSO is employed for optimization solution. A FWTP, sustaining two 1000 MW generation units in a coal-fired power plant, is taken as a study case. Its operation data is collected for following verification study. The results show that the optimized WD is effective to restrain the noises of T2 and SPE statistics, so as to improve the performance of PCA-WD algorithm. And, the parameter optimization enables PCA-WD to get its optimal parameters in an auto- matic way rather than on individual experience. The optimized PCA-WD is further compared with classical PCA and sliding window PCA (SWPCA), in terms of four cases as bias fault, drift fault, broken line fault and normal condition, respectively. The advantages of the optimized PCA-WD, against classical PCA and SWPCA, is finally convinced with the results.
Data-driven adaptive predictive control for an activated sludge processjournalBEEI
Data-driven control requires no information of the mathematical model of the controlled process. This paper proposes the direct identification of controller parameters of activated sludge process. This class of data-driven control calculates the predictive controller parameters directly using subspace identification technique. By updating input-output data using receding window mechanism, the adaptive strategy can be achieved. The robustness test and stability analysis of direct adaptive model predictive control are discussed to realize the effectiveness of this adaptive control scheme. The applicability of the controller algorithm to adapt into varying kinetic parameters and operating conditions is evaluated. Simulation results show that by a proper and effective excitation of direct identification of controller parameters, the convergence and stability of the implicit predictive model can be achieved.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Optimized robust fuzzy sliding mode control for efficient wastewater treatmen...IAESIJAI
Wastewater treatment plants (WWTPs) are plagued by nonlinearities, uncertainties, and disturbances that degrade control performance and may even lead to severe instability. The WWTP control issue has received a lot of research and development during the last several decades. One well-known way of designing a resilient control system is called sliding mode control (SMC). The SMC's greatest strength lies in its innate resistance to disturbances and uncertainty. Incorporating fuzzy SMC would eliminate the chattering effect, the primary drawback of traditional sliding-mode controller, without sacrificing robustness against parametric uncertainties, modeling errors, and variable dynamic loads. This article discusses the hybridization of fuzzy logic with sliding mode control to provide highly excellent stability and accuracy in a control system. As a means of optimizing the fuzzy SMC, the gradient-free optimization technique known as the Jaya algorithm is investigated. By repeatedly altering a population of individual solutions, this population-based method can deal with both limited and unbounded optimization issues.
Hydrological Calibration in the Mount Lofty Ranges using Source Paramenter Es...eWater
The catchments of the Mount Lofty Ranges (MLR) provide a crucial water resource for the rural and urban community of Adelaide. The Source Catchments model is one planning tool used to assess current and future water, sediment and nutrient yields from the catchment. The modelling is critical for future planning to ensure a safe and reliable water supply is maintained.
Linking PEST and Source Catchments for hydrology calibration was an efficient and repeatable method for calibration. Future work to improve the process could include calibrating with other rainfall runoff models such as Sacramento which better account for groundwater losses or explicit representation of groundwater in the model.
This project has focused solely on rainfall and runoff. It has not considered how external impacts (e.g. climate change) may change catchment hydrological characteristics or constituent characteristics such as EMC/DWC values. Future projects through the Goyder Water Research Institute aim to address some of these
issues. This project may produce data which will inform further work in this respect.
Turbidity is a measure of water quality. Excessive turbidity poses a threat to health and causes pollution. Most of the available mathematical models of water treatment plants do not capture turbidity. A reliable model is essential for effective removal of turbidity in the water treatment plant. This paper presents a comparison of Hammerstein Wiener and neural network technique for estimating of turbidity in water treatment plant. The models were validated using an experimental data from Tamburawa water treatment plant in Kano, Nigeria. Simulation results demonstrated that the neural network model outperformed the Hammerstein-Wiener model in estimating the turbidity. The neural network model may serve as a valuable tool for predicting the turbidity in the plant.
Fredrick Ishengoma - A Novel Design of IEEE 802.15.4 and Solar Based Autonomo...Fredrick Ishengoma
The recently advancement in Wireless Sensor Network (WSN) technology has brought new distributed sensing applications such as water quality monitoring. With sensing capabilities and using parameters like pH, conductivity and temperature, the quality of water can be known. This paper proposes a novel design based on IEEE 802.15.4 (Zig-Bee protocol) and solar energy called Autonomous Water QualityMonitoring Prototype (AWQMP). The prototype is designed to use ECHERP routing protocol and Adruino Mega 2560, an open-source electronic prototyping platform for data acquisition. AWQMP is expected to give real time data acquirement and to reduce the cost of manual water quality monitoring due to its autonomous characteristic. Moreover, the proposed prototype will help to study the behavior of aquatic animals in deployed water bodies.
Due to environmental concern and certain constraint on building a new power plant, renewable energy particularly distributed generation photovoltaic (DGPV) has becomes one of the promising sources to cater the increasing energy demand of the power system. Furthermore, with appropriate location and sizing, the integration of DGPV to the grid will enhance the voltage stability and reduce the system losses. Hence, this paper proposed a new algorithm for DGPV optimal location and sizing of a transmission system based on minimization of Fast Voltage Stability Index (FVSI) with considering the system constraints. Chaotic Mutation Immune Evolutionary Programming (CMIEP) is developed by integrating the piecewise linear chaotic map (PWLCM) in the mutation process in order to increase the convergence rate of the algorithm. The simulation was applied on the IEEE 30 bus system with a variation of loads on Bus 30. The simulation results are also compared with Evolutionary Programming (EP) and Chaotic Evolutionary Programming (CEP) and it is found that CMIEP performed better in most of the cases.
Identification and real time position control of a servo-hydraulic rotary act...ISA Interchange
This paper presents a new intelligent approach for adaptive control of a nonlinear dynamic system. A modified version of the brain emotional learning based intelligent controller (BELBIC), a bio-inspired algorithm based upon a computational model of emotional learning which occurs in the amygdala, is utilized for position controlling a real laboratorial rotary electro-hydraulic servo (EHS) system. EHS systems are known to be nonlinear and non-smooth due to many factors such as leakage, friction, hysteresis, null shift, saturation, dead zone, and especially fluid flow expression through the servo valve. The large value of these factors can easily influence the control performance in the presence of a poor design. In this paper, a mathematical model of the EHS system is derived, and then the parameters of the model are identified using the recursive least squares method. In the next step, a BELBIC is designed based on this dynamic model and utilized to control the real laboratorial EHS system. To prove the effectiveness of the modified BELBIC’s online learning ability in reducing the overall tracking error, results have been compared to those obtained from an optimal PID controller, an auto-tuned fuzzy PI controller (ATFPIC), and a neural network predictive controller (NNPC) under similar circumstances. The results demonstrate not only excellent improvement in control action, but also less energy consumption.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Performance Analysis of Faults Detection in Wind Turbine Generator Based on H...chokrio
Electrical energy production based on wind power has become the most popular renewable resources in the recent years because it gets reliable clean energy with minimum cost. The major challenge for wind turbines is the electrical and the mechanical failures which can occur at any time causing prospective breakdowns and damages and therefore it leads to machine downtimes and to energy production loss. To circumvent this problem, several tools and techniques have been developed and used to enhance fault detection and diagnosis to be found in the stator current signature for wind turbines generators. Among these methods, parametric or super-resolution frequency estimation methods, which provides typical spectrum estimation, can be useful for this purpose. Facing on the plurality of these algorithms, a comparative performance analysis is made to evaluate robustness based on different metrics: accuracy, dispersion, computation cost, perturbations and faults severity. Finally, simulation results in MATLAB with most occurring faults indicate that ESPRIT and R-MUSIC algorithms have high capability of correctly identifying the frequencies of fault characteristic components, a performance ranking had been carried out to demonstrate the efficiency of the studied methods in faults detecting.
Similar to Performance comparison of SVM and ANN for aerobic granular sludge (20)
Square transposition: an approach to the transposition process in block cipherjournalBEEI
The transposition process is needed in cryptography to create a diffusion effect on data encryption standard (DES) and advanced encryption standard (AES) algorithms as standard information security algorithms by the National Institute of Standards and Technology. The problem with DES and AES algorithms is that their transposition index values form patterns and do not form random values. This condition will certainly make it easier for a cryptanalyst to look for a relationship between ciphertexts because some processes are predictable. This research designs a transposition algorithm called square transposition. Each process uses square 8 × 8 as a place to insert and retrieve 64-bits. The determination of the pairing of the input scheme and the retrieval scheme that have unequal flow is an important factor in producing a good transposition. The square transposition can generate random and non-pattern indices so that transposition can be done better than DES and AES.
Hyper-parameter optimization of convolutional neural network based on particl...journalBEEI
Deep neural networks have accomplished enormous progress in tackling many problems. More specifically, convolutional neural network (CNN) is a category of deep networks that have been a dominant technique in computer vision tasks. Despite that these deep neural networks are highly effective; the ideal structure is still an issue that needs a lot of investigation. Deep Convolutional Neural Network model is usually designed manually by trials and repeated tests which enormously constrain its application. Many hyper-parameters of the CNN can affect the model performance. These parameters are depth of the network, numbers of convolutional layers, and numbers of kernels with their sizes. Therefore, it may be a huge challenge to design an appropriate CNN model that uses optimized hyper-parameters and reduces the reliance on manual involvement and domain expertise. In this paper, a design architecture method for CNNs is proposed by utilization of particle swarm optimization (PSO) algorithm to learn the optimal CNN hyper-parameters values. In the experiment, we used Modified National Institute of Standards and Technology (MNIST) database of handwritten digit recognition. The experiments showed that our proposed approach can find an architecture that is competitive to the state-of-the-art models with a testing error of 0.87%.
Supervised machine learning based liver disease prediction approach with LASS...journalBEEI
In this contemporary era, the uses of machine learning techniques are increasing rapidly in the field of medical science for detecting various diseases such as liver disease (LD). Around the globe, a large number of people die because of this deadly disease. By diagnosing the disease in a primary stage, early treatment can be helpful to cure the patient. In this research paper, a method is proposed to diagnose the LD using supervised machine learning classification algorithms, namely logistic regression, decision tree, random forest, AdaBoost, KNN, linear discriminant analysis, gradient boosting and support vector machine (SVM). We also deployed a least absolute shrinkage and selection operator (LASSO) feature selection technique on our taken dataset to suggest the most highly correlated attributes of LD. The predictions with 10 fold cross-validation (CV) made by the algorithms are tested in terms of accuracy, sensitivity, precision and f1-score values to forecast the disease. It is observed that the decision tree algorithm has the best performance score where accuracy, precision, sensitivity and f1-score values are 94.295%, 92%, 99% and 96% respectively with the inclusion of LASSO. Furthermore, a comparison with recent studies is shown to prove the significance of the proposed system.
A secure and energy saving protocol for wireless sensor networksjournalBEEI
The research domain for wireless sensor networks (WSN) has been extensively conducted due to innovative technologies and research directions that have come up addressing the usability of WSN under various schemes. This domain permits dependable tracking of a diversity of environments for both military and civil applications. The key management mechanism is a primary protocol for keeping the privacy and confidentiality of the data transmitted among different sensor nodes in WSNs. Since node's size is small; they are intrinsically limited by inadequate resources such as battery life-time and memory capacity. The proposed secure and energy saving protocol (SESP) for wireless sensor networks) has a significant impact on the overall network life-time and energy dissipation. To encrypt sent messsages, the SESP uses the public-key cryptography’s concept. It depends on sensor nodes' identities (IDs) to prevent the messages repeated; making security goals- authentication, confidentiality, integrity, availability, and freshness to be achieved. Finally, simulation results show that the proposed approach produced better energy consumption and network life-time compared to LEACH protocol; sensors are dead after 900 rounds in the proposed SESP protocol. While, in the low-energy adaptive clustering hierarchy (LEACH) scheme, the sensors are dead after 750 rounds.
Plant leaf identification system using convolutional neural networkjournalBEEI
This paper proposes a leaf identification system using convolutional neural network (CNN). This proposed system can identify five types of local Malaysia leaf which were acacia, papaya, cherry, mango and rambutan. By using CNN from deep learning, the network is trained from the database that acquired from leaf images captured by mobile phone for image classification. ResNet-50 was the architecture has been used for neural networks image classification and training the network for leaf identification. The recognition of photographs leaves requested several numbers of steps, starting with image pre-processing, feature extraction, plant identification, matching and testing, and finally extracting the results achieved in MATLAB. Testing sets of the system consists of 3 types of images which were white background, and noise added and random background images. Finally, interfaces for the leaf identification system have developed as the end software product using MATLAB app designer. As a result, the accuracy achieved for each training sets on five leaf classes are recorded above 98%, thus recognition process was successfully implemented.
Customized moodle-based learning management system for socially disadvantaged...journalBEEI
This study aims to develop Moodle-based LMS with customized learning content and modified user interface to facilitate pedagogical processes during covid-19 pandemic and investigate how teachers of socially disadvantaged schools perceived usability and technology acceptance. Co-design process was conducted with two activities: 1) need assessment phase using an online survey and interview session with the teachers and 2) the development phase of the LMS. The system was evaluated by 30 teachers from socially disadvantaged schools for relevance to their distance learning activities. We employed computer software usability questionnaire (CSUQ) to measure perceived usability and the technology acceptance model (TAM) with insertion of 3 original variables (i.e., perceived usefulness, perceived ease of use, and intention to use) and 5 external variables (i.e., attitude toward the system, perceived interaction, self-efficacy, user interface design, and course design). The average CSUQ rating exceeded 5.0 of 7 point-scale, indicated that teachers agreed that the information quality, interaction quality, and user interface quality were clear and easy to understand. TAM results concluded that the LMS design was judged to be usable, interactive, and well-developed. Teachers reported an effective user interface that allows effective teaching operations and lead to the system adoption in immediate time.
Understanding the role of individual learner in adaptive and personalized e-l...journalBEEI
Dynamic learning environment has emerged as a powerful platform in a modern e-learning system. The learning situation that constantly changing has forced the learning platform to adapt and personalize its learning resources for students. Evidence suggested that adaptation and personalization of e-learning systems (APLS) can be achieved by utilizing learner modeling, domain modeling, and instructional modeling. In the literature of APLS, questions have been raised about the role of individual characteristics that are relevant for adaptation. With several options, a new problem has been raised where the attributes of students in APLS often overlap and are not related between studies. Therefore, this study proposed a list of learner model attributes in dynamic learning to support adaptation and personalization. The study was conducted by exploring concepts from the literature selected based on the best criteria. Then, we described the results of important concepts in student modeling and provided definitions and examples of data values that researchers have used. Besides, we also discussed the implementation of the selected learner model in providing adaptation in dynamic learning.
Prototype mobile contactless transaction system in traditional markets to sup...journalBEEI
One way to prevent and reduce the spread of the covid-19 pandemic is through physical distancing program. This research aims to develop a prototype contactless transaction system using digital payment mechanisms and QR code technology that will be applied in traditional markets. The method used in the development of electronic market systems is a prototype approach. The application of QR code and digital payments are used as a solution to minimize money exchange contacts that are common in traditional markets. The results showed that the system built was able to accelerate and facilitate the buying and selling transaction process in traditional market environment. Alpha testing shows that all functional systems are running well. Meanwhile, beta testing shows that the user can very well accept the system that was built. The results of the study also show acceptance of the usefulness of the system being built, as well as the optimism of its users to be able to take advantage of this system both technologically and functionally, so its can be a part of the digital transformation of the traditional market to the electronic market and has become one of the solutions in reducing the spread of the current covid-19 pandemic.
Wireless HART stack using multiprocessor technique with laxity algorithmjournalBEEI
The use of a real-time operating system is required for the demarcation of industrial wireless sensor network (IWSN) stacks (RTOS). In the industrial world, a vast number of sensors are utilised to gather various types of data. The data gathered by the sensors cannot be prioritised ahead of time. Because all of the information is equally essential. As a result, a protocol stack is employed to guarantee that data is acquired and processed fairly. In IWSN, the protocol stack is implemented using RTOS. The data collected from IWSN sensor nodes is processed using non-preemptive scheduling and the protocol stack, and then sent in parallel to the IWSN's central controller. The real-time operating system (RTOS) is a process that occurs between hardware and software. Packets must be sent at a certain time. It's possible that some packets may collide during transmission. We're going to undertake this project to get around this collision. As a prototype, this project is divided into two parts. The first uses RTOS and the LPC2148 as a master node, while the second serves as a standard data collection node to which sensors are attached. Any controller may be used in the second part, depending on the situation. Wireless HART allows two nodes to communicate with each other.
Implementation of double-layer loaded on octagon microstrip yagi antennajournalBEEI
A double-layer loaded on the octagon microstrip yagi antenna (OMYA) at 5.8 GHz industrial, scientific and medical (ISM) Band is investigated in this paper. The double-layer consist of two double positive (DPS) substrates. The OMYA is overlaid with a double-layer configuration were simulated, fabricated and measured. A good agreement was observed between the computed and measured results of the gain for this antenna. According to comparison results, it shows that 2.5 dB improvement of the OMYA gain can be obtained by applying the double-layer on the top of the OMYA. Meanwhile, the bandwidth of the measured OMYA with the double-layer is 14.6%. It indicates that the double-layer can be used to increase the OMYA performance in term of gain and bandwidth.
The calculation of the field of an antenna located near the human headjournalBEEI
In this work, a numerical calculation was carried out in one of the universal programs for automatic electro-dynamic design. The calculation is aimed at obtaining numerical values for specific absorbed power (SAR). It is the SAR value that can be used to determine the effect of the antenna of a wireless device on biological objects; the dipole parameters will be selected for GSM1800. Investigation of the influence of distance to a cell phone on radiation shows that absorbed in the head of a person the effect of electromagnetic radiation on the brain decreases by three times this is a very important result the SAR value has decreased by almost three times it is acceptable results.
Exact secure outage probability performance of uplinkdownlink multiple access...journalBEEI
In this paper, we study uplink-downlink non-orthogonal multiple access (NOMA) systems by considering the secure performance at the physical layer. In the considered system model, the base station acts a relay to allow two users at the left side communicate with two users at the right side. By considering imperfect channel state information (CSI), the secure performance need be studied since an eavesdropper wants to overhear signals processed at the downlink. To provide secure performance metric, we derive exact expressions of secrecy outage probability (SOP) and and evaluating the impacts of main parameters on SOP metric. The important finding is that we can achieve the higher secrecy performance at high signal to noise ratio (SNR). Moreover, the numerical results demonstrate that the SOP tends to a constant at high SNR. Finally, our results show that the power allocation factors, target rates are main factors affecting to the secrecy performance of considered uplink-downlink NOMA systems.
Design of a dual-band antenna for energy harvesting applicationjournalBEEI
This report presents an investigation on how to improve the current dual-band antenna to enhance the better result of the antenna parameters for energy harvesting application. Besides that, to develop a new design and validate the antenna frequencies that will operate at 2.4 GHz and 5.4 GHz. At 5.4 GHz, more data can be transmitted compare to 2.4 GHz. However, 2.4 GHz has long distance of radiation, so it can be used when far away from the antenna module compare to 5 GHz that has short distance in radiation. The development of this project includes the scope of designing and testing of antenna using computer simulation technology (CST) 2018 software and vector network analyzer (VNA) equipment. In the process of designing, fundamental parameters of antenna are being measured and validated, in purpose to identify the better antenna performance.
Transforming data-centric eXtensible markup language into relational database...journalBEEI
eXtensible markup language (XML) appeared internationally as the format for data representation over the web. Yet, most organizations are still utilising relational databases as their database solutions. As such, it is crucial to provide seamless integration via effective transformation between these database infrastructures. In this paper, we propose XML-REG to bridge these two technologies based on node-based and path-based approaches. The node-based approach is good to annotate each positional node uniquely, while the path-based approach provides summarised path information to join the nodes. On top of that, a new range labelling is also proposed to annotate nodes uniquely by ensuring the structural relationships are maintained between nodes. If a new node is to be added to the document, re-labelling is not required as the new label will be assigned to the node via the new proposed labelling scheme. Experimental evaluations indicated that the performance of XML-REG exceeded XMap, XRecursive, XAncestor and Mini-XML concerning storing time, query retrieval time and scalability. This research produces a core framework for XML to relational databases (RDB) mapping, which could be adopted in various industries.
Key performance requirement of future next wireless networks (6G)journalBEEI
Given the massive potentials of 5G communication networks and their foreseeable evolution, what should there be in 6G that is not in 5G or its long-term evolution? 6G communication networks are estimated to integrate the terrestrial, aerial, and maritime communications into a forceful network which would be faster, more reliable, and can support a massive number of devices with ultra-low latency requirements. This article presents a complete overview of potential 6G communication networks. The major contribution of this study is to present a broad overview of key performance indicators (KPIs) of 6G networks that cover the latest manufacturing progress in the environment of the principal areas of research application, and challenges.
Noise resistance territorial intensity-based optical flow using inverse confi...journalBEEI
This paper presents the use of the inverse confidential technique on bilateral function with the territorial intensity-based optical flow to prove the effectiveness in noise resistance environment. In general, the image’s motion vector is coded by the technique called optical flow where the sequences of the image are used to determine the motion vector. But, the accuracy rate of the motion vector is reduced when the source of image sequences is interfered by noises. This work proved that the inverse confidential technique on bilateral function can increase the percentage of accuracy in the motion vector determination by the territorial intensity-based optical flow under the noisy environment. We performed the testing with several kinds of non-Gaussian noises at several patterns of standard image sequences by analyzing the result of the motion vector in a form of the error vector magnitude (EVM) and compared it with several noise resistance techniques in territorial intensity-based optical flow method.
Modeling climate phenomenon with software grids analysis and display system i...journalBEEI
This study aims to model climate change based on rainfall, air temperature, pressure, humidity and wind with grADS software and create a global warming module. This research uses 3D model, define, design, and develop. The results of the modeling of the five climate elements consist of the annual average temperature in Indonesia in 2009-2015 which is between 29oC to 30.1oC, the horizontal distribution of the annual average pressure in Indonesia in 2009-2018 is between 800 mBar to 1000 mBar, the horizontal distribution the average annual humidity in Indonesia in 2009 and 2011 ranged between 27-57, in 2012-2015, 2017 and 2018 it ranged between 30-60, during the East Monsoon, the wind circulation moved from northern Indonesia to the southern region Indonesia. During the west monsoon, the wind circulation moves from the southern part of Indonesia to the northern part of Indonesia. The global warming module for SMA/MA produced is feasible to use, this is in accordance with the value given by the validate of 69 which is in the appropriate category and the response of teachers and students through a 91% questionnaire.
An approach of re-organizing input dataset to enhance the quality of emotion ...journalBEEI
The purpose of this paper is to propose an approach of re-organizing input data to recognize emotion based on short signal segments and increase the quality of emotional recognition using physiological signals. MIT's long physiological signal set was divided into two new datasets, with shorter and overlapped segments. Three different classification methods (support vector machine, random forest, and multilayer perceptron) were implemented to identify eight emotional states based on statistical features of each segment in these two datasets. By re-organizing the input dataset, the quality of recognition results was enhanced. The random forest shows the best classification result among three implemented classification methods, with an accuracy of 97.72% for eight emotional states, on the overlapped dataset. This approach shows that, by re-organizing the input dataset, the high accuracy of recognition results can be achieved without the use of EEG and ECG signals.
Parking detection system using background subtraction and HSV color segmentationjournalBEEI
Manual system vehicle parking makes finding vacant parking lots difficult, so it has to check directly to the vacant space. If many people do parking, then the time needed for it is very much or requires many people to handle it. This research develops a real-time parking system to detect parking. The system is designed using the HSV color segmentation method in determining the background image. In addition, the detection process uses the background subtraction method. Applying these two methods requires image preprocessing using several methods such as grayscaling, blurring (low-pass filter). In addition, it is followed by a thresholding and filtering process to get the best image in the detection process. In the process, there is a determination of the ROI to determine the focus area of the object identified as empty parking. The parking detection process produces the best average accuracy of 95.76%. The minimum threshold value of 255 pixels is 0.4. This value is the best value from 33 test data in several criteria, such as the time of capture, composition and color of the vehicle, the shape of the shadow of the object’s environment, and the intensity of light. This parking detection system can be implemented in real-time to determine the position of an empty place.
Quality of service performances of video and voice transmission in universal ...journalBEEI
The universal mobile telecommunications system (UMTS) has distinct benefits in that it supports a wide range of quality of service (QoS) criteria that users require in order to fulfill their requirements. The transmission of video and audio in real-time applications places a high demand on the cellular network, therefore QoS is a major problem in these applications. The ability to provide QoS in the UMTS backbone network necessitates an active QoS mechanism in order to maintain the necessary level of convenience on UMTS networks. For UMTS networks, investigation models for end-to-end QoS, total transmitted and received data, packet loss, and throughput providing techniques are run and assessed and the simulation results are examined. According to the results, appropriate QoS adaption allows for specific voice and video transmission. Finally, by analyzing existing QoS parameters, the QoS performance of 4G/UMTS networks may be improved.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
2. Bulletin of Electr Eng and Inf ISSN: 2302-9285
Performance comparison of SVM and ANN for aerobic granular sludge (Nur Sakinah Ahmad Yasmin)
1393
outsized number of stoichiometric and kinetic parameters that should be determined by specialist process
operation, they may not be practical for on-line plant control. Besides, it may not provide good prediction of
plant if other current process such as sedimentation are not included in the overall plant evaluation.
A popular Artificial Intelligent (AI) such as artificial neural network (ANN) methods has been
widely used for prediction for WWTP due to its good capability in modelling and prediction of process
performance [2]. However, compared with multiple linear regression or other conventional methods [3-5],
ANN methods shows high accuracy but large number of data is needed as some shortcomings will appeare
from its theoretical statical basis for instance accuracy of the prediction is not satisfying when the training
data set is small. As in against Support Vector Machine (SVM), has been proposed to solve this type of
problems as SVM is based on statistical learning theory [6]. SVM has several merits compared to ANN such
as efficient utilization of high-dimensional feature space, distinctively in solvable optimization problem and
has a good ability in theoretical analysis using computional learning theory [4].
As a highly approach for the model with limited training samples set, the SVM has been applied in
many field such prediction [7], patern recognition problem [8], regression [9, 10], classification [11] and time
series analysis [11, 12]. In addition, SVM has been reported outperformed traditional statistical learning
methods such as ANN. Thus, more researcher are focusing on this method as SVM has been arousing more.
For example, in order to make a beginner learner to understand SVM regression and classification were
reviewed by Smola et al. [13] and Burges et al. [14], respectively. In addition, a library for SVM has been
develop by [15] in order to help beginners to easily apply SVM according to their field of application.
Furthermore, Noble described the explanation of SVM and its biological applications [16]. Specifically, Xu
et al. adopted SVM for chemometrics in term of classification [17]. While Jia. et al. showed that SVM was
able to predict the synthesis characteristic of hydraulic valve in industrial production [8]. Other than that,
Liang et al. had done an effective approach on SVM for content based on sketch retrival [18]. Evaluation of
using various method such as wavelets, PCA and SVM has been proposed by Gumus et al [19]. All these
studies show all the application and had make a good contribution of SVM.
The example of implementation of SVM with limited dataset in WWTP can be found in [20], which
developed SVM model to study the relationship between membrane bioreactor and affecting factors that
cause fouling. The study reported that SVM model can assure a good prediction result compared to RBFNN
model since the depending of the training dataset for SVM is lesser than RBFNN. Other than that, W. Li Juan
et al. [21] done a research on predicting effluent quality of WWTP plant with cyclic activated sludge process
(ASP) using SVM model. The SVM model was reported to gives a high accuracy in predicting quality
effluent with small learning ability and have a good generalization. X. Xi et al. [22], presented SVM model
that was applied to predict the permeate flux and rejection of Bovine serum albumin (BSA). The result shows
SVM model more accurate than ANN model. This is due to ANN model need to rely on the ones expertise
and experience while SVM is based on statistical theory. K. Gao et al. [23], developed SVM model for
membrane permeate flux during dead-end microfiltration of activated sludge reactor. The SVM model were
then compared with BPNN model shows SVM model obtained higher accuracy than BPNN. The researcher
concluded that in small sample size data, SVM yield better accuracy than BPNN model. This is because
SVM has a rigorous foundation either in mathematical or theoretical and it based on statistical theory despite
BPNN that needs to rely on the designer knowledge. Hence, that’s make SVM model only need small
number of data to achieved higher prediction performance. Therefore, this paper compares the SVM and
FFNN methods in term of their accuracy performance of the model using a limited experimental dataset of
SBR aerobic granular sludge nutrient removal process. The aim of this paper is to minimize the correlation
and mean squeare error of the training dataset. The 60 days’ data (with 21 samples) under the temperature of
50oC is utilized to develop the model for AGS.
This paper consists of five sections and are organized as follows. The first section will introduce on
background study and relavent literarature on previous study on the proposed method. The second section
will be presented SVM and FFNN structure with the flow of the modelling method. The detailed of the
experimental setup of this study is explained in the third section. In the fouth section is the result and
discussion. The result is compared and discussed in this section. The fifth section gives conclusion and some
future works.
2. EXPERIMENT SETUP
Experiment setup in this study were carried out in three double-walled cylindrical column bioreactor
with internal diameter of 6.5 cm and height of 100 cm. The sequencing bioreactor sequence (SBR) such as
aerator pump, influent feeding and effluent discharge were controlled by programmable logic controller
(PLC). The working temperature for the bioreactor was set to 30, 40 and 50 ± 1°C and controlled using
thermostat and water bath sleeves. The schematic diagram of the AGS system is shown in Figure 1.
3. ISSN: 2302-9285
Bulletin of Electr Eng and Inf, Vol. 8, No. 4, December 2019 : 1392 – 1401
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Figure 1. Schematic diagram of laboratory scale aerobic granular sludge bioreactor
In this work, the SBR operated at 50 ± 1°C is considered. The influent parameters that used as input
of the model are chemical oxygen demand (COD), total nitrogen (TN), total phosphorus (TP), amnonia
nitrogen (AN) and mixed liquor suspended solid (MLSS) while the same influent parameters are considered
as an effluent or output of the model (COD, TN, TP, AN and MLSS). Figure 2 shows (a) Total Phosphorus
(TP), Total Nitrogen (TN), and Amonia Nitrogen (AN) influent dataset, (b) Total Phosphorus (TP), Total
Nitrogen (TN), and Amonia Nitrogen (AN) effluent dataset, (c) Chemical Oxygen Demand (COD)
concentration dataset, (d) Mixed Liquor Suspended Solid (MLSS) dataset.
(a) (b)
4. Bulletin of Electr Eng and Inf ISSN: 2302-9285
Performance comparison of SVM and ANN for aerobic granular sludge (Nur Sakinah Ahmad Yasmin)
1395
(c) (d)
Figure 2. (a) Total Phosphorus (TP), Total Nitrogen (TN), and Amonia Nitrogen (AN) influent dataset, (b)
Total Phosphorus (TP), Total Nitrogen (TN), and Amonia Nitrogen (AN) effluent dataset, (c) Chemical
Oxygen Demand (COD) concentration dataset, (d) Mixed Liquor Suspended Solid (MLSS) dataset
3. RESULTS METHOD
Support Vector Machine (SVM) is first introduced by Vapnik [24] which is based on the idea of
structural risk management. SVM is basically a supervised learning method that is capable in generating
predictive models and used for classifiying unseen patterns into their categories. Quite recently, SVM method
has been extended in solving function estimation and regression problem. The algorithm of SVM method is
briefly explained in the following section.
3.1. Support vector machine
The basic theory of Support Vector Machine (SVM) regression function is expressed as in (1) [9].
y=f (x)=.x+b (1)
where y is scalar output, , x is a weight vector, x is multivariate input and b is bias. By introducing slack
variables, the SVM model can be expressed by (2) and (3).
Minimize ( ) ∑ ( ) (2)
Subject to: { (3)
The new predicted SVM for each xis determined by evaluating (4)
b
x
x
y
L
i
i
i
i
1
.
)
(
(4)
3.1.1. Validation of SVM model
The division processes of normalized sequencing batch reactor data are determined by three folds
cross validation concept based on two folds for training and onefold for testing. The cross validation is to
access the performance of the SVM model besides checking the suitable parameters of cost of penalty, and
gamma, value to be used. The best model is selected based on the accuracy performance correlation (R2) and
mean square error (MSE) value. Figure 3 shows the flowchart of SVM method.
5. ISSN: 2302-9285
Bulletin of Electr Eng and Inf, Vol. 8, No. 4, December 2019 : 1392 – 1401
1396
Figure 3. Flow chart of SVM prediction model
3.2. Feed-forward neural network
Basic theory of Feed forward neural network (FFNN) is the combination multilayer network. First
layer is where the input from external input received. The second layer will be connected from the previous
layer neurons. This layer will continue until it reached the final output layer neuron. Figure 4 shows the basic
FFNN structure.
Figure 4. Feed forward neural network structure
This network is represented by (5);
b
x
x
y
L
i
i
i
i
1
.
)
(
(5)
YES
Start
Load Data
Data Division
SVM configuration
(SVM kernel, cost and gamma)
SVM Training
SVM Testing
Data denormalization
End
Preprocesing Data
Test the model
NO
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Where )
(t
yi is the model output of FFNN, i
F is a function of the network, ij
W and ij
w is the connection
layer of biases and weight, is input layer. Figure 5 shows the flowchart of FFNN method.
Figure 5. Flow chart of SVM prediction model
4. RESULT AND DISCUSSION
In general, both SVM and ANN techniques can provide good results in training and testing,
although in terms of accuracy of the models, SVM is preferable to ANN. The model prediction of AGS
system considered in this work are COD, TP, AN and AN variables. Figure 6(a) shows the COD evaluation
results during training dataset. The SVM method predicted a good results as it achived 99.99% for R2 and
0.001 for MSE while FFNN achived slightly lower than SVM which is 92.52% for R2 and 0.0047 for MSE.
In COD estimation for testing data, SVM gives higher accurate result compared with FFNN method for all
evaluation criterion. Figure 6(b) shows the comparison prediction result performace of COD for testing
where SVM result score are 99.84% and 0.0 while FFNN score are 96.44% and 0.0021 for R2 and MSE
value, respectively. The COD estimation for overall training and testing performance result as shown in
Table 1.
Results for TP training data shows good accuracy performance for both model techniques. The R2
result for both model SVM and FFNN achieved more than 90%. The R2 value for SVM and FFNN are
96.34% and 93.36% while MSE are 0.0017 and 0.0047 respectively. Figure 7(a) shows the comparison graph
of TP for both model SVM and FFNN. The testing result for TP estimation shows higher prediction value for
SVM model compared to FFNN model. The scored of both testing value for R2 are 99.73% and 95.46%
while MSE results are 0.0003 and 0.0044 respectively. Figure 7(b) shows the testing data result. The
performance results of TP for both training and testing are shown in Table 2.
YES
Start
Load Data
Data Division
Select no. of neurons in hidden
layer
Create network
Train network
Data denormalization
End
Preprocesing Data (normalization)
Test the model
NO
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(a) (b)
Figure 6. (a) COD training result for SVM and ANN, and (b) COD testing result for SVM and ANN
Table 1. COD model evaluation for training and testing
Model Training Testing
R2
(%) MSE R2
(%) MSE
SVM 99.99 0.0001 99.84 0.0000
FFNN 92.53 0.0047 96.44 0.0021
Table 2. TP testing result for SVM and ANN
Model Training Testing
R2
(%) MSE R2
(%) MSE
SVM 96.34 0.0017 99.73 0.0003
FFNN 93.36 0.0047 95.46 0.0044
(a) (b)
Figure 7. (a) TP training result for SVM and ANN, (b) TP testing result for SVM and ANN
The TN results show good prediction result for SVM method compared to FFNN method. The R2
for training and testing of SVM method both achieved 90% above and MSE 0.0006 while for FFNN method,
the R2 and MSE is slightly lower prediction with 75.31% and 0.0022 respectively. During testing, R2 and
MSE achieved 95.69 and 0.002. Figure 8(a) and 8(b) show an overall performance for both methods. Table 3
presents the performance comparison of both methods.
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(a) (b)
Figure 8. (a) TN training result for SVM and ANN, (b) TN testing result for SVM and ANN
The result for AN estimation shows acceptable accuracy from both methods. During AN testing,
SVM methods achieved 95.09% and 0.0036 for R2 and MSE value, respectively. For FFNN, the achieved R2
and MSE are 86.06% and 0.0101, respectively. During testing, SVM achieved the R2 of 90.12% and MSE of
0.0012 which is more accurate than FFNN with R2 of 80.48% and MSE of 0.0023. Figure 9(a) and 9(b) show
the AN output response between SVM and FFNN during training and testing. Table 4 indicates an overall
performance for both methods.
(a) (b)
Figure 9. (a) AN training result for SVM and ANN, (b) AN testing result for SVM and ANN
Table 3. TN testing result for SVM and ANN
Model Training Testing
R2
(%) MSE R2
(%) MSE
SVM 96.34 0.0017 99.45 0.0003
FFNN 93.36 0.0047 95.69 0.0020
Table 4. AN testing result for SVM and ANN
Model Training Testing
R2
(%) MSE R2
(%) MSE
SVM 95.09 0.0036 90.12 0.0012
FFNN 86.06 0.0101 80.48 0.0023
5. CONCLUSION
This paper presents the modelling techniques for aerobic granular sludge using SVM and compared
with feed-forward neural network (FFNN). From the simulation results, it can be observed that the training
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result using SVM produces better accuracy of model for the prediction at the temperature of 50˚C. This
provide an evident that SVM can give better prediction compared to FFNN model for limited training data.
To improve the computational time of SVM parameters in future, external optimizations such as gravitational
search algorithm (GSA), and Particle Swarm Optimization (PSO) can be considered.
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BIOGRAPHIES OF AUTHORS
Nur Sakinah Ahmad Yasmin is Master student in School of Electrical Engineering, Faculty of
Engineering, Universiti Teknologi Malaysia, Skudai, Johor. Received her bachelor degree in
Electrical Control & Instrument in Universiti Teknologi Malaysia, Skudai, Johor. Her area of
interest is applying data mining technique in Wastewater Treatment fields and application.
Dr. Norhaliza Abdul Wahab is currently an Associate Professor at Universiti Teknologi
Malaysia (UTM). She is currently the Director for Control and Mechatronic Engineering at the
School of Electrical Engineering, UTM. She completed her PhD in Electrical Engineering
majoring in Control in July 2009. She is actively involved in researching and teaching in the
field of industrial process control. Her expertise is in modelling and control of industrial process
plant. Recently she has worked primarily on different types of domestic and industrial water and
wastewater treatment technology towards optimization and energy saving system.
Dr. Aznah Nor Anuar is currently a Senior Lecturer at Department of Environmental and Green
Technology, Malaysia-Japan International Institute of Technology, Universiti Teknologi
Malaysia (UTM). She completed her PhD in Environmental Engineering and her thesis is on
Development of Aerobic Granular Sludge Technology for Hot Climate Countries, graduated in
July 2009. Her recent research interests in Environmental Engineering are in the area of
wastewater-related disaster risk management, microbial granulation for wastewater treatment
and low cost-green-sustainable technology for water and wastewater management. At present,
she has published more than 15 journals, 15 expert reports and 70 proceedings that covers topic
of her research interests
Mustafa Bob has completed his PhD at the age of 35 years from The Ohio State University and
he was a postdoctoral fellow with the National Research Council (NRC) in USA. He is currently
an assistant professor at Taibah University in Saudi Arabia. He has published more than 10
papers in reputed journals and has been serving as a reviewer for a number of reputed journals.