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1
Mirbagheri, Sayed Ahmad, Majid Bagheri, Majid Ehteshami, Zahra Bagheri, and Masoud Pourasghar. "MODELING OF MIXED LIQUOR VOLATILE SUSPENDED SOLIDS AND PERFORMANCE EVALUATION FOR A SEQUENCING BATCH REACTOR." Journal of Urban and Environmental Engineering 9, no. 1 (December 27, 2015): 54–65. http://dx.doi.org/10.4090/juee.2015.v9n1.054065.
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This study examined carbon, nitrogen and phosphorous removal from municipal wastewater in a sequencing batch reactor and biokinetic coefficients were evaluated according to results of BOD and COD. Furthermore, the MLVSS in the aeration reactor was modeled by using multilayer perceptron and radial basis function artificial neural networks (MLPANN and RBFANN). The experiments were performed so that the cell retention time, filling time and intensity of aeration were (5, 10 and 15 d), (1, 2 and 3 h) and (weak, medium and strong) respectively. The result indicated that with cell retention time of 15 d, filling time of 1 h, aeration time of 6 h and settling time of 3 h the HRT is optimized at 10 h. The BOD5, COD, TP, TN and removal efficiencies were 97.13%, 94.58%, 94.27%, 89.7% and 92.75% respectively. The yield coefficient (Y), decay coefficient (Kd), maximum specific growth rate (K) and saturation constant (Ks) were 6.22 mgVSS/mgCOD, 0.002 1/d, 0.029 1/d and 20 mg COD/L according to COD experimental data. The values of the biokinetic coefficients were found to be as follows: Y = 10.45 mgVSS/mgBOD, Kd = 0.01 1/d, 0.014 1/d and 3.38 mgBOD/L according to BOD5 experimental data. The training procedures for simulation of MLVSS were highly collaborated for both RBFANN and MLPANN. The train and test models for both MLPANN and RBFANN demonstrated perfectly matched results between the experimental and the simulated values of MLVSS. The values of RMSE for train and test (verification) models obtained by MLPANN were 31.82 and 40.25 mg/L respectively, and the value of R2 was 0.99 for both models. The values of RMSE for train and test models obtained by MLPANN were 69.04 and 43.87 mg/L respectively, and the value of R2 was 0.99 for both models. It was observed that the MLPANN has stronger approximation and generalization ability than the RBFANN with regard to our experimental data for MLVSS.
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Mirbagheri, Sayed Ahmad, Majid Bagheri, Majid Ehteshami, Zahra Bagheri, and Masoud Pourasghar. "MODELING OF MIXED LIQUOR VOLATILE SUSPENDED SOLIDS AND PERFORMANCE EVALUATION FOR A SEQUENCING BATCH REACTOR." Journal of Urban and Environmental Engineering 9, no. 1 (December 27, 2015): 54–65. http://dx.doi.org/10.4090/juee.2015.v9n1.54-65.
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This study examined carbon, nitrogen and phosphorous removal from municipal wastewater in a sequencing batch reactor and biokinetic coefficients were evaluated according to results of BOD and COD. Furthermore, the MLVSS in the aeration reactor was modeled by using multilayer perceptron and radial basis function artificial neural networks (MLPANN and RBFANN). The experiments were performed so that the cell retention time, filling time and intensity of aeration were (5, 10 and 15 d), (1, 2 and 3 h) and (weak, medium and strong) respectively. The result indicated that with cell retention time of 15 d, filling time of 1 h, aeration time of 6 h and settling time of 3 h the HRT is optimized at 10 h. The BOD5, COD, TP, TN and removal efficiencies were 97.13%, 94.58%, 94.27%, 89.7% and 92.75% respectively. The yield coefficient (Y), decay coefficient (Kd), maximum specific growth rate (K) and saturation constant (Ks) were 6.22 mgVSS/mgCOD, 0.002 1/d, 0.029 1/d and 20 mg COD/L according to COD experimental data. The values of the biokinetic coefficients were found to be as follows: Y = 10.45 mgVSS/mgBOD, Kd = 0.01 1/d, 0.014 1/d and 3.38 mgBOD/L according to BOD5 experimental data. The training procedures for simulation of MLVSS were highly collaborated for both RBFANN and MLPANN. The train and test models for both MLPANN and RBFANN demonstrated perfectly matched results between the experimental and the simulated values of MLVSS. The values of RMSE for train and test (verification) models obtained by MLPANN were 31.82 and 40.25 mg/L respectively, and the value of R2 was 0.99 for both models. The values of RMSE for train and test models obtained by MLPANN were 69.04 and 43.87 mg/L respectively, and the value of R2 was 0.99 for both models. It was observed that the MLPANN has stronger approximation and generalization ability than the RBFANN with regard to our experimental data for MLVSS.
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Alizamir, Meysam, Zahra Kazemi, Zohre Kazemi, Majid Kermani, Sungwon Kim, Salim Heddam, Ozgur Kisi, and Il-Moon Chung. "Investigating Landfill Leachate and Groundwater Quality Prediction Using a Robust Integrated Artificial Intelligence Model: Grey Wolf Metaheuristic Optimization Algorithm and Extreme Learning Machine." Water 15, no. 13 (July 4, 2023): 2453. http://dx.doi.org/10.3390/w15132453.
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The likelihood of surface water and groundwater contamination is higher in regions close to landfills due to the possibility of leachate percolation, which is a potential source of pollution. Therefore, proposing a reliable framework for monitoring leachate and groundwater parameters is an essential task for the managers and authorities of water quality control. For this purpose, an efficient hybrid artificial intelligence model based on grey wolf metaheuristic optimization algorithm and extreme learning machine (ELM-GWO) is used for predicting landfill leachate quality (COD and BOD5) and groundwater quality (turbidity and EC) at the Saravan landfill, Rasht, Iran. In this study, leachate and groundwater samples were collected from the Saravan landfill and monitoring wells. Moreover, the concentration of different physico-chemical parameters and heavy metal concentration in leachate (Cd, Cr, Cu, Fe, Ni, Pb, Mn, Zn, turbidity, Ca, Na, NO3, Cl, K, COD, and BOD5) and in groundwater (Cd, Cr, Cu, Fe, Ni, Pb, Mn, Zn, turbidity, EC, TDS, pH, Cl, Na, NO3, and K). The results obtained from ELM-GWO were compared with four different artificial intelligence models: multivariate adaptive regression splines (MARS), extreme learning machine (ELM), multilayer perceptron artificial neural network (MLPANN), and multilayer perceptron artificial neural network integrated with grey wolf metaheuristic optimization algorithm (MLPANN-GWO). The results of this study confirm that ELM-GWO considerably enhanced the predictive performance of the MLPANN-GWO, ELM, MLPANN, and MARS models in terms of the root-mean-square error, respectively, by 43.07%, 73.88%, 74.5%, and 88.55% for COD; 23.91%, 59.31%, 62.85%, and 77.71% for BOD5; 14.08%, 47.86%, 53.43%, and 57.04% for turbidity; and 38.57%, 59.64%, 67.94%, and 74.76% for EC. Therefore, ELM-GWO can be applied as a robust approach for investigating leachate and groundwater quality parameters in different landfill sites.
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Mobasser, Farid, and Keyvan Hashtrudi-Zaad. "A Comparative Approach to Hand Force Estimation using Artificial Neural Networks." Biomedical Engineering and Computational Biology 4 (January 2012): BECB.S9335. http://dx.doi.org/10.4137/becb.s9335.
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In many applications that include direct human involvement such as control of prosthetic arms, athletic training, and studying muscle physiology, hand force is needed for control, modeling and monitoring purposes. The use of inexpensive and easily portable active electromyography (EMG) electrodes and position sensors would be advantageous in these applications compared to the use of force sensors which are often very expensive and require bulky frames. Among non-model-based estimation methods, Multilayer Perceptron Artificial Neural Networks (MLPANN) has widely been used to estimate muscle force or joint torque from different anatomical features in humans or animals. This paper investigates the use of Radial Basis Function (RBF) ANN and MLPANN for force estimation and experimentally compares the performance of the two methodologies for the same human anatomy, ie, hand force estimation, under an ensemble of operational conditions. In this unified study, the EMG signal readings from upper-arm muscles involved in elbow joint movement and elbow angular position and velocity are utilized as inputs to the ANNs. In addition, the use of the elbow angular acceleration signal as an input for the ANNs is also investigated.
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Alizamir, Meysam, Kaywan Othman Ahmed, Sungwon Kim, Salim Heddam, AliReza Docheshmeh Gorgij, and Sun Woo Chang. "Development of a robust daily soil temperature estimation in semi-arid continental climate using meteorological predictors based on computational intelligent paradigms." PLOS ONE 18, no. 12 (December 27, 2023): e0293751. http://dx.doi.org/10.1371/journal.pone.0293751.
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Changes in soil temperature (ST) play an important role in the main mechanisms within the soil, including biological and chemical activities. For instance, they affect the microbial community composition, the speed at which soil organic matter breaks down and becomes minerals. Moreover, the growth and physiological activity of plants are directly influenced by the ST. Additionally, ST indirectly affects plant growth by influencing the accessibility of nutrients in the soil. Therefore, designing an efficient tool for ST estimating at different depths is useful for soil studies by considering meteorological parameters as input parameters, maximal air temperature, minimal air temperature, maximal air relative humidity, minimal air relative humidity, precipitation, and wind speed. This investigation employed various statistical metrics to evaluate the efficacy of the implemented models. These metrics encompassed the correlation coefficient (r), root mean square error (RMSE), Nash-Sutcliffe (NS) efficiency, and mean absolute error (MAE). Hence, this study presented several artificial intelligence-based models, MLPANN, SVR, RFR, and GPR for building robust predictive tools for daily scale ST estimation at 05, 10, 20, 30, 50, and 100cm soil depths. The suggested models are evaluated at two meteorological stations (i.e., Sulaimani and Dukan) located in Kurdistan region, Iraq. Based on assessment of outcomes of this study, the suggested models exhibited exceptional predictive capabilities and comparison of the results showed that among the proposed frameworks, GPR yielded the best results for 05, 10, 20, and 100cm soil depths, with RMSE values of 1.814°C, 1.652°C, 1.773°C, and 2.891°C, respectively. Also, for 50cm soil depth, MLPANN performed the best with an RMSE of 2.289°C at Sulaimani station using the RMSE during the validation phase. Furthermore, GPR produced the most superior outcomes for 10cm, 30cm, and 50cm soil depths, with RMSE values of 1.753°C, 2.270°C, and 2.631°C, respectively. In addition, for 05cm soil depth, SVR achieved the highest level of performance with an RMSE of 1.950°C at Dukan station. The results obtained in this research confirmed that the suggested models have the potential to be effectively used as daily predictive tools at different stations and various depths.
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Hussain, Bydaa Ali, and Mohammed Sadoon Hathal. "Developing Arabic License Plate Recognition System Using Artificial Neural Network and Canny Edge Detection." Baghdad Science Journal 17, no. 3 (September 1, 2020): 0909. http://dx.doi.org/10.21123/bsj.2020.17.3.0909.
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In recent years, there has been expanding development in the vehicular part and the number of vehicles moving on the roads in all the sections of the country. Arabic vehicle number plate identification based on image processing is a dynamic area of this work; this technique is used for security purposes such as tracking of stolen cars and access control to restricted areas. The License Plate Recognition System (LPRS) exploits a digital camera to capture vehicle plate numbers is used as input to the proposed recognition system. Basically, the proposed system consists of three phases, vehicle license plate localization, character segmentation, and character recognition, the License Plate (LP) detection is presented using canny edge detection algorithm, Connect Component Analysis (CCA) have been exploited for segmenting characters. Finally, a Multi-Layer Perceptron Artificial Neural Network (MLPANN) model is utilized to identify and detect the vehicle license plate characters, and hence the results are displayed as a text on GUI. The proposed system successfully detects LP and recognizes multi-style Arabic characters with rates of 96% and 97.872% respectively under different conditions.
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Hussain, Bydaa Ali, and Mohammed Sadoon Hathal. "Development of Iraqi License Plate Recognition System based on Canny Edge Detection Method." Journal of Engineering 26, no. 7 (July 1, 2020): 115–26. http://dx.doi.org/10.31026/j.eng.2020.07.08.
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In recent years, there has been expanding development in the vehicular part and the number of vehicles moving on the road in all the sections of the country. Vehicle number plate identification based on image processing is a dynamic area of this work; this technique is used for security purposes such as tracking of stolen cars and access control to restricted areas. The License Plate Recognition System (LPRS) exploits a digital camera to capture vehicle plate numbers is used as input to the proposed recognition system. Basically, the developing system is consist of three phases, vehicle license plate localization, character segmentation, and character recognition, the License Plate (LP) detection is presented using canny Edge detection algorithm, Connect Component Analysis (CCA) have been exploited for segmenting characters. Finally, a Multi-Layer Perceptron Artificial Neural Network (MLPANN) model is utilized to recognize and detect the vehicle license plate characters, and hence the results are displayed as a text on GUI. The proposed system successfully identified and recognized multi_style Iraqi license plates using different image situations and it was evaluated based on different metrics performance, achieving an overall system performance of 91.99%. This results shows the effectiveness of the proposed method compared with other existing methods, whose average recognition rate is 86% and the average processing time of one image is 0.242s which proves the practicality of the proposed method.
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Prashanth, M., D. Madhu, K. Ramanarasimh, and R. Suresh. "Effect of Heat Input and Filling Ratio on Raise in Temperature of the Oscillating Heat Pipe with Different Working Fluids Using ANN Model." International Journal of Heat and Technology 40, no. 2 (April 30, 2022): 535–42. http://dx.doi.org/10.18280/ijht.400221.
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In the present article, feed forward multilayer perceptron neural network (FFMLPNN) model has been used to predict the rise in temperature in closed loop oscillating heat pipe filled with three different fluid i.e., Acetone, methanol and ethanol respectively. Experimental test was carried out for the inner diameter of 1.7mm copper tube for all the combinations of filling ratio, heat input and time taken to evaluate the performance of the OHP. Totally 2000 data sets have been used for Acetone and Methanol, 1500 data sets is used for ethanol in the present NN model. ANN model with FFMLPNN using three input parameter (Filling ratio, heat input and time taken) and rise in temperature has output parameter respectively. Levenberg-Marquardt algorithm with a 4-10 neurons has been used for the determination of optimal model. The 3-8-1 combinations predict the rise in temperature for ethanol and acetone whereas for methanol 3-7-1 is the optimal combinations was achieved. For all the combinations RMSE values are 0.3414, 0.1285 and 0.1237 (Training-70%), 0.3526, 0.1375, 0.1234 (testing-15%) and 0.3010, 0.1515, 0.1425 (validation-15%). The values for coefficient of determinations are 0.9941, 0.9975 and 0.9971 for methanol, acetone and ethanol was achieved. The results clearly indicated that the proposed MLPANN model can successfully predict the rise in temperature.
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Gebremariam, Gebrekiros Gebreyesus, J. Panda, and S. Indu. "Localization and Detection of Multiple Attacks in Wireless Sensor Networks Using Artificial Neural Network." Wireless Communications and Mobile Computing 2023 (January 10, 2023): 1–29. http://dx.doi.org/10.1155/2023/2744706.
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Security enhancement in wireless sensor networks (WSNs) is significant in different applications. The advancement of routing attack localization is a crucial security research scenario. Various routing attacks degrade the network performance by injecting malicious nodes into wireless sensor networks. Sybil attacks are the most prominent ones generating false nodes similar to the station node. This paper proposed detection and localization against multiple attacks using security localization based on an optimized multilayer perceptron artificial neural network (MLPANN). The proposed scheme has two major part localization techniques and machine learning techniques for detection and localization WSN DoS attacks. The proposed system is implemented using MATLAB simulation and processed with the IBM SPSS toolbox and Python. The dataset is classified into training and testing using the multilayer perceptron artificial neural network to detect ten classes of attacks, including denial-of-service (DoS) attacks. Using the UNSW-NB, WSN-DS, NSL-KDD, and CICIDS2018 benchmark datasets, the results reveal that the suggested system improved with an average detection accuracy of 100%, 99.65%, 98.95%, and 99.83% for various DoS attacks. In terms of localization precision, recall, accuracy, and f-score, the suggested system outperforms state-of-the-art alternatives. Finally, simulations are done to assess how well the suggested method for detecting and localizing harmful nodes performs in terms of security. This method provides a close approximation of the unknown node position with low localization error. The simulation findings show that the proposed system is effective for the detection and secure localization of malicious attacks for scalable and hierarchically distributed wireless sensor networks. This achieved a maximum localization error of 0.49% and average localization accuracy of 99.51% using a secure and scalable design and planning approach.
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SAHIN, CENK, SEYFETTIN NOYAN OGULATA, KEZBAN ASLAN, HACER BOZDEMIR, and RIZVAN EROL. "A NEURAL NETWORK-BASED CLASSIFICATION MODEL FOR PARTIAL EPILEPSY BY EEG SIGNALS." International Journal of Pattern Recognition and Artificial Intelligence 22, no. 05 (August 2008): 973–85. http://dx.doi.org/10.1142/s0218001408006594.
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Epilepsy is a disorder of cortical excitability and still an important medical problem. The correct diagnosis of a patient's epilepsy syndrome clarifies the choice of drug treatment and also allows an accurate assessment of prognosis in many cases. The aim of this study is to evaluate epileptic patients and classify subgroups of partial epilepsy by Multilayer Perceptron Neural Networks (MLPNNs). This is the first study to classify the partial epilepsy groups using the neural network according to EEG signals. 418 patients with epilepsy diagnoses according to International League against Epilepsy (ILAE, 1981) were included in this study. The epilepsy outpatients at the Neurology Department Clinic of Cukurova University Medical School between the years of 2002–2005 were examined and included in the study. The MLPNNs were trained by the parameters obtained from the EEG signals and clinical findings of the patients. Test results show that the MLPNN model is able to classify partial epilepsy with an accuracy of 91.5%. Moreover, new MLPNNs were constructed for determining significant variables on classification. The loss of consciousness in the course of seizure time variable caused the largest decrease in the classification accuracy when it was left out. In conclusion, we think that the classification performance of MLPNN model for partial epilepsy is satisfactory and this model may be used in clinical studies as a decision support tool to determine the partial epilepsy classification of the patients.
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Bensaoucha, Saddam, Youcef Brik, Sandrine Moreau, Sid Ahmed Bessedik, and Aissa Ameur. "Induction machine stator short-circuit fault detection using support vector machine." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 40, no. 3 (May 21, 2021): 373–89. http://dx.doi.org/10.1108/compel-06-2020-0208.
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Purpose This paper provides an effective study to detect and locate the inter-turn short-circuit faults (ITSC) in a three-phase induction motor (IM) using the support vector machine (SVM). The characteristics extracted from the analysis of the phase shifts between the stator currents and their corresponding voltages are used as inputs to train the SVM. The latter automatically decides on the IM state, either a healthy motor or a short-circuit fault on one of its three phases. Design/methodology/approach To evaluate the performance of the SVM, three supervised algorithms of machine learning, namely, multi-layer perceptron neural networks (MLPNNs), radial basis function neural networks (RBFNNs) and extreme learning machine (ELM) are used along with the SVM in this study. Thus, all classifiers (SVM, MLPNN, RBFNN and ELM) are tested and the results are compared with the same data set. Findings The obtained results showed that the SVM outperforms MLPNN, RBFNNs and ELM to diagnose the health status of the IM. Especially, this technique (SVM) provides an excellent performance because it is able to detect a fault of two short-circuited turns (early detection) when the IM is operating under a low load. Originality/value The original of this work is to use the SVM algorithm based on the phase shift between the stator currents and their voltages as inputs to detect and locate the ITSC fault.
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LIN, Mei-Mei, and Fu-Hsiang KUO. "Applying the Multi-Layer Perceptron Neural Network Model to Predicting School Closures: An Example of Taipei City." MANAGEMENT AND ECONOMICS REVIEW 8, no. 3 (October 31, 2023): 276–87. http://dx.doi.org/10.24818/mer/2023.10-02.
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In this study, we utilised multi-layer perceptron neural networks (MLPNNs) to assess the issue of school closures. Specifically, this study uses the MLPNN model to conduct learning assessments to identify schools that may suffer from poor management. The empirical findings are briefly summarised as follows: (1) The research shows that more than half of the private schools in this study will face bankruptcy. In Taipei City, out of the 23 existing private schools, only four operate normally, while the remaining 19 private vocational high schools require assistance. About 12 schools face severe problems in terms of poor management, accounting for 63% of the total, while seven schools had a prediction value below 50, indicating a severe problem. These schools have expressed an immediate need for government assistance. (2) According to the MLPNN model in this study, reducing the number of full-time teachers is a primary factor contributing to school closures. First, since full-time teachers are a fixed cost, the dismissal of teachers tends to be prioritised to bring down school management costs. This, in turn, reduces the teacher-student ratio. Other factors that contribute to school bankruptcy are dismissing staff and part-time teachers, reducing expenditure, and poor operational maintenance. When the above policies are implemented in schools with poor management, a vicious cycle is created, leading to the bankruptcy of schools.
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Muşat, Elena Camelia, and Stelian Alexandru Borz. "Learning from Acceleration Data to Differentiate the Posture, Dynamic and Static Work of the Back: An Experimental Setup." Healthcare 10, no. 5 (May 15, 2022): 916. http://dx.doi.org/10.3390/healthcare10050916.
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Information on body posture, postural change, and dynamic and static work is essential in understanding biomechanical exposure and has many applications in ergonomics and healthcare. This study aimed at evaluating the possibility of using triaxial acceleration data to classify postures and to differentiate between dynamic and static work of the back in an experimental setup, based on a machine learning (ML) approach. A movement protocol was designed to cover the essential degrees of freedom of the back, and a subject wearing a triaxial accelerometer implemented this protocol. Impulses and oscillations from the signals were removed by median filtering, then the filtered dataset was fed into two ML algorithms, namely a multilayer perceptron with back propagation (MLPBNN) and a random forest (RF), with the aim of inferring the most suitable algorithm and architecture for detecting dynamic and static work, as well as for correctly classifying the postures of the back. Then, training and testing subsets were delimitated and used to evaluate the learning and generalization ability of the ML algorithms for the same classification problems. The results indicate that ML has a lot of potential in differentiating between dynamic and static work, depending on the type of algorithm and its architecture, and the data quantity and quality. In particular, MLPBNN can be used to better differentiate between dynamic and static work when tuned properly. In addition, static work and the associated postures were better learned and generalized by the MLPBNN, a fact that could provide the basis for cheap real-world offline applications with the aim of getting time-scaled postural profiling data by accounting for the static postures. Although it wasn’t the case in this study, on bigger datasets, the use of MLPBPNN may come at the expense of high computational costs in the training phase. The study also discusses the factors that may improve the classification performance in the testing phase and sets new directions of research.
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Velliangiri, S., and R. Selvam. "Investigation Distributed Denial of Service Attack Classification Using MLPNN-BP and MLPNN-LM." Journal of Computational and Theoretical Nanoscience 15, no. 9 (September 1, 2018): 2764–68. http://dx.doi.org/10.1166/jctn.2018.7536.
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Wang, Guoqing, Changquan Wang, and Lihong Shi. "CO2 Corrosion Rate Prediction for Submarine Multiphase Flow Pipelines Based on Multi-Layer Perceptron." Atmosphere 13, no. 11 (November 3, 2022): 1833. http://dx.doi.org/10.3390/atmos13111833.
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The implementation of corrosion detection in submarine pipelines is difficult, and a combined PCA-MLP prediction model is proposed to improve the accuracy of corrosion prediction in submarine pipelines. Firstly, the corrosion rate of a submarine multiphase flow pipeline in the South China Sea is simulated by the De Waard 95 model in the multiphase flow transient simulation software OLGA and compared with the actual corrosion rate; then, according to the corrosion data simulated by OLGA, principal component analysis (PCA) is used to reduce the dimensionality of the corrosion factors in the pipeline, and the multiple linear regression model (MLR), multi-layer perceptron neural network (MLPNN), and radial basis function neural network (RBFNN) were optimized. The PCA-MLPNN model has an average relative error of 3.318%, an average absolute error of 0.0034, a root mean square error of 0.0082, a residual sum of squares of 0.0020, and a coefficient of determination of 0.8609. Compared with five models, including MLR, MLPNN, RBFNN, PCA-MLR, PCA-MLPNN, and PCA-RBFNN, PCA-MLPNN has higher prediction accuracy and better prediction performance. The above results indicate that the combined PCA-MLPNN model has a more reliable application capability in CO2 corrosion prediction of submarine pipelines.
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Hamadneh, Nawaf N. "Dead Sea Water Levels Analysis Using Artificial Neural Networks and Firefly Algorithm." International Journal of Swarm Intelligence Research 11, no. 3 (July 2020): 19–29. http://dx.doi.org/10.4018/ijsir.2020070102.
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In this study, the performance of adaptive multilayer perceptron neural network (MLPNN) for predicting the Dead Sea water level is discussed. Firefly Algorithm (FFA), as an optimization algorithm is used for training the neural networks. To propose the MLPNN-FFA model, Dead Sea water levels over the period 1810–2005 are applied to train MLPNN. Statistical tests evaluate the accuracy of the hybrid MLPNN-FFA model. The predicted values of the proposed model were compared with the results obtained by another method. The results reveal that the artificial neural network (ANN) models exhibit high accuracy and reliability for the prediction of the Dead Sea water levels. The results also reveal that the Dead Sea water level would be around -450 until 2050.
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Rezaie-Balf, Mohammad, and Ozgur Kisi. "New formulation for forecasting streamflow: evolutionary polynomial regression vs. extreme learning machine." Hydrology Research 49, no. 3 (March 27, 2017): 939–53. http://dx.doi.org/10.2166/nh.2017.283.
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Abstract Streamflow forecasting is crucial in hydrology and hydraulic engineering since it is capable of optimizing water resource systems or planning future expansion. This study investigated the performances of three different soft computing methods, multilayer perceptron neural network (MLPNN), optimally pruned extreme learning machine (OP-ELM), and evolutionary polynomial regression (EPR) in forecasting daily streamflow. Data from three different stations, Soleyman Tange, Perorich Abad, and Ali Abad located on the Tajan River of Iran were used to estimate the daily streamflow. MLPNN model was employed to determine the optimal input combinations of each station implementing evaluation criteria. In both training and testing stages in the three stations, the results of comparison indicated that the EPR technique would generally perform more efficiently than MLPNN and OP-ELM models. EPR model represented the best performance to simulate the peak flow compared to MLPNN and OP-ELM models while the MLPNN provided significantly under/overestimations. EPR models which include explicit mathematical formulations are recommended for daily streamflow forecasting which is necessary in watershed hydrology management.
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Al-Hashem, Mohammed Najeeb, Muhammad Nasir Amin, Waqas Ahmad, Kaffayatullah Khan, Ayaz Ahmad, Saqib Ehsan, Qasem M. S. Al-Ahmad, and Muhammad Ghulam Qadir. "Data-Driven Techniques for Evaluating the Mechanical Strength and Raw Material Effects of Steel Fiber-Reinforced Concrete." Materials 15, no. 19 (October 6, 2022): 6928. http://dx.doi.org/10.3390/ma15196928.
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Estimating concrete properties using soft computing techniques has been shown to be a time and cost-efficient method in the construction industry. Thus, for the prediction of steel fiber-reinforced concrete (SFRC) strength under compressive and flexural loads, the current research employed advanced and effective soft computing techniques. In the current study, a single machine learning method known as multiple-layer perceptron neural network (MLPNN) and ensembled machine learning models known as MLPNN-adaptive boosting and MLPNN-bagging are used for this purpose. Water; cement; fine aggregate (FA); coarse aggregate (CA); super-plasticizer (SP); silica fume; and steel fiber volume percent (Vf SF), length (mm), and diameter were the factors considered (mm). This study also employed statistical analysis such as determination coefficient (R2), root mean square error (RMSE), and mean absolute error (MAE) to assess the performance of the algorithms. It was determined that the MLPNN-AdaBoost method is suitable for forecasting SFRC compressive and flexural strengths. The MLPNN technique’s higher R2, i.e., 0.94 and 0.95 for flexural and compressive strength, respectively, and lower error values result in more precision than other methods with lower R2 values. SHAP analysis demonstrated that the volume of cement and steel fibers have the greatest feature values for SFRC’s compressive and flexural strengths, respectively.
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Ali, Zulifqar, Ijaz Hussain, Muhammad Faisal, Hafiza Mamona Nazir, Tajammal Hussain, Muhammad Yousaf Shad, Alaa Mohamd Shoukry, and Showkat Hussain Gani. "Forecasting Drought Using Multilayer Perceptron Artificial Neural Network Model." Advances in Meteorology 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/5681308.
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These days human beings are facing many environmental challenges due to frequently occurring drought hazards. It may have an effect on the country’s environment, the community, and industries. Several adverse impacts of drought hazard are continued in Pakistan, including other hazards. However, early measurement and detection of drought can provide guidance to water resources management for employing drought mitigation policies. In this paper, we used a multilayer perceptron neural network (MLPNN) algorithm for drought forecasting. We applied and tested MLPNN algorithm on monthly time series data of Standardized Precipitation Evapotranspiration Index (SPEI) for seventeen climatological stations located in Northern Area and KPK (Pakistan). We found that MLPNN has potential capability for SPEI drought forecasting based on performance measures (i.e., Mean Average Error (MAE), the coefficient of correlation (R), and Root Mean Square Error (RMSE)). Water resources and management planner can take necessary action in advance (e.g., in water scarcity areas) by using MLPNN model as part of their decision-making.
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El Aoumari, Abdelaziz, Hamid Ouadi, Jamal El-Bakkouri, and Fouad Giri. "Adaptive neural network observer for proton-exchange membrane fuel cell system." Clean Energy 7, no. 5 (October 1, 2023): 1078–90. http://dx.doi.org/10.1093/ce/zkad048.
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Abstract This paper develops an adaptive neural network (NN) observer for proton-exchange membrane fuel cells (PEMFCs). Indeed, information on the oxygen excess ratio (OER) value is crucial to ensure optimal management of the durability and reliability of the PEMFC. The OER indicator is computed from the mass of oxygen and nitrogen inside the PEMFC cathode. Unfortunately, the measurement process of both these masses is difficult and costly. To solve this problem, the design of a PEMFC state observer is attractive. However, the behaviour of the fuel cell system is highly non-linear and its modelling is complex. Due to this constraint, a multilayer perceptron neural network (MLPNN)-based observer is proposed in this paper to estimate the oxygen and nitrogen masses. One notable advantage of the suggested MLPNN observer is that it does not require a database to train the NN. Indeed, the weights of the NN are updated in real time using the output error. In addition, the observer parameters, namely the learning rate and the damping factor, are online adapted using the optimization tools of extremum seeking. Moreover, the proposed observer stability analysis is performed using the Lyapunov theory. The observer performances are validated by simulation under MATLAB®/Simulink®. The supremacy of the proposed adaptive MLPNN observer is highlighted by comparison with a fixed-parameter MLPNN observer and a classical high-gain observer (HGO). The mean relative error value of the excess oxygen rate is considered the performance index, which is equal to 1.01% for an adaptive MLPNN and 3.95% and 9.95% for a fixed MLPNN and HGO, respectively. Finally, a robustness test of the proposed observer with respect to measurement noise is performed.
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Zhu, Senlin, and Salim Heddam. "Prediction of dissolved oxygen in urban rivers at the Three Gorges Reservoir, China: extreme learning machines (ELM) versus artificial neural network (ANN)." Water Quality Research Journal 55, no. 1 (July 29, 2019): 106–18. http://dx.doi.org/10.2166/wqrj.2019.053.
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Abstract In the present study, two non-linear mathematical modelling approaches, namely, extreme learning machine (ELM) and multilayer perceptron neural network (MLPNN) were developed to predict daily dissolved oxygen (DO) concentrations. Water quality data from four urban rivers in the backwater zone of the Three Gorges Reservoir, China were used. The water quality data selected consisted of daily observed water temperature, pH, permanganate index, ammonia nitrogen, electrical conductivity, chemical oxygen demand, total nitrogen, total phosphorus and DO. The accuracy of the ELM model was compared with the standard MLPNN using several error statistics such as root mean squared error, mean absolute error, the coefficient of correlation and the Willmott index of agreement. Results showed that the ELM and MLPNN models perform well for the Wubu River, acceptably for the Yipin River and moderately for the Huaxi River, while poor model performance was obtained at the Tributary of Huaxi River. Model performance is negatively correlated with pollution level in each river. The MLPNN model slightly outperforms the ELM model in DO prediction. Overall, it can be concluded that MLPNN and ELM models can be applied for DO prediction in low-impacted rivers, while they may not be appropriate for DO modelling for highly polluted rivers. This article has been made Open Access thanks to the kind support of CAWQ/ACQE (https://www.cawq.ca).
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Narayanan, Alagusundari, and Dr Sivakumari Subramania Pillai. "A Novel Optimized Neural Network Model for Ink Selection in Printed Electronics." International Journal of Electrical and Electronics Research 11, no. 4 (December 2, 2023): 1103–9. http://dx.doi.org/10.37391/ijeer.110430.
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The field of Printed Electronics (PE) is experiencing significant growth in the industrial sector and generating considerable interest across various industries due to its ability to produce intricate components. The functionality of printed electronic products heavily relies on the utilization of conductive ink during the printing process, which plays a vital role in developing flexible electronic circuits and improving the communicative functionalities of objects. Selecting the right ink for printing is crucial to meet consumer requirements. However, the conventional approach to this process has been manual, labor-intensive, and time-consuming, relying on the expertise of designers. This paper presents an automated ink selection model for printed circuits. This novel method has been incorporated with Multilayer Perceptron Neural Network (MLPNN) and Particle Swarm Optimization (PSO), named PSO-MLPNN. A dataset containing material features is generated by gathering information from both literature and experimental observations. To ensure uniformity, the data undergoes preprocessing using the min-max method, which scales all features to a standardized range between 0 and 1. A four-layer MLPNN is constructed to choose the most suitable ink. The network is trained with the PSO algorithm. The bias and weight values of MLPNN are tuned using the PSO algorithm to attain high accuracy. The computed findings confirm that the ink selection is highly effective and more accurate when compared to both the standard MLPNN.
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Than, Nguyen Hien. "WATER QUALITY CLASSIFICATION BY ARTIFICIAL NEURAL NETWORK - A CASE STUDY OF DONG NAI RIVER, VIETNAM." Vietnam Journal of Science and Technology 55, no. 4C (March 24, 2018): 297. http://dx.doi.org/10.15625/2525-2518/55/4c/12167.
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The Dong Nai River is the main source of supplied water for Ho Chi Minh City, Dong Nai, Binh Duong province and other areas. However, the water quality state of the Dong Nai River has been heavily pressured by discharged sources from urban areas, industrial zones, agricultural, domestic activities, etc. In this paper, the authors employed the artificial neural network model (ANNs) to classify water quality of Dong Nai River that apply a new tool to assess water quality in Vietnam. The monitoring data were used for eight years from 2007 to 2014 with 23 monitoring stations. Two neural network models including a multi-layer perceptron (MLPNN) and a generalized regression network (GRNN) were employed to classify water quality of the Dong Nai River. The results of the study showed that GRNN and MLPNN classified excellently water quality. Optimal structure of the MLPNN was H8I4O1 with model error about 0.1268 while the GRNN was error about 0.00001615. Comparing the result of water quality classification between the ANNs and the fuzzy comprehensive evaluation indicated that they were in close agreement with the respective values (the accurate rate of GRNN 100% and 98,5 % of MLPNN).
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MÜGE, DURSUN, ŞENOL YAVUZ, BULGUN ENDER YAZGAN, and AKKAN TANER. "Neural network based thermal protective performance prediction of three-layered fabrics for firefighter clothing." Industria Textila 70, no. 01 (March 1, 2019): 57–64. http://dx.doi.org/10.35530/it.070.01.1527.
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The firefighter protective clothing is comprised of three main layers; an outer shell, a moisture barrier and a thermal liner. This three-layered fabric structure provides protection against the fire and extremely hot environments. Various parameters such as fabric construction, weight, warp/weft count, warp/weft density, thickness, water vapour resistance of the fabric layers have effect on the protective performance as heat transfer through the firefighter clothing. In this study, it is aimed to examine the predictability of the heat transfer index of three-layered fabrics, as function of the fabric parameters using artificial neural networks. Therefore, 64 different three layered-fabric assembly combinations of the firefighter clothing were obtained and the convective heat transfer (HTI) and radiant heat transfer (RHTI) through the fabric combinations were measured in a laboratory. Six multilayer perceptron neural networks (MLPNN) each with a single hidden layer and the same 12 input data were constructed to predict the convective heat transfer performance and the radiant heat transfer performance of three-layered fabrics separately. The networks 1 to 4 were trained to predict HTI12, HTI24, RHTI12, and RHTI24, respectively, while networks 5 and 6 had two outputs, HTI12 and HTI24, and RHTI12 and RHTI24, respectively. Each system indicates a good correlation between the predicted values and the experimental values. The results demonstrate that the proposed MLPNNs are able to predict the convective heat transfer and the radiant heat transfer effectively. However, the neural network with two outputs has slightly better prediction performance
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Jadidi, Aydin, Raimundo Menezes, Nilmar de Souza, and Antonio Cezar de Castro Lima. "Short-Term Electric Power Demand Forecasting Using NSGA II-ANFIS Model." Energies 12, no. 10 (May 17, 2019): 1891. http://dx.doi.org/10.3390/en12101891.
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Load forecasting is of crucial importance for smart grids and the electricity market in terms of the meeting the demand for and distribution of electrical energy. This research proposes a hybrid algorithm for improving the forecasting accuracy where a non-dominated sorting genetic algorithm II (NSGA II) is employed for selecting the input vector, where its fitness function is a multi-layer perceptron neural network (MLPNN). Thus, the output of the NSGA II is the output of the best-trained MLPNN which has the best combination of inputs. The result of NSGA II is fed to the Adaptive Neuro-Fuzzy Inference System (ANFIS) as its input and the results demonstrate an improved forecasting accuracy of the MLPNN-ANFIS compared to the MLPNN and ANFIS models. In addition, genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO), differential evolution (DE), and imperialistic competitive algorithm (ICA) are used for optimized design of the ANFIS. Electricity demand data for Bonneville, Oregon are used to test the model and among the different tested models, NSGA II-ANFIS-GA provides better accuracy. Obtained values of error indicators for one-hour-ahead demand forecasting are 107.2644, 1.5063, 65.4250, 1.0570, and 0.9940 for RMSE, RMSE%, MAE, MAPE, and R, respectively.
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Dong, Jingya, Bin Song, Fei He, Yingying Xu, Qiang Wang, Wanjun Li, and Peng Zhang. "Research on a Hybrid Intelligent Method for Natural Gas Energy Metering." Sensors 23, no. 14 (July 19, 2023): 6528. http://dx.doi.org/10.3390/s23146528.
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In this paper, a Comprehensive Diagram Method (CDM) for a Multi-Layer Perceptron Neuron Network (MLPNN) is proposed to realize natural gas energy metering using temperature, pressure, and the speed of sound from an ultrasonic flowmeter. Training and testing of the MLPNN model were performed on the basis of 1003 real data points describing the compression factors (Z-factors) and calorific values of the three main components of natural gas in Sichuan province, China. Moreover, 20 days of real tests were conducted to verify the measurements’ accuracy and the adaptability of the new intelligent method. Based on the values of the Mean Relative Errors and the Root Mean Square errors for the learning and test errors calculated on the basis of the actual data, the best-quality MLP 3-5-1 network for the metering of Z-factors and the new CDM methods for the metering of calorific values were experimentally selected. The Bayesian regularized MLPNN (BR-MLPNN) 3-5-1 network showed that the Z-factors of natural gas have a maximum relative error of −0.44%, and the new CDM method revealed calorific values with a maximum relative error of 1.90%. In addition, three local tests revealed that the maximum relative error of the daily cumulative amount of natural gas energy was 2.39%.
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Khan, Mehran, Jiancong Lao, and Jian-Guo Dai. "Comparative study of advanced computational techniques for estimating the compressive strength of UHPC." Journal of Asian Concrete Federation 8, no. 1 (June 30, 2022): 51–68. http://dx.doi.org/10.18702/acf.2022.6.8.1.51.
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The effect of raw materials on the compressive strength of concrete is a complex process, especially in the case of ultra-high-performance concrete (UHPC), where a higher number of inter-dependent parameters are involved in the strength development. In this era of digitalization, advanced machine learning methods are used to predict the material's mechanical characteristics because of their superior performance compared to conventional and nonlinear statistical regression models. Thus, the goal of the current study is to estimate the compressive strength of UHPC from the designed raw materials using advanced machine learning techniques. The compressive strength of UHPC is predicted from the 14 input parameters, i.e., cement, fly ash, slag, silica fume, nano-silica, limestone powder, sand, coarse aggregate, quartz powder, water, superplasticizer, PE fiber, steel fiber, and curing time. A total of eight machine learning models were compared that include multi-layer perceptron neural network (MLPNN), MLPNN Bootstrap aggregating (MLPNN-BA), MLPNN adaptive boosting (MLPNN-AB), Gradient boosting (GB), Decision tree (DT), DT Bootstrap aggregating (DT-BA), DT adaptive boosting (DT-AB) and Random Forest (RNF). The validation and performance evaluation of the above models were checked by using K-fold cross-validation, mean absolute error (MAE), root mean square error (RSME), coefficient of determination (R2), relative root mean square error (RRMSE), performance index (PI), and Nash Sutcliffe efficiency (NSE). The optimal model was selected based on the results of all statistical checks. It was found the ensembled machine learning models especially decision tree-based models outperform the neural network-based models with higher accuracy and low error. Thus, the recommended machine learning model is random forest having superior prediction capacity followed by DT Bootstrap aggregating and DT adaptive boosting.
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Nuanmeesri, S., and W. Sriurai. "Multi-Layer Perceptron Neural Network Model Development for Chili Pepper Disease Diagnosis Using Filter and Wrapper Feature Selection Methods." Engineering, Technology & Applied Science Research 11, no. 5 (October 12, 2021): 7714–19. http://dx.doi.org/10.48084/etasr.4383.
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The goal of the current study is to develop a diagnosis model for chili pepper disease diagnosis by applying filter and wrapper feature selection methods as well as a Multi-Layer Perceptron Neural Network (MLPNN). The data used for developing the model include 1) types, 2) causative agents, 3) areas of infection, 4) growth stages of infection, 5) conditions, 6) symptoms, and 7) 14 types of chili pepper diseases. These datasets were applied to the 3 feature selection techniques, including information gain, gain ratio, and wrapper. After selecting the key features, the selected datasets were utilized to develop the diagnosis model towards the application of MLPNN. According to the model’s effectiveness evaluation results, estimated by 10-fold cross-validation, it can be seen that the diagnosis model developed by applying the wrapper method along with MLPNN provided the highest level of effectiveness, with an accuracy of 98.91%, precision of 98.92%, and recall of 98.89%. The findings showed that the developed model is applicable.
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Djahafi, Fatiha, and Abdelkader Gafour. "Neuro-Immune Model Based on Bio-Inspired Methods for Medical Diagnosis." International Journal of Ambient Computing and Intelligence 13, no. 1 (January 2022): 1–18. http://dx.doi.org/10.4018/ijaci.293176.
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In this article, a hybrid bio-inspired algorithm called neuro-immune is proposed based on Multi-Layer Perceptron Neural Network (MLPNN) and the Clonal Selection Classification (CSC) principle of the Artificial Immune System (AIS) for the classifying and diagnosing of medical disease. The proposed approach consists in the first phase to code the weights and biases of MLPNN concatenation vector of the input samples into an antigen vector and to decompose it into new weights to generate population memory cells which will be applied by the processes of the CSC algorithm clone and mutate in the second phase, to optimize the accuracy class of data and updating the MLPNN weights to minimize the mean squared error. Experimental results show that the proposed hybrid neuro-immune model allows obtaining a high diagnosis performance on a set of medical data problems from the UCI repository with an improved classification accuracy compared to existing works in the literature.
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Rohman, Budiman Putra Asmaur, and Dayat Kurniawan. "Classification of Radar Environment Using Ensemble Neural Network with Variation of Hidden Neuron Number." Jurnal Elektronika dan Telekomunikasi 17, no. 1 (August 31, 2017): 19. http://dx.doi.org/10.14203/jet.v17.19-24.
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Target detection is a mandatory task of radar system so that the radar system performance is mainly determined by its detection rate. Constant False Alarm Rate (CFAR) is a detection algorithm commonly used in radar systems. This method is divided into several approaches which have different performance in the different environments. Therefore, this paper proposes an ensemble neural network based classifier with a variation of hidden neuron number for classifying the radar environments. The result of this research will support the improvement of the performance of the target detection on the radar systems by developing such an adaptive CFAR. Multi-layer perceptron network (MLPN) with a single hidden layer is employed as the structure of base classifiers. The first step of this research is the evaluation of the hidden neuron number giving the highest accuracy of classification and the simplicity of computation. According to the result of this step, the three best structures are selected to build an ensemble classifier. On the ensemble structure, all of those three MLPN outputs then be collected and voted for getting the majority result in order to decide the final classification. The three possible radar environments investigated are homogeneous, multiple-targets and clutter boundary. According to the simulation results, the ensemble MLPN provides a higher detection rate than the conventional single MLPNs. Moreover, in the multiple-target and clutter boundary environments, the proposed method is able to show its highest performance.
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Katipoglu, Okan Mert, Muhammet Ali Pekin, and Sercan Akil. "The Impact of Preprocessing Approaches on Neural Network Performance: A Case Study on Evaporation in Adana, a Mediterranean Climate." Indonesian Journal of Earth Sciences 3, no. 2 (December 29, 2023): A821. http://dx.doi.org/10.52562/injoes.2023.821.
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The application of artificial intelligence (AI) technologies is quickly expanding in water management. Additionally, the artificial neural network methodology has an advantage over traditional statistical approaches in that it does not need assumptions about the distribution of data and variables. These methods can be used if there is a large enough data collection and criteria relevant to the nature of the problem. Preprocessing data before utilizing it improves the performance of the AI model. Evaporation matters in water management, agriculture processes and soil science. It is critical to ensure proper estimation of evaporation losses for effective water resource planning and management particularly in drought-prone areas such as Adana. Artificial intelligence approaches can be applied successfully in evaporation calculation. In this research, we used the Standard scaler, power transformer, robust scaler quantile transformer (Uniform) and quantile transformer (Normal), and min-max scaler preprocessing techniques to preprocess the multilayer perceptron neural network (MLPNN). We also trained the MLPNN using unprocessed data and compared it to the results of the preprocessed model. In the setup of the model, daily temperature, pressure, wind, sunny hours, and humidity parameters covering the years 2018-2021 were presented as input to the MLPNN model. Consequently, we pinpoint that all preprocessing approaches produce better outcomes than unscaled. Although all models produced statistically high accuracy predictions according to statistical criteria, the MLPNN model established without transformation (test phase: r2: 0.93, NSE : 0.927, SMAPE: 10.77, RMSE: 1.2, MAE: 0.9) exhibited the lowest accuracy. The evaporation prediction model that was developed using the MLPNN-based standard scalar optimization algorithm exhibited the highest level of accuracy (test phase: r2: 0.978, NSE: 0.977, SMAPE: 5.93, RMSE: 0.68, MAE: 0.48). Power Transformer (test phase: r2: 0.978, NSE: 0.977, SMAPE: 5.81, RMSE: 0.67, MAE: 0.49) showed second-degree promising results. It can be concluded from these results that the estimation of meteorological variables requires the scaling and presentation of data in a uniform structure. Therefore, improving efficiency and productivity in water management or agricultural processes can be enhanced by making more accurate evaporation estimates.
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Lepore, Marco, Claudia de Lalla, S. Ramanjaneyulu Gundimeda, Heiko Gsellinger, Michela Consonni, Claudio Garavaglia, Sebastiano Sansano, et al. "A novel self-lipid antigen targets human T cells against CD1c+ leukemias." Journal of Experimental Medicine 211, no. 7 (June 16, 2014): 1363–77. http://dx.doi.org/10.1084/jem.20140410.
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T cells that recognize self-lipids presented by CD1c are frequent in the peripheral blood of healthy individuals and kill transformed hematopoietic cells, but little is known about their antigen specificity and potential antileukemia effects. We report that CD1c self-reactive T cells recognize a novel class of self-lipids, identified as methyl-lysophosphatidic acids (mLPAs), which are accumulated in leukemia cells. Primary acute myeloid and B cell acute leukemia blasts express CD1 molecules. mLPA-specific T cells efficiently kill CD1c+ acute leukemia cells, poorly recognize nontransformed CD1c-expressing cells, and protect immunodeficient mice against CD1c+ human leukemia cells. The identification of immunogenic self-lipid antigens accumulated in leukemia cells and the observed leukemia control by lipid-specific T cells in vivo provide a new conceptual framework for leukemia immune surveillance and possible immunotherapy.
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Ma, Yue, Xinyu Wu, Can Wang, Zhengkun Yi, and Guoyuan Liang. "Gait Phase Classification and Assist Torque Prediction for a Lower Limb Exoskeleton System Using Kernel Recursive Least-Squares Method." Sensors 19, no. 24 (December 10, 2019): 5449. http://dx.doi.org/10.3390/s19245449.
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The gait phase classification method is a key technique to control an exoskeleton robot. Different people have different gait features while wearing an exoskeleton robot due to the gap between the exoskeleton and the wearer and their operation habits, such as the correspondence between the joint angle and the moment at which the foot contacts the ground, the amplitude of the joint angle and others. In order to enhance the performance of the gait phase classification in an exoskeleton robot using only the angle of hip and knee joints, a kernel recursive least-squares (KRLS) algorithm is introduced to build a gait phase classification model. We also build an assist torque predictor based on the KRLS algorithm in this work considering the adaptation of unique gait features. In this paper, we evaluate the classification performance of the KRLS model by comparing with two other commonly used gait recognition methods—the multi-layer perceptron neural network (MLPNN) method and the support vector machine (SVM) algorithm. In this experiment, the training and testing datasets for the models built by KRLS, MLPNN and SVM were collected from 10 healthy volunteers. The gait data are collected from the exoskeleton robot that we designed rather than collected from the human body. These data depict the human-robot coupling gait that includes unique gait features. The KRLS classification results are in average 3% higher than MLPNN and SVM. The testing average accuracy of KRLS is about 86%. The prediction results of KRLS are twice as good as MLPNN in assist torque prediction experiments. The KRLS performs in a good, stable, and robust way and shows model generalization abilities.
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Xie, Nai-ming, Song-Ming Yin, and Chuan-Zhen Hu. "Estimating a civil aircraft’s development cost with a GM(1, N) model and an MLP neural network." Grey Systems: Theory and Application 7, no. 1 (February 6, 2017): 2–18. http://dx.doi.org/10.1108/gs-11-2016-0049.
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Purpose The purpose of this paper is to study a new approach by combining a multilayer perceptron neural network (MLPNN) algorithm with a GM(1, N) model in order to estimate the development cost of a new type of aircraft. Design/methodology/approach First, data about developing costs and their influencing factors were collected for several types of Boeing and Airbus aircraft. Second, a GM(1, N) model was constructed to simulate development costs for a civil aircraft. Then, an MLPNN algorithm was added to optimize and revise the simulative and forecasting values. Finally, a combined approach, using both a GM(1, N) model and an MLPNN algorithm was adopted to forecast development costs for new civil aircraft. Findings The results show that the proposed approach could do the work of cost estimation for new types of aircraft. Rather than using a single model, the combined approach could improve simulative and forecasting accuracy. Practical implications Scientific cost estimation could improve management efficiency and promote the success of a new type of civil aircraft development. Considering that China’s civil aircraft research and development is at its very beginning stages, only very limited data could be collected. The development costs for civil aircraft are affected by a series of factors. The approach outlined by this paper could be applied to development cost estimations in China’s civil aircraft industry. Originality/value The paper has succeeded by constructing a cost estimation index system and proposing a novel combined cost estimation approach comprised of a GM(1, N) model and an MLPNN. It has undoubtedly contributed to improving the accuracy of cost estimations.
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Tur, Rifat, and Serbay Yontem. "A Comparison of Soft Computing Methods for the Prediction of Wave Height Parameters." Knowledge-Based Engineering and Sciences 2, no. 1 (May 2, 2021): 31–46. http://dx.doi.org/10.51526/kbes.2021.2.1.31-46.
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In the previous studies on the prediction of wave height parameters, only the significant wave height has been considered as the unknown parameter to be predicted. However, the other wave height parameters, which may be required for the design of coastal structures depending on their importance level, have been neglected. Therefore, in this study, novel soft computing methods were used to predict all wave height parameters required for the design of coastal structures. To this end, wave data were derived from a buoy located in Southwest Black Sea Coast. Then, Multi-layer Perceptron Neural Network (MLPNN) and Adaptive-Neuro Fuzzy Inference System (ANFIS) models were developed to predict wave height parameters. Various input combinations were selected to create seven different sub-models. These sub-models were applied using developed MLPNN and ANFIS models. Accuracy of sub-models were evaluated for each wave height parameters in terms of performance evaluation criteria. The results showed that the wave height parameters predicted by the MLPNN and ANFIS methods are similar and both methods yield results acceptable for design purposes. However, for maximum wave height, Hmax, ANFIS sub-model yields slightly better results.
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Alsaffar, May Ali, Mohamed Abdel Rahman Abdel Ghany, Alyaa K. Mageed, Adnan A. AbdulRazak, Jamal Manee Ali, Khalid A. Sukkar, and Bamidele Victor Ayodele. "Effect of Textural Properties on the Degradation of Bisphenol from Industrial Wastewater Effluent in a Photocatalytic Reactor: A Modeling Approach." Applied Sciences 13, no. 15 (August 4, 2023): 8966. http://dx.doi.org/10.3390/app13158966.
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Conventional treatment methods such as chlorination and ozonation have been proven not to be effective in eliminating and degrading contaminants such as Bisphenol A (BPA) from wastewater. Hence, the degradation of BPA using a photocatalytic reactor has received a lot of attention recently. In this study, a model-based approach using a multilayer perceptron neural network (MLPNN) coupled with back-propagation, as well as support vector machine regression coupled with cubic kernel function (CSVMR) and Gaussian process regression (EQGPR) coupled with exponential quadratic kernel function, were employed to model the relationship between the textural properties such as pore volume (Vp), pore diameter (Vd), crystallite size, and specific surface area (SBET) of erbium- and iron-modified TiO2 photocatalysts in degrading BPA. Parametric analysis revealed that effective degradation of the Bisphenol up to 90% could be achieved using photocatalysts having textural properties of 150 m2/g, 8 nm, 7 nm, and 0.36 cm3/g for SBET, crystallite size, particle diameter, and pore volume, respectively. Fifteen architectures of the MPLNN models were tested to determine the best in terms of predictability of BPA degradation. The performance of each of the MLPNN models was measured using the coefficient of determination (R2) and root mean squared errors (RMSE). The MLPNN architecture comprised of 4 input layers, 14 hidden neurons, and 3 output layers displayed the best performance with R2 of 0.902 and 0.996 for training and testing. The 4-14-3 MLPNN robustly predicted the BPA degradation with an R2 of 0.921 and RMSE of 4.02, which is an indication that a nonlinear relationship exists between the textural properties of the modified TiO2 and the degradation of the BPA. The CSVRM did not show impressive performance as indicated by the R2 of 0.397. Therefore, appropriately modifying the textural properties of the TiO2 will significantly influence the BPA degradability.
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Waili, T., Md Gapar Md Johar, K. A. Sidek, N. S. H. Mohd Nor, H. Yaacob, and M. Othman. "EEG Based Biometric Identification Using Correlation and MLPNN Models." International Journal of Online and Biomedical Engineering (iJOE) 15, no. 10 (June 27, 2019): 77. http://dx.doi.org/10.3991/ijoe.v15i10.10880.
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<p class="0abstract">This study investigates the capability of electroencephalogram (EEG) signals to be used for biometric identification. In the context of biometric, recently, researchers have been focusing more on biomedical signals to substitute the biometric modalities that are being used nowadays as the signals obtained from our bodies is considered more secure and privacy-compliant. The EEG signals of 6 subjects were collected where the subjects were required to undergo two baseline experiments which are, eyes open (EO) and eyes closed (EC). The signals were processed using a 2nd order Butterworth filter to eliminate the unwanted noise in the signals. Then, Daubechies (db8) wavelet was applied to the signals in the feature extraction stage and from there, Power Spectral Density (PSD) of alpha and beta waves was computed. Finally, the correlation model and Multilayer Perceptron Neural Network (MLPNN) was applied to classify the EEG signals of each subject. Correlation model has yielded great significant difference of coefficient between autocorrelation and cross-correlation where it gives the coefficient value of 1 for autocorrelation and the coefficient value of less than 0.35 for cross-correlation. On the other hand, the MLPNN model gives an accuracy of 75.8% and 71.5% for classification during EO and EC baseline condition respectively. Therefore, these results support the usability of EEG signals in biometric recognition.</p>
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Achili, B., B. Daachi, Y. Amirat, A. Ali-Cherif, and M. E. Daâchi. "A stable adaptive force/position controller for a C5 parallel robot: a neural network approach." Robotica 30, no. 7 (January 17, 2012): 1177–87. http://dx.doi.org/10.1017/s0263574711001354.
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SUMMARYThis paper presents an adaptive force/position controller for a parallel robot executing constrained motions. This controller, based on an MLPNN (or Multi-Layer Perceptron Neural Network), does not require the inverse dynamic model of the robot to derive the control law. A neural identification of the dynamic model of the robot is proposed to determine the principal components of the MLPNN input vector. The latter is used to compensate the dynamic effects arising from the robot–environment interaction and its parameters are adjusted according to an adaptation law based on the Lyapunov-analysis methodology. The proposed controller is evaluated experimentally on the C5 parallel robot. This method is capable of tracking accurately the force/position trajectories and its stability robustness is proved.
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Przybył, Krzysztof, Krzysztof Koszela, Franciszek Adamski, Katarzyna Samborska, Katarzyna Walkowiak, and Mariusz Polarczyk. "Deep and Machine Learning Using SEM, FTIR, and Texture Analysis to Detect Polysaccharide in Raspberry Powders." Sensors 21, no. 17 (August 30, 2021): 5823. http://dx.doi.org/10.3390/s21175823.
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In the paper, an attempt was made to use methods of artificial neural networks (ANN) and Fourier transform infrared spectroscopy (FTIR) to identify raspberry powders that are different from each other in terms of the amount and the type of polysaccharide. Spectra in the absorbance function (FTIR) were prepared as well as training sets, taking into account the structure of microparticles acquired from microscopic images with Scanning Electron Microscopy (SEM). In addition to the above, Multi-Layer Perceptron Networks (MLPNs) with a set of texture descriptors (machine learning) and Convolution Neural Network (CNN) with bitmap (deep learning) were devised, which is an innovative attitude to solving this issue. The aim of the paper was to create MLPN and CNN neural models, which are characterized by a high efficiency of classification. It translates into recognizing microparticles (obtaining their homogeneity) of raspberry powders on the basis of the texture of the image pixel.
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Malik, Anurag, Anil Kumar, Priya Rai, and Alban Kuriqi. "Prediction of Multi-Scalar Standardized Precipitation Index by Using Artificial Intelligence and Regression Models." Climate 9, no. 2 (February 1, 2021): 28. http://dx.doi.org/10.3390/cli9020028.
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Accurate monitoring and forecasting of drought are crucial. They play a vital role in the optimal functioning of irrigation systems, risk management, drought readiness, and alleviation. In this work, Artificial Intelligence (AI) models, comprising Multi-layer Perceptron Neural Network (MLPNN) and Co-Active Neuro-Fuzzy Inference System (CANFIS), and regression, model including Multiple Linear Regression (MLR), were investigated for multi-scalar Standardized Precipitation Index (SPI) prediction in the Garhwal region of Uttarakhand State, India. The SPI was computed on six different scales, i.e., 1-, 3-, 6-, 9-, 12-, and 24-month, by deploying monthly rainfall information of available years. The significant lags as inputs for the MLPNN, CANFIS, and MLR models were obtained by utilizing Partial Autocorrelation Function (PACF) with a significant level equal to 5% for SPI-1, SPI-3, SPI-6, SPI-9, SPI-12, and SPI-24. The predicted multi-scalar SPI values utilizing the MLPNN, CANFIS, and MLR models were compared with calculated SPI of multi-time scales through different performance evaluation indicators and visual interpretation. The appraisals of results indicated that CANFIS performance was more reliable for drought prediction at Dehradun (3-, 6-, 9-, and 12-month scales), Chamoli and Tehri Garhwal (1-, 3-, 6-, 9-, and 12-month scales), Haridwar and Pauri Garhwal (1-, 3-, 6-, and 9-month scales), Rudraprayag (1-, 3-, and 6-month scales), and Uttarkashi (3-month scale) stations. The MLPNN model was best at Dehradun (1- and 24- month scales), Tehri Garhwal and Chamoli (24-month scale), Haridwar (12- and 24-month scales), Pauri Garhwal (12-month scale), Rudraprayag (9-, 12-, and 24-month), and Uttarkashi (1- and 6-month scales) stations, while the MLR model was found to be optimal at Pauri Garhwal (24-month scale) and Uttarkashi (9-, 12-, and 24-month scales) stations. Furthermore, the modeling approach can foster a straightforward and trustworthy expert intelligent mechanism for projecting multi-scalar SPI and decision making for remedial arrangements to tackle meteorological drought at the stations under study.
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Escobar-Avalos, Emmanuel, Martín A. Rodríguez-Licea, Horacio Rostro-González, Allan G. Soriano-Sánchez, and Francisco J. Pérez-Pinal. "A Comparison of Integrated Filtering and Prediction Methods for Smart Grids." Energies 14, no. 7 (April 2, 2021): 1980. http://dx.doi.org/10.3390/en14071980.
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The intelligent use of green and renewable energies requires reliable and preferably anticipated information regarding their availability and the behavior of meteorological variables in a scenario of natural intermittency. Examples of this are the smart grids, which can incorporate, among others, a charging system for electric vehicles and modern and predictive management techniques. However, some issues associated with such procedures are data captured by sensors and transducers with noise in their signals and low information repeatability under the same reading conditions. To tackle such problems, numerous filtering and data fitting techniques and various prediction methods have been developed, but an appropriate selection can be cumbersome. Also, some filtering techniques, such as RANdom SAmple Consensus (RANSAC) appear not to have been used in prediction scenarios for smart grids, to the authors’ knowledge. In this regard, this paper aims to present a comparison in terms of average error, determination coefficient, and cross validation that can be expected under prediction schemes as Multiple Linear Regression, Vector Support Machines and a Multilayer Perceptron Regression Neural Network (MLPRNN), with filtering/scaling methods such as Maximum and Minimum, L2 Norm, Standard Scale, and RANSAC. Cross validation allows to flag problems like overfitting or selection bias, and this comparison is another novelty for smart grid scenarios, to the authors’ knowledge. Although many combinations were analyzed, RANSAC, with L2 Norm filtering and an MLPRNN for prediction, generate the best results. RANSAC algorithm with L2 Norm is a novelty for filtering and predicting in smart grids, and through an MLPRNN, the R2 error can be reduced to 0.8843, the MSE to 0.8960, and the cross validation accuracy can be increased to 0.44 (±0.2).
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Ostrowski, Jennifer Lynn, and Cheree M. Iadevaia. "Characteristics and Program Decisions of Master's-Level Professional Athletic Training Students." Athletic Training Education Journal 9, no. 1 (May 1, 2014): 36–42. http://dx.doi.org/10.4085/090136.
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Context The number of master's-level professional athletic training programs (MLPATPs) has grown by over 400% in the past 10 years; however, little is known about the characteristics of the students who enroll in these programs or why they select this route to certification. Objective To describe, by exploring the characteristics of MLPATP students, the profile of students who enroll in MLPATPs, and to aid in recruitment of students by developing a greater understanding of why students select the MLPATP route to athletic training certification. Design Cross-sectional design involving online survey research. MLPATP directors were asked to forward the survey link to students enrolled in their programs. Participants Seventy-nine students enrolled in MLPATPs accredited by the Commission on Accreditation of Athletic Training (CAATE). Data Collection and Analysis Survey data were collected by Formstack.com. Open-ended questions were categorized based on common themes and then coded. Descriptive statistics and nonparametric correlations were calculated. Results MLPATP students were, on average, 24.7 years old; 68% were women, 85% were Caucasian. Forty-two percent earned their bachelor's degree in exercise/sports science. Nearly 80% of students decided they wanted to be an athletic trainer either prior to or during their undergraduate studies, and students enrolled in their MLPATP an average of 1.2 years after completing their bachelor's degree. The geographical area and an institution's reputation were the primary contributing factors in choosing an MLPATP. Following graduation, 93.5% plan to seek employment using their certified athletic trainer credential. Conclusions Understanding the characteristics of MLPATP students can help in the recruitment of students for MLPATPs as well as develop a greater understanding of the needs of these students. Additional lines of research would contribute to discussions regarding the future of athletic training education.
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Kamrud, Alexander, Brett Borghetti, Christine Schubert Kabban, and Michael Miller. "Generalized Deep Learning EEG Models for Cross-Participant and Cross-Task Detection of the Vigilance Decrement in Sustained Attention Tasks." Sensors 21, no. 16 (August 20, 2021): 5617. http://dx.doi.org/10.3390/s21165617.
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Tasks which require sustained attention over a lengthy period of time have been a focal point of cognitive fatigue research for decades, with these tasks including air traffic control, watchkeeping, baggage inspection, and many others. Recent research into physiological markers of mental fatigue indicate that markers exist which extend across all individuals and all types of vigilance tasks. This suggests that it would be possible to build an EEG model which detects these markers and the subsequent vigilance decrement in any task (i.e., a task-generic model) and in any person (i.e., a cross-participant model). However, thus far, no task-generic EEG cross-participant model has been built or tested. In this research, we explored creation and application of a task-generic EEG cross-participant model for detection of the vigilance decrement in an unseen task and unseen individuals. We utilized three different models to investigate this capability: a multi-layer perceptron neural network (MLPNN) which employed spectral features extracted from the five traditional EEG frequency bands, a temporal convolutional network (TCN), and a TCN autoencoder (TCN-AE), with these two TCN models being time-domain based, i.e., using raw EEG time-series voltage values. The MLPNN and TCN models both achieved accuracy greater than random chance (50%), with the MLPNN performing best with a 7-fold CV balanced accuracy of 64% (95% CI: 0.59, 0.69) and validation accuracies greater than random chance for 9 of the 14 participants. This finding demonstrates that it is possible to classify a vigilance decrement using EEG, even with EEG from an unseen individual and unseen task.
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Jadidi, Aydin, Raimundo Menezes, Nilmar de Souza, and Antonio de Castro Lima. "A Hybrid GA–MLPNN Model for One-Hour-Ahead Forecasting of the Global Horizontal Irradiance in Elizabeth City, North Carolina." Energies 11, no. 10 (October 2, 2018): 2641. http://dx.doi.org/10.3390/en11102641.
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The use of photovoltaics is still considered to be challenging because of certain reliability issues and high dependence on the global horizontal irradiance (GHI). GHI forecasting has a wide application from grid safety to supply–demand balance and economic load dispatching. Given a data set, a multi-layer perceptron neural network (MLPNN) is a strong tool for solving the forecasting problems. Furthermore, noise detection and feature selection in a data set with numerous variables including meteorological parameters and previous values of GHI are of crucial importance to obtain the desired results. This paper employs density-based spatial clustering of applications with noise (DBSCAN) and non-dominated sorting genetic algorithm II (NSGA II) algorithms for noise detection and feature selection, respectively. Tuning the neural network is another important issue that includes choosing the hidden layer size and activation functions between the layers of the network. Previous studies have utilized a combination of different parameters based on trial and error, which seems to be inefficient in terms of accurate selection of the desired features and also tuning of the neural network. In this research, two different methods—namely, particle swarm optimization (PSO) algorithm and genetic algorithm (GA)—are utilized in order to tune the MLPNN, and the results of one-hour-ahead forecasting of the GHI are subsequently compared. The methodology is validated using the hourly data for Elizabeth City located in North Carolina, USA, and the results demonstrated a better performance of GA in comparison with PSO. The GA-tuned MLPNN reported a normalized root mean square error (nRMSE) of 0.0458 and a normalized mean absolute error (nMAE) of 0.0238.
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Kamel, Nayra M., Maged W. Helmy, Magda W. Samaha, Doaa Ragab, and Ahmed O. Elzoghby. "Multicompartmental lipid–protein nanohybrids for combined tretinoin/herbal lung cancer therapy." Nanomedicine 14, no. 18 (September 2019): 2461–79. http://dx.doi.org/10.2217/nnm-2019-0090.
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Aim: Multicompartmental lipid–protein nanohybrids (MLPNs) were developed for combined delivery of the anticancer drugs tretinoin (TRE) and genistein (GEN) as synergistic therapy of lung cancer. Materials & methods: The GEN-loaded lipid core was first prepared and then coated with TRE-loaded zein shell via nanoprecipitation. Results: TRE/GEN-MLPNs demonstrated a size of 154.5 nm. In situ ion pair formation between anionic TRE and the cationic stearyl amine improved the drug encapsulation with enhanced stability of MLPNs. TRE/GEN-coloaded MLPNs were more cytotoxic against A549 cancer cells compared with combined free GEN/TRE. In vivo, lung cancer bearing mice treated with TRE/GEN-MLPNs displayed higher apoptotic caspase activation compared with mice-treated free combined GEN/TRE. Conclusion: TRE/GEN-MLPNs might serve as a promising parenteral nanovehicles for lung cancer therapy.
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Nafees, Afnan, Muhammad Faisal Javed, Sherbaz Khan, Kashif Nazir, Furqan Farooq, Fahid Aslam, Muhammad Ali Musarat, and Nikolai Ivanovich Vatin. "Predictive Modeling of Mechanical Properties of Silica Fume-Based Green Concrete Using Artificial Intelligence Approaches: MLPNN, ANFIS, and GEP." Materials 14, no. 24 (December 8, 2021): 7531. http://dx.doi.org/10.3390/ma14247531.
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Silica fume (SF) is a mineral additive that is widely used in the construction industry when producing sustainable concrete. The integration of SF in concrete as a partial replacement for cement has several evident benefits, including reduced CO2 emissions, cost-effective concrete, increased durability, and mechanical qualities. As environmental issues continue to grow, the development of predictive machine learning models is critical. Thus, this study aims to create modelling tools for estimating the compressive and cracking tensile strengths of silica fume concrete. Multilayer perceptron neural networks (MLPNN), adaptive neural fuzzy detection systems (ANFIS), and genetic programming are all used (GEP). From accessible literature data, a broad and accurate database of 283 compressive strengths and 149 split tensile strengths was created. The six most significant input parameters were cement, fine aggregate, coarse aggregate, water, superplasticizer, and silica fume. Different statistical measures were used to evaluate models, including mean absolute error, root mean square error, root mean squared log error and the coefficient of determination. Both machine learning models, MLPNN and ANFIS, produced acceptable results with high prediction accuracy. Statistical analysis revealed that the ANFIS model outperformed the MLPNN model in terms of compressive and tensile strength prediction. The GEP models outperformed all other models. The predicted values for compressive strength and splitting tensile strength for GEP models were consistent with experimental values, with an R2 value of 0.97 for compressive strength and 0.93 for splitting tensile strength. Furthermore, sensitivity tests revealed that cement and water are the determining parameters in the growth of compressive strength but have the least effect on splitting tensile strength. Cross-validation was used to avoid overfitting and to confirm the output of the generalized modelling technique. GEP develops an empirical expression for each outcome to forecast future databases’ features to promote the usage of green concrete.
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Amaro, V., S. Cavuoti, M. Brescia, C. Vellucci, C. Tortora, and G. Longo. "METAPHOR: Probability density estimation for machine learning based photometric redshifts." Proceedings of the International Astronomical Union 12, S325 (October 2016): 197–200. http://dx.doi.org/10.1017/s1743921317002186.
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AbstractWe present METAPHOR (Machine-learning Estimation Tool for Accurate PHOtometric Redshifts), a method able to provide a reliable PDF for photometric galaxy redshifts estimated through empirical techniques. METAPHOR is a modular workflow, mainly based on the MLPQNA neural network as internal engine to derive photometric galaxy redshifts, but giving the possibility to easily replace MLPQNA with any other method to predict photo-z’s and their PDF. We present here the results about a validation test of the workflow on the galaxies from SDSS-DR9, showing also the universality of the method by replacing MLPQNA with KNN and Random Forest models. The validation test include also a comparison with the PDF’s derived from a traditional SED template fitting method (Le Phare).
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Rahman, Md Mizanur, Chalie Charoenlarpnopparut, Prapun Suksompong, and Pisanu Toochinda. "Sensor Array Optimization for Complexity Reduction in Electronic Nose System." ECTI Transactions on Electrical Engineering, Electronics, and Communications 15, no. 1 (September 28, 2016): 49–59. http://dx.doi.org/10.37936/ecti-eec.2017151.171295.
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An Electronic nose (E-Nose) can be used to assess food quality and fruit ripeness without personal bias. A set of relevant sensors must be identified to design an effective E-Nose and reduce implementation cost and complexity. The analysis of tropical fruit odour in terms of pattern recognition errors is carried out to determine the minimum number of sensors and their combinations. Two new methods namely 1) principal component loading and mutual information between sensor data, and 2) threshold based approach are proposed in this work to evaluate and optimize the sensor set. Four pattern recognition methods, namely multilayer perceptron neural network (MLPNN), radial basis function neural network (RBFNN), support vector machine (SVM), and k-nearest neighbour (k-NN) are also compared in terms of classification performance. The pattern recognition error of SVM with the optimal set of sensors is as low as 2.78% and that of k-NN is 9.72%. The results conclude that the pattern classification error with MLPNN, and RBFNN is higher than the error from k-NN and SVM.
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Houichi, Larbi, Noureddine Dechemi, Salim Heddam, and Bachir Achour. "An evaluation of ANN methods for estimating the lengths of hydraulic jumps in U-shaped channel." Journal of Hydroinformatics 15, no. 1 (September 21, 2012): 147–54. http://dx.doi.org/10.2166/hydro.2012.138.
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Modelling of hydraulic characteristics of jump using theoretical and empirical models has always been a difficult task. The length of jump may be defined as the distance measured from the toe of the jump to the location of the surface rise. Due to high turbulence this length cannot be determined easily by theory. However, it has been investigated experimentally so as to design the stilling basins with hydraulic jumps. In this work, the control of a hydraulic jump by broad-crested sills in a U-shaped channel is recalled theoretically and experimentally examined. The study begins with a multiple regression (MR) analysis. Then, and in order to model the relative lengths of hydraulic jumps, we have implemented and evaluated two different artificial neural networks (ANN): multilayer perceptron neural network (MLPNN) and generalized regression neural network (GRNN). The results demonstrate the predictive strength of GRNN and its potential to predict hydraulic problems with an adaptive spread value. However, the MLPNN model remains best classified by these indexes of performance.
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Zhang, Zhe, Tuija Laakso, Zeyu Wang, Seppo Pulkkinen, Suvi Ahopelto, Kirsi Virrantaus, Yu Li, et al. "Comparative Study of AI-Based Methods—Application of Analyzing Inflow and Infiltration in Sanitary Sewer Subcatchments." Sustainability 12, no. 15 (August 3, 2020): 6254. http://dx.doi.org/10.3390/su12156254.
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Inflow and infiltration (I/I) is a common problem in sanitary sewer systems. The I/I rate is also considered to be an important indicator of the operational and structural condition of the sewer system. Situation awareness in sanitary sewer systems requires accurate wastewater-flow information at a fine spatiotemporal scale. This study aims to develop artificial intelligence (AI)-based models (adaptive neurofuzzy inference system (ANFIS) and multilayer perceptron neural network (MLPNN)) and to compare their performance for identifying the potential inflow and infiltration of the sanitary sewer subcatchment of two pumping stations. We tested the performance of these AI models by using data gathered from two pumping stations through a supervisory control and data acquisition (SCADA) system. As a result, these two AI models produced similar inflow and infiltration patterns—both subcatchments experienced inflow and infiltration. On the other hand, the ANFIS had overall higher performance than that of the MLPNN model for modelling the I/I situation for the catchments. The results of the research can be used to support spatial decision making in sewer system maintenance.