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Abstract This study introduces a high-performance 4-channel Metal-Insulator-Metal (MIM) diplexer, employing silver and Teflon, optimized for advanced photonic applications. The proposed diplexer, configured with two novel band-pass filters (BPFs), operates across four distinct wavelength bands (843 nm, 1090 nm, 1452 nm, 1675 nm) by precisely manipulating the passband dimensions. Utilizing Finite-Difference Time-Domain (FDTD) simulations, the designed diplexer achieves exceptional sensitivity values of 3500 nm/RIU, 4250 nm/RIU, 3375 nm/RIU, and 4003 nm/RIU, along with high figures of merit (FOM) ranging from 113.4 to 124.7 1/RIU. Also, the compact design (400 nm × 830 nm) underscores its suitability for integrated photonic circuits and advanced sensing applications. Furthermore, to further enhance accuracy in detecting refractive index (RI) changes, a multilayer perceptron (MLP) neural network was employed, ensuring the highest sensor accuracy. The accuracy of the MIM diplexer’s RI measurements was statistically validated through a one-sample t-test, confirming the sensor’s reliability. Comparative analysis with existing sensors highlights the diplexer’s superior sensitivity and efficiency, setting a new benchmark in optical communication and photonic sensing technologies. This work paves the way for future advancements in miniaturized, high-sensitivity optical devices, offering robust solutions for next-generation communication and sensing systems.

This study introduces a high-performance 4-channel Metal-Insulator-Metal (MIM) diplexer, employing silver and Teflon, optimized for advanced photonic applications. The proposed diplexer, configured with two novel band-pass filters (BPFs), operates across four distinct wavelength bands (843 nm, 1090 nm, 1452 nm, 1675 nm) by precisely manipulating the passband dimensions. Utilizing Finite-Difference Time-Domain (FDTD) simulations, the designed diplexer achieves exceptional sensitivity values of 3500 nm/RIU, 4250 nm/RIU, 3375 nm/RIU, and 4003 nm/RIU, along with high figures of merit (FOM) ranging from 113.4 to 124.7 1/RIU. Also, the compact design (400 nm × 830 nm) underscores its suitability for integrated photonic circuits and advanced sensing applications. Furthermore, to further enhance accuracy in detecting refractive index (RI) changes, a multilayer perceptron (MLP) neural network was employed, ensuring the highest sensor accuracy. The accuracy of the MIM diplexer's RI measurements was statistically validated through a one-sample t-test, confirming the sensor's reliability. Comparative analysis with existing sensors highlights the diplexer's superior sensitivity and efficiency, setting a new benchmark in optical communication and photonic sensing technologies. This work paves the way for future advancements in miniaturized, high-sensitivity optical devices, offering robust solutions for next-generation communication and sensing systems. [ABSTRACT FROM AUTHOR]

Easy clogging and short service life of mesh filters have posed great challenges in the irrigation water system. In this study, the pre- and post-pump mesh filters in series were combined to determine the interception of sediment and organic impurities at both pre- and post-pump filters. The indoor prototype tests were also carried out. The results showed that the distribution of the filter interception of sediment and organic impurities was related to the aperture of the mesh at pre- and postpump filters. The smaller the aperture of the post-pump filter was, the shorter the filtration time was. There was basically unchanged in the distribution of sawdust and sediment that was intercepted at pre- and post-pump filters. The smaller the aperture of the pre-pump filter was, the longer the filtration time was, and the smaller the percentage of sawdust intercepted by the filter. The percentage of sawdust intercepted by the pre-pump filter increased under the condition of the same screen aperture, with the increase of inlet water flow rate. It was recommended that the pre-pump filter with the screen aperture of 0.32 mm and the post-pump filter with the screen aperture of 0.20 mm were used for filtration when the content of organic impurities was high. Furthermore, the 0.32 and 0.20 mm mesh apertures of the pre- and post-pump filters were for filtration, when the mass ratio of organic impurities and sediment in the irrigation water source was less than 2. The 0.25-0.32 mm screen aperture of pre-pump filter prolonged the filtration time. There was a variation in the head loss with the filtration time, in order to clarify the influence of water conditions and screen aperture on the filtration time. Combined with the range analysis and multivariate analysis of variance (MANOVA), the initial and peak head loss were concentrated in the 2.43-5.87 and 13.92- 28.92 m, respectively, under different inlet flow conditions. The impurity ratio posed a smaller influence on the head loss. Additionally, the larger the inlet water flow rate was, the shorter the filtration time was. The empirical formula of head loss was established to fit the filtration time. The error between the fitting and the test was less than 7%, indicating the better suitable for the filtration time of tandem mesh filters under actual irrigation water. The best combination of the factor level for the orthogonal test was screened as the aperture D1（pre-pump is 0.32 mm, post-pump is 0.20 mm）, the sawdust to sediment mass ratio 1:1, sand content 0.12 g/L, and inlet flow 120 m3 /h. The influence of each factor on the filtration time was ranked in descending order: the impurity ratio, the screen aperture, the sand content, and the rate of inlet water flow. MLP neural network model was used to predict the filtration time. The error between the predicted and measured values was basically within 10%. The mean square error and the average relative error were 0.32% and 5.85%, respectively, suitable for the prediction of the filtration time under the complex water sources. The finding can also provide a strong reference to configuring the screen aperture of the pre- and post-pump filters in the tandem mesh filters for the irrigation projects. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Textiles & Engineers / Tekstil ve Mühendis is the property of Union of Chambers of Turkish Engineers & Architects, Chamber of Textile Engineers and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

While machine learning has become the new way of analyzing data, neutral networks form the basis of this revolutionary technology. In this work, we shall employ the power of neural networks to analyze and demystify the processes in nanofluids. By combining the precision of neural networks with the optimization capabilities of genetic algorithms, we aim to create a more accurate and efficient prediction model for MWCNT-alumina/water-ethylene glycol (80:20) hybrid antifreeze. Our approach entails using an MLP neural network and several training functions (LM, GD, BFGS, BN) with an adjustable number of neurons. The inputs of the network are φ (solid volume fraction or ϕ), temperature (T), and shear rate (γ), and the output is μnf of MWCNT-alumina/water-ethylene glycol (80:20) hybrid anti-freeze. To improve the accuracy of the final model, we use genetic optimization to make final adjustments to the parameters of the neural network. Utilizing the detailed analysis of the primary characteristics of these algorithms, we conclude that the BFGS function is the best to obtain neural network training. Steady performance achieved by this function—0.99828 of the R-value and RMSE value significantly equal to 0.213—illustrates good stability and accuracy of the suggested model. This work contributes to progressing the existing knowledge about the behavior of nanofluids and can stimulate further improvement in heat transfer and energy utilization.

The accurate estimation and measurement of coal ash are crucial for fuel selection, combustion efficiency assessment and quality control. However, the most widely used ash measurement method is combustion; this method is highly accurate but has a certain lag; additionally, an older radiation measurement method has a certain error. In this regard, in this study, a detection model and measurement method are proposed based on the combination of dual-energy X-ray measurement results and artificial intelligence algorithms, i.e. a coal ash detection model based on a multilayer perceptron (MLP) neural network. A model training database was created, and 6468 raw data points were measured with an experimental apparatus and organized. The results showed that the root-mean-square error between the predicted value and the true value of the trained model was 0.0857. By comparing several indices with the traditional backpropagation (BP) neural network, the root-mean-square error was reduced by 1.27%, and the model’s errors in different predicted output values were uniformly distributed without evident systematic deviation; these results demonstrated and confirmed that our proposed ash prediction model achieved high estimation accuracy and had strong robustness. [ABSTRACT FROM AUTHOR]

In this study, the Field Programmable Gate Array (FPGA) technology is employed to integrate multi-loop controllers for motion systems. To provide precise positioning and trajectory tracking for multi-axis systems, the proportional-integral (PI) control is used in the speed control loop and adaptive PID control in the position control loop. The motion system under consideration comprises an X-Y table driven by permanent magnet synchronous motors (PMSMs), and controlled by two programmable servo systems, each designed to regulate a separate axis. Each axis of this system consists of a motion planning module, a speed PI controller in the inner loop, and an adaptive PID position controller in the outer loop. The adaptive PID controller is specifically designed using a multilayer perceptron (MLP) neural network and parameter tuning methods. The control objective is to enhance trajectory tracking accuracy, especially in the presence of dynamic variations and uncertain disturbances. The Very High-speed IC Hardware Description Language (VHDL) is utilized to implement the desirable features of the control system. The control development is based on an FPGA device using Altera’s Quartus II and Nios II software environment. The VHDL designs are analyzed and synthesized within this software environment. Simulation results demonstrate that the on-chip control system can achieve accurate positioning and tracking performance for the X-Y table motion.

This paper proposes a novel approach to analyzing and managing electricity consumption using a clustering algorithm and a high-accuracy classifier for smart meter data. The proposed method utilizes a multilayer perceptron neural network classifier optimized by an Imperialist Competitive Algorithm (ICA) called ICA-optimized MLP, and a CD Index based on Fuzzy c-means to optimally determine representative load curves. A case study involving a real dataset of residential smart meters is conducted to validate the effectiveness of the proposed method, and the results demonstrate that the ICA-optimized MLP method achieves an accuracy of 98.62%, outperforming other classification methods. This approach has the potential to improve energy efficiency and reduce costs in the power system, making it a promising solution for analyzing and managing electricity consumption.

Magnetorheological (MR) dampers exhibit a complex nonlinear hysteresis which makes the modeling of their behavior with parametric or non-parametric models to be challenging. In case of parametric models, the generalization of the parameters identified for a particular excitation is difficult and requires high computation costs. On the other hand, non-parametric models are considered as black-box type with no association to physical phenomena. The objective of this study is to propose a new identification model combining the merits of a parametric model and neural network paradigm. The proposed model is a parametric type which exploits an algebraic model with a hyperbolic tangent hysteresis, while a series multilayer-perceptron (MLP) neural networks are used to identify the model parameters under different excitation conditions. This approach not only preserves the physical meanings of the model parameters but also prompts generalization to common excitation conditions. The data for training the MLP neural networks were generated from a test program on a RD-8041-1 MR damper covering a wide range of input conditions. Results show that the proposed sequential neural network model not only increases the accuracy of the predicted MR damper force but also exhibits higher robustness and better consistency under different excitation conditions than a conventional algebraic model. [ABSTRACT FROM AUTHOR]

This paper proposes an artificial neural network (ANN) architecture for solving nonlinear fractional differential equations. The proposed ANN algorithm is based on a truncated power series expansion to substitute the unknown functions in the equations in this approach. Then, a set of algebraic equations is resolved using the ANN technique in an iterative minimization process. Finally, numerical examples are provided to demonstrate the usefulness of the ANN architectures. The results verify that the suggestedANNarchitecture achieves high accuracy and good stability. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach to analyzing and managing electricity consumption using a clustering algorithm and a high-accuracy classifier for smart meter data. The proposed method utilizes a multilayer perceptron neural network classifier optimized by an Imperialist Competitive Algorithm (ICA) called ICA-optimized MLP, and a CD Index based on Fuzzy c-means to optimally determine representative load curves. A case study involving a real dataset of residential smart meters is conducted to validate the effectiveness of the proposed method, and the results demonstrate that the ICA-optimized MLP method achieves an accuracy of 98.62%, outperforming other classification methods. This approach has the potential to improve energy efficiency and reduce costs in the power system, making it a promising solution for analyzing and managing electricity consumption. [ABSTRACT FROM AUTHOR]

It is usually to conduct a full-scale three-dimensional flow analysis for a radial turbine to find a way to increase the efficiency of a Compressed Air Energy Storage (CAES) system. However, long solving time and huge consumption of computing resources become a major obstacle to the analysis. Therefore, in present study, a surrogate model with test data-based multi-layer perceptron (MLP) Neural Network is proposed to overcome the difficulty. Instead of complex flow field solving process, it provides reliable turbine aerodynamic performance and flow field distribution characteristics in a short solution time by "learning the measurement results". The validation results illustrated that the predicted maximum relative errors of isentropic efficiency, corrected mass flow rate and corrected power are only 0.03%, 0.22% and 0.26% respectively. The predicted flow distribution parameters in chamber, shroud cavity and outlet region of rotor are also basically consistent with the experimental results. In the chamber, it can be found that a pressure stagnation point is observed at circumferential angle of 270° when total pressure ratio is decreased. In the shroud cavity, obvious pressure variation is found near outlet of shroud cavity which although labyrinth seals exist. At outlet of rotor, obvious variations of velocity and pressure are found in the 0.0–0.4 and 0.6–0.8 of blade height. At the same time, obvious variations of velocity and pressure are found in the 0.0–0.4 and 0.6–0.8 of blade height and this is because the influence of upper passage vortex, lower passage vortex and end wall secondary flow. The present study can provide further reference for the dynamic performance evaluation of CAES radial inflow turbine. [ABSTRACT FROM AUTHOR]

Interface shear stiffness modulus (K) is one of the important bonding properties of layers. It is also used to evaluate interface shear strength between asphalt layers of asphalt pavement. Direct determination of K parameter in field or laboratory requires time, cost and special equipment. In this article, K has been estimated based on three interlayer shear strength affecting factors namely maximum size of the asphalt concrete aggregate (Dmax), normal pressure and temperature using Machine Learning (ML) methods such as Multilayer Perception Neural Network, Bagging Random Forest (Bagging-RF), and Bagging Reduced Error Pruning Tree (Bagging-REPT). The ML models for the prediction of shear strength were built based on the laboratory shear tests results of 180 double-layer asphalt samples. The data was divided randomly into a ratio of 70/30 to train and test model, respectively. Standard statistical measures were used to evaluate and validate the models' performance. All the developed models performed well in correctly predicting K value of AC, but performance of the Bagging-RF model is the best as it is giving Correlation Coefficient (R) value 0.88 between estimated value and determined value. The proposed ML predictive models will reduce the field and laboratory experimental efforts and increase the efficiency in estimating the K parameter for the safe designing, construction and maintenance of asphalt concrete pavements. [ABSTRACT FROM AUTHOR]

Inertial measurement unit (IMU) technology has gained popularity in human activity recognition (HAR) due to its ability to identify human activity by measuring acceleration, angular velocity, and magnetic flux in key body areas like the wrist and knee. It has propelled the extensive application of HAR across various domains. In the healthcare sector, HAR finds utility in monitoring and assessing movements during rehabilitation processes, while in the sports science field, it contributes to enhancing training outcomes and preventing exercise-related injuries. However, traditional sensor fusion algorithms often require intricate mathematical and statistical processing, resulting in higher algorithmic complexity. Additionally, in dynamic environments, sensor states may undergo changes, posing challenges for real-time adjustments within conventional fusion algorithms to cater to the requirements of prolonged observations. To address these limitations, we propose a novel hybrid human pose recognition method based on IMU sensors. The proposed method initially calculates Euler angles and subsequently refines them using magnetometer and gyroscope data to obtain the accurate attitude angle. Furthermore, the application of FFT (Fast Fourier Transform) feature extraction facilitates the transition of the signal from its time-based representation to its frequency-based representation, enhancing the practical significance of the data. To optimize feature fusion and information exchange, a group attention module is introduced, leveraging the capabilities of a Multi-Layer Perceptron which is called the Feature Fusion Enrichment Multi-Layer Perceptron (GAM-MLP) to effectively combine features and generate precise classification results. Experimental results demonstrated the superior performance of the proposed method, achieving an impressive accuracy rate of 96.13% across 19 different human pose recognition tasks. The proposed hybrid human pose recognition method is capable of meeting the demands of real-world motion monitoring and health assessment. [ABSTRACT FROM AUTHOR]

The goal of the present investigation is to assess the applicability of the Gig Economy Framework (GEF) to the nursing workforce in Saudi Arabia. In order to learn more about the viability of the gig economy paradigm for the nursing profession, this study employed a cross-sectional survey technique. The survey asked questions specific to the nursing profession in Saudi Arabia and the GEF, while also taking into account other relevant variables. This nurse survey was sent to 102 Saudi Arabian hospitals' HR departments. After removing invalid and missing data, 379 responses remained. The gig economy's impact on everyday living and professional growth differed significantly between groups. After processing the data, we inputted them into a multi-layer perceptron (MLP) neural network to find relationships between responses to surveys and compatibility with the GEF. There were 20 inputs to this neural network and four possible outputs. The results of the network are the answers to questions about how the gig economy might affect four areas—life, financial management, and personal and professional comfort and development. Outputs 1–4 were predicted with 96.5%, 96.5%, 99.2%, and 99.2% accuracy, respectively. The primary issues with the nursing workforce in Saudi Arabia may be addressed with the use of gig economy elements. As a result, it is crucial to provide a trustworthy, intelligent strategy for foreseeing the gig economy's framework's alignment. [ABSTRACT FROM AUTHOR]

This paper proposes a recommendation algorithm that combines MLP with the DIN model and conducts simulation experiments in the field of earthquake missing data prediction. The original DIN model may face challenges and weaknesses in earthquake monitoring data prediction, such as a limited capability in handling data loss or anomalies in seismic monitoring stations. To overcome these issues, we innovatively treat seismic monitoring stations as special users and historical data patterns as recommended items. Based on the DIN model, we implement data processing and prediction for seismic monitoring stations and introduce an attention mechanism based on MLP neural networks in the model, while leveraging the prior knowledge base to enhance predictive capabilities. Compared to the original DIN model, our proposed approach not only recommends sequence combinations that meet the demands of seismic monitoring stations but also enhances the matching between station behavior attributes and historical data characteristics, thereby significantly improving prediction accuracy. To validate the effectiveness of our method, we conducted comparative experiments. The results show that the GAUC achieved by the DIN–MLP model reaches 0.69, which is an 11 percent point improvement over the original DIN model. This highlights the remarkable advantages of our algorithm in earthquake missing data prediction. Furthermore, our research reveals the potential of the DIN–MLP algorithm in practical applications, providing more accurate data processing and time series combination recommendations for the field of earthquake monitoring stations, thus contributing to the improvement of monitoring efficiency and accuracy. [ABSTRACT FROM AUTHOR]

Over time, the accumulation of scale within the transmission pipeline results in a decrease in the internal diameter of the pipe, leading to a decline in efficiency and energy waste. The employment of a gamma ray attenuation system that is non-invasive has been found to be a highly precise diagnostic technique for identifying volumetric percentages across various states. The most appropriate setup for simulating a volume percentage detection system through Monte Carlo N particle (MCNP) simulations involves a system consisting of two NaI detectors and dual-energy gamma sources, namely 241Am and 133Ba radioisotopes. A three-phase flow consisting of oil, water, and gas exhibits symmetrical homogenous flow characteristics across varying volume percentages as it traverses through scaled pipes of varying thicknesses. It is worth mentioning that there is an axial symmetry of flow inside the pipe that creates a homogenous flow pattern. In this study, the experiment involved the emission of gamma rays from one end of a pipe, with photons being absorbed by two detectors located at the other end. The resulting data included three distinct features, namely the counts under the photopeaks of 241Am and 133Ba from the first detector as well as the total count from the second detector. Through the implementation of a two-output MLP neural network utilising the aforementioned inputs, it is possible to accurately forecast the volumetric percentages with an RMSE of under 1.22, regardless of the thickness of the scale. The minimal error value ensures the efficacy of the proposed technique and the practicality of its implementation in the domains of petroleum and petrochemicals. [ABSTRACT FROM AUTHOR]

The scaling of oil pipelines over time leads to issues including diminished flow rates, wasted energy, and decreased efficiency. To take appropriate action promptly and avoid the aforementioned issues, it is crucial to determine the precise value of the scale within the pipe. Non-invasive gamma attenuation systems are one of the most accurate detection methods. To accomplish this goal, the Monte Carlo N Particle (MCNP) algorithm was used to simulate a scale thickness measurement system, which included two sodium iodide detectors, a dual-energy gamma source (241 Am and 133 Ba radioisotopes), and a test pipe. Water, gas, and oil were all used to mimic a three-phase flow in the test pipe, with the volume percentages ranging from 10% to 80%. Moreover, a scale ranging in thickness from 0 to 3 cm was inserted into the pipe, gamma rays were shone on the pipe, and on the opposite side of the pipe, photon intensity was measured by detectors. There were 252 simulations run. Fifteen time and frequency characteristics were derived from the signals collected by the detectors. The ant colony optimisation (ACO)-based approach is used to pick the ideal inputs from among the extracted characteristics for determining the thickness of the scale within the pipe. This technique led to the introduction of thirteen features that represented the ideal combination. The features introduced by ACO were introduced as inputs to a multi-layer perceptron (MLP) neural network to predict the scale thickness inside the oil pipe in centimetres. The maximum error found in calculating scale thickness was 0.017 as RMSE, which is a minor error compared to earlier studies. The accuracy of the present study in detecting scale thickness has been greatly improved by using the ACO to choose the optimal features. [ABSTRACT FROM AUTHOR]

This paper introduces a nonlinear adaptive controller of unknown nonlinear dynamical systems based on the approximate models using a multi-layer perceptron neural network. The proposal of this study is to employ the structure of the Multi-Layer Perceptron (MLP) model into the NARMA-L2 structure in order to construct a hybrid neural structure that can be used as an identifier model and a nonlinear controller for the MIMO nonlinear systems. The big advantage of the proposed control system is that it doesn't require previous knowledge of the model. Our ultimate goal is to determine the control input using only the values of the input and output. The developed NARMA-L2 neural network model is tuned for its weights employing the backpropagation optimizer algorithm. Nonlinear autoregressive-moving average-L2 (NARMA-L2) neural network controller, based on the inputs and outputs from the nonlinear model, is designed to perform control action on the nonlinear for the attitude control of unmanned aerial vehicles (UAVs) model. Once the system has been modeled efficiently and accurately, the proposed controller is designed by rearranging the generalized submodels. The controller performance is evaluated by simulation conducted on a quadcopter MIMO system, which is characterized by a nonlinear and dynamic behavior. The obtained results show that the NARMA-L2-based neural network achieved good performances in modeling and control. [ABSTRACT FROM AUTHOR]

Friction stir welding (FSW) is a process by which a joint can be made in a solid state. The complexity of the process due to metallurgical phenomena necessitates the use of models with the ability to accurately correlate the process parameters with the joint properties. In the present study, a multilayer perceptron (MLP) artificial neural network (ANN) was used to model and predict the ultimate tensile strength (UTS) of the joint between the AA2024 and AA7075 aluminum alloys. Three pin geometries, pyramidal, conical, and cylindrical, were used for welding. The rotation speed varied between 800 and 1200 rpm and the welding speed varied between 10 and 50 mm/min. The obtained ANN model was used in a simulated annealing algorithm (SA algorithm) to optimize the process to attain the maximum UTS. The SA algorithm yielded the cylindrical pin and rotational speed of 1110 rpm to achieve the maximum UTS (395 MPa), which agreed well with the experiment. Tensile testing and scanning electron microscopy (SEM) were used to assess the joint strength and the microstructure of the joints, respectively. Various defects were detected in the joints, such as a root kissing bond and unconsolidated banding structures, whose formations were dependent on the tool geometry and the rotation speed. [ABSTRACT FROM AUTHOR]

What is presented in this research is an intelligent system for detecting the volume percentage of three-phase fluids passing through oil pipes. The structure of the detection system consists of an X-ray tube, a Pyrex galss pipe, and two sodium iodide detectors. A three-phase fluid of water, gas, and oil has been simulated inside the pipe in two flow regimes, annular and stratified. Different volume percentages from 10 to 80% are considered for each phase. After producing and emitting X-rays from the source and passing through the pipe containing a three-phase fluid, the intensity of photons is recorded by two detectors. The simulation is introduced by a Monte Carlo N-Particle (MCNP) code. After the implementation of all flow regimes in different volume percentages, the signals recorded by the detectors were recorded and labeled. Three frequency characteristics and five wavelet transform characteristics were extracted from the received signals of each detector, which were collected in a total of 16 characteristics from each test. The feature selection system based on the particle swarm optimization (PSO) algorithm was applied to determine the best combination of extracted features. The result was the introduction of seven features as the best features to determine volume percentages. The introduced characteristics were considered as the input of a Multilayer Perceptron (MLP) neural network, whose structure had seven input neurons (selected characteristics) and two output neurons (volume percentage of gas and water). The highest error obtained in determining volume percentages was equal to 0.13 as MSE, a low error compared with previous works. Using the PSO algorithm to select the most optimal features, the current research's accuracy in determining volume percentages has significantly increased. [ABSTRACT FROM AUTHOR]

Brain-computer interface (BCI) systems can be used to communicate and express desires from people with severe nervous system damage. Among BCI systems based on evoked responses, steady state visual evoked potential (SSVEP) responses are the most widely used. Canonical correlation analysis (CCA)-based methods have been widely used in SSVEP-based online BCIs due to their low computation and high speed, and many methods have been introduced to improve the results. In this research, a method for constructing reference signals used in CCA based on the amplified evoked response of brain is introduced. In the proposed method, after removing the latency in the training signals, to construct reference signals, multilayer perceptron neural networks of the fitting type are used instead of the usual sine/cosine signals. The results show the success of this method in boosting the evoked responses of brain. The detection accuracy in 100-second time windows was 100%, and the information transfer rate in the same period was 240 bits per minute. Making reference signals similar to the recorded electroencephalogram allowed us to make more similarities in the CCA between the signals under consideration, and the reference signals, and to dramatically improve the results.

This study uses a neural network to propose a methodology for creating digital bathymetric models for shallow water areas that are partially covered by a mix of hydroacoustic and photogrammetric data. A key challenge of this approach is the preparation of the training dataset from such data. Focusing on cases in which the training dataset covers only part of the measured depths, the approach employs generalized linear regression for data optimization followed by multilayer perceptron neural networks for bathymetric model creation. The research assessed the impact of data reduction, outlier elimination, and regression surface-based filtering on neural network learning. The average values of the root mean square (RMS) error were successively obtained for the studied nearshore, middle, and deep water areas, which were 0.12 m, 0.03 m, and 0.06 m, respectively; moreover, the values of the mean absolute error (MAE) were 0.11 m, 0.02 m, and 0.04 m, respectively. Following detailed quantitative and qualitative error analyses, the results indicate variable accuracy across different study areas. Nonetheless, the methodology demonstrated effectiveness in depth calculations for water bodies, although it faces challenges with respect to accuracy, especially in preserving nearshore values in shallow areas.

Nowadays, breast cancer is one of the leading causes of women's death in the world. If breast cancer is detected at the initial stages, it can ensure long-term survival. Numerous methods have been proposed for the early prediction of such cancer. However, efforts are still ongoing, given the importance of the problem. Artificial Neural Networks (ANN) are a prevalent machine learning algorithm, which is very popular for prediction and classification problems. In this paper, an Intelligent Ensemble Classification method based on Multi-Layer Perceptron neural network (IEC-MLP) is proposed for breast cancer diagnosis. The proposed method consists of two stages: parameters optimisation and ensemble classification. In the first stage, the MLP Neural Network (MLP-NN) parameters, including optimal features, hidden layers, hidden nodes and weights, are optimised with the help of an Evolutionary Algorithm (EA), aiming at maximising the classification accuracy. In the second stage, an ensemble classification algorithm of MLP-NN with optimised parameters is applied to classify the patients. Our proposed IEC-MLP method not only reduces the complexity of MLP-NN and effectively selects the optimal subset of features but also minimises the misclassification cost. The classification results have been evaluated using the IEC-MLP over different breast cancer datasets, and the prediction results have been auspicious (98.74% accuracy on the WBCD dataset). It is noteworthy that the proposed method outperforms the GAANN and CAFS algorithms and other state-of-the-art classifiers. In addition, IEC-MLP is also capable of being employed in diagnosing other cancer types. [ABSTRACT FROM AUTHOR]

Over time, oil pipes are scaled, which causes problems such as a reduction in the effective diameter of the oil pipe, an efficiency reduction, waste of energy, etc. Determining the exact value of the scale inside the pipe is very important in order to take timely action and to prevent the mentioned problems. One accurate detection methodology is the use of non-invasive systems based on gamma-ray attenuation. For this purpose, in this research, a scale thickness detection system consisting of a test pipe, a dual-energy gamma source (241Am and 133Ba radioisotopes), and two sodium iodide detectors were simulated using the Monte Carlo N Particle (MCNP) code. In the test pipe, three-phase flow consisting of water, gas, and oil was simulated in a stratified flow regime in volume percentages in the range from 10% to 80%. In addition, a scale with different thicknesses from 0 to 3 cm was placed inside the pipe, and gamma rays were irradiated onto the pipe; on the other side of the pipe, the photon intensity was recorded by the detectors. A total of 252 simulations were performed. From the signal received by the detectors, four characteristics were extracted, named the Photopeaks of 241Am and 133Ba for the first and second detectors. After training many different Multi-Layer Perceptron(MLP) neural networks with various architectures, it was found that a structure with two hidden layers could predict the connection between the input, extracted features, and the output, scale thickness, with a Root Mean Square Error (RMSE) of less than 0.06. This low error value guarantees the effectiveness of the proposed method and the usefulness of this method for the oil and petrochemical industry. [ABSTRACT FROM AUTHOR]

As time passes, scale builds up inside the pipelines that deliver the oil or gas product from the source to processing plants or storage tanks, reducing the inside diameter and ultimately wasting energy and reducing efficiency. A non-invasive system based on gamma-ray attenuation is one of the most accurate diagnostic methods to detect volumetric percentages in different conditions. A system including two NaI detectors and dual-energy gamma sources (241Am and 133Ba radioisotopes) is the recommended requirement for modeling a volume-percentage detection system using Monte Carlo N particle (MCNP) simulations. Oil, water, and gas form a three-phase flow in a stratified-flow regime in different volume percentages, which flows inside a scaled pipe with different thicknesses. Gamma rays are emitted from one side, and photons are absorbed from the other side of the pipe by two scintillator detectors, and finally, three features with the names of the count under Photopeaks 241Am and 133Ba of the first detector and the total count of the second detector were obtained. By designing two MLP neural networks with said inputs, the volumetric percentages can be predicted with an RMSE of less than 1.48 independent of scale thickness. This low error value guarantees the effectiveness of the intended method and the usefulness of using this approach in the petroleum and petrochemical industries. [ABSTRACT FROM AUTHOR]

Soil is an essential natural resource for life that provides the required substrate on which plants grow and flourish. One of the challenges for environmental specialists is to accurately estimate and control soil erosion. MPSIAC (Modified model of Pacific Southwest Inter-Agency Committee) is a common model for estimating erosion and sedimentation rate. In this study, we used MPSIAC, regression and artificial neural networks (ANN) to estimate sediment yield in Kordan Basin, a region in Alborz Province of Iran. The erosion and sedimentation data of the region were collated using the opinions of sedimentation experts. A linear regression was performed in Weka software to determine the factors influencing the sedimentation rate. Based on the results and the opinion of the experts, the factors with less impact on the sedimentation were removed. ANN was implemented using NeuroSolutions and Matlab software. The neural network was a Multi-Layer Perceptron (MLP) with one hidden layer and five neurons. The hidden layer consisted of tan-sigmoid activation function, and the output layer had a linear-sigmoid activation function. The algorithm used for training the neural network was Levenberg-Marquardt. The ANN results were superior to that of regression and the Matlab's output was more accurate than that of NeuroSolutions, with a mean square error of 0.009 for sediment yield. Finally, Matlab's neural network was extracted in the form of a function for later applications without the need to further training.

Inertial measurement unit (IMU) technology has gained popularity in human activity recognition (HAR) due to its ability to identify human activity by measuring acceleration, angular velocity, and magnetic flux in key body areas like the wrist and knee. It has propelled the extensive application of HAR across various domains. In the healthcare sector, HAR finds utility in monitoring and assessing movements during rehabilitation processes, while in the sports science field, it contributes to enhancing training outcomes and preventing exercise-related injuries. However, traditional sensor fusion algorithms often require intricate mathematical and statistical processing, resulting in higher algorithmic complexity. Additionally, in dynamic environments, sensor states may undergo changes, posing challenges for real-time adjustments within conventional fusion algorithms to cater to the requirements of prolonged observations. To address these limitations, we propose a novel hybrid human pose recognition method based on IMU sensors. The proposed method initially calculates Euler angles and subsequently refines them using magnetometer and gyroscope data to obtain the accurate attitude angle. Furthermore, the application of FFT (Fast Fourier Transform) feature extraction facilitates the transition of the signal from its time-based representation to its frequency-based representation, enhancing the practical significance of the data. To optimize feature fusion and information exchange, a group attention module is introduced, leveraging the capabilities of a Multi-Layer Perceptron which is called the Feature Fusion Enrichment Multi-Layer Perceptron (GAM-MLP) to effectively combine features and generate precise classification results. Experimental results demonstrated the superior performance of the proposed method, achieving an impressive accuracy rate of 96.13% across 19 different human pose recognition tasks. The proposed hybrid human pose recognition method is capable of meeting the demands of real-world motion monitoring and health assessment.

In diagnostic radiology, the air kerma is an essential parameter. Radiologists consider the air kerma, when calculating organ doses and dangers to patients. The intensity of the radiation beam is represented by the air kerma, which is the value of energy wasted by a photon as it travels through air. Because of the heel effect in X-ray sources, air kerma varies throughout the field of medical imaging systems. One possible contributor to this discrepancy is the X-ray tube's voltage. In this study, an approach has been proposed for predicting the air kerma anywhere inside the field of X-ray beams utilized in medical diagnostic imaging systems. As a first step, a diagnostic imaging system was modelled using the Monte Carlo N-Particle platform. We used a tungsten target and aluminum and beryllium filters of varying thicknesses to recreate the X-ray tube. The air kerma has been measured in different parts of the conical X-ray beam that is working at 30, 50, 70, 90, 110, 130, and 150 kV. This gives enough data for training neural networks. The voltage of the X-ray tube, filter type, filter thickness, and the coordinates of each point used to calculate the air kerma were all inputs to the MLP neural network. The MLP architecture, known for its significant advancements in research and expanding applications, was trained to predict the quantity of air kerma as its output. Specifically, by considering X-ray tube filters of varying thicknesses, the trained MLP model demonstrated its capability to accurately predict the air kerma at every point within the X-ray field for a range of X-ray tube voltages typically used in medical diagnostic radiography (30–150 kV). • A method for anticipating the air kerma at any location inside the field of X-ray beam. • A diagnostic imaging system was modelled using MCNP code. • The air kerma was calculated for different voltages to supply a data for training MLP. • The voltage, filter type and thickness, and the coordinates used as inputs of MLP. • The trained MLP model was able to forecast the air kerma at every location. [ABSTRACT FROM AUTHOR]

This research investigates the development of an advanced predictive model aimed at accurately determining the volumetric percentages of water, oil, and gas within oil pipeline systems. Utilizing an innovative approach that incorporates an X-ray source alongside two sodium iodide detectors, the study leverages the Monte Carlo N-Particle (MCNP) simulation code to model the behavior of three-phase fluids under varied conditions. The model meticulously simulates various volumetric configurations of water, oil, and gas, resulting in a comprehensive dataset that provides key spectral information. The initial phase involved the extraction of ten temporal and frequency-related features from each detector, culminating in a pool of twenty features. The analytical process then applied the Grey Wolf Optimization (GWO) algorithm to select the most indicative features for predictive modeling. Out of the initial set, seven features—short-time energy, frequency deviation, relative spectral density, spectral margin, main peak position, spectral coefficient, and frequency intensity—were identified as critical for enhancing model accuracy. These features were subsequently fed into a meticulously structured multilayer perceptron (MLP) neural network. This network, designed with two hidden layers containing 20 and 10 neurons, respectively, demonstrated exceptional capability, achieving a root mean square error (RMSE) of less than 0.06 in the prediction of oil and gas volumetric percentages. The study emphasizes the significant impact of integrating refined feature selection techniques and robust neural network architectures on the precision and reliability of volumetric predictions in multiphase flow systems within oil pipelines. This approach not only enhances predictive accuracy but also contributes to more efficient resource management and operational planning in the oil and gas industry. • 10 temporal and frequency-related features from recorded signals in each detector were extracted. • The features applied to the Grey Wolf Optimization (GWO) algorithm to select the most indicative ones. • The critical features were fed into a meticulously structured MLP neural network. • Oil and gas volumetric percentages were predicted with RMSE of less than 0.06 [ABSTRACT FROM AUTHOR]

Atmospheric effects are among the primary error sources affecting the accuracy of interferometric synthetic aperture radar (InSAR). The topography-dependent atmospheric effect is particularly noteworthy in reservoir areas for landslide monitoring utilizing InSAR, which must be effectively corrected to complete the InSAR high-accuracy measurement. This paper proposed a topography-dependent atmospheric correction method based on the Multi-Layer Perceptron (MLP) neural network model combined with topography and spatial data information. We used this proposed approach for the atmospheric correction of the interferometric pairs of Sentinel-1 images in the Baihetan dam. We contrasted the outcomes with those obtained using the generic atmospheric correction online service for InSAR (GACOS) correction and the traditional linear model correction. The results indicated that the MLP neural network model correction reduced the phase standard deviation of the Sentinel-1 interferogram by an average of 64% and nearly eliminated the phase-elevation correlation. Both comparisons outperformed the GACOS correction and the linear model correction. Through two real-world examples, we demonstrated how slopes with displacements, which were previously obscured by a significant topography-dependent atmospheric delay, could be successfully and clearly identified in the interferograms following the correction by the MLP neural network. The topography-dependent atmosphere can be better corrected using the MLP neural network model suggested in this paper. Unlike the previous model, this proposed approach could be adjusted to fit each interferogram, regardless of how much of the topography-dependent atmosphere was present. In order to improve the effectiveness of DInSAR and time-series InSAR solutions, it can be applied immediately to the interferogram to retrieve the effective displacement information that cannot be identified before the correction. [ABSTRACT FROM AUTHOR]

Currently， with the changes in living habits and eating habits of China， consumers have higher requirements for the wearing comfort and fit of head and face products such as helmets and masks. In addition， the outbreak of COVID-19 in 2019 has made suitable masks an important protective equipment for medical staff and the general population. How to improve the safety protection level of masks has also become a hot social issue of concern. The fit of the mask is directly related to the protection effectiveness， so it is urgent to measure， track and update human head and face data. The research on the characteristics and classification of human head and face is an important basis for the structural design， size formulation， fit research and plate shape optimization of masks and helmets. Multilayer perceptron is an ANN algorithm. With the development of neural network technology， it is gradually applied to prediction and classification. The model， with strong nonlinear approximation function， simple structure， controllable number of input variables， and strong operability， can be applied to the classification and prediction of human body shape. In order to improve the adaptability of head and face products， this paper took 189 female college students aged 18-26 as the research subjects and used the Martin measuring instrument to measure the head and face of the subjects. Feature factors affecting head and face shape were extracted by principal component analysis （PCA）， the K-Means method was used to classify the head and face morphology， index classification method was used to quantify head and face morphology. As a result， a head-face shape prediction model based on MLP-ANN was proposed to improve the problem of low production work efficiency caused by too many head and face sizes in classifying or selecting models with too many references. The study found that: through the analysis of head and face characteristics of 189 subjects， seven important characteristic factors affecting the head and face shape were extracted: head contour factor， morphological facial factor， morphological facial factor， eye factor， nose factor and mouth ＆ lip factor. The head and face shapes were divided into five sizes according to the clustering center value of each category: XS type/morphological index>93， S type/morphological index∈(88， 93]， M type/morphological index∈(84， 88]， L type/morphological index∈(79， 84]， XL type/morphological index≤79， and the M type was the most widely distributed and had a big coverage rate， so it can be used as an intermediate type. Then through the MLP neural network， seven head-face feature factors were used to predict Head-face shape classification. The generated model had a 93.42% correct prediction result， and the research results can provide a reference for the design and production of head and face products. This paper provides an objective method for the study of head and facial features， but there are still some limitations. In the future， we can continue to improve the classification of head and face shape by expanding the area and age of the experimental subjects for comparative research. We can apply the classification to the head and face product specification system， so as to accumulate morphological data for the study of the head and face characteristics of contemporary Chinese people and the design of head and face products such as masks for the Chinese market. [ABSTRACT FROM AUTHOR]

Brain-computer interface (BCI) systems can be used to communicate and express desires from people with severe nervous system damage. Among BCI systems based on evoked responses, steady state visual evoked potential (SSVEP) responses are the most widely used. Canonical correlation analysis (CCA)-based methods have been widely used in SSVEP-based online BCIs due to their low computation and high speed, and many methods have been introduced to improve the results. In this research, a method for constructing reference signals used in CCA based on the amplified evoked response of brain is introduced. In the proposed method, after removing the latency in the training signals, to construct reference signals, multilayer perceptron neural networks of the fitting type are used instead of the usual sine/cosine signals. The results show the success of this method in boosting the evoked responses of brain. The detection accuracy in 100-second time windows was 100%, and the information transfer rate in the same period was 240 bits per minute. Making reference signals similar to the recorded electroencephalogram allowed us to make more similarities in the CCA between the signals under consideration, and the reference signals, and to dramatically improve the results. [ABSTRACT FROM AUTHOR]

The Weibull distribution is regarded as among the finest in the family of failure distributions. One of the most commonly used parameters of the Weibull distribution (WD) is the ordinary least squares (OLS) technique, which is useful in reliability and lifetime modeling. In this study, we propose an approach based on the ordinary least squares and the multilayer perceptron (MLP) neural network called the OLSMLP that is based on the resilience of the OLS method. The MLP solves the problem of heteroscedasticity that distorts the estimation of the parameters of the WD due to the presence of outliers, and eases the difficulty of determining weights in case of the weighted least square (WLS). Another method is proposed by incorporating a weight into the general entropy (GE) loss function to estimate the parameters of the WD to obtain a modified loss function (WGE). Furthermore, a Monte Carlo simulation is performed to examine the performance of the proposed OLSMLP method in comparison with approximate Bayesian estimation (BLWGE) by using a weighted GE loss function. The results of the simulation showed that the two proposed methods produced good estimates even for small sample sizes. In addition, the techniques proposed here are typically the preferred options when estimating parameters compared with other available methods, in terms of the mean squared error and requirements related to time. [ABSTRACT FROM AUTHOR]

One of the factors that significantly affects the efficiency of oil and gas industry equipment is the scales formed in the pipelines. In this innovative, non-invasive system, the inclusion of a dual-energy gamma source and two sodium iodide detectors was investigated with the help of artificial intelligence to determine the flow pattern and volume percentage in a two-phase flow by considering the thickness of the scale in the tested pipeline. In the proposed structure, a dual-energy gamma source consisting of barium-133 and cesium-137 isotopes emit photons, one detector recorded transmitted photons and a second detector recorded the scattered photons. After simulating the mentioned structure using Monte Carlo N-Particle (MCNP) code, time characteristics named 4th order moment, kurtosis and skewness were extracted from the recorded data of both the transmission detector (TD) and scattering detector (SD). These characteristics were considered as inputs of the multilayer perceptron (MLP) neural network. Two neural networks that were able to determine volume percentages with high accuracy, as well as classify all flow regimes correctly, were trained. [ABSTRACT FROM AUTHOR]

Friction stir welding (FSW) is a process by which a joint can be made in a solid state. The complexity of the process due to metallurgical phenomena necessitates the use of models with the ability to accurately correlate the process parameters with the joint properties. In the present study, a multilayer perceptron (MLP) artificial neural network (ANN) was used to model and predict the ultimate tensile strength (UTS) of the joint between the AA2024 and AA7075 aluminum alloys. Three pin geometries, pyramidal, conical, and cylindrical, were used for welding. The rotation speed varied between 800 and 1200 rpm and the welding speed varied between 10 and 50 mm/min. The obtained ANN model was used in a simulated annealing algorithm (SA algorithm) to optimize the process to attain the maximum UTS. The SA algorithm yielded the cylindrical pin and rotational speed of 1110 rpm to achieve the maximum UTS (395 MPa), which agreed well with the experiment. Tensile testing and scanning electron microscopy (SEM) were used to assess the joint strength and the microstructure of the joints, respectively. Various defects were detected in the joints, such as a root kissing bond and unconsolidated banding structures, whose formations were dependent on the tool geometry and the rotation speed.

Due to their moderate sensitivity, extremely cheap cost, ease of fabrication method, and, more crucially, the fact that they are non-invasive, planar microwave sensors have attracted a lot of interest from both industries and academics over the past years. These intriguing properties drive this field's research toward opening up a wide range of applications that go beyond oil and gas to include biological, material sensing, pollution monitoring, and other industrial uses. The main focus of this research is on the simulation and fabrication of a high-sensitivity, very small, and repeatable microwave sensor to measure volume fractions of oil and water in real-time. This sensor is designed by Ansys HFSS software and is made on the RT/Duroid 5880 (with εr = 2.2, thickness = 0.787 mm, loss tangent of 0.0009). In a polylactic acid (PLA) box made using a 3D printer, oil and water with different volume percentages will be placed on the microwave sensor in non-contact conditions. To determine volume percentages independent of the volume of the samples, different samples were analyzed in volumes of 5 ml, 10 ml, and 15 ml. The developed sensor includes two passing bands, and when exposed to crude oil with varying amounts of water, the frequencies of these bands, their insertion loss, and their prominence in these frequencies change. Due to the non-linear variations in the insertion loss, frequency, and prominence value of the two passbands, the MLP neural network is used in this study over other approaches for identifying the objective parameter. The MLP neural network's output was the water volume percentage, and its inputs were variations in the frequency, insertion loss, and prominence of the two passbands in the transmission response. Thanks to microwave sensors and artificial neural networks, volume fractions could be detected with high accuracy, independent of the volume of samples. The suggested microwave sensor could be a highly effective way to measure volume percentages in the oil sector because of its high accuracy, compact size, simplicity of transportation, non-contact feature, etc. • Novel microstrip sensor designed for non-contact liquid composition measurement. • Experiments show sensor's versatility with various water and oil mixtures. • Advanced signal processing extracted six features from frequency response. • ANN used to predict water percentage with high accuracy. • Promising applications in industry, environmental sensing, and biomedical diagnostics. [ABSTRACT FROM AUTHOR]

Today, the use of intelligent systems in medical diagnosis is gradually increasing. These systems lead to a reduction in error, which may be experienced by inexperienced experts. In this study, the use of artificial intelligent systems in predicting and diagnosing breast cancer, which is one of the most common cancers among women, is being considered. In this research, the diagnosis of breast cancer is performed with a two-stage approach. In the first step, the two parameters of the effective properties and the number of secret layer nodes for optimizing the MLP neural network are simultaneously optimized by a genetic algorithm. Then, using selected features and number of hidden layer nodes, a MLP neural network modeling model is developed for diagnosis of breast cancer in the second step. Here, a FinGrain parallels genetic algorithm based on optimized parameters is used to adjust the weight of the MLP neural network. The evaluation of the experiments shows that the proposed method compared to the two GAANN and CAFS methods on the WBCD dataset yielded better results and reported an accuracy of 98.72% in the average time.