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The profile of Volatile Organic Compounds (VOCs) may help prioritise at-risk groups for early cancer detection. Urine sampling has been shown to provide good disease accuracy whilst being patient acceptable compared to faecal analysis. Thus, in this study, urine samples were examined using an electronic nose with metal oxide gas sensors and a solid-phase microextraction sampling system. A calibration dataset (derived from a previous study) with CRC-positive patients and healthy controls was used to train a radial basis function neural network. However, a blinded analysis failed to detect CRC accurately, necessitating an enhanced data-processing strategy. This new approach categorised samples by significant bowel diseases, including CRC and high-risk polyps. Retraining the neural network showed an area under the ROC curve of 0.88 for distinguishing CRC versus non-significant bowel disease (without CRC, polyps or inflammation). These findings suggest that, with appropriate training sets, urine VOC analysis could be a rapid, low-cost method for early detection of precancerous colorectal polyps and CRC. [ABSTRACT FROM AUTHOR]

During the interaction process of a manipulator executing a grasping task, to ensure no damage to the object, accurate force and position control of the manipulator's end-effector must be concurrently implemented. To address the computationally intensive nature of current hybrid force/position control methods, a variable-parameter impedance control method for manipulators, utilizing a gradient descent method and Radial Basis Function Neural Network (RBFNN), is proposed. This method employs a position-based impedance control structure that integrates iterative learning control principles with a gradient descent method to dynamically adjust impedance parameters. Firstly, a sliding mode controller is designed for position control to mitigate uncertainties, including friction and unknown perturbations within the manipulator system. Secondly, the RBFNN, known for its nonlinear fitting capabilities, is employed to identify the system throughout the iterative process. Lastly, a gradient descent method adjusts the impedance parameters iteratively. Through simulation and experimentation, the efficacy of the proposed method in achieving precise force and position control is confirmed. Compared to traditional impedance control, manual adjustment of impedance parameters is unnecessary, and the method can adapt to tasks involving objects of varying stiffness, highlighting its superiority. [ABSTRACT FROM AUTHOR]

This paper introduces a novel methodology for evaluating communication performance in rotating electric machines using Received Signal Strength Indication (RSSI) measurements coupled with artificial intelligence. The proposed approach focuses on assessing the quality of wireless signals in the complex, dynamic environment inside these machines, where factors like reflections, metallic surfaces, and rotational movements can significantly impact communication. RSSI is used as a key parameter to monitor real-time signal behavior, enabling a detailed analysis of communication reliability. The methodology comprises several stages, including data collection, preprocessing, feature extraction, and model training. Various machine learning models are implemented and evaluated. Among these, the SVM model with a Radial Basis Function (RBF) kernel outperforms others, achieving an accuracy of 97%, with high precision and recall scores, confirming its robustness in classifying RSSI data and handling complex signal behavior. The confusion matrix further supports the SVM model's accuracy, showing minimal misclassification. [ABSTRACT FROM AUTHOR]

The aim of this study is to introduce and evaluate a dual filter that combines Radial Basis Function neural networks and Kalman filters to enhance the accuracy of numerical wave prediction models. Unlike the existing methods, which focus solely on systematic errors, the proposed framework concurrently targets both systematic and non-systematic parts of forecast errors, significantly reducing the bias and variability in significant wave height predictions. The produced filter is self-adaptive, identifying optimal Radial Basis Function network configurations through an automated process involving various network parameters tuning. The produced computational system is assessed using a time-window procedure applied across divergent time periods and regions in the Aegean Sea and the Pacific Ocean. The results reveal a consistent performance, outperforming classic Kalman filters with an average reduction of 53% in bias and 28% in RMSE, underlining the dual filter's potential as a robust post-processing tool for environmental simulations. [ABSTRACT FROM AUTHOR]

Although integrated joint torque sensors in robots dispel the need for external force/torque sensors at the wrist to measure interactions, an inherent challenge is that they also measure the robot's intrinsic dynamics. This is especially problematic for delicate robot manipulation tasks, where interaction forces may be comparable to the robot intrinsic dynamics. Therefore, the intrinsic dynamics must first be experimentally estimated under no-load conditions, when the measurement only consists of torques due to the transmission of the robot actuator, before external interactions may be measured. In this work, we propose an approach for identifying and predicting the intrinsic dynamics using linear regression with non-linear radial basis functions. Then, we validate this regression on a wheel-bearing turning task, in which its friction is a measure of quality, and thus must be accurately measured. The results showed that the bearing torque measured by the joint 7 torque sensor was within an RMS error of 11% of the torque measured by the external force/torque sensor. This error is much lower than that before our proposed model in compensating the intrinsic dynamics of the robot arm. [ABSTRACT FROM AUTHOR]

This paper presents a new means to control the processes involving energy conversion. Electric machines fed by electronic converters provide a useful power defined by the inner product of two generalized energetic variables: effort and flow. The novelty in this paper is controlling the desired energetic variables by a Data-Driven Control (DDC) law, which comprises the effort and flow and the corresponding process control. The same desired useful power might be obtained with different controls at different efficiencies. Solving the regularization problem is based on building a knowledge database that contains the maximum efficiency points. Knowing a reasonable number of optimal efficiency operation points, an interpolation Radial Base Function (RBF) control was built. The RBF algorithm can be found by training and testing the optimal controls for any admissible operation points of the process. The control scheme developed for Permanent Magnet Synchronous Motor (PMSM) has an inner DDC loop that performs converter control based on measured speed and demanded torque by the outer loop, which handles the speed. A comparison of the DDC with the Model Predictive Control (MPC) of the PMSM highlights the advantages of the new control method: the method is free from the process nature and guarantees higher efficiency. [ABSTRACT FROM AUTHOR]

The flow dynamics of low-yield horizontal wells demonstrate considerable complexity and unpredictability, chiefly attributable to the structural attributes of the wellbore and the interplay of gas–water two-phase flow. In horizontal wellbores, precisely predicting flow patterns using conventional approaches is often problematic. Consequently, accurate monitoring and analysis of water holdup in gas–water two-phase flows are essential. This study performs a gas–water two-phase flow simulation experiment under diverse total flow and water cut conditions, utilizing air and tap water to represent downhole gas and formation water, respectively. The experiment relies on the measurement principles of an array fiber holdup meter (GAT) and the response characteristics of the sensors. In the experiment, GAT was utilized for real-time water holdup measurement, and the acquired sensor data were analyzed using three interpolation algorithms: simple linear interpolation, inverse distance weighted interpolation, and Gaussian radial basis function interpolation. The results were subsequently post-processed and visualized with 2020 version MATLAB software, generating two-dimensional representations of water holdup in the wellbore. The study findings demonstrate that, at total flow of 300 m3/d and 500 m3/d, the simple linear interpolation approach yields superior accuracy in water holdup calculations, with imaging outcomes closely aligning with the actual gas–water flow patterns and the authentic gas–water distribution. As total flow and water cut increase, the gas–water two-phase flow progressively shifts from stratified smooth flow to stratified wavy flow. In this paper, the Gaussian radial basis function and inverse distance weighted interpolation algorithms exhibit superior accuracy in water holdup calculations, effectively representing the fluctuating features of the gas–water interface and yielding imaging outcomes that align more closely with experimentally observed gas–water flow patterns. [ABSTRACT FROM AUTHOR]

The paper presents research related to coastal observation using a camera and LiDAR (Light Detection and Ranging) mounted on an unmanned surface vehicle (USV). Fusion of data from these two sensors can provide wider and more accurate information about shore features, utilizing the synergy effect and combining the advantages of both systems. Fusion is used in autonomous cars and robots, despite many challenges related to spatiotemporal alignment or sensor calibration. Measurements from various sensors with different timestamps have to be aligned, and the measurement systems need to be calibrated to avoid errors related to offsets. When using data from unstable, moving platforms, such as surface vehicles, it is more difficult to match sensors in time and space, and thus, data acquired from different devices will be subject to some misalignment. In this article, we try to overcome these problems by proposing the use of a point matching algorithm for coordinate transformation for data from both systems. The essence of the paper is to verify algorithms based on selected basic neural networks, namely the multilayer perceptron (MLP), the radial basis function network (RBF), and the general regression neural network (GRNN) for the alignment process. They are tested with real recorded data from the USV and verified against numerical methods commonly used for coordinate transformation. The results show that the proposed approach can be an effective solution as an alternative to numerical calculations, due to process improvement. The image data can provide information for identifying characteristic objects, and the obtained accuracies for platform dynamics in the water environment are satisfactory (root mean square error—RMSE—smaller than 1 m in many cases). The networks provided outstanding results for the training set; however, they did not perform as well as expected, in terms of the generalization capability of the model. This leads to the conclusion that processing algorithms cannot overcome the limitations of matching point accuracy. Further research will extend the approach to include information on the position and direction of the vessel. [ABSTRACT FROM AUTHOR]

Given the increased significance of electric vehicles in recent years, this study aimed to develop a novel form of direct yaw-moment control (DYC) to enhance the driving stability of four-wheel independent drive (4WID) electric vehicles. Specifically, this study developed an innovative non-singular fast terminal sliding mode control (NFTSMC) method that integrates NFTSM and a fast-reaching control law. Moreover, this study employed a radial basis function neural network (RBFNN) to approximate both the entire system model and uncertain components, thereby reducing the computational load associated with a complex system model and augmenting the overall control performance. Using the aforementioned factors, the optimal additional yaw moment to ensure the lateral stability of a vehicle is determined. To generate the additional yaw moment, we introduce a real-time optimal torque distribution method based on the vertical load ratio. The stability of the proposed approach is comprehensively verified using the Lyapunov theory. Lastly, the validity of the proposed DYC system is confirmed by simulation tests involving step and sinusoidal inputs conducted using Matlab/Simulink and CarSim software. Compared to conventional sliding mode control (SMC) and NFTSMC methods, the proposed approach showed improvements in yaw rate tracking accuracy for all scenarios, along with a significant reduction in the chattering phenomenon in control torques. [ABSTRACT FROM AUTHOR]

Aiming at the shortcomings of single-sensor sensing information characterization ability, which is easily interfered with by external environmental factors, a method of intelligent perception is proposed in this paper. This method integrates multi-source and multi-level information, including spindle temperature field, spindle thermal deformation, operating parameters, and motor current. Firstly, the internal and external thermal-error-related signals of the spindle system are collected by sensors, and the feature parameters are extracted; then, the radial basis function (RBF) neural network is utilized to realize the preliminary integration of the feature parameters because of the advantages of the RBF neural network, which offers strong multi-dimensional solid nonlinear mapping ability and generalization ability. Thermal-error decision values are then generated by a weighted fusion of different pieces of evidence by considering uncertain information from multiple sources. The spindle thermal-error sensing experiment was based on the spindle system of the VMC850 (Yunnan Machine Tool Group Co., LTD, Yunnan, China) vertical machining center of the Yunnan Machine Tool Factory. Experiments were designed for thermal-error sensing of the spindle under constant speed (2000 r/min and 4000 r/min), standard variable speed, and stepped variable speed conditions. The experiment's results show that the prediction accuracy of the intelligent-sensing model with multi-source information fusion can reach 98.1%, 99.3%, 98.6%, and 98.8% under the above working conditions, respectively. The intelligent-perception model proposed in this paper has higher accuracy and lower residual error than the traditional BP neural network perception and wavelet neural network models. The research in this paper provides a theoretical basis for the operation, maintenance management, and performance optimization of machine tool spindle systems. [ABSTRACT FROM AUTHOR]

Control design for the nonlinear cascaded system is challenging due to its complicated system dynamics and system uncertainty, both of which can be considered some kind of system nonlinearity. In this paper, we propose a novel nonlinearity approximation scheme with a simplified structure, where the system nonlinearity is approximated by a steady component and an alternating component using only local tracking errors. The nonlinearity of each subsystem is estimated independently. On this basis, a model-free adaptive control for a class of nonlinear cascaded systems is proposed. A squared-error correction procedure is introduced to regulate the weight coefficients of the approximation components, which makes the whole adaptive system stable even with the unmodeled uncertainties. The effectiveness of the proposed controller is validated on a flexible joint system through numerical simulations and experiments. Simulation and experimental results show that the proposed controller can achieve better control performance than the radial basis function network control. Due to its simplicity and robustness, this method is suitable for engineering applications. [ABSTRACT FROM AUTHOR]

Partial discharge detection is considered a crucial technique for evaluating insulation performance and identifying defect types in cable terminals of high-speed electric multiple units (EMUs). In this study, terminal samples exhibiting four typical defects were prepared from high-speed EMUs. A cable discharge testing system, utilizing high-frequency current sensing, was developed to collect discharge signals, and datasets corresponding to these defects were established. This study proposes the use of the convolutional neural network (CNN) for the classification of discharge signals associated with specific defects, comparing this method with two existing neural network (NN)-based classification models that employ the back-propagation NN and the radial basis function NN, respectively. The comparative results demonstrate that the CNN-based model excels in accurately identifying signals from various defect types in the cable terminals of high-speed EMUs, surpassing the two existing NN-based classification models. [ABSTRACT FROM AUTHOR]

Aerial manipulators expand the application scenarios of manipulators into the air. To complete various operations, the contact force between the aerial manipulator and the target must be precisely controlled. In this study, we first established the mathematical models of the multirotor and the manipulator separately. Their mutual influence is regarded as each other's disturbance, and the overall linkage mechanism is established through analysis. Then, a robust sliding mode control strategy is developed for accurate trajectory tracking. The controller is derived from Lyapunov theory, which can ensure the stability of the closed-loop system. To compensate for the effect of system uncertainty, an adaptive radial basis function neural network is devised to approximate the part of the controller containing the model information. In addition, an impedance controller is designed to convert force control into position control to make the manipulator contact with the target compliantly. Finally, the simulation and experimental results indicate that the proposed method can guarantee the accuracy of the contact force and has good robustness. [ABSTRACT FROM AUTHOR]

This study develops an adaptive sliding mode control approach for a drilling tool attitude adjustment system, aiming at solving the problems of model uncertainties and insufficient ability of disturbance suppression during the regulation behavior. To further improve the performance of the position-tracking loop in terms of response time, tracking accuracy, and robustness, a state observer based on an improved radial basis function is designed to approximate the model uncertainties, a valve dead-zone compensate controller is used to reduce control deviation, an adaptive sliding mode controller is designed to improve the position-tracking precision and attenuate sliding mode chattering. Finally, simulation and experimental results are carried out to verify the observability of the model uncertainties and position-tracking errors of the drilling tool attitude adjustment system, which can effectively improve the position-tracking performance and robustness of the drilling tool attitude adjustment system. [ABSTRACT FROM AUTHOR]

The torque is a significant indicator reflecting the comprehensive operational characteristics of a power system. Thus, accurate torque measurement plays a pivotal role in ensuring the safety and stability of the system. However, conventional torque measurement systems predominantly rely on strain gauges adhered to the shaft, often leading to reduced accuracy, poor repeatability, and non-traceability due to the influence of strain gauge adhesion. To tackle the challenge, this paper introduces a photoelectric torque measurement system. Quadrants of photoelectric sensors are employed to capture minute deformations induced by torque on the rotational axis, converting them into measurable voltage. Subsequently, the system employs the radial basis function neural network optimized by simulated annealing combined with particle swarm algorithm (SAPSO-RBF) to establish a correlation between measured torque values and standard references, thereby calibrating the measured values. Experimental results affirm the system's capability to accurately determine torque measurements and execute calibration, minimizing measurement errors to 0.92%. [ABSTRACT FROM AUTHOR]

For two-axis electro-optical measurement equipment, there are many error sources in parts manufacturing, assembly, sensors, calibration, and so on, which cause some random errors in the final measurement results of the target. In order to eliminate the random measurement error as much as possible and improve the measurement accuracy, an active compensation technique for target measurement error is proposed in this paper. Firstly, the error formation mechanism and error transfer model establishment of the two-axis electro-optical measurement equipment were studied, and based on that, three error compensation and correction methods were proposed: the least square (LS)-based error compensation method, adaptive Kalman filter(AKF)-based error correction method, and radial basis function neural network (RBFNN)-based error compensation method. According to the theoretical analysis and numerical simulation comparison, the proposed RBFNN-based error compensation method was identified as the optimal error compensation method, which can approximate the random error space surface more precisely, so that a more accurate error compensation value can be obtained, and in order to improve the measurement accuracy with higher precision. Finally, the experimental results proved that the proposed active compensation technology was valid in engineering applicability and could efficiently enhance the measurement accuracy of the two-axis electro-optical measurement equipment. [ABSTRACT FROM AUTHOR]

A camera equipped with a transparent shield can be modeled using the pinhole camera model and residual error vectors defined by the difference between the estimated ray from the pinhole camera model and the actual three-dimensional (3D) point. To calculate the residual error vectors, we employ sparse calibration data consisting of 3D points and their corresponding 2D points on the image. However, the observation noise and sparsity of the 3D calibration points pose challenges in determining the residual error vectors. To address this, we first fit Gaussian Process Regression (GPR) operating robustly against data noise to the observed residual error vectors from the sparse calibration data to obtain dense residual error vectors. Subsequently, to improve performance in unobserved areas due to data sparsity, we use an additional constraint; the 3D points on the estimated ray should be projected to one 2D image point, called the ray constraint. Finally, we optimize the radial basis function (RBF)-based regression model to reduce the residual error vector differences with GPR at the predetermined dense set of 3D points while reflecting the ray constraint. The proposed RBF-based camera model reduces the error of the estimated rays by 6% on average and the reprojection error by 26% on average. [ABSTRACT FROM AUTHOR]

Indoor positioning using smartphones has garnered significant research attention. Geomagnetic and sensor data offer convenient methods for achieving this goal. However, conventional geomagnetic indoor positioning encounters several limitations, including low spatial resolution, poor accuracy, and stability issues. To address these challenges, we propose a fusion positioning approach. This approach integrates geomagnetic data, light intensity measurements, and inertial navigation data, utilizing a hierarchical optimization strategy. We employ a Tent-ASO-BP model that enhances the traditional Back Propagation (BP) algorithm through the integration of chaos mapping and Atom Search Optimization (ASO). In the offline phase, we construct a dual-resolution fingerprint database using Radial Basis Function (RBF) interpolation. This database amalgamates geomagnetic and light intensity data. The fused positioning results are obtained via the first layer of the Tent-ASO-BP model. We add a second Tent-ASO-BP layer and use an improved Pedestrian Dead Reckoning (PDR) method to derive the walking trajectory from smartphone sensors. In PDR, we apply the Biased Kalman Filter–Wavelet Transform (BKF-WT) for optimal heading estimation and set a time threshold to mitigate the effects of false peaks and valleys. The second-layer model combines geomagnetic and light intensity fusion coordinates with PDR coordinates. The experimental results demonstrate that our proposed positioning method not only effectively reduces positioning errors but also improves robustness across different application scenarios. [ABSTRACT FROM AUTHOR]

A model-free adaptive positioning control strategy for piezoelectric stick–slip actuators (PSSAs) with uncertain disturbance is proposed. The designed controller consists of a data-driven self-learning feedforward controller and a model-free adaptive feedback controller with a radial basis function neural network (RBFNN)-based observer. Unlike the traditional model-based control methods, the model-free adaptive control (MFAC) strategy avoids the complicated modeling process. First, the nonlinear system of the PSSA is dynamically linearized into a data model. Then, the model-free adaptive feedback controller based on a data model is designed to avoid the complicated modeling process and enhance the robustness of the control system. Simultaneously, the data-driven self-learning feedforward controller is improved to realize the high-precision control performance. Additionally, the convergence of the tracking error and the boundedness of the control output signal are proved. Finally, the experimentally obtained results illustrate the advantages and effectiveness of the developed control methodology on the bidirectional stick–slip piezoelectric actuator with coupled asymmetric flexure-hinge mechanisms. The positioning error through the proposed controller reaches 30 nm under the low-frequency condition and 200 nm under the high-frequency condition when the target position is set to 100 μm. In addition, the target position can be accurately tracked in less than 0.5 s in the presence of a 100 Hz frequency. [ABSTRACT FROM AUTHOR]

In electrical impedance tomography (EIT) detection of industrial two-phase flows, the Gauss-Newton algorithm is often used for imaging. In complex cases with multiple bubbles, this method has poor imaging accuracy. To address this issue, a new algorithm called the artificial bee colony–optimized radial basis function neural network (ABC-RBFNN) is applied to industrial two-phase flow EIT for the first time. This algorithm aims to enhance the accuracy of image reconstruction in electrical impedance tomography (EIT) technology. The EIDORS-v3.10 software platform is utilized to generate electrode data for a 16-electrode EIT system with varying numbers of bubbles. This generated data is then employed as training data to effectively train the ABC-RBFNN model. The reconstructed electrical impedance image produced from this process is evaluated using the image correlation coefficient (ICC) and root mean square error (RMSE) criteria. Tests conducted on both noisy and noiseless test set data demonstrate that the ABC-RBFNN algorithm achieves a higher ICC value and a lower RMSE value compared to the Gauss–Newton algorithm and the radial basis function neural network (RBFNN) algorithm. These results validate that the ABC-RBFNN algorithm exhibits superior noise immunity. Tests conducted on bubble models of various sizes and quantities, as well as circular bubble models, demonstrate the ABC-RBFNN algorithm's capability to accurately determine the size and shape of bubbles. This outcome confirms the algorithm's generalization ability. Moreover, when experimental data collected from a 16-electrode EIT experimental device is employed as test data, the ABC-RBFNN algorithm consistently and accurately identifies the size and position of the target. This achievement establishes a solid foundation for the practical application of the algorithm. [ABSTRACT FROM AUTHOR]

The aim of this study was to use geometric features and texture analysis to discriminate between healthy and unhealthy femurs and to identify the most influential features. We scanned proximal femoral bone (PFB) of 284 Iranian cases (21 to 83 years old) using different dual-energy X-ray absorptiometry (DEXA) scanners and magnetic resonance imaging (MRI) machines. Subjects were labeled as "healthy" (T-score > −0.9) and "unhealthy" based on the results of DEXA scans. Based on the geometry and texture of the PFB in MRI, 204 features were retrieved. We used support vector machine (SVM) with different kernels, decision tree, and logistic regression algorithms as classifiers and the Genetic algorithm (GA) to select the best set of features and to maximize accuracy. There were 185 participants classified as healthy and 99 as unhealthy. The SVM with radial basis function kernels had the best performance (89.08%) and the most influential features were geometrical ones. Even though our findings show the high performance of this model, further investigation with more subjects is suggested. To our knowledge, this is the first study that investigates qualitative classification of PFBs based on MRI with reference to DEXA scans using machine learning methods and the GA. [ABSTRACT FROM AUTHOR]

Piezoelectric actuators (PEAs) have the benefits of a high-resolution and high-frequency response and are widely applied in the field of micro-/nano-high-precision positioning. However, PEAs undergo nonlinear hysteresis between input voltage and output displacement, owing to the properties of materials. In addition, the input frequency can also influence the hysteresis response of PEAs. Research on tracking the control of PEAs by using various adaptive controllers has been a hot topic. This paper presents a finite-time sliding-mode controller (SMC) based on the disturbance observer (DOB) and a radial basis function (RBF) neural network (NN) (RBF-NN). RBF-NN is used to replace the hysteresis model of the dynamic system, and a novel finite-time adaptive DOB is proposed to estimate the disturbances of the system. By using RBF-NN, it is no longer necessary to establish the hysteresis model. The proposed DOB does not rely on any priori knowledge of disturbances and has a simple structure. All the solutions of closed-loop systems are practical finite-time-stable, and tracking errors can converge to a small neighborhood of zero in a finite time. The proposed control method was compiled in C language in the VC++ environment. A series of comparative experiments were conducted on a platform of a commercial PEA to validate the method. According to the experimental results of the sinusoidal and triangular trajectories under the frequencies of 1, 50, 100, and 200 Hz, the proposed control method is feasible and effective in improving the tracking control accuracy of the PEA platform. [ABSTRACT FROM AUTHOR]

Fifth-generation (5G) networks have been deployed alongside fourth-generation networks in high-traffic areas. The most recent 5G mobile communication access technology includes mmWave and sub-6 GHz C-bands. However, 5G signals possibly interfere with existing radio systems because they are using adjacent and co-channel frequencies. Therefore, the minimisation of the interference of 5G with other signals already deployed for other services, such as fixed-satellite service Earth stations (FSS-Ess), is urgently needed. The novelty of this paper is that it addresses issues using measurements from 5G base stations (5G-BS) and FSS-ES, simulation analysis, and prediction modelling based on artificial neural network learning models (ANN-LMs). The ANN-LMs models are used to classify interference events into two classes, namely, adjacent and co-channel interference. In particular, ANN-LMs incorporating the radial basis function neural network (RBFNN) and general regression neural network (GRNN) are implemented. Numerical results considering real measurements carried out in Malaysia show that RBFNN evidences better accuracy with respect to its GRNN counterpart. The outcomes of this work can be exploited in the future as a baseline for coexistence and/or mitigation techniques. [ABSTRACT FROM AUTHOR]

We propose a new fault diagnosis model for rolling bearings based on a hybrid kernel support vector machine (SVM) and Bayesian optimization (BO). The model uses discrete Fourier transform (DFT) to extract fifteen features from vibration signals in the time and frequency domains of four bearing failure forms, which addresses the issue of ambiguous fault identification caused by their nonlinearity and nonstationarity. The extracted feature vectors are then divided into training and test sets as SVM inputs for fault diagnosis. To optimize the SVM, we construct a hybrid kernel SVM using a polynomial kernel function and radial basis kernel function. BO is used to optimize the extreme values of the objective function and determine their weight coefficients. We create an objective function for the Gaussian regression process of BO using training and test data as inputs, respectively. The optimized parameters are used to rebuild the SVM, which is then trained for network classification prediction. We tested the proposed diagnostic model using the bearing dataset of the Case Western Reserve University. The verification results show that the fault diagnosis accuracy is improved from 85% to 100% compared with the direct input of vibration signal into the SVM, and the effect is significant. Compared with other diagnostic models, our Bayesian-optimized hybrid kernel SVM model has the highest accuracy. In laboratory verification, we took sixty sets of sample values for each of the four failure forms measured in the experiment, and the verification process was repeated. The experimental results showed that the accuracy of the Bayesian-optimized hybrid kernel SVM reached 100%, and the accuracy of five replicates reached 96.7%. These results demonstrate the feasibility and superiority of our proposed method for fault diagnosis in rolling bearings. [ABSTRACT FROM AUTHOR]

Tunable Diode Laser Absorption Spectroscopy (TDLAS) has been widely applied in in situ and real-time monitoring of trace gas concentrations. In this paper, an advanced TDLAS-based optical gas sensing system with laser linewidth analysis and filtering/fitting algorithms is proposed and experimentally demonstrated. The linewidth of the laser pulse spectrum is innovatively considered and analyzed in the harmonic detection of the TDLAS model. The adaptive Variational Mode Decomposition-Savitzky Golay (VMD-SG) filtering algorithm is developed to process the raw data and could significantly eliminate the background noise variance by about 31% and signal jitters by about 12.5%. Furthermore, the Radial Basis Function (RBF) neural network is also incorporated and applied to improve the fitting accuracy of the gas sensor. Compared with traditional linear fitting or least squares method (LSM), the RBF neural network brings along the enhanced fitting accuracy within a large dynamic range, achieving an absolute error of below 50 ppmv (about 0.6%) for the maximum 8000 ppmv methane. The proposed technique in this paper is universal and compatible with TDLAS-based gas sensors without hardware modification, allowing direct improvement and optimization for current optical gas sensors. [ABSTRACT FROM AUTHOR]

Digital bottom models are commonly used in many fields of human activity, such as navigation, harbor and offshore technologies, or environmental studies. In many cases, they are the basis for further analysis. They are prepared based on bathymetric measurements, which in many cases have the form of large datasets. Therefore, various interpolation methods are used for calculating these models. In this paper, we present the analysis in which we compared selected methods for bottom surface modeling with a particular focus on geostatistical methods. The aim was to compare five variants of Kriging and three deterministic methods. The research was performed with real data acquired with the use of an autonomous surface vehicle. The collected bathymetric data were reduced (from about 5 million points to about 500 points) and analyzed. A ranking approach was proposed to perform a complex and comprehensive analysis integrating typically used error statistics—mean absolute error, standard deviation and root mean square error. This approach allowed the inclusion of various views on methods of assessment while integrating various metrics and factors. The results show that geostatistical methods perform very well. The best results were achieved with the modifications of classical Kriging methods, which are disjunctive Kriging and empirical Bayesian Kriging. For these two methods, good statistics were calculated compared to other methods (for example, the mean absolute error for disjunctive Kriging was 0.23 m, while for universal Kriging and simple Kriging, it was 0.26 m and 0.25 m, respectively). However, it is worth mentioning that interpolation based on radial basis function in some cases is comparable to Kriging in its performance. The proposed ranking approach was proven to be useful and can be utilized in the future for choosing and comparing DBMs, mostly in mapping and analyzing seabed changes, for example in dredging operations. The research will be used during the implementation of the new multidimensional and multitemporal coastal zone monitoring system using autonomous, unmanned floating platforms. The prototype of this system is at the design stage and is expected to be implemented. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the upstream manufacturer cannot accurately formulate the production plan after the link of the nonlinear supply chain system changes under emergencies, an optimization model of production change in a nonlinear supply chain system under emergencies is designed. Firstly, based on the structural characteristics of the supply chain system and the logical relationship between production, sales, and storage parameters, a three-level single-chain nonlinear supply chain dynamic system model containing producers, sellers, and retailers was established based on the introduction of nonlinear parameters. Secondly, the radial basis function (RBF) neural network and improved fast variable power convergence law were introduced to improve the traditional sliding mode control, and the improved adaptive sliding mode control is proposed so that it can have a good control effect on the unknown nonlinear supply chain system. Finally, based on the numerical assumptions, the constructed optimization model was parameterized and simulated for comparison experiments. The simulation results show that the optimized model can reduce the adjustment time by 37.50% and inventory fluctuation by 42.97%, respectively, compared with the traditional sliding mode control, while helping the supply chain system to return the smooth operation after the change within 5 days. [ABSTRACT FROM AUTHOR]

A class of heterogeneous second-order multi-agent consensus problems is studied, in which an event-triggered method is used to improve the feasibility of the control protocol. The sliding mode control method is used to achieve the robustness of the system. A special type of general radial basis function neural network is applied to estimate the uncertainties. The event-triggered mechanism is introduced to reduce the update frequency of the controller and the communication frequency among the agents. Zeno behavior is avoided by ensuring a lower bound between two adjacent trigger instants. Finally, the simulation results are provided to demonstrate that the time evolution of consensus errors eventually approaches zero. The consensus of multi-agent systems is achieved. [ABSTRACT FROM AUTHOR]

To realize high-performance line of sight (LOS) stabilization control of the optronic mast under high oceanic conditions and big swaying movements of platforms, a composite control method based on an adaptive radial basis function neural network (RBFNN) and sliding mode control (SMC) is proposed. The adaptive RBFNN is used to approximate the nonlinear and parameter-varying ideal model of the optronic mast, so as to compensate for the uncertainties of the system and reduce the big-amplitude chattering phenomenon caused by excessive switching gain in SMC. The adaptive RBFNN is constructed and optimized online based on the state error information in the working process; therefore, no prior training data are required. At the same time, a saturation function is used to replace the sign function for the time-varying hydrodynamic disturbance torque and the friction disturbance torque, which further reduce the chattering phenomenon of the system. The asymptotic stability of the proposed control method has been proven by the Lyapunov stability theory. The applicability of the proposed control method is validated by a series of simulations and experiments. [ABSTRACT FROM AUTHOR]

Galvanometric laser scanner (GLS) systems are widely used for materials processing due to their high precision, processing velocity, and repeatability. However, GLS systems generally suffer from scan field distortions due to joint and task space relationship errors. The problem is further pronounced in robotic applications, where the GLS systems are manipulated in space, as unknown errors in the relative pose of the GLS can be introduced. This paper presents an in situ, data-driven methodology for calibrating GLS systems using 3D scanning, emphasising the flexibility, generalisation, and automated industrial integration. Three-dimensional scanning serves two primary purposes: (1) determining the relative pose between the GLS system and the calibration plate to minimise calibration errors and (2) supplying an image processing algorithm with dense and accurate data to measure the scan field distortion based on the positional deviations of marked fiducials. The measured deviations are used to train a low-complexity Radial Basis Function (RBF) network to predict and correct the distorted scan field. The proposed method shows promising results and significantly reduces the scan field distortion without the use of specialised calibration tools and with limited knowledge of the optical design of the GLS system. [ABSTRACT FROM AUTHOR]

Contact force control for Unmanned Aerial Manipulators (UAMs) is a challenging issue today. This paper designs a new method to stabilize the UAM system during the formation of contact force with the target. Firstly, the dynamic model of the contact process between the UAM and the target is derived. Then, a non-singular global fast terminal sliding mode controller (NGFTSMC) is proposed to guarantee that the contact process is completed within a finite time. Moreover, to compensate for system uncertainties and external disturbances, the equivalent part of the controller is estimated by an adaptive radial basis function neural network (RBFNN). Finally, the Lyapunov theory is applied to validate the global stability of the closed-loop system and derive the adaptive law for the neural network weight matrix online updating. Simulation and experimental results demonstrate that the proposed method can stably form a continuous contact force and reduce the chattering with good robustness. [ABSTRACT FROM AUTHOR]

In this work, a new hydroelectric basin modelling approach is described and applied to the Pontecosi basin, Italy. Several types of data sources were used to learn the model: a number of weather stations, satellite observations, the reanalysis dataset, and basin data. With the goal of predicting the water level of the basin, the model was composed by three cascade modules. Firstly, different spatial interpolation methods, such as Kriging, Radial Basis Function, and Natural Neighbours, were compared and applied to interpolate the weather stations data nearby the basin area to infer the main environmental variables (air temperature, air humidity, precipitation, and wind speed) in the basin area. Then, using these variables as inputs, a neural network was trained to predict the mean soil moisture concentration over the area, also to improve the low availability due to satellite orbits. Finally, a non-linear auto regressive exogenous input (NARX) model was trained to simulate the basin level with different prediction horizons, using the data from the previous modules and past basin data (water level, discharge flow rate, and turbine flow rate). Accurate predictions of the basin water level were achieved within 1 to 6 h ahead, with mean absolute errors (MAE) between 2 cm and 10 cm, respectively. [ABSTRACT FROM AUTHOR]

Healthcare systems in recent times have witnessed timely diagnoses with a high level of accuracy. Internet of Medical Things (IoMT)-enabled deep learning (DL) models have been used to support medical diagnostics in real time, thus resolving the issue of late-stage diagnosis of various diseases and increasing performance accuracy. The current approach for the diagnosis of leukemia uses traditional procedures, and in most cases, fails in the initial period. Hence, several patients suffering from cancer have died prematurely due to the late discovery of cancerous cells in blood tissue. Therefore, this study proposes an IoMT-enabled convolutional neural network (CNN) model to detect malignant and benign cancer cells in the patient's blood tissue. In particular, the hyper-parameter optimization through radial basis function and dynamic coordinate search (HORD) optimization algorithm was used to search for optimal values of CNN hyper-parameters. Utilizing the HORD algorithm significantly increased the effectiveness of finding the best solution for the CNN model by searching multidimensional hyper-parameters. This implies that the HORD method successfully found the values of hyper-parameters for precise leukemia features. Additionally, the HORD method increased the performance of the model by optimizing and searching for the best set of hyper-parameters for the CNN model. Leukemia datasets were used to evaluate the performance of the proposed model using standard performance indicators. The proposed model revealed significant classification accuracy compared to other state-of-the-art models. [ABSTRACT FROM AUTHOR]

Positioning systems are used in a wide range of applications which require determining the position of an object in space, such as locating and tracking assets, people and goods; assisting navigation systems; and mapping. Indoor Positioning Systems (IPSs) are used where satellite and other outdoor positioning technologies lack precision or fail. Ultra-WideBand (UWB) technology is especially suitable for an IPS, as it operates under high data transfer rates over short distances and at low power densities, although signals tend to be disrupted by various objects. This paper presents a comprehensive study of the precision, failure, and accuracy of 2D IPSs based on UWB technology and a pseudo-range multilateration algorithm using Time Difference of Arrival (TDoA) signals. As a case study, the positioning of a 4 × 4 m 2 area, four anchors (transceivers), and one tag (receiver) are considered using bitcraze's Loco Positioning System. A Cramér–Rao Lower Bound analysis identifies the convex hull of the anchors as the region with highest precision, taking into account the anisotropic radiation pattern of the anchors' antennas as opposed to ideal signal distributions, while bifurcation envelopes containing the anchors are defined to bound the regions in which the IPS is predicted to fail. This allows the formulation of a so-called flyable area, defined as the intersection between the convex hull and the region outside the bifurcation envelopes. Finally, the static bias is measured after applying a built-in Extended Kalman Filter (EKF) and mapped using a Radial Basis Function Network (RBFN). A debiasing filter is then developed to improve the accuracy. Findings and developments are experimentally validated, with the IPS observed to fail near the anchors, precision around ± 3 cm , and accuracy improved by about 15 cm for static and 5 cm for dynamic measurements, on average. [ABSTRACT FROM AUTHOR]

This paper studies the cooperative control of multiple unmanned aerial vehicles (UAVs) with sensors and autonomous flight capabilities. In this paper, an architecture is proposed that takes a small quadrotor as a mission UAV and a large six-rotor as a platform UAV to provide an aerial take-off and landing platform and transport carrier for the mission UAV. The design of a tracking controller for an autonomous docking and landing trajectory system is the focus of this research. To examine the system's overall design, a dual-machine trajectory-tracking control simulation platform is created via MATLAB/Simulink. Then, an autonomous docking and landing trajectory-tracking controller based on radial basis function proportional–integral–derivative control is designed, which fulfills the trajectory-tracking control requirements of the autonomous docking and landing process by efficiently suppressing the external airflow disturbance according to the simulation results. A YOLOv3-based vision pilot system is designed to calibrate the rate of the aerial docking and landing position to eight frames per second. The feasibility of the multi-rotor aerial autonomous docking and landing technology is verified using prototype flight tests during the day and at night. It lays a technical foundation for UAV transportation, autonomous take-off, landing in the air, and collaborative networking. In addition, compared with the existing technologies, our research completes the closed loop of the technical process through modeling, algorithm design and testing, virtual simulation verification, prototype manufacturing, and flight test, which have better realizability. [ABSTRACT FROM AUTHOR]

This paper's novel focus is predicting the leaf nitrogen content of rice during growing and maturing. A multispectral image processing-based prediction model of the Radial Basis Function Neural Network (RBFNN) model was proposed. Moreover, this paper depicted three primary points as the following: First, collect images of rice leaves (RL) from a controlled condition experimental laboratory and new shoot leaves in different stages in the visible light spectrum, and apply digital image processing technology to extract the color characteristics of RL and the morphological characteristics of the new shoot leaves. Secondly, the RBFNN model, the General Regression Model (GRL), and the General Regression Method (GRM) model were constructed based on the extracted image feature parameters and the nitrogen content of rice leaves. Third, the RBFNN is optimized by and Partial Least-Squares Regression (RBFNN-PLSR) model. Finally, the validation results show that the nitrogen content prediction models at growing and mature stages that the mean absolute error (MAE), the Mean Absolute Percentage Error (MAPE), and the Root Mean Square Error (RMSE) of the RFBNN model during the rice-growing stage and the mature stage are 0.6418 (%), 0.5399 (%), 0.0652 (%), and 0.3540 (%), 0.1566 (%), 0.0214 (%) respectively, the predicted value of the model fits well with the actual value. Finally, the model may be used to give the best foundation for achieving exact fertilization control by continuously monitoring the nitrogen nutrition status of rice. In addition, at the growing stage, the RBFNN model shows better results compared to both GRL and GRM, in which MAE is reduced by 0.2233% and 0.2785%, respectively. [ABSTRACT FROM AUTHOR]

Classification of motor imagery (MI) tasks provides a robust solution for specially-abled people to connect with the milieu for brain-computer interface. Precise selection of uniform tuning parameters of tunable Q wavelet transform (TQWT) for electroencephalography (EEG) signals is arduous. Therefore, this paper proposes robust TQWT for automatically selecting optimum tuning parameters to decompose non-stationary EEG signals accurately. Three evolutionary optimization algorithms are explored for automating the tuning parameters of robust TQWT. The fitness function of the mean square error of decomposition is used. This paper also exploits channel selection using a Laplacian score for dominant channel selection. Important features elicited from sub-bands of robust TQWT are classified using different kernels of the least square support vector machine classifier. The radial basis function kernel has provided the highest accuracy of 99.78%, proving that the proposed method is superior to other state-of-the-art using the same database. [ABSTRACT FROM AUTHOR]

The performances of position synchronization and force interaction of the teleoperation system provide a safe and efficient way for operators to perform tasks in remote, hazardous environments. In practice, however, communication delays and dynamic uncertainties can impair the performance of position synchronization controls. Under the above factors, it is necessary to study and design appropriate bilateral control methods to achieve stable and effective position synchronization control. In this paper, a new adaptive control architecture based on velocity feedback filter and radial basis function neural network is proposed. In the proposed control scheme, only the position signal is transmitted during the communication process, and the speed feedback filter and compensation method are designed and adopted to avoid the use of acceleration signals. In addition, a new auxiliary variable with a tracking error integral term is used to reduce the steady-state error of position tracking under nonzero external environmental forces. Using the Lyapunov–Krasovskii method, the stability of closed-loop remote operating systems is demonstrated. In the simulation and experiment sections, the algorithm was verified separately and compared with other algorithms. The results of a master–slave robot system verify the tracking performance of our proposed control scheme. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of formation fault-tolerant control of multiple quadrotors (QRs) for a mobile sensing oriented application. The QRs subject to faults, input saturation and time-varying delays can be controlled to perform a target-enclosing and covering task while guaranteeing the state constraints will not be exceeded. A distributed formation control scheme is proposed, using a radial basis function neural network (RBFNN)-based time-delay position controller and an adaptive fault-tolerant attitude controller. The Lyapunov–Krasovskii approach is used to analyze the time-varying delay. Barrier Lyapunov function is deployed to handle the prescribed constraints, and an auxiliary system combined with a command filter is designed to resolve the saturation problem. An RBFNN and adaptive estimators are deployed to provide estimates of disturbances, fault signals and uncertainties. It is proven that all the closed-loop signals are bounded under the proposed protocol, while the prescribed constraints will not be violated, which enhances the flight safety and QR formation's applicability. Comparative simulations based on application scenarios further verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Diagnosis of cardiovascular diseases is an urgent task because they are the main cause of death for 32% of the world's population. Particularly relevant are automated diagnostics using machine learning methods in the digitalization of healthcare and introduction of personalized medicine in healthcare institutions, including at the individual level when designing smart houses. Therefore, this study aims to analyze short 10-s electrocardiogram measurements taken from 12 leads. In addition, the task is to classify patients with suspected myocardial infarction using machine learning methods. We have developed four models based on the k-nearest neighbor classifier, radial basis function, decision tree, and random forest to do this. An analysis of time parameters showed that the most significant parameters for diagnosing myocardial infraction are SDNN, BPM, and IBI. An experimental investigation was conducted on the data of the open PTB-XL dataset for patients with suspected myocardial infarction. The results showed that, according to the parameters of the short ECG, it is possible to classify patients with a suspected myocardial infraction as sick and healthy with high accuracy. The optimized Random Forest model showed the best performance with an accuracy of 99.63%, and a root mean absolute error is less than 0.004. The proposed novel approach can be used for patients who do not have other indicators of heart attacks. [ABSTRACT FROM AUTHOR]

When a vehicle is being driven, it is excited by the road roughness and generates its own vibration. In order to improve the vehicle's riding comfort and the physical–mental health of passengers in the vehicle, this paper proposes a formulation method for a comfortable speed strategy and the technical route of its application. According to international standard ISO 2631-1, the relationship between the weighted root-mean-square acceleration value and comfortable vehicle speed is analyzed. The simulation test platform of the road roughness signal and vehicle vibration signal is built by using the filtering white noise method and the second Lagrange equation through Matlab/Simulink. Combined with the simulation platform, this paper extracts seven characteristics with statistical properties from the time-domain signal and obtains 500 sample data. Random forest (RF), extreme learning machine (ELM), and radial basis function neural network (RBF-NN) are applied to identify roads. Two comfortable speed strategy formulation methods based on the improved simulated annealing (ISA) algorithm are proposed and compared according to the solution effect of each grade of comfortable speed. The results show that the simulated signals of each grade road roughness are accurate. Road recognition can be effectively carried out using the statistical characteristics of vehicle vibration acceleration signals. ELM has high recognition accuracy and fast execution speed. The ISA-II algorithm has a low solution error of comfortable speed and a low computation time. The comfortable speed of the research vehicle on different road grades showed a great difference. [ABSTRACT FROM AUTHOR]

The real-time, continuity, and accuracy of blast furnace stockline information are of great significance in reducing energy consumption and improving smelting efficiency. However, the traditional mechanical measurement method has the problem of measuring point discontinuity, while the radar measurement method exhibits problems such as weak anti-interference ability, low accuracy, and poor stability. Therefore, a high-dimensional, spatial feature stockline detection method based on the maximum likelihood radial basis function model (MLRBFM) and structural dynamic self-optimization RBF neural network (SDSO-RBFNN) is proposed. Firstly, the discrete time series joint partition method is used to extract the time dimension periodic features of the blast furnace stockline. Based on MLRBFM, the high-dimensional spatial features of the stockline are then obtained. Finally, an SDSO-RBFNN is constructed based on an eigen orthogonal matrix and a right triangular matrix decomposition (QR) direct clustering algorithm with spatial–temporal features as input, so as to obtain continuous, high-precision stockline information. Both the simulation results and industrial validation indicate that the proposed method can provide real-time and accurate stockline information, and has great practical value for industrial production. [ABSTRACT FROM AUTHOR]

This paper discusses a novel approach to an EEG (electroencephalogram)-based driver distraction classification by using brain connectivity estimators as features. Ten healthy volunteers with more than one year of driving experience and an average age of 24.3 participated in a virtual reality environment with two conditions, a simple math problem-solving task and a lane-keeping task to mimic the distracted driving task and a non-distracted driving task, respectively. Independent component analysis (ICA) was conducted on the selected epochs of six selected components relevant to the frontal, central, parietal, occipital, left motor, and right motor areas. Granger–Geweke causality (GGC), directed transfer function (DTF), partial directed coherence (PDC), and generalized partial directed coherence (GPDC) brain connectivity estimators were used to calculate the connectivity matrixes. These connectivity matrixes were used as features to train the support vector machine (SVM) with the radial basis function (RBF) and classify the distracted and non-distracted driving tasks. GGC, DTF, PDC, and GPDC connectivity estimators yielded the classification accuracies of 82.27%, 70.02%, 86.19%, and 80.95%, respectively. Further analysis of the PDC connectivity estimator was conducted to determine the best window to differentiate between the distracted and non-distracted driving tasks. This study suggests that the PDC connectivity estimator can yield better classification accuracy for driver distractions. [ABSTRACT FROM AUTHOR]

This paper investigates the adaptive fault-tolerant formation control scheme for heterogeneous multi-agent systems consisting of unmanned aerial vehicles (UAVs) and unmanned surface vehicles (USVs) with actuator faults, parameter uncertainties and external disturbances under directed communication topology. Firstly, the dynamic models of UAVs and USVs are introduced, and a unified heterogeneous multi-agent system model with actuator faults is established. Then, a distributed fault-tolerant formation controller is proposed for the unified model of UAVs and USVs in the X Y plane by using adaptive updating laws and radial basis function neural network. After that, a decentralized formation-tracking controller is designed for the altitude control system of UAVs. Based on the Lyapunov stability theory, it can be proved that the formation errors and tracking errors are uniformly ultimately bounded which means that the expected time-varying formation is achieved. Finally, a simulation study is given to demonstrate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

This paper proposes a new hybrid framework for short-term load forecasting (STLF) by combining the Feature Engineering (FE) and Bayesian Optimization (BO) algorithms with a Bayesian Neural Network (BNN). The FE module comprises feature selection and extraction phases. Firstly, by merging the Random Forest (RaF) and Relief-F (ReF) algorithms, we developed a hybrid feature selector based on grey correlation analysis (GCA) to eliminate feature redundancy. Secondly, a radial basis Kernel function and principal component analysis (KPCA) are integrated into the feature-extraction module for dimensional reduction. Thirdly, the Bayesian Optimization (BO) algorithm is used to fine-tune the control parameters of a BNN and provides more accurate results by avoiding the optimal local trapping. The proposed FE-BNN-BO framework works in such a way to ensure stability, convergence, and accuracy. The proposed FE-BNN-BO model is tested on the hourly load data obtained from the PJM, USA, electricity market. In addition, the simulation results are also compared with other benchmark models such as Bi-Level, long short-term memory (LSTM), an accurate and fast convergence-based ANN (ANN-AFC), and a mutual-information-based ANN (ANN-MI). The results show that the proposed model has significantly improved the accuracy with a fast convergence rate and reduced the mean absolute percent error (MAPE). [ABSTRACT FROM AUTHOR]

Cartilage loss due to osteoarthritis (OA) in the patellofemoral joint provokes pain, stiffness, and restriction of joint motion, which strongly reduces quality of life. Early diagnosis is essential for prolonging painless joint function. Vibroarthrography (VAG) has been proposed in the literature as a safe, noninvasive, and reproducible tool for cartilage evaluation. Until now, however, there have been no strict protocols for VAG acquisition especially in regard to differences between the patellofemoral and tibiofemoral joints. The purpose of this study was to evaluate the proposed examination and acquisition protocol for the patellofemoral joint, as well as to determine the optimal examination protocol to obtain the best diagnostic results. Thirty-four patients scheduled for knee surgery due to cartilage lesions were enrolled in the study and compared with 33 healthy individuals in the control group. VAG acquisition was performed prior to surgery, and cartilage status was evaluated during the surgery as a reference point. Both closed (CKC) and open (OKC) kinetic chains were assessed during VAG. The selection of the optimal signal measures was performed using a neighborhood component analysis (NCA) algorithm. The classification was performed using multilayer perceptron (MLP) and radial basis function (RBF) neural networks. The classification using artificial neural networks was performed for three variants: I. open kinetic chain, II. closed kinetic chain, and III. open and closed kinetic chain. The highest diagnostic accuracy was obtained for variants I and II for the RBF 9-35-2 and MLP 10-16-2 networks, respectively, achieving a classification accuracy of 98.53, a sensitivity of 0.958, and a specificity of 1. For variant III, a diagnostic accuracy of 97.79 was obtained with a sensitivity and specificity of 0.978 for MLP 8-3-2. This indicates a possible simplification of the examination protocol to single kinetic chain analyses. [ABSTRACT FROM AUTHOR]

Artificial Neural Networks are used to find the influence of habitat types on the quality of the environment expressed by the concentrations of toxic and harmful elements in avian tissue. The main habitat types were described according to the Corine Land Cover CLC2012 model. Eggs of free-living species of a colonial waterbird, the grey heron Ardea cinerea, were used as a biological data storing media for biomonitoring. For modeling purposes, pollution indices expressing the sum of the concentration of harmful and toxic elements (multi-contamination rank index) and indices for single elements were created. In the case of all the examined indices apart from Cd, the generated topologies were a multi-layer perceptron (MLP) with 1 hidden layer. Interestingly, in the case of Cd, the generated optimal topology was a network with a radial basis function (RBF). The data analysis showed that the increase in environmental pollution was mainly influenced by human industrial activity. The increase in Hg, Cd, and Pb content correlated mainly with the increase in the areas characterized by human activity (industrial, commercial, and transport units) in the vicinity of a grey heron breeding colony. The decrease in the above elements was conditioned by relative areas of farmland and inland waters. Pollution with Fe, Mn, Zn, and As was associated mainly with areas affected by industrial activities. As the location variable did not affect the quality of the obtained networks, it was removed from the models making them more universal. [ABSTRACT FROM AUTHOR]

Data streaming applications such as the Internet of Things (IoT) require processing or predicting from sequential data from various sensors. However, most of the data are unlabeled, making applying fully supervised learning algorithms impossible. The online manifold regularization approach allows sequential learning from partially labeled data, which is useful for sequential learning in environments with scarcely labeled data. Unfortunately, the manifold regularization technique does not work out of the box as it requires determining the radial basis function (RBF) kernel width parameter. The RBF kernel width parameter directly impacts the performance as it is used to inform the model to which class each piece of data most likely belongs. The width parameter is often determined off-line via hyperparameter search, where a vast amount of labeled data is required. Therefore, it limits its utility in applications where it is difficult to collect a great deal of labeled data, such as data stream mining. To address this issue, we proposed eliminating the RBF kernel from the manifold regularization technique altogether by combining the manifold regularization technique with a prototype learning method, which uses a finite set of prototypes to approximate the entire data set. Compared to other manifold regularization approaches, this approach instead queries the prototype-based learner to find the most similar samples for each sample instead of relying on the RBF kernel. Thus, it no longer necessitates the RBF kernel, which improves its practicality. The proposed approach can learn faster and achieve a higher classification performance than other manifold regularization techniques based on experiments on benchmark data sets. Results showed that the proposed approach can perform well even without using the RBF kernel, which improves the practicality of manifold regularization techniques for semi-supervised learning. [ABSTRACT FROM AUTHOR]

The use of beamforming for efficient transmission has already been successfully implemented in practical systems and is absolutely necessary to even further increase spectral and energy efficiencies in some configurations of the next-generation wireless systems and for low earth orbit satellites. A remarkable capacity increase is then achieved and spectral congestion is minimized. In this context, this article proposes a novel complex multiple-input multiple-output radial basis function neural network (CMM-RBF) for transmitter beamforming, based on the phase transmittance radial basis function neural network (PTRBFNN). The proposed CMM-RBF is compared with the least mean square (LMS) algorithm for beamforming with six dipoles arranged in a uniform and circular array and with 16 dipoles in a 2D-grid array. Simulation results show that the proposed solution presents lower steady-state mean squared error, faster convergence rate and enhanced half-power beamwidth (HPBW) when compared with the LMS algorithm in a nonlinear scenario. [ABSTRACT FROM AUTHOR]