Step Size Research Articles

FinTech forecasting using an evolving connectionist system for lenders and borrowers: ecosystem behavior

<span lang="EN-US">Financial Technology (FinTech) which is included in the development of digitalization in the financial sector in the industrial era 4.0. Fintech can make any transactions anywhere with the pillars of Peet-to-Peer (P2P) lending, merchants and crowdfunding. In the P2P Lending pillar, there are borrowers and lenders who are digitized in Fintech devices. Fintech in Indonesia is controlled by a state agency called the Otoritas Jasa Keuangan or Financial Services Authority (OJK). In the movement of P2P Lending, there are borrowers and lenders who can be said to be investors where these activities are reported to the OJK. This data can be forecasted using a neural network approach such as ECoS, which is a method capable of forecasting with learning that develops in the hidden layer. In this research article, we present results on forecasting borrowers with a Mean Absolute Percentage Error (MAPE) of 0.148% and forecasting lenders with an accuracy measurement with MAPE of 0.209% with a learning rate 1 = 0.6 and a learning rate 2 = 0.3. So, this forecasting model can be said as an optimization in FinTech activities on the behavior of borrowers and lenders.</span>

A convolution neural network integrating climate variables and spatial-temporal properties to predict influenza trends

The spread of influenza is contingent upon a multitude of outbreak-related factors, including viral mutation, climate conditions, acquisition of immunity, crowded environments, vaccine efficacy, social gatherings, and the health and age profiles of individuals in contact with infected individuals. An epidemic in the region impacted by spatial transmission risk from adjacent regions. A few influenzas epidemic models start highlighting the spatial correlations between influenza patients and geographically adjacent regions. The proposed model is based on the concept of climatic, immunization, and spatial correlations which are represented by a convolution neural network (CNN) for influenza epidemic forecasting. This study presents an integration of three determinants for predicting influenza outbreaks, multivariate climate data, spatial data on influenza vaccination, and spatial-temporal data of historical influenza patients. The performance of three comparison models, CNN, recurrent neural network (RNN), and long short-term memory (LSTM) was compared by the root mean squared error metric (RMSE). The findings revealed that the CNN model represents human interaction at intervals of 12, 16, 20, 24, and 28 weeks resulting in the best effectiveness of the lowest RMSE=0.00376 with learning rate=0.0001.

Enhancing photovoltaic system maximum power point tracking with fuzzy logic-based perturb and observe method

Photovoltaic systems have emerged as a promising energy resource that caters to the future needs of society, owing to their renewable, inexhaustible, and cost-free nature. The power output of these systems relies on solar cell radiation and temperature. In order to mitigate the dependence on atmospheric conditions and enhance power tracking, a conventional approach has been improved by integrating various methods. To optimize the generation of electricity from solar systems, the maximum power point tracking (MPPT) technique is employed. To overcome limitations such as steady-state voltage oscillations and improve transient response, two traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb and observe (P&O), have been modified. This research paper aims to simulate and validate the step size of the proposed modified P&O and FLC techniques within the MPPT algorithm using MATLAB/Simulink for efficient power tracking in photovoltaic systems.

Traffic Sign Recognition using HIICNN Stack Ensemble Method

Abstract: This project introduces a novel deep convolutional neural network (CNN) model aimed at enhancing traffic sign recognition for autonomous vehicle technology and road safety. Employing a stacking ensemble approach, we combine multiple CNNs with diverse improvement techniques, including preprocessing for adverse weather conditions and shooting errors, and data augmentation to handle class imbalance. Through adjustments in learning rates during model training, we mitigate overfitting. Our ensemble model achieves an exceptional 99.4 percent test accuracy on the German Traffic Sign Recognition Benchmark (GTSRB) dataset, surpassing prior studies. We utilize gradient-weighted Class Activation Mapping (Grad-CAM) for model explain- ability and an evidential deep learning approach to quantify classification uncertainty. Our framework underscores the efficacy of combining preprocessing, ensemble learning, and transfer learning for superior perfor- mance and reliability in traffic sign recognition systems, contributing significantly to autonomous driving and road safety.

Handwritten Digit Recognition

Abstract: This paper presents a powerful Handwritten Digit Recognition System that combines a Graphical User Interface (GUI) based on Tkinter with Convolutional Neural Networks (CNNs). Our approach, which makes use of the MNIST dataset, includes careful data pretreatment to facilitate efficient CNN model training. Convolutional and pooling layers are included in the design of the model, and they are optimized with the Adam optimizer for higher learning rates. The evaluation's findings demonstrate excellent memory, accuracy, and precision. Furthermore, real-time digit drawing is made possible via an intuitive Tkinter GUI, which confirms the model's applicability. By showing the effectiveness of CNNs and offering an interactive platform for natural user interaction, the research provides a holistic solution to handwritten digit recognition. This method is promising for various uses in digit recognition scenarios, highlighting its flexibility and usefulness in real-world situations.

Effect of manual manipulation on mechanical gait parameters.

A variety of manual manipulation techniques are utilized in clinical practice to alleviate pain and improve musculoskeletal function. Many manual practitioners analyze gait patterns and asymmetries in their assessment of the patient, and an increasing number of gait motion capture studies are taking place with recent improvements in motion capture technology. This study is the first systematic review of whether these manual modalities have been shown to produce an objectively measurable change in gait mechanics. This study was designed to perform a systematic review of the literature to assess the impact of manual medicine modalities on biomechanical parameters of gait. A master search term composed of keywords and Medical Subject Headings (MeSH) search terms from an initial scan of relevant articles was utilized to search six databases. We screened the titles and abstracts of the resulting papers for relevance and then assessed their quality with the Cochrane Risk of Bias Tool. Clinical trials that featured both a manual manipulation intervention and multiple mechanical gait parameters were included. Case reports and other studies that only measured gait speed or other subjective measures of mobility were excluded. We included 20 studies in our final analysis. They utilize manipulation techniques primarily from osteopathic, chiropractic, massage, and physiotherapy backgrounds. The conditions studied primarily included problems with the back, knee, and ankle, as well as healthy patients and Parkinson's patients. Control groups were highly variable, if not absent. Most studies measured their gait parameters utilizing either multicamera motion capture systems or force platforms. Twelve of 20 papers included in the final analysis demonstrated a significant effect of manipulation on gait variables, many of which included either step length, walking speed, or sagittal range of motion (ROM) in joints of the lower extremity. However, the results and study design are too heterogeneous to draw robust conclusions from these studies as a whole. While there are initial indications that certain modalities may yield a change in certain gait parameters, the quality of evidence is low and there is insufficient evidence to conclude that manual therapies induce changes in biomechanical gait parameters. Studies are heterogeneous with respect to the populations studied and the interventions performed. Comparators were variable or absent across the studies, as were the outcome variables measured. More could be learned in the future with consistent methodology around blinding and sham treatment, and if the gait parameters measured were standardized and of a more robust clinical significance.

Examination of clinical variables affecting resurgence: A reanalysis of 46 applications.

Despite the efficacy of behavioral interventions, resurgence of challenging behavior (e.g., aggression, self-injury) following successful treatment can still occur. Applied work has focused on identifying treatment-related variables thought to affect the occurrence and magnitude of resurgence. The current study describes the relation between several variables (i.e., phase duration, response rates in baseline and treatment, obtained rates of reinforcement, downshift in reinforcement step size) and resurgence in a retrospective consecutive controlled case series of 46 treatment applications for challenging behavior conducted in an inpatient setting. Only the downshift in reinforcement (e.g., schedule-thinning) step size was correlated with the magnitude of resurgence. These results are consistent with recent findings suggesting that treatment duration and other factors have inconsistent effects on resurgence of challenging behavior.

Prediction of total volatile basic nitrogen (TVB-N) in fish meal using a metal-oxide semiconductor electronic nose based on the VMD-SSA-LSTM algorithm.

The total volatile basic nitrogen (TVB-N) is the main indicator for evaluating the freshness of fish meal, and accurate detection and monitoring of TVB-N is of great significance for the health of animals and humans. Here, to realize fast and accurate identification of TVB-N, in this article, a self-developed electronic nose (e-nose) was used, and the mapping relationship between the gas sensor response characteristic information and TVB-N value was established to complete the freshness detection. The TVB-N variation curve was decomposed into seven subsequences with different frequency scales by means of variational mode decomposition (VMD). Each subsequence was modelled using different long short-term memory (LSTM) models, and finally, the final TVB-N prediction result was obtained by adding the prediction results based on different frequency components. To improve the performance of the LSTM, the sparrow search algorithm (SSA) was used to optimize the number of hidden units, learning rate and regularization coefficient of LSTM. The prediction results indicated that the high accuracy was obtained by the VMD-LSTM model optimized by SSA in predicting TVB-N. The coefficient of determination (R2), the root-mean-squared error (RMSE) and relative standard deviation (RSD) between the predicted value and the actual value of TVBN were 0.91, 0.115 and 6.39%, respectively. This method improves the performance of e-nose in detecting the freshness of fish meal and provides a reference for the quality detection of e-nose in other materials. © 2024 Society of Chemical Industry.

Modeling and simulation of three-phase IGBT full-bridge inverter circuit based on FPGA

The field of motor drive makes extensive use of electronic power modeling and simulation of three-phase IGBT full-bridge inverter circuits. The accuracy and computational efficiency of these models have a direct impact on the dependability of the motor control system. The majority of earlier research focused solely on static processes of turning on and off in IGBTs, disregarding the transient proesses that occur when three-phase IGBT full-bridge inverter circuits are switched at high frequencies. This has an impact on the circuits' accuracy in real-time simulation. Therefore, this paper proposes and builds a field-programmable logic gate array (FPGA)-based steady-state and transient dual-phase three-phase IGBT full-bridge inverter circuit model for the static and transient characteristics of the insulated gate bipolar transistor (IGBT) element in the circuit. Depending on whether or not the switching states of the six IGBTs in the three-phase IGBT full-bridge inverter circuit are altered, the simulation process is split into steady state and transient phases. In the steady state phase with large step size, the circuit is discretized using the binary L/C approach. In the transient phase, the transient process is divided into several small-step-long time domains. Real-time simulation waveforms are generated by interleaving and combining the multistage fitting method's solution of the circuit's transient waveforms at tiny step lengths with the steady state phase. Finally, in order to demonstrate the accuracy of the circuit model in this work, the simulation results of the two-stage three-phase IGBT full-bridge inverter circuit model based on FPGA are compared with those of the conventional ideal model for waveform comparison and data analysis.

Effects of flexor reflex stimulation on gait aspects in stroke patients: randomized clinical trial.

Gait deficits are very common after stroke and therefore an important aspect in poststroke rehabilitation. A currently little used method in gait rehabilitation after stroke is the activation of the flexor reflex (FR) by electrical stimulation of the sole of foot while walking. The aim of this study wasto investigate the effect of FR stimulation on gait performance and gait parameters in participants with stroke within a single session of flexor reflex stimulation using Incedo™. Twenty-five participants with subacute (n = 14) and chronic (n = 11) stroke were enrolled in the study. Motor functions were tested with a 10-m walk test (10mWT), a 2-min walk test (2minWT), and a gait analysis. These tests were performed with and without Incedo™ within a single session in randomized order. In the 10mWT, a significant difference was found between walking with Incedo™ (15.0 ± 8.5s) versus without Incedo™ (17.0 ± 11.4s, p = 0.01). Similarly, the 2minWT showed a significant improvement with Incedo™ use (90.0 ± 36.4m) compared to without Incedo™ (86.3 ± 36.8m, p = 0.03). These results indicate that while the improvements are statistically significant, they are modest and should be considered in the context of their clinical relevance. The gait parameters remained unchanged except for the step length. A subgroup analysis indicated that participants with subacute and chronic stroke responded similarly to the stimulation. There was a correlation between the degree of response to electrostimulation while walking and degree of improvement in 2minWT (r = 0.50, p = 0.01). This study is the first to examine FR activation effects in chronic stroke patients and suggests that stimulation effects are independent of the time since stroke. A larger controlled clinical trial is warranted that addresses issues as the necessary number of therapeutical sessions and for how long stimulation-induced improvements outlast the treatment period. The trial was retrospectively registered in German Clinical Trials Register. DRKS00021457. Date of registration: 29 June 2020.

Prediction of Greenhouse Microclimatic Parameters Using Building Transient Simulation and Artificial Neural Networks

In the realm of agricultural advancement, the relentless quest for agricultural efficiency amidst the vagaries of climate change has positioned greenhouse technology as a linchpin for secure and sustainable food production. The precise management of greenhouse microclimatic conditions i.e., the ability to accurately predict and maintain ideal temperature and relative humidity, is crucial for enhancing plant growth and health, optimizing resource use, and ensuring sustainable agricultural practices. However, maintaining optimal microclimatic conditions is a significant challenge due to the dynamic nature of external environmental influences. This study aims to address the critical need for advanced predictive tools that can enhance the control and management of greenhouse microclimates, thereby supporting sustainable agricultural practices and food security. Our research introduces a novel integration of building transient simulation (TRNSYS) and artificial neural networks (ANNs) to predict temperature and relative humidity inside a greenhouse across the calendar year, based on external atmospheric conditions. The TRNSYS model meticulously simulates the greenhouse’s thermal load, incorporating real-world data to ensure a high level of accuracy in describing the facility’s dynamic behavior. Our ANN model, composed of three layers, underwent optimization to identify the ideal number of neurons, learning rates, and epochs, settling on a model configuration that minimized prediction errors. The evaluation metrics, including root mean square error (RMSE) and mean absolute error (MAE), demonstrated the model’s effectiveness, with an RMSE of 0.3166 °C for temperature and 5.9% for relative humidity, and MAE values of 0.1002° and 3.4%, respectively. These findings underscore the model’s potential as a powerful tool for greenhouse climate control, offering substantial benefits in terms of energy efficiency, resource optimization, and overall sustainability in agriculture. By leveraging detailed dynamic simulations and advanced neural network algorithms, this study contributes significantly to the field of precision agriculture, presenting a novel approach to managing greenhouse environments in the face of changing climatic conditions.

A wearable sensor and machine learning estimate step length in older adults and patients with neurological disorders

Step length is an important diagnostic and prognostic measure of health and disease. Wearable devices can estimate step length continuously (e.g., in clinic or real-world settings), however, the accuracy of current estimation methods is not yet optimal. We developed machine-learning models to estimate step length based on data derived from a single lower-back inertial measurement unit worn by 472 young and older adults with different neurological conditions, including Parkinson’s disease and healthy controls. Studying more than 80,000 steps, the best model showed high accuracy for a single step (root mean square error, RMSE = 6.08 cm, ICC(2,1) = 0.89) and higher accuracy when averaged over ten consecutive steps (RMSE = 4.79 cm, ICC(2,1) = 0.93), successfully reaching the predefined goal of an RMSE below 5 cm (often considered the minimal-clinically-important-difference). Combining machine-learning with a single, wearable sensor generates accurate step length measures, even in patients with neurologic disease. Additional research may be needed to further reduce the errors in certain conditions.

Space-time CutFEM on overlapping meshes I: simple continuous mesh motion

AbstractWe present a cut finite element method for the heat equation on two overlapping meshes: a stationary background mesh and an overlapping mesh that moves around inside/“on top” of it. Here the overlapping mesh is prescribed by a simple continuous motion, meaning that its location as a function of time is continuous and piecewise linear. For the discrete function space, we use continuous Galerkin in space and discontinuous Galerkin in time, with the addition of a discontinuity on the boundary between the two meshes. The finite element formulation is based on Nitsche’s method and also includes an integral term over the space-time boundary between the two meshes that mimics the standard discontinuous Galerkin time-jump term. The simple continuous mesh motion results in a space-time discretization for which standard analysis methodologies either fail or are unsuitable. We therefore employ what seems to be a relatively uncommon energy analysis framework for finite element methods for parabolic problems that is general and robust enough to be applicable to the current setting. The energy analysis consists of a stability estimate that is slightly stronger than the standard basic one and an a priori error estimate that is of optimal order with respect to both time step and mesh size. We also present numerical results for a problem in one spatial dimension that verify the analytic error convergence orders.

Finite Elements with Switch Detection for direct optimal control of nonsmooth systems

AbstractThis paper introduces Finite Elements with Switch Detection (FESD), a numerical discretization method for nonsmooth differential equations. We consider the Filippov convexification of these systems and a transformation into dynamic complementarity systems introduced by Stewart (Numer Math 58(1):299–328, 1990). FESD is based on solving nonlinear complementarity problems and can automatically detect nonsmooth events in time. If standard time-stepping Runge–Kutta (RK) methods are naively applied to a nonsmooth ODE, the accuracy is at best of order one. In FESD, we let the integrator step size be a degree of freedom. Additional complementarity conditions, which we call cross complementarities, enable exact switch detection, hence FESD can recover the high order accuracy that the RK methods enjoy for smooth ODE. Additional conditions called step equilibration allow the step size to change only when switches occur and thus avoid spurious degrees of freedom. Convergence results for the FESD method are derived, local uniqueness of the solution and convergence of numerical sensitivities are proven. The efficacy of FESD is demonstrated in several simulation and optimal control examples. In an optimal control problem benchmark with FESD, we achieve up to five orders of magnitude more accurate solutions than a standard time-stepping approach for the same computational time.

Gradient-based adaptive neural network technique for two-dimensional local fractional elliptic PDEs

Abstract This paper introduces gradient-based adaptive neural networks to solve local fractional elliptic partial differential equations. The impact of physics-informed neural networks helps to approximate elliptic partial differential equations governed by the physical process. The proposed technique employs learning the behaviour of complex systems based on input-output data, and automatic differentiation ensures accurate computation of gradient. The method computes the singularity-embedded local fractional partial derivative model on a Hausdorff metric, which otherwise halts the computation by available approximating numerical methods. This is possible because the new network is capable of updating the weight associated with loss terms depending on the solution domain and requirement of solution behaviour. The semi-positive definite character of the neural tangent kernel achieves the convergence of gradient-based adaptive neural networks. The importance of hyperparameters, namely the number of neurons and the learning rate, is shown by considering a stationary anomalous diffusion-convection model on a rectangular domain. The proposed method showcases the network’s ability to approximate solutions of various local fractional elliptic partial differential equations with varying fractal parameters.

Adaptive Network-Based Fuzzy Inference System–Proportional–Integral–Derivative Controller Based on FPGA and Its Application in Radiofrequency Ablation Temperature Control

The radiofrequency ablation temperature system is characterised by its time-varying, non-linear, and hysteretic nature. The application of PID controllers to the control of radiofrequency ablation temperature systems has a number of challenges, including overshoot, dependence on high-precision mathematical models, and difficulty in parameter tuning. Therefore, in order to improve the effectiveness of radiofrequency ablation temperature control, an adaptive network-based fuzzy inference system combined with an incremental PID controller was used to optimise the shortcomings of the PID controller in radiofrequency ablation temperature control. At the same time, the learning rate at the time of updating the consequence parameters was set by segmentation to solve the problem of poor control accuracy when the ANFIS-PID controller is implemented based on FPGA fixed-point decimals. Based on FPGA-in-the-loop simulation experiments and ex vivo experiments, the effectiveness of the ANFIS-PID controller in the temperature control of radiofrequency ablation was verified and compared with the PID controller under the same conditions. The experimental results show that the ANFIS-PID controller has a superior performance in terms of tracking capability and stability compared with the PID controller.

Study on the Two-Step Construction Method of Super Large Cross-Section Tunnels Crossing Karst Cave Areas

To explore the solution of the two-step method applied in the rapid construction of super large cross-section tunnels passing through IV-and V-grade surrounding rock sections in karst cave areas, based on an engineering example of the Lianhuashan Tunnel, we use the numerical calculation method to analyze the stability of surrounding rock and the design parameters of the control measures for super large cross-section tunnels during the construction of the step method. The calculated results show that the working face of IV-grade surrounding rock can be stabilized by an advanced small pipe, and the stability of the supporting structure should be controlled mainly by IV-grade surrounding rock. In order to control the stability of the tunnel face, it is necessary to use an advanced large pipe shed in the surrounding V-grade rock. The reinforcement range of the advanced large pipe shed is 120° and the length is 20 m. This is the most economical design parameter of the advanced large pipe shed, ensuring the deformation control effect. For control of the stability of the supporting structure, under the condition that the working space is suitable for large machinery, the settlement of the arch of the supporting structure can be obviously reduced by shortening the step cycle footage and reducing the step length, and the peripheral convergence of the supporting structure can be obviously reduced by reducing the step height. After comprehensive analysis and considering the development of karst caves, the advanced support measures, design parameters, bench excavation design parameters, initial support measures, karst cave treatment measures, and bench construction process of IV- and V-grade surrounding rock is determined. The application verification shows that the research results have a good control effect on the stability of the surrounding rock and cave and are suitable for large-scale mechanical operations, which can significantly improve the excavation speed of the super large cross-section tunnel passing through the IV- and V-grade surrounding rock sections in the karst cave area.

Laser-induced breakdown spectroscopy as a method for millimeter-scale inspection of surface flatness

Abstract A non-contact method for millimeter-scale inspection of materials’ surface flatness via Laser-Induced Breakdown Spectroscopy (LIBS) is investigated experimentally. The experiment is performed using a planished surface of an alloy steel sample to simulate its various flatness, ranging from 0 to 4.4 mm, by adjusting the laser focal plane to the surface distance with a step length of 0.2 mm. It is found that LIBS measurements are successful in inspecting the flatness differences among these simulated cases, implying that the method investigated here is feasible. It is also found that, for achieving the inspection of surface flatness within such a wide range, when univariate analysis is applied, a piecewise calibration model has to be constructed due to the complex dependence of plasma formation conditions on the surface flatness, which inevitably complicates the inspection procedure. To solve the problem, a multivariate analysis with the help of Back-Propagation Neural Network (BPNN) algorithms is applied to further construct the calibration model. By detailed analysis of the model performance, we demonstrate that a unified calibration model can be well established based on BPNN algorithms for unambiguous millimeter-scale range inspection of surface flatness with a resolution of about 0.2 mm.

Designing optimal prototype filters for maximally decimated Cosine Modulated filter banks with rapid convergence

An analytic design of a prototype filter for M-channel maximally decimated cosine-modulated Near Perfect Reconstruction (NPR) filter banks is proposed in this work. The prototype filter is created using the least-square (CLS) method with weighted constraints, which is one-dimensional and requires single-parameter optimization. Compared to existing approaches, this suggested method achieves rapid convergence by analytically determining the optimal step size, ensuring the 3 dB cutoff frequency at π/2 M. The simulation results for design examples outperform the techniques in the available literature in terms of amplitude and aliasing distortion, reaching distortion around 2.4489 × 10−4 and 3.4907 × 10−9, respectively. This optimization algorithm's usefulness is further demonstrated with the sub-band coding of ECG signals. Implementing optimal prototype filters has tangible real-world effects, especially in critical sectors like healthcare and communications, improving diagnostics accuracy, data transmission efficiency, and overall performance.

Solving Bilevel quasimonotone variational inequality problem in Hilbert spaces

In this paper, we propose and study a Bilevel quasimonotone Variational Inequality Problem (BVIP) in the frame work of Hilbert space. We introduce a new modified inertial iterative technique with self-adaptive step size for approximating a solution of the BVIP. In addition, we established a strong convergence result of the proposed iterative technique with an adaptive step-size conditions without prior knowledge of Lipschitz’s constant of the cost operators as well as the strongly monotonicity coefficient under some standard mild assumptions. Finally, we provide some numerical experiments to demonstrate the efficiency of our proposed methods in comparison with some recently announced results in the literature.