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Greedy search (GS) or exhaustive search plays a crucial role in decision trees and their various extensions. We introduce an alternative splitting method called smooth sigmoid surrogate (SSS) in which the indicator threshold function used in GS is approximated by a smooth sigmoid function. This approach allows for parametric smoothing or regularization of the erratic and discrete GS process, making it more effective in identifying the true cutoff point, particularly in the presence of weak signals, as well as less prone to the inherent end-cut preference problem. Additionally, SSS provides a convenient means of evaluating the best split by referencing a parametric nonlinear model. Moreover, in many variants of recursive partitioning, SSS can be reformulated as a one-dimensional smooth optimization problem, rendering it computationally more efficient than GS. Extensive simulation studies and real data examples are provided to evaluate and demonstrate its effectiveness. [ABSTRACT FROM AUTHOR]

A binary artificial rabbit optimization algorithm is proposed to maximize the cognitive radio (CR) network performance and fairness among users by addressing the issue of low spectrum resource utilization during the CR spectrum allocation process. Building upon the graph coloring spectrum allocation model, Sigmoid is introduced to transform the algorithm solution space. Incorporating the Lévy flight strategy for adaptive step size adjustment enhances the algorithm's flexibility and convergence accuracy. Moreover, a selective opposition strategy based on Spearman's coefficient is integrated into the algorithm to improve population diversity and convergence accuracy through reverse learning. Applying the binary artificial rabbit optimization algorithm to the CR spectrum allocation problem in the same CR network environment, we compare network efficiency and inter-user fairness through simulation experiments with the artificial rabbits optimization algorithm seagull optimization algorithm and particle swarm optimization algorithms. The experimental results show that the binary artificial rabbit optimization algorithm has higher convergence performance and global exploration ability, improves the overall network efficiency and user fairness of CR networks, and alleviates the problem of low spectrum resource utilization. [ABSTRACT FROM AUTHOR]

In the present paper, we introduce a new subclass of normalized analytic and univalent functions in the open unit disk associated with Sigmoid function. Coefficient estimates, convolution conditions, convexity and some other geometric properties for functions in this class are investigated. Also, subordination and inclusion results are obtained.

In response to the strong multi-solution problem of conventional seismic exploration methods in geological sweet spot prediction of shale， this paper fully explored one-dimensional whole rock analysis data and well logging data， two-dimensional geological data， three-dimensional post-stack seismic data， and five-dimensional offset vector tile （OVT） orientation information. In addition， the paper researched geological sweet spot identification technology of shale based on fuzzy fusion prediction to improve the accuracy of geological sweet spot prediction of shale. Firstly， the approximate distribution direction of faults in the study area and the distribution area of favorable shale lithofacies were statistically analyzed， and the sensitive azimuthal section was selected to stack the OVT data based on azimuth. Then， the fractures on the plane were analyzed based on pre-stack preferred azimuth， and the two-dimensional prediction of favorable shale lithofacies was carried out using a feedforward neural network. Finally， a fuzzy fusion technology based on an improved Sigmoid and Takagi-Sugeno （TS） function was developed， which could weigh the significance of controlling factors in geological sweet spot identification of shale according to their degree of influence and effectively integrate the predicted results of azimuthal anisotropy fractures on the plane with those of neural network-based shale lithofacies， so as to realize decision-making integration for identifying geological sweet spots of shale. This technology has been applied in the classification and grading evaluation of geological sweet spots of shale in the Lower Submember of the 3rd Member of Shahejie formation （Es3U） in Bonan Depression. Areas with poorly developed fractures and unfavorable lithofacies that could interfere with the analysis of geological sweet spots of shale were filtered out. Based on the predictions of fractures and lithofacies， The geological sweet spots of shale in the study area were categorized into three classes. The areas characterized by the superposition of developed fractures and favorable lithofacies were classified as sweet spots of Class I， which showed a high degree of consistency with actual drilling results and achieved notable application effects. The research results indicate that the classification and grading evaluation can be achieved using the geological sweet spot identification technology of shale based on fuzzy fusion prediction， which improves prediction reliability and provides reliable technical support for shale oil exploration.

Copyright of Petroleum Geology & Recovery Efficiency is the property of Petroleum Geology & Recovery Efficiency and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Railway Standard Design is the property of Railway Standard Design Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the present paper, we introduce a new subclass of normal- ized analytic and univalent functions in the open unit disk associated with Sigmoid function. Coeficient estimates, convolution conditions, convex- ity and some other geometric properties for functions in this class are investigated. Also, subordination and inclusion results are obtained. [ABSTRACT FROM AUTHOR]

As water allocation (WA) is reduced due to water scarcity in a river basin, the benefit function for the domestic water users and the economic sectors declines accordingly. Using the sigmoid-type equation, which is ubiquitous in natural and man-made systems, this study shows that the S-curve behaviour can be seen in a broad range of basin WA scenarios. A questionnaire survey reveals that progressive water supply cutback results in a mild initial hassle but builds up to an elaborate inconvenience and subsequently a diminished shock to the water users. The economic benefit of water consumption based on 8-year data of states in Malaysia shows evidence of the S-curve characteristics where lesser developed states tend to benefit more as water consumption increases. The model allows the sectorial benefit (and impact) level to be approximated as a function of basin water availability. The mathematical quantification, in lieu of qualitative descriptors, is useful as an integral component in water prioritisation and WA decision-making to provide an empirical assessment of optimum basin-wide benefit. HIGHLIGHTS A sigmoid-type mathematical model for water allocation (WA) benefit is introduced.; We show that data of social and economic benefit evaluation fall into the S-curve pattern.; We identify the different zones of the S-curve with descriptive meaning.; The model can be used for the quantitative evaluation of WA benefits in decision-making.;

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this article, some mathematical properties of (i, x0)-generalized logistic-type function are presented. This four-parameter generalized function can be considered as a statistical phenomenon enhancing more vigorous survival analysis models. Moreover, the behaviors of the relevant parametric functions obtained are examined with graphics using computer programming language Python 3.9. [ABSTRACT FROM AUTHOR]

In this article, asymmetrical novel system with two exponential functions, which can show hyperchaotic behavior, has been proposed. Although new system possesses only one unstable equilibrium. The dynamical behaviors of such system are discovered by computing the Lyapunov exponents and bifurcation diagram. Furthermore, the synchronization of the proposed system are also presented by an adaptive synchronization approach of two identical hyperchaotic systems. An application to image encryption has been obtained.

Abstract The signals received by very low-frequency/extremely low-frequency nonlinear receivers are frequently affected by intense atmospheric pulse noise stemming from thunderstorms and global lightning activity. Current noise processing algorithms designed for nonlinear channels within these frequency ranges, which are predicated on fractional p-order moment alpha stable distribution criteria (where 0

In the link prediction problem a relevant algorithm running over a network attempts to determine whether a link between two nodes will exist in the future, given that it is not present at the moment. Most link prediction algorithms take into account the structure of the network on which they are applied and based on this, they attempt to predict the existence or not of future new edges in the network. However, many of them are quite standardized, applying the same concept and parametrization to all networks, thus not always achieving good results in every different network structure. Algorithms based on Graph Neural Networks (GNNs) are more adaptive to any network structure but they do not give appreciable results when the only information available is the network structure. In this paper, we propose a new approach to this problem that approximates the structure of a complex network by allowing adjusted weight to this network structure to create additional information, which we can embed into effective algorithms such as local and superposed random walk link prediction. To achieve this goal, we use well-known kernel functions such as Sigmoids, in which we fit their parameters appropriately by a genetic algorithm to achieve the best possible approximation. To demonstrate the effectiveness of our proposed method we have compared our prediction method results based on precision, AUC and AUPR on eleven selected networks of different structures and properties with seven well-known link prediction algorithms and one more utilizing GNNs. In every case, we have improved the results of random walk algorithms and in most cases we achieved better results from all employed benchmark algorithms.

Probability estimation plays a pivotal role across diverse domains, particularly in scenarios where the objective is to select non-repetitive units one at a time, with the option of replacement, from a predefined set of units. Traditional probability calculations in this scenario pose three challenges: the number of floating-point operations to be executed is directly proportional to the chosen set size, susceptibility to floating-point precision errors, and exponential growth in storage needs with increasing number of chosen units. In this scenario, the presented work aims to develop SPM: a sigmoid function-based model that estimates probabilities for such problems with a fixed number of calculations (independent of the input parameter), achieving a constant time complexity algorithm. The research methodology involves generating probability data points, selecting the optimal sigmoid function, augmenting additional data to enhance parameter estimation, identifying parameter estimation equations, and evaluating the model. Moreover, the study’s second objective includes training and comparing six established machine learning-based models (including Decision Tree, Random Forest, Support Vector, Linear Regression, Nearest Neighbour, and Artificial Neural Network) against the proposed SPM. The rigorous assessment of the model’s performance, utilising metrics including RMSE, MAE and $r^{2}$ across a wide range of scenarios involving varying values of the total units, affirms the model’s accuracy and resilience. The study findings can improve decision-making processes in various domains, including statistics, cryptography, machine learning and optimisation, by offering a faster, more adaptable solution for probability estimation in units’ selection with replacement.

Greedy search (GS) or exhaustive search plays a crucial role in decision trees and their various extensions. We introduce an alternative splitting method called smooth sigmoid surrogate (SSS) in which the indicator threshold function used in GS is approximated by a smooth sigmoid function. This approach allows for parametric smoothing or regularization of the erratic and discrete GS process, making it more effective in identifying the true cutoff point, particularly in the presence of weak signals, as well as less prone to the inherent end-cut preference problem. Additionally, SSS provides a convenient means of evaluating the best split by referencing a parametric nonlinear model. Moreover, in many variants of recursive partitioning, SSS can be reformulated as a one-dimensional smooth optimization problem, rendering it computationally more efficient than GS. Extensive simulation studies and real data examples are provided to evaluate and demonstrate its effectiveness.

Availability and continuous operation under critical conditions are very important in electric machine drive systems. Such systems may suffer from several types of failures that affect the electric machine or the associated voltage source inverter. Therefore, fault diagnosis and fault tolerance are highly required. This paper presents a new robust deep learning-based approach to diagnose multiple open-circuit faults in three-phase, two-level voltage source inverters for induction-motor drive applications. The proposed approach uses fault-diagnosis variables obtained from the sigmoid transformation of the motor stator currents. The open-circuit fault-diagnosis variables are then introduced to a bidirectional long short-term memory algorithm to detect the faulty switch(es). Several simulation and experimental results are presented to show the proposed fault-diagnosis algorithm's effectiveness and robustness. [ABSTRACT FROM AUTHOR]

The signals received by very low-frequency/extremely low-frequency nonlinear receivers are frequently affected by intense atmospheric pulse noise stemming from thunderstorms and global lightning activity. Current noise processing algorithms designed for nonlinear channels within these frequency ranges, which are predicated on fractional p-order moment alpha stable distribution criteria (where 0 < p < α < 2, and p and α denote distinct characteristic indices of alpha stable distribution noise), are constrained by their reliance on limited p-order moment statistics. As a result, the performance of low-frequency nonlinear channel receivers experiences significant degradation when confronted with robust pulse noise interference (0 < p < α < 2). To tackle this challenge, the present study introduces a novel variable step robust mixed norm (RMN) adaptive filtering algorithm, designated as SVS-RMN, which is based on the Sigmoid function. Leveraging the nonlinearity of the Sigmoid function and building upon the power function Hammerstein nonlinear channel model, the algorithm aims to enhance the RMN algorithm by deriving new cost functions and adaptive iteration formulas. The performance of the proposed algorithm is evaluated in comparison to conventional RMN algorithms based on fractional low-order moment (FLOM) criteria (0 < p < 2), as well as other algorithms employing variable step sizes and either FLOM or radial basis function (RBF) criteria, across various intensities of pulse noise and mixed signal-to-noise ratios. The experimental results reveal the following: (1) The proposed algorithm effectively mitigates strong pulse noise interference and significantly enhances the tracking performance of the RMN algorithm compared to conventional RMN algorithms based on FLOM criteria. (2) In terms of computational efficiency, simplicity of structure, convergence speed, and stability, the proposed algorithm surpasses other algorithms based on FLOM or RBF criteria. [ABSTRACT FROM AUTHOR]

The salp swarm algorithm (SSA) is a well-known optimization algorithm that is increasingly being utilized to solve many sorts of optimization problems. However, SSA may converge to sub-optimal solutions when it is applied to discrete problems such as the feature selection (FS) problem. This paper presents the enhanced opposition-based learning salp swarm algorithm (EOSSA), which is an improved SSA algorithm for solving the FS problem in intrusion detection systems (IDS). EOSSA incorporates four improvements into the original SSA algorithm. Firstly, the opposition-based learning (OBL) method is used in the initialization step of SSA to boost its population diversity. Secondly, the Elite opposition-based learning (EOBL) is used in the improvement loop of SSA to improve its exploration ability. Third, a variable neighborhood search (VNS) method is used in the improvement loop of SSA to improve its exploration mechanism to improve the local search space. Lastly, the Sigmoid binary transform function is used to convert the continuous candidate solutions produced by SSA into discrete binary solutions. EOSSA was evaluated against eighteen popular optimization algorithms (e.g., improved salp swarm algorithm based on opposition-based learning (ISSA), SSA, particle swarm algorithm (PSO), cuckoo search (CS), bat algorithm (BA), and Harris Hawk Optimization (HHO)) using eleven popular intrusion detection datasets (CICIDS2017, CSE-CIC-IDS2018, CICDDOS2019, CIRA-CIC-DoH, Intrusion detection 2018, UNSW-NB15, NSL-KDD, Phishing Legitimate, Malmem2022, IoT, and LUFlow Network) to Detect IoT Botnet Attacks. Moreover, EOSSA was compared with four machine learning algorithms (Decision Tree (DT), logistic regression (LR), Naive Bayes (NB), and K-Nearest Neighborhood (KNN)). The overall simulation results suggested that the proposed method is superior to the other algorithms in terms of the accuracy and number of selected features. The statistical analysis of the simulation results using the Friedman and Wilcoxon signed-rank test confirms the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Influenza-Like Illness (ILI) constitutes a significant global health concern characterized by its high infection rates and widespread distribution worldwide. While influenza viruses, primarily types A and B, are primary contributors to ILI cases, other respiratory viruses also play a role in its prevalence. Jakarta, Indonesia's largest and densely populated city, has consistently reported a notable weekly number of ILI cases from 2016 to mid-2022. Intriguingly, this pattern of cases is irregular and does not exhibit a direct association with seasonal climate fluctuations. In response to this complex scenario, we have developed a SLIR mathematical model featuring a cumulative generating operator in the form of a multiple-terms sigmoid function, obtained from weekly cumulative data to derive model solutions. A total of 12 terms within the sigmoid function yielded a decent fit to the actual data spanning 339 weeks. Our correlation analysis unveiled distinct temporal relationships within the model, revealing an 8-week time lag between the dynamics of the infection rate and the latent compartment, along with a 2-week lag marking the incubation period between the latent and infected compartments. Furthermore, the effective reproduction number displayed recurrent fluctuations around a threshold of 1, indicating the endemic characteristics where infection persists within the population. This in-depth comprehension of ILI transmission dynamics and effective reproduction numbers plays a significant role in devising control measures and informed policy-making decisions. [ABSTRACT FROM AUTHOR]

The countries are the units that procure the vaccines during the COVID-19 pandemic. The delivered quantities are huge. The countries must bear the inventory holding cost according to the variation of stock quantities. This cost depends on the speed of the vaccination in the country. This speed is time-dependent. The vaccinated portion of the population can be approximated by the cumulative distribution function of the Cauchy distribution. A model is provided for determining the minimal-cost inventory policy and its optimality conditions are provided. The model is solved for 20 countries for different numbers of procurements. The results reveal the individual behavior of each country. [ABSTRACT FROM AUTHOR]

This study presents a novel approach to the application of distributed logistic modeling for the enhancement of education and teaching in colleges and universities, particularly focusing on the pedagogical goals and curriculum content. Utilizing the abundance and accessibility of educational resources, this research harnesses data from diverse sources such as books, textbooks, catechisms, and blogs. Through the application of text mining and text recognition technologies, preliminary data are extracted for analysis. Subsequent data preprocessing steps involve the removal of extraneous information, null values, and outliers to refine the dataset. The research identifies key determinants influencing educational outcomes, establishing independent and dependent variables for these factors. The logistic and sigmoid functions are employed to compute the loss function, thereby addressing issues related to overfitting. Regularization transformations further optimize the results, culminating in the development of a robust distributed logistic model for education. An illustrative analysis demonstrates the model’s applicability and effectiveness in understanding and improving educational practices. Data presentation: P=0.026

MA algorithms have received extensive attention from scholars soon after they were proposed, and are rapidly developing in various fields such as the problem of project schedule and the optimal allocation of cloud resources. In this paper, firstly, to address the lack of performance of the ordinary MA algorithm, Kent chaotic mapping is used in the initialization position to improve the initial traversal ability, and Sigmoid function is introduced to optimize the step-size settings in different stages of the crawling process. Different test functions are chosen to compare the efficiency of the solution and verify the performance of the improved MA algorithm. Finally, the improved MA algorithm is applied to allocate human resources in a city called China Mobile and a multi-project environment. Searching for the optimal solution and solving for the optimal value is the strongest performance of the improved MA algorithm compared to the other three algorithms. The human resource balance metrics of a city China Mobile are optimized and the peak human resource demand reaches the project below 120, reducing the waste of resources by about 20 people. In a multi-project environment, where 4 employees perform four different tasks, the optimal solution was found, resulting in an improvement in profitability.

In this paper, a tourism revitalization model of intangible cultural heritage is constructed, and firstly, feature factors are extracted and multivariate features are combined using methods such as kernel density estimation to provide support for reliable prediction. Then decompose the MLP network structure, i.e. multilayer perceptron, and use Sigmoid function as the activation function. The importance of the feature factors is calculated using the random forest algorithm, and the collected tourism data are mined for the relationship between intangible cultural heritage and tourism revitalization to obtain the residual mean square of out-of-bag data. Finally, the coupling coordination degree is used to derive the development of ICH and tourism revitalization. The experimental results showed that 78% of the tourists were moderately to highly involved in the interactive participation of ICH, and only 22% were not interested in it. Therefore, the combination of ICH tourism and new media may become a new way of communication, allowing more general public to have the opportunity to approach and know ICH tourism.

With the progress of the information age, the evaluation of students in higher vocational colleges and universities is not single, and more and more colleges and universities pay more attention to the overall development of students and use diversified evaluation of students’ abilities. This paper constructs a multivariate evaluation model for English teaching in higher vocational school-enterprise cooperation based on deep learning networks and forest stochastic algorithms. By analyzing the application of convolutional neural networks in teaching quality evaluation, Sigmoid and Tanh functions are used to improve the efficiency of English teaching quality evaluation. The bagging method in integrated learning involves averaging the model prediction results of each subset to arrive at the final evaluation results. The multivariate evaluation model is utilized in English teaching evaluation to examine the impact of English teaching under multivariate evaluation. The results show that the evaluation accuracy of the pluralistic evaluation model for English teaching ideas, teaching objectives, teaching contents and teaching effects are 0.981, 0.893, 0.904, and 0.924, respectively, and the students’ liking degree of English is increased from 0.459 to 0.793 under the pluralistic evaluation, which is conducive to the improvement of the teaching effect of English in higher vocational colleges and universities, and provides a new reference for the teaching of English. Perspective.

The density peak clustering (DPC) is one of the most popular algorithms for segmenting images due to its simplicity and efficiency. Since DPC and its variants are not specifically designed for image segmentation, their segmentation results do not necessarily meet both subjective and objective metrics. We propose an expanded relative density-based clustering algorithm as a solution to the above problems, which can automatically determine the cluster number and make the image segmentation results more consistent with subjective criteria. First, the image is pre-segmented into superpixels using the simple linear iterative clustering algorithm, and the superpixels are represented by feature vectors containing color and texture information. Secondly, the expanded relative density of the data point is obtained by comparing the tightness of a mini-cluster with its neighboring mini-clusters. The Sigmoid function is then applied to the data point with small density but large relative distance to further adjust its relative distance so that the distribution of cluster centers matches the characteristics of the image. Next, the optimal cluster number is determined by the rate of change of the sum of squared errors. Finally, the cluster center pairs with smaller distances are merged using the cluster center merging algorithm. The experiments conducted on synthetic and real datasets demonstrate that the performance of the proposed algorithm outperforms the compared algorithms. [ABSTRACT FROM AUTHOR]

Monoclonal antibodies (mAbs), crucial in medicine and biopharmaceuticals, require optimized large-scale production to meet high clinical dosage demands. Most of the processes for industrial mAb production rely on fed-batch operations, resulting in significant downtime. Transitioning to fully continuous and integrated processes holds the potential to boost product yield, enhance quality, and reduce storage costs for intermediate products. The integrated continuous mAb production process can be divided into the upstream and downstream processes. One crucial aspect that ensures the continuity of the integrated process is the switching of the capture columns, which are typically chromatography columns operated in a fed-batch manner downstream. Due to the discrete nature of the switching operation, advanced process control algorithms such as economic MPC (EMPC) are computationally difficult to implement. This is because an integer nonlinear program (INLP) needs to be solved online at each sampling time. This paper introduces two computationally-efficient approaches for EMPC implementation, namely, a sigmoid function approximation approach and a rectified linear unit (ReLU) approximation approach. It also explores the application of deep reinforcement learning (DRL). These three methods are compared to the traditional switching approach which is based on a 1 % product breakthrough rule and which involves no optimization. • An optimal control formulation for the switching operation in mAb production. • Two approximation methods for solving the optimal control problem. • Extensive simulation results showing the properties of the solving methods. [ABSTRACT FROM AUTHOR]

Under α stable distribution impulse noise environment, the least mean p-power algorithm (LMP) cannot handle the instability of the algorithm well, due to the large amplitude of the input signal and the large number of useless small weight coefficients in the sparse channel, which delay the convergence speed of the algorithm. In this paper, we develop a new adaptive filtering algorithm, named the proportional normalization least mean p-power (PNLMP) adaptive filtering algorithm. We first introduce a step size control matrix to improve the overall convergence speed and convergence accuracy of the algorithm. Next, to reduce the influence of a large impulse response on the LMP algorithm, a high-order tongue-line function about error is introduced in the normalization processing. Finally, we extend the tongue-line function to ensure that the cost function can also switch freely between V-shaped and M-shaped. This new algorithm overcomes the problem that the traditional LMP algorithm is only applicable to a specific environment and solves the problem of slow convergence speed and low convergence accuracy. Under α stable distribution impulse noise environment, the simulations show that the PNLMP algorithm can improve the convergence speed and stronger system tracking capability compared with existing algorithms, overcoming the problems of slow overall convergence and low convergence accuracy in the traditional algorithm. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The Modular Multilevel Converter High Voltage Direct Current Transmission (MMC-HVDC) technology is considered to be the most feasible choice for high-voltage and high-power transmission systems, and its flexibility and high controllability provide a new solution for renewable energy grid integration. The MMC topology contains a large number of capacitors, which enables it to provide a certain active inertia support for the connected AC system. Different from a synchronous machine, the active inertia control of an MMC can flexibly adjust a system's inertia-supporting power by changing the control parameters. By introducing a variation of the Sigmoid function with amplitude-limiting capability, this paper proposes an adaptive active inertia control strategy for the MMC-HVDC system. The proposed scheme adjusts the inertia constant adaptively according to the frequency change rate of the AC system, which can better respond to the frequency recovery performance. Finally, the MMC-HVDC simulation model is established in PSCAD/EMTDC to verify the effectiveness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

To address the problems of uneven transition between foreground and sky regions, inaccurate transmission rate fusion, and unnatural color restoration in image dehazing, an image dehazing algorithm using weighted fusion with the Sigmoid function is proposed. This algorithm uses different attenuation constants in the RGB three channels, and uses Sigmoid as the weight function to fuse the estimated transmission rates of the sky region based on the color attenuation prior and the estimated transmission rates of the foreground region based on the dark channel prior. Finally, the haze-free image is restored through the atmospheric scattering model. Experimental results show that the dehazing effect of this algorithm is better for hazy images containing sky regions, and the restored images are clear and natural in color, improving the subjective visual effect of the images. Objective comparisons also confirm the effectiveness and applicability of this algorithm. [ABSTRACT FROM AUTHOR]

Planning an achievable trajectory for a mobile robot usually consists of two steps: (i) finding a path in the form of a sequence of discrete waypoints and (ii) transforming this sequence into a continuous and smooth curve. To solve the second problem, this paper proposes algorithms for automatic dynamic smoothing of the primary path using a tracking differentiator with sigmoid corrective actions. Algorithms for setting the gains of the differentiator are developed, considering a set of design constraints on velocity, acceleration, and jerk for various mobile robots. When tracking a non-smooth primary path, the output variables of the differentiator generate smooth trajectories implemented by a mechanical plant. It is shown that the tracking differentiator with a different number of blocks also generates derivatives of the smoothed trajectory of any required order, taking into account the given constraints. Unlike standard analytical methods of polynomial smoothing, the proposed algorithm has a low computational load. It is easily implemented in real time on the on-board computer. In addition, simple methods for modeling a safety corridor are proposed, taking into account the dimensions of the vehicle when planning a polygon with stationary obstacles. Confirming results of numerical simulation of the developed algorithms are presented. [ABSTRACT FROM AUTHOR]

The point features of low-texture images are insufficient and unreliable, so it is difficult to achieve good alignment and easy to damage the image structure. To solve these problems, in this paper, we propose a new image stitching method by using the sigmoid function to create perception mask. Firstly, the point features and line features are used to improve the accuracy of image registration and the naturalness of distortion. Secondly, an energy function is used to optimize the alignment model. Finally, we propose to use sigmoid function to create perception mask image to reduce artifacts and retain image structure. The gradient domain fusion algorithm is combined to achieve image fusion. Experimental results are provided to demonstrate that the proposed method is superior to some previous methods in reducing artifacts and maintaining image structure. [ABSTRACT FROM AUTHOR]

Text-to-speech synthesis technology is one of the most important elements in the field of working with human speech. This paper makes an introductory analysis of speaker's speech sets recorded with different emotional coloration. That enables the identification of patterns in the frequency dynamics of harmonics and the development of a method for the analytical description of the emotional coloration of speech. We propose a model that describes changes in frequency in the vowels and phonemes pronounced with an emotional connotation. The model is based on the use of sigmoid functions and the results of a technique that allows the synthesis of the signal of emotionally colored phonemes. [ABSTRACT FROM AUTHOR]

In numerous geometric and physical applications of complex analysis, estimating the sharp bounds of coefficient-related problems of univalent functions is very important due to the fact that these coefficients describe the core inherent properties of conformal maps. The primary goal of this paper was to calculate the sharp estimates of the initial coefficients and some of their combinations (the Hankel determinants, Zalcman's functional, etc.) for the class of symmetric starlike functions linked with the sigmoid function. Moreover, we also determined the bounds of second-order Hankel determinants containing coefficients of logarithmic and inverse functions of the same class. [ABSTRACT FROM AUTHOR]

The objective of this work is to mathematically model vehicular connectivity using ALOHA base function with modified logistic function as an adaptive control function. The focus of this work is to utilize the ALOHA protocol as a carrier sensing multiple access technique and add to it an intelligent part, which is the modified logistic function (MLF) to enable adaptive communication channel. The work correlates three time based parameters related to vehicle, communication, and human driver, which are associated with connected vehicles (driver response time, machine processing time, and channel communication time). The mathematical model showed that throughput for autonomous and connected vehicles is much higher than that for connected vehicles due to the elimination of human time factor, together with the evident low response time from sensors, electronics, and communication. In addition, a more stable connectivity is established if human element is not part of the model, as this eliminates both uncertainty and lengthy human response time. The ALOHA function role is to enable continuous tries by each node (vehicle) to establish communication with probability related to availability of communication channel and a back off functionality in case of collision. The proposed model provides dynamic and adaptive technique to adjust time slots using the continuous change in the vehicular connectivity environment, which affects the presented time parameters, thus changing frames delivery pattern and time slots utilization. The presented approach, supports better and more efficient use of communication channel, particularly in the case of autonomous and connected vehicles. It also, highlights through comparison the inhibitory effect driver actions can have on safe driving, through time delay, and low communication channel utilization. In addition, the work also focusses on the effect of processing time for sensors and electronics, which also have negative effect on channel usage and effectiveness, if there is a functional problem. [ABSTRACT FROM AUTHOR]

The sigmoid function increases the size of the hypothesis space that the network can represent. Neural networks can be used for complex learning tasks. It is therefore necessary to investigate the role of sigmoid function in geometric function theory. In this study, a new subclass of bi-starlike functions involving Sigmoid function and Bernol li Lemniscate was defined. Some coefficient bounds belonging to this newly defined subclass were also obtained by using subordination principle. The key tools in the proof of our main results are the coefficient Fekete-Szegö inequalities for this subclass. The results obtained agree and extend some earlier results. [ABSTRACT FROM AUTHOR]

The development of well‐devised irrigation scheduling methods is desirable from the perspectives of plant quality and water conservation. Accordingly, in this article, a mixed‐integer model predictive control system is proposed to address the daily irrigation scheduling problem. In this framework, a long short‐term memory (LSTM) model of the soil–crop–atmosphere system is employed to evaluate the objective of ensuring optimal water uptake in crops while minimizing total water consumption and irrigation costs. To enhance the computational efficiency of the proposed method, a heuristic method involving the logistic sigmoid function is used to approximate the binary variable that arises in the mixed‐integer formulation. Through computer simulations, the proposed scheduler is applied to homogeneous and spatially variable fields. The results of these simulation experiments reveal that the proposed method can prescribe optimal/near‐optimal irrigation schedules that are typical of irrigation practice within practical computational budgets. [ABSTRACT FROM AUTHOR]