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Earth observation satellite (EOS) scheduling is critical for improving the performance of Earth observation system. Most of the existing studies on satellite observation scheduling problems focus on static tasks and real-time dynamic tasks also known as emergency tasks while few attempts have been made for common dynamic tasks yet. The common dynamic tasks are characterized by uncertain arrival times and delayable observations while the emergency tasks require immediate observations. The number of common dynamic tasks is rapidly increasing with the expansion of satellite observation applications, which poses great challenges to EOS observation scheduling. To address this issue, this paper investigates the satellite observation scheduling problem with common dynamic tasks. Firstly, a centralized onboard dynamic scheduling framework based on a periodic-triggered rolling horizontal optimization (RHO) strategy is proposed and a novel two-stage mathematical model is established for the periodic rescheduling problem. Secondly, we propose a low-complexity onboard periodic rescheduling algorithm (OPR), which consists of a greedy-based task allocation algorithm, a pointer network (Ptr-network) based task scheduling algorithm and an iterative local search algorithm. In the greedy-based task allocation algorithm, we define a Task-EOS Fitness Indicator (TEFI) and each task is greedily allocated to the EOS with maximum TEFI. In the Ptr-network based task scheduling algorithm, the allocated task sets of all EOSs are fed into the Ptr-network in parallel to generate the observation scheduling result. Afterward, an iterative local search algorithm is proposed to further improve the quality of the observation scheduling result. Finally, extensive experiments are conducted to demonstrate the superiority of the OPR algorithm for the satellite observation scheduling problem with common dynamic tasks. [ABSTRACT FROM AUTHOR]

Exploring special multi-scroll chaotic systems is meaningful work. This paper studies an expanded multi-scroll chaotic system consisting of eight terms with one nonlinearity. It is generated by modifying the nonlinear term of the newly constructed chaotic system by a polynomial function. The unique mathematical model makes the unstable index-2 equilibria increase in four dimensions, which contributes to the number of scrolls expanding in each phase plane unidirectionally. Dynamic analysis finds that the system can yield complex nested multi-scroll attractors and has single-parameter-based synchronization control of amplitude and offset boosting behavior. Moreover, circuit implementation verifies the physical existence of the proposed system. Also, an image encryption algorithm for remote sensing images is established. Permutation and diffusion operations are performed using new pixel coordinates derived from the data of the chaotic matrix, which comes from the same position as the pixel matrix. Relevant tests suggest that the algorithm is secure and able to resist undesirable interference. • Simple multiscroll chaotic system with various signal control features is proposed. • Complex dynamics, circuit implementation of the proposed system is studied. • New chaos-based image encryption algorithm with high security is designed. [ABSTRACT FROM AUTHOR]

Interactive teaching is very popular in the field of education, especially in college preschool education teaching. In this study, the experimental performance of the evaluation system based on scientific emotion construction is analyzed by the machine learning algorithm. The experimental results show that the classification model realized by the machine learning algorithm is feasible and effective for the construction of an interactive design teaching evaluation system of preschool education in colleges and universities. The main research content of this experiment is for the teaching evaluation text, this paper intends to use NB, KNN, LR, and SVM algorithms as the meta-learning algorithm in the framework, the classification effect of the meta-classifier is ranked as SVM > LR > KNN > NB. From the above data comparison, the SVM meta-classifier in the algorithm in this paper has achieved the best classification effect. The SVM meta-classifier based on the algorithm in this paper performs well, and the classification accuracy rate reaches 92.1%. This shows that this model is very suitable for studying the. And the classification accuracy rate reaches 92.1%. This shows that this model is very suitable for studying the construction of interactive teaching evaluation systems under emotional education. • The experimental results show that the classification model realized by machine learning algorithm. • This shows that this model is very suitable for studying the construction of interactive teaching. • The main research content of this experiment is for the teaching evaluation text. • This paper intends to use NB, KNN, LR and SVM algorithms as the meta-learning algorithm. [ABSTRACT FROM AUTHOR]

A new improved three-dimensional continuous chaotic system (ImproBsys) is designed in this paper. It can achieve from an ordinary chaotic state to a hyperchaotic state, that is, chaotic behavior tends to become more complex. Furthermore, by using ImproBsys, this paper proposes a double image encryption algorithm based on compressive sensing and public key elliptic curve. First, the two plain images of the same size are excuted by discrete wavelet transformation (DWT), and then the DWT coefficients are thresholded. Second, the quantization matrix is compressed by compressive sensing, and the size is reduced to half of the original one, and then the two compressed matrices are spliced together to form a new matrix. Finally, the new matrix is encrypted by elliptic curve cipher to get the cipher image. Our contributions are: (1) A new ImproBsys is designed with better chaotic behavior, which has two positive Lyapunov exponents to show hyperchaos phenomenon. (2) Compressive sensing technique is employed to reduce the amount of data transmission for two single images, and then we use ImproBsys to control the measurement matrix. (3) The initial values of the ImproBsys depend on the information entropy of the plain image by a new constructed mathematical model. [ABSTRACT FROM AUTHOR]

Achieving precision in the prediction of buildings' dynamic heat load is crucial for the advancement of smart heating systems. This research highlights the urgent need to enhance the accuracy of models predicting dynamic heat load. Through literature review, distinguished machine learning and regression algorithms were chosen to formulate prediction models. These models employ a data time-step adaptive strategy, a physics-guided loss function, and fundamental principles of heat transfer. Optimization algorithms of a mathematical nature were utilized to fine-tune the parameters and the framework of long short-term memory (LSTM) and multi-layer perceptron (MLP) models. An analytical comparison was undertaken between physics-guided models and those not guided by physics. Principal conclusions are: 1) Pelican optimization algorithm (POA)-LSTM model emerges as superior in heat load prediction accuracy of an office building, with percentage errors for actual and simulated datasets ranging from −6.7 % to 5.8 % and −5.2 %–4.5 %, respectively, and the mean absolute percentage error (MAPE) standing at 2.3 % and 1.3 %. 2) The linear regression model exhibits the lowest precision, with a MAPE of 17.5 % and 4.0 % for the 7-day prediction results in the actual and simulated datasets, respectively. These findings provide support for improving heat load prediction in heating systems. • The authors have implemented a total of 20 physically-guided models for predicting building thermal load, among which the POA-LSTM model demonstrates the highest accuracy. • The disparities in thermal load prediction accuracy between the physically-guided machine learning models and the physically-guided mathematical regression models have been identified. • The accuracy of thermal load prediction was compared between the five physical guidance models proposed in this paper and the five non-physical guidance models mentioned in other papers. [ABSTRACT FROM AUTHOR]

Magnetic localization techniques are used in in vivo therapy, drug delivery, and surgical localization widely due to their ability to locate in an unobstructed environment precisely. The Levenberg-Marquardt (L-M) algorithm is one of the most common magnetic localization algorithms due to its fast optimization speed and high precision, but it requires high initial values such that improper initial values can easily lead to local optimization. In this paper, the performance of ten commonly used intelligent optimization algorithms is compared based on the mathematical model of the permanent magnet. The final position of the magnet is obtained by a fusion algorithm that uses the result of the intelligent optimization algorithm as the initial value of L-M. The real-time localization accuracy of this fusion algorithm is verified on a hardware platform and experiments demonstrate that the integration of the TSA algorithm and L-M algorithm fusion yields higher performance of the magnetic positioning system. [ABSTRACT FROM AUTHOR]

This research paper proposes a hybrid Whale Optimization Algorithm (WOA) variant based on Equilibrium Optimizer (EO), named Equilibrium Whale Optimization Algorithm (EWOA). The major finding lies in an efficient hybridization of bio-inspired (WOA) and physics-based (EO) metaheuristic algorithms. Upon mathematical modelling, EWOA proposes a main architecture that combines WOA's encircling and net-bubble attacking mechanisms via EO's weight balance strategy. The proposed algorithm was tested on 23 classical, 28 constrained CEC 2017, 30 unconstrained CEC 2017, 10 CEC 2019, and 10 CEC 2020 benchmark problems, in comparison with six recently proposed state-of-the-art algorithms (including WOA and EO). EWOA outperforms other algorithms with the best statistical mean performance on 46 out of 101 functions and the most promising clustering data in the graph, respectively. The fact that EWOA could achieve best statistical SD performance on 2 of the total 5 benchmark sets proves that EWOA is competitively robust. EWOA can converge to the optimum before 50% iterations of most benchmark functions, achieving the fastest convergence rate compared to other algorithms. The major contribution thereby lies in the successful development of this hybrid algorithm, which yields better optimization efficiency than the original and other state-of-the-art algorithms in terms of statistics, convergence and clustering. [ABSTRACT FROM AUTHOR]

In this paper, the application of spatial partial differential equation in user intent oriented information mining is proposed. The hybrid mining algorithm is used to avoid the defects of a single algorithm, which makes the discovery and identification of user intent efficient and practical. The v-svc algorithm can suppress the classification of errors, the hierarchical generation based on attributes can easily get the focus of services, and the fuzzy clustering method can get the service level or service quality of users. This method can be used for any complex object research similar to user intention and the evaluation mechanism of a research object. In view of the problems of traditional methods, this paper puts forward the technology of association mining based on the mathematical model of partial differential classification, and makes an experimental comparative analysis. The results show that the convergence, stability and performance of this method are good. [ABSTRACT FROM AUTHOR]

• Investigate the inland depot location problem of the inland transportation system. • Build a robust mathematical model that focuses on determining when and where consignees/shippers are assigned to. • Propose a branch-and-price algorithm that is based on lagrangian relaxation and column generation. In this paper, we investigate the inland depot location problem of the inland transportation system. In inland container transportation, empty containers are transported between depots and consignees/shippers, and empty containers should be repositioned after/before inbound/outbound full containers. We build a robust mathematical model that focuses on determining when and where consignees/shippers are assigned to. In addition, inter-depots empty container repositioning is implemented considering demand uncertainty. This paper proposes a branch-and-price algorithm that is based on Lagrangian relaxation and column generation. We show the optimality condition of the pricing subproblem and construct a simpler formulation in this paper. Computational experiments are performed with test instances that mimic real life. Our results also show that considering time compatibility of full and empty container routes is closer to reality and increases the utilization of empty containers in depots. The proposed algorithm yields promising solutions compared with CPLEX. [ABSTRACT FROM AUTHOR]

The multi-robot task allocation problem has garnered significant attention in research and development. This paper addresses the multi-robot task allocation problem by introducing a unified model that seamlessly transforms into four popular mathematical models through simple parameter adjustments. We propose an efficient indicator-based multi-objective evolutionary algorithm with a hybrid encoding scheme for task execution order and robot starting point information. The algorithm employs the hypervolume indicator for environmental selection to enhance convergence and the modified crowding distance for archive updates to promote diversity. Additionally, an adaptive archive update mechanism is designed for time efficiency. In experiments comparing our proposed algorithm with 8 state-of-the-art algorithms on 18 randomly generated instances of various sizes, along with 4 real-world problems from the TSPLIB benchmark, our algorithm consistently outperformed the comparison algorithms in five key indicators. These results underscore the effectiveness of our approach in addressing real-world multi-robot task allocation problems. [ABSTRACT FROM AUTHOR]

The electrodynamic tether (EDT) system is used as one of the end-of-life disposal technologies to deorbit defunct satellites with the benefit of Earth's magnetic field and plasma environment. However, due to the flexibility of the conductive tether, the orbit-attitude coupling effects as well as the influence of Multiphysics fields, this system suffers from time-consuming computations using complicated mathematical model in long-term simulations. Therefore, this paper proposes a high-fidelity high-efficiency dynamics model for the EDT system, in which the tether libration and transverse motions are described by the articulated model, and the system equations are formulated using recursive dynamics algorithm. Computational cost of the recursive method is compared with a nonrecursive method, and the result shows that with the number of tether elements larger than 15, the recursive method would show better computational efficiency. Since the conductive tether tends to be kilometers long in application, this new model would definitely contribute to the numerical efficiency. In addition, simulations are conducted with 2 and 16 tether elements solutions. By comparing the obtained results, it is found that the two cases show large differences over time. For a long tether, it is reasonable to use a larger number of elements in the dynamics analysis. Besides, in order to suppress the libration motion and avoid unstable states in the EDT system, a current control strategy is proposed and verified by a deorbit simulation in low Earth orbit. • A high-fidelity high-efficiency model is proposed for the electrodynamic tether system. • The recursive method shows better efficiency with the number of elements larger than 15. • Different tether elements would result in large differences in simulation. • A current control strategy is proposed to stabilize the libration motion. [ABSTRACT FROM AUTHOR]

In this paper we present a linear algorithm that estimates some physical parameters of a continuous-time system, described by an analytical mathematical model, when not all the state variables can be measured. The algorithm starts from the well-known subspace methods and applies some linear transformations to recover, at least partially, the estimated model in physical coordinates. Some analytical investigations and numerical experiments are shown for this method, which has general application within linear time-invariant (LTI) dynamical systems. [ABSTRACT FROM AUTHOR]

Abstract The paper discusses a niche area problem — the design of reduced-order multiple observers which can achieve the finite-time state reconstruction for Takagi–Sugeno multiple models with unknown inputs. The new reduced-order multiple observer obtained in this paper is only the second ever designed, the author of this paper continuing his work started one year ago with the introduction of the reduced-order multiple observer concept. The obtaining of the new reduced-order multiple observer is achieved by combining two classical reduced-order observers for linear time-invariant multivariable systems with unknown inputs and a full-order multiple observer for Takagi–Sugeno systems. The main innovative idea behind the design of the reduced-order multiple observer for Takagi–Sugeno systems described by the multiple models is the split of the multiple model into two subsystems: an unknown-input-free subsystem and an unknown-input-depending subsystem. The unknown-input-free subsystem is brought to the form of a standard multiple model and, in this situation, any algorithm to design full-order multiple observers can be used in the design of reduced-order multiple observers. The steps of the design procedure have been summarized and software implemented; then, the validation of the suggested algorithm has been done for two concrete cases associated to the motion of an aircraft during its landing Highlights • Paper discusses a niche area problem-design of reduced-order multiple observers. • The reduced-order observer designed is only the second one ever obtained. • The steps of the design procedure have been summarized and software implemented. • The observer's performances are compared with the ones of other observers. [ABSTRACT FROM AUTHOR]

Highlights • Comparison and application of data-driven methods for operational modal analysis. • Damping ratios extraction using Hilbert Transform and Random Decrement Technique. • Proposed methods are validated by a discrete system and a continuous steel plate. • Dynamic parameters (modal shapes, frequencies, damping ratios) are identified. • Present the results of research from the field of signal theory and engineering. Abstract The operational modal parameters identification under ambient excitation has been widely applied in structural dynamics analysis, where the input force is difficult to measure. This paper makes comprehensive and systematic comparisons of four statistical learning algorithms (PCA, ICA, SOBI, LLE) on analyzing their performance for resolving operational modal parameters identification. It mainly focuses on the comparison and evaluation of the four data-driven methods based for operational modal analysis and explores the use of Hilbert Transform and Random Decrement Technique for the identification of modal damping ratios. The further tests, the robustness of four algorithms, are conducted for investigating the influences of external noise to the performance of the algorithms. On the basis of the modal expansion theory, the vibration response signals could be decomposed into the inner product of modal shapes matrix and modal responses vector in the modal coordinate, from which the modal shapes, modal natural frequencies and modal damping ratios can be well estimated. Hence, a one-to-one mapping between the mathematical model of four statistical learning algorithms and physical model of dynamic systems is established. To validate the effectiveness of the proposed methods, performance and comparison of the proposed methods are studied using a discrete three degree-of-freedom (DOF) system and continuous cantilever steel plate. The simulation and experimental results show that, for the same experimental data, the SOBI algorithm has better performance than other algorithms, and it is more suitable for operational modal identification. Finally, the characteristics of four algorithms and further directions are concluded and discussed in this paper. [ABSTRACT FROM AUTHOR]

Highlights • The mathematical model of corrugated shells with special shapes is presented. • Effect of corrugation parameters on critical load is investigated. • Linear and nonlinear buckling analyses are conducted. • Critical load of corrugated shells is compared to smooth ones. • Specific range of corrugation parameters significantly increase the value of relative critical load. Abstract The problem of elastic stability of the shells with special shapes with corrugated middle surfaces under external pressure is debated in the presented paper. Solution of the problem is based on FEM study. Corrugated barrelled, pseudo-barrelled, and cylindrical shells of constant mass are considered. Geometrical modification of the middle surface geometry is based on sine wave along principal directions. Middle surface of the corrugated shells are described referring to differential geometry of surfaces by parametric functions in three-dimensional Euclidean space. Linear and nonlinear buckling analyses are conducted. Examples of buckling modes are presented, which differ significantly from those typical for shells of revolution with positive or zero Gaussian curvature. It is proven that corrugation may lead to serious increase or decrease of critical load for all types of presented shells. [ABSTRACT FROM AUTHOR]

Abstract This paper proposes methods for super resolving single noisy low resolution images. Even if single image super resolution has been a topic of research for last few decades, super resolution of noisy low resolution images is still a challenging problem. Most of the state of the art super resolution algorithms will fail to perform if significant amount of noise is present in the observed image. In this paper, we propose a denoised patch dictionary based single image super resolution algorithm. To enhance the robustness to noise performance, this method is further modified by using a wavelet based fusion algorithm which combines the result of proposed method with direct super resolved image, and super resolved image after denoising to preserve the finer details of the super resolved image. The proposed methods are applied on the commonly used test images. The results validate that the proposed methods show improvement over the existing techniques. [ABSTRACT FROM AUTHOR]

In order to solve the problem of estimating the model parameters of hemodynamic model under low frequency interference, a new model parameter estimation algorithm (CSCKS) is proposed in this paper for the hemodynamic model with low frequency interference. The algorithm is based on SCKS algorithm to improve, its estimation effect is obviously better than the existing SCKS algorithm and DEM algorithm. We simulate the proposed algorithm and some other traditional algorithms to evaluate the estimation performance of CSCKS proposed in this paper under different Signal to Interference Ratio (SIR) and different integration steps, and through the simulation results, the estimated results of the algorithm were analyzed and compared. [ABSTRACT FROM AUTHOR]

An intelligent detection of the communication channel by the transceiver is what is known as cognitive radio (CR), a sort of radio communication. Avoid using channels that are already in use to reduce interference and swiftly direct traffic to open channels. In cognitive wireless networks, spectrum allocation has long been a research hotspot as one of the key problems to increase spectrum efficiency. This article examines the issue of spectrum sharing, in which one primary user (PU) might rent out portions of their spectrum to numerous secondary users (SU) in order to share it. The suggested Fuzzy Ant Colony Optimization based Multiple Scheduling Resource Selection Algorithm (FAMSRSA) chooses the best network while maximising data rate and minimising interference and cost. Here, Nash's balanced multi-antenna-based spectrum sharing model (NE-MASS) is used to optimize and share the spectrum. In this paper, a mathematical model optimizes multiple network selection goals to maximize the overall bandwidth, and the total cost and idle spectrum assignment are determined to a minimum in the context of a comprehensive consideration of differences in the spectral resource characteristics of primary networks that differ in network domain and diversify the user's secondary requirements and analyze the complexity of the problem in the user domain. This approach provides an efficient way to access the spectrum available to both primary and secondary users. As a result of the experiment, this algorithm shows the algorithm's efficiency better than the four conventional algorithms in terms of performance metrics like Maximized Minimum Bandwidth, Maximized Network Efficiency, Maximized Proportion Fairness, Relative error and Spectrum Utilization Efficiency. The performance of the NE spectrum allocation algorithm was evaluated by extensive simulation. The proposed spectrum allocation algorithm has shown superior channel allocation performance than other conventional literature algorithms. [ABSTRACT FROM AUTHOR]

A fraction-order total variation blind image restoration algorithm based on L1-norm was proposed for restoring the images blurred by unknown point spread function (PSF) during imaging. According to the form of total variation, this paper introduced an arithmetic operator of fraction-order total variation and generated a mathematical model of cost. Semi-quadratic regularization was used to solve the model iteratively so that the solution of this algorithm became easier. This paper also analyzed the convergence of this algorithm and then testified its feasibility in theory. The experimental results showed the proposed algorithm can increase the PSNR of the restored image by 1 dB in relation to the first order total variation blind restoration method and Bayesian blind restoration method. The details in real blurred image were also pretty well restored. The effectiveness of the proposed algorithm revealed that it was practical in the blind image restoration. [ABSTRACT FROM AUTHOR]

Microbial electrolysis cell (MEC) is a promising reactor. However, currently, the reactor cannot be adapted for industrial-scale biohydrogen production. Nevertheless, this drawback can be overcome by modeling studies based on mathematical equations. The limitation of analytical instrumentation to record the non-linearity of the dynamic behavior for biohydrogen processes in an MEC has led to the introduction of computational approach that has the potential to reduce time constraints and optimize experimental costs. Reviews of comparative studies on bioelectrochemical models are widely reported, but there is less emphasis on the MEC model. Therefore, in this paper, a comprehensive review of the MEC mathematical model will be further discussed. The classification of the model with respect to the assumptions, model improvement, and extensive studies based on the model application will be critically analyzed to establish a methodology algorithm flow chart as a guideline for future implementation. [Display omitted] • Advancement of MEC mathematical models. • Classification of MEC mathematical models. • Proposed guideline for the MEC mathematical model implementation. [ABSTRACT FROM AUTHOR]

It is a challenging task to solve a large-scale Inter-Plant Heat Integration (IPHI) problem, especially for simultaneous optimization for intra- and inter-plant heat integration. In the companion paper (Part I), a novel screening algorithm named Nearest and Largest Q r e c -based Screening Algorithm (NLQSA) was proposed. It can be used to divide a large-scale IPHI problem into several small ones, each of which includes two or three plants, while keeping the theoretical maximum inter-plant heat recovery potential Q r e c max almost unchanged. NLQSA provides a prior solution before determination of inter-plant Heat Exchanger Network (HEN) configuration for each achieved small IPHI scheme. In this paper, a modified MINLP model with an objective of minimum Total Annual Cost (TAC) is proposed to determine the final inter-plant HEN configurations of achieved segregated IPHI schemes. With the addition of stream data extraction method and NLQSA which were proposed in Part I of this paper series, a complete three-step strategy is established in order to solve the large-scale IPHI problem. Theoretically, a large-scale IPHI problem can be solved no matter how many plants involved. A case study with seven plants is introduced to illustrate the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In real life data sets some attributes may have lower importance or even may be completely noninformative. The subspace clustering algorithms have been proposed to handle this. The soft subspace algorithms are vulnerable to noise and outliers. The paper presents a novel algorithm that handles both various importance of attributes and outliers. The proposed Fuzzy Weighted C-Ordered Mean (FWCOM) clustering algorithm elaborates clusters in soft subspaces. In each cluster each attribute is assigned a weight from interval [ 0 , 1 ] . Each attribute has its individual weight (importance) in each cluster. The proposed algorithm applies the ordering technique to effectively reduce the influence of outliers and noise. The paper is accompanied by numerical experiments. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel density-based fuzzy clustering algorithm based on Active Learning Method (ALM), which is a methodology of soft computing inspired by some hypotheses claiming that human brain interprets information in pattern-like images rather than numerical quantities. The proposed clustering algorithm, Fuzzy Unsupervised Active Learning Method (FUALM), is performed in two main phases. First, each data point spreads in the feature space just like an ink drop that spreads on a sheet of paper. As a result of this process, densely connected ink patterns are formed that represent clusters. In the second phase, a fuzzifying process is applied in order to summarize the effects of all members of each cluster. Finding arbitrary shaped clusters, noise robustness and proposing fuzzy clusters are some of the advantages of our proposed clustering algorithm. The algorithm is described in full details and its performance is evaluated and compared with well-known clustering algorithms on synthetic and real-world datasets. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive audio watermarking algorithm in the wavelet domain to optimize the payload under the perceptual transparency constraints of audio signal by strategically using some of its local features. Unlike existing algorithms, the watermark payload in this approach is made adaptive based on the nature of the audio signal. This localized feature based approach to determine the payload addresses the issue of over-loading and under-loading the audio signals with watermark data making the payload optimized for each individual audio host signal. Some audio features are strategically extracted and the most discriminatory features are selected using Principal Component analysis (PCA) approach. A mathematical model is designed using selected audio features like energy, zero cross mean and short time energy to evaluate the degree of embedding under perceptual transparency. It is used to estimate the number of watermarking bits to be inserted for a particular audio signal which makes the approach adaptive in nature optimizing the watermarking payload. At the embedding stage, watermark is embedded in the host audio signal in the third level detailed coefficient of wavelet domain which strikes a balance between the contradicting design parameters of perceptual transparency, robustness and optimized payload. Watermark extraction in this paper is blind with good robustness to signal processing attacks. Experimental results validate that the proposed adaptive algorithm provide good imperceptibility with good robustness against signal processing attacks at adjustable payload for different types of audio signals. Comparative analysis indicates that this proposed adaptive algorithm has better performance in terms of imperceptibility and robustness in comparison to uniform watermarking algorithm. [ABSTRACT FROM AUTHOR]

Determination of the creep model parameters is a challenging task particularly when a non-linear elastic visco-plastic (EVP) model is adopted, mainly due to the limited test duration as well as the assumption of the reference time. Therefore, this paper presents an innovative numerical solution to find the EVP model parameters applying the trustregion reflective least square optimization algorithm. The developed approach involves several available laboratory consolidation test results in the optimization procedure with the adopted commencing time to creep as a unit of time. In this paper, the laboratory results of Ottawa clay were employed to demonstrate the limitation of the recent method to obtain model parameters. Furthermore, the developed method is verified against Skå-Edeby clay in the laboratory conditions. The EVP model parameters are obtained by applying the developed method to the available laboratory consolidation results of clay samples. The analysis results of vertical strains and excess pore water pressures demonstrate that the developed method can be a feasible tool to estimate the settlement properties of clays. [ABSTRACT FROM AUTHOR]

Both the species strategy and the memory scheme are efficient methods for addressing dynamic optimization problems. However, the combination of these two efficient techniques has scarcely been studied. Thus, this paper focuses on how to hybridize these two methods. In this paper, a new swarm updating method is proposed to enhance a representative species-based algorithm, i.e., SPSO (Species-based Particle Swarm Optimization), and the new algorithm is named MSPSO. MSPSO has two characteristics. First, the number of replaced particles in the current swarm is set adaptively according to the number of species. To not substantially destroy the exploitation capability of each species, no more than one particle in each species is replaced by the memory. Second, the retrieved memory particles are categorized according to their fitness values and their distances to the seed of the closest species. Aimed at enhancing the search in both promising areas and existing species, each category is processed by different operations. The MPB, Cyclic MPB and DRPBG are used to test the performance of MSPSO. Experimental results demonstrate that MSPSO is competitive for dynamic optimization problems. [ABSTRACT FROM AUTHOR]

In the real world, the demands for some specific goods may increase in some special occasions. At such conditions, in-time delivery of the desired goods and minimization of the traveling costs will be found crucial importance for the customers and the suppliers, respectively. In this paper, a new mathematical model consisting of two objectives: minimizing traveling costs and maximizing customers' demand satisfaction was proposed through a multi-objective optimization approach. The nonlinear equations were converted to linear equations and the proposed model was then solved using CPLEX solver by GAMS23.6.3 software. Two approaches were proposed to solve the problem based on the NSGA-II algorithm with different modifications in the mutation operator. A two-row structure was also employed for the chromosomes. This research contributes to vehicle routing literature considering time windows, multiple demands, and two conflict objectives and proposes two metaheuristic algorithms. The algorithm results were also compared with two criteria: covered non-dominated solutions and spread solutions. The results show that the improvements were made with the variation in the mutation operator in both criteria. In summary, the developed vehicle routing mathematical model works effectively for some real-world logistic problems with occasional goods, and the customer satisfaction in due time is incorporated in the mathematical model. [ABSTRACT FROM AUTHOR]

The paper discusses the classic problem of designing automatic ship course-keeping control systems using a relatively new methodology called Active Disturbance Rejection Control (ADRC). A comparative analysis of simulation results obtained via ADRC versus a traditional autopilot based on the PID algorithm was performed. The novelty of this article lies in the use of a complete realistic mathematical model of the ship both as a simulation model and as a framework for derivation a simplified model structure, appropriate for controller design. The results obtained indicate higher efficiency of the ADRC autopilot compared to the PID variant. Another important feature of using ADRC control over PID is the elimination of the tuning problem. [ABSTRACT FROM AUTHOR]

In the area of human digital twins, the designed model should be as close as possible to the reality. The weight variable is one of the interested parameters of these mathematical models, as well as its interaction with other vital signals. The aim of this paper is including the information gathered from weight sensors of a person in its digital twin. To do this it is described an algorithm that filters and forecasts the human weight to use it in indexes such as BMI. It has been applied to a real sample and the results obtained are good. [ABSTRACT FROM AUTHOR]

• An adsorption model based on the real microstructure of desiccant coating is developed. • A self-refined algorithm, which can realize automatic mesh optimization is proposed. • A model, which can simulate the transient performances of different SDHP systems is established. • Three SDHP systems based on silica gel, MOF, and hygroscopic salt are simulated and analyzed. The solid desiccant heat pump (SDHP) is a promising air-handling method. Present simulation models of SDHP have two challenges. Owing to the fixed mesh structure, one model should be deeply revised to simulate another SDHP system. Besides, an adsorption dynamic model based on the real mass transfer network of desiccant coating is still lacking. This study proposes a universal SDHP model, which is based on the self-refined mesh algorithm and a new adsorption dynamic model (GP model). Self-refined mesh algorithm can automatically optimize the mesh structure, making it easy to simulate different SDHP systems in variable environmental conditions. GP model, based on the real microstructure of coating is demonstrated to be suitable for MOF, hygroscopic salt (LiCl), and silica gel coatings in our paper. Our model simulates the performances of three SDHP systems (coated with silica gel, MIL-101Cr, and LiCl) in sub-humid and humid conditions. Compared with LiCl, MIL-101Cr is more suitable for situations of sub-humid environment and high dehumidification quantity. For the sub-humid condition, the optimal switch-over period for MIL-101Cr-based SDHP is 13 min to owe the highest COP (5.6). For the humid conditions, LiCl-based SDHP shows the highest COP (10.7) when the switch-over period is 3 min. We believe this work can enlighten other studies on SDHP simulation and mass transfer in desiccants and guide the design of SDHP systems. [ABSTRACT FROM AUTHOR]

The index of the peak magnitude of the fractional Fourier transform (FrFT) of linear frequency modulated (LFM) signals has been widely used as a powerful chirp-rate estimator. In this paper, we propose analytical approximations for the peak FrFT magnitude (PFM) of mono and multicomponent LFM signals, both for noiseless and noisy cases. In addition, we present a novel coarse-to-fine FrFT-based algorithm designed specifically for chirp-rate estimation of multi-component LFM signals. Our approach entails an initial coarse estimation of the chirp-rates for each component by utilizing our proposed mathematical models. By leveraging these models, we achieve improved performance and a reduced signal-to-noise breakdown threshold. Furthermore, we incorporate a unique and efficient estimate-and-subtract strategy to refine the estimated parameters using our proposed models. Rather than removing the components from the LFM signal, we utilize the derived model to identify and remove peaks in the PFM. This strategy enhances the algorithm's capability to handle challenging scenarios. Extensive simulation results demonstrate that our proposed algorithm performs very close to the Cramér–Rao lower bound. It effectively eliminates the leakage effect between signal components, avoids error propagation, and maintains an acceptable computational cost compared to other state-of-the-art methods. • Analytical approximations for the peak FrFT magnitude of LFM signals are presented. • We propose a fine-to-coarse estimation approach, which uses our models in parameter estimation of multi-component LFM signals. • Simulations show that the algorithm performs very close to the Cramer–Rao bound. • The algorithm has a very low computational cost, compared to the other methods. [ABSTRACT FROM AUTHOR]

SMO (Sequential Minimal Optimization) is an outstanding SVM algorithm in efficiency and memory requirements. But it need cross validation to optimize parameters in the mathematical model to avoid the overfitting, which produces too much median classifiers, resulting in the decrease of the stability of algorithm and the increase of training time considerably. In this paper, by introducing the concept of “classification noise”, CNSMO (Classification Noise Detection based SMO algorithm) is proposed. In the CNSMO, an inseparable problem can be converted into a separable problem so that overfitting is not required to be taken into consideration. This makes cross validation can be avoided in the whole algorithm and the stability of the algorithm can be improved. The training time can be saved considerably. The penalty parameter in the CNSMO algorithm can be eliminated so that the model of the CNSMO is much simpler than other SMO algorithms. This paper presents that the proposed algorithm can reduce training time considerably without decreasing prediction accuracy. The effectiveness and efficiency of the CNSMO are demonstrated through experiments on public data sets. [ABSTRACT FROM AUTHOR]

This paper proposes a novel optimization algorithm for grouping problems, the Grouping Coral Reefs Optimization algorithm, and describes its application to a Mobile Network Deployment Problem (MNDP) under four optimization criteria. These criteria include economical cost and coverage, and also electromagnetic pollution control and capacity constraints imposed at the base stations controllers, which are novel in this study. The Coral Reefs Optimization algorithm (CRO) is a recently-proposed bio-inspired approach for optimization, based on the simulation of the processes that occur in coral reefs, including reproduction, fight for space or depredation. This paper presents a grouping version of the CRO, which has not previously evaluated before. Grouping meta-heuristics are characterized by variable-length encoding solutions, and have been successfully applied to a number of different optimization and assignment problems. The GCRO proposed is a novel contribution to the intelligent systems field, which is able to improve results obtained by two alternative grouping algorithms such as grouping genetic algorithms and grouping Harmony Search. The performance of the proposed GCRO and the algorithms for comparison has been tested with real data in a case study of a MNDP in Alcalá de Henares, Madrid, Spain. [ABSTRACT FROM AUTHOR]

Financial markets play an important role on the economical and social organization of modern society. In these kinds of markets, information is an invaluable asset. However, with the modernization of the financial transactions and the information systems, the large amount of information available for a trader can make prohibitive the analysis of a financial asset. In the last decades, many researchers have attempted to develop computational intelligent methods and algorithms to support the decision-making in different financial market segments. In the literature, there is a huge number of scientific papers that investigate the use of computational intelligence techniques to solve financial market problems. However, only few studies have focused on review the literature of this topic. Most of the existing review articles have a limited scope, either by focusing on a specific financial market application or by focusing on a family of machine learning algorithms. This paper presents a review of the application of several computational intelligent methods in several financial applications. This paper gives an overview of the most important primary studies published from 2009 to 2015, which cover techniques for preprocessing and clustering of financial data, for forecasting future market movements, for mining financial text information, among others. The main contributions of this paper are: (i) a comprehensive review of the literature of this field, (ii) the definition of a systematic procedure for guiding the task of building an intelligent trading system and (iii) a discussion about the main challenges and open problems in this scientific field. [ABSTRACT FROM AUTHOR]

Our paper introduces a new methodology to solve group decision-making problems under fuzzy and dynamic environment. The methodology takes group members’ linguistically defined pair wise preferences of alternatives in different time intervals and aggregates them across the intervals to obtain each member's net preference levels. Each member's net preference levels are again aggregated across the members to obtain the group's preference. Our paper attaches higher importance to the members whose involvement in the decision process is more recent than the members who opined their views in the past. The fuzzy aggregation operator, IOWA (Induced Ordered Weighted Average) is used to aggregate their views in accordance to their importance in the group. The Ranked_List algorithm, introduced in our paper, inputs the aggregated views of the members in pair wise form and produces the set of sequences of ranked list of alternatives representing the group's consensus view as output. The Ranked_List algorithm is validated and analyzed through a series of synthetic data sets and its results are compared with a movie selection case study. The methodology is illustrated with a numerical example. [ABSTRACT FROM AUTHOR]

Accredited laboratories are required to perform measurements in accordance with the highest standards of quality and reliability. The pursuit of this is associated with considerable effort and commitment on the part of each laboratory that wants to find recognition in the measurement services market. Undoubtedly, the laboratory needs to possess outstanding technical capability, participate in proficiency testing and inter-laboratory comparisons, and follow reliable and validated calibration methods. This paper presents a validation procedure using the specially developed universal algorithm which offers specific, step-by-step validation of coordinate measurement methods. The paper first discusses the selection of validation methods, and then proceeds to discuss the choice of a sophisticated multi-feature check and calibration measurements performed in chosen laboratory. The final part of this publication is devoted to the development of a validation model used for verifying measurement methods. [ABSTRACT FROM AUTHOR]

This paper presents mathematical models for predicting R z , R p , R v , R a , R S m and R m r ( c ) roughness parameters based on the kinematical–geometrical copying of the cutting tool onto the machined surface. The feed f , the radius r ε and the angles λ s , γ o , κ r have been used as input parameters in mathematical models. In order to increase the mathematical models’ accuracy, as well as their practical applicability, this paper proposes connecting the R-parameter prediction models with the condition of the surface roughness formation process. The parameter of statistic equality of sampling lengths in surface roughness measurement ( S E ) has been used for this purpose. The proposed mathematical models were verified using two different CNC lathes, working pieces made of the material 42CrMo (EN), using finishing inserts. The paper provides the theoretically calculated values, the measured values and the percentage differences between them for the considered R-parameters. The paper proposes an algorithm of steps for predicting and realizing the considered roughness parameters for industrial purposes. [ABSTRACT FROM AUTHOR]

The global drive for integration of RESs (renewable energy sources) means that TEP (transmission expansion planning) has to be carried out over geographically wide and large-scale networks under high levels of uncertainty. This leads to complex combinatorial TEP optimization problems, requiring a huge amount of OPF (optimal power flow) computations. The algorithm employed to calculate the OPF must be reasonably accurate but computationally very efficient, because it has to be run for a lot of operational conditions. Overly simplified OPF formulations are not adequate, since they neglect aspects which play a relevant role in TEP. Specifically, network losses significantly influence TEP solutions, but they are often disregarded due to their computational burden. Given their potential impact on the optimal TEP solution, the main focus of this paper is to find an appropriate losses model in the context of medium to long-term TEP for large-scale power systems. Keeping the balance between accuracy and computation time is essential in such problems. The paper presents two alternative linear losses models, as well as two variants of existing ones. These models are compared and tested using case studies, including small, medium and large-scale networks. Practical conclusions and recommendations are drawn from numerical results. [ABSTRACT FROM AUTHOR]

This paper introduces a hybrid algorithm of Nelder–Mead and scatter search algorithms called SSNM. Numerical results for some known test problems with varying dimensions from 2 to 100 variables prove its great performance for a bounded or constrained problem. This paper also has a considerable contribution to the joint economic lot sizing (JELS) problem literature. This problem is considered for a two-stage supply chain with price-sensitive demand for geometric, geometric-then-equal size and optimal shipment policies. Solution procedures are also developed which use the SSNM algorithm in their steps. These models and solution procedures are novel in the JELS literature. [ABSTRACT FROM AUTHOR]

Recently developed point-to-point relations have greatly extended the modeling capability of the Precedence Diagram Method. These relations can connect any inner (and extreme) points of the related activities. Furthermore, the definition of multiple relations is allowed between the same two activities. These features make point-to-point relations suitable for modeling overlapping activities more easily and with greater precision than it was possible before. However, there is a lot of work ahead. Although both proper mathematical modeling, and the algorithm that handles both minimal and maximal relations are available, re-investigation of well-known definitions for float, critical path, critical path metrics, classification of critical activities etc. are still missing. This paper deals with the investigation of the critical path. It describes the ways in which an activity can be part of the critical path. Collecting the possible cases provides the opportunity for classifying critical activities. The main result of the paper is that it provides a method for determining the type of the critical activity based on the relationships going in and out of the activity. [ABSTRACT FROM AUTHOR]

Air pollution models plays a critical role in atmospheric environment research. Chemical kinetic equations is an important component of air pollution models. The chemical equations is numerically sticky because of its stiffness, nonlinearity, coupling and nonnegativity of the exact solutions. Over the past decades, numerous papers about chemical equation solvers have been published. However, these solvers cannot preserve the nonnegativity of the exact solutions. Therefore, in the calculation, the negative numerical concentration values are usually set to zero artificially, which may cause simulation errors. To obtain real nonnegative numerical concentration values, very small step-size has to be adopted. Then enormous amount of CPU time is consumed to solve the chemical equations. In this paper, we revisit this topic and derive a new algorithm. Our algorithm Modified-Backward-Euler (MBE) Method can unconditionally preserve the nonnegativity of the exact solutions. MBE is a simple, robust and efficient solver. It is much faster and more precise than the traditional solvers such as LSODE and QSSA. The numerical results and parameter suggestions are shown at the end of the paper. MBE is based on the P-L structure of the chemical equations and a deeper view into the nature of Euler Methods. It cannot only be used to solve chemical equations, but can also be applied to conquer ordinary differential equations (ODEs) with similar P-L structure. [ABSTRACT FROM AUTHOR]

In this paper a novel nature-inspired optimization paradigm is proposed called Moth-Flame Optimization (MFO) algorithm. The main inspiration of this optimizer is the navigation method of moths in nature called transverse orientation. Moths fly in night by maintaining a fixed angle with respect to the moon, a very effective mechanism for travelling in a straight line for long distances. However, these fancy insects are trapped in a useless/deadly spiral path around artificial lights. This paper mathematically models this behaviour to perform optimization. The MFO algorithm is compared with other well-known nature-inspired algorithms on 29 benchmark and 7 real engineering problems. The statistical results on the benchmark functions show that this algorithm is able to provide very promising and competitive results. Additionally, the results of the real problems demonstrate the merits of this algorithm in solving challenging problems with constrained and unknown search spaces. The paper also considers the application of the proposed algorithm in the field of marine propeller design to further investigate its effectiveness in practice. Note that the source codes of the MFO algorithm are publicly available at http://www.alimirjalili.com/MFO.html . [ABSTRACT FROM AUTHOR]

This paper aims to develop a data-driven approximate causal inference model using the newly-proposed evidential reasoning (ER) rule. The ER rule constitutes a generic conjunctive probabilistic reasoning process and generalises Dempster’s rule and Bayesian inference. The belief rule based (BRB) methodology was developed to model complicated nonlinear causal relationships between antecedent attributes and consequents on the basis of the ER algorithm and traditional IF-THEN rule-based systems, and in essence it keeps methodological consistency with Bayesian Network (BN). In this paper, we firstly introduce the ER rule and then analyse its inference patterns with respect to the bounded sum of individual support and the orthogonal sum of collective support from multiple pieces of independent evidence. Furthermore, we propose an approximate causal inference model with the kernel mechanism of data-based approximate causal modelling and optimal learning. The exploratory approximate causal inference model inherits the main strengths of BN, BRB and relevant techniques, and can potentially extend the boundaries of applying approximate causal inference to complex decision and risk analysis, system identification, fault diagnosis, etc. A numerical study on the practical pipeline leak detection problem demonstrates the applicability and capability of the proposed data-driven approximate causal inference model. [ABSTRACT FROM AUTHOR]

• A hybrid method is used to extract the unknown parameters of double diode PV model. • The combination of analytical and social learning differential evolution (SL-DE) is used. • The performance of four different types of PV modules are examined. • Proposed methodology can be helpful to predict the performance under low irradiance conditions. The accurate and efficient model development for photovoltaic (PV) system is crucial for potential assessment under variable operating conditions. In order to predict the performance of a PV system, this paper presents a robust and reliable hybrid method which is a combination of analytical and social learning differential evolution (SL-DE) to extract the unknown parameters of double diode PV model. Since these parameters are unknown for accurate modeling of the SPV system, the accurate parameter extraction is essential. Under the present study, the performance of four different kinds of PV modules are evaluated. The experiments have been conducted under controlled and uncontrolled environmental conditions to verify the accuracy and efficiency of the proposed technique. The results obtained from the proposed methodology can be useful to effectively predict the performance under low irradiance conditions and also for partial shading conditions. An attempt has also been made to compare the proposed technique with other popular techniques which revealed that the proposed methodology is superior with the others under similar conditions. [ABSTRACT FROM AUTHOR]

• formulate and study a new and practical variant of static bike-sharing re-positioning problem (BSRP) with multiple vehicle depots and comprehensive objective function of traveling cost minimization and inventory cost minimization. • propose an effective clustering strategy that enables decomposition of the original problem into classical traveling salesman problem and multi-depot vehicle routing problem. • design tailored destroy-and-repair algorithm to conduct clustering optimization, and adaptive variable neighborhood search for routing optimization. • perform extensive computational studies to validate performance of the proposed algorithm, which demonstrates the algorithm's competitiveness in solving large-scale BSRPs when compared with CPLEX and state-of-the-art algorithms. This paper studies the bike-sharing re-positioning problem (BSRP) frequently encountered in modern bike-sharing systems that are widely deployed around the world. To cope with customer demand fluctuations and improve service level, BSRP aims to identify the optimal vehicle routes to visit bike-sharing stations in order to balance their inventories, picking up excess bikes from surplus stations and adding needed bikes to insufficient stations, with the objective of minimizing total traveling cost and inventory cost. The mathematical model of the studied problem is first given, detailing the considerations of multiple depots available for re-positioning vehicles and the extra objective of inventory cost minimization. An effective clustering strategy is then proposed to put bike-sharing stations into self-sufficient groups, which is shown to be able to greatly decompose the problem complexity for large-scale instances. A destroy-and-repair algorithm is developed to improve the clusters, and an adaptive variable neighborhood search algorithm is designed to conduct intra-cluster and inter-cluster vehicle routing optimization. Performance of the hybrid algorithm is validated on three sets of benchmark instances, and compared with CPLEX as well as state-of-the-art algorithms from the literature, which demonstrates that the proposed algorithm is highly competitive in solving BSRPs. [ABSTRACT FROM AUTHOR]

In this paper, neural algorithms, including the multi-layered perceptron (MLP) differential approximator, generalized hybrid power series, discrete Hopfield neural network, and the hybrid numerical, are used for constructing models that incorporate a priori knowledge in the form of differential equations for dynamic engineering processes. The properties of these approaches are discussed and compared to each other in terms of efficiency and accuracy. The presented algorithms have a number of advantages over other traditional mesh-based methods such as reduction of the computational cost, speed up of the execution time, and data integration with the a priori knowledge. Furthermore, the presented techniques are applicable when the differential equations governing a system or dynamic engineering process are not fully understood. The proposed algorithms learn to compute the unknown or free parameters of the equation from observations of the process behavior, hence a more precise theoretical description of the process is obtained. Additionally, there will be no need to solve the differential equation each time the free parameters change. The parallel nature of the approaches outlined in this paper make them attractive for parallel implementation in dynamic engineering processes. [ABSTRACT FROM AUTHOR]

In this paper a new hybrid Model Output Statistics (MOS), named MOS cascade, is developed to refine the day-ahead forecast of the global horizontal irradiance provided by the Weather Research and Forecast (WRF) model. The proposed approach is based on a sequence of two different MOS. The first, called MOSRH, is a new physically based algorithm, built to correct the treatment of humidity in the WRF radiation schemes. The second, called MOSNN, is based on artificial intelligence techniques and aims to correct the main systematic and learnable errors of the Numerical Weather Prediction output. The 1-day and 2-day forecast accuracies are analyzed via direct comparison with irradiance data measured in two sites, Rome and Lugano. The paper shows that a considerable reduction in error was achieved using MOSRH model and MOS cascade. The differences between the two sites are discussed in details. Finally, the results obtained are compared with the benchmark accuracy reached for the data available for the average climate in Southern Spain and Switzerland. [ABSTRACT FROM AUTHOR]