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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]

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]

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]

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]

• 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]

• 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]

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]

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]

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]

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 this paper, it is a kind of challenge to simplify the conventional pulse width modulation (PWM) algorithms for a three-level neutral-point-clamped inverter. Thus, the unified duty-cycle modulation (UDM) algorithm is presented. It is more attractive than the carrier-based PWM (CBPWM), space-vector PWM (SVPWM), and unified PWM algorithms with a simpler calculation by utilizing the usefulness of the SVPWM algorithm to synthesize the duty cycles in time-domain and the CBPWM algorithm to synthesize the duty cycles in frequency-domain with the lesser conditions and the blankness of the lookup tables, switching sequence design, and zero-sequence component finding. Besides, it is outstanding with three outcomes: (1) comprehensive mathematical models of the dwell times, (2) permanent mathematical models of the duty cycles in time-domain, and (3) simplified PWM algorithm, which has a better workflow with the reduction of the computation burden. As it is very easy to implement as the sinusoidal PWM technique while its performance is equivalent to the SVPWM algorithm, this makes the proposed UDM algorithm a good candidate to optimize between the performance and the cost. Both the simulation and experimental results can confirm the feasible practicability and performance. The comparison with the conventional algorithms can evidently emphasize the superiority. [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]

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]

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 deals with an application of the fault detection and diagnosis algorithm based on nonlinear Kalman filter methods for transient state of an open-cycle liquid propellant rocket engine. In order to develop the algorithm, we designed two types of the nonlinear Kalman filter which are the extended Kalman filter and unscented Kalman filter with non-linear model of the liquid propellant rocket engine. Then using the measurement data of some important parameters of the engine, the residuals from the nonlinear Kalman filters are obtained. Using the residuals, we can detect and diagnose faults in the components of the engine by using the multiple model method. To confirm the fault detection and diagnosis algorithm, we developed mathematical model of an open-cycle liquid propellant rocket engine and artificially injected various faults such as decreasing turbopump efficiency. And then, perform the fault detection and diagnosis algorithm and check the performance of the algorithm. This process is numerically demonstrated for the open-cycle liquid propellant rocket engine under start-up process by using the simulated measurement data from the mathematical model of the engine. • A mathematical model for the transient state of an open-cycle liquid propellant rocket engine (OPLPRE) is developed. • Two variations of the nonlinear Kalman filter are used for fault detection and diagnosis (FDD) algorithm of the OPLPRE. • The FDD algorithms can be applied to the transient state of the OPLPRE. • The performances of the FDD algorithms are demonstrated by numerical simulations. [ABSTRACT FROM AUTHOR]

We consider foliations given by deformations d F + ϵ ω of exact forms dF in C 2 in a neighborhood of a family of cycles γ (t) ⊂ F − 1 (t). In 1996 Françoise gave an algorithm for calculating the first nonzero term of the displacement function Δ along γ of such deformations. This algorithm recalls the well-known Godbillon–Vey sequences discovered in 1971 for investigation of integrability of a form ω. In this paper, we establish the correspondence between the two approaches and translate some results by Casale relating types of integrability for finite Godbillon–Vey sequences to the Françoise algorithm settings. [ABSTRACT FROM AUTHOR]

• Proposed model with harbor constraints can reduce the conflicts in harbor apron. • The model optimizes the efficiency of gate utilization while ensuring safety. • A better solution is obtained by an improved branch-and-price method. • Experiments are designed to verify the value of harbor safety constraints. The airport gate assignment problem is the main problem in airport operation management. Most existing studies on the airport gate assignment problem focus more on the improvement of gate utilization efficiency and ignore apron operation safety. Aiming at addressing this problem, an airport gate assignment problem with harbor safety constraints is proposed in this paper. A two-phase mathematical optimization model is constructed to optimize the efficiency of gate utilization considering the safety constraints in harbors. To the best of our knowledge, this is the first study to explicitly focus on harbor apron security in the gate assignment problem. Then, the exact branch-and-price method is improved by incorporating a label-based pricing algorithm and two acceleration strategies. These strategies include an upper bound prediction strategy for handling the large number of subproblems and a lower parameter symmetry elimination strategy to overcome column generation degradation. The results show that the proposed gate assignment model and improved branch-and-price method optimize the efficiency of gate utilization under the condition of avoiding security conflicts in harbor aprons. The improved branch-and-price method has advantages in terms of both accuracy and efficiency compared with those of other solvers. [ABSTRACT FROM AUTHOR]

The paper describes the continuation of studies carried out earlier in the application of system identification to the estimation of parameters of ship manoeuvring mathematical models. A new version of a versatile identification algorithm based on various metrics and global optimization schemes is described. The algorithm is tested on artificial training data obtained with an entirely different ship mathematical model. The presented results confirm sufficient effectiveness and robustness of the algorithm which is now considered as sufficiently matured for application to real-world experimental data. At the same time, the study demonstrates the importance of using sufficiently complete and detailed mathematical structures and of establishing appropriate and sufficiently large intervals for the model parameters' variations. • A new version of a versatile identification algorithm based on various metrics and global optimization schemes is described. • The algorithm is tested on artificial training data obtained with an entirely different ship mathematical model. • The presented results confirm sufficient effectiveness and robustness of the algorithm. • The study demonstrates the importance of using sufficiently complete and detailed mathematical structures. • The study demonstrates the need of establishing sufficiently large intervals for the model parameters' variations. [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]

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]

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]

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]

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]

Highlights • Dynamical analysis has been provided to describe nonlinear behavior of supercavitating vehicles. • Robust H∞ loop-shaping synthesis with modified PID algorithm is proposed to control the dive plane maneuver of the HSSV. • Multi-objective control problems are solved using BMI optimization of an equivalent Schur formula. • Control scheme has the low order structure and provides robustness against uncertainties. • Integrated robust controller can deal with high parametric uncertainties and suppress exogenous disturbances and sensor noises. Abstract The high-speed supercavitating vehicle (HSSV) utilizes advanced technology that enables an underwater vehicle to reach its unprecedented high speed. The vertical motion control of the HSSV is challenging problem because of its complex dynamics with nonlinear planing force, parametric uncertainties, external disturbances, actuator saturation, and sensor noises. This paper deals with dynamical analysis and a robust H∞ loop-shaping synthesis with modified PID (proportional-integral-derivative) algorithm to control the dive plane maneuver of the HSSV. Typically, the control scheme has the low order structure and provides robustness against dynamic uncertainties, which can be implemented using the bilinear matrix inequality (BMI) optimization of an equivalent Schur formula. Simulation results show that the controlled vehicle system provides good performance and high robustness against uncertainties, ensuring no-overshoot and fast in time-domain responses. In addition, the control algorithm can decouple the dynamic interactions in the multi-input multi-output (MIMO) system, overcoming parametric uncertainty, planing force, and actuator saturation while minimizing the effect of the strong external disturbances and measurement noises. [ABSTRACT FROM AUTHOR]

Abstract The recovery of 3D left ventricle(LV) shape using 2D echocardiography is very attractable topic in the field of ultrasound imaging. In this paper, we propose a mathematical model to determine the 3D position of LV contours extracted from multiple 2D echocardiography images. We formulate the proposed model as a non-convex constrained minimization problem. To solve it, we propose a proximal alternating minimization algorithm with a solver OPTI for quadratically constrained quadratic program. For validating the proposed model, numerical experiments are performed with real ultrasound data. The experimental results show that the proposed model is promising and available for real echocardiography data. [ABSTRACT FROM AUTHOR]

Abstract For autonomous driving area, the communication among autonomous vehicles is quite frequent and inevitable to perceive the surrounding environment. Massive data needs to be processed in time on the edge to control vehicles to make suitable strategies. For a group of vehicles, collaborative strategies are necessary to avoid unintentional congestions. However, less attention is paid to competition and cooperation between vehicle groups. In this paper, we explore the possibility of adopting game theory for decision making to obtain win-win between autonomous vehicles. The Lemke-Howson algorithm in game theory is the best known combinatorial algorithm that computes a Nash equilibrium of a bimatrix game. More importantly, we implement the Lemke-Howson algorithm on FPGA to accelerate the computation process. We explain the design challenges of solving the performance bottleneck and how to make optimizations. We implement the Lemke-Howson accelerator on a KCU116 board and obtain a speedup of about 2.4 times versus that running on a CPU. [ABSTRACT FROM AUTHOR]

Highlights • Analytical and numerical study results of Y-type self-centering eccentrically braced frame (SCEBF-Y) are presented. • Post-gap opening stiffness of SCEBF-Y depend on PT cable properties. • Finite element model of the SCEBF-Y is verified with analytical and experimental test data. • Post-gap-opening stiffness and equivalent yield strength of SCEBF-Y is adjustable. Abstract This paper presents the results from both analytical and numerical study of a Y-type self-centering eccentrically braced frame (SCEBF-Y) subjected to earthquake loading. In this study, a three-story SCEBF-Y frame building has been designed along with a three-story conventional D-type eccentrically braced frame (EBF-D) for a site located in Los Angeles, California. The analytical results reveal that initial stiffness and post-gap opening stiffness of SCEBF-Y depend on brace section properties and PT strand properties respectively. The finite element model of the SCEBF-Y frame has been verified with analytical results as well as experimental test data from a similar one-story one-bay D-type self-centering eccentrically braced frame specimen. The study results demonstrate that SCEBF-Y frame can be designed to the same strength and stiffness as conventional EBFs while it can re-center itself after design basis earthquake (DBE). In the SCEBF-Y system, replaceable energy dissipation devices termed RHD devices are installed to dissipate seismic energy, and the primary structural members are intended to remain elastic under the DBE earthquakes. The nonlinear time history analysis results for the prototype SCEBF-Y buildings show that not only the SCEBF-Y building has negligible residual drift, but also it has its major damage confined to replaceable energy dissipation devices. In the parametric study, three different levels of PT strands' initial stress along with different PT cable length has been considered and the parametric study suggests that by properly choosing cable length and cable's initial post-tensioning stress level, key structural system properties such as the post-gap-opening stiffness and the equivalent yield strength of SCEBF-Y can be adjusted accordingly. [ABSTRACT FROM AUTHOR]

Highlights • The damper has an excellent hysteretic energy dissipation capacity. • The damper exhibits a stable post-visible-rupture behavior. • The damper can be promptly repaired after a major earthquake. • The repaired damper behaved as the original one. • The model predicts acceptable results for yield and ultimate strengths of the damper. Abstract This research focused on a new type of seismic damper consisting of replaceable U-shaped steel plates. To investigate behavior of the damper, seven specimens were tested. The test parameters varied in these specimens included geometries and materials of the U-shaped steel plates and the loading protocols. Test results show that the specimens exhibited stable hysteretic behaviors and satisfactory energy dissipating capacities. In addition, this research provided clear evidence that the damper is robust and tolerant to the ruptures in the U-shaped steel plates. Some specimens continued to dissipate significant amounts of hysteretic energy even after formation of the visible ruptures in the U-shaped steel plates. Moreover, this research demonstrated that the damper repaired through replacement of the U-shaped steel plates following a severe earthquake remained satisfactory in a subsequent earthquake. Further, this paper presents the equations for estimating yield strength and ultimate strength of the damper. It was found that the derived equations provide reasonable predictions and can be used for future design. [ABSTRACT FROM AUTHOR]

Highlights • Partially bonded (PB)-FREBs can retain the same advantages of unbonded-FREBs. • No relative slip occurs under low-rate lateral displacements in a PB-bearing. • The influence of static rotations on the hysteresis loops of PB-FREBs has been investigated. • A new copolymer employed in the bearings, and its response was examined experimentally. • The PB-application of FREBs as seismic isolators/bridge bearings is feasible. Abstract There are two classical types of application for fiber reinforced elastomeric bearings (FREB), namely, "bonded (B)" and "unbonded (UB)" applications. Although the UB-application results in improved seismic isolation efficiency due to rollover deformations, UB-FREB are not capable of transferring tensile forces, and may experience slip under certain loading conditions. As such, a new application type called Partially Bonded (PB) has been introduced to overcome these two limitations. A PB-FREB is achieved by partially bonding the top and bottom surfaces of the bearing to its contact supports. This paper presents the results of a comparative experimental study between UB-FREB and PB-FREB. Heat accelerated deterioration tests based on Arrhenius law was also conducted on the PB-FREB. Additionally, the low-temperature horizontal response of the PB-FREB was investigated. Results show that the displacement characteristics and damping values, long-term performance, and low-temperature response of the PB-FREB investigated in this study comply with current standard requirements. [ABSTRACT FROM AUTHOR]

Highlights • Uncertainty of the Unified Method for TEMs characterization is carried out. • The uncertainty is Evaluated by means of a Monte Carlo Simulation. • A sensitivity analysis, gives a comprehensive insight into the most critical issues. • Experimental results show that two proposed stimuli provide compatible estimates. Abstract In this paper the uncertainty evaluation of the recently proposed Unified Method for Thermo-Electric Module characterization is carried out. The measurement model is detailed and individual uncertainty contributions are highlighted, with close reference to the instrumentation and measurement setup. The uncertainty evaluation is performed by means of a Monte Carlo Simulation. The same algorithm is used to perform a sensitivity analysis, giving a comprehensive insight into the most critical issues of the proposed method and assessing the performance of the two adopted electrical stimuli. The experimental results thereby obtained are discussed and improvements to the measurement setup and technique are finally proposed. [ABSTRACT FROM AUTHOR]

Highlights • A comprehensive and discussion of the pros and cons of the different approaches to deal with frame conditions in behavioral verification of UML/OCL models. • A description of conceptual ideas how to integrate frame conditions given by modifies only statements into a symbolic formulation as well as a precise SMT-LIB realization. • An evaluation of the actual implementation including a comparison with other approaches. Abstract Validation and verification of UML/OCL models is a crucial task in the design of complex software/hardware systems. The behavior in those models is expressed in terms of operations with pre- and postconditions. These, however, are often not precise enough to describe what may or may not be modified in a transition between two system states. This frame problem is commonly addressed by providing additional constraints in terms of so-called frame conditions and has already been considered in different research areas in the last decades—except for UML/OCL where corresponding approaches have been investigated only recently. Among these, the so-called modifies only statements constitute a very promising concept which is complementary to all other approaches. More precisely, instead of allowing arbitrary modifications in principle and prohibiting certain undesired behavior, the statements explicitly describe what is allowed to change. However, this approach to frame conditions has not been considered so far in any of the numerous approaches for the automatic validation and verification of the behavior in UML/OCL models that have been proposed in the last years. Most of these approaches rely on a symbolic formulation of all possible system states and transitions between them. Therefore, in this paper we explain how modifies only statements can be integrated into an existing symbolic formulation. Based on this, we evaluate the applicability of the presented concept and compare it to previous implementations of frame conditions. [ABSTRACT FROM AUTHOR]

Abstract Whether the n × n completely positive cone for n ≥ 5 is facially exposed is an open problem raised by Berman et al. in 2015. In this paper, we give a class of non-exposed faces of the 5 × 5 completely positive cone, which implies that the 5 × 5 completely positive cone is not facially exposed. By embedding the 5 × 5 completely positive cone into the n × n completely positive cone for n > 5 , we further show that the n × n completely positive cone is not facially exposed for n > 5. [ABSTRACT FROM AUTHOR]

Highlight • This paper proposes an improved grasshopper optimization strategy (GOA) for continuous optimization. • We have applied the improved GOA successfully to the financial stress prediction problem. • Levy flight, Gaussian mutation and opposition-based learning are embedded in GOA. • The experimental results reveal the improved performance of the proposed algorithm. Abstract This study proposed an improved grasshopper optimization algorithm (GOA) for continuous optimization and applied it successfully to the financial stress prediction problem. GOA is a recently proposed metaheuristic algorithm inspired by the swarming behavior of grasshoppers. This algorithm is proved to be efficient in solving global unconstrained and constrained optimization problems. However, the original GOA has some drawbacks, such as easy to fall into local optimum and slow convergence speed. To overcome these shortcomings, an improved GOA which combines three strategies to achieve a more suitable balance between exploitation and exploration was established. Firstly, Gaussian mutation is employed to increase population diversity, which can make GOA has stronger local search ability. Then, Levy-flight strategy was adopted to enhance the randomness of the search agent's movement, which can make GOA have a stronger global exploration capability. Furthermore, opposition-based learning was introduced into GOA for more efficient search solution space. Based on the improved GOA, an effective kernel extreme learning machine model was developed for financial stress prediction. As the experimental results show, the three strategies can significantly boost the performance of GOA and the proposed learning scheme can guarantee a more stable kernel extreme learning machine model with higher predictive performance compared to others. [ABSTRACT FROM AUTHOR]

Highlights • We propose to combine different advanced numerical methods in one algorithmic recipe for EDS-STEM tomographic reconstruction. • Combining the methods facilitates tailoring the algorithm for specific samples and datasets. • Compared to using these methods separately, tailored combined recipes improve the accuracy of reconstructions from limited data. Abstract Energy-dispersive X-ray spectroscopic (EDS) tomography is a powerful three-dimensional (3D) imaging technique for characterizing the chemical composition and structure of nanomaterials. However, the accuracy and resolution are typically hampered by the limited number of tilt images that can be measured and the low signal-to-noise ratios (SNRs) of the energy-resolved tilt images. Various sophisticated reconstruction algorithms have been proposed for specific types of samples and imaging conditions, yet deciding on which algorithm to use for each new case remains a complex problem. In this paper, we propose to tailor the reconstruction algorithm for EDS tomography in three aspects: (1) model the reconstruction problem based on an accurate assumption of the data statistics; (2) regularize the reconstruction to incorporate prior knowledge; (3) apply bimodal tomography to augment the EDS data with a high-SNR modality. Methods for the three aspects can be combined in one reconstruction procedure as three modules. Therefore, a reconstruction algorithm can be constructed as a 'recipe'. We also provide guidelines for preparing the recipe based on conditions and assumptions for the data. We investigate the effects of different recipes on both simulated data and real experimental data. The results show that the preferred recipe depends on both acquisition conditions and sample properties, and that the image quality can be enhanced using a properly tailored recipe. [ABSTRACT FROM AUTHOR]

Abstract This paper addresses the problem of controlling a Markov chain so as to minimize the long-run expected average cost per unit time when the invariant distribution is unknown but we know it belongs to a given uncertain set. The mathematical model used to describe this set is the total variation distance uncertainty. We show that the equilibrium control policy, which yields higher probability to the states with low cost and lower probability to the states with the high cost, is an optimal control policy that minimizes the average cost. Recognition of such a policy may be of value in practical situations with constraints consistent to those studied here when the invariant distribution is uncertain and deriving online an optimal control policy is required. [ABSTRACT FROM AUTHOR]

Plant-wide oscillation detection is an important task in the maintenance of large-scale industrial control systems, owing to the fact that in an interactive multi-loop environment oscillation generated in one loop may propagate to the different parts of the plant. In such a scenario, it is required that different loops oscillating due to a common cause and hence similar frequency may be grouped together. In this paper an adaptive method for plant-wide oscillation detection based on multivariate empirical mode decomposition (MEMD) along with a grouping algorithm is proposed. The method can identify multiple oscillation groups among different variables as well as variables with random noise only. The proposed method is also applicable to both non-linear and non-stationary time series where the techniques based on the conventional Fourier analysis are prone to errors. Within each group that oscillate due to a common cause, the method can also indicate the location of the probable root cause of oscillations. The efficacy of the proposed method is established with the help of both simulation and industrial case studies. [ABSTRACT FROM AUTHOR]

Motion detection is a challenging problem in video sequel with complex background. Different background subtraction techniques are introduced to detect moving objects from video sequel, but none of these has been adequate to address the complex nature of the dynamic scene in real-surveillance task. So a simple and efficient vector-based method is proposed to address real-surveillance challenges. In this paper, the concept of linear dependence of vectors is used to build background models corresponding to each pixel. Consecutively, linear independence is used to detect moving object from incoming video sequel. The proposed method is spatio-temporal based background subtraction method, as it uses spatial and temporal information for constructing background model. The background models are updated using an adaptive update rate with region diffusion to produce efficient results in the dynamic background. The benchmark datasets taken from complex scenario are experimented using the proposed algorithm and the generated results are compared with recent background subtraction methods. The presented method attains better performance in terms of qualitative and quantitative results. [ABSTRACT FROM AUTHOR]