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ABSTRACT This article is aimed to study the parameter estimation problems of a non‐commensurate fractional‐order system with saturation and dead‐zone nonlinearity. In order to reduce the structural complexity of the system, the model separation scheme is used to decompose the fractional‐order nonlinear system into two subsystems, one includes the parameters of the linear part and the other includes the parameters of the nonlinear part. Then, we derive an auxiliary model separable gradient‐based iterative algorithm with the help of the model separation scheme. In addition, to improve the utilization of the real time information, an auxiliary model separable multi‐innovation gradient‐based iterative algorithm is presented based on the sliding measurement window. Finally, the feasibility of the presented algorithms is validated by numerical simulations. [ABSTRACT FROM AUTHOR]

Seru Production is widely used in the Japanese electronics industry owing to its benefits. The total tardiness can be significantly reduced by Seru Production. We focus on investigating the fundamental principle of the total tardiness reduction brought by Seru Production. We formulate the seru system operation with minimising the total tardiness and analyse the solution space. We clarify that the model is non-linear. To exactly obtain the optimal solution of the non-linear model, we decompose the non-linear model into seru formation and seru scheduling which is formulated as a linear model. Thus, the small-scale seru system operation with minimising the total tardiness is solved exactly. For the large-scale problems, we propose a cooperative coevolution algorithm, where two evolution algorithms deal with the seru formation and seru scheduling. In the coevolution process, the two algorithms perform cooperation to seek the better solutions of seru system operation with minimising the total tardiness. Extensive experiments are tested to investigate how Seru Production reduces the total tardiness. [ABSTRACT FROM AUTHOR]

In recent years, the cuckoo search (CS) algorithm has been successfully applied to single-objective optimization problems. But in real life, most optimization problems are multi-objective optimization problems (MOPs). In order to enable CS to better solve MOPs, this paper proposes an elite-guided multi-objective cuckoo search algorithm based on cross-operation and information enhancement (CIE-MOCS). This algorithm first enhances its population diversity through crossover operation, then adds elite individuals to guide its update process to speed up the algorithm convergence speed. Finally, the method of information enhancement is adopted in the abandonment process, so that the algorithm is not easy to fall into the local optimum. In order to verify the performance of the algorithm, this paper uses a variety of benchmark functions and performance evaluation indicators to evaluate it, and provides a case to verify the effectiveness of the algorithm in practical applications. The experimental results show that CIE-MOCS has good performance compared with the contrasting algorithms. [ABSTRACT FROM AUTHOR]

For atmospheric correction over turbid waters, due to non-negligible water-leaving radiance (L w) in the near-infrared (NIR), measurements in the short-wave infrared (SWIR) are usually required to achieve reliable remote-sensing reflectance (R rs). But several ocean color satellite sensors, such as the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and other small satellites, have no bands in the SWIR domain. We here present an atmospheric correction algorithm (termed as ACA NIR-NN) based on NASA SeaDAS (version 7.5.3), which can achieve atmospheric correction seamlessly over clear and turbid waters, even for sensors having no spectral bands in SWIR. Specifically, ACA NIR-NN uses estimated R rs (NIR) from available R rs in the visible bands with a specifically designed artificial Neural Networks to carry out atmospheric correction, and the performance of ACA NIR-NN is evaluated over eight coastal locations having ground measurements by the Aerosol Robotic Network-Ocean Color (AERONET-OC) system. It is found that the Mean Absolute Percent Difference (MAPD) of R rs retrievals by ACA NIR-NN for this dataset is smaller by a factor of two or more than that by the standard SeaDAS algorithm (termed as ACA NIR-bio) for each band, especially for R rs (412) and R rs (443), which is 7.5% and 7.7%, respectively, from ACA NIR-NN , but they are 44.0% and 27.5% from ACA NIR-bio. We further demonstrated the applicability of ACA NIR-NN to SeaWiFS measurements over turbid waters, where consistent R rs products were also obtained compared to that generated from the same-day MODerate resolution Imaging Spectrometer (MODIS) measurements using SWIR bands. These results indicate that ACA NIR-NN can generate reliable R rs over turbid coastal areas, as well as clear ocean waters, for sensors having no SWIR bands. [ABSTRACT FROM AUTHOR]

Summary: This article focuses on the parameter estimation issues for a fractional‐order nonlinear system with autoregressive noise. In the process, the challenge and difficulty are to identify the parameters of the system as well as the order. To reduce the complexity of the structure, we split the system into two subsystems by utilizing the hierarchical identification principle and derive a two‐stage gradient‐based iterative (2S‐GI) algorithm by minimizing two criterion functions. Compared with the calculation amount of the gradient‐based iterative algorithm, the computation of the 2S‐GI algorithm is significantly reduced. Moreover, in order to improve the identification accuracy, we propose a two‐stage moving‐data‐window gradient‐based iterative algorithm. Finally, the simulation examples test the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

In this article, we use the maximum likelihood principle and the multi‐innovation identification theory to study the identification issue of a bilinear‐in‐parameter system with autoregressive moving average noise. A maximum likelihood multi‐innovation stochastic gradient algorithm is derived to estimate the model parameters, which uses not only the current innovation but also the past innovations to improve the parameter estimation accuracy. The maximum likelihood multi‐innovation stochastic gradient algorithm has higher parameter estimation accuracy than the stochastic gradient algorithm. The simulation examples indicate that the proposed methods work well. [ABSTRACT FROM AUTHOR]

Summary: This article considers the parameter estimation for a fractional‐order nonlinear finite impulse response system with colored noise. For the fractional‐order systems, the challenge and difficulty are to identify the order and parameters of the systems simultaneously under colored noise disturbances. In order to reduce the problem of redundant parameter estimation, the output form of the system can be expressed by a linear combination of unknown parameters through the separation of the key term separation. A key term separation auxiliary model gradient‐based iterative algorithm is derived by using the negative gradient search. Meanwhile, to achieve the higher estimation accuracy, we propose a key term separation auxiliary model multiinnovation gradient‐based iterative algorithm by utilizing the multiinnovation theory. Finally, the simulation results test the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we concentrate on the synchronization problem of fractional-order complex networks with general linear dynamics under connected topology. By introducing a pseudo-state transformation, the problem is converted into an equivalent simultaneous stabilization problem of independent subsystems, which is characterized by nonzero eigenvalues of the Laplacian matrix. Then, sufficient conditions in terms of linear matrix inequalities (LMIs) for synchronization are established, which can be easily solved by efficient convex optimization algorithms. Finally, three examples are provided to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract: We propose a novel shape descriptor for matching and recognizing 2D object silhouettes. The contour of each object is represented by a fixed number of sample points. For each sample point, a height function is defined based on the distances of the other sample points to its tangent line. One compact and robust shape descriptor is obtained by smoothing the height functions. The proposed descriptor is not only invariant to geometric transformations such as translation, rotation and scaling but also insensitive to nonlinear deformations due to noise and occlusion. In the matching stage, the Dynamic Programming (DP) algorithm is employed to find out the optimal correspondence between sample points of every two shapes. The height function provides an excellent discriminative power, which is demonstrated by excellent retrieval performances on several popular shape benchmarks, including MPEG-7 data set, Kimia’s data set and ETH-80 data set. [Copyright &y& Elsevier]

Abstract: Referring to the flight mechanism of wild goose flock, we propose a novel version of Particle Swarm Optimization (PSO) with a leader and followers. It is referred to as Goose Team Optimization (GTO). The basic features of goose team flight such as goose role division, parallel principle, aggregate principle and separate principle are implemented in the recommended algorithm. In GTO, a team is formed by the particles with a leader and some followers. The role of the leader is to determine the search direction. The followers decide their flying modes according to their distances to the leader individually. Thus, a wide area can be explored and the particle collision can be really avoided. When GTO is applied to four benchmark examples of complex nonlinear functions, it has a better computation performance than the standard PSO. [Copyright &y& Elsevier]

We propose an innovative multi-pixel atmospheric correction approach (MPACA) to process high-spatial-resolution satellite measurements over coastal waters based on a revised POLYMER model. MPACA assumes the aerosol type to be uniform within a relatively small region, while the aerosol load and water properties are allowed to vary. Landsat-8 OLI images over six coastal locations with various turbidities were utilized to evaluate the performance of MPACA. The retrieved remote sensing reflectance (R rs (λ)) by MPACA is validated with in situ matchups obtained from two sources: ship-based field campaigns and the AERONET-OC networks. It is found that, at each of OLI's four visible bands, MPACA provided accurate R rs (λ) products over such coastal environments, with the Root Mean Square Difference (RMSD) and Mean Absolute Percentage Difference (MAPD) less than 0.0006 sr−1 and 16.2%, respectively. In contrast, the R rs (λ) values retrieved with NASA's SeaDAS (v7.5), where each pixel was treated independently, showed RMSD and MAPD as ~0.0018 sr−1 and ~38.8%, respectively. Acolite-DSF, which assumed some spatial dependency, obtained MAPD almost two times that of SeaDAS for each visible band. Further, it appears that Acolite-EXP did not perform well for this evaluation dataset, where RMSD is ~0.0062 sr−1 and MAPD is ~228.2%. These results suggest that MPACA is a promising scheme for atmospheric correction in coastal waters, especially for measurements from multi-band satellites that have a high spatial resolution along with at least two bands in the NIR or SWIR domain. • An algorithm is developed to process high-resolution coastal turbid waters. • The algorithm uses multiple pixels to constrain aerosol properties. • The algorithm requires at least two bands in the NIR or SWIR domain. • Excellent agreements are found between Landsat-8 products and field measurements. [ABSTRACT FROM AUTHOR]

• Propose a novel bidding-model-based incentive mechanism (BIM) to encourage bike users to participate in bike rebalancing in a free-floating bike sharing system (FBSS) • Develop the improve GetThreshold algorithm to progressively obtain the optimal incentive price. • Compare the performance of BIM with post-price-model-based incentive mechanism (PIM) • Consider the impact of user irrationality on BIM's effectiveness. Along with the fast deployment of bike-sharing systems (BSS) nowadays is the bike imbalance problem induced by the demand variability. In this study, we design a user-based bike rebalancing (UBR) strategy to solve the bike imbalance problem in a free-floating bike sharing system (FBSS), where the user arrival and incentive budget allocation of the FBSS are both dynamic. Specifically, we design a dynamic bidding-model-based incentive mechanism (BIM) to progressively determine the incentive price, based on which users are assigned the rebalancing task. The proposed BIM is proved to be budget feasible, incentive compatible, and competitive. We use a set of bike sharing data to numerically verify the effectiveness of BIM in improving the service level of FBSS. Furthermore, we find that the BIM outperforms the post-price-based incentive mechanism (PIM), with its advantage negatively correlated with user irrationality. Some managerial insights and potential research opportunities are provided for urban planners, policy makers and BSS practitioners. [ABSTRACT FROM AUTHOR]

Due to the discreteness of integer data, there are a large number of gaps and continuous columns in the histogram based on integer data. Aiming at the characteristics, this paper presents a robust and reversible watermarking algorithm for a relational database based on continuous columns in histogram. Firstly, it groups the database tuples according to the watermark length and the grouping key. Secondly, it calculates the prediction errors and uses the absolute values of the prediction errors to construct the histogram. Thirdly, it traverses the histogram to find all the continuous columns and in turn, computes the sum of the height of each continuous column and selects the group of continuous columns that has the largest sum as the positions to embed the watermarks. FCTD (Forest cover type data set) is utilized for experimental verification. A large amount of experimental data shows that the method is effective and robust. Not only does the data distortion caused by shifting histogram columns not exist, but the robustness of the watermark is also greatly improved. [ABSTRACT FROM AUTHOR]

Highlights • Propose the first branch-and-price (BAP) algorithm for HFGVRPTW. • Develop a MVADP algorithm to speed up solution of the pricing problem. • Develop an integer branch method to quickly obtain a tighter upper bound. Abstract Heterogeneous fleet vehicles can be used to reduce carbon emissions. We propose an improved branch-and-price (BAP) algorithm to precisely solve the heterogeneous fleet green vehicle routing problem with time windows (HFGVRPTW). In the improved BAP, to speed up the solution for the pricing problem, we develop a multi-vehicle approximate dynamic programming (MVADP) algorithm that is based on the labeling algorithm. The MVADP algorithm reduces labels by integrating the calculation of pricing problems for all vehicle types. In addition, to rapidly obtain a tighter upper bound, we propose an integer branch method. For each branch, we solve the master problem with the integer constraint by the CPLEX solver using the columns produced by column generation. We retain the smaller of the obtained integer solution and the current upper bound, and the branches are thus reduced significantly. Extensive computational experiments were performed on the Solomon benchmark instances. The results show that the branches and computational time were reduced significantly by the improved BAP algorithm. [ABSTRACT FROM AUTHOR]

Coal consumption and free calcium oxide (f-CaO) content are two important production indicators in the cement calcination process, and its collaborative optimization is of great significance to improve the production performance. However, due to the multiple dynamic interferences and multiple conflicts between coal consumption and f-CaO, product quality is unstable and coal consumption becomes high. To address the problems, this paper proposes a multi-objective collaborative optimization method for cement calcination process. This method takes the coal consumption and f-CaO content as the optimization objectives to establish a multi-objective optimization model. In addition, to solve the problem that static single-step optimization is difficult to track the continuous change of working conditions, we propose a time domain rolling multi-objective Jaya algorithm (TDRM-Jaya) to optimize the model. The algorithm, involving a population update strategy, a selection strategy and a time domain rolling strategy, realizes the dynamic rolling optimization for the cement calcination process. The test comparison with several common intelligent optimization algorithms demonstrates the advantages of using TDRM-Jaya. Besides, practical industrial data validates the effectiveness of the proposed optimization method. [Display omitted] • A multi-objective optimization model of cement calcination process is developed. • A TDRM-Jaya algorithm is proposed applied to solve the optimization model. • The multi-objective collaborative optimization using data-driven model is studied. • Computational results show that the expected optimization effect can be achieved. [ABSTRACT FROM AUTHOR]