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The traditional method for transformer moisture diagnosis is to establish empirical equations between feature parameters extracted from frequency domain spectroscopy (FDS) and the transformer’s moisture content. However, the established empirical equation may not be applicable to a novel testing environment, resulting in an unreliable evaluation result. In this regard, it is acknowledged that FDS combined with machine learning is more suitable for estimating moisture content in a variety of test environments. Nonetheless, the accuracy of the estimation results obtained using the existing method is limited by the algorithm’s inability to generalize. To address this issue, we propose an AdaBoost algorithm-enhanced back-propagation neural network (BP_AdaBoost). This study creates a database by extracting feature parameters from the FDS that characterize the insulation states of the prepared samples. Then, using the BP_AdaBoost algorithm and the newly constructed database, the moisture estimation models are trained. Finally, the results of the estimation are discussed in terms of laboratory and field transformers. By comparing the proposed BP_AdaBoost algorithm to other intelligence algorithms, it is demonstrated that it not only performs better in generalization, but also maintains a high level of accuracy. [ABSTRACT FROM AUTHOR]

Variable (feature) selection plays an important role in data analysis and mathematical modeling. This paper aims to address the significant lack of formal evaluation benchmarks for feature selection algorithms (FSAs). To evaluate FSAs effectively, controlled environments are required, and the use of synthetic datasets offers significant advantages. We introduce a set of ten synthetically generated datasets with known relevance, redundancy, and irrelevance of features, derived from various mathematical, logical, and geometric sources. Additionally, eight FSAs are evaluated on these datasets based on their relevance and novelty. The paper first introduces the datasets and then provides a comprehensive experimental analysis of the performance of the selected FSAs on these datasets including testing the FSAs' resilience on two types of induced data noise. The analysis has guided the grouping of the generated datasets into four groups of data complexity. Lastly, we provide public access to the generated datasets to facilitate bench-marking of new feature selection algorithms in the field via our Github repository. The contributions of this paper aim to foster the development of novel feature selection algorithms and advance their study. [ABSTRACT FROM AUTHOR]

Truck hoisting detection constitutes a key focus in port security, for which no optimal resolution has been identified. To address the issues of high costs, susceptibility to weather conditions, and low accuracy in conventional methods for truck hoisting detection, a non-intrusive detection approach is proposed in this paper. The proposed approach utilizes a mathematical model and an extreme gradient boosting (XGBoost) model. Electrical signals, including voltage and current, collected by Hall sensors are processed by the mathematical model, which augments their physical information. Subsequently, the dataset filtered by the mathematical model is used to train the XGBoost model, enabling the XGBoost model to effectively identify abnormal hoists. Improvements were observed in the performance of the XGBoost model as utilized in this paper. Finally, experiments were conducted at several stations. The overall false positive rate did not exceed 0.7% and no false negatives occurred in the experiments. The experimental results demonstrated the excellent performance of the proposed approach, which can reduce the costs and improve the accuracy of detection in container hoisting. [ABSTRACT FROM AUTHOR]

This paper proposes the Love Evolution Algorithm (LEA), a novel evolutionary algorithm inspired by the stimulus–value–role theory. The optimization process of the LEA includes three phases: stimulus, value, and role. Both partners evolve through these phases and benefit from them regardless of the outcome of the relationship. This inspiration is abstracted into mathematical models for global optimization. The efficiency of the LEA is validated through numerical experiments with CEC2017 benchmark functions, outperforming seven metaheuristic algorithms as evidenced by the Wilcoxon signed-rank test and the Friedman test. Further tests using the CEC2022 benchmark functions confirm the competitiveness of the LEA compared to seven state-of-the-art metaheuristics. Lastly, the study extends to real-world problems, demonstrating the performance of the LEA across eight diverse engineering problems. Source codes of the LEA are publicly available at https://ww2.mathworks.cn/matlabcentral/fileexchange/159101-love-evolution-algorithm. [ABSTRACT FROM AUTHOR]

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]

In modern converter-dominated power systems, total inertia is very variable and depends on the share of power generated by renewable-based converter-interfaced generation (CIG) at each specific moment. As a result, the limits required by the grid codes on the rate of change of frequency and its nadir or zenith during disturbances become challenging to achieve with conventional control approaches. Therefore, the transition to a novel control strategy of CIG with a grid-forming power converter is relevant. For this purpose, a control algorithm based on a virtual synchronous generator (VSG) is used, which simulates the properties and capabilities of a conventional synchronous generation. However, due to continuously changing operating conditions in converter-dominated power systems, the virtual inertia formed by VSG must be adaptive. At the same time, the efficiency of adaptive algorithms strongly depends on the used VSG structure. In this connection, this paper proposes an enhanced VSG structure for which the transfer function of the active power control loop was formed. With the help of it, the advantages over the conventional VSG structure were proven, which are necessary for the effective adaptive control of the VSG parameters. Then, the analysis of the impact of the VSG parameters on the dynamic response using the transient characteristics in the time domain was performed. Based on the results obtained, adaptive algorithms for independent control of the virtual inertia and the parameters of the VSG damper winding were developed. The performed mathematical modeling confirmed the reliable and effective operation of the developed adaptive control algorithms and the enhanced VSG structure. The theoretical and experimental results obtained in this paper indicate the need for simultaneous development and improvement of both adaptive control algorithms and VSG structures used for this purpose. [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]

This paper deals with parameter estimation problem of large‐scale systems. A recursive distributed parameter estimation algorithm, based on the minimization of the prediction estimation error method, is developed. Specifically, the class of large‐scale systems that are composed of several interconnected sub‐systems is considered. Each interconnected sub‐system is modelled by a linear discrete‐time state space mathematical model with unknown parameters. The convergence analysis is then achieved using the Lyapunov approach. The theoretical analysis and simulation results prove the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Purpose. As a scientific field, bioinformatics has drawn remarkable attention from various fields, such as information technology, mathematics, and modern biological sciences, in recent years. The topic models originating from the field of natural language processing have become the focus of attention with the rapid accumulation of biological datasets. Thus, this research is aimed at modeling the topic content of the bioinformatics literature presented by Iranian researchers in the Scopus Citation Database. Methodology. This research was a descriptive-exploratory study, and the studied population included 3899 papers indexed in the Scopus database, which had been indexed in this database until March 9, 2022. The topic modeling was then performed on the abstracts and titles of the papers. A combination of LDA and TF-IDF was utilized for topic modeling. Findings. The data analysis with topic modeling resulted in identifying seven main topics "Molecular Modeling," "Gene Expression," "Biomarker," "Coronavirus," "Immunoinformatics," "Cancer Bioinformatics," and "Systems Biology." Moreover, "Systems Biology" and "Coronavirus" had the largest and smallest clusters, respectively. Conclusion. The present investigation demonstrated an acceptable performance for the LDA algorithm in classifying the topics included in this field. The extracted topic clusters indicated excellent consistency and topic connection with each other. [ABSTRACT FROM AUTHOR]

Simple Summary: Neuroblastoma is a rare type of cancer that usually affects children. The high-risk patients' expected survival rate is less than 50%. One reason is the lack of precision in the standard treatment protocol: a one-size-fits-all multi-modal therapy. The study presented in this paper was designed to address this deficit by optimising the use of two chemotherapeutic agents—vincristine and cyclophosphamide—during induction chemotherapy—the part of the protocol that shrinks the primary tumour before surgical removal. We combined a mathematical model and an optimisation algorithm to identify the best chemotherapy schedules for a cohort of virtual patients with different initial tumour compositions. Our results reveal novel strategies to exploit a pair of drugs with different levels of efficacy, provide a platform on which to individualise induction chemotherapy, and lay the foundation for a personalised therapy that leverages targeted therapies, multi-region sequencing, liquid biopsies, and modern computational methods to improve today's multi-modal therapy. Neuroblastoma is the most common extra-cranial solid tumour in children. Despite multi-modal therapy, over half of the high-risk patients will succumb. One contributing factor is the one-size-fits-all nature of multi-modal therapy. For example, during the first step (induction chemotherapy), the standard regimen (rapid COJEC) administers fixed doses of chemotherapeutic agents in eight two-week cycles. Perhaps because of differences in resistance, this standard regimen results in highly heterogeneous outcomes in different tumours. In this study, we formulated a mathematical model comprising ordinary differential equations. The equations describe the clonal evolution within a neuroblastoma tumour being treated with vincristine and cyclophosphamide, which are used in the rapid COJEC regimen, including genetically conferred and phenotypic drug resistance. The equations also describe the agents' pharmacokinetics. We devised an optimisation algorithm to find the best chemotherapy schedules for tumours with different pre-treatment clonal compositions. The optimised chemotherapy schedules exploit the cytotoxic difference between the two drugs and intra-tumoural clonal competition to shrink the tumours as much as possible during induction chemotherapy and before surgical removal. They indicate that induction chemotherapy can be improved by finding and using personalised schedules. More broadly, we propose that the overall multi-modal therapy can be enhanced by employing targeted therapies against the mutations and oncogenic pathways enriched and activated by the chemotherapeutic agents. To translate the proposed personalised multi-modal therapy into clinical use, patient-specific model calibration and treatment optimisation are necessary. This entails a decision support system informed by emerging medical technologies such as multi-region sequencing and liquid biopsies. The results and tools presented in this paper could be the foundation of this decision support system. [ABSTRACT FROM AUTHOR]

Numerous studies on project scheduling only consider a single factor, which fails to reflect the actual environment of project operations. In light of this issue, the article synthesizes multiple perspectives and proposes a multi-skill resource-based multi-modal project scheduling problem (MRCMPSP). This problem is described, modeled, and solved using the resource capability matrix and other constraints to minimize the project duration. To effectively solve MRCMPSP and enrich scheduling algorithms, the paper selects the hybrid quantum algorithm (HQPSO) based on the quantum particle swarm algorithm (QPSO). The HQPSO introduces various improvements such as the JAYA optimization search to improve the algorithm's performance. In order to verify the generality, superiority, and effectiveness of the algorithm, independent operation comparison experiments and practical application experiments of the algorithm are designed based on different case sizes and resource quantities. The experimental results demonstrate that the proposed algorithm has superior convergence performance and solution accuracy and can provide an effective scheduling solution for real cases. Additionally, the article provides targeted management suggestions based on the research findings. Overall, this study contributes a novel mathematical model, solution algorithm, optimization strategies, and managerial insights, advancing the field of project management research. [ABSTRACT FROM AUTHOR]

There is a trend to deploy neural network on edge devices in recent years. While the mainstream of research often concerns with single edge device processing and edge-cloud two-layer neural network collaborative computing, in this paper, we propose partitioning multi-layer edge network for neural network collaborative computing. With the proposed method, sub-models of neural network are deployed on multi-layer edge devices along the communication path from end users to cloud. Firstly, we propose an optimal path selection method to form a neural network collaborative computing path with lowest communication overhead. Secondly, we establish a time-delay optimization mathematical model to evaluate the effects of different partitioning solutions. To find the optimal partition solution, an ordered elitist genetic algorithm (OEGA) is proposed. The experimental results show that, compared with traditional cloud computing, single-device edge computing and edge-cloud collaborative computing, the proposed multi-layer edge network collaborative computing has a smaller runtime delay with limited bandwidth resources, and because of the pipeline computing characteristics, the proposed method has a better response speed when processing large number of requests. Meanwhile, the OEGA algorithm has better performance than conventional methods, and the optimized partitioning method outperforms other methods like random and evenly partition. [ABSTRACT FROM AUTHOR]

The performance growth in processors has been continuing toward increasing the number of processing cores on the chip and scaling the feature size of transistors. However, in the nanoera, side effects of the scaling, such as induced heterogeneities in the performance, power, and soft error rate of identically designed cores, prevent the potential performance from being fully utilized. In this paper, we harness the mentioned side effects in shared-memory multicore processors with unknown workloads by a dynamic heuristic scheduling algorithm called HDSAP. HDSAP aims to maximize performance, i.e., the average response time, under power and reliability constraints in presence of induced heterogeneities. In this regard, we use a mathematical model to quantify task to core assignments based on performance variation. We also consider the variation in power to change selected cores when the power constraint is missed. To meet the reliability constraint, we use N-modular redundancy while being aware of the variation in the soft error rate of cores to prevent under/over reliability estimation. To evaluate HDSAP, we run SPLASH benchmark suite on Sniper and MACPat simulators. As a result, the response time of HDSAP reduces by 6%, 8%, and 25% in comparison with similar algorithms under the same power and reliability constraints. [ABSTRACT FROM AUTHOR]

Detecting small targets and handling target occlusion and overlap are critical challenges in weld defect detection. In this paper, we propose the S-YOLO model, a novel weld defect detection method based on the YOLOv8-nano model and several mathematical techniques, specifically tailored to address these issues. Our approach includes several key contributions. Firstly, we introduce omni-dimensional dynamic convolution, which is sensitive to small targets, for improved feature extraction. Secondly, the NAM attention mechanism enhances feature representation in the region of interest. NAM computes the channel-wise and spatial-wise attention weights by matrix multiplications and element-wise operations, and then applies them to the feature maps. Additionally, we replace the SPPF module with a context augmentation module to improve feature map resolution and quality. To minimize information loss, we utilize Carafe upsampling instead of the conventional upsampling operations. Furthermore, we use a loss function that combines IoU, binary cross-entropy, and focal loss to improve bounding box regression and object classification. We use stochastic gradient descent (SGD) with momentum and weight decay to update the parameters of our model. Through rigorous experimental validation, our S-YOLO model demonstrates outstanding accuracy and efficiency in weld defect detection. It effectively tackles the challenges of small target detection, target occlusion, and target overlap. Notably, the proposed model achieves an impressive 8.9% improvement in mean Average Precision (mAP) compared to the native model. [ABSTRACT FROM AUTHOR]

As an important technology in 3D vision, point-cloud registration has broad development prospects in the fields of space-based remote sensing, photogrammetry, robotics, and so on. Of the available algorithms, the Iterative Closest Point (ICP) algorithm has been used as the classic algorithm for solving point cloud registration. However, with the point cloud data being under the influence of noise, outliers, overlapping values, and other issues, the performance of the ICP algorithm will be affected to varying degrees. This paper proposes a global structure and adaptive weight aware ICP algorithm (GSAW-ICP) for image registration. Specifically, we first proposed a global structure mathematical model based on the reconstruction of local surfaces using both the rotation of normal vectors and the change in curvature, so as to better describe the deformation of the object. The model was optimized for the convergence strategy, so that it had a wider convergence domain and a better convergence effect than either of the original point-to-point or point-to-point constrained models. Secondly, for outliers and overlapping values, the GSAW-ICP algorithm was able to assign appropriate weights, so as to optimize both the noise and outlier interference of the overall system. Our proposed algorithm was extensively tested on noisy, anomalous, and real datasets, and the proposed method was proven to have a better performance than other state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

An efficient and more accurate millimeter-wave imaging algorithm, applied to a close-range monostatic personnel screening system, with consideration of dual path propagation loss, is presented in this paper. The algorithm is developed in accordance with a more rigorous physical model for the monostatic system. The physical model treats incident waves and scattered waves as spherical waves with a more rigorous amplitude term as per electromagnetic theory. As a result, the proposed method can achieve a better focusing effect for multiple targets in different range planes. Since the mathematical methods in classical algorithms, such as spherical wave decomposition and Weyl identity, cannot handle the corresponding mathematical model, the proposed algorithm is derived through the method of stationary phase (MSP). The algorithm has been validated by numerical simulations and laboratory experiments. Good performance in terms of computational efficiency and accuracy has been observed. The synthetic reconstruction results show that the proposed algorithm has significant advantages compared with the classical algorithms, and the reconstruction by using full-wave data generated by FEKO further verifies the validity of the proposed algorithm. Finally, the proposed algorithm performs as expected over real data acquired by our laboratory prototype. [ABSTRACT FROM AUTHOR]

Modal-free optimization algorithms do not require specific mathematical models, and they, along with their other benefits, have great application potential in adaptive optics. In this study, two different algorithms, the single-dimensional perturbation descent algorithm (SDPD) and the second-order stochastic parallel gradient descent algorithm (2SPGD), are proposed for wavefront sensorless adaptive optics, and a theoretical analysis of the algorithms' convergence rates is presented. The results demonstrate that the single-dimensional perturbation descent algorithm outperforms the stochastic parallel gradient descent (SPGD) and 2SPGD algorithms in terms of convergence speed. Then, a 32-unit deformable mirror is constructed as the wavefront corrector, and the SPGD, single-dimensional perturbation descent, and 2SPSA algorithms are used in an adaptive optics numerical simulation model of the wavefront controller. Similarly, a 39-unit deformable mirror is constructed as the wavefront controller, and the SPGD and single-dimensional perturbation descent algorithms are used in an adaptive optics experimental verification device of the wavefront controller. The outcomes demonstrate that the convergence speed of the algorithm developed in this paper is more than twice as fast as that of the SPGD and 2SPGD algorithms, and the convergence accuracy of the algorithm is 4% better than that of the SPGD algorithm. [ABSTRACT FROM AUTHOR]

In order to improve the scientificity of sports action analysis, this paper constructs a sports action analysis model based on machine learning based on the greedy algorithm and the bat algorithm. According to the structural characteristics of the model, the structure of the model is reflected in the form of face order, that is, the face neighborhood structure. Moreover, this paper judges the degree of similarity between model faces through the pros and cons of the order and applies it to the structural similarity matrix between models. In addition, this paper establishes corresponding mathematical models for the shape and structure of the model and constructs the shape similarity matrix, the surface neighborhood structure similarity matrix, and the structure similarity matrix between the source model and the target model. Finally, this paper designs and implements CAD model retrieval methods based on greedy algorithm and bat algorithm and designs experiments to compare the performance of the algorithm proposed in this paper with traditional algorithms. The result of the experiment shows that the algorithm proposed in this paper has obvious advantages in sports action analysis compared with the traditional algorithm. [ABSTRACT FROM AUTHOR]

This paper adopts the method of psychological data analysis to conduct in-depth research and analysis on the correlation between teachers' classroom teaching behaviors and students' knowledge acceptance. Firstly, this paper proposes a health factor prediction model, which is specifically divided into clustering and then classification model and a clustering and classification synthesis model. The classroom learning process is coded, sampled, and quantified to obtain data on students' learning behaviors, and a visualization system based on classroom students' learning behaviors is designed and developed to record and analyze students' behaviors in the classroom learning process and grasp students' classroom learning. These two models use algorithms to fine-grained divide the dataset from the perspective of subject users and mental health factors, respectively, and then use decision tree algorithms to classify and predict the mental health factor information by the subject user base information. Second, based on the collected datasets, we designed comparison experiments to validate the clustering-then-classification model and the integrated clustering-classification model and selected the optimal model for comparison. Teachers should increase effective praise and encouragement behaviors; teachers should increase meaningful teacher-student interaction behaviors; teachers should be proficient in teaching media technology to reduce unnecessary time wastage. Strategies to enhance teachers' TPACK include enriching teachers' knowledge base of CK, TK, and PK; developing teachers' integration thinking; and enriching teachers' types of activities for integrating technology. [ABSTRACT FROM AUTHOR]

The development of artificial intelligence has brought rapid changes to human life and brought great convenience to human activities. The development of various modes of transportation has also brought convenience to people's travel and commodity transactions, but it has also added more issues that need to be carefully considered. Because of the diversification of transportation methods, transportation problems also arise many fields, such as air transportation, water transportation, and land transportation. The development of mathematical models and algorithms for transportation problems is also in full swing, and it is a major trend to introduce mathematical models and algorithms into the solution of transportation problems. This paper deals with the multi-index transportation problem by establishing a multi-index mathematical model and algorithm to find a scientific transportation method for the goods to be transported, so as to save the cost and time of transportation. Experiments show that the mathematical model established in this paper has high efficiency for solving the multi-index transportation problem. At the same time, the most suitable transportation method can also be selected for the transportation of goods, and the route planned by the mathematical model and algorithm can reduce the risk to 12.34%. [ABSTRACT FROM AUTHOR]

The generation of mathematical models for the analysis of buildings with multiple thermal zones is a large and complex task. Furthermore, the order and complexity of the dynamical model are increased by the number of included thermal zones. To overcome this problem, this paper presents an algorithm to define the mathematical model automatically, using the geometric and physics parameters as inputs. Additionally, the spatial position of each thermal zone must be recorded in an arrangement called a contact matrix. The algorithm for modeling systems with multiple thermal zones is the main contribution of this work. This algorithm is presented in pseudocode format and as an annex, an implementation in MATLAB software. One of the advantages of this methodology is that it allows us to work with parallelepipeds and not necessarily cubic thermal zones. The algorithm allows us to generate mathematical models with symbolic variables, starting from the knowledge of how many thermal zones compose the system and its geometric organization. This information must be organized in a matrix arrangement called a contact matrix. Different arrays of thermal zones were constructed with wooden boxes to verify the functionality of the models generated with the algorithm. Each case provided information that allowed us to adjust the mathematical models and their simulations, obtaining a range of errors between experimental and simulated temperatures from 2.08 to 5.6 , depending on the number of thermal zones studied. [ABSTRACT FROM AUTHOR]

Numerical methods have gained momentum among specific engineering problems that must be solved in such a manner that accuracy and speed are the two most important aspects to consider regarding the output. This paper presents a fast, semi-analytical method (SAM) and original mathematical algorithms to determine the pressure distribution and von Mises stress for spur gears' meshing teeth. The SAM begins with the Hartnett approach, based on Boussinesq's equation for the half-space theory of linear elasticity, which implicitly means an infinite width of the gear flank. To simulate more realistic quarter-space conditions, corrections based on virtual mirror pressure are introduced in the computational algorithm. Mathematical surfaces modeling is an important aspect for spur gears as an intermediate stage to determine the pressure distribution and von Mises stress. Shaft misalignment changes the contact problem from symmetric, in which the half- or quarter-space model can be used, to asymmetric. In the latter case, the model must determine the entire contact area. The obtained output is validated by comparisons between our original FEA results and results from the literature using SAMs and FEA. [ABSTRACT FROM AUTHOR]

The Unlimited Sensing Framework (USF) was recently introduced to overcome the sensor saturation bottleneck in conventional digital acquisition systems. At its core, the USF converts a continuous-time high-dynamic-range (HDR) signal into folded, low-dynamic-range, modulo samples and allows the recovery of the HDR signal via algorithmic unfolding. In hardware, however, implementing ideal modulo folding requires careful calibration, analog design and high precision. At the interface of theory and practice, this paper explores a computational sampling strategy that relaxes strict hardware requirements via a novel, mathematically guaranteed reconstruction. We start with a generalized model for USF with two new parameters modeling hysteresis and folding transients in addition to the modulo threshold. Hysteresis accounts for mismatches between the reset threshold and the amplitude displacement at the folding time and the folding transient is a continuous transition period in the implementation of a reset. Both these effects are motivated by our hardware experiments and also occur in previous, domain-specific applications. We show that hysteresis is beneficial for USF and leverage it to derive the first recovery guarantees in the context of our generalized USF model for a certain sampling rate regime. Additionally, we show how the sampling rate requirement can be greatly reduced via a direct generalization of the proposed recovery. Our theoretical work is corroborated by hardware experiments with a hysteresis enabled, modulo ADC testbed comprising off-the-shelf electronic components. Thus, by capitalizing on a collaboration between hardware and algorithms, our paper enables an end-to-end pipeline for HDR sampling allowing more flexible hardware implementations. [ABSTRACT FROM AUTHOR]

A huge amount of construction waste is generated in construction sites every day that needs to be transported by vehicle to disposal facilities for processing. Unlike in most typical transportation problems, once these vehicles are loaded with construction waste, they must travel directly to the disposal facility. Moreover, there are different types of construction waste that may require handling by different disposal facilities. In this paper, we develop a model and algorithm for identifying the optimal transportation routes specific to construction waste transportation. Our results can not only minimize the overall costs for both the logistics company and the contractor but also minimize the distance traveled, thus reducing urban traffic emissions. [ABSTRACT FROM AUTHOR]

In this paper, we study the quadratic assignment problem with a rank-one cost matrix (QAP-R1). Four integer-programming formulations are introduced of which three are assumed to have partial integer data. Unlike the standard quadratic assignment problem, some of our formulations can solve reasonably large instances of QAP-R1 with impressive running times and are faster than some metaheuristics. Pairwise relative strength of the LP relaxations of these formulations are also analyzed from theoretical and experimental points of view. Finally, we present a new metaheuristic algorithm to solve QAP-R1 along with its computational analysis. Our study offers the first systematic experimental analysis of integer-programming models and heuristics for QAP-R1. The benchmark instances with various characteristics generated for our study are made available to the public for future research work. Some new polynomially solvable special cases are also introduced. Summary of Contribution: This paper aims to advance our knowledge and ability in solving an important special case of the quadratic assignment problem. It shows how to exploit inherent properties of an optimization problem to achieve computational advantages, a strategy that was followed by researchers in model building and algorithm developments for decades. Our computational results attest to this time-tested general philosophy. The paper presents the first systematic computational study of the rank one quadratic assignment problem, along with new mathematical programming models and complexity analysis. We believe the theoretical and computational results of this paper will inspire further research on the topic and will be of significant value to practitioners using rank one quadratic assignment models. [ABSTRACT FROM AUTHOR]

This paper proposes a reconstruction algorithm for the geometric shapes of static overhead transmission lines that is based on the spatial coordinate data (obtained by field mapping) and the corresponding inclination data (obtained by attitude sensors). The algorithm takes the piecewise cubic Hermite interpolation function as the basic framework and corrects the aforementioned two types of data based on the “minimum energy principle” of the transmission line system. The introduction of a new energy function in modified energy method (MEM) overcame the shortcomings of conventional energy method (CEM) in fairing strategy, and the new Fairing Criterion made result curves more consistent with the mechanical properties of transmission lines. In the numerical simulation tests, the two energy methods were compared in terms of load type, horizontal span, elevation difference, and anti-disturbance ability, which showed that MEM has a higher reconstruction accuracy (average Euclidean distance is less than 1 cm) and better robustness than CEM. Meanwhile, the key parameters involved in the algorithm were discussed to guide the application in practical engineering. In addition, measurement tests under non-uniform icing were carried out in the field test station, and the results showed that the maximum relative error is less than 0.1%. [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]

The nanoindentation technique is employed to characterize the behaviors of biomaterials. Nevertheless, there is a lack of development of a miniaturized precise positioner for in situ nanoindentation. Besides, modeling behaviors of the positioner are restricted due to its complex kinematic characteristics. Therefore, this paper proposes a novel compliant two degrees of freedom (dof) stage for positioning a biomaterial sample in in situ nanoindentation. In addition, a new modeling and dimensional optimization synthesis method of the stage is developed. The proposed effective methodology is developed based on a kinetostatic analysis-based calculation method, the Lagrange approach, and a neural network algorithm. With an increased advance in artificial intelligence, a neural network algorithm is proposed to extend the applicability of artificial neural networks in optimizing the parameters of the stage. First, the 2-dof stage is built via a combination of an eight-lever displacement amplifier and a symmetric parallelogram mechanism. Second, a chain of mathematical equations of the 2-dof stage is constructed using a kinetostatic analysis-based method to calculate the ratio of displacement amplification and the input stiffness of the 2-dof stage. Then, the Lagrange method is utilized to formulate the dynamic equation of the 2-dof stage. Finally, a neural network algorithm is adopted to maximize the natural first frequency of the proposed stage. The optimal results determined that the frequency of the stage can achieve a high value of 112.0995 Hz. Besides, the formed mathematical models were relatively precise by comprising the simulation verifications. [ABSTRACT FROM AUTHOR]

Vehicle and airborne navigation users are facing more and more complex signal interference and even spoofing. If vehicles and aircraft do not strengthen their anti-spoofing ability, their navigation performance is bound to be adversely affected. global navigation satellite system (GNSS) spoofing technology has gradually become a preferred interference method for spoofer because of its high concealment and great harm. For spoofer, user terminal increasingly adopts GNSS with inertial measurement unit (IMU): tightly-coupled GNSS/IMU, on this basis, user also configures a variety of anti-spoofing techniques to effectively deal with spoofing. Even if spoofer slowly changes user's positioning, if spoofing strategy is unreasonable, it will lead to the violation of parameter rationality of coupled filter output parameters and spoofing observation, which greatly increases the difficulty of spoofing. In view of the above problems, from the perspective of spoofer, in order to effectively counter the non cooperative target of assembling tightly-coupled GNSS/IMU by using GNSS spoofing, this paper establishes GNSS spoofing mathematical model, and proposes a slowly varying spoofing algorithm to avoid tightly-coupled GNSS/IMU with multiple anti-spoofing techniques based on the analysis of the influence mechanism of spoofing on the positioning of tightly-coupled GNSS/IMU, the algorithm proposes a measurement deviation determination method to avoid a variety of anti-spoofing techniques, which can gradually pull the positioning results of coupled system, and successfully avoid anti-spoofing techniques detection of least squares residual receiver autonomous integrity monitoring (RAIM) and parameter rationality check. The experimental results show that the algorithm can gradually change positioning of tightly-coupled GNSS/IMU within 30 s, and the north, east and down displacements basically achieve the spoofing effect, the errors with the expected offset are −0.5 m, 1.9 m and 12.7 m respectively. At the same time, the detection of the above anti-spoofing techniques is avoided. The mean value of test statistics for tightly-coupled system is reduced by 75.4% and does not exceed the alarm threshold, so as to achieve the purpose of spoofing, the effectiveness and high concealment of the spoofing algorithm are proved. [ABSTRACT FROM AUTHOR]

As the competition between enterprises intensifies, employees have witnessed a decline of psychological health year by year and severe anxiety and depression. To ensure the normal work of employees, it is important to implement psychological occupational therapy intervention (POTI). The previous studies on corporate occupational values focus on the behavioral variables and attitude variables related to occupations. The research paradigm does not suit a specific group of employees. Therefore, this paper explores the individual differences in POTI guided by the realization of occupational values. For a specific group of employees, the correlations of the psychological problems with job attitude, job involvement, and psychological health level were reasoned logically, a theoretical model was established, and the full-model map was plotted to illustrate the influence of the realization of occupational values over POTI. Then, the evaluation data on the psychological intervention effect of 2,800 employees were explored based on multikernel learning, and an individual difference prediction model was constructed through multikernel learning. Finally, the predicted results of different groups of employees were compared, revealing the effectiveness of our algorithm. [ABSTRACT FROM AUTHOR]

The main semantic symbol systems for people to express their emotions include natural language and music. The analysis and establishment of semantic association between language and music is helpful to provide more accurate retrieval and recommendation services for text and music. Existing researches mainly focus on the surface symbolic features and association of natural language and music, which limits the performance and interpretability of applications based on semantic association of natural language and music. Emotion is the main meaning of music expression, and the semantic range of text expression includes emotion. In this paper, the semantic features of music are extracted from audio features, and the semantic matching model of audio emotion analysis is constructed to analyze ethnic music audio emotion through feature extraction ability of deep structure. The model is based on the framework of emotional semantic matching technology and realizes the emotional semantic matching of music fragments and words through semantic emotional recognition algorithm. Multiple experiments show that when W = 0.65 , the recognition rate of multichannel fusion model is 88.42%, and the model can reasonably realize audio emotion analysis. When the spatial dimension of music data changes, the classification accuracy reaches the highest when the spatial dimension is 25. Analysing the semantic association of audio promotes the application of folk music in occupational therapy. [ABSTRACT FROM AUTHOR]

Power flow analysis is a key tool for planning, operation and control of power systems. With future hybrid Supergrids in mind, a wide variety of power flow algorithms for multi-terminal VSC AC/DC systems have been proposed. However, the nonlinear nature of these formulations represents a challenge for its use in real power systems. Whereas in the study of AC networks linear approximations are frequently utilized to speed up multi-scenario steady-state analysis, an equivalent tool has not been proposed for hybrid AC/DC grids. This paper fills that gap by formulating a linear MW-only power flow algorithm with losses for multi-terminal VSC AC/DC systems. The proposed algorithm provides a consistent estimation of converters' losses and allows modelling of all branches within the converter stations. Moreover, two novel schemes for the inclusion of DC network losses are proposed and a circuit-equivalent model of the linear power flow problem in multi-terminal VSC AC/DC systems is suggested. [ABSTRACT FROM AUTHOR]

As the most important core process in the dyeing and finishing workshop of knitting companies, the dyeing process has the characteristics of multi-variety, small-batch, parallel machine processing of multiple types, and high cost in equipment cleaning, which render the dyeing scheduling problem a bottleneck in the production management of a dyeing and finishing workshop. In this paper, the dyeing process scheduling problem in dyeing and finishing workshops is described and abstracted, and an optimized mathematical model of dyeing scheduling is constructed with the goal of minimizing the delay cost and switching cost. Constraints such as multiple types of equipment, equipment capacity, weights of orders and equipment cleaning time are considered. For the sub-problem of equipment scheduling in the dyeing scheduling problem, a heuristic rule that considers equipment utilization and order delay is proposed. For the sub-problem of order sorting of the equipment in the dyeing scheduling problem, a hybrid genetic algorithm with a variable neighbourhood search strategy has been designed to optimize sorting. The algorithm proposed in this paper has been demonstrated via case simulation to be effective in solving the scheduling problem in dyeing and finishing workshops. [ABSTRACT FROM AUTHOR]

The purpose of this work is to present a new method for estimating the parameters of the biomass of agricultural crops based on Earth remote sensing (ERS) data. The method includes mathematical models and algorithms estimation and has been tested on the example of spring wheat sowing. Sowing biomass parameters are the basis for making management decisions aimed at obtaining a given crop yield. Currently, for these purposes, vegetation indices are most widely used. It is impossible to estimate the physical parameters of the crop sowing biomass using these indices, due to their scalar form and lack of dimension. The paper develops a classical approach to the problem of estimating the parameters of the state of agricultural crops, in which remote sensing data are considered as an indirect measurement of the estimated parameters. The basis for the implementation of the estimation method is the dynamic model of biomass parameters and the remote sensing model, which reflects the relationship between the spectral reflection parameters and the estimated parameters of the crop biomass. The parameters of the dynamic model and the remote sensing model are refined by selective ground measurements in separate elementary sections of the field. The difference between this article and previous works of a similar nature lies in the fact that agricultural crops with a more complex morphological structure are considered as the object of evaluation. In addition, such an important feature of agricultural objects as their spatial distribution is considered here. To take it into account, a new type of mathematical models is used, in which spatial coordinates are introduced. Due to the significant complication of modeling and estimation algorithms based on such models, simpler approximation schemes are proposed. The advantage of the proposed approach is that the assessment is considered as a dynamic process that meets the content of the task of monitoring crops. [ABSTRACT FROM AUTHOR]

In view of the poor performance of the original mathematical model of assembly construction project precost budget, a mathematical model of assembly construction project precost budget based on improved neural network algorithm is proposed. This paper investigates the cost content of assembly construction project and analyzes its early cost. It finds that the early cost of assembly construction project includes component production cost, transportation component cost, and installation component cost. Based on the improved neural network algorithm to build an improved neural network model, the improved neural network model to mine the cost data in the early stage of assembly construction project is used. In this paper, the earned value variable is introduced to transform the project duration and project cost in the early stage of the prefabricated construction project into quantifiable cost data, and the earned value analysis method is used to estimate the implementation cost of the prefabricated construction project. According to the result of cost estimation, the mathematical model of precost budget of prefabricated construction project is built based on the project parameters. In order to prove that the cost budget performance of the mathematical model based on the improved neural network algorithm in the early stage of assembly construction project is better, the original mathematical model is compared with the mathematical model, the experimental results show that the cost budget performance of the model is better than the original model, and the cost budget performance is improved. [ABSTRACT FROM AUTHOR]

The paper presents the results of a study done to find the optimal shapes of matrix element partitioning on three abstract heterogeneous processors when performing multiplication operations. An abstract processor model allows applying the research results in systems with different heterogeneous architectures. To determine the optimal partitioning shape, the work uses non-rectangular candidate shapes identified by Ashley DeFlumere in her work as a result of applying the technology of redistribution of matrix elements between the processors «push»: Square Corner, Rectangle Corner, Square Rectangle, Block Rectangle, L-Rectangle, Traditional 1D Rectangular. The optimality of shapes is determined for four classes of matrix multiplication algorithms: Serial Communication with Barrier (SCB), Parallel Communication with Barrier (PCB), Serial Communication with Bulk Overlap (SCO) and Parallel Communication with Overlap (PCO). The Hockney model was used to evaluate the communication complexity of algorithms. Mathematical models of the algorithm execution time were introduced in the paper for each considered candidate shape in all algorithms. Based on the developed mathematical models, software was developed that allows to select the form of elements partitioning between processors, depending on the ratio of their speeds and latency of the transmission medium. [ABSTRACT FROM AUTHOR]

In this paper, we propose to extend the standard Convolutional Dictionary Learning problem to a tensor representation where the activations are constrained to be “low-rank” through a Canonical Polyadic decomposition. We show that this additional constraint increases the robustness of the CDL with respect to noise and improve the interpretability of the final results. In addition, we discuss in detail the advantages of this representation and introduce two algorithms, based on ADMM or FISTA, that efficiently solve this problem. We show that by exploiting the low rank property of activations, they achieve lower complexity than the main CDL algorithms. Finally, we evaluate our approach on a wide range of experiments, highlighting the modularity and the advantages of this tensorial low-rank formulation. [ABSTRACT FROM AUTHOR]

This paper proposes a mixed decision strategy for freight and passenger transportation in metro systems during off-peak hours (MTS-OPH). The definition of the mixed decision strategy is proposed, and fixed and flexible loading modes are considered for different passenger flow volumes. A mathematical model of the MTS-OPH is proposed and solved using an improved variable neighborhood search algorithm. Case studies demonstrate the performance and applicability of the proposed model and algorithm, and the MTS-OPH is discussed for different delivery distances, passenger flows, and metro network types. The proposed strategy is suitable for long-distance delivery, and the proposed model framework can be applied to different types of metro networks with different levels of complexity. The mixed decision strategy provides a decision support tool for metro and freight companies and can propose corresponding solutions according to different passenger flows. [ABSTRACT FROM AUTHOR]

Many classes of engineering problems focus on the process of calibrating mathematical models using observed data. The enormous progress of scientific computation and data-mining techniques has allowed the search for accurate mathematical models from experimental data using algorithms. Among them, the evolutionary polynomial regression (EPR) is an artificial intelligence (AI) technique that merges genetic algorithms (GAs) and regression techniques such as ordinary least square (OLS). This paper presents a robust and well-conditioned EPR technique to remove potential outliers and leverage points included in any biased data set. This hybrid approach combines bisquare, Huber, and Cauchy robust multivariate techniques with GAs and the Akaike weight-based method to assess the optimal polynomial model while limiting the impact of the data bias. The robust techniques will define the parameters, the GAs will determine the exponents, and the Akaike weight-based method will evaluate the relative importance of each observed variable of the proposed model. As a case study, a shear strength data set of RC beams without stirrups is used to compare the standard EPR algorithm with the new proposed hybrid methodology. Furthermore, the optimal robust model is compared with different benchmark formulations to highlight its accuracy and consistency. The proposed hybrid technique can be adopted as a mathematical tool for many engineering problems, providing an unbiased prediction of the observed variable. Furthermore, the shear strength equation that provides the best compromise between accuracy and complexity allows its potential use in many engineering practices and building codes. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to develop a fixed-wing aircraft that has the abilities of both vertical take-off (VTOL) and a fixed-wing aircraft. To achieve this goal, a prototype of a fixed-wing gyroplane with two propellers is developed and a rotor can maneuver like a drone and also has the ability of vertical take-off and landing similar to a helicopter. This study provides guidance, navigation, and control algorithm for the gyrocopter. Firstly, this study describes the dynamics of the fixed-wing aircraft and its control inputs, i.e., throttle, blade pitch, and thrust vectors. Secondly, the inflow velocity, the forces acting on the rotor blade, and the factors affecting the rotor speed are analyzed. Afterward, the mathematical models of the rotor, dual engines, wings, and vertical and horizontal tails are presented. Later, the flight control strategy using a global processing system (GPS) module is designed. The parameters that are examined are attitude, speed, altitude, turn, and take-off control. Lastly, hardware in the loop (HWIL) based simulations proves the effectiveness and robustness of the navigation guidance and control mechanism. The simulations confirm that the proposed novel mechanism is robust and satisfies mission requirements. The gyrocopter remains stable during the whole flight and maneuvers the designated path efficiently. [ABSTRACT FROM AUTHOR]

In this paper, we present a new structure of the n-dimensional trigonometric cubic B-spline collocation algorithm, which we show in three different formats: one-, two-, and three-dimensional. These constructs are critical for solving mathematical models in different fields. We illustrate the efficiency and accuracy of the proposed method by its application to a few two- and three-dimensional test problems. We use other numerical methods available in the literature to make comparisons. [ABSTRACT FROM AUTHOR]

This paper proposes a new decentralized data-driven load restoration (DDLR) scheme for transmission and distribution (TD) systems with high penetration of wind power. Robust DDLR models are constructed in order to handle uncertainties and ensure the feasibility of decentralized schemes. The Wasserstein metric is used to describe the ambiguity sets of probability distributions in order to build the complete DDLR model and realize computationally tractable formulation. A data-driven model-nested analytical target cascading (DATC) algorithm is developed to obtain the final load restoration result by iteratively solving small-scale mathematical models. The proposed DDLR scheme provides load restoration results with adjustable robustness, and performance efficiency is independent from the amount of data. The DDLR scheme makes full use of the available data while respecting information privacy requirements of independent operated systems, and ensures the feasibility of a decentralized load restoration strategy even in the worst-case condition. The effectiveness of the proposed method is validated using a small-scale TD system and a large-scale system with the IEEE 118-bus TS and thirty IEEE-33 DSs, showing high computational efficiency and superior restoration performance. [ABSTRACT FROM AUTHOR]

Ship deck arrangement design is about determining the positions and dimensions of arranged objects. This paper presents the mathematical model for the ship deck arrangement optimization problem statement and how the individual's objective and constraint functions are computed. Moreover, an improved multiobjective hybrid genetic algorithm is redesigned to solve this complex nondeterministic problem and generate a set of diverse and rational deck arrangements in the early stage of ship design. An adaptive crossover operator and a novel topological replace operator invoked in this algorithm are described. Finally, the proposed algorithm is tested on a main deck arrangement optimization of an underwater detection ship. In the validation tests, the proposed algorithm is compared to the standard NSGA-II to determine its ability to produce a set of diverse and rational deck arrangements. Subsequently, the performance tests are used to determine the ability of the algorithm to work with the highly constrained arrangement problems and the efficiency of the adaptive crossover and topological replace operators. [ABSTRACT FROM AUTHOR]

Energy-saving production is one of the issues that must be paid attention to by today's manufacturing enterprises. Aiming at the problem of integrated process planning and scheduling (IPPS) in the manufacturing process, considering Time-of-Use (TOU) and tiered electricity price, this paper systematically studies the energy-saving scheduling problem in order to reduce the power consumption in processing and production. To establish the multiobjective optimization mathematical model of the problem, the load balancing problem of the equipment is considered, the minimization of the power consumption and the maximum load of the equipment are taken as the optimization objectives. Then, considering the constraints of resource and multiprocess, the switching strategy of the equipment in idle time is introduced, including shutdown and restart operations. In order to solve the model easily, a multiobjective firefly algorithm (MOFA) based on five-layer coding is designed, and the elite strategy is introduced to protect the excellent firefly individuals in the iterative population. Finally, through a specific example, the Pareto solution set is obtained, which provides a reference scheme for decision-makers, and verifies the correctness of the model and the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

In this paper, the problem of temperature distribution for convective straight fins with constant and temperature-dependent thermal conductivity is solved by using artificial neural networks trained by the biogeography-based heterogeneous cuckoo search (BHCS) algorithm. We have solved the integer and noninteger order energy balance equation in order to analyse the temperature distribution in convective straight fins. We have compared our results with homotopy perturbation method (HPM), variational iteration method (VIM), and homotopy perturbation Sumudu transform method (HPSTM). The results show that the ANN–BHCS algorithm gives better results than other analytical techniques. We have further checked the efficiency of the ANN–BHCS algorithm by using the performance metrics MAD, TIC, and ENSE. We have calculated the values of MAD, TIC, and ENSE for case 1 of the problem, and histograms of these metrics show the efficiency of our algorithm. [ABSTRACT FROM AUTHOR]

In this paper, a modified model of Nondominated Sorting Genetic Algorithm 2 (NSGA-II), which is one of the Multiobjective Evolutionary Algorithms, is proposed. This algorithm is a new model designed to make a trade-off between minimizing the cost of preventive maintenance (PM) and minimizing the time taken to perform this maintenance for a series-parallel system. In this model, the limitations of labor and equipment of the maintenance team and the effects of maintenance issues on manufacturing problems are also considered. In the mathematical model, finding the appropriate objective functions for the maintenance scheduling problem requires all maintenance costs and failure rates to be integrated. Additionally, the effects of production interruption during preventive maintenance are added to objective functions. Furthermore, to make a better performance compared with a regular NSGA-II algorithm, we proposed a modified algorithm with a repository to keep more unacceptable solutions. These solutions can be modified and changed with the proposed mutation algorithm to acceptable solutions. In this algorithm, modified operators, such as simulated binary crossover and polynomial mutation, will improve the algorithm to generate convergence and uniformly distributed solutions with more diverse solutions. Finally, by comparing the experimental solutions with the solutions of two Strength Pareto Evolutionary Algorithm 2 (SPEA2) and regular NSGA-II, MNSGA-II generates more efficient and uniform solutions than the other two algorithms. [ABSTRACT FROM AUTHOR]

For the quantitative applications of the Suomi National Polar-orbiting Partnership (SNPP) Advanced Technology Microwave Sounder (ATMS), the geolocation accuracy of its sensor data records must be quantified during its on-orbit operation. In this paper, a refined coastline inflection point method is used to evaluate the on-orbit geolocation accuracy of SNPP ATMS. It is disclosed that for SNPP ATMS, the static error term with scan-angle-dependent feature is a dominant part among all the geolocation error sources. A mathematical model is then developed to convert the in-track and cross-track geolocation errors to the beam pointing Euler angles defined in the spacecraft coordinate system, which can be further used to construct the correction matrix for on-orbit geolocation process. By using the correction matrix built in this paper, the geolocation error is obviously reduced both at nadir and at the edge of the scan. The total geolocation error at nadir before/after correction is 3.8/0.8 km at K-band, 5.6/0.8 km at Ka-band, 3.3/0.4 km at V-band, and 1.5/0.1 km at W-band. The geolocation bias at the edge of the scan line before/after correction is 4.6/1.3 km at K-band, 9.4/1.8 km at Ka-band, 4.4/2.4 km at V-band, and 3.2/0.8 km at W-band. After correction, the scan-angle-dependent feature in geolocation error is also largely reduced. [ABSTRACT FROM AUTHOR]