Adaptive Weighting Scheme Research Articles

Fault Assessment for Mechanical Equipment with Adaptive Weights Incorporating Sensitivity and Monotonicity

Driven by the increasing needs in industrial processes for condition-based maintenance, this paper present an adaptive weighting strategy based on sensitivity and monotonicity for fault assessment. Since the sensitivities of features vary with the changes of fault severity, adaptive weight coefficients are designed based on sensitivities to strengthen the feature information. Meanwhile, considering the irreversibility of fault evolution and the difference of sensitive features to different faults, unique monotonic multi-domain feature set with high sensitivity can be selected. Finally, a monotonic health index (HI) is fused based on adaptive weight coefficients for fault assessment which satisfies the needs for intuitiveness in industrial sites. Moreover, the effectiveness of the proposed method is demonstrated by rolling bearing test rig. Results show that the average assessment accuracy can reach 87.5%, 95.375% and 95.375%.

A cloud computing load prediction hybrid model with adaptive weight strategy

A cloud computing load prediction hybrid model with adaptive weight strategy

Aero-Engine Rotor Assembly Process Optimization Based on Improved Harris Hawk Algorithm

Multi-stage disc rotor assembly is an important part of the aero-engine rotor manufacturing process. To solve the problem that excessive unbalance of assembly affects the vibration of the whole machine, this paper presents an optimization method for aero-engine rotor assembly balance based on an improved Harris Hawk algorithm. Firstly, the assembly sequence model of the single-stage disc blade and the phase assembly model of a multi-stage disc of the engine rotor is established. Secondly, by using the initial population generation based on dynamic opposing learning and the escape energy function of the non-linear logarithmic convergence factor, the search mechanism of the whale optimization algorithm is introduced in the global exploration, and the adaptive weight strategy and mutation strategy of the genetic algorithm is introduced in the development to improve the algorithm. Then, the effectiveness of the algorithm is verified by experiments and compared with particle swarm optimization, genetic algorithm, and Harris Hawk algorithm, the unbalance of the optimal blade assembly sequence is reduced by 91.75%, 99.82%, and 83.39%, respectively. The algorithm comparison and analysis are carried out for all disc-blade assembly optimization of the rotor. The optimal unbalance of the improved Harris Hawk optimization algorithm is reduced by 79.71%, 99.48%, and 54.92% on average. The unbalance of the algorithm in this paper is the best. Finally, the improved Harris Hawk algorithm is used to find the best assembly phase, and the optimized unbalanced force and moment are reduced by 84.22% and 98.05%, respectively. The results of this study prove that the improved Harris Hawk algorithm for aero-engine rotor assembly balance optimization can effectively reduce the unbalance of rotor disc blade assembly and rotor unbalance and provide a powerful solution for solving engine vibration.

Improved Whale Optimization Algorithm for Solving Microgrid Operations Planning Problems

Microgrid operations planning is one of the keys to ensuring the safe and efficient outputs of distributed energy resources (DERs) and the stable operation of a power system in a microgrid (MG). In this study, for the symmetry in renewable energy and microgrid systems, and coordinated control based on a storage battery system, an MG dispatching model with DER conditions and integrated costs is established in grid-connected mode, on the basis of MG operation costs, interaction costs, and pollutant emissions costs. Moreover, an optimization objective for minimizing integrated costs is established. Therefore, based on the original whale optimization algorithm (WOA), an improved whale optimization algorithm (IWOA) with adaptive weight strategy and Levy flight trajectory is proposed in this paper, to solve the optimal operations planning problem of MGs. Finally, in computing comparisons with methods such as the genetic algorithm (GA), particle swarm algorithm (PSO), WOA, wild horse optimizer (WHO), and enhanced whale optimization algorithm (EWOA), the results show that the IWOA computation had lower integrated costs and higher operational efficiency. Moreover, it is verified that the IWOA performed better in solving the MG operations planning problem.

An Improved NSGA-II Algorithm Based on Adaptive Weighting and Searching Strategy

Non-dominated sorting genetic algorithm II is a classical multi-objective optimization algorithm but it suffers from poor diversity and the tendency to fall into a local optimum. In this paper, we propose an improved non-dominated sorting genetic algorithm, which aims to address the issues of poor global optimization ability and poor convergence ability. The improved NSGA-II algorithm not only uses Levy distribution for global search, which enables the algorithm to search a wider range, but also improves the local search capability by using the relatively concentrated search property of random walk. Moreover, an adaptive balance parameter is designed to adjust the respective contributions of the exploration and exploitation abilities, which lead to a faster search of the algorithm. It helps to expand the search area, which increases the diversity of the population and avoids getting trapped in a local optimum. The superiority of the improved NSGA-II algorithm is demonstrated through benchmark test functions and a practical application. It is shown that the improved strategy provides an effective improvement in the convergence and diversity of the traditional algorithm.

Particle swarm optimization with Chebychev functional-link network model for engineering design problems

In recent years, many improved particle swarm optimization (PSO) algorithms have been developed to improve the performance of PSO. These improved algorithms have greatly improved PSO performance, but PSO still has some shortcomings. So, an particle swarm optimization with Chebychev functional-link network model is proposed (APSOCFLN) in this paper. Firstly, in order to make up for the shortcomings of the canonical PSO update search, a novel Chebychev functional-link network (CFLN) elite guidance strategy is proposed. Two different update mechanisms are executed alternately, increasing the diversity of the algorithm population and making the algorithm more capable of jumping out of local optimization. Secondly, in order to better balance the exploration and exploitation of the algorithm, an adaptive probability strategy is proposed. Thirdly, an adaptive weighting strategy is proposed. It will be used for CFLN elite guidance strategy and give different weights to the two alternating update strategies, which can effectively solve the problem of premature convergence of PSO. Fourthly, the APSOCFLN and comparison algorithms are used to solve the practical engineering problems, and the results show that APSOCFLN has high precision, fast convergence , and excellent performance. Finally, the performance of the algorithm is tested with CEC2017 and CEC2022 benchmark functions. The comparative algorithm includes the classical PSO improvement algorithms and the classical other algorithms, and the experimental results show the effectiveness of the proposed strategy and the good performance of the improved algorithm.

A semi-supervised adaptive Markov Gaussian embedding process (SAMGEP) for prediction of phenotype event times using the electronic health record

While there exist numerous methods to identify binary phenotypes (i.e. COPD) using electronic health record (EHR) data, few exist to ascertain the timings of phenotype events (i.e. COPD onset or exacerbations). Estimating event times could enable more powerful use of EHR data for longitudinal risk modeling, including survival analysis. Here we introduce Semi-supervised Adaptive Markov Gaussian Embedding Process (SAMGEP), a semi-supervised machine learning algorithm to estimate phenotype event times using EHR data with limited observed labels, which require resource-intensive chart review to obtain. SAMGEP models latent phenotype states as a binary Markov process, and it employs an adaptive weighting strategy to map timestamped EHR features to an embedding function that it models as a state-dependent Gaussian process. SAMGEP’s feature weighting achieves meaningful feature selection, and its predictions significantly improve AUCs and F1 scores over existing approaches in diverse simulations and real-world settings. It is particularly adept at predicting cumulative risk and event counting process functions, and is robust to diverse generative model parameters. Moreover, it achieves high accuracy with few (50–100) labels, efficiently leveraging unlabeled EHR data to maximize information gain from costly-to-obtain event time labels. SAMGEP can be used to estimate accurate phenotype state functions for risk modeling research.

Marine Predators Algorithm Based on Adaptive Weight and Chaos Factor and Its Application

Aiming at the problems of uneven distribution of initialized populations and unbalanced exploration and exploitation leading to slow convergence, low convergence accuracy, and easy to fall into local optimality of marine predators algorithm (MPA), a marine predators algorithm based on adaptive weight and chaos factor is proposed (ACMPA), the algorithm is applied to the traveling salesman problem (TSP), and the shortest path planning and research are carried out for the traveling salesman problem. Firstly, the improved adaptive weight strategy is used to balance the exploration and exploitation stage of the algorithm and improve the convergence accuracy of the algorithm. Secondly, the chaos factor is used to replace the random factor, and the ergodicity of the chaos factor is used to make it easier for predators to jump out of local optimization and enhance the optimization ability of the algorithm. Finally, 10 benchmark test functions, the CEC2015 test set, and the CEC2017 test set are used to evaluate the effectiveness of the ACMPA. The results show that, compared with the other four intelligent optimization algorithms, the improved ACMPA achieves better results in both mean and standard deviation, and the algorithm has a better effect on the shortest path problem.

Algorithm 1028: VTMOP: Solver for Blackbox Multiobjective Optimization Problems

VTMOP is a Fortran 2008 software package containing two Fortran modules for solving computationally expensive bound-constrained blackbox multiobjective optimization problems. VTMOP implements the algorithm of [ 32 ], which handles two or more objectives, does not require any derivatives, and produces well-distributed points over the Pareto front. The first module contains a general framework for solving multiobjective optimization problems by combining response surface methodology, trust region methodology, and an adaptive weighting scheme. The second module features a driver subroutine that implements this framework when the objective functions can be wrapped as a Fortran subroutine. Support is provided for both serial and parallel execution paradigms, and VTMOP is demonstrated on several test problems as well as one real-world problem in the area of particle accelerator optimization.

Weighted deep factorizing heterogeneous molecular network for genome-phenome association prediction.

Genome-phenome association (GPA) prediction can promote the understanding of biological mechanisms about complex pathology of phenotypes (i.e., traits and diseases). Traditional heterogeneous network-based GPA approaches overwhelmingly need to project heterogeneous data toward homogeneous network for data fusion and prediction, such projections result in the loss of heterogeneous network structure information. Matrix factorization based data fusion can avoid such projection by integrating multi-type data in a coherent way, but they typically perform linear factorization and cannot mine the nonlinear relationships between molecules, which compromise the accuracy of GPA analysis. Furthermore, most of them can not selectively synergy network topology and node attribution information in a principle way. In this paper, we propose a weighted deep matrix factorization based solution (WDGPA) to predict GPAs by selectively and differentially fusing heterogeneous molecular network and diverse attributes of nodes. WDGPA firstly assigns weights to inter/intra-relational data matrices and attribute data matrices, and performs deep matrix factorization on these matrices of heterogeneous network in a cooperative manner to obtain the nonlinear representations of different nodes. In addition, it performs low-rank representation learning on the attribute data with the shared nonlinear representations. In this way, both the network topology and node attributes are jointly mined to explore the representations of molecules and complex interplays between molecules and phenotypes. WDGPA then uses the representational vectors of gene and phenotype nodes to predict GPAs. Experimental results on maize and human datasets confirm that WDGPA outperforms competitive methods by a large margin under different evaluation protocols.

Dual-Channel Multi-Task CNN for No-Reference Screen Content Image Quality Assessment

Nowadays the problem of image quality assessment (IQA) for screen content images (SCIs) has become a research hotspot as they are ubiquitous in multimedia applications. Although the quality assessment of natural images (NIs) has been continuously developed in the past few decades, few NI-oriented IQA methods can be directly applied on SCIs due to different visual characteristics between them. In this paper, we present a no-reference quality prediction approach considering the content information of SCIs, which is based on dual-channel multi-task convolutional neural network. First, we segment a SCI into small patches and classify them as the textual patches and the pictorial patches. Then, we devise a novel dual-channel convolutional neural network (CNN) to predict the quality of textual patches and pictorial patches. Finally, we propose an effective adaptive weighting strategy for quality score aggregation. The proposed CNN is built on an end-to-end multi-task learning framework, which assists the SCI quality prediction task through the histogram of oriented gradient (HOG) feature prediction task to learn a better mapping between the input patch and its quality score. The adaptive weighting strategy further improves the representation ability of each SCI patch. Experimental results on two largest SCI-oriented databases demonstrate that the proposed method outperforms most of the state-of-the-art no-reference IQA methods and the full-reference IQA methods.

Mind the Gap: Norm-Aware Adaptive Robust Loss for Multivariate Least-Squares Problems

Measurement outliers are unavoidable when solving real-world robot state estimation problems. A large family of robust loss functions (RLFs) exists to mitigate the effects of outliers, including newly developed adaptive methods that do not require parameter tuning. All of these methods assume that residuals follow a zero-mean Gaussian-like distribution. However, in multivariate problems the residual is often defined as a norm, and norms follow a Chi-like distribution with a non-zero mode value. This produces a “mode gap” that impacts the convergence rate and accuracy of existing RLFs. The proposed approach, “Adaptive MB,” accounts for this gap by first estimating the mode of the residuals using an adaptive Chi-like distribution. Applying an existing adaptive weighting scheme only to residuals greater than the mode leads to more robust performance and faster convergence times in two fundamental state estimation problems, point cloud alignment and pose averaging.

Selective weighted multi-scale morphological filter for fault feature extraction of rolling bearings

Morphological filtering shows effectiveness in vibration signal analysis because of its simplicity and efficiency. Considering that different structural elements have different effects on filtering results, a new multi-scale morphological filtering (MMF) method called selective weighted multi-scale morphological filter (SWMMF) is developed for integrating results of different scales based on adaptive weighting strategy. Firstly, four morphological operators (dilation–closing, closing–dilation, erosion–opening and opening–erosion) are integrated into a new combination difference morphological filter to strengthen effect of faulty component extraction. Secondly, this new morphological filter is further extended to multiple scales in order to overcome limitation of single scale filter. Finally, the filtered results of different scales are adaptively combined by using the whale optimization algorithm (WOA)-based selective weighting method. The effectiveness of multi-scale filter and selective weights is proved by comparing with single-scale and average weighting filter on simulation and real-world cases (bearing vibration signals with different defects). The testing results on vibration signals indicate that SWMMF is able to extract effectively defect frequency and the corresponding multiplication frequencies from bearing vibration signals with heavy noise. The testing results illustrate that SWMMF outperforms other representative MMFs (e.g., weighted multi-scale morphological gradient operator (WMMG), weighted multi-scale difference operator (WMDIF), weighted multi-scale average operator (WMAVG)) on impulsive feature extraction of bearing vibrations signals with various defects. Moreover, it is demonstrated that SWMMF has good applicability in bearing fault diagnosis due to setup of adaptive weights and selection of structure element.

An improved whale optimization algorithm based on multilevel threshold image segmentation using the Otsu method

In this paper, an improved multithreshold image segmentation method based on the whale optimization algorithm (RAV-WOA) is proposed, with the between-class variance (Otsu method) as the objective function. The proposed RAV-WOA is able to select satisfactory optimal thresholds while ensuring high efficiency and quality when performing image segmentation on grayscale and color images In the current work, a reverse learning strategy was introduced into the initialization of RAV-WOA populations to improve the quality of the initial population of whales. An adaptive weighting strategy was introduced into the RAV-WOA algorithm, which is influenced by the fitness value and the number of iterations, to balance the global search capability of the algorithm with the local exploitation capability. The proposed RAV-WOA is then applied to the Otsu method to solve the multilevel thresholding image segmentation problem. To better verify the effectiveness of the proposed method, this paper compares the RAV-WOA with some classical heuristic algorithms and performs image segmentation experiments on a set of benchmark images with low and high thresholds. The experimental results show that the convergence speed and convergence accuracy of RAV-WOA are significantly better than other algorithms, and the segmentation results of RAV-WOA in multithreshold image segmentation have better quality and stability than other algorithms.

A reinforced exploration mechanism whale optimization algorithm for continuous optimization problems

Whale optimization algorithm (WOA) is a meta-heuristic algorithm. In process of optimizing single-peak problem and multi-peak problem, it has the problems of weak exploration ability, poor convergence behavior and easy to fall into local optimum (LO). To solve these problems, we analyze WOA from the two aspects of global exploration efficiency and convergence characteristics. Correspondingly, the population redistribution and convergent adaptive weighting strategy are proposed. The population redistribution strategy can improve the space utilization of WOA in search process, enhance its exploration efficiency and prevent it from falling into LO. The purpose of the convergent adaptive weighting strategy is to improve convergence behavior and global exploitation efficiency. Experiment result shows that it can effectively enhance the exploration efficiency of WOA. The paper introduces the two strategies to reconstruct a reinforced exploration mechanism whale optimization algorithm (REM-WOA). In order to verify its performance, 36 well known benchmark functions are selected for experiment from the CEC2017. Compared with other 12 meta-heuristic algorithms, the REM-WOA shows obvious advantages. Compared with 9 WOA and its variant algorithms, the REM-WOA has the best convergence behavior and strong global exploration efficiency. For three real design cases studies, REM-WOA has the best exploration efficiency.

A modified power management algorithm with energy efficiency and GHG emissions limitation for hybrid power ship system

With the integration of energy storage system (ESS), photovoltaic cell (PV) and generator, hybrid power ship system (HPSS), as one of promising technology, is regarded as an advanced method to improve energy efficiency and marine environment quality. However, the computational complexity and non-convexity of energy scheduling in hybrid power ship system make it challenging to obtain the feasible solution. To address this crucial issue, a heuristic optimization algorithm named multi-populations particle swarm optimization (MPPSO) is proposed for economic and feasible energy scheduling. Firstly, a hybrid power ship system, comprising generator, ESS, PV, service loads and propulsion system, is formulated. On this basis, a load shedding coefficient is given for the secure and stable operation of hybrid power ship system under fault model. Then, to achieve energy scheduling, several improvements are proposed to enhance PSO. Considering the problem of premature, a nonlinear adaptive inertial weight strategy is proposed to improve the searching ability. With the fitness value of population, learning coefficients are adjusted in nonlinear so that particle can accurately learn from individual or population position. Further, a modified velocity update formula with the information of historical experience and center particle is proposed to employ the particle information fully. Finally, the effectiveness of MPPSO is illustrated on simulation experiment by three cases.

TCDesc: Learning Topology Consistent Descriptors for Image Matching

The triplet loss is widely used in learning the local descriptors for image matching. However, existing triplet loss-based methods, like HardNet and DSM, employ the point-to-point distance metric, which neglects the neighborhood information of descriptors. Considering the fact that local neighborhood structures of matching descriptors would be similar under the ideal condition, this paper aims to learn the neighborhood topology-consistent descriptors (TCDesc). To this end, we first propose the linear combination weight as the topology weight to depict the neighborhood topology for each descriptor, where the difference between the center descriptor and the linear combination of its neighbors is minimized. For the global comparison, we then define a global topology vector by using the local topology weights. Next, beyond the Euclidean distance, we define a topology distance with the topology vectors to indicate the topological difference between the matching descriptors. Furthermore, we propose an adaptive weighting strategy to jointly minimize the topology distance and Euclidean distance in triplet loss. Experimental results on four widely-used datasets, i.e., UBC PhotoTourism, HPatches, W1BS and Oxford, demonstrate that our method can effectively improve the performance of both HardNet and DSM.

Infrared and Visible Image Fusion Methods for Unmanned Surface Vessels with Marine Applications

Infrared and visible image fusion is a very effective way to solve the degradation of sea images for unmanned surface vessels (USVs). Fused images with more clarity and information are useful for the visual system of USVs, especially in harsh marine environments. In this work, three novel fusion strategies based on adaptive weight, cross bilateral filtering, and guided filtering are proposed to fuse the feature maps that are extracted from source images. First, the infrared and visible cameras equipped on the USV are calibrated using a self-designed calibration board. Then, pairs of images containing water scenes are aligned and used as experimental data. Finally, each proposed strategy is inserted into the neural network as a fusion layer to verify the improvements in quality of water surface images. Compared to existing methods, the proposed method based on adaptive weight provides a higher spatial resolution and, in most cases, less spectral distortion. The experimental results show that the visual quality of fused images obtained based on an adaptive weight strategy is superior compared to other strategies, while also providing an acceptable computational load.

Attention guided discriminative feature learning and adaptive fusion for grading hepatocellular carcinoma with Contrast-enhanced MR.

Multimodality medical imaging has played a significant role in lesion diagnosis and characterization. However, there are remaining challenges in the procedure of multimodality feature fusion based lesion characterization. First, large inter-modality variations make it difficult to harness the complementary information between modalities for better characterization. Subsequently, large intra-class and small inter-class variations due to the heterogeneity of neoplasm makes the classification more challenging. Finally, the relative importance of modalities for the characterization has not been thoroughly investigated, easily resulting in non-optimal fusion performance. In this study, we propose an attention guided discriminative and adaptive fusion (AGDAF) method based on deep learning architecture to address above three problems. Specifically, we first design a novel cross-modal intra- and inter-attention module to focus on learning both the intra-modality relations and inter-modality relations. Then, we introduce a discriminative feature learning loss to reduce the distance of features in the same class and increase the distance of features in different classes of neoplasm in single modalities. Finally, we design an adaptive weighting strategy to increase the contribution of modalities with relatively lower loss values and reduce the impact of modalities with large loss values for the final loss function. Experimental results of grading clinical hepatocellular carcinoma demonstrate that the proposed method significantly outperforms the previously reported multimodality feature fusion methods. In addition, ablation study also demonstrates the effectiveness of the proposed cross-modal intra- and inter-attention module, discriminative module, and adaptive weight adjustment module for multimodality feature fusion in lesion characterization.

Load frequency control based on an improved Chimp optimization algorithm using adaptive weight strategy

PurposeThis study aims to implement an improved version of the Chimp algorithm (IChimp) for load frequency control (LFC) of power system.Design/methodology/approachThis work was adopted by IChimp to optimize proportional integral derivative (PID) controller parameters used for the LFC of a two area interconnected thermal system.FindingsThe supremacy of proposed IChimp tuned PID controller over Chimp optimization, direct synthesis-based PID controller, internal model controller tuned PID controller and recent algorithm based PID controller was demonstrated.Originality/valueIChimp has good convergence and better search ability. The IChimp optimized PID controller is the proposed controlling method, which ensured better performance in terms of converging behaviour, optimizing controller gains and steady-state response.