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The complexity of composite grinding movement, the variety of machining features and available grinding wheels, and the changing working conditions pose a challenge to the rapid programming of safe and efficient composite grinding procedure. The procedure optimisation plays an important role in solving this difficult problem. Therefore, a procedure optimisation method is proposed for composite grinding based on a Digital Twin (DT) system, which takes procedure time as optimisation objective to achieve high efficiency and ensures process rationality and safety by constructing corresponding constraint conditions. Moreover, the actual working conditions mapped into the DT system, such as workpiece parameters, machining requirements, grinding wheel parameters and status, machine tool motion position, and so on, are obtained to update the parameters involved in the optimisation model. And thus, the proposed method has the ability to timely find the optimal procedure under changing working conditions. In addition, a combination algorithm based on genetic algorithm (GA) and dynamic programming is proposed, which greatly reduces the search space of GA and realises the two-class co-optimisation of grinding wheel selection and procedure. Finally, the case study verifies the effectiveness of the proposed method to reduce procedure time and the dynamic response ability to changing working conditions. [ABSTRACT FROM AUTHOR]

Objectives With the improvement of geodetic observation accuracy, higher requirements are put forward for the seismic inversion algorithm.Methods In view of this problem, we successfully develop a novel grey wolf optimizer (GWO) algorithm to invert the seismic source parameters. The weighted distance GWO (wdGWO) algorithm with the strategy of the nonlinear decreasing convergence factor based on the cosine law is proposed to instead that of the original linear decreasing. Subsequently, a combination approach with the improved wdGWO algorithm and the simplex algorithm is configured and the introduction of the latter algorithm is to stabilize the performance of the proposed wdGWO algorithm. Thus, the combination algorithm has better advantages for both convergence and stability. Finally, we achieve synthetic tests to evaluate the performance of the basic wdGWO algorithm, the genetic algorithm and the combination algorithm.Results The simulated experimental results show that the estimation of seismic source parameters via the proposed algorithm is superior to the wdGWO algorithm, which expresses excellent stability and accuracy. On the other hand, the stability of seismic source parameters is validated between the combination algorithm and the genetic algorithm, and we find the superiority of the combination algorithm. Furthermore, the availability of the combination algorithm is tested by the 2014 Napa earthquake and the 2017 Bodrum-Kos earthquake. The results show that the combination algorithm can achieve the inversion precision of genetic algorithm, and exhibit better parameters stability.Conclusions Considering the accuracy and stability of the inversion results is particularly important for the accurate determination of seismic source parameters, the combination algorithm has potential applications in the inversion of seismic source parameters. [ABSTRACT FROM AUTHOR]

In precision marketing for online communities, the existing text-based methods of user positioning cannot position new users rapidly, and they have low positioning efficiency when there is a large number of users. This research proposes a systematic method for the positioning of online community users. In this method, text mining and clustering algorithms are combined to cluster users, and then the user clusters are effectively matched with users' basic attributes through a multinomial logistic regression model. By this means, efficient positioning under the circumstances of a rapid increase in new users and a large number of users can be achieved. Calculation results from a real world example show that this method can effectively solve the problems found in traditional user positioning methods and provides a productive new approach to community user positioning. The study also offers suggestions for user classification management from the perspective of precision marketing. [ABSTRACT FROM AUTHOR]

Space robots have a broad application prospect in the aerospace industry. It is difficult for space robots to keep running for a long time due to limited fuel. In addition, it is impossible to replenish the fuel for space robots at any time due to the unique working environment. Therefore, a proper path is crucial for the effective operation of the space robot. In this paper, we investigate the allocation of exploration tasks and the path planning of space robots jointly. The goal is to minimize the completion latency of exploration tasks. We propose two algorithms named subbranch insertion task allocation (SI-TA) and parallel search task allocation (PS-TA) to solve the problem. We also customize an algorithm named random path planning task allocation (RTA) as the baseline. At last, we implement extensive experiments to demonstrate that proposed algorithms can obtain lower completion latency than RTA. Compared with RTA, the proposed algorithms SI-TA and PS-TA can reduce completion latency by at least 20% and 40%, respectively. Moreover, both algorithms work more stably than RTA.

The frequent start–stop scheme in the operation management of large pumping stations causes several problems, e.g., energy consumption and a potential safety hazard. We therefore established a mathematical model for the optimal operation of a large-scale pumping station considering the start-stop loss of machine. The objectives of the optimization model included electricity charges for a pumping station operation and the start-stop loss of a pumping station. The blade angle and start-stop scheme were taken into account as the double-decision variables, while the target water extraction (equality constraint), start-stop scheme, and power were the constraint conditions. A combination of an orthogonal experiment and a 0-1 integer programming algorithm was proposed to explore the optimal solution of the model. A real case of a single-unit pump station in China was applied to prove the effectiveness of the algorithm. The results showed that the optimization efficiency of the proposed algorithm was 4%–10% higher than that of the genetic algorithm. Additionally, the maximum optimization benefit was obtained when there were fewer than three start-stops during the operation process. The proposed algorithm provided a reference for similar complex nonlinear models. HIGHLIGHTS The start-stop loss of a pumping station was introduced to develop the optimization model.; A combination of an orthogonal experiment and a 0-1 integer programming algorithm was proposed.; The proposed algorithm could deal with the model with equality constraints.;

Oilfields in high or ultra-high water-cut period are of high nonlinearity and heterogeneity, thus complicated in its internal physics mechanism. Corresponding production data is often of small amount due to large timesteps. In addition, numerous mechanism factors that are usually a prerequisite for traditional data-driven prediction methods are to be determined with extra human effort on a well basis, deviating from the intention of oilfield digital transformation. In response to these obstacles, this research proposes an EMD (Empirical Mode Decomposition)-based variable-weight combination model algorithm (EMD-Combined model): It does not require inputs other than its own historical oil production time series, but rather automatically constructs and filters input features via EMD, time-lag reconstruction and Spearman correlation analysis instead. A dynamic Variable-Weight Combination Methods of four machine learning models with different strengths is constructed to carry out prediction. Experimental results show that the EMD-Combined model is more accurate than the solely use of any of the four single models listed above, or models solely exerted with EMD without model-wise combination. In terms of the ease of usage, deployment and “stability” of prediction quality, EMD-Combined model offers a possibility of improvement in real-life practice.

The frequency hopping pattern of the existing frequency hopping communication system is not designed according to the electromagnetic interference environment, resulting in blind anti-jamming. Therefore, to address this problem, a "three-variable" frequency-hopping pattern is proposed, where the frequency, hopping rate, and instantaneous bandwidth of the frequency-hopping signal vary randomly based on the background electromagnetic interference. The decision-making problem of the "three-variable" frequency-hopping pattern is modeled as a Markov decision process (MDP) by constructing the state-action-reward tuple. The designed frequency varies continuously within a small frequency band selected from a pseudo-random sequence to alleviate the problem of dimension explosion in decision-making. At the same time, discrete values for the hopping rate and instantaneous bandwidth are designed. To solve this MDP problem efficiently, a combined deep reinforcement learning algorithm (OC-CDRL) based on optimistic exploration and conservative estimation is proposed, which combines the features of TD3 and D3QN algorithms and designs the corresponding states, actions, and rewards to deal with continuous and discrete action spaces, respectively. To address the problem that the D3QN algorithm tends to fall into local optimal solutions, an optimistic exploration strategy (OES) for action selection is proposed to improve the degree of exploration. Moreover, the loss function is improved by conservatively estimating state–action pairs outside the experience replay buffer, reducing the overestimation of the optimistic action-value function and increasing the stability and convergence of the algorithm. Comparative simulation results of the algorithms in different electromagnetic interference environments show that the OC-CDRL algorithm effectively avoids most regions with higher interference and has better adaptability and anti-jamming capability. [ABSTRACT FROM AUTHOR]

Objective: Pulmonary function parameters play a pivotal role in the assessment of respiratory diseases. However, the accuracy of the existing methods for the prediction of pulmonary function parameters is low. This study proposes a combination algorithm to improve the accuracy of pulmonary function parameter prediction. Methods: We first established a system to collect volumetric capnography and then processed the data with a combination algorithm to predict pulmonary function parameters. The algorithm consists of three main parts: a medical feature regression structure consisting of support vector machines (SVM) and extreme gradient boosting (XGBoost) algorithms, a sequence feature regression structure consisting of one-dimensional convolutional neural network (1D-CNN), and an error correction structure using improved K-nearest neighbor (KNN) algorithm. Results: The root mean square error (RMSE) of the pulmonary function parameters predicted by the combination algorithm was less than 0.39L and the R2 was found to be greater than 0.85 through a ten-fold cross-validation experiment. Conclusion: Compared with the existing methods for predicting pulmonary function parameters, the present algorithm can achieve a higher accuracy rate. At the same time, this algorithm uses specific processing structures for different features, and the interpretability of the algorithm is ensured while mining the feature depth information. [ABSTRACT FROM AUTHOR]

This paper provides a detailed overview of active impulsive noise control (AINC) technology of α-stable distribution noise. The second-order moment of the non-Gaussian impulsive noise does not exist, so the adaptive algorithms based on second-order statistics would lead to poor noise reduction performance. On account of this defect, a series of AINC algorithms have been proposed over the past 25 years. In this paper, we briefly classified the AINC algorithms into the classic algorithms and the expanded algorithms depending on different criteria, and both the classic AINC algorithms and the expanded AINC algorithms are further divided. Afterward, we introduced the principles and characteristics of different AINC algorithms and listed the weights update formulas of those algorithms. Finally, the computational complexity comparisons of common AINC algorithms and further recommendations are given, and a brief review about the experimental testing of AINC systems is provided in order to assist related scholars to carry out AINC researches more conveniently. [ABSTRACT FROM AUTHOR]

This paper proposes a novel algorithm to combine two or more techniques of maximum power point tracking (MPPT) to increase the output dc power and the efficiency of photovoltaic (PV) arrays. Different MPPT methods have dissimilar responses for the same environmental circumstances. The combination of multiple methods has the advantage of ever acquiring maximum power as the environmental conditions change. The proposed algorithm is used to enhance the selection of the most suitable duty cycle of the combined methods, based on the power-voltage characteristics, to get the best tracking response. Multiple classical and/or artificial intelligence (AI)-based MPPT methods can be combined based on this selection algorithm. To demonstrate its effectiveness, two examples are illustrated. The first one is the combination of two classical MPPT methods, which are the incremental conductance (INC) and the perturb-and-observe (P&O). The second example is to combine two AI-based MPPT, which are the artificial neural network (ANN) and the fuzzy logic control (FLC). The simulation results of the application of the proposed algorithm to a grid-connected PV system model justify its capability to acquire better static and dynamic responses than the responses of individual MPPT methods. [ABSTRACT FROM AUTHOR]

Objective: Pulmonary function parameters play a pivotal role in the assessment of respiratory diseases. However, the accuracy of the existing methods for the prediction of pulmonary function parameters is low. This study proposes a combination algorithm to improve the accuracy of pulmonary function parameter prediction. Methods: We first established a system to collect volumetric capnography and then processed the data with a combination algorithm to predict pulmonary function parameters. The algorithm consists of three main parts: a medical feature regression structure consisting of support vector machines (SVM) and extreme gradient boosting (XGBoost) algorithms, a sequence feature regression structure consisting of one-dimensional convolutional neural network (1D-CNN), and an error correction structure using improved K-nearest neighbor (KNN) algorithm. Results: The root mean square error (RMSE) of the pulmonary function parameters predicted by the combination algorithm was less than 0.39L and the R2 was found to be greater than 0.85 through a ten-fold cross-validation experiment. Conclusion: Compared with the existing methods for predicting pulmonary function parameters, the present algorithm can achieve a higher accuracy rate. At the same time, this algorithm uses specific processing structures for different features, and the interpretability of the algorithm is ensured while mining the feature depth information.

Aim: The Enhanced Liver Fibrosis (ELF) test comprises a logarithmic algorithm combining three serum markers of hepatic extracellular matrix metabolism. We aimed to evaluate the performance of ELF for the diagnosis of liver fibrosis and to compare it with that of liver stiffness measurement (LSM) by FibroScan in non‐alcoholic fatty liver disease. Methods: ELF cut‐off values for the diagnosis of advanced fibrosis were obtained using receiver operating characteristic analysis in patients with biopsy‐confirmed non‐alcoholic fatty liver disease (training set; n = 200). Diagnostic performance was analyzed in the training set and in a validation set (n = 166), and compared with that of LSM in the FibroScan cohort (n = 224). Results: The area under receiver operating characteristic curve was 0.81 for the diagnosis of advanced fibrosis, and the ELF cut‐off values were 9.34 with 90.4% sensitivity and 10.83 with 90.6% specificity in the training set, and 89.8% sensitivity and 85.5% specificity in the validation set. There was no significant difference in the area under the receiver operating characteristic curve between ELF and LSM (0.812 and 0.839). A combination of ELF (cut‐off 10.83) and LSM (cut‐off 11.45) increased the specificity to 97.9% and the positive predictive value, versus ELF alone. Sequential use of the Fibrosis‐4 index (cut‐off 2.67) and ELF (cut‐off 9.34) increased the sensitivity to 95.9%. Conclusions: ELF can identify advanced liver fibrosis in non‐alcoholic fatty liver disease, and its diagnostic accuracy is comparable to that of FibroScan. According to the clinical setting, combinations or sequential procedures using other non‐invasive tests complement the diagnostic performance of ELF for the identification of advanced fibrosis. [ABSTRACT FROM AUTHOR]

Intensive Care Unit (ICU) Rooms usually have several detectors attached to each patient providing intensive care, and several processors control and interpret. If the processor detects an abnormality, the medical professional office will be alerted. Nevertheless, many patients with heart disease are concerned with day-to-day behaviors such as hard work, battle, exercise, shock, fight, and war. Become due to clinical depression and erectile impotence this induces anxiety and fear. The boundaries of your heart muscle and coronary strength are unclear. They want a warning that is quick and accurate before they lose control. We develop signal recognition for an algorithm that is very fast and accurate. It helps alert patients to avoid the operation of risk. Nevertheless, it is able to transfer information from the heartbeat network to the doctor's guidance system. The research would analyze the 200 signals from multiple ECG signals. Integration Component Diagnosis (ICD) is capable of extraordinary reliability of identification than the 17.10-41.93 average percentage of the Automata Matching Process which takes less time than the other 29.37 average percentage process and can alert within 12 seconds. It cannot be identified in a pattern other than without the detection of an ECG signal. In the experimental, the signal is used to distinguish 20 ECG signal patterns. [ABSTRACT FROM AUTHOR]

We consider a non-overlapping domain decomposition approach to approximate the solution of elliptic boundary value problems with high contrast in their coefficients. We propose a method such that initially local solutions subject to Robin boundary conditions in each primal subdomain are constructed with (locally conservative) finite element or finite volume methods. Then, a novel approach is introduced to obtain a (discontinuous) global solution in terms of linear combination of the local subdomain solutions. In the proposed algorithm the computation of local solutions for unions of subdomains are localized at nearest-neighbor subdomain boundaries, thus avoiding the solution of global interface problems. We remove discontinuities in a smoothing step that is defined on a staggered grid or dual subdomains. The resulting algorithm is naturally parallelizable and can be employed as a parallel direct solver, offering great potential for the numerical solution of large problems. In fact, subdomains can be considered small enough to fit well in GPUs and the proposed procedure can handle adaptive (in space) simulations effectively. Numerical simulations are presented and discussed. We demonstrate the effectiveness of the proposed approach with two and three dimensional high contrast and channelized coefficients, that lead to challenging approximation problems. The new procedure, although designed for parallel processing, is also of value for serial calculations. [ABSTRACT FROM AUTHOR]

The factors influencing residents health have become complex and intertwined with the development of economy and society. Traditional research with a single factor on health will not provide an accurate picture of the situation. This paper collects data on economic, environmental and social factors to estimate their impact on regional health. Considering the data is multi-source and complex, this paper proposes a combined feature importance algorithm, which weighted the feature importance of RF, XGB and SOIL. The algorithm does not depend on the data and adaptively approximates the true results. The results show that economic factors have a significant and direct impact on health, environmental factors have a lag correlation with health level, and social factors have a more complicated effect on health. Finally, we provide policy suggestions for health on economic, environmental, and social development. [ABSTRACT FROM AUTHOR]

The failure probability function (FPF) expresses the probability of failure as a function of the distribution parameters associated with the random variables of a reliability problem. Knowledge on this FPF is of much relevance for reliability sensitivity analysis and reliability-based design optimisation. However, its calculation is usually a challenging task. Therefore, this paper presents an efficient approach for estimating the FPF based on an adaptive strategy and a combination algorithm. The proposed approach involves three basic elements: (1) a Weighted Importance Sampling approach, which allows determining local FPF estimates; (2) an adaptive strategy for determining at which realisations of the distribution parameters it is necessary to perform local FPF estimation; and (3) an optimal combination algorithm, which allows to aggregate local FPF estimations together to form a global estimate of the FPF. Test and practical examples are presented to demonstrate the efficiency and feasibility of the proposed approach. • A global approach for failure probability function estimation is proposed. • It utilises weighted importance sampling to efficiently obtain local estimators. • It includes an adaptive active strategy to guide the local estimations. • It adopts an optimal combination algorithm to combine the local FPF estimators. [ABSTRACT FROM AUTHOR]