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The classification analysis of incomplete and imbalanced data is still a challenging task since these issues could negatively impact the training of classifiers, which were also found in our study on the physical fitness assessments of patients. And in fields such as healthcare, there are higher requirements for the accuracy of the generated imputation values. To train a high-performance classifier and pursue high accuracy, we attempted to resolve any potential negative impact by using a novel algorithmic approach based on the combination of multivariate imputation by chained equations and the ensemble learning method (MICEEN), which can solve the two problems simultaneously. We used multivariate imputation by chained equations to generate more accurate imputation values for the training set passed to ensemble learning to build a predictor. On the other hand, missing values were introduced into minority classes and used them to generate new samples belonging to the minority classes in order to balance the distribution of classes. On real-world datasets, we perform extensive experiments to assess our method and compare it to other state-of-the-art approaches. The advantages of the proposed method are demonstrated by experimental results for the benchmark datasets and self-collected datasets of physical fitness assessment of tumor patients with varying missing rates.

Background and Objective: Conflict may happen when more than one classifier is used to perform prediction or classification. The recognition model error leads to conflicting evidence. These conflicts can cause decision errors in a baby cry recognition and further decrease its recognition accuracy. Thus, the objective of this study is to propose a method that can effectively minimize the conflict among deep learning models and improve the accuracy of baby cry recognition., Methods: An improved Dempster-Shafer evidence theory (DST) based on Wasserstein distance and Deng entropy was proposed to reduce the conflicts among the results by combining the credibility degree between evidence and the uncertainty degree of evidence. To validate the effectiveness of the proposed method, examples were analyzed, and applied in a baby cry recognition. The Whale optimization algorithm-Variational mode decomposition (WOA-VMD) was used to optimally decompose the baby cry signals. The deep features of decomposed components were extracted using the VGG16 model. Long Short-Term Memory (LSTM) models were used to classify baby cry signals. An improved DST decision method was used to obtain the decision fusion., Results: The proposed fusion method achieves an accuracy of 90.15% in classifying three types of baby cry. Improvement between 2.90% and 4.98% was obtained over the existing DST fusion methods. Recognition accuracy was improved by between 5.79% and 11.53% when compared to the latest methods used in baby cry recognition., Conclusion: The proposed method optimally decomposes baby cry signal, effectively reduces the conflict among the results of deep learning models and improves the accuracy of baby cry recognition., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Medical image quality is highly relative to clinical diagnosis and treatment, leading to a popular research topic of medical image denoising. Image denoising based on deep learning methods has attracted considerable attention owing to its excellent ability of automatic feature extraction. Most existing methods for medical image denoising adapted to certain types of noise have difficulties in handling spatially varying noise; meanwhile, image detail losses and structure changes occurred in the denoised image. Considering image context perception and structure preserving, this paper firstly introduces a medical image denoising method based on conditional generative adversarial network (CGAN) for various unknown noises. In the proposed architecture, noise image with the corresponding gradient image is merged as network conditional information, which enhances the contrast between the original signal and noise according to the structural specificity. A novel generator with residual dense blocks makes full use of the relationship among convolutional layers to explore image context. Furthermore, the reconstruction loss and WGAN loss are combined as the objective loss function to ensure the consistency of denoised image and real image. A series of experiments for medical image denoising are conducted with the denoising results of PSNR = 33.2642 and SSIM = 0.9206 on JSRT datasets and PSNR = 35.1086 and SSIM = 0.9328 on LIDC datasets. Compared with the state-of-the-art methods, the superior performance of the proposed method is outstanding., Competing Interests: The authors declare that there is no conflict of interest regarding the publication of this paper., (Copyright © 2021 Yuqin Li et al.)

Knowledge of protein-ATP interaction can help for protein functional annotation and drug discovery. Accurately identifying protein-ATP binding residues is an important but challenging task to gain the knowledge of protein-ATP interactions, especially for the case where only protein sequence information is given. In this study, we propose a novel method, named DeepATPseq, to predict protein-ATP binding residues without using any information about protein three-dimension structure or sequence-derived structural information. In DeepATPseq, the HHBlits-generated position-specific frequency matrix (PSFM) profile is first employed to extract the feature information of each residue. Then, for each residue, the PSFM-based feature is fed into two prediction models, which are generated by the algorithms of deep convolutional neural network (DCNN) and support vector machine (SVM) separately. The final ATP-binding probability of the corresponding residue is calculated by the weighted sum of the outputted values of DCNN-based and SVM-based models. Experimental results on the independent validation data set demonstrate that DeepATPseq could achieve an accuracy of 77.71%, covering 57.42% of all ATP-binding residues, while achieving a Matthew's correlation coefficient value (0.655) that is significantly higher than that of existing sequence-based methods and comparable to that of the state-of-the-art structure-based predictors. Detailed data analysis show that the major advantage of DeepATPseq lies at the combination utilization of DCNN and SVM that helps dig out more discriminative information from the PSFM profiles. The online server and standalone package of DeepATPseq are freely available at: https://jun-csbio.github.io/DeepATPseq/for academic use., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Purpose: It is still not conclusive which four-dimensional computed tomography (4DCT)-based ventilation imaging algorithm is most accurate and efficient. In this study, we proposed a simplified algorithm (VIAAVG) which only requires the average computed tomography (AVG CT) as input, and quantitatively compared its accuracy and efficiency with three other popular algorithms., Material and Methods: Fifty patients with lung or esophageal cancer who underwent radiotherapy were enrolled. Single photon emission computed tomography (SPECT) ventilation images (VI-SPECT) and 4DCT were acquired 1-3 days before the first treatment session. The end of exhalation and the end of inhalation CT were registered to derive deformable vector field (DVF) using MIMvista. 4DCT-based ventilation images (CTVI) were first calculated respectively by means of four algorithms (VIAJAC, VIAHU, VIAPRO and VIAAVG). The computation times were compared using paired t-test. The corresponding CTVIs (CTVIJAC, CTVIHU, CTVIPRO and CTVIAVG) and VI-SPECT were segmented into three equal sub-volumes (high, medium and low function lung, respectively) after smoothing and normalization. The Dice Similarity Coefficients (DSCs) were calculated for each sub-volume between each CTVI and VI-SPECT. The average DSCs for high, medium and low function lung in different CTVIs for each patient were compared using paired t-test., Results: The mean DSCs for CTVIJAC, CTVIHU, CTVIPRO and CTVIAVG were 0.3255, 0.4465, 0.5865 and 0.5958, respectively. The average computation times for CTVIJAC, CTVIHU, CTVIPRO and CTVIAVG were 18.3 s, 24.2 s, 144.8 s and 15.0 s., Conclusion: VIAAVG is available for clinical use because of its high accuracy, improved efficiency and less input requirement compared to the other algorithms., (Copyright © 2019 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.)

Background: The recent advancement in array CGH (aCGH) research has significantly improved tumor identification using DNA copy number data. A number of unsupervised learning methods have been proposed for clustering aCGH samples. Two of the major challenges for developing aCGH sample clustering are the high spatial correlation between aCGH markers and the low computing efficiency. A mixture hidden Markov model based algorithm was developed to address these two challenges., Results: The hidden Markov model (HMM) was used to model the spatial correlation between aCGH markers. A fast clustering algorithm was implemented and real data analysis on glioma aCGH data has shown that it converges to the optimal cluster rapidly and the computation time is proportional to the sample size. Simulation results showed that this HMM based clustering (HMMC) method has a substantially lower error rate than NMF clustering. The HMMC results for glioma data were significantly associated with clinical outcomes., Conclusions: We have developed a fast clustering algorithm to identify tumor subtypes based on DNA copy number aberrations. The performance of the proposed HMMC method has been evaluated using both simulated and real aCGH data. The software for HMMC in both R and C++ is available in ND INBRE website http://ndinbre.org/programs/bioinformatics.php.

Background: Cancer is a heterogeneous disease caused by genomic aberrations and characterized by significant variability in clinical outcomes and response to therapies. Several subtypes of common cancers have been identified based on alterations of individual cancer genes, such as HER2, EGFR, and others. However, cancer is a complex disease driven by the interaction of multiple genes, so the copy number status of individual genes is not sufficient to define cancer subtypes and predict responses to treatments. A classification based on genome-wide copy number patterns would be better suited for this purpose., Method: To develop a more comprehensive cancer taxonomy based on genome-wide patterns of copy number abnormalities, we designed an unsupervised classification algorithm that identifies genomic subgroups of tumors. This algorithm is based on a modified genomic Non-negative Matrix Factorization (gNMF) algorithm and includes several additional components, namely a pilot hierarchical clustering procedure to determine the number of clusters, a multiple random initiation scheme, a new stop criterion for the core gNMF, as well as a 10-fold cross-validation stability test for quality assessment., Result: We applied our algorithm to identify genomic subgroups of three major cancer types: non-small cell lung carcinoma (NSCLC), colorectal cancer (CRC), and malignant melanoma. High-density SNP array datasets for patient tumors and established cell lines were used to define genomic subclasses of the diseases and identify cell lines representative of each genomic subtype. The algorithm was compared with several traditional clustering methods and showed improved performance. To validate our genomic taxonomy of NSCLC, we correlated the genomic classification with disease outcomes. Overall survival time and time to recurrence were shown to differ significantly between the genomic subtypes., Conclusions: We developed an algorithm for cancer classification based on genome-wide patterns of copy number aberrations and demonstrated its superiority to existing clustering methods. The algorithm was applied to define genomic subgroups of three cancer types and identify cell lines representative of these subgroups. Our data enabled the assembly of representative cell line panels for testing drug candidates.

In wireless networks, multiple monitoring nodes are used to collect users' transmission data in real time, which can be used for fault diagnosis and analytical feedback of the wireless network. Due to the limited number of monitoring nodes, key issues include how to optimize and schedule the channel resources of each node to cover more users, obtain more network data, and maximize the quality of network monitoring. In this paper, a channel allocation algorithm based on swarm intelligence—"discrete bacterial foraging optimization"—is proposed based on the classic bacterial foraging optimization algorithm. The position of each dimension in the iterative process is discretized to binary 0 or 1 to encode and express the channel allocation problem of wireless monitoring networks, and the channel allocation scheme is optimized by location updates guided by bacterial foraging. Many simulation and practical experiments have proved the effectiveness of the algorithm, and it also has low complexity and provable convergence. Compared with similar algorithms, this algorithm improves monitoring quality by 1.428% while boosting speed by up to 32.602%. The algorithm has lower complexity, higher performance, and can converge to the optimal solution at a faster rate. [ABSTRACT FROM AUTHOR]

In this paper, we consider the exact continuous relaxation model of ℓ 0 regularization problem, which was given by Bian and Chen (SIAM J Numer Anal 58:858–883, 2020) and propose a smoothing proximal gradient algorithm with extrapolation (SPGE) for this kind of problems. Under a general choice of extrapolation parameter, it is proved that all the accumulation points have a common support set, and the ability of the SPGE algorithm to identify the zero entries of the accumulation point within finite iterations is available. We show that any accumulation point of the sequence generated by the SPGE algorithm is a lifted stationary point of the relaxation model. Moreover, a convergence rate concerning proximal residual is established. Finally, we conduct three numerical experiments to illustrate the efficiency of the SPGE algorithm compared with the smoothing proximal gradient (SPG) algorithm proposed by Bian and Chen (2020). [ABSTRACT FROM AUTHOR]

Actual evapotranspiration (ET) is the key link between water and energy cycles. However, accurate evaporation estimation in alpine barren areas remains understudied. In this study, we aimed to improve the satellite-driven Process-based Land Surface ET/Heat fluxes algorithm (P-LSH) for better satellite retrieval of ET on the Tibetan Plateau by introducing two effective soil moisture constraint schemes in which normalized surface soil moisture and the ratio of cumulative antecedent precipitation to cumulative antecedent equilibrium evaporation are used to represent soil water stress, respectively, based on the intercomparison and knowledge-learning of the existing schemes. We first conducted intercomparison of six existing soil evaporation algorithms and sorted out the two most effective soil moisture constraint schemes. We then introduced the modified versions of the two constraint schemes into the P-LSH algorithm and further optimized the parameters using the differential evolution method. As a result, it formed two improved P-LSH algorithms. We systematically assessed the performances of the two improved P-LSH algorithms and six existing remote sensing ET retrieval algorithms on two barren-dominated basins of the Tibetan Plateau using reconstructed ET estimates derived from the terrestrial water balance method as a benchmark. The two moisture constraint schemes largely improved the performance of the P-LSH algorithm and showed better performance in both basins (root mean square error (RMSE) = 7.36 and 7.76 mm per month; R2=0.86 and 0.87), resulting in a higher simulation accuracy than all six existing algorithms. We used five soil moisture datasets and five precipitation datasets to further investigate the impact of moisture constraint uncertainty on the improved P-LSH algorithm. The ET estimates of the improved P-LSH algorithm, driven by the GLDAS_Noah soil moisture, performed best compared with those driven by other soil moisture and precipitation datasets, while ET estimates driven by various precipitation datasets generally showed a high and stable accuracy. These results suggest that high-quality soil moisture can optimally express moisture supply to ET, and that more accessible precipitation data can serve as a substitute for soil moisture as an indicator of moisture status for its robust performance in barren evaporation. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) have broad application prospects in various industries, and node localization technology is the foundation of WSN applications. Recently, many range-free node localization algorithms have been proposed, but most of them suffer from low accuracy. In order to improve the localization accuracy, in this paper we proposed the node localization algorithm based on multi-hop distance vector and error correction (MDV-EC). In terms of distance estimation, firstly the MDV-EC algorithm calculates the neighbor distance according to node neighbor relationship, then estimates the distance between unknown node and anchor node in multi-hop manner, and finally calibrates the distance refer to distance correction coefficient. In view of similarity of localization errors of nodes in similar regions, an error correction scheme is also investigated, which corrects the node initial estimated locations of nodes refer to the localization error vector of nearby anchor node. Simulation results show that our proposed MDV-EC has better performance than the other two algorithms in terms of node localization accuracy, and the error correction scheme can effectively reduce the localization errors. [ABSTRACT FROM AUTHOR]

By exploiting the simple random sampling and the greedy technique for capturing large residual entries, we put forth two multi-step greedy Kaczmarz algorithms for solving large linear systems. Both algorithms employ simple random sampling ab initio yet differ in the rules for choosing the working row. The new algorithms are proved to be convergent when the linear system is consistent. Some numerical experiments are carried out to verify the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Accurate location information is essential for the effective deployment of Wireless Sensor Networks (WSNs) applications. Recently, researchers have developed numerous localization algorithms, with multi-hop methods being particularly attractive due to their cost-effectiveness and robustness, but their accuracy is still unsatisfactory. For better localization accuracy, this paper presented a distance correction range-free localization algorithm (DCRA). Firstly, the proposed algorithm estimates the distance between neighboring nodes based on neighbor connectivity to improve the accuracy of single-hop distance estimation. Secondly, during the multi-hop distance estimation phase, path similarity metrics are employed to ascertain the optimal path correction coefficients, thereby minimizing multi-hop distance estimation errors. Finally, based on the estimated distances, the node localization is converted into an optimization problem, which is then solved using an enhanced particle swarm algorithm to obtain the node coordinates. Simulation results show that the distance estimation accuracy of the DCRA algorithm in different node density scenarios is improved by more than 31 % and 12 % compared to the DV-Hop and DV-RND algorithms, respectively; and in terms of localization accuracy the DCRA algorithm is improved by more than 58 % and 35 % compared to the DV-Hop and DV-RND algorithms, respectively. [ABSTRACT FROM AUTHOR]

Biclustering algorithm is an effective tool for processing gene expression datasets. There are two kinds of data matrices, binary data and non-binary data, which are processed by biclustering method. A binary matrix is usually converted from pre-processed gene expression data, which can effectively reduce the interference from noise and abnormal data, and is then processed using a biclustering algorithm. However, biclustering algorithms of dealing with binary data have a poor balance between running time and performance. In this paper, we propose a new biclustering algorithm called the Adjacency Difference Matrix Binary Biclustering algorithm (AMBB) for dealing with binary data to address the drawback. The AMBB algorithm constructs the adjacency matrix based on the adjacency difference values, and the submatrix obtained by continuously updating the adjacency difference matrix is called a bicluster. The adjacency matrix allows for clustering of gene that undergo similar reactions under different conditions into clusters, which is important for subsequent genes analysis. Meanwhile, experiments on synthetic and real datasets visually demonstrate that the AMBB algorithm has high practicability. [ABSTRACT FROM AUTHOR]

Collision prevention is critical for navigational safety at sea, which has developed rapidly in the past decade and attracted a lot of attention. In this article, an improved velocity obstacle (IVO) algorithm for intelligent collision avoidance of ocean-going ships is proposed in various operating conditions, taking into count both a ship's manoeuvrability and Convention on the International Regulations for Preventing Collisions at Sea (COLREGs). An integrated model combines a three-degree-of-freedom manoeuvring model with ship propeller characteristics to provide a precise prediction of ships in various manoeuvring circumstances. In the given case, what is different to present studies, this improved algorithm allows for decision-making in two ways: altering course and changing speed. The proposed technique is demonstrated in a variety of scenarios through simulation. The findings reveal that collision-avoidance decision-making can intelligently avoid collisions with the target ships (TSs) in multi-ship situations. [ABSTRACT FROM AUTHOR]

Nowadays, the Earth observation based on the Moon has attracted attention from many researchers and relevant departments. There also exists a considerable amount of interest in monitoring large-scale and long-term geoscience phenomena using the Moon-based SAR (MBS). However, the Earth's observation from MBS has long transmission time, and the relative motion of MBS with its Earth ground target (EGT) is much different to the space-borne SAR, the above reasons indicate that the traditional stop-and-go model is no longer suitable for MBS in frequency domain imaging. Here a dual-path separate calculation method for single pulse is presented in this paper for a better match of a real scenario, and then the slant range is fitted to a high-order polynomial series. The MBS's location, the synthetic aperture time and other factors have effects on length of the dual- path and fit bias. Without thoroughly investigated phase de-correlation processing in frequency domain, and to avoid computational costs in traditional back-projection (BP) algorithm, the paper first proposes a fast back-projection (FBP) algorithm in time domain for MBS, a platform that has long transmission time and long synthetic aperture time. In the FBP algorithm, the original method, that projected echo on all pixels in the imaging area, is changed to projected echo on a centerline instead. A suitable interpolation for points on the centerline is adopted to reduce the projected error; the synthetic aperture length and imaging area are also divided into subsections to reduce computation cost. The formula indicates that the range error could be control once the product of sub-imaging area's length and sub-aperture's length stay constant. Through the theoretical analysis, the detailed range difference mainly at apogee, perigee, ascending, and descending nodes indicate the necessity to separately calculate the dual-path for MBS's single pulse transmission in Earth-Moon motion, with real ephemeris been adopted; then, the high-order polynomial fitting will better describe the motion trajectory. Lastly, the FBP algorithm proposed is simulated in a specific scenario under acceptable resolution, and the result shows its feasibility for image compression. [ABSTRACT FROM AUTHOR]

With the rapid development of the Internet, more and more people pay attention to wireless sensor networks. Localization technology plays a vital role in wireless sensor networks. To reduce the localization error and improve the localization stability, a gray wolf localization algorithm based on beetle antennae search (BASGWO) is proposed, transforming the node localization problem into function constrained optimization. Firstly, the excellent point set method is used to initialize the gray wolf population, improving the richness. Secondly, the beetle antennae search mechanism with good global search ability is introduced into the gray wolf algorithm to avoid the gray wolf algorithm falling into local optimization in the late iteration. The gray wolf is the beetle antennae in search of excellence. The location of the gray wolf was updated according to the fitness value of the gray wolf and beetle antennae. The optimal global solution can be obtained, and then the unknown node coordinates can be obtained. The improved gray wolf algorithm improves the localization accuracy by 24% through simulation comparison and reduces the localization error fluctuation by 23%. Compared with the classical localization algorithm of WSN, the solution ability and localization accuracy of the BASGWO algorithm are improved. [ABSTRACT FROM AUTHOR]

An adaptive clamping method (SGD-MS) based on the radius of curvature is designed to alleviate the local optimal oscillation problem in deep neural network, which combines the radius of curvature of the objective function and the gradient descent of the optimizer. The radius of curvature is considered as the threshold to separate the momentum term or the future gradient moving average term adaptively. In addition, on this basis, we propose an accelerated version (SGD-MA), which further improves the convergence speed by using the method of aggregated momentum. Experimental results on several datasets show that the proposed methods effectively alleviate the local optimal oscillation problem and greatly improve the convergence speed and accuracy. A novel parameter updating algorithm is also provided in this paper for deep neural network. [ABSTRACT FROM AUTHOR]

In order to monitor the rail base, the dispersion characteristics and propagation properties of the guided wave are studied. Firstly, two modes named as Modes V1 and V2 are selected by the semianalytical finite element method (SAFE). The region at the bottom edge can be monitored by Mode V1, while the junction of the base edge and the flange can be detected by Mode V2. Then, the characteristics in the propagation process are analyzed using the finite element method (FEM). The two modes can be separated about 0.6 ms after they are excited. Thirdly, a wave attenuation algorithm based on mean is proposed to quantify the wave attenuation. Both waves can have weak attenuation and be detected within 5 m. Finally, a mode-identified experiment is performed to validate the aforementioned analysis. And a defect detection experiment is performed to demonstrate the excellent monitoring characteristics using Mode V2. These results can be used to monitor the rail base in practice engineering. [ABSTRACT FROM AUTHOR]

This paper investigates the attitude and position tracking control problem of the Lead–Wing close formation system with unknown multiplicative actuator faults. In close formation flight, the movement of the Wing unmanned aerial vehicle (UAV) is influenced by the vortex effects of the neighboring Lead UAV. This situation requires a modeling of the aerodynamic coupling vortex effects and linearization on the basis of optimal close formation geometry. Adaptive actuator fault parameters are identified by merging the improved unknown input observers, and adaptive laws with projection functions are presented for actuator fault tracking. The sufficient condition corresponding to the existence of unknown input observers is given. Then, an adaptive fault-tolerant H-infinity output feedback control scheme is developed to guarantee the asymptotic stability, H-infinity closed-loop system performance, and attitude and position tracking properties while the Lead UAV is being maneuvered. Simulation results of the Lead–Wing close formation flight validate the efficiency of the proposed fault-tolerant control algorithm. [ABSTRACT FROM AUTHOR]

Summary: This study investigates the consensus problem of multiple 3‐DOF laboratory helicopters modeled with system nonlinearity, uncertainty, and actuator faults. The simultaneous additives and partial loss of effectiveness actuator faults are considered. The fault detection hierarchy, the healthy control hierarchy, and the fault‐tolerant control hierarchy constitute the hierarchical structure of multihelicopter systems. The fault‐tolerant consensus protocol is switched from the healthy control hierarchy once the actuator fault is detected in the fault detection hierarchy. An adaptive fault‐tolerant consensus control scheme is developed on the basis of the instantaneous and integral estimations to compensate simultaneously for system nonlinearity, uncertainty, and actuator faults and to guarantee the mean‐square consensus in a completely distributed form. Simulation results are presented to validate the effectiveness of the proposed adaptive fault‐tolerant consensus control algorithm. [ABSTRACT FROM AUTHOR]

In this paper we aim to solve the problems of laser cladding remanufacturing of damaged complex components. First, a model of a workpiece to be repaired was pre-sliced finely by using the minimum layer thickness. On this basis, an algorithm for calculating step width was proposed. When a workpiece side angle meets |a − 180∣ not more than 40°, the distance from the projection point to the left or right lines of the closest contour point is calculated, and the distance closest to that of the previous contour point as the step width of the current point is noted. When a workpiece side angle meets |a − 180∣ more than 40°, the corner feature of the contour point is obvious, and the distance from the projection point to the corresponding point is taken directly as the step width of the current point. After which, the slices with the minimum layer thickness are merged layer by layer, according to the maximum step width required by the process and the maximum acceptable layer thickness. Finally, the algorithm was verified by using a laser repair example. The method of calculating the step width accurately reflected variations of the curvatures between adjacent layers, or the whole contour profile. Also, the adaptive slicing algorithm balanced the build time and repair qualities, further, which addressed the loss of curvature mutation in conventional stepwise uniform refinement and improved the slicing efficiency. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we propose an adaptive multi-objective evolutionary algorithm for multi-objective optimization problems (MOPs). In the algorithm, a clustering approach is employed to learn the Pareto optimal set's manifold structure adaptively, in accordance with the regularity property of MOPs, along the evolution. An advanced sampling strategy is developed for the generation of promising offspring from the learned structure. To generate trial solution, each non-dominated solution at present generation is Gaussian-perturbed using the variance-covariance matrix within its cluster. The other new features include 1) an adaptive hybridization of the developed sampling strategy with a differential evolution (DE) operator which aims to combine local and global information; 2) a reusing scheme which is to reduce the computational cost on modeling (clustering); and 3) an adaptive strength Pareto based approach which is to adaptively determine the contribution of the developed sampling strategy and the DE operator for balancing exploration and exploitation. The developed algorithm was empirically compared with four well-known MOEAs on a number of test instances with complex Pareto optimal set structure and complicated Pareto fronts. Experimental results suggest that it outperforms the compared algorithms on these test instances in terms of two commonly-used measure metrics. The effectiveness of the developed sampling strategy, the reusing scheme, the hybrid strategy, and the adaptive strategy was also empirically validated. [ABSTRACT FROM AUTHOR]

Quantum proxy signature(QPS) is one of the most important topics in quantum signature. In this paper, we propose a new and general model of QPS based on genuine entangled Nj-qubit (3 ≤ Nj ≤ 6,Nj ∈ N∗,j = 1,2,3,4.) states. In our model, only the teleportation of multiparty entangled states and quantum one-time pad(QOTP) encryption algorithm have been applied to ensure the security. We hope our results will be helpful to the research of quantum signature in future. [ABSTRACT FROM AUTHOR]

In this study, based on the finite-time unknown input observer (FT-UIO), the problem of distributed fault diagnosis (FD) for multi-agent systems (MAS) with actuator faults and disturbances is investigated. Firstly, using the communication topology and local information, the global multi-agent system dynamics are represented with respect to a certain agent. Then, in order to construct a FT-UIO at the selected agent, the system will be decomposed into three subsystems after coordinate transformation. Furthermore, constituted by two distinct observers, the FT-UIO is designed to converge in a pre-assigned finite time, which is chosen as the time delay of the observers. With the state estimation obtained from the FT-UIO in the selected agent, a FD algorithm is also proposed to diagnose the faults occurred in its neighbor agents. Finally, simulation results are presented to illustrate the effectiveness and advantages of the proposed FT-UIO-based distributed FD scheme for MAS. [ABSTRACT FROM AUTHOR]

A residual networks family with hundreds or even thousands of layers dominates major image recognition tasks, but building a network by simply stacking residual blocks inevitably limits its optimization ability. This paper proposes a novel residual network architecture, residual networks of residual networks (RoR), to dig the optimization ability of residual networks. RoR substitutes optimizing residual mapping of residual mapping for optimizing original residual mapping. In particular, RoR adds levelwise shortcut connections upon original residual networks to promote the learning capability of residual networks. More importantly, RoR can be applied to various kinds of residual networks (ResNets, Pre-ResNets, and WRN) and significantly boost their performance. Our experiments demonstrate the effectiveness and versatility of RoR, where it achieves the best performance in all residual-network-like structures. Our RoR-3-WRN58-4 + SD models achieve new state-of-the-art results on CIFAR-10, CIFAR-100, and SVHN, with the test errors of 3.77%, 19.73%, and 1.59%, respectively. RoR-3 models also achieve state-of-the-art results compared with ResNets on the ImageNet data set. [ABSTRACT FROM AUTHOR]

This paper investigates the attitude and position tracking control problem for Lead-Wing close formation systems in the presence of loss of effectiveness and lock-in-place or hardover failure. In close formation flight, Wing unmanned aerial vehicle movements are influenced by vortex effects of the neighbouring Lead unmanned aerial vehicle. This situation allows modelling of aerodynamic coupling vortex-effects and linearisation based on optimal close formation geometry. Linearised Lead-Wing close formation model is transformed into nominal robust H-infinity models with respect to Mach hold, Heading hold, and Altitude hold autopilots; static feedback H-infinity controller is designed to guarantee effective tracking of attitude and position while manoeuvring Lead unmanned aerial vehicle. Based on H-infinity control design, an integrated multiple-model adaptive fault identification and reconfigurable fault-tolerant control scheme is developed to guarantee asymptotic stability of close-loop systems, error signal boundedness, and attitude and position tracking properties. Simulation results for Lead-Wing close formation systems validate the efficiency of the proposed integrated multiple-model adaptive control algorithm. [ABSTRACT FROM PUBLISHER]

In order to generate efficient tool path with given precision requirements, scallop height should be kept under a given limit, while the tool path should be as short as possible to reduce machining time. Traditional methods generate CC curves one by one, which makes the final tool path far from being globally optimal. This paper presents an optimal tool path generation model for a ball-end tool which strives to globally optimize a tool path with various objectives and constraints. Two scalar functions are constructed over the part surface to represent the path intervals and the feedrate (with directions). Using the finite element method (FEM), the tool path length minimization model and the machining time minimization model are solved numerically. The proposed method is also suitable for tool path generation on mesh surfaces. Simulation results show that the generated tool path can be direction parallel or contour parallel with different boundary conditions. Compared to most of the conventional tool path generation methods, the proposed method is able to generate more effective tool paths due to the global optimization strategy. [ABSTRACT FROM AUTHOR]

Shuffled frog leaping algorithm (SFLA) has shown its good performance in many optimization problems. This paper proposes a Mnemonic Shuffled Frog Leaping Algorithm with Cooperation and Mutation (MSFLACM), which is inspired by the competition and cooperation methods of different evolutionary computing, such as PSO, GA, and etc. In the algorithm, shuffled frog leaping algorithm and improved local search strategy, cooperation and mutation to improve accuracy and that exhibits strong robustness and high accuracy for high-dimensional continuous function optimization. A modified shuffled frog leaping algorithm (MSFLA) is investigated that improves the leaping rule by combining velocity updating equation of PSO. To improve accuracy, if the worst position in the memeplex couldn't get a better position in the local exploration procedure of the MSFLA, the paper introduces cooperation and mutation, which prevents local optimum and updates the worst position in the memeplex. By making comparative experiments on several widely used benchmark functions, analysis results show that the performances of that improved variant are more promising than the recently developed SFLA for searching optimum value of unimodal or multimodal continuous functions. [ABSTRACT FROM AUTHOR]

Background: Many toxic environmental agents such as cadmium and arsenic are found to be epidemiologically linked to increasing rates of cancers. In vitro studies show that these toxic agents induced malignant transformation in human cells. It is not clear whether such malignant transformation induced by a single toxic agent is driven by a common set of genes. Although the advancement of high-throughput technology has facilitated the profiling of global gene expression, it remains a question whether the sample size is sufficient to identify this common driver gene set. Results: We have developed a statistical method, SOFLR, to predict the number of common activated genes using a limited number of microarray samples. We conducted two case studies, cadmium and arsenic transformed human urothelial cells. Our method is able to precisely predict the number of stably induced and repressed genes and the number of samples to identify the common activated genes. The number of independent transformed isolates required for identifying the common activated genes is also estimated. The simulation study further validated our method and identified the important parameters in this analysis. Conclusions: Our method predicts the degree of similarity and diversity during cell malignant transformation by a single toxic agent. The results of our case studies imply the existence of common driver and passenger gene sets in toxin-induced transformation. Using a pilot study with small sample size, this method also helps microarray experimental design by determining the number of samples required to identify the common activated gene set. [ABSTRACT FROM AUTHOR]

Following the philosophy of the block Kaczmarz methods, we propose a randomized block Kaczmarz method with the blocks determined by the k -means clustering (RBK(k)). It can be considered as an efficient variant of the relaxed greedy randomized Kaczmarz algorithm by using a practical probability criterion for selecting the working block submatrix per iteration. The new algorithm is proved to be convergent when the linear system is consistent. A practical variant of the new method is also given. Some numerical examples are given to verify the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

Achieving multi-scene electronic component detection is the key to automatic electronic component assembly. The study of a deep-learning-based multi-scene electronic component object detection method is an important research focus. There are many anchors in the current object detection methods, which often leads to extremely unbalanced positive and negative samples during training and requires manual adjustment of thresholds to divide positive and negative samples. Besides, the existing methods often bring a complex model with many parameters and large computation complexity. To meet these issues, a new method was proposed for the detection of electronic components in multiple scenes. Firstly, a new dataset was constructed to describe the multi-scene electronic component scene. Secondly, a K-Means-based two-stage adaptive division strategy was used to solve the imbalance of positive and negative samples. Thirdly, the EfficientNetV2 was selected as the backbone feature extraction network to make the method simpler and more efficient. Finally, the proposed algorithm was evaluated on both the public dataset and the constructed multi-scene electronic component dataset. The performance was outstanding compared to the current mainstream object detection algorithms, and the proposed method achieved the highest mAP (83.20% and 98.59%), lower FLOPs (44.26GMAC) and smaller Params (29.3 M). [ABSTRACT FROM AUTHOR]

As a feasible model for signing quantum messages, some cryptanalysis and improvement of arbitrated quantum signature (AQS) have received a great deal of attentions in recent years. However, in this paper we find the previous improvement is not suitable implemented in some typical AQS protocols in the sense that the receiver, Bob, can forge a valid signature under known message attack. We describe the forgery strategy and present some corresponding improved strategies to stand against the forgery attack by modifying the encryption algorithm, an important part of AQS. These works preserve the merits of AQS and lead some potential improvements of the security in quantum signature or other cryptography problems. [ABSTRACT FROM AUTHOR]

In this paper, an improved fast SPIHT algorithm has been presented. SPIHT and NLS (Not List SPIHT) are efficient compression algorithms, but the algorithms application is limited by the shortcomings of the poor error resistance and slow compression speed in the aviation areas. In this paper, the error resilience and the compression speed are improved. The remote sensing images are decomposed by Le Gall5/3 wavelet, and wavelet coefficients are indexed, scanned and allocated by the means of family blocks. The bit-plane importance is predicted by bitwise OR, so the N bit-planes can be encoded at the same time. Compared with the SPIHT algorithm, this improved algorithm is easy implemented by hardware, and the compression speed is improved. The PSNR of reconstructed images encoded by fast SPIHT is higher than SPIHT and CCSDS from 0.3 to 0.9db, and the speed is 4-6 times faster than SPIHT encoding process. The algorithm meets the high speed and reliability requirements of aerial applications. [ABSTRACT FROM AUTHOR]

This paper proposes a real-time design for accurate stereo matching on compute unified device architecture (CUDA). We present a leading local algorithm and then accelerate it by parallel computing. High matching accuracy is achieved by cost aggregation over shape-adaptive support regions and disparity refinement using reliable initial estimates. A novel sample-and-restore scheme is proposed to make the algorithm scalable, capable of attaining several times speedup at the expense of minor accuracy degradation. The refinement and the restoration are jointly realized by a local voting method. To accelerate the voting on CUDA, a graphics processing unit (GPU)-oriented bitwise fast voting method is proposed, faster than the traditional histogram-based approach with two orders of magnitude. The whole algorithm is parallelized on CUDA at a fine granularity, efficiently exploiting the computing resources of GPUs. Our design is among the fastest stereo matching methods on GPUs. Evaluated in the Middlebury stereo benchmark, the proposed design produces the most accurate results among the real-time methods. The advantages of speed, accuracy, and desirable scalability advocate our design for practical applications such as robotics systems and multiview teleconferencing. [ABSTRACT FROM AUTHOR]

Abstract: Aiming at the multi-disease diagnosis, a self-organizing map (SOM) is developed. In this paper the tomato disease features are extracted and a mapping relationship between the diseases and the features is created. The inaccurate clustering of traditional SOM algorithm is analyzed. According to the analysis, Euclidean distance is taken as the main discrimination, and the adjacent-searching algorithm is optimized. Using the optimized algorithm, the cluster results of input samples are obtained, features of diseases are mapped, and a multi-disease diagnosis model is developed. The proposed SOM-based model has two layers. The feature array of diseases can be accurately and rapidly sorted and clustered using this model. This model can achieve an accurate diagnosis of multi-diseases. The simulation results show that the proposed model performs well and the proposed multi-disease diagnosis is effective. [Copyright &y& Elsevier]

We applied a satellite remote sensing-based evapotranspiration (ET) algorithm to assess global terrestrial ET from 1983 to 2006. The algorithm quantifies canopy transpiration and soil evaporation using a modified Penman-Monteith approach with biome-specific canopy conductance determined from the normalized difference vegetation index (NDVI) and quantifies open water evaporation using a Priestley-Taylor approach. These algorithms were applied globally using advanced very high resolution radiometer (AVHRR) GIMMS NDVI, NCEP/NCAR Reanalysis (NNR) daily surface meteorology, and NASA/GEWEX Surface Radiation Budget Release−3.0 solar radiation inputs. We used observations from 34 FLUXNET tower sites to parameterize an NDVI-based canopy conductance model and then validated the global ET algorithm using measurements from 48 additional, independent flux towers. Two sets of monthly ET estimates at the tower level, driven by in situ meteorological measurements and meteorology interpolated from coarse resolution NNR meteorology reanalysis, agree favorably (root mean square error (RMSE) = 13.0-15.3 mm month−1; R2 = 0.80-0.84) with observed tower fluxes from globally representative land cover types. The global ET results capture observed spatial and temporal variations at the global scale and also compare favorably (RMSE = 186.3 mm yr−1; R2 = 0.80) with ET inferred from basin-scale water balance calculations for 261 basins covering 61% of the global vegetated area. The results of this study provide a relatively long term global ET record with well-quantified accuracy for assessing ET climatologies, terrestrial water, and energy budgets and long-term water cycle changes. [ABSTRACT FROM AUTHOR]

In order to realize high accuracy control for periodic motion, a hybrid controller with grey prediction was presented in this paper. Incorporating the grey prediction, repetitive control, and the traditional Proportional-Integral- Differential (PID) control, a design method of the grey prediction repetitive PID (GRPID) control algorithm was investigated, according to the characteristics of the periodic motion control. The hybrid control algorithm can estimate unsure parameters and disturbance of system using grey prediction, and compensate control in terms of the prediction results, and this may improve control quality and robustness of repetitive control for controlling periodic motion. An example was carried out to verify the feasibility of the controller. The simulation results show that this algorithm has better performances than that of the conventional repetitive control system. It indicates the presented control method is more suitable for control system of periodic motion. [ABSTRACT FROM AUTHOR]

Summary: We developed an evapotranspiration (ET) algorithm driven by satellite remote sensing inputs, including AVHRR GIMMS NDVI, MODIS land cover and NASA/GEWEX solar radiation and albedo, and regionally corrected NCEP/NCAR Reanalysis daily surface meteorology. The algorithm was used to assess spatial patterns and temporal trends in ET over the pan-Arctic basin and Alaska from 1983 to 2005. We then analyzed associated changes in the regional water balance defined as precipitation (P) minus ET, where monthly P was defined from Global Precipitation Climatology Project (GPCP) and Global Precipitation Climatology Center (GPCC) sources. Monthly ET results derived from both in situ meteorological measurements and coarse resolution model reanalysis inputs agreed well (RMSE=5.1–6.3mmmonth−1; R 2 =0.91–0.92) with measurements from eight independent flux towers representing regionally dominant land cover types. ET showed generally positive trends over most of the pan-Arctic domain, though negative ET trends occurred over 32% of the region, primarily in boreal forests of southern and central Canada. Generally positive trends in ET, P and available long-term river discharge measurements imply that the pan-Arctic terrestrial water cycle is intensifying despite uncertainty in regional P and associated water balance estimates. Increasing water deficits in eastern Alaska, Canadian Yukon and western Prairie Provinces, and Northern Mongolia agree with regional drought records and recent satellite observations of vegetation browning and productivity decreases. Our results indicate that the pan-Arctic water balance is responding to a warming climate in complex ways with direct links to terrestrial carbon and energy cycles. [Copyright &y& Elsevier]

To overcome the shortcoming of the algorithm based on the classical grey relational analysis in the transformation of matrix of pixels to sequence, the grey relational analysis is extended in this paper, and a matrix absolute degree of grey incidences is presented. Then a novel algorithm of image edge detection is proposed which is based on matrix absolute degree of grey incidences. In the new algorithm, the characteristics of pixels are analyzed to decide non-edge referential matrix and the matrix to be compared, and the matrix absolute degree of grey incidences is employed to distinguish non-edge pixels and edge pixels. Finally the results of the experiments show that the proposed algorithm can gain more precise edges than some traditional methods, and can also tolerate some noises. Furthermore the quantity of edges ca be controlled by adjusting threshold of degree of grey incidences. So the technique is a new effective adjustable method of edge detection. [ABSTRACT FROM AUTHOR]

Abstract: Coal mine industry, which produces the most abundant and widely-distributed fossil fuel, is the deadliest in casualties. A recent report shows that the casualty rates of coal production per million tones are 4.36 in China, 2.14 in Ukraine, 2.03 in Russia, 0.088 in USA and 0.08 in Australia respectively. Consequently, safety is a critical issue for the mining industry, especially for the underground mining companies. The modernization and informatization of mining companies have significantly improved the operational environments (and hence safety) for the underground miners. Some advanced technologies, such as the intelligent response and rescue systems, have contributed significantly to the reduction of mining fatalities and accidents. Typical underground positioning and three dimensional (3D) modelling techniques are discussed in this paper first. A RFID/INS integrated positioning system has been tested to evaluate its performance and different positioning algorithms have been implemented. Experiments show that the simple centre of origin (CoO) positioning algorithm can achieve a positioning accuracy of 7.9 m. The integrated fingerprinting algorithm can achieve 1.7 m accuracy by using an Extended Kalman Filter. A new underground structure modelling method and a collision detecting method are developed. A prototype intelligent response and rescue system has been implemented in Da Zhuang coal mine, China. The results indicate that both the underground positioning technique and the 3D modelling and representing technique developed can satisfy the requirements of underground rescue in an emergency situation. [Copyright &y& Elsevier]

Abstract: In this paper, we present a new one-step smoothing Newton method proposed for solving the non-linear complementarity problem with -function based on a new smoothing -function. We adopt a variant merit function. Our algorithm needs only to solve one linear system of equations and perform one line search per iteration. It shows that any accumulation point of the iteration sequence generated by our algorithm is a solution of . Furthermore, under the assumption that the solution set is non-empty and bounded, we can guarantee at least one accumulation point of the generated sequence. Numerical experiments show the feasibility and efficiency of the algorithm. [Copyright &y& Elsevier]

Due to the strong nonlinear nature and the mutual effect between the temperature field and the velocity field, it is very difficult to solve the natural convection heat transfer problem. To this end, the Gauss Seidel iterative algorithm based on the reduced-order model is proposed to decouple temperature and pressure. In this paper, the governing equations are discretized by finite element method using bilinear shape functions. To suppress the oscillation of the solution of the governing equations, Streamline Upwind Petrov-Galerkin method (SUPG) is adopted. The main idea of the Gauss Seidel iterative algorithm is: given an initial temperature, pressure and temperature are obtained successively by solving only two nonlinear equations, which greatly improves the computational efficiency. Compared with full order Navier-Stokes model, the Gauss Seidel iterative algorithm for reduced order model has similar results. Then, in order to generate accurate boundaries, Smooth-Edged Material Distribution for Optimizing Topology (SEMDOT) based on density method (SIMP) is studied. Finally, some numerical examples demonstrate the feasibility and effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

The paper explores a trust-region active-set algorithm for general nonlinear optimization with nonlinear equality and inequality constraints. In this algorithm, an active-set strategy is used together with trust-region methods to compute the trial step. penalty functions are employed to obtain the global convergence. The global convergence of this algorithm is proved under standard conditions. The numerical tests show the efficiency of the proposed algorithm. [Copyright &y& Elsevier]

Abstract: In this paper a new branch and bound algorithm based on the rectangular partition and the Lagrangian relaxation for solving sum of quadratic ratios problem with nonconvex quadratic constraints (QSRP) is proposed. Firstly, the problem QSRP is converted into an equivalent sum of linear ratios problem with quadratic constraints (LSRP). Then, the bounding procedure is investigated by minimizing the restricted Lagrangian function of LSRP with a given estimate of the Lagrange multipliers. Minimizing a linear relaxation of the restricted Lagrangian overcomes the difficulty that the restricted Lagrangian is often nonconvex and facilitates the use of Lagrangian duality within a global optimization framework. In the algorithm, the interval Newton method is used to facilitate convergence in the neighborhood of the global solution. The proposed algorithm is convergent to the global minimum through the successive refinement of the solutions of a series of linear programming problems. Finally, numerical experiments are reported to show the efficiency of our algorithm. [Copyright &y& Elsevier]

Abstract: In this paper a global optimization algorithm based on a parametric linearizing method for generalized quadratic programming (GQP), i.e., the quadratic programming problem with nonconvex quadratic constraints, is proposed. By utilizing the linearizing method initial nonconvex nonlinear problem GQP is reduced to a sequence of linear programming problems through the successive refinement of a linear relaxation of feasible region and of the objective function. The proposed algorithm is convergent to the global minimum of GQP by means of the subsequent solutions of a series of linear programming problems. Test results indicate that the proposed algorithm is extremely robust and can be used successfully to solve global minimum of GQP on a microcomputer. [Copyright &y& Elsevier]

Abstract: In this paper, we consider a deterministic global optimization algorithm for solving a general linear sum of ratios (LFP). First, an equivalent optimization problem (LFP1) of LFP is derived by exploiting the characteristics of the constraints of LFP. By a new linearizing method the linearization relaxation function of the objective function of LFP1 is derived, then the linear relaxation programming (RLP) of LFP1 is constructed and the proposed branch and bound algorithm is convergent to the global minimum through the successive refinement of the linear relaxation of the feasible region of the objection function and the solutions of a series of RLP. And finally the numerical experiments are given to illustrate the feasibility of the proposed algorithm. [Copyright &y& Elsevier]