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In this paper, a meshless Chebyshev collocation method (CCM) for the numerical solution of the eigenvalue problems of the Helmholtz equation is presented. The Chebyshev polynomials are employed for the efficient and accurate approximation of the eigenfunctions to ensure the pseudo-spectral convergence of the CCM. Two different approaches, namely the generalized eigenvalue approach and the standard eigenvalue approach, which convert the original eigenvalue problem into the generalized eigenvalue problem and the standard eigenvalue problem respectively, are implemented. Five benchmark numerical examples are presented and discussed to demonstrate the accuracy and efficiency of the proposed CCM.

Face Parsing aims to partition the face into different semantic parts, which can be applied into many downstream tasks, e.g., face mask up, face swapping, and face animation. With the popularity of cameras, it is easier to acquire facial images. However, pixel-wise manually labeling is time-consuming and labor-intensive, which motivates us to explore the unlabeled data. In this paper, we present a self-supervised learning method attempting to make full use of the unlabeled facial images for face parsing. In particular, we randomly mask some patches in the central area of facial images, and the model is required to reconstruct the masked patches. This self-supervised pretraining is capable of making the model capture facial feature representations through these unlabeled data. After self-supervised pretraining, the model is fine-tuned on a few labeled data for the face parsing task. Experimental results show that the model achieves better performance for face parsing assisted by the self-supervised pretraining, which greatly decreases the labeling cost. Our approach achieves 74.41 mIoU on the LaPa test set fine-tuned on only 0.2% of the labeled data of the whole training data, surpassing the model that is directly trained by a large margin of +5.02 mIoU. In addition, our approach achieves a new state-of-the-art on the LaPa and CelebAMask-HQ test set.

Optimization problems widely exist in many expert and intelligent systems, e.g., greenhouse intelligent control systems in agriculture, energy management systems for hybrid electric vehicle, and job shop scheduling systems in manufacture. For the optimization problems in these systems, the objective functions may change over time. This kind of problem is usually called as dynamic optimization problems (DOPs) or optimizing in dynamic environments. The optimization algorithm plays an important role in designing an expert and intelligent system. In this paper, we present a novel particle swarm optimizer for optimization in dynamic environments. We introduce two schemes to improve performance of particle swarm optimization in dynamic environments. Firstly, the classical particle swarm optimization is enhanced by a collaborative mechanism, in which a target particle learns from another randomly selected particle and the global best one in the swarm. Instead of moving to the new position directly, a worst replacement operator is used to update the swarm, whereby the worst particle in the swarm moves to the better newly generated position. During optimizing, the best solution in each generation is stored. When an environmental change is detected, the historical solutions are retrieved to collaborate with some newly generated solutions to adapt to the new environment. The performance of the proposed algorithm is compared with several reported algorithms over the benchmark problems. Experimental results indicate that the proposed algorithm offers superior performance compared with the competitors.

This paper presents a novel prediction model combined with a multiobjective evolutionary algorithm based on decomposition to solve dynamic multiobjective optimization problems. In our model, the motion of approximated Pareto-optimal solutions (POS) over time is represented by the motion of the centroid, and the other solutions are assumed to have the same motion as the centroid. A history of recent centroid locations is used to build a difference model to estimate the later motion of the centroid when an environmental change is detected, and then the new locations of the other solutions are predicted based on their current locations and the estimated motion. The predicted solutions, combined with some retained solutions, form a new population to explore the new environment, and are expected to track the new POS and/or Pareto-optimal front relatively well. The proposed algorithm is compared with four state-of-the-art dynamic multiobjective evolutionary algorithms through 20 benchmark problems with differing dynamic characteristics. The experimental studies show that the proposed algorithm is effective in dealing with dynamic problems and clearly outperforms the competitors.

This paper presents a novel Particle Swarm Optimization algorithm to address Dynamic Optimization Problems. The algorithm incorporates a neighbor-based learning strategy into the velocity update of Particle Swarm Optimization, in order to enhance the exploration and exploitation capabilities of particles. Unlike the traditional swarm update scheme, a “worst replacement” strategy is used to update the swarm, whereby the position of the worst particle in the swarm is replaced by a better newly generated position. The short-term memory is employed to store solutions with intermediate fitnesses from the most recent environment, and the long-term memory is to store the historical best solutions found in all previous environments. After an environmental change is detected, some particles’ positions in the swarm are replaced by the members of the short-term memory, and the best member in the long-term memory under the current environment is re-introduced to the active swarm along with its Gaussian neighborhood, then the remaining particles’ positions are re-initialized. The performance of the proposed algorithm is compared with six state-of-the-art dynamic algorithms over the Moving Peaks Benchmark problems and Dynamic Rotation Peak Benchmark Generator. Experimental results indicate that out algorithm obtains superior performance compared with the competitors.

Functionalization of π-conjugated polymers is dispensable for solubilization of the rigid and hydrophobic backbones in water. However, polymer aggregation is always present and leads to issues with complication and reproducibility in spectral properties. Herein, we reported a simple and robust method to make a series of conjugated polymer nanostructures by a crosslinking strategy. In favor of multivalency effect, polythionenes with various chain lengths were functionalized with branched polyethylenimine (PEI) via ester aminolysis reaction under mild conditions. Photophysical studies revealed the conjugated backbones could be well stabilized and dispersed in water. By taking advantage of intermolecular recognition interaction between copper ions and cationic PEI, we applied crosslinked polythiophenes as a nano probe at very low concentration (0.01 mg/mL) to fluorescently detect copper ions with high sensitivity up to 10 nM and selectivity over other metal ions in aqueous solutions, without occurrence of detectable aggregates. The overall performance of our nano probes outperforms reported water-soluble polymers-based probes, particularly in probe availability and manipulation as well as selective copper detection capability.

Evolutionary computation has shown great performance in solving many multi-objective optimization problems; in many such algorithms, non-dominated sorting plays an important role in determining the relative quality of solutions in a population. However, the implementation of non-dominated sorting can be computationally expensive, especially for populations with a large number of solutions and many objectives. The main reason is that most existing non-dominated sorting algorithms need to compare one solution with almost all others to determine its front, and many of these comparisons are redundant or unnecessary. Another reason is that as the number of objectives increases, more and more candidate solutions become non-dominated solutions, and most existing time-saving approaches cannot work effectively. In this paper, we present a novel non-dominated sorting strategy, called Hierarchical Non-Dominated Sorting (HNDS). HNDS first sorts all candidate solutions in ascending order by their first objective. Then it compares the first solution with all others one by one to make a rapid distinction between different quality solutions, thereby avoiding many unnecessary comparisons. Experiments on populations with different numbers of solutions, different numbers of objectives and different problems have been done. The results show that HNDS has better computational efficiency than fast non-dominated sort, Arena’s principle and deductive sort.

This paper focuses on the shift strategy of HMCVT (Hydro-Mechanical Continuously Variable Transmission). A kinetic model for HMCVT is deduced on the basis of transmission theory, and the ITI simulation model is used to study the shift strategy based on the physical parameters and shift time. There are 4 typical working conditions in the continuous shift process, and the shift process from Fi(N) to Fi+1(P) and from F(H) to F1(P) play decisive roles in the shift quality. Simulations are performed to analyze the shift strategy based on the physical parameters and shift time under the working conditions of F2(N)→F3(P) and F(H)→F1(P), which are the most typical conditions. Speed drop amplitude, dynamic load coefficient, maximum degree of shock, and shift time are regarded as evaluation indexes; physical parameters (engine rotating speed, load torque, main circuit pressure, speed valve flow rate) and shift time (switch time of clutches) are considered as the influencing factors of shift quality; the orthogonal test method (including the range method and the variance analysis method) is used to solve the control strategy problems by simulation study and experimental verification. The results show that there are some differences in the shift strategy based on the physical parameters and shift time of the two typical conditions. In the shift process from F2(N) to F3(P), the shift strategy based on the physical parameters is the lower engine rotating speed, smaller load torque and main circuit pressure, and larger speed valve flow rate, the shift strategy based on the shift time is that switch L6 and L7 firstly, then disengage L1 and L3, engage L2 and L4 lastly. In the shift process from F(H) to F1(P), the shift strategy based on the physical parameters is the lower engine rotating speed, smaller main circuit pressure, and larger load torque and speed valve flow rate, the shift strategy based on the shift time is that switch L2 and L8 at first, then engage L4, L5 successively.

This article focuses on the application of adaptive filter based on the LMS algorithm. An adaptive filter of the closed-loop system is introduced, including the elimination of interference signal, the prediction of useful signal, and the approximation of expected signal. LMS (Least Mean Square) algorithm is used to meet the optimum norm of error between estimated signal and expected signal. The structure of LMS algorithm is presented and the simulation of LMS algorithm is carried out. The results indicate that the convergence performances of LMS algorithm are prefect, and the input signal can converge to the expected signal. The application of adaptive filtering technology in this article includes the correction of channel mismatch by an adaptive linear filter, the improvement of system performance by an adaptive equalizer, and the filter of frequency signal by an adaptive notch filter. The analysis on adaptive linear filter shows that the constant channel mismatch can be corrected quite well by the correction algorithm. The analysis on adaptive equalizer shows that the error rate of system with an adaptive equalizer has significant improvement gains over that of system without an adaptive equalizer. The smaller the error rate, the larger the SNR. The relationship between error rate and multi-path loss show that the error rate is largest when the loss factor is 0.5. The analysis on adaptive notch filter shows that the interference signal with two different known frequencies can be eliminated effectively by the adaptive notch filter. The filtered signals accord with the corresponding useful signals very well.

In this paper, high-quality three-dimensional (3D) nanocomposites (Au nanoparticles anchored on graphene, denoted as Au-gra) were synthesized by a simple electrochemical approach, in which the reduction of Au ions and graphene oxide were achieved with a one-step process. The resulting hybrids were characterized by field-emission scanning electron microscopy (FE-SEM), Raman spectroscopy, X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS). It is found that Au nanoparticles have been successfully supported on the surface of graphene, and the nanocomposites uniformly modified on the electrode with 3D porous structures. Based on cyclic voltammetry and amperometric results, Au-gra efficiently catalyzed the oxidation of glucose in neutral media (0.1 M PBS, pH 7.4) and exhibited a rapid response time (about 3 s), a broad linear range (0.1–16 mM), good stability, and sensitivity estimated to be 4.56 μA cm−2 mM−1 (R = 0.992, 285.8 μA cm−2 mM−1 vs. geometric area). The electrochemical experiments were performed at a relatively low detection potential (i.e., 0 V), under which the impact from the oxidation of common interfering species could be effectively limited. These results indicated a great potential of Au-gra in fabricating novel non-enzymatic glucose sensors with high performance.

Dendrite-like gold nanostructures (DGNs) were directly electrodeposited onto the surface of a glassy carbon electrode (GCE) via the potentiostatic method without any templates, surfactants, or stabilizers. The effects of the deposition time, potential and the concentration of precursor solution on the evolution of the nanostructure and on the electrocatalytic activity of the DGNs were systematically investigated using scanning electron microscopy (SEM), transmission electron microscopy (TEM) and electrochemical methods including cyclic voltammetry, linear voltammetry and chronoamperometry. The results confirmed that DGNs have good electrocatalytic activity towards the electro-oxidation of glucose in a neutral phosphate buffer solution (PBS, pH 7.4). A non-enzymatic glucose sensor fabricated with the DGNs as an electrocatalyst showed a quick response (less than 2 s), a low detection limit (0.05 mM), a wide and valuable linear range (0.1 - 25 mM), a high sensitivity (190.7 μA cm −2 mM −1 ) and good repeatability and stability. In addition, the commonly interfering species, such as ascorbic acid (AA), uric acid (UA), and 4-acetaminophen (AP), did not cause obvious interference because of the use of a low detection potential (0.15 V vs. Ag/AgCl). This work demonstrates a simple and an effective sensing platform for the non-enzymatic detection of glucose.

The superplastic deformation behavior and mechanism of Ti600 alloy at elevated temperature were investigated. Results show that Ti600 alloy exhibits excellent superplastic behavior in the temperature range of 840–960 °C at 5×10 −4 s −1 and all of the tensile elongations exceed 220%. Optical microstructure shows that the grains still remain equiaxed and refined after deformation. However, primary α phase increases with the increasing of temperature. TEM observation indicates that the intragranular dislocation movement is very active and is accompanied by the occurrence of dynamic recrystallization, which is beneficial to promote the grainboundary sliding and to relieve the stress concentration. The superplastic deformation mechanism of Ti600 alloy is grainsgroup sliding accommodated by dislocation movement and dynamic recrystallization. The model of this mechanism is a corrected Ball–Hutchinson Model.

A meshless model based on radial basis function and method of fundamental solution (RBF–MFS) is developed to investigate bioheat transfer problems. First, A time-stepping θ-method is used in handling the time variable in the Pennes bioheat equation. Then, the particular solution is approximated by a linear combination of radial basis functions, and the homogeneous solution is approximately determined by the method of fundamental solution. The multi-subdomain RBF–MFS technique is implemented for analysing problems containing different materials and/or multi-connected regions. The efficiency of the proposed method is assessed by several examples including normal tissue, tissue with tumor and burned tissue.