Self-adaptive Method Research Articles

A Self-Adaptive Optimization Individual Tree Modeling Method for Terrestrial LiDAR Point Clouds

Individual tree modeling for terrestrial LiDAR point clouds always involves heavy computation burden and low accuracy toward a complex tree structure. To solve these problems, this paper proposed a self-adaptive optimization individual tree modeling method. In this paper, we first proposed a joint neighboring growing method to segment wood points into object primitives. Subsequently, local object primitives were optimized to alleviate the computation burden. To build the topology relation among branches, branches were separated based on spatial connectivity analysis. And then the nodes corresponding to each object primitive were adopted to construct the graph structure of the tree. Furthermore, each object primitive was fitted as a cylinder. To revise the local abnormal cylinder, a self-adaptive optimization method based on the constructed graph structure was proposed. Finally, the constructed tree model was further optimized globally based on prior knowledge. Twenty-nine field datasets obtained from three forest sites were adopted to evaluate the performance of the proposed method. The experimental results show that the proposed method can achieve satisfying individual tree modeling accuracy. The mean volume deviation of the proposed method is 1.427 m3. In the comparison with two other famous tree modeling methods, the proposed method can achieve the best individual tree modeling result no matter which accuracy indicator is selected.

Correction to: A self-adaptive three-term conjugate gradient method for monotone nonlinear equations with convex constraints

Correction to: A self-adaptive three-term conjugate gradient method for monotone nonlinear equations with convex constraints

Dynamic Range Compression Self-Adaption Method for SAR Image Based on Deep Learning

The visualization of synthetic aperture radar (SAR) images involves the mapping of high dynamic range (HDR) amplitude values to gray levels for lower dynamic range (LDR) display devices. This dynamic range compression process determines the visibility of details in the displayed result. It therefore plays a critical role in remote sensing applications. There are some problems with existing methods, such as poor adaptability, detail loss, imbalance between contrast improvement and noise suppression. To effectively obtain the images suitable for human observation and subsequent interpretation, we introduce a novel self-adaptive SAR image dynamic range compression method based on deep learning. Its designed objective is to present the maximal amount of information content in the displayed image and eliminate the contradiction between contrast and noise. Considering that, we propose a decomposition-fusion framework. The input SAR image is rescaled to a certain size and then put into a bilateral feature enhancement module to remap high and low frequency features to realize noise suppression and contrast enhancement. Based on the bilateral features, a feature fusion module is employed for feature integration and optimization to achieve a more precise reconstruction result. Visual and quantitative experiments on synthesized and real-world SAR images show that the proposed method notably realizes visualization which exceeds several statistical methods. It has good adaptability and can improve SAR images’ contrast for interpretation.

Self-Adaptive Method and Inertial Modification for Solving the Split Feasibility Problem and Fixed-Point Problem of Quasi-Nonexpansive Mapping

The split feasibility problem (SFP) has many practical applications, which has attracted the attention of many authors. In this paper, we propose a different method to solve the SFP and the fixed-point problem involving quasi-nonexpansive mappings. We relax the conditions of the operator as well as consider the inertial iteration and the adaptive step size. For example, the convergence generated by our new method is better than that of other algorithms, and the convergence rate of our algorithm greatly improves that of previous algorithms.

Self-adaptive subgradient extragradient-type methods for solving variational inequalities

In this paper, we introduce two subgradient extragradient-type algorithms for solving variational inequality problems in the real Hilbert space. The first one can be applied when the mapping f is strongly pseudomonotone (not monotone) and Lipschitz continuous. The first algorithm only needs two projections, where the first projection onto closed convex set C and the second projection onto a half-space C_{k}. The strong convergence theorem is also established. The second algorithm is relaxed and self-adaptive; that is, at each iteration, calculating two projections onto some half-spaces and the step size can be selected in some adaptive ways. Under the assumption that f is monotone and Lipschitz continuous, a weak convergence theorem is provided. Finally, we provide numerical experiments to show the efficiency and advantage of the proposed algorithms.

Self-adaptive loss balanced Physics-informed neural networks

Physics-informed neural networks (PINNs) have received significant attention as a representative deep learning-based technique for solving partial differential equations (PDEs). The loss function of PINNs is a weighted sum of multiple terms, including the mismatch of observed data, boundary and initial constraints, as well as PDE residuals. In this paper, we observe that the performance of PINNs is susceptible to the weighted combination of competitive multiple loss functions. Therefore, we establish Gaussian probabilistic models to define the self-adaptive loss function through the adaptive weights for each loss term. In particular, we propose a self-adaptive loss balanced method that automatically assigns the weights of losses by updating adaptive weights in each epoch based on the maximum likelihood estimation. Finally, we perform a series of numerical experiments with self-adaptive loss balanced physics-informed neural networks (lbPINNs), including solving Poisson, Burgers, Helmholtz, Navier–Stokes, and Allen–Cahn equations in regular and irregular areas. We also test the robustness of lbPINNs by varying the initial adaptive weights, numbers of observations, hidden layers, and neurons per layer. These experimental results demonstrate that lbPINNs consistently achieve better performance than PINNs, and reduce the relative L2 error by about two orders of magnitude.

Self-Adaptive Telemedicine Specialist Recommendation Considering Specialist Activity and Patient Feedback.

Purpose: With the rapid development of medical informatization, information overload and asymmetry have become major obstacles that limit patients’ ability to find appropriate telemedicine specialists. Although doctor recommendation methods have been proposed, they fail to address data sparsity and cold-start issues, and electronic medical records (EMRs), patient preferences, potential interest of service providers and the changes over time are largely under-explored. Therefore, this study develops a self-adaptive telemedicine specialist recommendation method that incorporates specialist activity and patient utility feedback from the perspective of privacy protection to fill the research gaps. Methods: First, text vectorization, view similarity and probabilistic topic model are used to construct the patient and specialist feature models based on patients’ EMRs and specialists’ long- and short-term knowledge backgrounds, respectively. Second, the recommended specialist candidate set and recommendation index are obtained based on the similarity between patient features. Then, the specialist long-term knowledge feature model is used to update the newly registered specialist recommendation index and the recommended specialist candidate set to overcome the data sparsity and cold-start issues, and the specialist short-term knowledge feature model is adopted to extend the recommended specialist candidate set at the semantic level. Finally, we introduce the specialists’ activity and patients’ perceived utility feedback mechanism to construct a closed-loop adjusted and optimized specialist recommendation method. Results: An empirical study was conducted integrating EMRs of telemedicine patients from the National Telemedicine Center of China and specialists’ profiles and ratings from an online healthcare platform. The proposed method successfully recommended relevant and active telemedicine specialists to the target patient, and increased the recommended opportunities for newly registered specialists to some extent. Conclusions: The proposed method emphasizes the adaptability and acceptability of the recommended results while ensuring their accuracy and relevance. Specialists’ activity and patients’ perceived utility jointly contribute to the acceptability of recommended results, and the recommendation strategy achieves the organic fusion of the two. Several comparative experiments demonstrate the effectiveness and operability of the hybrid recommendation strategy under the premise of data sparsity and privacy protection, enabling effective matching of patients’ demand and service providers’ capabilities, and providing beneficial insights for data-driven telemedicine services.

Research on a Real-Time Estimation Method of Vehicle Sideslip Angle Based on EKF.

In this article, a real-time vehicle sideslip angle state observer is proposed, which is based on the EKF algorithm. Firstly, based on a 2-DOF dynamical model and the tire lateral force model, the vehicle sideslip angle state observer model with a self-adapted truncation procedure is established by combining the EKF and the least squares methods. The calculation of the Jacobi matrix in the time domain is transformed into a calculation in the frequency domain. A self-adapted update noise estimation method and an initial value setting strategy are proposed as well. Finally, a hardware-in-the-loop simulation is carried out by Matlab/Simulink-CarSim-dSPACE, and the real-time reliability of the estimation method is verified and analyzed by RMSE.

EMG-driven fatigue-based self-adapting admittance control of a hand rehabilitation robot

Upper-limb rehabilitation therapy sessions for post-stroke people generally contain rhythmic hand movements in a tiresome manner to rebuild the injured neural circuits. Fatigue formation causes breaks in the training and limits the therapy duration. Therefore, it is essential to establish a correlation between the patient’s muscle condition and the rehabilitation exercises to improve the physiotherapy sessions. A self-adapting control method based on online fatigue detection in rhythmic arm movements is presented. Experimental tests were performed on twenty healthy subjects to validate the method’s feasibility. Electromyography (EMG) and force signals considered the interfaces between users and the robot. In the first stage of the experiment, utilizing the frequency features from EMG signals, a neural network for fatigue detection trained; however, in the end, it substituted with a simple function as a refinement in the time-consuming aspect for the online employment. The initiation of the fatigue process is followed by reducing the admittance controller damping term based on the EMG signal processing. Trajectory tracking with the robot employs the self-adapting admittance controller (SAAC) method and the non-adapting admittance controller (NAAC). Movement accuracy and smoothness were measured and showed a better performance of the SAAC method related to the NAAC. Simulations with two different stiffness levels were performed on an upper-limb OpenSim model to study a stroke-injured arm and evaluate the proposed method’s proficiency. The metabolic cost indicated the movement’s superiority in a fatigue situation for reduced environment stiffness.

A New Self-Adaptive Method for the Multiple-Sets Split Common Null Point Problem in Banach Spaces

A New Self-Adaptive Method for the Multiple-Sets Split Common Null Point Problem in Banach Spaces

Inertial Extragradient Method for Solving Variational Inequality and Fixed Point Problems of a Bregman Demigeneralized Mapping in a Reflexive Banach Spaces

In this paper, by employing a Bregman distance approach, we introduce a self-adaptive inertial extragradient method for finding a common solution of variational inequality problem involving a pseudo-monotone operator and fixed point problem of a Bregman demigeneralized mapping in a reflexive Banach spaces. Using a Bregman distance approach, we establish a strong convergence result for approximating the common solution of the aforementioned problems under some mild assumptions. We display some numerical examples to show the performance of our iterative method. The result presented in this paper extends and complements many related results in literature.

Joint Offloading and Resource Allocation Based on UAV-Assisted Mobile Edge Computing

Due to the birth of various new Internet of Things devices, the rapid increase of users, and the limited coverage of infrastructure, computing resources will inevitably become insufficient. Therefore, we consider an unmanned aerial vehicle (UAV)–assisted mobile edge computing system with multiple users, an edge server, a remote cloud server, and an UAV. A UAV, as a relay node, can provide users with extensive communications and certain computing capabilities. Our proposed scheme aims to optimize the unloading decision of the tasks among all users and the allocation of computing and communication resources to minimize overall energy consumption and costs of computing and maximum delay. To solve the joint optimization problem, we propose an efficient USS algorithm, which includes a UAV position optimization algorithm, semi-qualitative relaxation method, and self-adaptive adjustment method. Our numerical results show that the proposed algorithm can significantly reduce the unloading cost of multi-user tasks compared with four other unloading decisions, such as traditional cloud computing, which uses only the edge server.

A Color Spot Extraction Method Based on the Multifused Enhancement Algorithm

The color spot can reflect the skin status and the physiological change. It has been used in the evaluation of skin quality and medical diagnosis. However, the gray level of the color spot is very similar to that of normal skin. Because the shape, size, and position of the color spots are irregular. It is difficult to extract the accurate region of the color spot with the traditional methods. In order to extract the region of the color spot, we propose a multifused enhancement algorithm to enhance the feature of the color spot and extract the accurate region. The multifused enhancement algorithm mainly includes three components: the polarized image acquirement, the color model transformation, and the wavelet transform and enhancement. The color spot, which is in the intraepidermal basal cell, is captured by the polarized camera with the polarized light. The acquired method can remove the reflective light, covering the color spot. To further enhance the feature of the color spot, we use saturation instead of the gray level to characterize the color spot. The color spot is more conspicuous in the saturation image. Then, the wavelet transform and enhancement are used to enhance the feature of the color spot and reduce the effect of uneven illumination. The color spot is extracted by the proposed self-adaptive segmentation method. In addition, we discuss the performance with the different wavelets to choose the optimal wavelet. The actual reason for removing uneven illumination in the proposed algorithm is also analyzed in this work. The experimental results indicate that the proposed algorithm achieves the best accuracy among all the algorithms compared.

Strong convergence of a self-adaptive inertial Tseng's extragradient method for pseudomonotone variational inequalities and fixed point problems

Abstract In this paper, we study the problem of finding a common solution of the pseudomonotone variational inequality problem and fixed point problem for demicontractive mappings. We introduce a new inertial iterative scheme that combines Tseng’s extragradient method with the viscosity method together with the adaptive step size technique for finding a common solution of the investigated problem. We prove a strong convergence result for our proposed algorithm under mild conditions and without prior knowledge of the Lipschitz constant of the pseudomonotone operator in Hilbert spaces. Finally, we present some numerical experiments to show the efficiency of our method in comparison with some of the existing methods in the literature.

A self-adaptive method for the assessment of dynamic measurement uncertainty

Measurement uncertainty is as important as measurement in metrology and industry. The GUM and its supplements provide a widely accepted framework for evaluating measurement uncertainty; but don’t provide a reasonable assessment method for some special circumstances, especially for dynamic measurement. Several emerging methodologies with different mathematical approaches are used for evaluating the dynamic uncertainty in a specific application, such as knowing the characteristics of data. To expand the applicability, a self-adaptive method is proposed. This method evaluates measurement uncertainty by analyzing the compositions of dynamic data, regardless of linearity, stationarity, or stochasticity. Information entropy on spectra combined with EDM algorithms is presented to divide dynamic data into deterministic and stochastic components; and then a Bayesian model and a time-varying auto-regression model are used to analyze decomposed components, respectively. Synthetic noisy signals and experimental data from a double-rotor table are utilized to demonstrate the effectiveness of the proposed method.

An acoustic near surface soil profiler using surface wave method

An acoustic soil profiler, using a so-called the high-frequency multi-channel analysis of surface waves (HF-MASW) method, has been developed, which uses surface (Rayleigh) waves to measure soil profile in terms of the shear (S) wave velocity as a function of depth, up to a 2.5 m deep below the surface. Several practical techniques have been developed to enhance the HF-MASW method, including (1) a variable sensor spacing configuration, (2) the self-adaptive method, and (3) the phase-only signal processing. Fundamentally, the S-wave velocity is related to soil mechanical and hydrological properties through the principle of effective stress. Therefore, the measured two-dimensional S-wave velocity images reflect the temporal and spatial variations of soils due to weather effects, geological anomalies, and anthropologic activities. Several HF-MASW applications will be reported, including (1) near surface soil profiling, (2) a long-term-survey for studying weather and seasonal effects, (3) short-term monitoring rain fall events, (4) detecting fraigpan layers, and (5) a farmland compaction study. This acoustic soil profiler can be used for agricultural, environmental, civil engineering, and military applications.

A self-adaptive parallel subgradient extragradient method for finite family of pseudomonotone equilibrium and fixed point problems

A self-adaptive parallel subgradient extragradient method for finite family of pseudomonotone equilibrium and fixed point problems

A self-adaptive level-based learning artificial bee colony algorithm for feature selection on high-dimensional classification

Feature selection is an important data preprocessing method in data mining and machine learning, yet it faces the challenge of “curse of dimensionality” when dealing with high-dimensional data. In this paper, a self-adaptive level-based learning artificial bee colony (SLLABC) algorithm is proposed for high-dimensional feature selection problem. The SLLABC algorithm includes three new mechanisms: (1) A novel level-based learning mechanism is introduced to accelerate the convergence of the basic artificial bee colony algorithm, which divides the population into several levels and the individuals on each level learn from the individuals on higher levels, especially, the individuals on the highest level learn from each other. (2) A self-adaptive method is proposed to keep the balance between exploration and exploitation abilities, which takes the diversity of population into account to determine the number of levels. The lower the diversity is, the fewer the levels are divided. (3) A new update mechanism is proposed to reduce the number of selected features. In this mechanism, if the error rate of an offspring is higher than or is equal to that of its parent but selects more features, then the offspring is discarded and the parent is retained, otherwise, the offspring replaces its parent. Further, we discuss and analyze the contribution of these novelties to the diversity of population and the performance of classification. Finally, the results, compared with 8 state-of-the-art algorithms on 12 high-dimensional datasets, confirm the competitive performance of the proposed SLLABC on both classification accuracy and the size of the feature subset.

Threshold optimization method of single-bit quanta image sensor based on bit density

To improve the quality of reconstructed images of single-bit quanta image sensors, a self-adaptive threshold optimization method is proposed. The method can adaptively adjust the quantization threshold according to different light intensities, making the quality of the reconstructed image improved. First, the bitstream data from the quantization of real images are obtained by sensor model. Second, the optimal threshold value of each row is determined by calculating the bit-density, followed by the threshold value updated according to the deviation value between the bit-density of a single-pixel and 0.5. Finally, the optimal threshold matrix obtained by this method is used to reconstruct the image to improve imaging quality. Four identical test images were processed by the proposed method, the traditional method, and Elgendy’s variable threshold method, respectively. The results show that the proposed method can improve the peak signal-to-noise ratio of the reconstructed image in comparison with the traditional uniform threshold method. Compared with Elgendy’s method, the proposed method can effectively reduce the number of iterations under the same parameter conditions. These experiments prove that the proposed method can effectively improve the quality of reconstructed images with fewer iterations.

Accelerating the gradient projection algorithm for solving the non-additive traffic equilibrium problem with the Barzilai-Borwein step size

The non-additive traffic equilibrium problem (NaTEP) overcomes the inadequacies of the additivity assumption in traditional traffic equilibrium models by relaxing the cost incurred on each path that is not a simple sum of the link costs on that path. The computation of the NaTEP heavily depends on the efficiency of the step size determination. This paper aims to accelerate the path-based gradient projection (GP) algorithm for solving the NaTEP using the Barzilai-Borwein (BB) step size scheme. The GP algorithm with the BB step size scheme uses the solution information of the last two iterations to determine a suitable step size and avoids extra evaluations of the mapping value. The proposed algorithm only needs to perform one-time projection onto the nonnegative orthant at each iteration. Two approaches with and without column generation are considered in the GP algorithm implementation. A non-additive shortest path algorithm is adopted for the column generation approach. Numerical results on four transportation networks demonstrate the superior efficiency and robustness of the GP algorithm with the BB step size scheme over the self-adaptive GP method.