Direct Method Research Articles

Comprehensive Preview Decision Method for Direction and Speed of Intelligent Vehicle Based on Rules and Learning

Decision-making is a key part of autonomous driving systems. Human-like decision-making in complex scenarios is important for enhancing drivers’ trust in and acceptance of autonomous driving systems. In order to improve their human-like features and their adaptability to complex scenarios, this paper proposes a comprehensive preview decision method for direction and speed based on rules and learning. First, a decision-making structure including maneuvering ability judgment, motion trajectory prediction, safety judgment, legitimacy judgment, comprehensive performance evaluation, and parameters learning is presented. Second, a learning method for the longitudinal safety distance threshold based on a BP neural network is introduced. Third, inverse reinforcement learning (IRL) is used to learn the comprehensive performance evaluation weight coefficient for different driving styles. Finally, the DJI AD4CHE dataset is processed and used to train the parameters. Car-following and lane-changing scenarios are simulated to verify the effectiveness of decision-making. The simulation results show that the proposed method can reflect human-like decision-making in multiple scenarios.

A high-resolution method for direction of arrival estimation based on an improved self-attention module.

The high-resolution direction of arrival (DOA) estimation is a prominent research issue in underwater acoustics. The existing high-resolution methods include subspace methods and sparse representation methods. However, the performance of subspace methods suffers from low signal-to-noise ratio (SNR) and limited snapshots conditions, and the computational complexity of sparse representation methods is too high. The neural network methods are emerging high-resolution methods. However, insufficient support for big data is frequently observed in underwater acoustics, and conventional network structures present challenges in further enhancing performance. To address the aforementioned problems, we propose a neural network method based on an improved self-attention module to achieve high accuracy and robust DOA estimation. First, we design a multi-head self-attention module with large-scale convolutional kernels and residual structures to improve the estimated accuracy. Second, we propose an improved input feature to enhance the robustness to non-uniform noise and unequal-intensity targets. The simulations demonstrate that the proposed method exhibits superior angle resolution compared to sparse representation methods under the same simulation conditions. The proposed method demonstrates exceptional accuracy and robustness in DOA estimation under challenging conditions of low SNR, limited snapshots, and unequal-intensity targets. The experimental results further prove the effectiveness of the proposed method.

Study of the Physical Characteristics of Carbonate Samples from Archaeological Artifacts for EPR Dating

Investigation of the physical characteristics of mineral artifacts was undertaken with purpose of development of dating by the electron paramagnetic resonance (EPR) method in a new direction, by means of analysis of spectra of the carbonate probes from the surface of archaeological artifacts. The artifacts – samples of layers from the surface of ceramics and stone tools, containing impurities of quartz and alumosilicates, have been tested. For obtaining the physical characteristics and dating the complex of the methods and equipment has been used which allowed us to reveal specialties of mineral content and structure of the surface. There were chosen optimal conditions for registration, distinguishing and identification of the EPR signals from the paramagnetic centers. Applying of radiation of the ILU-10 accelerator with converter and consequent EPR-studying allowed to estimate the age of artifacts.

A novel approach on calibration of a laser line sensor’s position in the machine coordinate system

Abstract On-machine measurement facilitates real-time monitoring and feedback of the machining status of parts, thereby preventing secondary positioning errors in the offline measurement of parts. Calibrating spatial position of the measuring sensor is crucial for ensuring the accuracy of on-machine measurements. This paper introduces a novel approach to on-machine calibration of line laser displacement sensors, based on the fitted sphere-center method and iterative position in both height and width directions along the line laser. By constructing a transformation matrix and measuring multiple sets of data to determine the unknowns in the matrix, the spatial position of the laser displacement sensor can be calibrated. The results showed that the polar deviation of the standard ball radius was 0.0067 mm, confirming the feasibility and accuracy of the method.

Multiagent Trust Region Policy Optimization.

We extend trust region policy optimization (TRPO) to cooperative multiagent reinforcement learning (MARL) for partially observable Markov games (POMGs). We show that the policy update rule in TRPO can be equivalently transformed into a distributed consensus optimization for networked agents when the agents' observation is sufficient. By using a local convexification and trust-region method, we propose a fully decentralized MARL algorithm based on a distributed alternating direction method of multipliers (ADMM). During training, agents only share local policy ratios with neighbors via a peer-to-peer communication network. Compared with traditional centralized training methods in MARL, the proposed algorithm does not need a control center to collect global information, such as global state, collective reward, or shared policy and value network parameters. Experiments on two cooperative environments demonstrate the effectiveness of the proposed method.

SSGCNet: A Sparse Spectra Graph Convolutional Network for Epileptic EEG Signal Classification.

In this article, we propose a sparse spectra graph convolutional network (SSGCNet) for epileptic electroencephalogram (EEG) signal classification. The goal is to develop a lightweighted deep learning model while retaining a high level of classification accuracy. To do so, we propose a weighted neighborhood field graph (WNFG) to represent EEG signals. The WNFG reduces redundant edges between graph nodes and has lower graph generation time and memory usage than the baseline solution. The sequential graph convolutional network is further developed from a WNFG by combining sparse weight pruning and the alternating direction method of multipliers (ADMM). Compared with the state-of-the-art method, our method has the same classification accuracy on the Bonn public dataset and the spikes and slow waves (SSW) clinical real dataset when the connection rate is ten times smaller.

An adaptive lattice Green's function method for external flows with two unbounded and one homogeneous directions

We solve the incompressible Navier-Stokes equations using a lattice Green's function (LGF) approach, including immersed boundaries (IB) and adaptive mesh refinement (AMR), for external flows with one homogeneous direction (e.g. infinite cylinders of arbitrary cross-section). We hybridize a Fourier collocation (pseudo-spectral) method for the homogeneous direction with a specially designed, staggered-grid finite-volume scheme on an AMR grid. The Fourier series is also truncated variably according to the refinement level in the other directions. We derive new algorithms to tabulate the LGF of the screened Poisson operator and viscous integrating factor. After adapting other algorithmic details from the fully inhomogeneous case [1], we validate and demonstrate the new method with transitional and turbulent flows over a circular cylinder at Re=300 and Re=12,000, respectively.

Optimal error estimates of a non-uniform IMEX-L1 finite element method for time fractional PDEs and PIDEs

Stability and optimal convergence analysis of a non-uniform implicit-explicit L1 finite element method (IMEX-L1-FEM) is studied for a class of time-fractional linear partial differential/integro-differential equations with non-self-adjoint elliptic part having (space-time) variable coefficients. The proposed scheme is based on a combination of an IMEX-L1 method on graded mesh in the temporal direction and a finite element method in the spatial direction. With the help of a discrete fractional Grönwall inequality, global almost optimal error estimates in L2- and H1-norms are derived for the problem with initial data u0∈H01(Ω)∩H2(Ω). The novelty of our approach is based on managing the interaction of the L1 approximation of the fractional derivative and the time discrete elliptic operator to derive the optimal estimate in H1-norm directly. Furthermore, a super convergence result is established when the elliptic operator is self-adjoint with time and space varying coefficients, and as a consequence, an L∞ error estimate is obtained for 2D problems that too with the initial condition is in H01(Ω)∩H2(Ω). All results proved in this paper are valid uniformly as α→1−, where α is the order of the Caputo fractional derivative. Numerical experiments are presented to validate our theoretical findings.

Estimation of spatial demographic maps from polymorphism data using a neural network.

A fundamental goal in population genetics is to understand how variation is arrayed over natural landscapes. From first principles we know that common features such as heterogeneous population densities and barriers to dispersal should shape genetic variation over space, however there are few tools currently available that can deal with these ubiquitous complexities. Geographically referenced single nucleotide polymorphism (SNP) data are increasingly accessible, presenting an opportunity to study genetic variation across geographic space in myriad species. We present a new inference method that uses geo-referenced SNPs and a deep neural network to estimate spatially heterogeneous maps of population density and dispersal rate. Our neural network trains on simulated input and output pairings, where the input consists of genotypes and sampling locations generated from a continuous space population genetic simulator, and the output is a map of the true demographic parameters. We benchmark our tool against existing methods and discuss qualitative differences between the different approaches; in particular, our program is unique because it infers the magnitude of both dispersal and density as well as their variation over the landscape, and it does so using SNP data. Similar methods are constrained to estimating relative migration rates, or require identity by descent blocks as input. We applied our tool to empirical data from North American grey wolves, for which it estimated mostly reasonable demographic parameters, but was affected by incomplete spatial sampling. Genetic based methods like ours complement other, direct methods for estimating past and present demography, and we believe will serve as valuable tools for applications in conservation, ecology, and evolutionary biology. An open source software package implementing our method is available from https://github.com/kr-colab/mapNN .

Robust Corrupted Data Recovery and Clustering via Generalized Transformed Tensor Low-Rank Representation.

Tensor analysis has received widespread attention in high-dimensional data learning. Unfortunately, the tensor data are often accompanied by arbitrary signal corruptions, including missing entries and sparse noise. How to recover the characteristics of the corrupted tensor data and make it compatible with the downstream clustering task remains a challenging problem. In this article, we study a generalized transformed tensor low-rank representation (TTLRR) model for simultaneously recovering and clustering the corrupted tensor data. The core idea is to find the latent low-rank tensor structure from the corrupted measurements using the transformed tensor singular value decomposition (SVD). Theoretically, we prove that TTLRR can recover the clean tensor data with a high probability guarantee under mild conditions. Furthermore, by using the transform adaptively learning from the data itself, the proposed TTLRR model can approximately represent and exploit the intrinsic subspace and seek out the cluster structure of the tensor data precisely. An effective algorithm is designed to solve the proposed model under the alternating direction method of multipliers (ADMMs) algorithm framework. The effectiveness and superiority of the proposed method against the compared methods are showcased over different tasks, including video/face data recovery and face/object/scene data clustering.

Marginal Subspace Learning With Group Low-Rank for Unsupervised Domain Adaptation.

Unsupervised domain adaptation is intended to construct a reliable model for the unlabeled target samples using the well-labeled but differently distributed source samples. To tackle the domain shift issue, learning domain-invariant feature representations across domains is important, and most of the existing methods have concentrated on this goal. However, these methods rarely take into consideration the group discriminability of the feature representation, which is detrimental to the final recognition. Therefore, this article proposes a novel unsupervised domain adaptation method, named marginal subspace learning with group low-rank (MSL-GLR), to extract both domain-invariant and discriminative feature representations. Specifically, MSL-GLR uses the retargeting strategy to relax the regression matrix, such that the regression values would be forced to satisfy a margin maximization criterion for the requirement of correct classification. Moreover, MSL-GLR imposes a class-induced low-rank constraint, which enables the samples of each class to be located in their respective subspace. In this way, the distance between samples from the same class can be decreased and the discriminant ability of the projection is greatly improved. Furthermore, with the help of alternating direction method of multipliers (ADMM), an efficient algorithm is presented to solve the resulting optimization problem. Finally, the effectiveness of the proposed MSL-GLR is demonstrated by comprehensive evaluations on multiple domain adaptation benchmark datasets.

Tensor Ring Decomposition Guided Dictionary Learning for OCT Image Denoising.

Optical coherence tomography (OCT) is a non-invasive and effective tool for the imaging of retinal tissue. However, the heavy speckle noise, resulting from multiple scattering of the light waves, obscures important morphological structures and impairs the clinical diagnosis of ocular diseases. In this paper, we propose a novel and powerful model known as tensor ring decomposition-guided dictionary learning (TRGDL) for OCT image denoising, which can simultaneously utilize two useful complementary priors, i.e., three-dimensional low-rank and sparsity priors, under a unified framework. Specifically, to effectively use the strong correlation between nearby OCT frames, we construct the OCT group tensors by extracting cubic patches from OCT images and clustering similar patches. Then, since each created OCT group tensor has a low-rank structure, to exploit spatial, non-local, and its temporal correlations in a balanced way, we enforce the TR decomposition model on each OCT group tensor. Next, to use the beneficial three-dimensional inter-group sparsity, we learn shared dictionaries in both spatial and temporal dimensions from all of the stacked OCT group tensors. Furthermore, we develop an effective algorithm to solve the resulting optimization problem by using two efficient optimization approaches, including proximal alternating minimization and the alternative direction method of multipliers. Finally, extensive experiments on OCT datasets from various imaging devices are conducted to prove the generality and usefulness of the proposed TRGDL model. Experimental simulation results show that the suggested TRGDL model outperforms state-of-the-art approaches for OCT image denoising both qualitatively and quantitatively.

Determination of an unknown coefficient in the Korteweg–de Vries equation

Abstract In this paper, a space-time spectral method for solving an inverse problem in the Korteweg–de Vries equation is considered. Optimal order of convergence of the semi-discrete method is obtained in L 2 {L^{2}} -norm. The discrete schemes of the method are based on the modified Fourier pseudospectral method in spatial direction and the Legendre-tau method in temporal direction. The nonlinear term is computed via the fast Fourier transform and fast Legendre transform. The method is implemented by the explicit-implicit iterative method. Numerical results are given to show the accuracy and capability of this space-time spectral method.

Adaptive Joint Carrier and DOA Estimations of FHSS Signals Based on Knowledge-Enhanced Compressed Measurements and Deep Learning.

As one of the most widely used spread spectrum techniques, the frequency-hopping spread spectrum (FHSS) has been widely adopted in both civilian and military secure communications. In this technique, the carrier frequency of the signal hops pseudo-randomly over a large range, compared to the baseband. To capture an FHSS signal, conventional non-cooperative receivers without knowledge of the carrier have to operate at a high sampling rate covering the entire FHSS hopping range, according to the Nyquist sampling theorem. In this paper, we propose an adaptive compressed method for joint carrier and direction of arrival (DOA) estimations of FHSS signals, enabling subsequent non-cooperative processing. The compressed measurement kernels (i.e., non-zero entries in the sensing matrix) have been adaptively designed based on the posterior knowledge of the signal and task-specific information optimization. Moreover, a deep neural network has been designed to ensure the efficiency of the measurement kernel design process. Finally, the signal carrier and DOA are estimated based on the measurement data. Through simulations, the performance of the adaptively designed measurement kernels is proved to be improved over the random measurement kernels. In addition, the proposed method is shown to outperform the compressed methods in the literature.

The Orthodox Brotherhood of Bishop Makarii (Opotskii) in the Context of Persecution in the Twentieth Century

The religious life of Orthodox believers under the conditions of persecution in the 20th century, especially from the mid-20s to the first half of the 40s, has not been sufficiently studied. Many communities, including brotherhoods, were forced to go underground due to the repressive policies of civil authorities. This is associated with a number of problems in providing this topic with archival sources. The article examines the life of the Orthodox brotherhood founded in 1924 by Bishop Makarii (Opotskii), which existed in several cities in northwestern Russia until 1942. The source base was not only materials from state and departmental but also private archives, as well as the results of field research. The article is based on the principles of historicism, comprehensiveness and scientific objectivity; methods of the micro-historical direction are used: historical-genetic, comparative; oral history method – interviews. The study showed that the brotherhood was able to survive all the waves of persecution of the 1920s and 1930s. In the 1920s, it showed noticeable resistance to Cherepovets renovationism and carried out legal activities within the framework of what was permitted by the civil authorities. The main activity was education. In 1937 it became illegal. The principles thanks to which the brotherhood managed to maintain its functionality were identified: concern for the presence of leaders, maximum maintenance of communication regardless of location, mutual trust between members of the community. Thanks to this, the brotherhood was able to practice such forms of religious life as common worship, regular participation in the sacraments, reading of the Holy Scriptures, sermons and spiritual conversations. It seems promising to compare the brotherhood of Bishop Makarii (Opotskii) with one or more similar communities operating in the same period or on the same territory

EMG-Constrained and Ultrasound-Informed Muscle-Tendon Parameter Estimation in Post-Stroke Hemiparesis.

Secondary morphological and mechanical property changes in the muscle-tendon unit at the ankle joint are often observed in post-stroke individuals. These changes may alter the force generation capacity and affect daily activities such as locomotion. This work aimed to estimate subject-specific muscle-tendon parameters in individuals after stroke by solving the muscle redundancy problem using direct collocation optimal control methods based on experimental electromyography (EMG) signals and measured muscle fiber length. Subject-specific muscle-tendon parameters of the gastrocnemius, soleus, and tibialis anterior were estimated in seven post-stroke individuals and seven healthy controls. We found that the maximum isometric force, tendon stiffness and optimal fiber length in the post-stroke group were considerably lower than in the control group. We also computed the root mean square error between estimated and experimental values of muscle excitation and fiber length. The musculoskeletal model with estimated subject-specific muscle tendon parameters (from the muscle redundancy solver), yielded better muscle excitation and fiber length estimations than did scaled generic parameters. Our findings also showed that the muscle redundancy solver can estimate muscle-tendon parameters that produce force behavior in better accordance with the experimentally-measured value. These muscle-tendon parameters in the post-stroke individuals were physiologically meaningful and may shed light on treatment and/or rehabilitation planning.

Intranasal Radioiodinated Ferulic Acid Polymeric Micelles as the First Nuclear Medicine Imaging Probe for ETRA Brain Receptor.

The aim of this work was to prepare a selective nuclear medicine imaging probe for the Endothelin 1 receptor A in the brain. Ferulic acid (an ETRA antagonist) was radiolabeled using 131I by direct electrophilic substitution method. The radiolabeled ferulic acid was formulated as polymeric micelles to allow intranasal brain delivery. Biodistribution was studied in Swiss albino mice by comparing brain uptake of 131I-ferulic acid after IN administration of 131I-ferulic acid polymeric micelles, IN administration of 131I-ferulic acid solution and IV administration of 131I-ferulic acid solution. Successful radiolabeling was achieved with an RCY of 98 % using 200 μg of ferulic acid and 60 μg of CAT as oxidizing agents at pH 6, room temperature and 30 min reaction time. 131I-ferulic acid polymeric micelles were successfully formulated with the particle size of 21.63 nm and polydispersity index of 0.168. Radioactivity uptake in the brain and brain/blood uptake ratio for I.N 131I-ferulic acid polymeric micelles were greater than the two other routes at all periods. Our results provide 131I-ferulic acid polymeric micelles as a hopeful nuclear medicine tracer for ETRA brain receptor.

Full-length PacBio Amplicon Sequencing to Unveil RNA Editing Sites

Background: RNA editing enriches post-transcriptional sequence changes. Currently detecting RNA editing sites is mostly based on the Sanger sequencing platform and second-generation sequencing. However, detection with Sanger sequencing is limited by the disturbing background peaks using the direct sequencing method and the clone number using the clone sequencing method, while second-generation sequencing detection is constrained by its short read. Objective: We aimed to design a pipeline that can accurately detect RNA editing sites for full-length long-read amplicons to meet the requirement when focusing on a few specific genes of interest. Method: We developed a novel high-throughput RNA editing sites detection pipeline based on the PacBio circular consensus sequences sequencing which is accurate with high-throughput and long-read coverage. We tested the pipeline on cytosolic malate dehydrogenase in the hard-shelled mussel Mytilus coruscus and further validated it using direct Sanger sequencing. Results: Data generated from the PacBio circular consensus sequences (CCS) amplicons in three mussels were first filtered by quality and then selected by open reading frame. After filtering, 225-2047 sequences of the three mussels, respectively, were used to identify RNA editing sites. With corresponding genomic DNA sequences, we extracted 227-799 candidate RNA editing sites excluding heterozygous sites. We further figured out 7-11 final RESs using a new error model specially designed for RNA editing site detection. The resulting RNA editing sites all agree with the validation using the Sanger sequencing. Conclusion: We report a near-zero error rate method in identifying RNA editing sites of long-read amplicons with the use of PacBio CCS sequencing.

Visual midline gauge validity and repeatability: Comparison to a current clinical method.

Visual midline shifts are thought to occur post-stroke and be a risk factor for falls. This study investigates a new method for quantifying visual midline shifts, a first step toward developing greater understanding of visual midline shift. This study standardized the parameters of a novel visual midline gauge, compared the results with the current clinical method, and presents normative data and repeatability of both methods. Ninety-three participants without neurological or ocular problems were recruited in Canada and Hong Kong. In experiment 1, horizontal and vertical visual midlines were measured using the gauge for two speeds and two repositioning methods. In experiment 2, visual midline was measured for three distances using a target speed and repositioning method chosen based on the first experiment. Visual midlines were also measured using the current clinical method during both visits. There were no significant effects of age, speed, study location, or repositioning method on visual midline positions (all p>0.05). For the horizontal direction, measurements at 25 cm were different from those at 50 (p=0.03) and 100 cm (p=0.001). For the vertical direction, there was no such effect. The measurements were found to be repeatable to within approximately 3°. In both visits, there were significant correlations between measurements using the visual midline gauge and the clinical method for the vertical direction (all p<.001) but not for the horizontal direction (all p>0.05). The measurement of visual midline is tolerant of differences in target speed, testing method, and age of the participants, and the visual midline gauge measurements are repeatable.

Multichannel deconvolution with a high-frequency structural regularization

The resolution of seismic data determines the ability to characterize stratigraphic features from observed seismic records. Sparse spike inversion, as an important processing method, can effectively improve the band-limited property of the seismic data. However, this approach ignores the spatial information along seismic traces, which causes the unreliability of the reconstructed high-resolution data. In this paper, we develop a high-frequency structure-constrained multichannel deconvolution (HFSC-MD) to alleviate this issue. This method allows the cost function to incorporate high-frequency spatial information in the form of a prediction-error filter (PEF) to regularize the components of the result beyond the original frequency. The PEF, also called a high-frequency reflection structural characterization operator, is estimated from the mapping relationship of low- and high-frequency components. We adopt the alternating direction method of multipliers to solve the cost function in HFSC-MD. Synthetic and field data demonstrate that our method recovers more reliable high-resolution data and enriches reflective structures.