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Aiming at the problem of aggravated wear of coiled tubing caused by buckling in underground, a contact wear model between compression buckling coiled tubing and wellbore was established based on submodel method. Archard model was introduced to calculate the wear depth of coiled tubing. The removal process of material on the surface of coiled tubing was described by moving contact boundary node method to avoid mesh distortion.The wear of various compressive loads and annulus clearance are predicted.The results show that the coiled tubing is subjected to sinusoidal buckling and the wear parts are few, and the compression load and annular clearance have little influence on the wear of the coiled tubing.There are many and continuous wear parts in coiled tubing with spiral buckling, and the compression load and annulus clearance have great influence on the wear of coiled tubing This paper provides a calculation method for quantitative prediction of wear of buckling coiled tubing in wellbore.

In smart transportation, assisted driving relies on data integration from various sensors, notably LiDAR and cameras. However, their optical performance can degrade under adverse weather conditions, potentially compromising vehicle safety. Millimeter-wave radar, which can overcome these issues more economically, has been re-evaluated. Despite this, developing an accurate detection model is challenging due to significant noise interference and limited semantic information. To address these practical challenges, this paper presents the TC–Radar model, a novel approach that synergistically integrates the strengths of transformer and the convolutional neural network (CNN) to optimize the sensing potential of millimeter-wave radar in smart transportation systems. The rationale for this integration lies in the complementary nature of CNNs, which are adept at capturing local spatial features, and transformers, which excel at modeling long-range dependencies and global context within data. This hybrid approach allows for a more robust and accurate representation of radar signals, leading to enhanced detection performance. A key innovation of our approach is the introduction of the Cross-Attention (CA) module, which facilitates efficient and dynamic information exchange between the encoder and decoder stages of the network. This CA mechanism ensures that critical features are accurately captured and transferred, thereby significantly improving the overall network performance. In addition, the model contains the dense information fusion block (DIFB) to further enrich the feature representation by integrating different high-frequency local features. This integration process ensures thorough incorporation of key data points. Extensive tests conducted on the CRUW and CARRADA datasets validate the strengths of this method, with the model achieving an average precision (AP) of 83.99% and a mean intersection over union (mIoU) of 45.2%, demonstrating robust radar sensing capabilities. [ABSTRACT FROM AUTHOR]

This study focuses on the synthesis and characterization of recyclable nano-adsorbents of magnetic nanocomposites (Fe3O4@ZnO-RGO). The Fe3O4 nanoparticles were used as magnetic responders, ZnO coated on the surface of Fe3O4 not only captured Cu2+ but also prevented the oxidation of Fe3O4, and reduced graphene oxide (RGO) acted as Fe3O4@ZnO carrying platform could effectively adsorb Cu2+. The physical and chemical performance characterization suggested that the nano-adsorbents had stable structure, good hydrophilicity, and magnetic properties. Furthermore, the research results on adsorption performance indicated that the Fe3O4@ZnO-RGO had high adsorption properties for Cu2+, and the adsorption rate could reach more than 98%. The adsorption process conformed to the Langmuir model and second-order adsorption kinetics, and the adsorption of Cu2+ was mainly chemisorption, accompanied by physical adsorption. In addition, because of the special magnetic response performance, Fe3O4@ZnO-RGO could be quickly separated from the solutions for cyclic adsorption so as to avoid secondary pollution. This provided a valuable idea for the effective development of clean and efficient Cu2+ adsorbents, and the Fe3O4@ZnO-RGO had great application potential in the field of Cu2+ wastewater treatment. [ABSTRACT FROM AUTHOR]

This study focuses on the synthesis and characterization of recyclable nano-adsorbents of magnetic nanocomposites (Fe3O4@ZnO-RGO). The Fe3O4 nanoparticles were used as magnetic responders, ZnO coated on the surface of Fe3O4 not only captured Cu2+ but also prevented the oxidation of Fe3O4, and reduced graphene oxide (RGO) acted as Fe3O4@ZnO carrying platform could effectively adsorb Cu2+. The physical and chemical performance characterization suggested that the nano-adsorbents had stable structure, good hydrophilicity, and magnetic properties. Furthermore, the research results on adsorption performance indicated that the Fe3O4@ZnO-RGO had high adsorption properties for Cu2+, and the adsorption rate could reach more than 98%. The adsorption process conformed to the Langmuir model and second-order adsorption kinetics, and the adsorption of Cu2+ was mainly chemisorption, accompanied by physical adsorption. In addition, because of the special magnetic response performance, Fe3O4@ZnO-RGO could be quickly separated from the solutions for cyclic adsorption so as to avoid secondary pollution. This provided a valuable idea for the effective development of clean and efficient Cu2+ adsorbents, and the Fe3O4@ZnO-RGO had great application potential in the field of Cu2+ wastewater treatment. [ABSTRACT FROM AUTHOR]

With the rocketing progress of the Internet, it is easier for people to get information about the objects that they are interested in. However, this information usually has conflicts. In order to resolve conflicts and get the true information, truth discovery has been proposed and received widespread attention. Many algorithms have been proposed to adapt to different scenarios. This paper aims to investigate these algorithms and summarize them from the perspective of algorithm models and specific concepts. Some classic datasets and evaluation metrics are given in this paper. Some future directions for readers are also provided to better understand the field of truth discovery. [ABSTRACT FROM AUTHOR]

Graph pattern matching (GPM) can be used to mine the key information in graphs. Exact GPM is one of the most commonly used methods among all the GPMrelated methods, which aims to exactly find all subgraphs for a given query graph in a data graph. The exact GPM has been widely used in biological data analyses, social network analyses and other fields. In this paper, the applications of the exact GPM were first introduced, and the research progress of the exact GPM was summarized. Then, the related algorithms were introduced in detail, and the experiments on the state-of-the-art exact GPM algorithms were conducted to compare their performance. Based on the experimental results, the applicable scenarios of the algorithms were pointed out. New research opportunities in this area were proposed. [ABSTRACT FROM AUTHOR]

In the live broadcast process, eye movement characteristics can reflect people' s attention to the product. However, the existing interest degree predictive model research does not consider the eye movement characteristics. In order to obtain the users' interest in the product more effectively, we will consider the key eye movement indicators. We first collect eye movement characteristics based on the self-developed data processing algorithm fast discriminative model prediction for tracking (FDIMP), and then we add data dimensions to the original data set through information filling. In addition, we apply the deep factorization machine (DeepFM) architecture to simultaneously learn the combination of low-level and high-level features. In order to effectively learn important features and emphasize relatively important features, the multi-head attention mechanism is applied in the interest model. The experimental results on the public data set Criteo show that, compared with the original DeepFM algorithm, the area under curve ( AUC ) value was improved by up to 9. 32%. [ABSTRACT FROM AUTHOR]

Near-Earth asteroids are a great threat to the Earth, especially potential rendezvous and collision asteroids. To protect the Earth from an asteroid collision, it is necessary to investigate the asteroid defence problem. An asteroid terminal defence method based on multisatellite interception was designed in this study. For an asteroid intruding in the sphere of the gravitational influence of the Earth, multiple interceptor satellites are used to apply a kinetic energy impulse to deflect the orbit of the asteroid. First, the effects of planned interception time and planned interception position on the required impulse velocity increment applied to the asteroid are assessed for interception opportunity selection. Second, multiple interceptor satellites are selected to perform the defence task from the on-orbit available interceptor satellite formation. An improved contract net protocol algorithm considering the Lambert orbital manoeuvre is designed to fulfil the task allocation and satellite orbit planning. Finally, simulation experiments demonstrate the rationale and effectiveness of the proposed method, which provides support for asteroid terminal defence technology. [ABSTRACT FROM AUTHOR]

This letter studies the estimation of structured clutter covariance matrix (CCM) for space–time adaptive processing (STAP)-based airborne multiin multiout (MIMO) radar with limited training data. The Kronecker-product-expansion structure of the CCM is considered, where each term involves two Kronecker product operators. By exploiting the low-rankness of two permutations of the CCM, we propose a novel estimator based on least squares penalized by two nuclear norms. The estimator is also extended by considering the linear structures of the CCM. We examine the structure of the proposed solution and demonstrate its superior performance through simulation studies. [ABSTRACT FROM AUTHOR]

In this letter, a novel pattern synthesis algorithm that jointly optimizes the number, positions, and excitation of elements in a sparse linear array (SLA) is designed based on continuous compressed sensing. The pattern synthesis problem is first formulated as a sparse recovery problem, which can be solved using semidefinite programming involving atomic norm minimization (ANM). The parameters of the designed SLA, including the corresponding element positions and excitations, are determined via Vandermonde decomposition and least squares in the continuous domain. As ANM works directly in the continuous domain, the proposed algorithm is able to synthesize the desired patterns with a small mismatch and a small number of elements. A number of representative numerical experiments show the effectiveness of our algorithm. [ABSTRACT FROM AUTHOR]

This article considers the waveform design problem for airborne multiple-input-multiple-output (MIMO) radar systems with space-time adaptive processing (STAP). We choose the mutual information between the received signal and the target impulse response as the design metric to achieve enhanced detection performance under the influences from neighboring range cells. In order to solve the resulting optimization problem, we first decompose the objective function into two parts and consider their optimization individually. We optimize the first part by relaxing it using an upper bound and optimize the second part using a simple optimization procedure based on the majorization–minimization (MM) algorithm. The alternation direction method of multipliers (ADMM) framework is then adopted to derive the overall solution, where the MM algorithm is employed again to handle the fourth-order term of the waveform vector. Numerical results are provided to show that the proposed algorithm has better detection performance than that of the existing methods. [ABSTRACT FROM AUTHOR]

Graph Pattern based Node Matching (GPNM) has been proposed to find all the matches of the nodes in a data graph GD based on a given pattern graph GP. GPNM has been increasingly adopted in many applications such as group finding and expert recommendation, in which data graphs are frequently updated over time. Moreover, many typical pattern graphs frequently and repeatedly appear in users’ queries in a short period of time, e.g., social graph searches on Facebook. To deliver a GPNM result in such applications, the existing GPNM methods have to perform an incremental GPNM procedure for each of the updates in the data graph, which is computationally expensive. To address this problem, in this paper, we first analyze the elimination relationships between multiple updates in GD and the hierarchical structure between these elimination relationships. Then, we generate an Elimination Hierarchy Tree (EH-Tree) to index the elimination relationships and propose an EH-Tree based GPNM method, called EH-GPNM, considering the elimination relationships between multiple updates in GD. EH-GPNM first delivers the GPNM result of an initial query, and then delivers the GPNM result of a subsequent query, based on the initial GPNM result and the multiple updates of GD that occur between those two queries. The experimental results on five real-world social graphs demonstrate that our proposed EH-GPNM is much more efficient than the state-of-the-art GPNM methods. [ABSTRACT FROM AUTHOR]

As training samples are not always target-free in space–time processing for airborne radar, the traditional methods usually use the sample covariance matrix (SCM) as the test covariance matrix (TCM) to censor contaminated training samples. However, the SCM cannot represent the property of the cell under test (CUT) accurately, resulting in low selection efficiency. To deal with this problem, this letter proposes a novel training sample selection method based on covariance matrix whitening. Specifically, we utilize the reconstructed subaperture’s clutter covariance matrix (RSCCM) of the CUT as the TCM. The RSCCM is only determined by the CUT and can characterize the CUT directly. Then, we use the RSCCM to whiten the subaperture’s covariance matrix of the training sample. A criterion for selecting the training samples is derived based on the maximum eigenvalue of the whitened subaperture’s covariance matrix, which is related to the energy of the outliers and more stable than the statistic of the generalized inner product method. Simulations are conducted to evaluate the performance of the proposed method. [ABSTRACT FROM AUTHOR]

Long-time coherent integration can effectively improve the radar detection ability of a maneuvering target. However, with the increasing of integration time, radial velocity and radial acceleration of the moving target result in linear range migration (LRM), range curvature (RC), and Doppler frequency migration (DFM) of the received signal. These may degrade detection performance seriously. To solve these problems, an efficient coherent accumulation method based on improved time reversal transform (ITRT) and modified radon Fourier transform (MRFT) is proposed in this paper. Different from conventional methods, this approach firstly eliminates the acceleration effects by ITRT. It can remove RC and DFM simultaneously via multiplying its slow-time reversed conjugate data in range-frequency and azimuth-time domains. Then by employing a new Doppler filter bank (DFB), MRFT realizes the echoes spatial temporal decoupling as well as the coherent integration in searching-velocity and Doppler-frequency domains. The major advantages of the proposed method are that RC and DFM can be rectified without parameter estimation, and computational complexity can be optimized by the new FFT-based filter bank. In addition, due to the nonlinearity of ITRT operation, the influence of cross terms of multiple targets on detection performance is also discussed. Several simulations are provided to demonstrate the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

This paper aims to address grating-lobe clutter suppression in a uniform subarray (US) for the airborne radar system. Due to the sparsity at the subarray level and different scanning angles between the inner subarray and the subarray, grating lobes are produced. The grating-lobe clutter then enters the main lobe, defined as main grating-lobe clutter (MGLC). When the target falls into the MGLC with the same Doppler frequency, the clutter cannot be effectively suppressed by the conventional space-time adaptive processing (STAP) algorithm. Hence, we design a transmit beamforming (TB)-STAP algorithm based on a modified phased array (MPA) radar system, where TB suppresses the MGLC, while STAP suppresses the main-lobe and sidelobe clutter; the goal is to maximize the output signal-to-clutter-plus-noise ratio (SCNR). The optimal solution of TB-STAP can be obtained through several iterations, and in each iterative algorithm, the TB is a quadratically constrained quadratic problem (QCQP) solved via the semidefinite relaxation (SDR) technique, and the STAP can obtain a closed solution by the minimum variance distortionless response (MVDR) algorithm. The simulation results show that compared with traditional STAP in PA and multiple-input multiple-output (MIMO) radars, the proposed algorithm in MPA radar greatly improves the output SCNR. [ABSTRACT FROM AUTHOR]

Frequency-modulated continuous waveform signals are of great interest in automotive radar, perimeter radar, and wide-area surveillance radar applications. For automotive radars and wide-area surveillance radars, which generally utilize high Doppler frequency resolution for target recognition, the important potential target such as a walking person cannot be viewed as a point-like target due to different velocities of the reflection points of a pedestrian. The echoes of such targets have extended Doppler spectrum, and they appear as horizontal lines in the range-Doppler plane, rather than a single point considered in the traditional cases. However, the existing approaches do not make full use of the Doppler-spread characteristic to enhance the detection performance of pedestrians. In this paper, we will show how this characteristic can be used to enhance the detection performance for Doppler-spread targets. The main goal of this paper is pedestrian detection in wide-surveillance radar applications and the proposed approach can also be applied in automotive radar applications. Motivated by the Hough transform, the energies of most Doppler bins corresponding to the target will be accumulated to enhance the detection performance. The questions of how many and which cells should be accumulated in practical applications are discussed in detail. The proposed approach is based on the ordered statistical-constant false alarm rate but tailored to Doppler-spread targets. The detection performance of this algorithm is derived analytically, and verified via practical wide-area surveillance radar signals. [ABSTRACT FROM AUTHOR]

This letter proposes a new approach for knowledge-aided estimation of structured clutter covariance matrices (CCMs) in airborne radar systems with limited training data. First, we model the CCM in space–time adaptive processing (STAP) as a sum of low-rank Kronecker products. We then apply a permutation operation to convert the Kronecker factors into linear structures and propose a novel CCM estimation method under the maximum-likelihood framework. Employing a proximal gradient algorithm, the proposed method simultaneously exploits the knowledge about the clutter and the Kronecker structure of the CCM. We finally evaluate the performance of the proposed method using real data from airborne STAP. [ABSTRACT FROM PUBLISHER]

In this Letter, the authors propose an approach to evaluate the average signal‐to‐clutter ratio loss (ASCRL) of an adaptive radar filter with a parametrically constrained clutter covariance matrix (CCM). The approach is a two‐step strategy, the constrained Cramér–Rao bound of the CCM estimate is first derived, and then it is used to derive the ASCRL. More specifically, the ASCRL of an adaptive filter with a trace constrained CCM is derived. Simulation results show that our approach performs well in evaluating the trace constrained ASCRL for both complex Gaussian clutter and compound‐Gaussian clutter. [ABSTRACT FROM AUTHOR]

Crowdsourcing integrates human wisdom to solve problems. Tremendous research efforts have been made in this area. However, most of them assume that workers have the same credibility in different domains and workers complete tasks independently. This leads to an inaccurate evaluation of worker credibility, hampering crowdsourcing results. To consider the impact of worker domain expertise, we adopted a vector to more accurately measure the credibility of each worker. Based on this measurement and prior task domain knowledge, we calculated fine-grained worker credibility on each given task. To avoid tasks being assigned to dependent workers who copy answers from others, we conducted copier detection via Bayesian analysis. We designed a crowdsourcing system called SWWC composed of a task assignment stage and a truth discovery stage. In the task assignment stage, we assigned tasks wisely to workers based on worker domain expertise calculation and copier removal. In the truth discovery stage, we computed the estimated truth and worker credibility by an iterative method. Then, we updated the domain expertise of workers to facilitate the upcoming task assignment. We also designed initialization algorithms to better initialize the accuracy of new workers. Theoretical analysis and experimental results showed that our method had a prominent advantage, especially under a copying situation. [ABSTRACT FROM AUTHOR]