Time-consuming Problem Research Articles

Irregular Modular Subarrayed Phased Array Tiling by Algorithm X and Differential Evolution Algorithm

In this letter, an efficient and versatile method based on algorithm X and differential evolution algorithm is proposed to synthesize irregular modular subarrayed planar phased arrays with exact tiling. To alleviate the time-consuming problem of using algorithm X to synthesize large arrays, the proposed method obtains the subarray configuration with exact tiling through twice-partitioning operations. In the first partition process, the planar phased array is divided into several first-level subarrays. In the second partition process, the first-level subarrays are respectively divided into pre-specified irregular modular subarrays, called second-level subarrays. All feasible schemes for dividing the first-level subarrays and second-level subarrays can be obtained by algorithm X. Through the above partition process, the partitioning paradigm can be represented by only a few variables, which can be optimized by swarm intelligence algorithms. In this letter, the variables representing the subarray configuration are optimized by differential evolution algorithm to obtain excellent radiation performances. Three representative numerical examples are presented and discussed to assess the validity, efficiency, and reliability of the proposed method.

Fast inversion method for seepage parameters of core earth-rock dam based on LHS-SSA-MKELM fusion surrogate model

The inversion of seepage parameters is an important research topic in the safety evaluation of earth-rock dams. Core earth-rock dam is a commonly used structural type in earth-rock dams. Most of the existing inversion methods focus on concrete dams, and few of them are aimed at earth-rock dams. These methods have the problems of slow inversion speed or low accuracy. Aiming at the above problems, this paper proposes a novel fast inversion method for the seepage parameters of core earth-rock dams based on the LHS-SSA-MKELM fusion surrogate model. The proposed fusion inversion model optimizes the selection of combination models based on the surrogate model framework, making it more suitable for the inversion calculation of seepage parameters in earth-rock dams. First, based on the seepage theory of earth-rock dams, the finite element numerical simulation model of core earth-rock dam seepage is constructed. The Latin Hypercube Sampling (LHS) method is used to obtain the parameter sample dataset according to the range of the seepage parameters, and the sample dataset is substituted into the finite element model to obtain the corresponding theoretical seepage pressure of different datasets. Then, the Mixed Kernel Extreme Learning Machine (MKELM) is used to establish the mapping relationship between seepage parameters and the theoretical seepage pressure. The MKELM parameters were optimized by using the Sparrow Search Algorithm (SSA) to further improve the accuracy of the surrogate model. Finally, taking the minimum error between the theoretical value and the measured value of the surrogate model as the objective function, the SSA algorithm is used to derive the optimal inversion value of seepage parameters. The results of engineering examples show that the mean absolute percentage error between the measured value of the core earth-rock dam seepage pressure and the simulated value after inversion is only 4.958%. In addition, the average inversion time of the proposed surrogate model is only 6.6 s, which greatly reduces the time-consuming problem of numerical simulation. The proposed method provides a new way for accurate and fast inversion of seepage parameters of core earth-rock dams after long-term operation.

High-order parametrization of the hypergeometric-Meijer approximants

In this work, we introduce an extension to the hypergeometric algorithm we developed before for the resummation of divergent series. The extension overcome the time-consuming problem we face in the parametrization process of the hypergeometric approximants. In the previous version, the parametrization process results in a set of non-linear equations which is hard to solve (might be impossible) for relatively high orders (greater than six) using normal PCs with Mathematica software for instance. To solve this problem, we formulate an equivalent (order by order) linear set of equations which is easy to solve in an appropriate time using normal PCs. We also show that such extension of the hypergeometric resummation algorithm is able to employ non-perturbative information like strong-coupling and large-order asymptotic data which are always used to accelerate the convergence. We applied the algorithm for different orders (up to O(29)) of the ground state energy of the x4 anharmonic oscillator with and without the non-perturbative information. We also considered the available 20 orders for the ground state energy of the PT−symmetric ix3 anharmonic oscillator as well as the given 20 orders of its strong-coupling expansion or equivalently the Yang–Lee model. For high order weak-coupling parametrization, accurate results have been obtained for the ground state energy and the non-perturbative parameters describing strong-coupling and large-order asymptotic behaviors. The employment of the non-perturbative data accelerated the convergence very clearly. The High temperature expansion for the susceptibility within the SQ lattice has been also considered and led to accurate prediction for the critical exponent and critical temperature.

Ability-aware knowledge distillation for resource-constrained embedded devices

Deep Neural Network (DNN) models have notably improved the efficiency of machine learning tasks. However, their high storage and computational costs restrict their deployment on resource-limited embedded devices. Knowledge distillation (KD) has emerged as a promising approach for compressing DNN models. However, two challenges in KD, namely the capacity gap problem and the time-consuming redundancy problem, have hindered its performance and efficiency in compression. To alleviate these challenges, this paper proposes a novel framework, called Ability-Aware Knowledge Distillation (AAKD). AAKD introduces a knowledge sample selection strategy and an adaptive teacher switching strategy based on the dynamic awareness of the student’s ability. This enables the framework to automatically select suitable knowledge samples and teacher networks according to the increasing representation ability of students. Extensive experiments on different datasets and models have demonstrated that AAKD can enhance the performance of compact student models, significantly improve the efficiency of distillation, and lead to higher compression rates.

LSR-YOLO: A High-Precision, Lightweight Model for Sheep Face Recognition on the Mobile End.

The accurate identification of sheep is crucial for breeding, behavioral research, food quality tracking, and disease prevention on modern farms. As a result of the time-consuming, expensive, and unreliable problems of traditional sheep-identification methods, relevant studies have built sheep face recognition models to recognize sheep through facial images. However, the existing sheep face recognition models face problems such as high computational costs, large model sizes, and weak practicality. In response to the above issues, this study proposes a lightweight sheep face recognition model named LSR-YOLO. Specifically, the ShuffleNetv2 module and Ghost module were used to replace the feature extraction module in the backbone and neck of YOLOv5s to reduce floating-point operations per second (FLOPs) and parameters. In addition, the coordinated attention (CA) module was introduced into the backbone to suppress non-critical information and improve the feature extraction ability of the recognition model. We collected facial images of 63 small-tailed Han sheep to construct a sheep face dataset and further evaluate the proposed method. Compared to YOLOv5s, the FLOPs and parameters of LSR-YOLO decreased by 25.5% and 33.4%, respectively. LSR-YOLO achieved the best performance on the sheep face dataset, and the mAP@0.5 reached 97.8% when the model size was only 9.5 MB. The experimental results show that LSR-YOLO has significant advantages in recognition accuracy and model size. Finally, we integrated LSR-YOLO into mobile devices and further developed a recognition system to achieve real-time recognition. The results show that LSR-YOLO is an effective method for identifying sheep. The method has high recognition accuracy and fast recognition speed, which gives it a high application value in mobile recognition and welfare breeding.

Robust binary fringe pattern optimization for optical three-dimensional shape measurement

Current studies show that binary fringe patterns can be optimized in the phase or intensity domain. The problem with the former is the poor sensitivity to defocusing levels and the problem with the latter is that phase errors can not be restricted efficiently. In this paper, a binary fringe pattern optimization technology, combining intensity similarity and phase similarity, is proposed to improve fringe pattern structure similarity and defocusing robustness. Moreover, a binary patch optimization framework is also proposed to solve the time-consuming problem. Simulation and experiment results show that the proposed optimization can produce binary fringe patterns with high phase quality and defocusing robustness.

A paralleled embedding high-dimensional Bayesian optimization with additive Gaussian kernels for solving CNOP

Conditional Nonlinear Optimal Perturbation (CNOP) is widely used in atmospheric and oceanic predictability studies. Solving CNOP is essentially a nonlinear optimization problem with certain constraints. One method to solve CNOP is the intelligent optimization algorithm. But it may not always obtain the satisfactory solution and efficiency because of the local exploitation of individual particles. The Bayesian optimization algorithm can avoid this local stagnation by building a global probability grasp for the optimization space of CNOP. Nonetheless, the limit of approximately 20-dimensional optimization space hampers its application in solving CNOP of high-dimensional numerical models. To overcome this bottleneck, we propose a paralleled embedding high-dimensional Bayesian optimization with additive Gaussian kernels (PEBOA) algorithm, which mainly consists of the feature extraction process and low-dimensional optimization process. In PEBOA, a feature extraction method (deepFE) using the convolutional Autoencoder with residual connections and customized constraint-preserved loss function is designed, which compresses 10 million dimensional data to relatively low-dimensional search spaces, from tens to hundreds of dimensions. Then, we propose the strategy of additive Gaussian kernels, with which the Bayesian optimization algorithm can optimize in relatively low-dimensional search spaces. Concretely, a kernel handles a subspace, and a subspace can handle dimensions approximately not more than 20. Additionally, the modified acquisition function can sample multiple candidates simultaneously with the aim of accelerating the optimization process. We apply PEBOA to solve CNOP of Regional Ocean Modeling System (ROMS) for identifying optimal initial errors of upstream Kuroshio transport variation, which is a 10 million dimensional and time-consuming problem. The computational performance of PEBOA and physical mechanisms of the obtained CNOP are analyzed. Experimental results indicate that deepFE excels Principal Component Analysis (PCA) in terms of relative error ratio, the magnitude of objective function values, and the obtained CNOP pattern. Besides, compared to deepFE-based Particle Swarm Optimization, PEBOA has better solving efficiency with 3.4% larger objective function values and 2.2 times greater likelihood of obtaining the valid CNOP. Furthermore, the modified acquisition function reduces computation time by about 71.0% with four cores. The physical mechanism analysis shows that CNOPs obtained by PEBOA are almost identical to the adjoint method, which can cause an anomalous increase (decrease) in upstream Kuroshio transport and maintain physics consistency.

Self-supervised learning for heterogeneous graph via structure information based on metapath

Graph neural networks (GNNs) are the dominant paradigm for modeling and handling graph structural data by learning universal node representation. The traditional way of training GNNs depends on a great many labeled data, which is time-consuming and money-consuming. In some special scenes, it is even unavailable and impracticable. Self-supervised representation learning, which can generate labels by graph structural data itself, is a potential approach to tackle this problem. And turning to research self-supervised learning problems for heterogeneous graphs is more challenging than dealing with homogeneous graphs, there are fewer studies about it as well. In this paper, we propose a SElf-supervised learning method for heterogeneous graph via Structure Information based on Metapath (SESIM). Firstly, the pseudo-labels are constructed to train pretext tasks, using data itself and avoiding time-consuming manual labeling. Afterward, we use traditional graph neural networks to aggregate node features, obtaining the node embeddings. And then, the primary task and pretext tasks are designed by these node embeddings. The pretext tasks, i.e., jump numbers prediction between nodes in each metapath, can improve the representation ability of the primary task. Moreover, predicting jump numbers in each metapath can effectively utilize graph structural information, which is the essential property of nodes. Therefore, SESIM deepens the understanding of models for graph structure. At last, we train the primary task and pretext tasks jointly and balance the contributions of pretext tasks for the primary task. The key advantage of our proposed model is that we research self-supervised learning for the heterogeneous graph to address the time-consuming and money-consuming problem of obtaining labels. And we design a novel pretext task, i.e., jump numbers prediction in each metapath, via graph structural information based on the metapath. Empirical results validate the performance of the SESIM method and demonstrate that this method can improve the representation ability of traditional neural networks on link prediction tasks and node classification tasks.

GANAD: A GAN-based method for network anomaly detection

Cyber-intrusion always leads to severe threats to the network, i,e., system paralysis, information leaky, and economic losses. To protect network security, anomaly detection methods based on generative adversarial networks (GAN) for hindering cyber-intrusion have been proposed. However, existing GAN-based anomaly score methods built upon the generator network are designed for data synthesis, which would get unappealing performance on the anomaly detection task. Therefore, their low-efficient and unstable performance make detection tasks still quite challenging. To cope with these issues, we propose a novel GAN-based approach GANAD to address the above problems which is specifically designed for anomaly identification rather than data synthesis. Specifically, it first proposes a similar auto-encoder architecture, which makes up for the time-consuming problem of the traditional generator loss computation. In order to stabilize the training, the proposed discriminator training replaces JS divergence with Wasserstein distance adding gradient penalty. Then, it utilizes a new training strategy to better learn minority abnormal distribution from normal data, which contributes to the detection precision. Therefore, our approach can ensure the detection performance and overcomes the problem of unstable in the process of GAN training. Experimental results demonstrate that our approach achieves superior performance to state-of-the-art methods and reduces time consumption at the same time.

Electrospun-Aligned Gd-Doped In2O3 Nanofiber Field-Effect Transistors for Artificial DNA Detection

In view of the time-consuming and laborious problem of detecting biological genetic information by conventional methods, it is important to study high-performance deoxyribonucleic acid (DNA) biosensors. In this report, an aligned Gd-doped In2O3 nanofiber favoring carrier transport was prepared by electrospinning. When the Gd doping concentration is 0.5 mol %, the aligned In2O3 nanofiber field-effect transistors (FETs) exhibit an excellent mobility (μ = 8.7 cm2/Vs), suitable threshold voltage (VTH = 0.3 V), large on/off current ratio (Ion/Ioff = 4.1 × 107), and outstanding stability. The FET biosensor based on one-dimensional nanofibers has the advantages of simple structure, fast response, high sensitivity, and extraordinary potential for biosensor applications, where the current variation is quite pronounced at concentrations as low as 10 nM DNA immobilized in the active layer of the aligned In1.995Gd0.005O3 nanofiber FET, and this phenomenon becomes more pronounced as the concentration of immobilized DNA increases. The sensing mechanism of the current variation is mainly attributed to the oxidation of guanine nucleotides in the DNA molecular chain. The aligned Gd-doped In2O3 nanofiber FETs exhibit high sensitivity to DNA at room temperature, which provides a solution for the development of novel DNA biosensors.

AN INSIGHT INTO BLOG USE IN EFL READING CLASS

This study was performed concerning the usage of a blog to ascertain the perceptions of the students regarding the usage of blogs in Extensive Reading (hereafter ER) classes at one of Tasikmalaya’s universities. Braun and Clarke’s Descriptive Case Study Design and Thematic Analysis techniques were used in this study. The primary data source was semi-structured interviews with four participants, transcribed and analysed using Thematic Analysis. This study discovered that blogging has advantages and disadvantages in the ER class. The advantage of using blogs as media in Extensive Reading classes is that blogging is easy to use and can increase student creativity. Furthermore, blogging can increase student motivation to read, serve as a forum for reflection, and increase learning autonomy. However, based on the findings, it turns out that there are some disadvantages to using blogs as media in Extensive Reading classes, such as limited access to blogs, sluggish connectivity for submitting assignments, and causing time-consuming problems. Finally, the research provides information to lecturers so that lecturers can learn about the benefits and drawbacks of using blogs in ER learning.

Decomposed Two-Stage Prompt Learning for Few-Shot Named Entity Recognition

Named entity recognition (NER) in a few-shot setting is an extremely challenging task, and most existing methods fail to account for the gap between NER tasks and pre-trained language models. Although prompt learning has been successfully applied in few-shot classification tasks, adapting to token-level classification similar to the NER task presents challenges in terms of time consumption and efficiency. In this work, we propose a decomposed prompt learning NER framework for few-shot settings, decomposing the NER task into two stages: entity locating and entity typing. In training, the location information of distant labels is used to train the entity locating model. A concise but effective prompt template is built to train the entity typing model. In inference, a pipeline approach is used to handle the entire NER task, which elegantly resolves time-consuming and inefficient problems. Specifically, a well-trained entity locating model is used to predict entity spans for each input. The input is then transformed using prompt templates, and the well-trained entity typing model is used to predict their types in a single step. Experimental results demonstrate that our framework outperforms previous prompt-based methods by an average of 2.3–12.9% in F1 score while achieving the best trade-off between accuracy and inference speed.

A Pose Estimation Method Combining Instance Segmentation and Point Pair Features

For the traditional pose estimation method based on point pair features, both the preprocessing of scene point cloud and the construction of point pair features of scene point cloud have serious time-consuming problems, which cannot meet the needs of actual industry. Therefore, this paper proposes a pose estimation method that combines instance segmentation and point pair features. Therefore, this paper proposes a pose estimation method that combines instance segmentation and point pair features. First, use the Mask R-CNN-based instance segmentation network to obtain the location of the target object in the two-dimensional image of the scene; then, obtain the local point cloud data of the space where the target object is located from this position and the depth information of the scene; finally, the local the point cloud data is used as the scene point cloud based on point pair feature pose estimation to perform feature matching with the point cloud of the target object.

Automatic posture capture of sports movement based on sensor information fusion

Aiming at the problems of low accuracy and long time consumption of motion attitude information collection in the existing methods, a new automatic attitude capture method based on sensor information fusion is proposed. The line builds a motion attitude information acquisition architecture through five functional modules, including data acquisition and processing, laser sensor, data storage, voltage conversion and serial communication, to obtain motion attitude data. Then, the sensor image information collected by median filtering is processed, and the target image is obtained by fusing the binary image obtained by optical flow segmentation with the original image. Finally, the Mean-Shfit algorithm and particle filter are used to track the human moving target, and the self-similar matrix is used to automatically capture the motion posture. Experiments show that the method has high recall and accuracy for the collection results of motion attitude information, the accuracy rate of automatic motion attitude capture is as high as 95.5%, the maximum capture time is 79 ms, the time consumption is short, and the practical application effect is good.

A Novel Fault Diagnosis Method for Rotor-Bearing System Based on Instantaneous Orbit Fusion Feature Image and Deep Convolutional Neural Network

The rotor-bearing system of large rotating machinery has multiple bearings with complex vibration correlations, which significantly affect the effectiveness of intelligent diagnosis in industrial production. In this article, a new framework of fault diagnosis for the rotor with multiple bearings is proposed. The framework is composed of two parts: 1) instantaneous orbit feature fusion image construction; 2) the deep convolutional network based on transfer learning. The multivariate complex variational mode decomposition (MCVMD) is adopted to decompose the complex-valued signals of multiple bearings, which can make full use of the joint information between signals by considering the axis orbit of each bearing simultaneously. To our best knowledge, it is the first attempt of applying MCVMD to the field of fault diagnosis. Then, multiple orbit features are derived from the decomposed signals to reflect the transient state of vibration. Finally, the fusion feature images, constructed by the orbit features of multiple bearings, can exhaustively present the overall status of the rotor-bearing system. Parameter transfer is used for the deep convolutional network to solve the time-consuming training problem. The experiment and verification is carried out on three steam turbines and the pumped storage unit. The results demonstrate that the proposed method outperforms the existing approaches based on the original signal, frequency, or time-frequency features.

A Review on Peeling Techniques for Development of Shallot Peeling Machine

Abstract: Shallot is among the essential marketable vegetable and spice crops, extensively used in the south Indian kitchen for seasoning curries. Shallots are known for their subtle, delicate taste, and shallots can be pickled, caramelized, or roasted and used in salad dressings, gravies, and vinaigrettes. The shallots are peeled by hand before using in food operations. The peeling process faces a multitudinous problem of time consumption and is veritably important for minimal process and from a domestic point of view. Automation of processing operations plays a vital function in removing the negative attributes of traditional processing ways and promoting desired quality. The proposed system helps to identify the peeling method for effective peeling of shallot onion at the domestic level to acquire a quality product.

Decoupling calibration method of long focal length camera using vanishing point

Optical measurement and perception technology is widely used in the field of smart energy. The accurate calibration of the internal and external parameters of the camera in the optical system is very important for the application of the system in three-dimensional (3D) reconstruction and geometric measurement. At present, the mainstream camera calibration methods include Zhang’s calibration method and Tsai’s calibration method. These methods all choose to calculate the distortion parameters together with the camera’s internal parameters. For long focal length and narrow-field-of-view cameras with smaller perspective distortion, the coupling calculation of parameters may cause inaccurate calibration parameters and more time-consuming problems. To improve the calibration accuracy of the long focal length camera, we propose an efficient noniterative camera calibration method, based on the equation relationship between the vanishing point coordinates and the first-order single-parameter division lens distortion coefficient, and based on the radial distortion separation model as well as the corner subpixel coordinates and checkerboard 3D space points. The spatial point correspondence is solved to obtain the homography matrix to complete the calculation of the internal parameters of the camera. Our work has potential applications in photovoltaic troubleshooting and intelligent inspection. It may also contribute to the practical application of the sensor in intelligent energy.

Spherical Planting Inversion of GRAIL Data

In large-scale potential field data inversion, constructing the kernel matrix is a time-consuming problem with large memory requirements. Therefore, a spherical planting inversion of Gravity Recovery and Interior Laboratory (GRAIL) data is proposed using the L1-norm in conjunction with tesseroids. Spherical planting inversion, however, is strongly dependent on the correct seeds’ density contrast, location, and number; otherwise, it can cause mutual intrusion of anomalous sources produced by different seeds. Hence, a weighting function was introduced to limit the influence area of the seeds for yielding robust solutions; moreover, it is challenging to set customized parameters for each seed, especially for the large number of seeds used or complex gravity anomalies data. Hence, we employed the “shape-of-anomaly” data-misfit function in conjunction with a new seed weighting function to improve the spherical planting inversion. The proposed seed weighting function is constructed based on the covariance matrix for given gravity data and can avoid manually setting customized parameters for each seed. The results of synthetic tests and field data show that spherical planting inversion requires less computer memory than traditional inversion. Furthermore, the proposed seed weighting function can effectively limit the seed influence area. The result of spherical planting inversion indicates that the crustal thickness of Mare Crisium is about 0 km because the Crisium impact may have removed all crust from parts of the basin.

Kalman Filter for Artifact Reduction in MRI Imaging: A Literature Review

Background: The appearance of Noise Artifacts is admittedly very disturbing the quality of MRI diagnostic images. The application of BLADE and STIR sequences based on artificial intelligence technology has described in reference that it is able to suppress moving signals from the vascular and signals from fat tissue. However, the long consumption of scanning time is one of the drawbacks that arise, and subsequently affect the presence of noise in the image. The use of another technique, namely kallman filter with the Matlab (Matrix Laboratory) program, which is applied as part of post-scanning image processing will help reduce image noise values ​​that arise due to the problem of long scanning time consumption in both sequences. The aim of this literature review is to determine the potential of the filter kalman for the reduction of artifacts on MRI examinations. Methods: The search was conducted using google scholar, WILEY, IEE Explore, SPRINGER, Scopus and PERPUSNAS in English with the article period 2004-2020 using the keywords MRI artifacts, reducing artifacts and the Kalman filter algorithm. Result: The results of a review of 4 articles of kalman filter intervention on MRI Brain, MRI Abdomen and MR Cardiac showed that kalman filter was good enough in reducing artifacts and increasing anatomical information. Conclusion: Kalman filter has the potential to reduce artifacts, improve image quality and clarify anatomical images on MRI examinations.

Design of Loading and Unloading Mechanism of Spring Steel Cloth Fixture for Computer Embroidery Machine

At present, the computer embroidery machine industry mainly uses manpower to disassemble the spring steel cloth clamp, which is very inefficient. This research designed a spring steel cloth clamp loading and unloading mechanism to solve this problem. First of all, the scheme of the loading and unloading mechanism of the spring steel cloth clamp is proposed. The mathematical model is established, and the simulation verification is carried out on this basis. Then the structural design is carried out. Finally, the prototype of the loading and unloading mechanism of the spring steel cloth clamp was trial produced, and continuous loading and unloading tests were carried out. The results show that the loading and unloading mechanism can automatically and continuously load and unload the spring steel cloth clips, meeting the design requirements. It also provides a solution for the embroidery machine industry to solve the time-consuming problem of manually loading and unloading spring steel cloth clips.