Journal of Algorithms and Computational Technology

With the wide application of intelligent sensors and internet of things (IoT) in the smart job shop, a large number of real-time production data is collected. Accurate analysis of the collected data can help producers to make effective decisions. Compared with the traditional data processing methods, artificial intelligence, as the main big data analysis method, is more and more applied to the manufacturing industry. However, the ability of different AI models to process real-time data of smart job shop production is also different. Based on this, a real-time big data processing method for the job shop production process based on Long Short-Term Memory (LSTM) and Gate Recurrent Unit (GRU) is proposed. This method uses the historical production data extracted by the IoT job shop as the original data set, and after data preprocessing, uses the LSTM and GRU model to train and predict the real-time data of the job shop. Through the description and implementation of the model, it is compared with KNN, DT and traditional neural network model. The results show that in the real-time big data processing of production process, the performance of the LSTM and GRU models is superior to the traditional neural network, K nearest neighbor (KNN), decision tree (DT). When the performance is similar to LSTM, the training time of GRU is much lower than LSTM model.

Chuang Wang

Wenbo Du

Zhixiang Zhu

Zhifeng Yue

Journal of Algorithms & Computational Technology

The real-time big data processing method based on LSTM or GRU for the smart job shop production process

基于LSTM或GRU的智能作业车间生产过程实时大数据处理方法

In the era of intelligent manufacturing, the production mode of customer demand-pull has become predominant. However, this mode of production entails numerous uncertainties, necessitating accurate predictions in various aspects such as market demand, supply chain, warehousing, and workshop logistics. Therefore, it is of great engineering significance to establish a high-performance time forecasting model for enhancing logistics planning and operations. In this study, we propose a novel hybrid time series forecasting method. This method can select appropriate decomposition methods and prediction models based on the characteristics of the sequence itself, and use hyperparameter optimization to achieve the best prediction effect. The effectiveness of the proposed method is demonstrated through rigorous validation with diverse types of time series data. Consequently, this method holds promise as a suitable forecasting model for logistics planning and operations.

Fusheng Qiu

Hongjun Wang

Tang Tang

Liang Wang

Ming Chen

Research on time forecasting model in digital twin for logistic activity in intelligent manufactory

智能工厂物流活动数字孪生时间预测模型研究

Variational methods for image registration basically involve a regularizer to ensure that the resulting well-posed problem admits a solution. Different choices of regularizers lead to different deformations. On one hand, the conventional regularizers, such as the elastic, diffusion and curvature regularizers, are able to generate globally smooth deformations and generally useful for many applications. On the other hand, these regularizers become poor in some applications where discontinuities or steep gradients in the deformations are required. As is well-known, the total (TV) variation regularizer is more appropriate to preserve discontinuities of the deformations. However, it is difficult in developing an efficient numerical method to ensure that numerical solutions satisfy this requirement because of the non-differentiability and non-linearity of the TV regularizer. In this work we focus on computational challenges arising in approximately solving TV-based image registration model. Motivated by many efficient numerical algorithms in image restoration, we propose to use augmented Lagrangian method (ALM). At each iteration, the computation of our ALM requires to solve two subproblems. On one hand for the first subproblem, it is impossible to obtain exact solution. On the other hand for the second subproblem, it has a closed-form solution. To this end, we propose an efficient nonlinear multigrid (NMG) method to obtain an approximate solution to the first subproblem. Numerical results on real medical images not only confirm that our proposed ALM is more computationally efficient than some existing methods, but also that the proposed ALM delivers the accurate registration results with the desired property of the constructed deformations in a reasonable number of iterations.

Noppadol Chumchob

Ke Chen

An augmented Lagrangian method for solving total variation (TV)-based image registration model

一种求解基于全变差（TV）图像配准模型的增广拉格朗日方法

In this work, we introduce a new regularized logistic model for the supervised classification problem. Current logistic models have become the preferred tools for supervised classification in many situations. They mostly use either L 1 or L 2 regularization of the weight vector of parameters. Here we take a different approach by applying regularization not to the weight vector but to the gradient vector of the function representing the separating hyper-surface. We present the mathematical analysis of the model in its continuous setting and provide experimental evidence to show that the new model is competitive with state of the art models.

Carlos Brito-Pacheco

Carlos Brito-Loeza

Anabel Martin-Gonzalez

A regularized logistic regression based model for supervised learning

基于正则化逻辑回归的监督学习模型

Autonomy is considered an important criterion that characterizes the performance of electric vehicles. It is represented by the distance that could be traveled by a fully electric vehicle which mainly depends on several parameters such as the vehicle model, type of battery, type of motor, etc. In this context, to improve the autonomy of electric vehicles, this paper represents an optimization study for the electric motor based on two contributions. The first devise an energy optimization algorithm to reduce the motor losses by calculation of the stator flux reference according to the electromagnetic torque and the rotation speed. The second is concerned with controller parameters adjustment using the Particle Swarm Optimization (PSO) technique to improve the efficacy and robustness of the drive. The performance of this strategy is evaluated in terms of torque, flux ripples, and transient response to step variations of the torque control. A comparative study of the designed PI controllers based on PSO with four other control algorithms and tuning methods is established in order to prove the efficiency of PI_PSO. The analysis, modeling, and simulation results are presented to verify the validity of the proposed overall optimization study.

Habib Kraiem

Shaaban M Shaaban

Energy optimization of an electric car using losses minimization and intelligent predictive torque control

电动汽车能耗优化：基于损耗最小化和智能预测转矩控制

Image denoising is a fundamental tool in the fields of image processing and computer vision. With the rapid development of multimedia and cloud computing, it has become popular for resource-constrained users to outsource the storage and denoising of massive images. However, it may cause privacy concerns and response delays. In this scenario, we propose an efFicient privAcy-preseRving Image deNoising schEme (FARINE) for outsourcing digital images. By introducing a key conversion mechanism, FARINE allows removing noise from a given noisy image using a non-local mean way without leaking any information about the plaintext content. Due to its low computational latency/communication cost, edge computing is considered to improve the user experience. To achieve a dynamic user set efficiently, we design a fine-grained access control mechanism to support user authorization and revocation in multi-user scenarios. Extensive experiments over several benchmark data sets show that FARINE obtains comparable performance to plaintext image denoising.

Yibing Huang

Yongliang Xu

Hang Cheng

Fei Chen

Meiqing Wang

Edge-based secure image denoising scheme supporting flexible user authorization

基于边缘的安全图像去噪方案支持灵活的用户授权

A smart antenna, which consists of several antenna elements makes a gain at the desired direction, makes nulls at the direction of interferers. In this article, the bit error rate is measured in different directions and distances, and the method of the minimum variance is used with the concept of the link budget when the weights of the multiple input multiple output (MIMO) radar are calculated with the number of N receiver elements and with the number of M transmitter elements. The less the number of interferers is, or using MIMO, the better the system is.

Hyeon-Cheol Lee

Sang Gyu Lee

Haeng-Pal Heo

BER comparisons of MIMO radar with uniform linear arrays

MIMO雷达与均匀线性阵列的BER比较

The applications of non-square binary matrices span many domains including mathematics, error-correction coding, machine learning, data storage, navigation signals, and cryptography. In particular, they are employed in the McEliece and Niederreiter public-key cryptosystems. For the parity check matrix of these cryptosystems, a systematic non-square binary matrix [Formula: see text] with dimensions [Formula: see text], [Formula: see text], [Formula: see text], there exist [Formula: see text] distinct inverse matrices. This article presents an algorithm to generate these matrices as well as a method to construct a random inverse matrix. Then it is extended to non-square matrices in arbitrary fields. This overcomes the limitations of the Moore-Penrose and Gauss-Jordan methods. The application to public-key cryptography is also discussed.

Farshid Haidary Makoui

Thomas Aaron Gulliver

Inverse matrices with applications in public-key cryptography

具有公钥密码学应用的逆矩阵

A mixed Laguerre-Legendre spectral collocation schemes is constructed for initial-boundary value problem of the nonlinear Fokker-Planck nonhomogeneous equations and the linear Fokker-Planck homogeneous equation by using the Laguerre interpolation functions in velocity, and the Legendre interpolation functions in time, which is a bivariate Lagrange interpolation approximation function based on Legendre and Laguerre Gauss quadrature notes. The original problem is changed into a matrix equation and solved by Newton iteration. Numerical experiments show that the algorithm has high accuracy and efficiency.

Abbreviation
Journal Impact	0.69
Quartiles(Global)	COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS(Q4)

ISSN	1748-3018, 1748-3026
h-index	20

Publication Information	Publisher: SAGE Publications Inc.，Publishing cycle: ，Journal Type: journal，Open Access Journals: Yes
Basic data	Year of publication: 2011，Proportion of original research papers: 100.00%，Self Citation Rate:0.00%， Gold OA Rate: 98.28%
Journal Fees	$2000Details
Average review cycle	网友分享经验：15 Weeks

Journal of Algorithms and Computational Technology

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The real-time big data processing method based on LSTM or GRU for the smart job shop production process

Research on time forecasting model in digital twin for logistic activity in intelligent manufactory

An augmented Lagrangian method for solving total variation (TV)-based image registration model

A regularized logistic regression based model for supervised learning

Energy optimization of an electric car using losses minimization and intelligent predictive torque control

Edge-based secure image denoising scheme supporting flexible user authorization

Research on time forecasting model in digital twin for logistic activity in intelligent manufactory

BER comparisons of MIMO radar with uniform linear arrays

Inverse matrices with applications in public-key cryptography

Binary spectral collocation method for the nonlinear Fokker-Planck equation