Xiaohua Zhu^1,*, Li Zeng¹, Bo Li¹

CMES-Computer Modeling in Engineering & Sciences, Vol.121, No.2, pp. 631-660, 2019, DOI:10.32604/cmes.2019.06755

Abstract The complicated geological conditions will bring great challenges to the drillstring of horizontal wells for the reason that the increase of the well depth in SichuanChongqing region. Since drillstring failure and friction during drilling are generally caused by drillstring vibration, great importance must be attached to computer simulation methods for the prediction of drillstring vibration. A finite element model considering axial, lateral and torsional vibration is established. In order to verify the established numerical model, an indoor experimental device based on the similarity principle was established. The vibration characteristics of three shale gas horizontal wells drillstrings are described. The reasons… More >

Liang Ding^1,*, Lunjie Li¹, Jianghong Han¹, Yuqi Fan^2,*, Donghui Hu¹

CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 1285-1304, 2019, DOI:10.32604/cmc.2019.06160

Abstract Botnets often use domain generation algorithms (DGA) to connect to a command and control (C2) server, which enables the compromised hosts connect to the C2 server for accessing many domains. The detection of DGA domains is critical for blocking the C2 server, and for identifying the compromised hosts as well. However, the detection is difficult, because some DGA domain names look normal. Much of the previous work based on statistical analysis of machine learning relies on manual features and contextual information, which causes long response time and cannot be used for real-time detection. In addition, when a new family of… More >

Yatian Shen¹, Yan Li¹, Jun Sun^1,*, Wenke Ding¹, Xianjin Shi¹, Lei Zhang¹, Xiajiong Shen¹, Jing He²

CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 1261-1269, 2019, DOI:10.32604/cmc.2019.06104

Abstract On Twitter, people often use hashtags to mark the subject of a tweet. Tweets have specific themes or content that are easy for people to manage. With the increase in the number of tweets, how to automatically recommend hashtags for tweets has received wide attention. The previous hashtag recommendation methods were to convert the task into a multi-class classification problem. However, these methods can only recommend hashtags that appeared in historical information, and cannot recommend the new ones. In this work, we extend the self-attention mechanism to turn the hashtag recommendation task into a sequence labeling task. To train and… More >

Haiqi Zhu¹, Fanzhi Meng^2,*, Seungmin Rho³, Mohan Li^4,*, Jianyu Wang¹, Shaohui Liu¹, Feng Jiang¹

CMC-Computers, Materials & Continua, Vol.61, No.2, pp. 829-847, 2019, DOI:10.32604/cmc.2019.06115

Abstract In real-world many internet-based service companies need to closely monitor large amounts of data in order to ensure stable operation of their business. However, anomaly detection for these data with various patterns and data quality has been a great challenge, especially without labels. In this paper, we adopt an anomaly detection algorithm based on Long Short-Term Memory (LSTM) Network in terms of reconstructing KPIs and predicting KPIs. They use the reconstruction error and prediction error respectively as the criteria for judging anomalies, and we test our method with real data from a company in the insurance industry and achieved good… More >

Alex Kost¹, Wael A. Altabey^2,3,4, Mohammad Noori^1,2,*, Taher Awad⁴

Structural Durability & Health Monitoring, Vol.13, No.3, pp. 247-266, 2019, DOI:10.32604/sdhm.2019.07025

Abstract A proof-of-concept indirect tire-pressure monitoring system is developed using artificial neural networks to identify the tire pressure of a vehicle tire. A quarter-car model was developed with MATLAB and Simulink to generate simulated accelerometer output data. Simulation data are used to train and evaluate a recurrent neural network with long short-term memory blocks (RNN-LSTM) and a convolutional neural network (CNN) developed in Python with Tensorflow. Bayesian Optimization via SigOpt was used to optimize training and model parameters. The predictive accuracy and training speed of the two models with various parameters are compared. Finally, future work and improvements are discussed. More >

Yimeng Zhai¹, Aidong Deng^1,*, Jing Li^1,2, Qiang Cheng¹, Wei Ren³

Journal on Artificial Intelligence, Vol.1, No.1, pp. 19-27, 2019, DOI:10.32604/jai.2019.05817

Abstract In order to acquire the degradation state of rolling bearings and achieve predictive maintenance, this paper proposed a novel Remaining Useful Life (RUL) prediction of rolling bearings based on Long Short Term Memory (LSTM) neural net-work. The method is divided into two parts: feature extraction and RUL prediction. Firstly, a large number of features are extracted from the original vibration signal. After correlation analysis, the features that can better reflect the degradation trend of rolling bearings are selected as input of prediction model. In the part of RUL prediction, LSTM that making full use of the network’s memory in time… More >

Zhaowei Qu^1,*, Bingyu Cao¹, Xiaoru Wang¹, Fu Li², Peirong Xu¹, Luhan Zhang¹

CMC-Computers, Materials & Continua, Vol.59, No.2, pp. 575-589, 2019, DOI:10.32604/cmc.2019.05569

Abstract Images are complex multimedia data which contain rich semantic information. Most of current image description generator algorithms only generate plain description, with the lack of distinction between primary and secondary object, leading to insufficient high-level semantic and accuracy under public evaluation criteria. The major issue is the lack of effective network on high-level semantic sentences generation, which contains detailed description for motion and state of the principal object. To address the issue, this paper proposes the Attention-based Feedback Long Short-Term Memory Network (AFLN). Based on existing codec framework, there are two independent sub tasks in our method: attention-based feedback LSTM… More >

Xianyu Wu¹, Chao Luo¹, Qian Zhang², Jiliu Zhou¹, Hao Yang^{1, 3, *}, Yulian Li¹

CMC-Computers, Materials & Continua, Vol.61, No.1, pp. 289-300, 2019, DOI:10.32604/cmc.2019.05990

Abstract Words are the most indispensable information in human life. It is very important to analyze and understand the meaning of words. Compared with the general visual elements, the text conveys rich and high-level moral information, which enables the computer to better understand the semantic content of the text. With the rapid development of computer technology, great achievements have been made in text information detection and recognition. However, when dealing with text characters in natural scene images, there are still some limitations in the detection and recognition of natural scene images. Because natural scene image has more interference and complexity than… More >