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A real-time prediction method for tunnel boring machine cutter-head torque using bidirectional long short-term memory networks optimized by multi-algorithm

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Journal of Rock Mechanics and Geotechnical Engineering 2022年第3期14卷 798-812页

作者： Xing Huang Quantai Zhang Quansheng Liu Xuewei Liu Bin Liu Junjie Wang Xin Yin State Key Laboratory of Geomechanics and Geotechnical Engineering Institute of Rock and Soil MechanicsChinese Academy of SciencesWuhan430071China Key Laboratory of Geotechnical and Structural Engineering Safety of Hubei Province School of Civil EngineeringWuhan UniversityWuhan430072China The 2nd Engineering Company of China Railway 12th Bureau Group Taiyuan030032China

Based on data from the Jilin Water Diversion Tunnels from the Songhua River(China),an improved and real-time prediction method optimized by multi-algorithm for tunnel boring machine(TBM)cutter-head torque is ***,a function excluding invalid and abnormal data is established to distinguish TBM operating state,and a feature selection method based on the SelectKBest algorithm is ***,ten features that are most closely related to the cutter-head torque are selected as input variables,which,in descending order of influence,include the sum of motor torque,cutter-head power,sum of motor power,sum of motor current,advance rate,cutter-head pressure,total thrust force,penetration rate,cutter-head rotational velocity,and field penetration ***,a real-time cutterhead torque prediction model’s structure is developed,based on the bidirectional long short-term memory(BLSTM)network integrating the dropout algorithm to prevent ***,an algorithm to optimize hyperparameters of model based on Bayesian and cross-validation is *** stopping and checkpoint algorithms are integrated to optimize the training ***,a BLSTMbased real-time cutter-head torque prediction model is developed,which fully utilizes the previous time-series tunneling *** mean absolute percentage error(MAPE)of the model in the verification section is 7.3%,implying that the presented model is suitable for real-time cutter-head torque ***,an incremental learning method based on the above base model is introduced to improve the adaptability of the model during the TBM *** of the prediction performance between the base and incremental learning models in the same tunneling section shows that:(1)the MAPE of the predicted results of the BLSTM-based real-time cutter-head torque prediction model remains below 10%,and both the coefficient of determination(R^(2))and correlation coefficient(r)between measured and

关键词： Tunnel boring machine(TBM) Real-time cutter-head torque prediction bidirectional long short-term memory (BLSTM) Bayesian optimization Multi-algorithm fusion optimization Incremental learning

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Construction of Human Digital Twin Model Based on Multimodal Data and Its Application in Locomotion Mode Identifcation

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Chinese Journal of Mechanical Engineering 2023年第5期36卷 7-19页

作者： Ruirui Zhong Bingtao Hu Yixiong Feng Hao Zheng Zhaoxi Hong Shanhe Lou Jianrong Tan State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang UniversityHangzhou 310027China Engineering Research Center for Design Engineering and Digital Twin of Zhejiang Province Hangzhou 310027China Hangzhou Innovation Institute Beihang UniversityHangzhou 310052China School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingapore 637460Singapore

With the increasing attention to the state and role of people in intelligent manufacturing, there is a strong demand for human-cyber-physical systems (HCPS) that focus on human-robot interaction. The existing intelligent manufacturing system cannot satisfy efcient human-robot collaborative work. However, unlike machines equipped with sensors, human characteristic information is difcult to be perceived and digitized instantly. In view of the high complexity and uncertainty of the human body, this paper proposes a framework for building a human digital twin (HDT) model based on multimodal data and expounds on the key technologies. Data acquisition system is built to dynamically acquire and update the body state data and physiological data of the human body and realize the digital expression of multi-source heterogeneous human body information. A bidirectional long short-term memory and convolutional neural network (BiLSTM-CNN) based network is devised to fuse multimodal human data and extract the spatiotemporal features, and the human locomotion mode identifcation is taken as an application case. A series of optimization experiments are carried out to improve the performance of the proposed BiLSTM-CNN-based network model. The proposed model is compared with traditional locomotion mode identifcation models. The experimental results proved the superiority of the HDT framework for human locomotion mode identifcation.

关键词： Human digital twin Human-cyber-physical system bidirectional long short-term memory Convolutional neural network Multimodal data

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Multivariety and multimanufacturer drug identification based on near-infrared spectroscopy and recurrent neural network

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Journal of Innovative Optical Health Sciences 2022年第4期15卷 86-96页

作者： Wenjie Zeng Yunqi Qiu Yanting Huang Qingping Sun Zhuoya Luo School of Pharmaceutical Sciences Guangzhou Medical University 1 Xinzao Road Guangzhou 511436 P.R.China NMPA Key Laboratory of Rapid Drug Inspection Technology Guangdong Institute for Drug Control 766 Shenzhou Road Guangzhou 510663P.R.China

Near-infrared(NIR)spectral analysis,which has the advantages of rapidness,nondestruction and high-efficiency,is widely used in the detection of feed,food and *** terms of qualitative identification,it can also be used for the discriminant analysis of *** short-term memory(LSTM)neural network,bidirectional long short-term memory(BiLSTM)neural network and gated recurrent unit(GRU)network are variants of the recurrent neural network(RNN).The potential relationship between nonlinear features learned from the sequence by these variants is used to complete the missions infields such as natural language processing,signal classification and video *** the effect of these variants in drug identification is still to be studied,this paper constructs a multiclassifier of these three variants,using compoundα-keto acid tablets produced by four manufacturers and repaglinide tablets produced by five manufacturers as the research ***,the paper analyzes the impacts of seven different preprocessed methods on the drug NIR data by constructing different layers of LSTM,BiLSTM and GRU networks and compares different classification model indicators and training time of each *** the spectrum data are pre-processed by z-score normalization,the GRU-3 model has the best accuracy in all *** BiLSTM models are better for analyzing high coincidence *** method proposed in this paper can be further extended to other NIR spectroscopy data sets.

关键词： Near-infrared spectroscopy long short-term memory bidirectional long short-term memory gated recurrent unit multiple classifiers.

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Analyzing Arabic Twitter-Based Patient Experience Sentiments Using Multi-Dialect Arabic bidirectional Encoder Representations from Transformers

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Computers, Materials & Continua 2023年第7期76卷 195-220页

作者： Sarab AlMuhaideb Yasmeen AlNegheimish Taif AlOmar Reem AlSabti Maha AlKathery Ghala AlOlyyan Department of Computer Science College of Computer and Information SciencesKing Saud UniversityP.O.Box 266Riyadh 11362Saudi Arabia

Healthcare organizations rely on patients’feedback and experiences to evaluate their performance and services,thereby allowing such organizations to improve inadequate services and address any *** to the literature,social networks and particularly Twitter are effective platforms for gathering public ***,recent studies have used natural language processing to measure sentiments in text segments collected from Twitter to capture public opinions about various sectors,including *** present study aimed to analyze Arabic Twitter-based patient experience sentiments and to introduce an Arabic patient experience *** authors collected 12,400 tweets from Arabic patients discussing patient experiences related to healthcare organizations in Saudi Arabia from 1 January 2008 to 29 January *** tweets were labeled according to sentiment(positive or negative)and sector(public or private),and thereby the Hospital Patient Experiences in Saudi Arabia(HoPE-SA)dataset was produced.A simple statistical analysis was conducted to examine differences in patient views of healthcare *** authors trained five models to distinguish sentiments in tweets automatically with the following schemes:a transformer-based model fine-tuned with deep learning architecture and a transformer-based model fine-tuned with simple architecture,using two different transformer-based embeddings based on bidirectional Encoder Representations from Transformers(BERT),Multi-dialect Arabic BERT(MAR-BERT),and multilingual BERT(mBERT),as well as a pretrained word2vec model with a support vector machine *** is the first study to investigate the use of a bidirectional long short-term memory layer followed by a feedforward neural network for the fine-tuning of *** deep-learning fine-tuned MARBERT-based model—the authors’best-performing model—achieved accuracy,micro-F1,and macro-F1 scores of 98.71%,98.73%,and 98.63%,respectively.

关键词： Sentiment analysis patient experience healthcare Twitter MARBERT bidirectional long short-term memory support vector machine transformer-based learning deep learning

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Continuous Sign Language Recognition Based on Spatial-Temporal Graph Attention Network

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Computer Modeling in Engineering & Sciences 2023年第3期134卷 1653-1670页

作者： Qi Guo Shujun Zhang Hui Li College of Information Science and Technology Qingdao University of Science and TechnologyQingdao266061China

Continuous sign language recognition(CSLR)is challenging due to the complexity of video background,hand gesture variability,and temporal modeling *** work proposes a CSLR method based on a spatialtemporal graph attention network to focus on essential features of video *** method considers local details of sign language movements by taking the information on joints and bones as inputs and constructing a spatialtemporal graph to reflect inter-frame relevance and physical connections between *** graph-based multihead attention mechanism is utilized with adjacent matrix calculation for better local-feature exploration,and short-term motion correlation modeling is completed via a temporal convolutional *** adopted BLSTM to learn the long-termdependence and connectionist temporal classification to align the word-level *** proposed method achieves competitive results regarding word error rates(1.59%)on the Chinese Sign Language dataset and the mean Jaccard Index(65.78%)on the ChaLearn LAP Continuous Gesture Dataset.

关键词： Continuous sign language recognition graph attention network bidirectional long short-term memory connectionist temporal classification

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Transportation robot battery power forecasting based on bidirectional deep-learning method

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Transportation Safety and Environment 2019年第3期1卷 205-211页

作者： Kerstin Thurow Chao Chen Steffen Junginger Norbert Stoll Hui Liu Center for Life Science Automation University of RostockFriedrich-Barnewitz-StraBe 818119 RostockGermany School of Traffic and Transportation Engineering Central South UniversityChangsha410075 HunanChina and Institute of Automation University of RostockRichard-Wagner-StraBe 3118119 RostockGermany

This paper proposes a data-driven hybrid model for forecasting the battery power voltage of transportation robots by combining a wavelet method and a bidirectional deep-learning *** the proposed model,the on-board battery power data is measured and transmitted.A WPD(wavelet packet decomposition)algorithm is employed to decompose the original collected non-stationary series into several relatively more stable *** each subseries,a deep learning–based predictor–bidirectional long short-term memory(BiLSTM)–is constructed to forecast the battery power voltage from one step to three steps *** experiments verify the effectiveness and generalization ability of the proposed hybrid forecasting model,which shows the highest forecasting *** obtained forecasting results can be used to decide whether the robot can complete the given task or needs to be recharged,providing effective support for the safe use of transportation robots.

关键词： robotic power management transportation robot time series forecasting wavelet packet decomposition bidirectional long short-term memory

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