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A Study on the Work Process-Based Practical Training Model for Basic Nursing Skills in Vocational Colleges
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作者 Dan Li Huan Wei 《Journal of Clinical and Nursing Research》 2024年第9期128-132,共5页
Objective:To explore and analyze the work process-based practical training teaching model for basic nursing skills in vocational colleges and its implementation effects.Methods:A total of 82 nursing students from our ... Objective:To explore and analyze the work process-based practical training teaching model for basic nursing skills in vocational colleges and its implementation effects.Methods:A total of 82 nursing students from our school were selected for the study,which was conducted from April 2023 to April 2024.Using a random number table method,the students were divided into an observation group and a control group,each with 41 students.The control group received conventional practical training teaching,while the observation group followed the work process-based practical training model for basic nursing skills.The assessment scores and teaching satisfaction of the two groups were compared.Results:The comparison of assessment scores showed that the observation group performed significantly better than the control group(P<0.05).The comparison of teaching satisfaction also indicated that the observation group had significantly higher satisfaction than the control group(P<0.05).Conclusion:The work process-based practical training teaching model for basic nursing skills in vocational colleges can improve students’assessment scores and enhance teaching satisfaction,demonstrating its value for wider application. 展开更多
关键词 work processes Vocational colleges Basic nursing skills Practical training teaching model
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Detection of Oscillations in Process Control Loops From Visual Image Space Using Deep Convolutional Networks
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作者 Tao Wang Qiming Chen +3 位作者 Xun Lang Lei Xie Peng Li Hongye Su 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2024年第4期982-995,共14页
Oscillation detection has been a hot research topic in industries due to the high incidence of oscillation loops and their negative impact on plant profitability.Although numerous automatic detection techniques have b... Oscillation detection has been a hot research topic in industries due to the high incidence of oscillation loops and their negative impact on plant profitability.Although numerous automatic detection techniques have been proposed,most of them can only address part of the practical difficulties.An oscillation is heuristically defined as a visually apparent periodic variation.However,manual visual inspection is labor-intensive and prone to missed detection.Convolutional neural networks(CNNs),inspired by animal visual systems,have been raised with powerful feature extraction capabilities.In this work,an exploration of the typical CNN models for visual oscillation detection is performed.Specifically,we tested MobileNet-V1,ShuffleNet-V2,Efficient Net-B0,and GhostNet models,and found that such a visual framework is well-suited for oscillation detection.The feasibility and validity of this framework are verified utilizing extensive numerical and industrial cases.Compared with state-of-theart oscillation detectors,the suggested framework is more straightforward and more robust to noise and mean-nonstationarity.In addition,this framework generalizes well and is capable of handling features that are not present in the training data,such as multiple oscillations and outliers. 展开更多
关键词 Convolutional neural networks(CNNs) deep learning image processing oscillation detection process industries
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Transferable adversarial slow feature extraction network for few-shot quality prediction in coal-to-ethylene glycol process
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作者 Cheng Yang Chao Jiang +2 位作者 Guo Yu Jun Li Cuimei Bo 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2024年第7期258-271,共14页
In the coal-to-ethylene glycol(CTEG)process,precisely estimating quality variables is crucial for process monitoring,optimization,and control.A significant challenge in this regard is relying on offline laboratory ana... In the coal-to-ethylene glycol(CTEG)process,precisely estimating quality variables is crucial for process monitoring,optimization,and control.A significant challenge in this regard is relying on offline laboratory analysis to obtain these variables,which often incurs substantial monetary costs and significant time delays.The resulting few-shot learning scenarios present a hurdle to the efficient development of predictive models.To address this issue,our study introduces the transferable adversarial slow feature extraction network(TASF-Net),an innovative approach designed specifically for few-shot quality prediction in the CTEG process.TASF-Net uniquely integrates the slowness principle with a deep Bayesian framework,effectively capturing the nonlinear and inertial characteristics of the CTEG process.Additionally,the model employs a variable attention mechanism to identify quality-related input variables adaptively at each time step.A key strength of TASF-Net lies in its ability to navigate the complex measurement noise,outliers,and system interference typical in CTEG data.Adversarial learning strategy using a min-max game is adopted to improve its robustness and ability to model irregular industrial data accurately and significantly.Furthermore,an incremental refining transfer learning framework is designed to further improve few-shot prediction performance achieved by transferring knowledge from the pretrained model on the source domain to the target domain.The effectiveness and superiority of TASF-Net have been empirically validated using a real-world CTEG dataset.Compared with some state-of-the-art methods,TASF-Net demonstrates exceptional capability in addressing the intricate challenges for few-shot quality prediction in the CTEG process. 展开更多
关键词 Chemical process Neural networks Slowness principle Transfer learning Prediction
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Causal temporal graph attention network for fault diagnosis of chemical processes
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作者 Jiaojiao Luo Zhehao Jin +3 位作者 Heping Jin Qian Li Xu Ji Yiyang Dai 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2024年第6期20-32,共13页
Fault detection and diagnosis(FDD)plays a significant role in ensuring the safety and stability of chemical processes.With the development of artificial intelligence(AI)and big data technologies,data-driven approaches... Fault detection and diagnosis(FDD)plays a significant role in ensuring the safety and stability of chemical processes.With the development of artificial intelligence(AI)and big data technologies,data-driven approaches with excellent performance are widely used for FDD in chemical processes.However,improved predictive accuracy has often been achieved through increased model complexity,which turns models into black-box methods and causes uncertainty regarding their decisions.In this study,a causal temporal graph attention network(CTGAN)is proposed for fault diagnosis of chemical processes.A chemical causal graph is built by causal inference to represent the propagation path of faults.The attention mechanism and chemical causal graph were combined to help us notice the key variables relating to fault fluctuations.Experiments in the Tennessee Eastman(TE)process and the green ammonia(GA)process showed that CTGAN achieved high performance and good explainability. 展开更多
关键词 Chemical processes Safety Fault diagnosis Causal discovery Attention mechanism Explainability
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Flexible,thermal processable,self-healing,and fully bio-based starch plastics by constructing dynamic imine network
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作者 Xiaoqian Zhang Haishan Zhang +2 位作者 Guowen Zhou Zhiping Su Xiaohui Wang 《Green Energy & Environment》 SCIE EI CAS CSCD 2024年第10期1610-1618,共9页
The serious environmental threat caused by petroleum-based plastics has spurred more researches in developing substitutes from renewable sources.Starch is desirable for fabricating bioplastic due to its abundance and ... The serious environmental threat caused by petroleum-based plastics has spurred more researches in developing substitutes from renewable sources.Starch is desirable for fabricating bioplastic due to its abundance and renewable nature.However,limitations such as brittleness,hydrophilicity,and thermal properties restrict its widespread application.To overcome these issues,covalent adaptable network was constructed to fabricate a fully bio-based starch plastic with multiple advantages via Schiff base reactions.This strategy endowed starch plastic with excellent thermal processability,as evidenced by a low glass transition temperature(T_(g)=20.15℃).Through introducing Priamine with long carbon chains,the starch plastic demonstrated superior flexibility(elongation at break=45.2%)and waterproof capability(water contact angle=109.2°).Besides,it possessed a good thermal stability and self-adaptability,as well as solvent resistance and chemical degradability.This work provides a promising method to fabricate fully bio-based plastics as alternative to petroleum-based plastics. 展开更多
关键词 Bioplastic Covalent adaptable networks Schiff base chemistry Thermal processability SELF-HEALING
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A Comprehensive Image Processing Framework for Early Diagnosis of Diabetic Retinopathy
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作者 Kusum Yadav Yasser Alharbi +6 位作者 Eissa Jaber Alreshidi Abdulrahman Alreshidi Anuj Kumar Jain Anurag Jain Kamal Kumar Sachin Sharma Brij BGupta 《Computers, Materials & Continua》 SCIE EI 2024年第11期2665-2683,共19页
In today’s world,image processing techniques play a crucial role in the prognosis and diagnosis of various diseases due to the development of several precise and accurate methods for medical images.Automated analysis... In today’s world,image processing techniques play a crucial role in the prognosis and diagnosis of various diseases due to the development of several precise and accurate methods for medical images.Automated analysis of medical images is essential for doctors,as manual investigation often leads to inter-observer variability.This research aims to enhance healthcare by enabling the early detection of diabetic retinopathy through an efficient image processing framework.The proposed hybridized method combines Modified Inertia Weight Particle Swarm Optimization(MIWPSO)and Fuzzy C-Means clustering(FCM)algorithms.Traditional FCM does not incorporate spatial neighborhood features,making it highly sensitive to noise,which significantly affects segmentation output.Our method incorporates a modified FCM that includes spatial functions in the fuzzy membership matrix to eliminate noise.The results demonstrate that the proposed FCM-MIWPSO method achieves highly precise and accurate medical image segmentation.Furthermore,segmented images are classified as benign or malignant using the Decision Tree-Based Temporal Association Rule(DT-TAR)Algorithm.Comparative analysis with existing state-of-the-art models indicates that the proposed FCM-MIWPSO segmentation technique achieves a remarkable accuracy of 98.42%on the dataset,highlighting its significant impact on improving diagnostic capabilities in medical imaging. 展开更多
关键词 Image processing biological data PSO Fuzzy C-Means(FCM)
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Multi-Perspective Data Fusion Framework Based on Hierarchical BERT: Provide Visual Predictions of Business Processes
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作者 Yongwang Yuan Xiangwei Liu Ke Lu 《Computers, Materials & Continua》 SCIE EI 2024年第1期1227-1252,共26页
Predictive Business Process Monitoring(PBPM)is a significant research area in Business Process Management(BPM)aimed at accurately forecasting future behavioral events.At present,deep learning methods are widely cited ... Predictive Business Process Monitoring(PBPM)is a significant research area in Business Process Management(BPM)aimed at accurately forecasting future behavioral events.At present,deep learning methods are widely cited in PBPM research,but no method has been effective in fusing data information into the control flow for multi-perspective process prediction.Therefore,this paper proposes a process prediction method based on the hierarchical BERT and multi-perspective data fusion.Firstly,the first layer BERT network learns the correlations between different category attribute data.Then,the attribute data is integrated into a weighted event-level feature vector and input into the second layer BERT network to learn the impact and priority relationship of each event on future predicted events.Next,the multi-head attention mechanism within the framework is visualized for analysis,helping to understand the decision-making logic of the framework and providing visual predictions.Finally,experimental results show that the predictive accuracy of the framework surpasses the current state-of-the-art research methods and significantly enhances the predictive performance of BPM. 展开更多
关键词 Business process prediction monitoring deep learning attention mechanism BERT multi-perspective
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Big Data Application Simulation Platform Design for Onboard Distributed Processing of LEO Mega-Constellation Networks
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作者 Zhang Zhikai Gu Shushi +1 位作者 Zhang Qinyu Xue Jiayin 《China Communications》 SCIE CSCD 2024年第7期334-345,共12页
Due to the restricted satellite payloads in LEO mega-constellation networks(LMCNs),remote sensing image analysis,online learning and other big data services desirably need onboard distributed processing(OBDP).In exist... Due to the restricted satellite payloads in LEO mega-constellation networks(LMCNs),remote sensing image analysis,online learning and other big data services desirably need onboard distributed processing(OBDP).In existing technologies,the efficiency of big data applications(BDAs)in distributed systems hinges on the stable-state and low-latency links between worker nodes.However,LMCNs with high-dynamic nodes and long-distance links can not provide the above conditions,which makes the performance of OBDP hard to be intuitively measured.To bridge this gap,a multidimensional simulation platform is indispensable that can simulate the network environment of LMCNs and put BDAs in it for performance testing.Using STK's APIs and parallel computing framework,we achieve real-time simulation for thousands of satellite nodes,which are mapped as application nodes through software defined network(SDN)and container technologies.We elaborate the architecture and mechanism of the simulation platform,and take the Starlink and Hadoop as realistic examples for simulations.The results indicate that LMCNs have dynamic end-to-end latency which fluctuates periodically with the constellation movement.Compared to ground data center networks(GDCNs),LMCNs deteriorate the computing and storage job throughput,which can be alleviated by the utilization of erasure codes and data flow scheduling of worker nodes. 展开更多
关键词 big data application Hadoop LEO mega-constellation multidimensional simulation onboard distributed processing
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Optimization of extraction process for total flavonoids of Sophorae Flos for the treatment of hyperlipidemia based on network pharmacology and molecular docking
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作者 Jiale Mao Aijinxiu Ma +1 位作者 Lingling Wang Xu Zhao 《Journal of Polyphenols》 2024年第3期117-129,共13页
This study aimed to investigate the mechanism of action of Sophora Flos(SF)in the treatment of hyperlipidemia(HLP)using network pharmacology and molecular docking methods,and to optimize the extraction process of the ... This study aimed to investigate the mechanism of action of Sophora Flos(SF)in the treatment of hyperlipidemia(HLP)using network pharmacology and molecular docking methods,and to optimize the extraction process of the predicted active components.The STRING database was used for protein interaction analysis and PPI network construction via Cytoscape 3.9.1.Pymol was employed for docking and visualization.An extensive review of SF identifi ed 6 active ingredients,297 related objectives,84 disease objectives,and 57 total objectives.After protein interaction and topology analysis,18 core targets were identified.These included 146 gene function entries(P<0.05).Active compounds,mainly flavonoids,can modulate the expression of various proteins such as TNF,IL-6,IL-1β,PPARG,and TGFB1 to achieve therapeutic effects on HLP.The network pharmacology and molecular docking results suggested that the active fl avonoids component in SF may be related to the treatment of hyperlipidemia.Therefore,the orthogonal experiment method was used to optimize the extraction process of total fl avonoid from SF using ethanol refl ux extraction,based on a single factor experiment.The effects of refl ux time,solid-liquid ratio,ethanol concentration,and other factors on the extraction of total fl avonoid from SF were investigated.The optimum process conditions were refl ux time of 1.25 h,solid-liquid ratio of 1:15 g/mL and ethanol concentration of 60%.Using these conditions,the purity of total fl avonoid extracted from SF was 70.33±0.22%. 展开更多
关键词 Sophorae Flos total flavonoid process optimization network pharmacology HYPERLIPIDEMIA
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Mechanism of Rosae Rugosae Flos flavonoids in the treatment of hyperlipidemia and optimization of extraction process based on network pharmacology
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作者 Yunxiao Xia Aijinxiu Ma +1 位作者 Zihan Hou Xu Zhao 《Journal of Polyphenols》 2024年第2期65-77,共13页
This study aims to identify a natural plant chemical with hypolipidemic effects that can be used to treat high cholesterol without adverse reactions.Through network pharmacology screening,it was found that Rosae Rugos... This study aims to identify a natural plant chemical with hypolipidemic effects that can be used to treat high cholesterol without adverse reactions.Through network pharmacology screening,it was found that Rosae Rugosae Flos(RF)flavonoids had potential therapeutic effects on hyperlipidemia and its mechanism of action was discussed.TCMSP and GeneCards databases were used to obtain active ingredients and disease targets.Venn diagrams were drawn to illustrate the findings.The interaction network diagram was created using Cytoscape 3.8.0 software.The PPI protein network was constructed using String.GO and KEGG enrichment analysis was performed using Metascape.The results revealed 2 active flavonoid ingredients and 60 potential targets in RF.The key targets,including CCL2,PPARG,and PPARA,were found to play a role in multiple pathways such as the AGE-RAGE signaling pathway,lipid and atherosclerosis,and cancer pathway in diabetic complications.The solvent extraction method was optimized for efficient flavonoid extraction based on network pharmacology prediction results.This was achieved through a single factor and orthogonal test,resulting in an optimum process with a reflux time of 1.5 h,a solid-liquid ratio of 1:13 g/mL,and an ethanol concentration of 50%. 展开更多
关键词 Rosae Rugosae Flos FLAVONOIDS EXTRACTION process optimization network pharmacology HYPERLIPIDEMIA
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基于Arduino与Processing的心率检测计设计研究 被引量:1
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作者 臧红波 华拓 管志岳 《家电维修》 2024年第3期74-76,共3页
随着社会经济的高速发展,人们的物质生活也有了极大的提高,但同时也伴随着各种疾病的到来,身体健康已经成为人们普遍关注的焦点,因此,心率检测仪、血压计、血糖仪等各种家用医疗监测仪器已经逐渐融入日常生活。心脏病是人们难以预防的... 随着社会经济的高速发展,人们的物质生活也有了极大的提高,但同时也伴随着各种疾病的到来,身体健康已经成为人们普遍关注的焦点,因此,心率检测仪、血压计、血糖仪等各种家用医疗监测仪器已经逐渐融入日常生活。心脏病是人们难以预防的突发致命疾病之一,本文介绍的是一款基于Arduino【是基于易用硬件和软件的原型开源平台,包由可编程的电路板(简称微控制器),以及集成开发环境(称为Arduino IDE)的现成软件组成】Processing(开源编程语言,包括编辑器、编译器、展示器)的简易心率检测计系统,其功能实用、操作简单,可以测量心率,当超出正常心率范围时及时预警,是一款便携的实时心率测试仪。 展开更多
关键词 Arduino processing 心率检测计 设计研究
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Denoising Fault-Aware Wavelet Network:A Signal Processing Informed Neural Network for Fault Diagnosis 被引量:8
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作者 Zuogang Shang Zhibin Zhao Ruqiang Yan 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2023年第1期1-18,共18页
Deep learning(DL) is progressively popular as a viable alternative to traditional signal processing(SP) based methods for fault diagnosis. However, the lack of explainability makes DL-based fault diagnosis methods dif... Deep learning(DL) is progressively popular as a viable alternative to traditional signal processing(SP) based methods for fault diagnosis. However, the lack of explainability makes DL-based fault diagnosis methods difficult to be trusted and understood by industrial users. In addition, the extraction of weak fault features from signals with heavy noise is imperative in industrial applications. To address these limitations, inspired by the Filterbank-Feature-Decision methodology, we propose a new Signal Processing Informed Neural Network(SPINN) framework by embedding SP knowledge into the DL model. As one of the practical implementations for SPINN, a denoising fault-aware wavelet network(DFAWNet) is developed, which consists of fused wavelet convolution(FWConv), dynamic hard thresholding(DHT),index-based soft filtering(ISF), and a classifier. Taking advantage of wavelet transform, FWConv extracts multiscale features while learning wavelet scales and selecting important wavelet bases automatically;DHT dynamically eliminates noise-related components via point-wise hard thresholding;inspired by index-based filtering, ISF optimizes and selects optimal filters for diagnostic feature extraction. It’s worth noting that SPINN may be readily applied to different deep learning networks by simply adding filterbank and feature modules in front. Experiments results demonstrate a significant diagnostic performance improvement over other explainable or denoising deep learning networks. The corresponding code is available at https://github. com/alber tszg/DFAWn et. 展开更多
关键词 Signal processing Deep learning Explainable DENOISING Fault diagnosis
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Attention-based long short-term memory fully convolutional network for chemical process fault diagnosis 被引量:5
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作者 Shanwei Xiong Li Zhou +1 位作者 Yiyang Dai Xu Ji 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2023年第4期1-14,共14页
A correct and timely fault diagnosis is important for improving the safety and reliability of chemical processes. With the advancement of big data technology, data-driven fault diagnosis methods are being extensively ... A correct and timely fault diagnosis is important for improving the safety and reliability of chemical processes. With the advancement of big data technology, data-driven fault diagnosis methods are being extensively used and still have considerable potential. In recent years, methods based on deep neural networks have made significant breakthroughs, and fault diagnosis methods for industrial processes based on deep learning have attracted considerable research attention. Therefore, we propose a fusion deeplearning algorithm based on a fully convolutional neural network(FCN) to extract features and build models to correctly diagnose all types of faults. We use long short-term memory(LSTM) units to expand our proposed FCN so that our proposed deep learning model can better extract the time-domain features of chemical process data. We also introduce the attention mechanism into the model, aimed at highlighting the importance of features, which is significant for the fault diagnosis of chemical processes with many features. When applied to the benchmark Tennessee Eastman process, our proposed model exhibits impressive performance, demonstrating the effectiveness of the attention-based LSTM FCN in chemical process fault diagnosis. 展开更多
关键词 Safety Fault diagnosis process systems Long short-term memory Attention mechanism Neural networks
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A Dual Model Watermarking Framework for Copyright Protection in Image Processing Networks 被引量:1
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作者 Yuhang Meng Xianyi Chen +2 位作者 Xingming Sun Yu Liu Guo Wei 《Computers, Materials & Continua》 SCIE EI 2023年第4期831-844,共14页
Image processing networks have gained great success in many fields,and thus the issue of copyright protection for image processing networks hasbecome a focus of attention. Model watermarking techniques are widely used... Image processing networks have gained great success in many fields,and thus the issue of copyright protection for image processing networks hasbecome a focus of attention. Model watermarking techniques are widely usedin model copyright protection, but there are two challenges: (1) designinguniversal trigger sample watermarking for different network models is stilla challenge;(2) existing methods of copyright protection based on trigger swatermarking are difficult to resist forgery attacks. In this work, we propose adual model watermarking framework for copyright protection in image processingnetworks. The trigger sample watermark is embedded in the trainingprocess of the model, which can effectively verify the model copyright. And wedesign a common method for generating trigger sample watermarks based ongenerative adversarial networks, adaptively generating trigger sample watermarksaccording to different models. The spatial watermark is embedded intothe model output. When an attacker steals model copyright using a forgedtrigger sample watermark, which can be correctly extracted to distinguishbetween the piratical and the protected model. The experiments show that theproposed framework has good performance in different image segmentationnetworks of UNET, UNET++, and FCN (fully convolutional network), andeffectively resists forgery attacks. 展开更多
关键词 Image processing networks copyright protection model watermark
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Numerical‐discrete‐scheme‐incorporated recurrent neural network for tasks in natural language processing 被引量:1
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作者 Mei Liu Wendi Luo +3 位作者 Zangtai Cai Xiujuan Du Jiliang Zhang Shuai Li 《CAAI Transactions on Intelligence Technology》 SCIE EI 2023年第4期1415-1424,共10页
A variety of neural networks have been presented to deal with issues in deep learning in the last decades.Despite the prominent success achieved by the neural network,it still lacks theoretical guidance to design an e... A variety of neural networks have been presented to deal with issues in deep learning in the last decades.Despite the prominent success achieved by the neural network,it still lacks theoretical guidance to design an efficient neural network model,and verifying the performance of a model needs excessive resources.Previous research studies have demonstrated that many existing models can be regarded as different numerical discretizations of differential equations.This connection sheds light on designing an effective recurrent neural network(RNN)by resorting to numerical analysis.Simple RNN is regarded as a discretisation of the forward Euler scheme.Considering the limited solution accuracy of the forward Euler methods,a Taylor‐type discrete scheme is presented with lower truncation error and a Taylor‐type RNN(T‐RNN)is designed with its guidance.Extensive experiments are conducted to evaluate its performance on statistical language models and emotion analysis tasks.The noticeable gains obtained by T‐RNN present its superiority and the feasibility of designing the neural network model using numerical methods. 展开更多
关键词 deep learning natural language processing neural network text analysis
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Data-driven intelligent modeling framework for the steam cracking process 被引量:1
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作者 Qiming Zhao Kexin Bi Tong Qiu 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2023年第9期237-247,共11页
Steam cracking is the dominant technology for producing light olefins,which are believed to be the foundation of the chemical industry.Predictive models of the cracking process can boost production efficiency and prof... Steam cracking is the dominant technology for producing light olefins,which are believed to be the foundation of the chemical industry.Predictive models of the cracking process can boost production efficiency and profit margin.Rapid advancements in machine learning research have recently enabled data-driven solutions to usher in a new era of process modeling.Meanwhile,its practical application to steam cracking is still hindered by the trade-off between prediction accuracy and computational speed.This research presents a framework for data-driven intelligent modeling of the steam cracking process.Industrial data preparation and feature engineering techniques provide computational-ready datasets for the framework,and feedstock similarities are exploited using k-means clustering.We propose LArge-Residuals-Deletion Multivariate Adaptive Regression Spline(LARD-MARS),a modeling approach that explicitly generates output formulas and eliminates potentially outlying instances.The framework is validated further by the presentation of clustering results,the explanation of variable importance,and the testing and comparison of model performance. 展开更多
关键词 Mathematical modeling Data-driven modeling process systems Steam cracking CLUSTERING Multivariate adaptive regression spline
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Early identification of process deviation based on convolutional neural network
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作者 Fangyuan Ma Cheng Ji +1 位作者 Jingde Wang Wei Sun 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2023年第4期104-118,共15页
A novel process monitoring method based on convolutional neural network(CNN)is proposed and applied to detect faults in industrial process.By utilizing the CNN algorithm,cross-correlation and autocorrelation among var... A novel process monitoring method based on convolutional neural network(CNN)is proposed and applied to detect faults in industrial process.By utilizing the CNN algorithm,cross-correlation and autocorrelation among variables are captured to establish a prediction model for each process variable to approximate the first-principle of physical/chemical relationships among different variables under normal operating conditions.When the process is operated under pre-set operating conditions,prediction residuals can be assumed as noise if a proper model is employed.Once process faults occur,the residuals will increase due to the changes of correlation among variables.A principal component analysis(PCA)model based on the residuals is established to realize process monitoring.By monitoring the changes in main feature of prediction residuals,the faults can be promptly detected.Case studies on a numerical nonlinear example and data from two industrial processes are presented to validate the performance of process monitoring based on CNN. 展开更多
关键词 process monitoring RESIDUAL Principal component analysis process systems Systems engineering
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Constraints on Characteristics and Distribution of Gas Hydrate and Free Gas Using Broad-Band Processing of Three-Dimensional Seismic Data 被引量:2
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作者 WANG Xiujuan ZHOU Jilin +7 位作者 LI Sanzhong LI Lixia LI Jie LI Yuanping WANG Linfei SU Pibo JIN Jiapeng GONG Zhi 《Journal of Ocean University of China》 SCIE CAS CSCD 2024年第5期1233-1247,共15页
Gas hydrate drilling expeditions in the Pearl River Mouth Basin,South China Sea,have identified concentrated gas hydrates with variable thickness.Moreover,free gas and the coexistence of gas hydrate and free gas have ... Gas hydrate drilling expeditions in the Pearl River Mouth Basin,South China Sea,have identified concentrated gas hydrates with variable thickness.Moreover,free gas and the coexistence of gas hydrate and free gas have been confirmed by logging,coring,and production tests in the foraminifera-rich silty sediments with complex bottom-simulating reflectors(BSRs).The broad-band processing is conducted on conventional three-dimensional(3D)seismic data to improve the image and detection accuracy of gas hydratebearing layers and delineate the saturation and thickness of gas hydrate-and free gas-bearing sediments.Several geophysical attributes extracted along the base of the gas hydrate stability zone are used to demonstrate the variable distribution and the controlling factors for the differential enrichment of gas hydrate.The inverted gas hydrate saturation at the production zone is over 40% with a thickness of 90 m,showing the interbedded distribution with different boundaries between gas hydrate-and free gas-bearing layers.However,the gas hydrate saturation value at the adjacent canyon is 70%,with 30-m-thick patches and linear features.The lithological and fault controls on gas hydrate and free gas distributions are demonstrated by tracing each gas hydrate-bearing layer.Moreover,the BSR depths based on broad-band reprocessed 3D seismic data not only exhibit variations due to small-scale topographic changes caused by seafloor sedimentation and erosion but also show the upward shift of BSR and the blocky distribution of the coexistence of gas hydrate and free gas in the Pearl River Mouth Basin. 展开更多
关键词 gas hydrate free gas shift of BSR broad-band processing
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Design and optimization of a greener sinomenine hydrochloride preparation process considering variations among different batches of the medicinal herb 被引量:1
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作者 Dandan Ren Jiale Xie +2 位作者 Tianle Chen Haibin Qu Xingchu Gong 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2024年第7期77-90,共14页
The current methods used to industrially produce sinomenine hydrochloride involve several issues,including high solvent toxicity,long process flow,and low atomic utilization efficiency,and the greenness scores of the ... The current methods used to industrially produce sinomenine hydrochloride involve several issues,including high solvent toxicity,long process flow,and low atomic utilization efficiency,and the greenness scores of the processes are below 65 points.To solve these problems,a new process using anisole as the extractant was proposed.Anisole exhibits high selectivity for sinomenine and can be connected to the subsequent water-washing steps.After alkalization of the medicinal material,heating extraction,water washing,and acidification crystallization were carried out.The process was modeled and optimized.The design space was constructed.The recommended operating ranges for the critical process parameters were 3.0–4.0 h for alkalization time,60.0–80.0℃ for extraction temperature,2.0–3.0(volume ratio)for washing solution amount,and 2.0–2.4 mol·L^(-1) for hydrochloric acid concentration.The new process shows good robustness because different batches of medicinal materials did not greatly impact crystal purity or sinomenine transfer rate.The sinomenine transfer rate was about 20%higher than that of industrial processes.The greenness score increased to 90 points since the novel process proposed in this research solves the problems of long process flow,high solvent toxicity,and poor atomic economy,better aligning with the concept of green chemistry. 展开更多
关键词 Sinomenine hydrochloride process optimization ANISOLE
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UAV-assisted data collection for wireless sensor networks with dynamic working modes 被引量:1
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作者 Jie Chen Jianhua Tang 《Digital Communications and Networks》 SCIE CSCD 2024年第3期805-812,共8页
Wireless Sensor Network(WSN)is a cornerstone of Internet of Things(IoT)and has rich application scenarios.In this work,we consider a heterogeneous WSN whose sensor nodes have a diversity in their Residual Energy(RE).I... Wireless Sensor Network(WSN)is a cornerstone of Internet of Things(IoT)and has rich application scenarios.In this work,we consider a heterogeneous WSN whose sensor nodes have a diversity in their Residual Energy(RE).In this work,to protect the sensor nodes with low RE,we investigate dynamic working modes for sensor nodes which are determined by their RE and an introduced energy threshold.Besides,we employ an Unmanned Aerial Vehicle(UAV)to collect the stored data from the heterogeneous WSN.We aim to jointly optimize the cluster head selection,energy threshold and sensor nodes’working mode to minimize the weighted sum of energy con-sumption from the WSN and UAV,subject to the data collection rate constraint.To this end,we propose an efficient search method to search for an optimal energy threshold,and develop a penalty-based successive convex approximation algorithm to select the cluster heads.Then we present a low-complexity iterative approach to solve the joint optimization problem and discuss the implementation procedure.Numerical results justify that our proposed approach is able to reduce the energy consumption of the sensor nodes with low RE significantly and also saves energy for the whole WSN. 展开更多
关键词 Unmanned aerial vehicle Wireless sensor networks Cluster heads Dynamic working modes
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