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Flame smoke detection algorithm based on YOLOv5 in petrochemical plant

International Journal of Intelligent Computing and Cybernetics 2023年第3期16卷 502-519页

作者： Yueting Yang Shaolin Hu Ye Ke Runguan Zhou Guangdong University of Petrochemical Technology MaomingChina and Jilin Institute of Chemical TechnologyjilinChina Automation School Guangdong University of Petrochemical TechnologyMaomingChina

Purpose–Fire smoke detection in petrochemical plant can prevent fire and ensure production safety and life safety.The purpose of this paper is to solve the problem of missed detection and false detection in flame smoke detection under complex factory background.Design/methodology/approach–This paper presents a flame smoke detection algorithm based on YOLOv5.The target regression loss function(CIoU)is used to improve the missed detection and false detection in target detection and improve the model detection performance.The improved activation function avoids gradient disappearance to maintain high real-time performance of the algorithm.Data enhancement technology is used to enhance the ability of the network to extract features and improve the accuracy of the model for small target detection.Findings–Based on the actual situation of flame smoke,the loss function and activation function of YOLOv5 model are improved.Based on the improved YOLOv5 model,a flame smoke detection algorithm with generalization performance is established.The improved model is compared with SSD and YOLOv4-tiny.The accuracy of the improved YOLOv5 model can reach 99.5%,which achieves a more accurate detection effect on flame smoke.The improved network model is superior to the existing methods in running time and accuracy.Originality/value–Aiming at the actual particularity of flame smoke detection,an improved flame smoke detection network model based on YOLOv5 is established.The purpose of optimizing the model is achieved by improving the loss function,and the activation function with stronger nonlinear ability is combined to avoid over-fitting of the network.This method is helpful to improve the problems of missed detection and false detection in flame smoke detection and can be further extended to pedestrian target detection and vehicle running recognition.

关键词： Flame smoke detection Target recognition YOLOv5 Image detection Deep learning

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An improved morphological weighted dynamic similarity measurement algorithm for time series data

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International Journal of Intelligent Computing and Cybernetics 2018年第4期11卷 486-495页

作者： Ke Yi Zhou Shaolin Hu School of Automation and Information Engineering Xi’an University of TechnologyXi’anChina School of Automation Foshan UniversityFoshanChina

Purpose–The similarity measurement of time series is an important research in time series detection,which is a basic work of time series clustering,anomaly discovery,prediction and many other data mining problems.The purpose of this paper is to design a new similarity measurement algorithm to improve the performance of the original similarity measurement algorithm.The subsequence morphological information is taken into account by the proposed algorithm,and time series is represented by a pattern,so the similarity measurement algorithm is more accurate.Design/methodology/approach–Following some previous researches on similarity measurement,an improved method is presented.This new method combines morphological representation and dynamic time warping(DTW)technique to measure the similarities of time series.After the segmentation of time series data into segments,three parameter values of median,point number and slope are introduced into the improved distance measurement formula.The effectiveness of the morphological weighted DTW algorithm(MW-DTW)is demonstrated by the example of momentum wheel data of an aircraft attitude control system.Findings–The improved method is insensitive to the distortion and expansion of time axis and can be used to detect the morphological changes of time series data.Simulation results confirm that this method proposed in this paper has a high accuracy of similarity measurement.Practical implications–This improved method has been used to solve the problem of similarity measurement in time series,which is widely emerged in different fields of science and engineering,such as the field of control,measurement,monitoring,process signal processing and economic analysis.Originality/value–In the similarity measurement of time series,the distance between sequences is often used as the only detection index.The results of similarity measurement should not be affected by the longitudinal or transverse stretching and translation changes of the sequence,so it is necessary to incorporate themorphologica

关键词： Dynamic time warping Morphological representation Similarity measurement Time series data

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LSTM based prediction algorithm and abnormal change detection for temperature in aerospace gyroscope shell

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International Journal of Intelligent Computing and Cybernetics 2019年第2期12卷 274-291页

作者： Haoqiang Shi Shaolin Hu Jiaxu Zhang School of Automation and Information Engineering Xi’an University of TechnologyXi’anChina Automation School Guangdong University of Petrochemical TechnologyMaomingChina

Purpose–Abnormal changes in temperature directly affect the stability and reliability of a gyroscope.Predicting the temperature and detecting the abnormal change is great value for timely understanding of the working state of the gyroscope.Considering that the actual collected gyroscope shell temperature data have strong non-linearity and are accompanied by random noise pollution,the prediction accuracy and convergence speed of the traditional method need to be improved.The purpose of this paper is to use a predictive model with strong nonlinear mapping ability to predict the temperature of the gyroscope to improve the prediction accuracy and detect the abnormal change.Design/methodology/approach–In this paper,an double hiddenlayer long-shortterm memory(LSTM)is presentedto predicttemperature data for the gyroscope(including singlepoint andperiod prediction),and the evaluation index of the prediction effect is also proposed,and the prediction effects of shell temperature data are compared by BP network,support vector machine(SVM)and LSTM network.Using the estimated value detects the abnormal change of the gyroscope.Findings–By combined simulation calculation with the gyroscope measured data,the effect of different network hyperparameters on shell temperature prediction of the gyroscope is analyzed,and the LSTM networkcan beusedto predictthe temperature(timeseriesdata).Bycomparingthe performance indicatorsof different prediction methods,the accuracy of the shell temperature estimation by LSTM is better,which can meet the requirements of abnormal change detection.Quick and accurate diagnosis of different types of gyroscopefaults(stepsanddrifts)can beachievedbysettingreasonabledatawindowlengthsandthresholds.Practicalimplications–The LSTMmodelisa deepneuralnetworkmodelwithmultiplenon-linearmapping levels,and can abstract the input signal layer by layer and extract features to discover deeper underlying laws.The improved method has been used to solve the problem of strong non-linearity and random noise pollutionin time

关键词： Gyroscope LSTM Temperature prediction Recurrent neural network Abnormal change detection

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CLOF Based Outlier Detection Algorithm of Temperature Data for Ethylene Cracking Furnace

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Journal of Harbin Institute of Technology(New Series) 2023年第4期30卷 50-57页

作者： Yidan Xin Shaolin Hu Wenzhuo Chen He Song School of Automation and Information Engineering Xi’an University of TechnologyXi’an 710048China Guangdong Provincial Key Lab.of Petrochemical Equipment and Fault Diagnosis School of AutomationGuangdong University of Petrochemical TechnologyMaoming 525000GuangdongChina

The flue temperature is one of the important indicators to characterize the combustion state of an ethylene cracker furnace,the outliers of temperature data can lead to the false alarm.Conventional outlier detection algorithms such as the Isolation Forest algorithm and 3-sigma principle cannot detect the outliers accurately.In order to improve the detection accuracy and reduce the computational complexity,an outlier detection algorithm for flue temperature data based on the CLOF(Clipping Local Outlier Factor,CLOF)algorithm is proposed.The algorithm preprocesses the normalized data using the cluster pruning algorithm,and realizes the high accuracy and high efficiency outlier detection in the outliers candidate set.Using the flue temperature data of an ethylene cracking furnace in a petrochemical plant,the main parameters of the CLOF algorithm are selected according to the experimental results,and the outlier detection effect of the Isolation Forest algorithm,the 3-sigma principle,the conventional LOF algorithm and the CLOF algorithm are compared and analyzed.The results show that the appropriate clipping coefficient in the CLOF algorithm can significantly improve the detection efficiency and detection accuracy.Compared with the outlier detection results of the Isolation Forest algorithm and 3-sigma principle,the accuracy of the CLOF detection results is increased,and the amount of data calculation is significantly reduced.

关键词： temperature data outlier detection ethylene cracker furnace clustering data clipping LOF

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Fault-tolerant Mining Algorithm of Sampling Data from Dynamic System

Fault-tolerant Mining Algorithm of Sampling Data from Dynami...

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第25届中国控制与决策会议

作者： hu shaolin Li Ye Zhang Dong State Key Laboratory of Astronautics Automation School Xi'an University of Technology

ISBN: (纸本)9781467355339

Time series data mining is an useful tool for us to design data-driven condition monitoring as well as fault diagnosis system. Aiming at monitoring abnormal changes of dynamic process, a series of mining algorithms are built up to mine signal form, model structure of process and statistical properties of noise in sampling data series, the architecture of information mining system of sampling time series is set up. These algorithms are very fault tolerant for patchy outliers in sampling data set of the complex system. Results given in this paper can be used not only in the safety analysis and fault diagnosis of complicated dynamic process but also in change detection as well as other related fields.

关键词： Time Series Data Mining Knowledge Discovery Fault-tolerant Mining

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A Small-Scale Medication of Leflunomide as a Treatment of COVID-19 in an Open-Label Blank-Controlled Clinical Trial

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Virologica Sinica 2020年第6期35卷 725-733页

作者： Ke hu Mengmei Wang Yang Zhao Yunting Zhang Tao Wang Zhishui Zheng Xiaochen Li shaolin Zeng Dong Zhao Honglin Li Ke Xu Ke Lan Department of Respiratory and Critical Care Medicine Renmin Hospital of Wuhan UniversityWuhan 430060China Shanghai Key Laboratory of New Drug Design State Key Laboratory of Bioreactor EngineeringSchool of PharmacyEast China University of Science and TechnologyShanghai 200237China State Key Laboratory of Virology College of Life SciencesWuhan UniversityWuhan 430072China Frontier Science Center for Immunology and Metabolism Wuhan UniversityWuhan 430072China

We recently reported that inhibitors against human dihydroorotate dehydrogenase(DHODH)have broad-spectrum antiviral activities including their inhibitory efficacies on SARS-CoV-2 replication in infected cells.However,there are limited data from clinical studies to prove the application of DHODH inhibitors in Coronavirus disease 2019(COVID-19)patients.In the present study,we evaluated Leflunomide,an approved DHODH inhibitor widely used as a modest immune regulator to treat autoimmune diseases,in treating COVID-19 disease with a small-scale of patients.Cases of 10 laboratory-confirmed COVID-19 patients of moderate type with obvious opacity in the lung were included.Five of the patients were treated with Leflunomide,and another five were treated as blank controls without a placebo.All the patients accepted standard supportive treatment for COVID-19.The patients given Leflunomide had a shorter viral shedding time(median of5 days)than the controls(median of 11 days,P=0.046).The patients given Leflunomide also showed a significant reduction in C-reactive protein levels,indicating that immunopathological inflammation was well controlled.No obvious adverse effects were observed in Leflunomide-treated patients,and they all discharged from the hospital faster than controls.This preliminary study on a small-scale compassionate use of Leflunomide provides clues for further understanding of Leflunomide as a potential antiviral drug against COVID-19.

关键词： DHODH inhibitors Leflunomide Coronavirus disease 2019(COVID-19) Viral shedding time Inflammation

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Genomic Insight into the Antimicrobial Resistance of Streptococcus Suis-Six Countries,2011-2019

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China CDC weekly 2021年第47期3卷 994-998页

作者： Lichao Ma Wanjiang Zhang Jingyun Ma Qingpo Cui Chaoyang Zhang Peng Zhang Chengtao Sun huarun Sun Yao Zhu shaolin Wang Shuangyang Ding Gongzheng hu Zhangqi Shen College of Veterinary Medicine China Agricultural UniversityBeijingChina Harbin Veterinary Research Institute Chinese Academy of Agricultural SciencesHarbinHeilongjiangChina College of Animal Science South China Agricultural UniversityGuangzhouGuangdongChina College of Veterinary Medicine Henan Agricultural UniversityZhengzhouHenanChina

Summary What is already known on this topic?Streptococcus suis(S.suis)is a zoonotic pathogen causing disease in humans and animals,and the emergence of its increased resistance to antimicrobial agents has become a sig... 详细信息

关键词： suis Streptococcus resistance

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一种基于最大信息系数预处理的k-modes聚类方法

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系统仿真学报 2022年第10期34卷 2204-2212页

作者：李明媚文成林胡绍林杭州电子科技大学浙江杭州310018 广东石油化工学院广东茂名525000

为解决现有k-modes聚类方法因忽略了变量属性之间的弱相关性,常造成其在实际应用中聚类性能不佳的问题,提出一种包含属性弱相关性的新k-modes聚类方法。引入最大信息系数(maximum information coefficient,MIC)度量数据集中变量属性之... 详细信息

为解决现有k-modes聚类方法因忽略了变量属性之间的弱相关性,常造成其在实际应用中聚类性能不佳的问题,提出一种包含属性弱相关性的新k-modes聚类方法。引入最大信息系数(maximum information coefficient,MIC)度量数据集中变量属性之间的相关性;将得到的MIC值与原有距离进行融合,建立包含属性弱相关性信息的新度量方法,以增强变量属性间相关信息的完备性,建立更加精细的k-modes聚类方法;调用3种不同的数据集,将新方法与原有的k-modes聚类方法和其他改进k-modes聚类方法的性能进行对比,并通过仿真结果表明了新方法的有效性。

关键词：聚类方法 k-modes 最大信息系数距离度量变量属性

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崇义县城防洪工程实施性施工组织设计案例研究

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水利科学与寒区工程 2019年第6期2卷 85-88页

作者：胡绍林崇义县水务集团有限公司

为应对崇义县洪水暴涨暴落等现象,解决河流两岸堤段破损、河道淤塞、挡墙老化等问题,建设崇义中城河防洪工程对保障该区居民的生产生活安全有重要意义。本文综合考虑了防洪施工总体布置型式,对防洪工程的施工方案设计进行了分析,并对涉... 详细信息

为应对崇义县洪水暴涨暴落等现象,解决河流两岸堤段破损、河道淤塞、挡墙老化等问题,建设崇义中城河防洪工程对保障该区居民的生产生活安全有重要意义。本文综合考虑了防洪施工总体布置型式,对防洪工程的施工方案设计进行了分析,并对涉及到的挡墙稳定及应力、土层垫层置换以及堤岸冲刷等进行了计算论证,通过对施工组织方案的整体分析,编制可靠的施工方案,为工程高效优质实施提供技术保障。

关键词：防洪工程施工组织导流堤岸

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A survey of routing techniques for satellite networks

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Journal of Communications and Information Networks 2016年第4期1卷 66-85页

作者： QI Xiaogang MA Jiulong WU Dan LIU Lifang hu shaolin School of Mathematics and Statistics Xidian UniversityXi'an 710126China School of Computer Science and Technology Xidian UniversityXi'an 710071China Key Laboratory of Spacecraft Faults Diagnosis and Maintance Xi'an 710043China

Satellite networks have many advantages over traditional terrestrial networks.However,it is very difficult to design a satellite network with excellent performance.The paper briefly summarizes some existing satellite network routing technologies from the perspective of both single-layer and multilayer satellite constellations,and focuses on the main ideas,characteristics,and existing problems of these routing technologies.For single-layer satellite networks,two routing strategies are discussed,virtual node strategy and virtual topology strategy.Moreover,considering the deficiency of existing multilayer satellite network routing,we discuss the topic invulnerability.Finally,the challenges and problems faced by the satellite network are analyzed and the trend of future development is predicted.

关键词： satellite network routing technology virtual node virtual topology invulnerability

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